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.

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.

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 ...

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.

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

This data provides high-level data on historical registrations (or cases) and deaths, including information about the cancer types and breakdowns by gender variables.

Lung cancer had the highest rate of death among all cancer types worldwide in 2022. In that year, there were around 17 deaths from trachea, bronchus and lung cancer per 100,000 population. The death rate for all cancers was 91.1 per 100,000 population. This statistic shows the rate of cancer deaths worldwide in 2022, by type of cancer.

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).

Source: https://ourworldindata.org/cancer

The dataset titled "Cancer Types Causing Death," sourced from Our World in Data, provides a comprehensive overview of global cancer mortality trends. According to the dataset, lung cancer leads as the most fatal cancer worldwide, with approximately 1.8 million deaths in 2022, accounting for 18.7% of all cancer-related fatalities . Following lung cancer, colorectal cancer ranks second, causing about 900,000 deaths (9.3%), while liver cancer and breast cancer account for 760,000 (7.8%) and 670,000 (6.9%) deaths, respectively. Stomach cancer also remains a significant cause of death, with 660,000 fatalities (6.8%) .

The dataset highlights that lung cancer's prevalence is closely linked to tobacco use, particularly in regions like Asia. In contrast, breast cancer predominantly affects women, while colorectal cancer impacts both genders equally. Notably, the dataset indicates a decline in age-standardized death rates for certain cancers, such as stomach cancer, due to improved hygiene, sanitation, and antibiotic treatments targeting Helicobacter pylori infections . Our World in Data

Additionally, the dataset underscores the global disparity in cancer mortality, with approximately 70% of cancer deaths occurring in low- and middle-income countries . This disparity is attributed to factors like limited access to early detection, treatment, and preventive measures. The dataset serves as a valuable resource for understanding the global burden of cancer and the need for targeted public health interventions. World Health Organization

Content

In this dataset you'll find the deaths from cancer at hospitals in the respective 50 states in America.

Acknowledgements

This dataset comes from https://data.world/dartmouthatlas/cancer-patients-death.

Variables included in the best fitted models for different types of cancer morbidity; main variables are denoted as age (a), year (t), gender (g), region (r), deprivation (d), with corresponding interactions shown as, e.g., a:t.

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.

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.

Variables included in the best fitted models for different types of cancer mortality; main variables are denoted as age (a), year (t), gender (g), region (r), deprivation (d), average age-at-diagnosis (AAD), with corresponding interactions shown as, e.g., a:t.

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

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.

Legacy unique identifier: P00508

Hazard ratios, 95% confidence intervals, and significance values for each quintile increase in SES indices are given from Cox regression models for risk of cancer mortality. Covariates listed were force-entered in adjusted Cox regression models.*p≤0.05 †p≤0.01 SES = socioeconomic status; BMI = body mass index

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.