Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

• Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported.
• Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

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.

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

A quarter of the global human population is estimated to be latently infected by Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB). TB remains the global leading cause of death by a single pathogen and ranks among the top-10 causes of overall global mortality. Current immunodiagnostic tests cannot discriminate between latent, active and past TB, nor predict progression of latent infection to active disease. The only registered TB vaccine, Bacillus Calmette-Guérin (BCG), does not adequately prevent pulmonary TB in adolescents and adults, thus permitting continued TB-transmission. Several Mtb proteins, mostly discovered through IFN-γ centered approaches, have been proposed as targets for new TB-diagnostic tests or -vaccines. Recently, however, we identified novel Mtb antigens capable of eliciting multiple cytokines, including antigens that did not induce IFN-γ but several other cytokines. These antigens had been selected based on high Mtb gene-expression in the lung in vivo, and have been termed in vivo expressed (IVE-TB) antigens. Here, we extend and validate our previous findings in an independent Southern European cohort, consisting of adults and adolescents with either LTBI or TB. Our results confirm that responses to IVE-TB antigens, and also DosR-regulon and Rpf stage-specific Mtb antigens are marked by multiple cytokines, including strong responses, such as for TNF-α, in the absence of detectable IFN-γ production. Except for TNF-α, the magnitude of those responses were significantly higher in LTBI subjects. Additional unbiased analyses of high dimensional flow-cytometry data revealed that TNF-α+ cells responding to Mtb antigens comprised 17 highly heterogeneous cell types. Among these 17 TNF-α+ cells clusters identified, those with CD8+TEMRA or CD8+CD4+ phenotypes, defined by the expression of multiple intracellular markers, were the most prominent in adult LTBI, while CD14+ TNF-α+ myeloid-like clusters were mostly abundant in adolescent LTBI. Our findings, although limited to a small cohort, stress the importance of assessing broader immune responses than IFN-γ alone in Mtb antigen discovery as well as the importance of screening individuals of different age groups. In addition, our results provide proof of concept showing how unbiased multidimensional multiparametric cell subset analysis can identify unanticipated blood cell subsets that could play a role in the immune response against Mtb.

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
• ...

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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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.

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 

Tuberculosis (TB) is the leading infectious disease cause of death worldwide. In recent years, stringent measures to contain the spread of SARS-CoV-2 have led to considerable disruptions of healthcare services for TB in many countries. The extent to which these measures have affected TB testing, treatment initiation and outcomes has not been comprehensively assessed. We aimed to estimate TB healthcare service disruptions occurring during the COVID-19 pandemic in Brazil, India, and South Africa. We obtained country-level TB programme and laboratory data and used autoregressive integrated moving average (ARIMA) time-series models to estimate healthcare service disruptions with respect to TB testing, treatment initiation, and treatment outcomes. We quantified disruptions as the percentage difference between TB indicator data observed during the COVID-19 pandemic compared with values for a hypothetical no-COVID scenario, predicted through forecasting of trends during a three-year pre-pandemic period. Annual estimates for 2020–2022 were derived from aggregated monthly data. We estimated that in 2020, the number of bacteriological tests conducted for TB diagnosis was 24.3% (95% uncertainty interval: 8.4%;36.6%) lower in Brazil, 27.8% (19.8;3 4.8%) lower in India, and 32.0% (28.9%;34.9%) lower in South Africa compared with values predicted for the no-COVID scenario. TB treatment initiations were 17.4% (13.9%;20.6%) lower than predicted in Brazil, 43.3% (39.8%;46.4%) in India, and 27.0% (15.2%;36.3%) in South Africa. Reductions in 2021 were less severe compared with 2020. The percentage deaths during TB treatment were 13.7% (8.1%; 19.7%) higher than predicted in Brazil, 1.7% (-8.9%;14.0%) in India and 21.8% (7.4%;39.2%) in South Africa. Our analysis suggests considerable disruptions of TB healthcare services occurred during the early phase of the COVID-19 pandemic in Brazil, India, and South Africa, with at least partial recovery in the following years. Sustained efforts to mitigate the detrimental impact of COVID-19 on TB healthcare services are needed.

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...

Background Tuberculosis is responsible for more female deaths around the earth than any other infectious disease. Reports have suggested that responses to tuberculosis may differ between men and women. We investigated gender related differences in the presentation and one year outcomes of HIV-infected adults with initial episodes of pulmonary tuberculosis in Uganda.

   Methods
   We enrolled and followed up a cohort of 105 male and 109 female HIV-infected adults on treatment for initial episodes of culture-confirmed pulmonary tuberculosis between March 1993 and March 1995. A favorable outcome was defined as being cured and alive at one year while an unfavorable outcome was not being cured or dead. Subjects were followed-up by serial medical examinations, complete blood counts, serum β2 microglobulin, CD4+ cell counts, sputum examinations, and chest x-rays.


   Results
   Male patients were older, had higher body mass indices, and lower serum β2 microglobulin levels than female patients at presentation. At one year, there was no difference between male and female patients in the likelihood of experiencing a favorable outcome (RR 1.02, 95% CI 0.89–1.17). This effect persisted after controlling for symptoms, serum β2 microglobulin, CD4+ cell count, and severity of disease on chest x-ray (OR 1.07, 95% CI 0.54–2.13) with a repeated measures model.


   Conclusions
   While differences existed between males and females with HIV-associated pulmonary tuberculosis at presentation, the outcomes at one year after the initiation of tuberculosis treatment were similar in Uganda. Women in areas with a high HIV and tuberculosis prevalence should be encouraged to present for screening at the first sign of tuberculosis symptoms.

Background: Coronavirus disease 2019 (COVID-19) and tuberculosis (TB) are two major infectious diseases posing significant public health threats, and their coinfection (aptly abbreviated COVID-TB) makes the situation worse. This study aimed to investigate the clinical features and prognosis of COVID-TB cases.Methods: The PubMed, Embase, Cochrane, CNKI, and Wanfang databases were searched for relevant studies published through December 18, 2020. An overview of COVID-TB case reports/case series was prepared that described their clinical characteristics and differences between survivors and deceased patients. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) for death or severe COVID-19 were calculated. The quality of outcomes was assessed using GRADEpro.Results: Thirty-six studies were included. Of 89 COVID-TB patients, 19 (23.46%) died, and 72 (80.90%) were male. The median age of non-survivors (53.95 ± 19.78 years) was greater than that of survivors (37.76 ± 15.54 years) (p < 0.001). Non-survivors were more likely to have hypertension (47.06 vs. 17.95%) or symptoms of dyspnea (72.73% vs. 30%) or bilateral lesions (73.68 vs. 47.14%), infiltrates (57.89 vs. 24.29%), tree in bud (10.53% vs. 0%), or a higher leucocyte count (12.9 [10.5–16.73] vs. 8.015 [4.8–8.97] × 109/L) than survivors (p < 0.05). In terms of treatment, 88.52% received anti-TB therapy, 50.82% received antibiotics, 22.95% received antiviral therapy, 26.23% received hydroxychloroquine, and 11.48% received corticosteroids. The pooled ORs of death or severe disease in the COVID-TB group and the non-TB group were 2.21 (95% CI: 1.80, 2.70) and 2.77 (95% CI: 1.33, 5.74) (P < 0.01), respectively.Conclusion: In summary, there appear to be some predictors of worse prognosis among COVID-TB cases. A moderate level of evidence suggests that COVID-TB patients are more likely to suffer severe disease or death than COVID-19 patients. Finally, routine screening for TB may be recommended among suspected or confirmed cases of COVID-19 in countries with high TB burden.

BACKGROUND: Tuberculosis (TB) is believed to have caused more deaths than any other infection since records began. The “Sustainable Development Goals”, previous “Millennium Development Goals”, World Health Organisation “End TB Strategy” and the second and third “Global Plans to Stop TB” all prioritise(d) key targets to reduce deaths due to TB. However, there seems to be limited research evidence available to inform how this may best be achieved. We therefore aim to summarise, critically appraise, and synthesise the trial evidence that interventions decrease deaths due to TB. METHODS: We will follow the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. We will search the PubMed, Scopus and Web of Science databases for peer reviewed English/Spanish language publications focused on evaluating interventions to reduce deaths due to TB as primary or secondary trial outcomes. We plan to use the following search terms: tuberculosis OR TB; death OR mortality OR fatality OR survival; prevent* OR reduce* OR decrease*; AND trial. Eligible publications will be selected by two independent reviewers and a third will resolve any discrepancies. Key information will be extracted using a shared cloud-based spreadsheet, publications categorised and summarised and critically appraised. Key data will be extracted and synthesised. Meta-analysis will be carried out if there are three or more studies investigating similar interventions with a similar outcome. The quality of trial evidence and any risk of bias will be formally assessed using the Cochrane tools. This systematic review with meta-analysis is registered with the PROSPERO database (record number CRD42023387877). CONCLUSION: We report a protocol for a systematic review of the published literature involving trial evidence assessing whether interventions reduce deaths due to TB and a meta-analysis of the quantitative evidence. We aim to clarify research gaps and to synthesise evidence in order to guide future policy and research.

BackgroundThe Xpert MTB/RIF (Xpert) assay offers rapid and accurate diagnosis of tuberculosis (TB) but still suffers from imperfect sensitivity. The newer Xpert MTB/RIF Ultra cartridge has shown improved sensitivity in recent field trials, but at the expense of reduced specificity. The clinical implications of switching from the existing Xpert cartridge to the Xpert Ultra cartridge in different populations remain uncertain.Methods and findingsWe developed a Markov microsimulation model of hypothetical cohorts of 100,000 individuals undergoing diagnostic sputum evaluation with Xpert for suspected pulmonary TB, in each of 3 emblematic settings: an HIV clinic in South Africa, a public TB center in India, and an adult primary care setting in China. In each setting, we used existing data to project likely diagnostic results, treatment decisions, and ultimate clinical outcomes, assuming use of the standard Xpert versus Xpert Ultra cartridge. Our primary outcomes were the projected number of additional unnecessary treatments generated, the projected number of TB deaths averted, and the projected number of unnecessary treatments generated per TB death averted, if standard Xpert were switched to Xpert Ultra. We also simulated alternative approaches to interpreting positive results of the Ultra cartridge’s semi-quantitative trace call. Extensive sensitivity and uncertainty analyses were performed to evaluate the drivers and generalizability of projected results. In the Indian TB center setting, replacing the standard Xpert cartridge with the Xpert Ultra cartridge was projected to avert 0.5 TB deaths (95% uncertainty range [UR]: 0, 1.3) and generate 18 unnecessary treatments (95% UR: 10, 29) per 1,000 individuals evaluated—resulting in a median ratio of 38 incremental unnecessary treatments added by Ultra per incremental death averted by Ultra compared to outcomes using standard Xpert (95% UR: 12, indefinite upper bound). In the South African HIV care setting—where TB mortality rates are higher and Ultra’s improved sensitivity has greater absolute benefit—this ratio improved to 7 unnecessary treatments per TB death averted (95% UR: 2, 43). By contrast, in the Chinese primary care setting, this ratio was much less favorable, at 372 unnecessary treatments per TB death averted (95% UR: 75, indefinite upper bound), although the projected number of unnecessary treatments using Xpert Ultra was lower (with a possibility of no increased overtreatment) when using specificity data only from lower-burden settings. Alternative interpretations of the trace call had little effect on these ratios. Limitations include uncertainty in key parameters (including the clinical implications of false-negative results), the exclusion of transmission effects, and restriction of this analysis to adult pulmonary TB.ConclusionsSwitching from the standard Xpert cartridge to the Xpert Ultra cartridge for diagnosis of adult pulmonary TB may have different consequences in different clinical settings. In settings with high TB and HIV prevalence, Xpert Ultra is likely to offer considerable mortality benefit, whereas in lower-prevalence settings, Xpert Ultra will likely result in considerable overtreatment unless the possibility of higher specificity of Ultra in lower-prevalence settings in confirmed. The ideal use of the Ultra cartridge may therefore involve a more nuanced, setting-specific approach to implementation, with priority given to populations in which the anticipated prevalence of TB (and HIV) is the highest.

BackgroundEven in low and middle income countries most deaths occur in older adults. In Europe, the effects of better education and home ownership upon mortality seem to persist into old age, but these effects may not generalise to LMICs. Reliable data on causes and determinants of mortality are lacking. Methods and FindingsThe vital status of 12,373 people aged 65 y and over was determined 3–5 y after baseline survey in sites in Latin America, India, and China. We report crude and standardised mortality rates, standardized mortality ratios comparing mortality experience with that in the United States, and estimated associations with socioeconomic factors using Cox's proportional hazards regression. Cause-specific mortality fractions were estimated using the InterVA algorithm. Crude mortality rates varied from 27.3 to 70.0 per 1,000 person-years, a 3-fold variation persisting after standardisation for demographic and economic factors. Compared with the US, mortality was much higher in urban India and rural China, much lower in Peru, Venezuela, and urban Mexico, and similar in other sites. Mortality rates were higher among men, and increased with age. Adjusting for these effects, it was found that education, occupational attainment, assets, and pension receipt were all inversely associated with mortality, and food insecurity positively associated. Mutually adjusted, only education remained protective (pooled hazard ratio 0.93, 95% CI 0.89–0.98). Most deaths occurred at home, but, except in India, most individuals received medical attention during their final illness. Chronic diseases were the main causes of death, together with tuberculosis and liver disease, with stroke the leading cause in nearly all sites. ConclusionsEducation seems to have an important latent effect on mortality into late life. However, compositional differences in socioeconomic position do not explain differences in mortality between sites. Social protection for older people, and the effectiveness of health systems in preventing and treating chronic disease, may be as important as economic and human development. Please see later in the article for the Editors' Summary

This dataset provides an extensive view of global population statistics and health metrics across various countries from 2014 to 2024. It combines population data with vital health-related indicators, making it a valuable resource for understanding trends in population growth and health outcomes worldwide. Researchers, data scientists, and policymakers can utilize this dataset to analyze correlations between population dynamics and health performance at a global scale.

Key Features: - Country: Name of the country. - Year: Year of the data (2014–2024). - Population: Total population for the respective year and country. - Country Code: ISO 3-letter country codes for easy identification. - Health Expenditure (health_exp): Percentage of GDP spent on healthcare. - Life Expectancy (life_expect): Average life expectancy at birth in years. - Maternal Mortality (maternal_mortality): Maternal deaths per 100,000 live births. - Infant Mortality (infant_mortality): Deaths of infants under 1 year per 1,000 live births. - Neonatal Mortality (neonatal_mortality): Deaths of newborns (0–28 days) per 1,000 live births. - Under-5 Mortality (under_5_mortality): Deaths of children under 5 years per 1,000 live births. - HIV Prevalence (prev_hiv): Percentage of the population living with HIV. - Tuberculosis Incidence (inci_tuberc): Estimated new and relapse TB cases per 100,000 people. - Undernourishment Prevalence (prev_undernourishment): Percentage of the population that is undernourished.

Use Cases: - Health Policy Analysis: Understand trends in healthcare expenditure and its relationship to health outcomes. - Global Health Research: Investigate global or regional disparities in health and nutrition. - Population Studies: Analyze population growth trends alongside health indicators. - Data Visualization: Build visual dashboards for storytelling and impactful data representation.

ObjectiveTo assess sex, age, regional differences, and the changing trend in human immunodeficiency virus and tuberculosis (HIV-TB) in different regions from 1990 to 2021, and project future trends.MethodsGlobal Burden of Disease Study 2021 data were analyzed to assess HIV-TB incidence, death, prevalence, and DALY rates from 1990 to 2021, including different types of TB co-infections (drug-susceptible, multidrug-resistant, and extensively drug-resistant). Bayesian age-period-cohort models were used to forecast age-standardized DALY rates through 2035.ResultsIn 2021, there were approximately 1.76 million HIV-TB infections and 200,895 deaths globally. The highest burden of HIV-DS-TB and HIV-MDR-TB was found in Southern Sub-Saharan Africa, while HIV-XDR-TB was most prevalent in Eastern Europe. The co-infection burden was highest among individuals aged 30–49. Key risk factors were unsafe sex, drug use, and intimate partner violence, with regional variations. The global burden of HIV-TB remains high, and age-standardized DALY rates are expected to increase in the coming years, especially in regions with low socio-demographic indices (SDI).ConclusionThe burden of HIV-TB co-infection correlates with the socio-demographic index (SDI): countries with a low SDI have a higher burden. Therefore, clinical diagnosis and treatment in such areas are more challenging and may warrant more attention. High death rates underscore the importance of early management.

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.

Dataset Description

This dataset serves as a comprehensive repository of global development metrics, consolidating data from multiple international organizations into a single, unified structure. It provides a granular view of the state of health, economy, and nutrition across 193 countries over a 30-year period (1990–2019).

The data is organized by Country, Year, and Gender (Male, Female, and Both Sexes), making it a valuable resource for longitudinal studies, demographic analysis, and socio-economic research. It combines high-level economic indicators (like GDP) with granular health metrics (specific mortality rates) and detailed nutritional breakdowns (diet composition by food group).

Content Overview

The dataset covers a wide spectrum of categories:

• Demographics & Economy: Population stats, GNI, GDP, and poverty rates.
• Mortality & Life Expectancy: Survival rates at various ages, maternal mortality, and life expectancy.
• Public Health: Incidence of infectious diseases (Malaria, Tuberculosis, Hepatitis B) and prevalence of health risks (Tobacco, road traffic accidents).
• Environmental Health: Mortality attributed to air pollution, sanitation access, and clean fuel availability.
• Nutrition: Detailed caloric and quantity breakdown of food consumption (fruits, vegetables, cereals, meats, etc.).
• Healthcare Infrastructure: Coverage of essential health services and density of medical professionals.

Sources

The data was extracted and unified via an ETL process from the following organizations:

• World Bank: World Development Indicators
• WHO: Global Health Observatory Data
• UNICEF: Data Warehouse
• Our World In Data: Statistics and Research
• FAOSTAT: Food and Agriculture Data
• Kaggle: WHO World Health Statistics 2020 Complete

Data Dictionary

Index Columns

• Country: Name of the country.
• Year: The calendar year of the recorded data.
• Gender: The gender category for the data (Female, Male, or Both sexes).

Demographics & Health Metrics

• Life Expectancy: The average number of years a newborn is expected to live.
• Infant Mortality Rate: Number of infants dying before reaching one year of age, per 1,000 live births.
  • Includes Low/High Confidence Interval (CI) columns.
• Under 5 Mortality Rate: Probability of a child dying before reaching age 5, per 1,000 live births.
  • Includes Low/High CI columns.
• Neonatal Mortality Rate: Number of deaths during the first 28 days of life per 1,000 live births.
  • Includes Low/High CI columns.
• Maternal Mortality Ratio: Number of maternal deaths due to childbirth per 100,000 live births.
  • Includes Low/High CI columns.
• Birth Rate: Number of births per 1,000 inhabitants.
• Death Rate: Number of deaths per 1,000 inhabitants.
• Adolescent Birth Rate: Number of births by women aged 15 to 19 per 1,000 women in that age range.
• % Population Aged 0-14 / 15-64 / 65+: Percentage of the total population falling into these specific age brackets.
• % Population Aged 65-69 / 70-74 / 75-79 / 80+: Granular breakdown of the elderly population percentages.
• Total Population: Total number of inhabitants.

Causes of Death & Disease

• % Death Cardiovascular: Probability of dying from cardiovascular diseases, cancer, diabetes, or chronic respiratory diseases between ages 30 and 70.
  • Includes Low/High CI columns.
• Incidence of Malaria: Number of malaria cases per 1,000 inhabitants at risk per year.
• Incidence of Tuberculosis: Estimated cases of tuberculosis per 100,000 inhabitants.
  • Includes Low/High CI columns.
• Hepatitis B Surface Antigen: Prevalence of hepatitis B surface antigen.
  • Includes Low/High CI columns.
• Road Traffic Deaths: Number of deaths due to traffic accidents per 100,000 people.
• Poisoning Mortality Rate: Deaths attributed to unintentional poisoning per 100,000 people.
• Conflict and Terrorism Deaths: Number of deaths due to armed conflicts and terrorism.
• Battle Related Deaths: Number of deaths related to battles in an armed conflict.
• % Injury Deaths: Percentage of deaths caused by injuries.
• Suicides Rate: Number of deliberate deaths per 100,000 inhabitants.
• Homicide Rate: Number of homicides per 100,000 inhabitants.

Air Pollution Mortality

• Air Pollution Death Rate Total: Probability of dying fr...

Abstract

This is the microdataset used in the paper "SMS nudges as a tool to reduce Tuberculosis treatment delay and pretreatment loss to follow-up. A randomized controlled trial". We fielded two SMS interventions in three Cape Town clinics to see their effects on whether people returned to clinic, and how quickly. One was a simple reminder; the other aimed to overcome “optimism bias” by reminding people TB is curable and many millions die unnecessarily from it. Recruits were randomly assigned at the clinic level to a control group or one of the two SMS groups (1:2:2). In addition to estimating effects on the full sample, we also estimated effects on HIV-positive patients.

Geographic coverage

3 clinics in Greater Cape Town

Analysis unit

Patient

Kind of data

Clinical data [cli]

Sampling procedure

Patients not already being treated for TB arriving in TB waiting rooms of 3 clinics. Aimed to recruit > 90% of new patients over recruitment period. Inclusion criteria: Adult, provided consent, not already on treatment, waiting for a TB test or just had a TB test. Exclusion criteria: Adult, refused consent, already on treatment, not waiting for a TB test or just had a TB test. Recruitment was from 2 October 2017 until 15 December 2017. Fieldworkers continued visiting clinics and phoning patients until mid-February 2018 to collect data on patients’ return-to-clinic date, test results and treatment start date.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

CAPI interview at recruitment was based on a long questionnaire only a few questions from which were used in the present study. The questionnaire is therefore not attached to the current dataset.

Cleaning operations

Data-cleaning was done by staff at Stellenbosch University and the World Bank.