In 2025, it was estimated that there would be over 972 thousand new cancer cases among women in the United States. This statistic illustrates the estimated number of new cancer cases and deaths in the United States for 2025, by gender.

Number and rate of new cancer cases diagnosed annually from 1992 to the most recent diagnosis year available. Included are all invasive cancers and in situ bladder cancer with cases defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3). Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.

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).

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.

Age standardized rate of cancer incidence, by selected sites of cancer and sex, three-year average, census metropolitan areas.

Exploring County-Level Correlations in Cancer Rates and Trends

A Multivariate Ordinary Least Squares Regression Model

By Noah Rippner [source]

About this dataset

This dataset offers a unique opportunity to examine the pattern and trends of county-level cancer rates in the United States at the individual county level. Using data from cancer.gov and the US Census American Community Survey, this dataset allows us to gain insight into how age-adjusted death rate, average deaths per year, and recent trends vary between counties – along with other key metrics like average annual counts, met objectives of 45.5?, recent trends (2) in death rates, etc., captured within our deep multi-dimensional dataset. We are able to build linear regression models based on our data to determine correlations between variables that can help us better understand cancers prevalence levels across different counties over time - making it easier to target health initiatives and resources accurately when necessary or desired

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How to use the dataset

This kaggle dataset provides county-level datasets from the US Census American Community Survey and cancer.gov for exploring correlations between county-level cancer rates, trends, and mortality statistics. This dataset contains records from all U.S counties concerning the age-adjusted death rate, average deaths per year, recent trend (2) in death rates, average annual count of cases detected within 5 years, and whether or not an objective of 45.5 (1) was met in the county associated with each row in the table.

To use this dataset to its fullest potential you need to understand how to perform simple descriptive analytics which includes calculating summary statistics such as mean, median or other numerical values; summarizing categorical variables using frequency tables; creating data visualizations such as charts and histograms; applying linear regression or other machine learning techniques such as support vector machines (SVMs), random forests or neural networks etc.; differentiating between supervised vs unsupervised learning techniques etc.; reviewing diagnostics tests to evaluate your models; interpreting your findings; hypothesizing possible reasons and patterns discovered during exploration made through data visualizations ; Communicating and conveying results found via effective presentation slides/documents etc.. Having this understanding will enable you apply different methods of analysis on this data set accurately ad effectively.

Once these concepts are understood you are ready start exploring this data set by first importing it into your visualization software either tableau public/ desktop version/Qlikview / SAS Analytical suite/Python notebooks for building predictive models by loading specified packages based on usage like Scikit Learn if Python is used among others depending on what tool is used . Secondly a brief description of the entire table's column structure has been provided above . Statistical operations can be carried out with simple queries after proper knowledge of basic SQL commands is attained just like queries using sub sets can also be performed with good command over selecting columns while specifying conditions applicable along with sorting operations being done based on specific attributes as required leading up towards writing python codes needed when parsing specific portion of data desired grouping / aggregating different categories before performing any kind of predictions / models can also activated create post joining few tables possible , when ever necessary once again varying across tools being used Thereby diving deep into analyzing available features determined randomly thus creating correlation matrices figures showing distribution relationships using correlation & covariance matrixes , thus making evaluations deducing informative facts since revealing trends identified through corresponding scatter plots from a given metric gathered from appropriate fields!

Research Ideas

• Building a predictive cancer incidence model based on county-level demographic data to identify high-risk areas and target public health interventions.
• Analyzing correlations between age-adjusted death rate, average annual count, and recent trends in order to develop more effective policy initiatives for cancer prevention and healthcare access.
• Utilizing the dataset to construct a machine learning algorithm that can predict county-level mortality rates based on socio-economic factors such as poverty levels and educational attainment rates

Acknowledgements

If you use this dataset i...

Editor’s Choice

• Global Breast Cancer Market size is expected to be worth around USD 49.2 Bn by 2032 from USD 19.8 Bn in 2022, growing at a CAGR of 9.8% during the forecast period from 2022 to 2032.
• Breast cancer is the most common cancer among women worldwide. In 2020, there were about 2.3 million new cases of breast cancer diagnosed globally.
• Breast cancer is the leading cause of cancer-related deaths in women. In 2020, it was responsible for approximately 685,000 deaths worldwide.
• The survival rate of breast cancer has improved over the years. In the United States, the overall five-year survival rate of breast cancer is around 90%.
• The American Cancer Society recommends annual mammograms starting at age 40 for women at average risk.
• Although rare, breast cancer also occurs in men. Less than 1% of breast cancer cases are diagnosed in males.

(Source: WHO, American Cancer Society)

https://market.us/wp-content/uploads/2023/04/Breast-Cancer-Market-Value.jpg" alt="">

The rate of breast cancer deaths in the U.S. has dramatically declined since 1950. As of 2023, the death rate from breast cancer was **** per 100,000 population. However, cancer is a serious public health issue in the United States and is the second leading cause of death among women. Breast cancer incidence Breast cancer symptoms include lumps or thickening of the breast tissue and may include changes to the skin. Breast cancer is driven by many factors, but age is a known risk factor. Among all age groups, the highest number of invasive breast cancer cases were among those aged 60 to 69. The incidence rate of new breast cancer cases is higher in some ethnicities than others. White, non-Hispanic women have the highest incidence rate of breast cancer, followed by non-Hispanic Black women. Breast cancer treatment Breast cancer treatments usually involve several methods, including surgery, chemotherapy and biological therapy. Types of cancer diagnosed at earlier stages often require fewer treatments. A majority of early stage breast cancer cases in the U.S. receive breast conserving surgery and radiation therapy.

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

Cancer Rates for Lake County Illinois. Explanation of field attributes: Colorectal Cancer - Cancer that develops in the colon (the longest part of the large intestine) and/or the rectum (the last several inches of the large intestine). This is a rate per 100,000. Lung Cancer – Cancer that forms in tissues of the lung, usually in the cells lining air passages. This is a rate per 100,000. Breast Cancer – Cancer that forms in tissues of the breast. This is a rate per 100,000. Prostate Cancer – Cancer that forms in tissues of the prostate. This is a rate per 100,000. Urinary System Cancer – Cancer that forms in the organs of the body that produce and discharge urine. These include the kidneys, ureters, bladder, and urethra. This is a rate per 100,000. All Cancer – All cancers including, but not limited to: colorectal cancer, lung cancer, breast cancer, prostate cancer, and cancer of the urinary system. This is a rate per 100,000.

Age-standardised rate of mortality from oral cancer (ICD-10 codes C00-C14) in persons of all ages and sexes per 100,000 population.RationaleOver the last decade in the UK (between 2003-2005 and 2012-2014), oral cancer mortality rates have increased by 20% for males and 19% for females1Five year survival rates are 56%. Most oral cancers are triggered by tobacco and alcohol, which together account for 75% of cases2. Cigarette smoking is associated with an increased risk of the more common forms of oral cancer. The risk among cigarette smokers is estimated to be 10 times that for non-smokers. More intense use of tobacco increases the risk, while ceasing to smoke for 10 years or more reduces it to almost the same as that of non-smokers3. Oral cancer mortality rates can be used in conjunction with registration data to inform service planning as well as comparing survival rates across areas of England to assess the impact of public health prevention policies such as smoking cessation.References:(1) Cancer Research Campaign. Cancer Statistics: Oral – UK. London: CRC, 2000.(2) Blot WJ, McLaughlin JK, Winn DM et al. Smoking and drinking in relation to oral and pharyngeal cancer. Cancer Res 1988; 48: 3282-7. (3) La Vecchia C, Tavani A, Franceschi S et al. Epidemiology and prevention of oral cancer. Oral Oncology 1997; 33: 302-12.Definition of numeratorAll cancer mortality for lip, oral cavity and pharynx (ICD-10 C00-C14) in the respective calendar years aggregated into quinary age bands (0-4, 5-9,…, 85-89, 90+). This does not include secondary cancers or recurrences. Data are reported according to the calendar year in which the cancer was diagnosed.Counts of deaths for years up to and including 2019 have been adjusted where needed to take account of the MUSE ICD-10 coding change introduced in 2020. Detailed guidance on the MUSE implementation is available at: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/articles/causeofdeathcodinginmortalitystatisticssoftwarechanges/january2020Counts of deaths for years up to and including 2013 have been double adjusted by applying comparability ratios from both the IRIS coding change and the MUSE coding change where needed to take account of both the MUSE ICD-10 coding change and the IRIS ICD-10 coding change introduced in 2014. The detailed guidance on the IRIS implementation is available at: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/impactoftheimplementationofirissoftwareforicd10causeofdeathcodingonmortalitystatisticsenglandandwales/2014-08-08Counts of deaths for years up to and including 2010 have been triple adjusted by applying comparability ratios from the 2011 coding change, the IRIS coding change and the MUSE coding change where needed to take account of the MUSE ICD-10 coding change, the IRIS ICD-10 coding change and the ICD-10 coding change introduced in 2011. The detailed guidance on the 2011 implementation is available at https://webarchive.nationalarchives.gov.uk/ukgwa/20160108084125/http://www.ons.gov.uk/ons/guide-method/classifications/international-standard-classifications/icd-10-for-mortality/comparability-ratios/index.htmlDefinition of denominatorPopulation-years (aggregated populations for the three years) for people of all ages, aggregated into quinary age bands (0-4, 5-9, …, 85-89, 90+)

The rate of liver cancer diagnoses in the United States increases with age. As of 2022, those aged 80 to 84 years had the highest rates of liver cancer. Risk factors for liver cancer include smoking, drinking alcohol, being overweight or obese, and having diabetes. Who is most likely to get liver cancer? Liver cancer in the United States is much more common among men than women. In 2022, there were 12 new liver cancer diagnoses among men per 100,000 population, compared to just five new diagnoses per 100,000 women. Concerning race and ethnicity, non-Hispanic American Indians and Alaska Natives and Hispanics have the highest rates of new liver cancer diagnoses. The five-year survival rate for liver cancer in the United States is around 22 percent; however, this rate is much higher among non-Hispanic Asian and Pacific Islanders than other races and ethnicities. Non-Hispanic Asian and Pacific Islanders have a 33 percent chance of surviving the next five years after a liver cancer diagnosis. Deaths from liver cancer In 2023, there were an estimated 29,911 deaths in the United States due to liver cancer. However, the death rate for liver cancer has remained stable over the past few years. In 2023, the death rate for liver cancer was 6.6 deaths per 100,000 population. It is estimated that in 2025, there will be over 19,000 liver and intrahepatic bile duct cancer deaths among men in the United States and 10,800 such deaths among women.

This dataset provides detailed information on global cancer incidence rates and numbers for both males and females in the year 2022. It includes data on various types of cancer, both including and excluding non-melanoma skin cancer (NMSC). The dataset is organized into two CSV files:

Global cancer incidence in males and females (2022).csv: Contains detailed data for individual countries, including cancer incidence rates and numbers for both males and females, categorized by including and excluding NMSC. Overall global cancer incidence (2022).csv: Provides an aggregated view of global cancer incidence, summarizing key statistics across different regions and demographics.

This publication reports on newly diagnosed cancers registered in England in addition to cancer deaths registered in England during 2020. It includes this summary report showing key findings, spreadsheet tables with more detailed estimates, and a methodology document.

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 rates

Incidence 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

Annual percent change and average annual percent change in age-standardized cancer incidence rates since 1984 to the most recent diagnosis year. The table includes a selection of commonly diagnosed invasive cancers, as well as in situ bladder cancer. Cases are defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3) from 1992 to the most recent data year and on the International Classification of Diseases, ninth revision (ICD-9) from 1984 to 1991.

In 2022, 175 females per 100,000 population were registered in England as newly diagnosed with breast cancer. This was an overall increase in comparison to the last few years' rate of registration. This statistic shows the rate of newly diagnosed female cases of breast cancer per 100,000 population in England from 1995 to 2022.

Cancer Mortality & Incidence Rates: (Country LVL)

Investigating Cancer Trends over time

By Data Exercises [source]

About this dataset

This dataset is a comprehensive collection of data from county-level cancer mortality and incidence rates in the United States between 2000-2014. This data provides an unprecedented level of detail into cancer cases, deaths, and trends at a local level. The included columns include County, FIPS, age-adjusted death rate, average death rate per year, recent trend (2) in death rates, recent 5-year trend (2) in death rates and average annual count for each county. This dataset can be used to provide deep insight into the patterns and effects of cancer on communities as well as help inform policy decisions related to mitigating risk factors or increasing preventive measures such as screenings. With this comprehensive set of records from across the United States over 15 years, you will be able to make informed decisions regarding individual patient care or policy development within your own community!

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How to use the dataset

This dataset provides comprehensive US county-level cancer mortality and incidence rates from 2000 to 2014. It includes the mortality and incidence rate for each county, as well as whether the county met the objective of 45.5 deaths per 100,000 people. It also provides information on recent trends in death rates and average annual counts of cases over the five year period studied.

This dataset can be extremely useful to researchers looking to study trends in cancer death rates across counties. By using this data, researchers will be able to gain valuable insight into how different counties are performing in terms of providing treatment and prevention services for cancer patients and whether preventative measures and healthcare access are having an effect on reducing cancer mortality rates over time. This data can also be used to inform policy makers about counties needing more target prevention efforts or additional resources for providing better healthcare access within at risk communities.

When using this dataset, it is important to pay close attention to any qualitative columns such as “Recent Trend” or “Recent 5-Year Trend (2)” that may provide insights into long term changes that may not be readily apparent when using quantitative variables such as age-adjusted death rate or average deaths per year over shorter periods of time like one year or five years respectively. Additionally, when studying differences between different counties it is important to take note of any standard FIPS code differences that may indicate that data was collected by a different source with a difference methodology than what was used in other areas studied

Research Ideas

• Using this dataset, we can identify patterns in cancer mortality and incidence rates that are statistically significant to create treatment regimens or preventive measures specifically targeting those areas.
• This data can be useful for policymakers to target areas with elevated cancer mortality and incidence rates so they can allocate financial resources to these areas more efficiently.
• This dataset can be used to investigate which factors (such as pollution levels, access to medical care, genetic make up) may have an influence on the cancer mortality and incidence rates in different US counties

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: death .csv | Column name | Description | |:-------------------------------------------|:-------------------------------------------------------------------...