Evidence map›Paper›PMID 29037293›Full record

ArticleThe international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease2017

Community-based active case finding for tuberculosis in rural western China: a cross-sectional study.

C Chen, C-G Yang, X Gao, Z-Z Lu, F-X Tang, J Cheng, Q Gao, V Cárdenas

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 5 pooled it
1.1field-weighted citation impact, top 23% of its field
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

13 citing papers in PubMed, 5 syntheses or guidelines pooled it, 19 citations in OpenAlex.

  1. Active Case Finding for Tuberculosis in Migrants: a Systematic Review.Medical archives (Sarajevo, Bosnia and Herzegovina) · 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 at 7 institutions in 5 countries.

C ChenAeras, Beijing, China, Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
C-G YangKey Laboratory of Medical Molecular Virology of Ministries of Education and Health, School of Basic Medical Science, Fudan University, Shanghai, China, Department of Epidemiology of Microbial Diseases, School of Public Health, Yale University, New Haven, Connecticut, USA.
X GaoDivision of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany, Key Laboratory of Medical Molecular Virology of Ministries of Education and Health, School of Basic Medical Science, Fudan University, Shanghai, China.
Z-Z LuGuangxi Pingguo Center for Disease Control and Prevention, Pingguo, Guangxi.
F-X TangSichuan Wusheng Center for Disease Control and Prevention, Wusheng, Sichuan.
J ChengChinese Center for Disease Control and Prevention, Beijing, China.
Q GaoKey Laboratory of Medical Molecular Virology of Ministries of Education and Health, School of Basic Medical Science, Fudan University, Shanghai, China.
V CárdenasAeras, Rockville, Maryland, USA, The Aurum Institute, Johannesburg, South Africa.
Fudan University · CNAurum Institute · ZACenter for Disease Control · TWChinese Center For Disease Control and Prevention · CNGerman Cancer Research Center · DEHeidelberg University · DESichuan Center for Disease Control and Prevention · CN

Funding

University of California Launching Future Leaders in Global Health Research Training ProgramD43TW009343 · FIC · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CRAIG R COHEN, Sung-Jae Lee · 2017 to 2026
$14.7M
University of California Global Health Institute Program for Fellows and ScholarsR25TW009343 · FIC · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI COHEN, CRAIG R, STRATHDEE, STEFFANIE A. · 2012 to 2017
$6.4M
FIC NIH HHS D43 TW009343FIC NIH HHS R25 TW009343
6 · The paper itself

Abstract

settingCurrent passive case finding strategies are not effective at identifying tuberculosis (TB) patients in rural China.

objectiveTo evaluate a community-based, active case finding (ACF) scheme in identifying symptomatic individuals with TB.

designWe conducted door-to-door household visits of all residents aged 15 years at two rural sites to screen for TB symptoms. Individuals with symptoms were enrolled and asked to provide three sputum samples. All participants underwent chest X-ray, and microbiologic detection of Mycobacterium tuberculosis from sputum samples using microscopy, solid culture and Xpert® MTB/RIF was performed.

resultsAmong the 19 334 residents screened for TB symptoms, 865 (4.5%) reported having 1 symptom. A total of 52 TB cases were detected, 11 of whom had microbiologic confirmation. Xpert identified all five M. tuberculosis culture-positive cases and yielded an additional three diagnoses. Prevalence of newly detected TB at the two sites through ACF was respectively 475 and 196 per 100 000 population. These estimates are respectively four and eight times, on average, higher than those identified through passive surveillance during the previous 5-year period for the two sites.

conclusionCommunity-based symptom screening followed by laboratory tests was found to be feasible and effective in increasing TB case finding in rural China.

Indexed as

Rural PopulationAdultAgedBacteriological TechniquesChinaCross-Sectional StudiesFemaleHumansMaleMass ScreeningMicroscopyMiddle AgedMycobacterium tuberculosisPrevalenceSputumTuberculosis

Identifiers

PMID29037293
PMCPMC5901655
OpenAlexW2765366657

What OpenQuestion holds

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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.