March 5, 2019 e1 WRITING GROUP MEMBERS Emelia J. Benjamin, MD, ScM, FAHA, Chair Paul Muntner, PhD, MHS, FAHA, Vice Chair Alvaro Alonso, MD, PhD, FAHA Marcio S. Bittencourt, MD, PhD, MPH Clifton W. Callaway, MD, FAHA April P. Carson, PhD, MSPH, FAHA Alanna M. Chamberlain, PhD Alexander R. Chang, MD, MS Susan Cheng, MD, MMSc, MPH, FAHA Sandeep R. Das, MD, MPH, MBA, FAHA Francesca N. Delling, MD, MPH Luc Djousse, MD, ScD, MPH Mitchell S.V. Elkind, MD, MS, FAHA Jane F. Ferguson, PhD, FAHA Myriam Fornage, PhD, FAHA Lori Chaffin Jordan, MD, PhD, FAHA Sadiya S. Khan, MD, MSc Brett M. Kissela, MD, MS Kristen L. Knutson, PhD Tak W. Kwan, MD, FAHA Daniel T. Lackland, DrPH, FAHA Tené T. Lewis, PhD Judith H. Lichtman, PhD, MPH, FAHA Chris T. Longenecker, MD Matthew Shane Loop, PhD Pamela L. Lutsey, PhD, MPH, FAHA Seth S. Martin, MD, MHS, FAHA Kunihiro Matsushita, MD, PhD, FAHA Andrew E. Moran, MD, MPH, FAHA Michael E. Mussolino, PhD, FAHA Martin O’Flaherty, MD, MSc, PhD Ambarish Pandey, MD, MSCS Amanda M. Perak, MD, MS Wayne D. Rosamond, PhD, MS, FAHA Gregory A. Roth, MD, MPH, FAHA Uchechukwu K.A. Sampson, MD, MBA, MPH, FAHA Gary M. Satou, MD, FAHA Emily B. Schroeder, MD, PhD, FAHA Svati H. Shah, MD, MHS, FAHA Nicole L. Spartano, PhD Andrew Stokes, PhD David L. Tirschwell, MD, MS, MSc, FAHA Connie W. Tsao, MD, MPH, Vice Chair Elect Mintu P. Turakhia, MD, MAS, FAHA Lisa B. VanWagner, MD, MSc, FAST John T. Wilkins, MD, MS, FAHA Sally S. Wong, PhD, RD, CDN, FAHA Salim S. Virani, MD, PhD, FAHA, Chair Elect On behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee

Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association

Background: The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovas...

Heart Disease and Stroke Statistics—2020 Update: A Report From the American Heart Association

Background: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascul...

Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association.

The novel SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has led to a global pandemic manifested as coronavirus disease 2019 (COVID-19), with its most severe presentation being acute respiratory distress syndrome leading to severe complications and death. Select underlying medical comorbidities, older age, diabetes, obesity, and male sex have been identified as biological vulnerabilities for more severe COVID-19 outcomes.1 Geographic locations that reported data by race/ethnicity indicate that African American individuals and, to a lesser extent, Latino individuals bear a disproportionate burden of COVID-19–related outcomes. The pandemic has shone a spotlight on health disparities and created an opportunity to address the causes underlying these inequities.2 The most pervasive disparities are observed among African American and Latino individuals, and where data exist, American Indian, Alaska Native, and Pacific Islander populations. Preliminary prevalence

/pdf/covid-19-and-racial-ethnic-disparities-aqvw9e4q27.pdf

COVID-19 and Racial/Ethnic Disparities.

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy.Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions. 

https://www.ahajournals.org/doi/pdf/10.1161/CIR.0000000000001052

Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association

Generalized Linear Mixed Model (GLMM) has been widely used in small area estimation for health indicators. Bayesian estimation is usually used to construct statistical intervals, however, its computational intensity is a big challenge for large complex surveys. Frequentist approaches, such as bootstrapping, and Monte Carlo (MC) simulation, are also applied but not evaluated in terms of the interval magnitude, width, and the computational time consumed. The 2013 Florida Behavioral Risk Factor Surveillance System data was used as a case study. County-level estimated prevalence of three health-related outcomes was obtained through a GLMM; and their 95% confidence intervals (CIs) were generated from bootstrapping and MC simulation. The intervals were compared to 95% credential intervals through a hierarchial Bayesian model. The results showed that 95% CIs for county-level estimates of each outcome by using MC simulation were similar to the 95% credible intervals generated by Bayesian estimation and were the most computationally efficient. It could be a viable option for constructing statistical intervals for small area estimation in public health practice.

/pdf/constructing-statistical-intervals-for-small-area-estimates-1dgl9fzz.pdf

Constructing Statistical Intervals for Small Area Estimates Based on Generalized Linear Mixed Model in Health Surveys

We estimated the prevalence of short sleep duration (<7 hours per day) among US adults aged 18 years or older by using 2020 Behavioral Risk Factor Surveillance System data. Nationally, 33.2% of adults reported short sleep duration. We identified disparities across sociodemographic characteristics, including age, sex, race and ethnicity, marital status, education, income, and urbanicity. Counties with the highest model-based estimates of short sleep duration clustered in the Southeast and along the Appalachian Mountains. These findings identified subgroups and geographic areas in which tailored strategies for promotion of optimal sleep duration (≥7 hours per night) are most needed.

/pdf/prevalence-and-geographic-patterns-of-self-reported-short-20s6zqbk.pdf

Prevalence and Geographic Patterns of Self-Reported Short Sleep Duration Among US Adults, 2020

Bar graphs are generated by using SAS/Gchart to present the distribution of health behaviors or health outcomes among adults aged ≥18 years by selected characteristics and each of 50 states using 2011 Behavioral Risk Factor Surveillance Survey (BRFSS). Due to missing data or unreliable estimates of parameters, annotation facilities are utilized to make the charts more presentable by adding data labels and footnotes. Further, incorporating a SAS Macro variable into the program can definitely make the development of 50 charts for 50 states more achievable and efficient.

Annotation utilizations in customized SAS/Graph bar charts

CONTEXT Adults with unmet health care needs (UHCN) due to cost have fewer opportunities to receive behavioral counseling in clinical settings, which may be associated with a higher likelihood of having health-risk behaviors. OBJECTIVE This study assessed associations between UHCN and health-risk factors. DESIGN/SETTING We used 2016 Behavioral Risk Factor Surveillance System data to calculate age-adjusted weighted prevalence of 5 health-risk factors by UHCN and insurance status and to assess the association of UHCN with these factors using multivariable logistic regression. PARTICIPANTS US adults aged 18 to 64 years who participated in the survey (N = 301 035). MAIN OUTCOME MEASURES Five health-risk factors: obesity, current cigarette smoking, excessive alcohol use, sleeping less than 7 hours per 24-hour period, and no leisure-time physical activity within the past month. RESULTS In 2016, among adults aged 18 to 64 years, 11.2% of those with insurance and 40.1% of those without insurance (both age-adjusted) had UHCN. In both study populations, compared with adults with no UHCN, adults reporting UHCN were more likely to be a current cigarette smoker, report excessive alcohol use, and sleep less than 7 hours per 24-hour period. The prevalence of 3 or more health-risk factors was higher among adults with UHCN than among adults without UHCN (adults with insurance: adjusted prevalence ratio = 1.40; 95% confidence interval = 1.33-1.48; adults without insurance: adjusted prevalence ratio = 1.39; 95% confidence interval = 1.27-1.53). CONCLUSIONS Unmet health care needs was associated with more health-risk factors regardless of insurance status. Addressing cost barriers to behavioral counseling may be one approach to consider when seeking to reduce health-risk behaviors among high-risk populations.

Higher Prevalence of Health-Risk Factors Among US Adults With Unmet Health Care Needs Due to Cost, 2016.

Abstract Background There is a critical need for maternal and child health data at the local level (for example, county), yet most counties lack sustainable resources or capabilities to collect local-level data. In such case, model-based small area estimation (SAE) could be a feasible approach. SAE for maternal or infant health-related behaviors at small areas has never been conducted or evaluated. Methods We applied multilevel regression with post-stratification approach to produce county-level estimates using Pregnancy Risk Assessment Monitoring System (PRAMS) data, 2016–2018 ( n = 65,803 from 23 states) for 2 key outcomes, breastfeeding at 8 weeks and infant non-supine sleeping position. Results Among the 1,471 counties, the median model estimate of breastfeeding at 8 weeks was 59.8% (ranged from 34.9 to 87.4%), and the median of infant non-supine sleeping position was 16.6% (ranged from 10.3 to 39.0%). Strong correlations were found between model estimates and direct estimates for both indicators at the state level. Model estimates for both indicators were close to direct estimates in magnitude for Philadelphia County, Pennsylvania. Conclusion Our findings support this approach being potentially applied to other maternal and infant health and behavioral indicators in PRAMS to facilitate public health decision-making at the local level. 

/pdf/us-county-level-estimation-for-maternal-and-infant-health-twmng24y.pdf

US county-level estimation for maternal and infant health-related behavior indicators using pregnancy risk assessment monitoring system data, 2016–2018

Hua Lu

Papers

Constructing Statistical Intervals for Small Area Estimates Based on Generalized Linear Mixed Model in Health Surveys

Prevalence and Geographic Patterns of Self-Reported Short Sleep Duration Among US Adults, 2020

Annotation utilizations in customized SAS/Graph bar charts

Higher Prevalence of Health-Risk Factors Among US Adults With Unmet Health Care Needs Due to Cost, 2016.

US county-level estimation for maternal and infant health-related behavior indicators using pregnancy risk assessment monitoring system data, 2016–2018