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Background Disparities in cardiovascular disease (CVD) outcomes persist across the United States. Social determinants of health play an important role in driving these disparities. The current study aims to identify the most important social determinants associated with CVD mortality over time in US counties. Methods and Results The authors used the Agency for Healthcare Research and Quality's database on social determinants of health and linked it with CVD mortality data at the county level from 2009 to 2018. The age-standardized CVD mortality rate was measured as the number of deaths per 100 000 people. Penalized generalized estimating equations were used to select social determinants associated with county-level CVD mortality. The analytic sample included 3142 counties. The penalized generalized estimating equation identified 17 key social determinants of health including rural-urban status, county's racial composition, income, food, and housing status. Over the 10-year period, CVD mortality declined at an annual rate of 1.08 (95% CI, 0.74-1.42) deaths per 100 000 people. Rural counties and counties with a higher percentage of Black residents had a consistently higher CVD mortality rate than urban counties and counties with a lower percentage of Black residents. The rural-urban CVD mortality gap did not change significantly over the past decade, whereas the association between the percentage of Black residents and CVD mortality showed a significant diminishing trend over time. Conclusions County-level CVD mortality declined from 2009 through 2018. However, rural counties and counties with a higher percentage of Black residents continued to experience higher CVD mortality. Median income, food, and housing status consistently predicted higher CVD mortality.

Purpose: Socioeconomic and geographic determinants of medical school application and matriculation may help explain the unequal distribution of physicians in the United States. This study describes trends in MD-granting medical school application and matriculation rates and explores the relationship between county median family income, proximity to a medical school, and medical school application and matriculation rates., Method: Data were obtained from the Association of American Medical Colleges, including the age, gender, and Federal Information Processing Standards code for county of legal residence for each applicant and matriculant to U.S. MD-granting medical schools from 2001 through 2015. The application and matriculation rates in each county were calculated using the number of applicants and matriculants per 100,000 residents. Counties were classified into 4 groups according to the county median family income (high-income, middle-income, middle-low-income, low-income). The authors performed chi-square tests to assess trends across the study period and the association of county median family income with application and matriculation rates., Results: There were 581,833 applicants and 262,730 (45.2%) matriculants to MD-granting medical schools between 2001 and 2015. The application rates per 100,000 residents during 2001-2005, 2006-2010, and 2011-2015 were 57.2, 62.7, and 69.0, respectively, and the corresponding matriculation rates were 27.5, 28.1, and 29.8. The ratios of the application rate in high-income counties to that in low-income counties during the 3 time periods were 1.9, 2.4, and 2.8, respectively., Conclusions: The application and matriculation rates to MD-granting medical schools increased steadily from 2001 to 2015. Yet, applicants and matriculants disproportionately came from high-income counties. The differences in the application and matriculation rates between low-income and high-income counties grew during this period. Exploring these differences can lead to better understanding of the factors that drive geographic differences in physician access and the associated health disparities across the United States., (Copyright © 2021 by the Association of American Medical Colleges.)

Importance: Access to primary care clinicians, including primary care physicians and nonphysician clinicians (nurse practitioners and physician assistants) is necessary to improving population health. However, rural-urban trends in primary care access in the US are not well studied., Objective: To assess the rural-urban trends in the primary care workforce from 2009 to 2017 across all counties in the US., Design, Setting, and Participants: In this cross-sectional study of US counties, county rural-urban status was defined according to the national rural-urban classification scheme for counties used by the National Center for Health Statistics at the Centers for Disease Control and Prevention. Trends in the county-level distribution of primary care clinicians from 2009 to 2017 were examined. Data were analyzed from November 12, 2019, to February 10, 2020., Main Outcomes and Measures: Density of primary care clinicians measured as the number of primary care physicians, nurse practitioners, and physician assistants per 3500 population in each county. The average annual percentage change (APC) of the means of the density of primary care clinicians over time was calculated, and generalized estimating equations were used to adjust for county-level sociodemographic variables obtained from the American Community Survey., Results: The study included data from 3143 US counties (1167 [37%] urban and 1976 [63%] rural). The number of primary care clinicians per 3500 people increased significantly in rural counties (2009 median density: 2.04; interquartile range [IQR], 1.43-2.76; and 2017 median density: 2.29; IQR, 1.57-3.23; P < .001) and urban counties (2009 median density: 2.26; IQR. 1.52-3.23; and 2017 median density: 2.66; IQR, 1.72-4.02; P < .001). The APC of the mean density of primary care physicians in rural counties was 1.70% (95% CI, 0.84%-2.57%), nurse practitioners was 8.37% (95% CI, 7.11%-9.63%), and physician assistants was 5.14% (95% CI, 3.91%-6.37%); the APC of the mean density of primary care physicians in urban counties was 2.40% (95% CI, 1.19%-3.61%), nurse practitioners was 8.64% (95% CI, 7.72%-9.55%), and physician assistants was 6.42% (95% CI, 5.34%-7.50%). Results from the generalized estimating equations model showed that the density of primary care clinicians in urban counties increased faster than in rural counties (β = 0.04; 95% CI, 0.03 to 0.05; P < .001)., Conclusions and Relevance: Although the density of primary care clinicians increased in both rural and urban counties during the 2009-2017 period, the increase was more pronounced in urban than in rural counties. Closing rural-urban gaps in access to primary care clinicians may require increasingly intensive efforts targeting rural areas.