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Chronic diseases place a large burden on the healthcare system and are the leading cause of undermined quality of life, reduced life expectancy and mortality among Canadians. Identifying priority groups in the community is crucial for chronic disease prevention as it can facilitate strategic planning and resource allocation. Investigation can be challenging, however, due to small sample sizes and the absence of neighborhood factors such as cultural characteristics and local availability/accessibility to health facilities.
This research used an alternative modelling technique developed by Dr. Brumback from the University of Florida1 to examine local complex survey data (such as CCHS and RRFSS) by establishing a proportional odds model (ordinal outcomes) and assessing individual factors while controlling for neighborhood factors at the dissemination area (DA) level. Our findings demonstrate the utility of this type of modelling to identify the health risk behaviors and social determinants of health that contribute to chronic disease. The results can help identify priority populations for targeted chronic disease prevention initiatives at the local level.
1Am J Epidemiol, 2012;175(11):1133-1141
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Presenter: Martin Huang
Martin Huang is an epidemiologist from Brant County Health Unit, where he focuses on reportable disease surveillance, STI core group investigation, syndromic surveillance, life expectancy, priority group identification, and STI network detection. He holds a Bachelor of Medicine in Preventive Medicine from the Shanghai Medical University in China and a Master’s of Science in Community Health and Epidemiology from Queen’s University.
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