Abstract

Collected data from the South India Community Health Study (SICHS), which covers a rural population of 1.1 million individuals residing in Vellore district in Tamil Nadu. The study includes a census of all 298,000 households drawn from 57 castes and a detailed survey of 5,000 representative households. The census, completed in 2014, provides a comprehensive demographic and socioeconomic profile of the study area. The household survey, conducted in 2016, collected information on marriage patterns, including within-caste marriage, close-kin marriage, arranged marriage, and migration of spouses between villages.

The SICHS was designed to examine a variety of socioeconomic phenomena and health problems, including the treatment of tuberculosis. The study area thus comprises three Tuberculosis Units (TU’s) within Vellore district that were purposefully selected to be representative of rural South India.

Geographic coverage

Vellore district, Tamil Nadu, India.

Analysis unit

Households

Universe

Rural population of 1.1 million individuals in Vellore district, Tamil Nadu.

Kind of data

Sample survey data [ssd]

Sampling procedure

South India Community Health Study (SICHS) has two components:

A census of all 298,000 households drawn from 57 castes residing on the study area, completed in 2014. A detailed survey of 5,000 representative households, completed in 2016.

The sampling frame for the household survey included all ever-married men aged 25-60 in the SICHS census plus (a small number of) divorced or widowed women with “missing” husbands who would have been aged 25-60, based on the average age-gap between husbands and wives. The sample was subsequently drawn to be representative of each caste in the study area, excluding castes with less than 100 households in the census.

Research instrument

• Female primary respondent (FPR) questionnaire.
• Male primary respondent (MPR) questionnaire.
• Household roster questionnaire.
• Spouse (SPO) os MPR questionnaire.
• NIRT census questionnaire.

In 2019, the mid-year population of India was approximately **** billion people. Comparatively, the population of the Maldives was approximately five hundred thousand in mid-2019.

South Asia possesses a significant amount of genetic diversity due to considerable intergroup differences in culture and language. There have been numerous reports on the genetic structure of Asian Indians, although these have mostly relied on genotyping microarrays or targeted sequencing of the mitochondria and Y chromosomes. Asian Indians in Singapore are primarily descendants of immigrants from Dravidian-language–speaking states in south India, and 38 individuals from the general population underwent deep whole-genome sequencing with a target coverage of 30X as part of the Singapore Sequencing Indian Project (SSIP). The genetic structure and diversity of these samples were compared against samples from the Singapore Sequencing Malay Project and populations in Phase 1 of the 1,000 Genomes Project (1 KGP). SSIP samples exhibited greater intra-population genetic diversity and possessed higher heterozygous-to-homozygous genotype ratio than other Asian populations. When compared against a panel of well-defined Asian Indians, the genetic makeup of the SSIP samples was closely related to South Indians. However, even though the SSIP samples clustered distinctly from the Europeans in the global population structure analysis with autosomal SNPs, eight samples were assigned to mitochondrial haplogroups that were predominantly present in Europeans and possessed higher European admixture than the remaining samples. An analysis of the relative relatedness between SSIP with two archaic hominins (Denisovan, Neanderthal) identified higher ancient admixture in East Asian populations than in SSIP. The data resource for these samples is publicly available and is expected to serve as a valuable complement to the South Asian samples in Phase 3 of 1 KGP.

Quality filtered Human Origin array dataset of 70 individuals analysed in Mukhopadhyay et al., 2024 from Coorg, Karnataka, India.

It was estimated that by 2050, India's Muslim population would grow by ** percent compared to 2010. For followers of the Hindu faith, this change stood at ** percent. According to this projection, the south Asian country would be home not just to the world's majority of Hindus, but also Muslims by this time period. Regardless, the latter would continue to remain a minority within the country at ** percent, with ** percent or *** billion Hindus at the forefront by 2050.

Whereas the population is expected to decrease somewhat until 2100 in Asia, Europe, and South America, it is predicted to grow significantly in Africa. While there were 1.55 billion inhabitants on the continent at the beginning of 2025, the number of inhabitants is expected to reach 3.81 billion by 2100. In total, the global population is expected to reach nearly 10.18 billion by 2100. Worldwide population In the United States, the total population is expected to steadily increase over the next couple of years. In 2024, Asia held over half of the global population and is expected to have the highest number of people living in urban areas in 2050. Asia is home to the two most populous countries, India and China, both with a population of over one billion people. However, the small country of Monaco had the highest population density worldwide in 2024. Effects of overpopulation Alongside the growing worldwide population, there are negative effects of overpopulation. The increasing population puts a higher pressure on existing resources and contributes to pollution. As the population grows, the demand for food grows, which requires more water, which in turn takes away from the freshwater available. Concurrently, food needs to be transported through different mechanisms, which contributes to air pollution. Not every resource is renewable, meaning the world is using up limited resources that will eventually run out. Furthermore, more species will become extinct which harms the ecosystem and food chain. Overpopulation was considered to be one of the most important environmental issues worldwide in 2020.

This dataset provides a comprehensive look at population and migration trends in five South Asian countries: Afghanistan, Bangladesh, India, Pakistan, and Sri Lanka, covering the years 1960 to 2023. The data is sourced directly from the World Bank API and contains detailed statistics on total population and net migration for each year.

This dataset is ideal for:

• Time-series analysis to study population trends over six decades.
• Migration studies to assess policy impacts and demographic shifts.
• Data visualization for dashboards and presentations.
• Machine learning applications in predictive analytics.

Columns: - Country: Name of the country. - Year: Year of the recorded data. - Total Population: The total population of the country. - Net Migration: Net migration balance (positive for immigration surplus, negative for emigration surplus).

Key Insights: - Afghanistan: Significant migration shifts due to conflicts and crises. - India: Continuous population growth with varying migration trends. - Bangladesh: A history of large emigration and its impact on demographics. - Pakistan: Migration surpluses in some years and large outflows in others. - Sri Lanka: Gradual population growth and consistent emigration patterns.

Seven human-specific Alu markers were studied in 574 unrelated individuals from 10 endogamous groups and 2 hill tribes of Tamil Nadu and Kerala states. DNA was isolated, amplified by PCR-SSP, and subjected to agarose gel electrophoresis, and genotypes were assigned for various Alu loci. Average heterozygosity among caste populations was in the range of 0.292–0.468. Among tribes, the average heterozygosity was higher for Paliyan (0.3759) than for Kani (0.2915). Frequency differences were prominent in all loci studied except Alu CD4. For Alu CD4, the frequency was 0.0363 in Yadavas, a traditional pastoral and herd maintaining population, and 0.2439 in Narikuravars, a nomadic gypsy population. The overall genetic difference (Gst) of 12 populations (castes and tribes) studied was 3.6%, which corresponds to the Gst values of 3.6% recorded earlier for Western Asian populations. Thus, our study confirms the genetic similarities between West Asian populations and South Indian castes and tribes and supported the large scale coastal migrations from Africa into India through West Asia. However, the average genetic difference (Gst) of Kani and Paliyan tribes with other South Indian tribes studied earlier was 8.3%. The average Gst of combined South and North Indian Tribes (CSNIT) was 9.5%. Neighbor joining tree constructed showed close proximity of Kani and Paliyan tribal groups to the other two South Indian tribes, Toda and Irula of Nilgiri hills studied earlier. Further, the analysis revealed the affinities among populations and confirmed the presence of North and South India specific lineages. Our findings have documented the highly diverse (micro differentiated) nature of South Indian tribes, predominantly due to isolation, than the endogamous population groups of South India. Thus, our study firmly established the genetic relationship of South Indian castes and tribes and supported the proposed large scale ancestral migrations from Africa, particularly into South India through West Asian corridor.

This study aimed to better understand the epidemiology, sero-epidemiology, and transmission of scrub typhus, murine typhus and tick-borne spotted fever. The study is conducted in South India (Tamil Nadu) and follows up people living in affected villages. Participants will be followed up at 4 to 6 weeks intervals to ask for the occurrence of undifferentiated fever since the last contact. Participants will be followed up through annual serological testing to determine the incidence of serological infection. The cohort is open; participants newly moving or being born into a household already included in the study are enrolled.

While Indians between 12 and 34 years dominated internet use from 2013 to 2019 with about ** percent of the total market, this was projected to change by 2025. Between 2019 and 2025, it was estimated that age group 35 years and older would make up ** percent of internet usage in India. Gender and internet Among the total internet users in the country, it was found that only about ** percent were female users. While this was expected to change to ** percent male users and ** percent female users by 2020, it still showed a gender gap in internet accessibility in the south-Asian country. While several factors lead to this digital gender gap, economic and socio-cultural barriers stand out as the most compelling reasons. Older Indians part of digitalization The median age of India’s population was around 27 years in 2015, echoing the range of the country’s majority internet user base. The estimated shift, however, in the years to come would be the successful efforts towards digitalizing India. If done right, this would propel older adults to adopt and master new media technologies in their daily activities beyond social media and communication, including the use of financial services.

In India, the share of the population that earned at least the equivalent of the highest ** percent of global income earners as of 2022 in purchasing power parity (PPP) terms was ** percent. Hyderabad topped the list with the highest share of middle-class and above category of consumers. Cities from south India topped the list with the first four ranks, followed by the national capital, Delhi.

Census: Population: West Bengal: Chak South Enayetnagar: Female data was reported at 3,263.000 Person in 03-01-2011. This records an increase from the previous number of 2,802.000 Person for 03-01-2001. Census: Population: West Bengal: Chak South Enayetnagar: Female data is updated decadal, averaging 3,032.500 Person from Mar 2001 (Median) to 03-01-2011, with 2 observations. The data reached an all-time high of 3,263.000 Person in 03-01-2011 and a record low of 2,802.000 Person in 03-01-2001. Census: Population: West Bengal: Chak South Enayetnagar: Female 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.GAC037: Census: Population: By Towns and Urban Agglomerations: West Bengal.

In 2023, over 252 thousand refugees were residing in the South Asian country of India. This was an increase from the previous year. Post 1990, the refugee population hit the lowest number in 2005. Since then the population has steadily increased.

Census: Population: West Bengal: Chak South Enayetnagar data was reported at 6,754.000 Person in 03-01-2011. This records an increase from the previous number of 5,664.000 Person for 03-01-2001. Census: Population: West Bengal: Chak South Enayetnagar data is updated decadal, averaging 6,209.000 Person from Mar 2001 (Median) to 03-01-2011, with 2 observations. The data reached an all-time high of 6,754.000 Person in 03-01-2011 and a record low of 5,664.000 Person in 03-01-2001. Census: Population: West Bengal: Chak South Enayetnagar 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.GAC037: Census: Population: By Towns and Urban Agglomerations: West Bengal.

Snapshot img

Population Health Management Market Size 2025-2029

The population health management market size is valued to increase USD 19.40 billion, at a CAGR of 10.7% from 2024 to 2029. Rising adoption of healthcare IT will drive the population health management market.

Major Market Trends & Insights

North America dominated the market and accounted for a 68% growth during the forecast period.
By Component - Software segment was valued at USD 16.04 billion in 2023
By End-user - Large enterprises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 113.32 billion
Market Future Opportunities: USD 19.40 billion
CAGR : 10.7%
North America: Largest market in 2023

Market Summary

The market encompasses a continually evolving landscape of core technologies and applications, service types, and regulatory frameworks. With the rising adoption of healthcare IT solutions, population health management platforms are increasingly being adopted to improve patient outcomes and reduce costs. According to a recent study, The market is expected to witness a significant growth, with over 30% of healthcare organizations implementing these solutions by 2025. The focus on personalized medicine and the need to manage the rising cost of healthcare are major drivers for this trend. Core technologies such as data analytics, machine learning, and telehealth are transforming the way healthcare providers manage patient populations.
Despite these opportunities, challenges such as data privacy concerns, interoperability issues, and the high cost of implementation persist. The market is further shaped by regional differences in regulatory frameworks and healthcare infrastructure. For instance, in North America, the Affordable Care Act has fueled the adoption of population health management solutions, while in Europe, the European Medicines Agency's focus on personalized medicine is driving demand.

What will be the Size of the Population Health Management Market during the forecast period?

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How is the Population Health Management Market Segmented and what are the key trends of market segmentation?

The population health management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Software
  Services


End-user

  Large enterprises
  SMEs


Delivery Mode

  On-Premise
  Cloud-Based
  Web-Based
  On-Premise
  Cloud-Based


End-Use

  Providers
  Payers
  Employer Groups
  Government Bodies
  Providers
  Payers
  Employer Groups


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth, with the software segment playing a crucial role in this expansion. Currently, remote patient monitoring solutions are witnessing a 25% adoption rate, enabling healthcare providers to monitor patients' health in real-time and intervene promptly when necessary. Additionally, predictive modeling and risk stratification models are being utilized to identify high-risk patients and provide personalized care plans, contributing to a 21% increase in disease management efficiency. Furthermore, the integration of electronic health records, wellness programs, care coordination platforms, and value-based care models is fostering a data-driven approach to healthcare, leading to a 19% reduction in healthcare costs.

Health equity initiatives and healthcare data analytics are essential components of population health management, ensuring equitable access to care and improving healthcare quality metrics. Looking ahead, the market is expected to grow further, with utilization management and care management programs seeing a 27% increase in implementation. Preventive health programs and clinical decision support systems are also anticipated to experience a 24% surge in adoption, emphasizing the importance of proactive care and early intervention. Moreover, population health strategies are evolving to incorporate behavioral health integration, interoperability standards, and disease registry data to provide comprehensive care. The use of disease prevalence data and public health surveillance is becoming increasingly crucial in addressing population health challenges and improving overall health outcomes.

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The Software segment was valued at USD 16.04 billion in 2019 and showed a gradual increase during the forecast period.

In conclusion, the market is

While the BRICS countries are grouped together in terms of economic development, demographic progress varies across these five countries. In 2019, India and South Africa were the only BRICS countries with a fertility rate above replacement level (2.1 births per woman). Fertility rates since 2000 show that fertility in China and Russia has either fluctuated or remained fairly steady, as these two countries are at a later stage of the demographic transition than the other three, while Brazil has reached this stage more recently. Fertility rates in India are following a similar trend to Brazil, while South Africa's rate is progressing at a much slower pace. Demographic development is inextricably linked with economic growth; for example, as fertility rates drop, female participation in the workforce increases, as does the average age, which then leads to higher productivity and a more profitable domestic market.

South Asia is one of the most densely populated regions in the world. This dataset comprehensively collects historical materials related to the population of South Asia and previous research results (see data description documents and references for details), carefully examines and estimates the population of South Asia (now India, Pakistan, Nepal, Bangladesh) from 640 to 1801 AD, and connects it with the population census data of British India from 1871 to 1941 (Nepal's data comes from Nepal's census data) and the United Nations World Population Prospects data from 1950 to 2020, obtaining the population of South Asia for a total of 22 periods (640, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1595, 1750, 1801, 1871, 1901, 1921, 1941, 1960, 1980, 2000, 2010, 2020) from 640 to 2020. Next, based on geographic detectors, select the dominant environmental factors that affect the spatial distribution of population, collect historical data on the distribution of residential areas (see data description document and references for details), and use a random forest regression model to spatialize the population size. On the basis of excluding uninhabited areas such as water bodies, glaciers, and bare/unused land, and determining the maximum historical population distribution range, a 1km resolution population dataset for South Asia from 640 to 2020 was developed. The leave one method was used to test the model, and the variance explained was 0.81, indicating good model accuracy. Compared with the existing HYDE historical population dataset, this study incorporates more historical materials and the latest research results in estimating the historical population; In using random forest regression for historical population spatial simulation, this study considers the changes in South Asian settlements over the past millennium, while the HYDE dataset only considers natural elements and considers them stable and unchanged. Therefore, this dataset is more reliable than the HYDE dataset and can more reasonably reveal the spatiotemporal characteristics of population changes in South Asia during historical periods. It is the basic data for the long-term evolution of human land relations, climate change attribution, and ecological protection research in South Asia.

Analysis of variance (AMOVA) among draught type zebu cattle from South India.

AP RAVI_Outcomes_gee

Pairwise FST (upper triangle) and pairwise Nei’s genetic distance (lower triangle) among draught type zebu, taurine and crossbred cattle from South India.