Abstract 12361: Yelp Data on Fast Food and Exercise Facilities Predict Cardiovascular Events

Introduction: Yelp includes information on nearly all businesses and socioeconomic data in any region of the United States. This database maybe used to obtain socio-economic data that may affect the incidence of cardiovascular disease. This project focuses on access to poor quality food such as fast food and lack of exercise that may have a negative impact in cardiovascular disease. A dataset including the number of fast-food restaurants, non-fast-food restaurants, grocery stores, gyms, other fitness facilities, nursing homes, and pharmacies were aggregated by zip code. In addition, demographic variables such as gender, age, and comorbidities were included. Cardiovascular outcomes were obtained from the Myocardial Infarction Data Acquisition System (MIDAS) database of all hospitalizations in New Jersey. Ten percent of the zip codes that did not have sufficient data were excluded. Hypothesis: Yelp variables will predict the occurrence of stroke, myocardial infarction and heart failure requiring hospitalization. Methods: The yelp data were used to predict three outcomes as identified by the MIDAS database: stroke, myocardial infarction and heart failure requiring hospitalization. Linear models followed by stepwise selection of the predictors were used to define the final models used in these analyses. Results: The results of the regressions in the table show that the number of fast-food restaurants and nursing homes were associated with increased frequency of all 3 outcomes controlling for population size. Access to grocery stores, gyms, and other fitness facilities are associated with lower rate of stroke, myocardial infarction and heart failure requiring hospitalization. Conclusions: Socioeconomic variables obtained from the yelp database predicted the occurrence of stroke, myocardial infarction and heart failure requiring hospitalization with high degree of accuracy.