Neighbourhood-level risk factors of COVID-19 incidence and mortality

  1. Trevor van Ingen
  2. Samantha Akingbola
  3. Kevin A. Brown
  4. Nick Daneman
  5. Sarah A. Buchan
  6. Brendan T. Smith

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Abstract

Background

Racialized and low income communities face disproportionally high rates of coronavirus 2019 (COVID-19) infection and death. However, data on inequities in COVID-19 across granular categories of socio-demographic characteristics is more sparse.

Methods

Neighbourhood-level counts of COVID-19 cases and deaths in Ontario, Canada recorded as of July 28 th , 2020 were extracted from provincial and local reportable infectious disease surveillance systems. Associations between COVID-19 incidence and mortality and 18 neighbourhood-level measures of immigration, race, housing and socio-economic characteristics were estimated with Poisson generalized linear mixed models. Housing characteristic variables were subsequently added to models to explore if housing may have a confounding influence on the relationships between immigration, race, and socio-economic status and COVID-19 incidence.

Results

There were large inequities in COVID-19 incidence and mortality across the socio-demographic variables examined. Neighbourhoods having a higher proportion immigrants, racialized populations, large households and low socio-economic status were associated with COVID-19 risk. Adjusting for housing characteristics, especially unsuitably crowded housing, attenuated COVID-19 risks. However persistent risk remained for neighbourhoods having high proportions of immigrants, racialized populations, and proportion of Black, Latin American, and South Asian residents.

Conclusions

Socio-demographic factors account for some of the neighbourhood-level differences in COVID-19 across Ontario. Housing characteristics account for a portion, but not all, of the excess burden of COVID-19 experienced by immigrant, racialized, low income and low education populations.

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  1. ScreenIT

    SciScore for 10.1101/2021.01.27.21250618: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study received ethics approval from Public Health Ontario’s Research Ethics Board.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablePostal code of residence, age group (<15, 15-64, and ≥65 years), sex (male, female), case status, and outcome status were extracted from CCM plus.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    In the US, racialized individuals are more likely to be incarcerated, which in turn increases the likelihood of infection,[8] and racial discrimination in mortgage lending practices in urban areas may be a driving factor in residential crowding among Black Americans.[37] In the UK, non-White physicians comprise the vast majority of COVID-19 deaths among doctors and, compared to White physicians, are more likely to report being under pressure to attend to patients without receiving the necessary physical protection.[38] This study is subject to some limitations. First, the number of COVID-19 cases included in this study is an undercount of the true number of infections, and may be biased by changes in testing criteria during the study period and differences in testing patterns between various socio-demographic populations. However, recent Ontario research describing both the odds of having been tested for COVID-19 and the odds of having received a positive COVID-19 diagnosis suggest that selection bias is not driving socio-demographic inequities in COVID-19 incidence in the province.[39] Additionally, the use of neighbourhood-level measures of socio-demographic characteristics may dilute the real effect of these characteristics on COVID-19 risks, as individual cases may not reflect the characteristics of the neighbourhoods they live in. Previous Canadian studies comparing individual and area-level measures have shown that even with relatively poor agreement between measures, a...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.01.27.21250618 on medRxiv