In India, between 2031 and 2035, the increase in life expectancy among the elderly was expected to bring about a proportional increase in the crude death rates in all major states. Madhya Pradesh, Uttar Pradesh and Chhattisgarh were the exceptions that saw crude death rates of ***, *** and *** respectively during the same time period. A direct correlation appears to exist between the growing geriatric population of a society and the crude death rate.

In 2023, the death rate in deaths per 1,000 inhabitants in India stood at ****. Between 1960 and 2023, the figure dropped by *****, though the decline followed an uneven course rather than a steady trajectory.

The state with the highest IMR in India between 2011 and 2015 was Madhya Pradesh, which presented an IMR of ** per thousand infants. From 2031 to 2035, the highest projected IMRs were to be seen in Uttar Pradesh and Madhya Pradesh, with ** and ** deaths per thousand infants respectively. Countries with high IMR rates are indicative of subpar standards not only in terms of medical treatment, but also relative to sanitation, nutrition, and education. The infant mortality rate (IMR) is a health indicator of the general physical health of a society. The IMR measures the amount of human deaths that take place within a group, under the age of one year.

Vital Statistics: Infant Mortality Rate: per 1000 Live Births: Arunchal Pradesh: Urban data was reported at 13.000 NA in 2020. This records a decrease from the previous number of 24.000 NA for 2019. Vital Statistics: Infant Mortality Rate: per 1000 Live Births: Arunchal Pradesh: Urban data is updated yearly, averaging 14.000 NA from Dec 1998 (Median) to 2020, with 22 observations. The data reached an all-time high of 34.000 NA in 2017 and a record low of 10.000 NA in 2011. Vital Statistics: Infant Mortality Rate: per 1000 Live Births: Arunchal 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.GAH005: Vital Statistics: Infant Mortality Rate: by States.

In 2023, the infant mortality rate in India was at about 24.5 deaths per 1,000 live births, a significant decrease from previous years. Infant mortality as an indicatorThe infant mortality rate is the number of deaths of children under one year of age per 1,000 live births. This rate is an important key indicator for a country’s health and standard of living; a low infant mortality rate indicates a high standard of healthcare. Causes of infant mortality include premature birth, sepsis or meningitis, sudden infant death syndrome, and pneumonia. Globally, the infant mortality rate has shrunk from 63 infant deaths per 1,000 live births to 27 since 1990 and is forecast to drop to 8 infant deaths per 1,000 live births by the year 2100. India’s rural problemWith 32 infant deaths per 1,000 live births, India is neither among the countries with the highest nor among those with the lowest infant mortality rate. Its decrease indicates an increase in medical care and hygiene, as well as a decrease in female infanticide. Increasing life expectancy at birth is another indicator that shows that the living conditions of the Indian population are improving. Still, India’s inhabitants predominantly live in rural areas, where standards of living as well as access to medical care and hygiene are traditionally lower and more complicated than in cities. Public health programs are thus put in place by the government to ensure further improvement.

ObjectivesUnder the prevailing conditions of imbalanced life table and historic gender discrimination in India, our study examines crossover between life expectancies at ages zero, one and five years for India and quantifies the relative share of infant and under-five mortality towards this crossover.MethodsWe estimate threshold levels of infant and under-five mortality required for crossover using age specific death rates during 1981–2009 for 16 Indian states by sex (comprising of India’s 90% population in 2011). Kitagawa decomposition equations were used to analyse relative share of infant and under-five mortality towards crossover.FindingsIndia experienced crossover between life expectancies at ages zero and five in 2004 for menand in 2009 for women; eleven and nine Indian states have experienced this crossover for men and women, respectively. Men usually experienced crossover four years earlier than the women. Improvements in mortality below ages five have mostly contributed towards this crossover. Life expectancy at age one exceeds that at age zero for both men and women in India except for Kerala (the only state to experience this crossover in 2000 for men and 1999 for women).ConclusionsFor India, using life expectancy at age zero and under-five mortality rate together may be more meaningful to measure overall health of its people until the crossover. Delayed crossover for women, despite higher life expectancy at birth than for men reiterates that Indian women are still disadvantaged and hence use of life expectancies at ages zero, one and five become important for India. Greater programmatic efforts to control leading causes of death during the first month and 1–59 months in high child mortality areas can help India to attain this crossover early.

Vital Statistics: Infant Mortality Rate: per 1000 Live Births: Goa: Rural data was reported at 7.000 NA in 2020. This records a decrease from the previous number of 8.000 NA for 2019. Vital Statistics: Infant Mortality Rate: per 1000 Live Births: Goa: Rural data is updated yearly, averaging 10.000 NA from Dec 1998 (Median) to 2020, with 22 observations. The data reached an all-time high of 25.000 NA in 1998 and a record low of 6.000 NA in 2011. Vital Statistics: Infant Mortality Rate: per 1000 Live Births: Goa: Rural 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.GAH005: Vital Statistics: Infant Mortality Rate: by States.

The data shows the year-wise statistics for incidence of accidental deaths in different cities of India by natural or unnatural causes between 2009 and 2015.

Note: 1. Vasai Virar, Tiruchirappalli, Thrissur, Thiruvananthapuram, Ranchi, Srinagar, Raipur, Malappuram, Kozhikode, Kota, Kollam, Kannur, Jodhpur, Gwalior, Ghaziabad, Durg Bhilainagar, Aurangabad and Chandigarh (City) newly emerged Mega Cities as per Population Census 2011. 2. Poisoning includes the incidence due to food poisoning/accidental intake of insects, spurious/poisoning liquor, leakage of poisoning gases etc., snake bite/animal bite and others. 3. Traffic accidents includes Road accidents, Rail road accidents and other railway accidents. 4. Collapse of structure includes House, Building, Dam, Bridge others. 5. Sudden deaths include i) Heart Attacks ii) Epileptic fits/giddiness iii) Abortion/Childbirth iv) Influence of alcohol. 6. Fire includes i) Fireworks/crackers ii) Short-Circuit iii) Cooking Gas Cylinder/Stove burst iv) other fire accidents.

BackgroundDiverse socio-economic and cultural issues contribute to adverse health outcomes and increased mortality rates among rural Indian women across different age categories. The present study aims to comprehensively assess age-specific mortality rates (ASMR) and their temporal trends using a composite measure at the sub-national level for rural Indian females to capture cross-state differences.Materials and methodsA total of 19 states were included in the study to construct a composite age-specific mortality index for 2011 (base year) and 2018 (reference year) and examine the incremental changes in the index values across these years at the sub-national level in India. Sub-index values were calculated for each component age group and were subsequently used to compute the composite ASMR index using the geometric mean method. Based on the incremental changes, the performance of states was categorized into four different typologies.ResultsImprovement in mortality index scores in the 0–4 years age group was documented for all states. The mortality rates for the 60+ age group were recorded to be high for all states. Kerala emerged as the overall top performer in terms of mortality index scores, while Bihar and Jharkhand were at the bottom of the mortality index table. The overall mortality composite score has shown minor improvement from base year to reference year at all India level.ConclusionAn overall reduction in the mortality rates of rural Indian women has been observed over the years in India. However, in states like Bihar and Jharkhand, mortality is high and has considerable scope for improvement. The success of public health interventions to reduce the under-five mortality rate is evident as the female rural mortality rates have reduced sizably for all states. Nevertheless, there is still sizable scope for reducing mortality rates for other component age groups. Additionally, there is a need to divert attention toward the female geriatric (60+ years) population as the mortality rates are still high.

BackgroundThis paper examines if, when controlling for biophysical and geographical variables (including rainfall, productivity of agricultural lands, topography/temperature, and market access through road networks), socioeconomic and health care indicators help to explain variations in the under-five mortality rate across districts from nine high focus states in India. The literature on this subject is inconclusive because the survey data, upon which most studies of child mortality rely, rarely include variables that measure these factors. This paper introduces these variables into an analysis of 284 districts from nine high focus states in India. Methodology/Principal FindingsInformation on the mortality indicator was accessed from the recently conducted Annual Health Survey of 2011 and other socioeconomic and geographic variables from Census 2011, District Level Household and Facility Survey (2007–08), Department of Economics and Statistics Divisions of the concerned states. Displaying high spatial dependence (spatial autocorrelation) in the mortality indicator (outcome variable) and its possible predictors used in the analysis, the paper uses the Spatial-Error Model in an effort to negate or reduce the spatial dependence in model parameters. The results evince that the coverage gap index (a mixed indicator of district wise coverage of reproductive and child health services), female literacy, urbanization, economic status, the number of newborn care provided in Primary Health Centers in the district transpired as significant correlates of under-five mortality in the nine high focus states in India. The study identifies three clusters with high under-five mortality rate including 30 districts, and advocates urgent attention. ConclusionEven after controlling the possible biophysical and geographical variables, the study reveals that the health program initiatives have a major role to play in reducing under-five mortality rate in the high focus states in India.

In 2020, the infant mortality rate in the state of Haryana in India was about ** deaths per 1,000 live births. This value represented a significant decrease in infant mortality in Haryana from previous years.

The projected crude birth rate in India, at national level, was expected to decrease to about ** births per thousand people by 2031 to 2035 as opposed to the national crude birth rate from 2011 to 2015 which stood at more than ** births per thousand people. At state level, Bihar reflected the highest crude birth rate from 2011 to 2015 as well as the highest projected crude birth rate from 2031-2035. By contrast, the states with the lowest projected crude birth rates were Punjab, Tamil Nadu, and Andhra Pradesh during the same time period.

In 2023, the crude birth rate in live births per 1,000 inhabitants in India stood at 16.15. Between 1960 and 2023, the figure dropped by 26.75, though the decline followed an uneven course rather than a steady trajectory.

In 2020, the infant mortality rate in the state of Karnataka in India was about ** deaths per 1,000 live births. Infant mortality is measured by the number of deaths of children under one year of age per 1,000 live births.

• Per 1000 children.ΔPer 100,000 children.†Estimates based on a population of 42,066,431 children comprising <2 years of age in India [per the 2011 Census of India data [30]].¶Obtained from the World Health Statistics 2012 data [20].#Obtained from Walker et al. (2013) estimates of diarrhea proportionate mortality in Indian children [29].§Obtained from the GBD 2010 estimates [28]; 95% CI represents the uncertainty intervals estimated in the GBD 2010 study.‡Obtained by multiplying the unadjusted rate, and upper and lower boundaries of the 95% CI with a factor of 2.5 (to adjust for the increased proportion of diarrheal mortality in Indian children <5 years of age [1], [29]).

Life expectancy in India was 25.4 in the year 1800, and over the course of the next 220 years, it has increased to almost 70. Between 1800 and 1920, life expectancy in India remained in the mid to low twenties, with the largest declines coming in the 1870s and 1910s; this was because of the Great Famine of 1876-1878, and the Spanish Flu Pandemic of 1918-1919, both of which were responsible for the deaths of up to six and seventeen million Indians respectively; as well as the presence of other endemic diseases in the region, such as smallpox. From 1920 onwards, India's life expectancy has consistently increased, but it is still below the global average.

In 2020, the infant mortality rate in the state of Himachal Pradesh in India was about ** deaths per 1,000 live births. This value represented a significant decrease in infant mortality rate in Himachal Pradesh from previous years.

Data on total Accidental deaths and suicides for 2021

The projected median age of population in India, at national level, was expected to go up to more than 34 years by 2036 versus almost 25 years in 2011. At state level, Tamil Nadu reflected the highest projected median age with over 40 years in 2036 versus nearly 30 years in 2011.

The projected median age of population of a country is contingent upon several health metrics such as the fertility rate, birth rate, and mortality rate. For instance, if a country or state sees a lower fertility and mortality rate, the geriatric population is expected to increase proportionally.

In 2021, about ** people died across India due to cold waves. This was a significant decrease from the previous year's number of ***. The highest number of deaths was recorded in 2011 when *** people died.