A comprehensive record of Tuberculosis incidence across the nations of the world. Within a time range of 22 years, the features tell the incurrence rates, total incurrences, mortality rates, percentage of tb cases caused by HIV/AIDs.

Three year average incidence of TB per 100,000 population

Rationale Reducing TB incidence is a key indicator in the Tuberculosis Action plan for England, 2021 to 2026.

Definition of numerator Sum of the number of new TB notifications to the National Tuberculosis Surveillance system (NTBS) over a three year time period.

Definition of denominator Sum of the Office for National Statistics (ONS) midyear population estimates for each year of the three year time period.

Caveats Every effort is made to ensure accuracy and completeness of NTBS data, including web-based reporting, data cleaning, and data integrity checks for data quality. However, responsibility for the accuracy and completeness of the data lies with the reporting clinic. Data for all previous years are updated using the most recent TB notification dataset. This update means that the values for a given area and year may be different (either smaller or larger) when compared to what has been shown on this profile in the past.

3-year average of tuberculosis (TB) incidence per 100,000 population, Source: Health Protection Agency Publisher: Association of Public Health Observatories (APHO) Geographies: Local Authority District (LAD), County/Unitary Authority, Government Office Region (GOR), National Geographic coverage: England Time coverage: 2004-2006 Type of data: Administrative data

The three-year average incidence of TB per 100,000 population is calculated by dividing the numerator (the number of TB notifications in the 3-year period) by the denominator (the sum of the mid-year population estimates for the same 3-year period) and multiplying by 100,000.

Data for all previous years are updated using the most recent TB notification dataset. This update means that the values for a given area and year may be different (either smaller or larger) when compared to what has been shown on this profile in the past.Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

This dataset provides detailed estimates of tuberculosis (TB) incidence and mortality in India, categorized by different levels of uncertainty (low, middle, high). The data is modeled using an in-country approach.

This dataset includes 400,000 records with 22 variables that capture demographic, health, and socioeconomic factors influencing tuberculosis incidence across 70 countries. The data is designed to resemble real-world patterns observed in tuberculosis prevalence and healthcare indicators. It can be used for tasks such as descriptive analysis, machine learning, and public health research.

United States US: Incidence of Tuberculosis: per 100,000 People data was reported at 3.100 Ratio in 2016. This records a decrease from the previous number of 3.300 Ratio for 2015. United States US: Incidence of Tuberculosis: per 100,000 People data is updated yearly, averaging 4.900 Ratio from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 6.700 Ratio in 2000 and a record low of 3.100 Ratio in 2016. United States US: 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 United States – Table US.World Bank.WDI: 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;

This dataset presents the annual number of reported tuberculosis (TB) cases in the State of Qatar, along with the incidence rate per 100,000 population. It provides a time series overview of TB case counts and infection rates, enabling analysis of disease trends over time.This information is crucial for public health surveillance, planning, and evaluating tuberculosis control programs. It also supports monitoring national and global TB reduction targets in line with health development goals.

Context

Data provided by countries to WHO and estimates of TB burden generated by WHO for the Global Tuberculosis Report

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

Content

WHO TB burden estimates

This includes WHO-generated estimates of TB mortality, incidence (including disaggregation by age and sex and incidence of TB/HIV).

Acknowledgements

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

Photo by Markus Spiske on Unsplash

Inspiration

End TB Strategy.

Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org. The annual number of new cases of tuberculosis and average annual tuberculosis incidence rate (new cases per 100,000 residents) with corresponding 95% confidence intervals, by Chicago community area, for the years 2007 – 2011. See the full description at https://data.cityofchicago.org/api/assets/E0205898-C378-4299-97C1-F9F89AAF603C.

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).

Turkey TR: Incidence of Tuberculosis: per 100,000 People data was reported at 18.000 Ratio in 2016. This stayed constant from the previous number of 18.000 Ratio for 2015. Turkey TR: Incidence of Tuberculosis: per 100,000 People data is updated yearly, averaging 29.000 Ratio from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 33.000 Ratio in 2006 and a record low of 18.000 Ratio in 2016. Turkey TR: 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 Turkey – Table TR.World Bank: 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;

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

The Online Tuberculosis Information System (OTIS) on CDC WONDER contains information on verified tuberculosis (TB) cases reported to the Centers for Disease Control and Prevention (CDC) by state health departments, the District of Columbia and Puerto Rico since 1993. These data were extracted from the CDC national TB surveillance system. OTIS reports case counts, incidence rates, population counts, percentage of cases that completed therapy within 1 year of diagnosis, and percentage of cases tested for drug susceptibility. Data for 22 variables are included in the data set, including: age groups, race / ethnicity, sex, vital status, year reported, state, metropolitan area, several patient risk factors, directly observed therapy, disease verification criteria and multi-drug resistant TB. Each year these data are updated with an additional year of cases plus revisions to cases reported in previous years. OTIS is produced by the U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention (CDC), National Center for HIV/AIDS, viral Hepatitis, STD and TB Prevention (NCHHSTP).

Finland FI: Incidence of Tuberculosis: per 100,000 People data was reported at 4.700 Ratio in 2016. This records a decrease from the previous number of 5.600 Ratio for 2015. Finland FI: Incidence of Tuberculosis: per 100,000 People data is updated yearly, averaging 6.700 Ratio from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 12.000 Ratio in 2000 and a record low of 4.700 Ratio in 2016. Finland FI: 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 Finland – Table FI.World Bank.WDI: 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;

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.

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.

Bolivia BO: Incidence of Tuberculosis: per 100,000 People data was reported at 109.000 Ratio in 2021. This records an increase from the previous number of 103.000 Ratio for 2020. Bolivia BO: Incidence of Tuberculosis: per 100,000 People data is updated yearly, averaging 133.000 Ratio from Dec 2000 (Median) to 2021, with 22 observations. The data reached an all-time high of 184.000 Ratio in 2000 and a record low of 103.000 Ratio in 2020. Bolivia BO: 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 Bolivia – Table BO.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/].

Mexico MX: Incidence of Tuberculosis: per 100,000 People data was reported at 22.000 Ratio in 2017. This stayed constant from the previous number of 22.000 Ratio for 2016. Mexico MX: Incidence of Tuberculosis: per 100,000 People data is updated yearly, averaging 21.000 Ratio from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 23.000 Ratio in 2003 and a record low of 18.000 Ratio in 2004. Mexico MX: 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 Mexico – Table MX.World Bank.WDI: 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;

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) ...