Hidden COVID-19 deaths? Exploring the Spatial context of excess death rates during the COVID-19 pandemic

The COVID-19 pandemic caused substantial mortality in the United States with impacts unevenly distributed across the country. Official COVID-19-related death counts, however, almost certainly underrepresent the true impact of the pandemic due to underreporting, misclassification, and, particularly in the early stages of pandemic, limited testing and diagnosis [1]. Excess death rates, deaths above expected levels based on historical trends, arguably provide a more comprehensive measure of COVID-19 impacts by capturing both direct COVID-19 deaths and indirect fatalities related to pandemic disruptions. The goal of the study is to examine spatial and temporal disparities in COVID-19 excess mortality in 2020-2021 and 2021-2022 across the U.S., distinguishing between quantifiable sociodemographic influences and unmeasurable place-based factors through Multiscale Geographically Weighted Regression (MGWR).

Excess mortalities are examined in 2020-2021 and 2021-2022 to capture temporal and spatial shifts in COVID-19-related excess mortality patterns. MGWR is used to identify localized variations in the determinants of excess death rates using data on socioeconomic conditions, political affiliation, demographic factors, health status, and healthcare access.

We present the results of calibrating both a global and a local model of excess death rates during two phases of the COVID-19 pandemic. In terms of the global results, in both time periods excess death rates were significantly higher in counties with high percentages of people below the poverty line, Republican-leaning residents, high proportions of elderly population, high levels of deprivation, high unemployment, and relatively high proportions of residents with diabetes. Rates were also significantly higher in counties with relatively high proportions of residents without health insurance, where there were more females than males, and where there were fewer younger adults, although these effects were not as strong as the previous associations. However, these macro-level conditioned associations can hide important local variations in the determinants of severe COVID-19-related health outcomes. Because COVID-19-related excess death rates exhibit strong spatial patterns, any covariate sharing a similar spatial distribution, even if coincidental, might spuriously be reported to have a significant impact on excess dates rates when examined globally. To examine this possibility, a local statistical model is calibrated which suggests some alternative views on the determinants of COVID-19-relates deaths. For instance, although excess death rates were strongly linked to Republican party support across the whole country in the first phase of the pandemic, this relationship was limited to the eastern seaboard and the Deep South in the second phase. There was a significant conditioned relationship between excess deaths and the elderly only across the southern half of the country in both phases of the pandemic. The impacts of being without health insurance were only severe in the western half of the country and only in the first phase of the pandemic. In contrast to the global finding, the positive association with diabetes was only found along the east coast and only in the first phase of the pandemic. In the first phase of the pandemic, excess mortality was only significantly positively associated with the proportion of Hispanics in the Southwest and was insignificant elsewhere, In the second phase of the pandemic, there were no significant positive relationships reported locally but there were significant negative relationships across the upper Midwest, the Northeast, and in Texas. In distinct contrast to the global results, the local conditioned relationship between excess death rates and percentage Black population was significantly positive across the country in both phases of the pandemic. In the first phase of the pandemic, conditioning on all the covariates in the model, excess deaths from COVID-19 were lower than expected in most parts of the country except for a cone-shaped set of states from Nebraska to Texas; in the second phase the unseen benefits of location were only experienced in the upper Midwest. The results support the use of local models to better understand the nature of pandemics and also that COVID-19 impacts arose from a complex interaction between both measurable factors and localized, often unobservable, influences.

Disparities in excess deaths during the COVID-19 pandemic reflect a combination of structural vulnerabilities and unmeasured local influences. To effectively reduce mortality gaps and strengthen preparedness for future health crises, public health interventions must be geographically tailored, targeting both region-specific risk factors and the contextual conditions that shape local outcomes.