scholarly journals Case finding strategies under National Tuberculosis Elimination Programme (NTEP)

2020 ◽  
Vol 67 (4) ◽  
pp. S101-S106
Author(s):  
Varinder Saini ◽  
Kranti Garg
Keyword(s):  
Case Finding ◽  
National Tuberculosis ◽  
Elimination Programme

scholarly journals Active Case Finding for Tuberculosis in India: A Syntheses of Activities and Outcomes Reported by the National Tuberculosis Elimination Programme

2021 ◽  
Vol 6 (4) ◽  
pp. 206
Author(s):  
Sharath Burugina Nagaraja ◽  
Pruthu Thekkur ◽  
Srinath Satyanarayana ◽  
Prathap Tharyan ◽  
Karuna D. Sagili ◽  
...  
Keyword(s):  
Diagnostic Yield ◽  
Case Finding ◽  
Sputum Smear ◽  
X Rays ◽  
National Data ◽  
Community Based ◽  
Implementation Challenges ◽  
Number Needed To Screen ◽  
National Tuberculosis ◽  
Elimination Programme

India launched a national community-based active TB case finding (ACF) campaign in 2017 as part of the strategic plan of the National Tuberculosis Elimination Programme (NTEP). This review evaluated the outcomes for the components of the ACF campaign against the NTEP’s minimum indicators and elicited the challenges faced in implementation. We supplemented data from completed pretested data proformas returned by ACF programme managers from nine states and two union territories (for 2017–2019) and five implementing partner agencies (2013–2020), with summary national data on the state-wise ACF outcomes for 2018–2020 published in annual reports by the NTEP. The data revealed variations in the strategies used to map and screen vulnerable populations and the diagnostic algorithms used across the states and union territories. National data were unavailable to assess whether the NTEP indicators for the minimum proportions identified with presumptive TB among those screened (5%), those with presumptive TB undergoing diagnostic tests (>95%), the minimum sputum smear positivity rate (2% to 3%), those with negative sputum smears tested with chest X-rays or CBNAAT (>95%) and those diagnosed through ACF initiated on anti-TB treatment (>95%) were fulfilled. Only 30% (10/33) of the states in 2018, 23% (7/31) in 2019 and 21% (7/34) in 2020 met the NTEP expectation that 5% of those tested through ACF would be diagnosed with TB (all forms). The number needed to screen to diagnose one person with TB (NNS) was not included among the NTEP’s programme indicators. This rough indicator of the efficiency of ACF varied considerably across the states and union territories. The median NNS in 2018 was 2080 (interquartile range or IQR 517–4068). In 2019, the NNS was 2468 (IQR 1050–7924), and in 2020, the NNS was 906 (IQR 108–6550). The data consistently revealed that the states that tested a greater proportion of those screened during ACF and used chest X-rays or CBNAAT (or both) to diagnose TB had a higher diagnostic yield with a lower NNS. Many implementation challenges, related to health systems, healthcare provision and difficulties experienced by patients, were elicited. We suggest a series of strategic interventions addressing the implementation challenges and the six gaps identified in ACF outcomes and the expected indicators that could potentially improve the efficacy and effectiveness of community-based ACF in India.

scholarly journals Comparison of yield and relative costs of different screening algorithms for tuberculosis in active case-finding: a cross-section study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fei Zhao ◽  
Canyou Zhang ◽  
Chongguang Yang ◽  
Yinyin Xia ◽  
Jin Xing ◽  
...  
Keyword(s):  
Case Finding ◽  
Active Case Finding ◽  
Face To Face ◽  
Active Case ◽  
Symptom Screening ◽  
Significant Difference ◽  
National Tuberculosis ◽  
Study Population ◽  
The Difference ◽  
The Cost

Abstract Background Part of tuberculosis (TB) patients were missed if symptomatic screening was based on the main TB likely symptoms. This study conducted to compare the yield and relative costs of different TB screening algorithms in active case-finding in the whole population in China. Methods The study population was screened based on the TB likely symptoms through a face-to-face interview in selected 27 communities from 10 counties of 10 provinces in China. If the individuals had any of the enhanced TB likely symptoms, both chest X-ray and sputum tests were carried out for them furtherly. We used the McNemar test to analyze the difference in TB detection among four algorithms in active case-finding. Of four algorithms, two were from WHO recommendations including 1a/1c, one from China National Tuberculosis Program, and one from this study with the enhanced TB likely symptoms. Furthermore, a two-way ANOVA analysis was performed to analyze the cost difference in the performance of active case-finding adjusted by different demographic and health characteristics among different algorithms. Results Algorithm with the enhanced TB likely symptoms defined in this study could increase the yield of TB detection in active case-finding, compared with algorithms recommended by WHO (p < 0.01, Kappa 95% CI: 0. 93–0.99) and China NTP (p = 0.03, Kappa 95% CI: 0.96–1.00). There was a significant difference in the total costs among different three algorithms WHO 1c/2/3 (F = 59.13, p < 0.01). No significant difference in the average costs for one active TB case screened and diagnosed through the process among Algorithms 1c/2/3 was evident (F = 2.78, p = 0.07). The average costs for one bacteriological positive case through algorithm WHO 1a was about two times as much as the costs for one active TB case through algorithms WHO 1c/2/3. Conclusions Active case-finding based on the enhanced symptom screening is meaningful for TB case-finding and it could identify more active TB cases in time. The findings indicated that this enhanced screening approach cost more compared to algorithms recommend by WHO and China NTP, but the increased yield resulted in comparative costs per patient. And it cost much more that only smear/bacteriological-positive TB cases are screened in active case-finding.

scholarly journals Visceral Leishmaniasis Elimination Programme in India, Bangladesh, and Nepal: Reshaping the Case Finding/Case Management Strategy

2009 ◽  
Vol 3 (1) ◽  
pp. e355 ◽  
Author(s):  
Dinesh Mondal ◽  
Shri Prakash Singh ◽  
Narendra Kumar ◽  
Anand Joshi ◽  
Shyam Sundar ◽  
...  
Keyword(s):  
Visceral Leishmaniasis ◽  
Case Management ◽  
Management Strategy ◽  
Case Finding ◽  
Elimination Programme

scholarly journals How much do Indians pay for tuberculosis treatment? A cost analysis

2020 ◽  
Vol 10 (3) ◽  
pp. 110-117
Author(s):  
P. Sinha ◽  
M. Carwile ◽  
A. Bhargava ◽  
C. Cintron ◽  
C. Acuna-Villaorduna ◽  
...  
Keyword(s):  
Financial Distress ◽  
Indirect Costs ◽  
Direct Costs ◽  
Active Case Finding ◽  
Weighted Mean ◽  
Tb Treatment ◽  
Search Terms ◽  
National Tuberculosis ◽  
Elimination Programme ◽  
Diagnostic Delays

Setting: India’s National Tuberculosis Elimination Programme (NTEP) covers diagnostic and therapeutic costs of TB treatment. However, persons living with TB (PLWTB) continue to experience financial distress due to direct costs (payment for testing, treatment, travel, hospitalization, and nutritional supplements) and indirect costs (lost wages, loan interest, and cost of domestic helpers).Objective: To analyze the magnitude and pattern of TB-related costs from the perspective of Indian PLWTB.Design: We identified relevant articles using key search terms (‘tuberculosis,’ ‘India,’ ‘cost,’ ‘expenditures,’ ‘financing,’ ‘catastrophic’ and ‘out of pocket’) and calculated variance-weighted mean costs.Results: Indian patients incur substantial direct costs (mean: US$46.8). Mean indirect costs (US$666.6) constitute 93.4% of the net costs. Mean direct costs before diagnosis can be up to four-fold that of costs during treatment. Treatment in the private sector can result in costs up to six-fold higher than in government facilities. As many as one in three PLWTB in India experience catastrophic costs.Conclusion: PLWTB in India face high direct and indirect costs. Priority interventions to realize India’s goal of eliminating catastrophic costs from TB include decreasing diagnostic delays through active case finding, reducing the need for travel, improving awareness and perception of NTEP services, and ensuring sufficient reimbursement for inpatient TB care.

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