Health care input constraints and cost effectiveness analysis decision rules
Social Science & Medicine , Volume 200 p. 59- 64
Results of cost effectiveness analyses (CEA) studies are most useful for decision makers if they face only one constraint: the health care budget. However, in practice, decision makers wishing to use the results of CEA studies may face multiple resource constraints relating to, for instance, constraints in health care inputs such as a shortage of skilled labour. The presence of multiple resource constraints influences the decision rules of CEA and limits the usefulness of traditional CEA studies for decision makers. The goal of this paper is to illustrate how results of CEA can be interpreted and used in case a decision maker faces a health care input constraint. We set up a theoretical model describing the optimal allocation of the health care budget in the presence of a health care input constraint. Insights derived from that model were used to analyse a stylized example based on a decision about a surgical robot as well as a published cost effectiveness study on eye care services in Zambia. Our theoretical model shows that applying default decision rules in the presence of a health care input constraint leads to suboptimal decisions but that there are ways of preserving the traditional decision rules of CEA by reweighing different cost categories. The examples illustrate how such adjustments can be made, and makes clear that optimal decisions depend crucially on such adjustments. We conclude that it is possible to use the results of cost effectiveness studies in the presence of health care input constraints if results are properly adjusted.
|Cost-effectiveness analysis, Decision rules, Eye-care services, Health care input constraints, Human resource constraints, LMIC, Opportunity costs|
|Social Science & Medicine|
|Organisation||Erasmus School of Health Policy & Management (ESHPM)|
van Baal, P.H.M, Morton, A. (Alec), & Severens, J.L. (2018). Health care input constraints and cost effectiveness analysis decision rules. Social Science & Medicine, 200, 59–64. doi:10.1016/j.socscimed.2018.01.026