Informing Balanced Investment in Services and Health Systems: A Case Study of Priority Setting for Tuberculosis Interventions in South Africa.
Informing Balanced Investment in Services and Health Systems: A Case Study of Priority Setting for Tuberculosis Interventions in South Africa.
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DOI：
10.1016/j.jval.2020.05.021
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发表时间：
2020-11
期刊：
影响因子：
--
通讯作者：
Vassall A
中科院分区：
文献类型：
--
作者：
Bozzani FM;Sumner T;Mudzengi D;Gomez GB;White R;Vassall A
卫生系统面临非财政制约因素，这些制约因素可能影响干预措施的机会成本。然而，探索其影响的实证方法还不发达。我们开发了一个概念框架，用于定义卫生系统的约束条件和依赖于常规数据的经验估计方法。然后，我们提出了一个实证的方法，将非财务约束的成本效益模型的健康福利包的卫生部门。我们通过一个案例研究来说明这种方法的应用，该案例研究定义了南非结核病病例发现的一揽子服务。一个经济模型相结合的传输模型的输出与单位成本的替代筛选和诊断算法的成本效益进行了研究。制约因素被视为对可实现的覆盖率的限制，其依据是：（1）财政资源;（2）人力资源;（3）围绕诊断采购的政策制约。在一个“不受约束”和几个“受约束”的情况下评估了干预措施的成本效益。对于不受限制的设想，在考虑和不考虑“放松”限制的费用的情况下，对增量成本效益比率进行了估计。我们发现，在不同的情况下，增量成本效益比有很大的差异，导致优先干预措施的决策规则的变化。在限制性情景中，大多数干预措施的限制因素不是资金，而是人力资源的可用性。我们发现，在南非不同的结核病控制策略之间的最佳优先级的影响，是否以及如何考虑的限制。因此，我们证明了在卫生部门资源分配模型中考虑非财政约束的重要性和可行性。卫生系统的制约因素，如财政、人力资源和诊断投入不足，可能影响卫生干预措施的机会成本，并妨碍资源的有效分配。在确定优先事项时进行经验估计和纳入这些制约因素的方法还不发达，特别是在中低收入环境中的应用。我们提出了一种方法，可以应用于使用常规数据，至关重要的是，决策者提供了一个选择集，包括约束和不受约束的规模扩大的选项，以及至关重要的是，选择放松卫生系统的限制。该方法是说明使用的案例研究，在南非开发一个基本的结核病控制包。我们发现，考虑到卫生系统的限制因素改变了干预措施的成本效益排名，表明了它们对优先事项设定的重要性。我们证明了这种方法是可行的政策驱动的时间轴。
Health systems face nonfinancial constraints that can influence the opportunity cost of interventions. Empirical methods to explore their impact, however, are underdeveloped. We develop a conceptual framework for defining health system constraints and empirical estimation methods that rely on routine data. We then present an empirical approach for incorporating nonfinancial constraints in cost-effectiveness models of health benefit packages for the health sector. We illustrate the application of this approach through a case study of defining a package of services for tuberculosis case-finding in South Africa. An economic model combining transmission model outputs with unit costs was developed to examine the cost-effectiveness of alternative screening and diagnostic algorithms. Constraints were operationalized as restrictions on achievable coverage based on: (1) financial resources; (2) human resources; and (3) policy constraints around diagnostics purchasing. Cost-effectiveness of the interventions was assessed under one “unconstrained” and several “constrained” scenarios. For the unconstrained scenario, incremental cost-effectiveness ratios were estimated with and without the costs of “relaxing” constraints. We find substantial differences in incremental cost-effectiveness ratios across scenarios, leading to variations in the decision rules for prioritizing interventions. In constrained scenarios, the limiting factor for most interventions was not financial, but rather the availability of human resources. We find that optimal prioritization among different tuberculosis control strategies in South Africa is influenced by whether and how constraints are taken into consideration. We thus demonstrate both the importance and feasibility of considering nonfinancial constraints in health sector resource allocation models. Health system constraints such as financial, human resources, and diagnostic inputs scarcity can influence the opportunity costs of health interventions and prevent the efficient allocation of resources. Methods for empirical estimation and inclusion of these constraints in priority setting are underdeveloped, particularly for application in low- and middle-income settings. We propose an approach that can be applied using routine data and that, crucially, presents decision makers with a choice set including constrained and unconstrained scale-up options as well as, crucially, the option of relaxing the health system constraints. The approach is illustrated using a case study of developing an essential tuberculosis control package in South Africa. We find that taking health system constraints into account changes the cost-effectiveness ranking of intervention options, demonstrating their importance for priority setting. We demonstrate that the approach is feasible within a policy-driven timeline.
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影响因子：
16.1
作者：
Kelly, Sherrie L.;Martin-Hughes, Rowan;Wilson, David P.
通讯作者：
Wilson, David P.
影响因子：
5.4
作者：
Remme, Michelle;Siapka, Mariana;Vassall, Anna
通讯作者：
Vassall, Anna
在撒哈拉以南非洲的卫生系统限制下改变艾滋病毒治疗资格：投资需求，人口健康增长和成本效益。
DOI：
10.1097/qad.0000000000001190
发表时间：
2016-09-24
期刊：
AIDS (London, England)
影响因子：
--
作者：
Hontelez JA;Chang AY;Ogbuoji O;de Vlas SJ;Bärnighausen T;Atun R
通讯作者：
Atun R
DOI：
10.1016/s2214-109x(16)30265-0
发表时间：
2016-11
期刊：
The Lancet. Global health
影响因子：
--
作者：
Menzies NA;Gomez GB;Bozzani F;Chatterjee S;Foster N;Baena IG;Laurence YV;Qiang S;Siroka A;Sweeney S;Verguet S;Arinaminpathy N;Azman AS;Bendavid E;Chang ST;Cohen T;Denholm JT;Dowdy DW;Eckhoff PA;Goldhaber-Fiebert JD;Handel A;Huynh GH;Lalli M;Lin HH;Mandal S;McBryde ES;Pandey S;Salomon JA;Suen SC;Sumner T;Trauer JM;Wagner BG;Whalen CC;Wu CY;Boccia D;Chadha VK;Charalambous S;Chin DP;Churchyard G;Daniels C;Dewan P;Ditiu L;Eaton JW;Grant AD;Hippner P;Hosseini M;Mametja D;Pretorius C;Pillay Y;Rade K;Sahu S;Wang L;Houben RMGJ;Kimerling ME;White RG;Vassall A
通讯作者：
Vassall A
影响因子：
56.3
作者：
Turner, Hugo C.;Truscott, James E.;Anderson, Roy M.
通讯作者：
Anderson, Roy M.