Vital Statistics: Natural Growth Rate: per 1000 Population: Uttar Pradesh: Urban data was reported at 16.700 NA in 2020. This records a decrease from the previous number of 17.100 NA for 2019. Vital Statistics: Natural Growth Rate: per 1000 Population: Uttar Pradesh: Urban data is updated yearly, averaging 18.300 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 20.000 NA in 2004 and a record low of 16.700 NA in 2020. Vital Statistics: Natural Growth Rate: per 1000 Population: Uttar Pradesh: Urban 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.GAH004: Vital Statistics: Natural Growth Rate: by States.

Vital Statistics: Birth Rate: per 1000 Population: Uttar Pradesh data was reported at 25.100 NA in 2020. This records a decrease from the previous number of 25.400 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Uttar Pradesh data is updated yearly, averaging 28.700 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 32.800 NA in 2000 and a record low of 25.100 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: Uttar Pradesh 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.GAH002: Vital Statistics: Birth Rate: by States.

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

Vital Statistics: Natural Growth Rate: per 1000 Population: Uttar Pradesh data was reported at 18.700 NA in 2020. This records a decrease from the previous number of 18.900 NA for 2019. Vital Statistics: Natural Growth Rate: per 1000 Population: Uttar Pradesh data is updated yearly, averaging 20.500 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 22.500 NA in 2000 and a record low of 18.700 NA in 2020. Vital Statistics: Natural Growth Rate: per 1000 Population: Uttar Pradesh 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.GAH004: Vital Statistics: Natural Growth Rate: by States.

As of 2021, the population of Prayagraj in India was over 1.4 million. This was a significant increase from 2011, when the population in the Uttar Pradesh capital was just over one million, and reflected a decadal growth of more than 25 percent from 2011.

Vital Statistics: Birth Rate: per 1000 Population: Uttar Pradesh: Urban data was reported at 22.100 NA in 2020. This records a decrease from the previous number of 22.300 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Uttar Pradesh: Urban data is updated yearly, averaging 24.700 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 27.500 NA in 1999 and a record low of 22.100 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: Uttar Pradesh: Urban 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.GAH002: Vital Statistics: Birth Rate: by States.

237 882 725 (Persons) in décembre 2020.

In 2020, the northern state of Uttar Pradesh had the highest urban birth rate of 22.1 births per 1,000 inhabitants. It was followed by states of Bihar and Rajasthan. Among other states, Himachal Pradesh had the lowest birth rate in the urban areas that year.

Context

2011 India census data. Includes population/demographic data , housing data and socio economic data for each district.

Content

• india-districts-census-2011.csv - Population enumeration data with expanded columns.
• india_census_housing-hlpca-full.csv - Housing statistics for total (rural + urban) population by district.
• gdp_AndhraPradesh1.csv , gdp_AndhraPradesh2 : Contains GDP data for state AP.
• gdp_ArunachalPradesh.csv: Contains GDP data for state ArunachalPradesh.
• gdp_Assam1.csv, gdp_Assam2.csv : Contains GDP data for state Assam.
• gdp_Bihar1.csv , gdp_Bihar2.csv : Contains GDP data for state Bihar.
• gdp_Chattisgarh.csv : Contains GDP data for state Chattisgarh.
• gdp_Haryana.csv : Contains GDP data for state Haryana.
• gdp_HimachalPradesh.csv : Contains GDP data for state HimachalPradesh.
• gdp_Jharkhand.csv : Contains GDP data for state Jharkhand.
• gdp_Karnataka1.csv , gdp_Karnataka2.csv : Contains GDP data for state Karnataka.
• gdp_Kerala1.csv , gdp_Kerala2.csv : Contains GDP data for state Kerala.
• gdp_MadhyaPradesh.csv : Contains GDP data for state MadhyaPradesh.
• gdp_Maharashtra1.csv , gdp_Maharashtra2.csv : Contains GDP data for state Maharashtra.
• gdp_Manipur.csv : Contains GDP data for state Manipur.
• gdp_Meghalaya.csv : Contains GDP data for state Meghalaya.
• gdp_Mizoram.csv : Contains GDP data for state Mizoram.
• gdp_Odisha1.csv , gdp_Odisha2.csv : Contains GDP data for state Odhisha.
• gdp_Punjab1.csv , gdp_Punjab2.csv : Contains GDP data for state Punjab.
• gdp_Rajasthan1.csv , gdp_Rajasthan2.csv : Contains GDP data for state Rajasthan.
• gdp_Sikkim.csv : Contains GDP data for state Sikkim.
• gdp_Tamilnadu.csv : Contains GDP data for state Tamilnadu.
• gdp_Uttarakhand.csv : Contains GDP data for state Uttarakhand.
• gdp_UttarPradesh1.csv , gdp_UttarPradesh2.csv : Contains GDP data for state UttarPradesh.
• gdp_WestBengal1.csv , gdp_WestBengal2.csv : Contains GDP data for state West Bengal.

Acknowledgements

https://www.kaggle.com/danofer/india-census

https://www.kaggle.com/umeshnarayanappa/explore-census-2001-india

http://udise.in/drc.htm

https://data.gov.in/catalog/district-wise-gdp-and-growth-rate-current-price2004-05

https://data.gov.in/catalog/district-wise-gdp-and-growth-rate-constant-price1999-2000

Banner photo by @ishant_mishra54 from Unsplash.

Inspiration

What are the socioeconomic trends in different parts of India?

POPULATION PROIECTIONS FOR INDIA AND STATES 2011 – 2036 (Downscaled to District, Sub-Districts and Villages/Towns by Esri India)REPORT OF THE TECHNICAL GROUP ON POPULATION PROIECTTONSJuly, 2020The projected population figures provided by the Registrar General of India forms the basis for planning and implementation of various health interventions including RMNCH+A, which are aimed at improving the overall health outcomes by ensuring quality service provision to all the health beneficiaries. These interventions focus on antenatal, intranatal and neonatal care aimed at reducing maternal and neonatal morbidity and mortality; improving coverage and quality of health care interventions and improving coverage for immunization against vaccine preventable diseases. Further, these estimates would also enable us to tackle the special health care needs of various population age groups, thus gearing the system for necessary preventive, promotive, curative, and rehabilitative services for the growing population to this report. PREETI SUDAN, IAS SecretaryThe Cohort Component Method is the universally accepted method of making population projections because of the fact that the growth of population is determined by fertility, mortality, and migration rates. In this exercise, 20 States and two UTs have been applied the Cohort Component method. These are Andhra Pradesh, Assam, Bihar, Gujarat, Haryana, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Telangana, Uttar Pradesh, West Bengal, Jharkhand, Chhattisgarh, Uttarakhand, Jammu & Kashmir (UT) and NCT of Delhi. Based on the residual of the projected population of Jammu & Kashmir (State) and Jammu & Kashmir (UT), for which Cohort Component method has applied, projection of the Ladakh UT have been made. For the projections of Jammu & Kashmir (UT), SRS fertility and mortality estimates of Jammu & Kashmir (State) are used. The projection of the seven northeastern states (excluding Assam) has also been carried out as a whole using the Cohort Component Method. Separate projections for Andhra Pradesh and Telangana were done using the re-casted populations of these states. For the projections, for the years before 2014, combined SRS estimates of Andhra Pradesh and year 2014 onwards, separate SRS estimates of fertility and mortality of Andhra Pradesh and Telangana are used. For the remaining States and Union territories, Mathematical Method has been applied. The sources of data used are 2011 Census and Sample Registration System (SRS). SRS provides time series data of fertility and mortality, which has been used for predicting their future levelsEsri India Efforts:The Population Projections Report published by MoHFW contains output summary tables from series Table 8 to Table 14. Example: TABLE – 8: Projected total population by sex as on 1st March, 2011-2036: India, States and Union territories, TABLE – 9: Projected urban population by sex as on 1st March, 2011-2036: India, States and Union territories, etc. The parameters available with these census data tables are Census Year, Projected Total Persons with Gender categorization and Projected Urban Population from 2011 to 2036.By subtracting “Projected Urban Population” from “Projected Total Population”, a new data column has been added as “Projected Rural Population”. The data is available for all Union Territory and States for 25 years.A factor has been calculated by taking projected population and the base year population (2011). Subsequently, the factor is calculated for each year using the projected values provided by census of India. Projected Population by Sex as on 1st March - 2011 - 2036: India, States and Union Territories* ('000)YearGUJARAT GUJARAT URBANGUJARAT RURALPersonsMaleFemalePersonMaleFemalePersonMaleFemale2011 60,440 (A) 31,49128,94825,74513,69412,05134,69517,79716,8972012 61,383 (B)32,00729,37626,47214,08112,39134,91117,92616,985Factor has been applied below State level- Projected Population by Sex as on 1st March - 2011 - 2036: India, States and Union Territories* ('000)YearGUJARAT GUJARAT URBANGUJARAT RURALPersonsMaleFemalePersonMaleFemalePersonMaleFemale20121.01560225 (B/A)1.0163856341.0147851321.0282384931.0282605521.0282134261.0062256811.0072484131.005208025Esri India has access to SOI admin boundaries up-to district level and developed village, town and sub-district boundaries using census maps. The calculated factors have been applied to smallest geography at villages and towns and upscaled back to sub-district, district, state, and country. The derived values have been compared with the original values provided by census at state level and no deviation is confirmed.Data Variables: Year (2011-2036)Total Population MaleFemaleTotal Population UrbanMale UrbanFemale UrbanTotal Population RuralMale RuralFemale RuralData source: https://main.mohfw.gov.in/sites/default/files/Population Projection Report 2011-2036 - upload_compressed_0.pdfOther related contents are also available:Village Population Projections for India 2011-2036Sub-district Population Projections for India 2011-2036District Population Projections for India 2011-2036State Population Projections for India 2011-2036Country Population Projections for India 2011-2036This web layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.

Population: Uttar Pradesh data was reported at 237.082 Person mn in 2024. This records an increase from the previous number of 234.692 Person mn for 2023. Population: Uttar Pradesh data is updated yearly, averaging 192.325 Person mn from Mar 1994 (Median) to 2024, with 31 observations. The data reached an all-time high of 237.082 Person mn in 2024 and a record low of 140.030 Person mn in 1994. Population: Uttar Pradesh data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under Global Database’s India – Table IN.GBG001: Population. [COVID-19-IMPACT]

Abstract

An effective policy response to the economic impacts of the COVID-19 pandemic requires an enormous range of data to inform the design and response of programs. Public health measures require data on the spread of the disease, beliefs in the population, and capacity of the health system. Relief efforts depend on an understanding of hardships being faced by various segments of the population. Food policy requires measurement of agricultural production and hunger. In such a rapidly evolving pandemic, these data must be collected at a high frequency. Given the unexpected nature of the shock and urgency with which a response was required, Indian policymakers needed to formulate policies affecting India’s 1.4 billion people, without the detailed evidence required to construct effective programs. To help overcome this evidence gap, the World Bank, IDinsight, and the Development Data Lab sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.

Geographic coverage

Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, and Uttar Pradesh

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

This dataset includes observations covering six states (Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, Uttar Pradesh) and three survey rounds. The survey did not have a single, unified frame from which to sample phone numbers. The final sample was assembled from several different sample frames, and the choice of frame sample frames varied across states and survey rounds.

These frames comprise four prior IDinsight projects and from an impact evaluation of the National Rural Livelihoods project conducted by the Ministry of Rural Development. Each of these surveys sought to represent distinct populations, and employed idiosyncratic sample designs and weighting schemes.

A detailed note covering key features of each sample frame is available for download.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaires covered the following subjects:

1. Agriculture: COVID-19-related changes in price realisation, acreage decisions, input expenditure, access to credit, access to fertilisers, etc.

2. Income and consumption: Changes in wage rates, employment duration, consumption expenditure, prices of essential commodities, status of food security etc.

3. Migration: Rates of in-migration, migrant income and employment status, return migration plans etc.

4. Access to relief: Access to in-kind, cash and workfare relief, quantities of relief received, and constraints on the access to relief.

5. Health: Access to health facilities and rates of foregone healthcare, knowledge of COVID-19 related symptoms and protective behaviours.

While a number of indicators were consistent across all three rounds, questions were added and removed as and when necessary to account for seasonal changes (i.e: in the agricultural cycle).

Response rate

Round 1: ~55% Round 2: ~46% Round 3: ~55%

Context

Census of India is a rich database which can tell stories of over a billion Indians. It is important not only for research point of view, but commercially as well for the organizations that want to understand India's complex yet strongly knitted heterogeneity. However, nowhere on the web, there exists a single database that combines the district- wise information of all the variables (most include no more than 4-5 out of over 50 variables!). Extracting and using data from Census of India 2001 is quite a laborious task since all data is made available in scattered PDFs district wise. Individual PDFs can be extracted from http://www.censusindia.gov.in/(S(ogvuk1y2e5sueoyc5eyc0g55))/Tables_Published/Basic_Data_Sheet.aspx.

Content

This database has been extracted from Census of 2001 and includes data of 590 districts, having around 80 variables each.

In case of confusion regarding the context of the variable, refer to the following PDF and you will be able to make sense out of it: http://censusindia.gov.in/Dist_File/datasheet-2923.pdf

All the extraction work can be found @ https://github.com/preetskhalsa97/census2001auto The final CSV can be found at finalCSV/all.csv

The subtle hack that was used to automate extraction to a great extent was the the URLs of all the PDFs were same except the four digits (that were respective state and district codes).

A few abbreviations used for states:

AN- Andaman and Nicobar CG- Chhattisgarh D_D- Daman and Diu D_N_H- Dadra and Nagar Haveli JK- Jammu and Kashmir MP- Madhya Pradesh TN- Tamil Nadu UP- Uttar Pradesh WB- West Bengal

A few variables for clarification: Growth..1991...2001- population growth from 1991 to 2001 X0..4 years- People in age group 0 to 4 years SC1- Scheduled Class with highest population

Acknowledgements

Inspiration

This is a massive dataset which can be used to explain the interplay between education, caste, development, gender and much more. It really can explain a lot about India and propel data driven research. Happy Number Crunching!

Abstract

The National Family Health Survey (NFHS) was carried out as the principal activity of a collaborative project to strengthen the research capabilities of the Population Reasearch Centres (PRCs) in India, initiated by the Ministry of Health and Family Welfare (MOHFW), Government of India, and coordinated by the International Institute for Population Sciences (IIPS), Bombay. Interviews were conducted with a nationally representative sample of 89,777 ever-married women in the age group 13-49, from 24 states and the National Capital Territoty of Delhi. The main objective of the survey was to collect reliable and up-to-date information on fertility, family planning, mortality, and maternal and child health. Data collection was carried out in three phases from April 1992 to September 1993. THe NFHS is one of the most complete surveys of its kind ever conducted in India.

The households covered in the survey included 500,492 residents. The young age structure of the population highlights the momentum of the future population growth of the country; 38 percent of household residents are under age 15, with their reproductive years still in the future. Persons age 60 or older constitute 8 percent of the population. The population sex ratio of the de jure residents is 944 females per 1,000 males, which is slightly higher than sex ratio of 927 observed in the 1991 Census.

The primary objective of the NFHS is to provide national-level and state-level data on fertility, nuptiality, family size preferences, knowledge and practice of family planning, the potentiel demand for contraception, the level of unwanted fertility, utilization of antenatal services, breastfeeding and food supplemation practises, child nutrition and health, immunizations, and infant and child mortality. The NFHS is also designed to explore the demographic and socioeconomic determinants of fertility, family planning, and maternal and child health. This information is intended to assist policymakers, adminitrators and researchers in assessing and evaluating population and family welfare programmes and strategies. The NFHS used uniform questionnaires and uniform methods of sampling, data collection and analysis with the primary objective of providing a source of demographic and health data for interstate comparisons. The data collected in the NFHS are also comparable with those of the Demographic and Health Surveys (DHS) conducted in many other countries.

Geographic coverage

National

Analysis unit

• Household
• Data collected for women 13-49, indicators calculated for women 15-49

Universe

The population covered by the 1992-93 DHS is defined as the universe of all women age 13-49 who were either permanent residents of the households in the NDHS sample or visitors present in the households on the night before the survey were eligible to be interviewed.

Kind of data

Sample survey data

Sampling procedure

SAMPLE DESIGN

The sample design for the NFHS was discussed during a Sample Design Workshop held in Madurai in Octber, 1991. The workshop was attended by representative from the PRCs; the COs; the Office of the Registrar General, India; IIPS and the East-West Center/Macro International. A uniform sample design was adopted in all the NFHS states. The Sample design adopted in each state is a systematic, stratified sample of households, with two stages in rural areas and three stages in urban areas.

SAMPLE SIZE AND ALLOCATION

The sample size for each state was specified in terms of a target number of completed interviews with eligible women. The target sample size was set considering the size of the state, the time and ressources available for the survey and the need for separate estimates for urban and rural areas of the stat. The initial target sample size was 3,000 completed interviews with eligible women for states having a population of 25 million or less in 1991; 4,000 completed interviews for large states with more than 25 million population; 8,000 for Uttar Pradesh, the largest state; and 1,000 each for the six small northeastern states. In States with a substantial number of backward districts, the initial target samples were increased so as to allow separate estimates to be made for groups of backward districts.

The urban and rural samples within states were drawn separetly and , to the extent possible, sample allocation was proportional to the size of the urban-rural populations (to facilitate the selection of a self-weighting sample for each state). In states where the urban population was not sufficiently large to provide a sample of at least 1,000 completed interviews with eligible women, the urban areas were appropriately oversampled (except in the six small northeastern states).

THE RURAL SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

A two-stage stratified sampling was adopted for the rural areas: selection of villages followed by selection of households. Because the 1991 Census data were not available at the time of sample selection in most states, the 1981 Census list of villages served as the sampling frame in all the states with the exception of Assam, Delhi and Punjab. In these three states the 1991 Census data were used as the sampling frame.

Villages were stratified prior to selection on the basis of a number of variables. The firts level of stratification in all the states was geographic, with districts subdivided into regions according to their geophysical characteristics. Within each of these regions, villages were further stratified using some of the following variables : village size, distance from the nearest town, proportion of nonagricultural workers, proportion of the population belonging to scheduled castes/scheduled tribes, and female literacy. However, not all variables were used in every state. Each state was examined individually and two or three variables were selected for stratification, with the aim of creating not more than 12 strata for small states and not more than 15 strata for large states. Females literacy was often used for implicit stratification (i.e., the villages were ordered prior to selection according to the proportion of females who were literate). Primary sampling Units (PSUs) were selected systematically, with probaility proportional to size (PPS). In some cases, adjacent villages with small population sizes were combined into a single PSU for the purpose of sample selection. On average, 30 households were selected for interviewing in each selected PSU.

In every state, all the households in the selected PSUs were listed about two weeks prior to the survey. This listing provided the necessary frame for selecting households at the second sampling stage. The household listing operation consisted of preparing up-to-date notional and layout sketch maps of each selected PSU, assigning numbers to structures, recording addresses (or locations) of these structures, identifying the residential structures, and listing the names of the heads of all the households in the residentiak structures in the selected PSU. Each household listing team consisted of a lister and a mapper. The listing operation was supervised by the senior field staff of the concerned CO and the PRC in each state. Special efforts were made not to miss any household in the selected PSU during the listing operation. In PSUs with fewer than 500 households, a complete household listing was done. In PSUs with 500 or more households, segmentation of the PSU was done on the basis of existing wards in the PSU, and two segments were selected using either systematic sampling or PPS sampling. The household listing in such PSUs was carried out in the selected segments. The households to be interviewed were selected from provided with the original household listing, layout sketch map and the household sample selected for each PSU. All the selected households were approached during the data collection, and no substitution of a household was allowed under any circumstances.

THE RURAL URBAN SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

A three-stage sample design was adopted for the urban areas in each state: selection of cities/towns, followed by urban blocks, and finally households. Cities and towns were selected using the 1991 population figures while urban blocks were selected using the 1991 list of census enumeration blocks in all the states with the exception of the firts phase states. For the first phase states, the list of urban blocks provided by the National Sample Survey Organization (NSSSO) served as the sampling frame.

All cities and towns were subdivided into three strata: (1) self-selecting cities (i.e., cities with a population large enough to be selected with certainty), (2) towns that are district headquaters, and (3) other towns. Within each stratum, the cities/towns were arranged according to the same kind of geographic stratification used in the rural areas. In self-selecting cities, the sample was selected according to a two-stage sample design: selection of the required number of urban blocks, followed by selection of households in each of selected blocks. For district headquarters and other towns, a three stage sample design was used: selection of towns with PPS, followed by selection of two census blocks per selected town, followed by selection of households from each selected block. As in rural areas, a household listing was carried out in the selected blocks, and an average of 20 households per block was selected systematically.

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used in the NFHS: the Household Questionnaire, the Women's Questionnaire, and the Village Questionnaire. The overall content

With almost all major religions being practiced throughout the country, India is known for its religious diversity. Islam makes up the highest share among minority faiths in the country. According to the Indian census of 2011, the Muslim population in Uttar Pradesh more than ** million, making it the state with the most Muslims.

Socio-economic conditions of Muslims
Muslims seem to lag behind every other religious community in India in terms of living standards, financial stability, education and other aspects, thereby showing poor performance in most of the fields. According to a national survey, 17 percent of the Muslims were categorized under the lowest wealth index, which indicates poor socio-economic conditions.

Growth of Muslim population in India
Islam is one of the fastest-growing religions worldwide. According to India’s census, the Muslim population has witnessed a negative decadal growth of more than ** percent from 1951 to 1960, presumably due to the partitions forming Pakistan and Bangladesh. The population showed a positive and steady growth since 1961, making up ** percent of the total population of India . Even though people following Islam were estimated to grow significantly, they would still remain a minority in India compared to *** billion Hindus by 2050.

As of the year 2024, the population of the capital city of India, Delhi was over ** million people. This was a 2.63 percent growth from last year. The historical trends show that the population doubled between 1990 and 2010. The UN estimated that the population was expected to reach around ** million by 2030. Reasons for population growth   As per the Delhi Economic Survey, migration added over *** 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.

IntroductionAcute encephalitis syndrome (AES) is a major public health enigma in India and the world. Uttar Pradesh (UP) is witnessing recurrent and extensive seasonal AES outbreaks since 1978. Government of India and UP state government have devised various mitigation measures to reduce AES burden and AES associated mortality, morbidity and disability in Uttar Pradesh. The aim of this study was to describe the public health measures taken in order to control seasonal outbreaks of AES in UP between 1978 and 2020.MethodsWe used literature review as a method of analysis, including the Indian government policy documents. This review utilized search engines such as PubMed, Google Scholar, Research Gate, Cochrane, Medline to retrieve articles and information using strategic keywords related to Acute Encephalitis Syndrome. Data was also collected from progress reports of government schemes and websites of Indian Council of Medical Research (ICMR), National Vector Borne Disease Control Programme (NVBDCP) and Integrated Disease Surveillance Programmes (IDSP).ResultsThe incidence of AES cases in UP have declined from 18.2 per million population during 2005-2009 to 15 per million population during 2015-2019 [CI 12.6–20.6, P-value < 0.001] and case fatality rate (CFR) reduced from 33% during 1980-1984 to 12.6% during 2015-2019 [CI 17.4–30.98, P-value < 0.001]. AES incidence was 9 (2019) and 7 (2020) cases per million populations respectively and CFR was 5.8% (2019) and 5% (2020). This decline was likely due to active surveillance programs identifying aetiological agents and risk factors of AES cases. The identified etiologies of AES include Japanese encephalitis virus (5–20%), Enterovirus (0.1–33%), Orientia tsutsugamushi (45–60%) and other viral (0.2–4.2%), bacterial (0–5%) and Rickettsial (0.5–2%) causes. The aggressive immunization programs against Japanese encephalitis with vaccination coverage of 72.3% in UP helped in declining of JE cases in the region. The presumptive treatment of febrile cases with empirical Doxycycline and Azithromycin (EDA) caused decline in Scrub Typhus-AES cases. Decrease in incidence of vector borne diseases (Malaria, Dengue, Japanese Encephalitis and Kala Azar) i.e., 39.6/100,000 population in 2010 to 18/100,000 population in 2017 is highlighting the impact of vector control interventions. Strengthening healthcare infrastructure in BRD medical college and establishment of Encephalitis Treatment Centre (ETC) at peripheral health centres and emergency ambulance services (Dial 108) reduced the referral time and helped in early treatment and management of AES cases. The AES admissions increased at ETC centres to 60% and overall case fatality rate of AES declined to 3%. Under clean India mission and Jal Jeevan mission, proportion of population with clean drinking water increased from 74.3% in 1992 to 98.7% in 2020. The proportion of household having toilet facilities increased from 22.9% in 1992 to 67.4% in 2020. Provisions for better nutritional status under state and national nutrition mission helped in reducing the burden of stunting (52%) and wasting (53.4%) among under five children in 1992 to 38.8% (stunting) and 36.8% (wasting) in year 2018. These factors have all likely contributed to steady AES decline observed in UP.ConclusionThere is a recent steady decline in AES incidence and CFR since implementation of intensive AES surveillance system and JE immunization campaigns which is highlighting the success of interventions made by central and state government to control seasonal AES outbreaks in UP. Currently, AES incidence is 9 cases per million population (in year 2019) and mortality is 5.8%.

Abstract

An effective policy response to the economic impacts of the COVID-19 pandemic requires an enormous range of data to inform the design and response of programs. Public health measures require data on the spread of the disease, beliefs in the population, and capacity of the health system. Relief efforts depend on an understanding of hardships being faced by various segments of the population. Food policy requires measurement of agricultural production and hunger. In such a rapidly evolving pandemic, these data must be collected at a high frequency. Given the unexpected nature of the shock and urgency with which a response was required, Indian policymakers needed to formulate policies affecting India's 1.4 billion people, without the detailed evidence required to construct effective programs. To help overcome this evidence gap, researchers from the World Bank, in collaboration with IDinsight, the Development Data Lab, and John Hopkins University sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.

Geographic coverage

Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The samples for these surveys were drawn from surveys and impact evaluations previously conducted by the World Bank, the Ministry of Rural Development, India and IDInsight. A detailed note on the sampling frames is available for download.

Sampling deviation

Details will be made available after all rounds of data collection and analysis is complete.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Response rate

Approximately 55%

Census: Population: Uttar Pradesh: Agra data was reported at 1,585,704.000 Person in 03-01-2011. This records an increase from the previous number of 1,331,339.000 Person for 03-01-2001. Census: Population: Uttar Pradesh: Agra data is updated decadal, averaging 442,172.500 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 1,585,704.000 Person in 03-01-2011 and a record low of 185,449.000 Person in 03-01-1911. Census: Population: Uttar Pradesh: Agra 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.GAC035: Census: Population: By Towns and Urban Agglomerations: Uttar Pradesh.

Abstract

The objective of PLFS is primarily on two aspects. The first is to measure the dynamics in labour force participation and employment status in the short time interval of three months for the urban areas only in the Current Weekly Status (CWS). Thus, in every quarter, PLFS will bring out the level and change estimates of the key labour force indicators in CWS viz. Worker Population Ratio (WPR), Labour Force Participation Rate (LFPR), Unemployment Rate (UR). Secondly, for both rural and urban areas, level estimates of all important parameters in both usual status and CWS will be brought out annually.

Geographic coverage

The survey covers the whole of the Indian Union except the villages in Andaman and Nicobar Islands which remain extremely difficult to access throughout the year. 12800 FSUs (7024 villages and 5776 UFS blocks) are being covered annually at all-India level.

Sampling procedure

Rotational panel design for urban areas i.The initial rotational panel is for two years, where only 25% FSUs of urban annual allocation will be covered in the first quarter (Panel Ptwo-year period of rotation. 11) with detail listing and canvassing of visit 1 schedule in the selected households. ii. Another 25% FSUs will be covered in the second quarter (Panel P12) for taking up visit 1 schedule and revisit schedule will be canvassed in the selected households of Panel P11. iii. A new panel P13 of 25% FSUs will be surveyed in third quarter with visit 1 schedule and revisit schedules will be canvassed in the households of panels P11 & P12. iv. In the fourth quarter, households of panels P11, P12 & P13 will be surveyed with revisit schedule and a new panel P14 with 25% FSUs for visit 1 schedule. v. In the subsequent quarters of second year 75% FSUs (3 panels - P12, P13 & P14) will be common and an earlier panel (P11) will be replaced by a new panel (P15) for canvassing visit 1 schedule. This will continue till 8th quarter. vi. All the FSUs of the panels P11, P12, ...., P18 (each of which is with 25% of FSUs) will be selected before commencement of survey in the first quarter. vii. At the end of the second year of each two-year duration, updated frame will be used for both rural and urban areas. viii. FSUs of another set of panels P21, P22, ..., P28 selected from the updated frame will be made ready before commencement of first quarter of third year (first quarter of the second two-year duration). These panels P21 to P28 will take care of the changes in the urban frame during the intracensal period. ix. In the ninth quarter (first quarter of the second two-year duration), panel P21 selected from the updated frame will be introduced and the panels P16, P17 and P18 of the old frame will be surveyed. x. This scheme will continue for another 2 years with the introduction of panels P22 to P28 each in one quarter for the subsequent 7 quarters till the end of the fourth year (second year of the two-year period). xi. This scheme of rotation of panels will enable generation of estimates of change parameters with 75% matching and 25% of unmatched samples from fifth quarter onwards. xii. One of the main advantages of this plan of rotation is that there will not be any break in the series of estimates of the change parameters starting from 5th quarter. xiii. Since major changes in the rural-urban frame occurs in the Census years (say for the year 2023-24), provision is to be made to generate estimates without break in the series of estimates considering panels from pre and post-census frames.

1.3.3 Rural samples For rural areas, samples for all the 8 quarters have been selected before commencement of survey for each two-year period, while the frame remains same for this duration. In each quarter, only 25% FSUs of annual allocation (as is done in each sub-round of NSS rounds) are being covered in rural areas so that independent estimates can be generated for each quarter. For this purpose, quarterly allocation is multiple of 2 for drawing interpenetrating sub-samples. There will not be any revisit in the rural samples.

Outline of the design: A stratified multi-stage design has been adopted. The first stage units (FSU) are the Urban Frame Survey (UFS) blocks in urban areas and 2011 Population Census villages (Panchayat wards for Kerala) in rural areas. The ultimate stage units (USU) are households. As in usual NSS rounds, in the case of large FSUs one intermediate stage unit, called hamlet group/sub-block, will be formed. Periodic Labour Force Survey 4 Note on sample design and estimation procedure 1.3.7 Sampling Frame for First Stage Units: The list of latest available Urban Frame Survey (UFS) blocks is considered as the urban sampling frame. List of 2011 Population Census villages (Panchayat wards for Kerala) constitutes the rural sampling frame. Since the duration of rotational panel is of two-year, the urban sampling frame once updated incorporating the changes made in the current phase of UFS will remain unchanged for two years. Similarly the rural sampling frame with changes, if any, for urbanisation of village(s) will remain unchanged for two years. After completion of every two-year period, the frames will be updated for incorporating the changes likely to occur during this period. When next Population Census details will be available, the new frame will be used only when UFS blocks for all newly declared Census Towns and Statutory Towns are available for preparation of sampling frame, as the new list of census villages will not include those villages which will be considered as urban areas. ......

Mode of data collection

Face to Face