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标题：Costs and mortality associated with HIV: a machine learning analysis of the French national health insurance database
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作者：Martin Prodel ; Laurent Finkielsztejn ; Laëtitia Roustand 等
期刊名称：Journal of Public Health Research
印刷版ISSN：2279-9028
电子版ISSN：2279-9036
出版年度：2022
卷号：11
期号：2
DOI：10.4081/jphr.2021.2601
语种：English
出版社：PAGEPress Publications
摘要：Background: The objective is to characterise the economic burden to the healthcare system of people living with HIV (PLWHIV) in France and to help decision makers in identifying risk factors associated with high-cost and high mortality profiles. Design and methods: The study is a retrospective analysis of PLWHIV identified in the French National Health Insurance database (SNDS). All PLWHIV present in the database in 2013 were identified. All healthcare resource consumption from 2008 to 2015 inclusive was documented and costed (for 2013 to 2015) from the perspective of public health insurance. High-cost and high mortality patient profiles were identified by a machine learning algorithm. Results: In 2013, 96,423 PLWHIV were identified in the SNDS database, including 3,373 incident cases. Overall, 3,224 PLWHIV died during the three-year follow-up period (mean annual mortality rate: 1.1%). The mean annual per capita cost incurred by PLWHIV was € 14,223, corresponding to a total management cost of HIV of € 1,370 million in 2013. The largest contribution came from the cost of antiretroviral medication (M€ 870; 63%) followed by hospitalisation (M€ 154; 11%). The costs incurred in the year preceding death were considerably higher. Four specific patient profiles were identified for under/over-expressing these costs, suggesting ways to reduce them. Conclusions: Even though current therapeutic regimens provide excellent virological control in most patients, PLWHIV have excess mortality. Other factors such as comorbidities, lifestyle factors and screening for cancer and cardiovascular disease, need to be targeted in order to lower the mortality and cost associated with HIV infection. Significance for public health The authors believe that this study is important in that it provides for the first time an estimate of the most impacting factors of the total economic burden of HIV infections to public health insurance in France. The approach relies on a state of-the-art machine learning analysis. Regarding epidemiological results, although the prevalence and incidence of HIV infections in Western European countries are reasonably well documented, there is a paucity of information on its economic burden. We suggest ways that the economic burden of HIV (€ 1,400 million annually to public health insurance), as well as associated mortality, could be reduced. The main interest and novelty of the results from a public health perspective is the identification of patient profiles at risk of high costs. Such results provide clear and potentially actionable levers to decision makers, either for prevention plans, for patient pathways, or for reimbursement strategies.
关键词：Key wordsenHIVmachine learningcostclaim dataSNDSFrance