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A nonparametric vs. latent class model of general practitioner utilization: evidence from Canada
1Department of Public Health Sciences, School of Public Health, University of Alberta, 2-040 Li Ka Shing Centre for Health Research Innovation, Edmonton, Alberta T6G 2E1, Canada. logan.mcleod@ualberta.ca
Journal of Health Economics
|September 3, 2011
Summary
A new nonparametric model better predicts general practitioner (GP) visits than traditional methods. This advanced approach improves health economics analyses by accurately modeling patient utilization patterns.
Area of Science:
- Health Economics
- Econometrics
- Biostatistics
Background:
- Predicting healthcare utilization is crucial for health economics, including risk adjustment and equity measurement.
- Parametric count data models are commonly used for physician utilization, typically measured by the number of visits.
- Existing models may not fully capture the complexities of healthcare utilization patterns.
Purpose of the Study:
- To introduce and evaluate a nonparametric kernel conditional density estimator for modeling general practitioner (GP) utilization.
- To compare the performance of this novel estimator against a traditional latent class negative binomial model.
- To address the endogeneity between self-reported health status and GP visits using panel data.
Main Methods:
- Application of a nonparametric kernel conditional density estimator for GP visit prediction.
- Comparison with a latent class negative binomial model using goodness-of-fit metrics.
- Utilizing panel data to control for endogeneity between health status and utilization.
Main Results:
- The kernel conditional density estimator demonstrated a superior fit to the observed distribution of GP visits compared to the latent class negative binomial model.
- Significant differences were found in the predicted incremental effects (IE) of individual characteristics on GP visits between the two models.
- The latent class negative binomial model showed substantially larger IEs in the right tail of the distribution, up to 190 times greater than the kernel estimator.
Conclusions:
- The nonparametric kernel conditional density estimator offers a more accurate approach to modeling GP utilization.
- This method provides a better understanding of how individual characteristics influence healthcare seeking behavior, especially for high utilizers.
- Findings have implications for refining risk-adjustment mechanisms and promoting health equity in healthcare economics.
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