In the following maps, the U.S. states are divided into groups based on the rates at which people developed or died from cancer in 2013, the most recent year for which incidence data are available.

The rates are the numbers out of 100,000 people who developed or died from cancer each year.

Incidence Rates by State The number of people who get cancer is called cancer incidence. In the United States, the rate of getting cancer varies from state to state.

*Rates are per 100,000 and are age-adjusted to the 2000 U.S. standard population.

‡Rates are not shown if the state did not meet USCS publication criteria or if the state did not submit data to CDC.

†Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2016. Available at: http://www.cdc.gov/uscs.

Death Rates by State Rates of dying from cancer also vary from state to state.

*Rates are per 100,000 and are age-adjusted to the 2000 U.S. standard population.

†Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2016. Available at: http://www.cdc.gov/uscs.

Source: https://www.cdc.gov/cancer/dcpc/data/state.htm

The United States Cancer Statistics (USCS) online databases in WONDER provide cancer incidence and mortality data for the United States for the years since 1999, by year, state and metropolitan areas (MSA), age group, race, ethnicity, sex, childhood cancer classifications and cancer site. Report case counts, deaths, crude and age-adjusted incidence and death rates, and 95% confidence intervals for rates. The USCS data are the official federal statistics on cancer incidence from registries having high-quality data and cancer mortality statistics for 50 states and the District of Columbia. USCS are produced by the Centers for Disease Control and Prevention (CDC) and the National Cancer Institute (NCI), in collaboration with the North American Association of Central Cancer Registries (NAACCR). Mortality data are provided by the Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), National Vital Statistics System (NVSS).

This statistic shows the number of deaths from cardiovascular disease and from cancer in the U.S. in 2016, by age. In that year, there were approximately ****** deaths from cardiovascular disease and ****** deaths from cancer among those aged 44 years or younger.

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 graph displays the death rates for U.S. adults aged from 45 to 64 years due to cancer and heart disease from 1999 to 2017. The highest rate recorded was in the year 1999 for both cancer and heart disease, whereas the lowest rates for cancer and heart disease were recorded in the years 2017 and 2011, respectively. Overall, rates of death for both causes have declined.

It was estimated that in 2025 around ****** men in the United States would die of prostate cancer. This statistic shows the estimated number of cancer deaths among men in the United States in 2009 and 2025, by cancer type.

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information "The United States Cancer Statistics (USCS) are the official federal statistics on cancer incidence from registries having high-quality data and cancer mortality statistics for 50 states and the District of Columbia. USCS are produced by the Centers for Disease Control and Prevention (CDC) and the National Cancer Institute (NCI)." [Quote from: https://wonder.cdc.gov/cancer.htm]>

CDC's Division of Population Health provides cross-cutting set of 124 indicators that were developed by consensus and that allows states and territories and large metropolitan areas to uniformly define, collect, and report chronic disease data that are important to public health practice and available for states, territories and large metropolitan areas. In addition to providing access to state-specific indicator data, the CDI web site serves as a gateway to additional information and data resources.

The graph displays the death rates for women aged 45 to 64 in the U.S. due to cancer and heart disease from 1999 to 2017. The year 1999 saw the highest death rate for both causes. The lowest death rate due to cancer was recorded in 2017, whereas for heart disease it was in the years 2010 and 2011. Overall the death rates for both causes have declined.

BackgroundPrevious studies have demonstrated that patients with a cancer diagnosis have an elevated risk of suicide and cardiovascular death. However, the effects of the diagnosis of multiple primary cancers (MPCs) on the risk of suicide and cardiovascular death remain unclear. This study aimed to identify the risk of suicide and cardiovascular death among patients with MPCs in the United States.MethodsPatients with a single or MPC(s) between 1975 and 2016 were selected from the Surveillance, Epidemiology, and End Results database in a retrospective cohort study. Mortality rates and standardized mortality ratios (SMRs) of suicides and cardiovascular diseases among patients with MPCs were estimated.ResultsOf the 645,818 patients diagnosed with MPCs included in this analysis, 760 and 36,209 deaths from suicides and cardiovascular diseases were observed, respectively. The suicide and cardiovascular-disease mortality rates were 1.89- (95% CI, 1.76–2.02) and 1.65-times (95% CI, 1.63–1.67), respectively, that of the general population. The cumulative mortality rate from both suicides and cardiovascular diseases among patients with MPCs were significantly higher than those of patients with a single primary cancer (Both p < 0.001). In patients with MPCs diagnosed asynchronously, the cumulative incidence rates of suicides and cardiovascular deaths were higher than those diagnosed synchronously. Among all MPCs, cancers of the pancreas and esophagus had the highest SMRs of suicide (5.98 and 5.67, respectively), while acute myeloid leukemia and brain cancer had the highest SMRs of cardiovascular diseases (3.87 and 3.62, respectively). The SMR of suicide was highest within 1 year after diagnosis, while that of cardiovascular diseases was highest 5 years after diagnosis.ConclusionsThis study showed that the mortality rates from suicides and cardiovascular diseases among patients with MPCs were higher than those with a single primary cancer. Therefore, our results underscore the need for psychological assessment and targeted preventive interventions for suicides and cardiovascular diseases among patients with MPCs.

I was interested in investigating cancer incidence levels in the US by looking at how they vary by race or state. All the data is collected online from Centers for Disease Control and Prevention, State Cancer Profiles, and United States Census Bureau. This dataset can be used to answer questions on the correlation between poverty levels, insurance levels and cancer incidence levels. Further, one can find which cancers affect a certain race more or a certain state.

This dataset contains Cancer Incidence data for Breast 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 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.^ 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.

Context

This data comes from aggregation of the tables available on the NIH's National Cancer Institutes State Cancer Profiles, specifically with their incidence tables.

The objective of the State Cancer Profiles Web site is to provide a system to characterize the cancer burden in a standardized manner in order to motivate action, integrate surveillance into cancer control planning, characterize areas and demographic groups, and expose health disparities. The focus is on cancer sites for which there are evidence based control interventions. Interactive graphics and maps provide visual support for deciding where to focus cancer control efforts.

Content

This data has cancer Incidence rates broken down by US County and includes data aggregated from 2012-2016. It has both incidence rates per 100k as well as yearly totals averaged over that period

Potential Future Work

This data is summarized across other potentially illuminating fields. The State Cancer Profiles can be further broken down by cancer area, race/ethnicity, sex, age, and stage. If more fidelity on the data would be helpful please add it to the discussion section and I can work on adding it!

Data Use Restrictions

Read Carefully Before Using

By using these data, you signify your agreement to comply with the following statutorily based requirements.

The Public Health Service Act (42 U.S.C. 242m(d)) provides that the data collected by the National Center for Health Statistics (NCHS) may be used only for the purpose for which they were obtained; any effort to determine the identity of any reported cases, or to use the information for any purpose other than for statistical reporting and analysis, is against the law. The National Program of Cancer Registries (NPCR), Centers for Disease Control and Prevention (CDC), has obtained an assurance of confidentiality pursuant to Section 308(d) of the Public Health Service Act, 42 U.S.C. 242m(d). This assurance provides that identifiable or potentially identifiable data collected by the NPCR may be used only for the purpose for which they were obtained unless the person or establishment from which they were obtained has consented to such use. Any effort to determine the identity of any reported cases, or to use the information for any purpose other than statistical reporting and analysis, is a violation of the assurance.

Therefore users will: - Use the data for statistical reporting and analysis only. - Make no attempt to learn the identity of any person or establishment included in these data. - Make no disclosure or other use of the identity of any person or establishment discovered inadvertently, and advise the appropriate contact for the data provider. In addition to immediately notifying "Contact Us" of the potential disclosure, - For mortality data, notify the Confidentiality Officer at the National Center for Health Statistics (Alvan O. Zarate, Ph.D.), 3311 Toledo Road, Rm 7116, Hyattsville, MD 20782, Phone: 301-458-4601, Fax: 301-458-4021) - For incidence data notify both the Federal agency that provided the data and notify the relevant state or metropolitan area cancer registryExternal Web Site Policy, of any such discovery. - For CDC's National Program of Cancer Registries (NPCR) areas, notify the Associate Director for Science, Office of Science Policy and Technology Transfer, CDC, Mailstop D-50, 1600 Clifton Road, N.E., Atlanta, Georgia, 30333, Phone: 404-639-7240) - For NCI's Surveillance, Epidemiology, and End Results (SEER) Program registry areas, notify the Branch Chief of the Cancer Statistics Branch of the Surveillance Research Program, Division of Cancer Control and Population Sciences, NCI, BG 9609 MSC 9760, 9609 Medical Center Drive, Bethesda, MD 20892-9760, Phone: 301-496-8510, Fax: 301-496-9949.

United States US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data was reported at 11.800 NA in 2016. This records an increase from the previous number of 11.600 NA for 2015. United States US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data is updated yearly, averaging 11.800 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 14.600 NA in 2000 and a record low of 11.600 NA in 2015. United States US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

The United States Cancer Biomarkers Market is Segmented by Disease (Breast Cancer, Lung Cancer, Prostate Cancer, and More), Biomolecule Type (Protein Biomarkers and More), Profiling Technology (Omics Technologies, Imaging Technologies, and More), and End User (Hospitals & Clinics, and More). The Market and Forecasts are Provided in Terms of Value (USD).

This data package contains data on public health indicators, mortality and morbidity. Specifically this accelerator contains mortality and morbidity rates for groups of diseases in the United States by state and county from 1980 to 2014.

What are Cancer Statistics in US States?

The circled group of good survivors has genetic indicators of poor survivors (i.e. low ESR1 levels, which is typically the prognostic indicator of poor outcomes in breast cancer) – understanding this group could be critical for helping improve mortality rates for this disease. Why this group survived was quickly analysed by using the Outcome Column (here Event Death - which is binary - 0,1) as a Data Lens (which we term Supervised vs Unsupervised analyses).

How to use this dataset

• A network was built using only gene expression with 272 breast cancer patients (as rows), and 1570 columns.

• Metadata includes patient info, treatment, and survival.

• Each node is a group of patients similar to each other. Flares (left) represent sub-populations that are distinct from the larger population. (One differentiating factor between the two flares is estrogen expression (low = top flare, high = bottom flare)).

• A bottom flare is a group of patients with 100% survival. The top flare shows a range of survival – very poor towards the tip (red), and very good near the base (circled).

Acknowledgments

When we use this dataset in our research, we credit the authors as :

• License : CC BY 4.0.

• This data set is taken from https://query.data.world/s/yi422lv7mkhnydnt4ixrfujmoaglpk .

The main idea for uploading this dataset is to practice data analysis with my students, as I am working in college and want my student to train our studying ideas in a big dataset, It may be not up to date and I mention the collecting years, but it is a good resource of data to practice

In 2023, there were **** deaths from breast cancer per 100,000 population in the state of South Dakota, the lowest of any state that year. This statistic shows the death rate from breast cancer in the U.S. in 2023, by state.

ObjectiveBreast cancer (BC) is one of the most common cancers globally, placing a significant social burden. This study estimates the BC burden in the U.S. from 1990 to 2021 and projects future trends for the next 15 years.MethodsUsing data from the Global Burden of Disease (GBD) 2021 study, we analyzed four measures: prevalence, incidence, death, and disability-adjusted life years (DALYs), stratified by sex, age, U.S. states, and socio-demographic index (SDI).ResultsBC burden in the U.S. has decreased, with reductions in age-standardized rates of prevalence, incidence, mortality, and DALYs for both sexes. The overall age-standardized prevalence rate dropped from 695.0 (653.5–741.5)/100,000 in 1990 to 556.0 (525.2–584.7)/100,000 in 2021. The ASIR declined from 68.3 (65.1–70.3)/100,000 to 51.7 (48.4–54.1)/100,000. Death rates fell from 15.9 (14.9–16.5)/100,000 to 9.4 (8.5–9.9)/100,000, while DALYs decreased from 485.1 (462.9–507.0)/100,000 to 277.4 (260.1–294.8)/100,000 over the same period. Burden varies by state and SDI: in 2021, low-SDI states, Kentucky and Louisiana had the highest prevalence and incidence, while Louisiana and Mississippi had the highest mortality. Projections suggest a continued downward trend through 2036.ConclusionsBC burden in the U.S. decreased overall, but disparities persist across sex, age groups, and states with varying SDI levels. Addressing risk factors and improving healthcare access are essential to further reduce BC burden.

Abstract

The AURORA US Metastatic Breast Cancer project is funded by the Breast Cancer Research Foundation (BCRF) Evelyn H. Lauder Founder's Fund for Metastatic Breast Cancer Research. This multi-center effort was conducted within the Translational Breast Cancer Research Consortium (TBCRC) and cancer researchers to better understand the metastatic process through the study of both the primary and metastatic tissue. In the retrospective phase, 55 patients with 31 primary tissues and 102 metastases were profiled using whole genome DNA sequencing, whole exome DNA sequencing, DNA methylation arrays, and RNA sequencing. The related molecular data are hosted in dbGaP and GEO.

H&E slides are available for 184 specimens (17 samples have 2 images, 12 have 3 images). H&E were performed on 53 primary breast cancer tissues, 99 metastatic samples, and 32 adjacent normal tissues. HLA-A immunofluorescence was performed on 37 samples.

Introduction

The Aurora US metastatic breast cancer project is a partnership between the Breast Cancer Research Foundation (BCRF) and the Translational Breast Cancer Research Consortium (TBCRC). Metastatic breast cancer is currently incurable. To better understand why breast cancers spread and to improve treatment options for metastatic breast cancer, Aurora US proposed to study the metastatic process through molecular characterization of paired primary and metastatic samples. Limited prior research has been performed on large cohorts of paired samples. In the retrospective phase, investigators profiled 55 patients with 1-9 metastases to understand key molecular features associated with metastasis. This study found that subtype switching from primary to metastatic disease occurred in ~30% of cases. Interestingly, the basal-like molecular subtype rarely changed. HLA-A was found to be downregulated in metastasis compared to their paired primary tumors through a variety of mechanisms including, DNA hypermethylation or focal deletion.

A prospective clinical trial is underway to increase our cohort of metastatic breast cancer (NCT03737695). Additional information can be found at the Aurora US website: https://auroraus.org/about/

Methods

The following subsections provide information about how the data were selected, acquired and prepared for publication.

Subject Inclusion and Exclusion Criteria

TBCRC participating sites identified retrospective cases of individuals diagnosed with metastatic breast cancer with availability of tissue from both primary and metastatic specimens. Pathology quality control was performed on each specimen. H&E sections from each sample were subjected to independent pathology review to confirm the tumor specimen was histologically consistent to the reported histology. Tumor samples with ≥30% tumor nuclei and normal tissue with 0% tumor nuclei were submitted for nucleic acid extraction.

Data Acquisition

Histologic H&E images were scanned with a Leica Aperio scanner at 40X and the pixel aspect ratio varies by image. File type submitted is svs files.

HLA-A immunofluorescence was performed on 37 samples. FFPE tissue was sectioned at 4 µm and stained with a CK/HLA-A assay developed and optimized at Vanderbilt University Medical Center using tyramine signal amplification for increased antigen sensitivity. Sections were deparaffinized. Antigen retrieval was performed with citrate buffer at pH 6. Endogen peroxidase was blocked with hydrogen peroxide, and protein block was applied. Sections were then incubated with the first primary antibody, pan-cytokeratin (pan-CK) AE1/AE3 Biocare, at 1:1,600 overnight at 4 °C, followed by incubation with the secondary antibody conjugated with horseradish peroxidase. TSA reagent was applied according to manufacturer’s recommendations. After washing, antigen retrieval and protein block steps, the second primary antibody, HLA-A C6 Santa Cruz at 1:1,300, was incubated overnight as described. Counterstaining was performed with DAPI for nuclei identification. Tonsil and placenta tissue were used as positive- and negative-control tissues. Whole slide images were scanned with an Axioscan Z1 at 20X. File type submitted is tif files.

Clinical:

Clinical data for each case was captured in a redcap database. Data collected included details for patient demographics (age, gender, race, ethnicity, family history of breast or ovarian cancer, known BRCA1/2 mutations), primary diagnosis and clinical staging information, surgery and pathologic staging, metastatic diagnosis and pathology, and treatment information.

Data Analysis

H&E:

Pathology quality control (QC) was performed on each tumor specimen and normal tissue specimen as an initial QC step. Hematoxylin and eosin-stained sections from each sample were subjected to independent pathology review to confirm that the tumor specimen was histologically consistent to the reported histology. The percent tumor nuclei, percent necrosis and other pathology annotations were also assessed.

HLA-A immunofluorescence:

Automated quantification was performed via a pathologist-supervised machine learning algorithm using QuPath software. Cell segmentation was determined on DAPI. Object classifiers were trained on annotated training regions from control tissue and tumor samples to define cellular phenotypes. Tumor cells were defined by pan-CK expression and subcellular characteristics. Once the algorithm was performing at a satisfactory level, it was used for batch analysis. For cases with low, heterogenous or null CK expression in which the classifier performance was not optimal, tumor areas were manually annotated. Out-of-focus areas, tissue folds, necrosis, normal breast and in situ carcinoma were excluded from the analysis. Single-cell data were exported from QuPath, and mean HLA-A intensity on tumor cells was further calculated in R.

Usage Notes

File Naming Schema: