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.

Breast cancer was the cancer type with the highest rate of death among females worldwide in 2022. That year, there were around 13 deaths from breast cancer among females per 100,000 population. The death rate for all cancers among females was 76.4 per 100,000 population. This statistic displays the rate of cancer deaths among females worldwide in 2022, by type of cancer.

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.

Lung cancer was the cancer type with the highest rate of death among males worldwide in 2022. In that year there were around 25 deaths from trachea, bronchus and lung cancer among males per 100,000 population. The death rate for all cancers among males was 109 per 100,000 population. This statistic shows the rate of cancer deaths among males worldwide in 2022, by type of cancer.

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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Cancer survival statistics are typically expressed as the proportion of patients alive at some point subsequent to the diagnosis of their cancer. Statistics compare the survival of patients diagnosed with cancer with the survival of people in the general population who are the same age, race, and sex and who have not been diagnosed with cancer.

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.

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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.

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...

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.

This dataset contains estimates for 29 cancer-specific age-standardized mortality rates for specific cancer types at the county level for each state, the District of Columbia, and the United States as a whole for 1980-2014 (quinquennial), as well as the changes in rates during this period.

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)

Dataset

This dataset was created by Mathiew HErnandez

Contents

This data package contains information on cancer its type, its occurrence by age, type and site. It also provides detailed data on adult and childhood cancer survival rates and deaths caused by breast cancer in females.

Mortality from breast cancer (ICD-10 C50 equivalent to ICD-9 174). To reduce deaths from breast cancer. The next release date for this indicator is to be confirmed. Legacy unique identifier: P00147

AbstractIn Italy, approximately 400.000 new cases of malignant tumors are recorded every year. The average of annual deaths caused by tumors, according to the Italian Cancer Registers, is about 3.5 deaths and about 2.5 per 1,000 men and women respectively, for a total of about 3 deaths every 1,000 people. Long-term (at least a decade) and spatially detailed data (up to the municipality scale) are neither easily accessible nor fully available for public consultation by the citizens, scientists, research groups, and associations. Therefore, here we present a ten-year (2009–2018) database on cancer mortality rates (in the form of Standardized Mortality Ratios, SMR) for 23 cancer macro-types in Italy on municipal, provincial, and regional scales. We aim to make easily accessible a comprehensive, ready-to-use, and openly accessible source of data on the most updated status of cancer mortality in Italy for local and national stakeholders, researchers, and policymakers and to provide researchers with ready-to-use data to perform specific studies. Methods For a given locality, year, and cause of death, the SMR is the ratio between the observed number of deaths (Om) and the number of expected deaths (Em): SMR = Om/Em (1) where Om should be an available observational data and Em is estimated as the weighted sum of age-specific population size for the given locality (ni) per age-specific death rates of the reference population (MRi): Em = sum(MRi x ni) (2) MRi could be provided by a public health organization or be estimated as the ratio between the age-specific number of deaths of reference population (Mi) to the age-specific reference population size (Ni): MRi = Mi/Ni (3) Thus, the value of Em is weighted by the age distribution of deaths and population size. SMR assumes value 1 when the number of observed and expected deaths are equal. Following eqns. (1-3), the SMR was computed for single years of the period 2009-2018 and for single cause of death as defined by the International ICD-10 classification system by using the following data: age-specific number of deaths by cause of reference population (i.e., Mi) from the Italian National Institute of Statistics (ISTAT, (http://www.istat.it/en/, last access: 26/01/2022)); age-specific census data on reference population (i.e., Ni) from ISTAT; the observed number of deaths by cause (i.e., Om) from ISTAT; the age-specific census data on population (ni); the SMR was estimated at three different level of aggregation: municipal, provincial (equivalent to the European classification NUTS 3) and regional (i.e., NUTS2). The SMR was also computed for the broad category of malignant tumors (i.e. C00-C979, hereinafter cancer macro-type C), and for the broad category of malignant tumor plus non-malignant tumors (i.e. C00-C979 plus D0-D489, hereinafter cancer macro-type CD). Lower 90% and 95% confidence intervals of 10-year average values were computed according to the Byar method.

Mortality from stomach cancer (ICD-10 C16 equivalent to ICD-9 151). To reduce deaths from stomach cancer. Legacy unique identifier: P00666

Mortality rate ratios (MRRs) are adjusted for age at follow-up and calendar period at baseline. The reference group is Sweden-born men. MRR values significantly different from 1.0 are highlighted in bold.*Continents, regions, and countries with at least five cases of cancer mortality.aThe former Czechoslovakia includes Czechoslovakia, Slovakia, and the Czech Republic.bThe former Soviet Union includes Belarus, Moldova, Russian Federation, Soviet Union, and Ukraine.cThe former Yugoslavia includes Yugoslavia, Croatia, Macedonia, Serbia, Slovenia, and Montenegro.

*Total number of studied individuals excluding those who died due to each type of cancer.