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Plague in Madagascar – Watch – Level 1, Practice Usual Precautions – Travel Health Notices | Travelers’ Health

Warning – Level 3, Avoid Nonessential Travel

Alert – Level 2, Practice Enhanced Precautions

Watch – Level 1, Practice Usual Precautions

What is the current situation?

In early October, World Health Organization and the Madagascar Ministry of Health responded to an unusual outbreak of plague pneumonia in widespread areas of Madagascar, including in heavily populated cities of Antananarivo (the capital city and its suburbs) and Toamasina. The outbreak has been contained. However, bubonic plague occurs nearly every year in Madagascar, so travelers to the area should continue to take precautions to protect their health.

What is plague?

Plague is a bacterial infection that is usually spread through bites by infected fleas. When acquired by flea bite, plague causes symptoms of high fever and swollen and tender lymph nodes (bubonic plague) that usually occur 2–6 days after the bite. If it is not treated, the infection can spread to the lungs and cause pneumonia.

Plague pneumonia (or “pneumonic plague”) is the only form that can be directly transmitted from one person to another. In rare but serious cases, a person with severe plague pneumonia can spread the infection directly to others by coughing up droplets that contain the plague bacteria. These bacteria-containing droplets can cause pneumonic plague in another person if breathed in (more information). Symptoms of plague pneumonia typically appear 2–4 days after inhaling plague bacteria and usually include sudden onset of high fever and cough and other general symptoms such as headache, chills, and weakness.

Plague can be treated with antibiotics. However, without prompt treatment, plague can cause serious illness or death.

How can travelers protect themselves?

No vaccine is available to prevent plague. But travelers can take steps to prevent plague, and plague can be prevented with antibiotics. Travelers to Madagascar should

  • Use EPA-registered insect repellent that lists protection against fleas on the label and contains at least 25% DEET.
  • Avoid close contact with sick or dead animals.
  • Avoid close contact with seriously ill people, especially people who are coughing up blood.

Travelers who have had close contact with people with plague pneumonia should immediately notify a health care provider. They may need to take antibiotics to prevent plague.

During or after travel to Madagascar, travelers should be alert for symptoms of plague. If symptoms do appear, they should seek medical care and inform the provider about their travel to Madagascar.

Learn more about plague, how to prevent it, and what to do if you think you are infected at CDC’s plague page for travelers.

Traveler Information

Clinician Information

QuickStats: Percentage of Visits by Patients Aged ≥18 Years to

In 2015, the percentage of office-based physician visits by adults with two or more diagnosed chronic conditions was 53.1% for primary care physicians, 38.5% for medical specialists, and 23.9% for surgeons. This pattern was observed for each of the age groups studied. The percentage of visits increased with age group, regardless of specialty category.

Source: National Ambulatory Medical Care Survey, 2015 data. https://www.cdc.gov/nchs/ahcd/ahcd_questionnaires.htm.


Reported by: Brian W. Ward, PhD, ijz8@cdc.gov, 301-458-4568; Kelly L. Myrick, PhD; Donald K. Cherry, MS.

Suggested citation for this article: QuickStats: Percentage of Visits by Patients Aged ≥18 Years to Office-Based Physicians Made by Patients with ≥2 Selected Diagnosed Chronic Conditions, by Physician Specialty Category and Patient Age Group — National Ambulatory Medical Care Survey, 2015. MMWR Morb Mortal Wkly Rep 2017;66:1367. DOI: http://dx.doi.org/10.15585/mmwr.mm6649a9.

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2018 Winter Olympics (PyeongChang 2018) – Watch – Level 1, Practice Usual Precautions – Travel Health Notices | Travelers’ Health

Warning – Level 3, Avoid Nonessential Travel

Alert – Level 2, Practice Enhanced Precautions

Watch – Level 1, Practice Usual Precautions

What is the current situation?

The 2018 Winter Olympics will take place in PyeongChang, South Korea, from February 9 to February 25, 2018. The Paralympic Games are scheduled for March 9 to March 18, 2018. If you plan to travel to South Korea for the Olympics or Paralympics, follow the recommendations below to help you stay safe and healthy.

What can travelers do to protect themselves?

Before your trip:

  • Schedule an appointment with a travel medicine clinic or your health-care provider at least 4–6 weeks before you depart. Talk to the doctor or nurse about vaccines and medicines recommended for your destination. See the Find a Clinic webpage for help in finding a travel medicine clinic near you.
  • Consider purchasing travel health and medical evacuation insurance.
  • Pack your prescription and over-the-counter medicines (as well as other important supplies), as part of a travel health kit.
  • Familiarize yourself with local laws and social customs.
  • Monitor travel warnings and alerts from the US Department of State. Register your trip with the nearest US embassy or consulate through the Smart Traveler Enrollment Program (STEP) to get the latest safety and security information for your destination country.
  • Leave copies of your itinerary, contact information, credit cards, and passport with someone at home, in case you lose them during travel.

During your trip:

  • Follow security and safety guidelines. US travelers may be targets for criminals at mass gatherings.
    • If possible, don’t travel at night, avoid questionable areas, and travel with a companion.
    • If you drink alcohol, do it in moderation. People are more likely to hurt themselves or other people, engage in risky sex, or get arrested when they have been drinking.
    • Carry a photocopy of your passport and entry stamp; leave the actual passport securely in your hotel.
    • Carry the contact information for the nearest US embassy or consulate in South Korea with you. Call 112 for emergency assistance or to report a crime to local authorities. Call 02-397-4114 to contact the U.S. Embassy.
    • Follow all local laws and social customs.
    • Do not wear expensive clothing or jewelry, to avoid the risk of theft or loss.
    • Always keep hotel doors locked, and store valuables in secure areas.
    • If possible, choose hotel rooms on the second through the sixth floors. A room on the first floor of a hotel may provide easier access for criminals. Rooms on the seventh floor or above may be difficult to escape if there’s a fire.  
  • Protect yourself during cold temperatures. Average temperatures at the Winter Olympics are estimated to be around 33°F-35°F (1°C-2°C) during the day and 17°F (-8°C) during the night. Staying out in the cold too long can cause serious health problems such as frostbite, hypothermia, and other cold-related illnesses. Dress warmly with appropriate clothing and stay dry to reduce heat loss. Do not ignore shivering. It’s an important first sign that the body is losing heat. Read more about ways to stay healthy in cold weather.
  • Do not touch birds or other animals, and avoid farms and poultry markets. Bird flu viruses have been seen in poultry (no human cases) in South Korea. Read more about bird flu prevention.
  • Use condoms to reduce your risk of sexually transmitted diseases (STDs). The celebratory atmosphere at the Olympics may encourage travelers to engage in risky sex, especially if they are drinking or using drugs. Carry condoms that you purchased in the United States and store them in a dry and cool place (out of direct sunlight). Read more about how to prevent STDs by visiting the Traveler STD page.
  • Choose safe transportation. Motor vehicle crashes are the #1 killer of healthy US citizens in foreign countries. Read about ways to prevent transportation injuries by visiting the Road Safety page.
  • Reduce your exposure to germs. Wash your hands often, and avoid contact with people who are sick. Read more about reducing your exposure to germs in the Stay Healthy and Safe section of CDC’s South Korea page.

If you feel sick during your trip:

  • Talk to a doctor or nurse if you feel seriously ill, especially if you have a fever.
  • Western-style medical facilities are available in most large cities in South Korea. However, not all doctors and staff are proficient in English. Check the US Embassy website for a list of English-speaking physicians.
  • For more information about medical care abroad, see Getting Health Care Abroad.
  • Avoid contact with other people while you are sick, such as kissing, hugging, or sharing utensils or cups.
  • Wash your hands often. If soap and water aren’t available, use hand sanitizer (containing at least 60% alcohol) to clean hands.
  • Cover your mouth and nose with a tissue or your sleeve (not your hand) when coughing or sneezing.

After your trip:

  • If you are not feeling well after your trip, you may need to see a doctor. If you need help finding a travel medicine specialist, see Find a Clinic. Be sure to tell your doctor about your travel, including where you went and what you did on your trip. Also tell your doctor if you were bitten or scratched by an animal or were around any sick people while traveling. This will help your doctor understand your symptoms to exclude certain infections and avoid unnecessary testing.
  • For more information, see Getting Sick after Travel.

Traveler Information

Clinical Information

Plague in Madagascar – Watch – Level 1, Practice Usual Precautions – Travel Health Notices | Travelers’ Health

Warning – Level 3, Avoid Nonessential Travel

Alert – Level 2, Practice Enhanced Precautions

Watch – Level 1, Practice Usual Precautions

What is the current situation?

In early October, World Health Organization and the Madagascar Ministry of Health responded to an unusual outbreak of plague pneumonia in widespread areas of Madagascar, including in heavily populated cities of Antananarivo (the capital city and its suburbs) and Toamasina. The outbreak has been contained. However, bubonic plague occurs nearly every year in Madagascar, so travelers to the area should continue to take precautions to protect their health.

What is plague?

Plague is a bacterial infection that is usually spread through bites by infected fleas. When acquired by flea bite, plague causes symptoms of high fever and swollen and tender lymph nodes (bubonic plague) that usually occur 2–6 days after the bite. If it is not treated, the infection can spread to the lungs and cause pneumonia.

Plague pneumonia (or “pneumonic plague”) is the only form that can be directly transmitted from one person to another. In rare but serious cases, a person with severe plague pneumonia can spread the infection directly to others by coughing up droplets that contain the plague bacteria. These bacteria-containing droplets can cause pneumonic plague in another person if breathed in (more information). Symptoms of plague pneumonia typically appear 2–4 days after inhaling plague bacteria and usually include sudden onset of high fever and cough and other general symptoms such as headache, chills, and weakness.

Plague can be treated with antibiotics. However, without prompt treatment, plague can cause serious illness or death.

How can travelers protect themselves?

No vaccine is available to prevent plague. But travelers can take steps to prevent plague, and plague can be prevented with antibiotics. Travelers to Madagascar should

  • Use EPA-registered insect repellent that lists protection against fleas on the label and contains at least 25% DEET.
  • Avoid close contact with sick or dead animals.
  • Avoid close contact with seriously ill people, especially people who are coughing up blood.

Travelers who have had close contact with people with plague pneumonia should immediately notify a health care provider. They may need to take antibiotics to prevent plague.

During or after travel to Madagascar, travelers should be alert for symptoms of plague. If symptoms do appear, they should seek medical care and inform the provider about their travel to Madagascar.

Learn more about plague, how to prevent it, and what to do if you think you are infected at CDC’s plague page for travelers.

Traveler Information

Clinician Information

Time-Varying Effects of Parental Alcoholism on Depression

Sunita Thapa, MPH1; Arielle S. Selya, PhD2; Yvonne Jonk, PhD2 (View author affiliations)

Suggested citation for this article: Thapa S, Selya AS, Jonk Y. Time-Varying Effects of Parental Alcoholism on Depression. Prev Chronic Dis 2017;14:170100. DOI: http://dx.doi.org/10.5888/pcd14.170100.

MEDSCAPE CME

Medscape, LLC, is pleased to provide online continuing medical education (CME) for this journal article, allowing clinicians the opportunity to earn CME credit.

In support of improving patient care, this activity has been planned and implemented by Medscape, LLC, and Preventing Chronic Disease. Medscape, LLC, is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.

Medscape, LLC, designates this Journal-based CME activity for a maximum of 1.00 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

All other clinicians completing this activity will be issued a certificate of participation. To participate in this journal CME activity: (1) review the learning objectives and author disclosures; (2) study the education content; (3) take the post-test with a 75% minimum passing score and complete the evaluation at http://www.medscape.org/journal/pcd; (4) view/print certificate.

Release date: December 14, 2017; Expiration date: December 14, 2018

Learning Objectives

Upon completion of this activity, participants will be able to:

  1. Evaluate risk for lifetime major depressive disorder among children of alcoholic parents, based on a national database study using the National Epidemiological Survey on Alcohol and Related Conditions, wave III
  2. Determine risk for lifetime persistent depressive disorder among children of alcoholic parents, based on a national database study using the National Epidemiological Survey on Alcohol and Related Conditions, wave III
  3. Assess clinical implications regarding risk for lifetime depression among children of alcoholic parents, based on a national database study using the National Epidemiological Survey on Alcohol and Related Conditions, wave III

EDITOR

Camille Martin, RD, LD

Editor, Preventing Chronic Disease

Disclosure: Camille Martin, RD, LD, has disclosed no relevant financial relationships.

CME AUTHOR

Sunita Thapa, MPH

Department of Health Policy, Vanderbilt University School of Medicine, Nashville, Tennessee

Disclosure: Sunita Thapa, MPH, has disclosed no relevant financial relationships.

AUTHORS

Arielle S. Selya, PhD

Master of Public Health Program, Department of Population Health, University of North Dakota, Grand Forks, North Dakota

Disclosure: Arielle S. Selya, PhD, has disclosed the following relevant financial relationships:

Received grants for clinical research from: Sanford Research

Yvonne Jonk, PhD

Master of Public Health Program, Department of Population Health, University of North Dakota, Grand Forks, North Dakota

Disclosure: Yvonne Jonk, PhD, has disclosed no relevant financial relationships.

PEER REVIEWED

On This Page

Abstract

Introduction

Children of alcoholic parents are at increased risk for lifetime depression. However, little is known about how this risk may change in magnitude across age, especially in mid-adulthood and beyond.

Methods

We used a nationally representative sample (N = 36,057) of US adults from the National Epidemiologic Survey on Alcohol and Related Conditions, wave III. After adjusting for demographic characteristics, we examined the relationship between parental alcoholism and outcomes of 1) major depressive disorder, Diagnostic and Statistical Manual of Mental Disorders-5th edition (DSM-5) and 2) DSM-5 persistent depressive disorder. To examine continuous moderation of this relationship across participants’ age, we used time-varying effect models.

Results

Parental alcoholism was associated in general with a higher risk for both major depressive disorder (odds ratio [OR], 1.98, 95% confidence interval [CI], 1.85–2.11; P < .001) and persistent depressive disorder (OR, 2.28, 95% CI, 2.04–2.55; P < .001). The association between parental alcoholism and major depressive disorder was stable and positive across age, but the association with persistent depressive disorder significantly declined among older adults; respondents older than 73 years old were not at increased risk for persistent depressive disorder.

Conclusions

Findings from this study show that the risk of parental alcoholism on depression is significant and stable among individuals of a wide age range, with the exception of a decline in persistent depressive risk among older adults. These findings highlight the importance of screening for depression among adults with parental alcoholism.

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Introduction

Parental alcoholism has various negative physical, mental, and social consequences. Chief among these is depression; offspring of alcoholics are at heightened risk of depressive mood symptoms (1,2). The evidence for heightened depression among those exposed to parental alcoholism is particularly strong among young, college-aged adults (3,4).

Much of the research on the association between parental alcoholism and depression focuses on the question of resilience among adult children of alcoholics; that is, whether these individuals are ever able to overcome the challenges of parental alcoholism. Although some evidence suggests that older adults (those in their late 20s and early 30s) are more resilient than are young adults (those aged 18 through their early 20s) (5), there is little research on the effects of parental alcoholism among offspring of alcoholics in mid- to late adulthood, making their longer-term resilience unknown. Furthermore, the question of increased resilience at older ages assumes that the magnitude of the effect of parental alcoholism changes with increasing age; however, such age-varying effects have not yet been examined.

This study examined 1) the association between parental alcoholism and lifetime outcomes of both major depressive disorder (MDD) and persistent depressive disorder (PDD) among a full range of adults after controlling for demographic characteristics and 2) the age-varying effects of these associations (ie, how they may change in strength across participants’ ages). We used data from wave III of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC-III), a large nationally representative data set.

Methods

NESARC-III was sponsored, designed, and directed by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and conducted during 2012–2013. NESARC-III is a nationally representative sample of the civilian noninstitutionalized population of the United States aged 18 years or older; it had a 61.1% response rate and an original sample size of 36,309. The NIAAA collected information via questionnaires on alcohol and drug use and disorders, related risk factors, and associated physical and mental disabilities on the basis of NIAAA’s Alcohol Use Disorder and Associated Disabilities Interview Schedule. This study excluded respondents with missing information on parental alcoholism; the final sample size for this study was 36,057. We used existing data from human participants in NESARC, and the study was approved by the University of North Dakota institutional review board. We completed the final analyses in May of 2016.

Measures

Parental alcoholism

Parental alcoholism was based on the self-reported answer to the question “Before you were 18, parent/other adult living in home was a problem drinker/alcoholic?” as a binary response variable (yes or no).

Depression

We analyzed 2 depressive disorders, lifetime MDD and lifetime PDD, as separate outcomes. Each outcome was derived from detailed self-reported responses to questionnaire items on the basis of corresponding criteria from the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5)(6). Briefly, lifetime MDD is characterized by one or more discrete episodes of at least 2 weeks during which respondents had either a depressed mood or a loss of interest in nearly all activities at some time during their adult lives (6). Lifetime PDD is a milder but more chronic form of depression and can be diagnosed when the mood disturbance continues for at least 2 years at some time during an adult’s life (6). Both MDD and PDD exclude mood or anxiety disorders that are either substance-induced or due to a general medical condition.

Demographic characteristics

Age and sex were self-reported. Race/ethnicity was self-reported as white, black, Hispanic, American Indian, or Asian. Full-time employment was self-reported as working 35 or more hours per week or less than 35 hours per week.

Marital status was self-reported according to 6 response options, which were re-categorized as currently married (ie, married or living with someone as if married), not currently married (ie, widowed, divorced, or separated), and never married.

Education was self-reported with 14 response levels ranging from “no formal schooling” to “completed Master’s degree or higher,” and we re-categorized these into 3 levels: less than a high school diploma, high school diploma, and some college or more.

Annual household income was self-reported with 21 response categories ranging from less than $5,000 to $200,000 or more. We recoded these into a new numeric variable on the basis of midpoints of each category up to level 20; level 21 (≥$200,000) was recoded as $250,000, which is approximately the median income among households earning $200,000 or more (7).

Statistical analyses

We conducted weighted regressions using the statistical software R (The R Foundation) and its survey package to examine the association between parental alcoholism and outcomes of MDD and PDD, after adjusting for demographic characteristics.

We used time-varying effect models (TVEMs), an extension of regression modeling that allows coefficients to vary continuously over time (8), to assess how the association between parental alcoholism and depression outcomes varied across age of participants. In other words, TVEMs examine moderation across some continuous measure of time (eg, historical time, age, time from event). TVEMs are spline-based regression models, which estimate a lower-order polynomial trend within equal intervals on the basis of user-specified number of knots, k. On the basis of established standards for this methodology (9), 10 knots were specified, and P-spline estimation, which automatically finds the most parsimonious model (k ≤10), was used. We ran separate logistic TVEM models for outcomes of MDD and PDD after controlling for demographic characteristics. Each model included a time-varying intercept (to adjust for the overall prevalence of depression across age) and the time-varying predictor of age (to examine continuous moderation of the effect of parental alcoholism across ages). We performed TVEM analyses in SAS 9.3 (SAS Institute Inc) using a publicly available SAS macro (9), version 3.1.0. TVEM analyses were interpreted with respect to 1) overall significance of the effect at a given value of age (ie, whether the confidence bands overlap the odds ratio (OR) of 1.0), and 2) the change in the effect across different ages (ie, whether the confidence bands exclude each other at different ages). Although these methods of establishing significance are more conservative than conventional significance tests, we did this because P values were available only for time-invariant covariates.

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Results

Approximately 23% of respondents (n = 8,407) reported parental alcoholism. Respondents who reported parental alcoholism were significantly more likely than adults who did not report parental alcoholism to meet DSM-5 criteria for both MDD (29.6% vs 17.7%, P < .001) and PDD (9.3% vs 4.4%, P < .001) (Table). People who reported parental alcoholism were slightly but significantly younger (mean age, 44.8 y vs 45.9 y, P < .001); were more likely to be female (59.4% vs 55.4%, P < .001); had lower annual household incomes (median $32,500 vs $37,500, P < .001); were less likely to be never married (25.8% vs 28.4%, P < .001); were more likely to be not currently married (27.6% vs 25.4%, P < .001); were more likely to be white (57.8% vs 51.4%) or American Indian (2.1% vs 1.2%); and were less likely to be black (18.2% vs 22.3%) or Asian (1.9% vs 5.9%). The 2 groups did not significantly differ by education level (approximately 15% had

Additionally, compared with respondents who did not report parental alcoholism, those who reported parental alcoholism were slightly but significantly younger when they first had the first episode of MDD (median age, 27.8 y vs 30.5 y, P < .001) and PDD (median age, 27.9 y vs 30.6 y, P < .001) and had a significantly higher number of MDD episodes (median no., 4.6 vs 3.5, P < .001) and a nonsignificantly higher number of PDD episodes (median no., 2.1 vs 1.9). Respondents who reported parental alcoholism also talked to any health professional or therapist significantly more often to help improve their mood caused by MDD (63% vs 58%, P < .001) and nonsignificantly more often to help improve their mood caused by PDD (68% vs 64%) compared with respondents who did not report parental alcoholism. Respondents who reported parental alcoholism were significantly more likely to have symptoms of suicidal ideation (13% vs 8%, P < .001) and also meet DSM-5 criteria for other mental comorbidities such as anxiety (21% vs 11%, P < .001), personality disorders (27% vs 12%, P < .001), eating disorders (3% vs 1.5%, P < .001), substance use disorders (57% vs 37%, P < .001), and posttraumatic stress (12% vs 5%, P < .001).

Weighted regression analyses showed that parental alcoholism was associated with an approximately twofold increase in the odds of both MDD (OR, 1.84; 95% confidence interval [CI], 1.72–1.96; P < .001) and PDD (OR, 2.11; 95% CI, 1.88–2.37; P < .001), after controlling for demographics.

Parental alcoholism had a positive and stable effect on MDD across individuals throughout most of the age range of respondents aged 18 to 85 years (Figure 1). Participants between these ages were approximately 2 times as likely to have MDD as were participants who reported no parental alcoholism. Because of the small sample size of participants older than 85 years and the resulting widening of the confidence band (ie, the lower limit of the confidence band is less than the OR of 1), the relationship was no longer significant among these individuals, even though the point estimate remained stable.

Age-varying effects of parental alcoholism on lifetime major depressive disorder for respondents aged 18–90 years, National Epidemiologic Survey on Alcohol and Related Conditions, Wave III, 2012–2013. Age-varying effects are presented as odds ratios (ORs) across ages; the solid line represents the OR point estimates, and the surrounding shading represents 95% confidence intervals. The horizontal line represents an OR of 1.00.

Figure 1.
Age-varying effects of parental alcoholism on lifetime major depressive disorder for respondents aged 18–90 years, National Epidemiologic Survey on Alcohol and Related Conditions, Wave III, 2012–2013. Age-varying effects are presented as odds ratios (ORs) across ages; the solid line represents the OR point estimates, and the surrounding shading represents 95% confidence intervals. The horizontal line represents an OR of 1.00. [A tabular description of this figure is also available.]

Similarly, parental alcoholism had a positive effect on PDD across a wide age range (Figure 2). Participants aged 18 to 73 years were approximately 2 times as likely to have PDD as were participants who reported no parental alcoholism. The association was nonsignificant for those aged 74 years and older. Additionally, the effect of parental alcoholism among older individuals (eg, OR of 0.8 for participants aged 80 y) was significantly weaker than the effect among younger individuals (eg, OR of 2.3 for participants aged 60 y).

Age-varying effects of parental alcoholism on lifetime persistent depressive disorder for respondents aged 18–90 years, National Epidemiologic Survey on Alcohol and Related Conditions, Wave III, 2012–2013. Age-varying effects are presented as odds ratios (ORs) across ages; the solid line represents the OR point estimates, and the surrounding shading represents 95% confidence intervals. The horizontal line represents an OR of 1.00.

Figure 2.
Age-varying effects of parental alcoholism on lifetime persistent depressive disorder for respondents aged 18–90 years, National Epidemiologic Survey on Alcohol and Related Conditions, Wave III, 2012–2013. Age-varying effects are presented as odds ratios (ORs) across ages; the solid line represents the OR point estimates, and the surrounding shading represents 95% confidence intervals. The horizontal line represents an OR of 1.00. [A tabular description of this figure is also available.]

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Discussion

This study examined how the relationship between parental alcoholism and depression outcomes may change across individuals of different ages. Respondents who reported being exposed to parental alcoholism as children had approximately twice the risk of meeting criteria for lifetime MDD and PDD. Parental alcoholism had a positive and stable effect on the odds of lifetime MDD throughout most of the age range of the participants, although this association was no longer significant for those aged 85 years old or older. However, although the association with PDD was positive and stable across individuals in early and late adulthood, it significantly decreased in strength for those older than 73, such that parental alcoholism was no longer associated with a heightened risk for PDD.

Results of this study also showed that 23% of adults had a parent with alcohol problems before the age of 18; the 1988 National Health Interview Survey estimated that 18.1% of adults had a parent with alcohol problems before the age of 18 (10). Although there is a large gap in timeline, the prevalence of adults growing up with a parent with alcohol problems seems comparable. Although current data on the prevalence of adults who grew up with a parent with alcohol problems are not available, it is estimated that an annual average of 7.5 million US children (10.5% of all children) live with a parent who had an alcohol use disorder in the past year (11). Although this figure is lower than we report here, it includes only past-year alcohol use disorder, a severe form of problem drinking. Hence, assuming that this prevalence will increase under NESARC’s inclusion of other, less severe forms of problem drinking, the current prevalence rates are more consistent with those of previous reports.

Our findings confirm those of previous research that established that parental alcoholism is associated with an increased risk of depression among offspring (2,12,13). This study also extends this research in 2 important ways, given that many previous studies are limited to younger adults (2,3). Here, we examined the effects of parental alcoholism on depression among adults across a wide age range, and we rigorously examined the age-varying effects of parental alcoholism, showing that its effect is largely stable across individuals from early to late adulthood.

This study has limitations. First, the measure of parental alcoholism is limited in several ways. The single question that assessed parental alcoholism was proxy-reported by offspring. As a result, both the timing and the nature of the question may have created recall bias, in which those with depression are more likely to remember the drinking of their parents as problematic than those with no depression. Additionally, the wording of the question included parents as well as non-parental adults living in the household, although most participants reported living only with one or more biological parents. Thus, the wording of this question may have affected the results in unknown ways. Second, this study used cross-sectional data and thus cannot conclude that parental alcoholism causes depression among offspring.

Third, because we used cross-sectional data, the findings do not distinguish between true age and cohort when considering the age-varying effect of parental alcoholism. A true age-varying effect would capture data on the change in the effect of parental alcoholism as an individual ages, but these analyses examined the effect across individuals of different ages. This analysis introduces a cohort effect: the association between parental alcoholism and depression may change across individuals born in different years as a result of differences across time periods in, for example, the prevalence of parental alcoholism, the threshold at which participants consider alcohol consumption “problem drinking,” the prevalence of depression, or other associated risk and protective factors. It is likely that both an age effect (5) and a cohort effect (14,15) contribute to our findings, but this study cannot distinguish between them. Thus, the findings should not be interpreted as effects for a given individual across time. Future studies using longitudinal data are needed to separate true age-varying effects from cohort effects.

Strengths of this study include the large, nationally representative sample, the use of rigorous and well-validated DSM-5 measures of MDD and PDD, and the use of TVEMs, an innovative methodology for examining continuous moderation across age.

Parental alcoholism is stably associated with depression outcomes among offspring across a range of ages from early to late adulthood, with a decline in PDD among older adults. This finding implies that the effect of parental alcoholism on PDD may weaken among older adults (aged ≥60 y), making them more resilient than middle-aged and younger adults for PDD. Conversely, we found no evidence of resilience to MDD, as shown by a similar effect across ages. Despite this long-term effect of parental alcoholism, many adults with depression do not seek treatment because of a desire for self-reliance and the perceived stigma of mental health difficulties (16). Children of alcoholics often desire secrecy about their parents’ alcoholism (17), and this additional stigma may further compound the lack of treatment seeking among adult offspring of alcoholics. Our findings highlight the importance of screening for depression among offspring of alcoholics in health care settings to provide them with services and support to ultimately manage this mental health burden.

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Acknowledgments

The National Institute on Alcohol Abuse and Alcoholism (NIAAA) sponsored the National Epidemiologic Surveys on Alcohol and Related Conditions. We acknowledge the contribution of NIAAA funding support and support of the intramural program, NIAAA, National Institutes of Health. The manuscript for this article was prepared using a limited access dataset obtained from NIAAA and does not reflect the opinions or views of NIAAA or the US government.

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Author Information

Corresponding Author: Sunita Thapa, MPH, Department of Health Policy, Vanderbilt University School of Medicine, 2525 West End Ave, Suite 1200, Nashville, TN 37203. Telephone: 615-343-2934. Email: sunita.thapa@vanderbilt.edu.

Author Affiliations: 1Department of Health Policy, Vanderbilt University School of Medicine, Nashville, Tennessee. 2Master of Public Health Program, Department of Population Health, University of North Dakota, Grand Forks, North Dakota.

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References

  1. Christensen HB, Bilenberg N. Behavioural and emotional problems in children of alcoholic mothers and fathers. Eur Child Adolesc Psychiatry 2000;9(3):219–26. CrossRef PubMed
  2. Kelley ML, Braitman A, Henson JM, Schroeder V, Ladage J, Gumienny L. Relationships among depressive mood symptoms and parent and peer relations in collegiate children of alcoholics. Am J Orthopsychiatry 2010;80(2):204–12. CrossRef PubMed
  3. Chassin L, Pitts SC, DeLucia C, Todd M. A longitudinal study of children of alcoholics: predicting young adult substance use disorders, anxiety, and depression. J Abnorm Psychol 1999;108(1):106–19. CrossRef PubMed
  4. Kelley ML, Pearson MR, Trinh S, Klostermann K, Krakowski K. Maternal and paternal alcoholism and depressive mood in college students: parental relationships as mediators of ACOA-depressive mood link. Addict Behav 2011;36(7):700–6. CrossRef PubMed
  5. Jennison KM, Johnson KA. Parental alcoholism as a risk factor for DSM-IV-defined alcohol abuse and dependence in American women: the protective benefits of dyadic cohesion in marital communication. Am J Drug Alcohol Abuse 2001;27(2):349–74. CrossRef PubMed
  6. American Psychiatric Association. Diagnostic and statistical manual of mental disorders, fifth edition (DSM-5). Arlington (VA): American Psychiatric Association; 2013.
  7. US Census Bureau. Current population survey (CPS) annual social and economic (ASEC) supplement table HINC-06: income distribution to $250,000 or more for households 2014; 2015.
  8. Tan X, Shiyko MP, Li R, Li Y, Dierker L. A time-varying effect model for intensive longitudinal data. Psychol Methods 2012;17(1):61–77. CrossRef PubMed
  9. Li R, Dziak JD, Tan X, Huang L, Wagner AT, Yang J. TVEM (time-varying effect modeling) SAS macro users’ guide (version 3.1.0). University Park (PA): The Methodology Center, Penn State; updated 2015.
  10. Schoenborn CA. Exposure to alcoholism in the family: United States, 1988. Adv Data 1991;(205):1–13. PubMed
  11. Substance Abuse and Mental Health Services Administration. Data spotlight: more than 7 million children live with a parent with alcohol problems; 2012.
  12. Klostermann K, Chen R, Kelley ML, Schroeder VM, Braitman AL, Mignone T. Coping behavior and depressive symptoms in adult children of alcoholics. Subst Use Misuse 2011;46(9):1162–8. CrossRef PubMed
  13. Sher KJ. Psychological characteristics of children of alcoholics: overview of research methods and findings. In: M. Galanter, H. Begleiter, R. Deitrich, et al, editors. New York (NY): Plenum Press; 1991. p. 301–36.
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  16. Jennings KS, Cheung JH, Britt TW, Goguen KN, Jeffirs SM, Peasley AL, et al. How are perceived stigma, self-stigma, and self-reliance related to treatment-seeking? A three-path model. Psychiatr Rehabil J 2015;38(2):109–16. CrossRef PubMed
  17. Haverfield MC, Theiss JA. A theme analysis of experiences reported by adult children of alcoholics in online support forums. J Fam Stud 2014;20(2):166–84. CrossRef

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Table

Return to your place in the textTable. Descriptive Statistics of Sample (N = 36,057), Study on Effects of Parental Alcoholism on Depression, National Epidemiological Survey on Alcohol and Related Conditions, Wave III, 2012–2013
Measure Parental Alcoholisma
Yes No
Major depressive disorderb 29.6 17.7
Persistent depressive disorderb 9.3 4.4
Median (IQR), age, yc 44.0 (32–56) 44.0 (30–59)
Sexb
Female 59.4 55.4
Male 40.6 44.6
Education
15.7 14.8
High school diploma 22.4 22.7
Some college or more 61.9 62.4
Median (IQR) annual household income, $c 32,500 (17,500–65,000) 37,500 (17,500–65,000)
Full-time employment (≥35 h/wk) 43.2 44.2
Marital status
Currently married 46.6 46.2
Not currently marriedb 27.6 25.4
Never marriedb 25.8 28.4
Race/ethnicity
Whiteb 57.8 51.4
Blackb 18.2 22.3
American Indianb 2.1 1.2
Asianb 1.9 5.9
Hispanic 19.9 19.2

Abbreviation: IQR, interquartile range.
a Numeric variables presented as median (IQR), and categorical variables presented as percentages.
b χ2 significant in parental alcoholism status at P < .05. MDD is characterized by discrete episodes of at least 2 weeks during which respondents experienced either depressed mood or a loss of interest in nearly all activities in adults at some time in their lives. Lifetime PDD is a milder but more chronic form of depression and can be diagnosed when the mood disturbance continues for at least 2 years in adults at some time in their lives (6).
c Analysis of variance significant in parental alcoholism status at P < .05.

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Post-Test Information

To obtain credit, you should first read the journal article. After reading the article, you should be able to answer the following, related, multiple-choice questions. To complete the questions (with a minimum 75% passing score) and earn continuing medical education (CME) credit, please go to http://www.medscape.org/journal/pcd. Credit cannot be obtained for tests completed on paper, although you may use the worksheet below to keep a record of your answers.

You must be a registered user on http://www.medscape.org. If you are not registered on http://www.medscape.org, please click on the “Register” link on the right hand side of the website.

Only one answer is correct for each question. Once you successfully answer all post-test questions, you will be able to view and/or print your certificate. For questions regarding this activity, contact the accredited provider, CME@medscape.net. For technical assistance, contact CME@medscape.net. American Medical Association’s Physician’s Recognition Award (AMA PRA) credits are accepted in the US as evidence of participation in CME activities. For further information on this award, please go to https://www.ama-assn.org. The AMA has determined that physicians not licensed in the US who participate in this CME activity are eligible for AMA PRA Category 1 Credits™. Through agreements that the AMA has made with agencies in some countries, AMA PRA credit may be acceptable as evidence of participation in CME activities. If you are not licensed in the US, please complete the questions online, print the AMA PRA CME credit certificate, and present it to your national medical association for review.

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Post-Test Questions

Study Title: Time-Varying Effects of Parental Alcoholism on Depression

CME Questions

  1. Your patient is a 37-year-old man with a history of maternal alcoholism. On the basis of the national database study using the National Epidemiological Survey on Alcohol and Related Conditions, wave III, by Thapa and colleagues, which one of the following statements about risk for lifetime major depressive disorder (MDD) among children of alcoholic parents is correct?

    1. Parental alcoholism was associated with nearly twice the risk for MDD (OR, 1.98; 95% CI, 1.85-2.11; P < .001)

    2. The association between parental alcoholism and MDD declined with increasing age above 50 years

    3. Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5), criteria for MDD were met by 15% of those who reported parental alcoholism and by 6% of those who did not

    4. Age at first episode of MDD was not significantly different between those who reported parental alcoholism and those who did not

  2. According to the national database study using the National Epidemiological Survey on Alcohol and Related Conditions, wave III, by Thapa and colleagues, which one of the following statements about risk for lifetime persistent depressive disorder (PDD) among children of alcoholic parents is correct?

    1. Parental alcoholism was not associated with a significantly higher risk for PDD

    2. The association with PDD significantly declined among older adults, with those older than 73 years no longer at increased risk for PDD

    3. DSM-5 criteria for PDD were met by 12% of those who reported parental alcoholism and by 3% of those who did not

    4. Age at first episode of PDD was not significantly different between those who reported parental alcoholism and those who did not

  3. According to the national database study using the National Epidemiological Survey on Alcohol and Related Conditions, wave III, by Thapa and colleagues, which one of the following statements about clinical implications regarding risk for lifetime depression among children of alcoholic parents would be correct?

    1. The study proves that parental alcoholism causes depression among offspring

    2. Given the low prevalence of parental alcoholism, it is unlikely to cause a significant public health burden in the offspring

    3. Adult offspring of alcoholics are more likely to seek treatment for depression than those without this family history

    4. The findings support the need to screen adults with parental alcoholism for depression to provide them with services and support to manage this mental health burden

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Prevalence of and Trends in Diabetes Among Veterans, United States, 2005–2014

Ying Liu, PhD1; Sonica Sayam, MPH1; Xiaonan Shao, MS2; Kesheng Wang, PhD1; Shimin Zheng, PhD1; Ying Li, PhD3; Liang Wang, DrPH1 (View author affiliations)

Suggested citation for this article: Liu Y, Sayam S, Shao X, Wang K, Zheng S, Li Y, et al. Prevalence of and Trends in Diabetes Among Veterans, United States, 2005–2014. Prev Chronic Dis 2017;14:170230. DOI: http://dx.doi.org/10.5888/pcd14.170230.

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Abstract

Diabetes is a highly prevalent chronic disease among US adults, and its prevalence among US veterans is even higher. This study aimed to examine the prevalence of and trends in diabetes in US veterans by using data from the US National Health and Nutrition Examination Survey from 2005 through 2014. The overall prevalence of diabetes and undiagnosed diabetes was 20.5% and 3.4%, respectively, and increased from 15.5% in 2005–2006 to 20.5% in 2013–2014 (P = .04). Effective prevention and intervention approaches are needed to lower diabetes prevalence among US veterans and ultimately improve their health status.

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Objective

Diabetes was the seventh leading cause of death in the United States in 2013 (1). Approximately 30.3 million Americans had diabetes, including an estimated 7.2 million who had the disease but had not received a diagnosis (2). Diabetes is associated with multiple chronic conditions, including cardiovascular diseases, stroke, and disorders leading to amputation. The estimated annual cost of diabetes on the US health care system overall is $245 billion (2,3).

Diabetes is more prevalent among US veterans, who make up 9% of the civilian US population, than among the general population and affects nearly 25% of US Department of Veterans Affairs (VA) patients (4,5). The objective of this study was to assess the prevalence of diabetes among US veterans by using data from the National Health and Nutrition Examination Survey (NHANES).

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Methods

We used data from 5 NHANES cycles conducted from 2005 through 2014. NHANES uses a stratified multistage probability sampling approach to obtain representative samples from 50 states and the District of Columbia. Veteran status was self-identified through participant household interviews. The unweighted sample size for 2013–2014 was 491 and ranged from 472 to 685 for each cycle from 2005 through 2012.

Diabetes in a participant was defined as having at least 1 of 4 conditions: 1) a glycated hemoglobin A1c of 6.5% or higher, 2) fasting plasma glucose of 126 mg/dL or higher, 3) a 2-hour plasma glucose of 200 mg/dL or higher, or 4) a diagnosis of diabetes by a physician or other health care provider (2). We defined people without diabetes as those who had none of these conditions. A person with a body mass index (BMI; defined as the weight in kilograms divided by the square of the height in meters) of 30 or higher was classified as obese (2).

Demographic variables were age, sex, and race/ethnicity (white, African American, Hispanic, and other race). Age was sorted into 3 groups: 20 to 44 years, 45 to 64 years, and 65 years or older. Socioeconomic status included poverty level and education level. Poverty level is a ratio of annual income to the federal poverty level (FPL) adjusted for the number of people in the household and where they lived. We categorized poverty level into 3 groups: less than 100% of FPL, 100% to less than 300% of FPL, and 300% or more of FPL. Education was categorized by years of education: less than 12 years, 12 years, and more than 12 years.

Rao-Scott χ2 test measured the bivariate association of diabetes and each exploratory variable. We used the Cochran-Armitage trend test to assess prevalence of diabetes time-trends from 2005 through 2014, and the proportional test was used to compare prevalence differences. Significance was set at P less than .05. All analyses were performed with SAS version 9.4 (SAS Institute, Inc).

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Results

The overall pooled weighted prevalence of diabetes in NHANES for 2013–2014 was 20.5% (95% confidence interval [CI], 15.9–25.2%), and the prevalence of undiagnosed diabetes was 3.4% (95% CI, 1.1%–5.6%), (Table 1). Diabetes was most prevalent among veterans aged 65 years or older (27%), among male veterans (22%), among veterans with less than 12 years of education (33.5%) and among veterans with an annual income below the 100% FPL (23.8%). The highest prevalence of obesity was among veterans aged 45 to 64 years (53.1%), male veterans (41.1%), veterans with less than 12 years of education (51.4%), and veterans living below the 100% FPL (47.2%). Poverty level (P = .005) and education (P = .03) were significantly associated with the odds of diabetes. The highest prevalence of diabetes (25.7%) and obesity (43.5%) was observed among Hispanic veterans.

The overall prevalence trend of diabetes increased from 15.5% in 2005–2006 to 20.5% in 2013–2014 (P = .04 for trend test) and peaked in 2009–2010 (22.6%), (Table 2). The prevalence increased significantly among male veterans, from 16.5% in 2005–2006 to 22.0% in 2013–2014 (P = .04 for trend test). The prevalence of diabetes among veterans who had less than 12 years of education increased from 21.9% in 2005–2006 to 33.5% in 2013–2014 (P = .04 for trend test). Among veterans with more than 12 years of education, the prevalence increased from 12.3% in 2005–2006 to 19.9% in 2013–2014 (P = .03 for trend test). The increase in age-standardized prevalence was not significant for any of the 3 age groups.

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Discussion

Diabetes is more prevalent among US veterans than among the general population (3,5). This high prevalence is primarily attributable to the high prevalence of obesity among this population (5). Obesity and diabetes are genetically linked (6,7). People with obesity are more prone to the major contributors to type 2 diabetes — insulin resistance and β cell dysfunctions (8,9).

Diabetes is preventable for many people and treatable, but people with undiagnosed diabetes have delays in receiving effective treatment and, thus, are more likely to develop complications such as coronary artery diseases and retinal microvascular diseases than people with diabetes who receive treatment (10). However, in the United States the proportion of people with diabetes who had diabetes diagnosed decreased from 72.5% in 2005–2008 to 67.7% in 2009–2012 (7).

Overall prevalence of diabetes in the NHANES participants increased from 15.5% in 2005–2006 to 20.0% in 2007–2008 and remained stable at around 20.0% in NHANES surveys from 2009 through 2014. This change is partially because fewer people aged 65 years or older were included in the 2005–2006 survey (37.8%) than that in the 2007–2014 surveys (>40%). Unlike previously reported findings (3), our findings showed that the prevalence of diabetes by poverty level did not decline with increasing income, and this trend persisted over time. This persistence may be due to a small number diabetes cases and some unidentified confounders. The underlying reasons for this trend merit further investigation.

Our study had limitations. Statistical tests cannot be applied to data for female veterans because NHANES reported few or no female veterans with the disorder. In addition, NHANES data do not distinguish between type 1 and type 2 diabetes or gestational diabetes in the NHANES participants.

Certain factors limit the accuracy of the estimated prevalence of diabetes in US veterans when using VA databases. First, based on the eligibility criteria and capability of VA facilities, in fiscal year 2014 less than 30% of the total veteran population sought VA health care, and more than 70% of veterans sought care outside the VA system even though some of them were enrolled in the VA system (10). NHANES uses a complex, multistage, probability sampling design, which allows selected participants to represent noninstitutionalized US civilians. Consequently, our study sample represents US noninstitutionalized veterans. Therefore, NHANES is an appropriate resource to provide complementary results for VA data, and our findings can be used to determine the prevalence of diseases in the veteran population. Future research should combine nationwide data with VA data to obtain estimates that are more accurate. In spite of these limitations, the high prevalence of diabetes reported here calls for cost-effective strategies for prevention and intervention for US veterans.

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Acknowledgments

The authors thank the Centers for Disease Control and Prevention, National Center for Health Statistics, for providing the NHANES 2005–2014 data.

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Author Information

Corresponding Author: Ying Liu, PhD, Department of Biostatistics and Epidemiology, East Tennessee State University, PO Box 70259, Johnson City, TN 37614. Telephone: 423-439-6662. Email: liuy09@etsu.edu.

Author Affiliations: 1Department of Biostatistics and Epidemiology, East Tennessee State University, Johnson City, Tennessee. 2Carnegie Mellon University, Pittsburgh, Pennsylvania. 3Department of Environmental Health, East Tennessee State University, Johnson City, Tennessee.

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References

  1. Heron M. Deaths: leading causes for 2013. Natl Vital Stat Rep 2016;65(2):1–95. PubMed
  2. Centers for Disease Control and Prevention. National Diabetes Statistics Report, 2017: estimate of diabetes and its burden in the United States. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf. Accessed October 30, 2017.
  3. Menke A, Casagrande S, Geiss L, Cowie CC. Prevalence of and trends in diabetes among adults in the United States, 1988–2012. JAMA 2015;314(10):1021–9. CrossRef PubMed
  4. US Census Bureau. Veterans Day. https://www.census.gov/topics/population/veterans/about/veterans-day.html. Accessed October 30, 2017.
  5. US Department of Veterans Affairs. Veterans Health Administration: close to 25 percent of VA patients have diabetes. https://www.va.gov/health/NewsFeatures/20111115a.asp. Updated April 17, 2015. Accessed February 23, 2017.
  6. Eckel RH, Kahn SE, Ferrannini E, Goldfine AB, Nathan DM, Schwartz MW, et al. Obesity and type 2 diabetes: what can be unified and what needs to be individualized? J Clin Endocrinol Metab 2011;96(6):1654–63. CrossRef PubMed
  7. US Department of Health and Human Services, Office of Disease Prevention and Health Promotion. Healthy People 2020. https://www.healthypeople.gov/2020/data-search/Search-the-Data#objid=4112. Accessed March 26, 2017.
  8. Ray I, Mahata SK, De RK. Obesity: an immunometabolic perspective. Front Endocrinol (Lausanne) 2016;7:157. CrossRef PubMed
  9. Alarcon C, Boland BB, Uchizono Y, Moore PC, Peterson B, Rajan S, et al. Pancreatic β–cell adaptive plasticity in obesity increases insulin production but adversely affects secretory function. Diabetes 2016;65(2):438–50. CrossRef PubMed
  10. Erin Bagalman; US Congressional Research Service. The number of veterans that use VA health care services: a fact sheet. June 2014. R43579. https://fas.org/sgp/crs/misc/R43579.pdf. Accessed March 10, 2017.

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Tables

Return to your place in the textTable 1. Weighted Prevalence of Diabetes and Obesity in US Veterans Aged 20 Years or Older, National Health and Nutrition Examination Survey, 2013–2014
Variable (na) Diabetesb, % (95% CI) P Valuec Undiagnosed Diabetesb, % (95% CI) P Valuec Obesityd, % (95% CI) P Valuec
Overall (491) 20.5 (15.9–25.2) NA 3.4 (1.1–5.6) NA 40.7 (35.0–46.4) NA
Age, y
22–44 (78) 4.8 (0.0–10.4) <.001 NAe <.001 31.0 (18.6–43.3) <.001
45–64 (156) 20.8 (12.5–29.1) 4.5 (0.0–9.5) 53.1 (42.5–63.7)
≥65 (257) 27.0 (19.7–34.3) 4.0 (0.8–7.3) 36.3 (28.4–44.2)
Sex
Male (459) 22.0 (17.0–27.0) <.001 3.7 (1.2–6.1) .01 41.1 (35.2–47.1) .68
Female (32) 3.9 (0.0–8.8) NAe 35.8 (13.4–58.3)
Race
White (294) 20.3 (14.6–25.9) .79 3.7 (0.9–6.5) .34 40.7 (33.8–47.7) .07
Black (125) 22.8 (14.8–30.7) 2.1 (0.0–4.5) 40.6 (30.9–50.3)
Hispanic (47) 25.7 (12.8–38.6) 3.1 (0.0–7.9) 43.5 (26.8–60.2)
Other (25) 11.6 (0.0–23.5) 1.2 (0.0–3.7) 35.4 (9.1–61.6)
Federal poverty level
<100% (62) 23.8 (7.7–39.8) .005 2.1 (0.0–4.6) .30 47.2 (30.1–64.4) .08
100% to <300% (216) 21.3 (14.1–28.4) 3.06 (0.0–6.5) 42.8 (33.9–51.6)
≥300% (213) 19.4 (12.9–26.0) 3.8 (1.8–7.3) 37.9 (29.7–46.2)
Education, y
<12 (45) 33.5 (11.5–55.6) .03 2.0 (0.0–4.8) .25 51.4 (29.9–72.6) .70
12 (117) 18.5 (9.9–27.1) 2.7 (0.0–5.8) 42.2 (29.5–54.9)
>12 (329) 19.9 (14.4–25.4) 3.68 (0.7–6.7) 39.2 (32.5–45.9)

Abbreviations: CI, confidence interval; NA, not applicable.
a Unweighted total number of each category.
b Weighted prevalence of total diabetes cases. The presence of diabetes is defined as any participant who had at least 1 of 4 conditions: 1) a glycated hemoglobin A1c of 6.5% or higher, 2) fasting plasma glucose of 126 mg/dL or higher, 3) a 2-h plasma glucose of 200 mg/dL or higher, or 4) a diagnosis of diabetes by a physician or other health care provider.
cP values calculated by using Rao–Scott χ2 test for bivariate association.
d Weighted prevalence of obesity (body mass index, defined as weight in kilograms divided by height in m2) ≥30.
e Zero cases in the data set.

Return to your place in the textTable 2. Weighted Prevalence of Diabetesa in US Veterans Aged 20 Years or Older, National Health and Nutrition Examination Survey, 2005–2014
Variable 2005–2006 (n = 622) 2007–2008 (n = 670) 2009–2010 (n = 685) 2011–2012 (n = 472) 2013–2014 (n = 491) P Value for Trendb
No. with diabetesc 121 157 183 124 111 NA
Overall prevalence 15.5 (12.4–18.7) 20.0 (16.3–23.7) 22.6 (19.0–26.3) 20.8 (16.0–25.7) 20.5 (15.9–25.2) .04
Age, y
22–44 5.2 (0.4–9.7) 4.6 (0.0–10.3) 4.2 (0.3–8.2) 9.3 (0.0–19.3) 4.8 (0.0–10.4) .50
45–64 10.7 (6.2–15.1) 17.8 (11.8–23.8) 21.1 (14.5–27.7) 14.1 (7.2–20.9) 20.8 (12.5–29.1) .06
≥65 26.5 (20.7–32.3) 30.9 (24.9–36.9) 34.3 (28.5–40.1) 34.0 (25.8–42.2) 27.0 (19.7–34.3) .10
Sex
Male 16.5 (13.2–19.9) 21.5 (17.5–25.4) 24.1 (20.2–27.9) 22.1 (16.9–27.3) 22.0 (17.0–27.0) .04
Female 1.6 (0.0–5.0) 6.3 (0.0–14.7) 6.3 (0.0–12.9) 7.0 (0.0–16.0) 3.9 (0.0–8.8) .14
Race/ethnicity
White 15.0 (11.4–18.6) 20.9 (16.5–25.2) 22.8 (18.5–27.2) 19.0 (13.4–24.5) 20.2 (14.6–25.9) .16
Black 20.4 (13.4–27.5) 18.8 (12.1–25.5) 19.8 (13.3–26.2) 26.4 (18.6–34.1) 22.8 (14.8–30.7) .25
Hispanic 25.3 (7.3–43.2) 12.34 (5.1–19.6) 28.26 (5.3–38.7) 15.5 (5.7–25.2) 25.7 (12.8–38.6) .16
Other 2.0 (0.0–6.6) 10.5 (0.0–28.0) 19.1 (0.0–39.2) 39.3 (6.5–72.2) 11.6 (0.0–23.5) .11
Poverty level
<100% 14.7 (5.3–24.1) 23.7 (12.3–35.1) 12.6 (3.4–31.8) 21.2 (9.8–32.6) 23.8 (7.7–39.8) .27
100% to <300% 21.2 (15.6–36.8) 19.1 (14.1–24.1) 29.0 (22.6–35.4) 27.0 (18.2–35.7) 21.3 (14.1–28.4) .22
≥300% 11.7 (7.6–15.7) 20.3 (14.6–26.0) 19.9 (15.3–24.6) 16.65 (10.3–23.0) 19.4 (12.9–26.0) .06
Education, y
<12 21.9 (12.9–30.8) 18.1 (11.1–25.0) 23.2 (13.9–32.5) 32.1 (17.4–46.9) 33.5 (11.5–55.6) .04
12 19.3 (12.5–26.1) 28.1 (19.9–36.3 25.6 (17.8–33.5 16.0 (6.9–25.1) 18.5 (9.9–27.1) .43
>12 12.3 (8.5–16.1) 17.1 (12.3–21.8) 21.31 (16.7–25.9) 20.51 (14.4–26.7) 19.9 (14.4–25.4) .03

Abbreviation: NA, not applicable.
a The presence of diabetes is defined as any participant who had at least 1 of 4 conditions: 1) a glycated hemoglobin A1c of 6.5% or higher, 2) fasting plasma glucose of 126 mg/dL or higher, 3) a 2-h plasma glucose of 200 mg/dL or higher, or 4) a diagnosis of diabetes by a physician or other health care provider.
bP values calculated by using Cochran–Armitage trend test.
c Unweighted number of cases of diabetes.

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What Are the Barriers and Opportunities?

Daniel M. Finkelstein, PhD1; Dana M. Petersen, PhD2; Lisa S. Schottenfeld, MPH, MSW2 (View author affiliations)

Suggested citation for this article: Finkelstein DM, Petersen DM, Schottenfeld LS. Promoting Children’s Physical Activity in Low-Income Communities in Colorado: What Are the Barriers and Opportunities? Prev Chronic Dis 2017;14:170111. DOI: http://dx.doi.org/10.5888/pcd14.170111.

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Abstract

Introduction

Colorado has the highest rate of adult physical activity in the United States. However, children in Colorado have a lower rate of physical activity relative to other states, and the rate is lowest among children in low-income households. We conducted focus groups, surveys, and interviews with parents, youth, and stakeholders to understand barriers to physical activity among children in low-income households in Colorado and to identify opportunities to increase physical activity.

Methods

From April to July 2016, we recruited participants from 5 communities in Colorado with high rates of poverty, inactivity, and obesity; conducted 20 focus groups with 128 parents and 42 youth; and interviewed 8 stakeholders. All focus group participants completed intake surveys. We analyzed focus group and interviews by using constant comparison.

Results

We identified 12 themes that reflect barriers to children’s physical activity. Within the family context, barriers included parents’ work schedules, lack of interest, and competing commitments. At the community level, barriers included affordability, traffic safety, illicit activity in public spaces, access to high-quality facilities, transportation, neighborhood inequities, program availability, lack of information, and low community engagement. Survey respondents most commonly cited lack of affordable options and traffic safety as barriers. Study participants also identified recommendations for addressing these barriers. Providing subsidized transportation, improving parks and recreation centers, and making better use of existing facilities were all proposed as opportunities to improve children’s physical activity levels.

Conclusion

In this formative study of Colorado families, participants confirmed barriers to physical activity that previous research on low-income communities has documented, and these varied by geographic location. Participants proposed a set of solutions for addressing barriers and endorsed community input as an essential first step for planning community-level health initiatives.

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Introduction

Regular physical activity has important benefits for children’s health and well-being (1). Although Colorado has the highest rate of adult physical activity in the United States (2), children rank lower on physical activity, and this rate is lowest among children in low-income households (3). A foundation-funded effort is under way to increase physical activity among children in low-income families. Although barriers to physical activity in low-income communities are well-documented (4–7), the foundation recognizes Colorado’s unique demographics and geography and the need to invest in strategies that align with community priorities and characteristics. The foundation funded this formative study to understand families’ views about barriers to children’s physical activity in a sample of low-income Colorado communities — and their solutions for addressing these barriers — as an initial step for planning future investments. This type of community-informed approach is essential for identifying strategies that are aligned with a community’s needs, characteristics, and resources (8), and is considered a best practice for planning initiatives to improve community health (9).

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Methods

We conducted a primarily qualitative study that used 3 data sources: 1) focus groups with parents and youth, 2) interviews with community stakeholders, and 3) an intake survey of focus group participants.

We recruited study participants from 5 communities in Colorado with high rates of low-income households, physical inactivity, and child obesity. The sites we selected reflected the 4 geographic regions within the state: 2 communities were located in the same large city in the Urban Corridor, a small city was located in the Western Slope; and 2 small towns were located in each of the Mountain Range and Eastern Plains regions. Eligible parents were those who cared for a child aged 3 to 14 years, had an annual household income less than or equal to 200% of the federal poverty level, and spoke primarily English (or Spanish, in one Urban Corridor community). Youth were eligible if they were cared for by participating parents. A market research firm recruited families by calling residents whose names were in its proprietary database and advertising through social media and local organizations. For stakeholder interviews, we asked practitioners in the field to identify individuals involved with children’s physical activity in the study communities, which included staff members at recreation and health departments and nonprofit organizations.

In each community, we conducted 3 focus groups with parents (1 each with parents of children aged 3 to 7, 8 to 11, and 12 to 14); 1 focus group with 12- to 14-year-old youth from these families; and interviews with stakeholders. Researchers used semistructured interview guides to elicit discussion about barriers to and facilitators of physical activity and ways to increase children’s physical activity. The intake survey assessed participation in physical activities and perceived barriers to participation (6,10).

In total, we conducted 20 focus groups with 128 parents and 42 youth and interviews with 8 stakeholders from April to July 2016. Parents received $100 for participating and an additional $50 if their child participated. Participating youth and stakeholders received $50. We audio recorded and transcribed all focus groups and interviews. The Health Media Laboratory institutional review board approved all research protocols.

The 3 coauthors used thematic analysis to inductively analyze the qualitative data from focus groups and interviews. Applying the constant comparison method, we compared participants’ quotes and categorized them on the basis of their meaning (11). Next, we summarized findings for each group and community, reconciled discrepancies in meaning through discussion and systematic review, and merged findings across groups and communities under themes. We used SAS software version 9.4 (SAS Institute, Inc) to generate descriptive statistics for the survey data.

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Results

Most focus group parents were women (77%) (Table 1), half of families were white (54%), and a third of families were Hispanic (31%). Approximately 40% of the youth focus group participants were girls.

Parents had positive views toward physical activity; 81% reported that it is important that their child exercises regularly (data not shown). During focus groups, parents described their understanding of the value of regular physical activity, citing benefits to children’s physical health (maintaining a healthy weight and developing healthy habits), psychological well-being (improved mood and behavior), and social development (connecting with peers and developing social skills).

Focus groups and interviews

We grouped themes according to family and community contexts, a process that is consistent with ecological models of health behavior (12). Although themes cut across levels, we identified 3 related primarily to the family context and 9 related primarily to the community context. We summarize themes, provide illustrative quotes, and present solutions identified from the focus groups in Table 2. The themes generally cut across age groups and communities; we note instances in which we observed variation across subgroups.

Family context

Parents’ work schedule

Several parents indicated that their work schedules make it challenging to enroll children in organized activities or to be active with children at home. Many activities take place after school, which is not feasible for parents whose work schedule precludes them from transporting children to activities in the afternoon. Parents who work at night or on the weekends, which was common among parents in a rural Mountain Range community, have less time to be physically active with their children. Parents’ and stakeholders’ solutions for addressing scheduling constraints included offering more activities in the evening or during the weekend, offering transportation from school to recreational facilities, partnering with schools to expand offerings in school facilities during out-of-school time, and developing activities that serve children of multiple ages.

Child’s lack of interest or apprehension

Parents cited children’s lack of interest in available activities, preoccupation with electronic devices, and fear of being bullied as reasons they do not participate in physical activities. As solutions, parents, youth, and stakeholders recommended increasing activities that focus on fun and fitness, such as dance classes, or sports teams that emphasize social–emotional aspects rather than competition.

Youth’s school and family commitments

Youth described avoiding activities because they conflict with the time when they complete homework. Others said family obligations, such as caring for siblings or chores, limit their participation. To address these conflicts, parents suggested offering activities during non–after-school times that enable children to participate in academic work and physical activity.

Community context

Lack of affordable options

Parents in all communities and across age groups cited the high costs of enrolling in activities, purchasing equipment, and membership fees as barriers to children’s participation in physical activity. In the Urban Corridor and Mountain Range communities, parents reported that this barrier was exacerbated by the high cost of living in their communities. Some communities offer financial assistance to families, but parents and community stakeholders described the application as a complicated process that required families to divulge private information. As potential solutions, parents and stakeholders suggested that communities make these activities more affordable, for example, by offering low-cost or free activities sponsored by towns or community organizations, creating equipment exchanges, and offering financial aid that is accessible and noninvasive.

Traffic safety

Parents commonly cited safety hazards related to cars and traffic — particularly in the Western Slope and Urban Corridor communities — as barriers to their allowing children to play or travel outside. In particular, parents identified the absence or poor condition of sidewalks and crosswalks as a barrier to playing outside and biking and walking to parks. For example, one parent said, “The streets aren’t very safe. Cars drive by too fast. . . . Every day as soon as he comes home, [my son would like] to go out on his bike but it’s not very safe.” Parents suggested that sidewalk improvements, crosswalks, traffic-calming measures, and crossing guards near schools would ease traffic-related concerns.

Exposure to unsafe or illicit activity in public spaces

Concerns about neighborhood safety prevent parents from allowing children to play in parks and playgrounds, even with a supervising adult. Communities in all 4 regions reported this concern. Parents and youth expressed concerns about adults or older teens they perceived as threatening, peer violence, unleashed dogs, and poorly maintained public spaces and equipment. In the Urban Corridor community, parents expressed concern with drug use near parks, especially now that recreational use of marijuana is legal in Colorado. One stakeholder corroborated this concern, saying that in his community “parks have become the place where negative activity happens.”

Parents’ ideas for improving safety in outdoor spaces included organizing neighborhood watches to promote trust among neighbors and limiting the proximity of marijuana and liquor stores to parks. Parents had mixed opinions about the value of increasing police presence in their neighborhoods. Parents and stakeholders suggested making sure that public spaces are free of garbage, graffiti, and drug paraphernalia; improving lighting; and providing clean and safe restrooms. Stakeholders also proposed stationing child care professionals at parks to supervise and facilitate play.

Limited access to high-quality facilities

Across all communities, parents mentioned a lack of indoor recreational facilities as a barrier. Such facilities provide space for activity during colder months, host organized activities, and serve as a hub for families to socialize. Some parents in the Urban Corridor communities are reluctant to use local recreation centers, because they are poorly maintained or perceived to be unsafe because of surrounding neighborhoods. One mother described how illicit activity in the neighborhood, such as drug dealing or peer violence, spreads into the recreation center, commenting, “I don’t want to put my girls in that situation.” Parents in these communities also had concerns that facility staff do not have sufficient skills for working with children.

Parents in all communities expressed a need for more high-quality indoor facilities and improvements to existing facilities, including maintenance and modernization. They also recommended that centers hire better-qualified staff or improve training and supervision.

Transportation

Parents reported the need to travel to access physical activity programs or high-quality facilities; this was a barrier reported in all communities. Transportation-related challenges included time spent driving or riding public transportation and fuel and bus pass costs. Parents in the Urban Corridor communities said they travel to other sections of the city or suburban communities, whereas those in the Mountain Range and Eastern Plains rural communities travel longer distances to adjacent towns or states.

Parents recommended offering safe and subsidized options, such as school district–sponsored buses, for transporting children to activities after school. Community stakeholders and parents said increasing the frequency and number of bus routes and offering lower-cost transportation would improve children’s access to physical activity opportunities.

Neighborhood inequities

Parents and community stakeholders in the 2 Urban Corridor communities noted inequities in the quality of recreation centers and outdoor spaces in their communities relative to other neighborhoods. One parent said, “All the parks that are being built are in areas where the people have a higher income; I don’t know why this is, but that’s how it is.” Parents said their facilities were poorer-quality, and they had travel to other parts of the city to access higher-quality parks and playgrounds. One parent said that, as people of color, his family feels unwelcome in recreation centers in higher-income neighborhoods. Proposed solutions centered on improving parks and recreation centers so that children in these neighborhoods had the same opportunities as children in higher-income neighborhoods.

Limited program availability

Parents reported that there are limited program options during specific times of year (winter and summer) and for certain populations (preschool-age children). Registration often occurs during the workday, and programs fill up quickly. Parents in the Urban Corridor communities focused on the lack of summer offerings. To address the need for summer programming, one stakeholder highlighted a partnership with the local library that orients youth to new sports and offers supplies. To increase activities during the colder months, parents and stakeholders in the Western Slope community recommended using existing facilities such as schools or churches for indoor play spaces. For preschool-aged youth, parents recommended designing facilities for young children or offering dedicated preschool hours in existing facilities.

Lack of information

Parents reported challenges in finding complete information about opportunities for children’s physical activity. During the focus groups, parents said that there is no central repository for learning about children’s activities and that they have to rely on word of mouth. Parents suggested maintaining up-to-date electronic resources with listings of physical activity programs and publicizing opportunities in local newspapers and guides. Parents also recommended improving families’ abilities to communicate with one another through social media (eg, Facebook groups, email Listservs).

Limited engagement with community

Both parents and stakeholders across all communities indicated that program planners often design activities without input from parents and that this leads to underutilization of activities or facilities. One mother in the Urban Corridor community said she wished “that Parks and Recreation would take more into consideration the needs of the community, because many times . . . they don’t.” Community stakeholders indicated they had success when community leaders held meetings or “listening campaigns” with parents, youth, and other users of planned programs or facilities. Parents and stakeholders alike discussed that when this did not happen, investments fell short of their intended goals (eg, families underutilizing community activities).

Parent survey

The parent survey results demonstrate the frequency with which parents believe that 22 prespecified factors were challenges to children’s physical activity (Table 3). The most commonly cited barriers — that 60% or more of parents agreed limit their child’s physical activity — were cost, including enrollment fees and sports equipment; safety, including drivers not looking out for children and driving too fast; and access to indoor facilities near home. The least commonly cited barriers — that 20% or fewer cited as a barrier to physical activity — were access to parks and playgrounds to which children can walk or bike and lack of sidewalks.

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Discussion

This formative study of parents and children provides insight into the challenges low-income families in Colorado face in supporting children’s physical activity and describes their recommendations for addressing these barriers. It confirms several barriers already documented in the literature, such as neighborhood safety, program cost, and access to facilities (4,5). It also identifies less frequently documented factors, such as the difficulty obtaining financial aid and the lack of centralized information. Most of these barriers are at the community level rather than the family level, and nearly all solutions are at the community level. Although our study identified a common set of barriers across the Colorado regions, we also identified barriers that were most salient to families living in specific regions. For example, traffic safety was most frequently cited in the Urban Corridor communities, whereas distance to activities was cited in the rural Eastern Plains and Mountain Range communities.

One key finding is that this sample of parents recognized the importance of physical activity. Many physical activity interventions focus on individual-level factors, such as counseling families about the benefits of physical activity (13), but most parents indicated they believe it is important that their child exercises regularly. Despite this knowledge, their children are not as active as the parents would like, and this may be caused by barriers in their surrounding community, with 9 of the 12 barriers that parents identified being at the community level. This finding suggests that community-level interventions that address the affordability, accessibility, and safety of physical activity options may be more successful than those that target children’s or parents’ knowledge.

A strength of this study is that we collected information on parents’ views on barriers to physical activity through focus group discussions and a parent survey. The survey results reflect the magnitude of the concerns raised during the focus groups. Two prominent challenges raised during the focus groups — cost of activities and traffic safety — were the most frequently cited barriers in the parent survey, with more than 60% of parents endorsing 5 items related to these types of challenges. In contrast, 2 prominent barriers raised during the focus groups — having parks or playgrounds that are accessible by walking or bicycling and having sidewalks — were the least frequently cited barriers in the parent survey. It is unclear why these factors emerged as barriers during the focus groups and were not cited as frequently in the survey, but it is worth noting that the survey items address the proximity and presence of public spaces and sidewalks and not necessarily the quality and maintenance of this infrastructure. Our findings are consistent with those of previous research that suggest that in developing interventions to promote physical activity, addressing both proximity and quality is important (14,15).

This study also has limitations. We collected data from a small sample of families residing in 5 communities, and the groups may not be representative of the barriers faced by other families in Colorado or other states. Nevertheless, this project sampled participants in urban and rural locations, and our study findings mirror other findings of other studies about parents’ concerns with children’s safety because of traffic and financial barriers to participation (4–7).

A basic premise for this formative study of Colorado families is that community engagement and stakeholder input are essential for planning initiatives to improve community health. Parents and stakeholders validated this premise during the focus groups and interviews, emphasizing the importance of ensuring that specific investments are community-driven and describing instances when programs went underused because they did not incorporate community input. Both the methods used and findings of this study underscore the importance of funders and public health planners soliciting input from families and stakeholders when undertaking large-scale programs and initiatives to ensure that these plans meet the needs of their target population.

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Acknowledgments

This project was funded by the Colorado Health Foundation. The authors are especially grateful to the parents, youth, and stakeholders who participated in this study. We thank Kaye Boeke and Kelci Price at the Colorado Health Foundation for their guidance and collaboration and Leslie Foster at Mathematica for her thoughtful comments on the manuscript. We acknowledge the contributions of other Mathematica staff, including Martha Bleeker, who provided technical guidance on the study; Lauren Hula, who led stakeholder interviews; Raquel Af Ursin, Ebo Dawson-Andoh, Lauren Harris, and Galina Lapadatova, who led focus groups; and Molly McGlone, who analyzed survey data.

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Author Information

Corresponding Author: Daniel M. Finkelstein, Mathematica Policy Research, 955 Massachusetts Ave, Suite 801, Cambridge, MA 02139. Telephone: 617-588-6672. Email: dfinkelstein@mathematica-mpr.com.

Author Affiliations: 1Mathematica Policy Research, Cambridge, Massachusetts. 2Mathematica Policy Research, Oakland, California.

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References

  1. 2008 Physical activity guidelines for Americans. Atlanta (GA): US Department of Health and Human Services, Centers for Disease Control and Prevention; 2008.
  2. State indicator report on physical activity, 2014. Atlanta (GA): US Department of Health and Human Services, Centers for Disease Control and Prevention; 2014.
  3. US Department of Health and Human Services, Health Resources and Services Administration, Maternal and Child Health Bureau. The health and well-being of children: a portrait of states and the nation, 2011–2012. Rockville (MD): US Department of Health and Human Services; 2014.
  4. Taylor WC, Lou D. Do all children have places to be active? Disparities in physical activity environments in racial and ethnic minority and lower-income communities. Princeton (NJ): Active Living Research; 2011.
  5. Carver A, Timperio A, Crawford D. Playing it safe: the influence of neighbourhood safety on children’s physical activity. A review. Health Place 2008;14(2):217–27. CrossRef PubMed
  6. Davison KK. School performance, lack of facilities, and safety concerns: barriers to parents’ support of their children’s physical activity. Am J Health Promot 2009;23(5):315–9. CrossRef PubMed
  7. Gordon-Larsen P, Griffiths P, Bentley ME, Ward DS, Kelsey K, Shields K, et al. Barriers to physical activity: qualitative data on caregiver-daughter perceptions and practices. Am J Prev Med 2004;27(3):218–23. CrossRef PubMed
  8. Minkler M, Wallerstein N, editors. Community based participatory research for health. San Francisco (CA): Jossey-Bass; 2003.
  9. Community Health Assessment and Group Evaluation (CHANGE) action guide: building a foundation of knowledge to prioritize community needs. Atlanta (GA): US Department of Health and Human Services, Centers for Disease Control and Prevention; 2010.
  10. Harvard School of Public Health Northeastern University. Play across Boston student survey; 2002. https://cdn1.sph.harvard.edu/wp-content/uploads/sites/84/2012/09/PAB_Student_Survey.pdf. Accessed February 11, 2016.
  11. Glaser BG, Strauss AL. The discovery of grounded theory: strategies for qualitative research. London (UK): Wiedenfeld and Nicholson; 1967. p. 81.
  12. Sallis JF, Owen N, Fisher EB. Ecological models of health behavior. In: Glanz K, Rimer BK, Viswanath K, editors. Health behavior and health education: theory, research, and practice. 4th edition. San Francisco (CA): Jossey-Bass; 2008. p. 465-486.
  13. van Sluijs EMF, McMinn AM, Griffin SJ. Effectiveness of interventions to promote physical activity in children and adolescents: systematic review of controlled trials. BMJ 2007;335(7622):703. CrossRef PubMed
  14. Tappe KA, Glanz K, Sallis JF, Zhou C, Saelens BE. Children’s physical activity and parents’ perception of the neighborhood environment: neighborhood impact on kids study. Int J Behav Nutr Phys Act 2013;10(1):39. CrossRef PubMed
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Tables

Return to your place in the textTable 1. Characteristics of Parents (N = 128) Participating in a Qualitative Study of Challenges and Opportunities for Promoting Children’s Physical Activity, Colorado, 2016a
Characteristic Number of
Families
Percentage of Families
Total 128 100.0
Geographic location
Urban Corridor (English speaking) 25 19.5
Urban Corridor (Spanish speaking) 26 20.3
Mountain Range 15 11.7
Western Slope 29 22.7
Eastern Plains 33 25.8
Age of child, y
3–7 46 35.9
8–11 40 31.3
12–14 42 32.8
Sex of parent or caregiver
Female 98 76.6
Male 22 17.2
Not reported 8 6.3
Race/ethnicityb
White 69 53.9
Hispanic or Latino 40 31.3
Black or African American 10 7.8
Asian 1 0.8
Other 1 0.8
Not reported 8 6.3
Highest level of education
Less than high school 5 3.9
High school graduate 42 32.8
Some college 43 33.6
College graduate 30 23.4
Not reported 8 6.3
Number of children in family
1 59 46.1
2 29 22.7
3 19 14.8
≥4 13 10.2
Not reported 8 6.3

a Eight parents who were screened onsite for eligibility in the focus groups did not provide data to the focus group recruitment staff, so their information, except for age of child, was not reported.
b We asked parents, “What is your ethnic background?” Parents could respond with more than one race or ethnicity with which they identified.

Return to your place in the textTable 2. Barrier Themes, Illustrative Quotations, and Solutions Identified From Focus Groups With Parents, Youth, and Stakeholders, Qualitative Study of Challenges and Opportunities for Promoting Children’s Physical Activity, Colorado, 2016
Barrier Themes/Family Context Illustrative Quotations Solutions
Parents’ work schedule “I work full time, and me and my husband have one vehicle and we have 5 kids, and they’re all in sports. So we have to pick and choose who does what and when, and you know, to try to make it work, because there’s just no doing everything.” (Mother of 8- to 11-year-old) Offer activities at times that accommodate working parents
Encourage schools to host on-site activities after school
“If I had a magic wand, I would say get some kind of bus that can go to all the different areas, pick up kids, take ’em where they want to go. . . . It breaks my heart when [my child’s] friends say . . . ‘My mom has to work so she can’t take us.’” (Mother of 12- to 14-year-old) Provide subsidized transportation from school to activities
Offer activities that serve children of multiple ages
Child’s lack of interest or apprehension “One of our boys is bullied really bad at school, so he’s scared to play sports, because he doesn’t want to be on a team because of the kids that are bullying.” (Mother of 8- to 11-year-old) Provide noncompetitive physical activity options that focus on fun and fitness
“They’re so into their electronics, they want to sit at home and play the Xbox One and be on their tablet.” (Mother of 12- to 14-year-old)
“The group of kids that aren’t always into sports . . . those are the ones that get left out a little bit. [We need to] find alternative types of exercise and outdoor activities that they can do and that will help them get active.” (Community stakeholder)
Youth’s school and family commitments “I want to go to the park all the time, but I still have other priorities at home, like chores or homework or babysitting my younger siblings.” (12- to 14-year-old youth) Create programs during school and out-of-school time that incorporate academics and exercise
Community context
Lack of affordable options “I think the city managers stand up and tell everybody, ‘Just because you can’t afford [the activity], doesn’t mean you can’t play it.’” (Father of 3- to 7-year-old) Offer and publicize financial aid options that are accessible and noninvasive
“I have to work to survive. It’s difficult to pay for activities and also take time to be there with her, and that’s why it seems hard for me.” (Mother of 3- to 7-year-old, Spanish speaking)
“My son this fall would like to do football now too, and just that is $90 and that doesn’t include the cleats that he’s going to need. And mouth guards. . . . I’m trying to figure out how I’m going to afford $90. Then my daughter, she wants to do volleyball . . . and it’s like, I don’t know how I’m going to, where the money’s going to come from?” (Mother of 8- to 11-year-old) Create equipment exchanges
“[I’d like] a trade-off or an equipment share or sports swap . . . because for growing kids it’s just impossible. You very rarely can use the same thing from year to year just because they’re growing.” (Father of 8- to 11-year-old)
Traffic safety “I think the town needs to consider sidewalks . . . [when] kids are walking after school or riding their bike, they’re going down the middle of the street. . . . Our boys are only allowed to play outside when we’re with them . . . it’s not safe [without sidewalks].” (Mother of 8- to 11-year-old) Improve sidewalks and crosswalks
“Where I live, it’s not very good to ride a bike. . . . I mean, if the sidewalks were a bit wider, perhaps. I think we also need those barriers so that the cars won’t drive by so fast. Because I am telling you, there’s been a few accidents involving kids riding their bikes.” (Mother of 12- to 14-year-old, Spanish speaking) Implement traffic-calming measures
Provide crossing guards near schools
Exposure to unsafe or illicit activity
in public spaces
“They’re opening up [marijuana and alcohol shops] close to where the parks are. . . . They should be at a certain distance [from the parks].” (Father of 12- to 14-year-old) Limit proximity of marijuana and liquor stores to parks and schools
“[We] have a park across the street. . . . I prefer to go and walk with him there. . . . I don’t let him go by himself, I’m scared.” (Mother of 8- to 11-year-old, Spanish speaking) Plan neighborhood watches to improve safety and security
Improve lighting in public spaces
Keep parks free of garbage, graffiti, and drug paraphernalia
Provide clean and safe restrooms
Consider increasing police presence
Limited access to high-quality facilities “There’s rec centers [nearby], but I don’t want to put my girls in that situation, because they’re not the best rec centers. . . . So I have to look at other rec centers. Half the time, they’re full.” (Parent of 3- to 7-year-old) Improve quality of existing indoor facilities
“Sometimes at recreation centers that are a bit cheaper . . . you’re not so at ease because . . . the people that are teaching them let them do what they want and don’t pay attention to them.” (Mother of 3- to 7-year-old, Spanish speaking) Hire staff who are qualified to work with children or improve training and supervision of staff
Transportation “Transportation is a big thing, especially when you do have to go so far out of town ’cause we are in such a rural area. We have to get out of town to get our kids to play sports and play with other teams. That’s hard.” (Mother of 8- to 11-year-old) Increase frequency and number of bus routes
“I would love it if the schools coordinated with the rec centers and provided transportation. Wouldn’t it be great if from school, if there was a bus that went to the rec center?” (Father of 8- to 11-year-old) Provide subsidized transportation to recreation facilities and after-school activities
Neighborhood inequities “[It’s because] we’re of color; but you go down to the suburbs. . . . It’s kind of uncomfortable for the children to go down there. . . . It’s just that when you get out to certain areas, you’re not welcome… I told my daughter today, ‘Let’s go to one of the rec centers.’ She goes, ‘Nah.’ Just because . . . [she’s] not comfortable.” (Father of 8- to 11-year old) Create and improve the quality of indoor and outdoor facilities in lower-income neighborhoods
“We are campaigning to have a rec center in [a low-income] neighborhood. Despite having the most kids and the most child obesity, we do not have a community rec center. We’re one of the least served, so we are . . . hoping to have it on the next bond so that we get a rec center.” (Community stakeholder)
Limited program availability “There’s this small window of time on a certain day [when you can sign up for recreation center programs], and it’s always during the middle of the day. . . . And all of the times that are convenient for busy families are full within a half hour.” (Mother of 3- to 7-year-old) Offer more activities for young children
“It’s hard in the summer to get them where they need to be — all the things you want them to do so that they’re active and busy. And you know? That’s really stressful to me in the summertime. I’m working and he’s not doing anything. So again, one of those rec centers would be really nice.” (Father of 12- to 14-year-old) Expand use of existing facilities (schools, churches) to offer activities in winter
“Nearby my house there are 3 churches that are huge. . . . I ask myself, ‘That building is empty. . . . Why don’t they open it up so we could come and play, especially in the winter?’” (Mother of 12- to 14-year-old, Spanish speaking) Build partnerships with community organizations such as public libraries to promote physical activity in summer
Lack of information “I mean, there are options, but what I [am saying] is that you also have to make an effort and look for the places. . . . The school doesn’t announce the rec centers.” (Mother of 8- to 11-year-old) Create user-friendly, up-to-date electronic listings with current physical activity offerings and locations
“The city has a website and it has a calendar [of events] . . . but it’s really slow. It’s not user-friendly and I wish that they could have a kid’s corner. . . . They could pull . . . all the information together for children in one spot. . . . [Now, the site] seems geared toward the retirement community.” (Mother of 3- to 7-year-old) Publicize opportunities in local newspapers
“I think another thing [that would help me] is advertising or sending out information to get people to go. . . . I’ve lived here 2 years. I know where the rec center is, but I don’t know what they offer.” (Mother of 12- to 14-year-old) Support social media networks in which parents can share information with each other
Limited engagement with community “[Local planners] need to tailor to the needs of each community because something that works one place might not work somewhere else.” Solicit community input such as conducting “listening campaigns” when planning activities

 
 

Return to your place in the textTable 3. Parent Survey Results (N = 126) on Perceived Barriers to Physical Activity, Qualitative Study of Challenges and Opportunities for Promoting Children’s Physical Activity, Colorado, 2016
Barrier % of Parents Who
Strongly Agree or Agree That Item Is a Barrier to Their Child’s Physical Activity
Ranking of Frequency
Cost
I cannot afford enrollment fees for after-school programs/camps. 68.3 2
I cannot afford enrollment fees for sports and clubs. 67.5 3
I cannot afford equipment and gear for sports teams. 62.7 4
I cannot afford activity-related equipment such as bicycles. 43.7 7
Safety
Drivers don’t look out for children playing. 73.0 1
Cars drive too fast for my child to play near the road. 61.1 5a
There is too much traffic for my child to play outside. 33.3 14
It is unsafe for my child to play outside. 30.2 16
I worry that my child will get injured during sports and physical activities. 21.4 20
Access to parks and facilities
There are few indoor facilities near my home. 61.1 6a
I have no backyard for my child to play in. 31.0 15
There are no sidewalks for my child to walk or bike on. 18.3 21
There are no parks or playgrounds that my child can walk or bike to. 12.7 22
Availability of programs
Hours for after-school/summer programs are not flexible. 39.7 11
There aren’t many teams/programs in our neighborhood. 38.1 12
There are no teams/clubs for activities my child likes to do. 23.8 18
Parent schedules
I work and have little time at the end of the day. 40.5 9b
It is difficult to coordinate activities for children of different ages. 40.5 10b
I have no energy to help my child be active. 22.2 19
Information
There isn’t much information on sports/activities available. 42.9 8
I don’t know how to get my kids to be active in winter. 27.0 17
Other
There are no children with similar interests in our neighborhood. 34.1 13

a,b Two sets of barriers were tied in their ranking; these ties are denoted by footnotes a and b.

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Effects of a Tobacco-Free Work Site Policy on Employee Tobacco Attitudes and Behaviors, Travis County, Texas, 2010–2012

Sarah E. Seidel, DrPH1; Kristi Metzger, PhD, MPH1; Andrea Guerra, MPH1; Jessie Patton-Levine, MPH1; Sandeepkumar Singh, MD, MHA2; William T. Wilson, DrPH2; Philip Huang, MD, MPH1 (View author affiliations)

Suggested citation for this article: Seidel SE, Metzger K, Guerra A, Patton-Levine J, Singh S, Wilson WT, et al. Effects of a Tobacco-Free Work Site Policy on Employee Tobacco Attitudes and Behaviors, Travis County, Texas, 2010–2012. Prev Chronic Dis 2017;14:170059. DOI: http://dx.doi.org/10.5888/pcd14.170059.

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Abstract

Background

The adoption of tobacco-free policies in behavioral health settings is an important step in reducing staff tobacco use as well as the high rates of tobacco use among people with mental illness and behavioral disorders. Studies have demonstrated the importance of staff support when implementing tobacco-free workplace policies, but there is limited research examining tobacco use prevalence among staff and staff attitude before and after policy adoption.

Community Context

Integral Care, a local authority for behavioral health and developmental disabilities in Austin, Texas, and Austin Public Health embarked on a comprehensive planning process before implementing a 100% tobacco-free campus policy. The objectives were 1) assess staff tobacco use and attitudes toward a tobacco-free policy, 2) communicate policy to staff, 3) provide staff education and training, and 4) provide cessation resources.

Methods

Integral Care and Austin Public Health conducted a web-based employee survey 6 months before and 6 and 12 months after implementation of the policy to measure tobacco use prevalence and attitudes among employees.

Outcome

Employees had significant improvements in tobacco use prevalence and attitudes toward the tobacco-free policy from pre-implementation to post-implementation. Tobacco use prevalence among staff decreased from 27.6% to 13.8%, and support for the policy increased from 60.6% to 80.3% at 12 months post-implementation.

Interpretation

Adoption of 100% tobacco-free campus policies in behavioral health settings can result in significant reductions in staff tobacco use. Leadership should provide staff with education, training, and cessation support before adoption of tobacco-free work site policies to ensure success.

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Background

Tobacco-free workplace policies can decrease tobacco use among employees (1). The adoption of these policies in health care settings has the potential to reduce tobacco use among staff as well as the patients or clients they serve. Behavioral health care settings present an especially challenging and yet critical setting for the implementation of tobacco-free policies. Smoking rates in individuals with mental illness are 2 to 3 times higher than in the general population (2,3). Health care professionals other than physicians, and specifically health care professionals working long hours, report high rates of tobacco use (4,5). Behavioral health care professionals, in particular, have a smoking prevalence that exceeds that of the general population (6).

While behavioral health care professionals recognize the negative health effects of smoking and the importance of addressing tobacco use among their patients and clients (7,8), the establishment of tobacco-free policies in treatment settings has faced obstacles. Staff cite concerns that tobacco-free policies could negatively impact staff–client relationships (9–12). Organizational barriers include common practices such as promoting patient or client smoking for behavioral reinforcement and staff members and patients smoking together (6,13). Additionally, low levels of staff knowledge, confidence, skills, and perceived responsibility and a lack of training and tobacco use cessation support and resources for patients and staff (6,11) further impede policy adoption and implementation. Staff support is considered crucial to successfully implementing tobacco-free policies in behavioral health treatment settings (14,15). Yet, there is limited research examining 1) the use of participatory methods to address the abovementioned barriers to behavioral health staff support and 2) staff attitudes toward tobacco-free workplace policies and the prevalence of tobacco use among staff before and after policy adoption. We sought to determine whether comprehensive planning before the implementation of a tobacco-free work site policy could decrease employee tobacco use.

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Community Context

In Travis County, Texas, in 2010, 13.4% the population with frequent mental distress were current smokers, compared with 10.7% of the population not reporting mental distress (16). No local data on the prevalence of tobacco use among health care professionals in behavioral health care settings exist, though research suggests that the prevalence of smoking among nonphysician health care professionals, and specifically behavioral health care professionals, remains high (4–6,13).

On February 1, 2011, Integral Care (formerly Austin/Travis County Integral Care), a local authority for behavioral health and developmental disabilities in Austin, adopted a 100% tobacco-free campus policy. This policy prohibited the use of all forms of tobacco and covered all property owned, leased, or used by Integral Care, including indoor and outdoor spaces and common areas, parking lots and driveways (inside and outside personal vehicles), company vehicles, and residential treatment facilities in Travis County. At that time, Integral Care employed about 600 individuals and served about 27,000 consumers at 44 locations each year. Of the 612 staff employed by Integral Care in 2011, 73.2% were female and 26.6% were male, 53.9% were under the age of 40, and 67.7% held a bachelor’s degree or higher.

Before the 2011 policy, Austin Public Health, formerly Austin/Travis County Health and Human Services, engaged staff and administration at Integral Care in a comprehensive planning process comprising assessment, communication, training, and cessation resources. This process was part of a broader community-level effort in Austin and Travis County, under the Communities Putting Prevention to Work initiative from the Centers for Disease Control and Prevention to reduce tobacco use and prevent chronic disease through policy, systems, and environmental change (17). The objectives of this engagement and planning process were to 1) assess staff attitudes toward tobacco-free policies and their implementation; 2) design and promote early and extensive communication before policy implementation; 3) provide staff with training and organizational support to implement and enforce the policy and track patient tobacco use; and 4) provide staff and consumers with cessation resources. Outcomes of interest for the policy implementation were 1) improving staff attitudes toward tobacco-free policies, 2) reducing the prevalence of tobacco use among staff, and 3) reducing the prevalence of tobacco use among consumers (these data are not reported in this evaluation).

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Methods

Planning and engagement process

Six months before implementation, Integral Care conducted focus groups to inform staff about the impending policy change and to obtain staff input on the components of the implementation process. An online survey (with invitation to participate and survey link sent via employee email) was administered to determine baseline staff tobacco use rates and attitudes toward the upcoming policy change.

Early and extensive communication of the impending policy change to employees, consumers, and partners was an essential component of Integral Care’s implementation process. The Integral Care communication strategy began 5 months prior (Box) to the effective date of the tobacco-free campus policy to gradually introduce the policy, address staff and consumer resistance to the change, and give tobacco users time to prepare for the change. The internal communication strategy, “We Can Quit” (Figure), consisted of positive, nonpunitive messaging via multiple pathways with the goal of educating staff and encouraging tobacco use cessation. Integral Care created a policy change homepage on the employee intranet that included company and community cessation resources, information on the health consequences of tobacco use, and policy implementation updates. Integral Care also sent organization-wide emails on cessation success stories, policy updates, memoranda, and cessation resources. Brochures and flyers were distributed and signage was posted at all campus facilities. The external communication strategy comprised memoranda to Integral Care contractors and leased properties regarding the policy change as well as organizational newsletters and reports to partners in Austin/Travis County. Via a link on the Integral Care homepage, the information from the intranet page was available to the public. At the time, the local health department, Austin Public Health, was also implementing an extensive media campaign on the dangers of tobacco use and secondhand smoke exposure. Integral Care held a media launch event with Austin Public Health to celebrate the policy effective date.

Box. Timeline of Planning and Implementation for Integral Care’s Tobacco-Free Campus Policy

Year and Month Activity
2010
June Austin Public Health awards Integral Care with subrecipient grant from Communities Putting Prevention to Work
July Integral Care policy approved by board of trustees
August Integral Care staff survey (6 months pre-implementation)
September Internal communication begins (signage, brochures, intranet, cessation resources)
December External communication begins; signage posted on properties
2011
January Staff training and education; media event with Austin Public Health
February Implementation of tobacco-free workplace policy
March Tobacco Use Assessment (EHR) Implemented
August Integral Care staff survey (6 months post-implementation)
2012
February Integral Care staff survey (12 months post-implementation)

 

Poster used in the internal communication strategy, We Can Quit, for Integral Care’s tobacco-free campus policy, Austin, Texas, 2010–2012.

Figure.
Poster used in the internal communication strategy, We Can Quit, for Integral Care’s tobacco-free campus policy, Austin, Texas, 2010–2012. [A
text version of this figure is also available.]

Staff training began 90 days before policy implementation (Box) and included education on 1) assessing and treating tobacco use in Integral Care consumers within the clinical setting and 2) how to engage Integral Care staff and consumers about the policy outside the clinical setting. Integral Care implemented the tobacco treatment template from the American Academy of Family Physicians in electronic medical records and trained all clinicians in the Ask and Act treatment protocol (20), pharmacologic treatments, the epidemiology of tobacco use and mental illness or addiction comorbidity, and motivational interviewing techniques. Tobacco use cessation counselors were trained to provide brief (minimum of 15 minutes) counseling sessions for consumers, with up to 6 sessions available to each consumer. Treatment plans and billing reimbursements were adjusted to include tobacco cessation counseling, and staff were trained on accounting for this time. To assist in enforcement, Integral Care staff were also trained on engaging any staff, consumers, and visitors not complying with the policy using brief, nonconfrontational, scripted messages.

During the 6 months before policy implementation (Box), Integral Care also began a comprehensive education and cessation support program for both employees and consumers. Cessation support included free cessation counseling through the Employee Assistance Program and nicotine replacement therapy for both staff and consumers. Additionally, staff were eligible for reimbursement of their first tobacco cessation medical office visit and Zyban (GlaxoSmithKline; tobacco cessation prescription medicine) at no cost. Community cessation resources (eg, the Texas Quit Line) were also promoted by Integral Care.

Design and analysis

In August and September 2010, 6 months before policy implementation, an 18-question web-based pre-implementation survey was administered to all clinical and nonclinical employees at Integral Care; employees were sent an email with a hyperlink to the survey. The pre-implementation survey had 7 demographic and health questions and 11 questions regarding tobacco use, policy attitudes, and preferred cessation resources. The survey (including additional questions) was administered again at 6 and 12 months post-implementation. Pre- and post-implementation surveys asked Integral Care employees about current tobacco use (ever and while at work), knowledge about the health effects of tobacco use and secondhand smoke, desire to quit, preferred cessation methods, policy support, and willingness to enforce the policy. Response categories for policy support and for willingness to enforce the policy were collapsed from yes, no, and maybe into yes and no/maybe. The post-implementation surveys included 8 additional questions on previous quit attempts, awareness and use of cessation resources, and attitudes about training, enforcement, and compliance. Survey questions relating to the pre-implementation policy environment were modified in the post-implementation surveys to reflect the change in conditions. For example, the pre-implementation survey question “Would you support a tobacco-free policy?” was changed to “Do you support a tobacco-free policy?” The question “Would be willing to assist in the enforcement of the tobacco-free workplace policy?” was changed to “Do you assist in enforcing the tobacco-free workplace policy?”

Pre- and post-implementation survey respondents were not matched, but the only significant difference among the samples in the 3 surveys in terms of sex, age, education, or position was the proportion of staff with a high school diploma or general equivalency diploma as their highest level of education (pre-implementation vs 12 months post-implementation) (Table 1). In each survey, roughly half of respondents were younger than 40 years of age, and approximately three-quarters of respondents were women. The percentage of respondents with a bachelor’s degree or higher was 76% or higher across the 3 surveys. Additionally, survey participants’ demographic characteristics (sex, age, and education) were compared with demographics of the entire Integral Care staff (obtained from de-identified employee data). Race/ethnicity of employees was not provided and was not collected in the survey. No significant differences in sex, age, or education were observed between the survey participants and the study population.

Pearson χ2 tests were performed to compare survey samples and to compare pre- and post-implementation survey data on tobacco use prevalence, attitudes toward tobacco use, and tobacco-free workplace policy support and to compare tobacco use prevalence change for sex, age, education, and position subgroups. Significance was set at α = .01. Statistical analysis of survey data was performed using Stata 14.0 (StataCorp LP). Research was approved by Integral Care’s Board of Trustees and by the Integral Care Institutional Review Board.

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Outcome

Of approximately 612 eligible Integral Care staff, 246 employees completed the pre-implementation surveys in August and September 2010; 230 employees completed a post-implementation survey at 6 months in July and August 2011 and 234 employees completed a post-implementation survey at 12 months in February through April 2012 (Table 1). Response rates were 40.2%, 37.6%, and 38.2%, respectively. Demographic characteristics were missing for 21 respondents in the 6-month post-implementation survey and 12 respondents in the 12-month post-implementation survey. Descriptive statistics for each sample as well as selected characteristics from the survey population are presented in.

Tobacco use among staff declined significantly from 27.6% in the pre-implementation survey to 11.6% in the 6-month post-implementation survey (χ2 = 18.47; P < .001) and to 13.8% in the 12-month post-implementation survey (χ2 = 13.43; P < .001) (Table 2). There was no significant change in tobacco-use prevalence between the 6- and 12-month post-implementation surveys.

Staff support for the tobacco-free campus policy increased significantly from 60.6% to 80.3% from pre-implementation to 12 months post-implementation (Table 2) (χ2 = 22.42; P < .001). Policy support also increased significantly between the 6-month post-implementation (67.5%) and the 12-month post-implementation surveys (χ2 = 10.09; P = .001). Among tobacco users, there was a significant increase in support of the policy from the pre-implementation survey (26.5%) to the 6-month post-implementation survey (60.0%; χ2 = 8.98; P = .003) and to the 12-month post-implementation survey (64.5%; χ2 = 13.03; P < .001). Among non–tobacco users, support for the policy did not increase significantly between pre-implementation and 6 months post-implementation. Yet, support increased significantly between the 6-month (72.8%) and 12-month (87.1%) post-implementation surveys (χ2 = 12.20; P < .001). In all 3 surveys a higher percentage of staff who were non–tobacco users supported the policy than staff who used tobacco.

Most respondents were aware of the cessation services provided by Integral Care in both the 6-month (185; 80.4%) and 12-month (206; 88.0%) post-implementation surveys; tobacco users specifically reported only marginally higher awareness of resources in both post-implementation surveys. Cessation services were not offered to Integral Care staff at the time of the pre-implementation survey, and thus the question was not included in the pre-implementation survey.

In addition, 73% of respondents in the 6-month post-implementation survey and 69% in the 12-month post-implementation survey reported that they felt adequately trained or competent to engage consumers about the tobacco-free policy. There was a significant increase in the percentage of respondents who reported currently assisting in enforcement at 6 months (57.8%) and 12 months post-implementation (63.2%), compared with respondents who reported that they would be willing to assist in enforcement of the policy in the pre-implementation survey (48.0%) (Table 2). Among tobacco users specifically, this significant increase was observed between pre-implementation survey (26.5%) and 6 months post-implementation (64.0%) as well as between pre-implementation and 12 months post-implementation (54.8%). There was a small but nonsignificant decrease in the proportion of tobacco users willing to enforce the policy between 6 and 12 months post-implementation.

In the pre-, 6-month post-, and 12-month post-implementation surveys, 71.7%, 84.0%, and 74.2%, respectively, of tobacco users responded yes or maybe to the question of whether they wanted to quit using tobacco. Of tobacco users, 64.2%, 72.0%, and 64.5%, respectively, responded yes or maybe to the question of whether they were seriously considering quitting in the next 6 months. Approximately half of all tobacco users in the 6-month post-implementation and 12-month post-implementation surveys (56.0% and 45.2%, respectively) had made a quit attempt in the past 9 months (the question was not asked in the pre-implementation survey).

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Interpretation

This study evaluated the effects of a 100% tobacco-free campus policy at a large multisite provider of behavioral health and developmental disabilities services on staff tobacco use rates and staff attitudes toward a tobacco-free campus policy. The objectives of this engagement and planning process (assessment, communication, training, and cessation resources) were all met during the 18-month period. Assessment using web-based surveys of employees was carried out at 6 months before and 6 and 12 months following policy implementation. Regarding communication, survey results indicated that nearly 70% of respondents at both 6 and 12 months post-implementation felt adequately trained in engaging consumers regarding the policy; over 80% of respondents (and over 90% of tobacco users specifically) in both post-implementation surveys were aware of cessation resources. Outcomes of interest for the policy implementation also demonstrated improvement. Staff attitudes in support of tobacco-free policies increased significantly, and staff tobacco use prevalence decreased. Across all 3 surveys there was a high percentage of tobacco users (>64%) intending to quit tobacco in the next 6 months.

This evaluation demonstrates that a comprehensive implementation plan combining education, communication, and cessation support for staff before a tobacco-free policy adoption can contribute to reduced staff tobacco use and increased support for the policy after adoption. The components of education and training for staff regarding smoking behaviors and risks and smoking cessation treatment options for clinical populations have been recommended and linked to the success of smoke-free initiatives in inpatient mental health facilities (15,18). To our knowledge this is the first study of a policy implementation that has incorporated components to address staff tobacco use before implementing the policy in the patient population. Staff commonly cite low or lack of support for tobacco-free policies from the organization in which they work (12,19). Thus, addressing staff needs is an important first step to successfully implementing tobacco-free policies in behavioral health services settings.

Changes that were not significant at 6 months post-implementation (eg, support for the policy among non–tobacco users) were significant at 12 months post-implementation. This indicates that attitudes toward a tobacco-free policy can continue to improve after implementation and suggests that attitudes (and possibly social norms) may not change until after a policy is implemented and individuals can observe consequences or implications of the change.

This study has several limitations. Integral Care provides developmental disabilities services in addition to behavioral health services. Staff providing these services made up 18% of full-time employees in 2011 and may be different than staff working in traditional behavioral health care settings. Additionally, we did not obtain information on staff turnover. Staff may have not received the full intervention if they left or were hired during the 6 months before implementation during which communication, education, and training were conducted. Though survey response rates were average, staff who answered the surveys may have been systematically different than those who did not participate and may not represent the characteristics and attitudes of the Integral Care study population. We were also unable to link pre-implementation and post-implementation survey respondents. However, the only significant difference in demographic characteristics among the pre-implementation and post-implementation survey samples was in the proportion of staff with a high school diploma or general equivalency diploma as their highest level of education (pre-implementation vs 12 months post-implementation). There were no significant differences in the demographic characteristics between any pre-implementation or post-implementation survey samples and the entire Integral Care employee population. However, because employee race/ethnicity data were either not available or not collected, and disparities in tobacco use exist among racial/ethnic groups in the general population, we cannot determine the contribution of race/ethnicity to study findings. Additionally, tobacco users who quit may have been more willing to answer the post-implementation surveys than those who did not quit.

Finally, Integral Care was a sub-recipient of the Communities Putting Prevention to Work grant received by Austin Public Health in 2010. The grant funds provided Integral Care with funding for personnel, operating expenses (including signage), and indirect expenses to plan and execute the policy change. An intervention of this scale may not be feasible for smaller behavioral health providers.

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Acknowledgments

Funding for the original research was provided in part by Centers for Disease Control and Prevention Communities Putting Prevention to Work grant (1U58DP002587-01 REVISED) awarded to Austin/Travis County Health and Human Services (now Austin Public Health) March 2010–March 2012. Austin Travis County Integral Care (now Integral Care) was a subrecipient of the Communities Putting Prevention to Work grant.

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Author Information

Corresponding Author: Sarah Seidel, DrPH, Austin Public Health, RBJ Health Center — 4th Floor, Ste 413, 15 Waller St, Austin, TX 78702. Telephone: (512) 972-5146. Email: Sarah.Seidel@austintexas.gov.

Author Affiliations: 1Austin Public Health, Austin, Texas. 2Integral Care, Austin, Texas.

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References

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Tables

Return to your place in the textTable 1. Characteristics of Surveyed Staff at 6 Months Before and 6 and 12 Months After Implementation of a Tobacco-Free Workplace Policy at Integral Care, Austin, Texas, 2010–2012
Characteristic 6 Months Before Policy Implementation (July–August 2010), n (%) 6 Months After Policy Implementation (July–August 2011), n (%) 12 Months After Policy Implementation (February–April 2012), n (%)
Total 246 (100) 209 (100) 222 (100)
Sex
Male 58 (23.6) 49 (23.4) 58 (26.1)
Female 188 (76.4) 160 (76.6) 164 (73.9)
Age
≤30 60 (24.4) 48 (23.0) 54 (24.3)
31–40 61 (24.8) 61 (29.2) 63 (28.4)
41–50 55 (22.4) 43 (20.6) 46 (20.7)
51–60 48 (19.5) 37 (17.7) 43 (19.4)
≥60 22 (8.9) 20 (9.6) 16 (7.2)
Position at Integral Carea
Administration 108 (43.9) 99 (47.4) 86 (38.7)
Allied health professional 25 (10.2) 24 (11.5) 26 (11.7)
Direct care staff 83 (33.7) 69 (33.0) 90 (40.5)
Nursing 9 (3.7) 5 (2.4) 2 (0.9)
Physician 3 (1.2) 3 (1.4) 3 (1.4)
Other 18 (7.3) 9 (4.3) 15 (6.8)
Education
High school or general equivalency diploma 34 (13.8b) 19 (9.1) 12 (5.4)b
Associate’s degree 24 (9.8) 16 (7.7) 25 (11.3)
Bachelor’s degree 73 (29.7) 70 (33.5) 70 (31.5)
Master’s degree 95 (38.6) 87 (37.8) 97 (41.5)
MD or PhD degree 8 (3.3) 5 (2.4) 7 (3.2)
Other 12 (4.9) 12 (5.7) 11 (5.0)

a Job/position categories were chosen to determine employee involvement with Integral Care consumers and do not reflect education levels.
b χ2 = 9.32, df = 1; P = .002.

Return to your place in the textTable 2. Changes in Tobacco Use and Support for a Tobacco-Free Campus Policy at 6 Months Before and 6 and 12 Months After Implementation, Integral Care, Austin, Texas, 2010–2012
Characteristic 6 Months Before (July–August 2010) (N = 246) 6 Months After (July–August 2011) (N = 216) 12 Months After (February–April 2012) (N = 224) P Value
No. of Respondents % (n) No. of Respondents % (n) No. of Respondents % (n) 2010 vs 2011 2010 vs 2012 2011 vs 2012
Tobacco use prevalence 246 27.6 (68) 216 11.6 (25) 224 13.8 (31) <.001 <.001 .48a
Supports tobacco-free workplaceb 246 60.6 (149) 216 71.8 (155) 224 83.9 (188) .11 <.001 .002
Support among tobacco users 68 26.5 (18) 25 60.0 (15) 31 64.5 (20) .003 <.001 .73
Support among non–tobacco users 178 73.6 (131) 191 72.8 (139) 193 87.0 (168) .86c .001 <.001
Assists in enforcement of tobacco-free workplace policyd 246 48.0 (118) 216 61.6 (133) 224 66.1 (148) .003 <.001 .33
Willingness among tobacco users 68 26.5 (18) 25 64.0 (16) 31 54.8 (17) .001 .006 .49c
Willingness among non–tobacco users 178 56.2 (100) 191 60.7 (116) 193 67.9 (131) .38 .02 .14

a Nonsignificant increase in proportion observed.
b The survey 6 months before implementation asked, “Would you support a tobacco-free policy?” The surveys after implementation asked, “Do you support a tobacco-free policy?”
c Nonsignificant decrease in proportion observed.
d The survey 6 months before implementation asked, “Would be willing to assist in the enforcement of the tobacco-free workplace policy?” The surveys after implementation asked, “Do you assist in enforcing the tobacco-free workplace policy?”

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Daily Sugar-Sweetened Beverage Consumption, by Disability Status, Among Adults in 23 States and the District of Columbia

Sunkyung Kim, PhD1; Sohyun Park, PhD2; Dianna D. Carroll, PhD3,4; Catherine A. Okoro, PhD5 (View author affiliations)

Suggested citation for this article: Kim S, Park S, Carroll DD, Okoro CA. Daily Sugar-Sweetened Beverage Consumption, by Disability Status, Among Adults in 23 States and the District of Columbia. Prev Chronic Dis 2017;14:160606. DOI: http://dx.doi.org/10.5888/pcd14.160606.

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Abstract

Introduction

Information on dietary intake, including sugar-sweetened beverages (SSBs), for adults with disabilities is limited. Such information can inform interventions to prevent chronic disease and promote health among adults with disabilities. The objective of this study was to describe the associations between SSB consumption and disability among adults.

Methods

We examined data on adults aged 18 years or older in 23 states and the District of Columbia who participated in the 2013 Behavioral Risk Factor Surveillance System (n = 150,760). Participants who reported a limitation in any activity caused by physical, mental, or emotional problems or who reported use of special equipment were considered to have a disability (n = 41,199). Participants were classified as daily SSB consumers (≥1 time/d) and non-daily SSB consumers (<1 time/d). Multivariable logistic regression was used to examine associations between daily SSB intake and disability after controlling for sociodemographic characteristics. An interaction effect between disability and obesity status was tested to consider obesity status as a potential effect modifier.

Results

The prevalence of drinking SSBs at least once daily was significantly higher among adults with disabilities (30.3%) than among adults without disabilities (28.6%) (P = .01). After controlling for sociodemographic characteristics, among nonobese adults, the odds of daily SSB intake were significantly higher among adults with disabilities than among adults without disabilities (adjusted odds ratio = 1.27, P < .001). Among obese adults, daily SSB intake was not associated with disability status (adjusted odds ratio = 0.97; P = .58).

Conclusion

Our findings highlight the need for increased awareness of SSB consumption among adults with disabilities.

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Introduction

Various definitions of disability exist. The US Census Bureau defines a disability as an impairment or limitation in any activities caused by communicative, mental, or physical problems (1). Disabilities affect more than 56 million people in the United States, and the prevalence increases with increases in age (1). In 2010, 21.3% of people aged 15 years or older had a disability, whereas 49.8% of older adults (≥65 y) had a disability (1). Additionally, more than half of people with a disability had a severe disability in 2010, and disability-associated health care expenditures were estimated to be $400 billion among US adults in 2006 (1,2).

Eating a healthy diet is an important lifestyle behavior that contributes to overall health and nutritional well-being. However, people with disabilities may be at increased risk of nutritional deficiency, because barriers to eating a healthy diet often are biopsychosocial (eg, underlying physical or mental disease, loss of appetite, social isolation) (3,4). Adults with moderate to severe disabilities (eg, those who cannot walk independently, those who have significant cognitive limitations) may not be able to choose their foods independently, be able to cook for themselves, or have access barriers to healthy affordable food outlets. Instead, people with disabilities may consume processed foods or fast foods that have limited nutritional value more frequently than those without disabilities. These behaviors may negatively affect their health. The prevalence of obesity is significantly higher among adults with disabilities than among those without, and adults with disabilities are more likely to have risk factors for chronic diseases (5,6).

Sugar-sweetened beverages (SSBs) are defined as “liquids that are sweetened with various forms of added sugars. These beverages include, but are not limited to, soda (regular, not sugar-free), fruitades, sports drinks, energy drinks, sweetened waters, and coffee and tea beverages with added sugars” by the 2015–2020 Dietary Guidelines for Americans (7) and are a significant source of added sugars and energy in the diet of US adults (8). According to the 2011–2014 National Health and Nutrition Examination Survey (NHANES), approximately half of US adults drank SSBs on a given day and mean daily energy intake from SSBs was 145 kcal (9). SSBs provide calories with little or no nutritional value and are associated with increased health risks, including weight gain and obesity, cardiovascular disease, kidney disease, asthma, and type 2 diabetes as well as poor diet quality, physical inactivity, and smoking (10–16).

One study reported that older adults (≥65 y) with disabilities were less likely to have a healthy weight and engage in physical activity, but it also reported no difference in fruit and vegetable intake between older adults with disabilities and older adults without disabilities (17). Although general information on dietary intake among adults with disabilities is available (18), no details are available on the association between disability status and SSB consumption. To the best of our knowledge, our study is the first to investigate this topic.

Habitual SSB consumption among adults with disabilities can be more problematic than among adults without disabilities because the excess sugar intake, combined with limited physical activity, can expedite weight gain and increase the risk of chronic diseases. The prevalence of disability is expected to increase as the population ages, so a better understanding of SSB consumption among adults with disabilities could help in designing interventions and targeting messages about healthy dietary choices and further help to prevent chronic diseases among people with disabilities. The objectives of this study were to estimate the prevalence of daily SSB consumption by disability status and sociodemographic characteristics among US adults and to describe associations between SSB consumption and disability status while considering obesity status as a potential effect modifier.

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Methods

This cross-sectional study used data from the 2013 Behavioral Risk Factor Surveillance System (BRFSS) from 23 states and the District of Columbia. The BRFSS is the largest ongoing random-digit–dialed telephone health survey in the world; it is conducted via both landline and cellular telephones. For the landline telephone survey, data are collected from a randomly selected adult in a household. For the cellular telephone version, data are collected from an adult who participates by using a cellular telephone. It is a cross-sectional and state-based system of health surveys established in 1984 by the Centers for Disease Control and Prevention (CDC) (19). BRFSS surveys a representative sample of community-dwelling adults (aged ≥18 y) in all 50 states, the District of Columbia, and the US territories Guam and Puerto Rico to obtain information on health risk and behaviors, health practices for preventing disease, and health care access primarily related to chronic disease, injury, and death. In 2013, 23 states (Alaska, Arizona, California, Connecticut, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maryland, Minnesota, Mississippi, Nebraska, New Jersey, New York, North Carolina, Ohio, Oklahoma, South Carolina, Utah, Vermont, West Virginia, and Wisconsin) and the District of Columbia administered an optional module on sugar drinks. A total of 161,317 adults completed the optional module. For this analysis, participants with missing data on SSB intake (n = 2,471), disability status (n = 663), or self-reported weight or height (n = 7,423) were excluded, which resulted in a final analytic sample of 150,760 adults. The BRFSS has been reviewed by the Human Research Protection Office of CDC and determined to be exempt research.

Variables

The outcome variable was frequency of daily SSB consumption. The BRFSS module on sugar drinks consisted of 2 questions related to SSB intake: “During the past 30 days, how often did you drink regular soda or pop that contains sugar? Do not include diet soda or diet pop.” and “During the past 30 days, how often did you drink sugar-sweetened fruit drinks (such as Kool-Aid and lemonade), sweet tea, and sports or energy drinks (such as Gatorade and Red Bull)? Do not include 100% fruit juice, diet drinks, or artificially sweetened drinks.” For each question, respondents answered the number of times per day, per week, or per month they consumed a SSB. To convert frequency of weekly and monthly intake to frequency of daily intake, weekly frequency was divided by 7 and monthly frequency was divided by 30. The frequency of daily SSB intake was then calculated as the sum of the number of times daily that soda, fruit drink, sweet tea, and sports or energy drink were consumed. The participants were classified as daily SSB consumers (≥1 time/d) and non-daily SSB consumers (<1 time/d), which was used as the main outcome. At least once daily was used to define habitual SSB consumers (ie, daily intake) and was based on clinical research that showed increased risk for coronary heart disease and stroke with daily SSB intake (20,21).

The main exposure variable was disability status (yes or no). Those who reported a limitation in any activities due to physical, mental, or emotional problems or who reported special equipment use were defined as having a disability. Adults with disabilities were identified based on a response of yes to either of 2 questions: “Are you limited in any way in any activities because of physical, mental, or emotional problems?” and “Do you now have any health problem that requires you to use special equipment, such as a cane, a wheelchair, a special bed, or a special telephone?” Participants who answered no to both questions were considered to have no disability.

Covariates were the following sociodemographic factors: sex, age (18−24 y, 25−34 y, 35−44 y, 45−54 y, 55−64 y, and ≥65 y), race/ethnicity (non-Hispanic white, non-Hispanic black, non-Hispanic other, or Hispanic), annual household income (<$25,000, $25,000 to <$50,000, $50,000 to <$75,000, ≥$75,000, and unknown), educational attainment (high school diploma), marital status (married/couple, previously married, and never married). Body mass index (BMI, kg/m2), calculated by using self-reported data on height and weight, was used to dichotomize respondents into 2 groups: nonobese (BMI <30) and obese (BMI ≥30).

Statistical analysis

We calculated the unadjusted prevalence of daily SSB consumption, by disability status and sociodemographic variables, and assessed the differences in prevalence of daily SSB consumption between adults with disability and without disability for each demographic subgroup by using the χ2 test. To examine the adjusted association between SSB consumption and disability, we applied a multivariable logistic regression model to calculate adjusted odds ratios (ORs) and 95% confidence intervals (CIs). In the analysis, daily SSB consumption was treated as a binary outcome and all covariates were treated as categorical variables. Because previous studies showed significant associations between SSB consumption and obesity (17) and associations between disability and obesity (5), we tested for an interaction between disability and obesity status to consider obesity status as a potential effect modifier on the association between SSB consumption and disability in the multivariable regression. Because we found a significant interaction between disability and obesity status (P < .001), we tabulated the data on the association of SSB intake and disability by obesity status.

We considered P < .05 to be significant. To consider unequal selection probability and nonresponse differences, all analyses were conducted in SAS complex survey modules (version 9.3, SAS Institute Inc) by including sample weights, sampling strata, and primary sampling units in the analyses.

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Results

Of 150,760 survey participants in 23 states and the District of Columbia, 22.5% reported having a disability. Of all survey participants, 29.0% reported drinking SSB at least once daily; the prevalence of daily SSB consumption was slightly higher among adults with disabilities than among adults without disabilities (30.3% vs 28.6%, χ2= 7.4, P = .01) (Table 1). A significantly higher percentage of adults with disabilities than without disabilities were obese, female, older, had lower household income, had lower educational attainment, and were previously married. The prevalence of daily SSB intake decreased with age regardless of disability status, but it was significantly higher in adults with disabilities than in adults without disabilities in all age groups (Table 1).

Among nonobese adults, the odds of daily SSB intake were 1.27 times higher (95% CI, 1.17–1.38, P < .001) among adults with disabilities than among those without disabilities after controlling for sex, age, race/ethnicity, annual household income, educational attainment, and marital status (Table 2). However, among obese adults, we found no significant difference in the odds of daily SSB consumption by disability status (OR = 0.97; 95% CI, 0.86–1.09, P = .58). Results were the same when we further controlled for state.

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Discussion

The prevalence of consuming SSB at least once daily was 30.3% among adults with disability and 28.6% among adults with no disability in our study. The prevalence of disability for adults aged 18 years or older was 22.5% in our study, a prevalence similar to that reported in the 2010 Census Bureau, 21.3% for people aged 15 years or older, where disability was defined as having a difficulty in communicative, physical, or mental domains. The content of criteria used for each domain overlapped with the content of questions used to assess disability in the BRFSS (1).

Adults with disabilities are more likely than those without disabilities to have chronic diseases related to poor diet (5,6). However, among adults with disabilities, overall dietary intake is not well understood and the intake of foods or beverages that may contribute to poor diet quality, such as SSBs, is not documented. According to 2007–2010 NHANES data (18), the amount of saturated fat intake was likely to exceed the recommended daily limit among adults with disabilities, whereas the amount of fiber, vitamin A, vitamin C, calcium, and potassium intake was less likely to meet recommendations. Using 2011 BRFSS data, one study found that adults with disabilities consumed fruits and vegetables less frequently than adults without disabilities (22). In our study, the odds of consuming SSBs daily were 27% higher among nonobese adults with disabilities than among nonobese adults without disabilities. Because of disability-related limitations, such as severity of disabling condition, loss of appetite, and lack of physical energy, consumption of a healthy diet may be more challenging for adults with disabilities (3,4). These barriers may limit their ability to consume a healthy diet and may result in inadequate nutritional intake (eg, through consumption of processed food or fast food) (23). Eating fast food may be positively associated with increased SSB consumption among adults (24–26). One study found that among adults, fast food was associated with higher SSB intake — adults who ate fast foods drank about half of a serving more of SSBs than those who did not eat fast foods (24).

We found no significant association between disability and daily SSB intake among adults with obesity after controlling for sociodemographic factors. This finding might be due to the fact that our study was cross-sectional: adults with obesity may limit their SSB intake as a strategy for losing weight. A previous study reported that US adults who were trying to lose weight had lower SSB intake than those who were not trying to lose weight (16). Another possibility is that underlying health conditions or limitations related to obesity might have masked any associations between SSB intake and disability status.

Disability prevalence may be increasing because of advances in medical technologies (more years lived with disability), increased life expectancy, and an aging population. However, despite the nation’s progress in reducing health disparities among racial and ethnic minority groups (27,28), little attention has been given to the health disparities of people with disabilities. Identification of the factors related to poor dietary habits, including daily consumption of SSBs, among adults with disabilities can lead to strategies to improve nutrition and potentially reduce their health disparities.

Because sugar consumption increases energy intake but reduces nutritional caloric intake, the World Health Organization recommends reducing sugar intake, of which SSBs are a primary source (29). The negative affect of SSB intake on adults’ health (10–16) has not been examined exclusively among adults with disabilities, although a negative affect could be more substantial among this population than among adults without disabilities. Furthermore, identifying environmental factors associated with SSB intake among people with disabilities is needed to inform interventions to reduce SSB consumption. A healthy lifestyle includes healthy nutrition, and small lifestyle changes may especially affect the health of people with disabilities. A focus on the reduction of SSB consumption could aid in the prevention of related chronic diseases (30).

This study has several limitations. First, BRFSS data are based on self-report, which are subject to recall and reporting bias. Second, SSB intake was measured as a frequency in a food-frequency questionnaire (FFQ) rather than as an amount or as calories. However, although a direct comparison cannot be made with our study, a previous study found that estimates of beverage intake from a fully quantitative FFQ were similar to estimates from a 24-hour dietary recall (31). Third, because only 23 states and the District of Columbia participated in the optional BRFSS Sugar Drinks Module, our findings may not be generalizable to the entire US adult population. Fourth, the BRFSS disability questions do not assess disability severity and type or distinguish between mental and physical disabilities; these factors could influence beverage choices among adults with disabilities. For example, adults with severe disability might receive complete diet care from their caregivers, and the accurate report from people with mental disabilities can be difficult; such factors may confound the associations examined in our study. Fifth, about 13% of participants had unknown household income but were still included in the analysis so that we would not lose information on the main variables (ie, disability, SSB consumption, and obesity). Sixth, survey participants who use cellular telephones exclusively may have different characteristics than participants who do not, which may limit their representativeness. Finally, BRFSS data are cross-sectional, therefore, causation and directionality of association between SSB intake and disability by obesity status cannot be determined.

Our study found that 3 of 10 adults with disability consumed an SSB at least once daily. The prevalence of daily SSB consumption among adults with disabilities was slightly higher than among adults without disabilities; however, because this was one cross-sectional study, we do not know whether this difference is meaningfully significant. Moreover, nonobese adults with disability had higher odds of consuming an SSB at least once daily than nonobese adults without disability after controlling for sociodemographic factors. Our findings suggest that there is a need to increase awareness of SSB intake among adults with disabilities, because, given their possible limited mobility and other health conditions, adults with disabilities may be at even higher risk of developing chronic diseases than their counterparts. Health promotion program practitioners should be aware of the high prevalence of daily SSB intake in this population. Targeted intervention strategies may increase awareness that an unhealthy diet, consisting of frequent SSB intake, is associated with adverse health consequences. Finally, more research is needed to assess the effect of frequent SSB consumption on the increased chronic health risks among adults with disabilities.

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Acknowledgments

The authors thank the state health departments’ BRFSS coordinators for their participation in data collection and CDC’s staff in the Division of Population Health and Population Health Surveillance Branch for developing and maintaining the BRFSS data base. The authors also thank Dr Suzanne Whitmore for her valuable comments and David Flegel for his editorial contributions on this manuscript. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the CDC or Northrop Grumman. The authors have no conflicts of interest.

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Author Information

Corresponding Author: Sunkyung Kim, PhD, Northrop Grumman, 2800 Century Pkwy NE, Atlanta, GA 30345. Telephone: 770-488-4550. Email: wox0@cdc.gov.

Author Affiliations: 1Northrop Grumman, Atlanta, Georgia. 2Division of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia. 3Division of Human Development and Disability, National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia. 4Commissioned Corps Officer, US Public Health Service, Atlanta, Georgia. 5Population Health Surveillance Branch, Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia.

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Tables

Return to your place in the textTable 1. Sociodemographic Characteristics of Respondents, by Disability Status and Sugar-Sweetened Beverage (SSB)a Consumption, Among Adults in 23 States and the District of Columbia, Behavioral Risk Factor Surveillance System, 2013
Characteristic Adults With Disability Adults With No Disability P Valuec
n (%)b SSB Intake, % (SE) n (%)b SSB Intake, % (SE)
≥Once Daily ≥Once Daily
Total respondents 41,199 (100.0) 30.3 (0.5) 69.7 (0.5) 109,561 (100.0) 28.6 (0.3) 71.4 (0.3) .01
Obesity
No 24,287 (59.9) 30.8 (0.7) 69.2 (0.7) 81,380 (75.0) 27.3 (0.4) 72.7 (0.4) <.001
Yes 16,912 (40.1) 29.6 (0.9) 70.4 (0.9) 28,181 (25.0) 32.3 (0.6) 67.7 (0.6) .01
Sex
Male 16,088 (47.1) 33.5 (0.9) 66.5 (0.9) 46,751 (50.8) 34.0 (0.5) 66.0 (0.5) .63
Female 25,111 (52.9) 27.5 (0.7) 72.5 (0.7) 62,810 (49.2) 23.0 (0.4) 77.0 (0.4) <.001
Age, y
18–24 722 (5.9) 51.0 (3.6) 49.0 (3.6) 6,619 (14.3) 42.2 (1.2) 57.8 (1.2) .02
25–34 1,813 (9.5) 45.6 (2.5) 54.4 (2.5) 12,273 (18.0) 37.2 (0.8) 62.8 (0.8) .01
35–44 3,124 (11.2) 43.0 (1.8) 57.0 (1.8) 15,892 (17.7) 30.2 (0.7) 69.8 (0.7) <.001
45–54 6,713 (19.6) 34.8 (1.2) 65.2 (1.2) 20,060 (18.4) 26.5 (0.7) 73.5 (0.7) <.001
55–64 10,830 (23.6) 24.1 (0.9) 75.9 (0.9) 23,516 (15.3) 19.7 (0.6) 80.3 (0.6) <.001
≥65 17,997 (30.2) 18.7 (0.7) 81.3 (0.7) 31,201 (16.2) 16.0 (0.5) 84.0 (0.5) .01
Race/ethnicity
Non-Hispanic white 33,314 (70.9) 28.4 (0.5) 71.6 (0.5) 88,364 (65.3) 26.2 (0.3) 73.8 (0.3) <.001
Non-Hispanic black 4,667 (12.4) 40.3 (1.9) 59.7 (1.9) 10,687 (10.7) 39.7 (1.1) 60.3 (1.1) .81
Hispanic 1,203 (10.0) 31.6 (3.5) 68.4 (2.5) 5,396 (15.2) 36.6 (1.1) 63.4 (1.1) .01
Non-Hispanic other 2,015 (6.7) 29.4 (2.7) 70.6 (2.7) 5,114 (8.8) 18.7 (1.1) 81.3 (1.1) <.001
Annual household income, $
<25,000 16,382 (41.4) 35.9 (0.9) 64.1 (0.9) 20,773 (22.1) 39.0 (0.8) 61.0 (0.8) .01
25,000 to <50,000 8,994 (20.4) 28.7 (1.2) 71.3 (1.2) 24,912 (21.8) 33.6 (0.7) 66.4 (0.7) <.001
50,000 to <75,000 4,201 (10.4) 22.6 (1.5) 77.4 (1.5) 17,045 (14.5) 26.6 (0.8) 73.4 (0.8) .02
≥75,000 5,806 (15.3) 20.6 (1.3) 79.4 (1.3) 33,604 (30.3) 18.0 (0.5) 82.0 (0.5) .05
Unknown 5,816 (12.5) 32.5 (1.7) 67.5 (1.7) 13,227 (11.3) 29.3 (0.9) 70.7 (0.9) .08
Education level
5,131 (20.5) 40.8 (1.6) 59.2 (1.6) 6,842 (12.4) 42.9 (1.2) 57.1 (1.2) .31
High school diploma 13,041 (29.2) 33.6 (1.0) 66.4 (1.0) 30,282 (27.4) 36.4 (0.6) 63.6 (0.6) .02
>High school diploma 23,027 (50.3) 24.1 (0.6) 75.9 (0.6) 72,437 (60.2) 22.1 (0.4) 77.9 (0.4) .01
Marital status
Married/couple 18,551 (49.7) 27.7 (0.8) 72.3 (0.8) 64,991 (58.6) 25.2 (0.4) 74.8 (0.4) .01
Previously married 17,527 (32.1) 28.8 (0.8) 71.2 (0.8) 27,315 (16.5) 25.7 (0.6) 74.3 (0.6) .01
Never married 5,121 (18.2) 40.2 (1.7) 59.8 (1.7) 17,255 (24.9) 38.5 (0.8) 61.5 (0.8) .37

a SSBs include regular soda, fruit drink, sweet tea, and sports or energy drink.
b Unweighted sample size and weighted percentage.
c Differences in prevalence of daily SSB consumption between those with disabilities and those without disabilities determined by χ2 test.

Return to your place in the textTable 2. Adjusteda Odds Ratios and 95% Confidence Intervals for Consuming Sugar-Sweetened Beveragesb at Least Once Daily, by Disability and Obesity Status, Among Adults in 23 States and District of Columbia, Behavioral Risk Factor Surveillance System, 2013
Variable n Adjusted Odds Ratio (95% Confidence Interval) P Value
Disability by obesity status
Not obese
  No disability 81,380 1 [Reference]
  Disability 24,287 1.27 (1.17–1.38) <.001
Obese
  No disability 28,181 1 [Reference]
  Disability 16,912 0.97 (0.86–1.09) .58
Sex
Male 16,088 1.63 (1.54–1.72) <.001
Female 25,111 1 [Reference]
Age, y
18–24 722 4.35 (3.83–4.94) <.001
25–34 1,813 3.64 (3.30–4.02) <.001
35–44 3,124 2.95 (2.69–3.23) <.001
45–54 6,713 2.31 (2.12–2.52) <.001
55–64 10,830 1.49 (1.36–1.62) <.001
≥65 17,997 1 [Reference]
Race/ethnicity
Non-Hispanic white 33,314 1 [Reference]
Non-Hispanic black 4,667 1.39 (1.27–1.52) <.001
Hispanic 1,203 0.62 (0.54–0.71) .001
Non-Hispanic other 2,015 0.85 (0.77–0.95) <.001
Annual household income, $
0 to <25,000 16,382 2.12 (1.93–2.33) <.001
25,000 to <50,000 8,994 1.95 (1.78–2.12) <.001
50,000 to <75,000 4,201 1.52 (1.38–1.67) <.001
≥75,000 5,806 1 [Reference]
Unknown 5,816 1.66 (1.49–1.84) <.001
Education level
5,131 2.02 (1.83–2.23) <.001
High school diploma 13,041 1.65 (1.55–1.75) <.001
>High school diploma 23,027 1 [Reference]
Marital status
Married/couple 18,551 1 [Reference]
Previously married 17,527 1.03 (0.96–1.10) .44
Never married 5,121 0.95 (0.87–1.03) .22

a Multivariable logistic regression included disability, obesity, interaction of disability and obesity and controlled for sex, age, race/ethnicity, annual household income, educational attainment, and marital status.
b Sugar-sweetened beverages include regular soda, fruit drink, sweet tea, and sports or energy drink.

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State Laws Regarding Indoor Public Use, Retail Sales, and Prices of Electronic Cigarettes—U.S. States, Guam, Puerto Rico, and U.S. Virgin Islands, September 30, 2017

Discussion

Several states have enacted laws related to e-cigarettes in recent years, ranging from tobacco-21 laws in five states, DC, and Guam, to self-service display restrictions in approximately half of the states. Legislative activity increased during 2013–2015, peaked in 2015, and has since slowed. One third of states did not have any of the five assessed laws. State, local, and territorial strategies to reduce youths’ initiation of e-cigarettes and population exposure to e-cigarette aerosol, including educational initiatives, coupled with federal regulation of tobacco product manufacturing, labeling, and marketing, could help reduce the risks of e-cigarettes on population health, especially among young persons (2,5).

On October 23, 2017, New York became the ninth state to include e-cigarettes in its comprehensive smoke-free indoor air law.§ Thus, one third of the 27 states and DC with comprehensive smoke-free laws that prohibit conventional tobacco smoking in restaurants, worksites, and bars also prohibit e-cigarette use in these venues. Therefore, approximately 75.4% of the U.S. population (an estimated 243.6 million U.S. residents, including 55.7 million children) live in states in which bystanders can be exposed to secondhand e-cigarette aerosol in indoor public spaces. Previous research indicates that one in four U.S. middle and high school students reported past-month exposure to e-cigarette aerosol in a public place in 2015 (7). This exposure is of public health concern because the U.S. Surgeon General has concluded that e-cigarette aerosol is not harmless water vapor, and environmental studies have documented harmful and potentially harmful ingredients in secondhand e-cigarette aerosol, including nicotine, heavy metals, ultrafine particulate matter, and volatile organic compounds (2). Including e-cigarettes in comprehensive smoke-free laws can prevent involuntary exposures to secondhand e-cigarette aerosol, especially among vulnerable populations such as youths and pregnant women; simplify enforcement of smoke-free policies; and reduce the potential for the renormalization of tobacco product use (2).

The remaining types of laws assessed in this study leverage conventional smoking prevention strategies for youths, which have the potential to prevent youths’ e-cigarette access (2,5). Licensing requirements for tobacco retailers and manufacturers can increase the incentive to comply with tobacco-related laws, including those prohibiting sales to youths (2). In addition, restricting self-service tobacco displays can reduce youths’ tobacco access by reducing theft and increasing interactions between customers and retailers (8). Increasing the minimum age of tobacco product sales to 21 years is a potential prevention strategy, because 95% of adult smokers begin before age 21, and young adulthood represents a critical period when many smokers progress from experimental to regular tobacco use (9). Finally, substantial increases in conventional cigarette prices reduce consumption, especially among youths. To date, data are limited on the impact of e-cigarette taxes on conventional cigarette use; however, similar to conventional cigarettes, e-cigarette price increases would be expected to reduce use by youths (2,5). Further evaluations of the effectiveness of these strategies can help inform public health practice and planning (2,5).

FDA is authorized to regulate the manufacturing, sales, distribution, and marketing of tobacco products sold in the United States. In May 2016, the agency asserted jurisdiction over products that meet the definition of a tobacco product, including e-cigarettes. FDA generally cannot restrict public tobacco use, tax tobacco products, or establish a minimum age for tobacco sales above age 18 years (2). However, the Family Smoking Prevention and Tobacco Control Act ensures that localities, states, territories, and tribes can continue to play a central role in tobacco prevention and control policies by preserving their authority to regulate sales, marketing, advertising, and use of tobacco products by persons of any age. Thus, state, local, territorial, and tribal tobacco control strategies are an important complement to federal regulation, which can help reduce the public health risks of e-cigarettes, particularly among young persons (2).

The findings in this report are subject to at least two limitations. First, STATE does not account for local laws, bills under consideration, regulations, opinions of attorneys general, or case law decisions for tobacco control topics other than preemption. For example, at least 400 localities prohibit indoor e-cigarette use and smoking in worksites, restaurants, and bars,** and at least 200 localities have tobacco-21 laws.†† Second, statutory requirements and definitions vary across states. For example, although 26 states have laws or regulations prohibiting self-service displays of e-cigarettes, only three of these states (California, Iowa, and New Mexico) prohibit all self-service displays of e-cigarettes; the remaining 21 states restrict self-service displays to adult-only facilities or tobacco specialty stores and vape shops (6). Moreover, some states have regulated e-cigarettes by expanding the statutory definition of a tobacco product to include e-cigarettes, regardless of nicotine content, to simplify enforcement (2). However, some states define the products as alternative nicotine or vapor products that are exempt from other tobacco product laws, such as licensure requirements and taxes (2).

Given that cigarettes and other combusted tobacco products are responsible for the overwhelming burden of tobacco-related death and disease in the United States (5), the Surgeon General has recommended actions to uphold and accelerate strategies proven to prevent and reduce combustible tobacco smoking among youths and adults, while simultaneously preventing youths’ use of emerging tobacco products such as e-cigarettes (2). A comprehensive tobacco control framework, which includes strategies to prevent all tobacco product use by youths and public exposure to secondhand tobacco smoke and e-cigarette aerosol, is important to protect the public’s health (2,5).

Health and Development at Age 19–24 Months of 19 Children Who Were Bor

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Ashley Satterfield-Nash, DrPH1; Kim Kotzky, MPH1; Jacob Allen, MPH2; Jeanne Bertolli, PhD3; Cynthia A. Moore, MD, PhD3; Isabela Ornelas Pereira4; André Pessoa, MD5; Flavio Melo, MD6; Ana Carolina Faria e Silva Santelli, MD7; Coleen A. Boyle, PhD3; Georgina Peacock, MD3 (View author affiliations)

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Summary

What is already known about this topic?

Congenital Zika virus infection has been linked to increased rates of microcephaly and a unique pattern of birth defects among infants. Although children with microcephaly and laboratory evidence of Zika virus infection have been described in early infancy, the subsequent health and development in young children have not been well characterized, constraining planning for the care of these children.

What is added by this report?

The growth and development of 19 children, aged 19–24 months, with laboratory evidence of Zika virus infection were thoroughly assessed. All children had at least one adverse outcome including feeding challenges, sleeping difficulties, severe motor impairment, vision and hearing abnormalities, and seizures, and these outcomes tended to co-occur.

What are the implications for public health practice?

Children with microcephaly and laboratory evidence of Zika virus infection face medical and functional challenges that span many areas of development, some of which become more evident as children age. They will continue to require specialized care from clinicians and caregivers. These data allow for anticipation of medical and social services needs of affected children and families, such as early intervention services, and planning for resources to support these families in healthcare and community settings.

In November 2015, the Brazilian Ministry of Health (MOH) declared the Zika virus outbreak a public health emergency after an increase in microcephaly cases was reported in the northeast region of the country (1). During 2015–2016, 15 states in Brazil with laboratory-confirmed Zika virus transmission reported an increase in birth prevalence of microcephaly (2.8 cases per 10,000 live births), significantly exceeding prevalence in four states without confirmed transmission (0.6 per 10,000) (2). Although children with microcephaly and laboratory evidence of Zika virus infection have been described in early infancy (3), their subsequent health and development have not been well characterized, constraining planning for the care and support of these children and their families. The Brazilian MOH, the State Health Secretariat of Paraíba, and CDC collaborated on a follow-up investigation of the health and development of children in northeastern Brazil who were reported to national surveillance with microcephaly at birth. Nineteen children with microcephaly at birth and laboratory evidence of Zika virus infection were assessed through clinical evaluations, caregiver interviews, and review of medical records. At follow-up (ages 19–24 months), most of these children had severe motor impairment, seizure disorders, hearing and vision abnormalities, and sleep difficulties. Children with microcephaly and laboratory evidence of Zika virus infection have severe functional limitations and will require specialized care from clinicians and caregivers as they age.

The Zika Outcomes and Development in Infants and Children (ZODIAC) investigation sought to compile a comprehensive description of health and development among children aged >12 months who were born with microcephaly and participated in a 2016 case-control investigation. The case-control investigation assessed the association of Zika virus infection and microcephaly among children aged 1–7 months, living in Paraíba state. The children and their caregivers were evaluated by multidisciplinary teams at two state clinics in Campina Grande and João Pessoa (macroregions 1 and 2) in Paraíba state during August–October 2017. This report describes a subsample of 19 children, aged 19–24 months, who participated in ZODIAC and were born with microcephaly and with laboratory evidence of Zika virus infection.

All children in the ZODIAC investigation were born from October 1, 2015 through January 31, 2016, and were reported to the Registro de Eventos de Saúde Pública (RESP)—Microcefalias, Brazil’s national microcephaly registry. For infants to be eligible for the 2016 case-control investigation, their mothers must have resided in Paraíba state for at least 80% of their pregnancy. For the ZODIAC investigation, microcephaly was defined as head circumference below the third percentile for gestational age and sex, according to INTERGROWTH 21st standards (4). Subsequent measurements are reported in standard deviations (SD) to better characterize growth deficiencies (5). Laboratory evidence of Zika virus infection was defined as a positive test for Zika virus immunoglobulin M (IgM) and virus specific-neutralizing antibodies or a positive test for Zika virus-specific neutralizing antibodies in an infant sample (6). Samples were obtained at age 1–7 months in the 2016 case-control investigation, and any evidence of infection was assumed to be prenatal in origin. Results of prenatal and newborn testing to rule out other congenital infections were available for some infants and their mothers.

ZODIAC data were collected through clinical evaluations, caregiver interviews, and review of medical records. Licensed physicians performed growth, ophthalmologic and physical exams, and a neurologic assessment. Physicians were trained to use the Hammersmith Infant Neurological Examination (HINE), a standardized neurologic exam, to assess neuromotor function and visual and auditory responses (7). Trained interviewers administered screening and assessment instruments to the primary caregiver (usually the mother) regarding the child’s health and development, including a seizure screener (8), the Ages and Stages Questionnaires (ASQ-3),* and the Ages and Stages Social-Emotional Questionnaires (ASQ:SE). Data were captured in REDCap, a secure web application.

The families of 278 previously studied children residing in the ZODIAC investigation catchment area were eligible for inclusion; 122 children were enrolled, including 19 who were aged <24 months and who had both microcephaly at birth and laboratory evidence of Zika virus infection. Among the 19 children, 11 had a blood specimen that tested positive for Zika virus-specific IgM antibodies and neutralizing antibodies against Zika virus, and eight had only neutralizing antibodies against Zika virus. Among the eight with neutralizing antibodies only, seven had at least one test for other congenital infections; one had a positive Toxoplasma immunoglobulin G (IgG) antibody result and one had positive rubella virus and cytomegalovirus IgG results. Both had negative IgM antibody results for these infections; the first had brain imaging findings consistent with congenital Zika virus infection and the second had no record of imaging.

The median age at follow-up evaluation was 22 months (range = 19–24 months); 10 were male and nine were female. At the time of assessment, 15 children (seven males and eight females) had head circumference measurements more than 3 SDs below the mean for their age and sex (Table 1) (Table 2). Four children had an increase in head circumference for age from birth measurements: three males had head circumference within 1 SD below the mean and one female had head circumference within 1 SD above the mean. Thirteen children (six males and seven females) had length measurements 1–3 SDs below the mean, and 13 children (six males and seven females) had weight measurements 1 to >3 SDs below the mean for their age and sex.

Eleven children screened positive for nonfebrile seizures, indicating possible seizure disorder (Table 2) (Table 3). Caregivers reported that eight children were previously hospitalized, including six hospitalized for bronchitis/pneumonia, and that 10 children had frequent sleeping difficulties and nine had eating or swallowing challenges. Thirteen children had an impaired response to auditory stimuli. Four children had retinal abnormalities and 11 had an impaired response to visual stimuli. Fifteen children did not pass the ASQ-3 age interval questionnaire designed for a child aged 6 months. Fifteen children had a global score below 40 on the HINE, indicating severe motor impairment, including 14 who had findings consistent with cerebral palsy (7). Outcomes including feeding challenges, sleeping difficulties, severe motor impairment, vision and hearing abnormalities, and seizures tended to co-occur. All children had at least one of these outcomes, 12 had three to five of these outcomes, and two had all six outcomes. Four children (infant number 16, 17, 18, and 19) (Table 2) had typical growth and development at follow-up and might have been misclassified at birth.

Discussion

As of September 2017, 2,986 newborns with microcephaly in Brazil were reported to RESP and 2,959 cases are being monitored (9). Children with Zika virus–associated microcephaly face medical and functional challenges that span many areas of development. Previous reports established a baseline of poor health outcomes at birth, including severe brain and ophthalmologic abnormalities, and other serious central nervous system abnormalities (3). This report expands on initial findings by demonstrating that specific outcomes, such as severe motor impairment and impaired visual and auditory response to stimuli, affect the majority of children with evidence of congenital Zika virus infection and microcephaly and become more apparent as these children age. Approximately three quarters of young children affected by Zika virus infection in this analysis had at least three of the specified co-occurring outcomes. Many of the initial findings identified at birth remain present at ages 19–24 months, and these children are falling far behind in achievement of age-appropriate developmental milestones, indicating the need for long-term follow-up and support.

The findings in this report are subject to at least four limitations. First, although all children with microcephaly recruited into the 2016 case-control investigation from selected areas of Paraíba state were offered enrollment in the ZODIAC investigation, not all families chose to participate. Consequently, the findings might not be representative of all children with microcephaly associated with congenital Zika virus infection. Second, errors in head circumference measurement at birth and passive transfer of maternal antibodies might have led to misidentification and might explain the divergent observations for the four children showing more typical development. Additionally, some of the parent-assessment findings, such as those from the seizure screener, were not medically verified. Finally, the ages of infants in the original case-control investigation ranged from 1 to 7 months at the time of blood collection, and it is possible that the laboratory results for some infants reflected postnatal, rather than prenatal, exposure.

This report provides information on the ongoing challenges facing children with severe congenital Zika virus syndrome; these children will require specialized care from clinicians and caregivers as they age. These findings allow for anticipation of medical and social service needs of affected children and their families, including early intervention services, and planning for resources to support these families in health care and community settings in Brazil, the United States, and other countries. Children with disabilities related to congenital Zika virus infection will need multidisciplinary care from various pediatric subspecialists (10). Long-term follow-up and measurement of developmental progression of children affected by Zika virus can inform intervention services and sub-specialties needed to provide optimal care for these children.

Acknowledgments

Alanna dos Santos Delfino, Analine de Souza Bandeira Correia, Bruna Valerio Correia, Camila Carla de Arruda Silva, Camille Harden, Camille Smith, Charles Rose, Christine Coeli Moreira da Silva, Claudia Ferreira Rineiro Leao, Cláudia Luciana de Sousa Mascena Veras, Eric Dziuban, Erlane Marques Ribeiro, J. Erin Staples, Joria Guerreiro, Larissa Ribeiro Do Amaral, Maria Celeste Dantas Jotha De Lima, Myrian Carvalho, Daniele Ribeiro Magalhães Camelo, Eliza Gordon-Lipkin, Fabio Ramon Bezerra Clementino, Flawber Antonio Cruz, Georgia Medeiros Lopes De Souza Lucio, Isadora Silveira Xavier, Ivanice Jacinto da Silva, Jeanete Romao dos Santos, Jennita Reefhuis, Juliana Carneiro Monteiro Wanderley, Juliana Sousa Soares De Araujo, Kallytuana Mell Silva Sarmento, Karla Naraiane de Araujo, Karoline Marques Dantas, Mariana Bernardo Bezerra, Mariana Braatz Krueger, Michael Fox, Nathalie Maitre, Nevin Krishna, Pâmela Rodrigues Barbosa, Patricia Karla Guimaraes Brito, Priscila Leal Leite, Rafaela Domingos da Cunha, Rafaela Soares Barros de Menezes, Rafaella Alves Sarmento Costa, Rebecca Bitsko, Renato Girade, Renato Lima, Rogeirio Alves de Santana, Saile Cavalcante Kerbage, Suzanne Gilboa, Thalita Analyane Bezerra de Albuquerque, Thayse Elaine Costa Figueiredo, Vandezita Dantas De Medeiros Mazzaro, Virginia Batista de Morais.


Corresponding author: Ashley Satterfield-Nash, yev6@cdc.gov, 404-498-6084.

References

  1. Pan American Health Organization. Timeline of emergence of Zika virus in the Americas. Washington DC: Word Health Organization, Pan American Health Organization; 2016. http://www.paho.org/hq/index.php?option=com_content&view=article&id=11959:timeline-of-emergence-of-zika-virus-in-the-americas&Itemid=41711&lang=en
  2. Kleber de Oliveira W, Cortez-Escalante J, De Oliveira WT, et al. Increase in reported prevalence of microcephaly in infants born to women living in areas with confirmed Zika virus transmission during the first trimester of pregnancy—Brazil, 2015. MMWR Morb Mortal Wkly Rep 2016;65:242–7. CrossRef PubMed
  3. Moore CA, Staples JE, Dobyns WB, et al. Characterizing the pattern of anomalies in congenital Zika syndrome for pediatric clinicians. JAMA Pediatr 2017;171:288–95. CrossRef PubMed
  4. International Fetal and Newborn Growth Consortium for the 21st Century. Standards for newborns and references for very preterm infants. Oxford, United Kingdom: International Fetal and Newborn Growth Consortium for the 21st Century; 2017. https://intergrowth21.tghn.org/
  5. World Health Organization. Child growth standards. Head circumference for age. Geneva, Switzerland: World Health Organization; 2017. http://www.who.int/childgrowth/standards/hc_for_age/en/
  6. Martin DA, Muth DA, Brown T, Johnson AJ, Karabatsos N, Roehrig JT. Standardization of immunoglobulin M capture enzyme-linked immunosorbent assays for routine diagnosis of arboviral infections. J Clin Microbiol 2000;38:1823–6. PubMed
  7. Romeo DM, Ricci D, Brogna C, Mercuri E. Use of the Hammersmith Infant Neurological Examination in infants with cerebral palsy: a critical review of the literature. Dev Med Child Neurol 2016;58:240–5. CrossRef PubMed
  8. Douglass LM, Kuban K, Tarquinio D, et al. A novel parent questionnaire for the detection of seizures in children. Pediatr Neurol 2016;54:64–69.e1. CrossRef PubMed
  9. Secretaria de Vigilância em Saúde, Ministério da Saúde. Integrated monitoring of changes in growth and development related to Zika virus infection and other infectious etiologies, up to Epidemiological week 38 of 2017.Brasília, Brazil: Ministry of Health Brazil, Secretaria de Vigilância em Saúde, Ministério da Saúde; 2017. http://portalarquivos2.saude.gov.br/images/pdf/2017/novembro/16/2017-036.pdf
  10. Adebanjo T, Godfred-Cato S, Viens L, et al. Update: interim guidance for the diagnosis, evaluation, and management of infants with possible congenital Zika virus infection—United States, October 2017. MMWR Morb Mortal Wkly Rep 2017;66:1089–99. CrossRef PubMed
Return to your place in the textTABLE 1. Growth measurements* of children aged 19–24 months with confirmed or probable congenital Zika virus infection†,§ and microcephaly classification at birth¶,** — Paraíba, Brazil, August–October 2017
Growth No. (%)
Male (n = 10) Female (n = 9)
Head circumference††
>3 SD below mean for age and sex§§ 7 (70) 8 (89)
Length¶¶
1–3 SD below mean for age and sex*** 6 (60) 7 (78)
Weight†††
1 to >3 SD below mean for age and sex§§§ 6 (60) 7 (78)

Abbreviation: SD = standard deviation.
* http://www.who.int/childgrowth/standards/en.
Confirmed congenital Zika virus infection was indicated by a positive Zika virus-specific immunoglobulin M [IgM] capture enzyme-linked immunosorbent assay [MAC-ELISA] result on infant cerebrospinal fluid [CSF] or serum) and positive plaque reduction neutralization testing (PRNT). Serologic evidence without confirmation via PRNT indicated probable congenital Zika virus infection.
§http://jcm.asm.org/content/38/5/1823.full.pdf+html.
Microcephaly at birth was defined according to the internationally accepted definition, head circumference below the 3rd percentile for gestational age and sex, from the standards for newborns and references for very preterm infants compiled by the International Fetal and Newborn Growth Consortium for the 21st Century.
** https://intergrowth21.tghn.org/.
††http://www.who.int/childgrowth/standards/hc_for_age/en/.
§§ Of the remaining males, three (30%) had a head circumference equal to the mean or up to 1 SD below the mean, and of the remaining females, one (11%) had a head circumference equal to the mean or up to 1 SD above the mean.
¶¶http://www.who.int/childgrowth/standards/height_for_age/en/.
*** Of the remaining males, the length of 4 (40%) was equal to the mean or up to 3 SDs above the mean, and of the remaining females, the length of 2 (22%) was equal to the mean or up to 1 SD above the mean.
†††http://www.who.int/childgrowth/standards/weight_for_age/en/.
§§§ Of the remaining males, the weight of 3 (30%) was equal to the mean or up to 2 SDs above the mean; the weight of 1 (10%) male was >3 SDs above the mean. Of the remaining females, the weight of 2 (22%) was equal to the mean or up to 2 SDs above the mean.

Return to your place in the textTABLE 2. Growth parameters,* evaluations, and medical and developmental conditions for 19 infants aged 19–24 months with confirmed or probable congenital Zika virus infection,†,§ and microcephaly classification¶,** at birth — ZODIAC investigation, Paraíba, Brazil, August–October 2017
Infant no. Sex Birth HC** (%) ZODIAC HC†† (Z score) ZODIAC weight§§ (Z score) Brain imaging consistent with CZS Zika laboratory evidence Seizures Eating challenges Sleep challenges Severe motor impairment Vision limitation Hearing abnormalities ASQ-3 age interval¶¶
1 F <3rd -7.85 -1.68 Yes IgM +; NAb + Yes Yes Yes Yes Yes Yes <6 months
2 F <3rd -7.21 -0.98 Yes IgM +; NAb + No No Yes Yes Yes Yes <6 months
3 F <3rd -7.08 -4.47 Yes IgM +; NAb + Yes No Yes Yes No No <6 months
4 M <3rd -4.88 -2.40 Yes NAb + only No Yes No Yes No Yes <6 months
5 M <3rd -4.20 1.90 Yes NAb + only Yes No Yes Yes Yes Yes <6 months
6 F <3rd -5.36 -0.86 Yes IgM +; NAb + No No No Yes No Yes <6 months
7 F <3rd -8.02 -1.56 Yes NAb + only Yes Yes No Yes Yes No <6 months
8 M <3rd -5.75 -4.11 Yes IgM +; NAb + Yes No No Yes No Yes <6 months
9 M <3rd -5.83 -1.46 Yes IgM +; NAb + No Yes No Yes Yes Yes <6 months
10 F <3rd -6.65 -1.23 Yes IgM +; NAb + Yes Yes Yes Yes Yes Yes <6 months
11 F <3rd -5.67 -0.91 Yes NAb + only Yes Yes No Yes Yes Yes <6 months
12 M <3rd -3.69 3.52 Yes IgM +; NAb + Yes No Yes Yes Yes Yes <6 months
13 M <3rd -7.03 -2.36 Yes IgM +; NAb + Yes No Yes Yes Yes Yes <6 months
14 F <3rd -8.45 0.18 Yes IgM +; NAb + Yes Yes No Yes Yes Yes <6 months
15 M <3rd -6.29 -1.60 Yes IgM +; NAb + Yes Yes No Yes Yes Yes <6 months
16 M <3rd -0.68 1.52 No record NAb + only No No Yes No No No >6 months
17 M <3rd -0.18 -0.87 No record NAb + only No No Yes No No No >6 months
18 F <3rd 0.23 1.28 No anomaly NAb + only No Yes No No No No >6 months
19 M <3rd -0.09 1.14 No record NAb + only No No Yes No No No >6 months

Abbreviations: ASQ-3 = Ages and Stages-III Questionnaire; CZS = congenital Zika syndrome; F = female; HC = head circumference; IgM = immunoglobulin M; M = male; NAb = neutralizing antibodies; ZODIAC = Zika Outcomes and Development in Infants and Children.
* http://www.who.int/childgrowth/standards/en.
Confirmed congenital Zika virus infection was indicated by a positive Zika virus-specific IgM capture enzyme-linked immunosorbent assay result on infant cerebrospinal fluid or serum) and positive plaque reduction neutralization testing (PRNT). Serologic evidence without confirmation via PRNT indicated probable congenital Zika virus infection.
§http://jcm.asm.org/content/38/5/1823.full.pdf+html.
Microcephaly at birth was defined according to the internationally accepted definition, head circumference below the 3rd percentile for gestational age and sex from the standards for newborns and references for very preterm infants compiled by the International Fetal and Newborn Growth Consortium for the 21st Century.
** https://intergrowth21.tghn.org/.
††http://www.who.int/childgrowth/standards/hc_for_age/en/.
§§http://www.who.int/childgrowth/standards/weight_for_age/en/.
¶¶ The ASQ-3 is a series of 21 parent-completed questionnaires designed to screen the developmental performance of children aged 1–66 months in the areas of communication, gross motor skills, fine motor skills, problem solving, and personal-social skills (http://agesandstages.com); based on ASQ-3 screening, an age interval of <6 months indicates that the child’s parent-reported developmental progress has not advanced beyond that typical of an infant at age 6 months.

Return to your place in the textTABLE 3. Health and developmental outcomes of 19 children aged 19–24 months with confirmed or probable congenital Zika virus infection,*,† and microcephaly classification§,¶ at birth — Paraíba, Brazil, August–October 2017
Outcome No. (%)
Medical findings
Seizures**,†† 11 (58)
Retinal abnormalities§§ 4 (21)
Hospitalization** 8 (42)
Pneumonia/Bronchitis 6 (75)
Intestinal infection 1 (14)
High fever 1 (14)
Failure to thrive/feed 1 (14)
Functional outcomes
Sleeping difficulties** 10 (53)
Feeding difficulties** 9 (47)
Impaired response to auditory stimuli (hearing asymmetric or no response)¶¶ 13 (68)
Impaired response to visual stimuli¶¶ 11 (58)
Neurologic outcomes¶¶
Severe motor impairment¶¶ 15 (79)
Cerebral palsy*** 14 (74)

* Confirmed congenital Zika virus infection was indicated by a positive Zika virus-specific immunoglobulin M capture enzyme-linked immunosorbent assay result on infant cerebrospinal fluid or serum and positive plaque reduction neutralization testing (PRNT) at birth. Serologic evidence without confirmation via PRNT indicated probable congenital Zika virus infection.
http://jcm.asm.org/content/38/5/1823.full.pdf+html.
§ Microcephaly at birth was defined according to the internationally accepted definition, head circumference below the 3rd percentile for gestational age and sex from the standards for newborns and references for very preterm infants compiled by the International Fetal and Newborn Growth Consortium for the 21st Century.
https://intergrowth21.tghn.org/.
** Reported by the caregiver.
††https://doi.org/10.1016/j.pediatrneurol.2015.09.016.
§§ Retinal abnormalities were identified by ophthalmologic exam.
¶¶ Motor function, functional hearing, and functional vision were assessed using the Hammersmith Infant Neurologic Exam (HINE). A global score below 40 on the HINE is associated with severe motor impairment, according to findings published in 2016 (https://doi.org/10.1111/dmcn.12876).
*** Cerebral palsy was identified by neurologist.

Suggested citation for this article: Satterfield-Nash A, Kotzky K, Allen J, et al. Health and Development at Age 19–24 Months of 19 Children Who Were Born with Microcephaly and Laboratory Evidence of Congenital Zika Virus Infection During the 2015 Zika Virus Outbreak — Brazil, 2017. MMWR Morb Mortal Wkly Rep 2017;66:1347–1351. DOI: http://dx.doi.org/10.15585/mmwr.mm6649a2.

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Large Outbreak of Neisseria meningitidis Serogroup C — Nigeria, Decemb

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Chimeremma Nnadi, MD, PhD1; John Oladejo, MBBS2; Sebastian Yennan, MPH2; Adesola Ogunleye, DDS2; Chidinma Agbai, MPH3; Lawal Bakare, MBBS2; Mohammed Abdulaziz, MBBS4; Amina Mohammed, MBBS5; Mary Stephens, MBBS6; Kyadindi Sumaili, MPH7; Olivier Ronveaux, MBBS8; Helen Maguire, MBBS9; Debra Karch, PhD10; Mahmood Dalhat, MBBS11; Martin Antonio, PhD12; Andre Bita, MBBS13; Ifeanyi Okudo, MBBS6; Patrick Nguku, MBBCH11; Ryan Novak, PhD14; Omotayo Bolu, MD15; Faisal Shuaib, DrPH5; Chikwe Ihekweazu, MBBS2 (View author affiliations)

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Summary

What is already known about this topic?

Meningococcal disease caused by Neisseria meningitidis causes severe illness, and could lead to permanent disability or death if not quickly detected and treated. The largest global burden of meningococcal disease is in sub-Saharan Africa, where annual epidemics caused mainly by N. meningitidis serogroup A were previously common. After the introduction of meningococcal A vaccines in 2013, meningitis caused by serogroup A declined. However, N. meningitidis serogroup C (NmC) has now emerged as a cause of large outbreaks.

What is added by this report?

During December 2016–June 2017, the largest global epidemic of meningitis caused by NmC occurred in northern Nigeria, with 14,518 suspected cases and 1,166 deaths reported. An emergency operations center coordinated rapid development and implementation of an emergency outbreak response plan, including administration of meningococcal serogroup C–containing vaccines to >2 million persons. Multiple logistical challenges were encountered during the response; the outbreak was declared over in June 2017.

What are the implications for public health practice?

National and regional evaluations of the outbreak response have outlined recommendations for improving meningitis outbreak prevention, timely detection, and response in Nigeria. Implementation of these recommendations will be key to reducing future meningitis outbreaks. Expanding availability of multivalent vaccines that are effective against non-A serogroups of N. meningitidis might prevent future outbreaks in this region.

On February 16, 2017, the Ministry of Health in Zamfara State, in northwestern Nigeria, notified the Nigeria Centre for Disease Control (NCDC) of an increased number of suspected cerebrospinal meningitis (meningitis) cases reported from four local government areas (LGAs). Meningitis cases were subsequently also reported from Katsina, Kebbi, Niger, and Sokoto states, all of which share borders with Zamfara State, and from Yobe State in northeastern Nigeria. On April 3, 2017, NCDC activated an Emergency Operations Center (EOC) to coordinate rapid development and implementation of a national meningitis emergency outbreak response plan. After the outbreak was reported, surveillance activities for meningitis cases were enhanced, including retrospective searches for previously unreported cases, implementation of intensified new case finding, and strengthened laboratory confirmation. A total of 14,518 suspected meningitis cases were reported for the period December 13, 2016–June 15, 2017. Among 1,339 cases with laboratory testing, 433 (32%) were positive for bacterial pathogens, including 358 (82.7%) confirmed cases of Neisseria meningitidis serogroup C. In response, approximately 2.1 million persons aged 2–29 years were vaccinated with meningococcal serogroup C–containing vaccines in Katsina, Sokoto, Yobe, and Zamfara states during April–May 2017. The outbreak was declared over on June 15, 2017, after high-quality surveillance yielded no evidence of outbreak-linked cases for 2 consecutive weeks. Routine high-quality surveillance, including a strong laboratory system to test specimens from persons with suspected meningitis, is critical to rapidly detect and confirm future outbreaks and inform decisions regarding response vaccination.

Background

All northern Nigeria states lie within the sub-Saharan “Meningitis Belt,” a region of 26 countries that experiences the largest burden of meningococcal disease, with annual epidemics reported during the December–June dry season. Meningitis causes severe illness, and if not detected and treated quickly, could lead to permanent disability that puts a significant burden on families. In many settings, approximately 10% of meningitis cases ultimately result in death. Before introduction of the meningococcal serogroup A conjugate vaccine (MenAfriVac) in 2013 (1), Nigeria experienced some of the largest epidemics of meningococcal meningitis, including the 1996 N. meningitidis serogroup A (NmA) epidemic that resulted in 109,580 suspected cases and 11,717 reported deaths (2). In 2013, a new strain of N. meningitidis serogroup C (NmC) emerged in Nigeria, resulting in small focal outbreaks during 2014–2016 (3,4). In 2015, this strain of NmC entered neighboring Niger, resulting in the largest ever global epidemic of serogroup C meningitis (5), until the 2016–2017 Nigeria epidemic described in this report. Molecular sequencing of bacterial isolates from patients in the region has confirmed the expansion of this new strain of serogroup C in five countries in the region (Ryan Novak, National Center for Immunization and Respiratory Diseases, CDC, personal communication, 2017).

Case Definition and Incidence Thresholds for Response

A suspected case of meningitis was defined as the sudden onset of fever (>100.4°F [>38.0°C]) and at least one meningeal sign, including neck stiffness or altered consciousness in any person, or a bulging anterior fontanelle in children aged <18 months (6). Available cerebrospinal fluid (CSF) or blood specimens from patients meeting the suspected meningitis case definition were transported to a designated laboratory for confirmation by culture, latex agglutination, or real-time–polymerase chain reaction (PCR) tests. World Health Organization (WHO) Meningitis Outbreak Response Guidelines were used to identify geographic areas at risk for epidemics to guide response (6). Attack rates of suspected meningitis cases reported weekly by LGAs were calculated. WHO recommends that a set of preparedness activities be implemented when the attack rate of suspected meningitis in an LGA crosses a defined “Alert” threshold, and additional response activities at a defined “Epidemic” threshold (Table 1).

Outbreak Investigation

Two outbreak investigation teams were deployed to Zamfara and Sokoto states to augment routine surveillance, forward available CSF specimens to a designated laboratory for analysis, verify the extent of the outbreak, and gather specific information regarding the affected population to guide response. The first meningitis cases, a 21-case cluster in a village in Zurmi LGA of Zamfara State, were reported to the State Ministry of Health in December 2016; however, the cluster was not reported to NCDC until February 2017, after the outbreak had spread to four other LGAs in Zamfara, and to Katsina, Kebbi, Niger, and Sokoto states. During December 2016–June 2017, among Nigeria’s 37 state-level jurisdictions, 26 (70%) reported suspected meningitis cases, with peak incidence during reporting week 15 (April 16–22, 2017) (Figure). Meningitis incidence in 56 LGAs met the alert threshold and in 38 met the epidemic threshold. Overall, 14,518 suspected cases and 1,166 deaths (case-fatality ratio = 8.0%), were reported during the outbreak; 7,140 (49%) cases were reported from Zamfara State, and 6,792 (47%) occurred in children aged 5–14 years (Table 2). Confirmatory laboratory testing was conducted for specimens from 1,339 (9%) suspected meningitis patients; among these, 433 (32.3%) were laboratory-confirmed as bacterial meningitis, including 358 (82.7%) with NmC (Table 2).

Early Outbreak Response Activities

Following initial investigations, including health facility register reviews and analysis of community informant reports, NCDC activated the meningitis EOC on April 3, 2017 to coordinate outbreak response strategies and operations across the entire country in collaboration with country partner agencies, including WHO, CDC, the Africa Centre for Disease Control and Prevention, the United Nations Children’s Fund (UNICEF) and the Africa Field Epidemiology Network. To ensure that suspected meningitis cases were rapidly detected and investigated, meningitis surveillance, according to WHO’s Africa Region Guidelines for Enhanced Meningitis Surveillance, was strengthened in all states, regardless of whether states reported cases. EOCs were also activated to coordinate outbreak response activities in Sokoto and Zamfara states, the two states at the epicenter of the outbreak. Rapid response teams of epidemiologists and clinicians were deployed from the national EOC to support states with at least one LGA meeting the defined outbreak threshold.

Early outbreak response activities were hampered by difficulty in accessing some of the more rural and remote communities experiencing the outbreak. A limited capacity for CSF specimen collection among health care workers, deficiencies in the laboratory systems, including a lack of basic test kits and limited resources to support timely and appropriate specimen transportation from health facilities to a laboratory with PCR or culture capacity, contributed to delayed case identification. Additionally, the human resources needed to support effective outbreak detection and response were limited in some of the states with the largest case numbers, necessitating the recruitment and deployment of a large contingent of ad hoc technical support personnel from the national level to support outbreak control activities in these states.

Outbreak Response Vaccination

The National Primary Health Care Development Agency, responsible for vaccination activities in Nigeria, received meningococcal C–containing vaccines through the International Coordinating Group on Vaccine Provision in April 2017, 2 months after the outbreak was first widely reported. Because of limited vaccine supplies, vaccine use was prioritized to the most affected LGAs in Katsina, Sokoto, Yobe, and Zamfara states (6) where approximately 2.1 million (84.4%) of an estimated 2.5 million persons at risk (based on the WHO guidelines) aged 2–29 years were vaccinated. Extensive social mobilization activities, including outreach to community leaders and engagement on social and traditional media helped raise awareness and facilitate desired behavior change, including vaccine acceptance and avoidance of overcrowding, thereby reducing potential for continued transmission.

Discussion

The outbreak likely represents the largest global outbreak of NmC. Response measures implemented during the outbreak, including improved case finding and management as well as mass vaccination campaigns, might have contributed to the outbreak control. However, the large number of cases and prolonged duration of the outbreak highlight key lessons for meningitis outbreak prevention, detection, and response in Nigeria and other countries in the meningitis belt. Timely and appropriate use of meningococcal vaccines is effective in preventing and limiting the spread of meningococcal meningitis outbreaks. The introduction of the meningococcal A conjugate vaccine against NmA in Nigeria and other countries in the meningitis belt represents a major milestone in meningitis outbreak control and has contributed to significant reductions in NmA infections (7,8). However, laboratory data from this and other recent outbreaks point to the evolving regional meningitis epidemiology with increasing proportions of epidemics attributable to bacterial meningitis pathogens other than NmA, for which meningococcal A conjugate vaccine provides no protection (3,4). These findings suggest an urgent need to expand availability of multivalent vaccines that are effective against non-A serogroups.

In Nigeria, meningitis is classified as an epidemic-prone disease, requiring immediate notification, investigation, and necessary action (9); significant lapses in reporting in the early stages of this outbreak (from December 2016 to February 2017) might have contributed to its large size and wide reach. Additionally, limited capacity for CSF specimen collection, a lack of test kits, and inadequate resources to support timely and appropriate specimen transportation from health facilities to a laboratory with PCR or culture capacity contributed to the low percentage of confirmed meningitis cases. Similarly, delays in case finding, reporting and investigation, especially in the more remote areas, limited timely outbreak response. These meningitis surveillance system weaknesses merit further investigation, with remediating action implemented to prevent future reoccurrence. Because delayed access to meningococcal vaccines might have contributed to the prolonged outbreak duration, a careful examination of country vaccine requisition processes, and International Coordinating Group on Vaccine Provision protocols for vaccine requests, approval, delivery and use, is needed.

A surveillance and outbreak response system is most effective when the capacity to prevent, detect, and appropriately respond to outbreaks is available (10). In Nigeria, the human resource capacity to support an effective outbreak response varied widely within and between states, and was severely limited in some of the most at-risk states and LGAs. In low human resource capacity settings, evolving and refining new models for effective and timely outbreak detection and response, including scaling up emergency Rapid Response Team deployment where needed, is critical. In Nigeria, an opportunity exists for improved response coordination with lessons learned from EOCs established for coordination of polio eradication activities and response to Ebola virus disease, as well as leveraging trained personnel from the Nigeria Field Epidemiology and Laboratory Training Program. In the longer term, building adequate health care worker capacity at all national and subnational surveillance system levels will be essential to a timely and effective outbreak response. Functional laboratory systems are pivotal to meningitis case confirmation and provide guidance for critical outbreak response activities, including decisions on appropriate vaccine use.

With the outbreak now declared over, efforts to improve surveillance and outbreak preparedness for meningitis need to continue. Recently concluded national and regional evaluations of the outbreak response have articulated recommendations for improving meningitis outbreak prevention, timely detection, and response in Nigeria, and implementation of these recommendations is needed at all levels of the public health system. Additionally, conducting a review of the implementation of current meningitis outbreak alert and epidemic thresholds in Nigeria, including an assessment of sub-LGA–level sensitivity to outbreaks at the current thresholds could help to ensure optimal and timely detection at the lower levels. Developing and introducing conjugate vaccines effective against non-A meningococcal serogroups might help reduce the risk for future non-serogroup A meningococcal meningitis outbreaks.

Acknowledgments

Government and partner support staff members.


Corresponding author: Chimeremma Nnadi, wgq4@cdc.gov, 404-906-6316.

1Global Immunization Division, CDC; 2Nigeria Centre for Disease Control, Abuja, Nigeria; 3Federal Ministry of Health, Abuja, Nigeria; 4Africa Centre for Disease Control, Addis Ababa, Ethiopia; 5National Primary Health Care Development Agency, Abuja, Nigeria; 6Nigeria Country Office, World Health Organization, Abuja, Nigeria; 7Nigeria Country Office, United Nations Children’s Fund Abuja, Nigeria; 8World Health Organization, Geneva, Switzerland; 9Public Health England, London, United Kingdom; 10Center for Global Health, Global Rapid Response Team, CDC; 11Nigeria Office, Africa Field Epidemiology Network, Kampala, Uganda; 12Medical Research Council, Vaccines and Immunity Theme, Banjul, Gambia; 13World Health Organization Inter-country Support Team for West Africa, Ouagadougou, Burkina Faso; 14Meningitis and Vaccine Preventable Diseases Branch, National Center for Respiratory and Infectious Diseases, CDC; 15CDC, Nigeria Country Office, Abuja.

References

  1. World Health Organization. Meningococcal meningitis. Fact sheet. Geneva, Switzerland: World Health Organization; 2017. http://www.who.int/mediacentre/factsheets/fs141/en/
  2. Mohammed I, Nasidi A, Alkali AS, et al. A severe epidemic of meningococcal meningitis in Nigeria, 1996. Trans R Soc Trop Med Hyg 2000;94:265–70. CrossRef PubMed
  3. Chow J, Uadiale K, Bestman A, et al. Invasive meningococcal meningitis serogroup C outbreak in northwest Nigeria, 2015—third consecutive outbreak of a new strain. PLoS Curr 2016;8:8. PubMed
  4. Funk A, Uadiale K, Kamau C, Caugant DA, Ango U, Greig J. Sequential outbreaks due to a new strain of Neisseria meningitidis serogroup C in northern Nigeria, 2013–14. PLoS Curr 2014;6:6. PubMed
  5. Sidikou F, Zaneidou M, Alkassoum I, et al. Emergence of epidemic Neisseria meningitidis serogroup C in Niger, 2015: an analysis of national surveillance data. Lancet Infect Dis 2016;16:1288–94. CrossRef PubMed
  6. World Health Organization. Meningitis outbreak response in sub-Saharan Africa. WHO guideline. Geneva, Switzerland: World Health Organization; 2014. http://www.who.int/csr/resources/publications/meningitis/guidelines2014/en/
  7. Trotter CL, Lingani C, Fernandez K, et al. Impact of MenAfriVac in nine countries of the African meningitis belt, 2010–15: an analysis of surveillance data. Lancet Infect Dis 2017;17:867–72. CrossRef PubMed
  8. Sambo L, Chan M, Davis S, et al. A vaccine meets its promise: success in controlling epidemic meningitis in sub-Saharan Africa. Clin Infect Dis 2015;61(Suppl 5):S387–8. CrossRef PubMed
  9. Nigeria Centre for Disease Control. Technical guidelines for integrated disease surveillance and response in Nigeria. Abuja, Nigeria: Nigeria Centre for Disease Control; 2016. http://www.ncdc.gov.ng/themes/common/docs/protocols/4_1476085948.pdf
  10. Lo TQ, Marston BJ, Dahl BA, De Cock KM. Ebola: anatomy of an epidemic. Annu Rev Med 2017;68:359–70. CrossRef PubMed
Return to your place in the textTABLE 1. Guidelines for incidence thresholds and interventions for detection and control of epidemic meningococcal meningitis based on population size of the local government area in countries in Africa with endemic disease* — World Health Organization
Incidence threshold Population size Interventions
<30,000 30,000–100,000
Alert Two suspected cases in 1 week or increase in incidence compared with nonepidemic years Three suspected cases per 100,000 population per week (two or more cases in 1 week) 1) Inform authorities, 2) strengthen surveillance, 3) investigate, 4) confirm (including laboratory), 5) treat cases, 6) prepare for eventual response
Epidemic Five suspected cases in 1 week or doubling of number of cases in a 3-week period 10 suspected cases per 100,000 population per week 1) Conduct mass vaccination§ within 4 weeks of crossing epidemic threshold, 2) distribute treatment to health centers, 3) treat according to epidemic protocol, 4) inform the public

* Guidelines adapted from http://apps.who.int/iris/handle/10665/144727.
In special situations such as mass gatherings, refugees, displaced persons or closed institutions, two confirmed cases in a week should prompt mass vaccination.
§ If an area neighboring one targeted for vaccination is considered to be at risk (e.g., cases early in the dry season, no recent relevant vaccination campaign, or high population density), it should be included in a vaccination program.

Return to your place in the textFIGURE. Weekly number of suspected meningitis cases — Nigeria, December 2016–June 2017*

The figure above is a bar chart showing the weekly number of suspected meningitis cases in an outbreak in Nigeria during December 2016–June 2017.

* Reporting week 15 corresponds to April 16–22, 2017; week 21 corresponds to June 4–10, 2017.

 

Return to your place in the textTABLE 2. Characteristics of patients in 14,518 suspected cerebrospinal meningitis cases — Nigeria, December 2016–June 2017
Characteristic No. (%)
Sex
  Male 7,802 (53.7)
  Female 6,699 (46.2)
  Missing/Unknown 17 (0.1)
Age group (yrs)
  <1 219 (1.5)
  1–4 1,796 (12.4)
  5–14 6,792 (46.8)
  ≥15 5,667 (39.1)
  Missing/Unknown 44 (0.3)
State
  Zamfara 7,140 (49.2)
  Sokoto 4,980 (34.3)
  Katsina 915 (6.3)
  Yobe 415 (2.9)
  Kebbi 142 (1.0)
  Niger 131 (0.9)
  Other 795 (5.5)
Meningococcal serogroup or other identified organism*,†
  A 27 (6.2)
  B 1 (0.2)
  C 358 (82.7)
  W 1 (0.2)
  X§
  Y 0 (0)
  Unknown 32 (7.4)
  Haemophilus influenzae (type b) 5 (1.2)
  Streptococcus pneumoniae 9 (2.1)

*Total number of laboratory specimens tested = 1,339; 433 specimens yielded meningococcal or nonmeningococcal organisms. A total of 129 test results were invalid or missing, and the rest were classified as negative for any organisms tested.
Cases confirmed by any of the following tests: latex agglutination, polymerase chain reaction, or culture.
§ Laboratory tests not available to detect Neisseria meningitidis serogroup X.

Suggested citation for this article: Nnadi C, Oladejo J, Yennan S, et al. Large Outbreak of Neisseria meningitidis Serogroup C — Nigeria, December 2016–June 2017. MMWR Morb Mortal Wkly Rep 2017;66:1352–1356. DOI: http://dx.doi.org/10.15585/mmwr.mm6649a3.

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Introduction of Inactivated Poliovirus Vaccine and Impact on Vaccine-A

When included in a sequential polio vaccination schedule, inactivated polio vaccine (IPV) reduces the risk for vaccine-associated paralytic poliomyelitis (VAPP), a rare adverse event associated with receipt of oral poliovirus vaccine (OPV). During January 2014, the World Health Organization (WHO) recommended introduction of at least 1 IPV dose into routine immunization schedules in OPV-using countries (1). The Polio Eradication and Endgame Strategic Plan 2013–2018 recommended completion of IPV introduction in 2015 and globally synchronized withdrawal of OPV type 2 in 2016 (2). Introduction of 1 dose of IPV into Beijing’s Expanded Program on Immunization (EPI) on December 5, 2014 represented China’s first province-wide IPV introduction. Coverage with the first dose of polio vaccine was maintained from 96.2% to 96.9%, similar to coverage with the first dose of diphtheria and tetanus toxoids and pertussis vaccine (DTP) (96.5%–97.2%); the polio vaccine dropout rate (the percentage of children who received the first dose of polio vaccine but failed to complete the series) was 1.0% in 2015 and 0.4% in 2016. The use of 3 doses of private-sector IPV per child decreased from 18.1% in 2014, to 17.4% in 2015, and to 14.8% in 2016. No cases of VAPP were identified during 2014–2016. Successful introduction of IPV into the public sector EPI program was attributed to comprehensive planning, preparation, implementation, robust surveillance for adverse events after immunization (AEFI), and monitoring of vaccination coverage. This evaluation provided information that helped contribute to the expansion of IPV use in China and in other OPV-using countries.

OPV has been employed in China’s EPI system for decades, leading to certification of China’s polio-free status in 2000.* After elimination of wild-type polio in China, VAPP, a rare occurrence of paralysis associated with a mutated vaccine virus that occurs in an OPV recipient or a close unvaccinated or nonimmune contact of the OPV recipient, emerged as an unacceptable risk: during 2010–2014, an average of one case of VAPP (reporting rate 7.16 per million first OPV doses) occurred annually among previously healthy children in Beijing. A majority of VAPP occurs in infancy, associated with the first OPV dose (1). IPV provides immunity against wild polioviruses, but cannot cause VAPP and greatly reduces the risk for VAPP associated with subsequent OPV doses. Countries that have previously introduced at least 1 IPV dose before vaccination with OPV have rapidly eliminated VAPP (1). IPV has been available in China’s private sector since 2009. After completion of immunogenicity studies (35), Beijing introduced IPV into the public sector EPI program in December 2014 as part of a sequential schedule that included 1 dose of IPV at age 2 months, followed by 3 doses of trivalent OPV at ages 3, 4, and 48 months. After the global synchronized withdrawal of all Sabin type 2 vaccines in April 2016, trivalent OPV was replaced with bivalent OPV, which contains types 1 and 3 oral polio vaccine viruses.

Preparation for IPV introduction included addressing financial constraints, establishing a management structure, and developing an operational plan. The Beijing municipal government secured RMB18.9 million yuan ($US 2.9 million) for IPV procurement and program operations. During April–November 2014, the Beijing provincial health authorities developed a comprehensive work plan with technical guidelines for cold chain capacity assessment, training, risk communication, frequently asked questions, logistics materials (e.g., vaccines, forms), supply and distribution, and surveillance for polio vaccine utilization and AEFIs. During November 2014, health authorities issued an official circular that detailed responsibilities of various agencies and stated an objective to achieve 98.0% coverage with IPV. Information about the new IPV/OPV schedule was disseminated through the Beijing Municipal Authority’s website. Posters describing IPV and the availability of free vaccinations were posted on December 5, 2014, the first day that government-supplied IPV was offered. Health care workers were the primary sources of information about IPV introduction. Health care workers in vaccination clinic training workshops focused on immunogenicity, safety, and risk communication regarding the sequential schedule. Training materials included a polio fact sheet with frequently asked questions for parents, the new immunization schedule, eligibility criteria for IPV catch-up vaccination, and correct vaccine administration technique. Training was completed 2–7 days before IPV was introduced.

In December 2015, a program evaluation was conducted at the provincial level CDC (Beijing CDC), four subordinate district level CDCs, and 12 health facilities, by using the WHO Post Introduction Evaluation (PIE) tool (6). This tool is a systematic method for evaluating the effect of introducing a vaccine on a country’s existing immunization system. Beijing CDC surveyed 83 health care workers who were vaccinating children and 40 parents or guardians whose children were offered IPV. Polio vaccine utilization data were obtained from Beijing’s Immunization Planning Information System. Beijing CDC compared the proportions of eligible children receiving IPV and OPV before routine IPV introduction (December 2013–November 2014) and after IPV introduction (December 2014–November 2015) to assess utilization and preferences regarding polio vaccines and compared the polio vaccine and DTP dropout rates in 2015 and 2016 among children aged 1 year (born during October–November 2014 and 2015, respectively).

Adequate cold chain storage capacity was identified in all 12 surveyed sites. In addition to manual temperature recording, nine of the 12 surveyed health facilities were using a system that alerts vaccine mangers of temperature excursions. Oversight regarding IPV introduction was incorporated into routine supervision, with priority placed on vaccine usage and management. During the 6 months before the PIE, each surveyed health facility reported receiving 1–4 supervisory visits by district CDC personnel. Vaccine wastage data were reported by health facilities to district CDCs on a monthly basis. Median OPV and IPV wastage rates were 2.3% (range = 0%–5.3%) and 0.03% (range = 0%–1.2%), respectively.

Among the 83 health care worker survey respondents, 77 (93%) received training, and 80 (96%) responded correctly to questions about the immunization schedule, proper injection technique, contraindications to vaccination, and common AEFIs; all health care workers knew the appropriate anatomic site for injecting IPV. At least two of the following messages were relayed to parents by 72 (87%) health care workers: the vaccine name, the disease prevented, the sequential IPV/OPV schedule, the benefits of IPV, common AEFIs, how to report AEFIs, and the need to bring the child’s vaccination card to each visit. Among 40 parents or guardians whose children were offered IPV at the health facility, 13 (33%) knew what IPV and poliomyelitis were; among these 13 persons the primary sources of information about IPV were health care workers (seven), the Internet (four), and friends or relatives (two).

All surveyed sites reported that they had sufficient IPV and ancillary supplies (e.g., registration forms, certificates). Although new vaccination cards that included the IPV/OPV sequential schedule were issued to replace the previous cards, five (12.5%) surveyed parents still had the older vaccination cards on which IPV doses were recorded. Used needles and syringes were observed to have been discarded into safety boxes without recapping. Also, in five of the 12 health facilities, health care workers were observed to frequently manually disconnect the needle from syringe.

The existing acute flaccid paralysis (AFP) surveillance system, which needs to be sensitive enough to detect one case of AFP per 100,000 children aged <15 years, even in the absence of polio, has detected from 1.1 to 2.3 nonpolio AFP cases per 100,000 children aged <15 years annually during 2010–2016 in Beijing. VAPP cases were initially detected through this system. Since IPV introduction, clinicians, IPV suppliers, and district CDCs have reported any AEFI, including VAPP, after IPV administration through the existing passive, online AEFI surveillance system (7). During the first 2 years after IPV introduction, 115 mild adverse events (fever, local reaction, rash, or angioneurotic edema) and two rare adverse reactions (one case each of anaphylactoid purpura and thrombocytopenic purpura [both patients fully recovered]) were recorded. In addition, 22 adverse events that were determined, after expert panel review, to be unrelated to vaccination (i.e., coincidental events) occurred. These coincidental events included infections, allergies, thrombocytopenia, and infantile spasms. No case of VAPP has been reported since 2014 (Figure 1).

Administrative coverage rates with the first dose of polio vaccines during 2014, 2015, and 2016 were 96.2%, 96.9%, and 97.4%, respectively; these rates were similar to those for the first DTP dose during those years (96.5% [2014], 97.2% [2015], and 97.6% [2016]). The polio vaccine drop-out rate was 1.0% in 2015 and 0.4% in 2016, similar to that for DTP (1.5% [2015], 2.1% [2016]). Before introduction of the sequential IPV/OPV schedule in Beijing, parents could choose IPV or an IPV-containing combination vaccine, such as Pentavalent (Pentaxim, Sanofi Pasteur, France) (which protects against diphtheria, tetanus, pertussis, polio, and Haemophilus influenzae type b) for the second or third polio vaccine dose, at their expense. However, in June 2016, China’s national drug and health authorities prohibited IPV for all 3 doses in the private market because of a global IPV shortage, and to ensure that all children could get a first IPV dose. The use of 3 doses of private-sector IPV declined slightly from 18.1% in 2014 to 17.4% in 2015 and to 14.8% in 2016 (Figure 2).

Notes from the Field: Tuberculosis Control Activities After Hurricane Harvey — Texas, 2017

On September 14, 2017, the Texas Department of State Health Services (DSHS) reported that Hurricane Harvey had caused 82 deaths in Texas during August 25–August 30, 2017 (1), with property damage that could total $180 billion (2). Houston alone received 45 inches of rain from August 24 to September 1, 2017, and some parts of Texas received 60 inches or more. Dozens of inches of rain also fell on the cities of Port Arthur and Beaumont. Several local health departments experienced closures during the week of August 28 and resumed operations the week of September 5 under emergency conditions.

The Texas DSHS uses federal and state funding for tuberculosis (TB) surveillance, prevention, and control activities in eight DSHS health service regions, 31 local health departments, and four binational TB projects. In advance of major storms, TB programs have activated established protocols for providing patients with medications to take on their own, and for providing contact information to give to health departments in case patients become displaced. Line listings of patients are closely monitored to account for all patients after the storm, and treatment duration is frequently extended to allow for medication doses that were missed. Information exchange among neighboring local, regional, or state programs is often necessary.

Directly observed therapy (DOT) of patients taking each dose of their TB medications is a cornerstone of TB control activity, and video-enabled DOT using electronic devices, such as smart phones, has become a useful tool for patients who cannot visit, or be visited by, a health care provider (6). Lessons learned regarding management and follow-up of TB patients on treatment during Hurricane Katrina in 2005 (3) were applied during hurricanes Gustav and Ike in 2008 (4), Sandy in 2012 (5), and Harvey in 2017. Whereas approximately half of the TB patients in New Orleans, Louisiana, fled the state during Hurricane Katrina (3), TB patients in Texas during Hurricane Harvey typically remained close to their usual residence (at home, with relatives, or in shelters).

Immediately after Hurricane Harvey, the DSHS TB program directly contacted all affected regional and local health departments to determine the status of high-priority TB patients (persons with new TB diagnoses, infectious patients, and children), and relayed status of patient care, health care worker safety, and needs of local and regional health departments to CDC. In addition, surveillance questionnaires were distributed to temporary shelters to identify residents or volunteers exhibiting signs and symptoms of TB. Although TB control personnel in Texas were personally affected by the storm’s damage, they remained on duty, with some staff members traveling into flooded communities to follow up patients.

A total of 282 TB cases from 17 affected local or regional health departments, including 212 (75%) from one large urban county, were high priority TB cases with confirmed disease. Response efforts by affected local and regional health departments ensured that all but two of the 282 persons were accounted for within a week after the storm began. The remaining two were located the following week and connected to care. Sixty-one patients had already been placed on video-enabled DOT, 30 had TB disease (cases), and 31 had latent TB infection and needed DOT. Fifty-nine (97%) were monitored successfully and did not miss any medication doses. The aforementioned two patients who were lost during the storm and found a week later had TB disease (cases). Although respiratory illnesses among shelter residents were reported, no suspected cases of undiagnosed TB disease were identified.

Each year, the upcoming hurricane season provides opportunities to develop, test, and implement preparedness plans for continuity of patient care. During Hurricane Harvey, the high proportion of patients successfully managed through video-enabled DOT demonstrates that video-enabled DOT can help ensure TB treatment completion when regular treatment options have been disrupted by a major storm or other disasters.

Use of trade names and commercial sources is for identification only and does not imply endorsement by the U.S. Department of
Health and Human Services.
References to non-CDC sites on the Internet are
provided as a service to MMWR readers and do not constitute or imply
endorsement of these organizations or their programs by CDC or the U.S.
Department of Health and Human Services. CDC is not responsible for the content
of pages found at these sites. URL addresses listed in MMWR were current as of
the date of publication.

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Questions or messages regarding errors in formatting should be addressed to
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CDC – The Associate Opportunity – Become an Associate

Two Photos: First image shows two associates in the field preparing for helicopter transport. Second image shows a group of asociates in an office setting meeting with a health department official.

The Public Health Associate Program (PHAP) is a two-year training and service program that provides hands-on experience in public health. Explore these pages to learn how to apply, discover the benefits of joining PHAP, find answers to frequently asked questions, and read about a typical day in the life of an associate!

Norovirus Illness is Messy – Clean Up Right Away | | Blogs

Hand in pink protective glove wiping tiles with rag in the bathroom.

When norovirus strikes in your own home, you can be prepared by having the supplies you need to immediately clean up after a loved one vomits or has diarrhea.

Norovirus is a tiny germ that spreads quickly and easily. It causes vomiting and diarrhea that come on suddenly. A very small amount of norovirus can make you sick. The number of virus particles that fit on the head of a pin is enough to infect over 1,000 people.

You can get norovirus if poop or vomit from an infected person gets into your mouth. You can get it by:

  • Caring for a person who is infected with norovirus and then touching your hands to your mouth
  • Eating food or drinking liquids that are contaminated with norovirus
  • Touching surfaces or objects with norovirus on them and then putting your hands in your mouth

Clean up the splatter!

Vomiting and diarrhea are messy, especially with norovirus. If you get sick from norovirus, drops of vomit or poop might splatter for many feet in all directions.

It’s extremely important to clean up the entire area immediately after you or someone else vomits or has diarrhea. You must be very thorough so you don’t miss any drops of vomit or poop that you can’t see.

If you find yourself in this situation, follow these steps from start to finish to protect other people from getting sick with norovirus:

Step 1 – Put on disposable plastic gloves and a face maskNorovirus spreads when a person gets poop or vomit from an infected person in their mouth.

Step 2 – Wipe up vomit and poop with paper towels and throw them away

Step 3 – Clean all surfaces thoroughly with a bleach cleaner, or make your own solution (¾ cup of bleach plus 1 gallon of water)

Step 4 – Clean all surfaces again with hot water and soap

Step 5 – Remove your gloves, throw them away, and take out the trash

Step 6 – Wash all laundry that may have vomit or poop on them with hot water and soap

Step 7 – Wash your hands with soap and water

Thorough clean up helps prevent norovirus outbreaks

Cleaning-up immediately after someone with norovirus vomits or has diarrhea protects others from getting sick, and prevents norovirus outbreaks. It’s important for everyone to know the clean-up steps and other ways to prevent norovirus.

CDC and state and local health departments help to raise awareness among healthcare providers and the general public about norovirus and how to prevent it. Learn more about how health departments, CDC, and other agencies work to prevent and stop norovirus outbreaks.

To learn more about norovirus, see CDC’s norovirus website and infographics, videos, and other resources, and state and local health department websites.

Rural America in Crisis: The Changing Opioid Overdose Epidemic | | Blogs

The scenery aerial view of Poconos, Monroe County, Pennsylvania, USA.

In America, 15 out of 100 people live in a rural area.  I loved growing up in a rural community, where there were actually no stop lights, everyone knew their neighbors, and doors were always open. But, my years of working in public health has taught me rural areas are not that different from urban areas when it comes to the devastating impact of the opioid epidemic.

The rate of drug overdose deaths in rural areas has surpassed rates in urban areas, and it is a huge public health concern. Understanding how rural areas are different when it comes to drug use and drug overdose deaths, including opioids, can help public health professionals identify, monitor, and prioritize their response to this epidemic.

One Epidemic – Three Waves

Drug overdoses in the United States have now surpassed other leading causes of death like AIDS or motor vehicle crashes, even when they were at their peak.

The opioid overdose epidemic has come in three waves:Rural: Areas with low population, where there is a lot of space between residences. Urban: Refers to areas like cities, with high population and population density.

  1. Increases in deaths involving prescription opioids starting in 1999
  2. Increases in heroin-involved deaths starting in 2010
  3. Since 2013, we have seen more deaths involving synthetic opioids like illicitly manufactured fentanyl.

CDC is tracking how these waves of overdose deaths are affecting rural versus urban areas of the country to help states and public health departments identify, monitor, and prioritize customized prevention responses.

Rural Communities at Risk

Death rates for unintentional injuries like drug overdoses, falls, and motor vehicle crashes are around 50% higher in rural areas than in urban areas. In general, people who live in rural areas of the United States tend to be older, poorer, and sicker than those living in urban areas. Differences in socio-economic factors, health behaviors, and access to health care services contribute to these differences. From 1999 to 2015, the opioid death rates in rural areas have quadrupled among those 18-25 years old and tripled for females.

Preventing Overdose Deaths in Rural America

Overdose deaths can be prevented through improved public health programs. We can start addressing the opioid overdose epidemic and save lives by:

  • Understanding the differences in burden and context of drug use, drug use disorders, and fatal overdose, and identifying how to tailor prevention efforts to local situations between rural and urban areas.
  • Teaching healthcare providers about safer opioid prescribing practices and how to treat patients with opioid use disorder (addiction).
  • Considering non-opioid pain treatment options, like exercise and physical therapy, cognitive behavioral therapy, or more effective pain medicines (like acetaminophen, ibuprofen, and naproxen). Some of these options may actually work better and have fewer risks and side effects than opioids.
  • Supporting training and access to naloxone, a medication that can quickly stop an opioid overdose, for high-risk individuals, families, emergency responders, and law enforcement.
  • Increasing access to treatment for opioid use disorder (addiction) through medication-assisted treatment or comprehensive services to reduce infections from injection drug use, like HIV or Hepatitis C.
  • Working with public safety to share data, scale up evidence-based strategies, and decrease the illicit drug supply.

The landscape of drug overdoses in America is changing and affects everyone, no matter where they live. As the epidemic continues to evolve and change, we must understand the circumstances that contribute to opioid deaths and remain vigilant to prevent overdoses in our communities. The more we understand about this drug epidemic, the better prepared we all will be to stop it in its tracks and save lives.

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ABCs of Viral Hepatitis | | Blogs

Children playing chess with grandparents

Viral hepatitis is the term that describes inflammation of the liver that is caused by a virus. There are actually five types of hepatitis viruses; each one is named after a letter in the alphabet: A, B, C, D and E.

The most common types of viral hepatitis are A, B and C. These three viruses affect millions of people worldwide, causing both short-term illness and long-term liver disease. The World Health Organization estimates 325 million people worldwide are living with chronic hepatitis B or chronic hepatitis C. In 2015, 1.34 million died from viral hepatitis, a number that is almost equal to the number of deaths caused by tuberculosis and HIV combined.Know The ABC’s of Viral Hepatitis More than 4 million people in the US are living with viral hepatitis. Most don’t know it! A: Hepatitis A can be prevented with a safe, effective vaccine. B: Many people got infected with hepatitis B before the vaccine was widely available. C: Treatments are available that can cure hepatitis C. Take the CDC Online Risk Assessment to see if you should be vaccinated or tested for viral hepatitis: https://www.cdc.gov/hepatitis/riskassessment/

Hepatitis B and hepatitis C are the most common types of viral hepatitis in the United States, and can cause serious health problems, including liver failure and liver cancer. In the U.S., an estimated 3.5 million people are living with hepatitis C in the US and an estimated 850,000 are living with Hepatitis B. Unfortunately, new liver cancer cases and deaths are on the rise in the United States. This increase is believed to be related to infection with hepatitis B or hepatitis C.

Many people are unaware that they have been infected with hepatitis B and hepatitis C, because many people do not have symptoms or feel sick. CDC developed an online Hepatitis Risk Assessment to help determine if you should get tested or vaccinated for viral hepatitis. The assessment takes only five minutes and will provide personalized testing and vaccination recommendations for hepatitis A, hepatitis B, and hepatitis C.

Hepatitis A

Hepatitis A is a short-term disease caused by infection with the hepatitis A virus. Hepatitis A is usually spread when a person ingests the virus from contact with objects, food, or drinks contaminated by solid waste from an infected person. Hepatitis A was once very common in the United States, but now less than 3,000 cases are estimated to occur every year. Hepatitis A does not lead to liver cancer and most people who get infected recover over time with no lasting effects. However, the disease can be fatal for people in poor health or with certain medical conditions.

Hepatitis A is easily prevented with a safe and effective vaccine, which is believed to have caused the dramatic decline in new cases in recent years. The vaccine is recommended for all children at one year of age and for adults who may be at risk, including people traveling to certain international countries.

Hepatitis B

Hepatitis B is a liver disease that results after infection with the hepatitis B virus. Hepatitis B is common in many parts of the world, including Asia, the Pacific Islands and Africa. Like Hepatitis A, Hepatitis B is also preventable with a vaccine. The hepatitis B virus can be passed from an infected woman to her baby at birth, if her baby does not receive the hepatitis B vaccine. As a result, the hepatitis B vaccine is recommended for all infants at birth.

Unfortunately, many people got infected with hepatitis B before the vaccine was widely available. This is why CDC recommends anyone born in areas where hepatitis B is common, or who have parents who were born in these regions, get tested for hepatitis B. Treatments are available that can delay or reduce the risk of developing liver cancer.

Hepatitis C

Hepatitis C is a liver disease that results from infection with the hepatitis C virus. For reasons that are not entirely understood, people born from 1945 to 1965 are five times more likely to have hepatitis C than other age groups. In the past, hepatitis C was spread through blood transfusions and organ transplants. However, widespread screening of the blood supply in the United States began in 1990.The hepatitis C virus was virtually eliminated from the blood supply by 1992. Today, most people become infected with hepatitis C by sharing needles, syringes, or any other equipment to inject drugs. In fact, rates of new infections have been on the rise since 2010 in young people who inject drugs.

There is currently no vaccine to prevent hepatitis C. Fortunately, new treatments offer a cure for most people. Once diagnosed, most people with hepatitis C can be cured in just 8 to 12 weeks, which reduces their risk for liver cancer.

Find out if you should get tested or vaccinated for viral hepatitis by taking CDC’s quick online Hepatitis Risk Assessment.

For more information visit www.cdc.gov/hepatitis.

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Preparing to quit: 10 tips to help you quit smoking | | Blogs

Broken cigarette lies on a calendar sheet. Tobacco wake. On the calendar inscription marker.

Each year, on the third Thursday of November, the American Cancer Society encourages smokers to quit during the Great American Smokeout. Most people who smoke want to quit, but they also know quitting is hard…it can take several attempts to succeed.

Here are some tips to help you quit for good:

  1.  Find Your Reason to Quit
    To get motivated, find your reason to quit. It may be to protect your family from secondhand smoke. Or to lower your chance of getting cancer, heart disease, or some other serious health condition. Find a reason that is strong enough to outweigh the urge to light up.
  2. Set a Date
    Once you’ve made the decision to quit, set a “quit date” within the next month. Most smokers have tried to quit before, and sometimes people get discouraged thinking about previous attempts. Instead, treat them as steps on the road to success. Learn from what worked and what didn’t work, and apply these the next time you try to quit.
  3. Medication can help
    Using nicotine replacement products (such as nicotine gum and nicotine patches) or FDA-approved, non-nicotine cessation medications can help reduce withdrawal symptoms and increase the likelihood that you will quit. Ask your doctor about what option is best for you.It’s more than just tossing your cigarettes out. Cigarettes contain nicotine, which is highly addictive. Nearly all smokers have some feelings of nicotine withdrawal when they try to quit. Knowing this will help you deal with withdrawal symptoms that can occur, such as bad moods and really wanting to smoke.
  4. You don’t have to quit alone
    Telling friends and family that you’re trying to quit and getting their support will help the process. Expert help is available from a number of groups. 1-800-QUIT-NOW offers free telephone support; and Smokefree.gov is an on-line resource. There’s even a quit smoking app for your phone! Check out CDC Tobacco Free on Facebook for on-line support.
  5. Be prepared for challenges
    The urge to smoke doesn’t last long – usually only 3 to 5 minutes, but those moments can feel intense. Before you quit, plan new ways to occupy your time. You can exercise to blow off steam, listen to your favorite music, connect with friends, treat yourself to a massage, or make time for a hobby. Try to avoid stressful situations during the first few weeks after you stop smoking.
  6. Clean house
    Once you’ve smoked your last cigarette, remove any triggers or things that remind you of smoking. For example, throw out all your ashtrays and lighters. Wash any clothes that smell like smoke, and clean your carpets, draperies, and upholstery. If you smoked in your car, clean it out, too. It is best not to see or smell anything that reminds you of smoking.
  7. Get moving
    Some research shows that being active can help ease some withdrawal symptoms. When you feel the urge to reach for a cigarette, get active – try a yoga class or put on your jogging shoes instead. And you can burn calories, too!
  8. Quitting can save money
    In addition to all the health benefits, one of the perks of giving up cigarettes is the money you will save. There are online calculators that can help figure out how much you will save.
  9. It’s never too late to quit
    As soon as you quit, your health can immediately start to improve. After only 20 minutes without smoking, your heart rate drops. Within 12 hours, your blood’s carbon monoxide level falls back to normal. In just two to three months, your chance of having a heart attack starts to go down. In the long run, you will also lower your chance of getting cancer and other serious diseases. While it’s best to quit smoking as early as possible, quitting at any age will improve the length and quality of your life.
  10. Try and try again
    Most people make several attempts before giving up cigarettes for good. If you slip, don’t get discouraged. Instead, think about what led to your relapse. Use it as an opportunity to step up your commitment to quitting. Think about what helped you during those previous tries and what you’ll do differently the next time. Above all, don’t give up.

Remember this good news!

More than half of all adult smokers have quit, and you can, too. Millions of people have learned to live without cigarettes. Quitting smoking is an important step you can take to protect your health and the health of your family.

Resources:

Everyone can be a flu vaccine advocate! | | Blogs

Little girl getting a bandaid.
Children, especially those younger than 5 years, are at higher risk for serious flu-related complications. The flu vaccine offers the best defense against getting the flu and spreading it to others.

With the holidays quickly approaching, there will be more opportunities to spend time with family and friends.  Now is the time to ensure that you and those around you are protected from flu. Now is the time to get your seasonal flu vaccine if you haven’t already gotten it. It takes about two weeks after vaccination for antibodies that protect against flu to develop in the body.—so it’s  important to get vaccinated now, before the flu begins circulating in your community.

Whether you are a doctor, school nurse, grandchild, best friend, or coworker, you can play a role in reminding and encouraging  other people to get their flu vaccine. Get your flu shot and talk to others about the importance of everyone 6 months and older getting a flu shot every year.

Talking to Friends and Family about Flu ShotsGet yourself and your family vaccinated.

Need some tips for talking about the importance of flu vaccine? CDC is a great source of information about the serious risk of flu illness and the benefits of flu vaccination, as well as information to correct myths about the flu vaccine. Below are several examples of the benefits of flu shots and corrections of common flu myths. Find out more about the benefits of getting your annual flu vaccine on CDC’s Vaccine Benefits webpage, here.

  • Flu can be a serious illness, even for otherwise healthy children and adults. While most people will recover from flu without complications, anyone can experience severe illness, hospitalization, or death. Therefore, getting vaccinated is a safer choice than risking serious illness for yourself or those around you.
  • The flu vaccine CANNOT give you the flu. Flu shots do NOT contain flu viruses that could infect you and cause flu illness. Flu shots either contain flu vaccines viruses that have been “inactivated” (or killed) and therefore are not infectious, or they do not contain any flu vaccine viruses at all (recombinant influenza vaccine).
  • Flu vaccination can keep you from getting sick with flu. Flu vaccines can reduce your risk of illness, hospitalization.
  • Getting vaccinated yourself may also help protect people around you, including those who are more vulnerable to serious flu illness, like babies and children, older people, and people with certain chronic health conditions.

Making a Flu Vaccine Recommendation to Your Patients

Woman talking to her doctor
Talking to patients about vaccines can be difficult. CDC has resources to help you make a strong flu vaccine recommendation.

For health care providers, CDC suggests using the SHARE method to make a strong vaccine recommendation and to provide important information to help patients make informed decisions about vaccinations. Remind patients that it is not too late for them to get vaccinated, and follow the SHARE strategies below:

  • S- SHARE the reasons why the influenza vaccine is right for the patient given his or her age, health status, lifestyle, occupation, or other risk factors.
  • H- HIGHLIGHT positive experiences with influenza vaccines (personal or in your practice), as appropriate, to reinforce the benefits and strengthen confidence in flu vaccination.
  • A- ADDRESS patient questions and any concerns about the influenza vaccine, including side effects, safety, and vaccine effectiveness in plain and understandable language.
  • R- REMIND patients that influenza vaccines protect them and their loves ones from serious flu illness and flu-related complications.
  • E- EXPLAIN the potential costs of getting the flu, including serious health effects, time lost (such as missing work or family obligations), and financial costs.

Be an advocate for flu vaccination. Get your flu vaccine and remind those around you to do the same! Visit www.cdc.gov/flu for more information and tips on flu vaccination and prevention.

Interested in learning more about flu? Check out other CDC Flu Blog-a-thon post throughout the week for personal stories, advice, and tips on flu and flu prevention. You can see all the participating blogs here: https://www.cdc.gov/flu/toolkit/blog-a-thon.htm.


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Protect Yourself from Wildfire Smoke | | Blogs

Dry conditions in parts of the United States increase the potential for wildfires in or near wilderness areas. Stay alert for wildfire warnings and take action to protect yourself and your family from wildfire smoke.

When wildfires burn in your area, they produce smoke that may reach your community. Wildfire smoke is a mixture of gases and fine particles from burning trees and other plant materials. This smoke can hurt your eyes, irritate your respiratory system, and worsen chronic heart and lung diseases.

Who is at greatest risk from wildfire smoke?

  • People who have heart or lung diseases, like heart disease, chest pain, lung disease, or asthma, are at higher risk from wildfire smoke.
  • Older adults are more likely to be affected by smoke. This may be due to their increased risk of heart and lung diseases.
  • Children are more likely to be affected by health threats from smoke. Children’s airways are still developing and they breathe more air per pound of body weight than adults. Also, children often spend more time outdoors engaged in activity and play.
Stay alert for wildfire warnings.
Stay alert for wildfire warnings.

Take steps to decrease your risk from wildfire smoke.

  • Check local air quality reports. Listen and watch for news or health warnings about smoke. Find out if your community provides reports about the U.S. Environmental Protection Agency’s Air Quality Index (AQI) or check the report on AirNow.gov. In addition, pay attention to public health messages about safety measures.
  • Consult local visibility guides. Some communities have monitors that measure the amount of particles in the air. In the western United States, some states and communities have guidelines to help people determine if there are high levels of particulates in the air by how far they can see.
  • Take precautions to decrease risk from wildfire smoke.
    Take precautions to decrease risk from wildfire smoke.
  • Keep indoor air as clean as possible if you are advised to stay indoors. Keep windows and doors closed. Run an air conditioner, but keep the fresh-air intake closed and the filter clean to prevent outdoor smoke from getting inside. If you do not have an air conditioner and it is too warm to stay inside with the windows closed, seek shelter in a designated evacuation center or away from the affected area.
  • Avoid activities that increase indoor pollution. Burning candles, fireplaces, or gas stoves can increase indoor pollution. Vacuuming stirs up particles already inside your home, contributing to indoor pollution. Smoking also puts even more pollution into the air.
  • Prevent wildfires from starting. Prepare, build, maintain and extinguish campfires safely. Follow local regulations if you burn trash or debris. Check with your local fire department to be sure the weather is safe enough for burning.
  • Follow the advice of your doctor or other healthcare provider about medicines and about your respiratory management plan if you have asthma or another lung disease. Consider evacuating if you are having trouble breathing. Call your doctor for  advice if your symptoms worsen.
  • Do not rely on dust masks for protection. Paper “comfort” or “dust” masks commonly found at hardware stores are designed to trap large particles, such as sawdust. These masks will not protect your lungs from the small particles found in wildfire smoke.
  • Evacuate from the path of wildfires. Listen to the news to learn about current evacuation orders. Follow the instructions of local officials about when and where to evacuate. Take only essential items with you. Follow designated evacuation routes–others may be blocked–and plan for heavy traffic.

More Information

Tweet this: “Take action to protect yourself and your family from wildfire smoke. Learn more about CDCs recommended steps to take to decrease your risk from wildfire smoke at http://bit.ly/2kY0on1 #CDCEHblog via @CDCEnvironment ”

Springfield Smoked Fish Recalls Smoked Salmon Because Of Possible Health Risk

Springfield Smoked Fish of Springfield, Ma.is recalling its 1lb. packages Prescliced Nova Salmon because they have the potential to be contaminated with Listeria monocytogenes, an organism which can cause serious infections in young children, frail or elderly people, and others with weakened immune systems. Although healthy individuals may suffer only short-term symptoms such as high fever, severe headache, stiffness, nausea, abdominal pain and diarrhea.

The recalled packages were distributed to customers in Rhode Island and Connecticut through retail stores.

The product comes in a 1lb, clear plastic package marked with lot # 42173 on the back of the package and with an expiration date of 12/22/17. UPC 811907018018.

No illnesses have been reported to date in connection with this problem.

This issue was identified after FDA product sampling identified Listeria monocytogenes in the product.

The production of the product has been suspended while FDA and the company continue to investigate the source of the problem.

Consumers who have purchased the 1lb packages of the pre-sliced Nova Salmon are urged to return them to the place of purchase for a full refund. Consumers with questions may contact the company at 1-413-737-8693. Monday through Friday 8 am – 5 pm.

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Santa Fe Importers, Inc. Recalls Ready-To-Eat Pork Products due to Possible Extraneous Material Contamination

WASHINGTON, Dec. 12, 2017– Santa Fe Importers, Inc. a Long Beach, Calif. establishment, is recalling approximately 143 pounds of pork salami products that may be contaminated with extraneous materials, specifically metal shavings, the U.S. Department of Agriculture’s Food Safety and Inspection Service (FSIS) announced today.

The pork salami items were produced on Aug. 3, 2017. The following products are subject to recall: [View Labels (PDF Only)]

  • 3.5 to 4.0-lbs. random weight of plastic wrapped packages of “MARISA PREMIUM QUALITY GENOA SALAMI ITALIAN BRAND,” with a packaging date of Sept. 14, 2017 and a lot code of 257-100161 in the upper right hand corner.

The products subject to recall bear establishment number “EST. 4118” inside the USDA mark of inspection. These items were shipped to retail locations in California.

The problem was discovered after the company received a complaint from their retail customer and notified FSIS on Dec. 11, 2017.

There have been no confirmed reports of adverse reactions due to consumption of these products. Anyone concerned about an injury or illness should contact a healthcare provider.  

Consumers who have purchased these products are urged not to consume them. These products should be thrown away or returned to the place of purchase.

FSIS routinely conducts recall effectiveness checks to verify recalling firms notify their customers of the recall and that steps are taken to make certain that the product is no longer available to consumers.

Consumers with questions about the recall can contact Vincent Passanisi, president, at (562) 437-7775. Media with questions about the recall can contact Jorge Endara, general manager, at (562) 437-7775.

Consumers with food safety questions can “Ask Karen,” the FSIS virtual representative available 24 hours a day at AskKaren.gov or via smartphone at m.askkaren.gov. The toll-free USDA Meat and Poultry Hotline 1-888-MPHotline (1-888-674-6854) is available in English and Spanish and can be reached from 10 a.m. to 6 p.m. (Eastern Time) Monday through Friday. Recorded food safety messages are available 24 hours a day. The online Electronic Consumer Complaint Monitoring System can be accessed 24 hours a day at: http://www.fsis.usda.gov/reportproblem.

Contact with Animals in Public Settings | Healthy Pets Healthy People

children feeding aminals

Contact with animals in public settings (e.g., fairs, educational farms, petting zoos, schools) provides opportunities for entertainment and education. There are many wonderful benefits of human-animal contact. However, it is important to know that animals sometimes carry germs that could make people sick—even animals that look clean and healthy can still carry these harmful germs. Many outbreaks of illnesses spread between animals and people have been documented. These outbreaks have substantial medical, public health, legal, and economic effects.

State public health veterinarians are the local and state professionals who regularly consult with physicians, emergency rooms, legislators, local officials, schools, health departments, and the general public on preventing and controlling diseases that people can get from animals and animal products. CDC works closely with the National Association of State Public Health Veterinarians (NASPHV). NASPHV, in partnership with CDC and others, collaborate on a variety of guidance and recommendations related to the prevention and control of zoonoses.

The Compendium of Measures to Prevent Diseases Associated with Animals in Public Settings provides standardized recommendations for use by public health officials, veterinarians, animal venue operators, animal exhibitors, and others concerned with disease control and with minimizing risks associated with animals in public settings. To help prevent zoonotic diseases associated with animals in public settings, a toolkit of selected resources are available. This toolkit contains examples of regulations on animal exhibitions, printable posters with messages on how to stay safe while enjoying animals, and a check list of petting zoo best practices.

Harvesters Issues Safety Alert on Bibi Frutix Candy Possible Chemical Contamination

On Tuesday, December 5, a partner food pantry agency in Wamego, Kansas notified Harvesters—The Community Food Network of potentially contaminated Bibi Frutix candy product it received through Harvesters’ distribution program.

The candy product is likely contaminated with a chemical substance and is potentially harmful if eaten. To date, there has been a report of one illness. The candy product is the shape of a small baby bottle and labeled “Bibi Frutix.” The product was donated to Harvesters. Harvesters estimates six cases or fewer of the candy product was received.

Harvesters followed recall procedures, including issuing a recall notice to all partner agencies (food pantries) and checking to see if any product remained in its warehouses. To date, no product has been found beyond what was involved in the initially reported incident in Wamego, Kan.

Harvesters’ agencies that could have received the product are located in the following 19 counties of Harvesters’ service area: Kansas: Johnson, Wyandotte, Miami, Franklin, Douglas, Jefferson, Shawnee, Nemaha, Pottawatomie, and Wabaunsee. Missouri counties are:
Jackson, Platte, Clay, Ray, Lafayette, Johnson and Bates. Harvesters continues to cooperate with local and state authorities to determine the potential scope of the distribution.

This may be an isolated incident. Out of concern for public safety, Harvesters is alerting the public. The safety of the public, Harvesters’ agency partners, volunteers, clients and staff is most important.

The FDA has advised, if a member of the community has possession of the candy product, do not consume the product and dispose of it immediately. People who experience symptoms should contact their health care provider for treatment. If you believe product you received was contaminated, you may file a complaint with the FDA consumer complaint coordinator for Kansas and Missouri at 1-800-202-9780.

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Quickstats: Percentage of Children Aged 4–17 Years Who Had Ever …

During 2007–2016, the percentage of children aged 4–17 years who had ever had chickenpox decreased among both younger children (aged 4–11 years) and older children (aged 12–17 years). Among younger children, the percentage of children who had ever had chickenpox declined by 73.9%, from 16.1% in 2007 to 4.2% in 2016. Among older children the percentage who had ever had chickenpox declined by 76.9%, from 61.4% in 2007 to 14.2% in 2016. During 2007–2016, older children were more likely than younger children to have ever had chickenpox.

Source: National Center for Health Statistics, National Health Interview Survey, 2007–2016. https://www.cdc.gov/nchs/nhis.htm.


Reported by: Cynthia Reuben, MA, CReuben@cdc.gov, 301-458-4458; Mary Ann Bush, MS.

Suggested citation for this article: QuickStats: Percentage of Children Aged 4–17 Years Who Had Ever Had Varicella (Chickenpox), by Age Group — National Health Interview Survey, 2007–2016. MMWR Morb Mortal Wkly Rep 2017;66:1337. DOI: http://dx.doi.org/10.15585/mmwr.mm6648a7.

Use of trade names and commercial sources is for identification only and does not imply endorsement by the U.S. Department of
Health and Human Services.
References to non-CDC sites on the Internet are
provided as a service to MMWR readers and do not constitute or imply
endorsement of these organizations or their programs by CDC or the U.S.
Department of Health and Human Services. CDC is not responsible for the content
of pages found at these sites. URL addresses listed in MMWR were current as of
the date of publication.

All HTML versions of MMWR articles are generated from final proofs through an automated process.
This conversion might result in character translation or format errors in the HTML version.
Users are referred to the electronic PDF version (https://www.cdc.gov/mmwr)
and/or the original MMWR paper copy for printable versions of official text, figures, and tables.

Questions or messages regarding errors in formatting should be addressed to
mmwrq@cdc.gov.

QuickStats: Percentage of Adults Aged 20–64 Years With a Fasting Test

The percentage of U.S. adults aged 20–64 years who had a fasting test for high blood sugar or diabetes in the past 12 months increased from 39.7% in 2011 to 45.7% in 2016. From 2011 to 2016, there was an increase in the percentage for all racial/ethnic groups examined: Hispanic (38.3% to 43.0%), non-Hispanic white (39.6% to 46.5%), non-Hispanic black (41.2% to 44.9%), and non-Hispanic Asian (41.5% to 49.6%) adults. In 2011, there was no statistically significant difference among the four groups examined, but in 2016, Hispanic adults were less likely than non-Hispanic white and non-Hispanic Asian adults to have had a fasting test, and non-Hispanic Asian adults were more likely than non-Hispanic black adults to have had one.

Source: National Health Interview Survey, 2011 and 2016 data. https://www.cdc.gov/nchs/nhis.htm.


Reported by: Michael E. Martinez, MPH, MHSA, bmd7@cdc.gov, 301-458-4758; Maria A. Villarroel, PhD, Emily P. Zammitti, MPH.

Suggested citation for this article: QuickStats: Percentage of Adults Aged 20–64 Years With a Fasting Test in the Past 12 Months for High Blood Sugar or Diabetes, by Race/Ethnicity — National Health Interview Survey, United States, 2011 and 2016. MMWR Morb Mortal Wkly Rep 2017;66:1310. DOI: http://dx.doi.org/10.15585/mmwr.mm6647a7.

Use of trade names and commercial sources is for identification only and does not imply endorsement by the U.S. Department of
Health and Human Services.
References to non-CDC sites on the Internet are
provided as a service to MMWR readers and do not constitute or imply
endorsement of these organizations or their programs by CDC or the U.S.
Department of Health and Human Services. CDC is not responsible for the content
of pages found at these sites. URL addresses listed in MMWR were current as of
the date of publication.

All HTML versions of MMWR articles are generated from final proofs through an automated process.
This conversion might result in character translation or format errors in the HTML version.
Users are referred to the electronic PDF version (https://www.cdc.gov/mmwr)
and/or the original MMWR paper copy for printable versions of official text, figures, and tables.

Questions or messages regarding errors in formatting should be addressed to
mmwrq@cdc.gov.

ALDI Voluntarily Recalls Choceur Dark Chocolate Bar Due to Undeclared Nut Allergen Products Could Contain Almond Pieces Not Listed on Packaging

 In cooperation with Hofer KG ZNL Schokoladefab, ALDI has voluntarily recalled Choceur Dark Chocolate Bars as a precautionary measure due to the potential presence of almond pieces not listed on packaging. The recall was initiated after an ALDI employee identified almond pieces in the product. This product may cause an allergic reaction in customers who have a nut allergy.

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The product was available for purchase in the following states: Ohio, Illinois, Indiana, Wisconsin, Iowa, Kentucky, West Virginia, New York, Pennsylvania, Michigan, Maryland, Washington D.C., Virginia, Connecticut, Rhode Island, Massachusetts, New Hampshire, Vermont, Georgia, South Carolina, North Carolina, Tennessee, Florida and Texas.

The product was also available for purchase to ALDI customers in the Atlanta, Dallas and Los Angeles areas through the company’s partnership with Instacart, a grocery delivery service.

To date, no illnesses related to this product have been reported. No other ALDI products are affected by this recall.

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ALDI sincerely regrets any inconvenience and concern this voluntary recall may cause.

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Evaluating Cross-Cutting Approaches to Chronic Disease Prevention and Management: Developing a Comprehensive Evaluation

Marla Vaughan, MPH1; Jan Jernigan, PhD1; Seraphine Pitt Barnes, PhD, MPH, CHES1; Pat Shea, MPH, MA1; Rachel Davis, MPH1; Stephanie Rutledge, PhD, MA1 (View author affiliations)

Suggested citation for this article: Vaughan M, Jernigan J, Barnes SP, Shea P, Davis R, Rutledge S. Evaluating Cross-Cutting Approaches to Chronic Disease Prevention and Management: Developing a Comprehensive Evaluation. Prev Chronic Dis 2017;14:160499. DOI: http://dx.doi.org/10.5888/pcd14.160499.

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Abstract

We provide an overview of the comprehensive evaluation of State Public Health Actions to Prevent and Control Diabetes, Heart Disease, Obesity and Associated Risk Factors and Promote School Health (State Public Health Actions). State Public Health Actions is a program funded by the Centers for Disease Control and Prevention to support the statewide implementation of cross-cutting approaches to promote health and prevent and control chronic diseases. The evaluation addresses the relevance, quality, and impact of the program by using 4 components: a national evaluation, performance measures, state evaluations, and evaluation technical assistance to states. Challenges of the evaluation included assessing the extent to which the program contributed to changes in the outcomes of interest and the variability in the states’ capacity to conduct evaluations and track performance measures. Given the investment in implementing collaborative approaches at both the state and national level, achieving meaningful findings from the evaluation is critical.

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Background

State Public Health Actions to Prevent and Control Diabetes, Heart Disease, Obesity and Associated Risk Factors and Promote School Health (State Public Health Actions) is a program funded by the Centers for Disease Control and Prevention (CDC) to support the statewide implementation of strategies that promote health and prevent and control multiple chronic diseases and their risk factors (1). In the program, CDC partners with state health departments to address the 4 domains of chronic disease prevention: 1) epidemiology and surveillance, 2) environmental approaches, 3) health care system interventions, and 4) community programs linked to clinical services (2). Four divisions in the National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP) at CDC, the Division of Diabetes Translation (DDT), Division for Heart Disease and Stroke Prevention (DHDSP), Division of Nutrition, Physical Activity, and Obesity (DNPAO), and the School Health Branch (SHB) in the Division of Population Health, have collaborated to fund, implement, and evaluate State Public Health Actions.

Funding from the State Public Health Actions program has provided state health departments with an opportunity to address chronic diseases within their state at the individual level, such as by promoting health care interventions, and at the population level by developing policies and creating environments that promote health. This article is a companion to “Overview of State Public Health Actions to Prevent and Control Diabetes, Heart Disease, Obesity and Associated Risk Factors and Promote School Health,” which was published December 7, 2017, in Preventing Chronic Disease (3). Here we describe the approach taken to evaluate the collaborative, complex State Public Health Actions program to ensure its accountability by demonstrating health outcomes, assisting states and CDC in improving the implementation of programs, and expanding the body of practice-based evidence by identifying successful and replicable strategies.

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Evaluation Approach

Because State Public Health Actions is an innovative, cross-cutting program, it requires robust, multifaceted methods to evaluate it effectively. Although each of the 4 divisions conducted evaluations of their programs before State Public Health Actions, they took different approaches based on various factors, including the size and scale of the programs, the types of strategies being implemented (eg, policy, systems, and environmental changes, community-based and clinical interventions), and types of stakeholders engaged. Although evaluating large, federally funded public health programs is always challenging, the unique approach of State Public Health Actions compounded these challenges. Specifically, for State Public Health Actions there was a need to demonstrate to stakeholders its impact on disease-specific outcomes while implementing cross-cutting activities. Other challenges included coordinating across multiple chronic disease areas at the state and CDC level, accessing new partners and data sources, and the need to report performance measures that focused solely on outcomes.

These complex challenges required evaluators from each division to work together to design a comprehensive, multitiered approach to address the relevance, quality, and impact of State Public Health Actions. To begin, the evaluators followed standard practice by creating a logic model to highlight the inputs, activities, strategies, and outcomes of State Public Health Actions (Figure 1). The evaluators then designed the evaluation to assess and document the processes and outcomes of the program and to highlight how the implementation of the evidence-based strategies would lead to intended outcomes. The evaluation also examines the potential benefits and challenges of State Public Health Action’s approach of improving individual disease outcomes through the use of cross-cutting strategies.

Program logic model for State Public Health Actions to Prevent and Control Diabetes, Heart Disease, Obesity and Associated Risk Factors and Promote School Health program. Abbreviations: A1c, glycated hemoglobin A1c; CVD, cardiovascular disease; DDT, Division of Diabetes Translation; DHDSP, Division for Heart Disease and Stroke Prevention; DNPAO, Division of Nutrition, Physical Activity, and Obesity; DPH, Division of Population Health, School Health Branch; DSME, diabetes self-management education; K–12, kindergarten through 12th grade.

Figure 1.
Program logic model for State Public Health Actions to Prevent and Control Diabetes, Heart Disease, Obesity and Associated Risk Factors and Promote School Health program. Abbreviations: A1c, glycated hemoglobin A1c ; CVD, cardiovascular disease; DDT, Division of Diabetes Translation; DHDSP, Division for Heart Disease and Stroke Prevention; DNPAO, Division of Nutrition, Physical Activity, and Obesity; DPH, Division of Population Health, School Health Branch; DSME, diabetes self-management education; K–12, kindergarten through 12th grade. [A
text version of this figure is also available.]

The evaluation approach includes 4 primary components: conducting a national evaluation that assesses progress across all states; reporting by the states of performance measures to track the reach of individual strategies and disease-specific outcomes; conducting evaluations by the states to assess and improve programs at the state level and understand the facilitators of, and barriers to, program implementation; and providing evaluation technical assistance to enhance the capacity for evaluation at the local level and improve the reporting of data. CDC developed a structure to plan and implement the 4 components of the evaluation, which is to be carried out over a 5-year period. DHDSP was chosen to serve as the functional lead for evaluation in the administrative and management structure (3), while all 4 divisions identified a representative to act in a leadership role for evaluation-related decisions and the development of plans, processes, and guidance documents.

Four distinct evaluation workgroups were created to 1) oversee and implement the national evaluation; 2) collect, analyze, report, and provide guidance on performance measures; 3) provide guidance on planning and reporting the individual states’ evaluations; and 4) give technical assistance to build evaluation capacity among the states and ensure successful implementation of the 4 components of the evaluation (Figure 2). For each component, the workgroup members identified and addressed both common and unique challenges to developing and implementing that component.

Components of state public health actions evaluation, State Public Health Actions to Prevent and Control Diabetes, Heart Disease, Obesity and Associated Risk Factors and Promote School Health (State Public Health Actions).

Figure 2.
Components of state public health actions evaluation, State Public Health Actions to Prevent and Control Diabetes, Heart Disease, Obesity and Associated Risk Factors and Promote School Health (State Public Health Actions). [A text version of this figure is also available.]

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National Evaluation

The national evaluation is the key mechanism for understanding the progress, achievements, and challenges of the overall State Public Health Actions program. This component aims to not only assess the impact, effectiveness, and efficiencies of the program but also to determine the degree to which cross-cutting approaches affect outcomes for health promotion and chronic disease prevention.

Development

The national evaluation workgroup used the CDC evaluation framework (4) to guide the evaluation’s design and methods and to provide context for the findings. The workgroup developed 4 overarching evaluation questions that will be assessed throughout the 5-year span of State Public Health Actions:

  1. To what extent has the program been effective, as indicated by progress toward the intended accomplishments and outcomes?

  2. To what extent, if any, have state programs gained efficiencies (eg, in infrastructure, management, financial performance) through the implementation of the approach of State Public Health Actions?

  3. To what extent, if any, has CDC gained efficiencies by combining the efforts of 4 of its divisions within NCCDPHP?

  4. What promising and innovative strategies that could be replicated by state programs have been found effective and efficient?

The 5-year national evaluation plan comprises an examination of the collaborations, efficiencies, activities, and accomplishments of all awardees; an in-depth analysis of the implementation and effectiveness of specific strategies; and an examination of the efficiency of CDC’s internal coordination and the effectiveness of technical assistance to awardees.

Implementation

The national evaluation seeks to assess the implementation and outcomes of the program across all 50 states and the District of Columbia. Because the grantees are at different stages of implementation throughout the program period and because there are several potential focus areas and priorities, CDC evaluators develop an evaluation protocol for each year that incorporates programmatic priorities and subevaluation questions guided by the 4 overarching evaluation questions. Once a protocol is drafted, CDC obtains feedback from evaluators, states, CDC partners, and program staff members to ensure that the protocol is feasible and aligns with stakeholder needs. CDC relies on the primary and secondary collection of both quantitative and qualitative data. Specific data collection and analyses include conducting quantitative analyses of data on state performance measures; fielding surveys to assess the efficiency and collaboration of CDC and the states; implementing focus groups and key informant interviews; and reviewing training and technical assistance notes, state work plans, annual performance reports, and evaluation plans and reports written by the states. While the nature of evaluating a large program conducted by all the states limits the ability to attribute outcomes to the program because of the lack of comparison groups, the multiple sources of data collected allow for data triangulation to identify and assess trends and common themes in state progress. The evaluation of State Public Health Actions strives to show the reach of the program, methods of implementation, its synergy and coordination, and its impact in terms of contributing to improvements in disease-specific outcomes.

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Reporting of Performance Measures

Performance measures provide accountability by answering questions about what was achieved or, conversely, not achieved (5). For State Public Health Actions, performance measures provide key data for reporting outcomes to stakeholders and provide quantitative data that is incorporated into the national evaluation for assessing short-term and intermediate progress across each strategy being implemented by states. There were, however, several challenges to the implementation of reporting on performance measures. For example, previous programs funded by the 4 divisions within NCCDPHP did not require the reporting of outcome performance measures, many of the strategies that states are implementing as part of State Public Health Actions are new, many states were required to engage with new partners (eg, health care systems) and, as a group, states had varying capacity to collect and report measures and access data sources.

Development

To develop the performance measures, leadership from the DHDSP, DDT, DNPAO, and SHB reviewed the purpose and intended outcome of each strategy in the logic model (Figure 1) to determine the areas and type of performance measures needed. Each division pulled most of its measures from previously developed and pilot-tested measures. For example, of the measures selected by the DHDSP, all but one were chosen from a prior multiyear project working with stakeholders to develop a menu of indicators for the control of high blood pressure. For State Public Health Actions, each performance measure aligns with a strategy or intervention that focuses on outcomes relevant to specific disease outcomes and the interests of stakeholders.

To ensure the reporting of high-quality data and to build capacity to collect and report performance measures at the state level, CDC developed guidance documents and provided webinars related to calculating the reach of the intervention and developing baseline and target values. CDC also developed operational definitions, also called profiles, for each of the performance measures; each 2-page profile defines and describes the purpose of the measure, unit of analysis, target population, and setting. It also describes how to calculate the measure, including the data sources to be used and the frequency of data collection, and provides additional resources and references (Appendix). CDC worked with the states to review and finalize the profiles. Once the profiles were disseminated, a tip sheet and considerations for reporting were provided to assist states with the reporting of data on performance measures.

Implementation

In 2013, the states reported initial baselines and targets for strategy-specific performance measures. From 2015 until the end of the program (2018), states are required to report targets and annual progress for performance measures associated with their selected strategies and interventions. The states use a CDC-provided template that includes the measures required for a particular strategy, the prepopulated baseline (based on earlier reporting), targets for the current year and year 5, and actual data for the current year. Depending on the measure, the states report the data as a number, rate, percentage, or numerator and denominator. They also report the data source(s) and, as needed, provide notes that would give context to CDC for understanding the data during its analysis.

Each year, CDC’s performance measure workgroup assesses the quality of the state-reported data on these measures and the appropriateness of the analyses conducted (earlier, CDC had developed criteria for data quality and determined the type of analysis to be used for each performance measure). Data analysts at CDC use the criteria and inclusion and exclusion criteria for final cleaning and analysis of the data. Assessment of data quality also helps determine the performance measures for which the data are of sufficient quality to include them in the national evaluation and identifies measures that have widespread issues with quality. In addition, the process enables the provision of appropriate technical assistance to remedy those quality issues.

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Evaluations by the States

The evaluations performed by the individual states aim to provide data relevant to those states while also contributing to the national evaluation. States use data for purposes such as continuous program improvement and being responsive to local stakeholders. CDC uses these data for purposes such as synthesizing information on common strategies that states are using to identify and engage partners. This information provides a complete picture of progress on the performance measures, aids understanding of facilitators and barriers to implementation, and identifies potential best practices.

Development

Acknowledging the difficulties of aggregating results from evaluations conducted by the states and other challenges in reporting their data, including varying capacities and a lack of standard data collection methods, CDC developed a set of core process evaluation questions and division-specific core outcome evaluation questions to facilitate the aggregation and cross-analysis of findings from the states for the national evaluation. States were also encouraged to develop additional evaluation questions and indicators to meet their own evaluation needs. The core process evaluation questions were related to their coordination with critical partners, their work across areas of chronic disease, their type of organizational structure, and their increased efficiencies obtained. The division-specific core outcome evaluation questions were related to progress made and both the barriers that they encountered and facilitators that aided selected strategies (Table). To reduce their burden and to focus the evaluation, states were required to evaluate only 1 strategy for each CDC division. States could also select whether they were in the adoption or implementation phase of the strategy. CDC designed a template that states could use to provide background information on the particular approach and strategies of the program, the selection of activities implemented, settings and target populations, key stakeholders and partners involved in the program planning and implementation, indicators developed to monitor progress toward achieving an answer to the process evaluation question, and the synergistic approach used to implement the program.

The division-specific outcome evaluation sections of the template included additional information on barriers and facilitators, an indicator table, a findings and results section for each disease-specific core outcome evaluation question, and a plan to disseminate the results of their evaluation to internal and external stakeholders.

Implementation

States annually submit to CDC their plans for evaluation and the evaluation results obtained. Data are stored on an internal SharePoint (Microsoft Corp) site, where CDC evaluators review the data and determine how best to synthesize the data and pull out common themes. The data are triangulated with other data for the national evaluation and summarized. The information provided in the evaluation plans enables CDC evaluation technical assistance providers to understand the proposed methods and, thus, more effectively assist the states in conducting their own evaluations. Technical assistance providers can also provide information to the program team about common barriers and facilitators, which can be used to develop trainings and technical assistance to support and improve program implementation.

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Providing Evaluation Technical Assistance

The national evaluation, reporting of performance measures, and state evaluations all rely on data received by the states. Because the states have varying levels of capacity for evaluation, CDC must provide technical assistance to ensure effective reporting to the agency and to make sure that the state-level evaluations are providing information relevant to improving programs and meeting the needs of stakeholders. Because 4 divisions at CDC support the work of the state health departments, with each bringing its own body of expertise as it pertains to implementing disease-specific interventions, evaluators from all 4 of these divisions have worked collaboratively as part of regional teams that support the states to evaluate various strategies they are implementing.

Development

CDC’s technical assistance plan for the 5-year evaluation consists of evaluation capacity assessments, annual reviews of documents, the development of evaluation tools and resources, and other forms of technical assistance to the states. Evaluation capacity assessments were performed in the first year to understand the capacity of each state to conduct evaluations and to identify needs for technical assistance and types of trainings and resources that were needed for states to meet evaluation requirements. Ongoing assessments are also conducted to identify facilitators of and barriers to developing evaluation plans and tools, identifying appropriate indicators and data sources, and conducting data analysis for annual evaluation reporting. Evaluators at CDC maintain regular communication with evaluators at the state level and assist them with developing their evaluation plans, collecting and reporting performance measures, and reporting the results of their evaluations. CDC evaluators also assist both the states and project officers at CDC through the annual review of work plans, yearly performance reports, and evaluation reports to ensure that states are aligning activities with performance measures and accurately reporting data.

Implementation

Evaluation resources made available to the states by CDC include training opportunities such as cross-state peer-learning communities, evaluation guidance documents, sample data collection tools, and evaluation plan and report templates. The peer-learning communities meet monthly for presentations and facilitated discussion. In addition, there is a listserve on which community members can pose questions to other members about their experiences implementing their evaluations and can share information and documents. Additional evaluation guidance documents and tools developed by CDC include templates and helpful hints documents to support the states’ work throughout various phases of the program.

Consistent and coordinated communication with states and among CDC staff is important to reaching the goal of providing effective technical assistance. To standardize technical assistance, evaluators developed a guide designed to support consistent monitoring and documentation of evaluation technical assistance needs for a state during evaluation plan implementation and performance measure reporting. In collaboration with project officers, evaluators at CDC communicate with states at least monthly through regular calls with the regional team and ad hoc, evaluation-specific follow-up calls and email communication. Internally, CDC uses a performance-monitoring database to document progress on performance and evaluation activities and to track communication and follow-up activities between the states and CDC’s evaluation staff and project officers.

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Dissemination and Use of Evaluation Findings

CDC regularly disseminates findings related to the evaluation of State Public Health Actions to various stakeholders, including internal and external partners as well as the general public, through reports, executive summaries or briefs, presentations, and journal publications. Reports internal to CDC are used to understand how states are implementing programs and how well CDC is providing technical assistance to states and coordinating across divisions. Briefing documents, such as the State Public Health Actions Year 3 Performance Measures Snapshots (6), and the DNPAO state snapshots website (7), which report on highlights at the state level, are used to provide information on the program’s priorities and offer succinct outcomes that are relevant to stakeholders. Findings are also prepared for national partners and Congress to demonstrate accountability and program impact.

Presentations of findings are delivered internally to the CDC staff and externally to state health departments’ staff and other public health practitioners. For example, presentations were made at a meeting of grantees in Atlanta, Georgia, and to various diverse audiences at national conferences, such as those that were held by the American Public Health Association and the American Evaluation Association (8). Evaluation methods and findings obtained are also being shared through journal articles written by the CDC staff and state representatives (3, 9–11). In addition, CDC provides assistance to states in writing journal articles and finding strategies for dissemination.

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Conclusion

The approach to the evaluation of State Public Health Actions is intended to demonstrate the impact of the overall program while capturing unique cross-cutting aspects of the program and the disease-specific outcomes. Lessons learned and key findings from the national evaluation, performance measures data, and evaluations conducted by the states will be summarized throughout the 5 years of the program to assist with ongoing program improvement, report progress to stakeholders, identify successful strategies, and inform future decisions on funding. While the comprehensive evaluation strives to evaluate efficiency, effectiveness, and impact at the state and national levels, it faces numerous challenges.

Evaluations of large public health programs are difficult to conduct, with one of the big challenges being the inability to attribute successes or shortfalls wholly to the program, because there are often confounding factors, a lack of comparison groups, long time frames, or multiple interventions going on at once. The development and use of performance measures to assess outcomes for federal programs is also challenging because of issues such as the complexity of public health problems, which may have multiple determinants or outcomes that may take several years to achieve; the decentralized implementation of public health programs; and measurement issues related to a lack of reliable, timely, and consistent data sources (5). Also, to successfully aggregate standardized measures, it would be ideal, but not realistic, for the states to have similar capacities to access, collect, analyze, and report data. Finally, federal agencies are challenged by the limited resources available to provide state health departments with consistent and intensive technical assistance with evaluation to help them with collecting and reporting performance measures and evaluating their programs.

These common challenges are clearly applicable to State Public Health Actions, with the added complexity of working across multiple topic areas and attempting to evaluate cross-cutting strategies when most state health departments and CDC operate within distinct disease or topic areas. Each topic area has discrete funding streams and must demonstrate effectiveness in achieving outcomes for each of these areas. The State Public Health Actions program also expands funding to more states than were previously funded by each division, and oversight and management requires complex coordination. To accurately describe the implementation and outcomes of State Public Health Actions, assessing collaboration and coordination across topic areas at the state level and at CDC is an important part of the evaluation. Surveys, focus groups, key informant interviews, and results obtained from evaluations conducted by the states using a standard template are employed to highlight this unique aspect of State Public Health Actions. CDC evaluators provide proactive and intensive technical assistance to address challenges, but the complex, cross-topic, structure of technical assistance can be time-consuming.

Although there are challenges and limitations with the evaluation of State Public Health Actions, given CDC’s substantial investment in testing collaborative approaches and working across domains, striving to achieve meaningful findings from the evaluation is critical. Subsequent articles will highlight results achieved by the program and promising practices that can be implemented broadly.

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Acknowledgments

We are grateful for the contributions and hard work provided by members of the CDC evaluation workgroups across the Division for Heart Disease and Stroke Prevention; Division of Diabetes Translation; Division of Nutrition, Physical Activity, and Obesity; and the School Health Branch in the Division of Population Health. We also thank those state health department staff members who contributed to the development of the performance measure profiles. No financial support was received for this work.

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of CDC.

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Author Information

Corresponding Author: Marla Vaughan, MPH, Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention. 4770 Buford Hwy, Mailstop F-75, Atlanta, GA 30341-3717. Telephone: 770-488-4826. Email: mhv1@cdc.gov.

Author Affiliations: 1Division for Heart Disease and Stroke Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia.

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References

  1. Centers for Disease Control and Prevention. State Public Health Actions to Prevent and Control Diabetes, Heart Disease, Obesity and Associated Risk Factors and Promote School Health. https://www.cdc.gov/chronicdisease/about/state-public-health-actions.htm. Accessed May 11, 2017.
  2. Bauer UE, Briss PA, Goodman RA, Bowman BA. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet 2014;384(9937):45–52. CrossRef PubMed
  3. Park BZ, Cantrell L, Hunt H, Farris RP, Schumacher P, Bauer UE. Overview of State Public Health Actions to Prevent and Control Diabetes, Heart Disease, Obesity and Associated Risk Factors and Promote School Health. Prev Chronic Dis 2017;14:160437. . http://www.cdc.gov/pcd/issues/2017/16_0437.htm. CrossRef
  4. Centers for Disease Control and Prevention. Framework for program evaluation in public health. MMWR Recomm Rep 1999;48(RR-11):1–40. PubMed
  5. DeGroff A, Schooley M, Chapel T, Poister TH. Challenges and strategies in applying performance measurement to federal public health programs. Eval Program Plann 2010;33(4):365–72. CrossRef PubMed
  6. Centers for Disease Control and Prevention. State Public Health Actions (1305). Year 3 performance measures snapshot. Atlanta (GA): Centers for Disease Control and Prevention. https://wwwdev.cdc.gov/dhdsp/docs/1305-y3-performance-measures-snapshot.pdf. Accessed June 5, 2017.
  7. Division of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention. Funding by state. 2017. https://www.cdc.gov/nccdphp/dnpao/state-local-programs/funding.html. Accessed November 13, 2017.
  8. Jernigan JD. Evaluating cross-cutting approaches to addressing chronic disease: a comprehensive evaluation of program effectiveness and efficiency. Chicago (IL): American Evaluation Association; 2015.
  9. Mensa-Wilmot Y, Bowen S, Rutledge S, Morgan JM, Bonner T, Farris K, et al. Early results of states’ efforts to support, scale, and sustain the National Diabetes Prevention Program. Prev Chronic Dis 2017;14:170478. . http://www.cdc.gov/pcd/issues/2017/17_0478.htm. Accessed December 7, 2017. CrossRef
  10. Geary NA, Dooyema CA, Reynolds MA. Supporting obesity prevention in statewide quality rating and improvement systems: a review of state standards. Prev Chronic Dis 2017;14:160518. . http://www.cdc.gov/pcd/issues/2017/16_0518.htm. Accessed December 7, 2017. CrossRef
  11. Pitt Barnes S, Skelton-Wilson S, Cooper A, Merlo C, Lee S. Early outcomes of State Public Health Actions’ school nutrition strategies. Prev Chronic Dis 2017;14:170106. . http://www.cdc.gov/pcd/issues/2017/16_0518.htm. Accessed December 7, 2017. CrossRef

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Table

Return to your place in the textTable. Summary of Division-Specific Core Outcome Evaluation Questions for State Evaluations, the State Public Health Actions to Prevent and Control Diabetes, Heart Disease, Obesity and Associated Risk Factors and Promote School Health (State Public Health Actions) Program
Division Topic Area Outcome Evaluation Question
Nutrition, Physical Activity, and Obesity What are the key activities and/or resources considered critical to successful adoption/implementation of

  • Healthier food retail venues or farmers’ markets in underserved areas?
  • Food service guidelines/nutrition standards in priority settings?
  • Interventions to create or enhance access to places for physical activity with an emphasis on walking through either state policies or pedestrian/transportation plans?
  • Standards to increase physical activity in ECEs?
  • Breastfeeding policies and practices?

What are the major barriers and facilitators to adopting/implementing

  • Healthier retail food venues or farmers’ markets in underserved areas?
  • Food service guidelines/nutrition standards in priority settings?
  • Interventions to create or enhance access to places for physical activity with an emphasis on walking through either state policies or pedestrian/transportation plans?
  • Standards to increase physical activity in ECEs?
  • Breastfeeding policies and practices?
School Health What state activities have been effective in promoting

  • Nutrition policy development and nutrition practice adoption among districts and schools?
  • The development of CSPAPs among districts and schools?
  • The implementation of policies, processes, and protocols in schools to meet the management and care needs of students with chronic conditions?
What critical factors or activities influence the successful implementation of

  • Nutrition policy and nutrition practice?
  • CSPAP?
What are the major facilitators and barriers in helping districts and schools

  • Create supportive nutrition environments, such as partnerships (eg, MOUs) with the Department of Education? How were the barriers overcome?
  • Develop CSPAPs, such as partnerships (eg, MOUs) with the Department of Education? How were the barriers overcome?
  • Meet the management and care needs of students with chronic conditions? How were the barriers overcome?
To what extent has implementation of nutrition policies and nutrition practices increased

  • Access to healthier foods and beverages at school?
  • The number of physical activity opportunities available to students during the school day?
  • The management and care needs of students with chronic conditions?
Heart Disease and Stroke What were the major facilitators and barriers in promoting implementation of

  • Quality improvement processes, such as use of EHRs, in health care systems? How were the barriers overcome?
  • Team-based care in health systems? How were the barriers overcome?
How has the state promoted the use of health-care extenders in the community in support of self-management of high blood pressure? What were key facilitators and barriers?
To what extent has the state effectively promoted implementation

  • Of quality improvement processes, such as use of EHRs, in health care systems?
  • Of team-based care in health systems?
What factors at the state level are necessary to promote the use of health-care extenders in the community in support of self-management of high blood pressure?
How has the relationship between the state health department, health care systems, and other QI/HIT partners in the state changed as a result of State Public Health Actions? Include the following aspects:

  • The extent to which the state is able to obtain health systems data.
  • Key facilitators and barriers to strengthening these partnerships.
What policies/systems facilitated the support and promotion of

  • Team-based care?
  • The increased use of health-care extenders?
To what extent have the QI processes influenced the quality, delivery, and use of clinical services for hypertension management among health systems?
What policies/systems are needed for health care systems to effectively

  • Implement team-based care?
  • Increase the use of health-care extenders?
Diabetes What were the major facilitators and barriers in implementing the 4 drivers during the start-up/implementation phase? How were the barriers overcome?

  • For diabetes self-management education?
  • For lifestyle intervention programs?
What were the key activities critical to addressing disparities in the 4 drivers during the start-up/implementation phase?

  • For diabetes self-management education?
  • For lifestyle intervention programs?

Abbreviations: CSPAP, Comprehensive School Physical Activity Program; ECE, early care and education; EHR, electronic health record; HIT, health information technology; MOUs, memorandums of understanding; QI, quality improvement.

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Appendix: Sample Performance Measure Profile for State Public Health Actions to Prevent and Control Diabetes, Heart Disease, Obesity and Associated Risk Factors and Promote School Health (State Public Health Actions) Program

This appendix is available for download as a Microsoft Word document [DOC – 57 KB].

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Early Results of States’ Efforts to Support, Scale, and Sustain the National Diabetes Prevention Program

Yvonne Mensa-Wilmot, PhD, MPH1; Shelly-Ann Bowen, PhD, MS2; Stephanie Rutledge, PhD, MA1; Jennifer Murphy Morgan, MSPH1; Timethia Bonner, DPM, PhD3; Kimberly Farris, PhD, MPH, MSW1; Rachel Blacher, MPH1; Gia Rutledge, MPH1 (View author affiliations)

Suggested citation for this article: Mensa-Wilmot Y, Bowen S, Rutledge S, Morgan JM, Bonner T, Farris K, et al. Early Results of States’ Efforts to Support, Scale, and Sustain the National Diabetes Prevention Program. Prev Chronic Dis 2017;14:170478. DOI: http://dx.doi.org/10.5888/pcd14.170478.

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Abstract

The Centers for Disease Control and Prevention (CDC) developed a cooperative agreement with health departments in all 50 states and the District of Columbia to strengthen chronic disease prevention and management efforts through the implementation of evidence-based strategies, such as CDC’s National Diabetes Prevention Program. The National Diabetes Prevention Program supports organizations to deliver the year-long lifestyle change program that has been proven to prevent or delay the onset of type 2 diabetes among those at high risk. This article describes activities, barriers, and facilitators reported by funded states during the first 3 years (2013–2015) of a 5-year funding cycle.

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Introduction

Prediabetes is clinically known as the stage between normal blood glucose and severe glucose intolerance (1) where blood glucose or glycated hemoglobin A1C levels are elevated but not high enough to be diagnosed as diabetes. The Centers for Disease Control and Prevention (CDC) estimates that 84 million adults aged 18 years or older in the United States have prediabetes, nearly 90% of whom are unaware of their condition (2). Prediabetes increases the risk of developing not only type 2 diabetes but cardiovascular disease as well (3). The progression of prediabetes to type 2 diabetes can be prevented or delayed by lifestyle behavior modification addressing diet, exercise, and stress reduction that results in a 5% to 7% weight loss (3,4). On the basis of findings from the Diabetes Prevention Program research study and subsequent translation studies (5,6), Congress authorized CDC in 2010 to establish the National Diabetes Prevention Program (National DPP), which provides a framework for type 2 diabetes prevention efforts in the United States.

A key component of the National DPP is a structured, evidence-based, year-long lifestyle change program (LCP) to prevent or delay onset of type 2 diabetes in people with prediabetes or at risk of developing type 2 diabetes (7). The LCP is group-based program that is facilitated by a trained lifestyle coach, and uses a CDC-approved curriculum. The curriculum uses regular opportunities for direct interaction between the lifestyle coach and participants, builds peer support, and focuses on behavior modification through healthy eating, increasing physical activity, and managing stress. The program may be delivered in person, online, or through a combination of both delivery modes (8,9,10,).

CDC’s Division of Diabetes Translation works collaboratively to scale and sustain the National DPP through partnerships with public and private organizations at state and local levels (7). The Division of Diabetes Translation also manages the Diabetes Prevention Recognition Program (DPRP), the quality assurance arm of the National DPP. Through the DPRP, CDC awards recognition to organizations delivering the LCP that are able to meet national quality standards and achieve the outcomes proven to prevent or delay onset of type 2 diabetes (11).

In 2013, in an effort to promote an integrated model of chronic disease prevention and management, CDC’s National Center for Chronic Disease Prevention and Health Promotion developed the State Public Health Actions to Prevent and Control Diabetes, Heart Disease, Obesity and Associated Risk Factors and to Promote School Health (SPHA-1305) cooperative agreement. Under this 5-year cooperative agreement, all 50 state health departments and the District of Columbia were funded to implement strategies to reinforce health promotion, epidemiology, and surveillance activities and implement targeted strategies that would have a significant impact on school health, nutrition, obesity, diabetes, heart disease, and stroke.

We present preliminary findings from a collaborative effort between CDC and state health departments designed to scale and sustain the National DPP. Findings from the first 3 years are described with the goal of providing an in-depth understanding of types of activities implemented along with barriers and facilitators experienced. Comments from grantee reports are included to augment findings presented. The information described in this article was exempt from ethical research approval because it involved only a secondary analysis of state program reports. Grantees did not report data in year 1; thus, findings reflect years 2 and 3 of the funding cycle (reports submitted in 2015 and 2016).

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Implementation Framework To Scale And Sustain the National DPP

Through funding to state health departments, CDC works to promote awareness of prediabetes and the National DPP; increase prediabetes screening, testing, and referral; and increase program participation by facilitating relationships between government agencies, community-based organizations, insurance providers, private-sector employers, academia, and health care providers (8). State health departments also work to secure the program as a covered benefit for state employees and Medicaid beneficiaries at risk for type 2 diabetes.

The strategy for scaling and sustaining the National DPP is a set of recommended activities grouped into 4 drivers that are essential to long-term success: 1) support the efforts of partners to increase the availability of LCPs, 2) implement referral policies and mechanisms, 3) establish payers and payment mechanisms, and 4) identify and enroll people with prediabetes or at high risk for type 2 diabetes in LCPs (Table 1). When targeted individually and collectively, these drivers are designed to improve availability of programs; expand reimbursement and insurance coverage; increase the use of practices and policies within health care systems to screen, test, and refer patients; and increase willingness of people at high risk of developing type 2 diabetes to enroll.

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State Health Department Progress on Key Activities

State health departments provide an account of their progress for key activities and outcomes to CDC annually. Two CDC authors (Y.M. and S.R.) qualitatively analyzed data from grantee annual performance reports from years 2 and 3 to summarize the types of activities implemented. We developed structural codes from the drivers and analyzed 20 activities from each data set to ensure reliability of the codes. To ensure consistency in coding, authors then discussed their findings and refined codes to reach a consensus before independently analyzing the remaining data. During their discussion, authors added codes to capture activities that were inconsistent with the drivers. Activities that were dropped by state health departments were not included in the analysis.

Barriers and facilitators encountered by state health departments

State health departments selected 1 intervention strategy to evaluate over the cooperative agreement period. In year 2, California, Colorado, Florida, Kentucky, Maine, Maryland, Minnesota, Missouri, Montana, New Mexico, Nebraska, New York, North Carolina, Oregon, and Rhode Island chose to evaluate the National DPP strategy. In year 3, Nebraska elected not to evaluate its National DPP work. Evaluation reports from 15 states were included in the year 2 analysis, and reports from 14 states were included in year 3. Two authors (S.B. and Y.M.) analyzed the 2 data sets by using a multistage iterative process to develop a hierarchy of codes for the data. Authors determined a priori to use the drivers as basic codes, then conducted a thematic analysis coding of reported facilitators and barriers to each driver. After an initial analysis of 2 reports, authors compared findings and refined and added subcodes before proceeding to code the remaining reports. An additional “other” category was added to capture information not classified within the codes and subcodes.

The number of activities implemented by state health departments across all 4 categories of drivers doubled from year 2 to year 3, from 148 to 295. State health departments engaged partners to support the scaling of the National DPP gradually and strategically through the funding cycle. A summary of key facilitators and barriers with representative comments is presented (Table 2).

Support the efforts of partners to increase availability of LCPs

Thirty-five state health departments supported the implementation of activities of their partners to increase availability of programs in year 3, an increase of 133% from 15 state health departments in year 2 (Figure). The most commonly reported activities for this driver were creating a network of partners to develop a strategic plan to scale and sustain the National DPP, convening key stakeholders to address barriers affecting programs, examining state data to prioritize the location of new programs, establishing mechanisms to increase the availability of LCPs, identifying organizations with infrastructure and capacity to deliver programs, and leveraging state resources to support their partners to start new programs. State health departments partnered with community organizations, health care organizations, employers, private insurers, and government agencies to increase the availability of LCPs in the community.

Number of state and District of Columbia health departments (n = 51) implementing activities within each of 4 drivers essential to increasing enrollment of people with prediabetes or at high risk of developing type 2 diabetes into National Diabetes Prevention Program (National DPP) lifestyle change programs (LCPs), 2015–2016.

Figure.
Number of state and District of Columbia health departments (n = 51) implementing activities within each of 4 drivers essential to increasing enrollment of people with prediabetes or at high risk of developing type 2 diabetes into National Diabetes Prevention Program (National DPP) lifestyle change programs (LCPs), 2015–2016. [A tabular version of this figure is also available.]

California reported “a marked increase in the number of in-person and online LCPs due, in part, to organizations and businesses that were able to host LCPs in multiple locations.” The ability of these organizations and businesses to obtain funding was reported as another facilitator that “removed key barriers to the start-up of new programs” (Minnesota) and “supported program uptake” (Maryland). Nebraska reported that “The complex nature of evidence-based programs made timely, clear, and adequate technical assistance important to our programs and enabled us to continue with our implementation efforts.”

Although partnerships were vital, some state health departments faced several challenges in their partnerships, including a lack of accountability, predetermined reporting structure, clarity in partnership roles, follow-through with strategic planning efforts, decision-making power, clear partner priorities, and interest. All of these challenges impeded efforts to increase program uptake.

Implement referral policies and mechanisms

Forty-seven state health departments implemented activities to increase the number of provider referrals made to LCPs in year 3. This represented a 62% increase in number of state health departments from year 2 (Figure). Of the 11,385 participants enrolled in LCPs in year 3, 67.6% (7,700) were referred by a provider. The most common activities reported were promoting the adoption of the American Medical Association (AMA)/CDC provider tool kit (12), providing technical assistance on prediabetes screening and testing, integrating referrals into coordinated care models, and leveraging existing electronic health records (EHRs) as novel referral methods to increase participation in LCPs partnering with state and local medical and nonmedical associations to engage the clinical community. For example, in years 2 and 3, the Florida Department of Health, in partnership with the American Diabetes Association (ADA), provided mini-grants to 14 LCPs. These grantees were able to reach 503 health care practices and 955 physicians to discuss establishing processes or policies for referrals to LCPs, and 33 policies and 256 procedures were implemented to refer patients to LCPs. In the 14 LCPs, 336 participants achieved the desired weight loss outcome of 5% or more in year 3.

Facilitators to developing referral policies for LCPs were having the buy-in of hospital systems, partnering with providers to establish patient referral policies, delivering provider education through academic detailing (face-to-face education of providers by trained health care professionals), providing feedback to providers on referral status, and integrating or linking CDC-recognized lifestyle change programs to referring clinics. Having LCPs attached to primary health care settings was valuable. For example, “YMCAs that established referral policies with local hospitals or health care providers show greater success recruiting and filling workshop classes than those that did not” (New York State). Integration of prediabetes clinical measures into EHRs and providing prediabetes resources in patient waiting areas contributed to referral success. Lifestyle coaches and participants viewed health care providers and workplace health programs as effective referral mechanisms. Grantees also reported challenges to increasing referrals, such as low provider awareness, provider resistance to making referrals, and difficulty reaching providers to establish a feedback loop.

Establish payers and payment mechanisms

Activities to establish coverage through payers and payment mechanisms included convening stakeholders to develop a state-specific business case, recruiting champions, and engaging stakeholders to discuss coverage for state employees and Medicaid beneficiaries. By year 3, 42 state health departments were implementing activities around this driver, a threefold increase from 14 state health departments in year 2.

Establishing partnerships to address lack of coverage was key to increasing LCP reimbursement and enrollment for state health departments: “State employees began having the National DPP offered to them as a covered benefit. Our diabetes program has been working [for a while] with the State Employee Group Insurance Program to promote the ‘Prevent’ program within our agency” (Minnesota); “The National DPP was added as a covered health benefit for state employees enrolled in Kaiser Permanente and United Healthcare plans” (Colorado).

Lack of insurance coverage for the National DPP was reported as a significant barrier. One state health department expressed concern that the lack of insurance coverage for the National DPP transferred the implementation costs to delivery sites that depended on reimbursement to be sustainable. In this state, low or no availability of coverage is reportedly driven by a complex payer landscape where Medicaid reimburses for the National DPP, but not all employers offer insurance coverage for prediabetes. Another state health department established a formal relationship with the state governor’s office, which resulted in coverage for state employees. The governor subsequently established National DPP enrollment as a leading health metric for the state.

Identify and enroll people with prediabetes or at high risk for type 2 diabetes in LCPs

In year 3, 22 state health departments were involved in activities to increase enrollment of people at high risk of developing type 2 diabetes into LCPs (Figure). This was a 46% increase in the number of state health departments implementing activities to support this driver from year 2 to year 3. The most commonly reported activities were training lifestyle coaches, developing a marketing plan, and directing culturally appropriate marketing materials to people at high risk of developing type 2 diabetes.

State health departments reported that availability of culturally and linguistically aligned lifestyle coaches was a major facilitator for identification and enrollment of people with prediabetes or at high risk for type 2 diabetes into LCPs. Transportation, proximity to programs, awareness of programs, maintaining contact with program participants on a regular basis, and availability of low-cost or no-cost programs were also reported as facilitators to increasing enrollment. The Montana state health department reported increased enrollment and participation in the LCPs and concluded that incentives contributed significantly to participants’ weight loss outcomes. Barriers to participants’ willingness to enroll were transportation needs, cost, scheduling difficulties, nonadherence to care, LCP complexity and length, lack of perceived self-efficacy, lack of skills needed to track food intake and physical activity, and feelings of discomfort in group settings. Another state health department reported that “the challenge continues in assuring that both consumers and clinicians recognize that prediabetes is a considerable risk factor and one that can be reduced with participation in evidence-based programming” (Nebraska). Solutions reported were using Medicaid transportation assistance, adapting the curriculum without changing core elements, reiterating key session points, simplifying tracking tools, promoting coping skills, and providing ongoing support from multiple people beyond the coaches (case managers, doctors, therapists, family, and friends).

Limitations of this early analysis

Reporting from state health departments on their implementation of activities to scale and sustain the National DPP had some limitations. Because the evaluation of activities implemented to scale and sustain the National DPP was optional, only 15 state health departments in year 2 and 14 in year 3 elected to evaluate the impact of their activities. Thus, the discussion of barriers and facilitators to implementation represents what was reported by state health departments that evaluated their National DPP work. If state health departments opted to evaluate a strategy on the basis of how well they were doing, the overall results would appear more favorable than what actually took place. In addition, the variation in the level of detail provided in the annual performance reports and evaluation reports is a limitation of this study. Some grantee reports provided detailed accounts of activities, successes, and barriers, whereas other reports provided brief responses.

Despite aforementioned limitations, this report reflects efforts to promote an integrated model of chronic disease prevention and provides insights into ways to evaluate activities to support and scale a complex, multisector national program designed to stem the current and projected growth in new cases of type 2 diabetes. Our findings identify unique and innovative approaches for real-world program adoption and implementation — specifically, approaches that inform new ways of encouraging people in various sectors to work together to improve health. Our findings provide real-time insight that can be used to refine universal program implementation and increase opportunities for people at risk to be exposed to evidence-based interventions and to have good health outcomes.

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Implications for the Future

State health departments are effectively supporting evidence-based programs such as the National DPP to prevent or delay the onset of type 2 diabetes in people at high risk. Improving and sustaining collaborations between public health agencies and health systems is crucial to the success of this work. We present information on what works and also information for developing guidance on implementing activities that support and scale this evidence-based intervention in community settings. Understanding the activities being implemented, along with barriers and facilitators, has implications for technical assistance to support the expansion and sustainability of the National DPP. These results provide relevant data on state health departments’ progress and contribute to the identification of potential best practices. Furthermore, what is learned from states’ evaluations is critical to making adjustments midstream during implementation of activities to scale and sustain the National DPP. These early findings can inform the establishment of communities of practice, identify state health departments to lead peer-to-peer learning collaboratives, and shape guidance for scaling not just the National DPP but future public health practice.

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Acknowledgments

The authors acknowledge the state health departments for providing their insights related to implementing activities and describing barriers and facilitators as part of the SPHA 1305 cooperative agreement. The authors received no funding for the research described in this article and have no conflicts of interest to declare. The findings and conclusions in this report are those of the authors and do not necessarily represent the views of CDC.

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Author Information

Corresponding Author: Yvonne Mensa-Wilmot, PhD, MPH, Centers for Disease Control and Prevention, Division of Diabetes Translation, 4770 Buford Hwy NE, MS-F75, Atlanta, GA 30341-3717. Email: YMensaWilmot@cdc.gov.

Author Affiliations: 1Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, Georgia. 2ICF International, Atlanta, Georgia. 3Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee.

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References

  1. McCain J. Prediabetes: pre- does not mean preordained. Manag Care 2016;25(5):35–41. PubMed
  2. Centers for Disease Control and Prevention. National diabetes statistics report, 2017. Atlanta (GA): US Department of Health and Human Services, Centers for Disease Control and Prevention; 2017.
  3. Geiss LS, James C, Gregg EW, Albright A, Williamson DF, Cowie CC. Diabetes risk reduction behaviors among US adults with prediabetes. Am J Prev Med 2010;38(4):403–9. CrossRef PubMed
  4. Perreault L, Pan Q, Mather KJ, Watson KE, Hamman RF, Kahn SE; Diabetes Prevention Program Research Group. Effect of regression from prediabetes to normal glucose regulation on long-term reduction in diabetes risk: results from the Diabetes Prevention Program Outcomes Study. Lancet 2012;379(9833):2243–51. CrossRef PubMed
  5. Diabetes Prevention Program (DPP) Research Group. The Diabetes Prevention Program (DPP): description of lifestyle intervention. Diabetes Care 2002;25(12):2165–71. CrossRef PubMed
  6. Knowler WC, Fowler SE, Hamman RF, Christophi CA, Hoffman HJ, Brenneman AT, et al. 10-Year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet 2009;374(9702):1677–86. Erratum in Lancet 2009;374(9707):2054. CrossRef PubMed
  7. Centers for Disease Control and Prevention. The National Diabetes Prevention Program. https://www.cdc.gov/diabetes/prevention/index.html. Accessed September 24, 2017.
  8. Albright AL, Gregg EW. Preventing type 2 diabetes in communities across the US: the National Diabetes Prevention Program. Am J Prev Med 2013;44(4, Suppl 4):S346–51. CrossRef PubMed
  9. Sattin RW, Williams LB, Dias J, Garvin JT, Marion L, Joshua TV, et al. Community trial of a faith-based lifestyle intervention to prevent diabetes among African-Americans. J Community Health 2016;41(1):87–96. CrossRef PubMed
  10. Zhuo X, Zhang P, Gregg EW, Barker L, Hoerger TJ, Tony Pearson-Clarke , et al. A nationwide community-based lifestyle program could delay or prevent type 2 diabetes cases and save $5.7 billion in 25 years. Health Aff (Millwood) 2012;31(1):50–60. CrossRef PubMed
  11. Centers for Disease Control and Prevention. CDC Diabetes Prevention Recognition Program. https://www.cdc.gov/diabetes/prevention/lifestyle-program/requirements.html. Accessed September 24, 2017.
  12. American Medical Association, Centers for Disease Control and Prevention. Preventing type 2 diabetes: a guide to refer your patients with prediabetes to an evidence-based diabetes prevention program. Atlanta (GA): US Department of Health and Human Services, Centers for Disease Control and Prevention; 2015.

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Tables

Return to your place in the textTable 1. Abbreviated List of Activities from the National Diabetes Prevention Program Technical Assistance Guide
Driver Activities
Support the efforts of partners to increase the availability of LCPs
  • Integrate LCP planning and implementation with ongoing state/city diabetes coalition activities or state diabetes action plans.
  • Explain readiness criteria to organizations interested in becoming LCPs.
  • Use grant funds to help ADA/AADE DSME programs develop a strategic business plan to determine their capacity to offer a LCP.
Implement referral policies and mechanisms
  • Distribute the AMA/CDC provider tool kit, and engage health care systems and providers in using it; partner with state and local medical associations in reaching the clinical community.
  • Provide technical assistance, training, and academic detailing (face-to-face education of providers by trained health care professionals) on prediabetes screening, testing, and referrals to health care providers and care teams within existing LCP service areas.
  • Support health care systems in building EHRs or other systems to facilitate and track referrals and enhance decision support.
Establish payers and payment mechanisms
  • Develop a state-specific business case for the National Diabetes Prevention Program.
  • Work with state employee health plans and the state Medicaid agency to secure or extend coverage where needed.
  • Encourage LCP providers to connect with third-party administrators where necessary to facilitate billing and reimbursement.
Identify and enroll people with prediabetes or at high risk for type 2 diabetes in LCPs
  • Use strategic communication strategies (eg, customized waiting room advertising) to reach people at high risk about the importance of National Diabetes Prevention Program benefits and coverage.
  • Provide advanced training for lifestyle coaches (eg, motivational interviewing) to further strengthen group facilitation skills.
  • Provide materials and other resources to support existing LCP providers’ marketing efforts to recruit participants.

Abbreviations: AADE, American Association of Diabetes Educators; ADA, American Diabetes Association; AMA, American Medical Association; CDC, Centers for Disease Control and Prevention; DSME, diabetes self-management education; EHR, electronic health record; LCP, lifestyle change program.

Return to your place in the textTable 2. Facilitators and Barriers to Implementing the National Diabetes Prevention Program, 2015–2016
Themes Comments From State Health Department Representatives
Facilitators
Reimbursement availability
  • “State employees began having the National DPP lifestyle change program offered to them as a covered benefit. Our diabetes program has been working with the State Employee Group Insurance Program to promote the ‘Prevent’ program within our agency.” (Minnesota)
  • “A large employer and a large insurance company announced (2017) that the National DPP will become a covered benefit. Expansion in insurance coverage is due in part to California’s Department of Public Health’s PDSTAT statewide organization of stakeholders, which has been instrumental in educating payers and insurance companies about the need for and value of the National DPP. The US Preventive Services Task Force recommendations on diabetes screening, released in October 2015, were another factor in encouraging adoption of coverage for the National DPP.” (California)
Practice/provider referral policies “Based on CDC DPRP data, over 75% of participants in lifestyle change programs have enrolled based on a blood-based diagnostic test, which indicates that the majority of participants had a clinical test indicating prediabetes and were likely referred by a health care provider. YMCAs that established referral policies with local hospitals or health care providers show greater success in recruiting and filling classes than those that did not.” (New York State)
Program curriculum “Having standard curricula and referral policies helps facilitate dissemination of the National DPP lifestyle change program in community settings, particularly since coordinated care organizations want to implement evidence-based programs.” (Oregon)
Barriers
CDC recognition process “Paperwork and complicated processes, as well as the inability to use grant funds to support direct services, have been a challenge.” (Maryland)
Limited program resources
  • “Several health systems, clinics, and community-based organizations are linked to lifestyle change programs for delivery and referral. However, many do not have formal policies and bidirectional networks in place. Staff and funding aimed at enhancing these policies and networks have been essential to carry this work forward.” (Nebraska)
  • “These were the barriers to optimal National DPP implementation. There is a limited amount of wellness funding that has to be stretched across different priority areas.” (Colorado)
Reimbursement availability There is no standardized method of reimbursement, and confusion exists about who within the health system can apply for reimbursement: “Lack of insurance coverage for the program often shuts down conversations about referrals and is a constant barrier. Despite these obstacles, we do have some early adopters who are developing policies or willing to undergo practice change.” (Minnesota)
Obtaining referrals “Many lifestyle change programs report low enrollment and almost no referrals from physicians, even in cases where outreach was conducted to provider offices and larger health systems.” (California)
Participant cost “Lack of insurance coverage for lifestyle change programs statewide is most often cited as a reason for why providers are not diagnosing and referring patients and why patients are not attending (due to the high cost of the program). There are only a small handful of insurers in New York State that are covering the National DPP as a benefit for their members.” (New York State)
Lack of data “There is a lack of data on program completion rates, insurance information of enrollees, and measured health outcomes of program completers. Some insurers are aware of the benefit of the program but need more information on completers and outcomes to consider reimbursement.” (Rhode Island)
Lack of awareness “The majority of employees were not aware of the health and wellness policies in place in their departments.” (Colorado)

Abbreviations: CDC, Centers for Disease Control and Prevention; DPRP, Diabetes Prevention Recognition Program; National DPP, National Diabetes Prevention Program; PDSTAT, Prevent Diabetes Screen Test Act Today.

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