Optimal Allocation of Resources Across Four Interventions for Type 2 Diabetes

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Medical Decision Making (Impact Factor: 3.24). 10/2002; 22(5 Suppl):S80-91. DOI: 10.1177/027298902237704
Source: PubMed


Several interventions can be applied to prevent complications of type 2 diabetes. This article examines the optimal allocation of resources across 4 interventions to treat patients newly diagnosed with type 2 diabetes. The interventions are intensive glycemic control, intensified hypertension control, cholesterol reduction, and smoking cessation.
A linear programming model was designed to select sets of interventions to maximize quality-adjusted life years (QALYs), subject to varied budget and equity constraints.
For no additional cost, approximately 211,000 QALYs can be gained over the lifetimes of all persons newly diagnosed with diabetes by implementing interventions rather than standard care. With increased availability of funds, additional health benefits can be gained but with diminishing marginal returns. The impact of equity constraints is extensive compared to the solution with the same intervention costs and no equity constraint. Under the conditions modeled, intensified hypertension control and smoking cessation interventions were provided most often, and intensive glycemic control and cholesterol reduction interventions were provided less often.
A resource allocation model identifies trade-offs involved when imposing budget and equity constraints on care for individuals with newly diagnosed diabetes.

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Available from: Anke Richter, Nov 26, 2014
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    • "An alternative approach to economic assessment is optimization modeling applied previously in many different areas such as transport, agriculture, industry, and banking [22], and more recently in the health care sector [23-28]. This approach uses mathematical programming techniques to select the combination of alternative interventions that achieves the best clinical outcome while meeting pre-selected constraints on the available budget and on the feasibility of different coverage levels for the alternative interventions. "
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    ABSTRACT: Background This study aims to assess the most efficient combinations of vaccination and screening coverage for the prevention of cervical cancer (CC) at different levels of expenditure in Nigeria. Methods An optimization procedure, using a linear programming approach and requiring the use of two models (an evaluation and an optimization model), was developed. The evaluation model, a Markov model, estimated the annual number of CC cases at steady state in a population of 100,000 women for four alternative strategies: screening only; vaccination only; screening and vaccination; and no prevention. The results of the Markov model for each scenario were used as inputs to the optimization model determining the optimal proportion of the population to receive screening and/or vaccination under different scenarios. The scenarios varied by available budget, maximum screening and vaccination coverage, and overall reachable population. Results In the base-case optimization model analyses, with a coverage constraint of 20% for one lifetime screening, 95% for vaccination and a budget constraint of $1 per woman per year to minimize CC incidence, the optimal mix of prevention strategies would result in a reduction of CC incidence of 31% (3-dose vaccination available) or 46% (2-dose vaccination available) compared with CC incidence pre-vaccination. With a 3-dose vaccination schedule, the optimal combination of the different strategies across the population would be 20% screening alone, 39% vaccination alone and 41% with no prevention, while with a 2-dose vaccination schedule the optimal combination would be 71% vaccination alone, and 29% with no prevention. Sensitivity analyses indicated that the results are sensitive to the constraints included in the optimization model as well as the cervical intraepithelial neoplasia (CIN) and CC treatment cost. Conclusions The results of the optimization model indicate that, in Nigeria, the most efficient allocation of a limited budget would be to invest in both vaccination and screening with a 3-dose vaccination schedule, and in vaccination alone before implementing a screening program with a 2-dose vaccination schedule.
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    • "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. "
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    ABSTRACT: ABSTRACT: Diabetes mellitus brings an increased risk for cardiovascular complications and patients profit from prevention. This prevention also suits the general population. The question arises what is a better strategy: target the general population or diabetes patients. A mathematical programming model was developed to calculate optimal allocations for the Dutch population of the following interventions: smoking cessation support, diet and exercise to reduce overweight, statins, and medication to reduce blood pressure. Outcomes were total lifetime health care costs and QALYs. Budget sizes were varied and the division of resources between the general population and diabetes patients was assessed. Full implementation of all interventions resulted in a gain of 560,000 QALY at a cost of €640 per capita, about €12,900 per QALY on average. The large majority of these QALY gains could be obtained at incremental costs below €20,000 per QALY. Low or high budgets (below €9 or above €100 per capita) were predominantly spent in the general population. Moderate budgets were mostly spent in diabetes patients. Major health gains can be realized efficiently by offering prevention to both the general and the diabetic population. However, a priori setting a specific distribution of resources is suboptimal. Resource allocation models allow accounting for capacity constraints and program size in addition to efficiency.
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    • "Many have demonstrated that the standard decision rules in CEA may be optimal when there is a single budget constraint and perfect divisibility, or at least any indivisibilities are small relative to the budget (Epstein et al., 2007; Laska et al., 1999; Stinnett and Paltiel, 1996). MP techniques have been applied to some policy problems (Brandeau et al., 2003; Earnshaw et al., 2002; Zaric and Brandeau, 2001). All the work cited above assumes that costs and effects are known. "
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    ABSTRACT: The allocation problem in health care can be characterised as a mathematical programming problem but attempts to incorporate uncertainty in costs and effect have suffered from important limitations. A two-stage stochastic mathematical programming formulation is developed and applied to a numerical example to explore and demonstrate the implications of this more general and comprehensive approach. The solution to the allocation problem for different budgets, budgetary policies, and available actions are then demonstrated. This analysis is used to evaluate different budgetary policies and examine the adequacy of standard decision rules in cost-effectiveness analysis. The research decision is then considered alongside the allocation problem. This more general formulation demonstrates that the value of further research depends on: (i) the budgetary policy in place; (ii) the realisations revealed during the budget period; (iii) remedial actions that may be available; and (iv) variability in parameters values.
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