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.

Dataset Description This dataset contains information on cancer deaths by country, type, and year. It includes data on 18 different types of cancer, including liver cancer, kidney cancer, larynx cancer, breast cancer, thyroid cancer, stomach cancer, bladder cancer, uterine cancer, ovarian cancer, cervical cancer, prostate cancer, pancreatic cancer, esophageal cancer, testicular cancer, nasopharynx cancer, other pharynx cancer, colon and rectum cancer, non-melanoma skin cancer, lip and oral cavity cancer, brain and nervous system cancer, tracheal, bronchus, and lung cancer, gallbladder and biliary tract cancer, malignant skin melanoma, leukemia, Hodgkin lymphoma, multiple myeloma, and other cancers.

Data Fields The dataset includes the following data fields:

• Country: The country where the cancer death occurred.
• Code: The country code for the country where the cancer death occurred.
• Year: The year in which the cancer death occurred.
• Liver cancer: The number of cancer deaths from liver cancer in the country in the year.
• Kidney cancer: The number of cancer deaths from kidney cancer in the country in the year.
• Larynx cancer: The number of cancer deaths from larynx cancer in the country in the year.
• Breast cancer: The number of cancer deaths from breast cancer in the country in the year.
• Thyroid cancer: The number of cancer deaths from thyroid cancer in the country in the year.
• Stomach cancer: The number of cancer deaths from stomach cancer in the country in the year.
• Bladder cancer: The number of cancer deaths from bladder cancer in the country in the year.
• Uterine cancer: The number of cancer deaths from uterine cancer in the country in the year.
• Ovarian cancer: The number of cancer deaths from ovarian cancer in the country in the year.
• Cervical cancer: The number of cancer deaths from cervical cancer in the country in the year.
• Prostate cancer: The number of cancer deaths from prostate cancer in the country in the year.
• Pancreatic cancer: The number of cancer deaths from pancreatic cancer in the country in the year.
• Esophageal cancer: The number of cancer deaths from esophageal cancer in the country in the year.
• Testicular cancer: The number of cancer deaths from testicular cancer in the country in the year.
• Nasopharynx cancer: The number of cancer deaths from nasopharynx cancer in the country in the year.
• Other pharynx cancer: The number of cancer deaths from other pharynx cancer in the country in the year.
• Colon and rectum cancer: The number of cancer deaths from colon and rectum cancer in the country in the year.
• Non-melanoma skin cancer: The number of cancer deaths from non-melanoma skin cancer in the country in the year.
• Lip and oral cavity cancer: The number of cancer deaths from lip and oral cavity cancer in the country in the year.
• Brain and nervous system cancer: The number of cancer deaths from brain and nervous system cancer in the country in the year.
• Tracheal, bronchus, and lung cancer: The number of cancer deaths from tracheal, bronchus, and lung cancer in the country in the year.
• Gallbladder and biliary tract cancer: The number of cancer deaths from gallbladder and biliary tract cancer in the country in the year.
• Malignant skin melanoma: The number of cancer deaths from malignant skin melanoma in the country in the year.
• Leukemia: The number of cancer deaths from leukemia in the country in the year.
• Hodgkin lymphoma: The number of cancer deaths from Hodgkin lymphoma in the country in the year.
• Multiple myeloma: The number of cancer deaths from multiple myeloma in the country in the year.
• Other cancers: The number of cancer deaths from other cancers in the country in the year.

Data Source The data in this dataset was collected from the World Health Organization (WHO). The WHO collects data on cancer deaths from countries around the world.

Usage This dataset can be used to study cancer deaths by country, type, and year. It can also be used to compare cancer death rates between different countries or over time.

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The highest number of cancer deaths in the given period was 613,349, reported in 2023. The lowest number of cancer deaths was 549,829 in 1999. This statistic shows the total number of cancer deaths in the United States from 1999 to 2023.

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The "Cancer and Deaths Dataset: 1990~2019 Globally" is a comprehensive dataset containing information on cancer incidence and mortality rates across the world from 1990 to 2019.

The dataset is an excellent resource for researchers, healthcare professionals, and policymakers who are interested in understanding the global burden of cancer and its impact on populations.

[ Total 9 file , 160 columns, All the countries, 30Years data ]

>In 2017, 9.6 million people are estimated to have died from the various forms of cancer. Every sixth death in the world is due to cancer, making it the second leading cause of death – second only to cardiovascular diseases.1

Progress against many other causes of deaths and demographic drivers of increasing population size, life expectancy and — particularly in higher-income countries — aging populations mean that the total number of cancer deaths continues to increase. This is a very personal topic to many: nearly everyone knows or has lost someone dear to them from this collection of diseases.

## Data vastness of this dataset: 01. annual-number-of-deaths-by-cause data. 02. total-cancer-deaths-by-type data. 03. cancer-death-rates-by-age data. 04. share-of-population-with-cancer-types data. 05. share-of-population-with-cancer data. 06. number-of-people-with-cancer-by-age data. 07. share-of-population-with-cancer-by-age data. 08. disease-burden-rates-by-cancer-types data. 09. cancer-deaths-rate-and-age-standardized-rate-index data.

Lung cancer is the deadliest cancer worldwide, accounting for 1.82 million deaths in 2022. The second most deadly form of cancer is colorectum cancer, followed by liver cancer. However, lung cancer is only the sixth leading cause of death worldwide, with heart disease and stroke accounting for the highest share of deaths. Male vs. female cases Given that lung cancer causes the highest number of cancer deaths worldwide, it may be unsurprising to learn that lung cancer is the most common form of new cancer cases among males. However, among females, breast cancer is by far the most common form of new cancer cases. In fact, breast cancer is the most prevalent cancer worldwide, followed by prostate cancer. Prostate cancer is a very close second to lung cancer among the cancers with the highest rates of new cases among men. Male vs. female deaths Lung cancer is by far the deadliest form of cancer among males but is the second deadliest form of cancer among females. Breast cancer, the most prevalent form of cancer among females worldwide, is also the deadliest form of cancer among females. Although prostate cancer is the second most prevalent cancer among men, it is the fifth deadliest cancer. Lung, liver, stomach, colorectum, and oesophagus cancers all have higher deaths rates among males.

Decrease the cancer death rate from 185.7 per 100,000 in 2013 to 180.3 per 100,000 by 2019.

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.

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).

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

Lung Cancer Deaths reports the number, crude rate, and age-adjusted mortality rate (AAMR) of deaths due to lung cancer.

County Cancer Death Rates

County-level cancer death rates with related variables

By Noah Rippner [source]

About this dataset

This dataset provides comprehensive information on county-level cancer death and incidence rates, as well as various related variables. It includes data on age-adjusted death rates, average deaths per year, recent trends in cancer death rates, recent 5-year trends in death rates, and average annual counts of cancer deaths or incidence. The dataset also includes the federal information processing standards (FIPS) codes for each county.

Additionally, the dataset indicates whether each county met the objective of a targeted death rate of 45.5. The recent trend in cancer deaths or incidence is also captured for analysis purposes.

The purpose of the death.csv file within this dataset is to offer detailed information specifically concerning county-level cancer death rates and related variables. On the other hand, the incd.csv file contains data on county-level cancer incidence rates and additional relevant variables.

To provide more context and understanding about the included data points, there is a separate file named cancer_data_notes.csv. This file serves to provide informative notes and explanations regarding the various aspects of the cancer data used in this dataset.

Please note that this particular description provides an overview for a linear regression walkthrough using this dataset based on Python programming language. It highlights how to source and import the data properly before moving into data preparation steps such as exploratory analysis. The walkthrough further covers model selection and important model diagnostics measures.

It's essential to bear in mind that this example serves as an initial attempt at creating a multivariate Ordinary Least Squares regression model using these datasets from various sources like cancer.gov along with US Census American Community Survey data. This baseline model allows easy comparisons with future iterations intended for improvements or refinements.

Important columns found within this extensively documented Kaggle dataset include County names along with their corresponding FIPS codes—a standardized coding system by Federal Information Processing Standards (FIPS). Moreover,Met Objective of 45.5? (1) column denotes whether a specific county achieved the targeted objective of a death rate of 45.5 or not.

Overall, this dataset aims to offer valuable insights into county-level cancer death and incidence rates across various regions, providing policymakers, researchers, and healthcare professionals with essential information for analysis and decision-making purposes

How to use the dataset

• Familiarize Yourself with the Columns:

  • County: The name of the county.
  • FIPS: The Federal Information Processing Standards code for the county.
  • Met Objective of 45.5? (1): Indicates whether the county met the objective of a death rate of 45.5 (Boolean).
  • Age-Adjusted Death Rate: The age-adjusted death rate for cancer in the county.
  • Average Deaths per Year: The average number of deaths per year due to cancer in the county.
  • Recent Trend (2): The recent trend in cancer death rates/incidence in the county.
  • Recent 5-Year Trend (2) in Death Rates: The recent 5-year trend in cancer death rates/incidence in the county.
  • Average Annual Count: The average annual count of cancer deaths/incidence in the county.

• Determine Counties Meeting Objective: Use this dataset to identify counties that have met or not met an objective death rate threshold of 45.5%. Look for entries where Met Objective of 45.5? (1) is marked as True or False.

• Analyze Age-Adjusted Death Rates: Study and compare age-adjusted death rates across different counties using Age-Adjusted Death Rate values provided as floats.

• Explore Average Deaths per Year: Examine and compare average annual counts and trends regarding deaths caused by cancer, using Average Deaths per Year as a reference point.

• Investigate Recent Trends: Assess recent trends related to cancer deaths or incidence by analyzing data under columns such as Recent Trend, Recent Trend (2), and Recent 5-Year Trend (2) in Death Rates. These columns provide information on how cancer death rates/incidence have changed over time.

• Compare Counties: Utilize this dataset to compare counties based on their cancer death rates and related variables. Identify counties with lower or higher average annual counts, age-adjusted death rates, or recent trends to analyze and understand the factors contributing ...

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.

Lung Cancer Deaths reports the number, crude rate, and age-adjusted mortality rate (AAMR) of deaths due to lung cancer. Dimensions Year;Measure Type;Variable Full Description Lung cancer forms in tissues of the lung, usually in the cells lining air passages. Deaths with ICD-10 code C34 as the underlying cause of death are recorded as lung cancer deaths. Data are reported annually.

Information about the rates of cancer deaths in each state is reported. The data shows the total rate as well as rates based on sex, age, and race. Rates are also shown for three specific kinds of cancer: breast cancer, colorectal cancer, and lung cancer.

Original Data

Column Description

Key	List of...	Comment	Example Value
State	String	The name of a U.S. State (e.g., Virginia)	"Alabama"
Total.Rate	Float	Total Cancer Deaths (Rate per 100,000 Population, 2007-2013) 214.2	214.2
Total.Number	Float	Total Cancer Deaths (2007-2013)	71529.0
Total.Population	Float	Cumulative Population (Denominator Total_Cancer deaths total_) 2007-2013	33387205.0
Rates.Age.< 18	Float	Total Cancer Deaths (Under 18 Years, Rate per 100,000 Population, 2007-2013)	2.0
Rates.Age.18-45	Float	Total Cancer Deaths (18 to 44 Years, Rate per 100,000 Population, 2007-2013)	18.5
Rates.Age.45-64	Float	Total Cancer Deaths (45 to 64 Years, Rate per 100,000 Population, 2007-2013)	244.7
Rates.Age.> 64	Float	Total Cancer Deaths (65 Years and Over, Rate per 100,000 Population, 2007-2013)	1017.8
Rates.Age and Sex.Female.< 18	Float	Female under 18	2.0
Rates.Age and Sex.Male.< 18	Float	Male under 18	2.1
Rates.Age and Sex.Female.18 - 45	Float	Female 18 - 45	20.1
Rates.Age and Sex.Male.18 - 45	Float	Male 18 - 45	16.8
Rates.Age and Sex.Female.45 - 64	Float	Female 45 to 64 Years	201.0
Rates.Age and Sex.Male.45 - 64	Float	Male 45 to 64 Years	291.5
Rates.Age and Sex.Female.> 64	Float	Female 65 Years and Over	803.6
Rates.Age and Sex.Male.> 64	Float	Male 65 Years and Over	1308.6
Rates.Race.White	Float	Total Cancer Deaths (White, Rate per 100,000 Population, 2007-2013)	186.1
Rates.Race.White non-Hispanic	Float	Total Cancer Deaths (White non-Hispanic, Rate per 100,000 Population, 2007-2013)	187.5
Rates.Race.Black	Float	Total Cancer Deaths (Black or African American, Rate per 100,000 Population, 2007-2013)	216.1
Rates.Race.Asian	Float	Total Cancer Deaths (Asian or Pacific Islander, Rate per 100,000 Population, 2007-2013)	81.3
Rates.Race.Indigenous	Float	Total Cancer Deaths (American Indian or Alaska Native, Rate per 100,000 Population, 2007-2013)	69.9
Rates.Race and Sex.Female.White	Float	Female: White	149.2
Rates.Race and Sex.Female.White non-Hispanic	Float	Female: White non-Hispanic	150.2
Rates.Race and Sex.Female.Black	Float	Female: Black or African American	167.2
Rates.Race and Sex.Female.Black non-Hispanic	Float	Female: Black or African American non-Hispanic	167.9
Rates.Race and Sex.Female.Asian	Float	Female: Asian or Pacific Islander	84.9
Rates.Race and Sex.Female.Indigenous	Float	Female: American Indian or Alaska Native	53.8
...

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.

This dataset presents the mortality rate from cancer among individuals under the age of 75 within the Birmingham and Solihull area. It captures the number of deaths attributed to all cancers (classified under ICD-10 codes C00 to C97) and expresses this as a directly age-standardised rate per 100,000 population. The data is structured in quinary age bands and is available for both single-year and three-year rolling averages, providing a comprehensive view of premature cancer mortality trends in the region.

Rationale Reducing premature mortality from cancer is a key public health priority. This indicator helps track progress in lowering the number of cancer-related deaths among people under 75, supporting efforts to improve early diagnosis, treatment, and prevention strategies.

Numerator The numerator is the number of deaths from all cancers (ICD-10 codes C00 to C97) registered in the respective calendar years, for individuals aged under 75. These figures are aggregated into quinary age bands and sourced from the Death Register.

Denominator The denominator is the population of individuals under 75 years of age, also aggregated into quinary age bands. For single-year rates, the population for that year is used. For three-year rolling averages, the population-years are aggregated across the three years. The source of this data is the 2021 Census.

Caveats Data may not align exactly with published Office for National Statistics (ONS) figures due to differences in postcode lookup versions and the application of comparability ratios in Office for Health Improvement and Disparities (OHID) data. Users should be cautious when comparing this dataset with other national statistics.

External references Further information and related indicators can be found on the OHID Fingertips platform.

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

About Dataset

The dataset contains 2 .csv files This file contains various demographic and health-related data for different regions. Here's a brief description of each column:

File 1st

• avganncount: Average number of cancer cases diagnosed annually.

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

• target_deathrate: Target death rate due to cancer.

• incidencerate: Incidence rate of cancer.

• medincome: Median income in the region.

• popest2015: Estimated population in 2015.

• povertypercent: Percentage of population below the poverty line.

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

• binnedinc: Binned median income.

• medianage: Median age in the region.

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

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

• pctpubliccoverage: Percentage of population covered by public health insurance.

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

• pctwhite: Percentage of White population.

• pctblack: Percentage of Black population.

• pctasian: Percentage of Asian population.

• pctotherrace: Percentage of population belonging to other races.

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

File 2nd

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

• statefips: The FIPS code representing the state.

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

• avghouseholdsize: The average household size in the region.

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

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

Number of Cancer New Cases and Registered Deaths by Ten Leading Cancer Disease Group by Sex 2022

In 2025, it was estimated that around ****** women in the United States would die of lung and bronchial cancer. This statistic shows the estimated number of cancer deaths among women in the U.S. in 2009 and 2025, by cancer type.

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...