Introduction

• HIV (human immunodeficiency virus) is a virus that attacks the body's immune system. If HIV is not treated, it can lead to AIDS (acquired immunodeficiency syndrome) which currently has no cure. Once people get HIV, they have it for life. But with proper medical care, HIV can be controlled. Symptoms: Influenza-like illness; Fatigue… Treatments: Management of HIV/AIDS Type of infectious agent: Virus (Human Immunodeficiency Virus) • AIDS (acquired immune deficiency syndrome) is the name used to describe a number of potentially life-threatening infections and illnesses that happen when one’s immune system has been severely damaged by the HIV virus. While AIDS cannot be transmitted from 1 person to another, the HIV virus can.

Dataset

The data set contains data of the following:- 1. The top causes of deaths in the world 2. Total number of deaths due to HIV/AIDS 3. ART (Anti Retro-viral Therapy) coverage among people living with HIV 4. Knowledge among young citizens (15-24years) about HIV/AIDS 5. Population of HIV/AIDS patients living with TB and their death rate 6. Life expectancy rate among HIV/AIDS patients 7. HIV/AIDS Patients in different age groups 8. Women population living with HIV 9. Young women in India having the knowledge of HIV/AIDS 10. HIV/AIDS deaths in Indian states

Data was scrapped from the official website of UNICEF -https://data.unicef.org/ and https://data.gov.in/

Ask Phase

• Data gives the trend of increasing no. of HIV/AIDS patients across the world • The information available for each country is percentage of total Global AIDS patients • Time period traced is 2000-2019 • Key Questions to answer:  Which countries and regions are affected the most?  How are the different age groups affected?  How much is the ART (Anti Retro-viral Therapy) coverage among the patients and what is the life expectancy rate?  What percentage of the population is aware of the prevention and causes of HIV/AIDS

Prepare phase.

• By tabulating and filtering the data the required data was obtained to bring out observations. • Data was formatted to the desired format to perform further calculations. • Sorting of data region wise. • Columns with inconsistent and empty cells were deleted. • The data of India was extracted for further analysis • Duplicate entries and undesired data was removed

Process phase

For cleaning the dataset for further analysis MS Excel was used due to small data. • Used sumifs() functions to aggregate the data region wise • Used sumif() to segregate the no. of patients within different age groups • Used sumifs() to find the total number of TB patients among HIV deaths. • Used countif() to find the percentage of male and female patients. • Sorted data to find the top and bottom nation with most and least HIV/AIDS patients

Analyze phase

• Formed the following pivot tables to answer key target questions  Year v/s number of death rates  Country v/s death numbers to bring out nation wise deaths  Causes of death v/s the number of deaths to bring at which position AIDS causes causality  Year v/s percentage of life expectancy to observe the pattern of no. of survivors

Visualization phase

The data was visualized using Tableau.

Presentaion

The final presentation was prepared by accumulating all observations and inferences which is linked below https://docs.google.com/presentation/d/1NEX10Vz5u5Va3CrTLVbvsUHZjO-fn8EOeiOHkP03T3Q/edit?usp=sharing

This dataset contains death counts, crude rates and adjusted rates for selected causes of death by county and region. For more information, check out: http://www.health.ny.gov/statistics/vital_statistics/, or go to the "About" tab.

Contents

HIV/AIDS** data from the HIV Surveillance Annual Report * Note: Data reported to the HIV Epidemiology and Field Services Program by June 30, 2016. All data shown are for people ages 13 and older. Borough-wide and citywide totals may include cases assigned to a borough with an unknown UHF or assigned to NYC with an unknown borough, respectively. Therefore, UHF totals may not sum to borough totals and borough totals may not sum to citywide totals."

Dataset has 18 features including:

Year, Borough, UHF, Gender, Age, Race, HIV diagnoses, HIV diagnosis rate, Concurrent diagnoses, % linked to care within 3 months, AIDS diagnoses, AIDS diagnosis rate, PLWDHI prevalence, % viral suppression, Deaths, Death rate, HIV-related death rate, Non-HIV-related death rate

This dataset contains death counts and crude rates by region, age group, and selected cause of death. For more information, check out: http://www.health.ny.gov/statistics/vital_statistics/, or go to the "About" tab.

Overview: The AIDS Clinical Trials Group Study 175 Dataset, initially published in 1996, is a comprehensive collection of healthcare statistics and categorical information about patients diagnosed with AIDS. This dataset was created with the primary purpose of examining the performance of two different types of AIDS treatments: zidovudine (AZT) versus didanosine (ddI), AZT plus ddI, and AZT plus zalcitabine (ddC). The prediction task associated with this dataset involves determining whether each patient died within a specified time window.

Dataset Details: - Number of rows: 2139 - Number of columns: 24

Purpose of Dataset Creation: The dataset was created to evaluate the efficacy and safety of various AIDS treatments, specifically comparing the performance of AZT, ddI, and ddC in preventing disease progression in HIV-infected patients with CD4 counts ranging from 200 to 500 cells/mm3. This intervention trial aimed to contribute insights into the effectiveness of monotherapy versus combination therapy with nucleoside analogs.

Funding Sources: The creation of this dataset was funded by: - AIDS Clinical Trials Group of the National Institute of Allergy and Infectious Diseases - General Research Center units funded by the National Center for Research Resources

Instance Representation: Each instance in the dataset represents a health record of a patient diagnosed with AIDS in the United States. These records encompass crucial categorical information and healthcare statistics related to the patient's condition.

Study Design: - Study Type: Interventional (Clinical Trial) - Enrollment: 2100 participants - Masking: Double-Blind - Primary Purpose: Treatment - Official Title: A Randomized, Double-Blind Phase II/III Trial of Monotherapy vs. Combination Therapy With Nucleoside Analogs in HIV-Infected Persons With CD4 Cells of 200-500/mm3 - Study Completion Date: November 1995

Study Objectives: To determine the effectiveness and safety of different AIDS treatments, including AZT, ddI, and ddC, in preventing disease progression among HIV-infected patients with specific CD4 cell counts.

Additional Information: The dataset provides valuable insights into the HIV-related clinical trials conducted by the AIDS Clinical Trials Group, contributing to the understanding of treatment outcomes and informing future research in the field.

Attributes Description:

1. Patient Information:

2. Censoring Indicator (label):Binary indicator (1 = failure, 0 = censoring) denoting patient status.

3. Temporal Information:

4. Time to Event (time): Integer representing time to failure or censoring.

5. Treatment Features:

   • Treatment Indicator (trt): Categorical feature indicating the type of treatment received (0 = ZDV only, 1 = ZDV + ddI, 2 = ZDV + Zal, 3 = ddI only).

6. Baseline Health Metrics:

7. Age (age): Patient's age in years at baseline.

8. Weight (wtkg): Continuous feature representing weight in kilograms at baseline.

9. Hemophilia (hemo): Binary indicator of hemophilia status (0 = no, 1 = yes).

10. Sexual Orientation (homo): Binary indicator of homosexual activity (0 = no, 1 = yes).

11. IV Drug Use History (drugs): Binary indicator of history of IV drug use (0 = no, 1 = yes).

12. Karnofsky Score (karnof): Integer on a scale of 0-100 indicating the patient's functional status.

13. Antiretroviral Therapy History:

14. Non-ZDV Antiretroviral Therapy Pre-175 (oprior): Binary indicator of non-ZDV antiretroviral therapy pre-Study 175 (0 = no, 1 = yes).

15. ZDV in the 30 Days Prior to 175 (z30): Binary indicator of ZDV use in the 30 days prior to Study 175 (0 = no, 1 = yes).

16. ZDV Prior to 175 (zprior): Binary indicator of ZDV use prior to Study 175 (0 = no, 1 = yes).

17. Days Pre-175 Anti-Retroviral Therapy (preanti): Integer representing the number of days of pre-Study 175 anti-retroviral therapy.

18. Demographic Information:

19. Race (race): Integer denoting race (0 = White, 1 = non-white).

20. Gender (gender): Binary indicator of gender (0 = Female, 1 = Male).

21. Treatment History:

22. Antiretroviral History (str2): Binary indicator of antiretroviral history (0 = naive, 1 = experienced).

23. Antiretroviral History Stratification (strat): Integer representing antiretroviral history stratification.

24. Symptomatic Information:

25. Symptomatic Indicator (symptom): Binary indicator of symptomatic status (0 = asymptomatic, 1 = symptomatic).

26. Additional Treatment Attributes:

27. Treatment Indicator (treat): Binary indicator of treatment (0 = ZDV only, 1 = others).

28. Off-Treatment Indicator (offtrt): Binary indicator of being off-treatment before 96+/-5 weeks (0 = no, 1 = yes).

29. Immunological Metrics:

30. CD4 Counts (cd40, cd420): Integer values representing CD4 counts at baseline and 20+/-5 weeks.

31. CD8 Counts (cd80, cd820): Integer values representing CD8 counts at baseline and 20+/-5 weeks.

Original Dataset Website: [h...

Rate: Number of deaths (per 100,000) due to HIV disease

Definition: Deaths with human immunodeficiency virus (HIV) disease as the underlying cause (ICD-10 codes: B20-B24).

Data Sources:

(1) Centers for Disease Control and Prevention, National Center for Health Statistics

(2) Death Certificate Database, Office of Vital Statistics and Registry, New Jersey Department of Health

(3) Population Estimates, State Data Center, New Jersey Department of Labor and Workforce Development

No. of Deaths: Caused by: HIV Disease (Aids) data was reported at 547.000 Person in Sep 2024. This records a decrease from the previous number of 557.000 Person for Jun 2024. No. of Deaths: Caused by: HIV Disease (Aids) data is updated quarterly, averaging 558.000 Person from Mar 2017 (Median) to Sep 2024, with 30 observations. The data reached an all-time high of 659.000 Person in Mar 2018 and a record low of 461.000 Person in Sep 2020. No. of Deaths: Caused by: HIV Disease (Aids) data remains active status in CEIC and is reported by National Administrative Department of Statistics. The data is categorized under Global Database’s Colombia – Table CO.G012: Number of Deaths: Cause of Death.

Adjusted additionally for age, region of origin, delayed entry into care (≥3 mo between HIV diagnosis and first clinic visit), and HIV-exposure group. Late presentation: presenting for care with a CD4 count below 350/mm3 or presenting with an AIDS defining event regardless of the CD4 count, in the 6 mo following presentation. Advanced disease: presenting for care with a CD4 count below 200/mm3or presenting with an AIDS defining event, regardless of CD4 cell count, in the 6 mo following presentation.aFigures are n (%) of clinical events (AIDS/deaths) in late presenters or late presenters with advanced disease.

The mortality estimate methodology is fully described elsewhere and takes into consideration parameters such as ART coverage. For example, HIV associated mortality in Mozambique also reflects injection drug user driven epidemic.ART coverage calculated using 2013 reported people on ART/people estimated to be living with HIV in 2013.** Published guidelines as of December 2014; WHO 2013 Guidelines recommend

This dataset is compiled from the World Bank Open Data repository and provides a wide range of countrywise health, mortality, and population indicators spanning the years 2000–2025. The aim of creating this dataset is to bring together key global health statistics in a structured format that makes it easier for researchers, policymakers, and students to analyze trends, make comparisons, and draw insights.

The dataset was built to support research in public health, demographics, and sustainable development goals (SDGs). It contains indicators such as immunization coverage, sanitation access, drinking water services, health expenditures, hospital resources, disease incidence, mortality rates, fertility rates, HIV/AIDS data, maternal health, and many more. By providing this data in a single collection, the goal is to help users explore long-term global health patterns, identify disparities between rural and urban populations, and understand how healthcare systems affect life expectancy and mortality over time.

Context

A straightforward way to assess the health status of a population is to focus on mortality – or concepts like child mortality or life expectancy, which are based on mortality estimates. A focus on mortality, however, does not take into account that the burden of diseases is not only that they kill people, but that they cause suffering to people who live with them. Assessing health outcomes by both mortality and morbidity (the prevalent diseases) provides a more encompassing view on health outcomes. This is the topic of this entry. The sum of mortality and morbidity is referred to as the ‘burden of disease’ and can be measured by a metric called ‘Disability Adjusted Life Years‘ (DALYs). DALYs are measuring lost health and are a standardized metric that allow for direct comparisons of disease burdens of different diseases across countries, between different populations, and over time. Conceptually, one DALY is the equivalent of losing one year in good health because of either premature death or disease or disability. One DALY represents one lost year of healthy life. The first ‘Global Burden of Disease’ (GBD) was GBD 1990 and the DALY metric was prominently featured in the World Bank’s 1993 World Development Report. Today it is published by both the researchers at the Institute of Health Metrics and Evaluation (IHME) and the ‘Disease Burden Unit’ at the World Health Organization (WHO), which was created in 1998. The IHME continues the work that was started in the early 1990s and publishes the Global Burden of Disease study.

Content

In this Dataset, we have Historical Data of different cause of deaths for all ages around the World. The key features of this Dataset are: Meningitis, Alzheimer's Disease and Other Dementias, Parkinson's Disease, Nutritional Deficiencies, Malaria, Drowning, Interpersonal Violence, Maternal Disorders, HIV/AIDS, Drug Use Disorders, Tuberculosis, Cardiovascular Diseases, Lower Respiratory Infections, Neonatal Disorders, Alcohol Use Disorders, Self-harm, Exposure to Forces of Nature, Diarrheal Diseases, Environmental Heat and Cold Exposure, Neoplasms, Conflict and Terrorism, Diabetes Mellitus, Chronic Kidney Disease, Poisonings, Protein-Energy Malnutrition, Road Injuries, Chronic Respiratory Diseases, Cirrhosis and Other Chronic Liver Diseases, Digestive Diseases, Fire, Heat, and Hot Substances, Acute Hepatitis.

Dataset Glossary (Column-wise)

• 01. Country/Territory - Name of the Country/Territory
• 02. Code - Country/Territory Code
• 03. Year - Year of the Incident
• 04. Meningitis - No. of People died from Meningitis
• 05. Alzheimer's Disease and Other Dementias - No. of People died from Alzheimer's Disease and Other Dementias
• 06. Parkinson's Disease - No. of People died from Parkinson's Disease
• 07. Nutritional Deficiencies - No. of People died from Nutritional Deficiencies
• 08. Malaria - No. of People died from Malaria
• 09. Drowning - No. of People died from Drowning
• 10. Interpersonal Violence - No. of People died from Interpersonal Violence
• 11. Maternal Disorders - No. of People died from Maternal Disorders
• 12. Drug Use Disorders - No. of People died from Drug Use Disorders
• 13. Tuberculosis - No. of People died from Tuberculosis
• 14. Cardiovascular Diseases - No. of People died from Cardiovascular Diseases
• 15. Lower Respiratory Infections - No. of People died from Lower Respiratory Infections
• 16. Neonatal Disorders - No. of People died from Neonatal Disorders
• 17. Alcohol Use Disorders - No. of People died from Alcohol Use Disorders
• 18. Self-harm - No. of People died from Self-harm
• 19. Exposure to Forces of Nature - No. of People died from Exposure to Forces of Nature
• 20. Diarrheal Diseases - No. of People died from Diarrheal Diseases
• 21. Environmental Heat and Cold Exposure - No. of People died from Environmental Heat and Cold Exposure
• 22. Neoplasms - No. of People died from Neoplasms
• 23. Conflict and Terrorism - No. of People died from Conflict and Terrorism
• 24. Diabetes Mellitus - No. of People died from Diabetes Mellitus
• 25. Chronic Kidney Disease - No. of People died from Chronic Kidney Disease
• 26. Poisonings - No. of People died from Poisoning
• 27. Protein-Energy Malnutrition - No. of People died from Protein-Energy Malnutrition
• 28. Chronic Respiratory Diseases - No. of People died from Chronic Respiratory Diseases
• 29. Cirrhosis and Other Chronic Liver Diseases - No. of People died from Cirrhosis and Other Chronic Liver Diseases
• 30. Digestive Diseases - No. of People died from Digestive Diseases
• 31. Fire, Heat, and Hot Substances - No. of People died from Fire or Heat or any Hot Substances
• ...

The epidemiological surveillance of HIV in Belgium is based on several data collections carried out by Sciensano. National data are collected from the HIV reference centres (HRCs) and AIDS reference laboratories (ARLs): a) National data collection of all HIV diagnosed patients in Belgium; b) National data collection of all HIV patients in care, through an exhaustive data collection of all viral load measures performed in Belgium and a data collection of demographic, biological, immunological, treatment and death data of patients in care in the HRCs (around 80 % of all patients in care in Belgium); c) A laboratory data collection on viro-immunological follow-up of all new-borns from HIV positive mothers; d) A national data collection of post-exposure prophylaxis episodes. Since the beginning of the HIV epidemic, this surveillance enables the monitoring of the trends in number of people diagnosed with HIV and number of patients in medical follow-up, as well as to identify certain socio-demographic factors associated with the risk of HIV infection or of a pejorative clinical outcome. This information supports health authorities and HIV stakeholders to decide on evidence-based HIV prevention and care strategies and define target groups for tailored interventions. Statbel, the Belgian statistical office collects, produces and disseminates reliable and relevant figures on the Belgian economy, society and territory. The collection is based on administrative data sources and surveys. This project aims to link the HIV surveillance data with selected Statbel information. This will permit to greatly improve the quality of the HIV surveillance data by completing the data already collected by Sciensano with additional socio-economic and socio-demographic information on patients profiles, filling in missing data in the Sciensano database with demographics from Statbel, ascertaining vital status of lost-to-follow-up patients and completing the information on causes of death. Additionally, a linkage with the new-born registry would permit to have more demographic and clinical information on children born from HIV-positive women.

Abstract

The “Eligibility List” for the 2014 Round of the HIV Surveillance used 11 December 2014 as its reference date. The criteria for inclusion were all males and females aged 15 and over. The total number eligible is 36,970, so this is the number of rows in the dataset. The field visits occur from January 2014 to December 2014. At the time of visit some people included in the Eligibility List were found to in fact be ineligible - perhaps because they were dead, very sick, or had outmigrated - events either not known about at the time the Eligibility List was compiled, or occurring between the drawing up of the Eligibility List and the actual visit. These 'retrospective ineligibilities' are identifiable in the dataset as 'Premature Completions' with reasons such as “Death or “Outmigration”. There were also some individuals who could not be contacted, even after repeated visits and 'tracking' attempts for those who were reported to have moved. These are identifiable as PrematureCompletionReason = Non-Contact. Additionally, some people were contacted but refused to participate in the survey. They are identifiable by VisitType = Refusal. Finally, there are some who participated in the survey, but refused the HIV test offered. They are identifiable as HIVRefused = 'Y'. Individual Surveillance forms were redesigned in 2013. No questions were added or removed, but the BMF form was renamed as IHO - Individual Health Observation (image below). The Informed Consent questions, formerly on the CFZ form, were incorporated into the IHO form, and some of the questions previously on the BMF form were moved to the MGH/WGH forms. Finally, visit details and Premature Completion / refusal information is now collected just once on the IHO, not separately on both BMF and WGH/MGH.

Analysis unit

Individuals

About Dataset

Context:

A straightforward way to assess the health status of a population is to focus on mortality – or concepts like child mortality or life expectancy, which are based on mortality estimates. A focus on mortality, however, does not take into account that the burden of diseases is not only that they kill people, but that they cause suffering to people who live with them. Assessing health outcomes by both mortality and morbidity (the prevalent diseases) provides a more encompassing view on health outcomes. This is the topic of this entry. The sum of mortality and morbidity is referred to as the ‘burden of disease’ and can be measured by a metric called ‘Disability Adjusted Life Years‘ (DALYs).

DALYs are measuring lost health and are a standardized metric that allow for direct comparisons of disease burdens of different diseases across countries, between different populations, and over time. Conceptually, one DALY is the equivalent of losing one year in good health because of either premature death or disease or disability. One DALY represents one lost year of healthy life. The first ‘Global Burden of Disease’ (GBD) was GBD 1990 and the DALY metric was prominently featured in the World Bank’s 1993 World Development Report. Today it is published by both the researchers at the Institute of Health Metrics and Evaluation (IHME) and the ‘Disease Burden Unit’ at the World Health Organization (WHO), which was created in 1998. The IHME continues the work that was started in the early 1990s and publishes the Global Burden of Disease study.

Content:

In this Dataset, we have Historical Data of different cause of deaths for all ages around the World. The key features of this Dataset are: Meningitis, Alzheimer's Disease and Other Dementias, Parkinson's Disease, Nutritional Deficiencies, Malaria, Drowning, Interpersonal Violence, Maternal Disorders, HIV/AIDS, Drug Use Disorders, Tuberculosis, Cardiovascular Diseases, Lower Respiratory Infections, Neonatal Disorders, Alcohol Use Disorders, Self-harm, Exposure to Forces of Nature, Diarrheal Diseases, Environmental Heat and Cold Exposure, Neoplasms, Conflict and Terrorism, Diabetes Mellitus, Chronic Kidney Disease, Poisonings, Protein-Energy Malnutrition, Road Injuries, Chronic Respiratory Diseases, Cirrhosis and Other Chronic Liver Diseases, Digestive Diseases, Fire, Heat, and Hot Substances, Acute Hepatitis.

Dataset Glossary (Column-wise):

1. Country/Territory - Name of the Country/Territory
2. Code - Country/Territory Code
3. Year - Year of the Incident
4. Meningitis - No. of People died from Meningitis
5. Alzheimer's Disease and Other Dementias - No. of People died from Alzheimer's Disease and Other Dementias
6. Parkinson's Disease - No. of People died from Parkinson's Disease
7. Nutritional Deficiencies - No. of People died from Nutritional Deficiencies
8. Malaria - No. of People died from Malaria
9. Drowning - No. of People died from Drowning
10. Interpersonal Violence - No. of People died from Interpersonal Violence
11. Maternal Disorders - No. of People died from Maternal Disorders
12. Drug Use Disorders - No. of People died from Drug Use Disorders
13. Tuberculosis - No. of People died from Tuberculosis
14. Cardiovascular Diseases - No. of People died from Cardiovascular Diseases
15. Lower Respiratory Infections - No. of People died from Lower Respiratory Infections
16. Neonatal Disorders - No. of People died from Neonatal Disorders
17. Alcohol Use Disorders - No. of People died from Alcohol Use Disorders
18. Self-harm - No. of People died from Self-harm
19. Exposure to Forces of Nature - No. of People died from Exposure to Forces of Nature
20. Diarrheal Diseases - No. of People died from Diarrheal Diseases
21. Environmental Heat and Cold Exposure - No. of People died from Environmental Heat and Cold Exposure
22. Neoplasms - No. of People died from Neoplasms
23. Conflict and Terrorism - No. of People died from Conflict and Terrorism
24. Diabetes Mellitus - No. of People died from Diabetes Mellitus
25. Chronic Kidney Disease - No. of People died from Chronic Kidney Disease
26. Poisonings - No. of People died from Poisoning
27. Protein-Energy Malnutrition - No. of People died from Protein-Energy Malnutrition
28. Chronic Respiratory Diseases - No. of People died from Chronic Respiratory Diseases
29. Cirrhosis and Other Chronic Liver Diseases - No. of People died from Cirrhosis and Other Chronic Liver Diseases
30. Digestive Diseases - No. of People died from Digestive Diseases
31. Fire, Heat, and Hot Substances - No. of People died from Fire or Heat or any Hot Substances
32. Acute Hepatitis - No. of People died from Acute Hepatitis Structure of the Dataset

Acknowledgement:

This Dataset is created from Our World in Data. This Dataset falls under open access under the Creative Commons BY license. You can check the FAQ for more informa...

Data Set from the Russian Federation Federal State Statistics Service - Росстат. Collected, translated into English language and published. Mortality in Russia by cause of death in 2018 (absolute numbers).

Causes of death statistics are obtained from the inscriptions in medical death certificates filled in by a physician referring to disease, accident, homicide, suicide or any other external factor (injuries due to actions envisaged by the law, non-specified injuries, injuries caused by military actions) which led directly to death. Such inscriptions are used as a reason for classifying death causes in civil registration records of deaths.

Some of the presented causes of death: Cause of death, Cholera, Typhoid fever, Paratyphoid, Salmonella infections, Shigellosis, Food poisoning, Intestinal infections, Tuberculosis, Plague, Anthrax, Brucellosis, Leprosy, Tetanus, Diphtheria, Whooping cough Scarlet fever, Meningococcal infection, Sepsis, Erysipelas, Other bacterial infections, Syphilis, Sexually transmitted infections, Typhus, Poliomyelitis, Rabies, Viral encephalitis, Measles, Hepatitis A, Human Immunodeficiency Virus (HIV) Disease, Other diseases caused by viruses, Malaria, Leishmaniasis, Trypanosomiasis, Schistosomiasis, Malignant, Leukemia, Neoplasms, Diabetes, Diseases of the endocrine system, eating disorders and metabolic disorders, Mental disorders, Parkinson's disease, Alzheimer's disease, Multiple sclerosis, Hypertension, myocardial infarction, Myocardial infarction, Stroke, Urolithiasis, Birth injury, Intrauterine hypoxia and asphyxia in childbirth, Suicides, Murder, Firearm Accident, Other accidents, Causes of death due to alcohol, Drug-related causes of death, All types of transport accidents And many more causes of death.

Users can access data related to international women’s health as well as data on population and families, education, work, power and decision making, violence against women, poverty, and environment. Background World’s Women Reports are prepared by the Statistics Division of the United Nations Department for Economic and Social Affairs (UNDESA). Reports are produced in five year intervals and began in 1990. A major theme of the reports is comparing women’s situation globally to that of men in a variety of fields. Health data is available related to life expectancy, cause of death, chronic disease, HIV/AIDS, prenatal care, maternal morbidity, reproductive health, contraceptive use, induced abortion, mortality of children under 5, and immunization. User functionality Users can download full text or specific chapter versions of the reports in color and black and white. A limited number of graphs are available for download directly from the website. Topics include obesity and underweight children. Data Notes The report and data tables are available for download in PDF format. The next report is scheduled to be released in 2015. The most recent report was released in 2010.

††Number of Elites making it to 10 years follow up without experiencing composite endpoint and number subsequently experiencing composite endpoint.†Hazard ratios comparing ECs to Non-ECs (including those with unknown EC status) allowing for late entry at 10 years. For each definition, p-values were obtained from unadjusted log-rank test for time to composite endpoint and were all highly significant p

Effect of suicide rates on life expectancy dataset

Abstract In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy. The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

Ivory Coast CI: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data was reported at 28.200 NA in 2016. This records a decrease from the previous number of 28.500 NA for 2015. Ivory Coast CI: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data is updated yearly, averaging 27.700 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 28.500 NA in 2015 and a record low of 25.200 NA in 2000. Ivory Coast CI: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;