Data for deaths by leading cause of death categories are now available in the death profiles dataset for each geographic granularity.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Cause of death categories for years 1999 and later are based on tenth revision of International Classification of Diseases (ICD-10) codes. Comparable categories are provided for years 1979 through 1998 based on ninth revision (ICD-9) codes. For more information on the comparability of cause of death classification between ICD revisions see Comparability of Cause-of-death Between ICD Revisions.

This dataset presents the age-adjusted death rates for the 10 leading causes of death in the United States beginning in 1999. Data are based on information from all resident death certificates filed in the 50 states and the District of Columbia using demographic and medical characteristics. Age-adjusted death rates (per 100,000 population) are based on the 2000 U.S. standard population. Populations used for computing death rates after 2010 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for non-census years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Causes of death classified by the International Classification of Diseases, Tenth Revision (ICD–10) are ranked according to the number of deaths assigned to rankable causes. Cause of death statistics are based on the underlying cause of death. SOURCES CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. Murphy SL, Xu JQ, Kochanek KD, Curtin SC, and Arias E. Deaths: Final data for 2015. National vital statistics reports; vol 66. no. 6. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_06.pdf.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

What are people dying from?

This question is essential to guide decisions in public health, and find ways to save lives.

Many leading causes of death receive little mainstream attention. If news reports reflected what children died from, they would say that around 1,400 young children die from diarrheal diseases, 1,000 die from malaria, and 1,900 from respiratory infections – every day.

This can change. Over time, death rates from these causes have declined across the world.

A better understanding of the causes of death has led to the development of technologies, preventative measures, and better healthcare, reducing the chances of dying from a wide range of different causes, across all age groups.

In the past, infectious diseases dominated. But death rates from infectious diseases have fallen quickly – faster than other causes. This has led to a shift in the leading causes of death. Now, non-communicable diseases – such as heart diseases and cancers – are the most common causes of death globally.

More progress is possible, and the impact of causes of death can fall further.

On this page, you will find global data and research on leading causes of death and how they can be prevented.

This data can also help understand the burden of disease more broadly, and offer a lens to see the impacts of healthcare and medicine, habits and behaviours, environmental factors, health infrastructure, and more.

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

Historical chart and dataset showing World death rate by year from 1950 to 2025.

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Context

In the shadows of the Covid-19 pandemic, there is another global health crisis that has gone largely unnoticed. This is the Noncommunicable Disease (NCD) pandemic.

The WHO website describes NCDs as follows:

Noncommunicable diseases (NCDs), also known as chronic diseases, tend to be of long duration and are the result of a combination of genetic, physiological, environmental and behaviours factors.

The main types of NCDs are cardiovascular diseases (like heart attacks and stroke), cancers, chronic respiratory diseases (such as chronic obstructive pulmonary disease and asthma) and diabetes.

NCDs disproportionately affect people in low- and middle-income countries where more than three quarters of global NCD deaths – 32million – occur.

Key facts:

• Noncommunicable diseases (NCDs) kill 41 million people each year, equivalent to 71% of all deaths globally.
• Each year, 15 million people die from a NCD between the ages of 30 and 69 years; over 85% of these "premature" deaths occur in low- and middle-income > * countries.
• Cardiovascular diseases account for most NCD deaths, or 17.9 million people annually, followed by cancers (9.0 million), respiratory diseases (3.9million), and diabetes (1.6 million).
• These 4 groups of diseases account for over 80% of all premature NCD deaths.
• Tobacco use, physical inactivity, the harmful use of alcohol and unhealthy diets all increase the risk of dying from a NCD.
• Detection, screening and treatment of NCDs, as well as palliative care, are key components of the response to NCDs.

Content

This data repository consists of 3 CSV files: WHO-cause-of-death-by-NCD.csv is the main dataset, which provides the percentage of deaths caused by NCDs out of all causes of death, for each nation globally. Metadata_Country.csv and Metadata_Indicator.csv provide additional metadata which is helpful for interpreting the main CSV.

The data collected spans a period from 2000 to 2016. The main CSV has columns for every year from 1960 to 2019. It is advisable to drop all redundant columns where no data was collected.

Furthermore, it is advisable to merge Metadata_Country.csv with the main CSV as it provides valuable additional information, particularly on the economic situation of each nation.

Acknowledgements

This dataset has been extracted from The World Bank 'Cause of death, by non-communicable diseases (% of total)' Dataset, derived based on the data from WHO's Global Health Estimates. It is freely provided under a Creative Commons Attribution 4.0 International License (CC BY 4.0), with the additional terms as stated on the World Bank website: World Bank Terms of Use for Datasets.

Inspiration

I would be interested to see some good data wrangling (dropping redundant columns), as well as kernels interpreting additional information in 'SpecialNotes' column in Metadata_country.csv

It would also be great to see what different factors influence NCDs: most of all, the geopolitical factors. Would be great to see some choropleth visualisations to get an idea of which regions are most affected by NCDs.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf

Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics

Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differ from the timestamp in DPH's daily PDF reports.

Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

A dataset providing GP recorded coronary heart disease.

Coronary heart disease (CHD) is the leading cause of death both in the UK and worldwide.

It's responsible for more than 73,000 deaths in the UK each year. About 1 in 6 men and 1 in 10 women die from CHD.

In the UK, there are an estimated 2.3 million people living with CHD and around 2 million people affected by http://www.nhs.uk/conditions/Angina/Pages/Introduction.aspx">angina (the most common symptom of coronary heart disease).

CHD generally affects more men than women, although from the age of 50 the chances of developing the condition are similar for both sexes.

As well as angina (chest pain), the main symptoms of CHD are http://www.nhs.uk/conditions/heart-attack/Pages/Introduction.aspx">heart attacks and http://www.nhs.uk/conditions/Heart-failure/Pages/Introduction.aspx">heart failure. However, not everyone has the same symptoms and some people may not have any before CHD is diagnosed.

CHD is sometimes called ischaemic heart disease.

BackgroundColorectal cancer (CRC) is the second most common malignancy and the third leading cause of cancer-related deaths globally. Numerous studies have established a link between high body mass index (BMI) and CRC. However, a detailed analysis of the global disease burden of CRC attributable to high BMI has been lacking.ObjectiveThis study aimed to evaluate the spatiotemporal trends in mortality and disability-adjusted life years (DALYs) attributable to high BMI-related CRC at global, regional, and national levels from 1990 to 2021.MethodsEpidemiological data on the association between high BMI and CRC were extracted from the 2021 Global Burden of Disease (GBD) study. Data on mortality, DALYs, age-standardized mortality rate (ASMR), and age-standardized DALY rate (ASDR) were stratified by sex, year, age, country, and Socio-demographic Index (SDI). Estimated annual percentage changes (EAPC) were calculated to assess temporal trends in ASMR and ASDR attributable to high BMI from 1990 to 2021. Decomposition and frontier analyses were conducted to identify drivers of burden changes and top-performing countries. Inequality analysis was performed to assess burden disparities across different SDI levels. The Bayesian age-period-cohort (BAPC) model was used to predict disease burden up to 2050.ResultsDeaths and DALYs related to high BMI-associated CRC showed a robust upward trend, more than doubling in absolute numbers since 1990. Without intervention, similar patterns are projected to continue over the next 29 years. East Asia exhibited the highest risk of CRC deaths and DALYs attributable to high BMI, with the heaviest burden observed in China and the United States. High SDI regions demonstrated a higher burden, while low SDI regions faced higher EAPC.ConclusionThis study highlights high BMI as a significant risk factor for CRC, with notable regional heterogeneity in disease burden. Stratification by SDI and health inequality analysis underscore the need for tailored preventive strategies and health interventions targeting high BMI, particularly in different SDI regions.

Dataset Card for Breast Ultrasound Images Dataset

  Dataset Summary

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of early deaths. The data reviews the medical images of breast cancer using ultrasound scan. Breast Ultrasound Dataset is categorized into three classes: normal, benign, and malignant images. Breast ultrasound images can produce great results in classification, detection, and… See the full description on the dataset page: https://huggingface.co/datasets/ShivamRaisharma/breastcancer.

Lung Cancer Diagnostic Tests Market Outlook

The lung cancer diagnostic tests market size was valued at USD 2.5 billion in 2023 and is projected to reach USD 6.1 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 10.5% during the forecast period. This substantial growth can be attributed to the rising prevalence of lung cancer globally, advancements in diagnostic technologies, and increasing awareness regarding early detection and treatment of lung cancer. The growing aging population and the high incidence of smoking, which is a leading cause of lung cancer, further propel the demand for diagnostic tests.

The increasing prevalence of lung cancer is one of the primary drivers of market growth. Lung cancer remains the leading cause of cancer-related deaths worldwide, necessitating the development of more accurate and early diagnostic methods. With advancements in medical technology, such as molecular diagnostics and non-invasive imaging techniques, the accuracy and efficiency of lung cancer diagnosis have significantly improved. These innovations not only enhance the detection rate but also facilitate personalized treatment plans, thereby improving patient outcomes.

Furthermore, government initiatives and funding for cancer research play a crucial role in market expansion. Many countries are investing heavily in cancer research, leading to the development of new diagnostic tools and techniques. For instance, organizations such as the National Cancer Institute (NCI) in the United States provide substantial grants for lung cancer research, fostering innovations in diagnostics. In addition, public awareness campaigns and screening programs conducted by healthcare organizations and governments encourage early diagnosis, which is vital for successful treatment and survival rates.

The integration of artificial intelligence (AI) and machine learning in diagnostic tools is another significant factor contributing to market growth. AI algorithms can analyze medical images with high precision, aiding radiologists in identifying lung cancer at earlier stages. Moreover, AI-driven software can evaluate large datasets from genetic and molecular tests, providing insights into the most effective treatment options based on individual patient profiles. This technological advancement not only enhances the accuracy of diagnostics but also reduces the time required for analysis, thereby increasing the efficiency of healthcare services.

The EGFR Mutation Test is a pivotal advancement in the realm of lung cancer diagnostics, offering a more personalized approach to treatment. This test specifically identifies mutations in the Epidermal Growth Factor Receptor (EGFR) gene, which are often present in non-small cell lung cancer (NSCLC) patients. By detecting these mutations, healthcare providers can tailor therapies that target the specific genetic alterations, thereby improving treatment efficacy and patient outcomes. The growing adoption of EGFR Mutation Tests underscores the shift towards precision medicine, where treatments are increasingly customized based on individual genetic profiles. This approach not only enhances the effectiveness of therapies but also minimizes adverse effects, as treatments are more accurately aligned with the patient's unique genetic makeup.

Regionally, North America holds the largest share of the lung cancer diagnostic tests market, followed by Europe and Asia Pacific. The dominance of North America can be attributed to the presence of advanced healthcare infrastructure, high healthcare expenditure, and a robust research landscape. The Asia Pacific region, however, is expected to witness the highest growth rate during the forecast period, driven by increasing healthcare investments, growing awareness about lung cancer, and rising incidences of the disease in countries like China and India. The growing middle-class population and improving healthcare access in these countries further support market growth.

Test Type Analysis

The lung cancer diagnostic tests market is segmented by test type into imaging tests, sputum cytology, tissue biopsy, molecular tests, and others. Imaging tests are one of the most commonly used diagnostic methods for lung cancer detection. Techniques such as X-rays, CT scans, and PET scans provide detailed visuals of the lungs, helping in identifying abnormal growths or tumors. The non-invasive nature of these tests and their ability to provide quick results make them a preferred choice among healthcare

Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.

breastcanc-ultrasound-class

  Background

Cancer is the second leading cause of death worldwide, according to IHME - Global Burden of Disease, with 10.7 mln casualties in 2019.

Amongst the various types of cancer, a huge role is played by breast cancer, which stands in 4th position among the deadliest tumors, with more than 700.000 deaths during 2019 (IHME - Global Burden of Disease).

Moreover, breast cancer has the highest share of number of cases/100 people worldwide… See the full description on the dataset page: https://huggingface.co/datasets/as-cle-bert/breastcanc-ultrasound-class.

Cervical Cancer Risk Factors for Biopsy: This Dataset is Obtained from UCI Repository and kindly acknowledged! This file contains a List of Risk Factors for Cervical Cancer leading to a Biopsy Examination! About 11,000 new cases of invasive cervical cancer are diagnosed each year in the U.S. However, the number of new cervical cancer cases has been declining steadily over the past decades. Although it is the most preventable type of cancer, each year cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide. In the United States, cervical cancer mortality rates plunged by 74% from 1955 - 1992 thanks to increased screening and early detection with the Pap test. AGE Fifty percent of cervical cancer diagnoses occur in women ages 35 - 54, and about 20% occur in women over 65 years of age. The median age of diagnosis is 48 years. About 15% of women develop cervical cancer between the ages of 20 - 30. Cervical cancer is extremely rare in women younger than age 20. However, many young women become infected with multiple types of human papilloma virus, which then can increase their risk of getting cervical cancer in the future. Young women with early abnormal changes who do not have regular examinations are at high risk for localized cancer by the time they are age 40, and for invasive cancer by age 50. SOCIOECONOMIC AND ETHNIC FACTORS Although the rate of cervical cancer has declined among both Caucasian and African-American women over the past decades, it remains much more prevalent in African-Americans -- whose death rates are twice as high as Caucasian women. Hispanic American women have more than twice the risk of invasive cervical cancer as Caucasian women, also due to a lower rate of screening. These differences, however, are almost certainly due to social and economic differences. Numerous studies report that high poverty levels are linked with low screening rates. In addition, lack of health insurance, limited transportation, and language difficulties hinder a poor woman’s access to screening services. HIGH SEXUAL ACTIVITY Human papilloma virus (HPV) is the main risk factor for cervical cancer. In adults, the most important risk factor for HPV is sexual activity with an infected person. Women most at risk for cervical cancer are those with a history of multiple sexual partners, sexual intercourse at age 17 years or younger, or both. A woman who has never been sexually active has a very low risk for developing cervical cancer. Sexual activity with multiple partners increases the likelihood of many other sexually transmitted infections (chlamydia, gonorrhea, syphilis).Studies have found an association between chlamydia and cervical cancer risk, including the possibility that chlamydia may prolong HPV infection. FAMILY HISTORY Women have a higher risk of cervical cancer if they have a first-degree relative (mother, sister) who has had cervical cancer. USE OF ORAL CONTRACEPTIVES Studies have reported a strong association between cervical cancer and long-term use of oral contraception (OC). Women who take birth control pills for more than 5 - 10 years appear to have a much higher risk HPV infection (up to four times higher) than those who do not use OCs. (Women taking OCs for fewer than 5 years do not have a significantly higher risk.) The reasons for this risk from OC use are not entirely clear. Women who use OCs may be less likely to use a diaphragm, condoms, or other methods that offer some protection against sexual transmitted diseases, including HPV. Some research also suggests that the hormones in OCs might help the virus enter the genetic material of cervical cells. HAVING MANY CHILDREN Studies indicate that having many children increases the risk for developing cervical cancer, particularly in women infected with HPV. SMOKING Smoking is associated with a higher risk for precancerous changes (dysplasia) in the cervix and for progression to invasive cervical cancer, especially for women infected with HPV. IMMUNOSUPPRESSION Women with weak immune systems, (such as those with HIV / AIDS), are more susceptible to acquiring HPV. Immunocompromised patients are also at higher risk for having cervical precancer develop rapidly into invasive cancer. DIETHYLSTILBESTROL (DES) From 1938 - 1971, diethylstilbestrol (DES), an estrogen-related drug, was widely prescribed to pregnant women to help prevent miscarriages. The daughters of these women face a higher risk for cervical cancer. DES is no longer prsecribed.

Tobacco use is a leading cause of preventable deaths worldwide. Uganda enacted the Tobacco Control Act (TCA) 2015 to domesticate implementation of the World Health Organization (WHO) Framework Convention on Tobacco Control (FCTC) regulations. This study assessed the capacity to enforce the TCA and associated factors, as well as the level and barriers to its enforcement in Kampala, Uganda. A cross-sectional study using both quantitative and qualitative methods was employed. A structured questionnaire was used for quantitative data collection, and a WHO adapted capacity assessment open-ended guide was used for key informant interviews. Multivariable logistic regression was used to obtain odds ratios and 95% confidence intervals for the independent predictors for capacity to enforce the TCA. A total of 162 respondents from 5 institutions and six key informants were involved in the study. Findings established that only 23% (37/162) of the enforcers had the capacity to enforce the TCA. Male enforcers [OR = 0.16, 95% CI (0.05–0.55)], those who did not know when the law was enacted [OR = 0.19, 95% CI (0.07–0.52)], those with no plans to enforce the law [OR = 0.22, 95% CI (0.05–0.93)], and older enforcers (aged 31–40 years) [OR = 0.27, 95% CI (0.09–0.81)] were less likely to have the capacity to enforce the TCA. The level of enforcement of the TCA was mainly low to moderate for most of the institutions mandated to enforce it. Lack of knowledge about the law amongst the enforcers and general public, and inadequate funds were reported as major barriers to enforcement of the TCA. The capacity to enforce the TCA in Kampala was low. There is potential to enhance the capacity of enforcers through further dissemination of the Act, as well as sensitization of enforcers, institutional managers, and the general public about the legislation.

JP: Prevalence of Severe Wasting: Weight for Height: Male: % of Children under 5 data was reported at 0.300 % in 2010. JP: Prevalence of Severe Wasting: Weight for Height: Male: % of Children under 5 data is updated yearly, averaging 0.300 % from Dec 2010 (Median) to 2010, with 1 observations. JP: Prevalence of Severe Wasting: Weight for Height: Male: % of Children under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank: Health Statistics. Prevalence of severe wasting, male, is the proportion of boys under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

breastcanc-ultrasound-class

  Background

Cancer is the second leading cause of death worldwide, according to IHME - Global Burden of Disease, with 10.7 mln casualties in 2019.

Amongst the various types of cancer, a huge role is played by breast cancer, which stands in 4th position among the deadliest tumors, with more than 700.000 deaths during 2019 (IHME - Global Burden of Disease).

Moreover, breast cancer has the highest share of number of cases/100 people worldwide… See the full description on the dataset page: https://huggingface.co/datasets/as-cle-bert/breastcancer-auto-objdetect.