Quantifying lead-attributable cardiovascular disease burden in the United States.

Lead exposure has been linked to a myriad of cardiovascular diseases. Utilizing data from the 2019 Global Burden of Disease Study, we quantified age-standardized lead exposure-related mortality and disability-adjusted life years (DALYs) in the United States between 1990 and 2019. Our analysis revealed a substantial reduction in age-standardized cardiovascular disease (CVD) mortality attributable to lead exposure by 60 % (from 7.4 to 2.9 per 100,000), along with a concurrent decrease in age-standardized CVD DALYs by 66 % (from 143.2 to 48.7 per 100,000).

Sex-specific heart failure burden across the United States: Global burden of disease 1990-2019.

BACKGROUND: Heart failure (HF) has unique aspects that vary by biological sex. Thus, understanding sex-specific trends of HF in the US population is crucial to develop targeted interventions. We aimed to analyze the burden of HF in female and male patients across the US, from 1990 to 2019. METHODS: Using the Global Burden of Disease (GBD) study data from 2019, we performed an analysis of the burden of HF from 1990-2019, across US states and regions. The GBD defined HF through studies that used symptom-based criteria and expressed the burden of HF as the age-adjusted prevalence and years lived with disability (YLDs) rates per 100,000 individuals. RESULTS: The age-adjusted prevalence of HF for the US in 2019 was 926.2 (95% UI [799.6, 1,079.0]) for females and 1,291.2 (95% UI [1,104.1, 1,496.8]) for males. Notably, our findings also highlight cyclic fluctuations in HF prevalence over time, with peaks occurring in the mid-1990s and around 2010, while reaching their lowest points in around 2000 and 2018. Among individuals >70 years of age, the absolute number of individuals with HF was higher in females, and this age group doubled the absolute count between 1990 and 2019. Comparing 1990-1994 to 2015-2019, 10 states had increased female HF prevalence, while only 4 states increased male prevalence. Overall, Western states had the greatest relative decline in HF burden, in both sexes. CONCLUSION: The burden of HF in the US is high, although the magnitude of this burden varies according to age, sex, state, and region. There is a significant increase in the absolute number of individuals with HF, especially among women >70 years, expected to continue due to the aging population.

The Global Burden of premature cardiovascular disease, 1990-2019.

Aims: Premature cardiovascular disease (pCVD) definition varies in literature, with age cut-offs ranging from 50-65 years. While there is some literature available on pCVD in North America, comprehensive data on its global burden is still lacking which hinders the development of efficient strategies for early detection and prevention. In this study we aimed to investigate the global trends in pCVD related morbidity and mortality from 1990 to 2019. Methods: The 1990-2019 Global Burden of Disease (GBD) database was utilized to examine global trends in cardiovascular disease-related total mortality, mortality rates, and Disability-Adjusted Life Years (DALYs) within individuals aged 15-49 years. The findings were further analyzed based on factors such as age, sex, and Socio-Demographic Index (SDI). Results: From 1990 to 2019, the number of global annual pCVD deaths increased by 25%, from 992,067 (95% UI 1,042,261 - 946,383) to 1,241,484 (95% UI 1,339,193 -1,146,252). The rate of associated mortality decreased by 13%. Metabolic conditions were the most significant risk factors for pCVD mortality. Ischemic heart disease and stroke are the leading causes of death across all age groups. pCVD mortality presented progressive widening between high and low SDI regions. Additionally, sex-specific disparities in CVD mortality were significantly greater in the premature age group as compared to all-age groups. Conclusion: pCVD is an increasingly significant global cause of morbidity and mortality that disproportionately affects males and individuals living in less privileged regions. Furthermore, ischemic heart disease and stroke were identified as the main drivers of pCVD global burden.

Alzheimer`s disease mortality in the United States: Cross-sectional analysis of county-level socio-environmental factors.

BACKGROUND: Geographical disparities in mortality among Alzheimer`s disease (AD) patients have been reported and complex sociodemographic and environmental determinants of health (SEDH) may be contributing to this variation. Therefore, we aimed to explore high-risk SEDH factors possibly associated with all-cause mortality in AD across US counties using machine learning (ML) methods. METHODS: We performed a cross-sectional analysis of individuals ≥65 years with any underlying cause of death but with AD in the multiple causes of death certificate (ICD-10,G30) between 2016 and 2020. Outcomes were defined as age-adjusted all-cause mortality rates (per 100,000 people). We analyzed 50 county-level SEDH and Classification and Regression Trees (CART) was used to identify specific county-level clusters. Random Forest, another ML technique, evaluated variable importance. CART`s performance was validated using a "hold-out" set of counties. RESULTS: Overall, 714,568 individuals with AD died due to any cause across 2,409 counties during 2016-2020. CART identified 9 county clusters associated with an 80.1% relative increase of mortality across the spectrum. Furthermore, 7 SEDH variables were identified by CART to drive the categorization of clusters, including High School Completion (%), annual Particulate Matter 2.5 Level in Air, live births with Low Birthweight (%), Population under 18 years (%), annual Median Household Income in US dollars ($), population with Food Insecurity (%), and houses with Severe Housing Cost Burden (%). CONCLUSION: ML can aid in the assimilation of intricate SEDH exposures associated with mortality among older population with AD, providing opportunities for optimized interventions and resource allocation to reduce mortality among this population.

Geographical Patterns and Risk Factor Association of Cardio-Oncology Mortality in the United States.

Cardio-oncology mortality (COM) is a complex issue that is compounded by multiple factors that transcend a depth of socioeconomic, demographic, and environmental exposures. Although metrics and indexes of vulnerability have been associated with COM, advanced methods are required to account for the intricate intertwining of associations. This cross-sectional study utilized a novel approach that combined machine learning and epidemiology to identify high-risk sociodemographic and environmental factors linked to COM in United States counties. The study consisted of 987,009 decedents from 2,717 counties, and the Classification and Regression Trees model identified 9 county socio-environmental clusters that were closely associated with COM, with a 64.1% relative increase across the spectrum. The most important variables that emerged from this study were teen birth, pre-1960 housing (lead paint indicator), area deprivation index, median household income, number of hospitals, and exposure to particulate matter air pollution. In conclusion, this study provides novel insights into the socio-environmental drivers of COM and highlights the importance of utilizing machine learning approaches to identify high-risk populations and inform targeted interventions for reducing disparities in COM.