Development and application of a framework to estimate health care costs in China: The cervical cancer example

Yi-Jun Liu, Adam Keane, Kate T Simms, Jie-Bin Lew, Ju-Fang Shi, Carolyn Mazariego, Susan Yuill, Jose Jeronimo, You-Lin Qiao and Karen Canfell

PLOS ONE, 2019, vol. 14, issue 10, 1-13

Abstract: Objectives: Information on healthcare costs in low-and-middle-income countries is limited. This study presents a framework to perform healthcare cost estimates for each province in China. Methods: This study has two aims. Using cervical cancer as an example, the first aim is to use data (including micro-costing data) from one province to derive estimates for other provinces in China. This used provincial and national Chinese-language statistical reports and considered levels of service delivery, hospital-seeking behaviour, and the urban/rural population distribution. The second aim is to characterise the relationship between the reference costs estimated using the method mentioned above and two sets of cost estimates derived using simplified cost-scaling method with per capita Gross Domestic Product (GDP), and the Human Development Index (HDI). For simplified methods, regression modelling characterised the relationship between province-specific healthcare costs and macro-economic indicators, then we used the exponential fit to extrapolate costs. Results: Using the reference method, the estimated costs were found to vary substantially by urban/rural regions and between provinces; the ratios of highest to lowest provincial costs were 3.5 for visual inspection with acetic acid (VIA), 4.4 for cold knife conisation (CKC) and 4.6 for stage II cancer treatment. The HDI-based scaling method generally resulted in a better fit to reference costs than the GDP method. Conclusions: These reference costs for cervical cancer can inform cost-effectiveness evaluation of cervical screening and HPV vaccination in China. HDI-based methods for cost-scaling-based on social, as well as purely economic, factors-have potential to provide more accurate estimates.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0222760

DOI: 10.1371/journal.pone.0222760

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