Number needed to screen for TB in clinical, structural or occupational risk groups
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...
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Published in: | The international journal of tuberculosis and lung disease Vol. 26; no. 6; pp. 500 - 508 |
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Main Authors: | , , , , , , , |
Format: | Journal Article |
Language: | English |
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France
International Union Against Tuberculosis and Lung Disease
01-06-2022
International Union against Tuberculosis and Lung Disease (IUATLD) |
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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. |
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AbstractList | 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. 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. 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. 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. 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. 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. |
Author | Miller, C. R. Delgado-Barroso, P. Shapiro, A. E. Chaisson, L. H. Robsky, K. O. Golub, J. E. Naufal, F. Alvarez-Manzo, H. S. |
AuthorAffiliation | 6 Departments of Global Health and Medicine, University of Washington, Seattle, WA 2 Division of Infectious Diseases, Department of Medicine, University of Illinois at Chicago, Chicago, IL 4 Department of Molecular Microbiology and Immunology, Johns Hopkins University, Baltimore, MD, USA 7 Department of International Health, Johns Hopkins University, Baltimore, MD, USA 1 Department of Medicine, Johns Hopkins University, Baltimore, MD 5 World Health Organization, Geneva, Switzerland 3 Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA |
AuthorAffiliation_xml | – name: 1 Department of Medicine, Johns Hopkins University, Baltimore, MD – name: 5 World Health Organization, Geneva, Switzerland – name: 6 Departments of Global Health and Medicine, University of Washington, Seattle, WA – name: 7 Department of International Health, Johns Hopkins University, Baltimore, MD, USA – name: 4 Department of Molecular Microbiology and Immunology, Johns Hopkins University, Baltimore, MD, USA – name: 3 Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA – name: 2 Division of Infectious Diseases, Department of Medicine, University of Illinois at Chicago, Chicago, IL |
Author_xml | – sequence: 1 givenname: F. surname: Naufal fullname: Naufal, F. organization: Department of Medicine, Johns Hopkins University, Baltimore, MD – sequence: 2 givenname: L. H. surname: Chaisson fullname: Chaisson, L. H. organization: Division of Infectious Diseases, Department of Medicine, University of Illinois at Chicago, Chicago, IL – sequence: 3 givenname: K. O. surname: Robsky fullname: Robsky, K. O. organization: Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA – sequence: 4 givenname: P. surname: Delgado-Barroso fullname: Delgado-Barroso, P. organization: Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA – sequence: 5 givenname: H. S. surname: Alvarez-Manzo fullname: Alvarez-Manzo, H. S. organization: Department of Molecular Microbiology and Immunology, Johns Hopkins University, Baltimore, MD, USA – sequence: 6 givenname: C. R. surname: Miller fullname: Miller, C. R. organization: World Health Organization, Geneva, Switzerland – sequence: 7 givenname: A. E. surname: Shapiro fullname: Shapiro, A. E. organization: Departments of Global Health and Medicine, University of Washington, Seattle, WA – sequence: 8 givenname: J. E. surname: Golub fullname: Golub, J. E. organization: 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 |
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Copyright | Copyright International Union against Tuberculosis and Lung Disease (IUATLD) Jun 2022 |
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Snippet | BACKGROUND: Screening for active TB using active case-finding (ACF) may reduce TB incidence, prevalence, and mortality; however, yield of ACF interventions... Screening for active TB using active case-finding (ACF) may reduce TB incidence, prevalence, and mortality; however, yield of ACF interventions varies... BACKGROUND: Screening for active TB using active case-finding (ACF) may reduce TB incidence, prevalence, and mortality; however, yield of ACF interventions... |
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SubjectTerms | Active Case Finding Adult Diabetes mellitus Epidemiology Homelessness Humans Incidence Mass Screening - methods Mathematical analysis Medical personnel Number Needed To Screen Population studies Prevalence Prisoners Prisons Risk Risk Groups Screening Tuberculosis Tuberculosis, Pulmonary - diagnosis Tuberculosis, Pulmonary - epidemiology |
Title | Number needed to screen for TB in clinical, structural or occupational risk groups |
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