Vital Statistics: Birth Rate: per 1000 Population: West Bengal: Urban data was reported at 11.200 NA in 2020. This records a decrease from the previous number of 11.500 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: West Bengal: Urban data is updated yearly, averaging 12.100 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 15.200 NA in 1998 and a record low of 11.100 NA in 2014. Vital Statistics: Birth Rate: per 1000 Population: West Bengal: 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.

Vital Statistics: Natural Growth Rate: per 1000 Population: West Bengal: Urban data was reported at 5.400 NA in 2020. This records a decrease from the previous number of 6.100 NA for 2019. Vital Statistics: Natural Growth Rate: per 1000 Population: West Bengal: Urban data is updated yearly, averaging 5.800 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 8.200 NA in 1998 and a record low of 4.700 NA in 2014. Vital Statistics: Natural Growth Rate: per 1000 Population: West Bengal: 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.

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.

Government Doctor: West Bengal: Average Population Served per Doctor data was reported at 10,411.000 Person in 2014. This records an increase from the previous number of 9,618.000 Person for 2013. Government Doctor: West Bengal: Average Population Served per Doctor data is updated yearly, averaging 10,411.000 Person from Dec 2004 (Median) to 2014, with 9 observations. The data reached an all-time high of 27,473.000 Person in 2012 and a record low of 8,416.000 Person in 2011. Government Doctor: West Bengal: Average Population Served per Doctor 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.HLB002: Health Human Resources: Number of Doctors: Government.

In 1800, the population of the area of modern-day Bangladesh was estimated to be just over 19 million, a figure which would rise steadily throughout the 19th century, reaching over 26 million by 1900. At the time, Bangladesh was the eastern part of the Bengal region in the British Raj, and had the most-concentrated Muslim population in the subcontinent's east. At the turn of the 20th century, the British colonial administration believed that east Bengal was economically lagging behind the west, and Bengal was partitioned in 1905 as a means of improving the region's development. East Bengal then became the only Muslim-majority state in the eastern Raj, which led to socioeconomic tensions between the Hindu upper classes and the general population. Bengal Famine During the Second World War, over 2.5 million men from across the British Raj enlisted in the British Army and their involvement was fundamental to the war effort. The war, however, had devastating consequences for the Bengal region, as the famine of 1943-1944 resulted in the deaths of up to three million people (with over two thirds thought to have been in the east) due to starvation and malnutrition-related disease. As the population boomed in the 1930s, East Bengal's mismanaged and underdeveloped agricultural sector could not sustain this growth; by 1942, food shortages spread across the region, millions began migrating in search of food and work, and colonial mismanagement exacerbated this further. On the brink of famine in early-1943, authorities in India called for aid and permission to redirect their own resources from the war effort to combat the famine, however these were mostly rejected by authorities in London. While the exact extent of each of these factors on causing the famine remains a topic of debate, the general consensus is that the British War Cabinet's refusal to send food or aid was the most decisive. Food shortages did not dissipate until late 1943, however famine deaths persisted for another year. Partition to independence Following the war, the movement for Indian independence reached its final stages as the process of British decolonization began. Unrest between the Raj's Muslim and Hindu populations led to the creation of two separate states in1947; the Muslim-majority regions became East Pakistan (now Bangladesh) and West Pakistan (now Pakistan), separated by the Hindu-majority India. Although East Pakistan's population was larger, power lay with the military in the west, and authorities grew increasingly suppressive and neglectful of the eastern province in the following years. This reached a tipping point when authorities failed to respond adequately to the Bhola cyclone in 1970, which claimed over half a million lives in the Bengal region, and again when they failed to respect the results of the 1970 election, in which the Bengal party Awami League won the majority of seats. Bangladeshi independence was claimed the following March, leading to a brutal war between East and West Pakistan that claimed between 1.5 and three million deaths in just nine months. The war also saw over half of the country displaced, widespread atrocities, and the systematic rape of hundreds of thousands of women. As the war spilled over into India, their forces joined on the side of Bangladesh, and Pakistan was defeated two weeks later. An additional famine in 1974 claimed the lives of several hundred thousand people, meaning that the early 1970s was one of the most devastating periods in the country's history. Independent Bangladesh In the first decades of independence, Bangladesh's political hierarchy was particularly unstable and two of its presidents were assassinated in military coups. Since transitioning to parliamentary democracy in the 1990s, things have become comparatively stable, although political turmoil, violence, and corruption are persistent challenges. As Bangladesh continues to modernize and industrialize, living standards have increased and individual wealth has risen. Service industries have emerged to facilitate the demands of Bangladesh's developing economy, while manufacturing industries, particularly textiles, remain strong. Declining fertility rates have seen natural population growth fall in recent years, although the influx of Myanmar's Rohingya population due to the displacement crisis has seen upwards of one million refugees arrive in the country since 2017. In 2020, it is estimated that Bangladesh has a population of approximately 165 million people.

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:India Population Projections 2011-2036Village Population Projections for India 2011-2036Sub-district 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.

Government Hospital: West Bengal: Average Population Served per Hospital Bed data was reported at 1,170.000 Person in 2014. This records an increase from the previous number of 1,165.000 Person for 2013. Government Hospital: West Bengal: Average Population Served per Hospital Bed data is updated yearly, averaging 1,365.000 Person from Dec 2005 (Median) to 2014, with 8 observations. The data reached an all-time high of 1,734.000 Person in 2007 and a record low of 1,165.000 Person in 2013. Government Hospital: West Bengal: Average Population Served per Hospital Bed 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.HLA002: Health Infrastructure: Government Hospital Beds.

Background

Large carnivores maintain the stability and functioning of ecosystems. Currently, many carnivore species face declining population sizes due to natural and anthropogenic pressures. The leopard, Panthera pardus, is probably the most widely distributed and highly adaptable large felid globally, still persisting in most of its historic range. However, we lack subspecies-level data on country or regional scale on population trends, as ecological monitoring approaches are difficult to apply on such wide-ranging species. We used genetic data from leopards sampled across the Indian subcontinent to investigate population structure and patterns of demographic decline.

Methods

We collected faecal samples from the Terai-Arc landscape of north India and identified 56 unique individuals using a panel of 13 microsatellite markers. We merged this data with already available 143 leopard individuals and assessed genetic structure at country scale. Subsequently, we investigated the demographic history of each identified subpopulations and compared genetic decline analyses with countrywide local extinction probabilities.

Results

Our genetic analyses revealed four distinct subpopulations corresponding to Western Ghats, Deccan Plateau-Semi Arid, Shivalik and Terai region of the north Indian landscape, each with high genetic variation. Coalescent simulations with microsatellite loci revealed a possibly human-induced 75-90% population decline between ∼120-200 years ago across India. Population-specific estimates of genetic decline are in concordance with ecological estimates of local extinction probabilities in these subpopulations obtained from occupancy modeling of the historic and current distribution of leopards in India.

Conclusions

Our results confirm the population decline of a widely distributed, adaptable large carnivore. We re-iterate the relevance of indirect genetic methods for such species in conjunction with occupancy assessment and recommend that detailed, landscape-level ecological studies on leopard populations are critical to future conservation efforts. Our approaches and inference are relevant to other widely distributed, seemingly unaffected carnivores such as the leopard.

Methods Research permissions and ethical considerations

All required permissions for our field surveys and biological sampling were provided by the Forest Departments of Uttarakhand (Permit no: 90/5-6), Uttar Pradesh (Permit no: 1127/23-2-12(G) and 1891/23-2-12) and Bihar (Permit no: Wildlife-589). Due to non-invasive nature of sampling, no ethical clearance was required for this study.

Sampling

To detect population structure and past population demography it is important to obtain genetic samples from different leopard habitats all across the study area. In this study, we used leopard genetic data generated from non-invasive samples collected across the Indian subcontinent. We conducted extensive field surveys across the Indian part of Terai-Arc landscape (TAL) covering the north-Indian states of Uttarakhand, Uttar Pradesh and Bihar between 2016-2018. This region has already been studied for large carnivore occupancy using traditional camera trapping as well as field surveys (Johnsingh et al., 2004; Harihar et al., 2009; Jhala et al., 2015; Chanchani et al., 2016). We foot surveyed all existing trails covering the entire region to collect faecal samples. Number of trails walked in a particular area was decided based on existing knowledge of leopard presence by the local people and frontline staff members of the sampling team. We collected a total of 778 fresh large carnivore faecal samples. These samples were collected from both inside (n=469) and outside (n=309) protected areas from different parts of this landscape. In the field, the samples were judged as large carnivores based on several physical characteristics such as scrape marks, tracks, faecal diameter etc. All faecal samples were collected in wax paper and stored individually in sterile zip-lock bags and stored inside dry, dark boxes in the field for a maximum of two weeks period (Biswas et al., 2019). All samples were collected with GPS locations and were transferred to the laboratory and stored in -20°C freezers until further processing.

In addition to the north Indian samples collected in this study, we used genetic data previously described in Mondol et al. (2015), representing mostly the Western Ghats and central Indian landscape. The data was earlier used in forensic analyses to assign seized leopard samples to their potential geographic origins in India (Mondol et al., 2015). Out of the 173 individual leopards described in the earlier study, we removed data from related individuals and samples with insufficient data (n=30) and used the remaining 143 samples for analyses in this study. These samples were collected from the states of Kerala (n=5), Tamil Nadu (n=4), Karnataka (n=53), Andhra Pradesh (n=3), Madhya Pradesh (n=12), Maharashtra (n=46), Gujarat (n=2), Rajasthan (n=5), Himachal Pradesh (n=8), Jharkhand (n=1), West Bengal (n=2) and Assam (n=2), respectively. The sample locations are presented in Figure 1.

DNA extraction, species and individual identification

For all field-collected faecal samples, DNA extraction was performed using protocols described in Biswas et al. (2019). In brief, each frozen faeces was thawed to room temperature and the upper layer was swabbed twice with Phosphate buffer saline (PBS) saturated sterile cotton applicators (HiMedia). The swabs were lysed with 30 µl of Proteinase K (20mg/ml) and 300 µl of ATL buffer (Qiagen Inc., Hilden, Germany) overnight at 56°C, followed by Qiagen DNeasy tissue DNA kit extraction protocol. DNA was eluted twice in 100 µl preheated 1X TE buffer. For every set of samples, extraction negatives were included to monitor possible contaminations.

Species identification was performed using leopard-specific multiplex PCR assay with NADH4 and NADH2 region primers described in Mondol et al., (2014) and cytochrome b primers used in Maroju et al., (2016). PCR reactions were done in 10 µl volumes containing 3.5 µl multiplex buffer mix (Qiagen Inc., Hilden, Germany), 4 µM BSA, 0.2 µM primer mix and 3 µl of scat DNA with conditions including initial denaturation (95°C for 15 min); 40 cycles of denaturation (94°C for 30 s), annealing (Ta for 30 s) and extension (72°C for 35 s); followed by a final extension (72°C for 10 min). Negative controls were included to monitor possible contamination. Leopard faeces were identified by viewing species-specific bands of 130 bp (NADH4) and 190 bp (NADH2) (Mondol et al., 2014) and 277 bp (cytochrome b) (Maroju et al., 2016) in 2% agarose gel.

For individual identification, we used the same panel of 13 microsatellite loci previously used in Mondol et al. (2014) (Table 1). To generate comparable data with the samples used from earlier study by Mondol et al. (2014) we employed stringent laboratory protocols. All PCR amplifications were performed in 10 µl volumes containing 5 µl Qiagen multiplex PCR buffer mix (QIAGEN Inc., Hilden, Germany), 0.2 µM labelled forward primer (Applied Biosystems, Foster City, CA, USA), 0.2 µM unlabelled reverse primer, 4 µM BSA and 3 µl of the faecal DNA extract. The reactions were performed in an ABI thermocycler with conditions including initial denaturation (94°C for 15 min); 45 cycles of denaturation (94°C for 30 sec), annealing (Ta for 30 sec) and extension (72°C for 30 sec); followed by final extension (72°C for 30 min). Multiple primers were multiplexed to reduce cost and save DNA (Table 1). PCR negatives were incorporated in all reaction setups to monitor possible contamination. The PCR products were analyzed using an automated ABI 3500XL Bioanalyzer with LIZ 500 size standard (Applied Biosystems, Foster City, CA, USA) and alleles were scored with GENEMAPPER version 4.0 (Softgenetics Inc., State Collage, PA, USA). During data generation from field-collected samples we used one reference sample (genotyped for all loci) from the earlier study for genotyping. As the entire new data is generated along with the reference sample and the alleles were scored along with the reference genotypes, the new data (allele scores) were comparable with earlier data for analyses.

To ensure high quality multi-locus genotypes from faecal samples, we followed a modified multiple-tube approach in combination with quality index analyses (Miquel et al., 2006) as described previously for leopards by Mondol et al. (2009a, 2014). All faecal samples were amplified and genotyped four independent times for all the loci. Samples producing identical genotypes for at least three independent amplifications (or a quality index of 0.75 or more) for each loci were considered reliable and used for all further analysis, while the rest were discarded.

Approximately 70 percent of residents in the state of Haryana in India followed a vegetarian diet in 2014. West Bengal had only 1.4 percent of vegetarians during the time period.

The share of people who follow a vegetarian diet worldwide as of 2016, is highest in the Asia-Pacific region.

Government Dental Surgeon: West Bengal: Average Population Served per Dental Surgeon data was reported at 142,071.000 Person in 2014. This records an increase from the previous number of 140,838.000 Person for 2013. Government Dental Surgeon: West Bengal: Average Population Served per Dental Surgeon data is updated yearly, averaging 153,012.000 Person from Dec 2004 (Median) to 2014, with 9 observations. The data reached an all-time high of 341,163.000 Person in 2004 and a record low of 92,225.000 Person in 2006. Government Dental Surgeon: West Bengal: Average Population Served per Dental Surgeon 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.HLB004: Health Human Resources: Number of Dental Surgeons: Government.

Government Hospital: West Bengal: Average Population Served per Hospital data was reported at 58,697.000 Person in 2014. This records an increase from the previous number of 58,188.000 Person for 2013. Government Hospital: West Bengal: Average Population Served per Hospital data is updated yearly, averaging 135,800.000 Person from Dec 2005 (Median) to 2014, with 8 observations. The data reached an all-time high of 298,772.000 Person in 2009 and a record low of 58,188.000 Person in 2013. Government Hospital: West Bengal: Average Population Served per Hospital 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.HLA001: Health Infrastructure: Government Hospitals.

About ** percent of residents in the state of Telangana, West Bengal, and Andhra Pradesh in India were meat consumers. Rajasthan is the state where the lowest share of non-vegetarians came from, only ** percent of the population was not following a vegetarian diet. The share of people who follow a vegetarian diet worldwide as of 2016, is highest in the Asia-Pacific region.

Literacy in India has been increasing as more and more people receive a better education, but it is still far from all-encompassing. In 2023, the degree of literacy in India was about 77 percent, with the majority of literate Indians being men. It is estimated that the global literacy rate for people aged 15 and above is about 86 percent. How to read a literacy rateIn order to identify potential for intellectual and educational progress, the literacy rate of a country covers the level of education and skills acquired by a country’s inhabitants. Literacy is an important indicator of a country’s economic progress and the standard of living – it shows how many people have access to education. However, the standards to measure literacy cannot be universally applied. Measures to identify and define illiterate and literate inhabitants vary from country to country: In some, illiteracy is equated with no schooling at all, for example. Writings on the wallGlobally speaking, more men are able to read and write than women, and this disparity is also reflected in the literacy rate in India – with scarcity of schools and education in rural areas being one factor, and poverty another. Especially in rural areas, women and girls are often not given proper access to formal education, and even if they are, many drop out. Today, India is already being surpassed in this area by other emerging economies, like Brazil, China, and even by most other countries in the Asia-Pacific region. To catch up, India now has to offer more educational programs to its rural population, not only on how to read and write, but also on traditional gender roles and rights.

West Bengal: Tamluk: Total Votes Polled: Indian Union Muslim League data was reported at 1,500.000 Unit in 2014. West Bengal: Tamluk: Total Votes Polled: Indian Union Muslim League data is updated yearly, averaging 1,500.000 Unit from Mar 2014 (Median) to 2014, with 1 observations. West Bengal: Tamluk: Total Votes Polled: Indian Union Muslim League data remains active status in CEIC and is reported by Election Commission of India. The data is categorized under India Premium Database’s General Election – Table IN.GEA035: General Election: Loksabha: Election Outcome of Parliamentary Constituencies: West Bengal.

Government Dental Surgeon: West Bengal: Average Population Served per Dental Surgeon在2014达142,071.000人口，相较于2013的140,838.000人口有所增长。Government Dental Surgeon: West Bengal: Average Population Served per Dental Surgeon数据按每年更新，2004至2014期间平均值为153,012.000人口，共9份观测结果。该数据的历史最高值出现于2004，达341,163.000人口，而历史最低值则出现于2006，为92,225.000人口。CEIC提供的Government Dental Surgeon: West Bengal: Average Population Served per Dental Surgeon数据处于定期更新的状态，数据来源于Central Bureau of Health Intelligence，数据归类于India Premium Database的Health Sector – Table IN.HLB004: Health Human Resources: Number of Dental Surgeons: Government。