🔍 "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

This dataset is a comprehensive collection of data related to the spread of COVID-19 in India. It captures the number of confirmed cases and deaths in each state and union territory of India from the first reported case in January 2020 to the present day. The dataset was created to provide an understanding of the extent of the COVID-19 pandemic in India. It is important because it allows researchers, policy-makers and citizens to gain insights into the various factors that may be driving the spread of the virus in different states and regions of India. It also provides valuable information for researchers trying to understand the dynamics of the pandemic in India.

This dataset is important because it allows us to understand the current situation of the pandemic in India and to monitor the progress of the virus in each state. It can also be used to measure the effectiveness of the strategies implemented by the Indian Government to contain the spread of the virus. The dataset is applicable to anyone interested in understanding the dynamics of the COVID-19 pandemic in India, such as policy-makers, researchers, citizens, NGOs and media. It can be used to gain insights into the current situation and to track the progress of the virus in each state. It can also be used to monitor the effectiveness of the strategies implemented by the Indian Government to contain the spread of the virus.

Overall, this dataset provides a comprehensive view of the COVID-19 pandemic in India. It is updated on a daily basis, and provides essential information that is useful for researchers, policy-makers and citizens. It is an invaluable resource that can be used to understand the dynamics of the virus and to monitor the progress of the virus in each state.

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.

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.

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.

India recorded 44983152 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, India reported 531794 Coronavirus Deaths. This dataset includes a chart with historical data for India Coronavirus Cases.

This dataset provides values for CORONAVIRUS DEATHS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This data is scrapped from the National Center for Vector Borne Diseases Control. It's a website managed by the Ministry of Health & Family Welfare, Government of India. This data contains dengue cases and deaths happening in each state of India over the years.

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.

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.

The Third Plague Epidemic began in the mid-1800s in Yunnan, China, (an area that is still a natural reservoir for the Yersinia pestis bacteria) and had a huge death toll across Asia in the next century. While plague was confined to the Yunnan region for some decades, the mass displacement and social upheaval caused by the Taiping Rebellion saw millions flee the area , bringing the disease to other parts of the country. A plague epidemic then emerged in British-controlled Hong Kong in 1894, where merchants then unknowingly transported infected rats to other parts of the empire along various trade routes. Arrival in Bombay The first Indian cases were reported in Bombay (present-day Mumbai), and the Bombay Presidency suffered more losses than any other region in India (although there were some individual years where the state of Punjab reported more deaths). As with most disease or famine outbreaks in the region, the British authorities were slow to react, and their eventual response was in many ways too late. In some cases authorities even facilitated the spread of the disease; with multiple accounts of the military forcing healthy people into quarantine camps, evicting and burning homes of the afflicted, or by using such excessive force that the public would refuse medical help. Spread in India Lack of understanding among the Indian public was also to their own detriment. Some religions in India forbid the killing of rats, while some people simply refused to acknowledge that they were sick. As the plague in Bombay spiraled out of control, many fled to other parts of the country, taking the plague with them. It is estimated that there were over one million deaths in India by 1902, and almost one million further deaths in 1903 alone. The first four months of 1904 also saw over half a million deaths, almost matching the entire total for 1902. Plague would remain endemic to India for the next few decades, and there are varying reports of up to 10 or 12 million total plague deaths in this time. The public health measures taken to combat the plague in the early 20th century would mark the beginnings of India's public health system, and some of the quarantine measures put in place by the colonial government were even used in 2020 during the outbreak of the COVID-19 pandemic.

Indian Terrorism Deaths

Consequences of Terror Incidents in India

By CrowdFlower [source]

About this dataset

This dataset contains comprehensive information on Indian terrorism deaths, including death tolls due to violence, civilian deaths and militant/terrorist/insurgent fatalities. Accurate estimates from 27,233 sentences sourced and verified from the South Asia Terrorism Portal are provided for every incident. Each row of the dataset includes variables corresponding to the state, district, date reported on as well as features indicating accuracy of judgments for each row. Golden rows indicate maximum accuracy levels for these details and include totals for civilians killed or injured according to the gold standard. Additionally features such as trusted judgements count along with extracted subjects and objects of sentences can be derived from this data set making it a powerful interface that allows researchers to gain access into key aspects of India's current situation related to lethal force events

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How to use the dataset

This dataset provides information on deaths that have occurred in India due to terrorism, as well as the incident details. This can be a valuable source of information for researchers looking to better understand the impacts of terrorism on Indian society and the associated prevention measures.

Here’s how you can use this dataset:

• Analyze death tolls by type (civilian, militant/terrorist/insurgent, security forces):Use descriptive statistics functions to compare and contrast the number of deaths caused by civilian, militant/terrorist/insurgent, and security forces over time. You could also look for correlations between these types of incidents and other factors such as region or date.
• Explore different regions impacted by terrorism: Explore which states or districts in India are affected most adversely by terrorist activities using location data from this dataset. You could also examine trends related to where incidents take place over time as well as total cumulative death counts per region; these findings may help inform where intense anti-terrorism efforts are required most.
• Generate insight on key dates of events: Utilize date fields such as report date or last judgment at in order to pinpoint when certain major events have taken place related to terrorism in India; you could then dive deeper into any relevant context surrounding those dates that may spark further curiosity into the topic itself (e.g., who was involved? what was going on politically?)

Research Ideas

• Identifying trends in the number of deaths for different types of people over time in each district, state and country. This can be used to identify areas where violence is increasing or decreasing, and help develop interventions to reduce casualties from terrorism.
• Investigating correlations between the type of people killed (civilians, militants/terrorists/insurgents etc.) and other factors such as political instability or development levels in the region.
• Performing sentiment analysis on the sentences found in this dataset to measure how public opinion about terrorism is changing over time. This could be combined with other datasets such as media coverage to provide an even more comprehensive understanding of public attitudes towards terrorism

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: deaths-in-india-satp-dfe.csv | Column name | Description | |:-----------------------------------------|:--------------------------------------------------------------------------------------------------------------------| | _golden | A boolean value indicating whether the annotation is a golden annotation or not. (Boolean) | | _unit_state | A value indicating the state of the annotation unit. (String) | | _trusted_judgments | The number of trusted judgments for the annotation unit. (Integer) | | _last_judgment_at | The date and time of the last judgment for the annotation unit. (DateTime) ...

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.

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.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

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

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.

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.

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.