QuickStats: Percentage* of Emergency Department Visits …

From 2008–2011 to 2012–2015, the percentage of visits for acute viral upper respiratory tract infection that had an antimicrobial ordered or prescribed decreased from 37.1% to 25.5% among emergency departments (EDs) located in nonmetropolitan statistical areas, but this decline was not seen among EDs in metropolitan statistical areas. In 2008–2011, the percentage was higher among nonmetropolitan EDs than metropolitan EDs, but there was no difference in 2012–2015.

Source: National Center for Health Statistics, National Hospital Ambulatory Medical Care Survey, 2008–2015. https://www.cdc.gov/nchs/ahcd/ahcd_questionnaires.htm.


Reported by: Kari Yacisin, MD, kyacisin@cdc.gov, 301-458-4211; Akintunde Akinseye.

Suggested citation for this article: QuickStats: Percentage of Emergency Department Visits for Acute Viral Upper Respiratory Tract Infection That Had an Antimicrobial Ordered or Prescribed, by Metropolitan Statistical Area — United States, 2008–2015. MMWR Morb Mortal Wkly Rep 2018;67:111. DOI: http://dx.doi.org/10.15585/mmwr.mm6703a7.

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Early Outcomes, Activities, Facilitators, and Barriers

Jennifer Murphy Morgan, MSPH1; Yvonne Mensa-Wilmot, PhD, MPH1; Shelly-Ann Bowen, PhD, MS2; Monica Murphy, MPH2; Timethia Bonner, DPM, PhD3; Stephanie Rutledge, PhD, MA1; Gia Rutledge, MPH1 (View author affiliations)

Suggested citation for this article: Morgan JM, Mensa-Wilmot Y, Bowen S, Murphy M, Bonner T, Rutledge S, et al. Implementing Key Drivers for Diabetes Self-Management Education and Support Programs: Early Outcomes, Activities, Facilitators, and Barriers. Prev Chronic Dis 2018;15:170399. DOI: http://dx.doi.org/10.5888/pcd15.170399.

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Diabetes, a serious and costly condition, is characterized by illness and death from long-term microvascular and macrovascular complications (1). Additionally, numerous and well-known comorbidities can accompany diabetes, including cardiovascular disease, retinopathy, amputations, and nephropathy (1). Often these complications and comorbidities interfere with a person’s ability to self-manage their diabetes (2). The Centers for Disease Control and Prevention (CDC) projects that as many as 1 in 3 adults could have diabetes by 2050 (3). In 2012, the United States spent an estimated $245 billion on diabetes care, including $176 billion in direct medical costs and $69 billion in indirect costs from lost workdays, restricted activity, disability, and early death (4). Many costly complications among people with diabetes can be prevented or delayed with appropriate preventive care and self-management (5).

CDC’s National Center for Chronic Disease Prevention and Health Promotion leads efforts to address the chronic disease burden effectively and equitably in the US population. Generally positioned as the primary public health authority supporting the delivery of public health services within a state, the state health department is a unique partner for the collaborative implementation of population-focused interventions. The 5-year cooperative agreement SPHA1305 (State Public Health Actions to Prevent and Control Diabetes, Heart Disease, Obesity and Associated Risk Factors and Promote School Health) is such a partnership, involving 4 CDC divisions, all 50 state health departments, and the District of Columbia, to develop strategies to reduce the risk factors for obesity and the management and prevention of chronic conditions such as type 2 diabetes. Through this partnership, CDC’s Division of Diabetes Translation provides scientific leadership and technical expertise to support implementation of cross-cutting approaches to improve diabetes outcomes nationally. This essay reflects on the first 3 years of activity of the cooperative agreement.

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Clinical and Community Linkages To Support Diabetes Self-Management

One way to improve diabetes management is to increase linkages between community resources and clinical services. Diabetes self-management education and support (DSMES) programs connect people with diabetes to effective clinical services in their communities. DSMES is usually offered to patients at diagnosis, during annual assessments, and when transitions or new disease complications occur that influence self-management and is guided by evidence-based standards. DSMES is an individualized process in which health care providers incorporate information on the needs, goals, and life experiences of patients when imparting knowledge, teaching skills, and coaching for behavioral change necessary for diabetes self-care (6). Through an assessment of program structure, process, and outcomes, the American Diabetes Association (ADA) and the American Association of Diabetes Educators (AADE) recognize or accredit organizations providing DSMES programs to assure quality.

Studies show that participants in DSMES programs reduce glycosylated hemoglobin (HbA1c) levels, have fewer emergency department visits, and incur lower in-patient costs (7). Findings of a longitudinal study over a 10-year period showed that each 1% reduction in HbA1c was associated with reductions in risk of 21% for diabetes-related deaths, 14% for myocardial infarctions, and 37% for microvascular complications (8). Significant decreases in in-patient costs, a primary source of savings for Medicaid and commercial payers, have been attributed to DSMES (9).

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Assessing Key Activities Implemented by State Health Departments

Increasing the number of DSMES programs in communities and securing Medicaid reimbursement in states with no DSMES coverage for beneficiaries are critical goals of cooperative agreement SPHA1305. State health departments partner with health systems and community organizations to increase DSMES program access, patient referrals, and reimbursement. The partners’ activities are anchored in 4 promising practice areas known to drive implementation: 1) supporting organizations in establishing ADA-recognized or AADE-accredited DSMES programs, 2) securing Medicaid coverage for DSMES, 3) establishing referral policies and practices in health care systems to efficiently connect people to DSMES programs, and 4) raising awareness and enhancing the capacity of people with diabetes to participate in DSMES. Numerous state health departments have implemented such activities (Table 1).

Assessment of program activities to monitor and understand how the activities lead to improved health outcomes is critical to the success of any system-wide intervention. Performance monitoring provides useful and timely information on strengths and opportunities for improvement and on how to tailor technical assistance for midcourse corrections.

In year 3 of the 5-year cooperative agreement, we examined data on the progress made by analyzing the annual reports of the 51 grant recipients. We abstracted such data as quantitative performance measures describing the reach of activities, the number of ADA-recognized and AADE accredited DSMES programs, the proportion of counties with ADA-recognized and AADE accredited DSMES programs, the number of Medicaid recipients with DSMES as a covered Medicaid benefit, and the number of people with at least one encounter at an ADA-recognized or AADE-accredited DSMES program. Overall, 43 states were implementing activities to address DSMES access, participation, and/or coverage. Our analysis included data reported only by state health departments that provided data for a given measure for all 3 years: 2012, 2013, and 2014 (Table 2). The proportion of counties offering DSMES programs increased from 54.7% at baseline to 57.0% in year 3 (based on data from 38 states). The overall number of DSMES programs increased by 7.8% from 2,822 to 3,043 (based on data from 41 states). We also found a 12.6% increase in the number of Medicaid beneficiaries with DSMES as a covered benefit, from 1.26 million to 1.42 million (based on data from 20 states). The number of people with diabetes who had at least 1 session at an ADA-recognized or AADE-accredited DSMES program went up by 16.6%, from 906,402 at baseline to 1,057,194 by year 3 (based on data from 50 states and the District of Columbia) (Table 2).

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Supportive Partnerships in Diabetes Self-Management Education and Support

Analysis of information in the annual reports on the particular activities of 43 state health departments that implemented DSMES-related activities were coded according to the 4 promising practice areas known to drive implementation. In addition, barriers and facilitators reported by 16 state health departments that elected to evaluate their progress were analyzed.

Health departments and their partners undertook a wide range of activities. They worked to expand program locations to worksites and faith-based organizations; convened advisory groups to identify existing programs interested in obtaining ADA-recognition or AADE-accreditation; sponsored diabetes symposia to provide education for clinical staff, pharmacists, payers, and interested stakeholders on appropriate billing and coding for DSMES services, sustainability strategies, and reimbursement models; and worked with partners to survey health care providers to increase referrals to DSMES programs. Some state health departments developed data sharing agreements to automate DSMES program referrals through electronic health records, while others developed toolkits and educational materials for health care providers. Some developed radio public service announcements and engaged community health workers to raise awareness and increase program participation in the community. Additionally, several states posted maps of DSMES program locations on websites. Health departments entered into partnerships with Federally Qualified Health Centers (FQHCs), medical practices, diabetes coalitions, and pharmacists to advocate for adoption and sustainability of DSMES programs and provided technical assistance to programs seeking AADE accreditation or ADA recognition.

Health departments reported that the inclusion of DSMES as a preventive service in the state’s Medicaid expansion program was critical to success. Establishing champions and creating advocacy for policy change through statewide diabetes coalitions were also vital. Having similar software for electronic health records across FQHCs, using a statewide database of health information resources and programs, and having health care providers who were willing to refer patients to programs increased patient participation. DSMES programs that held classes in easily accessible locations and at convenient times and that used culturally and linguistically appropriate curricula increased participation rates.

Challenges that affected program availability and access included the application process for AADE accreditation and ADA recognition. Further analysis showed that state health departments have limited staff to support the processes of accreditation, recognition, and compliance. Other challenges were a lack of site-level assessment data on DSMES programs; clinicians’ concerns about low insurance reimbursement rates, not getting reimbursed, and complicated reimbursement processes; scheduling, transportation, and child care difficulties; and limited availability of culturally and linguistically appropriate programs.

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Future Directions

Assessment of the progress made in implementing DSMES programs under cooperative agreement SPHA1305 provides information to develop guidance for helping state health departments identify how to further improve results by the end of the SPHA1305 funding cycle. In addition, information on barriers and facilitators will inform and guide technical assistance and training provided by the Division of Diabetes Translation for the remainder of the cooperative agreement. The Division of Diabetes Translation developed a series of interactive webinars to build the evaluation capacity and enhance completeness and quality in data reporting. Topics included improving data quality along with developing and disseminating health impact statements and program success stories to various audiences. Continued attention to program activities and performance monitoring data with a goal of real-time action to overcome challenges and provide technical assistance will ensure that our partners promote sustainable strategies for improved health outcomes in diabetes management.

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Acknowledgments

The authors acknowledge the state health departments for providing their insights into identifying activities, barriers, and facilitators as part of the SPHA1305 cooperative agreement. The authors received no funding for the work 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 or the Agency for Toxic Substances and Disease Registry.

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Author Information

Corresponding Author: Yvonne Mensa-Wilmot, PhD, MPH, Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 4770 Buford Hwy NE, MS-F75 Atlanta, GA. E-mail: YMensaWilmot@cdc.gov.

Author Affiliations: 1Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia. 2Health, Research, Informatics and Technology: Public Health Division, ICF International, Atlanta, Georgia. 3Oak Ridge Institute for Science and Education, Atlanta, Georgia.

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References

  1. Kent D, D’Eramo Melkus G, Stuart PM, McKoy JM, Urbanski P, Boren SA, et al. Reducing the risks of diabetes complications through diabetes self-management education and support. Popul Health Manag 2013;16(2):74–81. CrossRef PubMed
  2. Haas L, Maryniuk M, Beck J, Cox CE, Duker P, Edwards L, et al. National standards for diabetes self-management education and support. Diabetes Care 2013;36(Suppl 1):S100–8. CrossRef PubMed
  3. Boyle JP, Thompson TJ, Gregg EW, Barker LE, Williamson DF. Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence. Popul Health Metr 2010;8(1):29. CrossRef PubMed
  4. American Diabetes Association. Economic costs of diabetes in the U.S. in 2012. Diabetes Care 2013;36(4):1033–46. CrossRef PubMed
  5. Nathan DM, Genuth S, Lachin J, Cleary P, Crofford O, Davis M, et al. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 1993;329(14):977–86. CrossRef PubMed
  6. Powers MA, Bardsley J, Cypress M, Duker P, Funnell MM, Fischl AH, et al. Diabetes self-management education and support in type 2 diabetes: a joint position statement of the American Diabetes Association, the American Association of Diabetes Educators, and the Academy of Nutrition and Dietetics. Clin Diabetes 2016;34(2):70–80. CrossRef PubMed
  7. Klein HA, Jackson SM, Street K, Whitacre JC, Klein J. Diabetes self-management education: miles to go. Nurs Res Pract 2013:581012.
  8. Stratton IM, Adler AI, Neil HA, Matthews DR, Manley SE, Cull CA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ 2000;321(7258):405–12. CrossRef PubMed
  9. Duncan I, Ahmed T, Li QE, Stetson B, Ruggiero L, Burton K, et al. Assessing the value of the diabetes educator. Diabetes Educ 2011;37(5):638–57. CrossRef PubMed

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Tables

Return to your place in the textTable 1. Examples of Diabetes Self-Management Education and Support Activities Implemented by State Health Departments, 2012–2014
Strategy Driver Example Activities
ADA-recognized or AADE-accredited DSMES programs established (primary or satellite sites) The Alabama Department of Public Health created an advisory group to work with department staff members to identify existing DSMES programs, areas of the state underserved or unserved by DSMES programs, and organizations interested in becoming providers of accredited or recognized programs, and to determine which strategies should be pursued in which areas to increase access and referrals to and use of DSMES programs.
Arizona Department of Health Services staff members provided technical assistance and training to 3 organizations in Arizona to obtain AADE accreditation (eg, capacity building within each organization, curriculum development, credentialing compliance, training of staff on evidence-based strategies).
Insurance coverage for DSMES The Illinois Department of Public Health engaged with Medicare/Medicaid Alignment Initiative Health Plans to discuss DSMES coverage for patients/members with diabetes.
Staff members of the Indiana State Department of Health provided live webinars on DSMES reimbursement for 20 hospital-based and 4 pharmacy-based DSMES programs.
Referral policies and practices in place in the health system to efficiently connect people with diabetes to DSMES programs Members of the Nevada Diabetes Education Stakeholder group, created by the Nevada Department of Health and Human Services, used a DSMES academic detailing toolkit to educate health care providers on ways to increase self-care options for patients and make referrals to ADA-recognized or AADE-accredited DSMES programs.
Maryland Department of Health and Mental Hygiene staff members designed and built an online self-management referral website that allows the public to search for DSMES classes and health care providers to refer patients to DSMES programs.
Awareness, capacity, and willingness of people with diabetes to attend DSMES programs when other drivers are in place Michigan Department of Health and Human Services staff members expanded media promotion of recognized or accredited DSMES programs through their diabetes program website and a statewide radio public service announcement.
The New York State Department of Health’s diabetes program partnered with the state’s arthritis program to develop a digital media campaign to promote DSMES among women aged 40 or older in 2 counties.

Abbreviations: ADA, American Diabetes Association; AADE, American Association of Diabetes Educators; DSMES, diabetes self-management education and support.

Return to your place in the textTable 2. Performance Measures for Diabetes Self-Management Education and Support (DSMES) Activities Implemented by State Health Departmentsa
Funding Year No. of ADA-Recognized and/or AADE-Accredited DSMES Programs Proportion of Counties with ADA-Recognized and/or AADE-Accredited DSMES Programs No. of Medicaid Recipients With DSMES as a Covered Medicaid Benefit No. of People With ≥1 Encounter at an ADA-Recognized and/or AADE-Accredited DSMES Program
No. of state health departments reporting data for all 3 years 41 38 20 51
Baseline (2012) 2,822 54.7 1,258,042 906,402
Year 2 (2013) 3,117 57.8 1,204,677 1,049,473
Year 3 (2014) 3,043 57.0 1,417,124 1,057,194
Percentage change from 2012 to 2014 7.8 4.2 12.6 16.6

ADA, American Diabetes Association; AADE, American Association of Diabetes Educators; DSMES, diabetes self-management education and support.
a Analysis included data reported only by state health departments that provided data for a given category for all 3 years (2012, 2013, and 2014).

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Coordinated Approaches to Strengthen State and Local Public Health Actions to Prevent Obesity, Diabetes, and Heart Disease and Stroke

Gia E. Rutledge, MPH1,2; Kimberly Lane, PhD, RDN2; Caitlin Merlo, MPH, RDN3; Joanna Elmi, MPH4 (View author affiliations)

Suggested citation for this article: Rutledge GE, Lane K, Merlo C, Elmi J. Coordinated Approaches to Strengthen State and Local Public Health Actions to Prevent Obesity, Diabetes, and Heart Disease and Stroke. Prev Chronic Dis 2018;15:170493. DOI: http://dx.doi.org/10.5888/pcd15.170493.

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Chronic diseases, including heart disease, stroke, cancer, diabetes, and obesity, are the leading causes of death in the United States and account for most of the nation’s health care costs (1). Heart disease is the leading cause of death among men and women in the United States, accounting for 1 of every 4 deaths (1). Approximately 140,000 Americans die each year from stroke, and it is a leading cause of long-term disability (2,3). It is estimated that more than 9% of the US population has diabetes, which is the leading cause of kidney failure, lower-limb amputations other than those caused by injury, and new cases of blindness among adults (4). Additionally, more than one-third of US adults have obesity, which is associated with several chronic conditions (5,6).

Chronic diseases are common and costly, but many are preventable. Although it is important to address the underlying risk factors for chronic diseases at the individual level, it is also critical to implement population-based interventions, including health-promoting policies and environments that affect where we work, live, play, and receive health care. This requires a multifaceted approach and the collective efforts of federal, state, local, private, and community-based organizations along with national partners.

The Centers for Disease Control and Prevention’s (CDC’s) mission is to prevent or control all diseases that affect Americans (7). CDC puts science into action by tracking diseases and determining their causes and by identifying the most effective ways to prevent and control them (7). This work entails tackling the major health problems that cause death and disability for Americans and promoting healthy and safe behaviors, communities, and environments (7).

The mission of CDC’s National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP) is to “help people and communities prevent chronic diseases and promote health and wellness for all” (8). NCCDPHP supports disease control efforts through 5-year term funding mechanisms called cooperative agreements that are awarded to state and local public health agencies to strengthen partnerships to improve health at the community level (9). In 2013, NCCDPHP developed the State Public Health Actions to Prevent Obesity, Diabetes, and Heart Disease and Stroke (State Public Health Actions [SPHA]-1305), a cooperative agreement that combined the efforts of 4 CDC divisions: the Division for Heart Disease and Stroke Prevention (DHDSP); the Division of Diabetes Translation (DDT); the Division of Nutrition, Physical Activity, and Obesity (DNPAO); and the Division of Population Health’s School Health Branch (SHB). The agreement funded 50 state health departments and the District of Columbia to implement strategies in health systems and communities to prevent chronic disease and reduce complications associated with them (10). State Public Health Actions provides examples of how mutually reinforcing strategies are implemented. Two tiers of strategies were recommended, basic and enhanced (Box 1).

Box 1. Strategies of State Public Health Actions to Prevent and Control Diabetes, Heart Disease, Obesity, and Associated Risk Factors and Promote School Health (SPHA-1305)
BASIC STRATEGIES
Promote the adoption of food service guidelines/nutrition standards, which include sodium
Promote the adoption of physical education/physical activity in schools
Promote adoption of physical activity in early care and education and worksites
Promote reporting of blood pressure and hemoglobin A1c measures; and as able, initiate activities that promote clinical innovations, team-based care, and self-monitoring of blood pressure
Promote awareness of high blood pressure among patients
Promote awareness of prediabetes among people at high risk for type 2 diabetes
Promote participation in diabetes self-management education programs
ENHANCED STRATEGIES
Environmental approaches to promote health and support and reinforce healthful behaviors
Access to healthy food and beverages
Food service guidelines/nutrition standards where foods and beverages are available. Guidelines and standards should address sodium
Supportive nutrition environments in schools
Physical activity access and outreach
Physical activity in early childhood education
Quality physical education and physical activity in kindergarten through 12th grade in schools
Access to breastfeeding-friendly environments
Health system interventions to improve the effective delivery and use of clinical and other preventive services
Quality improvement processes in health systems
Use of team-based care in health systems
Community–clinical linkages to support cardiovascular disease and diabetes prevention and control efforts
Use of diabetes self-management education programs in community settings
Use of CDC-recognized lifestyle intervention programs in community settings for the primary prevention of type 2 diabetes
Use of health-care extenders in the community in support of self-management of high blood pressure and diabetes
Use of chronic disease self-management programs in community settings
Placement of policies, processes, and protocols in schools to meet the management care needs of students with chronic conditions

Each of the 4 divisions focuses on a specific area of chronic disease. DHDSP provides public health leadership to improve cardiovascular health for all Americans and to reduce the burden and end disparities related to heart disease and stroke (www.cdc.gov/dhdsp/index.htm). DDT supports programs and activities to prevent or delay the onset of type 2 diabetes and to improve health outcomes for people diagnosed with diabetes (www.cdc.gov/diabetes/home/index.html). DNPAO focuses on decreasing obesity in the United States by encouraging regular physical activity and good nutrition at every stage of life. DNPAO supports healthy eating, active living, and obesity prevention by creating healthy child care centers, hospitals, schools, and worksites; building the capacity of state health departments and national organizations; and conducting research, surveillance, and evaluation studies (www.cdc.gov/nccdphp/dnpao/index.html). SHB’s aims are to improve the well-being of youth through healthy eating, physical education, and physical activity; to reduce risk factors associated with childhood obesity; and to manage chronic health conditions in schools (www.cdc.gov/healthyschools/stateprograms.htm).

The primary purpose of SPHA-1305 is to support state-level and statewide implementation of cross-cutting, evidence-based strategies to promote health and prevent and control chronic diseases and their risk factors (11). SPHA-1305 uses a collective approach to 1) improve environments in worksites, schools, early childhood education services, state and local government agencies, and community settings to promote healthy behaviors and expand access to healthy choices for people of all ages related to diabetes, cardiovascular health, physical activity, healthy foods and beverages, obesity, and breastfeeding; 2) improve the delivery and use of quality clinical and other health services aimed at preventing and managing high blood pressure and diabetes; and 3) increase links between community and clinical organizations to support prevention, self-management, and control of diabetes, high blood pressure, and obesity (10). The ultimate goal of SPHA-1305 is to make healthy living easier for all Americans. The following are primary outcomes of SPHA-1305:

  • Increased consumption of a healthy diet
  • Increased physical activity across the life span
  • Improved medication adherence for adults with high blood pressure or diabetes
  • Increased self-monitoring of high blood pressure tied to clinical support
  • Increased access to and participation in diabetes self-management programs and type 2 diabetes prevention programs
  • Increased breastfeeding

In 2014, CDC developed a second cooperative agreement, State and Local Public Health Actions to Prevent Obesity, Diabetes, and Heart Disease and Stroke (SLPHA-1422), a program designed for states and large cities to implement strategies to control and prevent chronic disease through a dual approach — targeting both the overall population and priority populations (groups of people who are at high risk of chronic disease, are experiencing a disproportionate incidence of chronic diseases and conditions, or are experiencing racial/ethnic or socioeconomic disparities). This competitive cooperative agreement combined the efforts of 3 NCCDPHP divisions (DDT, DNPAO, and DHDSP), and was awarded to 17 states and 4 large cities to implement additional evidence-based strategies to expand the reach and impact of SPHA-1305 with the aim of reducing health disparities and improving health equity among adults. SLPHA-1422 supports interventions to prevent obesity, type 2 diabetes, heart disease, and stroke (through control of high blood pressure) and to reduce health disparities in the prevalence of these among adults in the population overall and in priority populations (12). SLPHA-1422 awardees used the dual approach and mutually reinforcing strategies to maximize the impact of strategies implemented in SPHA-1305 by working with partners and funding subawardees at the local level. By applying the dual approach, states and large cities implemented strategies to improve the health of the whole population and of priority populations (12). The strategies are described as mutually reinforcing because they are implemented simultaneously and synergistically to address multiple risk factors and chronic diseases (12).

Three tiers of strategies make up SLPHA-1422, environmental strategies, health system strategies, and community–clinical linkage strategies. The purpose of SPHA-1422 environmental strategies is to “support environmental and system approaches to promote health, support and reinforce healthful behaviors, and build support for lifestyle improvements for the general population and particularly for those with uncontrolled high blood pressure and those at high risk for developing type 2 diabetes” (12). The purpose of community–clinical linkage strategies is to “support health system interventions and community–clinical linkages that focus on the general population and priority populations” (Box 2) (12). Environmental strategies were implemented in the same communities and jurisdictions as health system strategies and community–clinical linkage strategies, with local improvements supported by statewide efforts funded by this cooperative agreement as well as those supported by SPHA-1305. The following are primary outcomes of SLPHA-1422:

Box 2. State and Local Public Health Actions to Prevent Obesity, Diabetes, and Heart Disease and Stroke (SLPHA-1422) Strategies
COMPONENT 1
Environmental strategies to promote health and support and reinforce healthful behaviors
Implement food and beverage guidelines including sodium standards (ie, food service guidelines for cafeterias and vending machines) in public institutions, worksites, and other key locations, such as hospitals
Strengthen access to and sales of healthy foods (eg, fruit and vegetables, more low/no sodium options) in retail venues (eg, grocery stores, supermarkets, chain restaurants, markets) and community venues (eg, food banks) through increased availability and improved pricing, placement, and promotion
Strengthen community promotion of physical activity though signage, worksite policies, social support, and joint-use agreements
Develop and/or implement transportation and community plans that promote walking
Strategies to build support for lifestyle change, particularly for those at high risk, to support diabetes, heart disease, and stroke prevention efforts
Plan and execute strategic data-driven actions through a network of partners and local organizations to build support for lifestyle change. Implement evidence-based engagement strategies (eg, tailored communications, incentives) to build support for lifestyle change
Increase coverage for evidence-based supports for lifestyle change by working with network partners
COMPONENT 2
Health system interventions to improve the quality of health care delivery to populations with the highest hypertension and prediabetes disparities
Increase the adoption of electronic health records and the use of health information technology to improve performance (eg, implement advanced Meaningful Use data strategies to identify patient populations who experience cardiovascular disease–related disparities)
Increase the institutionalization and monitoring of aggregated/standardized quality measures at the provider level (eg, use dashboard measures to monitor health care disparities, implement activities to eliminate health care disparities)
Increase engagement of nonphysician team members (ie, nurses, pharmacists, dietitians, physical therapists and patient navigators/community health workers) in hypertension management in community health care systems
Increase use of self-measured blood pressure monitoring tied with clinical support
Implement systems to facilitate identification of patients with undiagnosed hypertension and people with prediabetes
Community–clinical linkage strategies to support heart disease, stroke, and type 2 diabetes prevention efforts
Increase engagement of community health workers to promote linkages between health systems and community resources for adults with high blood pressure and adults with prediabetes or at high risk for type 2 diabetes
Increase engagement of community pharmacists in the provision of medication self-management for adults with high blood pressure
Implement systems to facilitate bi-directional referral between community resources and health systems, including lifestyle change programs (eg, electronic health records, 800 numbers, 211 referral systems)
  • Increased consumption of nutritious food and beverages and increased physical activity
  • Increased engagement in lifestyle change to prevent type 2 diabetes
  • Improved medication adherence for adults with high blood pressure
  • Increased self-monitoring of high blood pressure tied to clinical support
  • Increased referrals to and enrollment in CDC-recognized lifestyle change programs to prevent type 2 diabetes

This special collection of articles in Preventing Chronic Disease describes how SPHA-1305 and SLPHA-1422 use a coordinated approach to chronic disease prevention and control. The collection describes an evaluation approach that was designed for state and local health departments with differing levels of evaluation capacity and highlights early outcomes at the national, state, and local levels. This special collection contains 12 articles: 4 by state health departments, 2 by one large city, and 6 authored by CDC staff members. Articles highlight a range of SPHA-1305 and SLPHA-1422 strategies. An article by Park et al describes in detail the foundations for SPHA-1305, the strategies recommended by each NCCDPHP division, the administrative and management structure, and the model for providing cross-division program and evaluation technical assistance (13). Given this complex approach to implementing a national chronic disease prevention initiative, it was imperative that the evaluation design use a robust, multi-tiered approach to accountability and learning. This comprehensive evaluation approach is described by Vaughan et al (14).

Smith et al summarize Maryland’s approach to improving implementation of quality improvement processes in Federally Qualified Health Centers through the use of health information technology and standardized reporting of clinical quality measures (15). Other states interested in learning how to harness the potential of electronic health records and how to use population health data to drive improvements in quality of care will appreciate this step-by-step explanation of how to gain the buy-in of health centers and how to build the operational structure of a data warehouse. The article also discusses challenges encountered in the process and plans for scaling up these efforts.

Oser et al describe how the Montana Department of Public Health and Human Services used SPHA-1305 funding to conduct an evaluation of a 3-year intervention among 25 community pharmacies in rural areas to improve adherence to blood pressure medication (16). In addition to patient-level data, Montana also implemented a statewide survey of pharmacists and identified barriers perceived from the pharmacy point of view. Results indicate that the intervention was successful with promising improvements in patient medication adherence.

Barragan et al focus on pharmacy-led strategies that the Los Angeles County Department of Public Health implemented with SLPHA-1422 funding (17). Authors report results from a community and stakeholder needs assessment for pharmacist services for management of hypertension medication therapy. The needs assessment included 3 components: 1) a policy context scan, 2) a survey of participants in a pharmacy leadership symposium, and 3) an internet public opinion survey of a final sample of more than 1,000 English- and Spanish-speaking Los Angeles County residents. A synthesis of results from these 3 assessments produced a list of needs and assets for scaling up and spreading pharmacy-led patient care services in Los Angeles County.

Mosst et al describe a practice-grounded framework used by the Los Angeles County Health Department to scale and sustain the National Diabetes Prevention Program (National DPP) by using a diverse partner network (18). By developing a 3-pronged framework (expanding outreach and education, improving health care referral systems and protocols, and increasing access to insurance coverage for the National DPP), Los Angeles County took an approach that other large jurisdictions can use to identify people with prediabetes and expand access to and use of CDC-recognized type 2 diabetes prevention programs.

Mensa-Wilmot et al use a mixed-method evaluation approach to describe preliminary findings of a collaborative effort between CDC and state health departments designed to scale and sustain the National DPP (19). Grantees reported reimbursement availability, practice and provider referral policies, and having standard curricula as facilitators to implementing the National DPP lifestyle change program. Understanding activities implemented by grantees and the barriers and facilitators they identify is critical for developing relevant and timely technical assistance and for understanding the impact of the program.

Morgan et al describe activities state health departments implemented to increase referrals to, coverage for, and availability of diabetes self-management education and support (DSMES) programs (20). By year 3 of SPHA-1305, more than 3,000 DSME programs had been established in 41 states. State health departments contributed to these increases by assisting organizations in establishing new DSME programs, providing technical assistance to providers, convening stakeholders to address gaps in DSME insurance coverage, and using marketing strategies to educate patients about the importance of DSME. Conducting early assessments of the activities implemented by state health departments and analyzing progress in performance measures associated with them provides early outcome results that can be used to develop technical assistance to help grantees identify where more focus is needed to further improve results by the end of the 5-year cooperative agreement.

An article by Fritz et al examines the SPHA-1305 strategy of increasing physical activity through community design (21). In this community case study, the authors describe how the Indiana State Department of Health used a workshop model to support communities with implementation of active-living opportunities in their communities to improve or increase access to physical activity. The authors report that providing a workshop model with follow-up support to the community resulted in policy adoption, the creation of new advisory committees, and new local funding allocations for active-living projects. These findings may inform efforts of other state health agencies as they collaborate with communities to improve physical access.

Geary et al describe the extent to which 38 states’ Quality Rating and Improvement Systems (QRIS) include obesity prevention content (22). States can use QRIS to set standards that define high-quality care and to award child care programs with a quality rating designation based on how well they meet these standards (eg, a star rating). The authors reviewed each state’s QRIS standards and compared them with the 47 “high impact” obesity prevention standards contained in Caring for Our Children: National Health and Safety Performance Standards; Guidelines for Early Care and Education Programs, 3rd Ed (Caring for Our Children) (23). The authors found that of 38 states with publically available standards, 20 included at least one standard with obesity prevention content; however, most had fewer than 5, suggesting room for states to embed additional obesity prevention standards into QRIS.

The article by Papa et al examines 5 of the child care standards of the Arizona Department of Health Services related to obesity prevention that are part of the Arizona Empower Program, a program that promotes healthy environments for children in Arizona’s licensed child care facilities (24). The authors examined 2 years of statewide data, tracked progress in implementing these 5 Empower standards, and identified areas in which facilities needed additional support to fully implement the standards. The results indicate that 1 in 5 facilities fully implemented all 5 standards, with the staff training standard having the highest level of implementation across facilities (77%) and the breastfeeding standard having the lowest implementation (44%). These findings can inform training and technical assistance efforts to further support the implementation of these standards in Arizona’s licensed child care facilities.

An article by Pitt Barnes et al examines performance measures and reported evaluation data from all 51 awardees to assess progress in improving the school nutrition environment and services over the first 4 years of the program (24). Findings indicated that, compared with year 2, by year 4 awardees made significant progress, especially related to providing professional development on strategies to improve the school nutrition environment, adopting and implementing policies to establish standards (including standards for sodium) for all competitive foods available during the school day, not selling unhealthy foods and beverages during the school day, placing fruits and vegetables near the cafeteria cashier where they are easy to access, and providing information to students or families on the nutrition, calorie, and sodium content of foods available. However, the data also show that only 33.5% of local education agencies adopted and implemented policies that prohibit all forms of advertising and promotion of unhealthy foods and beverages. Because the federal requirement for local school wellness policies now includes addressing the marketing of unhealthy foods, additional training, technical assistance, and guidance is likely needed to help districts adopt marketing policies.

This special collection describes overarching approaches and examples of interventions implemented by state and local health departments to prevent and manage obesity, diabetes, heart disease, and stroke. Readers should note that these articles represent early evaluation results of both SPHA-1305 and SLPHA-1422 and demonstrate promise that the implemented strategies are reaching populations in need and are beginning to have a population-wide impact. As of 2016, the 2 national programs are in the final year of funding. With ongoing analysis of performance-measure data, the impact of these programs will continue to be examined and reported.

Collectively, the work of SPHA-1305 and SLPHA-1422 demonstrates the barriers and facilitators that affect state and local program development, implementation, and evaluation of chronic disease prevention initiatives and describes a coordinated approach to implementing programs. This information will inform other state and local programs and further the potential reach of these approaches. The findings presented in this special collection contribute practice-based knowledge to the field of chronic disease prevention and management, evidence of combining different disease-specific funding streams to achieve early outcomes with greater efficiency, and lessons learned for future coordinated national chronic disease programs.

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Acknowledgments

This research received no grant from any funding agency in the public, commercial, or nonprofit sector.

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Author Information

Corresponding Author: Gia Rutledge, MPH, Lead Health Scientist, Division of Diabetes Translation, Health Education and Evaluation Branch, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 4770 Bufford Hwy NE, MS-75, Atlanta, GA 30341. Telephone: 770-488-5661. Email: hez6@cdc.gov.

Author Affiliations: 1Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 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 Population Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia. 4Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia.

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References

  1. Centers for Disease Control and Prevention, National Center for Health Statistics. CDC WONDER online database. Underlying cause of death, multiple cause of death, 1999–2013. Atlanta (GA): Centers for Disease Control and Prevention. https://wonder.cdc.gov/mcd.html. Accessed November 22, 2017.
  2. Yang Q, Tong X, Schieb L, Vaughan A, Gillespie C, Wiltz JL, et al. Vital signs: recent trends in stroke death rates — United States, 2000–2015. MMWR Morb Mortal Wkly Rep 2017;66(35):933–9. CrossRef PubMed
  3. Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, et al. ; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics — 2017 update: a report from the American Heart Association. Circulation 2017;135(10):e146–603. Errata in Circulation 2017; 135(10):e646 and Circulation 2017; 136(10):e196.PubMed CrossRef PubMed
  4. Centers for Disease Control and Prevention. National Diabetes Statistic Report, 2017. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf. Accessed November 22, 2017.
  5. Ogden CL, Carroll MD, Fryar CD, Flegal KM. Prevalence of obesity among adults and youth: United States, 2011–2014. NCHS data brief, no 219. https://www.cdc.gov/nchs/data/databriefs/db219.pdf. Accessed November 22, 2017.
  6. National Institutes of Health. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: the evidence report. NIH Publication no. 98-4083; September 1998. https://www.nhlbi.nih.gov/files/docs/guidelines/obesity_guidelines_archive.pdf. Accessed November 22, 2017.
  7. Centers for Disease Control and Prevention. Mission, Role, and Pledge Web site. https://www.cdc.gov/about/organization/mission.htm. Accessed November 22, 2017.
  8. Centers for Disease Control and Prevention. About the Center website. https://www.cdc.gov/chronicdisease/about/index.htm. Accessed November 22, 2017.
  9. Federal Grant and Cooperative Agreement Act. Pub. L. No. 95–224. 92 Stat. 3 (February 3, 1978).
  10. 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 (DP13-1305). https://www.cdc.gov/chronicdisease/about/state-public-health-actions.htm. Accessed November 22, 2017.
  11. US Department of Health and Human Services, Centers for Disease Control and Prevention. Placeholder for 1305 FOA. https://www.grants.gov/web/grants/search-grants.html?keywords=CDC-RFA-DP13-1305. Accessed November 22, 2017.
  12. US Department of Health and Human Services, Centers for Disease Control and Prevention. CDC-RFA-DP14-1422PPHF14 PPHF 2014: Heart disease & stroke prevention program and diabetes prevention. https://www.grants.gov/view-opportunity.html?oppId=255893. Accessed November 22, 2017.
  13. Park BZ, Cantrell L, Hunt H, Farris RP, Schumacher P, Bauer UE. 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:E127. CrossRef PubMed
  14. Vaughan M, Davis R, Pitt Barnes S, Jernigan J, Shea P, Rutledge S. Evaluating cross-cutting approaches to chronic disease prevention and management: developing a comprehensive evaluation. Prev Chronic Dis 2017;14:E131. CrossRef PubMed
  15. Smith EA, Lapinski J, Lichty-Hess J, Pier K. Using health information technology and data to improve chronic disease outcomes in Federally Qualified Health Centers in Maryland. Prev Chronic Dis 2016;13:E178. CrossRef PubMed
  16. Oser CS, Fogle CC, Bennett JA. A project to promote adherence to blood pressure medication among people who use community pharmacies in rural Montana, 2014-2016. Prev Chronic Dis 2017;14:E52. CrossRef PubMed
  17. Barragan NC, DeFosset AR, Torres J, Kuo T. Pharmacist-driven strategies for hypertension management in los angeles: a community and stakeholder needs assessment, 2014–2015. Prev Chronic Dis 2017;14:E54. CrossRef PubMed
  18. Mosst JT, DeFosset A, Gase L, Baetscher L, Kuo T. A framework for implementing the National Diabetes Prevention Program in Los Angeles County. Prev Chronic Dis 2017;14:E69. CrossRef PubMed
  19. Mensa-Wilmot Y, Bowen SA, 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:E130. CrossRef PubMed
  20. Morgan JM, Mensa-Wilmot Y, Bowen SA, Murphy M, Bonner T, Rutledge S, et al. Implementing key drivers for diabetes self-management education and support programs: early outcomes, activities, facilitators, and barriers. Prev Chronic Dis 2017;15:170399.
  21. Fritz PJ, Irwin K, Bouza L. Using a community workshop model to initiate policy, systems, and environmental change that support active living in Indiana, 2014–2015. Prev Chronic Dis 2017;14:E74. CrossRef PubMed
  22. 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:E129. https://doi.org/10.5888/pcd14.160518 CrossRef PubMed
  23. American Academy of Pediatrics, American Public Health Association, National Resource Center for Health and Safety in Child Care and Early Education. Caring for our children: national health and safety performance standards; guidelines for early care and education programs. 3rd edition. Elk Grove Village (IL): American Academy of Pediatrics; Washington (DC): American Public Health Association; 2011.
  24. Papa J, Agostinelli J, Rodriguez G, Robinson D. Implementation of best practices in obesity prevention in child care facilities: the Arizona Empower Program, 2013-2015. Prev Chronic Dis 2017;14:E75. CrossRef PubMed
  25. 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:E128. CrossRef PubMed

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Telemedicine in the Management of Type 1 Diabetes

Timothy Xu, BS1,2; Shreya Pujara, MD2; Sarah Sutton, PharmD3; Mary Rhee, MD, MS2,4 (View author affiliations)

Suggested citation for this article: Xu T, Pujara S, Sutton S, Rhee M. Telemedicine in the Management of Type 1 Diabetes. Prev Chronic Dis 2018;15:170168. DOI: http://dx.doi.org/10.5888/pcd15.170168.

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Abstract

Background

Veterans with type 1 diabetes who live in rural Alabama and Georgia face barriers to receiving specialty diabetes care because of a lack of endocrinologists in the Central Alabama Veterans Health Care System. Telemedicine is a promising solution to help increase access to needed health care. We evaluated telemedicine’s effectiveness in delivering endocrinology care from Atlanta-based endocrinologists.

Methods

We conducted a retrospective chart review of patients who were enrolled in the Atlanta VAMC Endocrinology Telehealth Clinic from June 2014 to October 2016. Outcomes of interest were hemoglobin A1c levels, changes in glycemic control, time savings for patients, cost savings for the US Veterans Health Administration, appointment adherence rates, and patient satisfaction with telehealth.

Results

Thirty-two patients with type 1 diabetes received telehealth care and in general received the recommended processes of diabetes care. Patients trended toward a decrease in mean hemoglobin A1c and glucose variability and a nonsignificant increase in hypoglycemic episodes. Patients saved 78 minutes of travel time (one way), and the VA saved $72.94 in travel reimbursements per patient visit. Patients adhered to 88% of scheduled telehealth appointments on average, and 100% of surveyed patients stated they would recommend telehealth to other veterans.

Conclusions

Specialty diabetes care delivered via telemedicine was safe and was associated with time savings, cost savings, high appointment adherence rates, and high patient satisfaction. Our findings support growing evidence that telemedicine is an effective alternative method of health care delivery.

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Introduction

The diabetes epidemic is continuously growing in America and affects 29.1 million Americans (9.3% of the US population) (1). The burgeoning prevalence of diabetes has created an increase in demand for specialty diabetes care. However, there is a nationwide shortage of approximately 1,500 full-time endocrinologists (2), creating a disparity between diabetes care and specialty diabetes providers.

Patients who live in rural areas, approximately 20% of the US population, have more barriers to receiving specialty care. Barriers such as long travel distances and costly expenses to urban areas where specialty care is often available (3,4) create challenges for these patients to achieve good health (4). Telemedicine, the exchange of medical information via electronic communications such as clinical video telehealth (CVT) (real-time videoconferencing between patients and providers), has emerged as a promising solution (5,6). The US Veterans Health Administration (VHA) created the Telehealth Services Program to increase access to specialty medical care for veterans with limited access (7). In 2014, the Atlanta Veterans Affairs Medical Center (VAMC) Endocrinology Telehealth Clinic was established to deliver specialty diabetes care to patients with type 1 diabetes in the Central Alabama Veterans Health Care System (CAVHCS); because the CAVHCS serves rural communities in Alabama and west Georgia, specialty diabetes care is often inaccessible for these patients.

We characterized the effectiveness of the Atlanta VAMC Endocrinology Telehealth Clinic in improving diabetes outcomes for patients with type 1 diabetes and increasing their access to specialty diabetes care. We studied patients with type 1 diabetes because the Atlanta VAMC Endocrinology Telehealth Clinic was created to increase access to specialty care for type 1 diabetes patients who manage their condition with insulin pump therapy. We hypothesized that management of type 1 diabetes via CVT leads to improvements in glycemic control, saves costs for the VHA, saves time for patients, and is associated with high appointment adherence and patient satisfaction.

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Methods

CAVHCS serves more than 134,000 veterans in 43 counties of Alabama and Georgia but does not employ a local endocrinologist. In 2014, the Atlanta VAMC Endocrinology Telehealth Clinic was established to increase access to specialty care for type 1 diabetes for CAVHCS patients. Without telehealth, CAVHCS patients have to travel to the Veterans Affairs (VA) medical centers in either Birmingham, Alabama, or Atlanta, Georgia, to receive in-person specialty care. With telehealth, patients travel to local community-based outpatient clinics for their telehealth appointment, where they check in as they would for a regular face-to-face appointment; they have their vital signs checked, go to a patient care room with a webcam or dedicated telehealth monitor, and have a CVT consultation from an Atlanta-based endocrinologist with in-person assistance from a telehealth pharmacist. Visits typically last 30 to 60 minutes.

We conducted a retrospective chart review of patients with type 1 diabetes who received care through the Atlanta VAMC Endocrinology Telehealth Clinic from June 2014 to October 2016. We collected data about changes in glycemic control, telemedicine’s capacity to save costs for the VHA and time for patients, patient adherence to telemedicine appointments, and patient satisfaction with telemedicine. Data were stored in REDCap, a secure web-based database application. Our use of REDCap was sponsored by the Atlanta Clinical and Translational Science Institute. This study was approved by the Emory institutional review board and the Atlanta VA Research and Development Committee.

To assess diabetes management, we collected data on recommended processes of diabetes care: blood pressure management, eye screening, urine microalbumin-to-creatinine ratio, and lipid panels (triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol). We also assessed whether patients received drug prescriptions for which they were eligible, specifically statins and aspirin.

To assess diabetes outcomes, we collected data on change in glycemic control, specifically hemoglobin A1c levels, 2-week frequency and severity of hypoglycemia, 2-week frequency and severity of hyperglycemia, and plasma glucose variability. Hemoglobin A1c indicates average plasma glucose concentration over 2 to 3 months and predicts diabetes complications (8,9). Hypoglycemia is defined as low plasma glucose concentration, and severe hypoglycemia may lead to unconsciousness (9). We defined hypoglycemia as a plasma glucose level of less than 70 mg/dL and severe hypoglycemia as less than 40 mg/dL. Hyperglycemia is defined as high plasma glucose concentration, which may lead to long-term complications such as diabetic retinopathy, nephropathy, and neuropathy (10). We defined hyperglycemia as a plasma glucose level of more than 250 mg/dL and severe hyperglycemia as more than 300 mg/dL. We reviewed patients’ insulin pump downloads or patients’ glucose logs over a 2-week period to determine frequency of hypoglycemia and hyperglycemia. Lastly, average glucose variability was defined as the standard deviation (SD) of all plasma glucose levels in the 2-week period. Data on glycemic control were collected at baseline visits, 6 month follow-up visits (±1 month), and 12 month follow-up visits (±1 month).

Cost savings for the VHA were calculated on the basis of the difference between patient travel reimbursement costs associated with in-person visits at VA medical centers in either Birmingham, Alabama, or Atlanta, Georgia, and costs associated with telemedicine visits at community-based outpatient clinics. Travel reimbursements were calculated using reimbursement rates published by the VHA’s Beneficiary Travel Benefits program, which was 41.5 cents per mile with a $6 patient deductible (11). Patients who traveled more than 75 miles one way were eligible for VA-reimbursed overnight lodging, and lodging costs of $75 were added to the travel cost for an in-person visit. Time savings for patients were calculated using Google Maps (Google Inc) and were based on the difference in estimated time to travel to community-based outpatient clinics versus the nearest VA medical center in either Atlanta, Georgia, or Birmingham, Alabama.

To evaluate telemedicine appointment adherence, we recorded the number of CVT appointments missed (patient did not show up), cancelled, and scheduled. Telemedicine appointment adherence was reported as the ratio of the number of CVT appointments in which the patient showed up to the number of CVT appointments scheduled, excluding the number of appointments cancelled by the patient in advance. To assess patient satisfaction with telemedicine, we administered via telephone a satisfaction survey published by the VA Telehealth Services Program. Patients were surveyed about telemedicine’s usability and convenience, and their satisfaction was measured using a Likert Scale with scores ranging from 1 through 5 (1 = “strongly agree” and 5 = “strongly disagree”).

Data analysis was performed using Microsoft Office Excel 2010 (Microsoft Corporation), SPSS version 23.0 (IBM Corp), and SAS version 9.4 (SAS Institute Inc). To analyze changes in diabetes outcomes, we conducted paired t tests from baseline data, 6-month follow-up data, and 12-month follow-up data. Significance was set at P < .05. To analyze patient satisfaction survey results, we calculated the median, mean, and SDs of patient responses to each survey question.

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Results

Demographic characteristics

Among 54 patients enrolled in the Atlanta VAMC Endocrinology Telehealth Clinic, 32 patients had type 1 diabetes (Figure). Of the 32 patients with type 1 diabetes, 17 had follow-up visits at 6 months, and 9 had follow-up visits at 12 months. Telehealth patients with type 1 diabetes were predominately male (n = 29, 91%) and white (n = 27, 84%) (Table 1). Mean age was 53.5 years and mean body mass index was 27.6 kg/m2. Comorbidities and diabetes complications were highly prevalent at baseline in this patient population; most patients had hyperlipidemia (n = 26, 81%) and diabetic neuropathy (n = 23, 72%).

Diagram showing criteria for inclusion in a study of patients (N = 32) enrolled in the Atlanta VA Telehealth Endocrine Clinic, June 2014 to October 2016. Abbreviation: VAMC, Veterans Affairs Medical Center.

Figure.
Diagram showing criteria for inclusion in a study of patients (N = 32) enrolled in the Atlanta VA Telehealth Endocrine Clinic, June 2014 to October 2016. Abbreviation: VAMC, Veterans Affairs Medical Center. [A text description of this figure is also available.]

Telehealth patients generally received the standard processes of diabetes care (Table 2) (12). At baseline, 94% patients (30 of 32) had a diabetic retinopathy eye screening within the preceding 2 years, and 100% (9 of 9) received the recommended eye screening at 12-month follow-up. Furthermore, 81% of patients (26 of 32) had their urine microalbumin-to-creatinine ratio measured at baseline, which increased to 89% (8 of 9) at 12-month follow-up. Of patients who were eligible for statin use, 89% (24 of 27) were prescribed a statin, and 64% patients who were eligible for aspirin use (14 of 22) were prescribed aspirin. At 12-month follow-up, 88% of eligible patients (7 of 8) were prescribed a statin, and 50% of eligible patients (1 of 2) were prescribed aspirin. When seen at baseline visits and at 6-month and 12-month follow-up visits, all patients had received the recommended blood pressure measurements and lipid panels.

Diabetes outcomes and glycemic control

Mean hemoglobin A1c levels decreased overall from baseline (8.7%) to 6-month (8.2%) and 12-month (8.1%) follow-up, although the change was not significant. After 6 months and 12 months, patients also had a mean increase in average frequency of hypoglycemia per 2 weeks of blood glucose levels less than 70 mg/dL and less than 40 mg/dL, although these trends were not significant. The mean frequency of hypoglycemia of glucose less than 70 mg/dL was 3.3 hypoglycemic episodes per 2 weeks at baseline, 3.3 at 6-month follow-up, and 6.2 at 12-month follow-up. The average frequency of hypoglycemic episodes per 2 weeks of glucose less than 40 mg/dL was 0.2 at baseline, 0.2 at 6-month follow-up, and 0.6 at 12-month follow-up. Clinically, the difference in severe hypoglycemia (<40 mg/dL) was insignificant, but hypoglycemia of glucose less than 70 mg/dL increased overall.

The average frequency of hyperglycemia every 2 weeks increased from baseline to 6-month follow-up but was stable after 12 months. This trend was observed in hyperglycemic episodes of glucose greater than 250 mg/dL and greater than 300 mg/dL but was not significant. The mean frequency of hyperglycemia greater than 250 mg/dL was 16.3 at baseline, 22.5 at 6-month follow-up, and 16.2 at 12-month follow-up. For hyperglycemic episodes greater than 300 mg/dL, the mean frequency was 4.0 at baseline, 5.4 at 6-month follow-up, and 3.8 at 12-month follow-up.

Lastly, there was a nonsignificant trend toward a decrease in mean 2-week blood glucose levels at 6-month and 12-month follow-up. Mean daily blood glucose level was 79.2 mg/dL (SD, 20.4 mg/dL; n = 27) at baseline, 76.2 mg/dL (SD, 15.7 mg/dL; n = 16) at 6 months, and 76.4 mg/dL (SD, 19.7 mg/dL; n = 9) at 12 months.

Time and cost savings

Patients saved a median of 78 minutes of one-way traveling time, and the VHA saved a median of $72.94 per patient visit in travel reimbursement. If Atlanta VAMC Endocrinology Telehealth patients received follow-up appointments every 3 months as recommended, each patient would save 624 minutes of traveling time per year, which corresponds with VHA savings of $9,336.32 per year in reimbursements to the 32 patients with type 1 diabetes.

Telehealth appointment adherence and patient satisfaction with telemedicine

Telehealth patients had a median of 5 scheduled appointments (range, 1–10 scheduled appointments). Patients were adherent to their telehealth appointments; at least half of the patients attended 100% of their appointments, and mean adherence rate was 87.8% (SD, 17.8%; range, 50.0%–100%).

Twenty-two (69%) telehealth patients with type 1 diabetes completed the survey about their satisfaction with telehealth care. Patients perceived the endocrinology care they received during their telemedicine appointments favorably; 100% of respondents agreed or strongly agreed that they were satisfied with telehealth (Table 3). Furthermore, 90.9% respondents strongly agreed with the statement that they would recommend telehealth to other veterans, and 90.9% respondents agreed or strongly agreed that they would rather use telehealth than travel long distances to see their providers. Two patients who preferred in-person care over telehealth stated that seeing their physician face-to-face was important to them.

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Discussion

Our findings suggest that telemedicine is a safe method of delivering type 1 diabetes care to rural patients. Telehealth patients in our study experienced improvements overall in diabetes outcomes, although our findings were not significant. Patients also had an increased mean frequency of hypoglycemia. Our observation of increased hypoglycemic episodes is consistent with literature that suggests improved glycemic control, indicated by lower hemoglobin A1c levels, is correlated with an increased frequency of hypoglycemia (13).

Our findings are in line with those of other studies that suggest that diabetes care via telemedicine is comparable to in-person diabetes care. For example, in a recent randomized controlled trial of 282 diabetes patients, those who received telemedicine consultation had a −1.01% decrease in hemoglobin A1c compared with a −0.68% decrease in hemoglobin A1c in those receiving in-person consultation, although the change was nonsignificant (14). Our findings, which demonstrated a 0.6% decrease in hemoglobin A1c at 12 months of telemedicine follow-up consultation, complement this study’s findings and growing evidence that suggests that telemedicine is a viable alternative for in-person care.

Previous studies also demonstrated telemedicine’s effectiveness in delivering diabetes care to rural patients. Wood et al described telemedicine’s use in pediatric type 1 diabetes care for patients in rural Wyoming, demonstrated equivalency between telemedicine and in-person visits, and found that patients received more follow-up visits after telemedicine’s implementation (15). Similarly, Wagnild et al described the use of telecommunications for diabetes patients in Montana and found that patients showed improvements in hemoglobin A1c levels, blood pressure, and diabetes knowledge (16). Our findings are consistent with literature that suggests that telemedicine may effectively deliver diabetes care to rural patients.

Our study has limitations. First, the referring diabetes specialty provider at CAVHCS also independently manages the diabetes treatment of many of the patients enrolled in the telehealth clinic, in some cases just before referral to the telehealth clinic but mostly with select patients between telehealth visits as needed. Thus, telehealth patients’ glycemic control before baseline visits and afterward may have been better than that of patients who receive care only from primary care providers (17). However, use of midlevel providers such as pharmacists and nurses is common across the VA health system, is an integral part of the VA-established Patient Aligned Care Team model, and may represent the patient-centered care model in use (18).

Another limitation was significant loss of follow-up. Many patients had follow-up visits that did not meet our study criteria of 6- and 12-month follow-up points. This apparent loss of follow-up may have been because the Atlanta VA Telehealth Endocrinology Clinic is available only once per week. As more patients enrolled in the clinic over time, the intervals between follow-up appointments necessarily increased. Therefore, some patients did not have an appointment scheduled at the 6-month point (5–7 months after baseline) or the 12-month point (11–13 months after baseline). Thus, if a patient had an appointment before 11 months or over 13 months after their initial appointment, they would not have been included for the 12-month follow-up analysis. Our follow-up data may have been further confounded by the possibility that patients with worse glycemic control needed more frequent follow-up and thus were more likely to have 12-month follow-up data.

Additionally, our study used convenience sampling of patients enrolled in the Atlanta VAMC Endocrinology Telehealth Clinic. Our findings may not accurately represent patients with type 1 diabetes in the general population because all our patients were veterans seen at the VA and most had insulin pumps, which are associated with better glycemic control compared with insulin injections (19). Furthermore, our evaluation of aspirin use may have been limited by inconsistent documentation of its use, because many patients purchase it over-the-counter at local drug stores, leading to an underestimation of its use.

Lastly, our limitations include self-selection bias and small sample size. Self-selection bias may have affected our satisfaction survey results because patients who prefer telemedicine may be more likely to enroll in telehealth clinics, whereas patients who prefer in-person care may be more likely travel to VA medical centers to receive treatment. Furthermore, our small sample size limited our statistical power and generalizability. However, these limitations were inherent in our study design, because we conducted a retrospective review of only patients enrolled in our telehealth clinic.

One of telemedicine’s most important benefits is its ability to increase access to health care. Distance is a significant factor for many veterans living in remote and rural areas seeking health care, because travel distance is negatively correlated with use of outpatient services (20). The VA has mitigated this issue by providing travel reimbursement and bus services for patients, but telemedicine further promotes health care accessibility for rural patients. Another important aspect of telemedicine is its acceptance by patients and providers. Our study demonstrates that most patients are satisfied with telemedicine care, believe that telemedicine appointments are convenient, and would recommend telemedicine to other veterans. Our findings are consistent with those of studies that report that both patients and providers are highly satisfied with telemedicine (21–24).

Lastly, our findings suggest that telemedicine leads to substantial cost savings and complement findings from studies that demonstrate telemedicine’s cost-saving capacity in larger health care systems. For example, the use of telemedicine in 7 rural hospital emergency departments in Mississippi decreased the hospitals’ expenditures from $7.6 million to $1.1 million during a 5-year period with no apparent effect on clinical outcomes (25). If the VHA implements telemedicine on a broader scale, veterans could receive more accessible patient-centered care, and the VHA could benefit from significant cost savings.

Our findings suggest that telemedicine delivers safe diabetes care to rural veterans and supports growing evidence that suggests that telemedicine is an effective alternative method of health care delivery. Additionally, telemedicine is associated with cost savings for the VHA, time savings for patients, high appointment adherence, and high patient satisfaction. Future studies with larger, more representative samples of patients with type 1 diabetes are needed to elucidate telemedicine’s effectiveness in providing health care to broader patient populations.

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Acknowledgments

This research was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award no. UL1TR000454.

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Author Information

Corresponding Author: Timothy Xu, Mayo Clinic School of Medicine, 200 1st St SW, Rochester, MN 55905. Email: xu.timothy@mayo.edu.

Author Affiliations: 1Mayo Clinic School of Medicine, Rochester, Minnesota. 2Emory University, Atlanta, Georgia. 3Central Alabama Veterans Health Care System, Montgomery, Alabama. 4Atlanta Veterans Affairs Medical Center, Decatur, Georgia.

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References

  1. Centers of Disease Control and Prevention. National diabetes statistics report: estimates of diabetes and its burden in the United States; 2014. http://www.cdc.gov/diabetes/pubs/statsreport14/national-diabetes-report-web.pdf. Accessed September 7, 2015.
  2. Endocrine Society. Endocrinologist workforce to see double digit shortage through 2025, 2014. https://www.endocrine.org/membership/email-newsletters/endocrine-insider/2014/july-10-2014/endocrinologist-workforce-to-see-doubledigit-shortage-through-2025. Accessed February 14, 2017.
  3. US Census Bureau. United States 2010 census urban and rural classification and urban area criteria. US Census Bureau; 2014. https://www.census.gov/geo/ reference/ua/urban-rural-2010.html. Accessed November 16, 2015.
  4. Hartley D. Rural health disparities, population health, and rural culture. Am J Public Health 2004;94(10):1675–8. CrossRef PubMed
  5. American Telehealth Association. What is telemedicine? 2012. http://www.americantelemed.org/about-telemedicine/what-is-telemedicine#.Ve4PaRFVhBd. Accessed November 29, 2015.
  6. US Department of Veterans Affairs. Real-time clinic based video telehealth; 2015. http://www.telehealth.va.gov/real-time/. Accessed February 1st, 2017.
  7. Darkins A. The growth of telehealth services in the Veterans Health Administration between 1994 and 2014: a study in the diffusion of innovation. Telemed J E Health 2014;20(9):761–8.
  8. Sacks DB, Arnold M, Bakris GL, Bruns DE, Horvath AR, Kirkman MS, et al. ; National Academy of Clinical Biochemistry. Position statement executive summary: guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Diabetes Care 2011;34(6):1419–23. CrossRef PubMed
  9. Albers JW, Herman WH, Pop-Busui R, Feldman EL, Martin CL, Cleary PA, et al. ; Diabetes Control and Complications Trial /Epidemiology of Diabetes Interventions and Complications Research Group. Effect of prior intensive insulin treatment during the Diabetes Control and Complications Trial (DCCT) on peripheral neuropathy in type 1 diabetes during the Epidemiology of Diabetes Interventions and Complications (EDIC) Study. Diabetes Care 2010;33(5):1090–6. CrossRef PubMed
  10. American Diabetes Association. Hyperglycemia (high blood glucosse), 2014. http://www.diabetes.org/living-with-diabetes/treatment-and-care/blood-glucose-control/hyperglycemia.html?referrer=https://www.google.com/. Accessed January 30, 2017.
  11. US Department of Veterans Affairs. Beneficiary travel; 2016. https://www.va.gov/healthbenefits/vtp/beneficiary_travel.asp. Accessed February 10, 2017.
  12. American Diabetes Association. 2016 Standards of care; 2016. http://care.diabetesjournals.org/content/suppl/2015/12/21/39…/2016-Standards-of-Care.full.pdf. Accessed January 30, 2017.
  13. DuBose SN, Weinstock RS, Beck RW, Peters AL, Aleppo G, Bergenstal RM, et al. Hypoglycemia in older adults with type 1 diabetes. Diabetes Technol Ther 2016;18(12):765–71.
  14. Sood A, Watts SA, Johnson JK, Hirth S, Aron DC. Telemedicine consultation for patients with diabetes mellitus: a cluster randomised controlled trial. J Telemed Telecare 2017:X17704346. PubMed
  15. Wood CL, Clements SA, McFann K, Slover R, Thomas JF, Wadwa RP. Use of telemedicine to improve adherence to American Diabetes Association standards in pediatric type 1 diabetes. Diabetes Technol Ther 2016;18(1):7–14. CrossRef PubMed
  16. Wagnild G, MacCart JG, Mitchell S, Tyabah K, Leenknecht C, Meszaros JF. A telecommunications intervention for frontier patients with diabetes. Telemed J E Health 2008;14(8):793–800. CrossRef PubMed
  17. Yang Y, Long Q, Jackson SL, Rhee MK, Tomolo A, Olson D, et al. Nurse practitioners, physician assistants, and physicians are comparable in managing the first five years of diabetes. Am J Med 2017;S0002-9343(17)30904-X; epub ahead of print. PubMed
  18. Collier IA, Baker DM. Implementation of a pharmacist-supervised outpatient diabetes treatment clinic. Am J Health Syst Pharm 2014;71(1):27–36. CrossRef PubMed
  19. Yeh HC, Brown TT, Maruthur N, Ranasinghe P, Berger Z, Suh YD, et al. Comparative effectiveness and safety of methods of insulin delivery and glucose monitoring for diabetes mellitus: a systematic review and meta-analysis. Ann Intern Med 2012;157(5):336–47. CrossRef PubMed
  20. Burgess JF Jr, DeFiore DA. The effect of distance to VA facilities on the choice and level of utilization of VA outpatient services. Soc Sci Med 1994;39(1):95–104. CrossRef PubMed
  21. Jaatinen PT, Aarnio P, Remes J, Hannukainen J, Köymäri-Seilonen T. Teleconsultation as a replacement for referral to an outpatient clinic. J Telemed Telecare 2002;8(2):102–6. CrossRef PubMed
  22. Park ES, Boedeker BH, Hemstreet JL, Hemstreet GP. The initiation of a preoperative and postoperative telemedicine urology clinic. Stud Health Technol Inform 2011;163(163):425–7. PubMed
  23. Mounessa JS, Chapman S, Braunberger T, Qin R, Lipoff JB, Dellavalle RP, et al. A systematic review of satisfaction with teledermatology. J Telemed Telecare 2017;X17696587. PubMed
  24. Becevic M, Boren S, Mutrux R, Shah Z, Banerjee S. User satisfaction with telehealth: study of patients, providers, and coordinators. Health Care Manag (Frederick) 2015;34(4):337–49. PubMed
  25. Duchesne JC, Kyle A, Simmons J, Islam S, Schmieg RE Jr, Olivier J, et al. Impact of telemedicine upon rural trauma care. J Trauma 2008;64(1):92–7, discussion 97–8. CrossRef PubMed

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Tables

Return to your place in the textTable 1. Demographic Characteristics of Patients, Study of Patients (N = 32) Enrolled in the Atlanta VA Telehealth Endocrine Clinic, June 2014 to October 2016
Characteristic Telehealth Patients With Type 1 Diabetes at Baseline (N = 32)a
Mean (SD) age, y 53.5
Sex
Male 90.6
Female 9.4
Race
White 84.4
Black 15.6
Primary care location
Montgomery, Alabama 75.0
Columbus, Georgia 25.0
Carrollton, Georgia 0
Mean (SD) body mass index, kg/m2 27.6
Mean (SD) duration of diabetes, y 24.7
Insulin pump use 75.0
Continuous glucose monitor use 18.8
Hypertension 46.9
Hyperlipidemia 81.3
Hypothyroidism 28.1
Tobacco use 21.9
Microvascular diseases
Neuropathy 71.9
Nephropathy 21.0
Retinopathy 40.6
Macrovascular diseases
Coronary Artery disease 25.0
Cerebrovascular disease 12.5
Peripheral vascular disease 3.1

a Values are percentages unless otherwise indicated.

Return to your place in the textTable 2. Maintenance of Standard Processes of Diabetes Care, Study of Patients (N = 32) Enrolled in the Atlanta VA Telehealth Endocrine Clinic, June 2014 to October 2016
American Diabetes Association 2016 Guideline Monitoring Percentagea of Patients With Recommended Care at Baseline Percentagea of Patients With Recommended Care at 6 Months Percentagea of Patients With Recommended Care at 12 Months
Blood pressure Every routine visit 100 (32 of 32) 100 (17 of 17) 100 (9 of 9)
Diabetic retinopathy eye exam Every 1 year 93.7 (30 of 32) 94.1 (16 of 17) 100 (9 of 9)
Urine microalbumin-to-creatinine ratio Every 1 year 81.3 (26 of 32) 88.2 (15 of 17) 88.9 (8 of 9)
Lipid panel (triglyceride, HDL, and LDL levels) Every 1 year 100 (32 of 32) 100 (17 of 17) 100 (9 of 9)
Statin use Eligibility: aged >40 y or history of CVD 88.9 (24 of 27) 100 (15 of 15) 87.5 (7 of 8)
Aspirin use Eligibility: aged >50 or history of CVD 63.6 (14 of 22) 69.2 (9 of 13) 50.0 (1 of 2)

Abbreviations: CVD, cardiovascular disease; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol.
a Values in parentheses are number of patients who adhered to recommendation out of total number.

Return to your place in the textTable 3. Patient Responses to Telehealth Satisfactiona Survey, Study of Patients With Type 1 Diabetes (N = 32) Enrolled in the Atlanta VA Telehealth Endocrine Clinic, June 2014 to October 2016
Telehealth Patient Satisfaction Survey Question Median Mean (SD)
I felt comfortable with the equipment used. 5.00 4.91 (0.29)
I was able to see the clinician clearly. 5.00 4.95 (0.21)
I was able to hear the clinician clearly. 5.00 5.00 (0)
There was enough technical assistance for my meeting with the clinician. 5.00 4.95 (0.21)
My relationship with the clinician was the same during this session as it is in person. 5.00 4.18 (1.01)
The location of the telehealth clinic is convenient for me. 5.00 4.68 (0.65)
My needs were met during the session. 5.00 4.95 (0.21)
I received good care during the session. 5.00 4.95 (0.21)
The telehealth clinic provided the care I expected. 5.00 4.95 (0.21)
Overall, I am satisfied with the telehealth session. 5.00 4.91 (0.29)
I would recommend this type of session to other veterans. 5.00 4.77 (0.75)
I would rather use telehealth to receive this service than travel long distance to see my provider. 5.00 4.59 (1.05)

a Patient satisfaction was measured using a Likert Scale (from 1 through 5), where 1 indicated “strongly agree” and 5 indicated “strongly disagree.”

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Identifying Financially Sustainable Pricing Interventions to Promote Healthier Beverage Purchases in Small Neighborhood Stores

Claudia Nau, PhD1; Shiriki Kumanyika, PhD2,3; Joel Gittelsohn, MS, PhD3,4; Atif Adam, MD, MPH, PhD3,4; Michelle S. Wong, PhD3,5; Yeeli Mui, MPH, PhD6; Bruce Y. Lee, MD, MBA3,7 (View author affiliations)

Suggested citation for this article: Nau C, Kumanyika S, Gittelsohn J, Adam A, Wong MS, Mui Y, et al. Identifying Financially Sustainable Pricing Interventions to Promote Healthier Beverage Purchases in Small Neighborhood Stores. Prev Chronic Dis 2018;15:160611. DOI: http://dx.doi.org/10.5888/pcd15.160611.

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Abstract

Introduction

Residents of low-income communities often purchase sugar-sweetened beverages (SSBs) at small, neighborhood “corner” stores. Lowering water prices and increasing SSB prices are potentially complementary public health strategies to promote more healthful beverage purchasing patterns in these stores. Sustainability, however, depends on financial feasibility. Because in-store pricing experiments are complex and require retailers to take business risks, we used a simulation approach to identify profitable pricing combinations for corner stores.

Methods

The analytic approach was based on inventory models, which are suitable for modeling business operations. We used discrete-event simulation to build inventory models that use data representing beverage inventory, wholesale costs, changes in retail prices, and consumer demand for 2 corner stores in Baltimore, Maryland. Model outputs yielded ranges for water and SSB prices that increased water demand without loss of profit from combined water and SSB sales.

Results

A 20% SSB price increase allowed lowering water prices by up to 20% while maintaining profit and increased water demand by 9% and 14%, for stores selling SSBs in 12-oz cans and 16- to 20-oz bottles, respectively. Without changing water prices, profits could increase by 4% and 6%, respectively. Sensitivity analysis showed that stores with a higher volume of SSB sales could reduce water prices the most without loss of profit.

Conclusion

Various combinations of SSB and water prices could encourage water consumption while maintaining or increasing store owners’ profits. This model is a first step in designing and implementing profitable pricing strategies in collaboration with store owners.

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Introduction

Researchers have pointed to the lack of obesity prevention strategies that consider retailer revenue and profit (1,2). Water consumption has been promoted to reduce calorie intake otherwise obtained from sugar-sweetened beverages (SSBs) (3–5) and to provide a range of other health benefits (5,6). Consumer demand for beverages is sensitive to price changes, and some obesity prevention interventions have lowered the price of bottled water to improve beverage consumption (7,8). However, lowering prices may negatively affect retailer profit if not compensated (9). Locally owned retail stores, in particular, operate under small profit margins and have limited resources and motivation to implement and maintain pricing interventions that may threaten their bottom line (10).

We assessed feasibility of coordinated price changes of bottled water and SSBs as a profitable public health strategy in small stores in low-income urban neighborhoods in Baltimore, Maryland. These small, privately owned stores — referred to here as “corner stores” because they are often situated at corner locations — play an important role as beverage providers in many low-income urban neighborhoods that do not have access to larger retail food stores (10,11). SSBs are among the best-selling products in these stores, constituting a significant part of the stores’ revenue (7,12).

To design a financially self-sustaining pricing intervention, we built simulation models that mimicked the day-to-day stocking and sales of SSBs and bottled water in 2 Baltimore corner stores. These 2 stores served as case studies to assess feasibility of our strategy. An explicit dual focus on public health goals and retailer profit allowed us to design an intervention that would be beneficial for community health and for retailers. Using simulation models allowed us to estimate sales and profit under different pricing scenarios without disrupting stores’ operations. We show how results from our models can serve as a first step toward implementing profitable pricing interventions in collaboration with store owners.

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Methods

Theoretical framework and approach

Our theoretical framework and modeling approach stem from inventory control theory, which is concerned with optimizing supply and retail processes (13). Inventory control theory focuses on factors such as wholesale prices, storage, and sales prices that regulate stocking and sale of a store’s inventory with the goal of maximizing sales and profits (13).We used discrete-event simulation (DES), an approach frequently used in inventory control research, to build inventory models that model the daily product flow of 2 beverage categories, bottled water and SSBs (13,14).

Inventory model

To identify pricing combinations that increase both demand for and profitability of healthier beverages (14), we built 2 inventory models that represented the beverage inventory and sales of 2 actual corner stores (store A and store B), in different low-income neighborhoods in Baltimore. Store A and store B were typical examples of Baltimore corner stores and were chosen because we were provided with information on their beverage sales by a prior survey they had participated in. They were comparable in size, and each had beverage coolers installed along the length of one of the side walls. We incorporated the average sales price of SSBs and 16-oz bottles of water sold in each store in the model as well as wholesaler costs. Each model also represented the demand for beverages of the community members living around both stores. Store A sold SSBs predominantly in 16- to 20-oz bottles (out of 5 coolers, only half a cooler was stocked with SSB cans); store B sold SSBs exclusively in 12-oz cans. Stores A and B thus serve as case studies for different inventory profiles. Each inventory profile was modeled separately, because sales prices of SSB bottles and cans were different, and thus, the same percentage change in sales prices yielded different absolute changes in sales prices, demand, and profit across stores. As we show below, the inventory of both SSBs and water was large compared with the demand, and store owners restocked products most days (10); therefore, running out of stock was not an issue. To calculate daily profit for changing prices, we assumed that restocking occurred the same day sales were made.

By using daily demand, average prices, and wholesaler costs of SSBs and water stocked in each store, we calculated baseline profit under current prices. We then used price elasticities, explained in detail below, to simulate demand and profit in each store when water prices decreased by $0.01 increments and SSB prices increased by $0.01 increments until SSB prices had increased by 20% and water prices decreased by 20%. We assessed all possible price combinations of SSBs and water, paying particular attention to the following 3 price combinations: 1) the price combination that maximized the demand for water while maintaining or increasing profit (the ideal price point from a public health point of view), 2) the price combination that maximized profit while at least maintaining current demand for water (the ideal price point from the store owner’s perspective), and 3) the pricing combination that produced an equal percentage increase of water demand and profit (as an example of a compromise price point for both retail and public health stakeholders).

Data sources

We collected detailed information on the beverage inventory, sales prices, and sales from store A and store B, including the number of items and the sales price of each SSB brand. Beverage wholesale costs were obtained by directly recording brand-specific prices from the 2 principal suppliers of Baltimore corner stores. Different prices for the same beverage were averaged between wholesalers. We only considered sodas and fruit drinks. We calculated an average wholesale cost and an average sales price for SSB bottles and cans and for water for each store. The average price was weighted by the number of bottles or cans of each specific beverage stocked and sold in each store.

We used store owner recall of sales for each beverage category to measure demand. Data from stores A and B were collected as part of a prior study (15).

To model the response of demand to price changes, we drew estimates of the effects of price changes on demand for SSBs and water in low-income populations — so-called own-price-elasticities — from a study by Lin et al (16). Lin et al used nationally representative data and found that in low-income populations, a 1% increase in water prices caused a 0.95% decrease in demand. Similarly, a 1% increase in SSB prices decreased demand by 0.72%. Lin et al found that water prices do not affect SSB demand and vice versa (16). In our data set, a 20% increase amounted to a maximum price of $1.26 for SSB bottles and $0.93 for cans. Analogously, for water, a price decrease of 20% lead to a sales price of $0.80 per 16-oz bottle. We limited price changes to a maximum of 20% because Lin et al found that demand within this range could be approximated by their estimates (B-H. Lin, email communication, September 9, 2014).

Model inputs

The inventory was set to 544 SSB bottles in model A (representing store A) and to 588 SSB cans in model B (representing store B), mirroring the actual number of SSBs in each store (Table 1). Starting values for sales prices and wholesale costs were set to the average sales price and wholesale cost calculated for SSB bottles in store A ($1.05 and $0.50, respectively) and SSB cans in store B ($0.77 and $0.36, respectively). In both stores, the inventory, price, and sales of 16-oz bottles of water were nearly identical and were set at 41 bottles in stock with a sales price of $1.00 and a per-item wholesale cost of $0.12 for both modeling scenarios. Baseline levels of daily demand for SSBs varied between stores (24 bottles for store A and 20 cans for store B); sales of bottled water were comparable and were set to be 15 per day for both stores. We calculated the current profit generated when prices were set to the current SSB and water sales prices by multiplying the number of sales with the average price and subtracting the wholesaler costs. Profit in model A was $26.38 and in model B was $21.53. Profit from any pricing intervention had to be equal to or higher than the current profit of each store. The discrete event simulation model was programmed in R 3.2.2 (R Foundation for Statistical Computing).

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Results

Price combinations of SSB bottles and water (model A)

Panel A in Figure 1 plots the daily demand of water bottles against all price combinations of water and SSB bottles. Our results showed that water demand increased as water prices decreased. However, SSB bottle prices had to increase simultaneously, or profit declined. Coordinated price changes of water and SSB bottles allowed water demand to increase from 15 to a maximum of 17.15 bottles per day at a sales price of $0.80.

Water demand and profit for coordinated price changes for selling bottles of sugar-sweetened beverages (SSBs) for corner store A, Baltimore, Maryland, 2014. Panel A shows absolute demand of water over prices of water and bottles of SSBs. Panel B shows only profitable price combinations for percentage change of water demand in relation to profit change.

Figure 1.
Water demand and profit for coordinated price changes for selling bottles of sugar-sweetened beverages (SSBs) for corner store A, Baltimore, Maryland, 2014. Panel A shows absolute demand of water over prices of water and bottles of SSBs. Panel B shows only profitable price combinations for percentage change of water demand in relation to profit change. [A
text version of this figure is also available.]

Figure 1, panel B, uses all price combinations within the profitable area of Figure 1, panel A (black symbols). For each price combination, we plotted the percentage increase in water demand against the percentage increase in profit that occurred at that price combination (Figure 1, panel B). We found that a price combination of $0.80 (20% decrease) for water and $1.26 for SSB bottles generated a maximum increase of 14.36% in water purchases (2.15 bottles per day) while maintaining the same level of profit as current prices. Maximum total profits are achieved when water prices remain at $1.00 and SSB prices are set to the maximum sales price of $1.26, resulting in a 6.00% profit increase. Equal percentage increases of profit (4.36%) and water demand (4.31%) are achieved at a price combination of $0.94 and $1.24 for water and SSB bottles, respectively.

Price combinations of SSB cans and water (model B)

Figure 2 represents results for store B selling SSB cans analogously to Figure 1. There are fewer profitable price combinations for Store B, as indicated by the smaller area of black symbols in Figure 2, panel A. Panel B of Figure 2 further shows that the maximum increase in water demand is smaller in store B (9.33%) compared with store A (14.36%). Prices that maximize water demand are $0.87 for water and $0.93 for SSB. Similarly, the maximum profit increase in store B was 4.25%, at prices of $1.00 for water and $0.93 for SSB cans, which is less than in store A (6.00%). Equal increases in demand and profit in store B were achieved at 2.87% when water and SSBs were priced $0.96 and $0.90, respectively.

Water demand and profit for coordinated price changes for selling cans of sugar-sweetened beverages (SSBs) for corner store B, Baltimore, Maryland, 2014. Panel A shows absolute demand of water over prices of water and cans of SSBs. Panel B shows only profitable price combinations for percentage change of water demand in relation to profit change.

Figure 2.
Water demand and profit for coordinated price changes for selling cans of sugar-sweetened beverages (SSBs) for corner store B, Baltimore, Maryland, 2014. Panel A shows absolute demand of water over prices of water and cans of SSBs. Panel B shows only profitable price combinations for percentage change of water demand in relation to profit change. [A text version of this figure is also available.]

Sensitivity analyses

The relative demand of water and SSBs at baseline (before any price change) determines how much the price of water can be lowered for a given increase in SSB price while maintaining profit. Therefore, the potential maximum improvement of profit and water demand also varies with the relative difference in the baseline demand of SSBs and water. To understand whether there are baseline conditions that do not allow improving water demand or profit, we used store B’s data to recalculate price combinations and maximum improvement of profit and demand under the assumption that, all else being equal, the demand of SSB cans was first increased by 1 unit increments from current levels to 10 times the initial demand. Then, keeping SSB demand constant at current levels, we allowed baseline demand of water to increase until demand was multiplied by a factor of 10. We chose store B because soda cans provide a smaller profit margin; therefore, they provide less buffer to counter-finance changes in water prices. As before, SSB prices were increased by no more than 20%.

In the current scenario, store B sold 1.3 SSB cans for each water bottle per day. Results from our sensitivity analysis (Table 2) show that doubling baseline SSB demand would increase the maximally attainable profit compared with current demand levels (6.13% vs 4.25%). The maximum attainable demand would increase from 9.33% to 15.80% at a sales price of $0.78 for water and $0.93 for SSBs.

If, all else being equal, the demand of water at baseline were higher than the current demand for water, then, to maintain profit, water prices could not be lowered as much as in the current scenario. If baseline water demand were doubled, profit could still be increased, but would be much less than under the current scenario (2.63% improvement of profit vs 4.25% in the current scenario) and maximum increase in water demand would be 5.03% compared with 9.33% (Table 2). An equal percentage improvement of demand and profit would be reached at approximately 1.45%. Even if the baseline demand for water were 10 times higher than in the original scenario (ie, if the store would sell 150 bottles of water and 20 SSB cans per day) water demand could be increased minimally by 0.72% at price points of $0.99 and $0.84 for water and SSBs, respectively, while profit would remain unchanged (results not shown).

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Discussion

To our knowledge, this is the first public health study to explore a corner store intervention that explicitly considers profitability for retailers. Our simulations allowed us to identify a range of plausible pricing combinations that are likely to improve beverage consumption and profit. We found that a store stocking SSB bottles could increase water demand by up to 14.36% and profit by up to 6.00% through coordinated price changes. A store selling SSB cans, which are cheaper and less profitable than SSB bottles, could increase demand by up to 9.33% and profit by 4.25%. Although the potential for improvement was smaller in the store selling SSB cans, our results indicate that our strategy can be successfully implemented in stores with different inventory profiles. Sensitivity analysis further showed that our pricing strategy was robust to changes in the demand structure of SSBs and water. We also found that our strategy would be most effective in stores where SSB demand and the need to incentivize water consumption are the highest.

Estimates of price elasticities vary slightly across studies. Additional sensitivity analysis assessed whether our coordinated pricing strategy was robust to different price elasticities. By using price elasticities for low-income populations of 2 other studies (17,18), we found that water demand might possibly be increased even more than our initial results suggested. Overall, we found that our pricing strategy worked under alternative demand elasticity scenarios (Appendix).

We demonstrated that coordinated price changes could improve water consumption while maintaining store profit under a wide range of scenarios. Prior research has shown that pricing is an effective tool that may lead not only to ad hoc, short-term changes in consumer behaviors (19) but also to habituation to healthier products over the long run (20). Pricing has been cited as a particularly important factor in purchasing decisions by low-income and African American customers (21,22), who are priority populations for public health interventions to lower SSB consumption (23).

We built a simulation model to identify plausible pricing combinations because conducting pricing experiments in corner stores is challenging. Optimizing a coordinated pricing strategy requires assessment of many different pricing combinations. Although store owners can set their own prices, most do not have digital cash registers that would allow tracking changes in demand in response to price changes (24). More importantly, increasing prices, even after a low-price promotion, causes customers to voice dissatisfaction and store owners to fear losing customers (25). Thus, a simulation model is an undisruptive first step to assess feasibility and eventually inform in-store experiments.

As a next step toward implementing our profitable pricing strategy, we plan to use stakeholder-involved modeling techniques to improve our model. Stakeholder-involved modeling will allow us to add mechanisms that store owners deem important and that are not yet captured in the model (26). Stakeholder-involved modeling has been found to increase model validity and stakeholder buy-in (27,28). We anticipate that issues such as the proximity to competitors are likely of concern for storeowners and need to be integrated into the model. The effect of other marketing and public health intervention tools, such as product placement or caloric information display, may also be incorporated.

Beyond the limitations of our model that can be addressed with stakeholder-involved modeling, there are other limitations that are inherent in our data and approach. For example, daily demand obtained from store owner recall may be subject to recall bias. Our results also depended on price elasticities that were derived from nationally representative data on beverage purchases from low-income customers for home consumption (16). Purchases of SSBs in 16- to 20-oz bottles or 12-oz cans in corner stores are more likely to be for immediate consumption away from home. These purchases might be more or less sensitive to price changes than those for home consumption. Furthermore, Lin et al combine fruit drinks and sodas into a single SSB category. This approach assumes that both beverages have the same price elasticity. Some research found moderate differences between the elasticities of these beverages (17,18).

Our study has an explicit dual focus on community health and retailer profit. Beyond deriving a strategy for self-sustaining promotions for bottled water, we introduce an intervention strategy that can be generalized to other products to ultimately improve the consumption patterns of low-income populations and support small businesses in low-income communities.

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Acknowledgments

The project described was supported by grant nos. U54HD070725 and U01HD086861 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) via the Global Obesity Prevention Center (GOPC). The U54 project is cofunded by the Office of Behavioral and Social Sciences Research (OBSSR). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NICHD or OBSSR. Dr Nau was supported by the training core of the Johns Hopkins GOPC.

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Author Information

Corresponding Author: Claudia Nau, PhD, Southern California Permanente Medical Group, Department for Research and Evaluation, Office 041R02, 100 S Los Robles, Pasadena CA 91101. Telephone: 626-564-5760. Email: Claudia.L.Nau@kp.org.

Author Affiliations: 1Kaiser Permanente Southern California, Department of Research and Evaluation, Pasadena, California. 2Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania. 3Global Obesity Prevention Center, Johns Hopkins University, Baltimore, Maryland. 4Center for Human Nutrition, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 5Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 6Food Systems Planning and Healthy Communities Lab, University at Buffalo, State University of New York, Buffalo, New York. 7Carey Business School, Johns Hopkins University, Baltimore, Maryland.

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References

  1. Gravlee CC, Boston PQ, Mitchell MM, Schultz AF, Betterley C. Food store owners’ and managers’ perspectives on the food environment: an exploratory mixed-methods study. BMC Public Health 2014;14(1):1031. CrossRef PubMed
  2. Bleich SN. Generating better evidence to engage local food outlets in obesity prevention research. Prev Med 2013;57(4):265–7. CrossRef PubMed
  3. Centers for Disease Control and Prevention. Rethink your drink — cutting calories. http://www.cdc.gov/healthyweight/healthy_eating/drinks.html. 2016. Accessed January 29, 2016.
  4. Tate DF, Turner-McGrievy G, Lyons E, Stevens J, Erickson K, Polzien K, et al. Replacing caloric beverages with water or diet beverages for weight loss in adults: main results of the Choose Healthy Options Consciously Everyday (CHOICE) randomized clinical trial. Am J Clin Nutr 2012;95(3):555–63. Erratum in: Am J Clin Nutr 2013;98(6):1599. CrossRef PubMed
  5. James J, Thomas P, Cavan D, Kerr D. Preventing childhood obesity by reducing consumption of carbonated drinks: cluster randomised controlled trial. BMJ 2004;328(7450):1237. Erratum in: BMJ 2004;328(7450):1236. CrossRef PubMed
  6. Let’s move drink up. A toast to the first anniversary of drink up with the top 5 moments from the past year. http://www.letsmove.gov/blog/2014/09/12/toast-first-anniversary-drink-top-5-moments-past-year. Accessed January 29, 2016.
  7. Gittelsohn J, Rowan M, Gadhoke P. Interventions in small food stores to change the food environment, improve diet, and reduce risk of chronic disease. Prev Chronic Dis 2012;9:E59. PubMed
  8. Escaron AL, Meinen AM, Nitzke SA, Martinez-Donate AP. Supermarket and grocery store-based interventions to promote healthful food choices and eating practices: a systematic review. Prev Chronic Dis 2013;10:E50. CrossRef PubMed
  9. Knight FH. Risk, uncertainty and profit. Mineola (NY): Dover Publications; 2012.
  10. Gittelsohn J, Franceschini MCT, Rasooly IR, Ries AV, Ho LS, Pavlovich W, et al. Understanding the food environment in a low-income urban setting: implications for food store interventions. J Hunger Environ Nutr 2008;2(2-3):33–50. CrossRef
  11. Borradaile KE, Sherman S, Vander Veur SS, McCoy T, Sandoval B, Nachmani J, et al. Snacking in children: the role of urban corner stores. Pediatrics 2009;124(5):1293–8. CrossRef PubMed
  12. Bodor JN, Ulmer VM, Dunaway LF, Farley TA, Rose D. The rationale behind small food store interventions in low-income urban neighborhoods: insights from New Orleans. J Nutr 2010;140(6):1185–8. CrossRef PubMed
  13. Sharma S. Operation research: inventory control and queuing theory. New Delhi (IN): Discovery Publishing House; 2006.
  14. Lee BY, Brown ST, Korch GW, Cooley PC, Zimmerman RK, Wheaton WD, et al. A computer simulation of vaccine prioritization, allocation, and rationing during the 2009 H1N1 influenza pandemic. Vaccine 2010;28(31):4875–9. CrossRef PubMed
  15. Gittelsohn J, Anderson Steeves E, Mui Y, Kharmats AY, Hopkins LC, Dennis D. B’More Healthy Communities for Kids: design of a multi-level intervention for obesity prevention for low-income African American children. BMC Public Health 2014;14(1):942–51. CrossRef PubMed
  16. Lin B-H, Smith TA, Lee J-Y, Hall KD. Measuring weight outcomes for obesity intervention strategies: the case of a sugar-sweetened beverage tax. Econ Hum Biol 2011;9(4):329–41. CrossRef PubMed
  17. Zhen C, Wohlgenant M, Karns S, Kaufman P. Habit formation and demand for sugar-sweetened beverages. Am J Agric Econ 2011;93(1):175–93. CrossRef
  18. Zhen C, Finkelstein EA, Nonnemaker J, Karns S, Todd JE. Predicting the effects of sugar-sweetened beverage taxes on food and beverage demand in a large demand system. Am J Agric Econ 2014;96(1):1–25. CrossRef PubMed
  19. Horgen KB, Brownell KD. Comparison of price change and health message interventions in promoting healthy food choices. Health Psychol 2002;21(5):505–12. CrossRef PubMed
  20. Zhen C, Wohlgenant MK, Karns S, Kaufman P. Habit formation and demand for sugar-sweetened beverages. Am J Agric Econ 2011;93(1):175–93. CrossRef
  21. DiSantis KI, Grier SA, Odoms-Young A, Baskin ML, Carter-Edwards L, Young DR, et al. What “price” means when buying food: insights from a multisite qualitative study with black Americans. Am J Public Health 2013;103(3):516–22. CrossRef PubMed
  22. DiSantis KI, Grier SA, Oakes JM, Kumanyika SK. Food prices and food shopping decisions of black women. Appetite 2014;77:104–12. CrossRef PubMed
  23. Han E, Powell LM. Consumption patterns of sugar-sweetened beverages in the United States. J Acad Nutr Diet 2013;113(1):43–53. CrossRef PubMed
  24. Dannefer R, Williams DA, Baronberg S, Silver L. Healthy bodegas: increasing and promoting healthy foods at corner stores in New York City. Am J Public Health 2012;102(10):e27–31. CrossRef PubMed
  25. Song H-J, Gittelsohn J, Kim M, Suratkar S, Sharma S, Anliker J. A corner store intervention in a low-income urban community is associated with increased availability and sales of some healthy foods. Public Health Nutr 2009;12(11):2060–7. CrossRef PubMed
  26. Epstein LH, Handley EA, Dearing KK, Cho DD, Roemmich JN, Paluch RA, et al. Purchases of food in youth. Influence of price and income. Psychol Sci 2006;17(1):82–9. CrossRef PubMed
  27. Hovmand PS. Group model building and community-based system dynamics process. In: Community based system dynamics. New York (NY): Springer; 2014 p. 17–30.
  28. Allender S, Owen B, Kuhlberg J, Lowe J, Nagorcka-Smith P, Whelan J, et al. A community based systems diagram of obesity causes. PLoS One 2015;10(7):e0129683. CrossRef PubMed

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Tables

Return to your place in the textTable 1. Number Stocked and Sold and Average Price and Wholesale Cost for Sugar-Sweetened Beverages (SSBs) (12-oz cans and 16- to 20-oz bottles), and Water (16-oz) in 2 Baltimore, Maryland, Corner Stores, 2014, and Inputs for Simulation Scenario for Each Store
Product Inventory Simulation
Store A, SSB Bottles Store B, SSB Cans Scenario A, SSB Bottles and Water Bottles Scenario B, SSB Cans and Water Bottles
SSB bottles
No. of SSB bottles stocked 544 NA 544 NA
Average sales price of SSB per bottlea, $ 1.05 1.05
Average cost at wholesalerb, $ 0.50 0.50
Average SSB sales per dayc, bottles 24 24
SSB cans
No. of SSB cans stocked NA 588 NA 588
Average sales price of SSB per cana, $ 0.77 0.77
Average cost at wholesalerb, $ 0.36 0.36
Average SSB sales per dayc, cans 20 20
Water bottles
No. of water bottles stocked 40 42 41 41
Average sales price of water per bottle, $ 1.00 1.00 1.00 1.00
Average cost at wholesaler, $ 0.12 0.12 0.12 0.12
Average water sales per day, bottles 15 14 15 15
Total daily profit at baseline prices NA NA 26.38 21.53

Abbreviation: NA, not applicable.
a Average sale price of SSB bottles and cans are the quantity-weighted average sale prices of specific beverages sold in each store. The data were collected by the authors.
b Data on average sales per day come from questionnaires of the B’More Healthy Communities for Kids study (15).
c In store A, out of 5 coolers with SSBs, less than half a cooler was stocked with SSB cans. Store B was carrying SSB cans exclusively. Therefore, the simplifying assumption is made that Store A stocked only SSB bottles.

Return to your place in the textTable 2. Sensitivity Analysis Showing the Potential for Improving Demand and Profit at Varying Starting Sales Volumes of Water and Sugar-Sweetened Beverage (SSB) Cans for Corner Store B in Baltimore, Maryland, 2014
Analysis Price of Water, $ Price of SSB, $ Percentage Improvement in Profit Percentage Improvement in Water Demand
Current demand store B: SSB cans sold = 20, water bottles = 15
Maximum increase in profit 1.00 0.93 4.25 0.00
Maximum increase in demand 0.87 0.93 0.07 9.33
Equal relative improvement of profit and demand 0.96 0.90 2.87 2.87
Double SSB demand in store B: SSB cans sold = 40, water bottles = 15
Maximum increase in profit 1.00 0.93 6.13 0.00
Maximum increase in demand 0.78 0.93 0.14 15.80
Equal relative improvement of profit and demand 0.94 0.89 4.26 4.31
Double water demand in store B: SSB cans sold = 20, water bottles = 30
Maximum increase in profit 1.00 0.93 2.63 0.00
Maximum increase in demand 0.93 0.93 0.03 5.03
Equal relative improvement of profit and demand 0.98 0.87 1.45 1.44

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Appendix

Return to your place in the text

This appendix is available for download as a Microsoft Word document [DOC – 28 KB].

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A Tool to Improve Community Health and Advance Health Equity

Vickie L. Boothe, MPH1; Leslie A. Fierro, PhD, MPH2; Amy Laurent, MSPH3; Margaret Shih, PhD4 (View author affiliations)

Suggested citation for this article: Boothe VL, Fierro LA, Laurent A, Shih M. Sub-County Life Expectancy: A Tool to Improve Community Health and Advance Health Equity. Prev Chronic Dis 2018;15:170187. DOI: http://dx.doi.org/10.5888/pcd15.170187.

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Abstract

Compared with people in other developed countries, Americans live shorter lives, have more disease and disability, and lag on most population health measures. Recent research suggests that this poor comparative performance is primarily driven by profound local place-based disparities. Several initiatives successfully used sub-county life expectancy estimates to identify geographic disparities, generate widespread interest, and catalyze multisector actions. To explore the feasibility of scaling these efforts, the Centers for Disease Control and Prevention and the Council of State and Territorial Epidemiologists initiated a multiphase project — the Sub-County Assessment of Life Expectancy. Phase I participants reviewed the literature, assessed and identified appropriate tools, calculated locally relevant estimates, and developed methodological guidance. Phase I results suggest that most state and local health departments will be able to calculate actionable sub-county life expectancy estimates despite varying resources, expertise, and population sizes, densities, and geographies. To accelerate widespread scaling, we describe several successful case examples, identify user-friendly validated tools, and provide practical tips that resulted from lessons learned.

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Need for Sub-County Population Health Indicators

Safer workplaces, vaccinations, improved motor-vehicle safety, and other twentieth-century public health achievements measurably improved health and increased longevity worldwide (1). In the United States, life expectancy at birth (LE), a key population health measure, increased steadily, reaching an all-time high of 78.8 years in 2012 (2). Since then, however, American LE has stalled. After increasing modestly from 2012 to 2014, LE unexpectedly declined to 78.8 years in 2015, adding to concerns about our nation’s health (2,3). Despite spending more than double on health care than other developed countries, Americans increasingly live shorter lives, experience more disease and disability across the lifespan, and lag on most population health measures (4).

Profound and persistent local geographic disparities are primary drivers of America’s poor performance (5,6). In 2010, LEs for females in Marin County, California (85.02 y), and males in Fairfax County, Virginia (81.67 y), were equivalent to the longest-lived countries of Japan and Switzerland. In contrast, LEs for males in McDowell County, West Virginia (63.90 y), and females in Perry County, Kentucky (72.65 y), were lower than estimates for Bangladesh and Nicaragua (5). Researchers suggest that these disparities are driven by several factors, including health care access; socioeconomic factors; and environmental, behavioral, and physiological risk factors (5).

Addressing America’s poor performance requires a shift in approach, which has focused historically on medical interventions, behaviors, and lifestyle choices (4). Accordingly, public health officials have called for development of locally relevant and timely neighborhood-level health and other indicators to drive actions that address underlying health determinants such as housing, economic development, and environment (7). This latest call to action adds to the growing body of literature documenting an urgent need for community-level health indicators. Without valid, reliable local indicators, health departments are constrained in their ability to detect disparity “hot spots,” identify correlated determinants, and catalyze effective, targeted, multisector actions (8–11).

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Advantages of Life Expectancy at Birth Compared with Other Local Measures

Unique mathematical and other properties suggest that local LE is better suited for driving actions than are other mortality measures (12). LE enables direct comparisons across time and geographic areas with diverse population structures and is easier to interpret than standardized mortality ratios or age-adjusted mortality rates (13–15). Stratifying LE by demographic characteristics such as race and income can elucidate disparities, inform resource allocation, and catalyze policy changes (9). LE has greater utility than modeled-based small-area estimates of national and state health survey data, which cannot be used to evaluate intervention effects (16) and can be affected by recall and selection bias (17). Furthermore, several studies document the feasibility of generating robust and accurate LE for small populations. Using Monte Carlo simulations, researchers evaluated methods for generating LE for the United Kingdom’s electoral wards, which in 2001 had a mean population of 5,959 (range, 995–35,770) (11,13,18). The adjusted Chiang II life table method was judged to produce accurate and reliable estimates for populations of 5,000 person-years-at-risk or more with standard errors of approximately 2 years. Because ward-level LE disparities were estimated to exceed 10 years, standard errors of 2 years and associated 95% confidence intervals of approximately 7 years allowed identification of wards with statistically different values (18). Subsequently, researchers evaluating methods for local jurisdictions in New South Wales and in Austria, Italy, Japan, Spain, Sweden, and the United Kingdom confirmed that populations smaller than 5,000 person-years-at-risk yielded biased LEs with standard errors too large for meaningful analysis (15,19). Other documented sources of LE bias include contextual factors such as large nursing home populations, which skew distribution of local population structures (15,19).

These findings hold promise for generating local LEs for most American populations. With average population sizes of 4,000 and a general range of 1,200 to 8,000 (20), census tracts are similar in size to United Kingdom wards. Also, US Census data on nursing home populations and other group quarters is readily available (21).

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Demonstrated Utility of Local Life Expectancy at Birth

Government agencies in England, Wales, Greece, and Australian New South Wales have used local LE for many public health applications, including identifying and tracking measurable reductions in health disparities (11,22), evaluating intervention effectiveness (14), and planning and funding local health services (14). Local LE has also been used to explore contributions of socioeconomic and environmental conditions to population health. For example, researchers exploring LE disparities in England and Wales reported the most important determinant to be “material poverty,” which is further influenced by sociodemographics, housing quality, and local economic policies (23,24).

Maps of LE help drive actions. Mapped LE inequalities between England’s northern and southern local authorities generated widespread media interest and catalyzed creation of Health Equity North, a collaboration of northern councils, the volunteer sector, National Health Service (NHS), and academia. Subsequent independent inquiry into root causes spurred national policy changes and increased community-centered investments focused on economic growth to reduce poverty; early childhood investments; transfer of authority and resources to local governments; and NHS services expanded beyond health care to address social determinants such as poverty, housing, education, and unemployment (25). For example, NHS partnered with Public Health England and others to fund 10 Healthy New Towns pilot sites, where 200,000 new housing units constructed in health-promoting neighborhoods will be monitored and evaluated for health effects (26).

In the United States, LE maps catalyzed local initiatives by highlighting disparities of up to 25 years across nearby neighborhoods in metropolitan areas including Oakland, California; Chicago, Illinois; Los Angeles, California; and New Orleans, Louisiana (9,10,27,28). Case examples from the Los Angeles County Department of Public Health (LACDPH) and Public Health–Seattle & King County (PHSKC) provide additional evidence of the utility of local LE maps.

Life expectancy at birth in Los Angeles County

In 2009, LACDPH examined LE disparities in the county. Although LE had increased steadily since 1991, large disparities were observed, including a nearly 18-year difference between black males (69.4 years) and Asian/Pacific Islander females (86.9 years). LACDPH recognized that actions addressing the underlying social and environmental health determinants were needed to reduce these disparities and advance equity. Partnerships with cities and unincorporated communities were established, and maps examining LE at matching geographic levels were created to increase engagement.

The adjusted Chiang II method was used to calculate single-year LE for 103 cities and unincorporated communities with populations greater than 15,000 (11,29). The Economic Hardship Index (EHI) was used to examine the relationship between LE and community-level social and economic conditions across communities (29). The EHI is a composite of 6 indicators (crowded housing, poverty, unemployment, educational attainment, population dependency, and income level) that provides a more complete picture of neighborhood conditions than any individual measure. The strong inverse relationship between the EHI score and LE prompted LACDPH to publish a report that ranks cities and communities by LE and economic hardship that was broadly disseminated via press releases and in print and electronic form to city mayors, council members, planners, and representatives from other health-related sectors such as education, housing, transportation, and business (27).

The report received substantial coverage in local, national, and international media and on local websites and blogs. Resulting increased awareness of the connection between social issues and health led to reframed city and community discussions around root causes of health and increased community engagement and motivation to act. For example, the report provided justification for a 2015 formal amendment to the Los Angeles General Plan, elevating health as a priority for the city’s future expansion and development. The amended plan includes a policy vision and measureable objectives for creating healthier communities through increased affordable housing, cleaner environments, and safer neighborhoods (30). Finally, the report strengthened LACDPH’s engagement with city and community leaders, education, business, and other nonhealth sectors and raised awareness of the importance of a Health-in-All-Policies approach, which considers the health implications of non-health–sector policies (31).

Life expectancy at birth in Seattle–King County

PHSKC staff calculated LE for King County using the adjusted Chiang II method (13) and 2012 mortality data. LE in King County (81.2 years) was substantially longer than LE in the United States. However, pronounced disparities across race/ethnicity and subregions were evident, so PHSKC staff examined census tract–level LE. In 2010, King County’s 398 tracts averaged 4,800 (range, 1,286–11,056) people. PHSKC used geocoded mortality data from 2008 through 2012 assigned to census tracts and locally generated population estimates to generate LE data. After suppressing cells with statistically unreliable estimates, results showed an LE gap of approximately 24 years between the shortest-lived and longest-lived tracts (Figure 1).

Estimated life expectancy at birth by census tract in King County, Washington, based on 2008–2012 mortality data.

Figure 1.
Estimated life expectancy at birth by census tract in King County, Washington, based on 2008–2012 mortality data. [A
text description of this figure is also available.]

PHSKC then used a Bayesian hierarchical model to generate mapped small-area estimates of modifiable risk factors such as adult obesity, smoking, adverse childhood experiences, preventable hospitalizations, poor housing conditions, high unemployment, low income, and adult frequent mental distress (32). Strikingly similar spatial patterns of disparities in LE and risk factors led to identification of potential communities for engagement; catalyzed an ongoing partnership between PHSKC, the Department of Community and Human Services, Seattle Foundation, and Living Cities; and led to formation of the Communities of Opportunity (COO) (33). COO focuses on improving equity in communities through system-level and policy-level solutions led by or engaging the local community. The COO collective impact framework includes community-identified achievement goals with identified indicators to measure progress. Desired results are that all people thrive economically; have quality, affordable housing; are healthy; and are connected to the community. To date, more than 90 community residents and 45 community organizations and their leaders have codesigned solutions (33).

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Resources for Calculating Sub-County Life Expectancy at Birth

In September 2014, the Centers for Disease Control and Prevention (CDC) and the Council of State and Territorial Epidemiologists (CSTE) initiated the multiyear Sub-County Assessment of Life Expectancy (SCALE) project. The goal of SCALE Phase I, which ended in June 2015, was to identify appropriate methods for calculating actionable sub-county LE and develop easy-to-use resources designed to assist other health departments. For LE to be considered actionable, the method needed to produce accurate estimates for most of the jurisdiction’s populations with standard errors and confidence intervals narrow enough to permit identification of areas with significantly higher or significantly lower LE values.

Phase I participants included a CDC senior scientist, an external evaluator, and scientists from LACDPH and PHSKC, recruited on the basis of their previous experience. Additionally, scientists from 6 state health departments (Florida, Maine, Massachusetts, New York, Washington, and Wisconsin) were invited to participate because they varied in size and resources, their jurisdictions represented diverse geographies and populations, and they had experience examining relationships between small-area health and environmental indicators through an initiative of the National Environmental Public Health Tracking Network (EPHTN).

Phase I participant activities included a literature review to identify feasible methods, successful case studies, and gold-standard parameters. After each jurisdiction independently tested various approaches, a consensus was reached to adopt the adjusted Chiang II method and associated software developed by the South East Public Health Observatory (34). Phase I participants also developed a draft guidance document (Guide) clarifying methodological decision points (eg, age categories, addressing zero cells, minimum population sizes) and sharing lessons learned. SCALE Phase I and subsequent activities are described in Table 1.

The initial objective was standardized calculation of census-tract LE estimates using 5 years of death data (2008–2012) and 2010 census or local population estimates. Because SCALE is a user-driven initiative with a primary goal of supporting local actions, participants were encouraged to adapt the proposed objective and methods to meet their unique needs.

To evaluate feasibility of generating sub-county LE, interviews with each jurisdiction were conducted using questions designed to answer the following questions:

  1. What resources are required for health departments with varying resources and diverse populations to calculate actionable sub-county LE for the majority of their jurisdiction?

  2. What methodological and data challenges were encountered and how were they addressed?

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SCALE Phase 1 Results and Lessons Learned

All jurisdictions reported successful calculation of actionable LE for most sub-county areas in less than 1.5 years, with 7 of the 8 participating jurisdictions completing calculations in less than 1 year.

Characteristics of participating jurisdictions and LE calculation approaches

Table 2 identifies characteristics of participating health departments and Table 3 describes the various LE approaches. Participating jurisdictions varied greatly on total expenditures, staffing, and total population size. Annual state health department expenditures for 2011 ranged from $108.08 million to $2.16 billion, and staffing ranged from 387 to 15,026 full-time equivalent employees (35). The 2016 Census population estimate for Maine of 1.3 million was smaller than those of the 2 county jurisdictions and approximately 16 times smaller than the estimated 20.6 million Florida residents (36).

Florida, Massachusetts, New York State, Washington, and PHSKC successfully calculated census tract–level LE with standard errors of less than 2 years for most of their populations using 5 years of data. All but one met the recommended minimum population size of 1,000 residents achieving 5,000 person-years-at-risk. Florida results included census tracts with the smallest and largest populations, 672 and 33,041 residents, respectively. Smaller and sparser Maine populations (37) required 10 years of data to generate LE with acceptable standard errors for most of their populations at the Minor Civil Divisions (MCDs) level. MCD is a US census bureau term for primary governmental divisions of a county such as townships. Wisconsin also required 10 years of data to calculate actionable LE at the zip code level. Aggregating data over time increases precision; however, the resulting LE may not reflect current conditions and increases the risk of numerator and denominator mismatch, which can bias standard errors (11,13,18). LACDPH chose to calculate single-year LE for areas with populations greater than 15,000.

Data challenges

Population and mortality data were readily available; however, some data sets were unsuitable or required additional manipulation. Florida explored the feasibility of calculating LE for inter-census periods, using American Community Survey (ACS) data. ACS data lacked population counts for the ideal age-intervals for calculating LE (<1 year and 1–4 year categories vs 0–4 years) and had high margins of error at the census tract level. As a result, LE estimates generated by using ACS data varied substantially from LE estimates generated by using 2010 Census data for the same sub-county area (46 years vs 65 years, respectively).

Erroneous and missing mortality data in some jurisdictions increased time and resource requirements. Special record requests were often necessary for residents dying in neighboring state jurisdictions. In Maine, mortality data lacked addresses before 2011; therefore, town of residence was used to assign deaths to MCDs. In New York State, mortality records required geocoding using varied batch and iterative techniques. Hospital records were used to correct incomplete or inaccurate address information. Geographic imputation techniques using age, race/ethnicity, town, and zip code were used to geocode remaining cases with missing addresses. Ultimately, census tracts were assigned to 99.97% of mortality records. However, these labor-intensive methods extended the project by several months. An article describing the New York State methods is under development.

Small number issues

Each jurisdiction included areas with populations too small to meet the recommended 5,000 person-years-at-risk. Florida, Massachusetts, PHSKC, Washington, and Wisconsin suppressed all LE values with standard errors greater than 2. Florida also suppressed improbable LE values of less than 66 years. The percentage of suppressed sub-county areas ranged from 3% to approximately 15%.

Before calculating LE, New York State excluded 18 census tracts that had no people and consisted of bodies of water, airports, and an uninhabited island. After exploratory analyses, tracts where more than 50% of the population lived in group quarters were also excluded. Consistent with the effects of nursing homes on LE values (16), improbable LE values were generated for tracts with large prison, military base, or college populations. Approximately 2.6% census tracts were ultimately excluded. Additionally, a New York State Geographic Aggregation Tool (38) was used to aggregate several neighboring census tracts until all had a minimum of 60 deaths and standard errors of less than 2 years.

Maine conducted exploratory analyses examining the effect of using a minimum standard error of 2 versus a standard error of 3 years, minimum number of deaths (>60), and minimum denominator (5,000 person-years). Depending on the rule(s), between 28% (standard error <3) and 46% (standard error <2 or deaths >60) of MCDs needed to be suppressed. Ultimately, Maine aggregated 10 years of data and several adjacent areas using the Geographic Aggregation Tool (38) until LE for all MCDs had a standard error of less than 2 years.

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Discussion

America’s lagging health status and persistent local disparities warrant bold actions that address all determinants of health, including social and environmental factors. Identifying and quantifying local disparities is a necessary first step for selecting, implementing, and documenting the impact of interventions (9,39). LACDPH and PHSKC and other case studies document the use of sub-county LE for quantifying disparities and catalyzing multisector actions (26,31,33).

Many LE methodological challenges were identified, such as small number issues, missing and erroneous data, and lack of suitable population data for noncensus years. Small, sparse populations in 2 jurisdictions prohibited the calculation of census tract–level LE using 5 years of data. All jurisdictions included areas requiring suppression of LE data or additional temporal or geographical aggregation. However, these solutions may not be as effective for even smaller or more sparsely populated jurisdictions. Finally, LE does not fully reflect health status or other dimensions of well-being through the life course (40).

As part of SCALE Phase II, initiated in June 2015, 17 additional health departments successfully calculated sub-county LE, pilot tested the Guide, and provided feedback on its usability and utility (Figure 2). In September 2016, CSTE launched a SCALE website (www.cste.org/page/SCALE/Sub-County-Assessment-of-Life-Expectancy-SCALE-Project.htm), which includes version 1.0 of the updated Guide and other user-friendly resources. A joint SCALE and EPHTN workshop was held in October 2016, with objectives of prioritizing future collective activities and supporting sub-county LE calculation by the 20 EPHTN grantees not previously engaged in SCALE.

United States map identifying health department jurisdictions of SCALE Phase I and II participants. Abbreviation: SCALE, Sub-County Assessment of Life Expectancy.

Figure 2.
United States map identifying health department jurisdictions of SCALE Phase I and II participants. Abbreviation: SCALE, Sub-County Assessment of Life Expectancy. [A
text description of this figure is also available.]

Prioritized future activities include identifying key local social determinant and health indicators for co-release with LE estimates; assessing feasibility of generating summary population measures that better reflect overall health, including health-adjusted life expectancy; identifying LE visualization and messaging best practices; and evaluating the utility of local LE for monitoring and evaluating the health effects of local policies and programs.

Current and planned SCALE resources directly respond to calls for locally relevant data capable of identifying geographic disparities, catalyzing multisector actions, and evaluating the effects of interventions designed to improve population health and advance equity. Lessons learned and user-friendly resources are provided to help accelerate widespread scaling of these efforts.

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Acknowledgments

The authors acknowledge the contribution and dedication of the SCALE Phase I Working Group members: Melissa Jordan (Florida Department of Health), Kristina Kintziger (formerly with Florida Department of Health), Douglas Done (New York State Department of Health), Tabassum Insaf (New York State Department of Health), Thomas Talbot (New York State Department of Health), Jessica Bonthius (Maine Department of Health and Human Services), Chris Paulu (Maine Department of Health and Human Services), Alicia Fraser (Massachusetts Department of Public Health), Robert Knorr (Massachusetts Department of Public Health), Glen Patrick (Washington State Department of Health), Henry Anderson (formerly with Wisconsin Division of Public Health), and Jessica Wurster (CSTE).

This publication was supported by Cooperative Agreement no. 1U38OT000143 awarded to CSTE by CDC. The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the official position of CDC.

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Author Information

Corresponding Author: Vickie L. Boothe, MPH, Division of Public Health Information Dissemination, Center for Surveillance, Epidemiology, and Laboratory Services, Centers for Disease Control and Prevention, 1600 Clifton Rd NE, Mail Stop E69, Atlanta, GA 30333. Telephone: 404-498-2826. Email: veb6@cdc.gov.

Author Affiliations: 1Centers for Disease Control and Prevention, Atlanta, Georgia. 2Claremont Graduate University, Los Angeles, California. 3Seattle and King County Public Health, Seattle, Washington. 4Los Angeles County Department of Public Health, Los Angeles, California.

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References

  1. Centers for Disease Control and Prevention (CDC). Ten great public health achievements—United States, 1900–1999. MMWR Morb Mortal Wkly Rep 1999;48(12):241–3. PubMed
  2. Kochanek KD, Murphy SL, Xu J, Tejada-Vera B. Deaths: final data for 2014. Natl Vital Stat Rep 2016;65(4):1–122. PubMed
  3. Xu JQ, Murphy SL, Kochanek KD, Arias E. Mortality in the United States, 2015. NCHS data brief, no 267. Hyattsville (MD): National Center for Health Statistics; 2016.
  4. Institute of Medicine, National Research Council. US health in international perspective: shorter lives, poorer health. Washington (DC): The National Academies Press; 2013.
  5. Wang H, Schumacher AE, Levitz CE, Mokdad AH, Murray CJ. Left behind: widening disparities for males and females in US county life expectancy, 1985–2010. Popul Health Metr 2013;11(1):8–8. CrossRef PubMed
  6. Cullen MR, Cummins C, Fuchs VR. Geographic and racial variation in premature mortality in the U.S.: analyzing the disparities. PLoS One 2012;7(4):e32930. CrossRef PubMed
  7. DeSalvo KB, O’Carroll PW, Koo D, Auerbach JM, Monroe JA. Public Health 3.0: time for an upgrade. Am J Public Health 2016;106(4):621–2. CrossRef PubMed
  8. Shah SN, Russo ET, Earl TR, Kuo T. Measuring and monitoring progress toward health equity: local challenges for public health. Prev Chronic Dis 2014;11(9):E159. PubMed
  9. Hunt BR, Tran G, Whitman S. Life expectancy varies in local communities in Chicago: racial and spatial disparities and correlates. J Racial Ethn Health Disparities 2015;2(4):425–33. CrossRef PubMed
  10. Schaff K, Desautels A, Flournoy R, Carson K, Drenick T, Fujii D, et al. Addressing the social determinants of health through the Alameda County, California, place matters policy initiative. Public Health Rep 2013;128(Suppl 3):48–53. CrossRef PubMed
  11. Eayres D, Williams ES. Evaluation of methodologies for small area life expectancy estimation. J Epidemiol Community Health 2004;58(3):243–9. CrossRef PubMed
  12. Auger N, Feuillet P, Martel S, Lo E, Barry AD, Harper S. Mortality inequality in populations with equal life expectancy: Arriaga’s decomposition method in SAS, Stata, and Excel. Ann Epidemiol 2014;24(8):575–80, 580.e1. CrossRef PubMed
  13. Silcocks PB, Jenner DA, Reza R. Life expectancy as a summary of mortality in a population: statistical considerations and suitability for use by health authorities. J Epidemiol Community Health 2001;55(1):38–43. CrossRef PubMed
  14. Stephens AS, Purdie S, Yang B, Moore H. Life expectancy estimation in small administrative areas with non-uniform population sizes: application to Australian New South Wales local government areas. BMJ Open 2013;3(12):e003710. CrossRef PubMed
  15. Jonker MF, van Lenthe FJ, Congdon PD, Donkers B, Burdorf A, Mackenbach JP. Comparison of Bayesian random-effects and traditional life expectancy estimations in small-area applications. Am J Epidemiol 2012;176(10):929–37. CrossRef PubMed
  16. Zhang X, Holt JB, Yun S, Lu H, Greenlund KJ, Croft JB. Validation of multilevel regression and poststratification methodology for small area estimation of health indicators from the Behavioral Risk Factor Surveillance System. Am J Epidemiol 2015;182(2):127–37. CrossRef PubMed
  17. Miller TM, Abdel-Maksoud MF, Crane LA, Marcus AC, Byers TE. Effects of social approval bias on self-reported fruit and vegetable consumption: a randomized controlled trial. Nutr J 2008;7(1):18. CrossRef PubMed
  18. Toson B, Baker A. Life expectancy at birth: methodological options for small populations. Norwich (UK): Office for National Statistics; 2003.
  19. Scherbov S, Ediev D. Significance of life table estimates for small populations: simulation-based study of estimation errors. Demogr Res 2011;24(22):527–50. CrossRef
  20. Krieger N, Chen JT, Waterman PD, Soobader MJ, Subramanian SV, Carson R. Choosing area based socioeconomic measures to monitor social inequalities in low birth weight and childhood lead poisoning: The Public Health Disparities Geocoding Project (US). J Epidemiol Community Health 2003;57(3):186–99. CrossRef PubMed
  21. National Research Council, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Small populations, large effects: improving the measurement of the group quarters population in the American Community Survey. In: Panel on statistical methods for measuring the group quarters population in the American Community Survey, Paul R. Voss, Krisztina Marton, editors. Washington (DC): The National Academies Press; 2012.
  22. Tsimbos C, Kalogirou S, Verropoulou G. Estimating spatial differentials in life expectancy in Greece at local authority level. Popul Space Place 2014;20(7):646–63. CrossRef
  23. Woods LM, Rachet B, Riga M, Stone N, Shah A, Coleman MP. Geographical variation in life expectancy at birth in England and Wales is largely explained by deprivation. J Epidemiol Community Health 2005;59(2):115–20. CrossRef PubMed
  24. Doran TF, Drever F, Whitehead M. Health underachievement and overachievement in English local authorities. J Epidemiol Community Health 2996;60(8):686–93.
  25. Johnstone P. Health, equity and the north of England: a case study on a new approach. Br Med Bull 2015;116(1):29–41. PubMed
  26. Norman H, McDonnell D. The NHS Healthy New Towns programme. Perspect Public Health 2017;137(1):29–30. CrossRef PubMed
  27. Life expectancy in Los Angeles County: how long do we live and why? A cities and communities health report. Los Angeles (CA): Los Angeles County Department of Public Health; 2010. http://www.publichealth.lacounty.gov/epi/docs/life%20expectancy%20final_web.pdf. Accessed October 11, 2016.
  28. Place Matters for Health in Orleans Parish: ensuring opportunities for good health for all. A report on health inequities in Orleans Parish, Louisiana. Joint Center for Political and Economic Studies; 2012. http://jointcenter.org/research/place-matters-health-orleans-parish-ensuring-opportunities-good-health-all. Accessed October 11, 2016.
  29. Montiel LM, Nathan RP, Wright DJ. An update on urban hardship. Albany (NY): Nelson A. Rockefeller Institute of Government; 2004. http://rockinst.org/pdf/cities_and_neighborhoods/2004-08-an_update_on_urban_hardship.pdf.
  30. Plan for a Healthy Los Angeles. A health and wellness element of the general plan. http://planning.lacity.org/cwd/gnlpln/PlanforHealthyLA.pdf. Accessed June 25, 2017.
  31. Fielding JE, Teutsch SM, Caldwell S. Public health practice: what works. New York (NY): Oxford University Press; 2012.
  32. Song L, Mercer L, Wakefield J, Laurent A, Solet D. Using small-area estimation to calculate the prevalence of smoking by subcounty geographic areas in King County, Washington, Behavioral Risk Factor Surveillance System, 2009–2013. Prev Chronic Dis 2016;13:150536. CrossRef PubMed
  33. Communities of opportunity. Public Health Seattle King County; 2016. http://www.kingcounty.gov/elected/executive/health-human-services-transformation/coo.aspx. Accessed June 25, 2017.
  34. Life expectancy calculator: LA and ward level; 2004. http://webarchive.nationalarchives.gov.uk/20160701122411/http://www.sepho.org.uk/viewResource.aspx?id=8943. Accessed June 25, 2017.
  35. Association of State and Territorial Health Officials. ASTHO profile of state public health, vol. 3. Washington (DC): Association of State and Territorial Health Officials; 2014.
  36. US Census Bureau. Quick facts. http://www.census.gov/quickfacts/table/PST045216/00. Accessed June 25, 2017.
  37. US Census Bureau. Population density by state, US Census Bureau. http://www.StateMaster.com. Accessed November 29, 2017.
  38. Talbot TO, LaSelva GD. Geographic aggregation tool for R, version 1.33. Albany (NY): New York State Health Department; 2015. http://www.albany.edu/faculty/ttalbot/GAT/. Accessed June 25, 2017.
  39. Clarke CA, Miller T, Chang ET, Yin D, Cockburn M, Gomez SL. Racial and social class gradients in life expectancy in contemporary California. Soc Sci Med 2010;70(9):1373–80. CrossRef PubMed
  40. Marmot M. Fair society, healthy lives: strategic review of health inequalities in England post 2010. London (UK): Institute of Health Equity; 2010.

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Tables

Return to your place in the textTable 1. SCALE Phase I and Phase II Activities, United States, 2015–2017
SCALE Phase I (January 2015–May 2015) SCALE Phase II (June 2015–June 2017)
Conducted a literature review to understand the approaches, available parameters, and lessons learned from previous efforts associated with constructing small-area LE estimates. Recruited and oriented new states/localities to methods and general project purpose/approach.
Reviewed common approaches used in the literature for calculating direct small-area LE estimates and arriving at initial decisions about methods. New state/localities pilot tested draft materials from Phase I and provided feedback through the evaluation.
Identified other existing tools for calculating LE that might easily be adopted/adapted (SEPHO). States/localities assessed potential refinements in methods to expand geographic coverage by performing several sensitivity analyses.
Compared calculations produced by SEPHO tool with other methods for generating LE estimates (SAS [SAS Institute, Inc] and STATA [Stata Corp, LP] code from previous LE efforts), and refined approach. Implemented evaluation.
Developed an evaluation plan for Phase II. Compiled lessons learned, refined tools and methodological recommendations, updated Guide and related resources, prioritized list of remaining issues and future actions.
Products included 1) drafted Guide for state/local health departments with SEPHO tool as approach used, 2) obtained sub-county estimates for Phase I states/localities, 3) held 2015 CSTE conference presentation, 4) made evaluation plan. Products included 1) created SCALE website, 2) revised tools for estimating LE, 3) revised/updated Guide, 4) held 2016 and 2017 CSTE conference presentations, 5) evaluated findings 6) completed manuscripts.

Abbreviations: CSTE, Council of State and Territorial Epidemiologists; LE, life expectancy at birth; SCALE, Sub-County Assessment of Life Expectancy; SEPHO, South East Public Health Observatory.

Return to your place in the textTable 2. SCALE Jurisdiction Characteristics, United States, 2015–2017
Jurisdiction Jurisdiction Characteristics
Total Expendituresa in 2011, $ Workforcea Full-Time Equivalents, 2011 Geographic Unit Population Sizeb in 2016, Millions Population Per Square Mileb in 2010
Florida Department of Health 2.16 billion 15,026 State 20.6 350.6
Los Angeles County Department of Public Health NA NA County 10.2 87.4
Maine Department of Health and Human Services 108.08 million 387 State 1.3 43.1
Massachusetts Department of Public Health 762.57 million 2,933 State 6.8 839.4
New York State Department of Health 1.72 billion 3,127 State 19.8 411.2
Public Health–Seattle & King County NA NA County 2.1 912.9
Washington State Department of Health 537.21 million 1,650 State 7.2 101.2
Wisconsin Department of Health Services 258.55 million 395 State 5.8 105.0

Abbreviations: NA, not available; SCALE, Sub-County Assessment of Life Expectancy.
a Source: Association of State and Territorial Health Officials (35).
b Source: US Census Bureau (36).

Return to your place in the textTable 3. Jurisdiction’s Life Expectancy Characteristics, United States, 2015–2017
Jurisdiction Characteristics of Life Expectancies (Standard Error, 2 Years)
Basic Geographic Units Number Years of Data Minimum Population Size Mean Population Size Maximum Population Size
Florida Department of Health Census tract 5 672 4,796 33,041
Zip code 5 295 21,138 72,248
Los Angeles County Department of Public Health Census tract 5 1,072 4,417 12,581
Maine Department of Health and Human Services Minor civil division 10 1,012 4,213 64,504
Massachusetts Department of Public Health Census tract 5 1,164 4,616 9,557
New York State Department of Health Census tract 5 NA NA NA
Public Health– Seattle & King County Census tract 5 1,280 5,248 10,776
Washington State Department of Health Census tract 5 1,112 4,795 13,201
Wisconsin Department of Health Services Zip code 10 540 7,488 60,953

Abbreviation: NA, not available.

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Short Sleep Duration Among Middle School and High School Students …

Insufficient sleep among children and adolescents is associated with increased risk for obesity, diabetes, injuries, poor mental health, attention and behavior problems, and poor academic performance (14). The American Academy of Sleep Medicine has recommended that, for optimal health, children aged 6–12 years should regularly sleep 9–12 hours per 24 hours and teens aged 13–18 years should sleep 8–10 hours per 24 hours (1). CDC analyzed data from the 2015 national, state, and large urban school district Youth Risk Behavior Surveys (YRBSs) to determine the prevalence of short sleep duration (<9 hours for children aged 6–12 years and <8 hours for teens aged 13–18 years) on school nights among middle school and high school students in the United States. In nine states that conducted the middle school YRBS and included a question about sleep duration in their questionnaire, the prevalence of short sleep duration among middle school students was 57.8%, with state-level estimates ranging from 50.2% (New Mexico) to 64.7% (Kentucky). The prevalence of short sleep duration among high school students in the national YRBS was 72.7%. State-level estimates of short sleep duration for the 30 states that conducted the high school YRBS and included a question about sleep duration in their questionnaire ranged from 61.8% (South Dakota) to 82.5% (West Virginia). The large percentage of middle school and high school students who do not get enough sleep on school nights suggests a need for promoting sleep health in schools and at home and delaying school start times to permit students adequate time for sleep.

The Youth Risk Behavior Surveillance System was designed to estimate the prevalence of health risk behaviors among students that contribute to the leading causes of death and disability in the United States at the national, state, territorial, tribal, and large urban school district levels.* Students complete an anonymous, voluntary, school-based paper-and-pencil questionnaire during a regular class period after the school obtains parental permission according to local procedures. The national high school YRBS is conducted by CDC. It uses a three-stage cluster sample design to obtain a nationally representative sample of students in public and private schools in grades 9–12 (5). In 2015, the student sample size was 15,624. The school and student response rates were 69% and 86%, respectively, resulting in an overall response rate of 60%.§

State and large urban school district high school and middle school surveys are conducted by health and education departments using a two-stage cluster sample designed to produce representative samples of students in each jurisdiction (5). These surveys are independent of CDC’s national YRBS and, unlike the national YRBS, are representative of only public school students, except in one state. To be included in this report, states and large urban school districts had to 1) have at least a 60% overall response rate, 2) include a question on sleep duration, and 3) provide permission for CDC to include their data. Thirty states and 16 large urban school districts administered a high school YRBS and met these criteria. Across these states, the student sample sizes ranged from 1,313 (South Dakota) to 55,596 (Maryland).The median overall response rate was 66.5% and ranged from 60% (Indiana and North Carolina) to 84% (Virginia). Across these large urban school districts, the high school student sample sizes ranged from 1,413 (Broward County, Florida) to 10,419 (District of Columbia). The median overall response rate was 76.5% and ranged from 64% (District of Columbia) to 88% (San Diego, California).

Nine states and seven large urban school districts administered a middle school YRBS and met these criteria. Across these states, the student sample sizes ranged from 1,640 (Kentucky) to 27,104 (Maryland). The median overall response rate was 76% and ranged from 68% (Maine) to 85% (Hawaii and Virginia). Across these large urban school districts, the middle school student sample sizes ranged from 1,333 (Los Angeles, California) to 4,533 (Duval County, Florida). The median overall response rate was 81% and ranged from 68% (San Francisco, California) to 86% (Orange County, Florida). All data sets were weighted to be representative of students in the jurisdiction.

All students in the national, state, and large urban school district surveys were asked to respond to this question about sleep duration: “On an average school night, how many hours of sleep do you get?” Possible responses were 4 or less hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, and 10 or more hours. Short sleep duration was defined as <9 hours for students aged 6–12 years and <8 hours for those aged 13–18 years. The analytic samples were composed of students who responded to both the sleep duration question and the age question.**

Prevalences and 95% confidence intervals (CIs) of short sleep duration on an average school night were calculated overall and by sex, grade, and race/ethnicity for the national high school YRBS and for a combined data set composed of data from the nine states that included the sleep duration question in a middle school YRBS. This combined data set is not nationally representative. The overall prevalence and 95% CI of short sleep duration also were calculated separately for each state and large urban school district at both middle school and high school levels. Pairwise differences in short sleep duration prevalence among sex, grade, and race/ethnicity subgroups were determined using t-tests; differences among estimates were considered statistically significant if the t-test p-value was <0.05. Analyses accounted for the weighting of the data and for the complex sampling designs.

The overall prevalence of short sleep duration among middle school students in the nine states combined was 57.8% (Table 1). The distribution of sleep duration was 5.9% for ≤4 hours, 6.0% for 5 hours, 11.0% for 6 hours, 20.0% for 7 hours, 29.9% for 8 hours, 17.2% for 9 hours, and 10.0% for ≥10 hours. The prevalence of short sleep duration in this combined sample was higher among female students (59.6%) than among male students (56.0%). The prevalence of short sleep duration also was highest among students in grade 6 (61.3%), lowest among students in grade 8 (53.1%), and higher among black (61.1%) and Native Hawaiian/Pacific Islander (64.2%) students than among white (56.6%), Hispanic (57.3%), and Asian (55.5%) students. State-specific estimates of short sleep duration ranged from 50.2% (New Mexico) to 64.7% (Kentucky). Prevalence estimates for the seven large urban school districts ranged from 50.2% (San Francisco, California) to 61.8% (Miami-Dade County, Florida).

At the high school level, nationwide, the prevalence of short sleep duration was 72.7% (Table 2). The distribution of sleep duration was 7.5% for ≤4 hours, 12.6% for 5 hours, 22.9% for 6 hours, 29.7% for 7 hours, 20.6% for 8 hours, 5.0% for 9 hours, and 1.7% for ≥10 hours. The prevalence of short sleep duration was higher among female students (75.6%) than among male students (69.9%), lower among students in grade 9 (65.6%) than in other grades (71.7%–77.6%), and higher among black (76.5%) and Asian (79.3%) students than white (72.0%) and Hispanic (70.2%) students. State-level estimates of short sleep duration for the 30 states ranged from 61.8% (South Dakota) to 82.5% (West Virginia) (Table 2) (Figure). Prevalence estimates for the 16 large urban school districts ranged from 69.9% (Los Angeles, California) to 85.6% (Broward County, Florida).

Discussion

Children and adolescents who do not get the recommended amount of sleep for their age are at increased risk for chronic conditions such as diabetes, obesity, and poor mental health, as well as injuries, attention and behavioral problems, and poor academic performance (14). In addition, short sleep duration has been found to be associated with engaging in health- and injury-related risk behaviors among high school students (6,7). The national high school YRBS has included a question about sleep duration since 2007, and it is used to track the progress of the Healthy People 2020 sleep objective for this population (Sleep Health Objective 3: Increase the proportion of students in grades 9 through 12 who get sufficient sleep).†† Nationally, no progress has been made toward this objective: the percentage of high school students who get sufficient sleep has substantially decreased from 30.9% in 2009, the baseline year for this objective, to 27.3% in 2015, the latest year of available data.§§ A question about sleep duration was included for the first time in 2015 in the standard middle school and high school YRBS questionnaires used as the starting point for the state and large urban school district YRBS questionnaires. As a result, evidence now exists that short sleep duration is prevalent among middle school students as well as high school students. In addition, at both middle and high school levels, in every state and large urban school district with YRBS data about sleep duration, a majority of students reported getting less than the recommended amount of sleep.

The findings in this report are subject to at least four limitations. First, sleep duration was obtained by self-report and was not verified by objective measures such as actigraphy (sensor measurement of motor activity) or polysomnography (sleep study). Second, a national YRBS is not conducted among middle school students. The middle school findings from the combined data set cannot be generalized to the entire United States. Third, at both middle and high school levels, state-level data are not available for states that did not administer the YRBS, did not include a question about sleep duration on their YRBS, or did not achieve a high enough overall response rate to obtain weighted data. Finally, YRBS data are representative only of students enrolled in school; in 2015, less than 5% of children aged 7–17 years were not enrolled in school.¶¶

To ensure their children get enough sleep, parents can support the practice of good sleep habits. One important habit is maintaining a consistent sleep schedule during the school week and weekends. Parent-set bedtimes have been linked to getting enough sleep among adolescents (8). Evening light exposure and technology use are also associated with less sleep among adolescents (9). Parents can limit children’s permitted use of electronic devices in terms of time (e.g., only before a specific time, sometimes referred to as a “media curfew”) and place (e.g., not in their child’s bedroom) Other tips for better sleep are available at https://www.cdc.gov/sleep/about_sleep/sleep_hygiene.html. One meta-analysis of school-based sleep education programs found that they produced significantly longer weekday and weekend total sleep time immediately after completion, but that these improvements were not maintained at follow-up (10). Researchers designing such programs might consider incorporating refresher sessions to maintain improvements in sleep duration and sleep hygiene (i.e., habits that support good sleep). School districts can also support adequate sleep among students by implementing delayed school start times as recommended by the American Academy of Pediatrics,*** the American Medical Association,††† and the American Academy of Sleep Medicine.§§§

Population-Based Surveillance of Birth Defects Potentially Related …

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Augustina Delaney, PhD1; Cara Mai, DrPH1; Ashley Smoots, MPH1; Janet Cragan, MD1; Sascha Ellington, MSPH1; Peter Langlois, PhD2; Rebecca Breidenbach, MPA2; Jane Fornoff, PhD3; Julie Dunn, PhD4; Mahsa Yazdy, PhD4; Nancy Scotto-Rosato, PhD5; Joseph Sweatlock, PhD5; Deborah Fox, MPH6; Jessica Palacios, MPH6; Nina Forestieri, MPH7; Vinita Leedom, MPH8; Mary Smiley, MS8; Amy Nance, MPH9; Heather Lake-Burger, MPH10; Paul Romitti, PhD11; Carrie Fall, MS11; Miguel Valencia Prado, MD12; Jerusha Barton, MPH13; J. Michael Bryan, PhD13; William Arias, MPH14; Samara Viner Brown, MS14; Jonathan Kimura, MPH15; Sylvia Mann, MS15; Brennan Martin, MPH16; Lucia Orantes, PhD16; Amber Taylor, MPH1; John Nahabedian, MS1; Amanda Akosa, MPH1; Ziwei Song, MPH1; Stacey Martin, MSc17; Roshan Ramlal, PhD1; Carrie Shapiro-Mendoza, PhD18; Jennifer Isenburg, MPH1; Cynthia A. Moore, MD, PhD1; Suzanne Gilboa, PhD1; Margaret A. Honein, PhD1 (View author affiliations)

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Summary

What is already known about this topic?

Data collected from three U.S. population-based birth defects surveillance systems from 2013 and 2014, before the introduction of Zika virus infection in the World Health Organization’s Region of the Americas, showed a baseline prevalence of birth defects potentially related to congenital Zika virus infection of 2.9 per 1,000 live births. Based on 2016 data from the U.S. Zika Pregnancy and Infant Registry, the risk for birth defects potentially related to Zika virus infection in pregnancies with laboratory evidence of possible Zika virus infection was approximately 20-fold higher than the baseline prevalence.

What is added by this report?

This report provides the first comprehensive data on the prevalence of birth defects (3.0 per 1,000 live births) potentially related to Zika virus infection in a birth cohort of nearly 1 million births in 2016. A significant increase in birth defects strongly related to Zika virus during the second half of 2016 compared with the first half was observed in jurisdictions with local Zika virus transmission. Only a small percentage of birth defects potentially related to Zika had laboratory evidence of Zika virus infection, and most were not tested for Zika virus.

What are the implications for public health practice?

Whereas the U.S. Zika Pregnancy and Infant Registry monitors women with laboratory evidence of possible Zika virus infection during pregnancy and their congenitally exposed infants, population-based birth defects surveillance systems make a unique contribution by identifying and monitoring all cases of these birth defects regardless of exposure or laboratory testing or results. Continued surveillance for birth defects potentially related to Zika virus infection is important because most pregnancies affected by Zika virus ended in 2017. These data will help communities plan for needed resources to care for affected patients and families and can serve as a foundation for linking and evaluating health and developmental outcomes of affected children.

Zika virus infection during pregnancy can cause serious birth defects, including microcephaly and brain abnormalities (1). Population-based birth defects surveillance systems are critical to monitor all infants and fetuses with birth defects potentially related to Zika virus infection, regardless of known exposure or laboratory evidence of Zika virus infection during pregnancy. CDC analyzed data from 15 U.S. jurisdictions conducting population-based surveillance for birth defects potentially related to Zika virus infection.* Jurisdictions were stratified into the following three groups: those with 1) documented local transmission of Zika virus during 2016; 2) one or more cases of confirmed, symptomatic, travel-associated Zika virus disease reported to CDC per 100,000 residents; and 3) less than one case of confirmed, symptomatic, travel-associated Zika virus disease reported to CDC per 100,000 residents. A total of 2,962 infants and fetuses (3.0 per 1,000 live births; 95% confidence interval [CI] = 2.9–3.2) (2) met the case definition. In areas with local transmission there was a non-statistically significant increase in total birth defects potentially related to Zika virus infection from 2.8 cases per 1,000 live births in the first half of 2016 to 3.0 cases in the second half (p = 0.10). However, when neural tube defects and other early brain malformations (NTDs)§ were excluded, the prevalence of birth defects strongly linked to congenital Zika virus infection increased significantly, from 2.0 cases per 1,000 live births in the first half of 2016 to 2.4 cases in the second half, an increase of 29 more cases than expected (p = 0.009). These findings underscore the importance of surveillance for birth defects potentially related to Zika virus infection and the need for continued monitoring in areas at risk for Zika.

In 2016, as part of the emergency response to the Zika virus outbreak in the World Health Organization’s Region of the Americas, population-based birth defects surveillance systems monitored fetuses and infants with birth defects potentially related to Zika virus infection using a standard case definition and multiple data sources. Medical records were abstracted for data on birth defects, congenital infections, pregnancy outcome, head circumference, vital status, and Zika laboratory test results, irrespective of maternal Zika virus exposure or infection. Verbatim text describing the birth defects was reviewed to identify those that met the case definition. Infants and fetuses were aggregated into four mutually exclusive categories: those with 1) brain abnormalities or microcephaly; 2) NTDs; 3) eye abnormalities without mention of a brain abnormality included in the two previously defined categories; and 4) other consequences of central nervous system (CNS) dysfunction, specifically joint contractures and congenital sensorineural deafness without mention of brain or eye abnormalities included in another category. Because the evidence linking NTDs and congenital Zika virus infection is weak, prevalence estimates per 1,000 live births were calculated both overall and excluding NTDs for each quarter in 2016; CIs were calculated using Poisson regression (1,2).

All 15 U.S. jurisdictions included in this report had existing birth defects surveillance systems that were rapidly adapted to monitor birth defects potentially related to Zika virus infection. These jurisdictions provided data on live births and pregnancy losses occurring from January 1–December 31, 2016. The jurisdictions were stratified into the following three groups: those with 1) confirmed local Zika virus transmission during 2016**; 2) one or more cases of confirmed, symptomatic, travel-associated Zika virus disease reported to CDC per 100,000 residents (i.e., “higher” Zika prevalence)††; and 3) less than one case per 100,000 residents of confirmed, symptomatic, travel-associated Zika virus disease reported to CDC (i.e., “lower” [low or no travel-associated] Zika prevalence)§§ (3).

Overall, 2,962 infants and fetuses with birth defects potentially related to Zika virus infection were identified (3.0 per 1,000 live births; CI = 2.9–3.2) (Table), including 1,457 (49%) with brain abnormalities or microcephaly, 581 (20%) with NTDs, 262 (9%) with eye abnormalities without mention of a brain abnormality, and 662 (22%) with other consequences of CNS dysfunction without mention of brain or eye abnormalities. Among the 2,962 infants and fetuses with defects potentially related to Zika virus infection, there were 2,716 (92%) live births. Laboratory evidence of possible Zika virus infection in maternal, placental, infant, or fetal specimens was present in 45 (1.5%) cases; 96 (3.2%) had negative tests for Zika virus, and 2,821 (95.2%) either had no testing performed or no results available.

The prevalence of reported birth defects cases potentially related to Zika virus infection increased in jurisdictions with confirmed local transmission, from 2.8 per 1,000 live births (182 cases) during the first half of 2016 to 3.0 per 1,000 live births (211 cases) during the second half (CI = 2.4-3.2 and CI = 2.6–3.4, respectively; p = 0.10). In “higher” Zika prevalence jurisdictions, the monitored birth defects prevalence was 3.0 per 1,000 live births in both the first (753 cases) and second (775 cases) halves of 2016. In “lower” prevalence jurisdictions, the monitored birth defects prevalence declined significantly from 3.4 per 1,000 live births (549 cases) during the first half of 2016 to 3.0 (492 cases) per 1,000 live births during the second half (CI = 3.2–3.7 and CI = 2.8–3.3, respectively; p = 0.002) (Figure 1).

When NTDs were excluded, the prevalence of birth defects potentially related to Zika virus infection in jurisdictions with local Zika transmission increased 21%, from 2.0 per 1,000 live births (CI = 1.7–2.4) to 2.4 (CI = 2.1–2.8) (Figure 2). This increase indicated there were 29 more infants and fetuses with birth defects than were expected in areas with local transmission in the second half of 2016 (169 observed cases compared with 140 expected, p = 0.009). The prevalence of birth defects excluding NTDs in “higher” prevalence jurisdictions did not change (2.4 per 1,000 live births) and the prevalence in the “lower” prevalence jurisdictions significantly decreased from 2.8 per 1,000 live births (CI = 2.5–3.0) to 2.4 (CI = 2.2-2.7). Among 393 infants and fetuses with birth defects potentially related to Zika virus infection in areas with local transmission, 32 (8.1%) had laboratory evidence of possible Zika virus infection in a maternal, placental, infant, or fetal sample, 59 (15.0%) had negative Zika virus test results, and 302 (76.81%) had no testing performed or no results available.

Discussion

Leveraging existing birth defects surveillance systems permitted rapid implementation of surveillance for birth defects potentially related to Zika virus infection early during the U.S. Zika virus outbreak. The prevalence of birth defects strongly linked to Zika virus infection increased significantly in areas with local Zika virus transmission (29 more than were expected in the second half of 2016 compared with observed prevalence in the first half). This finding underscores the importance of surveillance for birth defects potentially related to Zika virus infection and the need for continued monitoring in areas at risk for Zika transmission and exposure.

An increase in birth defects potentially related to Zika was only observed in jurisdictions with local Zika virus transmission, and this difference was significant when NTDs were excluded. Brain and eye abnormalities and consequences of CNS dysfunction have been most consistently described in cases of congenital Zika infection, whereas the evidence supporting a possible association between NTDs and Zika virus infection during pregnancy is weak (1,2). In jurisdictions with “lower” (low or no travel-associated) Zika prevalence, the reason for the significant decrease in prevalence of birth defects potentially related to Zika (both including NTDs and excluding NTDs) is not clear. However, birth defects surveillance data typically are not final until approximately 24 months after the end of the birth year, and this release of data only 12 months after the end of the birth year likely resulted in less complete ascertainment of birth defects in late 2016 compared with early 2016. Further case ascertainment from the final quarter of 2016 is anticipated in all jurisdictions. In addition, the peak occurrence of birth defects potentially related to Zika virus infection is expected to have occurred in the 2017 birth cohort because the peak of Zika virus transmission occurred in Puerto Rico in August 2016, and local transmission of Zika virus was identified in southern Florida in June 2016 and in southern Texas in November 2016 (47).

The overall prevalence of the birth defects in this analysis (3.0 per 1,000 live births) was similar to a previously published baseline prevalence of birth defects potentially related to Zika virus infection from 2013–14 (2.9 per 1,000 live births; 95% CI = 2.7–3.1) (8). The findings presented here included data from an additional 12 jurisdictions, which covers a larger birth cohort totaling nearly 1 million live births, representing approximately one fourth of the total live births in the U.S. states and territories.

The findings in this report are subject to at least three limitations. First, the three jurisdictions with local Zika virus transmission differed from one another in the scope and timing of identified local transmission of Zika virus. Whereas Puerto Rico experienced a widespread outbreak that began in early 2016, local transmission in Texas was not confirmed until November 2016. In addition, jurisdictions with local transmission also had a high prevalence of travel-related Zika virus disease in 2016 (3), which could have contributed to the observed increased prevalence in birth defects. Second, increased awareness of birth defects potentially related to Zika virus infection in areas with local transmission might have resulted in increased efforts focused on rapid and complete identification of these birth defects cases during the second half of 2016. However, a significant increase in NTD prevalence was not observed. Although more complete ascertainment might partially explain the increased prevalence observed in areas with local transmission, it is unlikely that it would lead to a significant change, given the longstanding, mature surveillance systems, the standardized case review process, and no observable change in the prevalence of NTDs. Finally, jurisdictions in this analysis might differ in population demographics and systematic case-finding methodology, contributing to differences in observed prevalences among the three groups (9). A comparison of the prevalences in the first and second halves of the year was used to partially control for regional differences and monitor trends for those specific jurisdictional groups rather than to compare one group with another.

Collaboration between state and territorial Zika pregnancy and infant registries and birth defects surveillance systems provides a model for using the complementary approach of a prospective, exposure-based surveillance and conventional disease-based surveillance to respond to an emerging public health threat. The U.S. Zika Pregnancy and Infant Registry¶¶ can provide an early alert mechanism regarding clinical characteristics and manifestations of infants and fetuses with potential congenital infection; over 7,000 pregnancies with laboratory evidence of Zika virus infection have been reported, and CDC is monitoring pregnancy and infant adverse outcomes (https://www.cdc.gov/pregnancy/zika/data/pregnancy-outcomes.html). Established birth defects surveillance systems can adapt to monitor other emerging pregnancy, infant, and newborn outcomes of concern beyond structural birth defects, including functional problems such as hearing loss, and can provide additional clinical information through standardized data collection and clinical review. Finally, birth defects surveillance systems can provide an important mechanism for facilitating timely access to services among infants with birth defects and serve as a resource for assessing subsequent health and developmental outcomes among these children. The unique contributions of ongoing birth defects surveillance and the U.S. Zika Pregnancy and Infant Registry are both critical to optimally monitoring pregnant women and infants from the threat of Zika virus infection and implementing appropriate prevention efforts (10).

Acknowledgments

Skyler Brennan, Amanda Feldpausch, Shawna Stuck, Ashton Thompson, Georgia Public Health Laboratory staff members, Georgia Department of Public Health; Adverse Pregnancy Outcomes Reporting System staff members, Illinois Department of Public Health; Daniel Bonthius, Florence Foo, Iowa Department of Health; Catherine Brown, Cathleen Higgins, Kayleigh Sandhu, Sarah Scotland, Susan Soliva, Massachusetts Department of Public Health; Lisa D’Amico, Mary Knapp, Cristina Suarez, Donna Williams, New Jersey Department of Health; Nina Ahmad, Sarah Bower, Laura Brady, Marilyn Browne, Sriharsha Kothuru, June Moore, Cristian Pantea, Elizabeth Rees, Amanda Stolz, New York State Department of Health; Ronna L. Chan, Robert E. Meyer, North Carolina Department of Health and Human Services; Camille Delgado-López, Alma Martinez-Quiñones, Leishla Nieves-Ferrer, Stephany Pérez-González, Puerto Rico Department of Health; Harley T. Davis, Daniel Drociuk, Kimberly A. Jenkins, South Carolina Department of Health and Environmental Control.


1Division of Congenital and Developmental Disorders, National Center on Birth Defects and Developmental Disabilities, CDC; 2Texas Department of State Health Services; 3Illinois Department of Public Health; 4Massachusetts Department of Public Health; 5New Jersey Department of Health; 6New York State Department of Health; 7North Carolina Department of Health and Human Services; 8South Carolina Department of Health and Environmental Control; 9Utah Department of Health; 10Florida Department of Health; 11University of Iowa; 12Puerto Rico Department of Health; 13Georgia Department of Public Health; 14Rhode Island Department of Health; 15Hawaii Department of Health; 16Vermont Department of Health; 17Division of Vector-Borne Diseases, National Center for Emerging and Zoonotic Infectious Diseases, CDC; 18Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, CDC.

References

  1. 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
  2. Honein MA, Dawson AL, Petersen EE, et al. ; US Zika Pregnancy Registry Collaboration. Birth defects among fetuses and infants of US women with evidence of possible Zika virus infection during pregnancy. JAMA 2017;317:59–68. CrossRef PubMed
  3. CDC. Zika virus: 2016 cases counts in the US. Laboratory-confirmed symptomatic Zika virus disease cases and presumptive viremic blood donors reported to ArboNET by states and territories—United States, 2016. Atlanta, GA: US Department of Health and Human Services, CDC; 2017. https://www.cdc.gov/zika/reporting/2016-case-counts.html
  4. Cuevas EL, Tong VT, Rozo N, et al. Preliminary report of microcephaly potentially associated with Zika virus infection during pregnancy—Colombia, January–November 2016. MMWR Morb Mortal Wkly Rep 2016;65:1409–13. CrossRef PubMed
  5. Lozier M, Adams L, Febo MF, et al. Incidence of Zika virus disease by age and sex—Puerto Rico, November 1, 2015–October 20, 2016. MMWR Morb Mortal Wkly Rep 2016;65:1219–23. CrossRef PubMed
  6. CDC. Guidance for travel and testing of pregnant women and women of reproductive age for Zika virus infection related to the investigation for local mosquito-borne Zika virus transmission in Miami-Dade and Broward counties, Florida. Atlanta, GA: US Department of Health and Human Services, CDC; 2016. https://emergency.cdc.gov/han/han00393.asp
  7. CDC. Guidance for travel and testing of pregnant women and women of reproductive age for Zika virus infection related to the investigation for local mosquito-borne Zika virus transmission in Brownsville, Cameron County, Texas. Atlanta, GA: US Department of Health and Human Services, CDC; 2016. https://emergency.cdc.gov/han/han00399.asp
  8. Cragan JD, Mai CT, Petersen EE, et al. Baseline prevalence of birth defects associated with congenital Zika virus infection—Massachusetts, North Carolina, and Atlanta, Georgia, 2013–2014. MMWR Morb Mortal Wkly Rep 2017;66:219–22. CrossRef PubMed
  9. Cragan JD, Isenburg JL, Parker SE, et al. ; National Birth Defects Prevention Network. Population-based microcephaly surveillance in the United States, 2009 to 2013: an analysis of potential sources of variation. Birth Defects Res A Clin Mol Teratol 2016;106:972–82. CrossRef PubMed
  10. Gilboa SM, Mai CT, Shapiro-Mendoza CK, et al. Population-based pregnancy and birth defects surveillance in the era of Zika virus. Birth Defects Res 2017;109:372–8. CrossRef PubMed

TABLE. Population-based counts of cases of infants and fetuses with birth defects potentially related to Zika virus infection and prevalence per 1,000 live births — 15 U.S. jurisdictions,* 2016
Characteristic Brain abnormalities or microcephaly (N = 1,457; 49%) Neural tube defects and other early brain malformations§ (N = 581; 20%) Eye abnormalities (N = 262; 9%) Consequences of CNS dysfunction** (N = 662; 22%) Total (N = 2,962; 100%)
Prevalence per 1,000 live births (95% CI) 1.5 (1.4–1.6) 0.60 (0.55–0.65) 0.27 (0.24-0.30) 0.68 (0.63–0.74) 3.0 (2.9–3.2)
Eye abnormalities No. (%) 144 (9.9) 24 (4.1) 0 430 (14.5)
Consequences of CNS dysfunction No. (%) 133 (9.1) 77 (13.3) 12 (4.6) 884 (29.8)
Pregnancy outcome††
Live births No. (%) 1,387 (95.2) 427 (73.5) 257 (98.1) 645 (97.4) 2,716 (91.7)
Neonatal death (≤28 days) No. 89 92 8 30 219
Pregnancy loss§§ No. (%) 65 (4.5) 149 (25.6) 5 (1.9) 16 (2.4) 235 (7.9)
Zika virus laboratory testing for infants or mothers
Positive No. (%) 29 (2.0) 4 (0.69) 10 (3.8) 2 (0.30) 45 (1.5)
Negative No. (%) 65 (4.5) 20 (3.4) 3 (1.1) 8 (1.2) 96 (3.2)
No testing performed/NA¶¶ No. (%) 1,363 (93.5) 557 (95.9) 249 (95.0) 652 (98.5) 2,821 (95.2)

Abbreviations: CI = confidence interval; CNS = central nervous system; NA = not available.
* 15 U.S. jurisdictions: Florida (selected southern counties), Georgia (selected metropolitan Atlanta counties), Hawaii, Iowa, Illinois, Massachusetts, New Jersey, New York (excluding New York City), North Carolina (selected regions), Puerto Rico, Rhode Island, South Carolina, Texas (Public Health Regions 1, 3, 9, and 11), Utah, and Vermont. Total live births = 971,685.
Brain abnormalities or microcephaly (congenital microcephaly [head circumference <3rd percentile for gestational age and sex], intracranial calcifications, cerebral atrophy, abnormal cortical gyral patterns [e.g., polymicrogyria, lissencephaly, pachygyria, schizencephaly, gray matter heterotopia], corpus callosum abnormalities, cerebellar abnormalities, porencephaly, hydranencephaly, ventriculomegaly/hydrocephaly [excluding “mild” ventriculomegaly without other brain abnormalities], fetal brain disruption sequence [collapsed skull, overlapping sutures, prominent occipital bone, scalp rugae], other major brain abnormalities).
§ Neural tube defects and other early brain malformations (anencephaly/acrania, encephalocele, spina bifida, and holoprosencephaly).
Structural eye abnormalities (microphthalmia/anophthalmia, coloboma, cataract, intraocular calcifications, and chorioretinal anomalies [e.g., atrophy and scarring, gross pigmentary changes, excluding retinopathy of prematurity]); optic nerve atrophy, pallor, and other optic nerve abnormalities.
** Consequences of CNS dysfunction (arthrogryposis, club foot with associated brain abnormalities, congenital hip dysplasia with associated brain abnormalities, and congenital sensorineural hearing loss).
†† 11 unknown pregnancy outcomes not included.
§§ Includes miscarriages, fetal deaths, and terminations.
¶¶ Includes cases linked to lab data where no testing was performed or there was unknown testing status.

Return to your place in the textFIGURE 1. Prevalence of birth defects cases potentially related to Zika virus infection, by Zika virus transmission characteristics and quarter —15 U.S. jurisdictions, 2016*,†,§

The figure above is a line graph showing the number of birth defects cases per 1,000 live births potentially related to Zika virus infection, by three groups of jurisdictions with varying prevalence and quarter, among 15 U.S. jurisdictions, in 2016.

* Local transmission jurisdictions included Florida (selected southern counties), Puerto Rico, and Texas (Public Health Region 11).

Higher travel-related Zika prevalence jurisdictions had one or more case of confirmed symptomatic travel-associated Zika virus disease reported to CDC per 100,000 residents. These jurisdictions included Georgia (selected metropolitan Atlanta counties), Massachusetts, New Jersey, New York (excluding New York City), Rhode Island, South Carolina, Texas (Public Health Regions 1, 3, and 9), and Vermont.

§ Low or no travel-related Zika prevalence jurisdictions had less than one case of confirmed symptomatic travel-associated Zika virus disease reported to CDC per 100,000 residents. These jurisdictions included Hawaii, Illinois, Iowa, North Carolina (selected regions), and Utah.

The figure above is a line graph showing the number of birth defects cases per 1,000 live births potentially related to Zika virus infection, by three groups of jurisdictions with varying prevalence and quarter, among 15 U.S., jurisdictions, in 2016.

Return to your place in the textFIGURE 2. Prevalence of birth defects cases* potentially related to Zika virus infection in U.S. jurisdictions with documented local transmission of Zika virus, by defect type and quarter, 2016

The figure above is a line graph showing the number of birth defects cases per 1,000 live births in three U.S. jurisdictions with documented local transmission of Zika virus, by defect type and quarter, in 2016.

*Fetuses and infants were aggregated into the following four mutually exclusive categories: those with 1) brain abnormalities with or without microcephaly (head circumference at delivery <3rd percentile for sex and gestational age); 2) NTDs and other early brain malformations; 3) eye abnormalities among those without mention of a brain abnormality included in the first two categories; and 4) other consequences of central nervous system dysfunction, specifically joint contractures and congenital sensorineural deafness, among those without mention of brain or eye abnormalities included in another category.

Jurisdictions with local transmission of Zika virus included Florida (selected southern counties), Puerto Rico, and Texas (Public Health Region 11).

The figure above is a line graph showing the number of birth defects cases per 1,000 live births in three U.S. jurisdictions with documented local transmission of Zika virus, by defect type and quarter, in 2016.

Suggested citation for this article: Delaney A, Mai C, Smoots A, et al. Population-Based Surveillance of Birth Defects Potentially Related to Zika Virus Infection — 15 States and U.S. Territories, 2016. MMWR Morb Mortal Wkly Rep 2018;67:91–96. DOI: http://dx.doi.org/10.15585/mmwr.mm6703a2.

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State-Specific Prevalence of Tobacco Product Use Among Adults …

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Satomi Odani, MPH1,2; Brian S. Armour, PhD1; Corinne M. Graffunder, DrPH1; Gordon Willis, PhD3; Anne M. Hartman, MS, MA3; Israel T. Agaku, DMD, PhD1 (View author affiliations)

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Summary

What is already known about this topic?

Tobacco use is the leading cause of preventable morbidity and mortality in the United States. Despite recent declines in cigarette smoking prevalence, the tobacco product landscape has shifted to include emerging tobacco products, such as electronic cigarettes and water pipes.

What is added by this report?

Analysis of data from the 2014–2015 Tobacco Use Supplement to the Current Population Survey found that the prevalence of ever-use of any tobacco product ranged from 27.0% (Utah) to 55.4% (Wyoming). Current (every day or some days) use of any tobacco product ranged from 10.2% (California) to 27.7% (Wyoming). Cigarettes were the most common currently used tobacco product. Among current cigarette smokers, the proportion who currently used ≥1 other tobacco products ranged from 11.5% (Delaware) to 32.3% (Oregon). Eight of the 10 states with the lowest prevalence of current use of any tobacco product have implemented policies that prohibit smoking in all indoor areas of workplaces, bars, and restaurants; seven of the 10 states with the highest prevalence have no such comprehensive smoke-free laws.

What are the implications for public health practice?

Differences in tobacco product use across states underscore the importance of implementing comprehensive tobacco control and prevention interventions to reduce tobacco use and tobacco-related disparities, including comprehensive smoke-free policies, tobacco product price increases, anti-tobacco mass media campaigns, and barrier-free access to clinical smoking cessation resources.

Despite recent declines in cigarette smoking prevalence, the tobacco product landscape has shifted to include emerging tobacco products* (1,2). Previous research has documented adult use of smokeless tobacco and cigarettes by state (3); however, state-specific data on other tobacco products are limited. To assess tobacco product use in the 50 U.S. states and the District of Columbia (DC), CDC and the National Cancer Institute analyzed self-reported use of six tobacco product types: cigarettes, cigars, regular pipes, water pipes, electronic cigarettes (e-cigarettes), and smokeless tobacco products among adults aged ≥18 years using data from the 2014–2015 Tobacco Use Supplement to the Current Population Survey (TUS-CPS). Prevalence of ever-use of any tobacco product ranged from 27.0% (Utah) to 55.4% (Wyoming). Current (every day or some days) use of any tobacco product ranged from 10.2% (California) to 27.7% (Wyoming). Cigarettes were the most common currently used tobacco product in all states and DC. Among current cigarette smokers, the proportion who currently used one or more other tobacco products ranged from 11.5% (Delaware) to 32.3% (Oregon). Differences in tobacco product use across states underscore the importance of implementing proven population-level strategies to reduce tobacco use and expanding these strategies to cover all forms of tobacco marketed in the United States. Such strategies could include comprehensive smoke-free policies, tobacco product price increases, anti-tobacco mass media campaigns, and barrier-free access to clinical smoking cessation resources (1,4).

The 2014–15 TUS-CPS was a household-based survey of adults aged ≥18 years in the 50 U.S. states and DC (5). A total of 163,920 respondents participated (response rate = 54.2%). Six tobacco product types were assessed: cigarettes, cigars (including regular cigars, cigarillos, or little filtered cigars), regular pipes, water pipes, e-cigarettes, and smokeless tobacco products (including moist snuff, dip, spit, chew tobacco, snus, or dissolvable tobacco).

For all tobacco product types except cigarettes,§ ever-users were defined as persons who had used the respective products one or more times during their lifetime; current users were persons who reported ever-use and who used the respective products “every day” or “some days” at the time of survey. Ever cigarette smokers were defined as persons who had smoked 100 or more cigarettes during their lifetime; current cigarette smokers were persons who reported ever cigarette smoking and smoked “every day” or “some days” at the time of survey. Any tobacco product use was defined as use of any of the six assessed tobacco products, and any combustible tobacco product use was defined as any use of cigarettes, cigars, regular pipes, or water pipes.** Data were weighted to yield state-representative estimates. Prevalence estimates with relative standard errors ≥30% were suppressed.

Prevalence of ever-use ranged from 27.0% (Utah) to 55.4% (Wyoming) for any tobacco product, from 25.8% (Utah) to 53.2% (Maine) for any combustible tobacco product, from 22.0% (Utah) to 44.3% (Maine) for cigarettes, from 10.6% (Utah) to 26.3% (Oregon) for cigars, from 4.3% (Delaware) to 14.2% (Wyoming) for e-cigarettes, from 2.7% (New Jersey) to 20.5% (Wyoming) for smokeless tobacco, from 3.2% (New Jersey) to 12.0% (Oregon) for regular pipes, and from 1.5% (Arkansas) to 16.7% (DC) for water pipes (Table 1).

In all states, cigarettes were the most commonly ever-used tobacco products, followed by cigars. The third most commonly reported ever-used product was e-cigarettes in 32 states (range for those states = 5.1% in New Jersey to 11.8% in Nebraska); smokeless tobacco in 14 states (9.1% in Pennsylvania to 20.5% in Wyoming); regular pipes in Delaware (4.3%), Maine (10.8%), and Vermont (11.1%); and water pipes in California (6.3%) and DC (16.7%).

Prevalence of current use of any tobacco product ranged from 10.2% (California) to 27.7% (Wyoming) (Table 2). Among respondents who had ever used any tobacco product, the proportion who were current users of any tobacco product ranged from 30.7% (California) to 57.7% (Mississippi) (not presented in Tables). Current use of any combustible tobacco product ranged from 8.9% (Utah) to 23.1% (West Virginia). Among respondents who had ever used any combustible tobacco product, the proportion who were current combustible tobacco product users ranged from 28.6% (California) to 53.0% (Mississippi). Current cigarette smoking prevalence ranged from 8.0% (Utah) to 21.7% (West Virginia); among ever cigarette smokers, the proportion who were current cigarette smokers ranged from 33.9% (California) to 57.3% (Louisiana). Prevalence of current cigar use ranged from 1.0% (Utah) to 3.5% (Alaska); among respondents who had ever smoked cigars, the proportion who were current cigar smokers ranged from 8.1% (Vermont) to 20.0% (New Jersey). The prevalence of current e-cigarette use ranged from 1.3% (Delaware) to 4.4% (Wyoming); among e-cigarette ever-users, the proportion who were current e-cigarette users ranged from 16.6% (DC) to 40.0% (Rhode Island). The prevalence of current smokeless tobacco use ranged from 0.6% (New York) to 6.4% (Wyoming); among respondents who had ever used smokeless tobacco, the proportion who were current smokeless tobacco users ranged from 6.7% (Maine) to 36.1% (Mississippi). The prevalence of current water pipe smoking prevalence ranged from 0.4% (Florida) to 1.9% (DC); among respondents who had ever smoked water pipes, the proportion who were current water pipe smokers ranged from 0.0% (Arkansas) and Oklahoma to 21.2% (Rhode Island). Finally, the prevalence of current regular pipe smoking ranged from 0.2% (Florida), to 1.0% (Oregon); among those who had ever smoked a regular pipe, the proportion who were current regular pipe smokers ranged from 2.9% (Georgia) to 13.0% (Utah).

Cigarettes were the most common currently used tobacco product in all states and DC. The second most common currently used product in 23 states was e-cigarettes (range = 1.8% in Vermont to 3.9% in Idaho), cigars in 18 states and DC (1.7% in California to 3.5% in Alaska), and smokeless tobacco in nine states (3.6% in Mississippi to 6.4% in Wyoming).

Among persons reporting current use of any tobacco product, the proportion reporting concurrent use of two or more tobacco products ranged from 11.5% (Delaware) to 27.0% (Oregon). The proportion of current cigarette smokers reporting concurrent use of a noncigarette tobacco product ranged from 11.5% (Delaware) to 32.3% (Oregon) (Figure).

Discussion

Ever-use of any tobacco product by adults aged ≥18 years ranged from 27.0% (Utah) to 55.4% (Wyoming), and current use ranged from 10.2% (California) to 27.7% (Wyoming); nine of the 10 states with the highest prevalence of current use of any tobacco product were in the Midwest or South, and seven of the 10 states with the lowest prevalence were in the Northeast or West. Apart from regional and demographic characteristics, the differences across states in tobacco use might, in part, reflect differences in tobacco control and prevention interventions. For example, eight of the 10 states with the lowest prevalence of current use of any tobacco product have implemented policies that prohibit smoking in all indoor areas of workplaces, bars, and restaurants. In contrast, seven of the 10 states with the highest prevalence have no such comprehensive smoke-free laws.†† Continued implementation of proven population-based interventions, including increasing tobacco product prices, implementing and enforcing comprehensive smoke-free laws, warning about the dangers of tobacco use, and increasing barrier-free access to cessation services, can help reduce tobacco use (1,4).

Cigarettes were the most commonly used tobacco product, and nearly one in five current cigarette smokers concurrently used another form of tobacco. Among ever-users of each of the six tobacco products assessed, the proportion of current users was highest for cigarettes, followed by e-cigarettes. Given that most tobacco initiation occurs in adolescence and young adulthood (6), and product trial is a critical step in initiating and maintaining tobacco use (7), intensified efforts to prevent experimentation could reduce the likelihood of a lifetime of tobacco addiction. In light of the ever-changing tobacco control landscape, it is important to expand surveillance, policy, and programs to cover the range of tobacco products being marketed and used among youth and adults (4). For example, eight U.S. states and DC have expanded their comprehensive smoke-free laws to include e-cigarettes (8), and California and several U.S. cities have enacted policies prohibiting smokeless tobacco use in public sport arenas, which include 14 of 30 major league baseball stadiums.§§

The findings in this report are subject to at least three limitations. First, tobacco use was self-reported and might be underreported. Second, small sample sizes for some tobacco product types within certain states resulted in imprecise estimates that could not be presented. Finally, “ever-use” thresholds were characterized as ≥100 cigarettes versus ≥1 lifetime use for all other products; thus potentially underestimating both ever and current cigarette smoking.

Adoption of evidence-based measures across all states could help decrease tobacco use (3,4). Furthermore, continued tobacco surveillance at the national and state levels can help guide public health programs and policy (4,8).


References

  1. US Department of Health and Human Services. The health consequences of smoking—50 years of progress: a report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, CDC; 2014. http://www.surgeongeneral.gov/library/reports/50-years-of-progress/full-report.pdf
  2. King BA, Patel R, Nguyen KH, Dube SR. Trends in awareness and use of electronic cigarettes among US adults, 2010–2013. Nicotine Tob Res 2015;17:219–27. CrossRef PubMed
  3. Nguyen KH, Marshall L, Brown S, Neff L. State-specific prevalence of current cigarette smoking and smokeless tobacco use among adults—United States, 2014. MMWR Morb Mortal Wkly Rep 2016;65:1045–51. CrossRef PubMed
  4. CDC. Best practices for comprehensive tobacco control programs—2014. Atlanta, GA: US Department of Health and Human Services; 2014. https://www.cdc.gov/tobacco/stateandcommunity/best_practices/pdfs/2014/comprehensive.pdf
  5. National Cancer Institute; US Census Bureau. Tobacco use supplement to the current population survey 2014–15. Rockville, MD: US Department of Health and Human Services, National Cancer Institute; Suitland, MD: US Census Bureau; 2016. https://cancercontrol.cancer.gov/brp/tcrb/tus-cps/.
  6. US Department of Health and Human Services. Preventing tobacco use among youth and young adults: a report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, CDC; 2012. https://www.ncbi.nlm.nih.gov/books/NBK99237/
  7. Kempf DS, Smith RE. Consumer processing of product trial and the influence of prior advertising: a structural modeling approach. J Mark Res 1998;35:325–38. CrossRef
  8. CDC. State tobacco activities tracking and evaluation (STATE) system. Atlanta, GA: US Department of Health and Human Services, CDC; 2014. https://www.cdc.gov/statesystem/

TABLE 1. Prevalence of ever-use of any tobacco product, combustible tobacco and six tobacco products types among U.S. adults aged ≥18 years,* by state and tobacco product type — Tobacco Use Supplement to the Current Population Survey, United States, 2014–2015
State Any tobacco Combustible tobacco§ Cigarettes Cigars** Regular pipe** Water pipe** Electronic cigarette** Smokeless tobacco**
% (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI)
Alabama 41.8 (39.6–43.9) 39.3 (37.2–41.4) 34.3 (32.3–36.3) 15.3 (13.7–16.9) 5.6 (4.6–6.6) 2.0 (1.3–2.7) 10.8 (9.3–12.2) 9.8 (8.4–11.3)
Alaska 51.5 (49.0–54.0) 49.1 (46.6–51.6) 39.2 (36.8–41.6) 24.3 (22.2–26.4) 9.1 (7.7–10.5) 6.6 (5.3–7.8) 11.7 (10.0–13.4) 15.0 (13.3–16.8)
Arizona 39.5 (37.4–41.7) 38.5 (36.4–40.7) 30.2 (28.3–32.2) 17.9 (16.1–19.6) 6.7 (5.7–7.8) 6.1 (5.0–7.3) 9.5 (8.1–10.9) 5.8 (4.8–6.8)
Arkansas 47.3 (45.1–49.4) 44.4 (42.2–46.5) 41.3 (39.2–43.4) 14.8 (13.3–16.3) 5.4 (4.5–6.3) 1.5 (0.9–2.0) 9.3 (8.1–10.6) 12.0 (10.5–13.4)
California 33.3 (32.4–34.2) 32.6 (31.8–33.5) 23.9 (23.1–24.6) 15.2 (14.5–15.8) 4.5 (4.1–4.9) 6.3 (5.8–6.8) 6.1 (5.7–6.6) 4.2 (3.8–4.6)
Colorado 49.4 (47.2–51.6) 47.7 (45.5–49.9) 35.8 (33.7–37.9) 24.2 (22.3–26.1) 8.3 (7.1–9.5) 7.6 (6.4–8.9) 9.9 (8.5–11.3) 13.1 (11.6–14.6)
Connecticut 43.5 (41.1–45.8) 42.6 (40.3–45.0) 35.6 (33.4–37.8) 18.7 (16.9–20.5) 6.1 (5.1–7.2) 4.7 (3.7–5.8) 6.9 (5.6–8.2) 4.8 (3.8–5.9)
Delaware 37.2 (35.1–39.4) 36.5 (34.4–38.7) 31.8 (29.8–33.8) 11.8 (10.4–13.3) 4.3 (3.4–5.2) 2.5 (1.7–3.2) 4.3 (3.3–5.2) 3.3 (2.5–4.1)
District of Columbia 46.9 (45.0–48.9) 46.3 (44.4–48.2) 28.6 (27.0–30.3) 22.5 (20.9–24.1) 5.7 (4.8–6.5) 16.7 (15.3–18.2) 7.6 (6.6–8.6) 4.9 (4.1–5.7)
Florida 34.9 (33.7–36.1) 34.2 (33.0–35.5) 30.2 (29.0–31.3) 12.0 (11.2–12.9) 3.8 (3.3–4.3) 3.0 (2.5–3.5) 5.8 (5.2–6.5) 3.4 (3.0–3.9)
Georgia 36.9 (35.2–38.6) 35.3 (33.6–37.0) 29.5 (27.9–31.1) 14.9 (13.6–16.1) 4.8 (4.0–5.5) 3.5 (2.8–4.2) 7.8 (6.8–8.8) 7.6 (6.6–8.5)
Hawaii 33.3 (31.0–35.5) 31.5 (29.3–33.7) 26.8 (24.7–28.9) 13.7 (12.0–15.3) 4.7 (3.7–5.7) 3.5 (2.5–4.4) 9.1 (7.5–10.6) 5.2 (4.1–6.2)
Idaho 42.5 (40.3–44.6) 41.3 (39.2–43.4) 32.3 (30.3–34.3) 21.0 (19.2–22.8) 7.5 (6.3–8.6) 5.5 (4.4–6.6) 10.3 (8.9–11.6) 10.5 (9.2–11.8)
Illinois 42.0 (40.5–43.5) 41.1 (39.6–42.6) 32.6 (31.2–34.0) 17.9 (16.8–19.1) 5.3 (4.7–6.0) 4.8 (4.1–5.6) 8.3 (7.4–9.1) 6.4 (5.6–7.1)
Indiana 49.7 (47.7–51.8) 48.5 (46.5–50.6) 40.2 (38.2–42.2) 21.4 (19.7–23.0) 7.1 (6.1–8.1) 4.7 (3.7–5.7) 10.6 (9.3–12.0) 9.2 (8.0–10.4)
Iowa 51.8 (49.6–54.0) 49.2 (46.9–51.4) 39.0 (36.9–41.2) 24.9 (22.9–26.8) 8.9 (7.6–10.2) 4.7 (3.7–5.8) 10.5 (9.0–11.9) 13.7 (12.2–15.3)
Kansas 46.3 (44.1–48.4) 44.1 (42.0–46.2) 35.3 (33.3–37.4) 21.0 (19.2–22.7) 7.1 (6.0–8.1) 5.3 (4.2–6.3) 12.3 (10.8–13.8) 11.2 (9.8–12.6)
Kentucky 49.4 (47.2–51.5) 47.3 (45.2–49.4) 41.8 (39.7–43.9) 17.5 (15.8–19.1) 6.6 (5.6–7.7) 3.1 (2.2–4.0) 11.0 (9.6–12.4) 11.3 (9.9–12.7)
Louisiana 38.5 (36.6–40.5) 36.9 (34.9–38.8) 32.7 (30.8–34.5) 12.2 (10.9–13.6) 4.1 (3.3–4.9) 2.1 (1.5–2.7) 8.3 (7.2–9.5) 6.9 (5.9–8.0)
Maine 54.1 (51.8–56.4) 53.2 (51.0–55.5) 44.3 (42.0–46.5) 23.9 (22.0–25.9) 10.8 (9.5–12.2) 4.7 (3.6–5.8) 9.7 (8.3–11.2) 8.3 (7.0–9.7)
Maryland 38.7 (36.7–40.7) 38.3 (36.3–40.3) 28.6 (26.7–30.4) 17.1 (15.6–18.7) 6.0 (5.0–7.0) 5.6 (4.6–6.7) 7.1 (6.0–8.3) 3.7 (2.9–4.5)
Massachusetts 41.9 (40.0–43.9) 41.4 (39.4–43.4) 32.5 (30.7–34.3) 17.4 (15.8–18.9) 5.5 (4.6–6.4) 6.2 (5.1–7.3) 6.6 (5.5–7.7) 3.4 (2.6–4.1)
Michigan 48.3 (46.6–50.0) 47.4 (45.7–49.1) 38.3 (36.7–39.9) 20.7 (19.3–22.1) 7.8 (6.9–8.7) 6.2 (5.3–7.2) 10.8 (9.6–11.9) 8.0 (7.0–8.9)
Minnesota 50.0 (48.0–51.9) 48.3 (46.4–50.3) 37.2 (35.3–39.0) 22.6 (20.9–24.2) 8.3 (7.2–9.3) 5.2 (4.3–6.2) 9.9 (8.7–11.1) 11.4 (10.2–12.7)
Mississippi 39.9 (37.9–41.9) 37.1 (35.1–39.0) 32.9 (31.0–34.8) 12.7 (11.3–14.0) 3.9 (3.1–4.6) 1.9 (1.3–2.4) 8.1 (7.0–9.3) 10.1 (8.8–11.4)
Missouri 49.0 (46.9–51.1) 47.2 (45.1–49.3) 39.3 (37.3–41.3) 20.5 (18.8–22.2) 7.3 (6.3–8.4) 4.0 (3.1–5.0) 9.9 (8.6–11.2) 9.8 (8.5–11.1)
Montana 50.7 (48.6–52.8) 47.9 (45.8–50.0) 36.5 (34.5–38.5) 24.2 (22.3–26.0) 10.2 (8.9–11.5) 5.6 (4.4–6.7) 9.6 (8.2–10.9) 16.0 (14.4–17.6)
Nebraska 46.9 (44.7–49.2) 45.0 (42.7–47.2) 36.3 (34.2–38.5) 21.1 (19.3–23.0) 6.2 (5.1–7.3) 4.4 (3.3–5.4) 11.8 (10.3–13.4) 10.5 (9.1–12.0)
Nevada 38.0 (35.8–40.2) 37.3 (35.2–39.5) 30.8 (28.8–32.8) 13.6 (12.1–15.1) 4.4 (3.6–5.3) 6.9 (5.6–8.1) 9.1 (7.7–10.4) 4.6 (3.6–5.5)
New Hampshire 49.6 (47.5–51.6) 49.0 (47.0–51.1) 41.5 (39.5–43.5) 20.0 (18.4–21.7) 7.7 (6.7–8.8) 4.6 (3.6–5.6) 7.8 (6.6–8.9) 6.5 (5.4–7.6)
New Jersey 34.1 (32.3–35.9) 33.8 (32.0–35.6) 28.9 (27.2–30.6) 11.4 (10.2–12.6) 3.2 (2.5–3.8) 2.6 (1.9–3.2) 5.1 (4.2–6.0) 2.7 (2.1–3.3)
New Mexico 38.8 (36.6–41.0) 37.6 (35.4–39.7) 31.3 (29.2–33.3) 14.8 (13.2–16.4) 4.5 (3.7–5.4) 3.5 (2.6–4.3) 7.3 (6.2–8.5) 5.9 (4.9–7.0)
New York 38.2 (36.8–39.5) 37.6 (36.3–39.0) 31.0 (29.8–32.2) 14.3 (13.4–15.3) 4.4 (3.9–4.9) 4.7 (4.1–5.4) 7.3 (6.6–8.1) 3.9 (3.3–4.4)
North Carolina 43.9 (42.1–45.7) 41.5 (39.7–43.3) 34.5 (32.8–36.2) 16.3 (14.8–17.7) 6.1 (5.2–7.1) 4.4 (3.6–5.3) 8.9 (7.8–10.1) 8.2 (7.1–9.2)
North Dakota 51.3 (49.1–53.5) 48.2 (45.9–50.4) 39.1 (36.9–41.2) 23.3 (21.4–25.2) 8.1 (6.9–9.4) 5.4 (4.2–6.5) 9.7 (8.4–11.1) 17.5 (15.7–19.2)
Ohio 50.2 (48.6–51.8) 48.7 (47.1–50.2) 39.6 (38.1–41.1) 22.2 (20.9–23.5) 7.3 (6.6–8.1) 4.5 (3.7–5.3) 11.2 (10.1–12.2) 9.6 (8.6–10.6)
Oklahoma 46.1 (44.0–48.2) 44.1 (42.0–46.3) 38.6 (36.5–40.7) 17.3 (15.7–19.0) 5.6 (4.7–6.5) 2.7 (2.0–3.5) 12.0 (10.6–13.4) 12.0 (10.6–13.4)
Oregon 49.7 (47.6–51.9) 48.3 (46.2–50.5) 37.6 (35.6–39.7) 26.3 (24.3–28.2) 12.0 (10.5–13.4) 6.4 (5.3–7.6) 10.1 (8.7–11.5) 11.7 (10.3–13.2)
Pennsylvania 48.4 (46.8–50.0) 46.9 (45.3–48.5) 38.5 (37.0–39.9) 20.5 (19.3–21.8) 7.1 (6.4–7.9) 4.2 (3.5–4.9) 9.1 (8.2–10.1) 9.1 (8.2–10.0)
Rhode Island 41.5 (39.0–44.0) 41.1 (38.6–43.5) 34.1 (31.8–36.4) 16.0 (14.1–17.8) 5.8 (4.7–6.8) 5.4 (4.0–6.7) 6.2 (4.9–7.5) 3.6 (2.6–4.6)
South Carolina 41.7 (39.7–43.8) 40.4 (38.4–42.4) 34.8 (32.9–36.7) 16.2 (14.7–17.8) 6.5 (5.4–7.5) 3.2 (2.4–4.0) 8.4 (7.2–9.6) 7.0 (6.0–8.0)
South Dakota 53.0 (50.7–55.3) 50.0 (47.7–52.3) 41.5 (39.2–43.8) 23.4 (21.4–25.4) 7.3 (6.1–8.4) 5.7 (4.5–6.9) 11.3 (9.7–12.8) 15.1 (13.4–16.8)
Tennessee 45.0 (43.0–47.0) 43.0 (41.1–45.0) 36.5 (34.6–38.4) 17.6 (16.1–19.1) 7.0 (6.0–7.9) 3.3 (2.6–4.1) 10.7 (9.4–11.9) 10.0 (8.8–11.1)
Texas 37.5 (36.3–38.6) 35.6 (34.5–36.7) 28.2 (27.2–29.2) 14.9 (14.1–15.7) 5.0 (4.5–5.5) 4.7 (4.1–5.2) 8.3 (7.6–8.9) 7.2 (6.6–7.8)
Utah 27.0 (25.0–29.0) 25.8 (23.8–27.8) 22.0 (20.1–23.9) 10.6 (9.2–12.1) 4.3 (3.4–5.3) 5.0 (3.9–6.0) 8.8 (7.4–10.1) 5.6 (4.6–6.7)
Vermont 52.6 (50.6–54.7) 51.7 (49.7–53.8) 42.8 (40.7–44.8) 21.9 (20.2–23.7) 11.1 (9.7–12.4) 5.1 (4.0–6.1) 8.7 (7.4–10.0) 8.7 (7.4–9.9)
Virginia 43.9 (42.1–45.7) 42.8 (41.0–44.7) 32.7 (31.0–34.4) 18.7 (17.3–20.1) 6.5 (5.6–7.4) 6.9 (5.9–7.9) 8.9 (7.8–10.0) 7.9 (6.9–8.9)
Washington 47.7 (45.8–49.6) 46.3 (44.4–48.2) 35.4 (33.7–37.2) 24.7 (23.0–26.3) 9.3 (8.2–10.4) 6.7 (5.7–7.7) 10.8 (9.6–12.0) 10.5 (9.4–11.7)
West Virginia 50.2 (48.1–52.3) 46.5 (44.4–48.6) 40.4 (38.3–42.4) 17.9 (16.1–19.6) 7.2 (6.0–8.3) 2.5 (1.7–3.3) 10.3 (9.0–11.7) 14.5 (12.9–16.1)
Wisconsin 50.2 (48.2–52.2) 48.9 (46.9–51.0) 37.8 (35.9–39.7) 24.4 (22.6–26.1) 7.5 (6.4–8.6) 4.3 (3.4–5.3) 9.2 (8.0–10.4) 10.0 (8.8–11.3)
Wyoming 55.4 (53.2–57.6) 51.3 (49.2–53.5) 40.6 (38.5–42.8) 23.9 (22.0–25.8) 9.9 (8.5–11.3) 6.9 (5.5–8.3) 14.2 (12.6–15.8) 20.5 (18.7–22.4)

Abbreviation: CI = confidence interval.
* n = 163,920. Data were weighted to adjust for nonresponse and to yield representative estimates at the state level.
Persons who reported ever use of at least one of the six tobacco products assessed (cigarettes, cigars, regular pipe, water pipe, e-cigarette, and smokeless tobacco).
§ Persons who reported having used cigarettes, cigars, regular pipe, or water pipe at least once during their lifetime.
Persons who reported having smoked ≥100 cigarettes during their lifetime.
** Persons who reported having used the respective product at least once during their lifetime. Cigars includes regular cigars, cigarillos, or little filtered cigars. Smokeless tobacco includes moist snuff, dip, spit, chew tobacco, snus, or dissolvable tobacco.


TABLE 2. Prevalence of current use of any tobacco product, combustible tobacco and six tobacco products types among adults aged ≥18 years,* by state and tobacco product type — Tobacco Use Supplement to the Current Population Survey, United States, 2014–2015
State Any tobacco Combustible tobacco§ Cigarettes Cigars** Regular pipe** Water pipe** Electronic cigarette** Smokeless tobacco**
% (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI)
Alabama 23.1 (21.2–24.9) 19.7 (17.9–21.5) 18.2 (16.5–19.9) 2.9 (2.2–3.7) †† †† 3.8 (3.0–4.6) 3.3 (2.5–4.0)
Alaska 21.4 (19.4–23.5) 18.5 (16.5–20.4) 16.2 (14.3–18.0) 3.5 (2.5–4.5) †† †† 2.4 (1.7–3.2) 3.5 (2.5–4.4)
Arizona 14.4 (12.9–15.9) 13.0 (11.5–14.5) 11.9 (10.5–13.3) 1.5 (0.9–2.0) †† 1.1 (0.5–1.6) 2.4 (1.7–3.1) 0.9 (0.5–1.4)
Arkansas 24.0 (22.2–25.8) 20.4 (18.7–22.1) 20.0 (18.3–21.7) 1.9 (1.3–2.5) †† †† 2.8 (2.0–3.5) 4.0 (3.1–4.8)
California 10.2 (9.6–10.8) 9.4 (8.8–9.9) 8.0 (7.5–8.5) 1.7 (1.4–1.9) 0.2 (0.1–0.3) 0.6 (0.5–0.8) 1.4 (1.1–1.6) 0.6 (0.4–0.8)
Colorado 16.9 (15.2–18.6) 14.9 (13.3–16.5) 13.1 (11.6–14.6) 2.1 (1.5–2.7) †† 0.8 (0.3–1.3) 2.6 (1.8–3.3) 1.8 (1.2–2.3)
Connecticut 15.4 (13.7–17.1) 14.3 (12.7–16.0) 12.3 (10.8–13.9) 3.0 (2.1–3.8) †† †† 2.3 (1.6–3.0) ††
Delaware 15.2 (13.6–16.8) 14.3 (12.8–15.9) 13.3 (11.8–14.8) 1.9 (1.2–2.5) †† †† 1.3 (0.8–1.8) ††
District of Columbia 15.8 (14.4–17.3) 15.5 (14.1–16.9) 12.2 (10.9–13.4) 3.0 (2.3–3.7) 0.5 (0.2–0.8) 1.9 (1.3–2.5) 1.3 (0.9–1.7) ††
Florida 14.4 (13.5–15.3) 13.2 (12.3–14.1) 12.1 (11.3–13.0) 2.1 (1.7–2.5) 0.2 (0.1–0.3) 0.4 (0.2–0.5) 1.8 (1.5–2.2) 0.8 (0.6–1.0)
Georgia 16.8 (15.5–18.2) 14.7 (13.4–15.9) 13.4 (12.2–14.7) 2.1 (1.5–2.6) †† 0.5 (0.3–0.8) 2.5 (1.9–3.0) 1.9 (1.4–2.4)
Hawaii 13.9 (12.2–15.6) 11.7 (10.1–13.2) 10.5 (9.0–12.0) 1.6 (1.0–2.1) †† †† 2.8 (1.9–3.6) 0.9 (0.5–1.4)
Idaho 17.4 (15.7–19.0) 14.7 (13.2–16.2) 13.3 (11.8–14.8) 2.1 (1.5–2.8) †† †† 3.9 (3.0–4.7) 2.2 (1.6–2.9)
Illinois 16.3 (15.1–17.4) 14.8 (13.7–15.8) 12.8 (11.8–13.8) 2.5 (2.0–3.1) 0.3 (0.1–0.5) 0.6 (0.3–0.8) 2.0 (1.6–2.4) 1.0 (0.7–1.3)
Indiana 22.5 (20.8–24.3) 20.3 (18.6–22.0) 18.9 (17.3–20.5) 3.1 (2.3–3.9) †† †† 3.1 (2.4–3.9) 2.2 (1.5–2.8)
Iowa 20.8 (18.9–22.6) 17.4 (15.7–19.1) 15.6 (14.0–17.2) 2.6 (1.8–3.3) †† †† 3.1 (2.4–3.9) 3.7 (2.8–4.6)
Kansas 22.1 (20.3–23.9) 19.5 (17.8–21.2) 17.6 (16.0–19.3) 3.0 (2.3–3.8) 0.5 (0.2–0.7) 0.9 (0.5–1.4) 3.5 (2.7–4.3) 2.9 (2.2–3.6)
Kentucky 26.2 (24.3–28.1) 22.4 (20.6–24.2) 21.1 (19.3–22.8) 2.5 (1.7–3.2) †† †† 3.7 (2.8–4.5) 3.8 (2.9–4.7)
Louisiana 21.6 (20.0–23.3) 19.5 (17.9–21.1) 18.6 (17.1–20.2) 2.1 (1.6–2.7) †† †† 2.5 (1.8–3.1) 2.4 (1.8–3.0)
Maine 18.6 (16.8–20.3) 17.8 (16.1–19.6) 16.1 (14.4–17.8) 2.6 (1.9–3.3) 0.8 (0.4–1.2) †† 1.8 (1.2–2.5) ††
Maryland 13.7 (12.3–15.2) 12.5 (11.1–13.9) 10.1 (8.8–11.4) 2.2 (1.6–2.8) †† †† 2.2 (1.5–3.0) ††
Massachusetts 13.3 (11.9–14.7) 12.5 (11.1–13.9) 11.2 (10.0–12.5) 1.8 (1.3–2.4) †† †† 1.6 (1.1–2.1) ††
Michigan 19.9 (18.5–21.3) 18.3 (17.0–19.7) 16.3 (15.0–17.6) 2.5 (1.9–3.1) 0.5 (0.3–0.7) 1.0 (0.5–1.4) 2.9 (2.3–3.4) 1.6 (1.1–2.1)
Minnesota 19.1 (17.6–20.7) 16.4 (14.9–17.8) 14.3 (12.9–15.7) 2.9 (2.2–3.6) 0.5 (0.2–0.8) 0.7 (0.3–1.1) 2.6 (1.9–3.2) 2.4 (1.8–3.0)
Mississippi 23.0 (21.3–24.7) 19.7 (18.1–21.3) 18.5 (17.0–20.1) 2.5 (1.9–3.1) †† †† 2.0 (1.5–2.6) 3.6 (2.8–4.5)
Missouri 20.7 (19.0–22.5) 18.0 (16.3–19.6) 16.9 (15.3–18.5) 2.0 (1.4–2.7) †† †† 3.1 (2.4–3.9) 2.0 (1.4–2.7)
Montana 21.8 (20.0–23.6) 18.5 (16.8–20.2) 16.3 (14.7–17.9) 2.8 (2.0–3.6) 0.9 (0.4–1.4) †† 1.9 (1.3–2.5) 3.8 (3.0–4.6)
Nebraska 19.8 (18.0–21.6) 17.0 (15.3–18.7) 15.3 (13.7–17.0) 2.2 (1.5–2.8) †† †† 3.2 (2.3–4.0) 2.5 (1.8–3.2)
Nevada 16.6 (14.9–18.3) 15.6 (13.9–17.2) 14.1 (12.6–15.7) 1.4 (0.9–1.9) †† 1.4 (0.7–2.1) 2.5 (1.9–3.2) 0.6 (0.2–0.9)
New Hampshire 17.3 (15.7–18.9) 15.9 (14.4–17.5) 14.1 (12.6–15.5) 2.4 (1.7–3.1) †† †† 2.2 (1.5–2.8) 1.0 (0.6–1.5)
New Jersey 12.2 (10.9–13.5) 11.9 (10.6–13.2) 10.1 (8.9–11.3) 2.3 (1.7–2.9) †† †† 1.5 (1.0–2.0) ††
New Mexico 17.1 (15.5–18.8) 15.2 (13.6–16.8) 13.7 (12.2–15.2) 1.9 (1.2–2.5) †† †† 2.5 (1.8–3.1) 1.4 (0.9–1.9)
New York 14.5 (13.5–15.5) 13.8 (12.9–14.8) 12.2 (11.3–13.1) 2.2 (1.8–2.7) 0.3 (0.2–0.5) 0.6 (0.3–0.8) 1.6 (1.2–1.9) 0.6 (0.3–0.8)
North Carolina 20.4 (18.9–21.9) 17.7 (16.3–19.1) 16.0 (14.7–17.4) 2.8 (2.0–3.5) †† 0.7 (0.3–1.0) 2.8 (2.2–3.4) 2.2 (1.6–2.8)
North Dakota 22.6 (20.7–24.5) 19.0 (17.2–20.7) 17.7 (16.0–19.4) 2.1 (1.5–2.7) †† 0.9 (0.4–1.4) 2.2 (1.5–3.0) 4.9 (3.9–6.0)
Ohio 23.8 (22.5–25.2) 20.8 (19.6–22.1) 19.0 (17.8–20.2) 2.6 (2.1–3.2) 0.3 (0.1–0.5) 0.6 (0.3–0.9) 3.2 (2.6–3.8) 2.8 (2.2–3.3)
Oklahoma 23.8 (22.0–25.7) 19.7 (17.9–21.4) 18.5 (16.8–20.2) 2.5 (1.8–3.2) †† †† 3.6 (2.8–4.3) 4.3 (3.4–5.2)
Oregon 17.3 (15.7–19.0) 15.5 (13.9–17.1) 13.9 (12.4–15.4) 2.9 (2.1–3.7) 1.0 (0.6–1.5) †† 3.6 (2.7–4.4) 2.1 (1.4–2.8)
Pennsylvania 20.5 (19.2–21.8) 18.1 (16.9–19.3) 15.8 (14.7–17.0) 3.2 (2.6–3.8) 0.4 (0.2–0.6) †† 2.8 (2.2–3.4) 2.6 (2.1–3.1)
Rhode Island 15.5 (13.6–17.3) 14.3 (12.5–16.1) 11.6 (10.0–13.1) 2.6 (1.8–3.4) †† †† 2.5 (1.7–3.3) ††
South Carolina 20.7 (19.0–22.4) 19.1 (17.5–20.7) 17.7 (16.1–19.2) 2.5 (1.8–3.2) 0.5 (0.2–0.7) †† 2.8 (2.1–3.4) 1.2 (0.8–1.6)
South Dakota 23.0 (21.0–25.1) 19.5 (17.6–21.4) 18.8 (16.9–20.7) 2.3 (1.6–3.1) †† 1.1 (0.6–1.7) 2.0 (1.4–2.7) 4.0 (2.9–5.0)
Tennessee 22.7 (21.1–24.4) 19.7 (18.1–21.3) 18.2 (16.7–19.7) 2.2 (1.6–2.8) †† †† 3.2 (2.5–3.9) 2.8 (2.1–3.5)
Texas 17.0 (16.2–17.9) 15.0 (14.1–15.8) 13.5 (12.7–14.3) 2.1 (1.7–2.4) 0.2 (0.1–0.3) 0.6 (0.3–0.8) 2.4 (2.1–2.8) 1.9 (1.6–2.2)
Utah 10.9 (9.5–12.4) 8.9 (7.6–10.2) 8.0 (6.8–9.2) 1.0 (0.5–1.5) †† 0.9 (0.4–1.3) 3.1 (2.2–3.9) 1.3 (0.7–1.8)
Vermont 18.2 (16.5–19.9) 16.5 (14.9–18.1) 14.8 (13.3–16.3) 1.8 (1.2–2.4) 0.4 (0.2–0.7) †† 1.8 (1.1–2.5) 1.8 (1.2–2.4)
Virginia 17.1 (15.7–18.5) 15.6 (14.2–16.9) 13.2 (12.0–14.5) 2.4 (1.8–3.0) †† 1.1 (0.7–1.6) 2.3 (1.7–2.8) 1.4 (1.0–1.9)
Washington 16.8 (15.3–18.2) 14.8 (13.4–16.1) 12.8 (11.5–14.1) 2.7 (2.0–3.3) 0.8 (0.5–1.2) 0.8 (0.4–1.2) 2.5 (1.9–3.1) 2.2 (1.6–2.7)
West Virginia 26.9 (25.0–28.8) 23.1 (21.2–24.9) 21.7 (19.9–23.5) 1.9 (1.3–2.5) †† †† 3.8 (2.9–4.8) 4.8 (3.9–5.7)
Wisconsin 19.1 (17.5–20.7) 16.8 (15.3–18.3) 15.3 (13.9–16.7) 2.4 (1.7–3.0) †† †† 2.1 (1.5–2.6) 2.2 (1.5–2.9)
Wyoming 27.7 (25.6–29.7) 22.2 (20.3–24.1) 20.2 (18.4–22.0) 2.9 (2.1–3.8) †† †† 4.4 (3.5–5.2) 6.4 (5.2–7.6)

Abbreviation: CI = confidence interval.
* n = 163,920. Data were weighted to adjust for nonresponse and to yield representative estimates at the state level.
Persons who reported ever use of at least one of the six tobacco products assessed (cigarettes, cigars, regular pipe, water pipe, e-cigarette, and smokeless tobacco), and reported using the respective product “every day” or “some days” at the time of the survey.
§ Persons who reported having used cigarettes, cigars, regular pipe, or water pipe at least once during their lifetime and used “every day” or “some days” at the time of the survey.
Persons who reported having smoked ≥100 cigarettes during their lifetime and smoked “every day” or “some days” at the time of survey.
** Persons who reported having used the respective product at least once during their lifetime and used “every day” or “some days” at the time of the survey. Cigars include regular cigars, cigarillos or little filtered cigars. Smokeless tobacco includes moist snuff, dip, spit, chew tobacco, snus, or dissolvable tobacco.
†† Estimates not presented because of relative standard error (RSE) ≥30%.

Return to your place in the textFIGURE. Proportion of current cigarette smokers* who reported concurrent use of noncigarette product— Tobacco Use Supplement to the Current Population Survey, United States, 2014–2015

The figure above is a map of the United States showing that the proportion of current cigarette smokers who reported concurrent use of a noncigarette tobacco product ranged from 11.5% in Delaware to 32.3% in Oregon.

Abbreviation: DC = District of Columbia.

* Current cigarette smokers were persons who reported having smoked ≥100 cigarettes during their lifetime and smoked “every day” or “some days” at the time of survey (n = 23,232). Data were weighted to adjust for nonresponse and to yield representative estimates at the state level. The proportion of current cigarette smokers that reported concurrent use of a noncigarette tobacco product ranged from 11.5% in Delaware to 32.3% in Oregon.

Noncigarette tobacco products were five tobacco product types assessed in Tobacco Use Supplement to the Current Population Survey (TUS-CPS): cigars (regular cigars, cigarillos, or little filtered cigars), regular pipes, water pipes, electronic cigarettes, and smokeless tobacco products (moist snuff, dip, spit, chew tobacco, snus, and dissolvable tobacco).

Suggested citation for this article: Odani S, Armour BS, Graffunder CM, Willis G, Hartman AM, Agaku IT. State-Specific Prevalence of Tobacco Product Use Among Adults — United States, 2014–2015. MMWR Morb Mortal Wkly Rep 2018;67:97–102. DOI: http://dx.doi.org/10.15585/mmwr.mm6703a3.

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.

Bulletproof 360, Inc. Issues Allergy Alert on Undeclared Milk in Collagen Protein Dietary Supplement

Out of an abundance of caution, Bulletproof 360, Inc. is voluntarily recalling one lot #1017088 of Bulletproof Collagen Protein dietary supplement due to undeclared milk.  People who have an allergy or severe sensitivity to milk run the risk of serious or life-threatening allergic reaction if they consume this product.

The recalled product is Bulletproof Collagen Protein dietary supplement, packaged in a 16-oz. composite-film bag and has the UPC 8 15709 02115 3.  The affected lot #1017088 EXP03/19 is found on the back panel of a bag, and EXP03/19 means expires by March 2019.  All other lots of Bulletproof Collagen Protein and other products purchased at bulletproof.com are not affected by this recall.

Affected Bulletproof Collagen Protein was sold directly to distributors in CA, CO, CT, FL, GA, IA, IN, NH, NY, OR, PA, SC, TX, WA, WI and product has been further distributed to retail stores nationwide.   

This voluntary recall was initiated after we discovered that bulk whey (milk) protein was mis-labeled as collagen protein by our third-party manufacturer during the manufacturing process.  As a result, the affected Bulletproof Collagen Protein product contains the whey (milk) protein and the finished product label does not declare milk. 

Thisrecall is being made with the knowledge of the U.S. Food and Drug Administration.

Consumers who have purchased the affected product (lot # 1017088) and have an allergy or severe sensitivity to milk are urged not to consume it and should return product to the original location of the purchase for a full refund.

Consumers with questions related to the recall can contact our Customer Support Team at: support@bulletproof.com or reach out to us via phone at: (877) 651-9482, Monday through Friday, 8:00 am – 5:00 pm Pacific Standard Time. 

###

Recommendations of the Advisory Committee on Immunization …

Summary of Findings

As a result of the GRADE process, key outcomes were designated as critical (prevention of herpes zoster and postherpetic neuralgia, serious adverse events following vaccination) or important (duration of protection, reactogenicity). All outcomes were considered for both RZV and ZVL compared with no vaccination. There were no clinical studies that compared the vaccines directly with one another (head-to-head). Supporting evidence for the Work Group’s findings is available online (https://www.cdc.gov/vaccines/acip/recs/grade/herpes-zoster.html) (22).

Recombinant Zoster Vaccine (RZV). Efficacy of RZV was evaluated in a two-part, phase III multicenter clinical trial which enrolled >30,000 participants, who were randomized 1:1 to receive vaccine or saline placebo (14,15). The median follow-up time was 3.2 years for Zoster Efficacy Study in Adults 50 Years of Age or Older (ZOE-50) (14), and 3.7 years for Zoster Efficacy Study in Adults 70 Years of Age or Older (ZOE-70) (15). The efficacy for the prevention of herpes zoster was 96.6% (95% confidence interval [CI] = 89.6–99.3) in persons aged 50–59 years and 97.4% (95% CI = 90.1–99.7) in persons aged 60–69 years (14). Using pooled data from both study arms, vaccine efficacy was 91.3% (95% CI = 86.8–94.5) in participants aged ≥70 years (15). Vaccine efficacy in the first year after vaccination was 97.6% (95% CI = 90.9–99.8) and was 84.7% (95% CI = 69.0–93.4) or higher for the remaining 3 years of the study in persons aged ≥70 years. Efficacy for prevention of postherpetic neuralgia was 91.2% (95% CI = 75.9–97.7) in adults aged ≥50 years and 88.8% (95% CI = 68.7–97.1) in those aged ≥70 years (15).

Serious adverse events (an undesirable experience associated with the vaccine that results in death, hospitalization, disability or requires medical or surgical intervention to prevent a serious outcome) were examined in eight studies sponsored by GSK, which included 29,965 subjects (15,264 RZV recipients) (22). Overall, rates of serious adverse events over the study periods were similar in the RZV and placebo groups.

Injection-site and systemic grade 3 solicited adverse events (reactions related to vaccination which were severe enough to prevent normal activities) were actively surveyed in eight studies involving 10,590 subjects (22). Among the subset of subjects completing the 7-day diary card for reactogenicity in phase III clinical trials (9,936), 16.5% of vaccine recipients reported any grade 3 adverse event compared with 3.1% of placebo recipients (14,15). Grade 3 injection-site reactions (pain, redness, and swelling) were reported by 9.4% of vaccine recipients, compared with 0.3% of placebo recipients and grade 3 solicited systemic events (myalgia, fatigue, headache, shivering, fever, and gastrointestinal symptoms) were reported by 10.8% of vaccine recipients and 2.4% of placebo recipients (14,15). Whereas there were no differences in the proportions of local grade 3 reactions between dose 1 and dose 2, systemic grade 3 reactions were reported more frequently after dose 2 (1). Overall, the most common solicited adverse reactions (grade 1–3) were pain (78%), myalgia (45%), and fatigue (45%) (1).

Zoster Vaccine Live (ZVL). Two randomized clinical trials and seven observational studies were reviewed to evaluate the performance of a single dose of ZVL in preventing herpes zoster (22). A randomized clinical trial in persons aged 50–59 years found that the efficacy was 70% (95% CI = 54–81) (median follow-up time was 1.3 years) (12). A randomized trial in persons aged ≥60 years found that the efficacy was 64% (95% CI = 56–71) in persons aged 60–69 years and 38% (95% CI = 25–48) in persons aged ≥70 (median follow-up time was 3.1 years) (4). Estimates from observational studies and randomized controlled trials (RCTs) are consistent; observational estimates are within the 95% CI of the RCT estimates (22). The duration of protection has been studied out to 11 years, including the first 4 years of the RCT and then follow-on, nonblinded studies which used a modeled control group from years 7–11 (4,10,11). Shorter follow-up periods have been evaluated in observational studies using administrative health data (22). Studies concur that there is a substantial decrease in effectiveness following the first year after receipt of ZVL, and, by 6 years postvaccination, vaccine effectiveness against herpes zoster is <35% (10,2325). During years 7–8 postvaccination, observational study estimates of effectiveness ranged from 21%–32% (23,24). In the longest study of ZVL, estimates of effectiveness were no longer statistically significant 9–11 years postvaccination (11). In a phase III clinical trial, vaccine efficacy against post herpetic neuralgia was 65.7% (95% CI = 20.4–86.7) in persons aged 60–69 years and 66.8% (95% CI = 43.3–81.3) in participants aged ≥70 years (median follow-up of 3.1 years) (4); these estimates are consistent with estimates from observational studies (22). Notably, in observational studies, vaccine effectiveness against postherpetic neuralgia was longer-lasting than effectiveness against herpes zoster itself (23,26).

Serious adverse events related to ZVL were examined in eight high quality RCTs, 13 RCTs with limitations, and an additional seven observational studies (22). Overall, serious adverse events occurred at similar rates in vaccinated and placebo groups. Whereas injection site reactions were reported in 48% of vaccine recipients and 17% of placebo recipients in phase III clinical trials, post hoc analysis indicates that no more than 0.9% of vaccine recipients reported any given injection site symptom as grade 3 (22). In addition, in rare instances, ZVL vaccine strain has been documented to cause disseminated rash as well as herpes zoster in immunocompetent recipients (22,27), and life-threatening and fatal complications in immunocompromised recipients (28,29).

Cost effectiveness. The CDC analysis was conducted from a societal perspective over a lifetime. It estimated that vaccination with RZV, compared with no vaccination, cost $31,000 per quality adjusted life year (QALY), on average, for immunocompetent adults aged ≥50 years. The numbers of persons needed to be vaccinated with RZV to prevent one case of herpes zoster and one case of postherpetic neuralgia are 11–17 and 70–187, respectively. Estimates of costs per QALY for vaccination with RZV 8 weeks following ZVL (estimated by immediate revaccination in the model) ranged from $15,000 per QALY in persons aged 80–89 years to $117,000 per QALY for persons aged 50–59 years. Under most assumptions, vaccination with RZV prevented more disease at lower overall costs than did vaccination with ZVL. In probabilistic sensitivity analyses, 73.5% 2-dose completion (range = 38.8%–96.3%) coupled with 1-dose initial effectiveness estimates of 90% and 69% were applied, and RZV remained the most cost-effective strategy (13).

ACIP also reviewed independent cost-effectiveness analyses by an academic group (18), GSK (19), and Merck (Merck, unpublished data, 2017). The academic group estimated RZV costs per QALY of $30,000 when vaccination occurred at age 60 years. The GSK model estimated RZV costs per QALY of $12,000, on average, for recipients aged ≥60 years. Although analytic approaches and model inputs differed, both groups found that RZV was more cost effective than ZVL. Merck modeled vaccination at age ≥60 years and estimated $107,000 per QALY for RZV and $83,000 per QALY for ZVL, with ZVL as the most cost-effective vaccine in most scenarios.

Notes from the Field: Errors in Administration of an Excess …

Yellow fever vaccine (YF-VAX, Sanofi Pasteur, Swiftwater, Pennsylvania) is a live, attenuated virus vaccine recommended for persons aged ≥9 months who are traveling to or living in areas with risk for yellow fever virus transmission (1). For persons of all ages for whom vaccination is indicated, a single subcutaneous injection of 0.5 mL of reconstituted vaccine is used. Because no specific treatment for yellow fever exists, prevention through vaccination is critical to reduce yellow fever–associated morbidity and mortality (2). YF-VAX is the only yellow fever vaccine licensed in the United States, and approximately 500,000 doses are distributed annually to vaccinate military and civilian travelers. Yellow fever vaccine is supplied only to designated Yellow Fever Vaccination Centers authorized to issue certificates of yellow fever vaccination. YF-VAX is available in single-dose and 5-dose vials. Single-dose vials of lyophilized (freeze-dried) vaccine are supplied in a package of five vials of vaccine (Figure); five vials of diluent are provided separately (each vial of diluent contains 0.6 mL sodium chloride for injection USP). Five-dose vials are supplied in a package containing one vial (Figure), and diluent is supplied separately in one vial containing 3 mL sodium chloride for injection USP. The manufacturer’s instructions specify that the vaccine is to be used within 60 minutes of reconstituting either the single-dose or the 5-dose vial.

In March 2017, four persons at a single military clinic were vaccinated in error, each receiving an entire 5-dose vial of YF-VAX reconstituted with 0.6 mL of diluent before administration. No specific adverse events were reported; all persons were evaluated in an emergency department (ED) and released. The error was reported to the Vaccine Adverse Event Reporting System (VAERS) (3), which prompted CDC to search the VAERS database for similar reports of incorrect dosage administration of YF-VAX. Eleven reports of similar errors in vaccine administration were identified, including a cluster of seven persons vaccinated at another military clinic in 2007 and four other reports (one from a public health clinic in 2010, two from separate military clinics in 2011 and 2013, and one from an unknown type of clinic in 2013). Among the 15 patients identified, five were evaluated in an ED, and one had a doctor’s evaluation in a clinic. Only one report described symptoms; a man aged 30 years was evaluated in an ED for intermittent upper abdominal pain and arm pain 1 day after inadvertent receipt of a 5-dose vial; his symptoms resolved following supportive intravenous treatment.

Reports of similar administration errors are rare. Three Brazilian reports involved multidose vials of 17-DD yellow fever vaccine (Bio-Manguinhos, Rio de Janeiro, Brazil) used in mass vaccination campaigns (46); 14 health care workers were asymptomatic following receipt of a 25-fold overdose (4); one person received a 12.5-fold overdose but was lost to follow up (5); and a 45-day clinical follow up of 49 persons who received a 10-fold overdose identified one child who was hospitalized for evaluation of possible acute viscerotropism and recovered (6).

Most reports did not involve an adverse event, but the error was costly in terms of follow-up medical evaluation and vaccine waste. Vaccine providers should follow the instructions provided with YF-VAX; preventive measures such as more distinctive packaging and in-service training in clinics that stock both the single and multidose vials might be helpful.

Acknowledgment

Paige Lewis MSPH, Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, CDC.


Corresponding author: Michael M. McNeil, mmm2@cdc.gov, 404-498-0661.

References

  1. Staples JE, Bocchini JA Jr, Rubin L, Fischer M. Yellow fever vaccine booster doses: recommendations of the Advisory Committee on Immunization Practices, 2015. MMWR Morb Mortal Wkly Rep 2015;64:647–50. PubMed
  2. Staples JE, Gershman M, Fischer M. Yellow fever vaccine: recommendations of the Advisory Committee on Immunization Practices (ACIP). MMWR Recomm Rep 2010;59(No. RR-7). PubMed
  3. Shimabukuro TT, Nguyen M, Martin D, DeStefano F. Safety monitoring in the Vaccine Adverse Event Reporting System (VAERS). Vaccine 2015;33:4398–405. CrossRef PubMed
  4. Rabello A, Orsini M, Disch J, et al. Low frequency of side effects following an incidental 25 times concentrated dose of yellow fever vaccine. Rev Soc Bras Med Trop 2002;35:177–80. CrossRef PubMed
  5. Nishioka SA, Lomônaco AF. Overdose of yellow fever vaccine: a preventable error? [Letter] [Portuguese]. Rev Soc Brasil Med Trop 2002;35:541–2. PubMed
  6. Carneiro M, Lara BS, Schimidt B, Gais L. Overdose of yellow fever vaccine in the southern region of Brazil [Portuguese]. Rev Soc Brasil Med Trop 2011;44:252–3. PubMed
Return to your place in the textFIGURE. Yellow fever vaccine (YF-VAX, Sanofi Pasteur, Swiftwater, Pennsylvania) supplied as five single-dose vials (top) and one 5-dose vial (bottom)*

The figure above consists of two photos of yellow fever vaccine packages. The top photo shows the package for five single-dose vials, and the bottom photo shows the package for the 5-dose vial.

Photo/Sanofi Pasteur

* Arrows indicate package identification of number of doses supplied.

The figure above consists of two photos of yellow fever vaccine packages. The top photo shows the package for five single-dose vials, and the bottom photo shows the package for the 5-dose vial.

Suggested citation for this article: McNeil MM, Hibbs BF, Miller ER, Cano MV. Notes from the Field: Errors in Administration of an Excess Dosage of Yellow Fever Vaccine — United States, 2017. MMWR Morb Mortal Wkly Rep 2018;67:109–110. DOI: http://dx.doi.org/10.15585/mmwr.mm6703a6.

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.
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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.

NIMH » Ask Suicide-Screening Questions (ASQ) Toolkit

Ask Suicide-Screening Questions

Overview

The Ask Suicide-Screening Questions (ASQ) Toolkit is a free resource for medical settings (emergency department, inpatient medical/surgical units, outpatient clinics/primary care) that can help nurses or physicians successfully identify youth at risk for suicide. The ASQ is a set of four screening questions that takes 20 seconds to administer. In an NIMH study, a “yes” response to one or more of the four questions identified 97% of youth (aged 10 to 21 years) at risk for suicide. By enabling early identification and assessment of young patients at high risk for suicide, the ASQ toolkit can play a key role in suicide prevention.

Background

Suicide is a global public health problem and the second leading cause of death for young people ages 10-24 worldwide. Suicide is also a major public health concern in the United States. According to the Centers for Disease Control and Prevention (CDC), more than 5,900 youths killed themselves in 2015. Even more common than death by suicide are suicide attempts and suicidal thoughts.

Screening for Suicide Risk

Early detection is a critical prevention strategy. The majority of people who die by suicide visit a healthcare provider within months before their death. This represents a tremendous opportunity to identify those at risk and connect them with mental health resources. Yet, most healthcare settings do not screen for suicide risk. In February 2016, the Joint Commission, the accrediting organization for health care programs in hospitals throughout the United States, issued a Sentinel Event Alert recommending that all medical patients in all medical settings (inpatient hospital units, outpatient practices, emergency departments) be screened for suicide risk. Using valid suicide risk screening tools that have been tested in the medical setting and with youth, will help clinicians accurately detect who is at risk and who needs further intervention.

About the Tool

Beginning in 2008, NIMH led a multisite study to develop and validate a suicide risk screening tool for youth in the medical setting called the Ask Suicide-Screening Questions (ASQ). The ASQ consists of four yes/no questions and takes only 20 seconds to administer. Screening identifies individuals that require further mental health/suicide safety assessment.

For medical settings, one of the biggest barriers to screening is how to effectively and efficiently manage the patients that screen positive. Prior to screening for suicide risk, each setting will need to have a plan in place to manage patients that screen positive. The ASQ Toolkit was developed to assist with this management plan and to aid implementation of suicide risk screening and provide tools for the management of patients who are found to be at risk.

Using the Toolkit

The Ask Suicide-Screening Questions (ASQ) toolkit is designed for screening youth ages 10-24 (for patients with mental health chief complaints, consider screening below age 10). The ASQ is free of charge and available in multiple languages, including Spanish, Portuguese, French, Arabic, Dutch, Hebrew, Mandarin, and Korean.

It is recommended that screening be conducted without the parent/guardian present. Refer to the nursing script for guidance on requesting that the parent/guardian leave the room during screening. If the parent/guardian refuses to leave or the child insists that they stay, conduct the screening with the parent/guardian present.

What happens if patients screen positive?

Patients who screen positive for suicide risk on the ASQ should receive a brief suicide safety assessment (BSSA) conducted by a trained clinician (e.g., social worker, nurse practitioner, physician assistant, physician, or other mental health clinicians) to determine if a more comprehensive mental health evaluation is needed. The BSSA should be brief and guides what happens next in each setting. Any patient that screens positive, regardless of disposition, should be given the Patient Resource List.

The ASQ toolkit is organized by the medical setting in which it will be used: emergency department, inpatient medical/surgical unit, and outpatient primary care and specialty clinics. For questions regarding toolkit materials or implementing suicide risk screening, please contact: Lisa Horowitz, PhD, MPH at horowitzl@mail.nih.gov or Debbie Snyder, MSW at DeborahSnyder@mail.nih.gov.

Emergency Department (ED/ER):

Inpatient Medical/Surgical Unit:

Outpatient Primary Care/Specialty Clinics:

*Note: The following materials remain the same across all medical settings. These materials can be used in other settings with youth (e.g. school nursing office, juvenile detention centers).

Suicide Prevention Resources

National Suicide Prevention Lifeline
1-800-273-TALK (8255)
Spanish/Español: 1-888-628-9454

Crisis Text Line
Text HOME to 741-741

Suicide Prevention Resource Center

National Institute of Mental Health

Substance Abuse and Mental Health Services Administration

References

Horowitz, L., Ballard, E., Teach, S. J., Bosk, A., Rosenstein, D. L., Joshi, P., & Dalton, M. E. (2010). Feasibility of screening patients with nonpsychiatric complaints for suicide risk in a pediatric emergency department: a good time to talk?. Pediatric emergency care, 26(11), 787.

Horowitz, L. M., Bridge, J. A., Teach, S. J., Ballard, E., Klima, J., Rosenstein, D. L., … & Joshi, P. (2012). Ask Suicide-Screening Questions (ASQ): a brief instrument for the pediatric emergency department. Archives of pediatrics & adolescent medicine, 166(12), 1170-1176.

Ballard, E. D., Bosk, A., Snyder, D., Bridge, J. A., Wharff, E. A., Teach, S. J., & Horowitz, L. (2012). Patients’ opinions about suicide screening in a pediatric emergency department. Pediatric emergency care, 28(1), 34.

Horowitz, L. M., Bridge, J. A., Pao, M., & Boudreaux, E. D. (2014). Screening youth for suicide risk in medical settings: time to ask questions. American journal of preventive medicine, 47(3), S170-S175.

Ross, A. M., White, E., Powell, D., Nelson, S., Horowitz, L., & Wharff, E. (2016). To ask or not to ask? Opinions of pediatric medical inpatients about suicide risk screening in the hospital. The Journal of pediatrics, 170, 295-300.

Ballard, E. D., Cwik, M., Van Eck, K., Goldstein, M., Alfes, C., Wilson, M. E., … & Wilcox, H. C. (2017). Identification of at-risk youth by suicide screening in a pediatric emergency department. Prevention science, 18(2), 174-182.

Newton, A. S., Soleimani, A., Kirkland, S. W., & Gokiert, R. J. (2017). A systematic review of instruments to identify mental health and substance use problems among children in the emergency department. Academic Emergency Medicine, 24(5), 552-568.

National Frozen Foods Corporation Recalls Frozen Green Beans and Frozen Mixed Vegetables Because of Possible Health Risk

National Frozen Foods Corporation (NFFC) is voluntarily recalling a limited quantity of Not-Ready-To Eat Individually Quick Frozen (IQF) green beans and IQF mixed vegetables because they have the potential to be contaminated with Listeria monocytogenes, an organism which can cause serious and sometimes fatal 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, listeria infection can cause miscarriages and stillbirths among pregnant women.

Following cooking preparation instructions on the labels of master cases or packages will effectively reduce the risk of exposure to this bacterium

This press release includes recalled products distributed to foodservice accounts in bulk and packaged containers in AK, AR, AZ, CA, CO, FL, ID, IL, KS, MI, MS, MT, ND, NE, NM, NV, OR, TN, TX, VA, and WA. The products being recalled listed below were distributed between August 18, 2017 and January 12, 2018.

The recalled items can be identified by the date codes printed on the back of the 32oz. sized bag or the side of the master case. Only the following codes are affected by this recall:

Brand Listed on Packaging Commodity Net Weight NFFC Item #
Customer #
Lot Information / Code Printed On Packaging
Bountiful Harvest Foundations Cut Green Beans 30 LB. 22486-11918
2229881
17100903A02
Bountiful Harvest Originals Cut Green Beans 30 LB. 22486-11908
2229871
17100903A02
Monarch Capri Blend 20 LB. 58108-00602
670229
38511-7H11G3N
Monarch Capri Blend 20 LB. 58108-00602
670229
38511-7H11H3N
NW Treasure Cut Green Beans 30 LB. 62406-90007
3828405
17102603A02
Simplot Classic Meadow Blend 32 oz. 71179-67166 965AUG081705H
Sysco Classic Cut Green Beans 32 oz. 74865-04977
1435197
17102703A03
Sysco Imperial Whole Green Beans 32 oz. 74865-24917
2101855
17102703A03
(No Brand Name) Cut Green Beans 30 LB. 15001-01070 38627-7H28A3N
(No Brand Name) Cut Green Beans 30 LB. 15001-01070 38627-7H28B3N
(No Brand Name) Cut Green Beans 30 LB. 15001-01070 38627-7H28C3N
(No Brand Name) Cut Green Beans 30 LB. 15001-01070 38627-7H28D3N
(No Brand Name) Cut Green Beans 30 LB. 15001-01070 38627-7H28E3N
Valamont* Cut Green Beans 32 oz. 72608-12082 38474-7H08F3N
Valamont Cut Green Beans 20 LB. 72608-12150 38510-7H11F3N
The World’s Harvest* Cut Green Beans 32 oz. WRH99-FV021 38475-7H08F3N
The World’s Harvest* Cut Green Beans 32oz. WRH99-FV021 38475-7H08G3N

* The 32 oz. inner clear poly bag has only lot code printed.

The recall was initiated based on a 3rd party test result of the IQF Green Beans only by a downstream customer that revealed that the finished products may potentially be contaminated with the bacteria. There has been no report of human illness to date.

The frozen green beans and frozen mixed vegetables are being recalled as a precaution with the health and safety of consumers as top priority and in full cooperation with the FDA. The recall has not yet been classified by the FDA.

Consumers should not consume these products. Consumers who purchased affected products may return them to the place of purchase for a full refund. Consumers with questions may contact the company at 1-800-253-8269, Monday – Friday 7:30 a.m. to 4 p.m. (Pacific Time).

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Rich Products Corporation Recalls Beef Products due to Possible Listeria Contamination

WASHINGTON, Jan. 24, 2018 – Rich Products Corporation, a Vineland, N.J. establishment, is recalling approximately 3,420 pounds of beef meatball products that may be adulterated with Listeria monocytogenes, the U.S. Department of Agriculture’s Food Safety and Inspection Service (FSIS) announced today.

The ready-to-eat (RTE) frozen beef meatball items were produced on Dec. 17, 2017. The following products are subject to recall: [View Label]

  • 36-lb. cases containing six 6-lb. bags of “Member’s Mark Casa DI BERTACCHI ITALIAN STYLE BEEF MEATBALLS” with a “Best if Used By 17 DEC 2018” label and a lot code of 15507351 on the packaging.

The products subject to recall bear establishment number “EST. 5336” inside the USDA mark of inspection. These items were shipped to distributors in Alabama, Florida, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Texas, and Virginia.                                 

The problem was discovered on Jan. 24, 2018 when FSIS received notification from the firm that they shipped adulterated product into commerce.

Consumption of food contaminated with L. monocytogenes can cause listeriosis, a serious infection that primarily affects older adults, persons with weakened immune systems, and pregnant women and their newborns. Less commonly, persons outside these risk groups are affected.

Listeriosis can cause fever, muscle aches, headache, stiff neck, confusion, loss of balance and convulsions sometimes preceded by diarrhea or other gastrointestinal symptoms. An invasive infection spreads beyond the gastrointestinal tract. In pregnant women, the infection can cause miscarriages, stillbirths, premature delivery or life-threatening infection of the newborn. In addition, serious and sometimes fatal infections in older adults and persons with weakened immune systems. Listeriosis is treated with antibiotics. Persons in the higher-risk categories who experience flu-like symptoms within two months after eating contaminated food should seek medical care and tell the health care provider about eating the contaminated food.

FSIS and the company are concerned that some product may be frozen and in consumers’ freezers.

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. When available, the retail distribution list(s) will be posted on the FSIS website at www.fsis.usda.gov/recalls.

Consumers with questions regarding the recall can contact Customer Care at Rich Products Corporation at 1-800-356-7094. Media with questions regarding the recall can contact Dwight Gram, Vice President Communications and Public Relations, at 716-878-8749.

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.

Product Label

Yellow Fever in Brazil – Alert – Level 2, Practice Enhanced Precautions – Travel Health Notices | Travelers’ Health

Warning – Level 3, Avoid Nonessential Travel

Alert – Level 2, Practice Enhanced Precautions

Watch – Level 1, Practice Usual Precautions

In early 2017, the Brazilian Ministry of Health reported outbreaks of yellow fever in several eastern states, including areas where yellow fever was not traditionally considered to be a risk. Although virus spread decreased by mid-2017, yellow fever cases have reappeared in several states since the end of 2017, especially in São Paulo State, including areas close to the city of São Paulo. In early 2018, the GeoSentinel Surveillance System reported a case of yellow fever in a Dutch traveler who had stayed near the São Paulo metropolitan region.

In response to the outbreak, the World Health Organization expanded the list of areas where yellow fever vaccination is recommended for international travelers to Brazil. Most recently, the city of São Paulo was added to this expanded list (below). Please note that this list contains temporary expanded vaccine recommendations.

In addition to areas in Brazil where yellow fever vaccination has been recommended since before the recent outbreaks, it is now also recommended for people who are traveling to or living in:

  • All of Espirito Santo State.
  • All of Rio de Janeiro State, including the city of Rio de Janeiro.
  • All of São Paulo State, including the entire city of São Paulo.
  • A number of cities in Bahia State.

The Brazilian Ministry of Health maintains a regular list of all other cities in Brazil for which yellow fever vaccination has been recommended since before the recent outbreaks. This list does not include recently added areas above. It is located at http://portalsaude.saude.gov.br/images/pdf/2015/novembro/19/Lista-de-Municipios-ACRV-Febre-Amarela-Set-2015.pdf.

Note: Because yellow fever vaccination was previously recommended (and continues to be recommended) in western parts of the states of São Paulo and Bahia, some cities in each of these states are included on the regular list.

Who should get this vaccine?

Anyone 9 months or older who travels to or lives in these areas should be vaccinated against yellow fever. Because of current limitations in the availability of yellow fever vaccine in the United States, travelers may need to contact a yellow fever vaccine provider well in advance of travel.

Expanded Yellow Fever Vaccine Recommendation Areas in Brazil

Clinician Information:

Additional Information:

Yellow Fever in Brazil – Alert – Level 2, Practice Enhanced Precautions – Travel Health Notices | Travelers’ Health

Warning – Level 3, Avoid Nonessential Travel

Alert – Level 2, Practice Enhanced Precautions

Watch – Level 1, Practice Usual Precautions

In early 2017, the Brazilian Ministry of Health reported outbreaks of yellow fever in several eastern states, including areas where yellow fever was not traditionally considered to be a risk. Although virus spread decreased by mid-2017, yellow fever cases have reappeared in several states since the end of 2017, especially in São Paulo State, including areas close to the city of São Paulo. In early 2018, the GeoSentinel Surveillance System reported a case of yellow fever in a Dutch traveler who had stayed near the São Paulo metropolitan region.

In response to the outbreak, the World Health Organization expanded the list of areas where yellow fever vaccination is recommended for international travelers to Brazil. Most recently, the city of São Paulo was added to this expanded list (below). Please note that this list contains temporary expanded vaccine recommendations.

In addition to areas in Brazil where yellow fever vaccination has been recommended since before the recent outbreaks, it is now also recommended for people who are traveling to or living in:

  • All of Espirito Santo State.
  • All of Rio de Janeiro State, including the city of Rio de Janeiro.
  • All of São Paulo State, including the entire city of São Paulo.
  • A number of cities in Bahia State.

The Brazilian Ministry of Health maintains a regular list of all other cities in Brazil for which yellow fever vaccination has been recommended since before the recent outbreaks. This list does not include recently added areas above. It is located at http://portalsaude.saude.gov.br/images/pdf/2015/novembro/19/Lista-de-Municipios-ACRV-Febre-Amarela-Set-2015.pdf.

Note: Because yellow fever vaccination was previously recommended (and continues to be recommended) in western parts of the states of São Paulo and Bahia, some cities in each of these states are included on the regular list.

Who should get this vaccine?

Anyone 9 months or older who travels to or lives in these areas should be vaccinated against yellow fever. Because of current limitations in the availability of yellow fever vaccine in the United States, travelers may need to contact a yellow fever vaccine provider well in advance of travel.

Expanded Yellow Fever Vaccine Recommendation Areas in Brazil

Clinician Information:

Additional Information:

Perdue Foods LLC Recalls Chicken Products due to Misbranding and Undeclared Allergens

WASHINGTON, January 23, 2018 – Perdue Foods LLC, a Monterey, Tenn. establishment, is recalling approximately 530 pounds of chicken products due to misbranding and undeclared allergens, the U.S. Department of Agriculture’s Food Safety and Inspection Service (FSIS) announced today. The product contains eggs, a known allergen which is not declared on the product label. 

The ready-to-cook chicken breast tenderloin fritters were inadvertently labeled with the Homestyle Chicken Tender Fritter label. The chicken breast tenderloin fritters contain egg whites and the Homestyle Chicken Tender Fritters do not. The ready-to-cook chicken tenderloin fritter items were produced on December 6, 2017. The following products are subject to recall: [View Label]

  • 10-lb. boxes containing two 5 lb. plastic bags with the box labeled “CHEF REDI HOMESTYLE CHICKEN TENDER FRITTER, RTC – LARGE” with the case code of 7374.

The products subject to recall bear establishment number “P-11507” inside the USDA mark of inspection. These items were shipped to food service locations in Florida, Maryland and Washington D.C.                     

The problem was discovered by the firm while performing routine label verification activities.

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. When available, the retail distribution list(s) will be posted on the FSIS website at www.fsis.usda.gov/recalls.

Consumers with questions about the recall can contact Perdue Consumer Relations, at 1-877-727-3447. Media with questions about the recall can contact Joe Forsthoffer, Director Corporate Communications, at (410) 341-2556.

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.

Product Label

Products Pacheco Inc Issues Allergy Alert For Undeclared Wheat, Soy, Egg, Milk and Undeclared Artificial Coloring in its Bakery Products

“Mallorca” Bread 16 oz 039941001098 Undeclared Allergen: Soy Bread 16 oz 039941001012 Undeclared Allergen: Wheat, Soy “Pan con Pasas” Raisin Bread 16 oz 039941001081 Undeclared Allergen: Wheat, Soy, Egg Mallorca Queso “Mallorca” Bread with Cheese 4 oz 039941001678 Undeclared Allergen: Soy, Wheat, Egg, Milk Mallorca Guava Queso “Mallorca” Bread with Guava and Cheese 4 oz 039941001647 Undeclared Allergen: Soy, Wheat, Egg, Milk “Polvorones” Cookies 4 oz 039941001241 Undeclared Allergen: Wheat, Soy Pound Cake 4 oz
12 oz 039941001265
039941001302 Undeclared Allergen: Wheat, Soy, Egg, Milk Bizcocho Zanahoria Carrot Cake 4 oz 039941001213 Undeclared Allergen: Wheat, Soy, Egg, Milk Bizcocho Maíz Corn Cake 4 oz 039941001203 Undeclared Allergen: Wheat, Soy, Egg, Milk Bizcocho Vainilla Queso Vanilla Cake with Cheese 4 oz
12 oz 039941001180
039941001180 Undeclared Allergen: Wheat, Soy, Egg, Milk
Color: Yellow #5, Yellow #6 Bizcocho Vainilla Guava Vanilla Cake with Guava 4 oz 039941001173 Undeclared Allergen: Wheat, Soy, Egg, Milk
Color: Red #40 Bizcocho Vainilla Queso Guava Vanilla Cake with Cheese and Guava

12 oz

039941001166 Undeclared Allergen: Soy, Wheat, Egg, Milk
Color: Red #40, Yellow #5, Yellow #6

Measles in Serbia – 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?

Health officials in Serbia have reported an outbreak of measles.

Measles is a disease caused by a virus that is spread through the air by breathing, coughing, or sneezing. Measles virus is highly contagious and can remain so for up to 2 hours in the air or on surfaces. Symptoms of measles are rash, high fever, cough, runny nose, and red, watery eyes.

CDC recommends that travelers to Serbia protect themselves by making sure they are vaccinated against measles with the MMR (measles, mumps, and rubella) vaccine. Before departure from the United States, infants (6 through 11 months of age) should have 1 dose of MMR vaccine, and adults and children over 1 year of age should have 2 doses of MMR vaccine separated by at least 28 days.

Clinicians should keep measles in mind when treating patients with fever and rash, especially if the patient has recently traveled internationally.

What can travelers do to protect themselves?

  • Get a measles vaccine, or make sure you have already been vaccinated.
  • Avoid contact with people who are sick.

Learn more about preventing measles and what to do if you think you have it on the measles page for travelers.

Traveler Information

Clinician Information

Radon: We Track That! | | Blogs

Tracking Network

CDC’s Tracking Network connects people with vital information on a variety of health and environmental topics. You can use data and information collected about radon to help determine individual and community risk for radon and inform community interventions.

Reduce Your Risk for Radon Exposure

In the United States, radon is the #2 cause of lung cancer after smoking and is estimated to cause over 20,000 deaths each year, according the U.S. Environmental Protection Agency.

Radon is a naturally occurring gas in rocks, soil, and groundwater that you cannot see, smell, or taste.

You can be exposed to radon primarily from breathing in radon that has comes in through cracks and gaps in buildings and homes.

Any home can have a radon problem. Testing is the only way to know if radon levels are high in your home. If radon levels in your home are above 4 picocuries per liter (pCi/L), the EPA recommends taking action [413 KB] to reduce your exposure.

Radon: We Track That

CDC funds 26 state and city health departments as part of the National Environmental Public Health Tracking Program, with the goal to better understand the connection between health and environment. Radon is one environmental health risk that many state tracking programs address. In collaboration with partners, the state tracking programs work to improve radon data to help identify communities at risk and inform testing. Some of the types of radon data that are collected include basic radon awareness, number of households that have tested for radon, households with elevated levels detected, and number of households that have fixed problems (mitigated) if they have high radon. The tracking programs display the data and information using interactive maps, charts, and tables.

Tracking Network map
Wisconsin Tracking map of radon test results over the radon action limit.

Explore state and local tracking programs’ radon data:

If your state is not listed here, you can find out more information about local radon zones and state contact information at EPA Map of Radon Zones.

Using Radon Data to Protect Communities

It’s not enough to just have data. Tracking programs have developed tools, applications, and products that go beyond data and help improve the health of communities. Here are a few examples:

  • New Hampshire Tracking increased radon testing through a radon testing media campaign.
  • Oregon Tracking created detailed radon hazard maps that increased radon testing.
  • See how the Washington Tracking Program’s improved radon exposure risk maps help keep residents safe in this Tracking in Action video.

For more information about the risks of radon, and the importance of getting your home tested for radon, visit CDC’s Radon Website.

Tweet this: “CDC’s Tracking Network. Radon: We Track That! It’s #RadonActionMonth! Protect Your Family. Learn steps to take to measure and reduce radon levels in your home: http://bit.ly/2AQ4yzz #CDCEHblog via @CDCEnvironment ”

CDC – Externship – Public Health Law

The word Internship written in chalk on a board
Interested in learning how law can be used as a tool to protect and promote the public’s health?

Law has been critical in achieving public health goals and serves as the foundation for governmental public health practice in the United States. Many of public health’s greatest successes, including high childhood immunization rates, improved motor vehicle safety, safer workplaces, and reduced tooth decay, are the result of legal interventions. Today, law plays an increasingly important role in addressing emerging public health threats such as childhood obesity, healthcare-associated infections, motor vehicle injuries, and prescription drug overdoses.

CDC’s Public Health Law Program (PHLP) is seeking motivated students who love a challenge and can bring a fresh perspective and innovative solutions to CDC. With rolling start and completion dates during the academic year, unpaid internships and externships with PHLP expose students to the public health field, allowing them to explore the role of law in advancing public health goals.

All of the following internships and externships consist of 9–14 weeks of professional work experience with PHLP in Atlanta, Georgia.

Public Health Law Internships and Externships

These internships and externships create formalized entry-level experience for rising and current third-year law students interested in exploring careers in public health law. This opportunity might be particularly appealing to law students who have conducted public health or other scientific research and/or worked with datasets and coding processes.

The internship and externship program features

  • Work experience on one or more core projects with mentorship from a PHLP staff attorney
  • Exposure to a complex, government work environment and a team of public health lawyers with diverse expertise
  • Involvement in work projects that impact the mission of PHLP and CDC
  • Opportunities to co-author published articles and other materials
  • Communication and mentorship with a dedicated preceptor throughout the externship experience
  • Active participation in weekly PHLP staff meetings
  • Opportunities to showcase experiences in an end-of-externship presentation

Tribal Public Health Law Internships and Externships

PHLP offers internships and externships in tribal public health law for current and rising third-year law students. PHLP maintains a core project track on tribal public health law, developing resources on both tribal and Indian law to support the use of law as a public health tool for tribes and American Indian and Alaska Native communities. As sovereign nations, tribes are uniquely situated to use law as a public health tool to promote the health and well-being of their communities. Federal law creates a framework that governs the relationships among tribes, states, and the federal government that can also affect tribal public health.

This internship and externship program features

  • Extensive research and writing experience on tribal and Indian law issues
  • Communication and mentorship with a dedicated project manager with experience in tribal and Indian law
  • Exposure to a complex, government work environment and a team of public health lawyers with diverse expertise
  • Involvement in work projects that carry out the mission of PHLP and CDC
  • Opportunities to co-author journal articles and other publications
  • Communication and mentorship with a dedicated preceptor throughout the externship experience
  • Opportunities to showcase experiences in an end-of-externship or end-of-internship presentation

Administrative and Communication Internships and Externships

PHLP is offering the Administrative and Communication Internship/Externship for students enrolled in masters-level programs earning degrees in public health, public policy, public administration, communication, business, or similar disciplines.

This internship/externship is an unpaid academic learning experience that offers an in-depth understanding of government agency operations and the role of law and policy in advancing public health. Interns are exposed to high-level strategic planning and other program functions, including marketing, communication, project management, and partner outreach and relations. 

Interns will help PHLP’s Workforce Development and Outreach Team leads with the day-to-day communication and training operations of a vigorous and dynamic government program and receive mentoring from PHLP’s director and other senior leaders.

Responsibilities

  • Help write and compile research for PHLP publications and communications, including Public Health Law News
  • Provide coverage highlights of state and federal congressional hearings, regulatory meetings, and other events driving news about public health
  • Help maintain listservs, communications archives, and PHLP’s website content
  • Help design and execute PHLP’s marketing and communication plans
  • Coordinate webinars and trainings with partners such as ChangeLab Solutions, American Bar Association, American Health Lawyers Association, and Network for Public Health Law  

How to Apply

To apply, please send a resume and cover letter to phlawprogram@cdc.gov. In the cover letter and email, indicate which internship or externship you are applying for. PHLP accepts applications by email only.

Only rising and current third-year law students will be considered for the internships and externships in public health law or in tribal public health law.

The PHLP administrative and communications internship/externship is for students enrolled in master’s-level programs earning a degree in public health, public policy, public administration, communications, business, or similar discipline.

Compensation

These internships/externships are unpaid. Internships are not tied to academic credit. Externship and practicum opportunities are available for students seeking academic credit as authorized by law schools or schools of public health.

Deadlines

  • Summer: January 31
  • Fall: April 30
  • Spring: October 1

Sun Noodle – New Jersey

Sun Noodle of Carlstadt, NJ is voluntarily recalling one lot of their retail Tonkotsu Ramen because it is mis-labeled. While it is labeled Tonkotsu, the actual flavor packet inside is Assari Shoyu, which contains an additional undeclared allergen of fish (sardines). Our concern is for those individuals who have an allergy or severe sensitivity to fish (sardines). They run the risk of a serious or life-threatening allergic reaction if they consume this product.

Only one lot of Tonkotsu Ramen was mislabeled, and was primarily sent to Asian food distributors in the New Jersey, Georgia, Illinois, Texas, and New York areas, and then on to retail stores. The product is packaged in a clear plastic clamshell, banded with a Sun Noodle label that reads “Tonkotsu Ramen.” The affected product contains 2 purple and white soup base packets, which correctly show the name “Assari Shoyu” flavor. The products affected by the recall are labeled with lot code 3117332.

No illnesses have been reported to date, and all affected product has already been removed from store shelves. We are sending out this release to alert any retail customers who may not have heard about it.

The decision to recall this product was initiated after it was discovered that the product had the wrong label applied to the product, and did not accurately represent what was packaged. Subsequent investigation indicated that the problem was caused by mis-labeling of the product at the New Jersey facility.

Note that only one particular lot of Tonkotsu Ramen was mislabeled. All other lots of Tonkotsu Ramen and all other products manufactured at other Sun Noodle locations are properly packed and labeled.

Consumers who have purchased any of the affected lot of Tonkotsu Ramen are urged to return the product to the original location of the purchase for a full refund. Consumers with questions may contact a toll free number at the company: 1-866-366-6353, Monday through Friday, 8:30 am – 5:30 pm Hawaii Standard Time.

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Trigo, Soya, Huevo, Leche Y Colorantes No Declarados En Sus Productos De Panaderia

Mallorca 16 oz 039941001098 Alergeno no declarado: Soya Pan 16 oz 039941001012 Alergeno no declarado: Trigo, Soya Pan con Pasas 16 oz 039941001081 Alergeno no declarado: Trigo, Soya, Huevo Mallorca Queso 4 oz 039941001678 Alergeno no declarado: Trigo, Soya, Leche, Huevo Mallorca Guava Queso 4 oz 039941001647 Alergeno no declarado: Trigo, Soya, Leche, Huevo Polvorones 4 oz 039941001241 Alergeno no declarado: Trigo, Soya Pound Cake 4 oz
12 oz 039941001265
039941001302 Alergeno no declarado: Trigo, Soya, Leche, Huevo Bizcocho Zanahoria 4 oz 039941001213 Alergeno no declarado: Trigo, Soya, Leche, Huevo Bizcocho Maíz 4 oz 039941001203 Alergeno no declarado: Trigo, Soya, Leche, Huevo Bizcocho Vainilla Queso 4 oz
12 oz 039941001180
039941001180 Alergeno no declarado: Trigo, Soya, Leche, Huevo
Color: Amarillo #5, Amarillo #6 Bizcocho Vainilla Guava 4 oz 039941001173 Alergeno no declarado: Trigo, Soya, Leche, Huevo
Color: Rojo #40 Bizcocho Vainilla Queso Guava

12 oz

039941001166 Alergeno no declarado: Trigo, Soya, Leche, Huevo
Color: Rojo #40, Amarillo #5, Amarillo #6

Yellow Fever in Brazil – Alert – Level 2, Practice Enhanced Precautions – Travel Health Notices | Travelers’ Health

Warning – Level 3, Avoid Nonessential Travel

Alert – Level 2, Practice Enhanced Precautions

Watch – Level 1, Practice Usual Precautions

In early 2017, the Brazilian Ministry of Health reported outbreaks of yellow fever in several eastern states, including areas where yellow fever was not traditionally considered to be a risk. Although virus spread decreased by mid-2017, yellow fever cases have reappeared in several states since the end of 2017, especially in São Paulo State, including areas close to the city of São Paulo. In early 2018, the GeoSentinel Surveillance System reported a case of yellow fever in a Dutch traveler who had stayed near the São Paulo metropolitan region.

In response to the outbreak, the World Health Organization expanded the list of areas where yellow fever vaccination is recommended for international travelers to Brazil. Most recently, the city of São Paulo was added to this expanded list (below). Please note that this list contains temporary expanded vaccine recommendations.

In addition to areas in Brazil where yellow fever vaccination has been recommended since before the recent outbreaks, it is now also recommended for people who are traveling to or living in:

  • All of Espirito Santo State.
  • All of Rio de Janeiro State, including the city of Rio de Janeiro.
  • All of São Paulo State, including the entire city of São Paulo.
  • A number of cities in Bahia State.

The Brazilian Ministry of Health maintains a regular list of all other cities in Brazil for which yellow fever vaccination has been recommended since before the recent outbreaks. This list does not include recently added areas above. It is located at http://portalsaude.saude.gov.br/images/pdf/2015/novembro/19/Lista-de-Municipios-ACRV-Febre-Amarela-Set-2015.pdf.

Note: Because yellow fever vaccination was previously recommended (and continues to be recommended) in western parts of the states of São Paulo and Bahia, some cities in each of these states are included on the regular list.

Who should get this vaccine?

Anyone 9 months or older who travels to or lives in these areas should be vaccinated against yellow fever. Because of current limitations in the availability of yellow fever vaccine in the United States, travelers may need to contact a yellow fever vaccine provider well in advance of travel.

Expanded Yellow Fever Vaccine Recommendation Areas in Brazil

Clinician Information:

Additional Information:

Break Ventures/California Basics Recalls “Zero For Him” 150ct Lot#1710-638 Because of Possible Health Risk

Break Ventures/California Basics is recalling its Dietary Supplement “Zero for Him 150ct” Lot#1710-638 (the “Product”) because it may be contaminated with Salmonella, an organism which can cause serious and sometimes fatal infections in young children, frail or elderly people, and others with weakened immune systems. Healthy persons infected with Salmonella often experience fever, diarrhea (which may be bloody), nausea, vomiting and abdominal pain. In rare circumstances, infection with Salmonella can result in the organism getting into the bloodstream and producing more severe illnesses such as arterial infections (i.e., infected aneurysms), endocarditis and arthritis.

The Product was distributed nationwide through Amazon. It comes in a 400cc, Light Amber PETE bottle marked with lot #1710-638 on the bottom and with an expiration date of November 2020 stamped on the side.

No illnesses have been reported to date in connection with the Product.

The potential for contamination was noted after routine testing by the company revealed the presence of Salmonellain 1 bottle. We have tested and re-tested the Product and no presence of Salmonellawas subsequently found. However, out of abundance of caution, we have decided to recall this Product.

Distribution of the Product has been suspended while FDA and the company continue their investigation as to the source of the Salmonellapresentin that aforementioned 1 bottle.

Consumers who have purchased Zero for Him Lot#1710-638 are urged to return them to the place of purchase for a full refund. Consumers with additional questions may contact the company at 323-375-5953.

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Arthri-D, LLC Recalls Arthri-D Lot#1701-092 Because of Possible Health Risk

Arthri-D, LLC is recalling its Dietary Supplement “Arthri-D 120ct” Lot#1701-092 (the “Product”) because it may be contaminated with Salmonella, an organism which can cause serious and sometimes fatal infections in young children, frail or elderly people, and others with weakened immune systems. Healthy persons infected with Salmonella often experience fever, diarrhea (which may be bloody), nausea, vomiting and abdominal pain. In rare circumstances, infection with Salmonella can result in the organism getting into the bloodstream and producing more severe illnesses such as arterial infections (i.e., infected aneurysms), endocarditis and arthritis.

The Product was distributed nationwide through mail orders. It comes in a 225 cc, white plastic HDPE bottle marked with lot #1701-092 on the label and with a manufacturing date of March 2017 stamped on the side.

No illnesses have been reported to date in connection with the Product.

The potential for contamination was noted after routine testing by the company revealed the presence of Salmonella in 1 bottle. We have tested and re-tested the Product and no presence of Salmonella was subsequently found. However, out of abundance of caution, we have decided to recall this Product.

Distribution of the Product has been suspended while FDA and the company continue their investigation as to the source of the Salmonella presentin that aforementioned 1 bottle.

Consumers who have purchased 120 count of Arthri-D lot#1701-092 are urged to return them to the place of purchase for a full refund. Consumers with additional questions may contact the company at 978-992-0505.

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JustFoodForDogs Voluntarily Recalls Three Daily Diets because of Possible Listeria monocytogenes Health Risk from Green Beans

JustFoodForDogs (JFFD) of Los Alamitos, CA, is voluntarily recalling its Beef & Russet Potato, Fish & Sweet Potato and Turducken dog food meals in all sizes. JFFD uses 100% USDA and other restaurant grade ingredients in all of its pet food, and because the green beans used in these meals may be contaminated with Listeria monocytogenes, the companyis alerting the public. Listeria monocytogenes can affect animals eating the product and there is risk to humans if they were to intentionally or unintentionally ingest the dog food or come into contact with contaminated feces from a pet that has eaten the food.

Listeriosis is rare in dogs and when infected most dogs have mild symptoms such as diarrhea and vomiting, however, more serious symptoms are possible, such as fever, muscular and respiratory signs, abortion and even death. In addition to the possibility of becoming sick, such infected animals can shed Listeria monocytogenes through their feces and thus serve as a source of infection to humans, especially if they have not thoroughly washed their hands. If your dog has consumed the recalled products and has these symptoms, please contact your veterinarian.

Healthy people infected with Listeria monocytogenes should monitor themselves for some or all of the following symptoms if they believe they may have consumed any of the above recalled items intentionally or unintentionally, or may have come into contact with contaminated feces from a pet that has eaten the food: nausea, vomiting, diarrhea, aches and fever. Rarely Listeria monocytogenes can result in more serious ailments including arterial infections, endocarditis and can be life threatening. Pregnant women are more susceptible to Listeria infection, which can result in abortion. The most common way that people are affected is by consumption of contaminated food. Consumers exhibiting these signs after consuming this product should contact their healthcare provider.

The recalled JFFD Beef & Russet Potato, Fish & Sweet Potato and Turducken was distributed only through 11 JFFD retail locations in Southern California, 3 Pet Food Express (PFE) locations in Southern California and 10 Northern California PFE locations. 

While testing demonstrates that only several dates of production were affected, out of an abundance of caution, JFFD is recalling all of the above mentioned products made from the dates of November 1, 2017 through January 14, 2018. These represent Best By lot code dates on all three of the JustFoodForDogs’ recalled products of 11/01/18 through 01/14/19.

The products being recalled were sold refrigerated or frozen and include all sizes offered – 7 oz, 14 oz, 18 oz and 72 oz. 

No confirmed Listeriosis has been reported but there have been reports of short-term symptoms in some dogs (diarrhea and vomiting).

There have been no reports of human illness to date.

The potential for contamination was discovered after a purchaser of JFFD products reported to JFFD veterinary medical staff that her dogs had become ill. Tests to date confirm that green beans purchased from a restaurant supply distributor were positive for Listeria monocytogenes.

The restaurant supplier/distributor of these green beans has voluntarily put a “product hold” on the distribution of these green beans to restaurants and other human food retailers.

JFFD is currently making these recipes available without green beans until the matter is resolved.

JFFD notified and is working with FDA on this matter.

Consumers who have purchased the recalled JFFD products from a JFFD store should contact JFFD at 866-726-9509, from 9:00 AM – 7:00PM PST seven days a week, for a full credit or refund. Consumers who have purchased the recalled products from Pet Food Express should return them to any Pet Food Express for a full credit or refund.

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Measles in Serbia – 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?

Health officials in Serbia have reported an outbreak of measles.

Measles is a disease caused by a virus that is spread through the air by breathing, coughing, or sneezing. Measles virus is highly contagious and can remain so for up to 2 hours in the air or on surfaces. Symptoms of measles are rash, high fever, cough, runny nose, and red, watery eyes.

CDC recommends that travelers to Serbia protect themselves by making sure they are vaccinated against measles with the MMR (measles, mumps, and rubella) vaccine. Before departure from the United States, infants (6 through 11 months of age) should have 1 dose of MMR vaccine, and adults and children over 1 year of age should have 2 doses of MMR vaccine separated by at least 28 days.

Clinicians should keep measles in mind when treating patients with fever and rash, especially if the patient has recently traveled internationally.

What can travelers do to protect themselves?

  • Get a measles vaccine, or make sure you have already been vaccinated.
  • Avoid contact with people who are sick.

Learn more about preventing measles and what to do if you think you have it on the measles page for travelers.

Traveler Information

Clinician Information

Yellow Fever in Brazil – Alert – Level 2, Practice Enhanced Precautions – Travel Health Notices | Travelers’ Health

Warning – Level 3, Avoid Nonessential Travel

Alert – Level 2, Practice Enhanced Precautions

Watch – Level 1, Practice Usual Precautions

In early 2017, the Brazilian Ministry of Health reported outbreaks of yellow fever in several eastern states, including areas where yellow fever was not traditionally considered to be a risk. Although virus spread decreased by mid-2017, yellow fever cases have reappeared in several states since the end of 2017, especially in São Paulo State, including areas close to the city of São Paulo. In early 2018, the GeoSentinel Surveillance System reported a case of yellow fever in a Dutch traveler who had stayed near the São Paulo metropolitan region.

In response to the outbreak, the World Health Organization expanded the list of areas where yellow fever vaccination is recommended for international travelers to Brazil. Most recently, the city of São Paulo was added to this expanded list (below). Please note that this list contains temporary expanded vaccine recommendations.

In addition to areas in Brazil where yellow fever vaccination has been recommended since before the recent outbreaks, it is now also recommended for people who are traveling to or living in:

  • All of Espirito Santo State.
  • All of Rio de Janeiro State, including the city of Rio de Janeiro.
  • All of São Paulo State, including the entire city of São Paulo.
  • A number of cities in Bahia State.

The Brazilian Ministry of Health maintains a regular list of all other cities in Brazil for which yellow fever vaccination has been recommended since before the recent outbreaks. This list does not include recently added areas above. It is located at http://portalsaude.saude.gov.br/images/pdf/2015/novembro/19/Lista-de-Municipios-ACRV-Febre-Amarela-Set-2015.pdf.

Note: Because yellow fever vaccination was previously recommended (and continues to be recommended) in western parts of the states of São Paulo and Bahia, some cities in each of these states are included on the regular list.

Who should get this vaccine?

Anyone 9 months or older who travels to or lives in these areas should be vaccinated against yellow fever. Because of current limitations in the availability of yellow fever vaccine in the United States, travelers may need to contact a yellow fever vaccine provider well in advance of travel.

Expanded Yellow Fever Vaccine Recommendation Areas in Brazil

Clinician Information:

Additional Information: