The Advantages of Comparative LISA Techniques in Spatial Inequality Research: Evidence from Poverty Change in the United States

Disparities in spatiotemporal clustering of maternal mental health conditions before and during the COVID-19 pandemic

Mounting evidence indicates the worsening of maternal mental health conditions during the COVID-19 pandemic. Mental health conditions are the leading cause of preventable death during the perinatal and postpartum periods. Our study sought to detect space-time patterns in the distribution of maternal mental health conditions in pregnant women before (2016-2019) and during (2020-2021) the COVID-19 pandemic in North Carolina, USA. Using the space-time Poisson model in SaTScan, we performed univariate and multivariate cluster analysis of emergency department (ED) visits for perinatal mood and anxiety disorders (PMAD), severe mental illness (SMI), maternal mental disorders of pregnancy (MDP), suicidal thoughts, and suicide attempts during the pre-pandemic and pandemic periods. Clusters were adjusted for age, race, and insurance type. Significant multivariate and univariate PMAD, SMI, and MDP clustering persisted across both periods in North Carolina, while univariate clustering for both suicide outcomes decreased during the pandemic. Local relative risk (RR) for all conditions increased drastically in select locations. The number of zip code tabulation areas (ZCTAs) included in clusters decreased, while the proportion of urban locations included in clusters increased for non-suicide outcomes. Average yearly case counts for all maternal mental health outcomes increased during the pandemic. Results provide contextual and spatial information concerning at-risk maternal populations with a high burden of perinatal mental health disorders before and during the pandemic and emphasize the necessity of urgent and targeted expansion of mental health resources in select communities.

Community social determinants and health outcomes drive availability of patient-centered medical homes

The collaborative design of America's patient-centered medical homes places these practices at the forefront of emerging efforts to address longstanding inequities in the quality of primary care experienced among socially and economically marginalized populations. We assessed the geographic distribution of the country's medical homes and assessed whether they are appearing within communities that face greater burdens of disease and social vulnerability. We assessed overlapping spatial clusters of mental and physical health surveys; health behaviors, including alcohol-impaired driving deaths and drug overdose deaths; as well as premature mortality with clusters of medical home saturation and community socioeconomic characteristics. Overlapping spatial clusters were assessed using odds ratios and marginal effects models, producing four different scenarios of resource need and resource availability. All analyses were conducted using county-level data for the contiguous US states. Counties having lower uninsured rates and lower poverty rates were the most consistent indicators of medical home availability. Overall, the analyses indicated that medical homes are more likely to emerge within communities that have more favorable health and socioeconomic conditions to begin with. These findings suggest that intersecting the spatial footprints of medical homes in relation to health and socioeconomic data can provide crucial information for policy makers and payers invested in narrowing the gaps between clinic availability and the communities that experience the brunt of health and social inequalities.