Suchergebnisse

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.

BACKGROUND AND OBJECTIVE: In health preference research, studies commonly hypothesize differences in parameters (i.e., differential or joint effects on attribute importance) and/or in choice predictions (marginal effects) by observable factors. Discrete choice experiments may be designed and conducted to test and estimate these observable differences. This guide covers how to explore and corroborate various observable differences in health preference evidence. METHODS: The analytical process has three steps: analyze the exploratory data, analyze the confirmatory data, and interpret and disseminate the evidence. In this guide, we demonstrate the process using dual samples (where exploratory and confirmatory samples were collected from different sources) on 2020 US COVID-19 vaccination preferences; however, investigators may apply the same approach using split samples (i.e., single source). RESULTS: The confirmatory analysis failed to reject ten of the 17 null hypotheses generated by the exploratory analysis (p < 0.05). Apart from demographic, socioeconomic, and geographic differences, political independents and persons who have never been vaccinated against influenza are among those least likely to be vaccinated (0.838 and 0.872, respectively). CONCLUSIONS: For all researchers in health preference research, it is essential to know how to identify and corroborate observable differences. Once mastered, this skill may lead to more complex analyses of latent differences (e.g., latent classes, random parameters). This guide concludes with six questions that researchers may ask themselves when conducting such analyses or reviewing published findings of observable differences.