This dataset contains the decadal growth rates of general population and elderly population in India during different census years.

In 2021, Kerala reflected the highest share of its population belonging to the elderly age group with 16.5 percent as opposed to only 10.5 percent in 2001. This was an increase in six percent in two decades.

India Population: Census: Age: 70 and Above data was reported at 39,730.350 Person th in 03-01-2011. This records an increase from the previous number of 29,299.000 Person th for 03-01-2001. India Population: Census: Age: 70 and Above data is updated decadal, averaging 29,299.000 Person th from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 39,730.350 Person th in 03-01-2011 and a record low of 21,074.000 Person th in 03-01-1991. India Population: Census: Age: 70 and Above data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAD001: Census: Population: by Age Group.

Elderly literacy rate, population 65+ years, both sexes (%) in India was reported at 45.38 % in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Elderly literacy rate, population 65+ years, both sexes - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Age dependency ratio, old (% of working-age population) in India was reported at 10.47 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Age dependency ratio, old (% of working-age population) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Graph and download economic data for Age Dependency Ratio: Older Dependents to Working-Age Population for India (SPPOPDPNDOLIND) from 1960 to 2024 about 64 years +, working-age, ratio, India, and population.

According to the last census data conducted across India in 2011, there were over 52 million elderly females and a little over 51 million elderly men in the country. Between 2001 and 2011, the number of elderly female population has gone up compared to the elderly male population. This might indicate a higher longevity among women in the country than the men.

India Population: Census: Age: 50 to 54 Year data was reported at 49,069.254 Person th in 03-01-2011. This records an increase from the previous number of 36,588.000 Person th for 03-01-2001. India Population: Census: Age: 50 to 54 Year data is updated decadal, averaging 36,588.000 Person th from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 49,069.254 Person th in 03-01-2011 and a record low of 31,114.000 Person th in 03-01-1991. India Population: Census: Age: 50 to 54 Year data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAD001: Census: Population: by Age Group.

Abstract

Purpose: The multi-country Study on Global Ageing and Adult Health (SAGE) is run by the World Health Organization's Multi-Country Studies unit in the Innovation, Information, Evidence and Research Cluster. SAGE is part of the unit's Longitudinal Study Programme which is compiling longitudinal data on the health and well-being of adult populations, and the ageing process, through primary data collection and secondary data analysis. SAGE baseline data (Wave 0, 2002/3) was collected as part of WHO's World Health Survey http://www.who.int/healthinfo/survey/en/index.html (WHS). SAGE Wave 1 (2007/10) provides a comprehensive data set on the health and well-being of adults in six low and middle-income countries: China, Ghana, India, Mexico, Russian Federation and South Africa. Objectives: To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions

Additional Objectives: To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes To develop a mechanism to link survey data to demographic surveillance site data To build linkages with other national and multi-country ageing studies To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults

Methods: SAGE's first full round of data collection included both follow-up and new respondents in most participating countries. The goal of the sampling design was to obtain a nationally representative cohort of persons aged 50 years and older, with a smaller cohort of persons aged 18 to 49 for comparison purposes. In the older households, all persons aged 50+ years (for example, spouses and siblings) were invited to participate. Proxy respondents were identified for respondents who were unable to respond for themselves. Standardized SAGE survey instruments were used in all countries consisting of five main parts: 1) household questionnaire; 2) individual questionnaire; 3) proxy questionnaire; 4) verbal autopsy questionnaire; and, 5) appendices including showcards. A VAQ was completed for deaths in the household over the last 24 months. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey.

Content Household questionnaire 0000 Coversheet 0100 Sampling Information 0200 Geocoding and GPS Information 0300 Recontact Information 0350 Contact Record 0400 Household Roster 0450 Kish Tables and Household Consent 0500 Housing 0600 Household and Family Support Networks and Transfers 0700 Assets and Household Income 0800 Household Expenditures 0900 Interviewer Observations

Individual questionnaire 1000 Socio-Demographic Characteristics 1500 Work History and Benefits 2000 Health State Descriptions and Vignettes 2500 Anthropometrics, Performance Tests and Biomarkers 3000 Risk Factors and Preventive Health Behaviours 4000 Chronic Conditions and Health Services Coverage 5000 Health Care Utilization 6000 Social Cohesion 7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method) 8000 Impact of Caregiving 9000 Interviewer Assessment

Geographic coverage

National coverage

Analysis unit

households and individuals

Universe

The household section of the survey covered all households in 19 of the 28 states in India which covers 96% of the population. Institutionalised populations are excluded. The individual section covered all persons aged 18 years and older residing within individual households.

Kind of data

Sample survey data [ssd]

Sampling procedure

World Health Survey Sampling India has 28 states and seven union territories. 19 of the 28 states were included in the design representing 96% of the population. India used a stratified multistage cluster sample design. Six states were selected in accordance with their geographic location and level of development. Strata were defined by the 6 states:(Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh and West Bengal), and locality (urban or rural). There are 12 strata in total. The 2000 Census demarcation was used as the sampling frame. Two stage and three stage sampling was adopted in rural and urban areas, respectively. In rural areas PSUs(villages) were selected probability proportional to size. The measure of size being the 2001 Census population in the village. SSUs (households) were selected using systematic sampling. TSUs (individuals) were selected using Kish tables. In urban areas, PSUs(city wards) were selected probability proportional to size. SSUs(census enumeration blocks), two were randomly selected from each PSU. TSU (households) were selected using systematic sampling. QSU (individuals) were selected as in rural areas. A sample of 379 EAs was selected as the primary sampling units(PSU).

SAGE Sampling The SAGE sample was pre-determined as all PSUs and households selected for the WHS/SAGE Wave 0 survey were included. Exceptions are three PSUs in Assam which were replaced as they were inaccessible due to flooding. And a further six PSUs were omitted for which the household roster information was not available. In each selected EA, a listing of the households was conducted to classify each household into the following mutually exclusive categories: 1)Households with a WHS/SAGE Wave 0 respondent aged 50-plus: all members aged 50-plus including the WHS/SAGE Wave 0 respondent were eligible for the individual interview. 2)Households with a WHS/SAGE Wave 0 respondent aged 47-49: all members aged 50-plus including the WHS/SAGE Wave 0 respondent aged 47-49 was eligible for the individual interview. 3)Households with a WHS/SAGE Wave 0 female respondent aged 18-46: all females members aged 18-49 including the WHS/SAGE Wave 0 female respondent aged 18-46 were eligible for the individual interview. 4)Households with a WHS/SAGE Wave 0 male respondent aged 18-46: three households were selected using systematic sampling and one male aged 18-49 was eligible for the individual interview. In the households not selected, all members aged 50-plus were eligible for the individual interview.

Stages of selection Strata: State, Locality=12 PSU: EAs=375 surveyed SSU: Households=10424 surveyed TSU: Individual=12198 surveyed

Mode of data collection

Face-to-face [f2f] PAPI

Research instrument

The questionnaires were based on the WHS Model Questionnaire with some modification and many new additions. A household questionnaire was administered to all households eligible for the study. A Verbal Autopsy questionnaire was administered to households that had a death in the last 24 months. An Individual questionniare was administered to eligible respondents identified from the household roster. A Proxy questionnaire was administered to individual respondents who had cognitive limitations. A Womans Questionnaire was administered to all females aged 18-49 years identified from the household roster. The questionnaires were developed in English and were piloted as part of the SAGE pretest in 2005. All documents were translated into Hindi, Assamese, Kanada and Marathi. SAGE generic questionnaires are available as external resources.

Cleaning operations

Data editing took place at a number of stages including: (1) office editing and coding (2) during data entry (3) structural checking of the CSPro files (4) range and consistency secondary edits in Stata

Response rate

Household Response rate=88% Cooperation rate=92%

Individual: Response rate=68% Cooperation rate=92%

Graph and download economic data for Population ages 65 and above for India (SPPOP65UPTOZSIND) from 1960 to 2024 about 65-years +, India, and population.

This map shows where senior populations are found throughout the world. Areas with more than 10% seniors are highlighted with a dark red shading while a dot representation reveals the number of seniors and their distribution in bright red.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

India Census: Population: Age: 85 data was reported at 1,264,798.000 Person in 2011. This records an increase from the previous number of 1,127,207.000 Person for 2001. India Census: Population: Age: 85 data is updated yearly, averaging 1,127,207.000 Person from Mar 1991 (Median) to 2011, with 3 observations. The data reached an all-time high of 1,264,798.000 Person in 2011 and a record low of 887,453.000 Person in 1991. India Census: Population: Age: 85 data remains active status in CEIC and is reported by Census of India. The data is categorized under India Premium Database’s Demographic – Table IN.GAD002: Census: Population: by Single Age.

Abstract

The Longitudinal Aging Study in India (LASI) aims to understand the situation of India’s elderly population by collecting data on their health, social situations, and economic circumstances. It will provide a foundation for innovative, rigorous, and multidisciplinary studies of aging in India that will inform policy and advance scientific knowledge. Its goal is to provide data harmonized with the Health and Retirement Study (HRS) and its sister studies around the world. A pilot study has been conducted that includes household survey data, Computer-Assisted Personal Interviews (CAPI) and molecular biomarkers. The results of the pilot study will inform the design of a full-scale, nationally representative LASI, with a sample of roughly 30,000 to be followed longitudinally (with refresher populations added as needed). Due to its harmonized design with parallel international studies, LASI will contribute to scientific insights and policy development in other countries as well. LASI will ultimately be part of a worldwide effort aimed at understanding how different institutions, cultures, and policies can understand and prepare for population ageing.

Documentation

You can download the pilot data at the Harvard Program on the Global Demography of Aging website

Methodology

The LASI pilot survey targeted 1,600 individuals aged 45 and older and their spouses, and will inform the design and rollout of a full-scale, nationally representative LASI survey. The expectation is that LASI will be a biennial survey and will be representative of Indians aged 45 and older, with no upper age limit.

1,600 age-qualifying individuals were drawn from a stratified, multistage area probability sampling design. After a series of pre-pilot studies designed to test the instrument and the key ideas behind it, pilot data were collected through face-to-face interviews over three month time periods. Descriptive analyses of the data will be performed and lessons will be drawn to inform the launching of a full-scale LASI survey.

The LASI pilot survey was conducted in four states: Karnataka, Kerala, Punjab, and Rajasthan. To capture regional variation we have included two northern states (Punjab and Rajasthan) and two southern states (Karnataka and Kerala). Karnataka and Rajasthan were included in the Study on Global AGEing and Adult Health (SAGE), which will enable us to compare our findings with the SAGE data. The inclusion of Kerala and Punjab demonstrates our aim to obtain a broader representation of India, where geographic variations accompanied by socioeconomic and cultural differences call for careful study and deliberation. Punjab is an example of an economically developed state, while Rajasthan is relatively poor, with very low female literacy, high fertility, and persisting gender disparities. Kerala, which is known for its relatively efficient health care system, has undergone rapid social development and is included as a potential harbinger of how other Indian states might evolve.

Population ages 80 and above, male (% of male population) in India was reported at 0.8939 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Population ages 80 and above, male (% of male population) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

India Population: Census: Age: 55 to 59 Year data was reported at 39,146.055 Person th in 03-01-2011. This records an increase from the previous number of 27,653.000 Person th for 03-01-2001. India Population: Census: Age: 55 to 59 Year data is updated decadal, averaging 27,653.000 Person th from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 39,146.055 Person th in 03-01-2011 and a record low of 21,473.000 Person th in 03-01-1991. India Population: Census: Age: 55 to 59 Year data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAD001: Census: Population: by Age Group.

This statistic depicts the age distribution of India from 2013 to 2023. In 2023, about 25.06 percent of the Indian population fell into the 0-14 year category, 68.02 percent into the 15-64 age group and 6.92 percent were over 65 years of age. Age distribution in India India is one of the largest countries in the world and its population is constantly increasing. India’s society is categorized into a hierarchically organized caste system, encompassing certain rights and values for each caste. Indians are born into a caste, and those belonging to a lower echelon often face discrimination and hardship. The median age (which means that one half of the population is younger and the other one is older) of India’s population has been increasing constantly after a slump in the 1970s, and is expected to increase further over the next few years. However, in international comparison, it is fairly low; in other countries the average inhabitant is about 20 years older. But India seems to be on the rise, not only is it a member of the BRIC states – an association of emerging economies, the other members being Brazil, Russia and China –, life expectancy of Indians has also increased significantly over the past decade, which is an indicator of access to better health care and nutrition. Gender equality is still non-existant in India, even though most Indians believe that the quality of life is about equal for men and women in their country. India is patriarchal and women still often face forced marriages, domestic violence, dowry killings or rape. As of late, India has come to be considered one of the least safe places for women worldwide. Additionally, infanticide and selective abortion of female fetuses attribute to the inequality of women in India. It is believed that this has led to the fact that the vast majority of Indian children aged 0 to 6 years are male.

According to data in India across different age groups from 60 to 85 years and above, the trend indicates a general fall in death rates in the country in the past ten years. In the age-group from 60-64 years there were approximately 22.5 deaths per thousand population in 2008, while in 2013 it was down to 18.4 and increasing slightly in 2018 to 19.5 deaths per thousand population. This was, however, still lower than in 2008.

Abstract

Purpose: The multi-country Study on Global Ageing and Adult Health (SAGE) is run by the World Health Organization's Multi-Country Studies unit in the Innovation, Information, Evidence and Research Cluster. SAGE is part of the unit's Longitudinal Study Programme which is compiling longitudinal data on the health and well-being of adult populations, and the ageing process, through primary data collection and secondary data analysis. INDEPTH SAGE Wave 1 (2006/7) provides data on the health and well-being of adults in: Ghana, India and South Africa.

Objectives: To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions

Additional Objectives: To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes To develop a mechanism to link survey data to demographic surveillance site data To build linkages with other national and multi-country ageing studies To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults

Methods: INDEPTH SAGE's first full round of data collection included persons aged 50 years and older in the health and demographic surveillance sites. All persons aged 50+ years (for example, spouses and siblings) were invited to participate. Standardized SAGE survey instruments were used in all countries consisting of two main parts: 1) household questionnaire; 2) individual questionnaire. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey.

Content - Household questionnaire 0000 Coversheet 0100 Sampling Information 0200 Geocoding and GPS Information 0300 Recontact Information 0350 Contact Record 0400 Household Roster 0450 Kish Tables and Household Consent 0500 Housing 0600 Household and Family Support Networks and Transfers 0700 Assets and Household Income 0800 Household Expenditures 0900 Interviewer Observations

• Individual questionnaire 1000 Socio-Demographic Characteristics 1500 Work History and Benefits 2000 Health State Descriptions and Vignettes 2500 Anthropometrics, Performance Tests and Biomarkers 3000 Risk Factors and Preventive Health Behaviours 4000 Chronic Conditions and Health Services Coverage 5000 Health Care Utilization 6000 Social Cohesion 7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method) 8000 Impact of Caregiving 9000 Interviewer Assessment

Geographic coverage

Rural subdistrict Mpumalanga Province

Analysis unit

household and individuals

Universe

Agincourt Health and Demographic Surveillance Site fifty plus population

Kind of data

Sample survey data [ssd]

Sampling procedure

Simple random sample of 575 persons 50 years and older with an oversample of women from the 2005 HDSS census.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires were based on the WHS Model Questionnaire with some modification and many new additions. A household questionnaire was administered to all households eligible for the study. An Individual questionnaire was administered to eligible respondents identified from the household roster. The questionnaires were developed in English and were piloted as part of the SAGE pretest. All documents were translated into Shangaan.

Cleaning operations

Data editing took place at a number of stages including: (1) office editing and coding (2) during data entry (3) structural checking of the CSPro files (4) range and consistency secondary edits in Stata

Response rate

86% of participants accepted to participate, 10% were not found and 4% refused to participate.

India Projection: Population: 10 Years: Female: Age: 85+ data was reported at 5,380,957.000 Person in 2031. This records an increase from the previous number of 3,842,066.000 Person for 2021. India Projection: Population: 10 Years: Female: Age: 85+ data is updated yearly, averaging 4,611,511.500 Person from Mar 2021 (Median) to 2031, with 2 observations. The data reached an all-time high of 5,380,957.000 Person in 2031 and a record low of 3,842,066.000 Person in 2021. India Projection: Population: 10 Years: Female: Age: 85+ data remains active status in CEIC and is reported by CEIC Data. The data is categorized under India Premium Database’s Demographic – Table IN.GAI002: Population Projection: 10 Years: by Age Group.

BackgroundThe ageing population in India is growing rapidly, but the decline in healthy life expectancy is more pronounced. This trend has been compounded and constituted by poor quality of life (QoL), with the salient underlying role of multimorbidity as the leading risk factor. This study primarily aimed to assess the intermediating role of multimorbidity as the risk factor for exogenous socioeconomic and demographic factors on QoL.MethodsThis study used data from 73,396 individuals aged 45 years and above from the Longitudinal Ageing Study in India (LASI), Wave – 1, 2017–18. Multimorbidity was defined as the simultaneous existence of two or more chronic conditions in an individual. The QoL score was constructed using Principal Component Analysis (PCA) by utilizing 21 factors under six domains (physical health, psychological health, social relationship, environmental satisfaction, life satisfaction and general health), with the composite QoL score ranging from 0 to 100. Further, the Structural equation model (SEM) was used to assess the role of multimorbidity as the intermediating risk factor for exogenous factors on QoL.ResultsDistributions of morbidities burden were skewed toward non-communicable diseases (NCDs) and sequentially escalated multimorbidity burden was observed among the oldest of old age groups. After the age of 75, there was a steep decline in the gradient of QoL score. The SEM results showed a substantial rise in multimorbidity burden leading to poor QoL with a magnitude of β = −2.39, p