Tuberculosis is one of the most common causes of death globally.

By Saloni Dattani, Fiona Spooner, Hannah Ritchie and Max Roser

Data description:

In richer countries, the impact of tuberculosis has been reduced significantly over history, but in poorer parts of our world, it continues to be a major challenge even today: it causes an estimated 1.2 million deaths annually.

Tuberculosis is caused by the bacteria Mycobacterium tuberculosis.

The bacteria spreads through respiratory particles and tends to cause tuberculosis in people with risk factors such as undernourishment, HIV/AIDS, smoking, and existing chronic conditions.

The disease involves symptoms like coughing, fatigue and night sweats, and can damage the lungs, the brain, kidneys and other organs, which can be fatal.

But it is treatable with a combination of specific antibiotics. Without being diagnosed correctly, however, people do not receive the proper treatment. This leaves them vulnerable, and also increases the risk that antibiotic-resistant strains of the bacteria will develop, which are much more difficult and expensive to treat.

With greater effort to tackle its risk factors and improve testing and treatment for the disease, the world can relegate tuberculosis to history — not just in the richer parts of the world, but for everyone.

Data number 1: Tuberculosis is still common in many parts of the world In high-income countries, tuberculosis is largely a disease of the past. Since the beginning of the 20th century, its impact has been significantly reduced with the development of antibiotics and improvements in healthcare and living standards.

Data number 2: Tuberculosis kills over a million people annually, most of whom are adults Tuberculosis kills over a million people each year, as you can see in the chart. The chart shows that most of those who die from tuberculosis are adults.

Data number 3: Many people with tuberculosis are undiagnosed Although tuberculosis is typically a disease of the lungs, the bacteria can affect many organs in the body, and people who are infected don’t always have respiratory symptoms. Instead, they may experience weight loss, breathlessness, fever, or night sweats.

Data number 4: Antibiotic resistance is an important consideration during treatment People with tuberculosis require treatment with a specific combination of antibiotic medications that can kill the bacteria.

Data number 5: HIV increases the risk of developing tuberculosis An HIV infection is a major risk factor for developing tuberculosis.

Good luck

This dataset includes two tables on tuberculosis (TB) in California: 1) TB cases and rates by place of birth, sex, age and race/ethnicity 2) TB cases by local health jurisdiction (LHJ). TB case reports are submitted to the California Department of Public Health (CDPH), TB Control Branch (TBCB), by 61 local health jurisdictions (58 counties, and the cities of Berkeley, Long Beach, and Pasadena).

I was seeking data on tuberculosis (TB) cases, along with information on each country's population size and income level, to conduct a comprehensive analysis. Unfortunately, I couldn’t locate this specific data on the WHO website, so I decided to devise a solution on my own. This involved a data pre-analysis step, which included merging multiple datasets and transforming them into the format I had envisioned. This notebook serves as the foundation for that preparation, and will be followed by another notebook dedicated to the actual data analysis. I obtained the WHO TB data from the WHO website, specifically the Case notifications [>2Mb] CSV file. The population data was sourced from the World Bank, as well as the income classification information for countries was also retrieved from the World Bank, extracted from the Current Classification by Income table in XLSX format. All relevant files are available in the data/raw_data folder.

WHO TB Dataset Variable Descriptions

Geo & Time Identifiers

• country, iso2, iso3, iso_numeric – Country and standard ISO codes
• g_whoregion – WHO region (AFR - African Region, AMR - Region of the Americas, EMR - Eastern Mediterranean Region, EUR - European Region, SEA - South-East Asia Region, WPR - Western Pacific Region)
• year – Reporting year
• population_size - The number of people living in that country at that particular year
• income_level - The income level of that country at that particular year. Low income (L), Lower middle income (LM), Upper middle income (UM), High income (H).

Case Counts by Type & Treatment Category
(numeric counts of cases reported in the given year)

• new_sp – New smear‑positive pulmonary TB
• new_sn – New smear‑negative pulmonary TB
• new_su – New pulmonary TB with unknown smear status
• new_ep – New extrapulmonary TB
• new_oth – New ‘other’ TB cases (unspecified/mixed)
• ret_rel – Relapse cases (previous treatment, now bacteriologically confirmed again)
• ret_taf – Retreatment after failure
• ret_tad – Retreatment after default (loss-to-follow-up)
• ret_oth – Other retreatment cases
• newret_oth – Other new/retreatment cases not covered above

Diagnostic Confirmation Indicators
(how cases were confirmed or diagnosed)

• new_labconf – New cases confirmed via laboratory (smear, culture or molecular)
• new_clindx – New cases diagnosed clinically (without lab confirmation)
• ret_rel_labconf, ret_rel_clindx – Relapse cases by confirmation method
• ret_rel_ep – Relapse extrapulmonary cases
• ret_nrel – Retreatment cases not relapse
• notif_foreign – Cases notified among foreign nationals
• c_newinc – Total new incident cases (across all types)

Age & Sex Disaggregated Counts
(cases broken down by age group & sex)

• new_sp_m04, new_sp_m514, … new_sp_f65 – New smear‑positive cases by age & sex
• Similar naming for new_sn_* (smear-negative) and new_ep_* (extrapulmonary)
• new_sp_mu, new_sn_mu, new_ep_mu – Male & unknown sex totals
• new_sp_fu, new_sn_fu, new_ep_fu – Female & unknown sex totals

Relapse by Age/Sex

• newrel_m04, newrel_f1524, etc. – Relapse cases by age group & sex
• rel_in_agesex_flg, agegroup_option – Flags for available disaggregation

Drug Resistance & Testing Indicators

• rdx_data_available – Is drug-resistance data present?
• newinc_rdx, newinc_pulm_labconf_rdx, etc. – New (and pulmonary) cases with drug-resistance testing
• rdxsurvey_newinc, rdxsurvey_newinc_rdx – Survey-derived drug resistance data
• rdst_new, rdst_ret, rdst_unk – DST status among new, retreatment, unknown
• conf_rrmdr, conf_mdr – Confirmed rifampicin-resistant/MDR cases
• rr_sldst, all_conf_xdr, etc. – SL-DST and XDR confirmation
• Numerous *_tx variables – Treatment counts for drug-resistant cases, by regimen type

TB & HIV Co-infection Indicators

• newrel_tbhiv_flg – Flag if relapse-TB HIV data available
• newrel_hivtest, newrel_hivpos, newrel_art – Among relapse cases: tested for HIV, positive, on antiretroviral therapy
• tbhiv_014_flg, newrel_hivtest_014, etc. – Same but for 0–14 age group
• hivtest, hivtest_pos, hiv_cpt, hiv_art, hiv_tbscr, hiv_reg, hiv_ipt, etc. – HIV-related services among TB patients (testing, prophylaxis, treatment, registration).

To see the code for how the data was obtained you can check it out on my github repo.

WHO has published a global TB report every year since 1997. The main aim of the report is to provide a comprehensive and up-to-date assessment of the TB epidemic, and of progress in prevention, diagnosis and treatment of the disease, at global, regional and country levels. This is done in the context of recommended global TB strategies and targets endorsed by WHO’s Member States, broader development goals set by the United Nations (UN) and targets set in the political declaration at the first UN high-level meeting on TB (held in September 2018) .

The 2019 edition of the global TB report was released on 17 October 2019. The report can be found at https://www.who.int/tb/publications/global_report/en/

Acknowlegement

Foto von CDC auf Unsplash

Global Health Outcomes Data

Impact on Mortality Rates and Malnutrition in Countries Around the World

By Humanitarian Data Exchange [source]

About this dataset

This dataset provides comprehensive insights into critical health conditions around the world, such as mortality rate, malnutrition levels, and frequency of preventable diseases. It documents the prevalence of life-threatening diseases like malaria and tuberculosis, and are tracked alongside key health indicators like adult mortality rates, HIV prevalence, physicians per 10,000 people ratio and public health expenditures. Such metrics provide us with an accurate picture of how developed healthcare systems are in certain countries which ultimately leads to improvements in public policy formation and awareness amongst decision-makers. With this data it is possible to observe disparities between different regions of the world which can help inform global strategies for providing equitable care globally. This dataset is a valuable source for researchers interested in understanding global health trends over time or seeking to evaluate regional differences within countries

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For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides comprehensive global health outcome data for countries around the world. It includes vital information such as infant mortality rates, child malnutrition rates, adult mortality rates, deaths due to malaria and tuberculosis, HIV prevalence rates, life expectancy at age 60 and public health expenditure. This dataset can be used to gain valuable insight into the challenges faced by different countries in providing a good quality of life for their citizens.

To use this dataset, first identify what questions you need answered and what outcomes you are looking to measure. You may want to look at specific disease-based indicators (e.g. malaria or tuberculosis), health-related indicators (e.g., nutrition), or overall population markers (e.g., life expectancy).

Then decide which data points from the provided fields will help answer your questions and provide the results needed - e.g,. infant mortality rate or HIV prevalence rate - extracting these values from relevant columns like “Infants lacking immunization (% of one-year-olds) Measles 2013” or “HIV prevalence, adult (% ages 15Ð49) 2013” respectively

Next extract other columnwise relevant information - e.g., country name — that could also aid your analysis using tools like Excel or Python's Pandas library; sorting through them based on any metric desired — e..g,, physicians per 10k people — while being mindful that some data points are missing in some cases (denoted by NA).

Finally perform basic analyses with either your own scripting language, like R/Python libraries' numerical functions with accompanying visuals/graphs etc if elucidating trends is desired; drawing meaningful conclusions about overall state of global health outcomes accordingly before making informed decisions thereafter if needed too!

Research Ideas

• Create a world health map to visualize the differences in health outcomes across different countries and regions.
• Develop an AI-based decision support tool that identifies optimal public health policies or interventions based on these metrics for different countries.
• Design a dashboard or web app that displays and updates this data in real-time, to allow users to compare the current state of global health indicators and benchmark them against historical figures

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: health-outcomes-csv-1.csv | Column name | Description | |:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------| | Country | The name of the country. (String) ...

BACKGROUND: Tuberculosis (TB) has killed more people than any other infection since records began. The Sustainable Development Goals and the World Health Organisation “End TB” Strategy prioritise key targets for reducing mortality due to TB. However, there seems to be limited research evidence available to inform how this target of reducing TB mortality may best be achieved. OBJECTIVES: We aim to describe and categorise the published literature concerning mortality due to TB and then to review, critically appraise and synthesise the evidence that interventions decrease mortality due to TB. METHODS: The Pubmed database will be searched. Screening and selection of eligible publications will be made by 2 independent reviewers and a third will be asked to resolve any discrepancies. Key information from selected publications will be extracted using a shared cloud-based spreadsheet. Quantitative assessments of the impacts of trial interventions on TB mortality will be extracted and synthesised using meta-analysis, if possible. When appropriate, the quality of trial evidence will be assessed.This systematic review and meta-analysis is registered with the PROSPERO database (CRD42023387877). CONCLUSIONS: We will review the current published evidence concerning TB mortality and how it may best be prevented. We aim to clarify research gaps and also to synthesise evidence in order to guide future policy and research.

BackgroundMortality from TB continues to be a global public health challenge. TB ranks alongside Human Immunodeficiency Virus (HIV) as the leading infectious causes of death globally. HIV is a major driver of TB related morbidity and mortality while TB is the leading cause of mortality among people living with HIV/AIDS. We sought to determine excess mortality associated with HIV and the effect of antiretroviral therapy on reducing mortality among tuberculosis patients in Kenya.MethodsWe conducted a retrospective analysis of Kenya national tuberculosis program data of patients enrolled from 2013 through 2014. We used direct standardization to obtain standardized mortality ratios for tuberculosis patients compared with the general population. We calculated the population attributable fraction of tuberculosis deaths due to HIV based on the standardized mortality ratio for deaths among TB patients with HIV compared to TB patients without HIV. We used Cox proportional hazards regression for assessing risk factors for mortality.ResultsOf 162,014 patients included in the analysis, 6% died. Mortality was 10.6 (95% CI: 10.4–10.8) times higher among TB patients than the general population; 42% of deaths were attributable to HIV infection. Patients with HIV who were not receiving ART had an over four-fold risk of death compared to patients without HIV (aHR = 4.2, 95% CI 3.9–4.6). In contrast, patients with HIV who were receiving ART had only 2.6 times the risk of death (aHR = 2.6, 95% CI 2.5–2.7).ConclusionHIV was a significant contributor to TB-associated deaths in Kenya. Mortality among HIV-infected individuals was higher among those not on ART than those on ART. Early initiation of ART among HIV infected people (a “test and treat” approach) should further reduce TB-associated deaths.

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
• ...

IntroductionTuberculosis incidence is disproportionately high among people in poverty. Cash transfer programs have become an important strategy in Brazil fight inequalities as part of comprehensive poverty alleviation policies. This study was aimed at assessing the effect of being a beneficiary of a governmental cash transfer program on tuberculosis (TB) treatment cure rates.MethodsWe conducted a longitudinal database study including people ≥18 years old with confirmed incident TB in Brazil in 2015. We treated missing data with multiple imputation. Poisson regression models with robust variance were carried out to assess the effect of TB determinants on cure rates. The average effect of being beneficiary of cash transfer was estimated by propensity-score matching.ResultsIn 2015, 25,084 women and men diagnosed as new tuberculosis case, of whom 1,714 (6.8%) were beneficiaries of a national cash transfer. Among the total population with pulmonary tuberculosis several determinants were associated with cure rates. However, among the cash transfer group, this association was vanished in males, blacks, region of residence, and people not deprived of their freedom and who smoke tobacco. The average treatment effect of cash transfers on TB cure rates, based on propensity score matching, found that being beneficiary of cash transfer improved TB cure rates by 8% [Coefficient 0.08 (95% confidence interval 0.06–0.11) in subjects with pulmonary TB].ConclusionOur study suggests that, in Brazil, the effect of cash transfer on the outcome of TB treatment may be achieved by the indirect effect of other determinants. Also, these results suggest the direct effect of being beneficiary of cash transfer on improving TB cure rates.

The indicator measures the standardised death rate of tuberculosis, HIV and hepatitis (International Classification of Diseases (ICD) codes A15-A19_B90, B15-B19_B942 and B20-B24). The rate is calculated by dividing the number of people dying due to selected communicable diseases by the total population. Data on causes of death (COD) refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. The data are presented as standardised death rates, meaning they are adjusted to a standard age distribution in order to measure death rates independently of different age structures of populations. This approach improves comparability over time and between countries. The standardised death rates used here are calculated on the basis of the standard European population referring to the residents of the countries. Copyright notice and free re-use of data on: https://ec.europa.eu/eurostat/about-us/policies/copyright

Data were collected as part of a prospective, cross-sectional study of 2,009 enrolled participants recruited from 4 healthcare settings in Cambodia, 1 in Thailand, and 3 in Vietnam from September 2006 through July 2008. Eligible participants were ≥7 years of age, not screened for TB in last 3 months, had not received TB treatment or isoniazid therapy within the last year, and not on antiretroviral therapy (ART) for HIV. After participants gave consent, they were administered a standardized, paper-based questionnaire supplemented by medical chart review assessing demographic characteristics, HIV risk factors, medical history and symptoms of illness); provided a physical examination and chest radiography; and participants provided sputum, blood, urine, stool samples, and lymph node aspirates (if indicated) for testing. The primary outcome was to construct algorithms for TB screening and diagnosis prior to initiation of ART and compare findings with the 2007 World Health Organization (WHO) algorithm used for TB screening and diagnosis in people with HIV.

BackgroundTuberculosis remains a major public-health problem in the world, despite several efforts to improve case identification and treatment compliance. It is well known cause of ill-health among millions of people each year and ranks as the second leading cause of death from infectious disease worldwide. Despite implementation of the World health organization recommended strategy, the reductions in the incidence of TB have been minimal in high burden countries.Objectives and methodsA case control study was carried out to assess the risk factors of TB, where cases were newly registered bacteriologically confirmed pulmonary TB patients with age greater than 15 years who present at twenty health centres in Addis Ababa. Controls were age and sex matched attendees who presented in the same health centers for non-TB health problems.ResultsA total of 260 cases and 260 controls were enrolled in the study and 45.8% of cases and 46.2% of controls were in the 26–45 years age bracket. According to the multivariable logistic regression analysis, seven variables were found to be independent predictors for the occurrence of TB after controlling possible confounders. Patients who live in house with no window or one window were almost two times more likely to develop tuberculosis compared to people whose house has multiple windows (AOR = 1.81; 95% CI:1.06, 3.07). Previous history of hospital admission was found to pose risk almost more than three times (AOR = 3.39; 95% CI: 1.64–7.03). Having a household member who had TB was shown to increase risk of developing TB by three fold (AOR = 3.00; 95% CI: 1.60, 5.62). The study showed that illiterate TB patients were found to be more than twice more likely to develop TB compared to subjects who can atleast read and write (AOR, 95% CI = 2.15, 1.05, 4.40). Patients with household income of less than 1000 birrs per month were more than two times more likely to develop TB compared to those who had higher income (AOR = 2.2; 95% CI: 1.28, 3.78). Smoking has also been identified as important risk factor for developing TB by four times (AOR = 4.43; 95% CI: 2.10, 9.3). BCG was found to be protective against TB reducing the risk by one-third (AOR = 0.34; 95% CI: 0.22, 0.54).ConclusionThis study showed that TB is more common among the most agile and economically active age group, and number of windows, history of hospital admission, a household member who had TB, illiteracy, low household income and smoking and lack of BCG scar were identified as independent risk factors. Therefore it is imperative that the TB control effort need a strategy to address socio economic issues such as poverty, overcrowding, smoking, and infection control at health care facilities level is an important intervention to prevent transmission of TB within the facilities.

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...

This dataset provides comprehensive insights into global tuberculosis (TB) trends from the year 2000 to 2024 across multiple countries and regions. It includes 3,000 records covering TB incidence, mortality, treatment success, drug-resistant cases, and healthcare access, making it an invaluable resource for public health analysis, epidemiological research, and predictive modeling.

Key Features: Global Coverage: Includes data from multiple countries across different income levels.

Longitudinal Analysis: Spans over two decades (2000-2024).

Epidemiological Metrics: TB cases, deaths, incidence/mortality rates, treatment success rates, and drug-resistant cases.

Health & Socioeconomic Factors: GDP per capita, healthcare expenditure, urbanization, malnutrition, and smoking prevalence.

Healthcare Accessibility: Number of TB doctors, hospitals, and access to health services.

Vaccination & HIV Testing: BCG vaccination coverage and HIV testing rates for TB patients.

This dataset is ideal for policymakers, researchers, and data analysts aiming to study TB trends, evaluate healthcare interventions, and develop predictive models for disease control.

Every year, over 10 million people worldwide contract tuberculosis (TB). The 2024 World Health Organisation TB global report indicated that 32% of the total deaths were children and adolescents under 15 years old. The scale of TB highlights the urgent need for action to end the global epidemic by 2030. This study aims to evaluate the mortality rate, survival probabilities, and factors associated with mortality among children and adolescents with TB in Tanzania. A retrospective cohort study was conducted from the Tanzania National Tuberculosis and Leprosy Programme data, which included individuals under 15 years old who began TB treatment between 1st January 2023 and 31st December 2023. The last patient’s end-of-follow-up time was on 16th June 2024. The primary outcome of interest in our study was death. We calculated overall and covariate-specific TB mortality rates per 1,000 person-months. The Kaplan-Meier curve was employed to estimate survival probabilities. A total of 10,491 children and adolescents receiving TB treatment were included, nearly half of whom, 5,940 (56.62%), were under age 5 years. A total of 177 (1.69%) died, resulting in a crude mortality rate of 2.86 per 1,000 person-months. Furthermore, TB and HIV co-infection individuals had five times the risk of death (aHR = 5.03, 95% CI = 3.40-7.47, p 

Mycobacterium tuberculosis (M.tb) is the second leading infectious cause of death worldwide and the primary cause of death in people living with HIV/AIDS. There are several excellent animal models employed to study tuberculosis (TB), but many have limitations for reproducing human pathology and none are amenable to the direct study of HIV/M.tb co-infection. The humanized mouse has been increasingly employed to explore HIV infection and other pathogens where animal models are limiting. Our goal was to develop a small animal model of M.tb infection using the bone marrow, liver, thymus (BLT) humanized mouse. NOD-SCID/γcnull mice were engrafted with human fetal liver and thymus tissue, and supplemented with CD34+ fetal liver cells. Excellent reconstitution, as measured by expression of the human CD45 pan leukocyte marker by peripheral blood populations, was observed at 12 weeks after engraftment. Human T cells (CD3, CD4, CD8), as well as natural killer cells and monocyte/macrophages were all observed within the human leukocyte (CD45+) population. Importantly, human T cells were functionally competent as determined by proliferative capacity and effector molecule (e.g. IFN-γ, granulysin, perforin) expression in response to positive stimuli. Animals infected intranasally with M.tb had progressive bacterial infection in the lung and dissemination to spleen and liver from 2–8 weeks post infection. Sites of infection in the lung were characterized by the formation of organized granulomatous lesions, caseous necrosis, bronchial obstruction, and crystallization of cholesterol deposits. Human T cells were distributed throughout the lung, liver, and spleen at sites of inflammation and bacterial growth and were organized to the periphery of granulomas. These preliminary results demonstrate the potential to use the humanized mouse as a model of experimental TB.

Canada CA: Incidence of Tuberculosis: per 100,000 People data was reported at 5.800 Ratio in 2023. This stayed constant from the previous number of 5.800 Ratio for 2022. Canada CA: Incidence of Tuberculosis: per 100,000 People data is updated yearly, averaging 5.500 Ratio from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 6.500 Ratio in 2001 and a record low of 4.600 Ratio in 2010. Canada CA: Incidence of Tuberculosis: per 100,000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Social: Health Statistics. Incidence of tuberculosis is the estimated number of new and relapse tuberculosis cases arising in a given year, expressed as the rate per 100,000 population. All forms of TB are included, including cases in people living with HIV. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously.;World Health Organization, Global Tuberculosis Report.;Weighted average;Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization. This is the Sustainable Development Goal indicator 3.3.2[https://unstats.un.org/sdgs/metadata/].

Dataset Overview 📝

This dataset includes key health indicators for over 200 countries, covering the period from 1999 to 2023.

Health Indicators:

• Current Health Expenditure (% of GDP): Shows the percentage of a country’s GDP allocated to health expenditure.
• Life Expectancy at Birth (Total Years):The average number of years a newborn is expected to live, assuming age-specific mortality rates remain constant.
• Maternal Mortality: The number of maternal deaths per 100,000 live births.
• Infant Mortality Rate:The number of infant deaths (under 1 year) per 1,000 live births.
• Neonatal Mortality Rate: The number of deaths of children under 28 days of age per 1,000 live births.
• Under-5 Mortality Rate: The number of deaths of children under 5 years of age per 1,000 live births.
• Prevalence of HIV (% of population): The percentage of the population aged 15-49 years living with HIV.
• Incidence of Tuberculosis (per 100,000 people):The number of incidence of tuberculosis per 100,000 people.
• Prevalence of Undernourishment (% of population):The percentage of the population whose caloric intake is below the minimum required for a healthy life.

Data Source 🌐

World Bank: This dataset is compiled from the World Bank's health database, providing reliable, updated statistics on health indicators worldwide.

Potential Use Cases 🔍

• Health Policy Research: This dataset is ideal for understanding how different countries allocate resources to health and how these investments correlate with health outcomes such as life expectancy and mortality rates.
• Global Health Trends: Analyzing trends in health spending, mortality rates, and disease prevalence across various regions.
• Predictive Modeling: Building models to predict health outcomes based on historical trends and identifying potential health disparities.
• Health Interventions: Understanding the impact of government spending on health and how it affects different demographics.

Key Questions You Can Explore 🤔

• How does health expenditure correlate with life expectancy across different countries?
• What are the trends in maternal, infant, and under-5 mortality rates over time?
• How do HIV prevalence and tuberculosis incidence vary by region, and what factors contribute to these differences?
• Can we predict which countries are likely to achieve universal health coverage based on current trends?

Important Note ⚠️

• Missing Data: Some values may be missing for certain years or countries, particularly for specific health indicators. Consider using techniques like forward filling, backward filling, or interpolation when performing time series analysis.

Goal 3Ensure healthy lives and promote well-being for all at all agesTarget 3.1: By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live birthsIndicator 3.1.1: Maternal mortality ratioSH_STA_MORT: Maternal mortality ratioIndicator 3.1.2: Proportion of births attended by skilled health personnelSH_STA_BRTC: Proportion of births attended by skilled health personnel (%)Target 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live birthsIndicator 3.2.1: Under-5 mortality rateSH_DYN_IMRTN: Infant deaths (number)SH_DYN_MORT: Under-five mortality rate, by sex (deaths per 1,000 live births)SH_DYN_IMRT: Infant mortality rate (deaths per 1,000 live births)SH_DYN_MORTN: Under-five deaths (number)Indicator 3.2.2: Neonatal mortality rateSH_DYN_NMRTN: Neonatal deaths (number)SH_DYN_NMRT: Neonatal mortality rate (deaths per 1,000 live births)Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseasesIndicator 3.3.1: Number of new HIV infections per 1,000 uninfected population, by sex, age and key populationsSH_HIV_INCD: Number of new HIV infections per 1,000 uninfected population, by sex and age (per 1,000 uninfected population)Indicator 3.3.2: Tuberculosis incidence per 100,000 populationSH_TBS_INCD: Tuberculosis incidence (per 100,000 population)Indicator 3.3.3: Malaria incidence per 1,000 populationSH_STA_MALR: Malaria incidence per 1,000 population at risk (per 1,000 population)Indicator 3.3.4: Hepatitis B incidence per 100,000 populationSH_HAP_HBSAG: Prevalence of hepatitis B surface antigen (HBsAg) (%)Indicator 3.3.5: Number of people requiring interventions against neglected tropical diseasesSH_TRP_INTVN: Number of people requiring interventions against neglected tropical diseases (number)Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-beingIndicator 3.4.1: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory diseaseSH_DTH_NCOM: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (probability)SH_DTH_NCD: Number of deaths attributed to non-communicable diseases, by type of disease and sex (number)Indicator 3.4.2: Suicide mortality rateSH_STA_SCIDE: Suicide mortality rate, by sex (deaths per 100,000 population)SH_STA_SCIDEN: Number of deaths attributed to suicide, by sex (number)Target 3.5: Strengthen the prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcoholIndicator 3.5.1: Coverage of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services) for substance use disordersSH_SUD_ALCOL: Alcohol use disorders, 12-month prevalence (%)SH_SUD_TREAT: Coverage of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services) for substance use disorders (%)Indicator 3.5.2: Alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcoholSH_ALC_CONSPT: Alcohol consumption per capita (aged 15 years and older) within a calendar year (litres of pure alcohol)Target 3.6: By 2020, halve the number of global deaths and injuries from road traffic accidentsIndicator 3.6.1: Death rate due to road traffic injuriesSH_STA_TRAF: Death rate due to road traffic injuries, by sex (per 100,000 population)Target 3.7: By 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmesIndicator 3.7.1: Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methodsSH_FPL_MTMM: Proportion of women of reproductive age (aged 15-49 years) who have their need for family planning satisfied with modern methods (% of women aged 15-49 years)Indicator 3.7.2: Adolescent birth rate (aged 10–14 years; aged 15–19 years) per 1,000 women in that age groupSP_DYN_ADKL: Adolescent birth rate (per 1,000 women aged 15-19 years)Target 3.8: Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for allIndicator 3.8.1: Coverage of essential health servicesSH_ACS_UNHC: Universal health coverage (UHC) service coverage indexIndicator 3.8.2: Proportion of population with large household expenditures on health as a share of total household expenditure or incomeSH_XPD_EARN25: Proportion of population with large household expenditures on health (greater than 25%) as a share of total household expenditure or income (%)SH_XPD_EARN10: Proportion of population with large household expenditures on health (greater than 10%) as a share of total household expenditure or income (%)Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contaminationIndicator 3.9.1: Mortality rate attributed to household and ambient air pollutionSH_HAP_ASMORT: Age-standardized mortality rate attributed to household air pollution (deaths per 100,000 population)SH_STA_AIRP: Crude death rate attributed to household and ambient air pollution (deaths per 100,000 population)SH_STA_ASAIRP: Age-standardized mortality rate attributed to household and ambient air pollution (deaths per 100,000 population)SH_AAP_MORT: Crude death rate attributed to ambient air pollution (deaths per 100,000 population)SH_AAP_ASMORT: Age-standardized mortality rate attributed to ambient air pollution (deaths per 100,000 population)SH_HAP_MORT: Crude death rate attributed to household air pollution (deaths per 100,000 population)Indicator 3.9.2: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services)SH_STA_WASH: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (deaths per 100,000 population)Indicator 3.9.3: Mortality rate attributed to unintentional poisoningSH_STA_POISN: Mortality rate attributed to unintentional poisonings, by sex (deaths per 100,000 population)Target 3.a: Strengthen the implementation of the World Health Organization Framework Convention on Tobacco Control in all countries, as appropriateIndicator 3.a.1: Age-standardized prevalence of current tobacco use among persons aged 15 years and olderSH_PRV_SMOK: Age-standardized prevalence of current tobacco use among persons aged 15 years and older, by sex (%)Target 3.b: Support the research and development of vaccines and medicines for the communicable and non-communicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade-Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for allIndicator 3.b.1: Proportion of the target population covered by all vaccines included in their national programmeSH_ACS_DTP3: Proportion of the target population with access to 3 doses of diphtheria-tetanus-pertussis (DTP3) (%)SH_ACS_MCV2: Proportion of the target population with access to measles-containing-vaccine second-dose (MCV2) (%)SH_ACS_PCV3: Proportion of the target population with access to pneumococcal conjugate 3rd dose (PCV3) (%)SH_ACS_HPV: Proportion of the target population with access to affordable medicines and vaccines on a sustainable basis, human papillomavirus (HPV) (%)Indicator 3.b.2: Total net official development assistance to medical research and basic health sectorsDC_TOF_HLTHNT: Total official development assistance to medical research and basic heath sectors, net disbursement, by recipient countries (millions of constant 2018 United States dollars)DC_TOF_HLTHL: Total official development assistance to medical research and basic heath sectors, gross disbursement, by recipient countries (millions of constant 2018 United States dollars)Indicator 3.b.3: Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basisSH_HLF_EMED: Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis (%)Target 3.c: Substantially increase health financing and the recruitment, development, training and retention of the health workforce in developing countries, especially in least developed countries and small island developing StatesIndicator 3.c.1: Health worker density and distributionSH_MED_DEN: Health worker density, by type of occupation (per 10,000 population)SH_MED_HWRKDIS: Health worker distribution, by sex and type of occupation (%)Target 3.d: Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risksIndicator 3.d.1: International Health Regulations (IHR) capacity and health emergency preparednessSH_IHR_CAPS: International Health Regulations (IHR) capacity, by type of IHR capacity (%)Indicator 3.d.2: Percentage of bloodstream infections due to selected antimicrobial-resistant organismsiSH_BLD_MRSA: Percentage of bloodstream infection due to methicillin-resistant Staphylococcus aureus (MRSA) among patients seeking care and whose

Context

Data provided by countries to WHO and estimates of TB burden generated by WHO for the Global Tuberculosis Report. TB expenditure and utilization on health services in fiscal year 2017.

https://www.who.int/tb/country/data/download/en/

Content

Acknowledgements

https://www.who.int/tb/country/data/download/en/

Photo by Morning Brew on Unsplash

Inspiration

The End TB Strategy and Covid-19 Pandemic