Introducing the Canadian Area-Level Social Determinants of Health Indicators (CASDOHI)
Notice bibliographique
Résumé
Abstract There is a growing demand for social data to support evidence-based health planning in Canada. Although Statistics Canada has made small-area Canadian census data available to researchers online for over a decade, it remains underutilized due to its cumbersome data structure and the challenges in processing and linking it with other data sources. This paper introduces the Canadian Area-Level Social Determinants of Health Indicators (CASDOHI), a user-friendly dataset that includes over 100 standardized indicators at the Dissemination Area (DA) level, harmonized between the 2011, 2016, and 2021 Census Profiles. The indicators included in CASDOHI cover various factors such as income, education, labour force, housing, and ethnocultural characteristics. The dataset also features intercensal estimates, which help researchers track changes over time. CASDOHI allows researchers to examine specific social determinants of health (SDOH) and link them to health and administrative datasets without needing advanced coding or geospatial skills. This paper also provides practical guidance for integrating these indicators with health data to support health inequality research. CASDOHI addresses a gap in Canadian health research by providing an accessible and long-running dataset of small area social determinants, facilitating the study of health inequalities, and supporting evidence-based health policymaking and planning across Canada. Extended Abstract Introduction The increasing demand for high-quality social data to inform local health planning and policy-making in Canada is challenged by the technical complexity of available census data. Although Statistics Canada provides small-area level census data, their use remains limited in health research due to the resources and high data skills required to work with them. Objectives This paper presents the development and use of CASDOHI, a comprehensive dataset of over 100 indicators related to social determinants of health, harmonized across the years 2011 to 2021. It explains the construction of CASDOHI, demonstrates its relevance for researching health inequalities, and offers guidance for linking it with external health datasets. The overarching aim is to support the integration of social and health data to advance research on health disparities. Methods We developed CASDOHI using publicly available Census Profiles from 2011, 2016, and 2021 provided by Statistics Canada, as well as the NHS Profile obtained through a formal request. Additionally, we estimated CASDOHI indicators for the intercensal years from 2012 to 2020 using the linear interpolation method, while controlling for boundary changes. We selected the indicators based on a review of seven Canadian deprivation indices, with additions informed by health inequality literature and frameworks. All indicators are reported at the DA level—the smallest standard geographic unit used for census dissemination. Results CASDOHI includes indicators across multiple domains, including income, education, housing, labour force, and ethnocultural characteristics. Where available, we reported the variables disaggregated by sex. This paper outlines how CASDOHI can be linked to external datasets using geographic correspondence tools such as PCCF+ and areal interpolation. Conclusion CASDOHI addresses longstanding challenges in using census data for health research by offering a preprocessed, comprehensive, and accessible dataset covering the years 2011 to 2021. It supports nuanced analyses of health inequalities and fosters evidence-informed policy-making at local, regional, and national levels. Highlights CASDOHI is a user-friendly, open-access dataset that provides over 100 standardized indicators of social determinants of health at the DA level, such as income, education, labour force, housing, and ethnocultural characteristics. We built CASDOHI using the Census Profiles 2011, 2016, 2021, and NHS Profile. By eliminating the need for manual data manipulation, we made census data more accessible, allowing users to focus on analysis and interpretation. We released CASDOHI in CSV files, with a separate table for each year from 2011 to 2021. This paper guides linking CASDOHI indicators to external datasets, such as health administrative data, to support diverse public health and research applications.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,011 | 0,025 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,006 | 0,001 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,003 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».