Untested : Exploring SARS-Cov-2 Diagnostic Test Allocation and Uptake

Abstract

The scarcity of SARS-Cov-2 diagnostic tests during the pandemic resulted in an urgent need to efficiently allocate these tests. When making these allocation decisions one should rely on values such as maximizing benefits and thus consider who is likely to be infected or experience mortality. To see if these values are reflected in reality, I use ZIP Code level testing data from the City of Chicago for the first 31 weeks of the pandemic as well as demographic data to conduct a multivariant regression analysis of the testing allocation and uptake determinants. My findings indicate that high-risk groups or characteristics are not significantly correlated with the number of tests conducted. It is also important to consider what groups may be unlikely to seek a test due to their low risk perception or lack of trust in health care. In this regard, my regression found a significant negative correlation between the percent of a ZIP Code who voted for Donald Trump in the 2016 Presidential Election and the number of tests conducted on its residents. This suggests that an individual’s trust in the Trump Administration may negatively influence their health-seeking behavior. I also examined the effect of Chicago’s decision to expand testing eligibility at their city-run test sites to include asymptomatic individuals with a known exposure using a difference-in-difference regression with nearest-neighbor matching, but I found no impact. This suggests that the eligibility expansion did not independently impact the number of tests conducted on the residents of the ZIP Codes with the testing sites. While distributing scarce medical resources is not an issue exclusive to the SARS-Cov-2 pandemic, the lessons learned from the pandemic will inform how governments respond to future public health crises.