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

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

2021· article· en· W3142779045 sur OpenAlexaffabout
Kwame McKenzie

Notice bibliographique

RevueEClinicalMedicine · 2021
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueGlobal Public Health Policies and Epidemiology
Établissements canadiensUniversity of TorontoWellesley Institute
Organismes subventionnairesnon disponible
Mots-clésPopulationPovertyCoronavirus disease 2019 (COVID-19)Equity (law)Government (linguistics)Health equityMedicinePublic healthDemographyGeographyPolitical scienceSociologyEnvironmental healthLaw

Résumé

récupéré en direct d'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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,012
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,425
Score d'incertitude au seuil0,996

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,012
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,186
Tête enseignante GPT0,480
Écart entre enseignants0,295 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations36
Publié2021
Routes d'admission2
Résumé présentoui

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