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).

Users can access data about cancer statistics in the United States including but not limited to searches by type of cancer and race, sex, ethnicity, age at diagnosis, and age at death. Background Surveillance Epidemiology and End Results (SEER) database’s mission is to provide information on cancer statistics to help reduce the burden of disease in the U.S. population. The SEER database is a project to the National Cancer Institute. The SEER database collects information on incidence, prevalence, and survival from specific geographic areas representing 28 percent of the United States population. User functionality Users can access a variety of reso urces. Cancer Stat Fact Sheets allow users to look at summaries of statistics by major cancer type. Cancer Statistic Reviews are available from 1975-2008 in table format. Users are also able to build their own tables and graphs using Fast Stats. The Cancer Query system provides more flexibility and a larger set of cancer statistics than F ast Stats but requires more input from the user. State Cancer Profiles include dynamic maps and graphs enabling the investigation of cancer trends at the county, state, and national levels. SEER research data files and SEER*Stat software are available to download through your Internet connection (SEER*Stat’s client-server mode) or via discs shipped directly to you. A signed data agreement form is required to access the SEER data Data Notes Data is available in different formats depending on which type of data is accessed. Some data is available in table, PDF, and html formats. Detailed information about the data is available under “Data Documentation and Variable Recodes”.

Context

This data comes from aggregation of the tables available on the NIH's National Cancer Institutes State Cancer Profiles, specifically with their incidence tables.

The objective of the State Cancer Profiles Web site is to provide a system to characterize the cancer burden in a standardized manner in order to motivate action, integrate surveillance into cancer control planning, characterize areas and demographic groups, and expose health disparities. The focus is on cancer sites for which there are evidence based control interventions. Interactive graphics and maps provide visual support for deciding where to focus cancer control efforts.

Content

This data has cancer Incidence rates broken down by US County and includes data aggregated from 2012-2016. It has both incidence rates per 100k as well as yearly totals averaged over that period

Potential Future Work

This data is summarized across other potentially illuminating fields. The State Cancer Profiles can be further broken down by cancer area, race/ethnicity, sex, age, and stage. If more fidelity on the data would be helpful please add it to the discussion section and I can work on adding it!

Data Use Restrictions

Read Carefully Before Using

By using these data, you signify your agreement to comply with the following statutorily based requirements.

The Public Health Service Act (42 U.S.C. 242m(d)) provides that the data collected by the National Center for Health Statistics (NCHS) may be used only for the purpose for which they were obtained; any effort to determine the identity of any reported cases, or to use the information for any purpose other than for statistical reporting and analysis, is against the law. The National Program of Cancer Registries (NPCR), Centers for Disease Control and Prevention (CDC), has obtained an assurance of confidentiality pursuant to Section 308(d) of the Public Health Service Act, 42 U.S.C. 242m(d). This assurance provides that identifiable or potentially identifiable data collected by the NPCR may be used only for the purpose for which they were obtained unless the person or establishment from which they were obtained has consented to such use. Any effort to determine the identity of any reported cases, or to use the information for any purpose other than statistical reporting and analysis, is a violation of the assurance.

Therefore users will: - Use the data for statistical reporting and analysis only. - Make no attempt to learn the identity of any person or establishment included in these data. - Make no disclosure or other use of the identity of any person or establishment discovered inadvertently, and advise the appropriate contact for the data provider. In addition to immediately notifying "Contact Us" of the potential disclosure, - For mortality data, notify the Confidentiality Officer at the National Center for Health Statistics (Alvan O. Zarate, Ph.D.), 3311 Toledo Road, Rm 7116, Hyattsville, MD 20782, Phone: 301-458-4601, Fax: 301-458-4021) - For incidence data notify both the Federal agency that provided the data and notify the relevant state or metropolitan area cancer registryExternal Web Site Policy, of any such discovery. - For CDC's National Program of Cancer Registries (NPCR) areas, notify the Associate Director for Science, Office of Science Policy and Technology Transfer, CDC, Mailstop D-50, 1600 Clifton Road, N.E., Atlanta, Georgia, 30333, Phone: 404-639-7240) - For NCI's Surveillance, Epidemiology, and End Results (SEER) Program registry areas, notify the Branch Chief of the Cancer Statistics Branch of the Surveillance Research Program, Division of Cancer Control and Population Sciences, NCI, BG 9609 MSC 9760, 9609 Medical Center Drive, Bethesda, MD 20892-9760, Phone: 301-496-8510, Fax: 301-496-9949.

This statistic shows the number of brain and other nervous system cancer deaths in the United States from 1999 to 2023. The highest number of brain and nervous system cancer deaths was ******, reported in 2023.

This is a linked dataset between drinking water data and cancer data. Drinking Water Data: County-level concentrations of arsenic from CWSs between 2000 and 2010 were collected from the Center for Disease Control and Prevention’s (CDC) National Environmental Public Health Tracking Network (NEPHTN) (Centers for Disease Control and Prevention, 2018a). Annual mean drinking water arsenic concentrations from 2000 to 2010 were available for a total of 87,662 samples from 75,453 CWS from 26 states, representing 1,425 counties. For samples identified as non-detects, the most frequently reported values were 0.5 ppb and 1 ppb, with a range of 0 ppb to 10 ppb. For non-detect samples reported as zero, the value was substituted with a constant of 0.25 ppb (Almberg et al., 2017; Bulka et al., 2016). Of the samples that were reported as non-detects, 10.87% were reported as zeros. Cancer Data: County-level cancer counts and incidence rates for bladder, colorectal, and kidney cancers were acquired from the National Cancer Institute (NCI) and CDC’s State Cancer Profiles for 2011 through 2015 for adults (age ≥ 50) to match the counties with exposure data (National Cancer Institute and Centers for Disease Control and Prevention, 2018a). We utilized the time period 2011-2015 to provide a lag following the exposure period of 2000-2010. The State Cancer Profiles provide age-adjusted county-level cancer incidence, prevalence, mortality rates and average annual counts for 20 different types of cancers and select demographics (National Cancer Institute and Centers for Disease Control and Prevention, 2018b). Counties where there were less than 16 reported cases in a specific county, sex, and/or race category were suppressed to ensure confidentiality and stability of rate estimates (National Cancer Institute and Centers for Disease Control and Prevention, 2018a). This dataset is associated with the following publication: Krajewski, A., M. Jimenez, K. Rappazzo, D. Lobdell, and J. Jagai. Aggregated Cumulative County Arsenic in Drinking Water and Associations with Bladder, Colorectal, and Kidney Cancers, Accounting for Population Served. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 31(6): 979-989, (2021).

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.

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).

This study aims to evaluate the feasibility of applying a method of estimating the incidence of cancer to regions of the state of São Paulo, Brazil, from real data (not estimated) and retrospectively comparing the results obtained with the official estimates. A method based on mortality and on the incidence to mortality (I/M) ration was used according to sex, age, and tumor location. In the I/M numerator, new cases of cancer were used from the population records of Jaú and São Paulo from 2006-2010; in the denominator, deaths from 2006-2010 in the respective areas, extracted from the national mortality system. The estimates resulted from the multiplication of I/M by the number of cancer deaths in 2010 for each region. Population data from the 2010 Demographic Census were used to estimate incidence rates. For the adjustment by age, the world standard population was used. We calculated the relative differences between the gross incidence rates estimated in this study and the official ones. Age-adjusted cancer incidence rates were 260.9/100,000 for men and 216.6/100,000 for women. Prostate cancer was the most common in males, whereas breast cancer was most common in females. Differences between the rates of this study and the official rates were 3.3% and 1.5% for each sex. The estimated incidence was compatible with the officially presented state profile, indicating that the application of real data did not alter the morbidity profile, while it did indicate different risk magnitudes. Despite the over-representativeness of the cancer registry with greater population coverage, the selected method proved feasible to point out different patterns within the state.

This statistic shows the rate of brain and other nervous system cancer deaths in the United States from 1999 to 2023. The maximum rate within the given period was 4.6 per 100,000 inhabitants in 1999, while the minimum rate stood at 4.2 in 2006, 2007, and 2010.

Oncology Information Systems Market Outlook

According to the latest research conducted in 2025, the global Oncology Information Systems market size reached USD 3.6 billion in 2024, demonstrating robust growth driven by increasing cancer prevalence and technological advancements in healthcare IT. The market is projected to expand at a CAGR of 7.8% over the forecast period, reaching an estimated USD 7.1 billion by 2033. This growth is primarily fueled by the rising demand for integrated healthcare solutions, the need for efficient cancer care management, and the ongoing digital transformation of healthcare infrastructure worldwide.

A significant growth factor for the Oncology Information Systems (OIS) market is the mounting global cancer burden. With cancer cases expected to rise significantly over the next decade, healthcare providers are increasingly recognizing the value of comprehensive oncology information systems in streamlining clinical workflows, enhancing patient data management, and improving treatment outcomes. The integration of advanced analytics, artificial intelligence, and interoperability features into OIS platforms enables clinicians to make more informed decisions, personalize treatment plans, and monitor patient progress with greater accuracy. Furthermore, the shift towards value-based care models and the growing emphasis on patient-centered approaches are compelling healthcare organizations to adopt sophisticated information systems that facilitate multidisciplinary collaboration and optimize resource utilization.

Another key driver of market expansion is the rapid evolution of digital health technologies and the proliferation of cloud-based solutions. Oncology information systems are being increasingly equipped with cloud capabilities, offering scalability, remote access, and enhanced data security. This transition is particularly advantageous for large hospital networks and oncology clinics that require seamless data sharing and real-time collaboration across multiple locations. Additionally, the integration of OIS with electronic medical records (EMRs), treatment planning systems, and telemedicine platforms is creating a holistic ecosystem that supports the continuum of cancer care, from diagnosis through survivorship. The ongoing investments in healthcare IT infrastructure by governments and private entities are further accelerating the adoption of these advanced systems.

Regulatory compliance and the need for standardized data management are also propelling the adoption of oncology information systems. Stringent regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in Europe mandate robust data security and privacy measures, compelling healthcare providers to implement comprehensive OIS solutions. These systems not only facilitate compliance but also reduce the risk of data breaches, improve auditability, and enhance the overall quality of care. As the complexity of oncology treatment protocols increases, the demand for systems capable of supporting evidence-based practices, clinical trial management, and outcomes reporting is expected to rise, further fueling market growth.

From a regional perspective, North America currently holds the largest share of the global oncology information systems market, driven by advanced healthcare infrastructure, high adoption rates of digital technologies, and significant investments in cancer research. Europe follows closely, benefiting from supportive government initiatives and a strong focus on healthcare digitization. The Asia Pacific region is emerging as a lucrative market, characterized by a rapidly growing patient population, increasing healthcare expenditure, and expanding access to modern cancer care facilities. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by improving healthcare systems and rising awareness of the benefits of oncology information systems.

Cancer Registry Software is becoming an essential component of oncology information systems, as it plays a crucial role in the systematic collection, storage, and analysis of cancer patient data. This software aids in tracking cancer incidence, prevalence, and survival rates, providing valuable insights

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Demographic Summary of Available Imaging

Characteristic	Value (N = 26254)
Age (years)	Mean ± SD: 61.4± 5 Median (IQR): 60 (57-65) Range: 43-75
Sex	Male: 15512 (59%) Female: 10742 (41%)
Race	White: 23969 (91.3%) Black: 1135 (4.3%) Asian: 547 (2.1%) American Indian/Alaska Native: 88 (0.3%) Native Hawaiian/Other Pacific Islander: 87 (0.3%) Unknown: 428 (1.6%)
Ethnicity	Not Available

Background: The aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. The advent of low-dose helical computed tomography (CT) altered the landscape of lung-cancer screening, with studies indicating that low-dose CT detects many tumors at early stages. The National Lung Screening Trial (NLST) was conducted to determine whether screening with low-dose CT could reduce mortality from lung cancer.

Methods: From August 2002 through April 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 U.S. medical centers. Participants were randomly assigned to undergo three annual screenings with either low-dose CT (26,722 participants) or single-view posteroanterior chest radiography (26,732). Data were collected on cases of lung cancer and deaths from lung cancer that occurred through December 31, 2009. This dataset includes the low-dose CT scans from 26,254 of these subjects, as well as digitized histopathology images from 451 subjects.

Results: The rate of adherence to screening was more than 90%. The rate of positive screening tests was 24.2% with low-dose CT and 6.9% with radiography over all three rounds. A total of 96.4% of the positive screening results in the low-dose CT group and 94.5% in the radiography group were false positive results. The incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose CT group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [CI], 1.03 to 1.23). There were 247 deaths from lung cancer per 100,000 person-years in the low-dose CT group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose CT screening of 20.0% (95% CI, 6.8 to 26.7; P=0.004). The rate of death from any cause was reduced in the low-dose CT group, as compared with the radiography group, by 6.7% (95% CI, 1.2 to 13.6; P=0.02).

Conclusions: Screening with the use of low-dose CT reduces mortality from lung cancer. (Funded by the National Cancer Institute; National Lung Screening Trial ClinicalTrials.gov number, NCT00047385).

Data Availability: A summary of the National Lung Screening Trial and its available datasets are provided on the Cancer Data Access System (CDAS). CDAS is maintained by Information Management System (IMS), contracted by the National Cancer Institute (NCI) as keepers and statistical analyzers of the NLST trial data. The full clinical data set from NLST is available through CDAS. Users of TCIA can download without restriction a publicly distributable subset of that clinical data, along with the CT and Histopathology images collected during the trial. (These previously were restricted.)

IntroductionLung cancer is a leading cause of cancer incidence and death in the United States. Although most firefighters are fit and do not smoke, they are exposed to many known carcinogens during and in the aftermath of firefighting activities. Comprehensive epidemiologic investigations on lung cancer survival for both career and volunteer firefighters have not been undertaken.MethodsData from the Florida Cancer Data System (1981–2014) were linked with firefighter certification records from the Florida State Fire Marshal’s Office to identify all patients of this occupational group; lung cancer cause-specific survival data were compared with other occupational groups using Cox regression models with occupation as the main effect. Adjusted hazard ratios (aHR) and 95% confidence intervals (95% CI) were calculated.ResultsOut of 210,541 male lung cancer cases diagnosed in Florida (1981–2014), 761 were firefighters (604 career, 157 volunteer). Lung cancer death was similar between volunteer (75.2%) and career firefighters (74.0%) but lower than non-firefighters (80.0%). Survival at 5 years was higher among firefighters (29.7%; career: 30.3%; volunteer: 27.4%) than non-firefighters (23.8%). In a multivariable model, compared with non-firefighters, firefighters have significantly higher cause-specific survival (aHR = 0.84; 95% CI: 0.77–0.91; p < 0.001). However, there were no significant survival differences between career and volunteer firefighters (1.14; 0.93–1.39; p = 0.213). In a separate multivariable model with firefighters as the comparator, other broad occupational groups had significantly lower cause-specific survival [white collar: 1.11 (1.02–1.21); blue collar: 1.15 (1.05–1.25); service: 1.13 (1.03–1.25); others/unknown: 1.21 (1.12–1.32); all p-values < 0.02].ConclusionLung cancer survival is significantly higher among firefighters compared with non-firefighters, but there is no significant difference between career and volunteer firefighters. Improved survival for firefighters might be due to a healthy worker effect, lower smoking prevalence relative to other worker groups, and possibly superior treatment adherence and compliance. Many firefighters are cross-trained as EMTs/paramedics and possess a level of medical knowledge that may favorably impact treatment engagement and better navigation of complex cancer care.

This dataset contains model-based census tract estimates. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 36 measures: 13 for health outcomes, 9 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, and 3 for health status. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2021 or 2020 data, Census Bureau 2010 population data, and American Community Survey 2015–2019 estimates. The 2023 release uses 2021 BRFSS data for 29 measures and 2020 BRFSS data for seven measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places.

Context

The Behavioral Risk Factor Surveillance System (BRFSS) is the Unites States’s premier system of health-related telephone surveys that collect state data about U.S. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.

By collecting behavioral health risk data at the state and local level, BRFSS has become a powerful tool for targeting and building health promotion activities.

2011 to present. BRFSS combined land line and cell phone prevalence data. BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. Data will be updated annually as it becomes available. Detailed information on sampling methodology and quality assurance can be found on the BRFSS website (http://www.cdc.gov/brfss).

Dataset

2,289,902 rows by 27 columns

Each entry contains the number and percent of responses to a survey question for a given year and demographic category sub-group.

Demographic categories:

• Overall
• Gender
• Age Group
• Race/Ethnicity
• Education Attained
• Household Income

Question categories:

• Alcohol Consumption
• Cholesterol Awareness
• Chronic Health Indicators
• Colorectal Cancer Screening
• Demographics
• Fruits and Vegetables
• Health Care Access/Coverage
• Health Status
• HIV-AIDS
• Hypertension Awareness
• Immunization
• Injury
• Oral Health
• Overweight and Obesity (BMI)
• Physical Activity
• Prostate Cancer
• Tobacco Use
• Women's Health
• E-Cigarette Use
• Days of Poor Health

Source

Methodology Glossary Original data source Date Created: June 4, 2015 Last Updated: October 21, 2022

This data comes under public domain licensing. Please use it responsibly and ethically. Thank you :)

Thumbnail image thanks to: https://unsplash.com/photos/QsBfOwMoPNY?utm_source=unsplash&utm_medium=referral&utm_content=creditShareLink

The Behavioral Risk Factor Surveillance System (BRFSS) is a collaborative project between all of the states in the United States and participating US territories and the Centers for Disease Control and Prevention (CDC).

BRFSS’s objective is to collect uniform state-specific data on health risk behaviors, chronic diseases and conditions, access to health care, and use of preventive health services related to the leading causes of death and disability in the United States. BRFSS conducts both landline and mobile phone-based surveys with individuals over the age of 18. General factors assessed by the BRFSS in 2020 included health status and healthy days, exercise, insufficient sleep, chronic health conditions, oral health, tobacco use, cancer screenings, and access to healthcare.

Section Names:

1. Record Identification -> Columns 0 to 8
2. Land Line Introduction -> Columns 9 to 20
3. Cell Phone Introduction -> Columns 21 to 30
4. Respondent Sex -> Column 31
5. Health Status -> Column 32 a. Healthy Days -> Columns 33 to 35 b. Health Care Access -> Columns 36 to 39 c. Exercise -> Column 40 d. Inadequate Sleep -> Column 41 e. Chronic Health Conditions -> Columns 42 to 54 f. Oral Health -> Columns 55 and 56
6. Demographics -> Columns 57 to 69
7. Disability -> Columns 70 to 75
8. Tobacco Use -> Columns 71 to 75
9. Alcohol Consumption -> Columns 76 to 79
10. Immunization -> Columns 80 to 83
11. Falls -> Columns 84 and 85
12. Seatbelt Use and Drinking and Driving -> Columns 86 and 87
13. Breast and Cervical Cancer Screening -> Columns 88 to 94
14. Prostate Cancer Screening -> Columns 95 to 100
15. Colorectal Cancer Screening -> Columns 101 to 110
16. HIV/AIDS -> Columns 111 to 113
17. Diabetes -> Columns 114 to 124
18. ME/CFS -> Columns 125 to 127
19. Hepatitis Treatment -> Columns 128 to 133
20. Health Care Access -> Column 134
21. Cognitive Decline -> Columns 135 to 140
22. Caregiver -> Columns 141 to 149
23. E-Cigarettes -> Columns 150 and 151
24. Marijuana Use -> Columns 152 to 154
25. Lung Cancer Screening -> Columns 155 to 158
26. Cancer Survivorship: a. Type of Cancer -> Columns 159 to 162 b. Course of Treatment -> Columns 163 to 170 c. Pain Management -> Columns 171 and 172
27. Prostate Cancer Screening Decision Making -> Columns 173 and 174
28. HPV Vaccination -> Columns 175 and 176
29. Tetanus Diphtheria (Tdap) (Adults) -> Column 177
30. Place of Flu Vaccination -> Column 178
31. Sex at Birth -> Column 179
32. Sexual Orientation and Gender Identity (SOGI) -> Columns 180 to 182
33. Adverse Childhood Experience -> Columns 183 to 193
34. Random Child Selection -> Columns 194 and 195
35. Childhood Asthma Prevalence -> Columns 196 and 197
36. Questionnaire Version -> Column 198
37. Questionnaire Language -> Column 199
38. Urban Rural -> Columns 200 and 201
39. Weighting Variables -> Columns 202 to 207, 212 to 215
40. Child Demographic Variables -> Columns 208 to 210
41. Child Weighting Variables -> Column 211
42. Calculated Variables -> Columns 216 to 228, 236 to 278
43. Calculated Race Variables -> Columns 229 to 235

Acknowledgements

This dataset has been published annually by the CDC since 1984. You can find the original dataset as a ASCII format and past years data from here

Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, [2020].

This dataset contains model-based place (incorporated and census designated places) level estimates for the PLACES 2021 release. PLACES is the expansion of the original 500 Cities Project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. The dataset includes estimates for 29 measures: 4 chronic disease-related risk behaviors, 13 health outcomes, 3 health status, and 9 on using preventive services. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2019 or 2018 data, Census Bureau 2010 population data, and American Community Survey (ACS) 2015–2019 or 2014–2018 estimates. The 2021 release uses 2019 BRFSS data for 22 measures and 2018 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours a night). Seven measures are based on the 2018 BRFSS because the relevant questions are only asked every other year in the BRFSS. More information about the methodology can be found at www.cdc.gov/places.

This dataset contains model-based place (incorporated and census-designated places) estimates. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 36 measures: 13 for health outcomes, 9 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, and 3 for health status. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2021 or 2020 data, Census Bureau 2010 population data, and American Community Survey 2015–2019 estimates. The 2023 release uses 2021 BRFSS data for 29 measures and 2020 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places.

This dataset contains model-based ZIP Code Tabulation Area (ZCTA) level estimates. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 36 measures: 13 for health outcomes, 9 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, and 3 for health status. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2021 or 2020 data, Census Bureau 2010 population data, and American Community Survey 2015–2019 estimates. The 2023 release uses 2021 BRFSS data for 29 measures and 2020 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places.

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information. This dataset was created by the US Centers for Disease Control and Prevention (CDC) and is hosted and maintained here: https://data.cdc.gov/500-Cities-Places/PLACES-Local-Data-for-Better-Health-Census-Tract-D/em5e-5hvn/about_data If you have questions about underlying source data, contact PLACES at places@cdc.gov. For questions about metadata or this extracted data contact CAFÉ (climatecafe@bu.edu). CDC describes the dataset as follows: "This dataset contains model-based census tract estimates. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 36 measures: 13 for health outcomes, 9 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, and 3 for health status. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2021 or 2020 data, Census Bureau 2010 population data, and American Community Survey 2015–2019 estimates. The 2023 release uses 2021 BRFSS data for 29 measures and 2020 BRFSS data for seven measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places." [Quote from CDC PLACES Data - 2023 Release]

Demographic Trends and Health Outcomes in the U.S

Inequalities,Risk Factors and Access to Care

By Data Society [source]

About this dataset

This dataset contains key demographic, health status indicators and leading cause of death data to help us understand the current trends and health outcomes in communities across the United States. By looking at this data, it can be seen how different states, counties and populations have changed over time. With this data we can analyze levels of national health services use such as vaccination rates or mammography rates; review leading causes of death to create public policy initiatives; as well as identify risk factors for specific conditions that may be associated with certain populations or regions. The information from these files includes State FIPS Code, County FIPS Code, CHSI County Name, CHSI State Name, CHSI State Abbreviation, Influenza B (FluB) report count & expected cases rate per 100K population , Hepatitis A (HepA) Report Count & expected cases rate per 100K population , Hepatitis B (HepB) Report Count & expected cases rate per 100K population , Measles (Meas) Report Count & expected cases rate per 100K population , Pertussis(Pert) Report Count & expected case rate per 100K population , CRS report count & expected case rate per 100K population , Syphilis report count and expected case rate per 100k popuation. We also look at measures related to preventive care services such as Pap smear screen among women aged 18-64 years old check lower/upper confidence intervals seperately ; Mammogram checks among women aged 40-64 years old specified lower/upper conifence intervals separetly ; Colonosopy/ Proctoscpushy among men aged 50+ measured in lower/upper limits ; Pneumonia Vaccination amongst 65+ with loewr/upper confidence level detail Additionally we have some interesting trend indicating variables like measures of birth adn death which includes general fertility ratye ; Teen Birth Rate by Mother's age group etc Summary Measures covers mortality trend following life expectancy by sex&age categories Vressionable populations access info gives us insight into disablilty ratio + access to envtiromental issues due to poor quality housing facilities Finally Risk Factors cover speicfic hoslitic condtiions suchs asthma diagnosis prevelance cancer diabetes alcholic abuse smoking trends All these information give a good understanding on Healthy People 2020 target setings demograpihcally speaking hence will aid is generating more evience backed policies

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How to use the dataset

What the Dataset Contains

This dataset contains valuable information about public health relevant to each county in the United States, broken down into 9 indicator domains: Demographics, Leading Causes of Death, Summary Measures of Health, Measures of Birth and Death Rates, Relative Health Importance, Vulnerable Populations and Environmental Health Conditions, Preventive Services Use Data from BRFSS Survey System Data , Risk Factors and Access to Care/Health Insurance Coverage & State Developed Types of Measurements such as CRS with Multiple Categories Identified for Each Type . The data includes indicators such as percentages or rates for influenza (FLU), hepatitis (HepA/B), measles(MEAS) pertussis(PERT), syphilis(Syphilis) , cervical cancer (CI_Min_Pap_Smear - CI_Max\Pap \Smear), breast cancer (CI\Min Mammogram - CI \Max \Mammogram ) proctoscopy (CI Min Proctoscopy - CI Max Proctoscopy ), pneumococcal vaccinations (Ci min Pneumo Vax - Ci max Pneumo Vax )and flu vaccinations (Ci min Flu Vac - Ci Max Flu Vac). Additionally , it provides information on leading causes of death at both county levels & national level including age-adjusted mortality rates due to suicide among teens aged between 15-19 yrs per 100000 population etc.. Furthermore , summary measures such as age adjusted percentage who consider their physical health fair or poor are provided; vulnerable populations related indicators like relative importance score for disabled adults ; preventive service use related ones ranging from self reported vaccination coverage among men40-64 yrs old against hepatitis B virus etc...

Getting Started With The Dataset

To get started with exploring this dataset first your need to understand what each column in the table represents: State FIPS Code identifies a unique identifier used by various US government agencies which denote states . County FIPS code denotes counties wi...