Available online 19 April 2024

Author links open overlay panel, , , , , , , , , ABSTRACTPurpose

Methods for assessing the structural mechanisms of health inequity are not well established. This study applies a phased approach to modeling racial, occupational, and rural disparities on the county level.

Methods

Rural counties with disparately high rates of COVID-19 incidence or mortality were randomly paired with in-state control counties with the same rural-urban continuum code. Analysis was restricted to the first six months of the pandemic to represent the baseline structural reserves for each county and reduce biases related to the disruption of these reserves over time. Conditional logistic regression was applied in two phases—first, to examine the demographic distribution of disparities and then, to examine the relationships between these disparities and county-level social and structural reserves.

Results

In over 200 rural county pairs (205 for incidence, 209 for mortality), disparities were associated with structural variables representing economic factors, healthcare infrastructure, and local industry. Modeling results were sensitive to assumptions about the relationships between race and other social and structural variables measured at the county level, particularly in models intended to reflect effect modification or mediation.

Conclusions

Multivariable modeling of health disparities should reflect the social and structural mechanisms of inequity and anticipate interventions that can advance equity.

Section snippetsINTRODUCTION

The COVID-19 pandemic constituted a widespread shock throughout the United States (U.S.), 1, 2, 3 yet its impacts across the population were uneven. Marginalized racial and ethnic groups, rural populations, frontline workers, and communities with limited pre-pandemic socioeconomic reserves had disparately high rates of COVID-19 and its complications 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14—a finding that reflects long-standing inequities in health and portends potential inequities in the post-acute

Data Sources

County-level data depicting COVID-19 incidence and mortality, population characteristics, and rural-urban classification for all 50 states were obtained from publicly available sources (Table 1). County-level data for other social and structural variables were obtained as summarized in the Supplementary Information (Table A1). The Institutional Review Board reviewed the study and determined it not to be human subjects research.

Study Period

Our objective was to assess the relationship of pre-pandemic, social

Descriptive Characteristics

Average rural COVID-19 incidence and mortality rates differed across the 50 U.S. states (Table 2). A total of 217 and 220 counties were identified as having county-level inequities in incidence and mortality, respectively. After matching with in-state controls, a total of 205 and 209 county pairs were available for the incidence and mortality analyses.

Phase 1: Demographic Variables

In unadjusted models, disparities in COVID-19 incidence were associated with age, sex, and race variables, whereas mortality disparities were

DISCUSSION

This study offers important methodological perspectives for population-level assessments of health inequity. When complex, socially constructed variables such as race are used to describe the distribution of inequity in populations, epidemiological models must determine the modifiable social and structural factors underlying these disparities. In this analysis focused within rural U.S. counties, the county-level “race” variable is modeled as a heterogeneous composite representing resource

CONCLUSIONS

The advancement of health equity requires rigorous examination of the presumed relationships between socially constructed variables like race, which are frequently used to describe disparities in epidemiological analyses, and the social and structural variables signifying the mechanisms by which disparities occur. As we have shown here (and as summarized in Fig. 1), conventional approaches to multivariable models that contain socially constructed variables may systematically overlook or

CRediT authorship contribution statement

Martha L. Carvour: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. Matida Bojang: Conceptualization, Investigation, Methodology, Writing – review & editing. Hannah Zadeh: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review

Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Martha Carvour reports financial support was provided by National Institutes of Health. Hannah Zadeh reports financial support was provided by National Institutes of Health. Matida Bojang reports financial support was provided by National Institutes of Health.

ACKNOWLEDGEMENTS AND FUNDING

Removed from deidentified version

Acknowledgements:

Support was provided by T32 GM139776 (Hannah Zadeh), T37 MD001453 (Matida Bojang), and KL2 TR002536 (Martha Carvour) from the National Institutes of Health. The content in this report is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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