Addressing spatial misalignment in population health research: a case study of US congressional district political metrics and county health data (preprint)

Areal spatial misalignment, which occurs when data on multiple variables are collected using mismatched boundary definitions, is a ubiquitous obstacle to data analysis in public health and social science research. As one example, the emerging sub-field studying the links between political context and health in the United States faces significant spatial misalignment-related challenges, as the congressional districts (CDs) over which political metrics are measured and administrative units, e.g., counties, for which health data are typically released, have a complex misalignment structure. Standard population-weighted data realignment procedures can induce measurement error and invalidate inference, which has prompted the development of fully model-based approaches for analyzing spatially misaligned data. One such approach, atom-based regression models (ABRM), holds particular promise but has scarcely been used in practice due to the lack of appropriate software or examples of implementation. ABRM use "atoms", the areas created by intersecting all sets of units on which variables of interest are measured, as the units of analysis and build models for the atom-level data, treating the atom-level variables (generally unmeasured) as latent variables. In this paper, we demonstrate the feasibility and strengths of the ABRM in a case study of the association between political representatives' voting behavior (CD-level) and COVID-19 mortality rates (county-level) in a post-vaccine period. The adjusted ABRM results suggest that more conservative voting record is associated with an increase in COVID-19 mortality rates, with estimated associations smaller in magnitude but consistent in direction with those of standard realignment methods. The results also indicate that ABRM may enable more robust confounding adjustment and more realistic uncertainty estimates, properly representing the uncertainties arising from all analytic procedures. We also implement the ABRM in modern optimized Bayesian computing programs and make our code publicly available, which may enable these methods to be more widely adopted.

Understanding Complex Patterns in Social, Geographic, and Economic Inequities in COVID-19 Mortality at the County Level in the US Using Generalized Additive Models (preprint)

I present three types of applications of generalized additive models (GAMs) to COVID-19 mortality rates in the US for the purpose of advancing methods to document inequities with respect to which communities suffered disproportionate COVID-19 mortality rates at specific times during the first three years of the pandemic. First, GAMs can be used to describe the changing relationship between COVID-19 mortality and county-level covariates (sociodemographic, economic, and political metrics) over time. Second, GAMs can be used to perform spatiotemporal smoothing that pools information over time and space to address statistical instability due to small population counts or stochasticity resulting in a smooth, dynamic latent risk surface summarizing the mortality risk associated with geographic locations over time. Third, estimation of COVID-19 mortality associations with county-level covariates conditional on a smooth spatiotemporal risk surface allows for more rigorous consideration of how socio-environmental contexts and policies may have impacted COVID-19 mortality. Each of these approaches provides a valuable perspective to documenting inequities in COVID-19 mortality by addressing the question of which populations have suffered the worst burden of COVID-19 mortality taking into account the nonlinear spatial, temporal, and social patterning of disease.

The Evolving Roles of US Political Partisanship and Social Vulnerability in the COVID-19 Pandemic from February 2020 - February 2021 (preprint)

The COVID-19 pandemic has had intense, heterogeneous impacts on different communities and geographies in the United States. We explore county level associations between COVID-19 attributed deaths and social, demographic, vulnerability, and political variables to develop a better understanding of the evolving roles these variables play in relation to mortality. We focus on the role of political variables, as captured by support for either the Republican or Democrat presidential candidates in the 2020 elections and the stringency of state-wide governor mandates, during three non-overlapping time periods between February 2020 and February 2021. We find that during the first three months of the pandemic, Democratic-leaning and internationally-connected urban counties were affected. During subsequent months (between May and September, 2020), Republican counties with high percentages of Hispanic and Black populations were most hardly hit. Importantly, in the time period between October 2020 and February 2021, when the effectiveness of non-pharmaceutical interventions, such as social distancing and wearing masks indoors, had been well-established. During this period, we find that Republican-leaning counties experienced up to 3 times higher death rates than Democratic-leaning counties, even after controlling for multiple social vulnerability factors. We also find that Republican-leaning counties in states with less strict mandates experienced the most severe outbreaks. Our findings suggest that ideologies promoted by prominent political actors may not align with efforts to mitigate the impact of the ongoing pandemic and prevent deaths.