Abstract
AbstractWe investigated the additional predictive value of an individual’s neighbourhood (quality and location), and of changes therein on his/her healthcare costs. To this end, we combined several Dutch nationwide data sources from 2003 to 2014, and selected inhabitants who moved in 2010. We used random forest models to predict the area under the curve of the regular healthcare costs of individuals in the years 2011–2014. In our analyses, the quality of the neighbourhood before the move appeared to be quite important in predicting healthcare costs (i.e. importance rank 11 out of 126 socio-demographic and neighbourhood variables; rank 73 out of 261 in the full model with prior expenditure and medication). The predictive performance of the models was evaluated in terms ofR2(or proportion of explained variance) and MAE (mean absolute (prediction) error). The model containing only socio-demographic information improved marginally when neighbourhood was added (R2+0.8%, MAE −€5). The full model remained the same for the study population (R2 = 48.8%, MAE of €1556) and for subpopulations. These results indicate that only in prediction models in which prior expenditure and utilization cannot or ought not to be used neighbourhood might be an interesting source of information to improve predictive performance.
Highlights
This paper aims to improve the prediction of healthcare costs by introducing a new level: the neighbourhood
The full model remained the same for the study population (R2 = 48.8%, MAE of €1556) and for subpopulations. These results indicate that only in prediction models in which prior expenditure and utilization cannot or ought not to be used neighbourhood might be an interesting source of information to improve predictive performance
Causality is not guaranteed, we may assume that the neighbourhood affects health (Diez Roux and Mair 2010; Ellen and Turner 2003; Sampson, Morenoff, and Gannon-Rowley 2002) and since health is a major determinant of healthcare demand, we hypothesize that the exposure to the neighbourhood translates from healthcare demand to healthcare utilization and to healthcare costs
Summary
This paper aims to improve the prediction of healthcare costs by introducing a new level: the neighbourhood. Despite the large set of variables included in risk adjustment models, these models still undercompensate insurers/healthcare providers for certain types of insured/patients (Buchner, Wasem, and Schillo 2017; Eijkenaar, van Vliet, Neighborhood & Healthcare Expenditure Prediction and van Kleef 2018; Sibley and Glazier 2012; Van Veen et al 2017). For this reason, it is important to find new variables for risk adjustment models that improve the compensation for expensive insured/patients. This study gives insight in whether neighbourhood variables may be of additional value for risk adjustment models
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