Addressing Non-Communicable Diseases in Maharashtra: The Case for Policy Reform and Care Innovation
Non-communicable diseases (NCDs) are India's most pressing public health challenge. Per the ICMR-INDIAB Study, the national weighted prevalence of diabetes stands at 11.4%, pre-diabetes at 15.3%, hypertension at 35.5%, generalised obesity at 28.6%, and abdominal obesity at 39.5%. Prevalence is high across urban centres in all states, and pre-diabetes prevalence is higher even in rural areas.[1] Among the broader NCD burden, diabetes, hypertension, and obesity are the big three demanding immediate attention. Addressing them requires state-level health policy targeted at three areas: increasing screening, improving drug procurement to make essential and newer drugs more accessible and affordable, and incentivising innovations in care models that better track treatment compliance and improve patient outcomes.
In Maharashtra, prevalence of diabetes is 12.4% among women and 13.6% among men. Hypertension affects 23.1% of women and 24.4% of men. Indicators of obesity are elevated in 44.5% of women and 40.7% of men, reflecting a growing burden, especially in urban areas.[2]
The Government of India launched the National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke (NPCDCS) in 2010, since renamed the National Programme for Prevention and Control of Non-Communicable Diseases (NP-NCD). More than 15 years on, positive effects are not visible, nor is the programme popularly known. NP-NCD has been scaled across all districts in Maharashtra, and its programmatic design presents a seemingly perfect decentralisation plan on paper, through district NCD cells, NCD clinics, and population-based screening services.[3] In practice, outputs are limited to brief reports on screening by ASHA workers and yoga and AYUSH integration for NCD prevention. This gap between design and outcomes points to the need for a new health policy and amended programmatic approach, designed at the state level and responsive to evolving epidemiological patterns.
The core flow for addressing NCDs runs from Screening and Diagnosis – > Patient Education – > Treatment – > Patient support –> Treatment compliance, and management of complications – > Prevention of morbidity and mortality. Approximately 80% of patients seek NCD treatment from private sector providers and do not interface with NP-NCD at all. [4] Private providers treat patients who visit them, but rely very little on counselling or comprehensive care models, and have limited incentive to develop them.
Documented successes, such as South-Korea's population-wide hypertension programme, demonstrate what is achievable. [5] However, intervention for diabetes and obesity is considerably more difficult than for hypertension. Rather than developing a perfect programme, piloting, and then scaling, Maharashtra needs more experimentation with care models and treatment options across different parts of the state, calibrated to local prevalence.
Two policy levers are essential. The first urgently concerns drug policy. Health policy action is needed to improve procurement and ensure newer, safer drugs are available through Essential Drugs Lists, increasing affordability and accessibility. This includes options for different insulins or GLP-1 agonists for diabetes and severe obesity, distributed per prevalence patterns across districts.
The second lever is care model innovation through small grants. The government should address the private sector's incentive gap and public sector’s challenge of decentralisation of trust, financing and governance by creating fixed-term primary healthcare innovation grants open to public and private providers alike. Both sectors should compete to develop comprehensive care models facilitating easier medicine collection, quick follow-up visits, disease education, and counselling for treatment compliance. These grants, of approximately 2 to 3 crore rupees over 2 to 3 years, awarded to 10 to 20 projects annually, would test multiple approaches to improving efficiency and outcomes of primary healthcare delivery across different regions. By decentralising problem identification and solution design, the state can generate low-cost experimentation and evidence for scalable reforms while limiting financial and administrative risk.
Grant projects can contribute to learning health systems with constant feedback on what is working at different levels and under different scenarios. Running these projects can help state government build confidence in gradually devolving power, funds, responsibility, and risks to the local level, creating autonomous, decentralised pilots that feed into a continuously learning and adaptive feedback loop. Eventually a adaptative, comprehensive intervention program and policy can be built based on the success at steps before. Small Grant Projects centres the concepts of learning health systems and feedback loops in implementation science, and directed improvisations and adaptive political economy from public policy, specially focusing on LMICs like China, also applicable to a country as vast and populous as India. [6] [7]
Recent progress in small grant design internationally supports this approach. The success of Emergent Ventures, USAID's Development Innovation Ventures, the EU4Health grants launched in 2021, and federal grants financing Federally Qualified Health Centers in the United States all point to the same conclusion: well-designed small grants can meaningfully improve NCD outcomes at the level of primary care. Maharashtra has both the epidemiological urgency and a growing evidence base to act now!
(PS: In this essay, I introduce two policy levers for addressing NCDs in India. The first is the concept of Small Grant Projects, which State governments can undertake to promote innovation in care models across both private and public healthcare settings. The second establishes that there are lessons for public health in implementing a directed improvement approach and adaptive political economy. These ideas will be explored independently in subsequent essays.
This essay was originally written for the Takshashila Institute course application. I had been thinking about the need for Small Grant Projects and their centrality in encouraging innovation in care models and health service delivery. However, while writing a policy essay for this application, I connected the idea that Small Grant Projects might be better focused on three specific NCDs (Diabetes, Hypertension, and Obesity) rather than on broad-spectrum primary healthcare.)
References
1. Anjana R, Unnikrishnan R, Deepa M et al. Metabolic non-communicable disease health report of India: the ICMR-INDIAB national cross-sectional study (ICMR-INDIAB-17). The Lancet Diabetes & Endocrinology, 2023; 11, 474-489
2. Kshirsagar MV, Ashturkar MD. Prevalence of lifestyle diseases in Maharashtra: A comparison between NFHS-5 and NFHS-4 surveys. J Family Med Prim Care. 2022 Jun;11(6):2474-2478. doi: 10.4103/jfmpc.jfmpc_1944_21. Epub 2022 Jun 30. PMID: 36119353; PMCID: PMC9480665.
4. Koya S, Pilakkadavath Z, Chandran P et al. Hypertension control rate in India: systematic review and meta-analysis of population-level non-interventional studies, 2001–2022 The Lancet Regional Health - Southeast Asia, 2022; 9
5. Frieden T. How South Korea Cut Stroke Deaths https://open.substack.com/pub/tomfrieden/p/how-south-korea-cut-stroke-deaths?utm_source=share&utm_medium=android&r=91mtn
6. Ang Y Y, Is Complex Another Word for Complicated? No, and Why it Matters. https://open.substack.com/pub/polytunity/p/complex-for-complicated?utm_source=share&utm_medium=android&r=91mtn
7. Ang, Yuen Yuen, YYA Glossary | Directed Improvisation (February 12, 2026). Available at SSRN: https://ssrn.com/abstract=6232019 or http://dx.doi.org/10.2139/ssrn.6232019
*Disclosure of Delegation to Generative AI
The author declares the use of generative AI in the research and writing process. According to the GAIDeT taxonomy (2025), the following tasks were delegated to GAI tools under full human supervision: - Proofreading and editing - Summarizing text. The GAI tool used was: Claude, Chat-GPT, Grammarly. Responsibility for the final essay/write-up lies entirely with the author. GAI tools are not listed as authors and do not bear responsibility for the final outcomes. Declaration submitted by: Saniya S. Additional note: Used ChatGPT and Claude's web search function to cross-check information, and the latest updates