Tuberculosis is the leading cause of death from a single infectious agent, and among the top 10 causes of death, according to the WHO. In 2024, over a million people died from TB, making it a significant public health challenge. The disease burden, however, is skewed. Over a quarter of the world’s estimated TB cases are in India — each year, approximately 2.7 million new cases are being reported.
Despite the country making substantial progress in recent years, the scale of the problem remains immense. Those living with the disease may have decreased by 21 percent in 2024 — nearly twice the global rate of decline — but over 3 lakh people are still succumbing to it every year.
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Public health experts suggest that drugs and diagnostics apart, tuberculosis patients need nutritious meals; undernutrition weakens immune responses and slows recovery from disease. For patients undergoing long TB treatment regimens spread over months, poor nutrition can also make it harder to adhere to medication schedules, or even complete the therapy.
In India, the overlap between TB and undernutrition is significant. In 2022 alone, nearly 7.44 lakh TB patients were estimated to be undernourished. To ensure TB patients eat well, each month, the government transfers Rs 1,000 to their accounts. Since 2018, the government has transferred ₹4,400 crore to over 1.37 crore beneficiaries. But policy measures such as these demand measurable outcomes. Does the cash transfer ensure TB patients eat well, and ultimately, help them recover?
It’s a question mathematical modelling can help answer.
During COVID-19, such models helped public health experts anticipate how COVID could spread among different populations. Now, using similar approaches, researchers including this author are trying to quantify the impact of cash transfers on tuberculosis patients — by translating policy mechanisms into measurable parameters, and exploring questions that are difficult to answer through observational data alone.
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Effective policymaking on tuberculosis
Mathematical models are commonly used in epidemiology to understand how diseases spread and interventions influence outcomes. However, social support programs such as nutritional assistance are rarely incorporated directly into these models.
Instead, they are often treated as background conditions or external assumptions.
The current study, done in partnership with Indian Institute of Public Health, Delhi, takes a different approach by explicitly integrating nutrition support into the dynamics of tuberculosis treatment outcomes. The goal is to examine how timely payments can influence treatment success, common issues such losing patients to follow-up, and deaths.
What sets this work apart from similar modelling efforts is that it incorporates real world scenarios in program implementation. Typically, epidemiological models are designed with ideal conditions in mind. But once programs are implemented, several issues crop up — payments get delayed, coverage and patient experiences are rarely homogenous.
Our team’s model includes such issues — partial coverage of the nutrition scheme, delays in transfers, and the different experiences of patients in responding to nutritional assistance.
By accounting for these uncertainties, the attempt is to be as close to the actual functioning of the program, rather than a theoretical best-case scenario.
Besides, this model emphasises quantities that policymakers can use directly, instead of abstract variables. For example, values such as the proportion of patients receiving nutritional support on time, or the degree to which improved nutrition affects their ability to stick to the treatment schedules, allow researchers to test practical questions that decision-makers face. Is it, for instance, more effective to expand coverage of nutrition payments, or to improve the speed with which payments reach patients? Or, which operational improvements could produce the greatest reduction in treatment failures or deaths?
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Delays cost lives of tuberculosis patients
The research is ongoing but early findings reveal important issues that need to be tackled.
First, delays in payments can reduce treatment success rates; good food strengthens immune function, and helps patients maintain the stability needed to adhere to long treatment regimens. Besides, nutritional support also encourages patients to remain engaged with treatment. Thus, delays in benefit transfers are not merely bureaucratic issues, and could well be translated into measurable health consequences.
Second, the model indicates that small gaps in coverage, or number of beneficiaries, can sometimes lead to disproportionately higher numbers of patients lost to follow-up, or even deaths. This indicates that even moderate improvements in program delivery could produce meaningful gains in public health outcomes.
For policymakers, these findings suggest that nutrition programs such as the Nikshay Poshan Yojana should not be viewed as peripheral welfare measures but seen as integral components of the TB treatment strategy.
Here, mathematical modelling can help policymakers understand the connections between an intervention and measurable outcomes by simulating different program scenarios.
By comparing outcomes across different combinations of the number of beneficiaries, payment delays, and improvements in adherence, such models can help identify which operational improvements might save the greatest number of lives.
Local models, cost-effective solutions
This team’s work also points to further avenues for research including adapting the model to state and district-level data. TB burden and program performance vary widely across India, and localised modelling could help identify regions where improvements in nutrition delivery might have the greatest impact.
Another important area that researchers could look into is an analysis of cost-effectiveness. Comparing investments in nutrition delivery systems with other tuberculosis interventions—such as diagnostics or treatment innovations—could help policymakers allocate limited public health resources more efficiently.
The modelling framework could also be expanded to include other social determinants of health. Factors such as cramped housing conditions, income support, and comorbidities such as diabetes —which affect more than one lakh TB patients in India—may interact with nutritional status and have an impact on how patients respond to the treatment. Using data on age, gender and risk groups (such as those living with HIV, for instance) can also help policymakers identify who would benefit the most from timely nutrition support.
Finally, integrating the model with real-time program data from the government’s system could eventually enable early warning mechanisms for program underperformance, helping health authorities respond more quickly when gaps emerge.
Dr Palak Goel is Assistant Professor, BML Munjal University, Haryana. This study is being conducted in collaboration with the Indian Institute of Public Health, Delhi. The research is ongoing, and the findings presented here are preliminary and subject to revision.