The current study used a resource allocation model to analyze prevention of diabetes and its complications in the Netherlands. Optimal resource allocations were computed over a set of 12 interventions aiming to reduce the risk for diabetes and/or cardiovascular disease, either in the general population or in diabetes patients. While for small and high budgets the majority of money would go to interventions in the general population, moderately high budgets were mostly spent in diabetes patients.
Strengths of the resource allocation approach were that it was relatively straightforward to account for constraints and analyze their effects. These constraints are for instance due to limited capacity to provide interventions. Removing constraints on intervention supply increased maximal additional expenditure from €640 to €1200 per capita and almost doubled maximal potential health gains. The constraints were more limiting for prevention in the general population than for interventions in diabetes patients. This makes sense, since the group of diabetes patients is much smaller.
The model used to evaluate long term health effects took into account limited effectiveness and adherence, competing risks, and relapse. Hence, the estimates took care not to overestimate health effects. Our special attention went to the health care costs to be included in the budget allocation model. In this study, costs consisted of intervention costs plus the full long term effects of prevention on health care costs. Alternatively only intervention costs could be included in the budgets. While the latter may result in numbers that are closer to common sense ideas about the sizes of the budgets at stake, it is inconsistent from a long term perspective . Changing to short term budgets increased the variability of choices between prevention in the general population and targeted prevention (results not shown).
Another distinctive feature of our modeling exercise is that we accounted for quality of life decreases with advancing age. This is important, since obviously most of the life years gained occur at advanced ages.
While the current results were specific for the Netherlands, the general approach could be applied to any setting. This would require either an existing disease model comparable to the RIVM chronic disease model, or a transfer of this model to the appropriate setting, replacing prevalence, incidence and mortality parameters by setting specific estimates. Furthermore, the cost estimates of the interventions, as well as the estimates of capacities and further constraints should be adjusted if they were expected to differ from the Dutch estimates.
Similar recent applications of resource allocation in diabetes and in obesity prevention have appeared in the UK and in Australia [23, 26]. The study by Segal focused on prevention in the general population, especially different types of overweight control. Indirect medical costs were not included and costs were computed per life year gained, ignoring effects on quality of life. The study by Earnshaw only considered prevention in the diabetes population. In contrast, the current study also included interventions in the general population and therefore allowed to explore the trade off between both types of prevention. Furthermore, Earnshaw used a full experimental design to directly compute results for any combination of prevention interventions. In the current paper, a simpler approach was applied with only single intervention policies modeled, assuming additive health effects. Third, Earnshaw focused on intervention costs only, which implies the implicit assumption that health care cost effects would be the same for all interventions. That is clearly not the case for interventions on overweight versus smoking cessation or statin treatment. Finally, they did not incorporate age effects on quality of life, which is important if trade-offs are made between age groups.
While a number of diabetes models have been published in recent years,  for the current application we preferred to use the RIVM Chronic Disease Model (CDM). While this model maybe less well known, all parameters estimates are accessible and the general structure of the CDM as well as relevant applications have been published in peer reviewed journals [20, 27–33]. The most important advantage of this model for our current purpose was that it allows evaluating interventions in the general population and in diabetes patients using the same model.
Some assumptions in our current study require further discussion. First of all, combinations of interventions were assumed to have no specific interaction effects, that is, the health gains in terms of life years and QALYs gained were assumed additive. This same assumption was made for instance in the global burden of disease study . It probably implies an overestimation of total health effects if persons receive more than one intervention. This assumption is a bit more problematic in the diabetes population than in the general population. Thus the effects of the diabetes interventions may have been overestimated as compared to interventions in the general population, implying that the optimal shares of money spent in the general population might be higher than our results indicated. Second, another assumption applied in the current paper was the possibility to offer interventions to a population of variable size, by varying the budget spent on each intervention. Some resource allocation models pay specific attention to the consequences of having indivisible interventions of fixed sizes . The optimization problem then changes into a so called integer programming problem. The question whether program size is variable or not depends on the interventions at stake. For the current interventions, it was rather easy to vary sizes by having more or less money available for them, because most of them were supply driven and addressed people that are not yet acutely ill. For curative interventions, varying program size may be more problematic, since it would imply that some actual patients would receive improper treatment.
While we did provide sensitivity analyses for the effects of discount rates, time horizon and budgetary constraints, a more extensive uncertainty analysis would improve insight into the robustness of our outcomes. This requires the use of stochastic programming techniques and we would like to address this issue in future research.
A further advantage of the resource allocation approach is that once the model has been formulated, it is easy to vary constraints and objectives, for instance on indivisible programs or equity [23, 24]. The current results on capacity constraints might help to focus efforts to extend prevention capacity to those areas where it would be most worthwhile, using the shadow prices of the constraints.
A drawback of resource allocation models may be seen in their data greediness. However, most of these data would be needed for careful priority setting anyway. The only additional requirement for a budget allocation model is that all data used are consistent and can be sensibly combined in the same model. Therefore, using a resource allocation model forces to seek for consistent, well comparable data, and that maybe considered an advantage rather than a drawback .