Anousheh Marouzi

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Two decades of research have highlighted persistent income-related health inequities in Canada across municipal, provincial, and national levels. While there is broad consensus among researchers, advocates, and health professionals that social determinants are the primary drivers of health, the empirical foundation supporting this remains relatively limited. A current renaissance in health system data access offers an opportunity to assess the multilevel impact of social factors on health inequalities, yet this potential remains underused. Objective: This project aims to examine how social, economic, and political conditions shape health inequalities and investigate how structural and intermediate determinants explain disparities across national, provincial, city, neighborhood, and individual levels. Methods: We will create the Canadian Social Determinants Urban Laboratory (CSDUL), a multilevel, longitudinal, virtual data environment that integrates 15 existing databases from Statistics Canada, the Canadian Institute for Health Information, the Canadian Urban Environmental Health Research Consortium, and DMTI Spatial. Guided by the World Health Organization social determinants of health framework, CSDUL will initially cover 2011 to 2015 due to data completeness and expand as additional years become available. CSDUL builds on Statistics Canada’s Canadian Population Health Survey and will link survey data to administrative and health records, including hospital discharges, ambulatory care, mortality, cancer registries, and longitudinal tax files. Area-level indicators will be added using historical postal codes and geospatial boundaries. Organized through a hub-and-node model, CSDUL includes a central hub and 5 research nodes. We will develop and validate area-based indicators to study social determinants at micro (individual), meso (neighborhood, city, and province), and macro (national) levels. A core deliverable is to assess the strengths and limitations of survey and administrative data for health research and derive variables accordingly. After developing CSDUL, we will replicate World Health Organization Regional Office for Europe income-related health inequality analysis for urban Canada and analyze the impact of social determinants on outcomes. We will apply a 2-fold Oaxaca-Blinder decomposition between the lowest and highest urban income quintiles. A major strength of CSDUL is its capacity to analyze how diverse determinants shape health across subgroups (eg, gender), identifying key drivers of health outcomes. Results: The indicators to be used in CSDUL are being developed and validated by the contributing nodes. In collaboration with node 3, we are constructing measures of social capital using DMTI Spatial Points of Interest data. A prototype version of CSDUL incorporating a limited set of indicators has been developed in Statistics Canada’s Research Data Centre. We anticipate receiving the finalized indicators from the nodes by August 2025 to September 2025 and aim to complete the decomposition analysis by December 2025. Conclusions: Multisectoral interventions are most effective when they are customized to meet the unique needs of specific subpopulations using robust and multilevel data sources such as CSDUL.

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 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.

Accurate small-area population estimates are essential for health research and social policy development. While Statistics Canada provides census-year population counts for all geographic units, it does not produce intercensal estimates for Dissemination Areas (DA) and Census Tracts (CT). This study addresses this gap by estimating DA- and CT-level population counts for 2011 to 2021 using census data and interpolation techniques. Population counts were derived from Statistics Canada’s Geographic Attribute Files for 2011, 2016, and 2021. We applied linear interpolation to estimate intercensal population counts (2012–2015 and 2017–2020). We used an areal-weighted interpolation technique to account for boundary shifts due to census geography changes, utilizing Statistics Canada’s Correspondence Files. The final datasets provide consistent population estimates across census cycles, enabling longitudinal and neighbourhood-level analyses. The methodology and accompanying R script, available as supplementary materials, can be adapted for other intercensal periods and other demographic information, promoting transparency and reproducibility in demographic research. This study facilitates data-driven decision-making in public health and policy development by providing a reliable and scalable methodology for estimating intercensal population counts. About the Research Department: The Saskatchewan Health Authority Research Department leads collaborative research to enhance Saskatchewan’s health and healthcare. We provide diverse research services to SHA staff, clinicians, and team members, including surveys, study design, database development, statistical analysis, and assistance with research funding. We also spearhead our own research programs to strengthen research and analytic capability and learning within Saskatchewan’s health system. Disclaimer: This working paper is for discussion and comment purposes. It has not been peer-reviewed nor been subject to review by Research Department staff or executives. Any opinions expressed in this paper are those of the author(s) and not those of the Saskatchewan Health Authority. Suggested Citation: Marouzi Anousheh, Plante Charles. 2025. “Estimating Population Counts for Dissemination Area and Census Tracts in Canada from 2011 to 2021.” MedRxiv. Author Contributions: AM conducted the data analysis and prepared the first draft of the article. AM and CP designed the study and directed its implementation, including quality assurance and control. CP supervised the data analysis. CP reviewed, edited, and finalized the text. CP provided the overall guidance and funding for the research project. All authors approved the final version of the manuscript. Funding Statement: This research was funded by the Saskatchewan Health Research Foundation (SHRF). Ethics Declaration: This study exclusively utilizes publicly available, de-identified population data obtained from Statistics Canada. No human participants, personal identifiers, or confidential information were involved in this research, and therefore, ethical approval was not required. Conflict of Interest: The authors declare that they have no conflict of interest. Data Availability: All data used in this study is for public use and can be accessed through the Statistics Canada website. Code Availability: Codes are available as a supplementary file to this working paper.

BACKGROUND Two decades of research have highlighted persistent income-related health inequities in Canada across municipal, provincial, and national levels. While there is broad consensus among researchers, advocates, and health professionals that social determinants are the primary drivers of health, the empirical foundation supporting this remains relatively limited. A current renaissance in health system data access offers an opportunity to assess the multilevel impact of social factors on health inequalities, yet this potential remains underused. OBJECTIVE This project aims to examine how social, economic, and political conditions shape health inequalities and investigate how structural and intermediate determinants explain disparities across national, provincial, city, neighborhood, and individual levels. METHODS We will create the Canadian Social Determinants Urban Laboratory (CSDUL), a multilevel, longitudinal, virtual data environment that integrates 15 existing databases from Statistics Canada, the Canadian Institute for Health Information, the Canadian Urban Environmental Health Research Consortium, and DMTI Spatial. Guided by the World Health Organization social determinants of health framework, CSDUL will initially cover 2011 to 2015 due to data completeness and expand as additional years become available. CSDUL builds on Statistics Canada’s Canadian Population Health Survey and will link survey data to administrative and health records, including hospital discharges, ambulatory care, mortality, cancer registries, and longitudinal tax files. Area-level indicators will be added using historical postal codes and geospatial boundaries. Organized through a hub-and-node model, CSDUL includes a central hub and 5 research nodes. We will develop and validate area-based indicators to study social determinants at micro (individual), meso (neighborhood, city, and province), and macro (national) levels. A core deliverable is to assess the strengths and limitations of survey and administrative data for health research and derive variables accordingly. After developing CSDUL, we will replicate World Health Organization Regional Office for Europe income-related health inequality analysis for urban Canada and analyze the impact of social determinants on outcomes. We will apply a 2-fold Oaxaca-Blinder decomposition between the lowest and highest urban income quintiles. A major strength of CSDUL is its capacity to analyze how diverse determinants shape health across subgroups (eg, gender), identifying key drivers of health outcomes. RESULTS The indicators to be used in CSDUL are being developed and validated by the contributing nodes. In collaboration with node 3, we are constructing measures of social capital using DMTI Spatial Points of Interest data. A prototype version of CSDUL incorporating a limited set of indicators has been developed in Statistics Canada’s Research Data Centre. We anticipate receiving the finalized indicators from the nodes by August 2025 to September 2025 and aim to complete the decomposition analysis by December 2025. CONCLUSIONS Multisectoral interventions are most effective when they are customized to meet the unique needs of specific subpopulations using robust and multilevel data sources such as CSDUL. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/71929

Introduction: Avoidable mortality is widely used by public health researchers to measure population health, and many related methodologies have been proposed for doing so. This scoping review presents a comprehensive view of global peer-reviewed and grey literature exploring the association between socioeconomic status (SES) and avoidable mortality. Methods: We searched Ovid Medline, Scopus, and Web of Science to find articles that investigated SES inequalities in avoidable mortality. We limited our review to articles in English published between 2000 and 2020. For grey literature, we searched leading global and Canadian health information websites. We extracted data on different study characteristics, avoidable mortality definition, SES indicator, method of analysis of the association between avoidable mortality and SES, and main findings of the studies. Results: We identified 34 articles to review, including 29 scientific papers and 5 grey literature documents. The findings of the selected articles consistently indicate a negative association between SES and avoidable mortality rates. Studies have not all used the same definitions of avoidable mortality or SES nor operationalized them in the same way. Conclusion: Our review highlights the absence of a globally standard definition in avoidable mortality health equity research. Additional work to establish a standardized definition is crucial for supporting global comparability.

Research on substance use harm in Saskatchewan has faced challenges due to an absence of linked data to analyze and report on the social drivers of substance use harm. This study uses the Canadian Census Health and Environment Cohorts (CanCHECs) 2006 to create, describe, and validate a cohort of Saskatchewan residents focusing on substance use. We achieved validation by comparing our descriptive findings with those from other Canadian studies on substance use. The second objective of this study was to underscore the potential CanCHEC holds in studying substance use, especially by bridging the gap in data concerning the linkage of social determinants of health and administrative health data. Additionally, to facilitate further research using this rich national data source, we share our Stata do-file, providing a detailed walkthrough for creating national or provincial substance use cohorts. About the Research Department: The Saskatchewan Health Authority Research Department leads collaborative research to enhance Saskatchewan’s health and healthcare. We provide diverse research services to SHA staff, clinicians, and team members, including surveys, study design, database development, statistical analysis, and assistance with research funding. We also spearhead our own research programs to strengthen research and analytic capability and learning within Saskatchewan’s health system. Disclaimer: This working paper is for discussion and comment purposes. It has not been peer-reviewed nor been subject to review by Research Department staff or executives. Any opinions expressed in this paper are those of the author(s) and not those of the Saskatchewan Health Authority. Suggested Citation: Marouzi Anousheh, Plante Charles, and Fornssler Barbara. 2024. “Creating an 11-year longitudinal substance use harm cohort from linked health and census data to analyze social drivers of health.” MedRxiv. Extended Background: Research on substance use harm in Saskatchewan has been hampered by an absence of linked data to analyze and report on the social drivers of substance use harm. This study aims to create, describe, and validate a cohort of Saskatchewan residents by linking their sociodemographic data to their health outcomes using line-level data made available by Statistics Canada’s Research Data Centres (RDC) program. Methods: We used Canadian Census Health and Environment Cohorts (CanCHECs) 2006 to create a cohort of Saskatchewanians followed from 2006 to 2016. We linked sociodemographic information of the 2006 Census (long-form) respondents to their hospitalization data captured in the Discharge Abstract Database (DAD) (2006 to 2016) and their mortality records in the Canadian Vital Statistics Death Database (CVSD) (2006 to 2016.) We developed an algorithm to identify Saskatchewanians who experienced a substance use harm event. We validated the cohort by comparing our descriptive findings with those from other Canadian studies on substance use. Results: We used CanCHEC, a national data resource, whereas most previous studies have used provincial data resources. Despite this difference in constructing the cohorts, our results showed trends consistent with previous studies, including an overrepresentation of individuals with lower socioeconomic status within the PESUH group. Similar to other Canadian studies, our results indicate an increasing rate of substance use harm from 2006 to 2016. To facilitate further research using CanCHEC, we share our Stata do-file, providing a detailed walkthrough so other researchers can create national or provincial substance use cohorts. Conclusion: Using CanCHEC to create substance use cohorts will enable health researchers to provide a province-wide, population-level, and longitudinal perspective on substance use harm. This comprehensive view is crucial in effectively contextualizing smaller-scale and local studies, allowing us to disentangle the “fundamental causes” of health within the region. Key Messages: CanCHEC provides researchers with an excellent opportunity to measure and examine health inequalities across socioeconomic and ethnocultural dimensions for different periods and locations in Canada. There has been a steady increase in people who experienced substance use harm in Saskatchewan, from 2006 to 2016. People who experienced substance use harm between 2006 and 2016 were overrepresented among individuals with an education level below high school, those in the lowest income quintile, residents of rural areas, and Indigenous population. This study provides a Stata do-file, including a detailed walkthrough for using CanCHEC to create national or provincial substance use cohorts.

IntroductionResearch on substance use harm in Canada has been hampered by an absence of linked data to analyse and report on the social drivers of substance use harm. ObjectivesThis study aims to address this gap by providing a fully annotated Stata do-file that links sociodemographic data to 11 years of hospitalisation and death outcomes. This do-file will greatly facilitate the creation of provincial and national substance use cohorts using line-level data available through Statistics Canada's Research Data Centres (RDC) program. MethodsWe used Canadian Census Health and Environment Cohorts (CanCHEC) 2006 to create a cohort of Saskatchewanians followed from 2006 to 2016. We linked sociodemographic information of the 2006 Census (long-form) respondents to their hospitalisation data captured in the Discharge Abstract Database (DAD) (2006 to 2016) and their mortality records in the Canadian Vital Statistics Death Database (CVSD) (2006 to 2016). We developed an algorithm to identify Saskatchewanians who experienced a substance use harm event. We validated the cohort by comparing our descriptive findings with those from other Canadian studies on substance use. ResultsWe used CanCHEC, a national data resource, whereas most previous studies have used provincial data resources. Despite this difference in constructing the cohorts, our results showed trends consistent with previous studies, including an overrepresentation of individuals with lower socioeconomic status among the people who experienced substance use harm (PESUH). Similar to other Canadian studies, our results indicate an increasing rate of substance use harm from 2006 to 2016. ConclusionThis study provides a Stata do-file that compiles a validated substance use cohort using CanCHEC, enabling comprehensive substance use research by linking sociodemographic data with health outcomes. The do-file is likely to save researchers hundreds of hours and accelerate research on the drivers of substance use harms in Canada.

Introduction Avoidable mortality is widely used by public health researchers to measure population health, and many related methodologies have been proposed for doing so. This scoping review presents a comprehensive view of global peer-reviewed and grey literature exploring the association between socioeconomic status (SES) and avoidable mortality. Methods We searched Ovid Medline, Scopus, and Web of Science to find articles that investigated SES inequalities in avoidable mortality. We limited our review to articles in English published between 2000 and 2020. For grey literature, we searched leading global and Canadian health information websites. We extracted data on different study characteristics, avoidable mortality definition, SES indicator, method of analysis of the association between avoidable mortality and SES, and main findings of the studies. Results We identified 34 articles to review, including 29 scientific papers and 5 grey literature documents. The findings of the selected articles consistently indicate a negative association between SES and avoidable mortality rates. Studies have not all used the same definitions of avoidable mortality or SES nor operationalized them in the same way. Conclusion Our review highlights the absence of a globally standard definition in avoidable mortality health equity research. Additional work to establish a standardized definition is crucial for supporting global comparability.

There is a growing desire to use social data to support local evidence-based health planning and decision-making. However, the geographic boundaries which social data are disseminated for do not usually align exactly with boundaries used by local health organizations. In this paper, we propose a method we call “pseudo-geography” to estimate counts for locally-defined geographic boundaries using data on smaller spatial units. We compared six different pseudo-geography methods, using data in Saskatoon, and identified the most accurate one, which incorporates the area-weighted spatial join technique. We further found that the pseudo-geography method can be refined by eliminating the areas with few or no residents before carrying out any spatial joins. We expect this method to be more accurate in larger cities and when the ratio of the locally-defined area to the smaller spatial units gets larger.