Abstract
BackgroundPremature mortality is a meaningful indicator of both population health and health system performance, which varies by geography in Ontario. We used the Local Health Integration Network (LHIN) sub-regions to conduct a spatial analysis of premature mortality, adjusting for key population-level demographic and behavioural characteristics.MethodsWe used linked vital statistics data to identify 163,920 adult premature deaths (deaths between ages 18 and 74) registered in Ontario between 2011 and 2015. We compared premature mortality rates, population demographics, and prevalence of health-relevant behaviours across 76 LHIN sub-regions. We used Bayesian hierarchical spatial models to quantify the contribution of these population characteristics to geographic disparities in premature mortality.ResultsLHIN sub-region premature mortality rates ranged from 1.7 to 6.6 deaths per 1000 per year in males and 1.2 to 4.8 deaths per 1000 per year in females. Regions with higher premature mortality had fewer immigrants and higher prevalence of material deprivation, excess body weight, inadequate fruit and vegetable consumption, sedentary behaviour, and ever-smoked status. Adjusting for all variables eliminated close to 90% of geographic variation in premature mortality, but did not fully explain the spatial pattern of premature mortality in Ontario.ConclusionsWe conducted the first spatial analysis of mortality in Ontario, revealing large geographic variations. We demonstrate that well-known risk factors explain most of the observed variation in premature mortality. The result emphasizes the importance of population health efforts to reduce the burden of well-known risk factors to reduce variation in premature mortality.
Highlights
Dying prematurely—before an expected or average age of death—is a signal of unfulfilled life expectancy
Data sources We identified premature deaths from the Ontario Registrar General’s death file (ORG-D), a comprehensive database of population mortality data linked at Institute for Clinical Evaluative Sciences (ICES) [12]
Excess deaths by Local Health Integration Network (LHIN) sub-region are mapped in Additional file 1: Figure S1
Summary
Dying prematurely—before an expected or average age of death—is a signal of unfulfilled life expectancy. Gaps between regions were shown to have increased between 1992 and 2015 [6] These large variations signal where the health system may be falling short or where upstream determinants can be better addressed. Ontario’s Patients First: Action Plan for Health Care acknowledged that access to health care varies across the (2019) 17:9 province, and committed the provincial government to improving health system access and delivery [7]. To support this goal, 76 LHIN sub-regions were formalized in 2017 to serve as the focal point for local populationbased planning, performance improvement and service integration [8]. We used the Local Health Integration Network (LHIN) subregions to conduct a spatial analysis of premature mortality, adjusting for key population-level demographic and behavioural characteristics
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