Cancer was responsible for around *** deaths per 100,000 population in the United States in 2023. The death rate for cancer has steadily decreased since the 1990’s, but cancer still remains the second leading cause of death in the United States. The deadliest type of cancer for both men and women is cancer of the lung and bronchus which will account for an estimated ****** deaths among men alone in 2025. Probability of surviving Survival rates for cancer vary significantly depending on the type of cancer. The cancers with the highest rates of survival include cancers of the thyroid, prostate, and testis, with five-year survival rates as high as ** percent for thyroid cancer. The cancers with the lowest five-year survival rates include cancers of the pancreas, liver, and esophagus. Risk factors It is difficult to determine why one person develops cancer while another does not, but certain risk factors have been shown to increase a person’s chance of developing cancer. For example, cigarette smoking has been proven to increase the risk of developing various cancers. In fact, around ** percent of cancers of the lung, bronchus and trachea among adults aged 30 years and older can be attributed to cigarette smoking. Other modifiable risk factors for cancer include being obese, drinking alcohol, and sun exposure.

You can see the numbers by sex, age, race and ethnicity, trends over time, survival, and prevalence.Link: https://gis.cdc.gov/Cancer/USCS/#/AtAGlance

Medical Service Study Areas (MSSAs)As defined by California's Office of Statewide Health Planning and Development (OSHPD) in 2013, "MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data" (Source). Each census tract in CA is assigned to a given MSSA. The most recent MSSA dataset (2014) was used. Spatial data are available via OSHPD at the California Open Data Portal. This information may be useful in studying health equity.Age-Adjusted Incidence Rate (AAIR)Age-adjustment is a statistical method that allows comparisons of incidence rates to be made between populations with different age distributions. This is important since the incidence of most cancers increases with age. An age-adjusted cancer incidence (or death) rate is defined as the number of new cancers (or deaths) per 100,000 population that would occur in a certain period of time if that population had a 'standard' age distribution. In the California Health Maps, incidence rates are age-adjusted using the U.S. 2000 Standard Population.Cancer incidence ratesIncidence rates were calculated using case counts from the California Cancer Registry. Population data from 2010 Census and SEER 2015 census tract estimates by race/origin (controlling to Vintage 2015) were used to estimate population denominators. Yearly SEER 2015 census tract estimates by race/origin (controlling to Vintage 2015) were used to estimate population denominators for 5-year incidence rates (2013-2017)According to California Department of Public Health guidelines, cancer incidence rates cannot be reported if based on <15 cancer cases and/or a population <10,000 to ensure confidentiality and stable statistical rates.Spatial extent: CaliforniaSpatial Unit: MSSACreated: n/aUpdated: n/aSource: California Health MapsContact Email: gbacr@ucsf.eduSource Link: https://www.californiahealthmaps.org/?areatype=mssa&address=&sex=Both&site=AllSite&race=&year=05yr&overlays=none&choropleth=Obesity

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 State Cancer Profiles (SCP) web site provides statistics to help guide and prioritize cancer control activities at the state and local levels. SCP is a collaborative effort using local and national level cancer data from the Centers for Disease Control and Prevention's National Program of Cancer Registries (NPCR) and National Cancer Institute's Surveillance, Epidemiology and End Results Registries (SEER). SCP address select types of cancer and select behavioral risk factors for which there are evidence-based control interventions. The site provides incidence, mortality and prevalence comparison tables as well as interactive graphs and maps and support data. The graphs and maps provide visual support for deciding where to focus cancer control efforts.

Medical Service Study Areas (MSSAs)As defined by California's Office of Statewide Health Planning and Development (OSHPD) in 2013, "MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data" (Source). Each census tract in CA is assigned to a given MSSA. The most recent MSSA dataset (2014) was used. Spatial data are available via OSHPD at the California Open Data Portal. This information may be useful in studying health equity.Age-Adjusted Incidence Rate (AAIR)Age-adjustment is a statistical method that allows comparisons of incidence rates to be made between populations with different age distributions. This is important since the incidence of most cancers increases with age. An age-adjusted cancer incidence (or death) rate is defined as the number of new cancers (or deaths) per 100,000 population that would occur in a certain period of time if that population had a 'standard' age distribution. In the California Health Maps, incidence rates are age-adjusted using the U.S. 2000 Standard Population.Cancer incidence ratesIncidence rates were calculated using case counts from the California Cancer Registry. Population data from 2010 Census and SEER 2015 census tract estimates by race/origin (controlling to Vintage 2015) were used to estimate population denominators. Yearly SEER 2015 census tract estimates by race/origin (controlling to Vintage 2015) were used to estimate population denominators for 5-year incidence rates (2013-2017)According to California Department of Public Health guidelines, cancer incidence rates cannot be reported if based on <15 cancer cases and/or a population <10,000 to ensure confidentiality and stable statistical rates.Spatial extent: CaliforniaSpatial Unit: MSSACreated: n/aUpdated: n/aSource: California Health MapsContact Email: gbacr@ucsf.eduSource Link: https://www.californiahealthmaps.org/?areatype=mssa&address=&sex=Both&site=AllSite&race=&year=05yr&overlays=none&choropleth=Obesity

Radon is an odorless and invisible radioactive gas that is naturally released from rocks, soil, and water. According to the U.S. Environmental Protection Agency (EPA) and the U.S. Surgeon General, radon is the second leading cause of lung cancer in the United States. Recent epidemiological studies have also identified linkages between radon exposure and cerebrovascular diseases including stroke. The Indoor Radon Abatement Act of 1988 (Public Law 100-551) directed the EPA to identify areas of the United States that have the potential to produce harmful levels of indoor radon, based on geological data and on indoor radon levels in homes and other structures. As part of this effort, the U.S. Geological Survey (USGS) prepared radon potential estimates for the United States, based on the Radon Index (RI), a composite score derived from the semi-quantitative ranking of five factors: geology, soil permeability, aerial gamma radioactivity, home architecture, and screening indoor radon data. The RI scores were grouped into three geologic radon potential (GRP) zones for compatibility with EPA's "Map of Radon Zones". The GRP maps were originally released as page-sized maps for each state in USGS Open-File Report 93-292. These maps were digitized and merged into a national-scale GIS database.

This map service portrays the number of deaths per 100,000 people per square mile from lung and colon cancer. It displays the distribution of lung and colon cancer across the United States. Pop-ups show attributes such as state name, county name, number of colon or lung cancer deaths, and square miles per area.Lung Cancer: Death due to malignant neoplasm of the trachea, bronchus and lung.Colon Cancer: Death due to malignant neoplasm of the colon, rectum and anus.This data was sourced from: Community Health Status Indicators_Other Health Datapalooza focused content that may interest you: Health Datapalooza Health Datapalooza

T1WI and T2WI = T1- and T2-weighted images, TR = repetition time, TE = echo time, msec = millisecond, FOV = field of view, DWI = diffusion weighted images, ADC = apparent diffusion coefficient, DCE = dynamic contrast enhanced, Ktrans = transfer constant.* The fitting of concentration versus time curves was performed based on theoretical models by Tofts. Perfusion-related parameters including Ktrans were derived by the curves [18]. We used commercial software (Tissue4D; Siemens healthcare, Erlangen, Germany) in the construction of perfusion map images.MR protocol of multiparametric MRI of the prostate who underwent contrast-enhanced US guided biopsy.

Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%) in United States was reported at 13.7 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

This map service displays all air-related layers used in the USEPA Community/Tribal-Focused Exposure and Risk Screening Tool (C/T-FERST) mapping application (http://cfpub.epa.gov/cferst/index.cfm). The following data sources (and layers) are contained in this service: USEPA's 2005 National-Scale Air Toxic Assessment (NATA) data. Data are shown at the census tract level (2000 census tract boundaries, US Census Bureau) for Cumulative Cancer and Non-Cancer risks (Neurological and Respiratory) from 139 air toxics. In addition, individual pollutant estimates of Ambient Concentration, Exposure Concentration, Cancer, and Non-Cancer risks (Neurological and Respiratory) are provided for: Acetaldehyde, Acrolein, Arsenic, Benzene, 1,3-Butadiene, Chromium, Diesel PM, Formaldehyde, Lead, Naphthalene, and Polycyclic Aromatic Hydrocarbon (PAH). The original Access tables were downloaded from USEPA's Office of Air and Radiation (OAR) http://www.epa.gov/ttn/atw/nata2005/tables.html. The data classification (defined interval) for this map service was developed for USEPA's Office of Research and Development's (ORD) Community-Focused Exposure and Risk Screening Tool (C-FERST) per guidance provided by OAR. The 2005 NATA provides information on 177 of the 187 Clean Air Act air toxics (http://www.epa.gov/ttn/atw/nata2005/05pdf/2005polls.pdf) plus diesel particulate matter (diesel PM was assessed for non-cancer only). For additional information about NATA, go to http://www.epa.gov/ttn/atw/nata2005/05pdf/nata_tmd.pdf or contact Ted Palma, USEPA (palma.ted@epa.gov). NATA data disclaimer: USEPA strongly cautions that these modeling results are most meaningful when viewed at the state or national level, and should not be used to draw conclusions about local exposures or risks (e.g., to compare local areas, to identify the exact location of "hot spots", or to revise or design emission reduction programs). Substantial uncertainties with the input data for these models may cause the results to misrepresent actual risks, especially at the census tract level. However, we believe the census tract data and maps can provide a useful approximation of geographic patterns of variation in risk within counties. For example, a cluster of census tracts with higher estimated risks may suggest the existence of a "hot spot," although the specific tracts affected will be uncertain. More refined assessments based on additional data and analysis would be needed to better characterize such risks at the tract level. (http://www.epa.gov/ttn/atw/nata2005/countyxls/cancer_risk02_county_042009.xls). Note that these modeled estimates are derived from outdoor sources only; indoor sources are not included in these examples, but may be significant in some cases. The modeled exposure estimates are for a median individual in the geographic area shown. Note that in some cases the estimated relationship between human exposure and health effect may be calculated as a high end estimate, and thus may be more likely to overestimate than underestimate actual health effects for the median individual in the geographic area shown. Other limitations to consider when looking at the results are detailed on the EPA 2005 NATA website. For these reasons, the NATA maps included in C-FERST are provided for screening purposes only. See the 2005 National Air Toxic Assessment website for recommended usage and limitations on the estimated cancer and noncancer data provided above. USEPA's NonAttainment areas data. C-FERST displays Ozone for 8-hour Ozone based on the 1997 standard for reporting and Particulate Matter PM-2.5 based on the 2006 standard for reporting. These are areas of the country where air pollution levels consistently exceed the national ambient air quality standards. Details about the USEPA's NonAttainment data are available at http://www.epa.gov/airquality/greenbook/index.html. Center of Disease Control's (CDC) Environmental Public Health Tracking (EPHT) data. Averaged over three years (2004 - 2006). The USEPA's ORD calculated a three-year average (2004 - 2006) using the values for Ozone (number of days with the maximum 8-hour average above the National Ambient Air Quality Standards (NAAQS)) and PM 2.5 (annual ambient concentration). These data were extracted by the CDC from the USEPA's ambient air monitors and are displayed at the county level. USEPA received the Monitor and Modeled data from the CDC and calculated the three year average displayed in the web service. For more details about the CDC EPHT data, go to http://ephtracking.cdc.gov/showHome.action.

Radon, a naturally occurring radioactive gas, is the second leading cause of lung cancer after tobacco smoke and the leading cause of lung cancer in nonsmokers in the United States. Radon is an under-recognized health concern in Alaska. This online map serves as a guide to where radon may occur; however, indoor radon concentrations can vary greatly from building to building. The only way to know if your home contains radon is to test. More information is available at http://dggs.alaska.gov/hazards/radon.html.The State of Alaska, Division of Geological & Geophysical Surveys is growing an Alaska Radon Database that contains radon test results from buildings located in communities all over the State. This map contains four layers of hexagons displaying test-result statistics at the scales of 1,024, 256, 64, and 16 square kilometers. These layers display in turn as you zoom into the map. Hexagons are colored based on the average (mean) of the indoor-air radon test results that are located in each hexagon. Where multiple test results are available at an individual test location (building), the maximum value is used. As of September 30, 2019, test results were available for 1,737 individual buildings statewide. Hexagons containing fewer than 10 results are labelled with “LC”, meaning “Low Count”. Hexagons may be selected to show additional statistics.The map also contains a radon-potential layer modeled from available test results and three statewide datasets, uranium in soils and sediments, depth to water table (and permafrost), and geology. More information about this layer will be available in forthcoming metadata. This map should not be used to determine whether to test your home. No matter where you live, test your home for radon. Buildings located in any radon-potential zone may concentrate radon gas, leading to significant levels of indoor radon at that site.This web map is included in the following web app: https://geoportal.dggs.dnr.alaska.gov/portal/home/item.html?id=8ed4e400e2d9460c8cf959deb91ee22b

Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%) in North America was reported at 13.17 % in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. North America - Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Cancer Imaging System Market report cover PEST Analysis, PORTER’s five forces analysis, opportunity map analysis, drivers and restraints impact analysis, and market attractiveness index.

This dataset contains summary data visualizations and clinical data from a broad sampling of 182 esophageal adenocarcinomas.
TCGA Esophageal Carcinoma . Source data from GDAC Firehose. Previously known as TCGA Provisional. The data was gathered as part of the Broad Institute of MIT and Harvard Firehose initiative, a cancer analysis pipeline. his dataset contains summary data visualizations and clinical data from a broad sampling of 186 carcinomas from 185 patients. The clinical data includes mutation count, information about mutated genes, patient demographics, disease status, tumor typing, numbers of samples per patient, Adjuvant Postoperative Pharmaceutical Therapy Administered Indicator, Alcohol Consumption Frequency, Alcohol History Documented, American Joint Committee on Cancer Lymph Node Stage Code, American Joint Committee on Cancer Metastasis Stage Code, American Joint Committee on Cancer Publication Version Type, American Joint Committee on Cancer Tumor Stage Code, Antireflux treatment type, and the presence of Barrett's esophagus. The data set also includes copy-number segment data downloadable as .seg files and viewable via the Integrative Genomics Viewer. The dataset includes Next-Generation Clustered Heat Maps (NG-CHM) viewable via an embedded NG-CHM Heat Map Viewer, provided my MD Anderson Cancer Center, which provides a graphical environment for exploration of clustered or non-clustered heat map data. The data set also includes copy-number segment data downloadable as .seg files and viewable via the Integrative Genomics Viewer.

TCGA Testicular Germ Cell Cancer. Source data from GDAC Firehose. Previously known as TCGA Provisional. This dataset contains summary data visualizations and clinical data from a broad sampling of 156 carcinomas from 150 patients. The data was gathered as part of the Broad Institute of MIT and Harvard Firehose initiative, a cancer analysis pipeline. The clinical data includes mutation count, information about mutated genes, patient demographics, sample type, disease code, Adjuvant Postoperative Pharmaceutical Therapy Administered Indicator, American Joint Committee on Cancer Lymph Node Stage Code, American Joint Committee on Cancer Lymph Node Stage Code.1, American Joint Committee on Cancer Metastasis Stage Code, American Joint Committee on Cancer Publication Version Type, American Joint Committee on Cancer Tumor Stage Code, Bilateral Diagnosis Timing Type, Cause of death source, and Days to bilateral tumor dx. The dataset includes Next-Generation Clustered Heat Maps (NG-CHM) viewable via an embedded NG-CHM Heat Map Viewer, provided my MD Anderson Cancer Center, which provides a graphical environment for exploration of clustered or non-clustered heat map data. The data set also includes copy-number segment data downloadable as .seg files and viewable via the Integrative Genomics Viewer.

United States - Producer Price Index by Industry: Pharmaceutical Preparation Manufacturing: Cancer Therapy Products was 1001.14000 Index Jun 1981=100 in June of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Industry: Pharmaceutical Preparation Manufacturing: Cancer Therapy Products reached a record high of 1001.14000 in January of 2025 and a record low of 100.00000 in June of 1981. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Industry: Pharmaceutical Preparation Manufacturing: Cancer Therapy Products - last updated from the United States Federal Reserve on August of 2025.

United States - Producer Price Index by Commodity: Chemicals and Allied Products: Cancer Therapy Products was 124.06200 Index Dec 2009=100 in July of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Chemicals and Allied Products: Cancer Therapy Products reached a record high of 124.06200 in July of 2025 and a record low of 86.00000 in August of 2013. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Chemicals and Allied Products: Cancer Therapy Products - last updated from the United States Federal Reserve on August of 2025.

MMWR Surveillance Summary 66 (No. SS-1):1-8 found that nonmetropolitan areas have significant numbers of potentially excess deaths from the five leading causes of death. These figures accompany this report by presenting information on potentially excess deaths in nonmetropolitan and metropolitan areas at the state level. They also add additional years of data and options for selecting different age ranges and benchmarks. Potentially excess deaths are defined in MMWR Surveillance Summary 66(No. SS-1):1-8 as deaths that exceed the numbers that would be expected if the death rates of states with the lowest rates (benchmarks) occurred across all states. They are calculated by subtracting expected deaths for specific benchmarks from observed deaths. Not all potentially excess deaths can be prevented; some areas might have characteristics that predispose them to higher rates of death. However, many potentially excess deaths might represent deaths that could be prevented through improved public health programs that support healthier behaviors and neighborhoods or better access to health care services. Mortality data for U.S. residents come from the National Vital Statistics System. Estimates based on fewer than 10 observed deaths are not shown and shaded yellow on the map. Underlying cause of death is based on the International Classification of Diseases, 10th Revision (ICD-10) Heart disease (I00-I09, I11, I13, and I20–I51) Cancer (C00–C97) Unintentional injury (V01–X59 and Y85–Y86) Chronic lower respiratory disease (J40–J47) Stroke (I60–I69) Locality (nonmetropolitan vs. metropolitan) is based on the Office of Management and Budget’s 2013 county-based classification scheme. Benchmarks are based on the three states with the lowest age and cause-specific mortality rates. Potentially excess deaths for each state are calculated by subtracting deaths at the benchmark rates (expected deaths) from observed deaths. Users can explore three benchmarks: “2010 Fixed” is a fixed benchmark based on the best performing States in 2010. “2005 Fixed” is a fixed benchmark based on the best performing States in 2005. “Floating” is based on the best performing States in each year so change from year to year. 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 Moy E, Garcia MC, Bastian B, Rossen LM, Ingram DD, Faul M, Massetti GM, Thomas CC, Hong Y, Yoon PW, Iademarco MF. Leading Causes of Death in Nonmetropolitan and Metropolitan Areas – United States, 1999-2014. MMWR Surveillance Summary 2017; 66(No. SS-1):1-8. Garcia MC, Faul M, Massetti G, Thomas CC, Hong Y, Bauer UE, Iademarco MF. Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States. MMWR Surveillance Summary 2017; 66(No. SS-2):1–7.

Tn antigen (CD175), recognized as the precursor monosaccharide (α-GalNAc) of mucin O-glycan, is a well-known tumor-associated carbohydrate antigen (TACA). It has emerged as a potential biomarker for cancer diagnosis and prognosis. However, the role it plays in cancer biology remains elusive due to the absence of a sensitive and selective detection method. In this study, we synthesized two new probes based on a unique uridine-5′-diphospho-α-d-galactose (UDP-Gal) derivative, each functionalized with either a fluorescence or a cleavable biotin tag, to develop an innovative one-step enzymatic labeling strategy, enabling the visualization, enrichment, and site-specific mapping of the Tn antigen with unparalleled sensitivity and specificity. Our versatile strategy has been successfully applied to detect and image Tn antigen across various samples, including the complex cell lysates, live cells, serum, and tissue samples. Compared to the traditional lectin method, this one-step enzymatic method is simpler and more efficient (>10/100-fold in sensitivity). Furthermore, it allowed us to map 454 Tn-glycoproteins and 624 Tn-glycosylation sites from HEK293FTn+ and Jurkat cells. Therefore, our strategy provides an exceptionally promising tool for revealing the biological functions of the Tn antigen and advancing cancer diagnostics.