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Disparities in premature cardiovascular mortality (PCVM) have been associated with socioeconomic, behavioral, and environmental risk factors. Understanding the “phenotypes”, or combinations of characteristics associated with the highest risk of PCVM, and the geographic distributions of these phenotypes is critical to targeting PCVM interventions. This study applied the classification and regression tree (CART) to identify county phenotypes of PCVM and geographic information systems to examine the distributions of identified phenotypes. Random forest analysis was applied to evaluate the relative importance of risk factors associated with PCVM. The CART analysis identified seven county phenotypes of PCVM, where high-risk phenotypes were characterized by having greater percentages of people with lower income, higher physical inactivity, and higher food insecurity. These high-risk phenotypes were mostly concentrated in the Black Belt of the American South and the Appalachian region. The random forest analysis identified additional important risk factors associated with PCVM, including broadband access, smoking, receipt of Supplemental Nutrition Assistance Program benefits, and educational attainment. Our study demonstrates the use of machine learning approaches in characterizing community-level phenotypes of PCVM. Interventions to reduce PCVM should be tailored according to these phenotypes in corresponding geographic areas.

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Background Extreme temperatures are increasingly experienced as a result of climate change. Both high and low temperatures, impacted by climate change, have been linked with cardiovascular disease (CVD). Global estimates on non-optimal temperature-related CVD are not known. Objectives The authors investigated global trends of temperature-related CVD burden over the last 3 decades. Methods The authors utilized the 1990-2019 global burden of disease methodology to investigate non-optimal temperature, low temperature-, and high temperature-related CVD deaths and disability-adjusted life years (DALYs) globally. Non-optimal temperatures were defined as above (high temperature) or below (low temperature) the location-specific theoretical minimum-risk exposure level, or the temperature associated with the lowest mortality rates. Analyses were later stratified by sociodemographic index (SDI) and world regions. Results In 2019, non-optimal temperature contributed to 1,194,196 (95% uncertainty interval [UI]: 963,816 to 1,425,090) CVD deaths and 21,799,370 (95% UI: 17,395,761 to 25,947,499) DALYs. low temperature contributed to 1,104,200 (95% UI: 897,783 to 1,326,965) CVD deaths and 19,768,986 (95% UI: 16,039,594 to 23,925,945) DALYs. High temperature contributed to 93,095 (95% UI: 10,827 to 158,386) CVD deaths and 2,098,989 (95% UI: 146,158 to 3,625,564) DALYs. Between 1990 and 2019, CVD deaths related to non-optimal temperature increased by 45% (95% UI: 32% to 63%), low temperature by 36% (95% UI: 25% to 48%), and high temperature by 600% (95% UI: -1879% to 2027%). Non-optimal temperature and high temperature-related CVD deaths increased more in countries with low income than countries with high income. Conclusion Non-optimal temperatures are significantly associated with global CVD deaths and DALYs, underscoring the significant impact of temperature on public health.