Geographic access to family physicians in urban areas across Canada
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Primary health care (PHC) is a term used to refer to the parts of the health system that people interact with most of the time when health care is needed. It is considered the first point of contact for health services in Canada. Access to PHC services is an important issue regarding health care delivery in Canada today. There is a need to advance current understanding of access to PHC providers at local scales such as neighbourhoods. The primary objective of this study is to examine the variation in geographic (spatial) accessibility to permanently located primary care services in the Canadian urban environment. Furthermore, the analysis of spatial patterns of accessibility, both visually and statistically using GIS, is to provide a better understanding of among and between neighbourhood variations. This research took place in the 14 urban areas across Canada: Victoria and Vancouver, British Columbia; Calgary and Edmonton, Alberta; Saskatoon, Saskatchewan; Winnipeg, Manitoba; Hamilton, and Toronto, Ontario; Montréal and Québec, Quebec; Halifax, Nova Scotia; St. John’s, Newfoundland; Saint John, New Brunswick; and Ottawa–Gatineau, Ontario and Quebec. A GIS based method, the Three-Step Floating Catchment Area (3SFCA), was applied to determine the spatial accessibility to PHC services (accessibility score). First, for increasing geocoding match rates with reduced positional uncertainty, an integrated geocoding technique was developed after an empirical comparison of the geocoding results based on manually built and online geocoding services and subsequently applied to generate geographic coordinates of PHC practices which are an essential element for measuring potential access to health care. Next, the results of the Three-Step Floating Catchment Area (3SFCA) method was compared with simpler approachs to calculate the City level physician-to-population ratios and this research highlights the benefit of using the 3SFCA method over simpler approaches in urban areas by providing similar or comparable results of City level physician-to-population ratios with the advantage of intra-urban measurements. Further, the results point out that considerable spatial variation in geographical accessibility to PHC services exists within and across Canadian urban areas and indicate the existence of clusters of poorly served neighbourhoods in all urban areas. In order to investigate the low accessibility scores in relation to population health care needs, spatial statistical modeling techniques were applied that revealed variations in geographical accessibility to PHC services by comparing the accessibility scores to different socio-demographic characteristics across Canadian urban settings. In order to analyse how these relationships between accessibility and predictors vary at a local scale within an urban area, a local spatial regression technique (i.e., geographically weighted regression or GWR) was applied in two urban areas. The results of GWR modelling demonstrates intra-urban variations in the relationships between socio-demographic variables and the geographic accessibility to PHC services. In addition, the influences of “unit of analysis” on accessibility score were analyzed using spatial statistical modeling that emphasize the use of units of analysis that are pertinent to policy and planning purposes such as city defined neighbourhoods. Overall, this research shows the importance of measuring geographic accessibility of PHC services at local levels for decision makers, planners, researchers, and policy makers in the field of public health and health geography. This dissertation will advance current understanding of access to primary care in Canadian urban settings from the perspective of the neighbourhood.
DegreeDoctor of Philosophy (Ph.D.)
DepartmentGeography and Planning
CommitteeMuhajarine, Nazeem; Shahab, Saqib; Hackett, Paul; Walker, Ryan
Copyright DateJune 2014
Spatial accessibility, neighbourhood, primary healthcare, health geography, urban geography, Spatial Statistics