Evidence map›Paper›PMID 30904025›Full record

ArticleInfectious diseases of poverty2019

Impact of an innovative financing and payment model on tuberculosis patients' financial burden: is tuberculosis care more affordable for the poor?

Wei-Xi Jiang, Qian Long, Henry Lucas, Di Dong, Jia-Ying Chen, Li Xiang, Qiang Li, Fei Huang, Hong Wang, Chris Elbers and 2 more

Abstract readComparative Study
In one paragraph

Article in Infectious diseases of poverty, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Wei-Xi JiangGlobal Health Research Center, Duke Kunshan University, Kunshan, 215316, Jiangsu, China.
Qian LongGlobal Health Research Center, Duke Kunshan University, Kunshan, 215316, Jiangsu, China.
Henry LucasInstitute of Development Studies, University of Sussex, Brighton, BN1 9RE, UK.
Di DongGlobal Health Research Center, Duke Kunshan University, Kunshan, 215316, Jiangsu, China.
Jia-Ying ChenSchool of Policy & Management, Nanjing Medical University, Nanjing, 211166, Jiangsu, China.
Li XiangTongji Medical College of Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Qiang LiSchool of Public Health, Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China.
Fei HuangNational Center for Tuberculosis Control and Prevention, China CDC, Beijing, 102206, China.
Hong WangBill & Melinda Gates Foundation, Seattle, WA, USA.
Chris ElbersFaculty of Economics and Business Administration, Vrije Universiteit Amsterdam, Amsterdam, 1081, HV, the Netherlands.
Frank CobelensThe Amsterdam Institute for Global Health and Development, Amsterdam, 1105, BP, the Netherlands.
Sheng-Lan TangDuke Global Health Institute, Duke University, Durham, NC, 27710, USA. shenglan.tang@duke.edu.

Funding

Bill and Melinda Gates Foundation #23386
6 · The paper itself

Abstract

backgroundIn response to the high financial burden of health services facing tuberculosis (TB) patients in China, the China-Gates TB project, Phase II, has implemented a new financing and payment model as an important component of the overall project in three cities in eastern, central and western China. The model focuses on increasing the reimbursement rate for TB patients and reforming provider payment methods by replacing fee-for-service with a case-based payment approach. This study investigated changes in out-of-pocket (OOP) health expenditure and the financial burden on TB patients before and after the interventions, with a focus on potential differential impacts on patients from different income groups.

methodsThree sample counties in each of the three prefectures: Zhenjiang, Yichang and Hanzhong were chosen as study sites. TB patients who started and completed treatment before, and during the intervention period, were randomly sampled and surveyed at the baseline in 2013 and final evaluation in 2015 respectively. OOP health expenditure and percentage of patients incurring catastrophic health expenditure (CHE) were calculated for different income groups. OLS regression and logit regression were conducted to explore the intervention's impacts on patient OOP health expenditure and financial burden after adjusting for other covariates. Key-informant interviews and focus group discussions were conducted to understand the reasons for any observed changes.

resultsData from 738 (baseline) and 735 (evaluation) patients were available for analysis. Patient mean OOP health expenditure increased from RMB 3576 to RMB 5791, and the percentage of patients incurring CHE also increased after intervention. The percentage increase in OOP health expenditure and the likelihood of incurring CHE were significantly lower for patients from the highest income group as compared to the lowest. Qualitative findings indicated that increased use of health services not covered by the standard package of the model was likely to have caused the increase in financial burden.

conclusionsThe implementation of the new financing and payment model did not protect patients, especially those from the lowest income group, from financial difficulty, due partly to their increased use of health service. More financial resources should be mobilized to increase financial protection, particularly for poor patients, while cost containment strategies need to be developed and effectively implemented to improve the effective coverage of essential healthcare in China.

Indexed as

AdultAgedChinaComorbidityCosts and Cost AnalysisFemaleHealth Care CostsHealth ExpendituresHumansInsurance, HealthInterviews as TopicMaleMiddle AgedPovertyRegression AnalysisSocioeconomic FactorsCase-based paymentFinancing and payment modelHealth insuranceTuberculosis

Identifiers

PMID30904025
PMCPMC6431427

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.