USING BIG DATA TO UNDERSTAND CVD RISK AT THE POPULATION LEVEL

Abstract

Disclosure: This study is funded by NIH Award # 1362134. Therapeutic Area: ASCVD/CVD Risk Factors Background: In 2019, cardiovascular disease (CVD) led to over 240 deaths and 5081 years of disability adjusted life years (DALY) per 100,000 individuals globally. Previous studies have shown that along with individual factors (genetics, individual habits), factors at the populational-level, such as pollution, ambient temperature, business density, green spaces, and grocery store proximity, can also impact an individual's cardiovascular health. Intervention at the individual level is available, but personalized healthcare is costly and inaccessible to many. Thus, greater understanding of the modifiable CVD risk factors at the population-level is necessary for the development of broad-scale interventions. Prior studies have used Big Data from Electronic Health Records to improve cardiovascular care for individuals with CVD, but few have analyzed Big Data related to CVD risk at the population-level. This study analyzed the risk factors associated with CVD mortality in places with low CVD mortality (cold-spots) using Big Data to understand the underlying factors related to CVD at the spatial geographic context. Methods: Data was obtained from the 2017-2018 HRSA Area Health Resource File and CDC WONDER compressed mortality file (National Center for Health Statistics. Compressed Mortality File) and from the National Association for Public Health Statistics and Information Systems (NAPHSIS). Getis-Ord Gi* statistic and Pearson product moment correlation were performed followed by Graph Analysis and Factor Analysis to elucidate the multifactorial connections within geographic CVD, develop cold spots, and determine potentially modifiable risk factors of CVD. Results: Sixteen cold-spot multiple variable paracliques were determined with 104 variables associated with CVD mortality. Ten latent constructs were generated, encompassing factors such as population density, age group, gender, house ownership; smoking prevalence; obesity and physical inactivity; higher wages; business density and grocery store proximity; fine particulate matter; poverty. Conclusion: CVD mortality increases with high population and business density, long term smoking, empty calorie consumption, high heat indexes, particulate matter pollution, lower household incomes, and Black homeownership. Findings from this study can be used to develop passive population-based interventions that have a broad impact on city and building planning to reduce CVD risk at the community level.