ArticleInternational journal of population data science2023
Advancing cross-sectoral data linkage to understand and address the health impacts of social exclusion: Challenges and potential solutions.
Article in International journal of population data science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed, 13 citations in OpenAlex.
- Accuracy of administrative data in ascertaining health conditions: a systematic review.JAMIA open · 2026Review
- Addressing social determinants of health and equity in early childhood: a qualitative document analysis of national policies in Ecuador.International journal for equity in health · 2026Article
- Data for Equity: Can Linked Administrative Data Inform Pathways to More Equitable Child Health?The Medical journal of Australia · 2026Article
- Strengthening non-communicable diseases monitoring systems in Europe through a multistakeholder collaborative approach: a key priority for advancing data-driven policymaking.The Lancet regional health. Europe · 2026Review
- Co-creating an Inclusion, Diversity, Equity, and Accessibility (IDEA) Strategy in a Pan-Canadian Health Data Research Network.International journal of population data science · 2026Article
- Article
- Health and incarceration research in Australia: a scoping review.The Lancet regional health. Western Pacific · 2025Review
- Exploring unregulated substance use health data in Ontario, Canada: Identifying gaps, addressing challenges, and uncovering opportunities.Frontiers in public health · 2025Article
- Public sector health analytics capacity before and after Covid-19: A case study of manager perspectives in New Brunswick, Canada.International journal of population data science · 2024Article
- Response to: 'Small numbers, big impact: making a utilitarian case for the contribution of inclusion health to population health in England' by ZhangJournal of epidemiology and community health · 2023Article
- Implementing the English health inequalities agenda: addressing challenges to person-level, cross-sectoral data linkage, access and routine use for local authority public health.International journal of population data science · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of administrative health data for research, monitoring, and quality improvement has proliferated in recent decades, leading to improvements in health across many disease areas and across the life course. However, not all populations are equally visible in administrative health data, and those that are less visible may be excluded from the benefits of associated research. Socially excluded populations - including the homeless, people with substance dependence, people involved in sex work, migrants or asylum seekers, and people with a history of incarceration - are typically characterised by health inequity. Yet people who experience social exclusion are often invisible within routinely collected administrative health data because information on their markers of social exclusion are not routinely recorded by healthcare providers. These circumstances make it difficult to understand the often complex health needs of socially excluded populations, evaluate and improve the quality of health services that they interact with, provide more accessible and appropriate health services, and develop effective and integrated responses to reduce health inequity. In this commentary we discuss how linking data from multiple sectors with administrative health data, often called
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.