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By correlating the values estimated by the model and the real value, a correlation of 25% was obtained.ĭiffering by type of expenditure, CRG showed a greater explanatory capacity in outpatient pharmaceutical spending and a lower in hospital expenditure.Ĭonclusions: Multimorbidity factors have a greater impact on the explanation of health expenditure than demographic variables.ĭealing with missing data in practice: methods, applications and implications for HIV cohort studies Belén Alejos Ferreras The model with the highest statistical significance was the one that used the combination of the variables age, sex, CRG health status and severity level, whose Akaike information criterion was 14.2. The GLM with logarithmic-gamma distributions have different iterations, depending on the dependent variable, the total health expenditure and as independent variables Age, gender and membership of the CRG in order to select the model that best explains the behavior of health expenditure. There was a database of 156,811 inhabitants of the Denia health department, which included age, Clinical Risk Group (CRG), total health expenditure, among other variables. Methods: Observational, descriptive, retrospective and cross-sectional study on total health expenditure using explanatory-predictive-stratified models. Objectives: To implement a health expenditure prediction system based on morbidity and to analyze its goodness of fit. Universidad Politécnica de Valencia and Universidad Politécnica de Madrid Guadalajara, Alexander Zlotnik, Isabel Barrachina Generalized linear models (GLM) applied to the prediction of health expenditure Vicente Caballer, David Vivas, N. We will introduce these topics and discuss examples using Stata. Special cases of those are finite mixture models, which can also be fit using the new prefix fmm. The new features added in Stata 15 to the gsem command allow us to fit a wide array of latent class models. Often, those classes are determined by heterogeneity on regression models, where the relationship of a dependent variable (or variables) with a group of covariates varies from group to group. Latent class analysis deals with these problems. Sometimes, we are interested in identifying and understanding different groups in a population, even though we cannot directly observe which group each individual belongs to. Latent class analysis and finite mixture models with Stata Isabel Cannete In other words, we may use -margins- after -npregress- to conduct semiparametric analysis. We may use this function to compute marginal effects, counterfactuals, and other statistics of interest. We fit the regression function that relates the outcome of interest and the covariates, and then we graph. Nonparametric analysis has been traditionally descriptive. Now what do I do with this function? Pinzón, Enrique