Is White Evangelical Antistructural Theology Related to Poor Health Outcomes?

Policy Points White evangelical theology has an "antistructural" component. Counties with a high percentage of White evangelicals have higher mortality rates and more persons with fair/poor health. The potential influence of antistructural components in evangelical theology on decision making and resource allocation and, ultimately, the length and quality of life of community members presents a point of intervention for religious leaders and policymakers to improve population health. CONTEXT: Structural factors are important determinants of health. Because antistructuralism has been identified as a tenet of White evangelical theology, we explored if there is an association of the percentage of White evangelicals in a US county with two county health outcomes: premature mortality and percentage of fair/poor health. METHODS: Regression analysis was performed with data from 2022 County Health Rankings and the American Value Atlas from the Public Religion Research Institute. FINDINGS: Every percent of evangelicals in a county is associated with 4.01 more premature deaths per 100,000 population and 0.13% fair/poor health. After controlling for income, education, political ideology, and county school funding adequacy (a proxy for antistructuralism), the associations remain positive and significant. CONCLUSIONS: We hope these findings could inform dialogue and critical analysis among individuals of evangelical faith, particularly fundamental and Pentecostal subsets, regarding a belief system that is inclusive of individual dimensions and health-promoting structural policies like school funding, Medicaid expansion, and antipoverty programs. These findings also demonstrate the importance of considering cultural factors like religion and political ideology in population health outcomes research.

Discounting Health Grades for Disparity: The 2021 Wisconsin Population Health and Equity Report Card.

We describe updates to the University of Wisconsin Population Health Institute's methodology for a state health report card, first described in Preventing Chronic Disease in 2010, and the considerations that were weighed in making those updates. These methods have been used since 2006 to issue a periodic report entitled Health of Wisconsin Report Card. The report highlights Wisconsin's standing among other states and serves as an example for others seeking to measure and improve their population's health. For 2021, we revisited our approach with an increased emphasis on disparities and health equity, which required many choices about data, analysis, and reporting methods. In this article, we outline the decisions, rationale, and implications of several choices we made in assessing Wisconsin's health by answering several questions, among them: Who is the intended audience and which measures of length (eg, mortality rate, years of potential life lost) and quality of life (eg, self-reported health, quality-adjusted life years) are most relevant to them? Which subgroups should we report disparities about, and which metric is most easily understood? Should disparities be summarized with overall health or reported separately? Although these decisions are applicable to 1 state, the rationale for our choices could be applied to other states, communities, and nations. Consideration of the purpose, audience, and context for health and equity policy making is important in developing report cards and other tools that can improve the health of all people and places.

Ecological Study of Urbanicity and Self-reported Poor Mental Health Days Across US Counties.

Geography may influence mental health by inducing changes to social and physical environmental and health-related factors. This understanding is largely based on older studies from Western Europe. We sought to quantify contemporary relationships between urbanicity and self-reported poor mental health days in US counties. We performed regression on US counties (n = 3142) using data from the County Health Rankings and Roadmaps. Controlling for state, age, income, education, and race/ethnicity, large central metro counties reported 0.24 fewer average poor mental health days than small metro counties (t = - 5.78, df = 423, p < .001). Noncore counties had 0.07 more average poor mental health days than small metro counties (t = 3.06, df = 1690, p = 0.002). Better mental health in large central metro counties was partly mediated by differences in the built environment, such as better food environments. Poorer mental health in noncore counties was not mediated by considered mediators.