Suchergebnisse

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.

Abstract Background Comprehensive information about national spending on prevention is crucial for health policy development and evaluation. This study provides a comprehensive overview of prevention spending in the Netherlands, including those activities beyond the national health accounts. Methods National spending on health-related primary and secondary preventive activities was examined by funding source with the use of national statistics, government reports, sector reports, and data from individual health associations and corporations, public services, occupational health services, and personal prevention. Costs were broken down by diseases, age groups and gender using population-attributable risks and other key variables. Results Total expenditures on prevention were €12.5 billion or €769 per capita in the Netherlands in 2003, of which 20% was included in the national health accounts. 82% was spent on health protection, 16% on disease prevention, and 2% on health promotion activities. Most of the spending was aimed at the prevention of infectious diseases (34%) and acute physical injuries (29%). Per capita spending on prevention increased steeply by age. Conclusion Total expenditure on health-related prevention is much higher than normally reported due to the inclusion of health protection activities beyond the national health accounts. The allocative efficiency of prevention spending, particularly the high costs of health protection and the low costs of health promotion activities, should be addressed with information on their relative cost effectiveness.

<b>Objective:</b> Evaluation of the costs, effects and savings of three strategies for female fragile X premutation and full mutation carrier screening in the general population. <b>Methods:</b> We calculated the costs, effects and savings by using a general model for prenatal, preconceptional, and school carrier screening. Assumptions were based on literature data, expert opinions, prices and tariffs. <b>Results:</b> Prenatal screening will detect most carriers and will lead to the highest number of avoided fragile X syndrome patients. The costs per detected carrier are quite similar for all screening programmes (around USD 45,000). All screening strategies have a favourable cost-savings balance (USD 14 million for prenatal screening, USD 9 million for preconceptional screening and USD 2 million for school screening). <b>Conclusions:</b> From an economic point of view, there is no obstacle to fragile X screening. The decision to screen or not can (and should) therefore concentrate on discussion of medical, social, psychological and ethical considerations.

It is widely acknowledged that in order to promote public health and prevent diseases, a wide range of scientific disciplines and sectors beyond the health sector need to be involved. Evidence-based interventions, beyond preventive health interventions targeting disease risk factors and interventions from other sectors, should be developed and implemented. Investing in these preventive health policies is challenging as budgets have to compete with other governmental expenditures. The current study aimed to identify, compare and rank cost-effective preventive interventions targeting metabolic, environmental, occupational and behavioral risk factors. To identify these interventions, a literature search was performed including original full economic evaluations of Western country interventions that had not yet been implemented in the Netherlands. Several workshops were held with experts from different disciplines. In total, 51 different interventions (including 13 cost saving interventions) were identified and ranked based on their incremental cost-effectiveness ratio (ICER) and potential averted disability-adjusted life years (DALYs), resulting in two rankings of the most cost-effective interventions and one ranking of the 13 cost saving interventions. This approach, resulting in an intersectoral ranking, can assist policy makers in implementing cost-effective preventive action that considers not only the health sector, but also other sectors.