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, 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 16.9% (95% CrI 16.1-17.8%) infection rate and 34.1% (95% CrI 32.4-35.8%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (8.0% [95% CrI 7.5-8.6%] vs 18.1% [95% CrI 17.2-19.2%]), but more likely to be hospitalized (1,381 per 100,000 vs 319 per 100,000) and have their infections reported (51% [95% CrI 48-55%] vs 33% [95% CrI 31-35%]). Children under 18, who make up 20.3% of the local population, accounted for only 5.5% (95% CrI 3.8-7.7%) of all infections between March 1 and May 1, 2020 compared with 20.4% (95% CrI 17.3-23.9%) between December 1, 2020 and February 1, 2021. 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 61%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. For example, the ratio in infection rates between the more and less vulnerable communities declined from 12.3 (95% CrI 8.8-17.1) to 4.0 (95% CrI 3.0-5.3) to 2.7 (95% CrI 2.0-3.6), from April to August to December of 2020, respectively. Our results suggest that public health efforts to mitigate COVID-19 disparities were only partially effective and that the CDCs social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited.