Population based cancer incidence rates were abstracted from National Cancer Institute, State Cancer Profiles for all available counties in the United States for which data were available. This is a national county-level database of cancer data that are collected by state public health surveillance systems. All-site cancer is defined as any type of cancer that is captured in the state registry data, though non-melanoma skin cancer is not included. All-site age-adjusted cancer incidence rates were abstracted separately for males and females. County-level annual age-adjusted all-site cancer incidence rates for years 2006–2010 were available for 2687 of 3142 (85.5%) counties in the U.S. Counties for which there are fewer than 16 reported cases in a specific area-sex-race category are suppressed to ensure confidentiality and stability of rate estimates; this accounted for 14 counties in our study. Two states, Kansas and Virginia, do not provide data because of state legislation and regulations which prohibit the release of county level data to outside entities. Data from Michigan does not include cases diagnosed in other states because data exchange agreements prohibit the release of data to third parties. Finally, state data is not available for three states, Minnesota, Ohio, and Washington. The age-adjusted average annual incidence rate for all counties was 453.7 per 100,000 persons. We selected 2006–2010 as it is subsequent in time to the EQI exposure data which was constructed to represent the years 2000–2005. We also gathered data for the three leading causes of cancer for males (lung, prostate, and colorectal) and females (lung, breast, and colorectal). The EQI was used as an exposure metric as an indicator of cumulative environmental exposures at the county-level representing the period 2000 to 2005. A complete description of the datasets used in the EQI are provided in Lobdell et al. and methods used for index construction are described by Messer et al. The EQI was developed for the period 2000– 2005 because it was the time period for which the most recent data were available when index construction was initiated. The EQI includes variables representing each of the environmental domains. The air domain includes 87 variables representing criteria and hazardous air pollutants. The water domain includes 80 variables representing overall water quality, general water contamination, recreational water quality, drinking water quality, atmospheric deposition, drought, and chemical contamination. The land domain includes 26 variables representing agriculture, pesticides, contaminants, facilities, and radon. The built domain includes 14 variables representing roads, highway/road safety, public transit behavior, business environment, and subsidized housing environment. The sociodemographic environment includes 12 variables representing socioeconomics and crime. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., L. Messer, K. Rappazzo , C. Gray, S. Grabich , and D. Lobdell. County-level environmental quality and associations with cancer incidence#. Cancer. John Wiley & Sons Incorporated, New York, NY, USA, 123(15): 2901-2908, (2017).

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

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.

The graph displays the total number of new cancer cases in the U.S. from 1999 to 2022. The x-axis represents the years, while the y-axis shows the total number of new cancer cases reported each year. The data shows a steady increase in new cancer cases over the years, with the highest number recorded in 2021, at 1,869,872 cases. The lowest number occurred in 1999, with 1,304,271 cases. A notable dip in 2020 reflects the impact of the COVID-19 pandemic, with cases dropping to 1,718,755 before rising again in 2021. The data highlights the growing prevalence of cancer cases in the U.S. over two decades.

In 2022, Utah had the lowest death rate from cancer among all U.S. states with around 116 deaths per 100,000 population. The states with the highest cancer death rates at that time were Mississippi, Kentucky and West Virginia. This statistic shows cancer death rates in the United States in 2022, by state.

This dataset contains Age-Adjusted Rate, Confidence Interval, Average Annual Count, and Trend field information for US States for the average 5 year span from 2012 to 2016.Data is segmented by sex and age, with fields describing the sex and age group tabulated.For more information, visit statecancerprofiles.cancer.gov Data NotationsState Cancer Registries may provide more current or more local data.† Incidence rates (cases per 100,000 population per year) are age-adjusted to the 2000 US standard population seer.cancer.gov/stdpopulations/stdpop.19ages.html. Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Rates calculated using SEER*Stat. [seer.cancer.gov/seerstat]Population counts for denominators are based on Census populations as modified [seer.cancer.gov/popdata] by NCI. The 1969-2016 US Population Data File [seer.cancer.gov/popdata] is used for SEER and NPCR incidence rates.‡ Incidence data come from different sources. Due to different years of data availability, most of the trends are AAPCs based on APCs but some are APCs calculated in SEER*Stat. Please refer to the source for each area for additional information. Rates and trends are computed using different standards for malignancy. For more information see malignant.html.^ All Stages refers to any stage in the Surveillance, Epidemiology, and End Results (SEER) summary stage [seer.cancer.gov/tools/ssm].Healthy People 2020 Objectives [www.healthypeople.gov]provided by the Centers for Disease Control and Prevention [www.cdc.gov]. Michigan Data do not include cases diagnosed in other states for those states in which the data exchange agreement specifically prohibits the release of data to third parties.Trend Data not available for Nevada.Data Source Field Key:(1) Source: CDC's National Program of Cancer Registries Cancer Surveillance System (NPCR-CSS) November 2018 data submission and SEER November 2018 submission as published in United States Cancer Statistics nccd.cdc.gov/uscs Source: State Cancer Registry and the CDC's National Program of Cancer Registries Cancer Surveillance System (NPCR-CSS) November 2018 data submission. State rates include rates from metropolitan areas funded by SEER [seer.cancer.gov/registries].(6) Source: State Cancer Registry and the CDC's National Program of Cancer Registries Cancer Surveillance System (NPCR-CSS) November 2018 data submission.(7) Source: SEER November 2018 submission.8 Source: Incidence data provided by the SEER Program. [seer.cancer.gov] AAPCs are calculated by the Joinpoint Regression Program [surveillance.cancer.gov/joinpoint] and are based on APCs. Data are age-adjusted to the 2000 US standard population www.seer.cancer.gov/stdpopulations/single_age.html. Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Population counts for denominators are based on Census populations as modified by NCI. The 1969-2017 US Population Data [seer.cancer.gov/popdata] File is used with SEER November 2018 data. Please note that the data comes from different sources. Due to different years [statecancerprofiles.cancer.gov/historicaltrend/differences.html] of data availability, most of the trends are AAPCs based on APCs but some are APCs calculated in SEER*Stat. [seer.cancer.gov/seerstat] Please refer to the source for each graph for additional information. Some data are not available [http://statecancerprofiles.cancer.gov/datanotavailable.html] for combinations of geography, cancer site, age, and race/ethnicity.

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated on 8/14/2024

Cancer Mortality Rate - This indicator shows the age-adjusted mortality rate from cancer (per 100,000 population). Maryland’s age adjusted cancer mortality rate is higher than the US cancer mortality rate. Cancer impacts people across all population groups, however wide racial disparities exist. https://health.maryland.gov/pophealth/Documents/SHIP/SHIP%20Lite%20Data%20Details/Cancer%20Mortality%20Rate.pdf"/> Link to Data Details

US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data was reported at 17.500 NA in 2016. This records an increase from the previous number of 17.200 NA for 2015. US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data is updated yearly, averaging 17.500 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 21.600 NA in 2000 and a record low of 17.200 NA in 2015. US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Dataset Card for Lung Cancer

  Dataset Summary

The effectiveness of cancer prediction system helps the people to know their cancer risk with low cost and it also helps the people to take the appropriate decision based on their cancer risk status. The data is collected from the website online lung cancer prediction system .

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/virtual10/lungs_cancer.

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.

Prostate cancer incidence rates in the United States vary significantly across racial and ethnic groups, with Non-Hispanic Black men facing the highest risk. According to recent data, Non-Hispanic Black males have an incidence rate of 194.8 per 100,000 population, which is substantially higher than the overall rate of 120.2 per 100,000. This stark disparity highlights the importance of targeted screening and prevention efforts to address this health inequality. Incidence and mortality trends The burden of prostate cancer in the U.S. has grown in recent years. In 2025, approximately 313,780 men were projected to be diagnosed with prostate cancer, representing a significant increase from previous years. Despite this rising incidence, mortality rates have shown improvement. In 2022, the prostate cancer death rate was 18.7 per 100,000 men, compared to a rate of almost 39 per 100,000 in the year 1990. This decrease reflects advancements in treatment and early detection. Risk factors and survival rates Age remains a critical risk factor for prostate cancer, with men aged 65 to 84 having a 10.6 percent chance of developing the disease. However, there is encouraging news regarding survival rates. From 2014 to 2020, the five-year relative survival rate for prostate cancer patients in the U.S. was an impressive 97 percent. This high survival rate underscores the importance of early detection and the effectiveness of current treatment options.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

SEER collects cancer incidence data from population-based cancer registries covering approximately 47.9 percent of the U.S. population. The SEER registries collect data on patient demographics, primary tumor site, tumor morphology, stage at diagnosis, and first course of treatment, and they follow up with patients for vital status.There are two data products available: SEER Research and SEER Research Plus. This was motivated because of concerns about the increasing risk of re-identifiability of individuals. The Research Plus databases require more rigorous process for access that includes user authentication through Institutional Account or multiple-step request process for Non-Institutional users.

United States US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data was reported at 14.600 % in 2016. This records an increase from the previous number of 14.300 % for 2015. United States US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data is updated yearly, averaging 14.600 % from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 18.000 % in 2000 and a record low of 14.300 % in 2015. United States US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted Average;

United States US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data was reported at 11.800 NA in 2016. This records an increase from the previous number of 11.600 NA for 2015. United States US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data is updated yearly, averaging 11.800 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 14.600 NA in 2000 and a record low of 11.600 NA in 2015. United States US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

WONDER online databases include county-level Compressed Mortality (death certificates) since 1979; county-level Multiple Cause of Death (death certificates) since 1999; county-level Natality (birth certificates) since 1995; county-level Linked Birth / Death records (linked birth-death certificates) since 1995; state & large metro-level United States Cancer Statistics mortality (death certificates) since 1999; state & large metro-level United States Cancer Statistics incidence (cancer registry cases) since 1999; state and metro-level Online Tuberculosis Information System (TB case reports) since 1993; state-level Sexually Transmitted Disease Morbidity (case reports) since 1984; state-level Vaccine Adverse Event Reporting system (adverse reaction case reports) since 1990; county-level population estimates since 1970. The WONDER web server also hosts the Data2010 system with state-level data for compliance with Healthy People 2010 goals since 1998; the National Notifiable Disease Surveillance System weekly provisional case reports since 1996; the 122 Cities Mortality Reporting System weekly death reports since 1996; the Prevention Guidelines database (book in electronic format) published 1998; the Scientific Data Archives (public use data sets and documentation); and links to other online data sources on the "Topics" page.

One woman in nine can expect to develop breast cancer during her lifetime and one in 25 will die from the disease. Statistically low incidences of breast cancer are found in Newfoundland and Labrador, the territories, and northern areas of most provinces. Otherwise, each province has one or more pockets of significantly high breast cancer incidence. These are often located in more southerly areas, but they do not seem to be restricted to either urban or rural areas alone. Breast cancer rates are a health status indicator. They can be used to help assess health conditions. Health status refers to the state of health of a person or group, and measures causes of sickness and death. It can also include people’s assessment of their own health.

Some racial and ethnic categories are suppressed for privacy and to avoid misleading estimates when the relative standard error exceeds 30% or the unweighted sample size is less than 50 respondents. Margins of error are estimated at the 90% confidence level.

Data Source: Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey (BRFSS) Data

Why This Matters

Colorectal cancer is the third leading cause of cancer death in the U.S. for men and women. Although colorectal cancer is most common among people aged 65 to 74, there has been an increase in incidences among people aged 40 to 49.

Nationally, Black people are disproportionately likely to both have colorectal cancer and die from it. Hispanic residents, and especially those with limited English proficiency, report having the lowest rate of colorectal cancer screenings.

Racial disparities in education, poverty, health insurance coverage, and English language proficiency are all factors that contribute to racial gaps in receiving colorectal cancer screenings. Increased colorectal cancer screening utilization has been shown to nearly erase the racial disparities in the death rate of colorectal cancer.

The District Response

The Colorectal Cancer Control Program (DC3C) aims to reduce colon cancer incidence and mortality by increasing colorectal cancer screening rates among District residents.

DC Health’s Cancer and Chronic Disease Prevention Bureau works with healthcare providers to improve the use of preventative health services and provide colorectal cancer screening services.

DC Health maintains the District of Columbia Cancer Registry (DCCR) to track cancer incidences, examine environmental substances that cause cancer, and identify differences in cancer incidences by age, gender, race, and geographical location.

IntroductionSociodemographic disparities in genitourinary cancer-related mortality have been insufficiently studied, particularly across multiple cancer types. This study aimed to investigate gender, racial, and geographic disparities in mortality rates for the most common genitourinary cancers in the United States.MethodsMortality data for prostate, bladder, kidney, and testicular cancers were obtained from the Centers for Disease Control and Prevention (CDC) WONDER database between 1999 and 2020. Age-adjusted mortality rates (AAMRs) were analyzed by year, gender, race, urban–rural status, and geographic region using a significance level of p < 0.05.ResultsOverall, AAMRs for prostate, bladder, and kidney cancer declined significantly, while testicular cancer-related mortality remained stable. Bladder and kidney cancer AAMRs were 3–4 times higher in males than females. Prostate cancer mortality was highest in black individuals/African Americans and began increasing after 2015. Bladder cancer mortality decreased significantly in White individuals, Black individuals, African Americans, and Asians/Pacific Islanders but remained stable in American Indian/Alaska Natives. Kidney cancer-related mortality was highest in White individuals but declined significantly in other races. Testicular cancer mortality increased significantly in White individuals but remained stable in Black individuals and African Americans. Genitourinary cancer mortality decreased in metropolitan areas but either increased (bladder and testicular cancer) or remained stable (kidney cancer) in non-metropolitan areas. Prostate and kidney cancer mortality was highest in the Midwest, bladder cancer in the South, and testicular cancer in the West.DiscussionSignificant sociodemographic disparities exist in the mortality trends of genitourinary cancers in the United States. These findings highlight the need for targeted interventions and further research to address these disparities and improve outcomes for all populations affected by genitourinary cancers.

Age-adjusted mortality rates for the contiguous United States in 2000–2005 were obtained from the Wide-ranging Online Data for Epidemiologic Research system of the U.S. Centers for Disease Control and Prevention (CDC) (2015). Age-adjusted mortality rates were weighted averages of the age-specific death rates, and they were used to account for different age structures among populations (Curtin and Klein 1995). The mortality rates for counties with < 10 deaths were suppressed by the CDC to protect privacy and to ensure data reliability; only counties with ≥ 10 deaths were included in the analyses. The underlying cause of mortality was specified using the World Health Organization’s International Statistical Classification of Diseases and Related Health Problems (10th revision; ICD-10). In this study, we focused on the all-cause mortality rate (A00-R99) and on mortality rates from the three leading causes: heart disease (I00-I09, I11, I13, and I20-I51), cancer (C00-C97), and stroke (I60- I69) (Heron 2013). We excluded mortality due to external causes for all-cause mortality, as has been done in many previous studies (e.g., Pearce et al. 2010, 2011; Zanobetti and Schwartz 2009), because external causes of mortality are less likely to be related to environmental quality. We also focused on the contiguous United States because the numbers of counties with available cause-specific mortality rates were small in Hawaii and Alaska. County-level rates were available for 3,101 of the 3,109 counties in the contiguous United States (99.7%) for all-cause mortality; for 3,067 (98.6%) counties for heart disease mortality; for 3,057 (98.3%) counties for cancer mortality; and for 2,847 (91.6%) counties for stroke mortality. The EQI includes variables representing five environmental domains: air, water, land, built, and sociodemographic (2). The domain-specific indices include both beneficial and detrimental environmental factors. The air domain includes 87 variables representing criteria and hazardous air pollutants. The water domain includes 80 variables representing overall water quality, general water contamination, recreational water quality, drinking water quality, atmospheric deposition, drought, and chemical contamination. The land domain includes 26 variables representing agriculture, pesticides, contaminants, facilities, and radon. The built domain includes 14 variables representing roads, highway/road safety, public transit behavior, business environment, and subsidized housing environment. The sociodemographic environment includes 12 variables representing socioeconomics and crime. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jian, Y., L. Messer, J. Jagai, K. Rappazzo, C. Gray, S. Grabich, and D. Lobdell. Associations between environmental quality and mortality in the contiguous United States 2000-2005. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 125(3): 355-362, (2017).