Strengthening the Collection and Use of Disaggregated Data to Understand and Monitor the Risk and Burden of COVID-19 Among Racialized Populations

There is growing evidence that the risk and burden of COVID-19 infections are not equally distributed across population subgroups and that racialized communities are experiencing disproportionately higher morbidity and mortality rates. However, due to the absence of large-scale race-based data, it is impossible to measure the extent to which immigrant and racialized communities are experiencing the pandemic and the impact of measures taken (or not) to mitigate these impacts, especially at a local level. To address this issue, the Ottawa Local Immigration Partnership partnered with the Collaborative Critical Research for Equity and Transformation in Health lab at the University of Ottawa and the Canadians of African Descent Health Organization to implement a project to build local organizational capacities to understand, monitor, and mitigate the impact of the COVID-19 pandemic on immigrant and racialized populations. This research note describes the working framework used for this project, proposed indicators for measuring the determinants of health among immigrant and racialized populations, and the data gaps we encountered. Recommendations are made to policymakers, and community and health stakeholders at all levels on how to collect and use data to address COVID-19 health inequities, including data collection strategies aimed at community engagement in the collection of disaggregated data, improving methods for collecting and analyzing data on immigrants and racialized groups and policies to enable and enhance data disaggregation.RésuméDes plus en plus d’études montrent que le risque et le fardeau des infections à la COVID-19 ne sont pas également répartis dans la population et que les communautés racialisées connaissent des taux de morbidité et de mortalité disproportionnellement plus élevés. Cependant, en raison de l’absence de données ventilés selon le statut ethnique, il est impossible de mesurer comment les communautés immigrantes et racialisées vivent la pandémie et quel est l’impact des mesures prises (ou non) pour atténuer ces effets, surtout à un niveau local. Pour résoudre ce problème, le Partenariat local pour l’immigration d’Ottawa (PLIO) s’est associé au Laboratoire de recherche critique collaborative pour l’équité et la transformation en santé (CO-CREATH) de l’Université d’Ottawa et l’Organisation de la santé des Canadiens d’ascendance africaine (CADHO) aux fins de mettre en œuvre un projet visant à renforcer les capacités organisationnelles locales pour comprendre, surveiller et atténuer l’impact de la pandémie de la COVID-19 sur les populations immigrantes et racialisées. Cette note de recherche décrit le cadre de travail utilisé pour ce projet, les indicateurs proposés pour mesurer les déterminants de la santé chez les populations immigrantes et racialisées, et les lacunes que nous avons identifiés dans les données existants. Des recommandations sont faites aux décideurs politiques et aux acteurs communautaires et de la santé à tous les niveaux sur comment collecter et utiliser les données pour remédier aux inégalités en matière de santé liées à la COVID-19. Ces recommandations font référence aux stratégies de collecte de données visant à impliquer les communautés, à l’amélioration des méthodes de collecte et d’analyse des données sur les immigrants et les groupes racialisés, et aux politiques nécessaires pour permettre et améliorer la désagrégation des données selon le statut ethnique.

Geospatial Data Disaggregation through Self-Trained Encoder–Decoder Convolutional Models

Datasets collecting demographic and socio-economic statistics are widely available. Still, the data are often only released for highly aggregated geospatial areas, which can mask important local hotspots. When conducting spatial analysis, one often needs to disaggregate the source data, transforming the statistics reported for a set of source zones into values for a set of target zones, with a different geometry and a higher spatial resolution. This article reports on a novel dasymetric disaggregation method that uses encoder–decoder convolutional neural networks, similar to those adopted in image segmentation tasks, to combine different types of ancillary data. Model training constitutes a particular challenge. This is due to the fact that disaggregation tasks are ill-posed and do not entail the direct use of supervision signals in the form of training instances mapping low-resolution to high-resolution counts. We propose to address this problem through self-training. Our method iteratively refines initial estimates produced by disaggregation heuristics and training models with the estimates from previous iterations together with relevant regularization strategies. We conducted experiments related to the disaggregation of different variables collected for Continental Portugal into a raster grid with a resolution of 200 m. Results show that the proposed approach outperforms common alternative methods, including approaches that use other types of regression models to infer the dasymetric weights.

Practical Guidebook on Data Disaggregation for the Sustainable Development Goals

The “leave no one behind” principle espoused by the 2030 Agenda for Sustainable Development requires measures of progress for different segments of the population. This entails detailed disaggregated data to identify subgroups that might be falling behind, to ensure progress toward achieving the Sustainable Development Goals (SDGs). The Asian Development Bank and the Statistics Division of the United Nations Department of Economic and Social Affairs developed this practical guidebook with tools to collect, compile, analyze, and disseminate disaggregated data. It also provides materials on issues and experiences of countries regarding data disaggregation for the SDGs. This guidebook is for statisticians and analysts from planning and sector ministries involved in the production, analysis, and communication of disaggregated data.

Financial Challenges and Capacity among African Refugees in the Southern USA: A Study of Socio-demographic Differences

Refugees’ successful integration into US society requires adaptation to economic, financial and social norms. Despite the importance of considering financial challenges (financial stress and financial anxiety) and financial capacity (financial literacy and financial self-efficacy) in reaching personal financial goals, literature examining the relationship between financial challenges and capacity—critical in refugee resettlement and integration—is sparse and fragmented. This study explored financial challenges and capacity amongst resettled African refugees (N = 130) in the southern USA using data from a larger community-based participatory research study that used a mixed-methods approach. We explored socio-demographic differences in financial stress, financial anxiety, financial literacy and financial self-efficacy across African refugee subpopulation groups. Our study highlights the importance of social work advocacy for data disaggregation, which helps establish the scope of the problem, unmask subpopulation differences and make vulnerable groups more visible to facilitate the development of tailored programmes and services to reach economic integration goals. We provide social work implications for data disaggregation in the current corona virus context, which will leave long-term financial scars on refugee subpopulations.