Severity of Disability in India: Insights from NFHS-5 Data

India’s disability landscape is far more nuanced than a single number can capture. The National Family Health Survey-5 (NFHS-5), conducted between 2019 and 2021 across 636,699 households, was the first NFHS round to systematically collect data on disability – covering five types: locomotor, mental, speech, vision, and hearing. What it revealed is that disability in India does not affect all people equally. Age, gender, education, geography, and socioeconomic standing each shape who is affected, how severely, and how much support they can access. Understanding these patterns is essential for designing interventions that actually reach those who need them most.

Age and disability severity
Gender and educational disparities
The gender gap in disability prevalence
Education as a protective factor
Regional variations in disability type and severity
Locomotor disability: a northern and western concentration
Mental disability: the northeastern picture
Socioeconomic factors: marital status, caste, and wealth
Marital status and disability risk
Caste and wealth as structural determinants
The intersectional picture

Age and disability severity

Research based on NFHS-5 data confirms a clear and consistent pattern: disability prevalence rises sharply with age. While the overall population-level prevalence stands at 0.93%, the rate climbs steeply in older age groups, with those aged 75 and above showing the highest burden. Compared to the 0-14 age group, individuals in the 75+ bracket had an adjusted prevalence ratio (aPR) of 26.35 – a stark figure that reflects just how concentrated disability risk becomes in later life.

This is not surprising from a biological standpoint. As people age, they face increasing chances of becoming disabled, and once disabled, there are increased chances of deterioration with a lower likelihood of full recovery. Degenerative musculoskeletal conditions, sensory decline, and chronic non-communicable diseases (NCDs) such as cardiovascular disease and diabetes all converge at older ages to produce compounding functional limitations. NCDs like cardiovascular and musculoskeletal disorders account for 66.5% of disability-adjusted life years (DALYs) in low and middle-income countries, and India’s ongoing epidemiological shift means this burden is growing.

The demographic projection adds urgency to this concern. By 2050, an estimated 323 million people – approximately 19.1% of India’s total population – will be aged 60 or above. An aging nation with a disability-support infrastructure that is still developing is a significant public health challenge. UN Population forecasts indicate the share of Indians aged 60 and above will rise from 8% currently to 19% by 2050, underscoring the urgency of addressing age-related disability at a policy level. Importantly, aging should not be treated as synonymous with disability – many older Indians live in good health – but the data makes it clear that preventive healthcare and early intervention for the elderly are critical.

Gender and educational disparities

The gender gap in disability prevalence

NFHS-5 data shows that males experience higher disability prevalence than females, with an adjusted prevalence ratio of 1.02 compared to females. Census 2011 data similarly found disability rates were higher in males than females (5,314 vs. 5,045 per 100,000 elderly population), and were also higher in rural compared to urban areas. This pattern may reflect greater occupational exposure to injury and physical hazard among men in India’s workforce, particularly in construction, agriculture, and manual labour – sectors that carry a disproportionately high risk of locomotor and sensory disability.

However, while men report higher disability rates overall, the experience of disability for women is compounded by unequal access to care. Women with disability face particular challenges in accessing reproductive and sexual health services and information. Female disability may also be undercounted due to underreporting in household surveys, where responses are often provided by the head of the family. This means real-world gender disparities in disability experience could be more complex than the headline numbers suggest.

Education as a protective factor

One of the most striking findings from NFHS-5 is the relationship between education and disability. People with no formal education showed an adjusted prevalence ratio of 1.62 compared to those with higher education – indicating significantly elevated disability risk among those who never attended school. This relationship holds across age groups and disability types.

Education shapes health outcomes through multiple pathways: it improves health literacy, enabling people to recognise symptoms early and seek timely care. It expands economic opportunities, raising income and therefore access to nutrition, housing, and medical services. It also increases awareness of government welfare schemes and entitlements. Research consistently shows that education is strongly linked with disability, and experts argue for a shift toward health education through Information, Education and Communication (IEC) and Behaviour Change Communication (BCC) strategies. Studies from urban resettlement colonies in Delhi confirm that disability levels decrease when participants are literate and when their past occupation was in formal employment – reinforcing the link between educational attainment, economic mobility, and reduced disability risk.

Regional variations in disability type and severity

India’s geographic diversity extends to how disability is distributed across states and union territories. The overall disability prevalence is highest in Lakshadweep (1.68%), followed by Tamil Nadu (1.26%) and Karnataka (1.22%). However, looking at specific disability types reveals more granular and policy-relevant patterns.

Locomotor disability: a northern and western concentration

Locomotor disability – which includes impairments in physical movement and mobility – is the most common disability type in India, accounting for 44.73% of all disabilities recorded in NFHS-5. Its geographic distribution is uneven: the highest prevalence of locomotor disability was recorded in Delhi at 57.03%, followed by Punjab at 55.51% and Madhya Pradesh at 53.47%. Urban states like Delhi likely see higher rates due to road traffic injuries, which are a leading cause of locomotor disability among working-age adults. According to the 2019 Global Burden of Disease report, road accidents accounted for nearly 5.1% of DALYs among people aged 25 to 49.

Mental disability: the northeastern picture

The highest prevalence of mental disability was recorded in Mizoram at 42.51%, followed by Lakshadweep and Goa. Speech disability showed a different geographic pattern, with Sikkim recording the highest rates. These state-level variations likely reflect differences in awareness, diagnostic capacity, reporting practices, and population composition rather than a simple statement about disease burden. In some smaller states and union territories, where populations are smaller, even a modest number of cases can produce high percentage figures.

What this regional picture tells policymakers is that a one-size-fits-all national programme for disability support is insufficient. States with high locomotor disability require strong orthopaedic rehabilitation services and road safety interventions. States with high mental disability prevalence need community mental health infrastructure and destigmatisation campaigns. The spatial mapping of disability – as done using QGIS analysis in the NFHS-5 disability study – provides the kind of granular evidence that can inform targeted resource allocation.

Socioeconomic factors: marital status, caste, and wealth

Marital status and disability risk

Among all the sociodemographic determinants assessed in NFHS-5, marital status stands out for the magnitude of its association with disability. Unmarried individuals showed an adjusted prevalence ratio of 1.76 compared to married individuals, meaning their disability prevalence was considerably higher. This relationship is likely bidirectional: some individuals remain unmarried because of pre-existing disabilities that limit social participation and marriage prospects, while others experience worsened health outcomes as a result of lacking the social and economic support that marriage often provides in the Indian context.

Married individuals frequently have access to informal caregiving, shared household resources, and a built-in advocate for healthcare access. Data from the 2011 Census confirms that currently married populations had lower disability rates than their unmarried counterparts. In a society where formal social safety nets remain limited, these informal support systems have a tangible effect on health outcomes.

Caste and wealth as structural determinants

NFHS-5 analysis confirms disparities in disability prevalence by wealth index and caste, consistent with the broader literature on health inequity in India. Individuals from Scheduled Caste (SC) and Scheduled Tribe (ST) communities face structural barriers to healthcare – including geographic isolation, financial exclusion, and social discrimination – that increase both the risk of developing disabling conditions and the difficulty of managing them effectively once they arise.

Research on elderly disability in India finds that the proportion of people with multiple disabilities is almost double among the lowest wealth quintile compared to the highest, and similarly elevated among SC and ST communities relative to other caste groups. This reflects how economic deprivation limits access to preventive care, nutrition, occupational safety, and rehabilitation services. Those in higher wealth quintiles can afford early diagnostics and interventions that prevent conditions from progressing to disabling severity. For those at the bottom of the wealth distribution, even basic assistive devices or medicines may be financially out of reach.

The intersectional picture

What makes disability severity in India particularly complex is how these factors compound one another. An elderly person from a marginalised community, without formal education, living in a rural area with limited healthcare access, faces multiple overlapping risks that amplify one another rather than simply adding up. Research using WHO SAGE data confirms that the prevalence of symptomatic, undiagnosed non-communicable diseases was highest among the lowest two wealth quintiles – suggesting that these populations not only face greater disability risk but are also least likely to have their conditions formally identified and treated.

The Rights of Persons with Disabilities (RPWD) Act 2016 represents India’s legislative commitment to equitable services for people with disabilities. But legislation alone does not close the gap. Translating policy into accessible, community-level services – particularly for rural, low-income, and marginalised populations – remains the central challenge. The NFHS-5 data provides a clear roadmap: interventions need to be targeted by age, geography, educational status, and socioeconomic context to be effective.

What do you think? Given that education significantly reduces disability risk, how should India’s public health strategy prioritise literacy and schooling as disability-prevention tools? And with regional variations so pronounced – from locomotor disability concentrated in Delhi to mental disability peaks in Mizoram – do you think a decentralised, state-specific disability policy would be more effective than a uniform national approach?

References

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Table of Contents

Age and disability severity 🔗

Gender and educational disparities 🔗

The gender gap in disability prevalence 🔗

Education as a protective factor 🔗

Regional variations in disability type and severity 🔗

Locomotor disability: a northern and western concentration 🔗

Mental disability: the northeastern picture 🔗

Socioeconomic factors: marital status, caste, and wealth 🔗

Marital status and disability risk 🔗

Caste and wealth as structural determinants 🔗

The intersectional picture 🔗