Biology of sex differences · 2025
A comparison of social drivers of health identification and intervention rates by sex among patients receiving primary care.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
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Holcomb Leah A, Killen Elizabeth Crabtree, Ryan Kelsey R, McRae-Clark Aimee L, Seipel Stacey, Aidoo Rita, Guille Constance
Аннотация
BACKGROUND: Social drivers of health (SDOH) significantly influence health behaviors and outcomes, yet sex-based disparities in these domains remain underexplored. Identifying these differences is essential for guiding equitable, evidence-based interventions.
METHODS: We analyzed electronic health record (EHR) data from all patients with a documented male or female sex who had a primary care visit or inpatient stay at the Medical University of South Carolina (MUSC) between January 1, 2023, and December 31, 2024 (n = 493,920). SDOH screening responses were categorized as "affirmative" (at risk) or "negative" (not at risk) across 17 predefined domains using Epic's logic-based risk classification. Descriptive statistics were calculated, and z-tests for proportions were used to assess sex-based differences. Race and ethnicity were included as descriptive variables; no inferential tests by race/ethnicity were conducted.
RESULTS: Females were significantly more likely to report financial strain (7.96%), food insecurity (4.44%), housing instability (3.72%), intimate partner violence (2.03%), transportation barriers (2.20%), depression (3.93%), and stress (14.10%). Despite these risks, females also reported higher rates of protective behaviors such as physical activity (74.2%) and social connectedness (14.22%). In contrast, males had higher rates of alcohol use (4.67%), tobacco use (35.6%), and adolescent substance use (2.14%). Notably, White/Caucasian males reported the highest alcohol use (6.23%), and both White and Black males reported the highest tobacco use (42%).
CONCLUSIONS: Sex-based disparities in SDOH reflect broader structural and social inequities. Health systems should implement routine, EHR-integrated SDOH screening and use this data to inform tailored, gender-responsive interventions-such as increasing access to mental health support for women and addressing substance use among men-while also considering how intersecting factors like race, income, and caregiving burden compound these risks.
METHODS: We analyzed electronic health record (EHR) data from all patients with a documented male or female sex who had a primary care visit or inpatient stay at the Medical University of South Carolina (MUSC) between January 1, 2023, and December 31, 2024 (n = 493,920). SDOH screening responses were categorized as "affirmative" (at risk) or "negative" (not at risk) across 17 predefined domains using Epic's logic-based risk classification. Descriptive statistics were calculated, and z-tests for proportions were used to assess sex-based differences. Race and ethnicity were included as descriptive variables; no inferential tests by race/ethnicity were conducted.
RESULTS: Females were significantly more likely to report financial strain (7.96%), food insecurity (4.44%), housing instability (3.72%), intimate partner violence (2.03%), transportation barriers (2.20%), depression (3.93%), and stress (14.10%). Despite these risks, females also reported higher rates of protective behaviors such as physical activity (74.2%) and social connectedness (14.22%). In contrast, males had higher rates of alcohol use (4.67%), tobacco use (35.6%), and adolescent substance use (2.14%). Notably, White/Caucasian males reported the highest alcohol use (6.23%), and both White and Black males reported the highest tobacco use (42%).
CONCLUSIONS: Sex-based disparities in SDOH reflect broader structural and social inequities. Health systems should implement routine, EHR-integrated SDOH screening and use this data to inform tailored, gender-responsive interventions-such as increasing access to mental health support for women and addressing substance use among men-while also considering how intersecting factors like race, income, and caregiving burden compound these risks.