Electronic Health Records for Population Health Management: Comparison of Electronic Health Record-Derived Hypertension Prevalence Measures Against Established Survey Data.

BACKGROUND: Hypertension is the most prevalent risk factor for mortality globally. Uncontrolled hypertension is associated with excess morbidity and mortality, and nearly one-half of individuals with hypertension do not have the condition under control. Data from electronic health record (EHR) systems may be useful for community hypertension surveillance, filling a gap in local public health departments' community health assessments and supporting the public health data modernization initiatives currently underway. To identify patients with hypertension, computable phenotypes are required. These phenotypes leverage available data elements-such as vitals measurements and medications-to identify patients diagnosed with hypertension. However, there are multiple methodologies for creating a phenotype, and the identification of which method most accurately reflects real-world prevalence rates is needed to support data modernization initiatives. OBJECTIVE: This study sought to assess the comparability of 6 different EHR-based hypertension prevalence estimates with estimates from a national survey. Each of the prevalence estimates was created using a different computable phenotype. The overarching goal is to identify which phenotypes most closely align with nationally accepted estimations. METHODS: Using the 6 different EHR-based computable phenotypes, we calculated hypertension prevalence estimates for Marion County, Indiana, for the period from 2014 to 2015. We extracted hypertension rates from the Behavioral Risk Factor Surveillance System (BRFSS) for the same period. We used the two 1-sided t test (TOST) to test equivalence between BRFSS- and EHR-based prevalence estimates. The TOST was performed at the overall level as well as stratified by age, gender, and race. RESULTS: Using both 80% and 90% CIs, the TOST analysis resulted in 2 computable phenotypes demonstrating rough equivalence to BRFSS estimates. Variation in performance was noted across phenotypes as well as demographics. TOST with 80% CIs demonstrated that the phenotypes had less variance compared to BRFSS estimates within subpopulations, particularly those related to racial categories. Overall, less variance occurred on phenotypes that included vitals measurements. CONCLUSIONS: This study demonstrates that certain EHR-derived prevalence estimates may serve as rough substitutes for population-based survey estimates. These outcomes demonstrate the importance of critically assessing which data elements to include in EHR-based computer phenotypes. Using comprehensive data sources, containing complete clinical data as well as data representative of the population, are crucial to producing robust estimates of chronic disease. As public health departments look toward data modernization activities, the EHR may serve to assist in more timely, locally representative estimates for chronic disease prevalence.

The synchronicity of COVID-19 disparities: Statewide epidemiologic trends in SARS-CoV-2 morbidity, hospitalization, and mortality among racial minorities and in rural America.

BACKGROUND: Early studies on COVID-19 identified unequal patterns in hospitalization and mortality in urban environments for racial and ethnic minorities. These studies were primarily single center observational studies conducted within the first few weeks or months of the pandemic. We sought to examine trends in COVID-19 morbidity, hospitalization, and mortality over time for minority and rural populations, especially during the U.S. fall surge. METHODS: Data were extracted from a statewide cohort of all adult residents in Indiana tested for SARS-CoV-2 infection between March 1 and December 31, 2020, linked to electronic health records. Primary measures were per capita rates of infection, hospitalization, and death. Age adjusted rates were calculated for multiple time periods corresponding to public health mitigation efforts. Comparisons across time within groups were compared using ANOVA. RESULTS: Morbidity and mortality increased over time with notable differences among sub-populations. Initially, hospitalization rates among racial minorities were 3-4 times higher than whites, and mortality rates among urban residents were twice those of rural residents. By fall 2020, hospitalization and mortality rates in rural areas surpassed those of urban areas, and gaps between black/brown and white populations narrowed. Changes across time among demographic groups was significant for morbidity and hospitalization. Cumulative morbidity and mortality were highest among minority groups and in rural communities. CONCLUSIONS: The synchronicity of disparities in COVID-19 by race and geography suggests that health officials should explicitly measure disparities and adjust mitigation as well as vaccination strategies to protect those sub-populations with greater disease burden.

Current smoking and quit-attempts among US adults following Medicaid expansion.

The objective of this study was to estimate the influence of the Affordable Care Act (ACA) Medicaid Expansion on current smoking and quit attempts in expanded and non-expanded states. We analyzed data from the Behavioral Risk Factor Surveillance System (BRFSS) between 2003 through 2015 to evaluate changes in current smoking and quit attempts using multivariable logistic regression and generalized estimating equations (GEE), adjusting for socioeconomic factors. Time periods evaluated were: 2003-2009 (pre-expansion) and 2011-2015 (post-expansion), and in supplemental analysis, also 2011-2017. Overall, smoking prevalence among adults in expanded and non-expanded states were 16% and 17% (p < 0.001), respectively, and quit attempt prevalence for expanded and non-expanded states were 56% and 57% (p = 0.05), respectively. In adjusted models comparing post- versus pre- expansion periods, current smoking declined by 6% in both expanded (RR: 0.94, 95% CI: 0.93-0.94) and non-expanded (RR: 0.94, 95% CI: 0.94-0.95) states. Quit attempts increased by 4% (RR: 1.04, 95% CI: 1.04-1.05) in expanded states, and by 3% (RR: 1.03, 95% CI: 1.02-1.03) in non-expanded states. States that imposed barriers to utilization of smoking cessation services e.g. prior authorization, saw only a 3% increase in quit attempts regardless of expansion status, while expanded states that did not impose barriers experienced a 6% (RR: 1.06, 95% CI: 1.05-1.06) increase in quit attempts. Reducing administrative barriers to smoking cessation programs may enhance further declines in smoking rates among US adults.

Medicaid Expansion and Breast Cancer Screening in Appalachia and Non-Appalachia, United States, BRFSS 2003 to 2015.

BACKGROUND: Prior data suggests that breast cancer screening rates are lower among women in the Appalachian region of the United States. This study examined the changes in breast cancer screening before and after the implementation of the Affordable Care Act Medicaid expansion, in Appalachia and non-Appalachia states. METHODS: Data from the Behavioral Risk Factor Surveillance System between 2003 and 2015 were analyzed to evaluate changes in breast cancer screening in the past 2 years among US women aged 50-74 years. Multivariable adjusted logistic regression and generalized estimating equation models were utilized, adjusting for sociodemographic, socioeconomic, and health-care characteristics. Data were analyzed for 2 periods: 2003 to 2009 (pre-expansion) and 2011 to 2015 (post-expansion) comparing Appalachia and non-Appalachia states. RESULTS: The prevalence for of self-reported breast cancer screening in Appalachia and non-Appalachia states were 83% and 82% ( P < .001), respectively. In Appalachian states, breast cancer screening was marginally higher in non-expanded versus expanded states in both the pre-expansion (relative risk [RR]: 1.002, 95% confidence interval [CI]: 1.002-1.003) and post-expansion period (RR: 1.001, 95% CI: 1.001-1.002). In non-Appalachian states, screening was lower in non-expanded states versus expanded states in both the pre-expansion (RR: 0.98, 95% CI: 0.97-0.98) and post-expansion period (RR: 0.95, 95% CI: 0.95-0.96). There were modest 3% to 4% declines in breast cancer screening rates in the pos-texpansion period regardless of expansion and Appalachia status. CONCLUSIONS: Breast cancer screening rates were higher in Appalachia versus non-Appalachia US states and higher in expanded versus nonexpanded non-Appalachia states. There were modest declines in breast cancer screening rates in the post-expansion period regardless of expansion and Appalachia status, suggesting that more work may be needed to reduce administrative, logistical, and structural barriers to breast cancer screening services.