Use of micro-data to determine effective indicators of fee-for-service general practitioner utilization in Newfoundland

• Main Author: Dunne, Tobias M. (Tobias Matthew)
• Format: Thesis
• Language: English
• Published: Memorial University of Newfoundland 2009
• Subjects: Newfoundland
• Online Access: https://research.library.mun.ca/9005/; https://research.library.mun.ca/9005/1/Dunne_TobiasM.pdf

In Newfoundland and Labrador health care resources are currently distributed to Regional Health Authorities (RHAs) largely according to their previous expenditure levels. It is suggested that this historical distribution may not be the most equitable way to distribute resources because these allocation levels may not be indicative of needs of the population living in the area. As such, some communities do not receive an adequate share of resources, resulting in a violation of the concept of vertical equity. -- The primary objective of this research is to determine which population characteristics (demographic and health) are the best indicators of FFS GP utilization. To do this, multiple linear regression was used to estimate the dollar value of GP resources consumed based on the demographic and health characteristic information of the population. Data were collected from: the Medical Care Plan (MCP) administration data for the years 1996-2004, the 1995 Newfoundland Panel on Health and Medical Care - Adult Health Survey (AHS), and the 2001 Canadian Community Health Survey (CCHS). -- It is recommended that, as a minimum, age and gender variables be used as the main predictors for GP resource utilization. However, it is also recommended that the number of chronic conditions and self assessed health status be used to compensate for all other variables outside of age and gender demographics. The health practices and socioeconomic indicators as measured in this study did not hold strong statistical significance and showed unexpected and inconsistent resource allocation values. -- These results could be used as part of a capitation formula which would assign funding to communities based on expected future need and thus result in a fairer allocation of scarce resources.