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.

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.

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 | |:-------------------------------------------|:-------------------------------------------------------------------...

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 ...

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

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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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...

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

In the period 2018 to 2022, a total of approximately *** men per 100,000 inhabitants died of cancers of all kinds in the United States, compared to an overall cancer death rate of *** per 100,000 population among women. This statistic shows cancer death rates in the U.S. for the period from 2018 to 2022, by type and gender.

In 2023, Hawaii had the lowest death rate from cancer among all U.S. states, with around 119 deaths per 100,000 population. The states with the highest cancer death rates at that time were Kentucky, West Virginia, and Mississippi. This statistic shows cancer death rates in the United States in 2023, by state.

Cancer death rates by county, all races (includes Hispanic/Latino), all sexes, all ages, 2019-2023. Death data were provided by the National Vital Statistics System. Death rates (deaths per 100,000 population per year) are age-adjusted to the 2000 US standard population (20 age groups: <1, 1-4, 5-9, ... , 80-84, 85-89, 90+). Rates calculated using SEER*Stat. Population counts for denominators are based on Census populations as modified by the National Cancer Institute. The US Population Data File is used for mortality data. The Average Annual Percent Change is based onthe APCs calculated by the Joinpoint Regression Program (Version 4.9.0.0). Due to data availability issues, the time period used in the calculation of the joinpoint regression model may differ for selected counties. Counties with a (3) after their name may have their joinpoint regresssion model calculated using a different time period due to data availability issues.

BackgroundThe success of the “war on cancer” initiated in 1971 continues to be debated, with trends in cancer mortality variably presented as evidence of progress or failure. We examined temporal trends in death rates from all-cancer and the 19 most common cancers in the United States from 1970–2006.Methodology/Principal FindingsWe analyzed trends in age-standardized death rates (per 100,000) for all cancers combined, the four most common cancers, and 15 other sites from 1970–2006 in the United States using joinpoint regression model. The age-standardized death rate for all-cancers combined in men increased from 249.3 in 1970 to 279.8 in 1990, and then decreased to 221.1 in 2006, yielding a net decline of 21% and 11% from the 1990 and 1970 rates, respectively. Similarly, the all-cancer death rate in women increased from 163.0 in 1970 to 175.3 in 1991 and then decreased to 153.7 in 2006, a net decline of 12% and 6% from the 1991 and 1970 rates, respectively. These decreases since 1990/91 translate to preventing of 561,400 cancer deaths in men and 205,700 deaths in women. The decrease in death rates from all-cancers involved all ages and racial/ethnic groups. Death rates decreased for 15 of the 19 cancer sites, including the four major cancers, with lung, colorectum and prostate cancers in men and breast and colorectum cancers in women.Conclusions/SignificanceProgress in reducing cancer death rates is evident whether measured against baseline rates in 1970 or in 1990. The downturn in cancer death rates since 1990 result mostly from reductions in tobacco use, increased screening allowing early detection of several cancers, and modest to large improvements in treatment for specific cancers. Continued and increased investment in cancer prevention and control, access to high quality health care, and research could accelerate this progress.

Annual percent change and average annual percent change in age-standardized cancer mortality rates since 1984 to the most recent data year. The table includes a selection of commonly diagnosed invasive cancers and causes of death are defined based on the World Health Organization International Classification of Diseases, ninth revision (ICD-9) from 1984 to 1999 and on its tenth revision (ICD-10) from 2000 to the most recent year.

Rate: Number of deaths due to cancer of the trachea, bronchus, and lung per 100,000 Population.

Definition: Number of deaths per 100,000 with malignant neoplasm (cancer) cancer of the trachea, bronchus, and lung as the underlying cause (ICD-10 codes: C33-C34).

Data Sources:

(1) Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File. CDC WONDER On-line Database accessed at http://wonder.cdc.gov/cmf-icd10.html

(2) Death Certificate Database, Office of Vital Statistics and Registry, New Jersey Department of Health

(3) Population Estimates, State Data Center, New Jersey Department of Labor and Workforce Development

Rate: Number of deaths due to all kinds of Cancer per 100,000 Population.

Definition: Number of deaths per 100,000 with malignant neoplasm (cancer) as the underlying cause (ICD-10 codes: C00-C97).

Data Sources:

(1) Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File. CDC WONDER On-line Database accessed at http://wonder.cdc.gov/cmf-icd10.html

(2) Death Certificate Database, Office of Vital Statistics and Registry, New Jersey Department of Health

(3) Population Estimates, State Data Center, New Jersey Department of Labor and Workforce Development

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Mortality Rates for Lake County, Illinois. Explanation of field attributes: Average Age of Death – The average age at which a people in the given zip code die. Cancer Deaths – Cancer deaths refers to individuals who have died of cancer as the underlying cause. This is a rate per 100,000. Heart Disease Related Deaths – Heart Disease Related Deaths refers to individuals who have died of heart disease as the underlying cause. This is a rate per 100,000. COPD Related Deaths – COPD Related Deaths refers to individuals who have died of chronic obstructive pulmonary disease (COPD) as the underlying cause. This is a rate per 100,000.

Legacy unique identifier: P00624

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.

Death rate has been age-adjusted by the 2000 U.S. standard population. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Lung cancer is a leading cause of cancer-related death in the US. People who smoke have the greatest risk of lung cancer, though lung cancer can also occur in people who have never smoked. Most cases are due to long-term tobacco smoking or exposure to secondhand tobacco smoke. Cities and communities can take an active role in curbing tobacco use and reducing lung cancer by adopting policies to regulate tobacco retail; reducing exposure to secondhand smoke in outdoor public spaces, such as parks, restaurants, or in multi-unit housing; and improving access to tobacco cessation programs and other preventive services.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.