India Vital Statistics: Death Rate: per 1000 Population: Rural data was reported at 6.900 NA in 2016. This records a decrease from the previous number of 7.100 NA for 2015. India Vital Statistics: Death Rate: per 1000 Population: Rural data is updated yearly, averaging 10.500 NA from Dec 1970 (Median) to 2016, with 47 observations. The data reached an all-time high of 18.900 NA in 1972 and a record low of 6.900 NA in 2016. India Vital Statistics: Death Rate: per 1000 Population: Rural data remains active status in CEIC and is reported by Census of India. The data is categorized under Global Database’s India – Table IN.GAH001: Vital Statistics.

This study estimates the economic losses (GDP), particularly the impact of COVID-19 deaths on non-health components of GDP in West Bengal state. The NHGDP losses were evaluated using cost-of-illness approach. Future NHGDP losses were discounted at 3%. Excess death estimates by the World Health Organisation (WHO) and Global Burden of Disease (GBD) were used. Sensitivity analysis was carried out by varying discount rates and Average Age of Death (AAD). 21,532 deaths in West Bengal since 17th March 2020 till 31st December 2022 decreased the future NHGDP by $0.92 billion. Nearly 90% of loss was due to deaths occurring in above 30 years age-group. The majority of the loss was borne among the 46–60 years age-group. The NHGDP loss/death was $42,646, however, the average loss/death declined with a rise in age. The loss increased to $9.38 billion and $9.42 billion respectively based on GBD and WHO excess death estimates. The loss increased to $1.3 billion by considering the lower age of the interval as AAD. At 5% and 10% discount rates, the losses reduced to $0.769 billion and $0.549 billion respectively. Results from the study suggest that COVID-19 contributed to major economic loss in West Bengal. The mortality and morbidity caused by COVID-19, the substantial economic costs at individual and population levels in West Bengal, and probably across India and other countries, is another argument for better infection control strategies across the globe to end the impact of this epidemic. Methods Various open domains were used to gather data on COVID-19 deaths in West Bengal and the aforementioned estimates. Economic losses in terms of Non-Health Gross Domestic Product (NHGDP)among six age-group brackets viz. 0–15, 16–30, 31–45, 46–60, 61–75 and 75 and above were estimated to facilitate comparisons and to initiate advocacy for an increase in health investments against COVID-19. This study used midpoint age as the age of death for all the age brackets. The legal minimum age for working i.e., 15 years. A sensitivity analysis was conducted to determine the effect of age on the overall total NHGDP loss estimate. The model was re-estimated assuming an average age at death to be the starting age of each age-group bracket. Based on existing literature discounted rate of interest to measure the value of life is taken as 2.9%. As a sensitivity analysis, NHGDP loss has also been computed using 5% and 10% of discounted rates of interest.

This study estimates the economic losses (GDP), particularly the impact of COVID-19 deaths on non-health components of GDP in West Bengal state. Economic losses in terms of Non-Health Gross Domestic Product (NHGDP)among six age-group brackets viz. 0-15, 16-30, 31-45, 46-60, 61-75 and 75 and above were estimated to facilitate comparisons and to initiate advocacy for an increase in health investments against COVID-19. This study used midpoint age as the age of death for all the age brackets. The legal minimum age for working i.e., 15 years. A sensitivity analysis was conducted to determine the effect of age on the overall total NHGDP loss estimate. The model was re-estimated assuming an average age at death to be the starting age of each age-group bracket. Based on existing literature discounted rate of interest to measure the value of life is taken as 2.9%. As a sensitivity analysis NHGDP loss has also been computed using 5% and 10% of discounted rates of interest.

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.

Data from the Global Burden of Disease (GBD) database, and other open databases. We analysed death and attributable economic costs for male smokers, as female smokers comprise only 2% in India. Cost-of-Illness model was used to estimate the economic costs attributable to smoking across all states of India.

India: Infant deaths per 1000 live births: The latest value from 2022 is 26 deaths per 1000 live births, a decline from 27 deaths per 1000 live births in 2021. In comparison, the world average is 19 deaths per 1000 live births, based on data from 187 countries. Historically, the average for India from 1967 to 2022 is 80 deaths per 1000 live births. The minimum value, 26 deaths per 1000 live births, was reached in 2022 while the maximum of 143 deaths per 1000 live births was recorded in 1967.

India Vital Statistics: Death Rate: per 1000 Population data was reported at 6.000 NA in 2020. This stayed constant from the previous number of 6.000 NA for 2019. India Vital Statistics: Death Rate: per 1000 Population data is updated yearly, averaging 9.000 NA from Dec 1970 (Median) to 2020, with 51 observations. The data reached an all-time high of 16.900 NA in 1972 and a record low of 6.000 NA in 2020. India Vital Statistics: Death Rate: per 1000 Population data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under Global Database’s India – Table IN.GAH001: Vital Statistics.

Vital Statistics: Death Rate: per 1000 Population: Delhi data was reported at 3.600 NA in 2020. This records an increase from the previous number of 3.200 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Delhi data is updated yearly, averaging 4.400 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 5.300 NA in 1998 and a record low of 3.200 NA in 2019. Vital Statistics: Death Rate: per 1000 Population: Delhi 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.GAH003: Vital Statistics: Death Rate: by States.

This statistic represents crude death rate from diabetes in India in 2016, by ETL groups. The high ETL group at 43.2 percent, had the highest crude death rate for diabetes during the measured time period.

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.

This dataset provides underlying data and calculated novel estimates for Economic Burden of Suicide Deaths in India in 2019. Dataset and data dictionary are provided.

India IN: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data was reported at 18.600 Ratio in 2016. India IN: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data is updated yearly, averaging 18.600 Ratio from Dec 2016 (Median) to 2016, with 1 observations. India IN: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population 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: Health Statistics. Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

India Vital Statistics: Death Rate: per 1000 Population: Urban data was reported at 5.400 NA in 2016. This stayed constant from the previous number of 5.400 NA for 2015. India Vital Statistics: Death Rate: per 1000 Population: Urban data is updated yearly, averaging 6.700 NA from Dec 1970 (Median) to 2016, with 47 observations. The data reached an all-time high of 10.300 NA in 1972 and a record low of 5.400 NA in 2016. India Vital Statistics: Death Rate: per 1000 Population: Urban data remains active status in CEIC and is reported by Census of India. The data is categorized under Global Database’s India – Table IN.GAH001: Vital Statistics.

WHO: Influenza A (H1N1): Number of Deaths: India data was reported at 0.000 Person in 06 Jul 2009. This stayed constant from the previous number of 0.000 Person for 05 Jul 2009. WHO: Influenza A (H1N1): Number of Deaths: India data is updated daily, averaging 0.000 Person from Apr 2009 (Median) to 06 Jul 2009, with 74 observations. The data reached an all-time high of 0.000 Person in 06 Jul 2009 and a record low of 0.000 Person in 06 Jul 2009. WHO: Influenza A (H1N1): Number of Deaths: India data remains active status in CEIC and is reported by World Health Organization. The data is categorized under High Frequency Database’s Disease Outbreaks – Table WHO.D002: World Heath Organization: Influenza A (H1N1): By Countries.

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.

The region of present-day China has historically been the most populous region in the world; however, its population development has fluctuated throughout history. In 2022, China was overtaken as the most populous country in the world, and current projections suggest its population is heading for a rapid decline in the coming decades. Transitions of power lead to mortality The source suggests that conflict, and the diseases brought with it, were the major obstacles to population growth throughout most of the Common Era, particularly during transitions of power between various dynasties and rulers. It estimates that the total population fell by approximately 30 million people during the 14th century due to the impact of Mongol invasions, which inflicted heavy losses on the northern population through conflict, enslavement, food instability, and the introduction of bubonic plague. Between 1850 and 1870, the total population fell once more, by more than 50 million people, through further conflict, famine and disease; the most notable of these was the Taiping Rebellion, although the Miao an Panthay Rebellions, and the Dungan Revolt, also had large death tolls. The third plague pandemic also originated in Yunnan in 1855, which killed approximately two million people in China. 20th and 21st centuries There were additional conflicts at the turn of the 20th century, which had significant geopolitical consequences for China, but did not result in the same high levels of mortality seen previously. It was not until the overlapping Chinese Civil War (1927-1949) and Second World War (1937-1945) where the death tolls reached approximately 10 and 20 million respectively. Additionally, as China attempted to industrialize during the Great Leap Forward (1958-1962), economic and agricultural mismanagement resulted in the deaths of tens of millions (possibly as many as 55 million) in less than four years, during the Great Chinese Famine. This mortality is not observable on the given dataset, due to the rapidity of China's demographic transition over the entire period; this saw improvements in healthcare, sanitation, and infrastructure result in sweeping changes across the population. The early 2020s marked some significant milestones in China's demographics, where it was overtaken by India as the world's most populous country, and its population also went into decline. Current projections suggest that China is heading for a "demographic disaster", as its rapidly aging population is placing significant burdens on China's economy, government, and society. In stark contrast to the restrictive "one-child policy" of the past, the government has introduced a series of pro-fertility incentives for couples to have larger families, although the impact of these policies are yet to materialize. If these current projections come true, then China's population may be around half its current size by the end of the century.

Vital Statistics: Death Rate: per 1000 Population: Uttar Pradesh: Urban data was reported at 5.400 NA in 2020. This records an increase from the previous number of 5.300 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Uttar Pradesh: Urban data is updated yearly, averaging 6.300 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 8.100 NA in 1999 and a record low of 5.300 NA in 2019. Vital Statistics: Death 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.GAH003: Vital Statistics: Death Rate: by States.

Vital Statistics: Death Rate: per 1000 Population: Nagaland: Urban data was reported at 3.500 NA in 2020. This records an increase from the previous number of 2.600 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Nagaland: Urban data is updated yearly, averaging 2.800 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 4.100 NA in 2006 and a record low of 1.700 NA in 1998. Vital Statistics: Death Rate: per 1000 Population: Nagaland: 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.GAH003: Vital Statistics: Death Rate: by States.

Vital Statistics: Death Rate: per 1000 Population: Delhi: Urban data was reported at 3.500 NA in 2020. This records an increase from the previous number of 3.200 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Delhi: Urban data is updated yearly, averaging 4.300 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 5.300 NA in 1998 and a record low of 3.200 NA in 2019. Vital Statistics: Death Rate: per 1000 Population: Delhi: 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.GAH003: Vital Statistics: Death Rate: by States.

India Exports: Volume: HS: 21022000: Inactive Yeasts, Other Single-Cell Dead Micro-organisms data was reported at 49.750 kg th in 2018. This records a decrease from the previous number of 64.480 kg th for 2017. India Exports: Volume: HS: 21022000: Inactive Yeasts, Other Single-Cell Dead Micro-organisms data is updated yearly, averaging 61.310 kg th from Mar 1998 (Median) to 2018, with 21 observations. The data reached an all-time high of 276.300 kg th in 2013 and a record low of 0.100 kg th in 2001. India Exports: Volume: HS: 21022000: Inactive Yeasts, Other Single-Cell Dead Micro-organisms data remains active status in CEIC and is reported by Ministry of Commerce and Industry. The data is categorized under India Premium Database’s Foreign Trade – Table IN.JAX003: Foreign Trade: Harmonized System 8 Digits: By Commodity: HS21: Miscellaneous Edible Preparations: Exports: Volume.