Evidence map›Paper›PMID 31215476›Full record

ArticleInfectious diseases of poverty2019

Inequity in catastrophic costs among tuberculosis-affected households in China.

Cai-Hong Xu, Kathiresan Jeyashree, Hemant Deepak Shewade, Yin-Yin Xia, Li-Xia Wang, Yan Liu, Hui Zhang, Li Wang

Abstract read
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 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. Cost-effectiveness of latent tuberculosis infection testing and treatment with 6-week regimen among key population in rural communities in China: a decision analysis study.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2024
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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

8 authors.

Cai-Hong XuNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 100226, China.
Kathiresan JeyashreeVelammal Medical College Hospital and Research Institute, Madurai, 625009, India.
Hemant Deepak ShewadeInternational Union Against Tuberculosis and Lung Disease (The Union), South-East Asia Office, New Delhi, 110016, India.
Yin-Yin XiaNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 100226, China.
Li-Xia WangNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 100226, China.
Yan LiuDepartment of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, 100005, China.
Hui ZhangNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 100226, China. zhanghui@chinacdc.cn.
Li WangDepartment of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, 100005, China. liwang@ibms.pumc.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere are limited nationally representative studies globally in the post-2015 END tuberculosis (TB) era regarding wealth related inequity in the distribution of catastrophic costs due to TB care. Under the Chinese national tuberculosis programme setting, we aimed to assess extent of equity in distribution of total TB care costs (pre-treatment, treatment and overall) and costs as a proportion of annual household income (AHI), and describe and compare equity in distribution of catastrophic costs (pre-treatment, treatment and overall) across population sub-groups.

methodsAnalytical cross-sectional study using data from national TB patient cost survey carried out in 22 counties from six provinces in China in 2017. Drug-susceptible pulmonary TB registered under programme, who had received at least 2 weeks of intensive phase therapy were included. Equity was depicted using concentration curves and concentration indices were compared using dominance test.

resultsOf 1147 patients, the median cost of pre-treatment, treatment and overall care, were USD 283.5, USD 413.1 and USD 965.5, respectively. Richer quintiles incurred significantly higher pre-treatment and treatment costs compared to poorer quintiles. The distribution of costs as a proportion of AHI and catastrophic costs were significantly pro-poor overall as well as during pre-treatment and treatment phase. All the concentration curves for catastrophic costs (due to pre-treatment, treatment and overall care) stratified by region (east, middle and west), area of residence (urban, rural) and type of insurance (new rural co-operative medical system [NCMS], non-NCMS) also exhibited a pro-poor pattern with statistically significant (P <  0.01) concentration indices. The pro-poor distribution of the catastrophic costs due to TB treatment was significantly more inequitable among rural, compared to urban patients, and NCMS compared to non-NCMS beneficiaries.

conclusionsThere is inequity in the distribution of catastrophic costs due to TB care. Universal health coverage, social protection strategies complemented by quality TB care is vital to reduce inequitable distribution of catastrophic costs due to TB care in China.

Indexed as

AdolescentAdultAgedAged, 80 and overCatastrophic IllnessChinaCross-Sectional StudiesFemaleHealth Care CostsHumansIncomeMaleMiddle AgedTuberculosis, PulmonaryYoung AdultCatastrophic health expenditure, tuberculosisEquityPatient costSocial protectionUniversal health coverage

Identifiers

PMID31215476
PMCPMC6582572

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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.