Evidence map›Paper›PMID 28594824›Full record

ArticlePloS one2017

Comparing yield and relative costs of WHO TB screening algorithms in selected risk groups among people aged 65 years and over in China, 2013.

Canyou Zhang, Yunzhou Ruan, Jun Cheng, Fei Zhao, Yinyin Xia, Hui Zhang, Ewan Wilkinson, Mrinalini Das, Jie Li, Wei Chen and 3 more

Abstract readComparative Study
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

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

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  11. The Impact of Funding on Childhood TB Case Detection in Pakistan.Tropical medicine and infectious disease · 2019
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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

13 authors.

Canyou ZhangNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Yunzhou RuanNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Jun ChengNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Fei ZhaoNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Yinyin XiaNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Hui ZhangNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Ewan WilkinsonInstitute of Medicine, University of Chester, Chester, United Kingdom.
Mrinalini DasMédecins Sans Frontières (MSF), Delhi, India.
Jie LiNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Wei ChenNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Dongmei HuNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Kathiresan JeyashreeVelammal Medical College Hospital & Research Institute, Madurai, India.
Lixia WangNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo calculate the yield and cost per diagnosed tuberculosis (TB) case for three World Health Organization screening algorithms and one using the Chinese National TB program (NTP) TB suspect definitions, using data from a TB prevalence survey of people aged 65 years and over in China, 2013.

methodsThis was an analytic study using data from the above survey. Risk groups were defined and the prevalence of new TB cases in each group calculated. Costs of each screening component were used to give indicative costs per case detected. Yield, number needed to screen (NNS) and cost per case were used to assess the algorithms.

findingsThe prevalence survey identified 172 new TB cases in 34,250 participants. Prevalence varied greatly in different groups, from 131/100,000 to 4651/ 100,000. Two groups were chosen to compare the algorithms. The medium-risk group (living in a rural area: men, or previous TB case, or close contact or a BMI <18.5, or tobacco user) had appreciably higher cost per case (USD 221, 298 and 963) in the three algorithms than the high-risk group (all previous TB cases, all close contacts). (USD 72, 108 and 309) but detected two to four times more TB cases in the population. Using a Chest x-ray as the initial screening tool in the medium risk group cost the most (USD 963), and detected 67% of all the new cases. Using the NTP definition of TB suspects made little difference.

conclusionsTo "End TB", many more TB cases have to be identified. Screening only the highest risk groups identified under 14% of the undetected cases,. To "End TB", medium risk groups will need to be screened. Using a CXR for initial screening results in a much higher yield, at what should be an acceptable cost.

Indexed as

AlgorithmsHealth Care CostsMass ScreeningWorld Health OrganizationAgedChinaDemographyFemaleHealth Care SurveysHumansMalePrevalenceRisk FactorsTuberculosis

Identifiers

PMID28594824
PMCPMC5464530

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Registered trials

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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.