To develop predictive models for early and overall tuberculosis (TB) deaths for prospective use at TB diagnosis in resource-constrained TB programme settings.

Statewide cohort study using routinely captured secondary data.

With the majority of TB deaths being early (within 2 months), India’s TB programme’s information management system (Ni-kshay)-dependent death prediction models (using age, gender, TB site, previous treatment, microbiological confirmation, HIV, diabetes and bank account availability) are not feasible for prospective use, as few variables are captured at diagnosis. Utilising routinely captured triage variables for severe illness at diagnosis (body mass index, pedal oedema, respiratory rate, oxygen saturation and ability to stand without support) from an ongoing statewide and state-specific differentiated TB care initiative to reduce TB deaths in Tamil Nadu state (southern India, 80 million population with 0.1 million annual notifications), robust models for prospective use were developed.

Adults (aged ≥15 years) with TB (not known to be drug-resistant at diagnosis) that were notified from public facilities of Tamil Nadu from July 2022 to June 2023.

Early and overall (within 12 months of notification) TB deaths. Area under the receiver operating characteristic curve (AUC) was used to assess accuracy of models built using modified Poisson regression.

Among 55 971 adults, the overall death rate was 7.4%, and 67.9% of the deaths were early. In predicting overall deaths, accuracy of the model using all Ni-kshay variables (AUC 0.716 (95% CI 0.707 to 0.725)) was as good as the model using triage variables for severe illness only (AUC 0.701 (95% CI 0.691 to 0.711)). To the latter, adding potentially capturable Ni-kshay variables at diagnosis (age, gender, TB site, previous treatment and microbiological confirmation) significantly improved model accuracy (AUC 0.754 (95% CI 0.745 to 0.763)). Further addition of remaining Ni-kshay variables did not improve accuracy significantly. Death prediction equations were generated for these models.

Simple and easily measurable triage variables for severe illness should be routinely captured at TB diagnosis. A death prediction calculator (http://44.208.93.99/) based on these variables (specifically triage variables for severe illness combined with age, gender, TB site, previous treatment and microbiological confirmation) may be used by Indian states and high TB burden countries seeking scalable, data-driven interventions to reduce TB deaths.

Dietary modification, particularly low-carbohydrate diet, and diabetes self-management education (DSME) have shown promise in improving glycaemic control among persons with type 2 diabetes mellitus (T2DM). However, real-world evidence from India is limited. This protocol describes the methods of a cluster randomised trial to determine the effectiveness and feasibility of adopting a low-carbohydrate diet among persons with T2DM.

Our cluster-randomised trial with a mixed-method process evaluation will use computer-generated block randomisation sequence to randomise Urban Primary Health Centres (UPHCs) (n=16) to either continue delivering the usual guideline-based care under the National Programme for Prevention and Control of Non-Communicable Diseases (NPNCD) or our study intervention. The study intervention will comprise a personalised nutrition counselling focusing on (i) low-carbohydrate diet (

We will include persons with T2DM, over the age of 30 years and above, irrespective of comorbidities, registered in the selected UPHC under care for diabetes for at least a month and with an glycated haemoglobin (HbA1c) level ≥6.5% during the screening test. We will collect data electronically using semistructured questionnaires and measure HbA1c, blood pressure, lipid profile, serum creatinine and body weight at baseline, 3, 6, 9 and 12 months after enrolment. We will use a difference in difference analysis, adjusted for clustering, to compare the change in HbA1c at the follow-up visits compared with baseline across the two study arms. We will conduct both intention-to-treat and per-protocol analysis, exploring reasons for differences in effect size.

The study protocol was reviewed and approved by the Scientific Advisory Committee/Institutional Human Ethics Committee of the research institution (NIE/IHEC/202302-03). The findings of this study will be disseminated through publication in peer-reviewed journals.

Clinical Trials Registry-India (CTRI/2024/02/062202).