TY - JOUR
T1 - A systematic review and meta-analysis of active case finding for tuberculosis in India
T2 - Active case finding for TB in India
AU - Garg, Tushar
AU - Chaisson, Lelia H.
AU - Naufal, Fahd
AU - Shapiro, Adrienne E.
AU - Golub, Jonathan E.
N1 - Funding Information:
We acknowledge Cecily R. Miller at the World Health Organization for her inputs. We acknowledge Lori Rosman at the Welch Medical Library, Johns Hopkins University, and Pamela Delgado-Barroso and Hector Alvarez-Manzo at the Johns Hopkins University for their assistance with the database search. TG acknowledges the support of Fulbright-Nehru Master's Fellowship. AES is supported by a National Institutes of Health (NIH) grant K23AI140918.
Funding Information:
We acknowledge Cecily R. Miller at the World Health Organization for her inputs. We acknowledge Lori Rosman at the Welch Medical Library, Johns Hopkins University, and Pamela Delgado-Barroso and Hector Alvarez-Manzo at the Johns Hopkins University for their assistance with the database search. TG acknowledges the support of Fulbright-Nehru Master's Fellowship. AES is supported by a National Institutes of Health (NIH) grant K23AI140918.
Publisher Copyright:
© 2022 The Author(s)
PY - 2022/12
Y1 - 2022/12
N2 - Background: Active case finding (ACF) for tuberculosis (TB) is the cornerstone case-finding strategy in India's national TB policy. However, ACF strategies are highly diverse and pose implementation challenges in routine programming. We reviewed the literature to characterise ACF in India; assess the yield of ACF for different risk groups, screening locations, and screening criteria; and estimate losses to follow-up (LTFU) in screening and diagnosis. Methods: We searched PubMed, EMBASE, Scopus, and the Cochrane library to identify studies with ACF for TB in India from November 2010 to December 2020. We calculated 1) weighted mean number needed to screen (NNS) stratified by risk group, screening location, and screening strategy; and 2) the proportion of screening and pre-diagnostic LTFU. We assessed risk of bias using the AXIS tool for cross-sectional studies. Findings: Of 27,416 abstracts screened, we included 45 studies conducted in India. Most studies were from southern and western India and aimed to diagnose pulmonary TB at the primary health level in the public sector after screening. There was considerable heterogeneity in risk groups screened and ACF methodology across studies. Of the 17 risk groups identified, the lowest weighted mean NNS was seen in people with HIV (21, range 3–89, n=5), tribal populations (50, range 40–286, n=3), household contacts of people with TB (50, range 3-undefined, n=12), people with diabetes (65, range 21-undefined, n=3), and rural populations (131, range 23–737, n=5). ACF at facility-based screening (60, range 3-undefined, n=19) had lower weighted mean NNS than at other screening locations. Using the WHO symptom screen (135, 3-undefined, n=20) had lower weighted mean NNS than using criteria of abnormal chest x-ray or any symptom. Median screening and pre-diagnosis loss-to-follow-up was 6% (IQR 4.1%, 11.3%, range 0–32.5%, n=12) and 9.5% (IQR 2.4%, 34.4%, range 0–86.9%, n=27), respectively. Interpretation: For ACF to be impactful in India, its design must be based on contextual understanding. The narrow evidence base available currently is insufficient for effectively targeting ACF programming in a large and diverse country. Achieving case-finding targets in India requires evidence-based ACF implementation. Funding: WHO Global TB Programme.
AB - Background: Active case finding (ACF) for tuberculosis (TB) is the cornerstone case-finding strategy in India's national TB policy. However, ACF strategies are highly diverse and pose implementation challenges in routine programming. We reviewed the literature to characterise ACF in India; assess the yield of ACF for different risk groups, screening locations, and screening criteria; and estimate losses to follow-up (LTFU) in screening and diagnosis. Methods: We searched PubMed, EMBASE, Scopus, and the Cochrane library to identify studies with ACF for TB in India from November 2010 to December 2020. We calculated 1) weighted mean number needed to screen (NNS) stratified by risk group, screening location, and screening strategy; and 2) the proportion of screening and pre-diagnostic LTFU. We assessed risk of bias using the AXIS tool for cross-sectional studies. Findings: Of 27,416 abstracts screened, we included 45 studies conducted in India. Most studies were from southern and western India and aimed to diagnose pulmonary TB at the primary health level in the public sector after screening. There was considerable heterogeneity in risk groups screened and ACF methodology across studies. Of the 17 risk groups identified, the lowest weighted mean NNS was seen in people with HIV (21, range 3–89, n=5), tribal populations (50, range 40–286, n=3), household contacts of people with TB (50, range 3-undefined, n=12), people with diabetes (65, range 21-undefined, n=3), and rural populations (131, range 23–737, n=5). ACF at facility-based screening (60, range 3-undefined, n=19) had lower weighted mean NNS than at other screening locations. Using the WHO symptom screen (135, 3-undefined, n=20) had lower weighted mean NNS than using criteria of abnormal chest x-ray or any symptom. Median screening and pre-diagnosis loss-to-follow-up was 6% (IQR 4.1%, 11.3%, range 0–32.5%, n=12) and 9.5% (IQR 2.4%, 34.4%, range 0–86.9%, n=27), respectively. Interpretation: For ACF to be impactful in India, its design must be based on contextual understanding. The narrow evidence base available currently is insufficient for effectively targeting ACF programming in a large and diverse country. Achieving case-finding targets in India requires evidence-based ACF implementation. Funding: WHO Global TB Programme.
KW - ACF
KW - Active case finding
KW - India
KW - Number needed to screen
KW - Screening
KW - Tuberculosis
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U2 - 10.1016/j.lansea.2022.100076
DO - 10.1016/j.lansea.2022.100076
M3 - Article
C2 - 37383930
AN - SCOPUS:85150861954
SN - 2772-3682
VL - 7
JO - The Lancet Regional Health - Southeast Asia
JF - The Lancet Regional Health - Southeast Asia
M1 - 100076
ER -