The statistic shows the total population of India from 2019 to 2029. In 2023, the estimated total population in India amounted to approximately 1.43 billion people.

Total population in India

India currently has the second-largest population in the world and is projected to overtake top-ranking China within forty years. Its residents comprise more than one-seventh of the entire world’s population, and despite a slowly decreasing fertility rate (which still exceeds the replacement rate and keeps the median age of the population relatively low), an increasing life expectancy adds to an expanding population. In comparison with other countries whose populations are decreasing, such as Japan, India has a relatively small share of aged population, which indicates the probability of lower death rates and higher retention of the existing population.

With a land mass of less than half that of the United States and a population almost four times greater, India has recognized potential problems of its growing population. Government attempts to implement family planning programs have achieved varying degrees of success. Initiatives such as sterilization programs in the 1970s have been blamed for creating general antipathy to family planning, but the combined efforts of various family planning and contraception programs have helped halve fertility rates since the 1960s. The population growth rate has correspondingly shrunk as well, but has not yet reached less than one percent growth per year.

As home to thousands of ethnic groups, hundreds of languages, and numerous religions, a cohesive and broadly-supported effort to reduce population growth is difficult to create. Despite that, India is one country to watch in coming years. It is also a growing economic power; among other measures, its GDP per capita was expected to triple between 2003 and 2013 and was listed as the third-ranked country for its share of the global gross domestic product.

India population health management market size is projected to exhibit a growth rate (CAGR) of 18.80% during 2024-2032. The rising focus of government bodies on preventative care, personalized interventions, and improved overall health outcomes of individuals is primarily driving the market growth.

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2024-2032. Our report has categorized the market based on component, mode of delivery, and end user.

The share of projected population increase in Uttar Pradesh, India from 2011 until 2036 is expected to grow by nearly 20 percent. By contrast, the estimated population increase in Uttarakhand is expected to be less than one percent during the same time period.

Why project population?
Population projections for a country are becoming increasingly important now than ever before. They are used primarily by government policy makers and planners to better understand and gauge future demand for basic services that predominantly include water, food and energy. In addition, they also support in indicating major movements that may affect economic development and in turn, employment and labour productivity. Consequently, this leads to amending policies in order to better adapt to the needs of society and to various circumstances.

Demographic projections and health interventions Demographic figures serve the foremost purpose of improving health and health related services among the population. Some of the government interventions include antenatal and neonatal care with the aim of reducing maternal and neonatal mortality and morbidity rates. In addition, it also focuses on improving immunization coverage across the country. Further, demographic estimates help in better preempting the needs of growing populations, such as the geriatric population within a country.

As of the year 2024, the population of the capital city of India, Delhi was over 33 million people. This was a 2.63 percent growth from last year. The historical trends show that the population doubled between 1990 and 2010. However, the population growth rate in the last three years has been the lowest since the recorded period starting in 1960. The UN estimated that the population was expected to reach around 35 million by 2030. Reasons for population growth   As per the Delhi Economic Survey, migration added over 200 thousand people to Delhi’s population in 2022. The estimates showed relative stability in natural population growth for a long time before the pandemic. The numbers suggest a sharp decrease in birth rates from 2020 onwards and a corresponding increase in death rates in 2021 due to the Covid-19 pandemic. The net natural addition or the remaining growth is attributed to migration. These estimates are based on trends published by the Civil Registration System. National Capital Region (NCR) Usually, population estimates for Delhi represent the urban agglomeration of Delhi, which includes Delhi and some of its adjacent suburban areas. The National Capital Region or NCR is one of the largest urban agglomerations in the world. It is an example of inter-state regional planning and development, centred around the National Capital Territory of Delhi, and covering certain districts of neighbouring states Haryana, Uttar Pradesh, and Rajasthan. Noida, Gurugram, and Ghaziabad are some of the key cities of NCR. Over the past decade, NCR has emerged as a key economic centre in India.

Owing to its high copy number and its small size, mtDNA analysis is the most reliable choice when biological materials from crime scenes are degraded or have mixed STR profiles. To examine the occurrence of heteroplasmy along with its frequency and pattern in both HV1 and HV2 regions of the mtDNA among unrelated individuals from India. Mitochondrial DNA control region [hypervariable region one (HV1) and hypervariable region two (HV2)] were analysed in blood and buccal tissues of 104 unrelated individuals from the Indian state of Gujarat. A high frequency of point heteroplasmy (PH) and length heteroplasmy (LH) was revealed. PH was detected in 7.69% of the population, with a higher frequency observed in blood than in buccal samples. However, there were no statistically significant differences in PH between the two tissues (Chi-square = 0.552, p ≥ 0.05). A total of six PH positions were detected: three at HV1, and another three at HV2. The studied population showed 46.15% LH in the HV1 and HV2 regions of both tissues. The LH positions observed in the Gujarat population were the same as those previously reported at HV1 np16184–16193 and HV2 np303–315. Our findings suggest that differences in the pattern of heteroplasmy found in different tissues can complicate the forensic analysis, on the other hand, the probability of a match between the questioned and reference samples increases when the heteroplasmy is identical in both tissues. Variability of PH among persons and even within tissues recommends analysing multiple tissue samples before drawing a conclusion in forensic mtDNA analyses.

According to latest figures, the Chinese population decreased by 1.39 million to around 1.408 billion people in 2024. After decades of rapid growth, China arrived at the turning point of its demographic development in 2022, which was earlier than expected. The annual population decrease is estimated to remain at moderate levels until around 2030 but to accelerate thereafter. Population development in China China had for a long time been the country with the largest population worldwide, but according to UN estimates, it has been overtaken by India in 2023. As the population in India is still growing, the country is very likely to remain being home of the largest population on earth in the near future. Due to several mechanisms put into place by the Chinese government as well as changing circumstances in the working and social environment of the Chinese people, population growth has subsided over the past decades, displaying an annual population growth rate of -0.1 percent in 2024. Nevertheless, compared to the world population in total, China held a share of about 18 percent of the overall global population in 2022. China's aging population In terms of demographic developments, the birth control efforts of the Chinese government had considerable effects on the demographic pyramid in China. Upon closer examination of the age distribution, a clear trend of an aging population becomes visible. In order to curb the negative effects of an aging population, the Chinese government abolished the one-child policy in 2015, which had been in effect since 1979, and introduced a three-child policy in May 2021. However, many Chinese parents nowadays are reluctant to have a second or third child, as is the case in most of the developed countries in the world. The number of births in China varied in the years following the abolishment of the one-child policy, but did not increase considerably. Among the reasons most prominent for parents not having more children are the rising living costs and costs for child care, growing work pressure, a growing trend towards self-realization and individualism, and changing social behaviors.

Background

The world is rapidly becoming urban with the global population living in cities projected to double by 2050. This increase in urbanization poses new challenges for the spread and control of communicable diseases such as malaria. In particular, urban environments create highly heterogeneous socio-economic and environmental conditions that can affect the transmission of vector-borne diseases dependent on human water storage and waste water management. Interestingly India, as opposed to Africa, harbors a mosquito vector, Anopheles stephensi, which thrives in the man-made environments of cities and acts as the vector for both Plasmodium vivax and Plasmodium falciparum, making the malaria problem a truly urban phenomenon. Here we address the role and determinants of within-city spatial heterogeneity in the incidence patterns of vivax malaria, and then draw comparisons with results for falciparum malaria.

Methodology/principal findings

Statistical analyses and a phenomenological transmission model are applied to an extensive spatio-temporal dataset on cases of Plasmodium vivax in the city of Ahmedabad (Gujarat, India) that spans 12 years monthly at the level of wards. A spatial pattern in malaria incidence is described that is largely stationary in time for this parasite. Malaria risk is then shown to be associated with socioeconomic indicators and environmental parameters, temperature and humidity. In a more dynamical perspective, an Inhomogeneous Markov Chain Model is used to predict vivax malaria risk. Models that account for climate factors, socioeconomic level and population size show the highest predictive skill. A comparison to the transmission dynamics of falciparum malaria reinforces the conclusion that the spatio-temporal patterns of risk are strongly driven by extrinsic factors.

Conclusion/significance

Climate forcing and socio-economic heterogeneity act synergistically at local scales on the population dynamics of urban malaria in this city. The stationarity of malaria risk patterns provides a basis for more targeted intervention, such as vector control, based on transmission ‘hotspots’. This is especially relevant for P. vivax, a more resilient parasite than P. falciparum, due to its ability to relapse and the operational shortcomings of delivering a “radical cure”.

Poverty and empowerment impacts of the Bihar Rural Livelihoods Project: Evidence from a Mixed-Methods Cluster-Randomized Trial Jeevika is a World Bank assisted project focussed (now under the umbrella of the NRLM) on building networks of women's self-help credit and savings groups,and then using them as a base of other "vertical" interventions. This houshold and village survey data was collected over two rounds to conduct an impact evaluation of Phase 2 of the project with random assignment of the project over a two year period. Collaboration: World Bank Social Observatory team with Government of Bihar. Evaluation design, methods and implementation In order to evaluate the impacts of Jeevika, 180 panchayats were randomly selected from within 16 blocks in seven districts where scale-up of the project was planned but had not yet occurred. Some of these blocks were in districts relatively far from Patna, which had not yet been entered by the project (Madhepura, Saharsa, Supaul), while others were within the larger districts within which Jeevika was already operating (Gaya, Nalanda, Madhubani, Muzaffarpur). The project had already entered these districts in Phase 1, but had not yet expanded to all blocks due to (project) capacity constraints. Within each of the study villages, hamlets (tolas) in which the majority of the population belonged to a scheduled caste or scheduled tribe were identified. This was the same procedure as used by Jeevika to identify the target population (of poor women) for mobilization into the project. Tolas were identified through a focus group discussion held in each village, along with the population of target castes (SC/STs) within each. In Bihar, tola boundaries are easily distinguishable. Field teams would enter the tola at a random point, determine the skip pattern based on the population size and target sample size, and select households through a random walk. Survey staff aimed to include 70% SC/ST households, and 30% households from other castes in each village, in order to ensure variation in socio-economic status within the sample. If the households in selected tolas included fewer SC/ST households than this, households from nearby non-SC/ST majority tolas were also included in the sample. Interviews for the quantitative study were conducted using a structured paper survey form. Baseline and follow up surveys included detailed questions on debt, asset holdings, consumption expenditures, livelihood activities, and women’s mobility, role in household decisions, and aspirations. In addition, in each village, a focus group discussion was conducted, through which data were collected on village level attributes such as local sources of credit, interest rates from each source, local wage rates, and the presence of or distance to markets and other institutions and amenities. Respondents were not compensated for their time. If a respondent was unavailable during initial field visit, the supervisor recorded contact details and returned with interviewers at a later date. As long as the survey team was in that district, repeat visits were undertaken, keeping attrition to a minimum. If a household could not be re-surveyed at endline, it was replaced with another household in the same village. Short re-surveys containing a subset of questions from the main survey were conducted by supervisors for 10% of the sample. Staff from the project also conducted occasional visits after the survey was completed in a village to confirm that all modules had been covered by survey staff. Data was entered in duplicate using CSPro and any discrepancies were corrected based on the paper form. Following the baseline survey, panchayats were stratified on the 16 administrative blocks in the sample and the panchayat-level mean of outstanding high cost (monthly interest rate of 4% or higher) debt held by households at baseline. They were then randomly assigned to an early rollout group or a late rollout group using the random number generator within the Stata statistical analysis software package. The baseline survey was administered to 8988 households across 333 villages in 179 panchayats. The target number of households per panchayat was 50, but there was some variation around this in reality. The lowest number of households in a given panchayat was 49 (9 panchayats), and the largest number was 53 households (3 panchayats). To ensure that control panchayats were not entered by the project, Jeevika held a quarterly ""evaluation panchayat"" meeting, which block project managers of the 16 blocks were required to attend. At these meetings the project M&E team checked whether any village in a control panchayat had been entered, and received an update on progress in treatment panchayats. This procedure was successful in maintaining adherence to randomized treatment assignment throughout the evaluation period. Of the 4,472 households in the sample across 89 panchayats allocated to receive the SHG intervention, 2,722 reported that one of their members belonged to an SHG by endline, constituting 61% of the sample. Since SHG membership was optional, approximately 38% of households in treatment group panchayats had no member in an SHG by endline. The remaining 56 households (across 39 panchayats) did not answer this question or were lost to follow-up (only one such household was not replaced). Although it was possible for those residing in control areas to join (non-Jeevika) SHGs, the proportion of households group in this area containing SHG members remained minimal at endline, with only 460 households (just over 10% of the total sample) reporting SHG membership. Attrition (and replacement) were similar in control and treatment arms, with 132 treatment group baseline households not reached for a follow-up interview and all but one of these replaced, and 128 not reached and thus replaced in the control group. The qualitative evaluation draws on data collected from 2011 to early 2015 in six villages, two where Jeevika had been operating since 2006, two it entered during Phase II, and two where it had not yet intervened by the end of data collection. The Phase I treatment villages were selected at random from the set of previously entered villages in two different districts – Muzaffarpur and Madhubani. Each treatment village was then matched with a set of control villages using propensity score matching methods (Imbens and Rubin 2015) on the basis of village level data from the 2001 government census on literacy, caste composition, landlessness, levels of outmigration, and the availability of infrastructure. In order to find the closest treatment-control match, field investigators then visited the set of possible controls for two days for visual inspection and qualitative assessment. This combined quantitative and qualitative matching method yielded three matched pairs of phase I treatment, phase II treatment, and control villages, with each pair located within the same district. This method of sample selection allows comparison of villages receiving the intervention at each stage with their statistical clones that received it at a different stage or had not received it at all, allowing us to draw causal inferences about the effects induced by Jeevika during the different phases of its expansion. For the purpose of keeping their identity anonymous, we refer to the villages in Madhubani district as Ramganj (Phase I treatment), Nauganj (Phase II treatment) and Virganj (control) and the villages in Muzaffarpur district Saifpur (Phase I treatment), Raipur (Phase II treatment) and Bhimpur (Control). Villages in Madhubani are divided into segregated and caste-homogenous tolas. Brahmins are a majority in these villages, and their tolas are located close to the main resources of the village: the temple, pond and school. All other tolas extend southwards in decreasing order of status in the caste hierarchy, with the Schedule Caste (SC) communities being located farthest south. Each of these communities is also spatially segregated. The SC communities of these villages are mainly comprised of Musahar, Pasi, Ram, and Dhobi subcastes, and the other backward caste communities are comprised of Yadav, Mandal, Badhai, Hajaam, and Teli subcastes. The only big difference between Ramganj and Virganj is that the former has a sizeable Muslim population, comprising Sheikhs, Ansaris, Nutts and Pamariyas, while in the latter, there is only one Muslim (Sheikh) family in the entire village. Inhabitants of these villages primarily depend on agriculture and related activities for their livelihood. The villages in Muzaffarpur district are largely similar to the ones in Madhubani with the important differences being that they are primarily bazaar (market)-centric and the dominant caste is the Chaudhury, who belong to the business community. In each of these villages, first, preliminary studies were conducted using several participatory rural appraisal methods to gain an understanding of the layout of the village. Following this, a team of four field investigators (recruited from a local research-based NGO) accompanied by one of the three principal researchers would visit the villages every three to four months for a cycle of data collection (11 in total over the study period). During every cycle, the ethnographers would enter a different tola in the village for a week (there are roughly 10 tolas in each village). The ethnographers spoke to as many respondents as possible across the village and also returned to the first few respondents in the concluding cycles of data collection. These repeat interviews allowed us to see how respondents reflected on changes experienced as a result of the project [or otherwise] over the four-year period. The first set of participants was selected to be representative of different socioeconomic strata in the village, and subsequent participants were selected via a mixture of purposive and snowball sampling. We

Snapshot img

Population Health Management Market Size 2025-2029

The population health management market size is forecast to increase by USD 19.40 billion at a CAGR of 10.7% between 2024 and 2029.

Population health management (PHM) In the healthcare sector has witnessed significant growth due to several catalysts. The adoption of healthcare IT is a major driving factor, enabling the collection, integration, and analysis of large volumes of data from various sources. This data, including electronic health records (EHR), point of care data management software, and remote patient monitoring, is utilized for healthcare analytics and data analytics.

The rise in chronic diseases necessitates proactive care and management, which is addressed through predictive analytics and chronic disease management. Additionally, advancements in healthcare IT, such as telehealth, artificial intelligence, and cloud computing, facilitate remote patient care and improved patient engagement. Genetic testing and data security are essential components of PHM, ensuring personalized medicine and data privacy. The focus on cost reduction and the increasing cost of installing PHM platforms further fuel market growth.

What will be the Size of the Population Health Management Market During the Forecast Period?

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The market is experiencing significant growth and transformation as healthcare providers shift towards patient-centered care and value-based models. Telemedicine adoption, patient satisfaction surveys, and remote patient monitoring are key trends driving market expansion. Patient segmentation, healthcare cost transparency, medical device integration, and precision medicine are also critical components of population health management. Healthcare data security and interoperability standards are essential considerations as healthcare organizations leverage healthcare analytics and data analytics to improve patient outcomes and reduce costs. Additionally, medical billing optimization, healthcare policy analysis, predictive modeling, clinical decision support, financial data, and healthcare workforce development are essential elements of this evolving market.
The future of population health management will be shaped by healthcare innovation, including virtual care, patient safety, personalized medicine, patient engagement, healthcare technology, quality improvement, patient needs, and precision healthcare. Telehealth, predictive analytics, cost-effective care, and digital health solutions are also expected to play a significant role In the market's development.

How is this Population Health Management Industry segmented and which is the largest segment?

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.ComponentSoftwareServicesEnd-userLarge enterprisesSMEsDelivery ModeOn-PremiseCloud-BasedEnd-UseProvidersPayersEmployer GroupsGeographyNorth AmericaCanadaUSEuropeGermanyUKFranceItalyAsiaChinaIndiaJapanSouth KoreaRest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period. The market's software segment is a significant and evolving sector, facilitating healthcare organizations in managing and enhancing the health outcomes of diverse populations. Software solutions collect, analyze, and utilize health data for informed decision-making. A population health management platform aggregates and integrates data from sources such as electronic health records, claims data, and patient-generated data. Advanced analytics enable the identification of at-risk populations, addressing chronic conditions, and improving patient outcomes. Value-based reimbursement models, such as fee-for-service and value-based payment, are driving the adoption of these platforms. Healthcare IT services, including data-driven healthcare, precision medicine, and care coordination, are integral to the software segment's growth.

Government bodies and healthcare providers, including hospitals, clinics, and healthcare facilities, are strategic collaborators in implementing population health management solutions. The software segment's innovation is fueled by technical knowledge, AI diagnostics, machine learning, edge computing, wearable devices, and IoT integration. Security risks, such as data encryption, predictive models, and cybersecurity, are addressed through interoperability tools and health metrics. The market's growth is influenced by healthcare expenditures, big data, and tailored interventions for chronic conditions.

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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.

Machine l

The graph shows the population growth in China from 2000 to 2024. In 2024, the Chinese population decreased by about 0.1 percent or 1.39 million to around 1.408 billion people. Declining population growth in China Due to strict birth control measures by the Chinese government as well as changing family and work situations of the Chinese people, population growth has subsided over the past decades. Although the gradual abolition of the one-child policy from 2014 on led to temporarily higher birth figures, growth rates further decreased in recent years. As of 2024, leading countries in population growth could almost exclusively be found on the African continent and the Arabian Peninsula. Nevertheless, as of mid 2024, Asia ranked first by a wide margin among the continents in terms of absolute population. Future development of Chinese population The Chinese population reached a maximum of 1,412.6 million people in 2021 but decreased by 850,000 in 2022 and another 2.08 million in 2023. Until 2022, China had still ranked the world’s most populous country, but it was overtaken by India in 2023. Apart from the population decrease, a clear growth trend in Chinese cities is visible. By 2024, around 67 percent of Chinese people lived in urban areas, compared to merely 36 percent in 2000.

Introduction: Hepatitis C virus (HCV) infection prevalence is believed to be elevated in Punjab, India; however, state-wide prevalence data are not available. An understanding of HCV prevalence, risk factors and genotype distribution can be used to plan control measures in Punjab. Methods: A cross-sectional, state-wide, population-based serosurvey using a multi-stage stratified cluster sampling design was conducted October 2013 to April 2014. Children aged >5 years and adults were eligible to participate. Demographic and risk behavior data were collected, and serologic specimens were obtained and tested for anti-HCV antibody, HCV Ribonucleic acid (RNA) on anti-HCV positive samples, and HCV genotype. Prevalence estimates and adjusted odds ratios for risk factors were calculated from weighted data and stratified by urban/rural residence. Results: 5,543 individuals participated in the study with an overall weighted anti-HCV prevalence of 3.6% (95% Confidence Interval [CI]: 3.0%-4.2%) and chronic infection (HCV Ribonucleic acid test positive) of 2.6% (95% CI: 2.0%-3.1%). Anti-HCV was associated with being male (adjusted odds ratio 1.52; 95% CI: 1.08-2.14), living in a rural area (adjusted odds ratio 2.53; 95% CI: 1.62-3.95) and was most strongly associated with those aged 40-49 (adjusted odds ratio 40-49 vs 19-29-year-olds 3.41; 95% CI: 1.90-6.11). Anti-HCV prevalence increased with each blood transfusion received (adjusted odds ratio 1.36; 95% CI: 1.10-1.68) and decreased with increasing education, (adjusted odds ratio 0.37 for graduate-level vs. primary school/no education; 95% CI: 0.16-0.82). Genotype 3 (58%) was most common among infected individuals. Discussion: The study findings, including the overall prevalence of chronic HCV infection, associated risk factors and demographic characteristics, and genotype distribution can guide prevention and control efforts, including treatment provision. In addition to high-risk populations, efforts targeting rural areas and adults aged >40 would be the most effective for identifying infected individuals.

Impact Evaluation Framework and Results: Odisha Rural Livelihoods Project This is a baseline panel survey of households from rural Odisha collected to evaluate the impact of the TRIPTI Livelihoods project using a quasi-RDD design. The data were also used, after merging it with information on rainfall patterns, to assess the impact of TRIPTI on mitigating the effects of Hurricane Phailin. Collaboration: World Bank Social Observatory in collaboration with the Government of Odisha. EVALUATION DESIGN This evaluation was designed in 2011 after the project areas were chosen, which meant that a “gold standard” impact evaluation with a Randomized Control Trial method was not feasible. The selection rule for project areas however allowed for the next best available tool of Regression Discontinuity Design (See below). Using this design, the difference in difference approach, which measures the change in outcomes between project or “treatment” and comparable non-project or “control” areas over the evaluation time period can be used to evaluate the impact the project. Selection of TRIPTI Blocks - In each TRIPTI district, 4 blocks were to be chosen for project ""treatment"" using a ""backwardness"" selecton rule - All blocks were given a score that gave weightage to block level development indices (Ghadei Committee Index), SHG coverage, total population and SC/ST Populations - Program blocks then ranked in descending order of scores, and the 4 blocks with highest backwardness score wee chosen for the program Selection of Evaluation Blocks - In each district, the non- program or ""control"" block was chosen to the block that had the closest score to the last of the 4 program blocks - A pair of blocks- one program or “treatment” block, and non- program or “control” blocks) were chosen to be part of the evaluation sample in every district Selection of GPs, Villages and Households - Treatment is universal at the level of the block, which implies that at sub-block units, or Gram Panchayats (GPs) receive TRIPTIs interventions. > 4 GPs randomly chosen in each block > 2 vilages randomly chosen in each GP - All targeted households in a TRIPTI GP are eligible TRIPTI interventions > 15 households randonly chosen in each village > Oversampling of SC/ST housholds to proxy for target housholds EVALUATION DATA The data used in this evaluation come from the first (baseline) of two surveys commissioned by TRIPTI with technical assistance from the World Bank. An independent survey firm implemented both surveys. The baseline survey was completed before the initiation of TRIPTI in the evaluation sample area, between September-November 2011; and the follow up survey was implemented over the same month in 2014. This data therefore covers a 3-year period during which TRIPTI was in operation. The data collected focused on four modules. A general household module collected data on household consumption expenditures (following the same format as India’s National Sample Surveys that are used to measure poverty); and detailed information on the livelihoods portfolio and debt profile of households. A woman’s module was also administered to an adult married woman in each household. This module measured different metrics of women’s empowerment; and included questions on decision-making within the household, and on women’s participation in local public action. Two focus group discussions with the village in general, and women in the villages separately were also implemented in order to understand key elements related to local politics and civic action. In addition, a GPLF survey module- that covered 58 project Gram Panchayats - was implemented during the follow up survey. As part of this evaluation, data was to be collected from a sample of 3000 households selected at random from these 160 villages twice: once before the launch of project interventions in these 80 GPs at baseline (2011), and once at the end of the project. Due to some missing data, the baseline survey included in the end a total of 2875 households and the end line survey included a total of 2,874 households. The working sample is the total set of these households with reliable data. In each round of the survey, each household is linked to village-level data from that round. This evaluation report is an output of the Social Observatory Team of the World Bank and the Orissa Rural Livelihoods Project (TRIPTI), and it was financed by the South Asia Food and Nutrition Security Initiative (SAFANSI). There are two parts to this repot- an executive summary, and a technical paper that is authored by Shareen Joshi (Georgetown University), Nethra Palaniswamy (World Bank), and Vijayendra Rao (World Bank). Discussions with the TRIPTI project team led by the Additional Project Director Babita Mohapatra, and the World Bank task team led by Samik Das were critical to the design of this evaluation. Support from Arvind Padhee and DV Swamy who served as Project Directors of TRIPTI; from...

According to a survey conducted between 2019 to 2021 in India, the prevalence of hypertension was significantly higher among the population in the fourth and highest wealth quintile. The prevalence of hypertension was highest with over 29 percent among men in the highest wealth quintile.

Causes of high blood pressure

Over 230 million Indians suffer from high blood pressure of which over 11 percent lie between the ages of 15 to 49 years. The younger generation today is succumbing to fast-paced and stressful lives and is hence, at risk of developing high blood pressure or being diagnosed with prehypertension which can be the onset of hypertension in the future.

In addition, unhealthy diets compounded with obesity, physical inactivity, and poor stress management can increase the risk of hypertension. Further, the use of tobacco and alcohol consumption can damage blood vessels increasing the risk of developing cardiovascular diseases.

Mitigation and control strategies

The government of India set a target of reducing the prevalence of hypertension by 25 percent by 2025, making it a public health priority. Only 12 percent of people with hypertension have self-monitoring devices and, hence, have their levels under control. Uncontrolled blood pressure is known to be the main cause of cardiovascular disease in India. Under the India Hypertension Control Initiative, procurement of anti-hypertension medicines, building capacity of health care providers, and monitoring patients through digital apps was a successful step toward prevention and control.

The brown planthopper (BPH; Nilaparvata lugens) is one of India’s most destructive pests of rice. BPH, a monophagous migratory insect, reported from all major rice-growing ecosystems of the country, is capable of traversing large distances and causing massive crop loss. A crucial step for developing viable management strategies is understanding its population dynamics. Very few reliable markers are currently available to screen BPH populations for their diversity. In the current investigation, we developed a combinatorial approach using the polymorphism present within the mitochondrial Control Region of BPH and in the nuclear genome (genomic simple sequence repeats; gSSRs) to unravel the diversity present in BPH populations collected from various rice-growing regions of India. Using two specific primer pairs, the complete Control Region (1112 to 2612 bp) was PCR amplified as two overlapping fragments, cloned and sequenced from BPH individuals representing nine different populations. Results revealed extensive polymorphism within this region due to a variable number of tandem repeats. The three selected gSSR markers also exhibited population-specific amplification patterns. Overall genetic diversity between the nine populations was high (>5%). Further, in silico double-digestion of the consensus sequences of the Control Region, with HpyCH4IV and Tsp45I restriction enzymes, revealed unique restriction fragment length polymorphisms (digital-RFLPs; dRFLPs) that differentiated all the nine BPH populations. To the best of our knowledge, this is the first report of markers developed from the Control Region of the BPH mitogenome that can differentiate populations. Eventually, such reliable and rapid marker-based identification of BPH populations will pave the way for an efficient pest management strategy.

In the century between Napoleon's defeat and the outbreak of the First World War (known as the "Pax Britannica"), the British Empire grew to become the largest and most powerful empire in the world. At its peak in the 1910s and 1920s, it encompassed almost one quarter of both the world's population and its land surface, and was known as "the empire on which the sun never sets". The empire's influence could be felt across the globe, as Britain could use its position to affect trade and economies in all areas of the world, including many regions that were not part of the formal empire (for example, Britain was able to affect trading policy in China for over a century, due to its control of Hong Kong and the neighboring colonies of India and Burma). Some historians argue that because of its economic, military, political and cultural influence, nineteenth century Britain was the closest thing to a hegemonic superpower that the world ever had, and possibly ever will have. "Rule Britannia" Due to the technological and logistical restrictions of the past, we will never know the exact borders of the British Empire each year, nor the full extent of its power. However, by using historical sources in conjunction with modern political borders, we can gain new perspectives and insights on just how large and influential the British Empire actually was. If we transpose a map of all former British colonies, dominions, mandates, protectorates and territories, as well as secure territories of the East India Trading Company (EIC) (who acted as the precursor to the British Empire) onto a current map of the world, we can see that Britain had a significant presence in at least 94 present-day countries (approximately 48 percent). This included large territories such as Australia, the Indian subcontinent, most of North America and roughly one third of the African continent, as well as a strategic network of small enclaves (such as Gibraltar and Hong Kong) and islands around the globe that helped Britain to maintain and protect its trade routes. The sun sets... Although the data in this graph does not show the annual population or size of the British Empire, it does give some context to how Britain has impacted and controlled the development of the world over the past four centuries. From 1600 until 1920, Britain's Empire expanded from a small colony in Newfoundland, a failing conquest in Ireland, and early ventures by the EIC in India, to Britain having some level of formal control in almost half of all present-day countries. The English language is an official language in all inhabited continents, its political and bureaucratic systems are used all over the globe, and empirical expansion helped Christianity to become the most practiced major religion worldwide. In the second half of the twentieth century, imperial and colonial empires were eventually replaced by global enterprises. The United States and Soviet Union emerged from the Second World War as the new global superpowers, and the independence movements in longstanding colonies, particularly Britain, France and Portugal, gradually succeeded. The British Empire finally ended in 1997 when it seceded control of Hong Kong to China, after more than 150 years in charge. Today, the United Kingdom consists of four constituent countries, and it is responsible for three crown dependencies and fourteen overseas territories, although the legacy of the British Empire can still be seen, and it's impact will be felt for centuries to come.

The Defibrillator market was valued at $13.16 billion in 2022 and will reach $23.1 billion by 2030, with a CAGR of 5.8% during the forecast period.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of XX % from 2024 to 2031. The Asia Pacific region is the fastest-growing market with a CAGR of XX% from 2024 to 2031 and it is projected that it will grow at a CAGR of XX% in the future. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million. Latin America had a market share for more than XX% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031. The Defibrillator Market held the highest market revenue share in 2024. Market Dynamics of the Defibrillator Market

Key Drivers for The Defibrillator Market

The growing senior population in emerging economies fuels, the growth of the defibrillator market 

Emerging economies are acknowledged as one of the key areas of medical device growth. The growing patient base of people with degenerative diseases, orthopedic ailments, cardiovascular diseases, and other conditions can be greatly assisted by more advanced technology-based implantable devices, particularly for conditions for which pharmaceutical treatment is effective. For instance, as per the Asia Aging International Population Reports 2022, it is anticipated that the population of those 65 years of age and over will increase from 730 million in 2020 to around 2 billion by 2060. The prevalence of chronic illnesses is rising as a result of population growth. The Centers for Disease Control and Prevention (CDC) and the National Association of Chronic Disease Directors estimate that one or more of the five chronic diseases—diabetes, cancer, heart disease, stroke, and chronic obstructive pulmonary disease—cause more than two-thirds of all fatalities. Consequently, as the population grows and the prevalence of cardiovascular disorders rises in emerging nations like Brazil and India, healthcare spending rises as well, driving up the need for defibrillators and also serves as an opportunity for the key players in emerging economies. Source:(https://www.weforum.org/agenda/2023/02/world-oldest-populations-asia-health/) Thus, the rising geriatric population in emerging economies is a significant driver for the growth of the defibrillator market. As these populations age, the incidence of cardiac-related issues increases, creating a greater demand for defibrillators in healthcare settings to address cardiac emergencies and improve patient outcomes.

Market demand for defibrillators is driven by the growing need for portable defibrillators due to the prevalence of cardiovascular diseases. 

Heart disease, stroke, peripheral vascular disease, and other cardiovascular disorders are the main causes of mortality worldwide, particularly in the United States and other industrialized nations. The primary contributing factors are obesity and its aftereffects, which include dyslipidemia, hypertension, and insulin resistance, which can result in diabetes. In 2020, 19.1 million deaths worldwide were linked to cardiovascular disease (CVD), according to the American Heart Association (AHA). Furthermore, more people die from cardiovascular illnesses annually than from all cancers combined with chronic lower respiratory ailments. Defibrillators are implanted devices that can be placed either within or outside the body to treat certain cardiac occurrences, such as arrhythmias. Ventricular tachycardia (VT) or ventricular fibrillation (VF), two types of fast, chaotic heart activity, are the primary cause of most cardiac events. If treated within minutes, most sufferers of cardiac arrest can recover; however, administering an electrical tremor is the only therapy that works. Various kinds of defibrillators are employed to manage arrhythmia and avert severe consequences. For instance, in October 2023, The FDA in the United States approved the Aurora EV-IVD MRI SureScan and Epsila EV MRI SureScan defibril...

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

India’s per capita net national income or NNI was around 200 thousand rupees in financial year 2025. The annual growth rate was 8.6 percent as compared to the previous year. National income indicators   While GNI (Gross National Income) and NNI are both indicators for a country’s economic performance and welfare, the GNI is related to the GDP plus the net receipts from abroad, including wages and salaries, property income, net taxes and subsidies receivable from abroad. On the other hand, the NNI of a country is equal to its GNI net of depreciation. In 2020, India ranked second amongst the Asia Pacific countries in terms of its gross national income. This has been possible due to a favorable GDP growth in India. Measuring wealth versus welfare   National income per person or per capita is often used as an indicator of people's standard of living and welfare. However, critics object to this by citing that since it is a mean value, it does not reflect the real income distribution. In other words, a small wealthy class of people in the country can skew the per capita income substantially, even though the average population has no change in income. This is exemplified by the fact that in India, the top one percent of people, control over 40 percent of the country’s wealth.

As of 2023, the bulk of the Chinese population was aged between 25 and 59 years, amounting to around half of the population. A breakdown of the population by broad age groups reveals that around 61.3 percent of the total population was in working age between 16 and 59 years in 2023. Age cohorts below 25 years were considerably smaller, although there was a slight growth trend in recent years. Population development in China Population development in China over the past decades has been strongly influenced by political and economic factors. After a time of high fertility rates during the Maoist regime, China introduced birth-control measures in the 1970s, including the so-called one-child policy. The fertility rate dropped accordingly from around six children per woman in the 1960s to below two at the end of the 20th century. At the same time, life expectancy increased consistently. In the face of a rapidly aging society, the government gradually lifted the one-child policy after 2012, finally arriving at a three-child policy in 2021. However, like in most other developed countries nowadays, people in China are reluctant to have more than one or two children due to high costs of living and education, as well as changed social norms and private values. China’s top-heavy age pyramid The above-mentioned developments are clearly reflected in the Chinese age pyramid. The age cohorts between 30 and 39 years are the last two larger age cohorts. The cohorts between 15 and 24, which now enter childbearing age, are decisively smaller, which will have a negative effect on the number of births in the coming decade. When looking at a gender distribution of the population pyramid, a considerable gender gap among the younger age cohorts becomes visible, leaving even less room for growth in birth figures.

BackgroundThe immunomodulatory effects of vitamin D are widely recognized and a few studies have been conducted to determine its utility in the treatment of tuberculosis, with mixed results. This study was conducted to see if vitamin D supplementation in patients with active pulmonary tuberculosis (PTB) in the Indian population contributed to sputum smear and culture conversion as well as the prevention of relapse.MethodsThis randomized double-blind placebo-controlled trial was conducted in three sites in India. HIV negative participants aged 15–60 years with sputum smear positive PTB were recruited according to the Revised National Tuberculosis Control Program guidelines and were randomly assigned (1:1) to receive standard anti-tubercular treatment (ATT) with either supplemental dose of oral vitamin D3 (60,000 IU/sachet weekly for first two months, fortnightly for next four months followed by monthly for the next 18 months) or placebo with same schedule. The primary outcome was relapse of PTB and secondary outcomes were time to conversion of sputum smear and sputum culture.ResultsA total of 846 participants were enrolled between February 1, 2017 to February 27, 2021, and randomly assigned to receive either 60,000 IU vitamin D3 (n = 424) or placebo (n = 422) along with standard ATT. Among the 697 who were cured of PTB, relapse occurred in 14 participants from the vitamin D group and 19 participants from the placebo group (hazard risk ratio 0.68, 95%CI 0.34 to 1.37, log rank p value 0.29). Similarly, no statistically significant difference was seen in time to sputum smear and sputum culture conversion between both groups. Five patients died each in vitamin D and placebo groups, but none of the deaths were attributable to the study intervention. Serum levels of vitamin D were significantly raised in the vitamin D group as compared to the placebo group, with other blood parameters not showing any significant difference between groups.ConclusionsThe study reveals that vitamin D supplementation does not seem to have any beneficial effect in the treatment of PTB in terms to the prevention of relapse and time to sputum smear and culture conversion.Trial registrationCTRI/2021/02/030977 (ICMR, Clinical trial registry-India).