🔍 "Unraveling India's Mortality Mysteries: A Comprehensive Dataset on Causes of Death, 2009-2020" 📊

This unique dataset, sourced directly from the official Indian Census website, offers a deep dive into the intricate patterns and trends of mortality in India over the past decade. 🌍

Covering a wide range of data points, including:

Detailed breakdown of causes of death 🩺 Age-wise distribution of fatalities 👨‍🦳👧 Year-over-year reporting of mortality statistics 📈 Comprehensive sex-wise analysis 👨‍🌾👩‍🔬 This comprehensive dataset is a must-have for researchers, policymakers, and public health experts seeking to uncover the hidden narratives behind India's evolving health landscape. 🔍💡

Dive into this treasure trove of insights and unlock the keys to understanding the complex tapestry of life and death in the world's second-most populous nation. 🇮🇳🔑

Anyone need the data in the form of excel please make request in the suggestion box . I will upload the excel form of the data

Actual value and historical data chart for India Death Rate Crude Per 1 000 People

India IN: Probability of Dying at Age 20-24 Years: per 1000 data was reported at 6.000 Ratio in 2019. This records a decrease from the previous number of 6.100 Ratio for 2018. India IN: Probability of Dying at Age 20-24 Years: per 1000 data is updated yearly, averaging 10.350 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 14.000 Ratio in 1990 and a record low of 6.000 Ratio in 2019. India IN: Probability of Dying at Age 20-24 Years: per 1000 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Health Statistics. Probability of dying between age 20-24 years of age expressed per 1,000 youths age 20, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

This dataset provides a comprehensive state-wise report of deaths in India from the year 2009 to 2020. This data is crucial for conducting in-depth analysis, identifying trends, making predictions, and developing technologies aimed at health improvement and prevention strategies. By examining this dataset, researchers can uncover the underlying factors contributing to mortality rates and address the challenges in public health.

Dataset Summary Time Period: 2009-2020 Geographical Coverage: All Indian states and Union Territories Data Sources: Official government records, public health databases, and verified statistical reports. Features State/UT: The name of the state or union territory. Year: The reporting year ranging from 2009 to 2020. Total Deaths: The total number of deaths reported in the respective year. Causes of Death: Categorized causes of death (e.g., natural causes, accidents, diseases, etc.) Age Groups: Death count categorized by different age groups. Gender: Gender-wise death distribution (Male, Female, Others). Urban/Rural: Distinction between deaths in urban and rural areas. Additional Notes: Any additional notes or anomalies for specific years or states. Potential Applications Trend Analysis: Identify trends in mortality rates over the years and analyze the possible reasons for any significant changes. Predictive Modeling: Develop predictive models to forecast future death rates and potential public health crises. Health Policy Development: Assist policymakers in formulating effective health policies and intervention strategies. Technology Development: Inspire technological innovations geared towards health monitoring, early warning systems, and improving healthcare services. Sociodemographic Research: Study the impact of sociodemographic factors on mortality rates, including effects of urbanization, economic status, and healthcare access. Mystery Behind the Deaths The dataset can also be a valuable resource in uncovering the mysteries behind deaths in India, enabling researchers to:

Investigate the causes behind unusually high death rates in certain states or periods. Study the impact of natural disasters, pandemics, and other calamities on mortality. Analyze the correlation between healthcare infrastructure and death rates. Examine gender disparities and their causes. Understand the impact of public health initiatives and their effectiveness. Data Collection and Accuracy The data has been meticulously collected from various reliable sources, ensuring high accuracy and consistency. Any discrepancies or missing data have been noted in the 'Additional Notes' column to maintain transparency.

How to Use This Dataset Exploration: Initial exploration and summarization of the data using statistical tools and visualizations. Cleaning: Undertake any necessary data cleaning to handle missing or anomalous values. Analysis: Conduct detailed analysis using statistical methods or machine learning models. Reporting: Generate reports and visualizations to convey findings effectively. Sharing: Share insights and findings with other researchers, policymakers, or the public to drive informed decision-making.

Vital Statistics: Death Rate: per 1000 Population: Uttar Pradesh data was reported at 6.500 NA in 2020. This stayed constant from the previous number of 6.500 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Uttar Pradesh data is updated yearly, averaging 8.200 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 10.500 NA in 1999 and a record low of 6.500 NA in 2020. Vital Statistics: Death 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.GAH003: Vital Statistics: Death Rate: by States.

This comprehensive dataset provides a deep dive into the infant mortality rates (IMR) in India, tracing its trajectory through various decades. It offers valuable insights into health indicators, socio-economic factors, and policy initiatives, showcasing how India has evolved in its approach to child health and safety. Researchers, policymakers, and enthusiasts can tap into this rich resource to gain a better understanding of the challenges and progress made in the realm of infant health in India.

It's worth noting that while the dataset is expansive, there are multiple null values for data points prior to the 1990s. This underscores the limitations in the available data from that period, and users are advised to exercise caution when making historical comparisons or drawing conclusions from these early years. Regardless, this dataset stands as a testament to the strides India has made and the distances yet to be covered in ensuring the well-being of its youngest citizens.

Source

This dataset is about countries per year in India. It has 1 row and is filtered where the date is 2021. It features 3 columns: country, and death rate.

The dataset contains year-wise data on the number of registered and medically certified deaths in India, compiled from Medical Certification of Cause of Death (MCCD) annual reports which contain registered deaths and other data obtained through the Civil Registration System (CRS) under the Registration of Births and Deaths Act, 1969

UNICEF's country profile for India, including under-five mortality rates, child health, education and sanitation data.

Vital Statistics: Death Rate: per 1000 Population: Punjab: Rural data was reported at 8.300 NA in 2020. This records an increase from the previous number of 8.000 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Punjab: Rural data is updated yearly, averaging 7.700 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 8.300 NA in 2020 and a record low of 6.600 NA in 2016. Vital Statistics: Death Rate: per 1000 Population: Punjab: 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.GAH003: Vital Statistics: Death Rate: by States.

Vital Statistics: Death Rate: per 1000 Population: West Bengal: Rural data was reported at 5.300 NA in 2020. This records an increase from the previous number of 5.200 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: West Bengal: Rural data is updated yearly, averaging 6.200 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 7.700 NA in 1998 and a record low of 5.200 NA in 2019. Vital Statistics: Death Rate: per 1000 Population: West Bengal: 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.GAH003: Vital Statistics: Death Rate: by States.

BackgroundOver 75% of the annual estimated 9.5 million deaths in India occur in the home, and the large majority of these do not have a certified cause. India and other developing countries urgently need reliable quantification of the causes of death. They also need better epidemiological evidence about the relevance of physical (such as blood pressure and obesity), behavioral (such as smoking, alcohol, HIV-1 risk taking, and immunization history), and biological (such as blood lipids and gene polymorphisms) measurements to the development of disease in individuals or disease rates in populations. We report here on the rationale, design, and implementation of the world's largest prospective study of the causes and correlates of mortality. Methods and FindingsWe will monitor nearly 14 million people in 2.4 million nationally representative Indian households (6.3 million people in 1.1 million households in the 1998–2003 sample frame and 7.6 million people in 1.3 million households in the 2004–2014 sample frame) for vital status and, if dead, the causes of death through a well-validated verbal autopsy (VA) instrument. About 300,000 deaths from 1998–2003 and some 700,000 deaths from 2004–2014 are expected; of these about 850,000 will be coded by two physicians to provide causes of death by gender, age, socioeconomic status, and geographical region. Pilot studies will evaluate the addition of physical and biological measurements, specifically dried blood spots. Preliminary results from over 35,000 deaths suggest that VA can ascertain the leading causes of death, reduce the misclassification of causes, and derive the probable underlying cause of death when it has not been reported. VA yields broad classification of the underlying causes in about 90% of deaths before age 70. In old age, however, the proportion of classifiable deaths is lower. By tracking underlying demographic denominators, the study permits quantification of absolute mortality rates. Household case-control, proportional mortality, and nested case-control methods permit quantification of risk factors. ConclusionsThis study will reliably document not only the underlying cause of child and adult deaths but also key risk factors (behavioral, physical, environmental, and eventually, genetic). It offers a globally replicable model for reliably estimating cause-specific mortality using VA and strengthens India's flagship mortality monitoring system. Despite the misclassification that is still expected, the new cause-of-death data will be substantially better than that available previously.

Vital Statistics: Death Rate: per 1000 Population: Bihar data was reported at 5.400 NA in 2020. This records a decrease from the previous number of 5.500 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Bihar data is updated yearly, averaging 7.000 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 9.400 NA in 1998 and a record low of 5.400 NA in 2020. Vital Statistics: Death Rate: per 1000 Population: Bihar 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 marks one COVID-19 death every 5 minutes

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Content

People across India scrambled for life-saving oxygen supplies on Friday and patients lay dying outside hospitals as the capital recorded the equivalent of one death from COVID-19 every five minutes.

For the second day running, the country’s overnight infection total was higher than ever recorded anywhere in the world since the pandemic began last year, at 332,730.

India’s second wave has hit with such ferocity that hospitals are running out of oxygen, beds, and anti-viral drugs. Many patients have been turned away because there was no space for them, doctors in Delhi said.

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Mass cremations have been taking place as the crematoriums have run out of space. Ambulance sirens sounded throughout the day in the deserted streets of the capital, one of India’s worst-hit cities, where a lockdown is in place to try and stem the transmission of the virus. source

Dataset

The dataset consists of the tweets made with the #IndiaWantsOxygen hashtag covering the tweets from the past week. The dataset totally consists of 25,440 tweets and will be updated on a daily basis.

The description of the features is given below | No |Columns | Descriptions | | -- | -- | -- | | 1 | user_name | The name of the user, as they’ve defined it. | | 2 | user_location | The user-defined location for this account’s profile. | | 3 | user_description | The user-defined UTF-8 string describing their account. | | 4 | user_created | Time and date, when the account was created. | | 5 | user_followers | The number of followers an account currently has. | | 6 | user_friends | The number of friends an account currently has. | | 7 | user_favourites | The number of favorites an account currently has | | 8 | user_verified | When true, indicates that the user has a verified account | | 9 | date | UTC time and date when the Tweet was created | | 10 | text | The actual UTF-8 text of the Tweet | | 11 | hashtags | All the other hashtags posted in the tweet along with #IndiaWantsOxygen | | 12 | source | Utility used to post the Tweet, Tweets from the Twitter website have a source value - web | | 13 | is_retweet | Indicates whether this Tweet has been Retweeted by the authenticating user. |

Acknowledgements

https://globalnews.ca/news/7785122/india-covid-19-hospitals-record/ Image courtesy: BBC and Reuters

Inspiration

The past few days have been really depressing after seeing these incidents. These tweets are the voice of the indians requesting help and people all over the globe asking their own countries to support India by providing oxygen tanks.

And I strongly believe that this is not just some data, but the pure emotions of people and their call for help. And I hope we as data scientists could contribute on this front by providing valuable information and insights.

According to data in India across different age groups from 60 to 85 years and above, the trend indicates a general fall in death rates in the country in the past ten years. In the age-group from 60-64 years there were approximately 22.5 deaths per thousand population in 2008, while in 2013 it was down to 18.4 and increasing slightly in 2018 to 19.5 deaths per thousand population. This was, however, still lower than in 2008.

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 Dataset contains date of death, state, gender, age and tiger reserve location wise deaths of Tigers. The dataset also includes core area and buffer area of each tiger reserve

Note: Adult Age Group: 3 to 10 Years Sub-Adult Age Group: 1 to 3 Years Cub Age Group: less than 1 Year

The India Road Accident Dataset provides a comprehensive view of road accidents across various states and cities in India. The dataset includes 3,000 accident records spanning from 2018 to 2023, with detailed attributes such as accident severity, weather conditions, road type, vehicle involvement, casualties, and more.

This dataset is ideal for predictive modeling, risk assessment, trend analysis, and policy-making related to road safety in India.

Key Features 📌 State & City-Level Data – Covers multiple Indian states and cities, allowing for regional accident analysis. 📌 Time-Based Analysis – Includes year, month, day of the week, and time of the accident. 📌 Accident Severity Levels – Categorized as Fatal, Serious, or Minor. 📌 Vehicle & Driver Insights – Includes vehicle types involved, driver age, gender, and license status. 📌 Environmental & Road Conditions – Captures weather, lighting, road type, and speed limits at accident locations. 📌 Alcohol Involvement – Identifies whether the accident was linked to drunk driving.

Potential Use Cases ✅ Predictive Modeling: Build machine learning models to predict accident hotspots. ✅ Trend Analysis: Identify seasonal, temporal, or geographical trends in road accidents. ✅ Policy Making & Road Safety Improvements: Assist governments and NGOs in designing safety measures. ✅ Data Visualization & Dashboarding: Create interactive reports for accident trends.

This dataset consists of the number of deaths due to heatwaves reported by different agencies/organizations. These are listed below: 1. MoSPI or MoES: Ministry of Statistics and Programme Implementation (MoSPI) published data on heat wave deaths in its annual Envistats report until 2021. Since 2022, the data has been collated from the Ministry of Earth Sciences since in the Envistats report, the source is mentioned as the India Meteorological Department (IMD), Ministry of Earth Sciences. 2. National Disaster Management Authority (NDMA): The data reported by this organization in some of its reports and workshop content has been collated. Values shared by Ministry of Health in the Parliament , which started recording the figures since 2015, is same as this until 2022. 3. World Meteorological Organization (WMO) 4. National Crime Records Bureau (NCRB)'s Accidental Deaths and Suicides India report: Data on heat stroke deaths reported by police departments at state level is presented in the report, which has been collated in the dataset. 5. IMD: Data on heatwave deaths reported by the IMD in its annual reports has been collated separately since the figures are slightly different from that reported by MoSPI/MoES.

The child mortality rate in India, for children under the age of five, was 509 deaths per thousand births in 1880. This means that over half of all children born in 1880 did not survive past the age of five, and it remained this way until the twentieth century. From 1900 until today, the child mortality rate has fallen from over 53 percent in 1900, to under four percent in 2020. Since 1900, there were only two times where the child mortality rate increased in India, which were as a result of the Spanish Flu pandemic in the 1910s, and in the 1950s as India adjusted to its newfound independence.