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

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 Tropic of Cancer lies at 23d 26' 22" (23.4394 degrees) north of the Equator and marks the most northerly latitude at which the sun can appear directly overhead at noon. This event occurs at the June solstice, when the northern hemisphere is tilted towards the sun to its maximum extent. The Earth's tropical zone ("the tropics") includes everything between the Tropic of Cancer and the Tropic of Capricorn.

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.

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.

This dataset contains model-based county-level estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2021 or 2020 data, Census Bureau 2021 or 2020 county population estimates, and American Community Survey (ACS) 2017–2021 or 2016–2020 estimates. The 2023 release uses 2021 BRFSS data for 29 measures and 2020 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours) that the survey collects data on every other year. These data can be joined with the census 2020 county boundary file in a GIS system to produce maps for 36 measures at the county level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

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

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

The PLACES (Population Level Analysis and Community Estimates) is an expansion of the original 500 Cities project and is a collaboration between the CDC, the Robert Wood Johnson Foundation (RWJF), and the CDC Foundation (CDCF). The original 500 Cities Project provided city- and census tract-level estimates for chronic disease risk factors (5), health outcomes (13), and clinical preventive services use (9) for the 500 largest US cities. The PLACES Project extends these estimates to all counties, places (incorporated and census designated places), census tracts and ZIP Code Tabulation Areas (ZCTA) across the United States. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Data sources used to generate these measures include BRFSS data (2018 or 2017), Census Bureau 2010 census population data or annual population estimates for county vintage 2018 or 2017, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates.The health outcomes include arthritis, current asthma, high blood pressure, cancer (excluding skin cancer), high cholesterol, chronic kidney disease, chronic obstructive pulmonary disease (COPD), coronary heart disease, diagnosed diabetes, mental health not good for >=14 days, physical health not good for >=14 days, all teeth lost and stroke.The preventive services uses include lack of health insurance, visits to doctor for routine checkup, visits to dentist, taking medicine for high blood pressure control, cholesterol screening, mammography use for women, cervical cancer screening for women, colon cancer screening, and core preventive services use for older adults (men and women).The unhealthy behaviors include binge drinking, current smoking, obesity, physical inactivity, and sleeping less than 7 hours.For more information about the methodology, visit https://www.cdc.gov/places or contact places@cdc.gov.CDC's source webpage.CDC's feature service.

BackgroundIt is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods.Methodology/Principal FindingsUsing melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer.Conclusions/SignificanceWe thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-througput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases.

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.

All mortality data come from the Indicator-Based Information System for Public Health Web site: http://ibis.health.state.nm.usOriginal sources: the New Mexico Death Certificate Database, Office of Vital Records and Statistics, New Mexico Department of Health; with Population (denominator) Estimates from the University of New Mexico, Geospatial and Population Studies (GPS) Program, http://bber.unm.edu/bber_research_demPop.html. See US trends at Age-Adjusted Death Rates for Heart Disease and Cancer, by Sex — United States, 1980–2011

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer-related deaths in the United States. The purpose of this study was to evaluate the gene expression differences in different stages of CRC. Gene expression data on 433 CRC patient samples were obtained from The Cancer Genome Atlas (TCGA). Gene expression differences were evaluated across CRC stages using linear regression. Genes with p≤0.001 in expression differences were evaluated further in principal component analysis and genes with p≤0.0001 were evaluated further in gene set enrichment analysis. A total of 377 patients with gene expression data in 20,532 genes were included in the final analysis. The numbers of patients in stage I through IV were 59, 147, 116 and 55, respectively. NEK4 gene, which encodes for NIMA related kinase 4, was differentially expressed across the four stages of CRC. The stage I patients had the highest expression of NEK4 genes, while the stage IV patients had the lowest expressions (p = 9*10−6). Ten other genes (RNF34, HIST3H2BB, NUDT6, LRCh4, GLB1L, HIST2H4A, TMEM79, AMIGO2, C20orf135 and SPSB3) had p value of 0.0001 in the differential expression analysis. Principal component analysis indicated that the patients from the 4 clinical stages do not appear to have distinct gene expression pattern. Network-based and pathway-based gene set enrichment analyses showed that these 11 genes map to multiple pathways such as meiotic synapsis and packaging of telomere ends, etc. Ten of these 11 genes were linked to Gene Ontology terms such as nucleosome, DNA packaging complex and protein-DNA interactions. The protein complex-based gene set analysis showed that four genes were involved in H2AX complex II. This study identified a small number of genes that might be associated with clinical stages of CRC. Our analysis was not able to find a molecular basis for the current clinical staging for CRC based on the gene expression patterns.

Comparitive study of classification models on the ISIC 2018 dataset.

Categorical segregation of predictions made on our test data.

Comparative analysis between different explainability techniques.