Chronic Disease Indicators: 2022-2024 Refresh and Modernization of the Web Tool.

Easy access and display of state-level estimates of the prevalence of chronic diseases and their risk factors can guide evidence-based decision-making, policy development, and tailored efforts to improve population health outcomes; however, these estimates are often presented across multiple websites and reports. The Chronic Disease Indicators (CDI) web tool (www.cdc.gov/cdi) disseminates state-level data compiled from various data sources, including surveys, vital records, and administrative data, and applies standardized definitions to estimate and track a wide range of key indicators of chronic diseases and their risk factors. In 2022-2024, the indicators were refreshed to include 113 measures across 21 topic areas, and the web tool was modernized to enhance its key features and functionalities, including standardized indicator definitions; interactive charts, graphs, and maps that present data in a visually appealing format; an easy-to-use web-based interface for users to query and extract the data they need; and state comparison reports to identify geographic variations in disease and risk factor prevalence. National and state-level estimates are provided for the overall population and, where applicable, by sex, race and ethnicity, and age. We review the history of CDIs, describe the 2022-2024 refresh process, and explore the interactive features of the CDI web tool with the goal of demonstrating how practitioners, policymakers, and other users can easily examine and track a wide range of key indicators of chronic diseases and their risk factors to support state-level public health action.

Linking Local-Level Chronic Disease and Social Vulnerability Measures to Inform Planning Efforts: A COPD Example.

INTRODUCTION: Data are publicly available to identify geographic differences in health outcomes, including chronic obstructive pulmonary disease (COPD), and social vulnerability; however, examples of combining data across sources to understand disease burden in the context of community vulnerability are lacking. METHODS: We merged county and census tract model-based estimates of COPD prevalence from PLACES (www.cdc.gov/PLACES) with social vulnerability measures from the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry Social Vulnerability Index (https://www.atsdr.cdc.gov/placeandhealth/svi), including 4 themes (socioeconomic, household composition and disability, minority status and language, and housing type and transportation), and the overall Social Vulnerability Index (SVI). We used the merged data set to create vulnerability profiles by COPD prevalence, explore joint geographic patterns, and calculate COPD population estimates by vulnerability levels. RESULTS: Counties and census tracts with high COPD prevalence (quartile 4) had high median vulnerability rankings (range: 0-1) for 2 themes: socioeconomic (county, 0.81; tract, 0.77) and household composition and disability (county, 0.75; tract, 0.81). Concordant high COPD prevalence and vulnerability for these themes were clustered along the Ohio and lower Mississippi rivers. The estimated number of adults with COPD residing in counties with high vulnerability was 2.5 million (tract: 4.7 million) for the socioeconomic theme and 2.3 million (tract: 5.0 million) for the household composition and disability theme (high overall SVI: county, 4.5 million; tract, 4.7 million). CONCLUSION: Data from 2 publicly available tools can be combined, analyzed, and visualized to jointly examine local COPD estimates and social vulnerability. These analyses can be replicated with other measures to expand the use of these cross-cutting tools for public health planning.

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

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.