Variation in hypertension control by race and ethnicity, and geography in US Veterans

Abstract: Background: Hypertension control and related cardiovascular outcomes among Americans remain suboptimal, and differ by race, ethnicity, and geography. Healthcare access is one of multiple critical factors in hypertension control. Understanding the degree to which healthcare access, versus other factors, produce these outcomes can inform policies and interventions to improve cardiovascular outcomes and reduce disparities. Department of Veterans Affairs Healthcare System data provide a unique opportunity to understand residual racial and ethnic differences in hypertension control after accounting for healthcare access. Our objective was to describe pre-pandemic post-Affordable Care Act implementation hypertension control by geographic sector and race and ethnicity, and assess spatial clustering of hypertension control. Methods and results: A secondary data analysis of hypertension control among US veterans (n=1 619 414) nationwide and in 4 US territories was conducted using electronic health record data. Age- and sex-adjusted regression models estimated overall and race- and ethnicity-specific rates by geographic sector. We created choropleth maps of hypertension control rates and assessed spatial autocorrelation. Hypertension control rates varied across sectors by race and ethnicity (range, 44.1%-97.5%); Black veterans, followed by American Indian or Alaska Native veterans, had the lowest mean control rates (72.5% and 75.4%, respectively). There was clustering of low hypertension control rates for Black veterans in the Pacific Northwest, Southwest, Missouri, Kansas, and Arkansas, and for American Indian or Alaska Native veterans in the West and Southwest. Conclusions: Hypertension control rates varied geographically for veteran groups experiencing racial and ethnic disparities. Geographic areas with concentrations of low rates of hypertension control should be a focus for interventions to address racial and ethnic disparities.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.

    Mental Health Peer reviewed