Suchergebnisse

Abstract
We 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 of R
2 (or proportion of explained variance) and MAE (mean absolute (prediction) error). The model containing only socio-demographic information improved marginally when neighbourhood was added (R
2 +0.8%, MAE −€5). The full model remained the same for the study population (R
2 = 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.

Background: Active disinvestment of healthcare interventions (i.e. discontinuing reimbursement by means of a policy decision) has received limited public support in the past. Previous research has identified four viewpoints on active disinvestment among citizens in the Netherlands. However, it remained unclear how strong these viewpoints are supported by society, and by whom. Therefore, the current study aimed to 1) measure the support for these four viewpoints and 2) assess whether support is associated with background characteristics of citizens. Method: In an online survey, a representative sample of adult citizens in the Netherlands (n = 1794) was asked to rate their agreement with short narratives of the four viewpoints on a 7-point Likert scale. The survey also included questions on sociodemographic characteristics, health status, healthcare utilization, and opinions about responsibility and costs in the healthcare context. Logistic regression models were estimated for each viewpoint to assess the association between viewpoint support and these characteristics. Results: The support for the different viewpoints varied between 46.8% and 57.7% of the sample. Viewpoint support was associated with participants' age, gender, educational level, financial situation, healthcare utilization, opinion on the responsibility of the government for the health of citizens, and opinion on whether the increase in healthcare expenditure and health insurance premiums is considered a problem. Conclusion: Resistance to active disinvestment may partially be explained by the consequences of disinvestment citizens anticipate experiencing themselves. Citizens considering the increase in healthcare expenditure a larger problem were more supportive of disinvestment than those considering it less of a problem.