Background: The rapid growing proportion of elderly people on world’s population poses several challenges, especially related to public health. Among these, the increase in chronic diseases can be noted, which recent evidence attributes also to the lowering of the subjective assessment of wellbeing. Therefore, being able to predict changes in wellbeing could be key to manage this problem. In this study, we investigate for the first time the possibility of developing a purely predictive model of perceived wellbeing for population aged over 50.
Methods: Data from the European SHARE project were used, specifically the demographic, health, social and financial variables of 9649 subjects. The subjective wellbeing was measured through the CASP-12 scale. Study outcome was defined binary, i.e., worsening/not worsening of the variation of CASP-12 in2 years. Logistic regression, logistic regression with LASSO regularization, and random forest were considered as candidate models. Performance was assessed in terms of accuracy in correctly predicting the outcome and area under the curve(AUC).
Results: The best performing model was the logistic regression, achieving accuracy = 0.659 and AUC = 65.99%. All models proved to be able to generalize both across subjects and over time. The most predictive variables were CASP-12score at baseline, the presence of depression and financial difficulties.
Conclusions: Predicting 2-year variations in wellbeing via modeling techniques ispossible, albeit with some limitations, probably originating from the subjectivenature of the outcome.