Population based cancer incidence rates were abstracted from National Cancer Institute, State Cancer Profiles for all available counties in the United States for which data were available. This is a national county-level database of cancer data that are collected by state public health surveillance systems. All-site cancer is defined as any type of cancer that is captured in the state registry data, though non-melanoma skin cancer is not included. All-site age-adjusted cancer incidence rates were abstracted separately for males and females. County-level annual age-adjusted all-site cancer incidence rates for years 2006–2010 were available for 2687 of 3142 (85.5%) counties in the U.S. Counties for which there are fewer than 16 reported cases in a specific area-sex-race category are suppressed to ensure confidentiality and stability of rate estimates; this accounted for 14 counties in our study. Two states, Kansas and Virginia, do not provide data because of state legislation and regulations which prohibit the release of county level data to outside entities. Data from Michigan does not include cases diagnosed in other states because data exchange agreements prohibit the release of data to third parties. Finally, state data is not available for three states, Minnesota, Ohio, and Washington. The age-adjusted average annual incidence rate for all counties was 453.7 per 100,000 persons. We selected 2006–2010 as it is subsequent in time to the EQI exposure data which was constructed to represent the years 2000–2005. We also gathered data for the three leading causes of cancer for males (lung, prostate, and colorectal) and females (lung, breast, and colorectal). The EQI was used as an exposure metric as an indicator of cumulative environmental exposures at the county-level representing the period 2000 to 2005. A complete description of the datasets used in the EQI are provided in Lobdell et al. and methods used for index construction are described by Messer et al. The EQI was developed for the period 2000– 2005 because it was the time period for which the most recent data were available when index construction was initiated. The EQI includes variables representing each of the environmental domains. The air _domain includes 87 variables representing criteria and hazardous air pollutants. The water _domain includes 80 variables representing overall water quality, general water contamination, recreational water quality, drinking water quality, atmospheric deposition, drought, and chemical contamination. The land _domain includes 26 variables representing agriculture, pesticides, contaminants, facilities, and radon. The built _domain includes 14 variables representing roads, highway/road safety, public transit behavior, business environment, and subsidized housing environment. The sociodemographic environment includes 12 variables representing socioeconomics and crime. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., L. Messer, K. Rappazzo , C. Gray, S. Grabich , and D. Lobdell. County-level environmental quality and associations with cancer incidence#. Cancer. John Wiley & Sons Incorporated, New York, NY, USA, 123(15): 2901-2908, (2017).

Users can access data about cancer statistics in the United States including but not limited to searches by type of cancer and race, sex, ethnicity, age at diagnosis, and age at death. Background Surveillance Epidemiology and End Results (SEER) database’s mission is to provide information on cancer statistics to help reduce the burden of disease in the U.S. population. The SEER database is a project to the National Cancer Institute. The SEER database collects information on incidence, prevalence, and survival from specific geographic areas representing 28 percent of the United States population. User functionality Users can access a variety of reso urces. Cancer Stat Fact Sheets allow users to look at summaries of statistics by major cancer type. Cancer Statistic Reviews are available from 1975-2008 in table format. Users are also able to build their own tables and graphs using Fast Stats. The Cancer Query system provides more flexibility and a larger set of cancer statistics than F ast Stats but requires more input from the user. State Cancer Profiles include dynamic maps and graphs enabling the investigation of cancer trends at the county, state, and national levels. SEER research data files and SEER*Stat software are available to download through your Internet connection (SEER*Stat’s client-server mode) or via discs shipped directly to you. A signed data agreement form is required to access the SEER data Data Notes Data is available in different formats depending on which type of data is accessed. Some data is available in table, PDF, and html formats. Detailed information about the data is available under “Data Documentation and Variable Recodes”.

The dataset contains 2 .csv files

This file contains various demographic and health-related data for different regions. Here's a brief description of each column:

File 1st

avganncount: Average number of cancer cases diagnosed annually.

avgdeathsperyear: Average number of deaths due to cancer per year.

target_deathrate: Target death rate due to cancer.

incidencerate: Incidence rate of cancer.

medincome: Median income in the region.

popest2015: Estimated population in 2015.

povertypercent: Percentage of population below the poverty line.

studypercap: Per capita number of cancer-related clinical trials conducted.

binnedinc: Binned median income.

medianage: Median age in the region.

pctprivatecoveragealone: Percentage of population covered by private health insurance alone.

pctempprivcoverage: Percentage of population covered by employee-provided private health insurance.

pctpubliccoverage: Percentage of population covered by public health insurance.

pctpubliccoveragealone: Percentage of population covered by public health insurance only.

pctwhite: Percentage of White population.

pctblack: Percentage of Black population.

pctasian: Percentage of Asian population.

pctotherrace: Percentage of population belonging to other races.

pctmarriedhouseholds: Percentage of married households. birthrate: Birth rate in the region.

File 2nd

This file contains demographic information about different regions, including details about household size and geographical location. Here's a description of each column:

statefips: The FIPS code representing the state.

countyfips: The FIPS code representing the county or census area within the state.

avghouseholdsize: The average household size in the region.

geography: The geographical location, typically represented as the county or census area name followed by the state name.

Each row in the file represents a specific region, providing details about household size and geographical location. This information can be used for various demographic analyses and studies.

SEER Limited-Use cancer incidence data with associated population data. Geographic areas available are county and SEER registry. The Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute collects and distributes high quality, comprehensive cancer data from a number of population-based cancer registries. Data include patient demographics, primary tumor site, morphology, stage at diagnosis, first course of treatment, and follow-up for vital status. The SEER Program is the only comprehensive source of population-based information in the United States that includes stage of cancer at the time of diagnosis and survival rates within each stage.

This dataset contains Cancer Incidence data for Breast Cancer (Late Stage^) including: Age-Adjusted Rate, Confidence Interval, Average Annual Count, and Trend field information for US States for the average 5 year span from 2016 to 2020.Data are for females segmented by age (All Ages, Ages Under 50, Ages 50 & Over, Ages Under 65, and Ages 65 & Over), with field names and aliases describing the sex and age group tabulated.For more information, visit statecancerprofiles.cancer.govData NotationsState Cancer Registries may provide more current or more local data.TrendRising when 95% confidence interval of average annual percent change is above 0.Stable when 95% confidence interval of average annual percent change includes 0.Falling when 95% confidence interval of average annual percent change is below 0.† Incidence rates (cases per 100,000 population per year) are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84, 85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Rates calculated using SEER*Stat. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used for SEER and NPCR incidence rates.‡ Incidence Trend data come from different sources. Due to different years of data availability, most of the trends are AAPCs based on APCs but some are APCs calculated in SEER*Stat. Please refer to the source for each area for additional information.Rates and trends are computed using different standards for malignancy. For more information see malignant.^ Late Stage is defined as cases determined to be regional or distant. Due to changes in stage coding, Combined Summary Stage (2004+) is used for data from Surveillance, Epidemiology, and End Results (SEER) databases and Merged Summary Stage is used for data from National Program of Cancer Registries databases. Due to the increased complexity with staging, other staging variables maybe used if necessary.Data Source Field Key(1) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(5) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(6) Source: National Program of Cancer Registries SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention (based on the 2022 submission).(7) Source: SEER November 2022 submission.(8) Source: Incidence data provided by the SEER Program. AAPCs are calculated by the Joinpoint Regression Program and are based on APCs. Data are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84,85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used with SEER November 2022 data.Some data are not available, see Data Not Available for combinations of geography, cancer site, age, and race/ethnicity.Data for the United States does not include data from Nevada.Data for the United States does not include Puerto Rico.

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated on 8/14/2024

Cancer Mortality Rate - This indicator shows the age-adjusted mortality rate from cancer (per 100,000 population). Maryland’s age adjusted cancer mortality rate is higher than the US cancer mortality rate. Cancer impacts people across all population groups, however wide racial disparities exist. https://health.maryland.gov/pophealth/Documents/SHIP/SHIP%20Lite%20Data%20Details/Cancer%20Mortality%20Rate.pdf"/> Link to Data Details

This dataset contains Cancer Incidence data for Lung Cancer (All Stages^) including: Age-Adjusted Rate, Confidence Interval, Average Annual Count, and Trend field information for US States for the average 5 year span from 2016 to 2020.Data are segmented by sex (Both Sexes, Male, and Female) and age (All Ages, Ages Under 50, Ages 50 & Over, Ages Under 65, and Ages 65 & Over), with field names and aliases describing the sex and age group tabulated.For more information, visit statecancerprofiles.cancer.govData NotationsState Cancer Registries may provide more current or more local data.TrendRising when 95% confidence interval of average annual percent change is above 0.Stable when 95% confidence interval of average annual percent change includes 0.Falling when 95% confidence interval of average annual percent change is below 0.† Incidence rates (cases per 100,000 population per year) are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84, 85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Rates calculated using SEER*Stat. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used for SEER and NPCR incidence rates.‡ Incidence Trend data come from different sources. Due to different years of data availability, most of the trends are AAPCs based on APCs but some are APCs calculated in SEER*Stat. Please refer to the source for each area for additional information.Rates and trends are computed using different standards for malignancy. For more information see malignant.^ All Stages refers to any stage in the Surveillance, Epidemiology, and End Results (SEER) summary stage.Data Source Field Key(1) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(5) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(6) Source: National Program of Cancer Registries SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention (based on the 2022 submission).(7) Source: SEER November 2022 submission.(8) Source: Incidence data provided by the SEER Program. AAPCs are calculated by the Joinpoint Regression Program and are based on APCs. Data are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84,85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used with SEER November 2022 data.Some data are not available, see Data Not Available for combinations of geography, cancer site, age, and race/ethnicity.Data for the United States does not include data from Nevada.Data for the United States does not include Puerto Rico.

BackgroundDelays in time to treatment initiation (TTI) for new cancer diagnoses cause patient distress and may adversely affect outcomes. We investigated trends in TTI for common solid tumors treated with curative intent, determinants of increased TTI and association with overall survival.Methods and findingsWe utilized prospective data from the National Cancer Database for newly diagnosed United States patients with early-stage breast, prostate, lung, colorectal, renal and pancreas cancers from 2004–13. TTI was defined as days from diagnosis to first treatment (surgery, systemic or radiation therapy). Negative binomial regression and Cox proportional hazard models were used for analysis. The study population of 3,672,561 patients included breast (N = 1,368,024), prostate (N = 944,246), colorectal (N = 662,094), non-small cell lung (N = 363,863), renal (N = 262,915) and pancreas (N = 71,419) cancers. Median TTI increased from 21 to 29 days (P

This project presents a dataset that is assembled from multiple sources between 2013 and 2017, including contaminants in drinking water, cancer incidence rates, public perception of the relationship between water contaminants and cancer on Twitter, and census data covering the population living in the United States. The units of analysis are 3,219 counties and 33,144 zip codes. The users of this dataset can address model-driven questions regarding water contaminants and cancer incidence rates in a geographic area, as well as how water contaminant levels and cancer incidence rates are directly and indirectly influenced by local population, economic, and social characteristics.

This is the Google Search interest data that powers the Visualisation Searching For Health. Google Trends data allows us to see what people are searching for at a very local level. This visualization tracks the top searches for common health issues in the United States, from Cancer to Diabetes, and compares them with the actual location of occurrences for those same health conditions to understand how search data reflects life for millions of Americans.

How does search interest for top health issues change over time? From 2004–2017, the data shows that search interest gradually increased over the past few years. Certain regions show a more significant increase in search interest than others. The increase in search activity is greatest in the Midwest and Northeast, while the changes are noticeably less dramatic in California, Texas, and Idaho. Are people generally becoming more aware of health conditions and health risks?

The search interest data was collected using the Google Trends API. The visualisation also brings in incidences of each condition so they can be compared. The health conditions were hand-selected from the Community Health Status Indicators (CHSI) which provides key indicators for local communities in the United States. The CHSI dataset includes more than 200 measures for each of the 3,141 United States counties. More information about the CHSI can be found on healthdata.gov.

Many striking similarities exist between searches and actual conditions—but the relationship between the Obesity and Diabetes maps stands out the most. “There are many risk factors for type 2 diabetes such as age, race, pregnancy, stress, certain medications, genetics or family history, high cholesterol and obesity. However, the single best predictor of type 2 diabetes is overweight or obesity. Almost 90% of people living with type 2 diabetes are overweight or have obesity. People who are overweight or have obesity have added pressure on their body's ability to use insulin to properly control blood sugar levels, and are therefore more likely to develop diabetes.” —Obesity Society via obesity.org

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

Leading Causes of Death US

1980–2009 by Sex, Race, and Hispanic Origin

By Health [source]

About this dataset

This fascinating dataset takes a look at the leading causes of death in the United States from 1980-2009, broken down by sex, race, and Hispanic origin. This data sheds light on how mortality in the US has changed over time among these categories. Accounting for everything from heart disease to cancer to suicide, this insight can be used by health researchers and policy makers to gain a better understanding of disparities in healthcare and deaths across different groups. Whether studying questions related to public health or more targeted population issues such as gender biases in death rates, this dataset provides an important resource for anyone interested in examining mortality across demographic lines

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How to use the dataset

This dataset can be used to explore some of the leading causes of death in the United States from 1980 to 2009, broken down by sex, race, and Hispanic origin. This data can be used to better understand mortality trends and risk factors associated with different populations in America.

By using this dataset you can compare and contrast mortality rates across different gender, racial, and ethnic groups during this time period. You can also compare different causes of death within these demographic categories to see if there are any patterns over time or notable differences between groups.

You could even use this data to track changes across population groups as a whole or look at details for specific years or types of causes of death in particular groups. With this information one may gain insight into health disparities across population segments in America— aiding advocates for social change & public policy shifts toward improved health outcomes for all Americans!

Research Ideas

• Analyzing regional or state-level differences in mortality rates over time.
• Examining the beahvioral factors or risk factors associated with each cause of death for different genders and populations.
• Examining the prevalence of each cause of death as a proportion to an overall population trend in different socio-economic categories such as race or income level

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Selected_Trend_Table_from_Health_United_States_2011._Leading_causes_of_death_and_numbers_of_deaths_by_sex_race_and_Hispanic_origin_United_States_1980_and_2009.csv | Column name | Description | |:-------------------|:---------------------------------------------------------------------------------------------------------| | Group | The group of people the cause of death applies to (e.g. men, women, whites, blacks, hispanics). (String) | | Year | The year the cause of death was recorded. (Integer) | | Cause of death | The cause of death. (String) | | Flag | A flag indicating whether the cause of death is considered a leading cause. (Boolean) | | Deaths | The number of deaths attributed to the cause of death. (Integer) |

Acknowledgements

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

ObjectiveTriple negative breast cancer (TNBC) is a more aggressive subtype resistant to conventional treatments with a poorer prognosis. This study was to update the status of TNBC and the temporal changes of its incidence rate in the US.MethodsWomen diagnosed with breast cancer during 2011–2019 were obtained from the National Program of Cancer Registries (NPCR) and Surveillance, Epidemiology and End Results (SEER) Program SEER*Stat Database which covers the entire population of the US. The TNBC incidence and its temporal trends by race, age, region (state) and disease stage were determined during the period.ResultsA total of 238,848 (or 8.8%) TNBC women were diagnosed during the study period. TNBC occurred disproportionally higher in women of Non-Hispanic Black, younger ages, with cancer at a distant stage or poorly/undifferentiated. The age adjusted incidence rate (AAIR) for TNBC in all races decreased from 14.8 per 100,000 in 2011 to 14.0 in 2019 (annual percentage change (APC) = −0.6, P = 0.024). Incidence rates of TNBC significantly decreased with APCs of −0.8 in Non-Hispanic White women, −1.3 in West and −0.7 in Northeastern regions. Women with TNBC at the age of 35–49, 50–59, and 60–69 years, and the disease at the regional stage displayed significantly decreased trends. Among state levels, Mississippi (20.6) and Louisiana (18.9) had the highest, while Utah (9.1) and Montana (9.6) had the lowest AAIRs in 2019. New Hampshire and Indiana had significant and highest decreases, while Louisiana and Arkansas had significant and largest increases in AAIR. In individual races, TNBC displayed disparities in temporal trends among age groups, regions and disease stages. Surprisingly, Non-Hispanic White and Hispanic TNBC women (0–34 years), and Non-Hispanic Black women (≥70 years) during the entire period, as well as Asian or Pacific Islander women in the South region had increased trends between 2011 and 2017.ConclusionOur study demonstrates an overall decreased trend of TNBC incidence in the past decade. Its incidence displayed disparities among races, age groups, regions and disease stages. Special attention is needed for a heavy burden in Non-Hispanic Black and increased trends in certain groups.

This is a linked dataset between drinking water data and cancer data. Drinking Water Data: County-level concentrations of arsenic from CWSs between 2000 and 2010 were collected from the Center for Disease Control and Prevention’s (CDC) National Environmental Public Health Tracking Network (NEPHTN) (Centers for Disease Control and Prevention, 2018a). Annual mean drinking water arsenic concentrations from 2000 to 2010 were available for a total of 87,662 samples from 75,453 CWS from 26 states, representing 1,425 counties. For samples identified as non-detects, the most frequently reported values were 0.5 ppb and 1 ppb, with a range of 0 ppb to 10 ppb. For non-detect samples reported as zero, the value was substituted with a constant of 0.25 ppb (Almberg et al., 2017; Bulka et al., 2016). Of the samples that were reported as non-detects, 10.87% were reported as zeros. Cancer Data: County-level cancer counts and incidence rates for bladder, colorectal, and kidney cancers were acquired from the National Cancer Institute (NCI) and CDC’s State Cancer Profiles for 2011 through 2015 for adults (age ≥ 50) to match the counties with exposure data (National Cancer Institute and Centers for Disease Control and Prevention, 2018a). We utilized the time period 2011-2015 to provide a lag following the exposure period of 2000-2010. The State Cancer Profiles provide age-adjusted county-level cancer incidence, prevalence, mortality rates and average annual counts for 20 different types of cancers and select demographics (National Cancer Institute and Centers for Disease Control and Prevention, 2018b). Counties where there were less than 16 reported cases in a specific county, sex, and/or race category were suppressed to ensure confidentiality and stability of rate estimates (National Cancer Institute and Centers for Disease Control and Prevention, 2018a). This dataset is associated with the following publication: Krajewski, A., M. Jimenez, K. Rappazzo, D. Lobdell, and J. Jagai. Aggregated Cumulative County Arsenic in Drinking Water and Associations with Bladder, Colorectal, and Kidney Cancers, Accounting for Population Served. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 31(6): 979-989, (2021).

To investigate the global incidence of prostate cancer with special attention to the changing age structures. Data regarding the cancer incidence and population statistics were retrieved from the International Agency for Research on Cancer in World Health Organization. Eight developing and developed jurisdictions in Asia and the Western countries were selected for global comparison. Time series were constructed based on the cancer incidence rates from 1988 to 2007. The incidence rate of the population aged ≥ 65 was adjusted by the increasing proportion of elderly population, and was defined as the “aging-adjusted incidence rate”. Cancer incidence and population were then projected to 2030. The aging-adjusted incidence rates of prostate cancer in Asia (Hong Kong, Japan and China) and the developing Western countries (Costa Rica and Croatia) had increased progressively with time. In the developed Western countries (the United States, the United Kingdom and Sweden), we observed initial increases in the aging-adjusted incidence rates of prostate cancer, which then gradually plateaued and even decreased with time. Projections showed that the aging-adjusted incidence rates of prostate cancer in Asia and the developing Western countries were expected to increase in much larger extents than the developed Western countries.

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated 8/14/2024. Definition of "All Cancer Sites": ICD-O-3 Topography (Site) Codes C00.0 – C80.9 with histology codes including all invasive cancers of all sites except basal and squamous cell skin cancers, and in situ cancer cases of the urinary bladder. Rates are per 100,000 population and are age-adjusted to 2000 U.S. standard population. Rates based on case counts of 1-15 are suppressed per DHMH/MCR Data Use Policy and Procedures.

Source: Single age and year state specific mortality data for cancer and all causes of death from the National Center for Health Statistics (NCHS), and respective populations from the US Census Bureau, are available for calendar years 1969-2007 from SEER*Stat. The population projections were obtained from the US Census Bureau.
Comments: Methodoloy: Cancer prevalence estimates are derived from state specific cancer mortality and survival data using a statistical package called MIAMOD (Mortality-Incidence Analysis MODEL). Cancer survival models are derived from the Surveillance, Epidemiology, and End Results (SEER) Program data and adjusted to represent state specific survival.
Subject: Methodoloy: Cancer prevalence estimates are derived from state specific cancer mortality and survival data.

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Demographic Summary of Available Imaging

Characteristic	Value (N = 26254)
Age (years)	Mean ± SD: 61.4± 5 Median (IQR): 60 (57-65) Range: 43-75
Sex	Male: 15512 (59%) Female: 10742 (41%)
Race	White: 23969 (91.3%) Black: 1135 (4.3%) Asian: 547 (2.1%) American Indian/Alaska Native: 88 (0.3%) Native Hawaiian/Other Pacific Islander: 87 (0.3%) Unknown: 428 (1.6%)
Ethnicity	Not Available

Background: The aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. The advent of low-dose helical computed tomography (CT) altered the landscape of lung-cancer screening, with studies indicating that low-dose CT detects many tumors at early stages. The National Lung Screening Trial (NLST) was conducted to determine whether screening with low-dose CT could reduce mortality from lung cancer.

Methods: From August 2002 through April 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 U.S. medical centers. Participants were randomly assigned to undergo three annual screenings with either low-dose CT (26,722 participants) or single-view posteroanterior chest radiography (26,732). Data were collected on cases of lung cancer and deaths from lung cancer that occurred through December 31, 2009. This dataset includes the low-dose CT scans from 26,254 of these subjects, as well as digitized histopathology images from 451 subjects.

Results: The rate of adherence to screening was more than 90%. The rate of positive screening tests was 24.2% with low-dose CT and 6.9% with radiography over all three rounds. A total of 96.4% of the positive screening results in the low-dose CT group and 94.5% in the radiography group were false positive results. The incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose CT group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [CI], 1.03 to 1.23). There were 247 deaths from lung cancer per 100,000 person-years in the low-dose CT group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose CT screening of 20.0% (95% CI, 6.8 to 26.7; P=0.004). The rate of death from any cause was reduced in the low-dose CT group, as compared with the radiography group, by 6.7% (95% CI, 1.2 to 13.6; P=0.02).

Conclusions: Screening with the use of low-dose CT reduces mortality from lung cancer. (Funded by the National Cancer Institute; National Lung Screening Trial ClinicalTrials.gov number, NCT00047385).

Data Availability: A summary of the National Lung Screening Trial and its available datasets are provided on the Cancer Data Access System (CDAS). CDAS is maintained by Information Management System (IMS), contracted by the National Cancer Institute (NCI) as keepers and statistical analyzers of the NLST trial data. The full clinical data set from NLST is available through CDAS. Users of TCIA can download without restriction a publicly distributable subset of that clinical data, along with the CT and Histopathology images collected during the trial. (These previously were restricted.)

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

This dataset presents the footprint of cancer incidence data in Australia for all cancers combined, and six selected cancers (female breast cancer, colorectal cancer, cervical cancer, lung cancer, melanoma of the skin, and prostate cancer) with their respective ICD-10 codes. The data spans the years 2009 to 2013 and is aggregated to the 2015 Primary Health Network (PHN) geographic areas based on the 2011 Australian Statistical Geography Standard (ASGS). The source of the incidence data is the 2014 Australian Cancer Database (ACD). The ACD is compiled by the Australian Institute of Health and Wellbeing (AIHW) from data provided by the state and territory population-based cancer registries. For further information about this dataset, please visit: AIHW - Cancer Incidence and Mortality in Australia Data Tables 2014 Australian Cancer Database Data Quality Statement Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. Where records are null, data was not publishable because of small numbers, confidentiality or other concerns about the quality of the data.