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.

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

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.

This dataset is about countries per year in Burkina Faso. It has 64 rows. It features 4 columns: country, incidence of HIV, and death rate.

Analysis of ‘HIV AIDS Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/imdevskp/hiv-aids-dataset on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

In the time of epidemics, what is the status of HIV AIDS across the world, where does each country stands, is it getting any better. The data set should be helpful to explore much more about above mentioned factors.

Content

The data set contains data on

1. No. of people living with HIV AIDS
2. No. of deaths due to HIV AIDS
3. No. of cases among adults (19-45)
4. Prevention of mother-to-child transmission estimates
5. ART (Anti Retro-viral Therapy) coverage among people living with HIV estimates
6. ART (Anti Retro-viral Therapy) coverage among children estimates

Acknowledgements / Data Source

• WHO : https://www.who.int/hiv/data/en/
• UNESCO : https://data.unicef.org/resources/dataset/hiv-aids-statistical-tables/

Collection methodology

https://github.com/imdevskp/hiv_aids_who_unesco_data_cleaning

Cover Photo

Photo by Anna Shvets from Pexels https://www.pexels.com/photo/red-ribbon-on-white-surface-3900425/

Similar Datasets

• COVID-19 - https://www.kaggle.com/imdevskp/corona-virus-report
• MERS - https://www.kaggle.com/imdevskp/mers-outbreak-dataset-20122019
• Ebola Western Africa 2014 Outbreak - https://www.kaggle.com/imdevskp/ebola-outbreak-20142016-complete-dataset
• H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset
• SARS 2003 Pandemic - https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset
• HIV AIDS - https://www.kaggle.com/imdevskp/hiv-aids-dataset

--- Original source retains full ownership of the source dataset ---

This dataset is about countries per year in Jordan. It has 64 rows. It features 4 columns: country, incidence of HIV, and death rate.

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

The research on life expectancy in countries takes the spotlight in the notebook's machine learning model. Substantial data analysis and predictive algorithms are used to uncover the reasons causing differences in longevity among countries. With the aid of strong statistical tools, valuable insights into the complex link between healthcare, socioeconomic factors, and life expectancy are sought |Description|Column| |:------:|:--------:| |Country under study|Country| |year|Year| |Status of the country's development|Status| |Population of country|Population| |Percentage of people finally one year old who were immunized against hepatitis B|Hepatitis B| |The number of reported measles cases per 1000 people|Measles| |Percentage of 1-year-olds immunized against polio|Polio| |Percentage of people finally one year old who were immunized against diphtheria|Diphtheria| |The number of deaths caused by AIDS of the last 4-year-olds who were born alive per 1000 people|HIV/AIDS| |The number of infant deaths per 1000 people|infant deaths| |he number of deaths of people under 5 years old per 1000 people|under-five deaths| |The ratio of government medical-health expenses to total government expenses in percentage|Total expenditure| |Gross domestic product|GDP| |The average body mass index of the entire population of the country|BMI| |Prevalence of thinness among people 19 years old in percentage|thinness 1-19 years| |Liters of alcohol consumption among people over 15 years old|Alcohol| |The number of years that people study|Schooling| |Country life expectancy|Life expectancy [target variable]|

This dataset is about countries per year in Lebanon. It has 64 rows. It features 4 columns: country, incidence of HIV, and death rate.

Infection with HIV (human immunodeficiency virus) can lead to AIDS (acquired immunodeficiency syndrome). AIDS results in a gradual and persistent decline and failure of the immune system, resulting in a heightened risk of life-threatening infection and cancers.

In the majority of cases, HIV is a sexually transmitted infection. However, HIV can also be transmitted from mother to child, during pregnancy or childbirth, or through breastfeeding. Non-sexual transmission can also occur by sharing injection equipment such as needles.

Other research and writing on HIV/AIDS on Our World in Data:

Antiretroviral therapy has saved millions of lives from AIDS and could save more See all interactive charts on HIV/AIDS ↓

HIV/AIDS is one of the world's most fatal infectious disease More than three-quarters of a million people die from HIV/AIDS each year; in some countries, it's the leading cause of death HIV/AIDS is one of the world's most fatal infectious diseases – particularly across Sub-Saharan Africa, where the disease has had a massive impact on health outcomes and life expectancy in recent decades.

The Global Burden of Disease is a major global study on the causes of death and disease published in the medical journal The Lancet.1 These estimates of the annual number of deaths by cause are shown here. This chart shows the global total but can be explored for any country or region using the "Change country" button.

This dataset is about countries per year in Albania. It has 64 rows. It features 4 columns: country, incidence of HIV, and death rate.

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

Much of the information on national HIV prevalence in Tanzania derives from surveillance of HIV in special populations, such as women attending antenatal clinics and blood donors. For example, Mainland Tanzania currently maintains a network of 134 antenatal care (ANC) sites from which HIV prevalence estimates are generated. However, these surveillance data do not provide an estimate of the HIV prevalence among the general population. HIV prevalence is higher among individuals who are employed (6 percent) than among those who are not employed (3 percent) and is higher in urban areas than in rural areas (7percent and 4 percent, respectively). In Mainland Tanzania, HIV prevalence is markedly higher than in Zanzibar (5 percent versus 1 percent). Differentials by region are large. Among regions on the Mainland,Njombe has the highest prevalence estimate (15 percent), followed by Iringa and Mbeya (9 percent each);Manyara and Tanga have the lowest prevalence (2 percent). Among the five regions that comprise Zanzibar, all have HIV prevalence estimates at 1 percent or below. Consistent with the overall national estimate among men and women, HIV prevalence is higher among women than men in nearly all regions of Tanzania.

ART not only saves lives but also gives a chance for people living with HIV/AIDS to live long lives. Without ART very few infected people survive beyond ten years.1

Today, a person living in a high-income country who started ART in their twenties can expect to live for another 46 years — that is well into their 60s.2

While the life expectancy of people living with HIV/AIDS in high-income countries has still not reached the life expectancy of the general population, we are getting closer to this goal.3

The combination of antiretroviral drugs which make-up ART have progressively improved. Recent research shows that a person who started ART in the late 1990s would be expected to live ten years less than a person who started ART in 2008.4 This increase goes beyond the general increase in life expectancy in that period and reflects the improvements in ART — fewer side effects, more people following the prescribed treatment, and more support for the people in need of ART.

Previously, we reported that HIV-1 patients infected with CRF07_BC had significantly lower viral loads than those infected with subtype B. Since HIV-1 viral load is associated with AIDS disease progression, the current study was to link multiple clinical and molecular databases, and compare clinical outcomes of HIV-1patients infected with CRF07_BC, CRF01_AE and subtype B in Taiwan. Molecular genotyping data of 2,982 HIV-1/AIDS patients were submitted to Taiwan CDC HIV-1/AIDS case management database. Then the database was linked to Taiwan National Health Insurance Research database and National Cause of Death database from 2000−2016. Subsequently, a subtype-based HIV/AIDS clinical database containing 1,605 patients including 858 (53.5%) subtype B, 690 (43.0%) CRF07_BC and 57 (3.5%) CRF01_AE patients was successfully established and the clinical outcomes and survival of these patients were analyzed. Analysis of transmission route showed this HIV-1/AIDS cohort consists of 761 (47.4%) men who have sex with men (MSM), 132 (8.2%) heterosexuals and 712 (44.4%) injection drug user (IDUs). Survival analysis showed subtype B patients had a significantly lower death rate (8.2%) than CRF07_BC and CRF01-AE patients (22.8% and 22.8%, respectively). The higher death rate for CRF07_BC versus subtype B patients could be largely influenced by transmission route (IDU: 95.7% vs. 3.9%; MSM: 85.8% vs. 2.0%), as well as lower ART uptake rates (69.9% vs. 96.3%). Indeed, subset analysis among IDU patients, CRF07_BC-infected patients had a better 16-year survival rate than patients infected with subtype B (74.3% vs. 45.7%, p < 0.05). Our findings suggest that the transmission route is one major factor influencing death rate, while ART treatment and HIV-1 subtypes may also play important roles. Our study is the largest long-term cohort study of patients infected with CRF07_BC and subtype B in the same geographic region.

This dataset is about countries per year in Namibia. It has 64 rows. It features 4 columns: country, incidence of HIV, and death rate.

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.

IntroductionAlthough AIDS-related deaths have had significant economic and social impact following an increased disease burden internationally, few studies have evaluated the cause of AIDS-related deaths among patients with AIDS on combination anti-retroviral therapy (cART) in China. This study examines the causes of death among AIDS-patients in China and uses a methodology to increase data accuracy compared to the previous studies on AIDS-related mortality in China, that have taken the reported cause of death in the National HIV Registry at face-value.MethodsDeath certificates/medical records were examined and a cross-sectional survey was conducted in three provinces to verify the causes of death among AIDS patients who died between January 1, 2010 and June 30, 2011. Chi-square analysis was conducted to examine the categorical variables by causes of death and by ART status. Univariate and multivariate logistic regression were used to evaluate factors associated with AIDS-related death versus non-AIDS related death.ResultsThis study used a sample of 1,109 subjects. The average age at death was 44.5 years. AIDS-related deaths were significantly higher than non-AIDS and injury-related deaths. In the sample, 41.9% (465/1109) were deceased within a year of HIV diagnosis and 52.7% (584/1109) of the deceased AIDS patients were not on cART. For AIDS-related deaths (n = 798), statistically significant factors included CD4 count <200 cells/mm3 at the time of cART initiation (AOR 1.94, 95%CI 1.24–3.05), ART naïve (AOR 1.69, 95%CI 1.09–2.61; p = 0.019) and age <39 years (AOR 2.96, 95%CI 1.77–4.96).ConclusionFor the AIDS patients that were deceased, only those who initiated cART while at a CD4 count ≥200 cells/mm3 were less likely to die from AIDS-related causes compared to those who didn’t initiate ART at all.