Use of medical services in Chile: How sensitive are the results to different econometric models?

Background: We compared different econometric specifications to model the use of medical services in Chile, focussing on visits to general practitioners and specialist physicians. Methods: The evaluated models are the Poisson, Negative Binomial, Zero Inflated Poisson and Negative Binomial, two-step Hurdle model, sample-selection Poisson, and Latent Class model. These models were estimated using Chilean data for the years 2009 and 2015, separated by gender. Results: Unlike previous literature that supported the use of the latent class model, our results show that the latent class model is not always the model with the best goodness of fit. Furthermore, the model with the best fit is not necessarily the model with the best predictive power. For instance, depending on the year and medical services, either the latent class model or the sample-selection Poisson model performs better than the other models. The results also show that the selection of the econometric model may have implications for the estimated influence that variables such as age, income, or affiliation to the public versus private sector have on the use of medical services. Conclusion: Using Chilean data, we have tested that the selection of an econometric method to model the use of medical services is not a problem with a unique answer. We recommend performing a sensitivity analysis of goodness of fit and predictive power between gender, healthcare services, or different years of datasets in future applications to be sure about the best model specification in each context.