ArticleBMJ open2025
Economic evaluation of integrating nutritional support intervention in India's National Tuberculosis Elimination Programme: implications for low-income and middle-income countries.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to evaluate the cost-effectiveness of integrating nutritional support into India's National Tuberculosis Elimination Programme (NTEP) using the MUKTI initiative.
designEconomic evaluation.
settingPrimary data on the cost of delivering healthcare services, out-of-pocket expenditure and health-related quality of life among patients with tuberculosis (TB) were collected from Dhar district of Madhya Pradesh, India.
interventionIntegration of nutritional support (MUKTI initiative) into the NTEP of India. CONTROL: Routine standard of care in the NTEP of India. PRIMARY OUTCOME MEASURE: Incremental cost per quality-adjusted life year (QALY) gained.
methodsA mathematical model, combining a Markov model and a compartmental susceptible-infected-recovered model, was used to simulate outcomes for patients with pulmonary TB under NTEP and MUKTI protocols. Primary data collected from 2615 patients with TB, supplemented with estimates from published literature, were used to model progression of disease, treatment outcomes and community transmission dynamics over a 2-year time horizon. Health-related quality of life was assessed using the EuroQol 5-Dimension 5-Level scale. Costs to the health system and out-of-pocket expenditures were included. A multivariable probabilistic sensitivity analysis was undertaken to estimate the effect of joint parameter uncertainty. A scenario analysis explored outcomes without considering community transmission. Results are presented based on health-system and abridged societal perspectives.
resultsOver 2 years, patients in the NTEP plus MUKTI programme had higher life years (1.693 vs 1.622) and QALYs (1.357 vs 1.294) than those in NTEP alone, with increased health system costs (₹11 538 vs ₹6807 (US$139 vs US$82)). Incremental cost per life year gained and QALY gained were ₹67 164 (US$809) and ₹76 306 (US$919), respectively. At the per capita gross domestic product threshold of ₹161 500 (US$1946) for India, the MUKTI programme had a 99.9% probability of being cost-effective but exceeded the threshold when excluding community transmission.
conclusionThe findings highlight the potential benefits of a cost-effective, holistic approach that addresses socio-economic determinants such as nutrition. Reduction in community transmission is the driver of cost-effectiveness of nutritional interventions in patients with TB.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.