Changes in the Geographic Patterns of Heart Disease Mortality in the United States Epidemiology and Prevention

changes in the geographic patterns of heart disease mortality. Methods and Results —Age-adjusted and spatially smoothed county-level heart disease death rates were calculated for 2-year intervals from 1973 to 1974 to 2009 to 2010 for those aged ≥ 35 years. Heart disease deaths were defined according to the International Classification of Diseases codes for diseases of the heart in the eighth, ninth, and tenth revisions of the International Classification of Diseases . A fully Bayesian spatiotemporal model was used to produce precise rate estimates, even in counties with small populations. A substantial shift in the concentration of high-rate counties from the Northeast to the Deep South was observed, along with a concentration of slow-decline counties in the South and a nearly 2-fold increase in the geographic inequality among counties. Conclusions dramatic change in the geographic patterns of heart disease mortality during 40 years highlights the importance of small-area to reveal patterns that are at the national level, gives communities the historical context for understanding their current burden of heart disease, and provides important clues for understanding the determinants of the disparities mortality among counties by using the coefficient of variation (CoV). 21 We calculated the CoV because it uses the full distribution of county-specific rates to measure the amount of geographic inequality rather than relying on the tails of the distribution for calculating a disparity ratio. The CoV is defined as the standard deviation of county-level heart disease death rates for each 2-year time interval divided by the mean rate for the respective time period. A large CoV indicates more inequality in the rates among counties. We then assessed the temporal change (measured as percent change) in the CoV for the entire study period.The Bayesian statistical model used in this analysis to generate the county rates and the LISA statistics was implemented using R sta- tistical software, the maps were created using ESRI ArcMap software (version 10.1), and data management was performed using SAS. 22–24 Details for computing all the summary statistics can be found in the online-only Data Supplement. slowest declines in heart disease mortality, suggest these patterns did not occur randomly. the emphasize the importance of place for health outcomes and suggest that there may have been systematic spatiotemporal changes in conditions that affect heart disease death rates. Studies have identified many interwoven dimensions of place that have implications for the burden of heart disease, including social and economic conditions, 27–29 the built environment, 30 healthy public policies, 31 prevalence of biomedical and behavioral risk factors, 32,33 of disparity in heart disease mortality among counties. These results emphasize the importance of geography for heart disease prevention and treatment, provide insights into new hypotheses regarding the determinants of declining heart disease mortality, and provide important information for tailoring clinical heart disease prevention and treatment services and policies to the communities with the greatest burden.