Regional variation of premature mortality in Ontario, Canada: a spatial analysis

Abstract Background Premature 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 pop...

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Main Authors: Emmalin Buajitti (Author), Tristan Watson (Author), Todd Norwood (Author), Kathy Kornas (Author), Catherine Bornbaum (Author), David Henry (Author), Laura C. Rosella (Author)
Format: Book
Published: BMC, 2019-07-01T00:00:00Z.
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LEADER 00000 am a22000003u 4500
001 doaj_a0845e9fc6d0410fbdbde9bfb62fe9fe
042 |a dc 
100 1 0 |a Emmalin Buajitti  |e author 
700 1 0 |a Tristan Watson  |e author 
700 1 0 |a Todd Norwood  |e author 
700 1 0 |a Kathy Kornas  |e author 
700 1 0 |a Catherine Bornbaum  |e author 
700 1 0 |a David Henry  |e author 
700 1 0 |a Laura C. Rosella  |e author 
245 0 0 |a Regional variation of premature mortality in Ontario, Canada: a spatial analysis 
260 |b BMC,   |c 2019-07-01T00:00:00Z. 
500 |a 10.1186/s12963-019-0193-9 
500 |a 1478-7954 
520 |a Abstract Background Premature 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. Methods We 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. Results LHIN 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. Conclusions We 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. 
546 |a EN 
690 |a Epidemiology 
690 |a Health policy 
690 |a Public health 
690 |a Geography 
690 |a Premature mortality 
690 |a Computer applications to medicine. Medical informatics 
690 |a R858-859.7 
690 |a Public aspects of medicine 
690 |a RA1-1270 
655 7 |a article  |2 local 
786 0 |n Population Health Metrics, Vol 17, Iss 1, Pp 1-8 (2019) 
787 0 |n http://link.springer.com/article/10.1186/s12963-019-0193-9 
787 0 |n https://doaj.org/toc/1478-7954 
856 4 1 |u https://doaj.org/article/a0845e9fc6d0410fbdbde9bfb62fe9fe  |z Connect to this object online.