Poverty and Inequality Raised COVID-19 Deaths Once Geography Is Counted

COVID-19 did not strike every community equally. A study in the Journal of Applied Statistics asks how much of that difference traces to economic conditions. The answer, it turns out, depends on how the analysis treats geography. When the models account for the influence of neighboring areas, worsening poverty, income, and inequality line up with higher death rates.

Two of the three authors are McCoy College of Business faculty: Rasim M. Musal, Associate Professor of Information Systems and Analytics, and Tahir Ekin, Fields Chair in Business Analytics and Professor of Analytics. Their co-author is Tevfik Aktekin of the University of New Hampshire.

Why geography matters

Counties and other spatial units are not islands. People commute across their borders, hospitals serve regions, and an outbreak in one place spills into the next. A model that treats each unit as independent can blur or hide the effect of local conditions.

The researchers address this with Bayesian spatial models. Bayesian methods combine prior knowledge with data to produce a full range of plausible estimates rather than a single point. Spatial models add a structure that lets each unit’s outcome depend in part on its neighbors. Spatio-temporal models extend that structure to track change over time.

The study

The authors built three families of models: non-spatial, spatial, and spatio-temporal. They applied them to publicly available data merged from federal and state sources, with an emphasis on California. The socioeconomic factors examined included poverty, income level, and income inequality. The analysis covered different phases of the pandemic rather than a single snapshot.

The paper also examines how changes in socioeconomic conditions over time related to mortality across the spatial units. The results therefore speak to trends as well as levels.

What the researchers found

The central finding is that deteriorating socioeconomic factors led to higher mortality rates. This relationship becomes clear when the models account for spatial effects across neighboring units. In other words, the non-spatial approach understates or muddles the connection. The authors present the results as insights for policymakers and public health decision makers.

What it means for policymakers

For public health agencies, the study makes a methodological point with practical weight. Resource allocation during an emergency depends on knowing where need is greatest. If the models used to find those places ignore geography, they may point to the wrong ones. Spatial modeling is a well-developed tool and the data it requires are public.

For economic policymakers, the finding adds to evidence that poverty and inequality carry health costs that surface sharply in a crisis. Investments that reduce hardship are also investments in resilience for the next emergency.

For business leaders and insurers, the results are a reminder that local economic conditions and health outcomes are linked. Those links cross jurisdictional lines. Planning that stops at a county boundary will miss part of the picture.

This summary is based on the paper’s abstract. The full article reports the data, methods, and detailed results.

What it means for managers

• Geography changes the answer. The link between socioeconomic hardship and COVID-19 deaths shows up clearly only when models account for spillovers between neighboring areas.
• Poverty, income, and inequality all mattered, and they mattered across pandemic phases, not just in the first wave.
• Public health planners can use spatial models to target resources. Models that ignore neighbors risk misjudging where the greatest need is.

Musal, R. M., Aktekin, T., & Ekin, T. (2025). Bayesian spatial and spatio-temporal analysis of socioeconomic determinants on COVID-19 mortality. Journal of Applied Statistics, 53(12), 2328-2364. 10.1080/02664763.2025.2593322