4.3 Article

Estimating health care costs at scale in low- and middle-income countries: Mathematical notations and frameworks for the application of cost functions

期刊

HEALTH ECONOMICS
卷 32, 期 10, 页码 2216-2233

出版社

WILEY
DOI: 10.1002/hec.4722

关键词

cost functions; econometrics; health economics; low- and middle-income countries; microeconomics; production costs

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Appropriate costing and economic modeling are crucial for the successful scale-up of health interventions. However, different cost functions can lead to inconsistent cost projections. This study reviewed existing methods and proposed new mathematical notations and cost function frameworks to improve the analysis of healthcare costs in LMICs. These frameworks consider variable returns to scale, which are often neglected in current studies, and aim to balance simplicity and accuracy while increasing transparency in reporting methods.
Appropriate costing and economic modeling are major factors for the successful scale-up of health interventions. Various cost functions are currently being used to estimate costs of health interventions at scale in low- and middle-income countries (LMICs) potentially resulting in disparate cost projections. The aim of this study is to gain understanding of current methods used and provide guidance to inform the use of cost functions that is fit for purpose. We reviewed seven databases covering the economic and global health literature to identify studies reporting a quantitative analysis of costs informing the projected scale-up of a health intervention in LMICs between 2003 and 2019. Of the 8725 articles identified, 40 met the inclusion criteria. We classified studies according to the type of cost functions applied-accounting or econometric-and described the intended use of cost projections. Based on these findings, we developed new mathematical notations and cost function frameworks for the analysis of healthcare costs at scale in LMICs setting. These notations estimate variable returns to scale in cost projection methods, which is currently ignored in most studies. The frameworks help to balance simplicity versus accuracy and increase the overall transparency in reporting of methods.

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