How Modicare Is A Rare Opportunity To Help Map And Cure Caste-based Genetic Diseases In Indians

The Narendra Modi government is currently in the process of rolling out the largest healthcare programme in the world, insuring 50 crore people with a cover of Rs 500,000 each per year. It’s one of the most complex and dynamic policies launched by any Indian government since Independence and will have far-reaching consequences, impacting politics, health sector, fiscal math, insurance industry, welfare, data collection and analysis, privacy, supply of doctors and many other areas that even the policymakers may not have fathomed.

With a programme of such scale, there is a great opportunity before India to leverage India’s caste system to deliver more targeted healthcare and ensure prevention of diseases for millions of citizens. How? To comprehend that, we need to first understand two components: the tech behind the programme which will help collect and analyse vast amounts of health data; and certain diseases and caste correlation.

The health insurance scheme, termed Ayushman Bharat in the government circles or Modicare in media and popular use, will use India Stack, a set of digital public goods, comprising four essential layers: presence-less, paperless, cashless and consent (discussed in detail here). India Stack stands on the shoulders of JAM trinity (Jan Dhan bank accounts, Aadhaar, Mobile) and can be implemented across various sectors. India Health Stack (IHS) using the aforementioned four layers of India Stack will form the IT backbone of Ayushman Bharat. National Health Registries (NHR) is the basic layer, which will house the master data under different heads, most important of which are Federated Personal Health Records (PHR) Framework and a National Health Analytics Framework (NHAF).

The PHR framework will have data relating to medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal stats such as age and weight, demographics and billing information, etc. It will be patient-controlled as far as access is concerned but shareable based on his/her consent with other entities - hospitals, insurers, research firms, etc. The Analytics Framework will help analyse aggregate datasets at the state, district and national level and assist in targeted policy-making. For instance, it can tell most commonly occurring diseases in the country (or a state or a district) or treatment characteristics and procedures and much more.

As the programme stabilises, it can also focus on expanding further and include many features such as disease surveillance, predicting epidemics, classifying and clustering population segments for proactive care, nutrition, or other health schemes.

Imagine the possibilities data analytics brings to the table. And this is also where India’s caste system can be leveraged in the area of preventive, proactive and targeted healthcare.

The millions of people in the Indian caste structure today are products of what scientists call “population bottlenecks” or strong founder-member effects, which can perpetuate over long periods of time due to various factors, mainly social or geographical. Socially, how? Let’s assume that some time back in history, due to some reason, various groups with comparatively small numbers but many offsprings get stratified and become endogamic, then as a result, their descendants over centuries can become isolated genetically from other groups even when they lived in the same geographic location. This is what happened in India as well.

A 2009 paper by David Reich and Kumarasamy Thangaraj, published in Nature, showed the extent to which the modern-day castes corresponded to genetic patterns. The results that came out effectively proved that the caste-system wasn’t a British construct, Indian society got stratified around 2,000 years back before mixing freely for nearly same number of years. Their research showed that allele frequency differences between groups in India were three times larger than in European groups separated by similar geographic distances “reflecting strong founder effects whose signatures have been maintained for thousands of years due to endogamy.” Due to this, there is excess of heritable diseases in India, transferring from one generation to another. Different caste groups have different recessive diseases.

Reich explains, “a high reproductive rate among a small number of individuals caused the rare mutations carried in those individuals to rise in frequency in their descendants.”

Due to endogamy, rare disease causing mutations tend to increase at high frequencies. These mutations aren’t harmful if a person inherits one copy, i.e. from a single parent, but if inherited from both, can be lethal. The chance of the latter situation is much higher in groups that are victims of population bottlenecks.

Ashkenazi Jews, a group that stratified around 600 years ago, has a high incidence of lethal Tay-Sachs and Riley-Day syndrome because these diseases were prevalent in founder members. As the Reich and Thangaraj 2009 paper illustrated, members of the Vysya, a middle caste group in Andhra Pradesh, which is estimated to have stratified around 3,000 years ago (one of the oldest and tightly held genetic groups in India) are known to have a rare mutation occurring at 20 per cent frequency, which makes those affected by it to react poorly to anesthesia given before a surgery and those injected with it can even suffer from prolonged muscle paralysis. That’s why doctors who are aware of this avoid this method for the Vysya members.

A lot of work is underway in the west to identify genes and groups known to have certain diseases due to founder members effect. It has met with considerable success. The scope for India is much bigger, given the much higher number of more strictly stratified groups in India, each comprising of millions of people.

Reich in his book Who we are and How we got here writes that the cost for carrying out genetic mapping is not a big factor. All it will need is a small sample in a caste group with disease whose genomes can be sequenced and thus certain diseases and malformations which occur more frequently in some groups than in others can be identified.

Given that Indians still marry overwhelmingly within the confines of their own caste, it is critical that two members of the same groups carrying disease-causing mutations do not marry. Apart from horoscope and manglik/non-manglik-ness, maybe it’s time parents also make sure that the future bride and groom don’t have the rare recessive disease-causing mutations before striking a match. This may be discomforting to some but has many positive implications for the health of lakhs of children.

While it will need some investment to carry out genetic analysis of thousands of caste groups, the analytics pillar of National Health Stack can leverage the information to put it in a personalised health database of individuals so that the diagnosing authority which he or she gives consent to access can avoid methods and procedures that can fall foul as we saw in the Vysya community example.

Data analytics under Modicare can leverage India’s caste system to map diseases caste-wise or geography-wise. Once this is done, specific genes causing the mutations can be researched on and drugs developed to neutralise their lethal effects. Taking this path will greatly increase the chances of delivering targeted, preventive and proactive healthcare for millions.