Quantile Composed-based Path Modeling complements the classical PLS Path Modeling. The latter is widely used to model relationships among latent variables and between the manifest variables and their corresponding latent variables. Since it essentially exploits classical least square regressions, PLS Path Modeling focuses on the effect the predictors exert on the conditional means of the different outcome variables involved in models. Quantile Composed-based Path Modeling extends the analysis to the whole conditional distributions of the outcomes. This paper proposes a procedure to estimate the conditional quantiles for the manifest variables of the outcome blocks. Starting from the information related to a grid of conditional quantiles, it is possible to define the most accurate model for each health indicator and the best predictive model for each Italian province. The proposed method is shown in action both on artificial and real data. The real data concerns the prediction of health indicators.