Exploring Loneliness and Living Alone Through Linked Administrative and Survey Data in Wales, UK

BackgroundLoneliness is a public health priority, yet population-scale evidence that distinguishes types of loneliness and links with living alone circumstances is scarce. Population-wide linked administrative data within the Secure Anonymised Information Linkage (SAIL) Databank provide an opportunity to investigate these relationships across Wales. Objectives(1) Describe long-term living alone trends; (2) develop indicators for reason for living alone; and (3) examine associations between living alone (including derived indicators) and emotional, social and overall loneliness, alongside demographic, health and place-based factors. MethodsWe conducted a two-phase observational study in Wales. Phase 1 used annual, population level snapshots (2008--2024) to quantify living alone trends and correlates. Phase 2 linked individual level data in SAIL to National Survey for Wales loneliness data (De Jong Gierveld 6-item) to model adjusted associations. We inferred living alone reasons (partner loss, separation, unassigned) from life events data. Models adjusted for age and gender interaction, health, tenure, housing type, deprivation, and built environment indicators. ResultsLiving alone households increased from 27.0% (314,806) in 2008 to 29.6% (363,133) in 2024. In adjusted models, living alone was strongly associated with loneliness risk, especially emotional loneliness. These associations were modified by reason for living alone, with the largest effects observed following recent partner loss. Younger adults reported highest overall loneliness; men showed higher social loneliness around midlife; women showed higher emotional loneliness. Built environment measures showed little evidence of independent association after adjustment. ConclusionsPopulation-scale linkage can generate robust, policy-relevant indicators of living alone dynamics. Such indicators support monitoring and targeting within health and social care, complementing national statistics and enabling future linked data studies.