Disproportionate impacts of COVID-19 in a large US city

Abstract

COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021 and fine-grain infection hospitalization rates, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 23.7% (95% CrI: 22.5–24.8%) infection rate and 29.4% (95% CrI: 28.0–31.0%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (11.2% [95% CrI: 10.3–12.0%] vs 25.1% [95% CrI: 23.7–26.4%]), but more likely to be hospitalized (1,965 per 100,000 vs 376 per 100,000) and have their infections reported (53% [95% CrI: 49–57%] vs 28% [95% CrI: 27–30%]). We used a mixed effect poisson regression model to estimate disparities in infection and reporting rates as a function of social vulnerability. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0–3.0) times the infection rate and only 70% (95% CrI: 60%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Our results suggest that further public health efforts are needed to mitigate local COVID-19 disparities and that the CDC’s social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited. COVID-19 disproportionately impacted communities based on their socioeconomic and racial composition. Studies have documented catastrophic disparities at multiple geographic scales, but have not yet tracked how they evolved over time. Here, we use fine-grain epidemiological data to estimate the time-varying disparate burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate that 23.7% (95% CrI: 22.5–24.8%) of the population was infected and 29.4% (95% CrI: 28.0–31.0%) of those infections were reported. Infections were not spread evenly across the region. Individuals over 65 were significantly less likely to be infected than younger age groups (11.2% [95% CrI: 10.3–12.0%] vs 25.1% [95% CrI: 23.7–26.4%]), suggesting that efforts to protect those populations may have been effective. We found that the most vulnerable ZIP codes in the region faced 2.5 times the infection risks compared with the least vulnerable ZIP codes, and that infections were only 70% as likely to be reported in vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Further public health efforts are needed to address local COVID-19 disparities.