AbstractChronic non-communicable diseases (CNCDs) are the largest contributors to the population health burden and it is expected that their incidence will keep on rising in the next decades, mainly due to ageing of the population and changing health behaviours. Not only population health is affected by these CNCDs, but also the health budget. Health care costs as well as costs due to productivity loss are putting the public budget under huge pressure. Health expenditure has been rising more steeply than the total economy, mainly due to a rise in the incidence of CNCDs, technological progress, growing patient expectations and inefficient use of the budget (Pammolli et al., 2012; WHO, 2015b). As such, the concern has been raised on the financial sustainability of the health care system. The World Health Organization recommends to promote public health interventions that prevent and control CNCDs in order to lessen their global health and economic impact. Governments should invest in cost-effective prevention interventions, by considering health economic evidence in the decision-making process. Cost-effectiveness analyses can inform policy makers on the value for money of interventions. Although cost-effectiveness evidence for public health interventions is modest compared to that for treatment interventions, it has grown in the last decade and shows favourable results.
The first aim of this PhD research was to assess the cost-effectiveness of eight public health interventions in the continuum of chronic disease prevention, that hold some promise to reduce the health burden at a reasonable cost or even lead to cost-savings. Universal prevention interventions included in this PhD research comprised the prevention of obesity in pre-schoolers, as well as a sensitisation campaign and a total ban on sunbed use to prevent skin cancer. The evaluation of interventions categorised as selective prevention consisted of a biennial mammography screening program for the early detection of breast cancer, a biennial faecal immunological test for the early detection of colorectal cancer, a total-body examination and a lesion-directed screening for the early detection of skin cancer. Finally, the indicated prevention intervention was a suicide helpline for the prevention of suicide. All interventions, except for the one aiming to prevent pre-schooler obesity, showed promising results for increasing population health while controlling the health care budget. These interventions were found to result in incremental cost-effectiveness ratios being below the assumed threshold of €35,000/QALY gained, which means that public investment in these interventions offers good value for money.
However, cost-effectiveness analyses include inherent uncertainties concerning the model structure (structural uncertainty), availability of data (parameter uncertainty) and the methodological choices (methodological uncertainty), which should be explored and reported. Therefore, our second aim was to inform researchers as well as policy makers and other stakeholders on the interpretation of cost-effectiveness results. This was performed by reporting and reflecting on the main uncertainties that we encountered in the included cost-effectiveness analyses. These uncertainties were largely (although not exclusively) associated with the particularities of the public health field. Structural uncertainty was related to the issue of providing indirect evidence based on the extrapolation of intermediate intervention outcomes to long term costs and health benefits. Parameter uncertainty included mainly the availability of data. Methodological uncertainty in the model arose in the choices on the methods, such as what health effects (and potential harms of an intervention) and what costs to include, how to value productivity loss, the length of the model time horizon and evidence on the long term sustainability of the intervention effect.
The results produced by this PhD research can be informing for several stakeholders who may be performing, funding, participating in, or making use of economic evaluations.
Researchers performing cost-effectiveness analyses should explore to what extent uncertainties are present in the analysis and to what extent there is an impact on the cost-effectiveness result. Transparency and validation are important to enhance the credibility of cost-effectiveness models. Model structure, calculations, parameters values and assumptions need to be clearly described so that interested parties are able to understand the main building blocks, as well as strengths and limitations of the analysis. Additionally, reporting validation efforts is desirable to further enhance policy makers' (and other stakeholders’) confidence in health economic models and their outcomes. Researchers should be aware that mean (base case) cost-effectiveness results provide relevant information, but that results from sensitivity analyses and model validation exercises are even more informing. Furthermore, cooperation with relevant stakeholders such as (clinical) experts in the field, data registries and policy makers can be beneficial in the development of the cost-effectiveness analysis and for distributing the results to the wider public.
Health professionals, developing and implementing prevention interventions, should keep in mind the factors with the highest impact on the cost-effectiveness of the particular intervention, shown from studies such as those included in this PhD research. For example, the intervention cost should be minimised, while maximising the intervention effects, as it has been shown that the intervention cost can have a great influence on the cost-effectiveness result (cf. ToyBox-analysis). Or, in the example of the screening programs, the major influencing parameters were shown to be the impact of a false-positive screening result on an individual’s quality of life, the productivity loss due to undergoing the screening test, the adherence to follow-up examinations, and the screening test accuracy. Health professionals should focus on these key parameters in order to increase or at least maintain good balance between the extra costs and health benefits of the screening program.
Policy makers should facilitate the use of cost-effectiveness evidence as a means of identifying the most valuable preventive services by funding research producing such evidence, by assessing the transferability of international cost-effectiveness studies, by having health economic training in order to better understand cost-effectiveness analyses, by disseminating results of cost-effectiveness studies to relevant stakeholders and by using such evidence as information when making a policy decision. Besides, policy could support health economic research not only by funding research projects, but also by investing in improving and integrating databases and by facilitating access to those data for research aims. Furthermore policy makers have to be aware that cost-effectiveness analyses include uncertainty. Whether a model is sufficiently valid or accurate for a particular application must be determined by those who use its results. The important question is not whether it is scientific to make decisions based on predictions, but how much uncertainty the policy maker is willing to accept (Threlfall et al., 2015). It is assumed that in case of prevention within public health, more uncertainty is allowed, as long term evidence is more difficult to assess. Finally, policy makers should be more transparent in the decision making criteria and the relative importance of the different criteria in each decision.
|Date of Award||16 Nov 2016|