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Record W3142779045 · doi:10.1016/j.eclinm.2021.100812

Socio-demographic data collection and equity in covid-19 in Toronto

2021· article· en· W3142779045 on OpenAlexaffabout
Kwame McKenzie

Bibliographic record

VenueEClinicalMedicine · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversity of TorontoWellesley Institute
Fundersnot available
KeywordsPopulationPovertyCoronavirus disease 2019 (COVID-19)Equity (law)Government (linguistics)Health equityMedicinePublic healthDemographyGeographyPolitical scienceSociologyEnvironmental healthLaw

Abstract

fetched live from OpenAlex

Toronto, Ontario, Canada is home to 8% of Canada's population and 11% of Canada's coronavirus cases [[1]City of Toronto: Covid-19 dashboard status of cases. Accessed 3rd February 2021 https://www.toronto.ca/home/covid-19/covid-19-latest-city-of-toronto-news/covid-19-status-of-cases-in-toronto/Google Scholar]. There is significant income inequality; 25% of children and 20% of adults live in poverty [[2]City of Toronto. Poverty reduction strategy. Accessed 3rd February 2021 https://www.toronto.ca/city-government/council/2018-council-issue-notes/poverty-reduction/Google Scholar]. 52% of the population is racialized. Income and race are risk factors for covid-19 so a pandemic strategy needs to be equitable to be effective [[1]City of Toronto: Covid-19 dashboard status of cases. Accessed 3rd February 2021 https://www.toronto.ca/home/covid-19/covid-19-latest-city-of-toronto-news/covid-19-status-of-cases-in-toronto/Google Scholar]. To flatten the curve, we needed to focus on who is under the curve, but, at the start of the pandemic, little routine socio-demographic data was being collected by public health. Reports of higher rates covid-19 in Black populations in the USA and UK and the rise of Black Lives Matter in spring 2020 led Toronto communities to question whether similar disparities were present locally. An open letter to the Government of Ontario calling for race based data collection [[3]Alliance for Healthier CommunitiesOpen letter to premier doug ford, deputy Christine Elliott and Dr David Williams regarding the need to collect and socio-demographic and race based data. Alliance for Healthier Communities, 2021https://www.allianceon.org/news/Letter-Premier-Ford-Deputy-Premier-Elliott-and-Dr-Williams-regarding-need-collect-and-use-socioGoogle Scholar], newspaper op-eds and multi-media interviews crystalized in the development of a backbone organization the Black Health Equity Working Group (BHEWG) which linked Black communities, academics, service providers and policy specialists. BHEWG developed a strategy for the collection and use of socio-demographic data including race/ethnicity and income in which initial analysis of existing area-based data was used as way of highlighting the need for individual level data collection at testing, tracing and hospitalization. A longer-term goal was for socio-demographic data collection when people renew their Ontario Health Insurance Plan cards. The strategy included suggested tools for data collection and the development of a data governance framework (available on request). The aim was to use data to improve equity by changing practice in all parts of the system involved in pandemic: public health units, City of Toronto, the Province of Ontario and Federal Government. Encouraging government analysts and policy organizations to use existing area based data from the census to map disparities was a vital first step. These analyses reported covid-19 rates 10 times higher in some areas and the best predictors were the percentage of racialized populations in an area and income [[4]Public Health OntarioEnhanced epidemiological summary. covid-19 in ontario – a focus on diversity. Public Health Ontario, 2020https://www.publichealthontario.ca/-/media/documents/ncov/epi/2020/06/covid-19-epi-diversity.pdf?la=enGoogle Scholar]. The analyses maintained media interest and pressure on government and public health. Neighbouring public health units (Peel and Middlesex London) and one Province (Manitoba) started collecting race based data in April 2020. Toronto Public Health started collecting race/ethnicity, income, housing data at the time of tracing in May 2020 [[5]McKenzie K. Race and ethnicity data collection during covid-19in canada; if you are not counted you cannot count on the pandemic response. Royal Society of Canada, 2020https://rsc-src.ca/en/race-and-ethnicity-data-collection-during-covid-19-in-canada-if-you-are-not-counted-you-cannot-countGoogle Scholar]. By June, the Ontario Government changed the law so that socio-demographic data would be collected at tracing by all public health units. Tracing information would be linked so that hospitalization rates could be measured. Testing sites were set up to be, quick, low barrier and easy to implement; because of this socio-demographic data collection was considered too onerous [[6]Public Health OntarioData collection resource.Introducing race income household size and language data collection; a resource for case managers. Public Health Ontario, 2021https://www.publichealthontario.ca/-/media/documents/ncov/main/2020/06/introducing-race-income-household-size-language-data-collection.pdf?la=enGoogle Scholar]. To achieve a more equitable pandemic, data has to be analysed and used. And, the publication of the data ensures transparency and accountability. In July, Toronto's Mayor joined the Medical Officer of Health to present the first analyses of socio-demographic disaggregated individual level data by Toronto Public Health. Racialized groups were over represented in covid-19 cases and hospitalizations; and Black populations, and Latino populations had covid-19 case rates 6–11 times that of the White population. The City announced immediate interventions for hard hit areas which started in July 2020 and a public consultation focussed on improving the equity of the response [[7]City of TorontoToronto public health releases new socio-demographic covid-19 data. Media room /News Releases and Media Advisories, 2020https://www.toronto.ca/news/toronto-public-health-releases-new-socio-demographic-covid-19-data/Google Scholar]. Interventions included community based multi-lingual public health campaigns, community testing and pop-up testing sites, free masks, free voluntary isolation sites, eviction prevention advocacy, food security programs, free digital access and emergency child-care [[1]City of Toronto: Covid-19 dashboard status of cases. Accessed 3rd February 2021 https://www.toronto.ca/home/covid-19/covid-19-latest-city-of-toronto-news/covid-19-status-of-cases-in-toronto/Google Scholar]. Focussed strategies for the Black population were deployed following the community consultation in August [[8]City of Toronto. Executive Committee Minutes 17.3 Appendix C – Confronting Anti-Black Racism Unit covid-19 response summary. https://www.toronto.ca/legdocs/mmis/2020/ec/bgrd/backgroundfile-157933.pdfGoogle Scholar]. Monthly data analysis and reporting has monitored progress, kept the issue visible and some may argue offers some evidence that the public health and social support changes may have been partially effective. The Latino population had the highest rate-ratio of covid-19 compared to the White population in June but this decreased as area based strategies were brought in. The rate-ratio in the Black population has decreased steadily; from 9 in August to 2.2 by end December 2020 (Fig. 1). By the end of 2020, the Province of Ontario had announced its own assistance to support pandemic response in hard hit areas [[9]Office of the PremierBackgrounder. Ontario supporting high priority communities. Province of Ontario Newsroom, 2020https://news.ontario.ca/en/backgrounder/59793/ontario-supporting-high-priority-communitiesGoogle Scholar], and were investigating socio-demographic data collection for the vaccine roll-out. In addition, the Federal Government announced a national socio-demographic data collection initiative and a pandemic equity model [[10]Public Health Agency of Canada. Chief Public Health Officer of Canada's report on the state of public health in Canada 2020. From Risk to resilience: An Equity approach to covid-19 https://www.canada.ca/en/public-health/corporate/publications/chief-public-health-officer-reports-state-public-health-canada/from-risk-resilience-equity-approach-covid-19.htmlGoogle Scholar]. The call for disaggregated data aligned community, academics, clinicians and policy makers. The collection, analysis and presentation of data led to changes in the public health response and may have improved the equity of the response. The equity of the response improved following both area focussed and sub-population-based approaches. Further evidence will be needed to determine which changes can be linked to improved pandemic equity. The positive experience of the collection and use of disaggregated data collection and use during covid-19 has increased the appetite for a longer-term strategy for socio-demographic data collection. No interests to declare

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.425
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.186
GPT teacher head0.480
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations36
Published2021
Admission routes2
Has abstractyes

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