Individual and social determinants of SARS-CoV-2 testing and positivity in Ontario,Canada: a population-wide study

BACKGROUND:Optimizing the public health response to reduce the burden of COVID-19 necessitates characterizing population-level heterogeneity of risks for the disease. However, heterogeneity in SARS-CoV-2 testing may introduce biased estimates depending on analytic design. We aimed to explore the potential for collider bias in a large study of disease determinants, and evaluate individual, environmental and social determinants associated with SARS-CoV-2 testing and diagnosis among residents of Ontario, Canada.METHODS:We explored the potential for collider bias and characterized individual, environmental and social determinants of being tested and testing positive for SARS-CoV-2 infection using cross-sectional analyses among 14.7 million community-dwelling people in Ontario, Canada. Among those with a diagnosis, we used separate analytic designs to compare predictors of people testing positive versus negative; symptomatic people testing positive versus testing negative; and people testing positive versus people not testing positive (i.e., testing negative or not being tested). Our analyses included tests conducted between Mar. 1 and June 20, 2020.RESULTS:Of 14 695 579 people, we found that 758 691 were tested for SARS-CoV-2, of whom 25 030 (3.3%) had a positive test result. The further the odds of testing from the null, the more variability we generally observed in the odds of diagnosis across analytic design, particularly among individual factors. We found that there was less variability in testing by social determinants across analytic designs. Residing in areas with the highest household density (adjusted odds ratio [OR] 1.86, 95% confidence interval [CI] 1.75–1.98), highest proportion of essential workers (adjusted OR 1.58, 95% CI 1.48–1.69), lowest educational attainment (adjusted OR 1.33, 95% CI 1.26–1.41) and highest proportion of recent immigrants (adjusted OR 1.10, 95% CI 1.05–1.15) were consistently related to increased odds of SARS-CoV-2 diagnosis regardless of analytic design.INTERPRETATION:Where testing is limited, our results suggest that risk factors may be better estimated using population comparators rather than test-negative comparators. Optimizing COVID-19 responses necessitates investment in and sufficient coverage of structural interventions tailored to heterogeneity in social determinants of risk, including household crowding, occupation and structural racism.

The spread of SARS-CoV-2, the virus causing COVID-19, has resulted in a pandemic with heterogeneity in exposure and risk of transmission.^1–4Heterogeneity in social determinants of COVID-19 may exist at the individual and community (e.g., by housing density^5–7) levels. In addition, social determinants of health, including barriers to health care, occupation, structural racism and xenophobia, have been implicated in COVID-19 risk.^8,9 Environmental determinants such as ambient air pollution may also play a role, as evidence indicates that higher ambient air pollution increases risk for infection with other respiratory viruses^10,11 and the development of severe COVID-19.^12,13 Environmental factors are linked with structural racism (e.g., in the context of low-quality housing).^12,14Using observational data to identify risk factors for COVID-19 relies on SARS-CoV-2 testing, a service that is not equally distributed.¹⁵ Differential testing introduces the potential for selection biases,^16,17 including collider bias.¹⁷ Collider bias may be introduced into epidemiologic studies of COVID-19 risk factors if the factors under investigation are related both to developing an infection and to the likelihood of being tested.^17–19 For example, data suggest that people with diabetes are more likely to develop severe COVID-19 if infected with SARS-CoV-2.^20,21 Thus, if infected, people with diabetes may be more likely to be tested, and consequently, diabetes may appear to be associated with a diagnosis of COVID-19 in studies of those tested for SARS-CoV-2, even if diabetes is not a risk factor for infection.¹⁷ The opposite may occur with underlying respiratory diseases (e.g., asthma) that have symptoms similar to those caused by SARS-CoV-2, leading to the appearance of potentially “protective” associations with COVID-19.²²Our objectives were to explore the potential for collider bias in a large study of COVID-19 determinants and examine individual, environmental and social determinants associated with testing and diagnosis among 14.7 million people in Ontario, Canada.¹⁷