Performance of algorithms for tuberculosis active case finding in underserved high-prevalence settings in Cambodia: a cross-sectional study
Background: Most studies evaluate active case findings (ACF) for bacteriologically confirmed TB. Adapted diagnostic approaches are needed to identify cases with lower bacillary loads. Objectives: To assess the likelihood of diagnosing all forms of TB, including clinically diagnosed pulmonary and ext...
Main Authors: | , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2019-01-01
|
Series: | Global Health Action |
Subjects: | |
Online Access: | http://dx.doi.org/10.1080/16549716.2019.1646024 |
id |
doaj-7017f2df3ee94b9ca9b9a2339d79c001 |
---|---|
record_format |
Article |
spelling |
doaj-7017f2df3ee94b9ca9b9a2339d79c0012020-11-25T03:41:19ZengTaylor & Francis GroupGlobal Health Action1654-98802019-01-0112110.1080/16549716.2019.16460241646024Performance of algorithms for tuberculosis active case finding in underserved high-prevalence settings in Cambodia: a cross-sectional studyKimcheng Choun0Tom Decroo1Tan Eang Mao2Natalie Lorent3Lisanne Gerstel4Jacob Creswell5Andrew J. Codlin6Lutgarde Lynen7Sopheak Thai8Sihanouk Hospital Center of HOPEInstitute of Tropical MedicineNational Center for Tuberculosis and Leprosy ControlUniversity Hospitals LeuvenKIT HealthStop TB PartnershipStop TB PartnershipInstitute of Tropical MedicineSihanouk Hospital Center of HOPEBackground: Most studies evaluate active case findings (ACF) for bacteriologically confirmed TB. Adapted diagnostic approaches are needed to identify cases with lower bacillary loads. Objectives: To assess the likelihood of diagnosing all forms of TB, including clinically diagnosed pulmonary and extra-pulmonary TB, using different ACF algorithms in Cambodia. Methods: Clients were stratified into ‘high-risk’ (presumptive TB plus TB contact, or history of TB, or presumptive HIV infection; n = 12,337) and ‘moderate-risk’ groups (presumptive TB; n = 28,804). Sputum samples were examined by sputum smear microscopy (SSM) or Xpert MTB/RIF (Xpert). Initially, chest X-ray using a mobile radiography unit was a follow-up test after a negative sputum examination [algorithms A (Xpert/X-ray) and B (SSM/X-ray)]. Subsequently, all clients received an X-ray [algorithms C (X-ray+Xpert) and D (Xray+SSM/Xpert)]. X-rays were interpreted on the spot. Results: Between 25 August 2014 and 31 March 2016, 2217 (5.4%) cases with all forms of TB cases were diagnosed among 41,141 adults. The majority of TB cases (1488; 67.1%) were diagnosed using X-ray. When X-rays were taken and interpreted the same day the sputum was collected, same-day diagnosis more than doubled. Overall, the number needed to test (NNT) to diagnose one case was 18.6 (95%CI:17.9–19.2). In the high-risk group the NNT was lower [algorithm D: NNT = 17.3(15.9–18.9)] compared with the ‘moderate-risk group’ [algorithm D: NNT = 20.8(19.6–22.2)]. In the high-risk group the NNT was lower when using Xpert as an initial test [algorithm A: NNT = 12.2(10.8–13.9) or algorithm C: NNT = 11.2(9.6–13.0)] compared with Xpert as a follow-up test [algorithm D: NNT = 17.3(15.9–18.9)]. Conclusion: To diagnose all TB forms, X-ray should be part of the diagnostic algorithm. The combination of X-ray and Xpert testing for high-risk clients was the most effective ACF approach in this setting.http://dx.doi.org/10.1080/16549716.2019.1646024outreachchest x-rayxpert mtb/rifsputum smear microscopyclinical diagnosis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kimcheng Choun Tom Decroo Tan Eang Mao Natalie Lorent Lisanne Gerstel Jacob Creswell Andrew J. Codlin Lutgarde Lynen Sopheak Thai |
spellingShingle |
Kimcheng Choun Tom Decroo Tan Eang Mao Natalie Lorent Lisanne Gerstel Jacob Creswell Andrew J. Codlin Lutgarde Lynen Sopheak Thai Performance of algorithms for tuberculosis active case finding in underserved high-prevalence settings in Cambodia: a cross-sectional study Global Health Action outreach chest x-ray xpert mtb/rif sputum smear microscopy clinical diagnosis |
author_facet |
Kimcheng Choun Tom Decroo Tan Eang Mao Natalie Lorent Lisanne Gerstel Jacob Creswell Andrew J. Codlin Lutgarde Lynen Sopheak Thai |
author_sort |
Kimcheng Choun |
title |
Performance of algorithms for tuberculosis active case finding in underserved high-prevalence settings in Cambodia: a cross-sectional study |
title_short |
Performance of algorithms for tuberculosis active case finding in underserved high-prevalence settings in Cambodia: a cross-sectional study |
title_full |
Performance of algorithms for tuberculosis active case finding in underserved high-prevalence settings in Cambodia: a cross-sectional study |
title_fullStr |
Performance of algorithms for tuberculosis active case finding in underserved high-prevalence settings in Cambodia: a cross-sectional study |
title_full_unstemmed |
Performance of algorithms for tuberculosis active case finding in underserved high-prevalence settings in Cambodia: a cross-sectional study |
title_sort |
performance of algorithms for tuberculosis active case finding in underserved high-prevalence settings in cambodia: a cross-sectional study |
publisher |
Taylor & Francis Group |
series |
Global Health Action |
issn |
1654-9880 |
publishDate |
2019-01-01 |
description |
Background: Most studies evaluate active case findings (ACF) for bacteriologically confirmed TB. Adapted diagnostic approaches are needed to identify cases with lower bacillary loads. Objectives: To assess the likelihood of diagnosing all forms of TB, including clinically diagnosed pulmonary and extra-pulmonary TB, using different ACF algorithms in Cambodia. Methods: Clients were stratified into ‘high-risk’ (presumptive TB plus TB contact, or history of TB, or presumptive HIV infection; n = 12,337) and ‘moderate-risk’ groups (presumptive TB; n = 28,804). Sputum samples were examined by sputum smear microscopy (SSM) or Xpert MTB/RIF (Xpert). Initially, chest X-ray using a mobile radiography unit was a follow-up test after a negative sputum examination [algorithms A (Xpert/X-ray) and B (SSM/X-ray)]. Subsequently, all clients received an X-ray [algorithms C (X-ray+Xpert) and D (Xray+SSM/Xpert)]. X-rays were interpreted on the spot. Results: Between 25 August 2014 and 31 March 2016, 2217 (5.4%) cases with all forms of TB cases were diagnosed among 41,141 adults. The majority of TB cases (1488; 67.1%) were diagnosed using X-ray. When X-rays were taken and interpreted the same day the sputum was collected, same-day diagnosis more than doubled. Overall, the number needed to test (NNT) to diagnose one case was 18.6 (95%CI:17.9–19.2). In the high-risk group the NNT was lower [algorithm D: NNT = 17.3(15.9–18.9)] compared with the ‘moderate-risk group’ [algorithm D: NNT = 20.8(19.6–22.2)]. In the high-risk group the NNT was lower when using Xpert as an initial test [algorithm A: NNT = 12.2(10.8–13.9) or algorithm C: NNT = 11.2(9.6–13.0)] compared with Xpert as a follow-up test [algorithm D: NNT = 17.3(15.9–18.9)]. Conclusion: To diagnose all TB forms, X-ray should be part of the diagnostic algorithm. The combination of X-ray and Xpert testing for high-risk clients was the most effective ACF approach in this setting. |
topic |
outreach chest x-ray xpert mtb/rif sputum smear microscopy clinical diagnosis |
url |
http://dx.doi.org/10.1080/16549716.2019.1646024 |
work_keys_str_mv |
AT kimchengchoun performanceofalgorithmsfortuberculosisactivecasefindinginunderservedhighprevalencesettingsincambodiaacrosssectionalstudy AT tomdecroo performanceofalgorithmsfortuberculosisactivecasefindinginunderservedhighprevalencesettingsincambodiaacrosssectionalstudy AT taneangmao performanceofalgorithmsfortuberculosisactivecasefindinginunderservedhighprevalencesettingsincambodiaacrosssectionalstudy AT natalielorent performanceofalgorithmsfortuberculosisactivecasefindinginunderservedhighprevalencesettingsincambodiaacrosssectionalstudy AT lisannegerstel performanceofalgorithmsfortuberculosisactivecasefindinginunderservedhighprevalencesettingsincambodiaacrosssectionalstudy AT jacobcreswell performanceofalgorithmsfortuberculosisactivecasefindinginunderservedhighprevalencesettingsincambodiaacrosssectionalstudy AT andrewjcodlin performanceofalgorithmsfortuberculosisactivecasefindinginunderservedhighprevalencesettingsincambodiaacrosssectionalstudy AT lutgardelynen performanceofalgorithmsfortuberculosisactivecasefindinginunderservedhighprevalencesettingsincambodiaacrosssectionalstudy AT sopheakthai performanceofalgorithmsfortuberculosisactivecasefindinginunderservedhighprevalencesettingsincambodiaacrosssectionalstudy |
_version_ |
1724530387941588992 |