Costs of Health Care Across Primary Care Models In Ontario

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Speaker: Maude Laberge

Date/Time: March 13, 10 AM – 12 PM


Objectives: This study analyzed the relationship between Ontario primary care remuneration models (fee-for-service (FFS), enhanced-FFS, and blended capitation) and primary care and total health care costs.
Approach: Utilization data for one year was examined using administrative databases at the Institute for Clinical Evaluative Sciences for a 10% random sample selected from the Ontario adult population eligible for public health insurance (n=1,171,019). Primary care and total health care costs were calculated at the individual level including costs from physician services, hospital visits and admissions, long term episodes, drugs, home care, rehabilitation, lab tests, and visits to non-medical health care providers. The effects of the primary care models on costs were analyzed with generalized linear model regressions.
Results: Primary care and total health care costs were significantly different across Ontario primary care models. Using the traditional FFS as the reference, analyses that adjusted for patient factors showed that patients in enhanced-FFS models had the lowest primary care and total health care costs while patients in blended capitation models had higher primary care but lower total health care costs.​ FFS Patients were younger, more likely to be males and of lower income; they also had higher health care costs, which were mainly driven by higher long term episodes and hospital costs. Patients in blended capitation models were healthier and wealthier than those of other primary care models. Primary care and health care costs increased with patients’ age, morbidity, lower income quintile and for females.
Conclusion: Incremental costs for primary care in blended capitation models appear to be offset with lower total health system care costs. The differences in patients’ characteristics across models (selection bias) suggest that case-mix variables included as risk adjustment in these analyses may not fully capture patients’ complexity.


Maude Laberge is a Ph.D. candidate at the Institute of Health Policy, Management and Evaluation at the University of Toronto. Maude’s professional experience in Canada and internationally includes work in health care policy, health care systems analysis, primary care performance measurement and decision support, accreditation of health services, as well as health promotion and health education. Maude’s research interests include performance measurement, primary care and efficiency analysis. Her thesis is looking at the performance of primary care models and practices in Ontario with a focus on effectiveness, costs and efficiency. She also holds a M.Sc. in Health Administration with a specialty in quality management as well as a B.Sc. in Biomedical Sciences, both from the Universite de Montreal.