Using cross-sectoral data linkage to understand the health of people experiencing multiple exclusion

Abstract

Abstract Background People affected by the intersection of homelessness, drug use, and/or serious mental illness have high rates of mortality and morbidity. However, they are often missed from routine information sources on population health, such as surveys and censuses. In many countries, administrative data are available which could help address this knowledge gap. We created a novel virtual cohort using cross-sectoral data linkage 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. Lessons Administrative data linkage is a feasible approach to understanding the health of people affected by multiple exclusionary processes, but requires robust and timely governance. Our initiative can support service planning and evaluation of future policy or service changes. Key messages We describe the creation and characteristics of a novel virtual cohort of people affected by multiple exclusionary processes, using record linkage of administrative datasets. Cross-sectoral linkage has international potential for enhancing public health intelligence, especially for population groups who may be missed from surveys and censuses.