RESOURCE ALLOCATION DECISIONS AND BUDGETARY POLICIES UNDER CONDITIONS OF UNCERTAINTY
Method(s): We use the example of a fixed HIV budget to be allocated across six regions. The model reflects a sub-Saharan African setting with a generalised HIV epidemic in which regions differ by HIV prevalence. Decision makers can choose to invest in one or more interventions (of late ART, male voluntary circumcision or early ART) in each region at a range of coverage levels. Costs and QALYs are predicted using a transmission model and the optimal resource allocation is identified using mathematical programming. Uncertainty is propagated through the model using Monte Carlo simulation. We assess the impact of three budgetary policies: (i) fixed budgets at the regional level, (ii) more modest planned programmes coupled with a contingency fund to preserve planned programmes, and (iii) a national budget policy which allows transfers between regions. We explore the implications of having perfect information, to represent an upper bound on the health that could be generated by data collection.
Result(s): Standard cost-effectiveness analyses overestimate the health generated under fixed regional budgets by up to 23%. This occurs as planned programmes cannot be implemented in all realisations of uncertainty. The contingency fund generates more health than regional budgets. This suggests that preserving more cost-effective interventions across realisations of uncertainty can be valuable, and that basing plans on expected costs and effects can be sub-optimal. The national budget policy outperforms these policies by allowing decision makers to maintain planned programmes via regional transfers. Perfect information outperforms the other fixed budget policies, and performs as well as soft budgets even though there is no possibility for national HIV budget over-runs.
Conclusion(s): This work shows that fixed budgets reduce health in a way that is not currently reflected in cost-effectiveness analysis. This can be mitigated via careful resource allocation decisions, budgetary policy design and information collection.