Author: Hasina Khatun

PC Mahalanobis gave the world a statistical tool that is, by almost any measure, more useful today than when he first proposed it in the 1930s. The Mahalanobis distance — a method for measuring how far a data point lies from a distribution, while accounting for the shape and spread of that data — has become a foundational technique in modern data science, machine learning and anomaly detection.

This blog explains what the Mahalanobis distance is, how it works, where it is used and why the man behind it deserves a more prominent place in the history of data science than he typically receives.

Who is PC Mahalanobis — Life and Legacy

Prasanta Chandra Mahalanobis was born on 29 June 1893 in Calcutta, into a family with deep roots in Bengal’s intellectual and reform traditions. He studied physics at Presidency College, Calcutta, where his teachers included Jagadish Chandra Bose, before travelling to King’s College, Cambridge, to continue his education in mathematics and physics.

It was during this period in England that a passing introduction to statistics changed the course of his life. He returned to India with a new conviction: that rigorous data collection and statistical reasoning could be applied to real, pressing problems in agriculture, anthropology, economics and public policy.

What followed was a career of extraordinary breadth. PC Mahalanobis founded the Indian Statistical Institute in Calcutta in 1931, established India’s first statistical journal and built the survey methods that laid the groundwork for India’s National Sample Survey — systems that continue to inform government policy to this day. He also played a central role in shaping India’s Second Five Year Plan, where his macro-economic model prioritised heavy industry investment as the foundation for long-term growth.

He was elected a Fellow of the Royal Society in 1945, received the Weldon Medal from Oxford University in 1944 and was awarded the Padma Vibhushan in 1968. He died in June 1972, one day before what would have been his seventy-ninth birthday.

Founding of the Indian Statistical Institute

The Indian Statistical Institute began in December 1931, growing out of an informal statistical laboratory that Mahalanobis had set up in his own room at Presidency College. Initially registered as a non-profit learned society in April 1932, it operated on a budget of just 238 rupees in its first year.

Over the following decades, ISI expanded into a globally recognised centre for statistical and quantitative research. In 1933, it launched Sankhya — India’s first academic statistics journal, still published today — which carried many of Mahalanobis’s landmark papers, including the original formulation of what would become the Mahalanobis distance.

ISI’s Kolkata campus became a hub for interdisciplinary research spanning economics, genetics, computer science and sociology. It trained generations of statisticians who went on to shape statistical practice in India and internationally. The Institute remains one of the most rigorous quantitative research institutions in the world.

What is Mahalanobis Distance — Simple Explanation

The Mahalanobis distance answers a deceptively simple question:

how unusual is this data point, given everything we know about the distribution it comes from?

Imagine you are looking at the heights and weights of a large group of people. You want to know whether a specific individual’s measurements are typical or unusual. The challenge is that height and weight are correlated — taller people tend to weigh more — and they are measured in completely different units. A simple measurement of distance from the average would ignore both of these facts.

The Mahalanobis distance solves this by accounting for the correlation between variables and the scale of each variable simultaneously. It transforms the space in which the measurement exists, stretching and rotating it to reflect the actual shape of the data, and then measures distance within that corrected space. The result is a single number that tells you, in a statistically meaningful way, how far a point lies from the centre of its distribution.

A Mahalanobis distance of zero means the point is exactly at the mean of the distribution. The larger the value, the more unusual the observation — regardless of how many variables are involved.

The Formula and How It Works

The Mahalanobis distance between a point x and a distribution with mean vector mu and covariance matrix S is defined as:

D(x) = sqrt [ (x – mu)^T * S^-1 * (x – mu) ]

Breaking this down:

• (x – mu) is the difference between the point and the mean of the distribution, a vector when multiple variables are involved.
• S^-1 is the inverse of the covariance matrix — the key term that accounts for how variables are spread and correlated. This is what distinguishes the Mahalanobis distance from simpler distance measures.
• The transpose and multiplication combine these to produce a single scalar value.

In practical terms, the covariance matrix describes the shape of the data cloud. If two variables are highly correlated, the data cloud is elongated in a particular direction. The inverse covariance matrix essentially normalises this shape, so that what appears to be a large distance in one direction is correctly recognised as not unusual if it follows the natural spread of the data.

The result is unitless meaning it does not depend on the original scale of any individual variable and can be compared directly across observations, regardless of the number of variables involved.

Mahalanobis Distance vs Euclidean Distance

The Euclidean distance is the straightforward geometric distance between two points — the kind taught in secondary school geometry. It works reliably when variables are uncorrelated and measured on the same scale.

Real-world data almost never meets both conditions simultaneously.

Consider a dataset where one variable ranges from 0 to 1 and another ranges from 0 to 10,000. Euclidean distance would be dominated almost entirely by the larger-scale variable, making the smaller one irrelevant. Mahalanobis distance normalises for this automatically.

Similarly, if two variables are highly correlated, a Euclidean measurement might flag a point as an outlier simply because it deviates from the mean in both variables simultaneously — even if that joint deviation is perfectly consistent with the correlation structure of the data. Mahalanobis distance would correctly identify this as a typical observation.

This is why data scientists prefer Mahalanobis distance for multivariate problems and why it has remained a standard tool more than 85 years after it was first proposed.

Applications of Mahalanobis Distance in Data Science

The Mahalanobis distance has found a wide range of practical applications in modern data science, reflecting both its mathematical rigour and its intuitive logic.

Outlier Detection, Clustering and Machine Learning

Outlier and anomaly detection is perhaps the most common application. In any dataset, an observation with a high Mahalanobis distance from the rest of the data is, by definition, statistically unusual. This makes the measure particularly useful in:

• Financial fraud detection — identifying transactions that deviate significantly from a customer’s normal pattern of behaviour, accounting for correlations between transaction amount, time, location and merchant type simultaneously.
• Medical diagnostics — detecting patients whose combination of clinical markers falls unusually far from reference population norms, even when each individual marker might appear borderline.
• Manufacturing quality control — identifying products whose combination of measurable properties falls outside acceptable limits, even when no single property is individually defective.
• Cybersecurity — flagging network activity that deviates from established behavioural baselines in multivariate ways that simple threshold rules would miss.

In machine learning, Mahalanobis distance appears across several core techniques:

• In Linear Discriminant Analysis (LDA), classifying a new observation into one of several groups involves comparing its Mahalanobis distance from each group’s centroid. The observation is assigned to the group whose centroid it is closest to, in the Mahalanobis sense.
• In k-Nearest Neighbour algorithms, replacing Euclidean distance with Mahalanobis distance often improves classification accuracy on correlated, multi-scale datasets.
• In Gaussian mixture models and cluster analysis, Mahalanobis distance is used to assign observations to clusters in a way that respects the shape of each cluster rather than assuming circular symmetry.

In clinical research, the Mahalanobis distance is used to assess whether individuals or groups are genuinely comparable — a technique called propensity score matching — ensuring that treated and control groups in observational studies are appropriately balanced across multiple background characteristics simultaneously.

PC Mahalanobis and the Second Five-Year Plan

Beyond his statistical contributions, PC Mahalanobis played a defining role in shaping India’s post-independence economic strategy. As a member of the Planning Commission, he developed what became known as the Mahalanobis Model — the strategic framework behind India’s Second Five Year Plan (1956 to 1961).

The model argued that sustained economic growth required prioritising investment in heavy industry and capital goods, rather than concentrating resources on consumer goods production. By building domestic productive capacity — steel mills, machine tools, power generation — India would create the foundation for long-term industrialisation rather than remaining dependent on imported capital equipment.

The model drew on his statistical background in a direct way, that is, built on input-output analysis and aggregate growth accounting, applying quantitative reasoning to economic planning at a national scale. While economic historians have debated its consequences, the model reflected a genuine and sophisticated attempt to apply statistical thinking to one of the most consequential policy decisions of newly independent India.

Why India Celebrates National Statistics Day on His Birthday

In 2006, the Government of India announced that 29 June, the birthday of PC Mahalanobis, would be observed annually as National Statistics Day. The first official celebration took place in 2007.

The choice acknowledges not only his specific contributions — the Mahalanobis distance, the Indian Statistical Institute, the National Sample Survey methodology — but the broader idea he embodied: that rigorous, evidence-based statistical reasoning is essential for good governance, sound policy and equitable development.

National Statistics Day is observed each year with seminars, academic events and policy discussions organised by the Ministry of Statistics and Programme Implementation, typically focused on a theme relevant to current data and development priorities.

As India moves deeper into a data-driven economy where decisions in agriculture, public health, urban planning and financial services increasingly rely on statistical inference, the foundations that PC Mahalanobis built become more relevant, not less.

FAQs — Mahalanobis Distance and PC Mahalanobis

Who is PC Mahalanobis and what is he known for?

Prasanta Chandra Mahalanobis was an Indian statistician who founded the Indian Statistical Institute, developed the Mahalanobis distance and played a central role in India’s post-independence economic planning. He is widely regarded as the father of statistics in India and his birthday is celebrated as National Statistics Day on 29 June each year.

What is Mahalanobis distance in simple terms?

It is a measure of how unusual a data point is relative to a distribution, accounting for the correlation and scale of all variables involved. Unlike simpler distance measures, it correctly handles situations where variables are correlated or measured in different units.

How is Mahalanobis distance different from Euclidean distance?

Euclidean distance measures straight-line distance between points but ignores correlation between variables and differences in scale. Mahalanobis distance corrects for both, making it more accurate and meaningful for multivariate, real-world datasets.

What is the formula for Mahalanobis distance?

D(x) = sqrt [ (x – mu)^T * S^-1 * (x – mu) ], where x is the data point, mu is the mean vector of the distribution, and S^-1 is the inverse of the covariance matrix. The covariance matrix is what accounts for the correlation structure and scale of the data.

How is Mahalanobis distance used in machine learning?

It is used in outlier detection, Linear Discriminant Analysis, k-Nearest Neighbour classification and cluster analysis. In each case, it provides a more accurate measure of statistical distance than Euclidean distance when variables are correlated or on different scales.

What is the Indian Statistical Institute and who founded it?

The Indian Statistical Institute was founded by PC Mahalanobis in Calcutta in 1931. It grew from a small statistical laboratory at Presidency College into a globally recognised centre for statistical and quantitative research, and remains one of the leading such institutions in the world.

How did PC Mahalanobis contribute to India’s economic planning?

He developed the Mahalanobis Model, which shaped India’s Second Five Year Plan (1956 to 1961) by arguing for prioritised investment in heavy industry as the foundation for long-term economic growth. He also pioneered the large-scale sample survey methods used by India’s National Sample Survey to collect economic and demographic data.

Why is National Statistics Day celebrated on June 29?

29 June is the birthday of PC Mahalanobis. The Government of India designated it as National Statistics Day in 2006 to recognise his contributions to statistical science, survey methodology and evidence-based governance.

Where is Mahalanobis distance used in real-world data science?

Applications include financial fraud detection, medical diagnostics, manufacturing quality control, cybersecurity anomaly detection, propensity score matching in clinical research, and classification tasks in machine learning wherever multiple correlated variables need to be assessed jointly.

What is the significance of PC Mahalanobis in modern statistics?

His development of the Mahalanobis distance gave statisticians and data scientists a tool for multivariate analysis that remains widely used nearly 90 years later. His institutional legacies like ISI, Sankhya, the National Sample Survey built the statistical infrastructure that modern India relies on for governance, planning and research.

Quick Summary

• Entrepreneurship development equips individuals with the skills, knowledge and confidence to start and grow businesses.
• Women entrepreneurs contribute significantly to economic growth but continue to face barriers related to finance, markets, skills and mobility.
• Effective entrepreneurship development programmes combine training, mentorship, financial literacy, market access and business support.
• Digital tools, government schemes, and ecosystem linkages are expanding opportunities for women-led enterprises.
• Investing in women entrepreneurs creates ripple effects across families, communities and local economies.

Why Entrepreneurship Development Matters More Than Ever

India could add USD 0.7 trillion to its GDP. The barrier is not ambition — it is access.

Women entrepreneurs reinvest up to 90% of their income back into their families and communities. The evidence for their economic multiplier effect is overwhelming. And yet in India, women account for only 20% of all MSMEs, and the majority of those operate informally, on a micro scale, in low-return sectors.

The McKinsey Global Institute has estimated that closing India’s gender gap in workforce participation could add USD 0.7 trillion to the country’s GDP by 2025. That number represents millions of women who have the drive and the ideas but not the credit history, the digital skills, the market access or the mentorship to turn potential into a sustainable business.

Despite significant policy advances — Skill India, PMKVY, MUDRA Yojana, Stand-Up India and Startup India — systemic barriers persist, especially for women from rural, low-income and marginalised backgrounds. These aren’t individual failures but structural gaps that require structural solutions.

Six barriers Smile Foundation is working to dismantle

The solution

A funnel-based, four-year-tested model that takes women from mobilisation to market

Since 2005, Smile Foundation’s Swabhiman programme has been building the infrastructure for women’s economic empowerment — reaching over 1,90,000 women across rural villages and urban slums in 26 states. Two complementary programmes now sit at the heart of this work: the Livelihood and Enterprise Development Program (LEDP) and the Women Entrepreneurship Development Program (WEDP).

Both follow a structured, funnel-based approach — beginning with community outreach, moving through intensive training, and culminating in seed-funded enterprise launches with sustained mentorship. The model is not a single intervention but an integrated system. It recognises that a woman who receives entrepreneurship training without access to capital will stall. A woman who receives capital without market linkages will plateau. Only when all the pieces connect does a micro-enterprise become sustainable.

“The programme promotes self-reliance and long-term economic empowerment of women through inclusive and sustainable enterprise development.”

Programme components

What each phase of the programme delivers

Target and outcomes

Who benefits — and what success looks like

Each programme cycle directly reaches 300 women from low-income and marginalised communities, with an indirect reach of 1,500+ household members and community stakeholders. The outcomes are defined, tracked, and reported — not aspirational.

Why this matters

Economic independence is not a women’s issue. It is an economic imperative.

The case for investing in women’s entrepreneurship is both moral and economic. When women control income, they spend it differently — on children’s nutrition, on healthcare, on education. The multiplier effects are documented across decades of development research. India’s MSME sector, which employs over 110 million people, cannot reach its potential while 80% of its enterprises are led by men alone.

LEDP and WEDP are not standalone training programmes. They are an integrated ecosystem — one that addresses capital, skills, market access, mentorship, and digital literacy in a single, sustained engagement. The funnel model is deliberate: not every woman who enters the programme will launch an enterprise, but every woman will emerge more financially literate, more digitally capable, and more connected to formal support systems than when she entered.

That shift — from economic invisibility to recognised participation — is itself transformative. And for the 60 women in each cycle who do launch enterprises, the potential ripple effects into their households and communities make every investment in this programme not just a social good, but a sound one.

“SSP is not only creating industry-ready engineers, but also empowering a generation of leaders who will break down barriers and pave the way for others to follow.”

Frequently Asked Questions (FAQs)

1. What is entrepreneurship development?

Entrepreneurship development is the process of building entrepreneurial skills, knowledge, and capabilities to help individuals start, manage and grow successful businesses.

2. Why is entrepreneurship development important for women?

It helps women achieve financial independence, access income opportunities, strengthen leadership skills and contribute to household and community development.

3. What skills are taught in entrepreneurship development programmes?

Common areas include business planning, financial literacy, digital marketing, market research, enterprise management and customer engagement.

4. How does entrepreneurship development contribute to economic growth?

It creates new businesses, generates employment, increases incomes and stimulates local economic activity.

5. What are the biggest challenges faced by women entrepreneurs?

Key challenges include limited access to finance, low financial literacy, restricted market access, digital barriers and social or cultural constraints.

Scale is not a number.
It is a system.

At a glance

• Smile Foundation and HDFC Parivartan launched eight Mobile Medical Care Units (MMCUs) in March 2026, targeting four lakh people in remote communities across J&K, Ladakh, Punjab and Uttarakhand.
• The two-year initiative goes beyond treatment — it integrates preventive screenings, maternal and child health, and disease awareness to address health vulnerabilities before they become crises.
• The programme is embedded in a wider public health ecosystem, with referral linkages to PHCs, CHCs and government schemes under the National Health Mission, following ICMR protocols.
• The partnership reflects a wider shift in CSR from one-time philanthropy to long-term developmental infrastructure — and offers a replicable model for what impact at scale genuinely requires.

Impact at Scale: Eight vans. Four lakh people. One larger question.

In March 2026, Smile Foundation and HDFC Bank’s CSR initiative, HDFC Parivartan, announced the launch of eight Mobile Medical Care Units (MMCUs) across Jammu & Kashmir, Ladakh, Punjab and Uttarakhand. The programme aims to deliver integrated primary healthcare services to more than four lakh people living in geographically remote communities over the next two years.

The mobile units will provide OPD consultations, diagnostics, medicines, maternal and child healthcare, disease screenings and health awareness programmes delivered directly at the community level, without asking people to travel to institutions that may be hours away or seasonally inaccessible.

At first glance, this may appear like yet another CSR-led healthcare intervention in rural India. There have been many. But the significance of this partnership lies less in the number of vans deployed and more in what the initiative reveals about the realities of delivering impact at scale in a country as vast and unequal as India. Because scale, in development work, is not simply about expansion. It is about systems.

In pursuit of last-mile access

India’s healthcare infrastructure has improved substantially over the last decade. But access remains deeply uneven. In remote terrains such as Ladakh or the hilly districts of Uttarakhand, healthcare facilities are often separated from communities by long travel times, poor road connectivity, seasonal weather conditions and chronic shortages of trained medical personnel.

For vulnerable populations, healthcare is not always absent — it is frequently inaccessible. This distinction matters enormously. Absence implies a gap that more facilities could fill. Inaccessibility implies a structural problem that requires structural solutions — mobile infrastructure, community-embedded care and sustained engagement rather than episodic outreach.

This is where mobile healthcare models become genuinely important. Rather than expecting communities to navigate fragile infrastructure in search of care, the infrastructure itself moves toward them. The MMCUs launched under the Smile Foundation–HDFC Parivartan partnership are designed precisely around this principle of last-mile delivery.

The idea of last-mile access has become central to contemporary development discourse. But implementing it consistently is far more difficult than the phrase suggests. Reaching remote populations reliably requires logistical planning, local partnerships, medical staffing, data systems, and sustained financial commitment. It also requires interventions flexible enough to adapt to geography, culture, and resource limitations without compromising quality of care.

Why collaborations like this one are becoming essential for impact at scale

Corporate social responsibility programmes often possess the financial capacity to fund large-scale interventions. But funding is not the same as execution. This is why collaborations between corporations and implementation-focused nonprofits are increasingly indispensable to achieving impact at scale.

Organisations like Smile Foundation bring what cannot easily be purchased or replicated: operational knowledge of local ecosystems. How communities interact with healthcare systems. What barriers prevent uptake. How trust is built over time. What forms of outreach actually work on the ground as opposed to what looks effective in a programme document.

“Lasting impact requires patient infrastructure. It requires partnerships that combine funding with implementation expertise, mobility with continuity, and service delivery with systems integration.”— Smile Foundation Programme Framework, 2026

This partnership also reflects a growing shift within CSR itself — away from short-term philanthropy and toward long-term developmental infrastructure. The healthcare programme will run from April 2026 to March 2028, indicating a model built on sustained presence rather than one-time visibility.

That distinction is not rhetorical. Healthcare outcomes are rarely immediate. Building preventive health habits, ensuring continuity of treatment, and improving maternal or child health indicators require long-term engagement. A community that receives a health camp once every six months is not the same as one that has reliable access to primary care. The MMCU model is designed around the latter.

Prevention as infrastructure — the underemphasised half of healthcare

India’s public health conversation is dominated by treatment. How many hospital beds, how many doctors per thousand people, how many tertiary care facilities. These are important metrics. But they systematically undervalue the dimension of care that has the highest long-term return: prevention.

In underserved communities, preventive healthcare can significantly reduce both financial distress and health complications. Conditions like anaemia or hypertension frequently remain undiagnosed for years because communities lack regular access to screenings and basic primary care. By the time they are identified, the cost of treatment — personal, economic and social — has multiplied.

The MMCUs address this directly. Adult screenings for hypertension, diabetes, and anaemia are built into every unit’s service delivery. School health sessions extend the model to younger populations. The result is a programme that expands the very ambit of healthcare — from treatment alone to the full spectrum of prevention, early intervention, and awareness.

Ecosystem thinking — why integration matters more than coverage

Healthcare delivery often fails not because services are entirely absent, but because systems remain fragmented. A patient diagnosed through a mobile unit still requires continuous care — referrals, access to medicines, institutional follow-up and sometimes hospitalisation. If that pathway is broken, the diagnosis itself provides limited long-term value.

The Smile Foundation–HDFC Parivartan initiative addresses this explicitly. The programme integrates referral linkages with Primary Health Centres, Community Health Centres and government schemes under the National Health Mission, while adhering to protocols set by the Indian Council of Medical Research. This is the architecture that makes impact at scale possible rather than merely impressive.

Effective social interventions depend on how well different systems — nonprofit, corporate and public — work together. The MMCU model is designed as a connector: it reaches communities that public infrastructure struggles to serve consistently, while feeding into the public system rather than operating parallel to it. That integration is what allows individual encounters to become sustained healthcare relationships.

Beyond healthcare, the broader Smile Foundation–HDFC Parivartan partnership also extends into skilling and livelihoods. In Bareilly, Uttar Pradesh, the two organisations recently launched a Parivartan Skilling Centre, training underserved youth for employment in BFSI and retail sectors. This reflects an increasingly important understanding in development practice: social vulnerabilities are interconnected. Healthcare outcomes are linked to livelihoods, nutrition, education, mobility, and financial stability. Addressing one in isolation rarely achieves the depth of change that communities actually need.

The real meaning of impact at scale

What emerges from this partnership, then, is not simply a story about mobile healthcare vans. It is a larger lesson about how change transforms into impact at scale, and why the phrase deserves more scrutiny than it typically receives.

Impact at scale is frequently discussed as if it were a question of reach alone. How many people, how many districts, how many units. But reach without continuity is outreach. Reach without integration is fragmentation. Reach without trust is low uptake. The Smile Foundation–HDFC Parivartan model is notable precisely because it takes all of these seriously, not as afterthoughts, but as foundational design principles.

In a country where millions still remain outside the effective reach of quality healthcare, the systems that enable consistent access matter as much as the services themselves. Transformation — in public health, in poverty reduction, in human development broadly — rarely happens because of one large announcement. More often, it happens through sustained, repetitive acts of access. Through the van that shows up every month. Through the health worker who knows the community. Through the referral that actually connects to the right facility.

“Scale is not only about reaching more people. It is about building systems capable of reaching people consistently — and with dignity.”— Smile Foundation, Programme Philosophy

That, perhaps, is the most important lesson from this initiative: that the ambition to achieve impact at scale must be matched by the patience to build the infrastructure that makes it real — and the humility to recognise that in development work, consistency is a form of excellence.

Frequently asked questions (FAQs)

What exactly are Mobile Medical Care Units and what services do they provide? ▾

Mobile Medical Care Units (MMCUs) are fully equipped vehicles designed to bring primary healthcare directly to communities that lack easy access to health facilities. Each unit provides OPD consultations, basic diagnostics, medicines, maternal and child healthcare, disease screenings — including for hypertension, diabetes, and anaemia — and health awareness sessions covering hygiene, nutrition, and menstrual health. Unlike one-time health camps, MMCUs operate on regular schedules in fixed community locations, making healthcare engagement predictable and routine rather than crisis-driven.

Why were Jammu & Kashmir, Ladakh, Punjab and Uttarakhand chosen for this initiative? ▾

These states represent some of India’s most geographically challenging terrains for healthcare delivery. In Ladakh and the hilly districts of Uttarakhand, extreme altitude, seasonal road closures, and long travel distances make consistent access to health facilities structurally difficult — particularly for elderly, pregnant, or seriously ill community members. Jammu & Kashmir has remote communities where healthcare infrastructure, while present in theory, remains functionally inaccessible to large portions of the population. Punjab, despite better connectivity, has rural pockets where preventive healthcare and early diagnostics remain significantly underutilised.

How does this programme connect to the wider public health system — does it operate independently? ▾

No — integration with the public health system is central to its design. The programme establishes referral linkages with Primary Health Centres (PHCs) and Community Health Centres (CHCs), and connects patients to government health schemes under the National Health Mission. Medical protocols follow ICMR guidelines. This is deliberate: the MMCUs are not a parallel healthcare system but a connector — designed to reach communities that public infrastructure serves inconsistently, while ensuring patients are channelled into public systems for continuous care, follow-up, and hospitalisation where needed.

What makes this different from a typical CSR health camp or outreach programme? ▾

Three things distinguish it from conventional outreach. First, duration: the programme runs for two years (April 2026 – March 2028), not a single day or season. Second, prevention focus: it doesn’t only treat existing illness — it screens for undiagnosed conditions, conducts school health sessions, and builds health literacy in communities over time. Third, systems integration: it is linked to public health infrastructure through referrals and government schemes, ensuring patients can access continuous care beyond what the MMCU itself provides. Together, these features shift the model from outreach to infrastructure.

How does Smile Foundation’s role in this partnership differ from that of HDFC Parivartan? ▾

HDFC Parivartan — HDFC Bank’s CSR initiative — provides the financial capacity that enables the programme to operate at the scale and duration required for meaningful impact. Smile Foundation brings implementation expertise: knowledge of local ecosystems, experience building community trust, understanding of what forms of healthcare outreach actually work on the ground in specific geographies, and the operational infrastructure to manage staffing, logistics, and quality across multiple remote locations simultaneously. This combination — funding with field knowledge — is increasingly recognised as the minimum viable model for impact at scale in development work.

Smile Foundation

Smile Foundation is an Indian development organisation working across health, education and livelihood sectors. This article was produced by the Smile Foundation analysis desk. For partnership enquiries, contact cp@smilefoundationindia.org.