Evidence map›Paper›PMID 34388976›Full record

ArticleBMC infectious diseases2021

Comparison of yield and relative costs of different screening algorithms for tuberculosis in active case-finding: a cross-section study.

Fei Zhao, Canyou Zhang, Chongguang Yang, Yinyin Xia, Jin Xing, Guolong Zhang, Lin Xu, Xiaomeng Wang, Wei Lu, Jianwei Li and 11 more

Abstract read
In one paragraph

Article in BMC infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

21 authors.

Fei ZhaoNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China.
Canyou ZhangNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China.
Chongguang YangDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.
Yinyin XiaNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China.
Jin XingInstitute of Tuberculosis Control and Prevention, Henan Provincial Center for Disease Control and Prevention, Zhengzhou, Henan, People's Republic of China.
Guolong ZhangInstitute of Tuberculosis Control and Prevention, Henan Provincial Center for Disease Control and Prevention, Zhengzhou, Henan, People's Republic of China.
Lin XuDivision of Tuberculosis Control and Prevention, Yunnan Provincial Center for Disease Control and Prevention, Kunming, Yunnan, People's Republic of China.
Xiaomeng WangInstitute of TB Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, People's Republic of China.
Wei LuDepartment of Chronic Communicable Disease, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, Jiangsu, People's Republic of China.
Jianwei LiCenter for Tuberculosis Control of Guangdong Province, Guangzhou, Guangdong, People's Republic of China.
Feiying LiuGuangxi Provincial Center for Disease Control and Prevention, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China.
Dingwen LinGuangxi Provincial Center for Disease Control and Prevention, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China.
Jianlin WuSichuan Provincial Center for Disease Control and Prevention, Chengdu, Sichuan, People's Republic of China.
Xin ShenShanghai Municipal Center for Disease Control and Prevention, Shanghai, People's Republic of China.
Shuangyi HouHubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, People's Republic of China.
Yanling YuHeilongjiang Provincial Center for Tuberculosis Control and Prevention, Harbin, Heilongjiang, People's Republic of China.
Dongmei HuNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China.
Chunyi FuDepartment of Emergency Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, People's Republic of China.
Lixia WangNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China.
Jun ChengNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China. chengjun@chinacdc.cn.ORCID http://orcid.org/0000-0001-7952-4517
Hui ZhangNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China. zhanghui@chinacdc.cn.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
National Major Science and Technology Projects of China 2013ZX10003-004-001National Major Science and Technology Projects of China 2017ZX10201-302-001NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

backgroundPart of tuberculosis (TB) patients were missed if symptomatic screening was based on the main TB likely symptoms. This study conducted to compare the yield and relative costs of different TB screening algorithms in active case-finding in the whole population in China.

methodsThe study population was screened based on the TB likely symptoms through a face-to-face interview in selected 27 communities from 10 counties of 10 provinces in China. If the individuals had any of the enhanced TB likely symptoms, both chest X-ray and sputum tests were carried out for them furtherly. We used the McNemar test to analyze the difference in TB detection among four algorithms in active case-finding. Of four algorithms, two were from WHO recommendations including 1a/1c, one from China National Tuberculosis Program, and one from this study with the enhanced TB likely symptoms. Furthermore, a two-way ANOVA analysis was performed to analyze the cost difference in the performance of active case-finding adjusted by different demographic and health characteristics among different algorithms.

resultsAlgorithm with the enhanced TB likely symptoms defined in this study could increase the yield of TB detection in active case-finding, compared with algorithms recommended by WHO (p < 0.01, Kappa 95% CI: 0. 93-0.99) and China NTP (p = 0.03, Kappa 95% CI: 0.96-1.00). There was a significant difference in the total costs among different three algorithms WHO 1c/2/3 (F = 59.13, p < 0.01). No significant difference in the average costs for one active TB case screened and diagnosed through the process among Algorithms 1c/2/3 was evident (F = 2.78, p = 0.07). The average costs for one bacteriological positive case through algorithm WHO 1a was about two times as much as the costs for one active TB case through algorithms WHO 1c/2/3.

conclusionsActive case-finding based on the enhanced symptom screening is meaningful for TB case-finding and it could identify more active TB cases in time. The findings indicated that this enhanced screening approach cost more compared to algorithms recommend by WHO and China NTP, but the increased yield resulted in comparative costs per patient. And it cost much more that only smear/bacteriological-positive TB cases are screened in active case-finding.

Indexed as

AdolescentAdultAgedAged, 80 and overAlgorithmsChildChinaCross-Sectional StudiesFemaleHumansInfantInfant, NewbornMaleMass ScreeningMiddle AgedSputumActive case-findingCost-effectivenessScreeningTuberculosis

Identifiers

PMID34388976
PMCPMC8361931

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.