Karen Li

Accessibility to Primary Care Physicians Among On-Reserve Aboriginals in Northern Ontario: A Three Zone 2SFCA Approach

Aboriginals account for 4% of Canada’s total population.  They have a significantly higher prevalence of obesity, diabetes, and poorer health status than non-Aboriginal Canadians.  This study examines the distribution and shortage of primary care physicians in Northern Ontario and how they affect the spatial accessibility of on-reserve Aboriginals to primary care physicians.  Northern Ontario was divided into three travel zones or land classifications; urban, rural and Aboriginal.  Data analyzed includes the 2010 Canadian Medical Directory (CMD), College of Physicians and Surgeons of Ontario (CPSO) in 2010, and geo-referenced 2006 adjusted Canadian census from Environics Analytics.  This study utilized the original two-step floating catchment area (2SFCA) and developed a three-zone 2SFCA accessibility model to calculate spatial accessibility for Northern Ontarians to primary care physicians in seven different scenarios using vary8ing travel thresholds.  A correlation analysis was carried out comparing the accessibility indices with the proportion of Aboriginal population.  The study revealed that access to primary care physicians Aboriginal reserves increased as travel threshold increases.  Dissemination areas (Das) with the highest accessibility scores were directly related to areas with a higher proportion of Aboriginal population, including Aboriginal reserves, only when travel threshold was increased to two hours.  The findings suggested that Aboriginal reserves are situated in such remote regions of Northern Ontario, that accessibility to primary care physicians would require a travel time of two hours or more, and that on-reserve Aboriginals in Northern Ontario do not receive equality of healthcare services which might be leading to the documented poor health status among Aboriginals.  The results of this study highlighted opportunities for further enhancement of spatial accessibility modeling to analyze the dynamics between healthcare service provision and demand.