Research
Open Access

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

Maria E. Sundaram, Andrew Calzavara, Sharmistha Mishra, Rafal Kustra, Adrienne K. Chan, Mackenzie A. Hamilton, Mohamed Djebli, Laura C. Rosella, Tristan Watson, Hong Chen, Branson Chen, Stefan D. Baral and Jeffrey C. Kwong
CMAJ May 17, 2021 193 (20) E723-E734; DOI: https://doi.org/10.1503/cmaj.202608

Abstract

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.1–4

Heterogeneity in social determinants of COVID-19 may exist at the individual and community (e.g., by housing density5–7) 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 viruses10,11 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).12,14

Using 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-19.22

Our 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

Methods

Study population, setting and design

We conducted an observational study using data from population-based laboratory and health administrative databases in Ontario. Ontario’s health system provides universal access to hospital and physician services23 and laboratory testing.24 We used data from people who were tested between Mar. 1 and June 20, 2020, to identify determinants associated with testing, and then used 3 analytic designs to identify determinants associated with a positive result for SARS-CoV-2 testing.

Data sources, linkages and inclusion criteria

We identified testing status using data from the Ontario Laboratories Information System (OLIS) and linked this information to relevant health-related data sets containing demographic, health care use and area-level information. These data sets were linked using unique encoded identifiers and analyzed at ICES.25

The OLIS captured about 88% of all laboratory-identified SARS-CoV-2 reported by the province during the study period (calculated by cases identified in OLIS divided by the number of cases reported by Ontario’s COVID-19 dashboard in the same time frame). The OLIS records included specimen collection date, results and a text field for symptoms that was completed by health care providers at the time of sampling. We obtained individual- and area-level demographic and environmental information from the Registered Persons Database; the Canadian Institute for Health Information’s Discharge Abstract Database, Same Day Surgery Database and National Ambulatory Care Reporting System; the Ontario Health Insurance Program; the Ontario Mental Health Reporting System; the Ontario Population Health and Environment Cohort; and the 2016 Canada Census26 (Appendix 1, Supplemental Table 1, available at www.cmaj.ca/lookup/doi/10.1503/cmaj.202608/tab-related-content).

For people with more than 1 test in OLIS, we used the first positive or indeterminate test, or the first negative test if all tests during the study period were negative. We included people who were not tested during the study period if they were not recorded as deceased before or born after Mar. 1, 2020. To assess determinants of testing and diagnosis, we included people who underwent polymerase chain reaction testing for SARS-CoV-2 infection and were not residing in a long-term care facility as of Mar. 1, 2020.

Selection and definition of potential determinants of positive results for SARS-CoV-2 testing

We included sex, age group, underlying health conditions and previous use of health care as individual-level determinants. We selected underlying health conditions as those identified in both the peer- and non-peer-reviewed literature as being associated with COVID-19 severity2,27–30 or with symptoms similar to those of COVID-19, because severity and symptoms may lead to differential testing and thus, collider bias;31–36 or conditions that increase the need for personal care support (e.g., dementia), thereby reflecting an intersection with occupational risks among essential care providers.37,38

We hypothesized that health care use would increase access to testing and signal a marker for comorbidities; we measured health care use by the number of hospital admissions in the past 3 years, number of outpatient physician visits in the past year and influenza vaccination in the 2019–2020 season. We also included the Johns Hopkins ACG System39 Aggregated Diagnostic Groups (ADGs)40 as a composite measure of comorbidities.

Environmental determinants included fine particulate matter (PM2.5) using satellite-derived estimates41 and a land-use regression model for NO242 at the postal code level.

We conceptualized social determinants as area-based variables that might signal contact rates in communities (household density; apartment building density; and uncoupled status, e.g., not married);43,44 contact rates at work (“essential workers”);16,45 socioeconomic barriers to health care access or housing (household income and educational attainment);46,47 and factors related to race and ethnicity (visible minority status and recent immigration).8,9 We derived these variables from the 2016 Canada Census at the level of dissemination areas (DAs), the smallest geographic unit at which Census data are collected.48 We ranked dissemination areas at the city level (for median per-person income equivalent) or at the provincial level (for all other social determinants) and then categorized them into quintiles. For apartment building density and recent immigration status, the high frequency of zeros permitted the creation of only 3 categories (i.e., the lower 3 quintiles combined, and the fourth and fifth quintiles).

Statistical analysis

We defined the testing outcome as receipt of at least 1 SARS-CoV-2 test during the study period. The comparator group comprised Ontario residents who had no record of testing during the study period. We evaluated determinants of testing in unadjusted, age- and sex-adjusted, and fully adjusted logistic regression models that included all determinants. The fully adjusted model also included a fixed-effect covariate for public health region. Public health regions are geographic areas in which public health measures were differentially applied,49 and in which there may be variability in measured and unmeasured social determinants.50

To address the potential for collider bias, we compared the odds of a positive test result for SARS-COV-2 derived from unadjusted, age- and sex-adjusted, and fully adjusted logistic regression models (including all determinants and public health regions) using 3 study designs. The “pseudo-test-negative” design compared people who tested positive to people who tested negative; the “true test-negative” design was restricted to tested people who were recorded as having symptomatic illness;51 and the “case–control” design compared all people with a positive test result with all people without a positive test result (i.e., people with a negative test result or who were not tested).

To identify the determinants of being tested for SARS-CoV-2 and being positive for the virus, we focused on the results from fully adjusted logistic regression models from the pseudo-test-negative and case–control designs; the results using the true test-negative design are provided in Appendix 1. We interpreted each set of determinants as independent analyses based on directed acyclic graphs (Appendix 1, Supplemental Figure 1). We believed that the case–control design had the least potential for collider bias.

We conducted the statistical analysis using SAS version 9.4. To assess for collinearity, we evaluated tolerances and variance inflation factors.

Ethical approval

The use of data in this project was authorized under Section 45 of Ontario’s Personal Health Information Protection Act, which does not require review by a Research Ethics Board.

Results

Of 758 691 people tested during the study period, 25 030 (3.3%) tested positive for SARS-CoV-2 (Figure 1). Only 11.8% of those tested had a symptom recorded by the provider, 13.6% were considered asymptomatic, and 74.6% were missing symptom information. Descriptive characteristics of our study population are reported in Table 1 and Appendix 1, Supplemental Table 2.

Figure 1:
Figure 1:

Flow chart showing inclusion and exclusion criteria and resulting analytic data sets.

View this table:
Table 1:

Characteristics of the study population tested and not tested for SARS-CoV-2 in Ontario (Mar. 1 to June 20, 2020)

Determinants of testing for SARS-CoV-2

In the fully adjusted analysis, we found that the odds of being tested increased with age (Table 2 and Appendix 1, Supplemental Table 3). Males had lower odds of testing than females. We also found that nearly every underlying health condition and most measures of previous use of health care were associated with increased odds of testing. In contrast, higher ambient air pollution was associated with reduced odds of testing. There was little variability in the odds of testing by most area-based social determinants of health. However, areas with higher visible minority populations had lower odds of testing, whereas areas with higher household income and greater percentages of uncoupled people had higher odds of testing. The estimates of the odds of being tested for most social determinants of health appeared to be progressively attenuated from unadjusted to age- and sex-adjusted, to fully adjusted regression models. Notably, the direction of the association between testing and income quintile changed direction after adjustment (Figure 2 and Appendix 1, Supplemental Table 3).

View this table:
Table 2:

Odds of ever being tested for SARS-CoV-2 and of COVID-19 diagnosis in Ontario between Mar. 1 and June 20, 2020, in fully adjusted analyses using 2 analytic designs

Figure 2:
Figure 2:

Unadjusted and fully adjusted association between social determinants of health and SARS-CoV-2 testing in Ontario (Mar. 1 to June 20, 2020) using the case–control design. Variables in this figure are listed as covariates and described in Table 2. Note: Ref. = reference category.

Variability in determinants of a positive result for SARS-CoV-2 testing across analytic designs

Our comparison of results using the different analytic designs showed important differences in individual-level determinants and fewer differences in social determinants (Table 2 and Appendix 1, Supplemental Tables 4–6). Variables that were associated with testing tended to show different relations with SARS-CoV-2 positivity across study designs. For example, the adjusted odds of being tested for adults aged 85 years or older compared with those younger than 5 years was 5.60 (95% confidence interval [CI] 5.47–5.73), and the adjusted odds of being positive for SARS-CoV-2 infection was 1.76 (95% CI, 1.51–2.06) with the pseudo-test-negative design and 7.26 (95% CI, 6.23–8.46) for the case–control design (Table 2). Some health conditions associated with higher odds of testing, such as chronic respiratory conditions and indicators of prior health care use, appeared protective against being positive using the pseudo-test-negative design, but showed no association or increased odds of being positive using the case–control design. Our results from the true test-negative design were largely similar to results from the pseudo-test-negative design with wider CIs, with the exceptions that odds of being positive were higher for people of older age using the true test-negative design compared with the pseudo-test-negative design, and lower for higher quintiles of essential workers in the true test-negative design compared with the pseudo-test-negative design (Appendix 1, Supplemental Tables 4 and 5).

Determinants of a positive test result for SARS-CoV-2 using the case–control design

Using the case–control design, we found that older age, certain comorbidities (i.e., hypertension, diabetes, congestive heart failure, dementia, chronic kidney disease and ischemic stroke/transient ischemic attack) and increased previous use of health care were associated with increased odds of a positive SARS-CoV-2 test result. Other comorbidities (i.e., asthma, cancer, ischemic heart disease and substance abuse) and receipt of influenza vaccine in the 2019–2020 season were associated with reduced odds of a positive test result (Table 2 and Appendix 1, Supplemental Table 6).

The 2 highest categories of PM2.5 exposure were associated with increased odds of being positive, whereas no categories of exposure to NO2 were associated with increased odds of positivity.

We also found that higher household density, increased apartment building density, greater percentages of uncoupled people and greater percentages of essential workers were associated with greater odds of a positive SARS-CoV-2 test. Lower educational attainment was related to increased odds, but there was no statistically consistent relation with household income. We also determined that being in the highest quintile of neighbourhoods with visible minorities and greater percentages of recent immigrants were associated with greater odds of a positive SARS-CoV-2 test result. Associations were attenuated after adjustment for all social determinants except for household density and essential work (Figure 3 and Appendix 1, Supplemental Table 6).

Figure 3:
Figure 3:

Unadjusted and fully adjusted association between social determinants of health and testing positive for SARS-CoV-2 in Ontario (Mar. 1 to June 20, 2020) using the case–control design. Variables in this figure are listed as covariates and described in Table 2. Note: Ref. = reference category.

Our evaluation of collinearity diagnostics found that all tolerances were below 1 and all variance inflation factors were below 5 (Appendix 1, Supplemental Table 7).

Interpretation

We found that our 3 analytic designs identified different individual determinants of positive test results for SARS-CoV-2, likely because of collider bias. Using the case–control analysis, which we considered the least biased, we identified particular individual, environmental, and social determinants of health as key determinants for testing positive for SARS-CoV-2.

Using the true test-negative and pseudo-test-negative designs we found a high potential for erroneously identifying some individual determinants, such as underlying health conditions as protective against testing positive for SARS-CoV-2, although they were associated with higher rates of being tested. These health conditions are associated with COVID-19 severity2 and may have been prone to collider bias, where the direction of effect measures changes based on model choice. Similar results were found with health care use variables. Thus, assessment of determinants for SARS-CoV-2 positive test results require careful interpretation by evaluation of the reasons for testing.17 In the context of low overall levels of testing, the case–control design appears to have mitigated some potential sources of collider bias, with the assumption that those not tested are similar to those who tested negative.16,17

We found that some underlying health conditions remained associated with diagnoses using the case–control design, reflecting either unmeasured confounding or possible biological susceptibility to infection if exposed.10,11,20,53,54 For example, dementia and frailty remained independently associated with diagnosis, which may have been due to unmeasured confounding such as higher rates of contacts with caregivers or residence in other types of congregate settings such as retirement homes. Thus, underlying health conditions like dementia and frailty represent targets for prevention with strategies tailored to reduce exposures among people characterized by these individual determinants.

During the study period, the testing criteria for SARS-CoV-2 in Ontario shifted from a focus on returning symptomatic travellers to people who were severely symptomatic and those with occupational exposure to additional testing of people who were asymptomatic.31–34,36 These changes may have created differences among people who were tested and had symptoms compared with all people who were tested. In our study, the restriction of the test-negative design to people with symptoms did not yield substantially different results than the test-negative design that included symptomatic and asymptomatic people for most determinants, but this may have been due, in part, to the high proportion of people who were missing symptom information (74.6%).

The independent association between high PM2.5 and diagnosis may reflect unmeasured social determinants of health.55,56 However, studies have also implicated environmental pollution as having a biological relation to the risk and severity of COVID-19.10–12

We identified increased odds of a positive test result for SARS-CoV-2 associated with household density, apartment building percentage, uncoupled status, essential work, educational attainment and recent immigration, consistent with findings from other settings.50,57,58 Household size has been shown to be a consistent risk factor across a broad range of settings.59,60 These higher infection rates are likely due to prolonged and physically closer in-person contacts occurring more frequently within the household.60 Essential services and occupations have also been associated with higher exposure risk,61 either because such jobs cannot be done feasibly with proper protections or because protective policies and materials are not issued, leaving workers at high risk.62,63

We found that higher percentages of recent immigrants in an area were associated with a positive test result for SARS-CoV-2, even after adjustment, although the percentage of visible minorities was not. Both variables might represent residual measures of structural racism, potentiating increased risk of SARS-CoV-2 exposure and COVID-19 severity,64–66 including hospital admission and death related to COVID-19.9,16,28,58 We found the association between visible minority status and diagnosis was attenuated after adjustment for individual, environmental and other social determinants of health. These findings likely reflect what is already known about race and ethnicity as social constructs and social determinants of health.67 Finally, because there was little association between most social determinants and the odds of testing suggests that testing resources may not be adequately prioritized to people at greatest risk.68

Our findings suggest a need to increase and redirect resources that specifically address social determinants such as household density47,69 (e.g., voluntary isolation centres70 and wrap-around services71), occupational risk62,66 (e.g., paid sick leave,72 workplace testing73 and improved ventilation62) and other mediators of structural racism68,74,75 (e.g., community-led outreach testing76). Our findings also suggest prioritizing COVID-19 vaccination strategies that reach communities and workplaces having the highest rates of cases. Although the Chief Public Health Officer of Canada has suggested an equity lens to the public health response to COVID-19,45 much of the response on COVID-19 equity and outreach to marginalized communities to date has been accomplished through smaller independent groups, including volunteer organizations.77–79

Limitations

Our determination of positive test results for SARS-CoV-2 was restricted to laboratory-confirmed cases and to the 88% of total provincial diagnoses that were available via OLIS. We assumed that determinants remained constant across the study period, whereas surveillance data suggest shifts in how infections propagate between social networks.80 Future analysis should evaluate changes in the direction and size of determinants over the course of the outbreak. Our models also adjusted for public health region, within which many social determinants cluster,50 and we cannot infer from our results how social determinants of diagnosis may vary among and within these geographic regions. We measured social determinants at the area level and these determinants were not available at the individual level; however, by describing individuals’ neighbourhoods, our analysis reflected the role of structural and environmental determinants for people living in them. We may have over-adjusted in the fully adjusted models in our analysis because of the large number of covariates. However, the directions of effect estimates generally remained the same after full adjustment, and the sample size of our analyses provided adequate statistical power. Finally, some relevant determinants, such as obesity,22,80 were not available for our study.81

Conclusion

We found that demographic and health-related risks for positive test results for SARS-CoV-2, which generally have been the targets of response strategies against COVID-19 to date, appeared subject to collider bias. However, we observed consistent relations between testing positive for SARS-CoV-2 and key social determinants of health, including essential worker status, number of people living in a household and educational attainment. Effective responses to COVID-19 require that the social determinants associated with access to testing and SARS-CoV-2 transmission risks be characterized and addressed using risk-tailored, community-based interventions.

Acknowledgements

The authors thank IQVIA Solutions Canada for use of their Drug Information File, as well as Owen Langman for technical assistance. Finally, the authors are grateful to the 14.7 million Ontario residents without whom this research would be impossible.

Footnotes

  • Competing interests: Adrienne Chan is a member of the board of Partners in Health Canada. Mackenzie Hamilton is currently serving an internship at AstraZeneca Canada in support of health research initiatives in lupus and severe asthma. No other competing interests were declared.

  • This article has been peer reviewed.

  • Contributors: Jeffrey Kwong, Sharmistha Mishra and Stefan Baral designed the study. Andrew Calzavara conducted all data analyses (data set and variable creation and statistical modelling). Jeffrey Kwong, Sharmistha Mishra, Stefan Baral, Rafal Kustra and Andrew Calzavara designed the analysis plans and conducted variable selection, with input from Hong Chen and Adrienne Chan on variable selection and definitions. Mackenzie Hamilton, Mohamed Djebli, Laura Rosella and Tristan Watson contributed to analytic plans related to collider bias. Branson Chen contributed to data analyses and data preparation for the symptomatic data set. Maria Sundaram, Jeffrey Kwong, Stefan Baral and Sharmistha Mishra wrote the manuscript. All of authors interpreted the data, critically reviewed and edited the manuscript, gave final approval of the version to be published and agreed to be accountable for all aspects of the work. Sharmistha Mishra and Stefan Baral are co–senior authors.

  • Funding: This study was funded by the St. Michael’s Hospital Research Innovation Council COVID-19 Research Grant. Stefan Baral, Jeffery Kwong, Sharmistha Mishra and Maria Sundaram received a research operating grant (VR5-172683) from the Canadian Institutes of Health Research. Sharmistha Mishra is supported by a Tier 2 Canada Research Chair in Mathematical Modeling and Program Science. Jeffrey Kwong is supported by a Clinician-Scientist Award from the University of Toronto Department of Family and Community Medicine.

  • Data sharing: The data set from this study is held securely in coded form at ICES. While legal data-sharing agreements between ICES and data providers (e.g., health care organizations and government) prohibit ICES from making the data set publicly available, access may be granted to those who meet prespecified criteria for confidential access, available at https://www.ices.on.ca/DAS (email: das{at}ices.on.ca). The full dataset creation plan and underlying analytic code are available from the authors upon request, understanding that the computer programs may rely upon coding templates or macros that are unique to ICES and are therefore either inaccessible or may require modification.

  • Disclaimers: The opinions, results, and conclusions reported in this paper are those of the authors and are independent from the funding sources. Parts of this material are based on data and/or information compiled and provided by the Canadian Institute for Health Information (CIHI) and by Cancer Care Ontario (CCO). However, the analyses, conclusions, opinions, and statement expressed herein are those of the authors, and not necessarily those of CIHI or CCO. No endorsement by ICES, the Ministries of Health and Long-Term Care (MOHLTC), CIHI or CCO is intended or should be inferred.

  • This study was supported by ICES, which is funded by an annual grant from the Ontario MOHLTC. The study sponsors did not participate in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; or the decision to submit the manuscript for publication

  • Accepted April 6, 2021.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY-NC-ND 4.0) licence, which permits use, distribution and reproduction in any medium, provided that the original publication is properly cited, the use is noncommercial (i.e., research or educational use), and no modifications or adaptations are made. See: https://creativecommons.org/licenses/by-nc-nd/4.0/

References

  1. ↵
    1. Mishra S,
    2. Kwong JC,
    3. Chan AK,
    4. et al
    . Understanding hetergeneity to inform public health response to COVID-19 in Canada. CMAJ 2020;192:E684–5.
    OpenUrlFREE Full Text
  2. ↵
    Report of the WHO–China joint mission on coronavirus disease 2019 (COVID-19). Geneva: World Health Organization; 2020.
    1. Rosenberg ES,
    2. Dufort EM,
    3. Blog DS,
    4. et al.
    New York State Coronavirus 2019 Response Team. COVID-19 testing, epidemic features, hospital outcomes, and household prevalence, New York State: March 2020. Clin Infect Dis 2020;71:1953–9.
    OpenUrl
  4. ↵
    1. Jing Q-L,
    2. Liu M-J,
    3. Yuan J,
    4. et al
    . Household secondary attack rate of COVID-19 and associated determinants [preprint]. MedRxiv 2020 Apr. 15. doi: 10.1101/2020.04.11.20056010.
    OpenUrlCrossRef
  5. ↵
    1. Bhala N,
    2. Curry G,
    3. Martineau AR,
    4. et al
    . Sharpening the global focus on ethnicity and race in the time of COVID-19. Lancet 2020;395:1673–6.
    OpenUrlCrossRefPubMed
    1. Chung H,
    2. Fung K,
    3. Ferreira-Legere LE,
    4. et al
    . COVID-19 Laboratory testing in Ontario: patterns of testing and characteristics of individuals tested, as of April 30, 2020. Toronto: ICES; 2020.
  7. ↵
    1. Wang L,
    2. Ma H,
    3. Yiu KCY,
    4. et al
    . Heterogeneity in risk, testing and outcome of COVID-19 across outbreak settings in the Greater Toronto Area, Canada: an observational study. CMAJ Open 2020;8:E627–36.
    OpenUrlAbstract/FREE Full Text
  8. ↵
    1. Price-Haywood EG,
    2. Burton J,
    3. Fort D,
    4. et al
    . Hospitalization and mortality among black patients and white patients with COVID-19. N Engl J Med 2020;382:2534–43.
    OpenUrlCrossRefPubMed
  9. ↵
    1. Yehia BR,
    2. Winegar A,
    3. Fogel R,
    4. et al
    . Association of race with mortality among patients hospitalized with coronavirus disease 2019 (COVID-19) at 92 US hospitals. JAMA Netw Open 2020;3:e2018039.
    OpenUrl
  10. ↵
    1. Feng C,
    2. Li J,
    3. Sun W,
    4. et al
    . Impact of ambient fine particulate matter (PM2.5) exposure on the risk of influenza-like-illness: a time-series analysis in Beijing, China. Environ Health 2016;15:17.
    OpenUrlPubMed
  11. ↵
    1. Zoran MA,
    2. Savastru RS,
    3. Savastru DM,
    4. et al
    . Assessing the relationship between surface levels of PM2.5 and PM10 particulate matter impact on COVID-19 in Milan, Italy. Sci Total Environ 2020;738:139825.
    OpenUrlCrossRefPubMed
  12. ↵
    1. Wu X,
    2. Nethery RC,
    3. Sabath BM,
    4. et al
    . Exposure to air pollution and COVID-19 mortality in the United States: a nationwide cross-sectional study. Sci Adv 2020;6:eabd4049.
    OpenUrlFREE Full Text
  13. ↵
    1. Liang D,
    2. Shi L,
    3. Zhao J,
    4. et al
    . Urban air pollution may enhance COVID-19 case-fatality and mortality rates in the United States. Innovation (NY) 2020;1:100047.
    OpenUrl
  14. ↵
    The Lancet Planetary Health. Environmental racism: time to tackle social injustice. Lancet Planet Health 2018;2:e462.
    OpenUrl
  15. ↵
    1. Stall NM,
    2. Wu W,
    3. Lapointe-Shaw L,
    4. et al
    . Sex- and age-specific differences in COVID-19 testing, cases and outcomes: a population-wide study in Ontario, Canada. J Am Geriatr Soc 2020;68:2188–91.
    OpenUrl
  16. ↵
    1. Niedzwiedz CL,
    2. O’Donnell CA,
    3. Jani BD,
    4. et al
    . Ethnic and socioeconomic differences in SARS-CoV-2 infection: prospective cohort study using UK Biobank. BMC Med 2020;18:160.
    OpenUrlCrossRefPubMed
  17. ↵
    1. Griffith GJ,
    2. Morris TT,
    3. Tudball MJ,
    4. et al
    . Collider bias undermines our understanding of COVID-19 disease risk and severity. Nat Commun 2020;11:5749.
    OpenUrlCrossRefPubMed
    1. Hernán MA,
    2. Robins JM
    . Causal inference: what if. Boca Raton (FL): Chapman & Hall/CRC; 2020.
  19. ↵
    1. Elwert F,
    2. Winship C
    . Endogenous selection bias: the problem of conditioning on a collider variable. Annu Rev Sociol 2014;40:31–53.
    OpenUrlCrossRef
  20. ↵
    1. Hussain A,
    2. Bhowmik B,
    3. do Vale Moreira NC
    . COVID-19 and diabetes: knowledge in progress. Diabetes Res Clin Pract 2020;162:108142.
    OpenUrlCrossRefPubMed
  21. ↵
    1. Kumar A,
    2. Arora A,
    3. Sharma P,
    4. et al
    . Is diabetes mellitus associated with mortality and severity of COVID-19? A meta-analysis. Diabetes Metab Syndr 2020;14:535–45.
    OpenUrlPubMed
  22. ↵
    1. Palaiodimos L,
    2. Kokkinidis DG,
    3. Li W,
    4. et al
    . Severe obesity, increasing age and male sex are independently associated with worse in-hospital outcomes, and higher in-hospital mortality, in a cohort of patients with COVID-19 in the Bronx, New York. Metabolism 2020;108:154262.
    OpenUrlCrossRefPubMed
  23. ↵
    Health care in Ontario: OHIP. Toronto: Government of Ontario. Available: www.ontario.ca/page/health-care-ontario (accessed 2020 Oct. 29).
  24. ↵
    Schedule of benefits for laboratory services. Toronto: Ministry of Health, Ontario Health Insurance Plan, Laboratories and Genetics Branch; 2020. Available: www.health.gov.on.ca/en/pro/programs/ohip/sob/lab/lab_mn2020.pdf (accessed 2021 Apr. 12).
  25. ↵
    ICES Data. Discovery, Better Health. Toronto: ICES; 2020. Available: www.ices.on.ca/Data-and-Privacy/ICES-data/Types-of-ICES-Data (accessed 2020 Oct. 29).
  26. ↵
    Data products, 2016 Census. Ottawa: Statistics Canada; modified 2021 Feb. 8. Available: www12.statcan.gc.ca/census-recensement/2016/dp-pd/index-eng.cfm (accessed 2020 Oct. 29).
  27. ↵
    1. Pennington E
    . Asthma increases risk of severity of COVID-19. Cleve Clin J Med 2020 May 5 [Epub ahead of print]. doi: 10.3949/ccjm.87a.ccc002.
    OpenUrlAbstract/FREE Full Text
  28. ↵
    1. Williamson EJ,
    2. Walker AJ,
    3. Bhaskaran K,
    4. et al
    . OpenSAFELY: factors associated with COVID-19 death in 17 million patients. Nature 2020;584:430–6.
    OpenUrlCrossRefPubMed
    1. Fang L,
    2. Karakiulakis G,
    3. Roth M
    . Are patients with hyperetension and diabetes mellitus at increased risk for COVID-19 infection? Lancet Respir Med 2020;8:e21.
    OpenUrl
  30. ↵
    1. Merkler AE,
    2. Parikh NS,
    3. Mir S,
    4. et al
    . Risk of ischemic stroke in patients with COVID-19 versus patients with influenza [preprint]. medRxiv 2020 May 21. doi: 10.1101/2020.05.18.20105494.
    OpenUrlCrossRef
  31. ↵
    COVID-19 provincial testing guidance update. V. 4.0, May 14, 2020. Toronto: Ministries of Health and Long-Term Care.
  32. COVID-19 provincial testing guidance update: May 2, 2020. Toronto: Ministries of Health and Long-Term Care.
  33. COVID-19 provincial testing guidance update: April 15, 2020. Toronto: Ministries of Health and Long-Term Care.
  34. ↵
    COVID-19 provincial testing guidance update: May 28, 2020. Toronto: Ministries of Health and Long-Term Care.
  35. COVID-19 quick reference public health guidance on testing and clearance. Toronto: Ministries of Health and Long-Term Care; 2020.
  36. ↵
    COVID-19 provincial testing guidance update: April 8, 2020. Toronto: Ministries of Health and Long-Term Care.
  37. ↵
    1. Tolea MI,
    2. Morris JC,
    3. Galvin JE
    . Trajectory of mobility decline by type of dementia. Alzheimer Dis Assoc Disord 2016;30:60–6.
    OpenUrl
  38. ↵
    1. Chiao C-Y,
    2. Wu H-S,
    3. Hsiao C-Y
    . Caregiver burden for informal caregivers of patients with dementia: a systematic review. Int Nurs Rev 2015;62:340–50.
    OpenUrlCrossRefPubMed
  39. ↵
    The Johns Hopkins ACG system. Baltimore: Johns Hopkins University. Available: www.hopkinsacg.org/ (accessed 2020 Nov. 1).
  40. ↵
    1. Austin PC,
    2. van Walraven C,
    3. Wodchis WP,
    4. et al
    . Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a general adult population cohort in Ontario, Canada. Med Care 2011;49:932–9.
    OpenUrlCrossRefPubMed
  41. ↵
    1. van Donkelaar A,
    2. Martin RV,
    3. Brauer M,
    4. et al
    . Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter. Environ Health Perspect 2015;123:135–43.
    OpenUrlCrossRefPubMed
  42. ↵
    1. Hystad P,
    2. Setton E,
    3. Cervantes A,
    4. et al
    . Creating national air pollution models for population exposure assessment in Canada. Environ Health Perspect 2011;119:1123–9.
    OpenUrlCrossRefPubMed
  43. ↵
    1. Walker PGT,
    2. Whittaker C,
    3. Watson OJ,
    4. et al
    . The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries. Science 2020;369:413–22.
    OpenUrlAbstract/FREE Full Text
  44. ↵
    1. Park SY,
    2. Kim Y-M,
    3. Yi S,
    4. et al
    . Coronavirus disease outbreak in call center, South Korea. Emerg Infect Dis 2020;26:1666–70.
    OpenUrlPubMed
  45. ↵
    Chief Public Health Officer of Canada. From risk to resilience: an equity approach to COVID-19. Ottawa: Public Health Agency of Canada; 2020.
  46. ↵
    1. Karaye IM,
    2. Horney JA
    . The impact of social vulnerability on COVID-19 in the U.S.: an analysis of spatially varying relationships. Am J Prev Med 2020;59:317–25.
    OpenUrlCrossRefPubMed
  47. ↵
    1. Rodriguez-Diaz CE,
    2. Guilamo-Ramos V,
    3. Mena L,
    4. et al
    . Risk for COVID-19 infection and death among Latinos in the United States: examining heterogeneity in transmission dynamics. Ann Epidemiol 2020;52:46–53.e2.
    OpenUrl
  48. ↵
    Dictionary, Census of Population, 2016: dissemination area (DA). Ottawa: Statistics Canada; 2016, modified 2019 Jan. 3. Available: www12.statcan.gc.ca/census-recensement/2016/ref/dict/geo021-eng.cfm (accessed 2020 Oct. 13).
  49. ↵
    Public health units. Toronto: Government of Ontario; 2014, updated 2019 Mar. 22. Available: www.ontario.ca/page/public-health-units (accessed 2020 Oct. 29).
  50. ↵
    Enhanced epidemiological summary: COVID-19 in Ontario — a focus on diversity. Toronto: Public Health Ontario; 2020.
  51. ↵
    1. Sullivan SG,
    2. Tchetgen EJ,
    3. Cowling BJ
    . Theoretical basis of the test-negative study design for assessment of influenza vaccine effectiveness. Am J Epidemiol 2016;184:345–53.
    OpenUrlCrossRefPubMed
  52. NAAQS Table. Washington (DC): United States Environmental Protection Agency. Available: www.epa.gov/criteria-air-pollutants/naaqs-table (accessed 2020 Sept. 21).
  53. ↵
    1. Nishiga M,
    2. Wang DW,
    3. Han Y,
    4. et al
    . COVID-19 and cardiovascular disease: from basic mechanisms to clinical perspectives. Nat Rev Cardiol 2020;17:543–58.
    OpenUrlPubMed
  54. ↵
    1. Elliot AJ,
    2. Harcourt SE,
    3. Hughes HE,
    4. et al
    . The COVID-19 pandemic: a new challenge for syndromic surveillance. Epidemiol Infect 2020;148:e122.
    OpenUrlCrossRef
  55. ↵
    1. Morelli X,
    2. Rieux C,
    3. Cyrys J,
    4. et al
    . Air pollution, health and social deprivation: a fine-scale risk assessment. Environ Res 2016;147:59–70.
    OpenUrl
  56. ↵
    1. Næss O,
    2. Piro FN,
    3. Nafstad P,
    4. et al
    . Air pollution, social deprivation, and mortality. Epidemiology 2007;18:686–94.
    OpenUrlCrossRefPubMed
  57. ↵
    1. Lin SL
    . Intersectionality and inequalities in medical risk for severe COVID-19 in the Canadian Longitudinal Study on Aging. Gerontologist 2020 Sept. 24 [Epub ahead of print]. doi: 10.1093/geront/gnaa143.
    OpenUrlCrossRef
  58. ↵
    1. Drefahl S,
    2. Wallace M,
    3. Mussino E,
    4. et al
    . A population-based cohort study of sociodemographic risk factors for COVID-19 deaths in Sweden. Nat Commun 2020;11:5097.
    OpenUrlCrossRefPubMed
  59. ↵
    1. Luo L,
    2. Liu D,
    3. Liao X,
    4. et al
    . Contact settings and risk for transmission in 3410 close contacts of patients with COVID-19 in Guangzhou, China: a prospective cohort study. Ann Intern Med 2020;173:879–87.
    OpenUrlPubMed
  60. ↵
    1. Jing Q-L,
    2. Liu M-J,
    3. Zhang Z-B,
    4. et al
    . Household secondary attack rate of COVID-19 and associated determinants in Guangzhou, China: a retrospective cohort study. Lancet Infect Dis 2020;20:1141–50.
    OpenUrlCrossRefPubMed
  61. ↵
    Workplace safety & prevention services guidance on health and safety for the personal care service sector during COVID-19. Mississauga (ON): Workplace Safety & Prevention Services; 2020.
  62. ↵
    1. Dyal JW,
    2. Grant MP,
    3. Broadwater K,
    4. et al
    . COVID-19 among workers in meat and poultry processing facilities: 19 states, April 2020. MMWR Morb Mortal Wkly Rep 2020;69:557–61.
    OpenUrlCrossRefPubMed
  63. ↵
    1. Contrera J
    . The N95 shortage America can’t seem to fix. The Washington Post 2020 Sept. 21. Available: www.washingtonpost.com/graphics/2020/local/news/n-95-shortage-covid/ (accessed 2020 Sept. 21).
  64. ↵
    1. Vahidy FS,
    2. Nicolas JC,
    3. Meeks JR,
    4. et al
    . Racial and ethnic disparities in SARS-CoV-2 pandemic: analysis of a COVID-19 observational registry for a diverse US metropolitan population. BMJ Open 2020;10:e039849.
    OpenUrlAbstract/FREE Full Text
    1. de Lusignan S,
    2. Dorward J,
    3. Correa A,
    4. et al
    . Risk factors for SARS-CoV-2 among patients in the Oxford Royal College of General Practitioners Research and Surveillance Centre primary care network: a cross-sectional study. Lancet Infect Dis 2020;20:1034–42.
    OpenUrlPubMed
  66. ↵
    1. Liem A,
    2. Wang C,
    3. Wariyanti Y,
    4. et al
    . The neglected health of international migrant workers in the COVID-19 epidemic. Lancet Psychiatry 2020;7:e20.
    OpenUrl
  67. ↵
    1. Ordóñez CE
    . Not race, racism: concerns of COVID-19 affecting African Americans. Anthropological Responses to Heatlh Emergencies; 2020.
  68. ↵
    1. Dodds C,
    2. Fakoya I
    . COVID-19: ensuring equality of access to testing for ethnic minorities. BMJ 2020;369:m2122.
    OpenUrlFREE Full Text
  69. ↵
    1. Brandén M,
    2. Aradhya S,
    3. Kolk M,
    4. et al
    . Residential context and COVID-19 mortality among adults aged 70 years and older in Stockholm: a population-based, observational study using individual-level data. Lancet Healthy Longev 2020;1:e80–8.
    OpenUrlCrossRefPubMed
  70. ↵
    1. Jordan-Martin NC,
    2. Madad S,
    3. Alves L,
    4. et al
    . Isolation hotels: a community-based intervention to mitigate the spread of the COVID-19 pandemic. Health Secur 2020 Sept. 25 [Epub ahead of print]. doi: 10.1089/hs.2020.0123.
    OpenUrlCrossRef
  71. ↵
    1. Madad S,
    2. Nuzzo JB,
    3. Bordeaux M
    . The missing piece in America’s COVID-19 isolation and quarantine strategy: wraparound services [blog]. Health Affairs 2020 Dec. 10. Available: www.healthaffairs.org/do/10.1377/hblog20201207.458415/full/ (accessed 2021 Mar. 9).
  72. ↵
    1. Vazquez J,
    2. Islam T,
    3. Beller J,
    4. et al
    . Expanding paid sick leave as a public health tool in the COVID-19 pandemic. J Occup Environ Med 2020;62:e598–9.
    OpenUrl
  73. ↵
    Rapid COVID-19 testing a potential game-changer in worker protection. Toronto: Institute for Work and Health; 2020. Available: www.iwh.on.ca/newsletters/at-work/102/rapid-covid-19-testing-potential-game-changer-in-worker-protection (accessed 2021 Mar. 9).
  74. ↵
    1. Webb Hooper M,
    2. Napoles AM,
    3. Perez-Stable EJ
    . COVID-19 and racial/ethnic disparities. JAMA 2020;323:2466–7.
    OpenUrlCrossRefPubMed
  75. ↵
    1. Yang T-C,
    2. Choi S-WE,
    3. Sun F
    . COVID-19 cases in US counties: roles of racial/ethnic density and residential segregation. Ethn Health 2021;26:11–21.
    OpenUrlPubMed
  76. ↵
    1. Wallis G,
    2. Siracusa F,
    3. Blank M,
    4. et al
    . Experience of a novel community testing programme for COVID-19 in London: lessons learnt. Clin Med (Lond) 2020;20:e165–9.
    OpenUrl
  77. ↵
    Good Neighbour Project [home page]. Available: www.goodneighbourproject.com (accessed 2021 Feb. 22).
    1. Marwaha S,
    2. Vohra-Miller S,
    3. Grewal R
    . We started the South Asian COVID Task Force because Ontario failed to address inequities. In a short time, we’ve seen more people get tested. Toronto Star 2020 Dec. 17. Available: www.thestar.com/opinion/contributors/2020/12/15/we-started-the-south-asian-covid-task-force-because-ontario-failed-to-address-inequities-in-a-short-time-weve-been-able-to-make-great-strides.html (accessed 2021 Feb. 22).
  79. ↵
    1. Jabakhanji S
    . Meet the Toronto Indigenous organizations bringing COVID-19 testing, food directly to people’s doorsteps. CBC News Toronto 2020 Dec. 19. Available: www.cbc.ca/news/canada/toronto/anishnawbe-health-toronto-mobile-unit-covid19-1.5841393 (accessed 2021 Feb. 22).
  80. ↵
    1. Kwok S,
    2. Adam S,
    3. Ho JH,
    4. et al
    . Obesity: a critical risk factor in the COVID-19 pandemic. Clin Obes 2020;10:e12403.
    OpenUrl
  81. ↵
    ICES Data Dictionary. Toronto: ICES. Available: www.ices.on.ca/Data-and-Privacy/ICES-data/Data-dictionary (accessed 2020 Nov. 1).
Back to top

In this issue

Canadian Medical Association Journal: 193 (20)
CMAJ
Vol. 193, Issue 20
17 May 2021

Article tools

Respond to this article
Print
Download PDF
Article Alerts
Email Article
Citation Tools
Individual and social determinants of SARS-CoV-2 testing and positivity in Ontario, Canada: a population-wide study
Maria E. Sundaram, Andrew Calzavara, Sharmistha Mishra, Rafal Kustra, Adrienne K. Chan, Mackenzie A. Hamilton, Mohamed Djebli, Laura C. Rosella, Tristan Watson, Hong Chen, Branson Chen, Stefan D. Baral, Jeffrey C. Kwong
CMAJ May 2021, 193 (20) E723-E734; DOI: 10.1503/cmaj.202608
Digg logo Reddit logo Twitter logo Facebook logo Google logo Mendeley logo

Jump to section

Collections