A 2023 report on data breaches in the healthcare system in the United States revealed that in most incidents, the leaked data was located in the network server, with almost 70 percent of data breaches indicating this location. The second-most common location of breached data was e-mail, with over 18 percent of the cases, followed by paper or films, with nearly six percent of the cases.

At a time of digital transformation, correlating as much relevant data as possible can provide a powerful lever in the fight against fraud. Focusing on the issue of the sources of this data, it appears that ** percent of the players in the French healthcare ecosystem who responded to the survey in 2017 placed their partners and peers as the primary source of data collection. It was also found that open data occupied an equivalent place to data obtained from patients, clients and insured persons.

This report compares estimates of adult mental health from the 2009 National Survey on Drug Use and Health (NSDUH) with estimates of similar measures from 2001 to 2003 National Comorbidity Survey Replication (NCS-R), 2001 to 2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), 2007 Behavioral Risk Factor Surveillance System (BRFSS), 2008 National Health Interview Survey (NHIS), 2008 Medical Expenditure Panel Survey (MEPS), and 2008 Uniform Reporting System (URS).

The Public Health Emergency (PHE) declaration for COVID-19 expired on May 11, 2023. As a result, the Aggregate Case and Death Surveillance System will be discontinued. Although these data will continue to be publicly available, this dataset will no longer be updated.

On October 20, 2022, CDC began retrieving aggregate case and death data from jurisdictional and state partners weekly instead of daily.

This dataset includes the URLs that were used by the aggregate county data collection process that compiled aggregate case and death counts by county. Within this file, each of the states (plus select jurisdictions and territories) are listed along with the county web sources which were used for pulling these numbers. Some states had a single statewide source for collecting the county data, while other states and local health jurisdictions may have had standalone sources for individual counties. In the cases where both local and state web sources were listed, a composite approach was taken so that the maximum value reported for a location from either source was used. The initial raw data were sourced from these links and ingested into the CDC aggregate county dataset before being published on the COVID Data Tracker.

Health data sources: Situation in countries and required actions.

This report compares specific health conditions, overall health, and health care utilization prevalence estimates from the 2006 National Survey on Drug Use and Health (NSDUH) and other national data sources. Methodological differences among these data sources that may contribute to differences in estimates are described. In addition to NSDUH, three of the data sources use respondent self-reports to measure health characteristics and service utilization: the National Health Interview Survey (NHIS), the Behavioral Risk Factor Surveillance System (BRFSS), and the Medical Expenditure Panel Survey (MEPS). One survey, the National Health and Nutrition Examination Survey (NHANES), conducts initial interviews in respondents\' homes, collecting further data at nearby locations. Five data sources provide health care utilization data extracted from hospital records; these sources include the National Hospital Discharge Survey (NHDS), the Nationwide Inpatient Sample (NIS), the Nationwide Emergency Department Sample (NEDS), the National Health and Ambulatory Medical Care Survey (NHAMCS), and the Drug Abuse Warning Network (DAWN). Several methodological differences that could cause differences in estimates are discussed, including type and mode of data collection; weighting and representativeness of the sample; question placement, wording, and format; and use of proxy reporting for adolescents.

Doorda's UK Health Data provides a comprehensive database covering 1.8M postcodes sourced from 20 data sources, offering unparalleled insights for local area health insights and analytics purposes.

Volume and stats: - 1.8M Postcodes - UK Coverage - Age and Gender bands

Our Health Data offers a multitude of use cases: - Market Analysis - Geodemographic Insights - Risk Management - Location Planning

The key benefits of leveraging our Health Data include: - Data Accuracy - Informed Decision-Making - Competitive Advantage - Efficiency - Single Source

Covering a wide range of industries and sectors, our data empowers organisations to make informed decisions, uncover market trends, and gain a competitive edge in the UK market.

Trauma means an emotional response to a deeply distressing or disturbing event like loss of a loved one or an accident.A trauma patient will get data from multiple sources like neurocognitive, physiologic data from various medical tests.The neuro cognitive data comprises of EEG signal data like Amplitude,Delta,Theta,Alpha and Beta Values.The physiologic data comprises of heart_rate,skin_conductance,skin_temperature,cortisol_level, Systolic_BP and Diastolic_BP. The In the existing system, all thoses data are analyzed by medical experts in order to arrive at the conditional severity of the Trauma patient. But, it is difficult for the experts to correlate data from multiple sources, and arrive at a decision on severity. This dataset is useful at classifying the Severity of Trauma patients.

Electronic Health Legal Data

Exploring Laws and Regulations

By US Open Data Portal, data.gov [source]

About this dataset

This Electronic Health Information Legal Epidemiology dataset offers an extensive collection of legal and epidemiological data that can be used to understand the complexities of electronic health information. It contains a detailed balance of variables, including legal requirements, enforcement mechanisms, proprietary tools, access restrictions, privacy and security implications, data rights and responsibilities, user accounts and authentication systems. This powerful set provides researchers with real-world insights into the functioning of EHI law in order to assess its impact on patient safety and public health outcomes. With such data it is possible to gain a better understanding of current policies regarding the regulation of electronic health information as well as their potential for improvement in safeguarding patient confidentiality. Use this dataset to explore how these laws impact our healthcare system by exploring patterns across different groups over time or analyze changes leading up to new versions or updates. Make exciting discoveries with this comprehensive dataset!

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How to use the dataset

• Start by familiarizing yourself with the different columns of the dataset. Examine each column closely and look up any unfamiliar terminology to get a better understanding of what the columns are referencing.

• Once you understand the data and what it is intended to represent, think about how you might want to use it in your analysis. You may want to create a research question, or narrower focus for your project surrounding legal epidemiology of electronic health information that can be answered with this data set.

• After creating your research plan, begin manipulating and cleaning up the data as needed in order to prepare it for analysis or visualization as specified in your project plan or research question/model design steps you have outlined .

4 .Next, perform exploratory data analysis (EDA) on relevant subsets of data from specific countries if needed on specific subsets based on targets of interests (e.g gender). Filter out irrelevant information necessary for drawing meaningful insights; analyze patterns and trends observed in your filtered datasets ; compare areas which have differing rates e-health related rules and regulations tying decisions made by elected officials strongly driven by demographics , socioeconomics factors ,ideology etc.. . Look out for correlations using statistical information as needed throughout all stages in process from filtering out dis-informative subgroups from full population set til generating visualizations(graphs/ diagrams) depicting valid insight leveraging descriptive / predictive models properly validate against reference datasets when available always keep openness principal during gathering info especially when needs requires contact external sources such validating multiple sources work best provide strong seals establishing validity accuracy facts statement representing humans case scenarios digital support suitably localized supporting local languages culture respectively while keeping secure datasets private visible limited particular users duly authorized access 5 Finally create concrete summaries reporting discoveries create share findings preferably infographics showcasing evidence observances providing overall assessment main conclusions protocols developed so far broader community indirectly related interested professionals able benefit those results ideas complete transparently freely adapted locally ported increase overall global society level enhancing potentiality range impact derive conditions allowing wider adoption increased usage diffusion capture wide spread change movement affect global e-health legal domain clear manner

Research Ideas

• Studying how technology affects public health policies and practice - Using the data, researchers can look at the various types of legal regulations related to electronic health information to examine any relations between technology and public health decisions in certain areas or regions.
• Evaluating trends in legal epidemiology – With this data, policymakers can identify patterns that help measure the evolution of electronic health information regulations over time and investigate why such rules are changing within different states or countries.
• Analysing possible impacts on healthcare costs – Looking at changes in laws, regulations, and standards relate...

Background: Digital data sources have become ubiquitous in modern culture in the era of digital technology but often tend to be under-researched because of restricted access to data sources due to fragmentation, privacy issues, or industry ownership, and the methodological complexity of demonstrating their measurable impact on human health. Even though new big data sources have shown unprecedented potential for disease diagnosis and outbreak detection, we need to investigate results in the existing literature to gain a comprehensive understanding of their impact on and benefits to human health.Objective: A systematic review of systematic reviews on identifying digital data sources and their impact area on people's health, including challenges, opportunities, and good practices.Methods: A multidatabase search was performed. Peer-reviewed papers published between January 2010 and November 2020 relevant to digital data sources on health were extracted, assessed, and reviewed.Results: The 64 reviews are covered by three domains, that is, universal health coverage (UHC), public health emergencies, and healthier populations, defined in WHO's General Programme of Work, 2019–2023, and the European Programme of Work, 2020–2025. In all three categories, social media platforms are the most popular digital data source, accounting for 47% (N = 8), 84% (N = 11), and 76% (N = 26) of studies, respectively. The second most utilized data source are electronic health records (EHRs) (N = 13), followed by websites (N = 7) and mass media (N = 5). In all three categories, the most studied impact of digital data sources is on prevention, management, and intervention of diseases (N = 40), and as a tool, there are also many studies (N = 10) on early warning systems for infectious diseases. However, they could also pose health hazards (N = 13), for instance, by exacerbating mental health issues and promoting smoking and drinking behavior among young people.Conclusions: The digital data sources presented are essential for collecting and mining information about human health. The key impact of social media, electronic health records, and websites is in the area of infectious diseases and early warning systems, and in the area of personal health, that is, on mental health and smoking and drinking prevention. However, further research is required to address privacy, trust, transparency, and interoperability to leverage the potential of data held in multiple datastores and systems. This study also identified the apparent gap in systematic reviews investigating the novel big data streams, Internet of Things (IoT) data streams, and sensor, mobile, and GPS data researched using artificial intelligence, complex network, and other computer science methods, as in this domain systematic reviews are not common.

Real World Evidence Solutions Market size was valued at USD 1.30 Billion in 2024 and is projected to reach USD 3.71 Billion by 2032, growing at a CAGR of 13.92% during the forecast period 2026-2032.Global Real World Evidence Solutions Market DriversThe market drivers for the Real World Evidence Solutions Market can be influenced by various factors. These may include:Growing Need for Evidence-Based Healthcare: Real-world evidence (RWE) is becoming more and more important in healthcare decision-making, according to stakeholders such as payers, providers, and regulators. In addition to traditional clinical trial data, RWE solutions offer important insights into the efficacy, safety, and value of healthcare interventions in real-world situations.Growing Use of RWE by Pharmaceutical Companies: RWE solutions are being used by pharmaceutical companies to assist with market entry, post-marketing surveillance, and drug development initiatives. Pharmaceutical businesses can find new indications for their current medications, improve clinical trial designs, and convince payers and providers of the worth of their products with the use of RWE.Increasing Priority for Value-Based Healthcare: The emphasis on proving the cost- and benefit-effectiveness of healthcare interventions in real-world settings is growing as value-based healthcare models gain traction. To assist value-based decision-making, RWE solutions are essential in evaluating the economic effect and real-world consequences of healthcare interventions.Technological and Data Analytics Advancements: RWE solutions are becoming more capable due to advances in machine learning, artificial intelligence, and big data analytics. With the use of these technologies, healthcare stakeholders can obtain actionable insights from the analysis of vast and varied datasets, including patient-generated data, claims data, and electronic health records.Regulatory Support for RWE Integration: RWE is being progressively integrated into regulatory decision-making processes by regulatory organisations including the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA). The FDA's Real-World Evidence Programme and the EMA's Adaptive Pathways and PRIority MEdicines (PRIME) programme are two examples of initiatives that are making it easier to incorporate RWE into regulatory submissions and drug development.Increasing Emphasis on Patient-Centric Healthcare: The value of patient-reported outcomes and real-world experiences in healthcare decision-making is becoming more widely acknowledged. RWE technologies facilitate the collection and examination of patient-centered data, offering valuable insights into treatment efficacy, patient inclinations, and quality of life consequences.Extension of RWE Use Cases: RWE solutions are being used in medication development, post-market surveillance, health economics and outcomes research (HEOR), comparative effectiveness research, and market access, among other healthcare fields. The necessity for a variety of RWE solutions catered to the needs of different stakeholders is being driven by the expansion of RWE use cases.

Background: In Brazil, studies that map electronic healthcare databases in order to assess their suitability for use in pharmacoepidemiologic research are lacking. We aimed to identify, catalogue, and characterize Brazilian data sources for Drug Utilization Research (DUR).Methods: The present study is part of the project entitled, “Publicly Available Data Sources for Drug Utilization Research in Latin American (LatAm) Countries.” A network of Brazilian health experts was assembled to map secondary administrative data from healthcare organizations that might provide information related to medication use. A multi-phase approach including internet search of institutional government websites, traditional bibliographic databases, and experts’ input was used for mapping the data sources. The reviewers searched, screened and selected the data sources independently; disagreements were resolved by consensus. Data sources were grouped into the following categories: 1) automated databases; 2) Electronic Medical Records (EMR); 3) national surveys or datasets; 4) adverse event reporting systems; and 5) others. Each data source was characterized by accessibility, geographic granularity, setting, type of data (aggregate or individual-level), and years of coverage. We also searched for publications related to each data source.Results: A total of 62 data sources were identified and screened; 38 met the eligibility criteria for inclusion and were fully characterized. We grouped 23 (60%) as automated databases, four (11%) as adverse event reporting systems, four (11%) as EMRs, three (8%) as national surveys or datasets, and four (11%) as other types. Eighteen (47%) were classified as publicly and conveniently accessible online; providing information at national level. Most of them offered more than 5 years of comprehensive data coverage, and presented data at both the individual and aggregated levels. No information about population coverage was found. Drug coding is not uniform; each data source has its own coding system, depending on the purpose of the data. At least one scientific publication was found for each publicly available data source.Conclusions: There are several types of data sources for DUR in Brazil, but a uniform system for drug classification and data quality evaluation does not exist. The extent of population covered by year is unknown. Our comprehensive and structured inventory reveals a need for full characterization of these data sources.

Evaluating Health Home Care Quality

CMS Core Set and Health Home SPA Measures

By Health Data New York [source]

About this dataset

This dataset provides comprehensive measures to evaluate the quality of medical services provided to Medicaid beneficiaries by Health Homes, including the Centers for Medicare & Medicaid Services (CMS) Core Set and Health Home State Plan Amendment (SPA). This allows us to gain insight into how well these health homes are performing in terms of delivering high-quality care. Our data sources include the Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Inform Incentive Program (DSRIP) Data Warehouse. With this data set you can explore essential indicators such as rates for indicators within scope of Core Set Measures, sub domains, domains and measure descriptions; age categories used; denominators of each measure; level of significance for each indicator; and more! By understanding more about Health Home Quality Measures from this resource you can help make informed decisions about evidence based health practices while also promoting better patient outcomes

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How to use the dataset

This dataset contains measures that evaluate the quality of care delivered by Health Homes for the Centers for Medicare & Medicaid Services (CMS). With this dataset, you can get an overview of how a health home is performing in terms of quality. You can use this data to compare different health homes and their respective service offerings.

The data used to create this dataset was collected from Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Incentive Program (DSRIP) Data Warehouse sources.

In order to use this dataset effectively, you should start by looking at the columns provided. These include: Measurement Year; Health Home Name; Domain; Sub Domain; Measure Description; Age Category; Denominator; Rate; Level of Significance; Indicator. Each column provides valuable insight into how a particular health home is performing in various measurements of healthcare quality.

When examining this data, it is important to remember that many variables are included in any given measure and that changes may have occurred over time due to varying factors such as population or financial resources available for healthcare delivery. Furthermore, changes in policy may also affect performance over time so it is important to take these things into account when evaluating the performance of any given health home from one year to the next or when comparing different health homes on a specific measure or set of indicators over time

Research Ideas

• Using this dataset, state governments can evaluate the effectiveness of their health home programs by comparing the performance across different domains and subdomains.
• Healthcare providers and organizations can use this data to identify areas for improvement in quality of care provided by health homes and strategies to reduce disparities between individuals receiving care from health homes.
• Researchers can use this dataset to analyze how variations in cultural context, geography, demographics or other factors impact delivery of quality health home services across different locations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: health-home-quality-measures-beginning-2013-1.csv | Column name | Description | |:--------------------------|:----------------------------------------------------| | Measurement Year | The year in which the data was collected. (Integer) | | Health Home Name | The name of the health home. (String) | | Domain | The domain of the measure. (String) | | Sub Domain | The sub domain of the measure. (String) | | Measure Description | A description of the measure. (String) | | Age Category | The age category of the patient. (String) | | Denominator | The denominator of the measure. (Integer) | | Rate | The rate of the measure. (Float) | | Level of Significance | The level of significance of the measure. (String) | | Indicator | The indicator of the measure. (String) |

Acknowledgements

...

This report compares adult mental health prevalence estimates generated from the 2009 National Survey on Drug Use and Health (NSDUH) with estimates of similar measures generated from other national data sources. It also describes the methodologies of the different data sources and discusses the differences in survey design and estimation that may contribute to differences among these estimates. The other data systems discussed include the 2001 to 2003 National Comorbidity Survey Replication (NCS-R), 2001 to 2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), 2007 Behavioral Risk Factor Surveillance System (BRFSS), 2008 National Health Interview Survey (NHIS), 2008 Medical Expenditure Panel Survey (MEPS), and 2008 Uniform Reporting System (URS).

Agreement rates between ethnicity data recorded in health-related administrative data sources with Census 2021, by sociodemographic characteristics.

Context:

This synthetic healthcare dataset has been created to serve as a valuable resource for data science, machine learning, and data analysis enthusiasts. It is designed to mimic real-world healthcare data, enabling users to practice, develop, and showcase their data manipulation and analysis skills in the context of the healthcare industry.

Inspiration:

The inspiration behind this dataset is rooted in the need for practical and diverse healthcare data for educational and research purposes. Healthcare data is often sensitive and subject to privacy regulations, making it challenging to access for learning and experimentation. To address this gap, I have leveraged Python's Faker library to generate a dataset that mirrors the structure and attributes commonly found in healthcare records. By providing this synthetic data, I hope to foster innovation, learning, and knowledge sharing in the healthcare analytics domain.

Dataset Information:

Each column provides specific information about the patient, their admission, and the healthcare services provided, making this dataset suitable for various data analysis and modeling tasks in the healthcare domain. Here's a brief explanation of each column in the dataset - - Name: This column represents the name of the patient associated with the healthcare record. - Age: The age of the patient at the time of admission, expressed in years. - Gender: Indicates the gender of the patient, either "Male" or "Female." - Blood Type: The patient's blood type, which can be one of the common blood types (e.g., "A+", "O-", etc.). - Medical Condition: This column specifies the primary medical condition or diagnosis associated with the patient, such as "Diabetes," "Hypertension," "Asthma," and more. - Date of Admission: The date on which the patient was admitted to the healthcare facility. - Doctor: The name of the doctor responsible for the patient's care during their admission. - Hospital: Identifies the healthcare facility or hospital where the patient was admitted. - Insurance Provider: This column indicates the patient's insurance provider, which can be one of several options, including "Aetna," "Blue Cross," "Cigna," "UnitedHealthcare," and "Medicare." - Billing Amount: The amount of money billed for the patient's healthcare services during their admission. This is expressed as a floating-point number. - Room Number: The room number where the patient was accommodated during their admission. - Admission Type: Specifies the type of admission, which can be "Emergency," "Elective," or "Urgent," reflecting the circumstances of the admission. - Discharge Date: The date on which the patient was discharged from the healthcare facility, based on the admission date and a random number of days within a realistic range. - Medication: Identifies a medication prescribed or administered to the patient during their admission. Examples include "Aspirin," "Ibuprofen," "Penicillin," "Paracetamol," and "Lipitor." - Test Results: Describes the results of a medical test conducted during the patient's admission. Possible values include "Normal," "Abnormal," or "Inconclusive," indicating the outcome of the test.

Usage Scenarios:

This dataset can be utilized for a wide range of purposes, including: - Developing and testing healthcare predictive models. - Practicing data cleaning, transformation, and analysis techniques. - Creating data visualizations to gain insights into healthcare trends. - Learning and teaching data science and machine learning concepts in a healthcare context. - You can treat it as a Multi-Class Classification Problem and solve it for Test Results which contains 3 categories(Normal, Abnormal, and Inconclusive).

Acknowledgments:

• I acknowledge the importance of healthcare data privacy and security and emphasize that this dataset is entirely synthetic. It does not contain any real patient information or violate any privacy regulations.
• I hope that this dataset contributes to the advancement of data science and healthcare analytics and inspires new ideas. Feel free to explore, analyze, and share your findings with the Kaggle community.

Image Credit:

Image by BC Y from Pixabay

Social Determinants of Health Data Platforms Market Outlook

According to our latest research, the global Social Determinants of Health (SDOH) Data Platforms market size reached USD 3.2 billion in 2024. The market is expected to grow at a robust CAGR of 18.7% during the forecast period, reaching a projected value of USD 15.1 billion by 2033. This significant growth is primarily driven by the increasing recognition of how non-clinical factors—such as economic stability, education, neighborhood, and social context—profoundly impact health outcomes and healthcare costs worldwide.

One of the most compelling growth factors for the Social Determinants of Health Data Platforms market is the intensifying focus on value-based care and population health management among healthcare stakeholders. As healthcare systems globally transition from traditional fee-for-service models to value-based care, there is a growing need to incorporate SDOH data into clinical workflows, risk stratification, and care coordination. Payers, providers, and government agencies are investing in platforms that aggregate, analyze, and operationalize diverse data sources, including demographic, socioeconomic, and behavioral factors. This integration enables healthcare organizations to identify at-risk populations, personalize interventions, and ultimately reduce costly health disparities, fueling substantial market demand.

Another pivotal driver is the expanding regulatory and policy support for addressing social determinants in healthcare delivery. Government agencies, especially in North America and Europe, are enacting mandates and incentives to encourage the collection and utilization of SDOH data. For instance, the Centers for Medicare & Medicaid Services (CMS) in the United States has introduced new requirements and payment models that reward the integration of social risk factors into patient assessments and care planning. Similarly, the World Health Organization (WHO) and other international bodies are emphasizing the importance of SDOH in achieving equitable health outcomes. These regulatory tailwinds are prompting healthcare organizations to adopt advanced SDOH data platforms, further accelerating market growth.

Technological advancements in data analytics, artificial intelligence, and interoperability are also propelling the Social Determinants of Health Data Platforms market forward. Modern SDOH data platforms leverage machine learning algorithms and predictive analytics to derive actionable insights from vast, complex datasets. Enhanced interoperability standards, such as FHIR (Fast Healthcare Interoperability Resources), are making it easier to integrate SDOH data with electronic health records (EHRs) and other health IT systems. These innovations are not only improving the accuracy and timeliness of SDOH data capture but also enabling real-time decision support for clinicians and care managers. As a result, healthcare organizations are increasingly deploying sophisticated SDOH data platforms to gain a competitive edge and improve patient outcomes.

From a regional perspective, North America currently dominates the Social Determinants of Health Data Platforms market, accounting for the largest share in 2024, followed by Europe and the Asia Pacific. The United States, in particular, is at the forefront due to its advanced healthcare IT infrastructure, proactive regulatory environment, and substantial investments in population health initiatives. However, the Asia Pacific region is expected to register the fastest CAGR during the forecast period, driven by rising healthcare digitization, growing awareness of health disparities, and supportive government policies. Europe is also witnessing steady growth, bolstered by cross-border health data initiatives and strong public health systems. Latin America and the Middle East & Africa are gradually emerging as promising markets as healthcare modernization efforts gain momentum.

The integration of Social Determinants of Health Analytics AI is becoming increasingly vital in the healthcare industry. By leveraging artificial intelligence, healthcare providers can analyze vast amounts of SDOH data to uncover patterns and insights that were previously unattainable. AI-driven analytics enable the identification of at-risk populations more accurately and efficiently

Comparing the quality of ethnicity data recorded in health-related administrative data sources with Census 2021.

An inventory of data sources to be used in the Digivet case studies, along with project documentation that gives them context. FAIRer datasets, in which context and data are stored in linked formats will be produced in next steps of the project, and also published in the Digivet community.

PLACES (Population Level Analysis and Community Estimates) is an extension of the original 500 Cities Project and is a collaboration between CDC, the Robert Wood Johnson Foundation (RWJF), and the CDC Foundation (CDCF). The original 500 Cities Project provided city- and census tract-level estimates for chronic disease risk factors, health outcomes, and clinical preventive services use for the 500 largest US cities. PLACES extends these estimates to all counties, places (incorporated and census designated places), census tracts, and ZIP Code Tabulation Areas (ZCTA) across the United States. Data were provided by CDC, Division of Population Health, Epidemiology and Surveillance Branch. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2018 or 2017), Census Bureau 2010 census population data or annual population estimates for county vintage 2018 or 2017, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates.For more information about the methodology, visit https://www.cdc.gov/places or contact places@cdc.gov.