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OP20 Using cross-sectoral administrative data linkage to understand the health of people experiencing multiple exclusion
  1. EJ Tweed1,
  2. A Leyland1,
  3. DS Morrison2,
  4. SV Katikireddi1
  1. 1MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK
  2. 2Information Services Division, NHS National Services Scotland, Edinburgh, UK


Background People affected by the intersection of homelessness, drug use, and/or serious mental illness have high rates of mortality and morbidity. However, a recent systematic review found important limitations in the evidence base on this topic, in particular in relation to people with more than two of these experiences and for health outcomes other than infections. This situation is exacerbated by the under-ascertainment of these populations in routine information sources on population health needs, such as surveys and censuses. In many countries, administrative data are available which could help address these knowledge gaps. We describe the creation and characteristics of a novel virtual cohort using cross-sectoral linkage of administrative datasets, in order to inform policy and practice responses to these co-occurring issues.

Methods Individual-level data from local authority homelessness services (HL), opioid substitution therapy dispensing (OST), and a psychosis case register (PSY) in Glasgow, Scotland between 2011–15 were confidentially linked to National Health Service records, using a mix of probabilistic and deterministic linkage. A de-identified dataset was made available to researchers through a secure analysis platform. Demographic characteristics associated with different exposure combinations were analysed using descriptive statistics.

Results Linkage created a cohort of 24,767 unique individuals with any one of the experiences of interest between 2011–15. Preliminary results suggest that 89.2% of the cohort had one experience; 10.6% two; and 0.2% all three. The most common combination was HL & OST (n=2,150; 8.7%), with other combinations much less frequent (HL & PSY, n=279, 1.1%; OST & PSY, n=188, 0.8%; HL & OST & PSY, n=51, 0.2%). The odds of male gender increased with number of exposures (2 exposures, OR 2.1, 95% CI 1.9–2.2; 3 exposures, OR 4.1, 95% CI 2.3–7.2), but there was little difference in age. Work is ongoing to incorporate into the cohort additional datasets on criminal justice involvement.

Discussion Administrative data linkage is a feasible approach to understanding the health of people affected by multiple exclusionary processes, addressing problems of recruitment and retention affecting traditional cohort studies in this field. As well as improving the validity of descriptive epidemiology for these populations, this study offers a foundation for evaluating future policy or service interventions. In order for the benefits of administrative data research to be realised, robust and timely governance and linkage processes are required.

  • data linkage
  • health inequalities
  • social epidemiology

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