Rousson Valentin, Rossel Jean-Benoît, Eggli Yves
Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland.
Health Serv Res Manag Epidemiol. 2019 Dec 3;6:2333392819891005. doi: 10.1177/2333392819891005. eCollection 2019 Jan-Dec.
We consider the nontrivial problem of estimating the health cost repartition among different diseases in the common case where the patients may have multiple diseases. To tackle this problem, we propose to use an iterative proportional repartition (IPR) algorithm, a nonparametric method which is simple to understand and to implement, allowing (among other) to avoid negative cost estimates and to retrieve the total health cost by summing up the estimated costs of the different diseases. This method is illustrated with health costs data from Switzerland and is compared in a simulation study with other methods such as linear regression and general linear models. In the case of an additive model without interactions between disease costs, a situation where the truth is clearly defined such that the methods can be compared on an objective basis, the IPR algorithm clearly outperformed the other methods with respect to efficiency of estimation in all the settings considered. In the presence of interactions, the situation is more complex and will deserve further investigation.
我们考虑在患者可能患有多种疾病的常见情况下，估计不同疾病间医疗费用分配这一重要问题。为解决此问题，我们建议使用迭代比例分配（IPR）算法，这是一种非参数方法，易于理解和实施，（除其他优点外）能够避免出现负的费用估计，并通过汇总不同疾病的估计费用来得出总医疗费用。该方法通过瑞士的医疗费用数据进行说明，并在模拟研究中与其他方法（如线性回归和一般线性模型）进行比较。在疾病费用之间不存在交互作用的加性模型情况下（这种情况下事实明确，可在客观基础上对方法进行比较），在所有考虑的设置中，就估计效率而言，IPR算法明显优于其他方法。在存在交互作用的情况下，情况更为复杂，值得进一步研究。
