Wayne H Giles’s research while affiliated with Office of Disease Prevention, National Institutes of Health and other places

Cardiovascular disparities remain pervasive in the United States. Unequal disease burden is evident among population groups based on sex, race, ethnicity, socioeconomic status, educational attainment, nativity, or geography. Despite the significant declines in cardiovascular disease mortality rates in all demographic groups during the last 50 years, large disparities remain by sex, race, ethnicity, and geography. Recent data from modeling studies, linked micromap plots, and small-area analyses also demonstrate prominent variation in cardiovascular disease mortality rates across states and counties, with an especially high disease burden in the southeastern United States and Appalachia. Despite these continued disparities, few large-scale intervention studies have been conducted in these high-burden populations to examine the feasibility of reducing or eliminating cardiovascular disparities. To address this challenge, on June 22 and 23, 2017, the National Heart, Lung, and Blood Institute convened experts from a broad range of biomedical, behavioral, environmental, implementation, and social science backgrounds to summarize the current state of knowledge of cardiovascular disease disparities and propose intervention strategies aligned with the National Heart, Lung, and Blood Institute mission. This report presents the themes, challenges, opportunities, available resources, and recommended actions discussed at the workshop.

Background: Although the overall life expectancy at birth has increased for both blacks and whites and the gap between these populations has narrowed, disparities in life expectancy and the leading causes of death for blacks compared with whites in the United States remain substantial. Understanding how factors that influence these disparities vary across the life span might enhance the targeting of appropriate interventions. Methods: Trends during 1999-2015 in mortality rates for the leading causes of death were examined by black and white race and age group. Multiple 2014 and 2015 national data sources were analyzed to compare blacks with whites in selected age groups by sociodemographic characteristics, self-reported health behaviors, health-related quality of life indicators, use of health services, and chronic conditions. Results: During 1999-2015, age-adjusted death rates decreased significantly in both populations, with rates declining more sharply among blacks for most leading causes of death. Thus, the disparity gap in all-cause mortality rates narrowed from 33% in 1999 to 16% in 2015. However, during 2015, blacks still had higher death rates than whites for all-cause mortality in all groups aged <65 years. Compared with whites, blacks in age groups <65 years had higher levels of some self-reported risk factors and chronic diseases and mortality from cardiovascular diseases and cancer, diseases that are most common among persons aged ≥65 years. Conclusions and implications for public health practice: To continue to reduce the gap in health disparities, these findings suggest an ongoing need for universal and targeted interventions that address the leading causes of deaths among blacks (especially cardiovascular disease and cancer and their risk factors) across the life span and create equal opportunities for health.

Problem/Condition: Persons living in rural areas are recognized as a health disparity population because the prevalence of disease and rate of premature death are higher than for the overall population of the United States. Surveillance data about health-related behaviors are rarely reported by urban-rural status, which makes comparisons difficult among persons living in metropolitan and nonmetropolitan counties. Reporting Period: 2013. Description of System: The Behavioral Risk Factor Surveillance System (BRFSS) is an ongoing, state-based, random-digit-dialed landline- and cellular-telephone survey of noninstitutionalized adults aged ≥18 years residing in the United States. BRFSS collects data on health-risk behaviors, chronic diseases and conditions, access to health care, and use of preventive health services related to the leading causes of death and disability. BRFSS data were analyzed for 398,208 adults aged ≥18 years to estimate the prevalence of five self-reported health-related behaviors (sufficient sleep, current nonsmoking, nondrinking or moderate drinking, maintaining normal body weight, and meeting aerobic leisure time physical activity recommendations) by urban-rural status. For this report, rural is defined as the noncore counties described in the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties. Results: Approximately one third of U.S. adults practice at least four of these five behaviors. Compared with adults living in the four types of metropolitan counties (large central metropolitan, large fringe metropolitan, medium metropolitan, and small metropolitan), adults living in the two types of nonmetropolitan counties (micropolitan and noncore) did not differ in the prevalence of sufficient sleep; had higher prevalence of nondrinking or moderate drinking; and had lower prevalence of current nonsmoking, maintaining normal body weight, and meeting aerobic leisure time physical activity recommendations. The overall age-adjusted prevalence of reporting at least four of the five health-related behaviors was 30.4%. The prevalence among the estimated 13.3 million adults living in noncore counties was lower (27.0%) than among those in micropolitan counties (28.8%), small metropolitan counties (29.5%), medium metropolitan counties (30.5%), large fringe metropolitan counties (30.2%), and large metropolitan centers (31.7%). Interpretation: This is the first report of the prevalence of these five health-related behaviors for the six urban-rural categories. Nonmetropolitan counties have lower prevalence of three and clustering of at least four health-related behaviors that are associated with the leading chronic disease causes of death. Prevalence of sufficient sleep was consistently low and did not differ by urban-rural status. Public Health Action: Chronic disease prevention efforts focus on improving the communities, schools, worksites, and health systems in which persons live, learn, work, and play. Evidence-based strategies to improve health-related behaviors in the population of the United States can be used to reach the Healthy People 2020 objectives for these five self-reported health-related behaviors (sufficient sleep, current nonsmoking, nondrinking or moderate drinking, maintaining normal body weight, and meeting aerobic leisure time physical activity recommendations). These findings suggest an ongoing need to increase public awareness and public education, particularly in rural counties where prevalence of these health-related behaviors is lowest.

Introduction: Five key health-related behaviors for chronic disease prevention are never smoking, getting regular physical activity, consuming no alcohol or only moderate amounts, maintaining a normal body weight, and obtaining daily sufficient sleep. The objective of this study was to estimate the clustering of these 5 health-related behaviors among adults aged 21 years or older in each state and the District of Columbia and to assess geographic variation in clustering. Methods: We used data from the 2013 Behavioral Risk Factor Surveillance System (BRFSS) to assess the clustering of the 5 behaviors among 395,343 BRFSS respondents aged 21 years or older. The 5 behaviors were defined as currently not smoking cigarettes, meeting the aerobic physical activity recommendation, consuming no alcohol or only moderate amounts, maintaining a normal body mass index (BMI), and sleeping at least 7 hours per 24-hour period. Prevalence of having 4 or 5 of these behaviors, by state, was also examined. Results: Among US adults, 81.6% were current nonsmokers, 63.9% obtained 7 hours or more sleep per day, 63.1% reported moderate or no alcohol consumption, 50.4% met physical activity recommendations, and 32.5% had a normal BMI. Only 1.4% of respondents engaged in none of the 5 behaviors; 8.4%, 1 behavior; 24.3%, 2 behaviors; 35.4%, 3 behaviors; and 24.3%, 4 behaviors; only 6.3% reported engaging in all 5 behaviors. The highest prevalence of engaging in 4 or 5 behaviors was clustered in the Pacific and Rocky Mountain states. Lowest prevalence was in the southern states and along the Ohio River. Conclusion: Additional efforts are needed to increase the proportion of the population that engages in all 5 health-related behaviors and to eliminate geographic variation. Collaborative efforts in health care systems, communities, work sites, and schools can promote all 5 behaviors and produce population-wide changes, especially among the socioeconomically disadvantaged.

Objective: To determine variability of arthritis prevalence in 4 US population health surveys. Methods: We estimated annualized arthritis prevalence in 2011/12, among adults ≥ 20 years, using 2 definition methods, both based on self-report: 1) doctor/health care provider diagnosed arthritis in the Behavioral Risk Factor Surveillance Survey (BRFSS), National Health and Nutrition Examination Survey (NHANES), National Health Interview Survey (NHIS), and Medical Expenditure Panel Survey (MEPS); and 2) three ICD-9-CM based arthritis definitions in MEPS (National Arthritis Data Workgroup Arthritis and Rheumatic Conditions [NADW-AORC], Clinical Classification System [CCS], and Centers for Disease Control and Prevention [CDC]). Results: Diagnosed arthritis prevalence percentages were within 3 percentage points (BRFSS= 26.2% [99% CI=26.0 - 26.4], MEPS= 26.1 [99% CI=25.0-27.2], NHIS=23.5 [99% CI = 22.9-24.1], NHANES=23.0% [99% CI=19.2-26.8]) and ICD-9-CM within 5 (CCS=25.8%; 99% CI=24.6-27.1; CDC=28.3%; 99% CI=27.0-29.6; and NADW=30.7%; 95% CI=29.4-32.1). Range in estimated number (in millions) affected with diagnosed arthritis was 7.8 (BRFSS=58.5 [99% CI=58.1-59.1]; MEPS=59.3 [99% CI=55.6-63.1]; NHANES=51.5 [99% CI=37.2-65.5], and NHIS=52.6 [99% CI=50.9-54.4]) and ICD-9-CM definitions was 11.1 (CCS=58.7 [99% CI=54.5-62.9]; CDC=64.3 [99% CI=59.9-68.6], and NADW=69.9 [99% CI=65.2-74.5]). Most (57% to 70%) reporting diagnosed arthritis also reported ICD-9-CM arthritis; respondents reporting diagnosed arthritis were older than those meeting ICD-9-CM definitions. Proxy response status affected arthritis prevalence differently across surveys. Conclusion: Public health practitioners and decision makers are frequently charged with choosing a single number to represent arthritis prevalence in the US population. We encourage them to consider the surveys' purpose, design, measurement methods, and statistical precision when choosing an estimate. This article is protected by copyright. All rights reserved.

Mental health and sleep are intricately linked. This study characterized associations of psychological distress with short (≤6 hours) and long (≥9 hours) sleep duration among adults aged ≥18 years. 2013 Behavioral Risk Factor Surveillance System data ( n = 36 ,859) from Colorado, Minnesota, Nevada, Tennessee, and Washington included the Kessler 6 (K6) scale, which has been psychometrically validated for measuring severe psychological distress (SPD); three specifications were evaluated. Overall, 4.0% of adults reported SPD, 33.9% reported short sleep, and 7.8% reported long sleep. After adjustment, adults with SPD had 1.58 (95% CI: 1.45, 1.72) and 1.39 (95% CI: 1.08, 1.79) times higher probability of reporting short and long sleep duration, respectively. Using an ordinal measure showed a dose-response association with prevalence ratios of 1.00, 1.16, 1.38, 1.67, and 2.11 for short sleep duration. Each additional point added to the K6 scale was associated with 1.08 (95% CI: 1.07, 1.10) and 1.02 (95% CI: 1.00, 1.03) times higher probability of reporting short and long sleep duration, respectively. Some results were statistically different by gender. Any psychological distress, not only SPD, was associated with a higher probability of short sleep duration but not long sleep duration. These findings highlight the need for interventions.

Background: Studies have linked adverse childhood experiences (ACE) to harmful health behaviors and the development of chronic diseases in adulthood. However, gender differences have not been comprehensively evaluated in a population-based sample. Methods: Using data from 15,844 women and 10,134 men aged ≥18 years in 4 states of the 2012 Behavioral Risk Factor Surveillance System, we estimated adjusted prevalence ratios (PR) and 95% confidence intervals (CI) for associations between ACE and arthritis, asthma, cancer, chronic obstructive pulmonary disease (COPD), coronary heart disease (CHD), depression, diabetes, kidney disease, and stroke, adjusting for sociodemographic characteristics. Results: Some 61.0% of women and 59.8% of men reported ≥ 1 ACE. Women who had experienced ≥ 5 ACE, compared to those who had experienced none, reported more arthritis (PR=1.54, 95% CI: 1.38, 1.72), asthma (PR=1.70, 95% CI: 1.39, 2.09), cancer (PR=1.68, 95% CI: 1.31, 2.16), COPD (PR=2.26, 95% CI: 1.82, 2.82), CHD (PR=1.56, 95% CI: 1.17, 2.07), depression (PR=3.24, 95% CI: 2.85, 3.70), kidney disease (PR=2.09, 95% CI: 1.39, 3.17), and stroke (PR=2.11, 95% CI: 1.42, 3.13). Men who had experienced ≥ 5 ACE, compared to those who had experienced none, reported more arthritis (PR=1.45, 95% CI: 1.22, 1.73), asthma (PR=1.67, 95% CI: 1.19, 2.33), COPD (PR=2.11, 95% CI: 1.45, 3.06), CHD (PR=1.59, 95% CI: 1.16, 2.19), and depression (PR=3.12, 95% CI: 2.54, 3.84). Conclusions: ACEs were associated with more chronic diseases in women than in men. Policy and intervention solutions that decrease exposure to maltreatment and household or family challenges during childhood are warranted.

Background: Improving mental health through prevention and increasing public knowledge of how adequate sleep improves health are Healthy People 2020 goals. Serious psychological distress (SPD) is associated with depression, anxiety, and mood disorders. However, it is not known whether SPD is linked to short sleep duration or long sleep duration in the general population. We examined these associations in a population-based sample. Methods: We analyzed cross-sectional data from 36,859 US adults aged 18 years or older in the 2013 Behavioral Risk Factor Surveillance System (BRFSS). SPD was assessed using the Kessler 6 (K6) scale of nonspecific psychological distress ranging from no symptoms to clinically significant symptoms. Five states (Colorado, Minnesota, Nevada, Tennessee, and Washington) included the optional BRFSS mental illness and stigma module including the K6 scale. To estimate prevalence ratios (PR) for the associations of self-reported SPD with short sleep duration (≤ 6 hours) and long sleep duration (≥ 9 hours), Poisson regression models with robust variance were used, adjusting for age, gender, race/ethnicity, marital status, educational attainment, income, employment, and health insurance coverage. Results: Overall, 4.0% of adults reported SPD, 33.9% reported short sleep, and 7.8% reported long sleep. Compared to those who did not report SPD, adults with SPD were significantly more likely to report short sleep (PR=1.56, 95% CI: 1.43, 1.69) and long sleep (PR=1.42, 95% CI: 1.10, 1.83) controlling for sociodemographic characteristics. Conclusions: Our study found that adults with SPD are more likely to report both short sleep duration and long sleep duration, highlighting the need for interventions that promote mental health and sufficient sleep. Future research to determine the temporal associations between SPD and sleep duration is necessary for developing and offering effective interventions.

Contemporary public health professionals must address the health needs of a diverse population with constrained budgets and shrinking funds. Economic evaluation contributes to evidence-based decision making by helping the public health community identify, measure, and compare activities with the necessary impact, scalability, and sustainability to optimize population health. Asking “how do investments in public health strategies influence or offset the need for downstream spending on medical care and/or social services?” is important when making decisions about resource allocation and scaling of interventions.