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Background: Reducing malnutrition through food supplementation is a critical component of the WHO End Tuberculosis (TB) strategy. A results-based financing (RBF) initiative in Madhya Pradesh, India—called Mukti—introduced an intensive nutrition intervention, including home visits, counseling, food basket distribution, and assistance in obtaining government benefits. Phase 1 of the program (Dhar District), implemented by ChildFund India (ChildFund) and funded by USAID, coincided with the COVID-19 lockdown in 2020. Under an RBF reimbursement scheme, ChildFund was paid based on treatment retention for 6 months and weight gain of 6 kg for adults. Methods: The evaluation used a mixed methods approach. Qualitative components included interviews with key informants and focus groups with program participants. Quantitative components included an analysis of program data (i.e., patient demographics, receipt of program services, and weight gain). An impact analysis of retention in treatment used data from a government database. A difference-in-differences model was used to compare results from baseline data and the program period for Dhar District to similar data for the adjacent Jhabua District. Results: The program was well implemented and appreciated by patients and providers. Patients received an average of 10.2 home visits and 6.2 food baskets. While all age and sex groups gained weight significantly over their 6-month treatment period, there was no program impact on treatment retention. Seventy-six percent of patients achieved both outcome goals. And though average program costs were under budget, ChildFund experienced a loss in the results-based financing scheme, which was covered by USAID to continue program expansion. Conclusions: Implementing a nutrition supplementation and education program for TB patients in India is feasible. The intervention improved weight gain despite COVID-19-related lockdowns. The Mukti program did not impact treatment retention, which was already high at baseline. Program costs were modest, but the results-based financing reimbursement scheme resulted in a loss for the implementer. Overall, the RBF model led to an increased focus on outcomes for program staff and other stakeholders, which led to more efficient service delivery. Future research should examine total costs (including donated staff time) more extensively to determine the cost-effectiveness of Mukti and similar interventions. [ABSTRACT FROM AUTHOR]

India has been implementing active case-finding (ACF) for TB among marginalised and vulnerable (high-risk) populations since 2017. The effectiveness of ACF cycle(s) is dependent on the use of appropriate screening and diagnostic tools and meeting quality indicators. To determine the number of ACF cycles implemented in 2021 at national, state (n = 36) and district (n = 768) level and quality indicators for the first ACF cycle. In this descriptive study, aggregate TB program data for each ACF activity that was extracted was further aggregated against each ACF cycle at the district level in 2021. One ACF cycle was the period identified to cover all the high-risk populations in the district. Three TB ACF quality indicators were calculated: percentage population screened (≥10%), percentage tested among screened (≥4.8%) and percentage diagnosed among tested (≥5%). We also calculated the number needed to screen (NNS) for diagnosing one person with TB (≤1538). Of 768 TB districts, ACF data for 111 were not available. Of the remaining 657 districts, 642 (98%) implemented one, and 15 implemented two to three ACF cycles. None of the districts or states met all three TB ACF quality indicators' cut-offs. At the national level, for the first ACF cycle, 9.3% of the population were screened, 1% of the screened were tested and 3.7% of the tested were diagnosed. The NNS was 2824: acceptable (≤1538) in institutional facilities and poor for population-based groups. Data were not consistently available to calculate the percentage of i) high-risk population covered, ii) presumptive TB among screened and iii) tested among presumptive. In 2021, India implemented one ACF cycle with sub-optimal ACF quality indicators. Reducing the losses between screening and testing, improving data quality and sensitising stakeholders regarding the importance of meeting all ACF quality indicators are recommended. [ABSTRACT FROM AUTHOR]