In 2018, over *** million people died due to poor quality of care in hospitals in the south Asian country of India. Furthermore, over *** people died due to insufficient access to healthcare in the country during that time.

India IN: Number of Deaths Ages 20-24 Years data was reported at 146,669.000 Person in 2019. This records a decrease from the previous number of 148,431.000 Person for 2018. India IN: Number of Deaths Ages 20-24 Years data is updated yearly, averaging 219,654.500 Person from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 223,796.000 Person in 2003 and a record low of 146,669.000 Person in 2019. India IN: Number of Deaths Ages 20-24 Years data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Health Statistics. Number of deaths of youths ages 20-24 years; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Sum; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Around *** million people were covered under government sponsored health insurance schemes in India as of financial year 2018. This number was expected to increase to over *** million people by financial year 2030 in the south Asian country.

In financial year 2024, the estimated total budget allotted for the Indian health sector amounted to about ****** billion Indian rupees. Since the financial year 2018, the allocation of the health budget has consistently increased. Healthcare in India The Indian healthcare system consists of a network of private and public healthcare sectors that offer a variety of medical services to over *** billion individuals. Most Indians prefer private healthcare over public healthcare in terms of hospitals, services, and physicians. Additionally, the overall size of the healthcare market stood at *** billion U.S. dollars in 2022 and is likely to grow in the future. A new horizon of opportunities and investments has emerged in pharmaceuticals, medical technology, and services for the advancement of this sector. Private healthcare expenditure Even though the government has increased the spending of country’s GDP spending on healthcare to over *** percent, patients pay for more than half of medical expenses directly at the time of treatment. As a result, the out-of-pocket expenses were roughly about ** percent of the current health expenditure in India. These expenditures still make up a large portion of health spending in the country.

India IN: Number of Deaths Ages 5-9 Years data was reported at 67,196.000 Person in 2019. This records a decrease from the previous number of 72,012.000 Person for 2018. India IN: Number of Deaths Ages 5-9 Years data is updated yearly, averaging 180,128.000 Person from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 310,340.000 Person in 1990 and a record low of 67,196.000 Person in 2019. India IN: Number of Deaths Ages 5-9 Years data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Health Statistics. Number of deaths of children ages 5-9 years; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Sum; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

India IN: Number of Deaths Ages 10-14 Years data was reported at 68,681.000 Person in 2019. This records a decrease from the previous number of 71,179.000 Person for 2018. India IN: Number of Deaths Ages 10-14 Years data is updated yearly, averaging 119,467.500 Person from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 140,520.000 Person in 1995 and a record low of 68,681.000 Person in 2019. India IN: Number of Deaths Ages 10-14 Years data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Health Statistics. Number of deaths of adolescents ages 10-14 years; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Sum; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

India IN: Probability of Dying at Age 15-19 Years: per 1000 data was reported at 4.000 Ratio in 2019. This records a decrease from the previous number of 4.100 Ratio for 2018. India IN: Probability of Dying at Age 15-19 Years: per 1000 data is updated yearly, averaging 7.800 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 10.500 Ratio in 1990 and a record low of 4.000 Ratio in 2019. India IN: Probability of Dying at Age 15-19 Years: per 1000 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Health Statistics. Probability of dying between age 15-19 years of age expressed per 1,000 adolescents age 15, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

India IN: Number of Deaths Ages 15-19 Years data was reported at 99,801.000 Person in 2019. This records a decrease from the previous number of 103,186.000 Person for 2018. India IN: Number of Deaths Ages 15-19 Years data is updated yearly, averaging 180,343.000 Person from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 189,432.000 Person in 2000 and a record low of 99,801.000 Person in 2019. India IN: Number of Deaths Ages 15-19 Years data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Health Statistics. Number of deaths of adolescents ages 15-19 years; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Sum; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Abstract

The overarching goal of this study is to generate baseline estimates for various healthcare performance indicators to help the authorities understand the current status of the sampled villages. The findings generated out of this study will serve as a crucial input for designing social accountability strategies and interventions for bringing about quality improvements in the health service delivery. Under the purview of this overarching goal, this study will seek to achieve the following specific objectives

• To evaluate the effectiveness of VHSNCs and PRIs for planning and implementing various interventions for improving the healthcare scenario of the village.

• To assess the functioning of VHSNCs in terms of regularity in carrying out the activities mandated for them.

• To generate various health indicators for the village residents to inform the authorities about the healthcare needs of the people in the selected locations.

• To take anthropometric measurements of the children under 5 years of age in order to assess their nutritional status and assess the level of malnourishment in the population.

• To bring to light various socio-economic and demographic characteristics of the sampled villages to help the authorities develop a better understanding of the overall status of these villages.

• To assess the level of awareness regarding social accountability mechanisms among the general population, PRI members, VHSNC members and healthcare staff.

• To evaluate the BPHCs for the availability of requisite healthcare infrastructure for offering quality healthcare services to the population in their catchment area.

• To appraise the utilization of key health services, especially services that would be made available to all recipients irrespective of health/illness conditions such as vaccinations and ANC services, etc.

• To study the obstetric history of ever married women between 15-49 years of age, including associated infant/child survival to develop a better understanding of the maternal and child health scenario.

Geographic coverage

State of Uttar Pradesh, India. The end line was conducted in sample village across 12 districts.

The actual implementation of the baseline followed the proposed design and included 120 villages randomly selected from the two IE districts, but also included an addition 120 GPs across ten other districts of UP, selected by UPHSSP in consultation with their Technical Assistance Provider (TAP) and the World Bank team.

Analysis unit

• Households

• Villages

Kind of data

Sample survey data [ssd]

Sampling procedure

• Sample size: 4,443 households in 120 villages in the IE districts

• Selection process: A full listing was conducted again at follow-up in the 120 IE district villages. Using the same eligibility criteria as at baseline, we selected 40 eligible households (or total eligible households if <40) for the survey.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

HH schedule and FGD checklist

Response rate

100%

India IN: Probability of Dying at Age 5-9 Years: per 1000 data was reported at 2.800 Ratio in 2019. This records a decrease from the previous number of 2.900 Ratio for 2018. India IN: Probability of Dying at Age 5-9 Years: per 1000 data is updated yearly, averaging 7.200 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 13.800 Ratio in 1990 and a record low of 2.800 Ratio in 2019. India IN: Probability of Dying at Age 5-9 Years: per 1000 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Health Statistics. Probability of dying between age 5-9 years of age expressed per 1,000 children aged 5, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

The Indian population is not only tech-savvy but also health-conscious. As of 2021, the number of health and fitness app downloads across the country tallied over *** million in total. The second quarter of that year saw the highest number of downloads, grossing ** million, second only to the number of downloads during the same period in 2020. Meditation, mental health and fitness were the prime focus among Indians when using these apps.

Rural Area: Maharashtra: Number of Health Workers: Male data was reported at 7,670.000 Person in 2022. This records a decrease from the previous number of 8,102.000 Person for 2021. Rural Area: Maharashtra: Number of Health Workers: Male data is updated yearly, averaging 6,685.000 Person from Mar 2005 (Median) to 2022, with 17 observations. The data reached an all-time high of 9,956.000 Person in 2008 and a record low of 4,570.000 Person in 2018. Rural Area: Maharashtra: Number of Health Workers: Male data remains active status in CEIC and is reported by Central Bureau of Health Intelligence. The data is categorized under India Premium Database’s Health Sector – Table IN.HLB009: Health Human Resources: Rural Area: Number of Health Assistants and Workers.

India Number of Doctors: Registered: Medical Councils: Total data was reported at 54,238.000 Person in 2019. This records an increase from the previous number of 53,600.000 Person for 2018. India Number of Doctors: Registered: Medical Councils: Total data is updated yearly, averaging 34,172.500 Person from Dec 2002 (Median) to 2019, with 18 observations. The data reached an all-time high of 54,238.000 Person in 2019 and a record low of 21,687.000 Person in 2002. India Number of Doctors: Registered: Medical Councils: Total data remains active status in CEIC and is reported by Central Bureau of Health Intelligence. The data is categorized under India Premium Database’s Health Sector – Table IN.HLB001: Health Human Resources: Number of Doctors: Registered.

Abstract

What do we know about incentives and norms in health bureaucracies and service delivery points at various levels of a state in India? For example, the logic of economic theory suggests that governments should be direct providers of services when there is a role for attracting intrinsically motivated agents (Francois, 2000), but we have no empirical evidence on integrity and public service motivation among state personnel across different cadres of service delivery. The available research has focused on documenting evidence of weak incentives and low accountability for service delivery in the public sector, and thence on evaluating interventions targeted at strengthening incentives, such as making some part of pay conditional on performance indicators (for example, Singh and Masters, 2017). But what is available is barely scratching the surface of knowledge needed to help reform leaders think about how to structure government bureaucracies and assign tasks to leverage intrinsic motivation and to reduce reliance on high-powered incentives. Even when increasing the power of incentives has been shown to “work”, the authors of those findings concede that implementing optimal incentive contracts at scale can place significant demands on state capacity (Muralidharan and Sundararaman, 2011). There is even less evidence available about the incentives and motivation of mid-level bureaucrats within the health system, compared to a growing body of research on frontline providers such as doctors and community health workers. Finally, the logic of economic theory, and growing international evidence in support of it, further suggests that politics casts a long shadow on culture in the bureaucracy, but we have no rigorous evidence for this claim for India.

To address these knowledge gaps we designed and implemented a complex survey of multiple types of respondents across districts, blocks (administrative sub-units within districts) and village governments (Gram Panchayats or GPs) in Bihar, one of the poorest states of India and with some of the worst statistics of child malnourishment.

Geographic coverage

16 study districts, from among the 38 of Bihar, selected to represent the 9 administrative divisions of Bihar: Patna, Tirhut, Darbhanga, Kosi, Purnia, Saran, Bhagalpur, Munger, Magadh

Analysis unit

Households Health Staff Politicians Bureaucrats

Universe

Citizens, Within the category of citizens, the survey additionally targeted office-bearing members of women’s Self Help Groups (SHG) under a rural livelihoods program in Bihar known as Jeevika. Politicians Bureaucrats Public Providers of Health Services

Kind of data

Sample survey data [ssd]

Sampling procedure

Budget and implementation constraints required us to select a sample of districts rather than covering all 38 districts of Bihar. At the same time, we needed a large sample to be representative of the diversity within the state, and allow us to capture some variation across district-level institutional characteristics. These constraints led us to determine 16 as the number of districts in which to undertake the survey. The purposive selection of which 16 study districts, from among the 38 of Bihar, was made using the following criteria:

• represent the 9 administrative divisions of Bihar: Patna, Tirhut, Darbhanga, Kosi, Purnia, Saran, Bhagalpur, Munger, Magadh • represent both border and interior districts • select "old" and "new" districts (those which were created after 1991) because district age might matter in interesting ways for their capacity to deliver (to be discussed further) • select districts which might vary in historical institutions that shape norms.

We first explored an established literature in India which finds that there are persistent effects on current service delivery of the long-gone historical institution of the Zamindari system of land revenue (Pandey, 2010; Banerjee and Iyer, 2005). However, since all of the districts of Bihar are classified as belonging to the Zamindari system, we could not use this established measure of historical institutions in selecting the study districts. We then turned to a newer literature which examines the early construction of railway lines in the late 1800s in the United States and India as a potential source of institutional variation (Donaldson, 2018; Donaldson and Hornbeck, 2016; Atack, Haines and Margo, various). The 16 districts in our study include those through which passed the first railway lines in Bihar, and those that received railway lines a decade or so later.

Within each of the 16 districts, 4 blocks were selected using a random number generator,after stratifying by proximity to the main railway line. Within each block, 4 Gram Panchayats (GPs) were selected using a random number generator. However, in one block each in the districts of Lakhisarai and Buxar, 3 GPs instead of 4 were selected because the sampling protocol required a sufficient number of replacement respondents to be available, and these districts only had 3 GPs fulfilling the replacement requirement (more details in section on Respondents below). This yields a sample of respondents drawn from 16 districts, 64 blocks from within those districts, and 254 Gram Panchayats (GPs) from within those blocks.

Citizen Survey: The citizen survey was aimed at respondents from 16 households residing in each GP area. The survey firm was provided with a list of respondents (with replacements) drawn randomly from the electoral rolls available of all voting-age adults in Bihar's population. The target sample size is thus 4064 citizens (16 each from 254 GPs). Within the category of citizens, the survey additionally targeted office-bearing members of women's Self Help Groups (SHG) under a rural livelihoods program in Bihar known as Jeevika. However, we had no lists available with names of SHG leaders of the village-level organziations across GPs. In the absence of these lists, we relied on the survey firm to ensure that enumerator teams would identify SHG leaders during their field-work. The data from SHG leaders that has been provided to us is thus subject to a greater than usual caveat: the risk of whether the enumerator teams accuratelyidentified and obtained interviews with the targeted SHG respondents. The instructions provided to the survey teams was to ask the GP Mukhiya and other GPlevel respondents (such as the ANM, ASHA and AWW) about the GP-level federated organzation of all the SHGs across the GP's communities to identify its President,Secretary and Treasurer. That is, 3 SHG leaders were targeted for each GP, for a total sample of 762 (3 each from 254 GPs) SHG leaders.

Politician Survey: Lists were provided to the survey teams of all incumbent Mukhiyas to be interveiwed, and a random selection (with replacement) of 3 Ward members and 3 candidates from among those who contested the previous GP elections of 2016. The targeted sample size of GP politicians is thus 1778 (7 each from 254 GPs)

Bureaucrats: The survey firm was responsible for identifying and interviewing the respondents holding these positions. The final data submitted by the survey firm contains 293 respondents in supervisory or management positions, including: 13 Civil Surgeons,11 Chief Medical Officers (including 4 who were in Acting capacity), 23 Superintendents (including 13 in Deputy or Acting capacity), 9 District Programme Officers- NHM, 4 District RCH and Immunization In-charge, 7 District Community Mobilizers, 58 MOICs, 58 Acting Facility Incharge, 43 Block Program Managers-NHM, 29 Block RCH Programme officers, and 35 Block Community Mobilizers.

Public Providers of Health Services: The survey team was provided a list (with replacements) of 3 AWW workers to interveiw per GP, for a targeted sample of 762 AWW respondents. The survey team was provided with a list of randomly selected candidates for the categories of respondents for all the PHCs and higher-level health facilities (such as District Hospitals) across the 64 blocks of the study area.

Sampling deviation

Block Level: The survey firm was responsible for identifying the block-level politicians targeted to be interviewed. The targeted sample size of Block-Panchayat (Panchayat Samiti) elected members’ is 128 respondents (2 each from 64 blocks). The 57 MLAs across the 64 blocks of the study area were also identified by the survey firm. However, because of problems of reaching politicians at a time that was close to the 2019 elections in India, the survey firm was able to complete interviews with only 39 MLAs (of the targeted 57) , and with 119 Panchayat Samiti members (of the targeted 128).

District Level: The survey firm was responsible for identifying the MPs from constituencies within the 16 study districts, and the 32 respondents of the District-Panchayat (Zilla Parishad). Again, because of problems reaching political leaders at election time, the survey firm was able to interviewonly 9 MPs, and 28 Zilla Parishad members.

Public Providers of Health Care Services: The survey team was provided with a list of randomly selected candidates for the categories of respondents for all the PHCs and higher-level health facilities (such as District Hospitals) across the 64 blocks of the study area. However, the survey team reports substantial difficulty in adhering to this list because the personnel were not found at the health facilities. The survey team was not able to reach a random sample of providers appointed at these positions.

India All States: Capital Expenditures: Loans and Advances: Developmental: Social Services: Medical and Public Health data was reported at 5,978.500 INR mn in 2019. This records a decrease from the previous number of 6,152.100 INR mn for 2018. India All States: Capital Expenditures: Loans and Advances: Developmental: Social Services: Medical and Public Health data is updated yearly, averaging 1,080.500 INR mn from Mar 2005 (Median) to 2019, with 15 observations. The data reached an all-time high of 6,152.100 INR mn in 2018 and a record low of 370.900 INR mn in 2016. India All States: Capital Expenditures: Loans and Advances: Developmental: Social Services: Medical and Public Health data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Government and Public Finance – Table IN.FI001: Capital Expenditure: All States.

This dataset provides underlying data and calculated novel estimates for cost-benefit analysis of scaling up cataract surgeries for universal cataract coverage in India (2018-19). Both dataset and data dictionary are provided.

India Central Government Health Scheme: Facilities: Number of Beneficiaries data was reported at 3,247,783.000 Person in 2018. This records an increase from the previous number of 3,104,589.000 Person for 2017. India Central Government Health Scheme: Facilities: Number of Beneficiaries data is updated yearly, averaging 3,191,131.000 Person from Mar 2006 (Median) to 2018, with 13 observations. The data reached an all-time high of 3,411,365.000 Person in 2007 and a record low of 2,580,301.000 Person in 2011. India Central Government Health Scheme: Facilities: Number of Beneficiaries data remains active status in CEIC and is reported by Central Bureau of Health Intelligence. The data is categorized under India Premium Database’s Health Sector – Table IN.HLA004: Health Infrastructure: Central Government Health Scheme.

India New Capital Raised: Value: Health Care and Pharma data was reported at 0.000 INR mn in Oct 2018. This stayed constant from the previous number of 0.000 INR mn for Sep 2018. India New Capital Raised: Value: Health Care and Pharma data is updated monthly, averaging 0.000 INR mn from Oct 1997 (Median) to Oct 2018, with 252 observations. The data reached an all-time high of 29,668.600 INR mn in Feb 2018 and a record low of 0.000 INR mn in Oct 2018. India New Capital Raised: Value: Health Care and Pharma data remains active status in CEIC and is reported by Securities and Exchange Board of India. The data is categorized under Global Database’s India – Table IN.ZA036: New Capital Raised: by Industry.

India IN: Mortality from Exposure to Lead: per 1 000 000 Inhabitants data was reported at 170.210 Person in 2019. This records an increase from the previous number of 169.680 Person for 2018. India IN: Mortality from Exposure to Lead: per 1 000 000 Inhabitants data is updated yearly, averaging 148.770 Person from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 170.210 Person in 2019 and a record low of 131.740 Person in 1990. India IN: Mortality from Exposure to Lead: per 1 000 000 Inhabitants data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s India – Table IN.OECD.GGI: Social: Air Quality and Health: Non OECD Member: Annual.

In 2021, the market size of the preventive health tech sector was ** billion U.S. dollars, with the fitness and wellness sector dominating the market share. It is estimated that the country's preventive health-tech sector will grow at a compound annual growth rate of ** percent, reaching a market size of *** billion dollars by 2025.