Epidemiological Analysis of SARS-CoV-2: Three Papers Examining Health Status, Response Bias, and Strategies for Engagment

Abstract

The emergence of the global SARS-CoV-2 pandemic created tremendous impact on humanity beginning in late 2019. Public health researchers at Indiana University Richard M. Fairbanks School of Public Health responded by conducting research into the etiological profile of the virus, including a large Indiana state-wide population-based prevalence study in early 2020. Methods Data on demographics, tobacco use, health status, and reasons for participating in the population prevalence study were used to conduct three retrospective cross-sectional studies. The first study assessed the association of self-reported health and tobacco behaviors with COVID-19 infection (n=8,241). The second study used successive wave analysis to assess nonresponse bias (n=3,658). Finally, participants demographics were characterized by who responded to text, email, phone calls, or postcards and by the number of prompts needed to elicit participation (n= 3,658). Results The first study found self-identified health status of those reporting “poor, “fair” or good” had a higher risk of past or current infections compared to “very good” or “excellent” health status (P <0.02). Positive smoking status was inversely associated with SARS-CoV-2 infection (p <0.001). When assessing the sample for non-response bias (n=3,658), 40.9% responded in wave 1 of recruitment, 34.1% in wave 2 and 25.0% in wave 3 for an overall participation rate of 23.6%. There were no significant differences in response by waves and demographics, being recently exposed or reasons for participating. In the final study, compared to males, females made up 54.6% of the sample and responded at a higher rate to postcards (8.2% vs. 7.5%) and text/emails (28.1 vs. 24.6%, 2= 7.43, p 0.025); and responded at a higher percentage after 1 contact (21.4 vs. 17.9%, 2 = 7.6, p 0.023). Conclusion This research contributed to the scientific understanding of the etiological picture of SARS-CoV-2. Additionally, the current study used a novel method that public health practitioners can easily implement to detect non-response bias in primary data collection without advanced statistical methods. Finally, the current study allows researchers to focus not only on the modality of inviting participants, but the frequency of invitations needed to secure specific populations, reducing time and resources.