F Naufal1, L H Chaisson2, K O Robsky3, P Delgado-Barroso3, H S Alvarez-Manzo4, C R Miller5, A E Shapiro6, J E Golub7. 1. Department of Medicine, Johns Hopkins University, Baltimore, MD. 2. Division of Infectious Diseases, Department of Medicine, University of Illinois at Chicago, Chicago, IL. 3. Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA. 4. Department of Molecular Microbiology and Immunology, Johns Hopkins University, Baltimore, MD, USA. 5. World Health Organization, Geneva, Switzerland. 6. Departments of Global Health and Medicine, University of Washington, Seattle, WA. 7. Division of Infectious Diseases, Department of Medicine, University of Illinois at Chicago, Chicago, IL, Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA, Department of International Health, Johns Hopkins University, Baltimore, MD, USA.
Abstract
BACKGROUND: Screening for active TB using active case-finding (ACF) may reduce TB incidence, prevalence, and mortality; however, yield of ACF interventions varies substantially across populations. We systematically reviewed studies reporting on ACF to calculate the number needed to screen (NNS) for groups at high risk for TB. METHODS: We conducted a literature search for studies reporting ACF for adults published between November 2010 and February 2020. We determined active TB prevalence detected through various screening strategies and calculated crude NNS for - TB confirmed using culture or Xpert® MTB/RIF, and weighted mean NNS stratified by screening strategy, risk group, and country-level TB incidence. RESULTS: We screened 27,223 abstracts; 90 studies were included (41 in low/moderate and 49 in medium/high TB incidence settings). High-risk groups included inpatients, outpatients, people living with diabetes (PLWD), migrants, prison inmates, persons experiencing homelessness (PEH), healthcare workers, and miners. Screening strategies included symptom-based screening, chest X-ray and Xpert testing. NNS varied widely across and within incidence settings based on risk groups and screening methods. Screening tools with higher sensitivity (e.g., Xpert, CXR) were associated with lower NNS estimates. CONCLUSIONS: NNS for ACF strategies varies substantially between adult risk groups. Specific interventions should be tailored based on local epidemiology and costs.
BACKGROUND: Screening for active TB using active case-finding (ACF) may reduce TB incidence, prevalence, and mortality; however, yield of ACF interventions varies substantially across populations. We systematically reviewed studies reporting on ACF to calculate the number needed to screen (NNS) for groups at high risk for TB. METHODS: We conducted a literature search for studies reporting ACF for adults published between November 2010 and February 2020. We determined active TB prevalence detected through various screening strategies and calculated crude NNS for - TB confirmed using culture or Xpert® MTB/RIF, and weighted mean NNS stratified by screening strategy, risk group, and country-level TB incidence. RESULTS: We screened 27,223 abstracts; 90 studies were included (41 in low/moderate and 49 in medium/high TB incidence settings). High-risk groups included inpatients, outpatients, people living with diabetes (PLWD), migrants, prison inmates, persons experiencing homelessness (PEH), healthcare workers, and miners. Screening strategies included symptom-based screening, chest X-ray and Xpert testing. NNS varied widely across and within incidence settings based on risk groups and screening methods. Screening tools with higher sensitivity (e.g., Xpert, CXR) were associated with lower NNS estimates. CONCLUSIONS: NNS for ACF strategies varies substantially between adult risk groups. Specific interventions should be tailored based on local epidemiology and costs.
Authors: Julius Matthias Weinrich; Roland Diel; Markus Sauer; Frank Oliver Henes; Karen Meywald-Walter; Gerhard Adam; Gerhard Schön; Peter Bannas Journal: Eur Radiol Date: 2017-01-03 Impact factor: 5.315
Authors: Robert W Aldridge; Dominik Zenner; Peter J White; Morris C Muzyamba; Miranda Loutet; Poonam Dhavan; Davide Mosca; Andrew C Hayward; Ibrahim Abubakar Journal: Lancet Infect Dis Date: 2016-03-21 Impact factor: 25.071
Authors: L Telisinghe; M Ruperez; M Amofa-Sekyi; L Mwenge; T Mainga; R Kumar; M Hassan; L H Chaisson; F Naufal; A E Shapiro; J E Golub; C Miller; E L Corbett; R M Burke; P MacPherson; R J Hayes; V Bond; C Daneshvar; E Klinkenberg; H M Ayles Journal: EClinicalMedicine Date: 2021-09-22