Brand performance data collected from AI search platforms for the query "social determinants of health data sources".

AHRQ's database on Social Determinants of Health (SDOH) was created under a project funded by the Patient Centered Outcomes Research (PCOR) Trust Fund. The purpose of this project is to create easy to use, easily linkable SDOH-focused data to use in PCOR research, inform approaches to address emerging health issues, and ultimately contribute to improved health outcomes.The database was developed to make it easier to find a range of well documented, readily linkable SDOH variables across domains without having to access multiple source files, facilitating SDOH research and analysis.Variables in the files correspond to five key SDOH domains: social context (e.g., age, race/ethnicity, veteran status), economic context (e.g., income, unemployment rate), education, physical infrastructure (e.g, housing, crime, transportation), and healthcare context (e.g., health insurance). The files can be linked to other data by geography (county, ZIP Code, and census tract). The database includes data files and codebooks by year at three levels of geography, as well as a documentation file.The data contained in the SDOH database are drawn from multiple sources and variables may have differing availability, patterns of missing, and methodological considerations across sources, geographies, and years. Users should refer to the data source documentation and codebooks, as well as the original data sources, to help identify these patterns

This archived SDOH Database (beta version) is available for reference. The most recent version of the SDOH Database replaces the beta version and is available on the main page. To ensure consistency in variable names and construction, analyses should not combine data from the beta version and the updated database.Download DataThe SDOH Data Source Documentation (PDF, 1.5 MB) file contains information for researchers about the structure and contents of the database and descriptions of each data source used to populate the database.The Variable Codebook (XLSX, 494 KB) Excel file provides descriptive statistics for each SDOH variable by year.

This dataset includes materials for an undergraduate learning activity focused on exploring the Social Determinants of Health (SDOH) through applied data analysis and mapping from Coastal Carolina University - Department of Nursing and Health Sciences. The download contains two files:a written assignment with step-by-step instructions, andan Excel file containing county-level health and SDOH data for South Carolina. The data were compiled from three sources (CDC PLACES, US Census Bureau's American Community Survey, Feeding America's Map the Meal Gap)Students use these materials to create maps, correlation matrices, and scatterplots in Microsoft Excel, enabling them to examine relationships between health outcomes and social factors such as poverty, education, and food access.

Social Determinants of Health Market Outlook

According to our latest research, the global Social Determinants of Health (SDOH) market size reached USD 7.2 billion in 2024, reflecting robust momentum driven by the integration of advanced analytics and digital health solutions across healthcare ecosystems. The market is anticipated to expand at a CAGR of 22.8% from 2025 to 2033, with the total market size expected to reach USD 59.6 billion by 2033. This accelerated growth is primarily fueled by the increasing recognition of the critical impact that social, economic, and environmental factors have on health outcomes, as well as the growing adoption of value-based care models globally. As per our latest research, the demand for holistic patient care and the need to address health disparities are the main catalysts propelling the SDOH market forward.

The surge in the Social Determinants of Health market is fundamentally driven by the global shift towards preventive healthcare and population health management. Healthcare organizations are increasingly recognizing that clinical care alone accounts for only a fraction of overall health outcomes, with social determinants such as housing, education, employment, and food security playing a pivotal role. This realization is prompting investments in SDOH data collection, analytics, and intervention programs that enable healthcare providers and payers to identify at-risk populations, design targeted interventions, and ultimately improve health equity. The proliferation of electronic health records (EHRs) and interoperable data platforms is further facilitating the integration of SDOH insights into clinical workflows, enhancing the ability to deliver personalized and effective care.

Another major growth driver for the SDOH market is the transition to value-based care and risk-based reimbursement models. Governments and private payers worldwide are incentivizing healthcare organizations to focus on outcomes rather than volume, which necessitates a comprehensive understanding of the social and environmental factors influencing patient health. As a result, there is a growing demand for advanced analytics, machine learning, and artificial intelligence solutions that can process and interpret large volumes of SDOH data. These technologies are enabling stakeholders to stratify risk, predict adverse health events, and allocate resources more efficiently, thereby reducing costs and improving quality of care. The increasing availability of real-time data from wearable devices, mobile applications, and community sources is also expanding the scope and effectiveness of SDOH initiatives.

Furthermore, regulatory mandates and policy initiatives are playing a crucial role in accelerating the adoption of SDOH solutions. In the United States, for instance, the Centers for Medicare & Medicaid Services (CMS) and other agencies have introduced guidelines and incentive programs that require healthcare organizations to screen for and address social determinants as part of routine care. Similar efforts are being observed in Europe and Asia Pacific, where governments are prioritizing health equity and social inclusion in their public health agendas. These policies are not only driving demand for SDOH data analytics and intervention platforms but are also fostering collaboration between healthcare providers, payers, community organizations, and technology vendors, thereby creating a vibrant and dynamic market landscape.

From a regional perspective, North America continues to dominate the SDOH market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, is at the forefront due to its advanced healthcare infrastructure, strong regulatory support, and early adoption of health IT solutions. However, Asia Pacific is expected to witness the fastest growth over the forecast period, driven by rapid urbanization, rising healthcare expenditures, and increasing awareness of social health disparities. Europe also presents significant opportunities, especially with the implementation of digital health strategies and cross-sector collaborations aimed at addressing the root causes of health inequities. Latin America and the Middle East & Africa are gradually catching up, supported by government-led health reforms and international investments in healthcare infrastructure.

Miami Valley block group boundaries and associated data utilized in the Miami Valley Regional Planning Commission's PLAN4Health - Miami Valley initiative. This shapefile constitutes the majority of the data and analysis done in project 1A (Health Environment Assessment) of PLAN4Health Miami Valley. The geography utilized is 2010 Census boundaries. Data source years range from 2000 for trend analysis, to 2020 for most recent study sources.The PLAN4Health Health Environment Assessment findings can be viewed here.For more information about PLAN4Health - Miami Valley, visit the initiative hub site here.For any questions related to this dataset, please contact Milo Simpson, Planner I at MVRPC. msimpson@mvrpc.org

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

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The IPUMS Contextual Determinants of Health (CDOH) data series provides access to measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. The Race and Ethnicity measure in this release is an indicator of income inequity which is measured using the index of concentration at the extremes (ICE). ICE is a measure of social polarization within a particular geographic unit. It shows whether people or households in a geographic unit are concentrated in privileged or deprived extremes. The privileged group in this study is the number of households with a householder identifying as White alone, not Hispanic or Latino, with an income equal to or greater than $100,000. The deprived group in this study is the number of households with a householder identifying as a different race/ethnic group (e.g., Black alone, Asian alone, Hispanic or Latino), with an income equal to or less than $25,000. To work with the IPUMS CDOH data, researchers will need to use the variable MATCH_ID to merge the data in DS1 with NCHAT surveys within the virtual data enclave (VDE).

The IPUMS Contextual Determinants of Health (CDOH) data series provides access to measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. Sexual and Gender Minority measures in this release include county-level summary data on the proportion of same-sex households in the United States, as reported in the 2020 Decennial Census. To work with the IPUMS CDOH data, researchers will need to use the variable MATCH_ID to merge the data in DS1 with NCHAT surveys within the virtual data enclave (VDE).

The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updatesTitle: HSD Data Book 2022Item Type: URLSummary: Health and Human Services Department of New Mexico 2022 Data Book. For a link to the download page with County Fact Sheets, and other data, see here: https://www.hsd.state.nm.us/2022-data-book/Notes: Link to PDF maintained by HSDPrepared by: Uploaded by EMcRae_NMCDCSource: Health and Human Services Dept Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=ba99c113ced244bbbeb4026da10bb98aUID: 73Data Requested: Annual data book on socioeconomic population healthMethod of Acquisition: Published publicly online by HSDDate Acquired: May 2022Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 6Tags: PENDING

Real‑time Health Data Analytics Market Outlook

According to our latest research, the global real-time health data analytics market size reached USD 16.2 billion in 2024, and is projected to grow at a robust CAGR of 18.4% from 2025 to 2033, reaching an estimated value of USD 80.2 billion by 2033. This substantial growth is primarily driven by the increasing adoption of digital health solutions, the proliferation of connected medical devices, and the rising demand for instant, actionable healthcare insights to improve patient outcomes and operational efficiency worldwide.

One of the primary growth factors fueling the real-time health data analytics market is the rapid digitization of healthcare systems. Hospitals, clinics, and other healthcare providers are increasingly deploying electronic health records (EHRs), wearable devices, and remote monitoring solutions that generate vast volumes of real-time patient data. These technologies enable continuous tracking of vital signs, medication adherence, and other health metrics, allowing clinicians to make timely decisions and intervene early in case of anomalies. The integration of artificial intelligence (AI) and machine learning (ML) algorithms with real-time analytics platforms further enhances the ability to detect patterns, predict adverse events, and personalize treatment plans. As healthcare organizations strive to transition from reactive to proactive care models, the demand for sophisticated real-time analytics solutions is expected to surge.

Another significant driver for the real-time health data analytics market is the increasing emphasis on value-based care and population health management. Governments and payers across the globe are incentivizing healthcare providers to improve quality while reducing costs, which necessitates the use of advanced analytics for tracking patient outcomes, identifying high-risk populations, and optimizing resource allocation. Real-time analytics platforms empower healthcare professionals to aggregate and analyze data from multiple sources, including EHRs, claims, and social determinants of health, providing a holistic view of patient populations. By enabling early identification of trends and gaps in care, these solutions facilitate targeted interventions, reduce hospital readmissions, and support evidence-based decision-making, thereby aligning with the objectives of value-based healthcare delivery.

Moreover, the ongoing COVID-19 pandemic has underscored the critical importance of real-time health data analytics in managing public health crises. Governments and healthcare organizations worldwide have leveraged real-time analytics to monitor the spread of the virus, allocate resources, and optimize vaccination campaigns. The pandemic has accelerated the adoption of telemedicine, remote patient monitoring, and cloud-based analytics platforms, further expanding the scope of real-time data utilization. As the world continues to face emerging infectious diseases and chronic health challenges, the ability to rapidly analyze and act upon real-time health data will remain a strategic priority for both public and private sector stakeholders.

From a regional perspective, North America currently dominates the real-time health data analytics market, accounting for the largest revenue share in 2024, driven by advanced healthcare infrastructure, widespread adoption of digital health technologies, and strong regulatory support for interoperability and data sharing. Europe follows closely, with significant investments in health IT modernization and data-driven healthcare initiatives. The Asia Pacific region is poised for the fastest growth during the forecast period, fueled by expanding healthcare access, increasing government spending on digital health, and a burgeoning population of tech-savvy consumers. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by ongoing healthcare reforms and rising awareness regarding the benefits of real-time analytics in improving care delivery.

Component Analysi

OBJECTIVES: To determine the association between area and individual measures of social disadvantage and infant health in the United Kingdom (UK). DESIGN: Systematic review and meta-analyses. DATA SOURCES: 26 databases and web sites, reference lists, experts in the field and hand-searching. STUDY SELECTION: 36 prospective and retrospective observational studies with socio-economic data and health outcomes for infants in the UK, published from 1994 to May 2011. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers assessed the methodological quality of the studies and abstracted data. Where possible, study outcomes were reported as odds ratios for the highest versus the lowest deprivation quintile. RESULTS: In relation to the highest versus lowest area deprivation quintiles the odds of adverse birth outcomes were 1.81 (1.71 to 1.92) for low birth weight, 1.67 (1.42 to 1.96) for premature birth and 1.54 (1.39 to 1.72) for still birth. For infant mortality rates the odds ratios were 1.7...

ObjectiveTo describe the development of an area-level measure of children's opportunity, the Ohio Children's Opportunity Index (OCOI).Data Sources/Study SettingSecondary data were collected from US census based-American Community Survey (ACS), US Environmental Protection Agency, US Housing and Urban Development, Ohio Vital Statistics, US Department of Agriculture-Economic Research Service, Ohio State University Center for Urban and Regional Analysis, Ohio Incident Based Reporting System, IPUMS National Historical Geographic Information System, and Ohio Department of Medicaid. Data were aggregated to census tracts across two time periods.Study DesignOCOI domains were selected based on existing literature, which included family stability, infant health, children's health, access, education, housing, environment, and criminal justice domains. The composite index was developed using an equal weighting approach. Validation analyses were conducted between OCOI and health and race-related outcomes, and a national index.Principal FindingsComposite OCOI scores ranged from 0–100 with an average value of 74.82 (SD, 17.00). Census tracts in the major metropolitan cities across Ohio represented 76% of the total census tracts in the least advantaged OCOI septile. OCOI served as a significant predictor of health and race-related outcomes. Specifically, the average life expectancy at birth of children born in the most advantaged septile was approximately 9 years more than those born in least advantaged septile. Increases in OCOI were associated with decreases in proportion of Black (48 points lower in the most advantaged vs. least advantaged septile), p < 0.001) and Minority populations (54 points lower in most advantaged vs. least advantaged septile, p < 0.001). We found R-squared values > 0.50 between the OCOI and the national Child Opportunity Index scores. Temporally, OCOI decreased by 1% between the two study periods, explained mainly by decreases in the children health, accessibility and environmental domains.ConclusionAs the first opportunity index developed for children in Ohio, the OCOI is a valuable resource for policy reform, especially related to health disparities and health equity. Health care providers will be able to use it to obtain holistic views on their patients and implement interventions that can tackle barriers to childhood development using a more tailored approach.

The size of the Data-Driven Healthcare Market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of XXX% during the forecast period. Data-driven healthcare refers to the integration of data, analytics, and technology into the healthcare system to enhance decision-making, improve patient outcomes, streamline operations, and reduce costs. This approach leverages vast amounts of data generated from various sources, including electronic health records (EHRs), medical imaging, wearable devices, genomic data, and patient feedback, to gain actionable insights that support clinical and operational decision-making. Recent developments include: In April 2024, Inovalon launched a new cloud-based Software as a Service (SaaS) solution, Converged Submissions, designed to streamline and improve the process of submitting encounter data for risk adjustment programs to ensure accurate submissions to regulatory agencies such as the Centers for Medicare & Medicaid Services (CMS)., In February 2024, Persistent Systems collaborated with Microsoft to launch a Generative AI-powered Population Health Management (PHM) Solution. This solution was used to identify Social Determinants of Health (SDoH) from Electronic Health Records (EHR) data with the aim of enabling personalized care recommendations and cost-effective interventions..

Health, United States is the report on the health status of the country. Every year, the report presents an overview of national health trends organized around four subject areas: health status and determinants, utilization of health resources, health care resources, and health care expenditures and payers.

The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. The Gender measures in this release include the state-level poverty ratio, which compares the proportion of females living in poverty to the proportion of males living in poverty in a given state in a given year. To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable.

Healthcare Provider Population Health Management Software Market Outlook

According to our latest research, the global Healthcare Provider Population Health Management Software market size reached USD 15.2 billion in 2024. The market is projected to expand at a robust CAGR of 13.8% from 2025 to 2033, reaching approximately USD 47.2 billion by 2033. This impressive growth is primarily driven by the rising demand for value-based care, increasing healthcare data volumes, and the critical need for efficient patient management across diverse healthcare settings. The ongoing digital transformation in healthcare, coupled with regulatory mandates for data interoperability and quality reporting, continues to accelerate the adoption of advanced population health management solutions among providers worldwide.

One of the most significant growth factors propelling the Healthcare Provider Population Health Management Software market is the global shift from fee-for-service to value-based care models. Healthcare systems and providers are under increasing pressure to improve patient outcomes while controlling costs, necessitating robust tools for data aggregation, risk stratification, and care coordination. Population health management (PHM) software enables providers to analyze large datasets, identify at-risk populations, and proactively manage chronic diseases. The integration of electronic health records (EHRs), claims data, and social determinants of health into PHM platforms allows for a more holistic approach to patient care, driving better clinical and financial outcomes. Additionally, government initiatives and reforms, such as the Affordable Care Act in the United States and similar policies in Europe and Asia Pacific, are further incentivizing the adoption of PHM solutions by linking reimbursement to quality metrics and patient satisfaction.

Another critical driver is the rapid advancement of healthcare IT infrastructure and the proliferation of digital health technologies. The increasing adoption of cloud computing, artificial intelligence, and machine learning in healthcare is transforming the way providers manage patient populations. Modern PHM software platforms leverage these technologies to deliver predictive analytics, automate care management workflows, and facilitate real-time decision support. This technological evolution enables healthcare organizations to efficiently aggregate and analyze disparate data sources, streamline patient engagement, and optimize resource allocation. The growing emphasis on interoperability and data exchange standards, such as HL7 FHIR, is also fostering a more connected and integrated healthcare ecosystem, further enhancing the value proposition of PHM software.

The COVID-19 pandemic has also played a pivotal role in accelerating the adoption of population health management solutions. The need for remote patient monitoring, telehealth, and coordinated care during the pandemic highlighted the importance of robust PHM platforms. Providers leveraged these tools to track disease outbreaks, manage high-risk patient cohorts, and allocate resources more effectively. As healthcare systems continue to adapt to the post-pandemic landscape, the focus on preventive care, chronic disease management, and population-level analytics is expected to remain strong, sustaining the long-term growth trajectory of the market. Furthermore, the increasing prevalence of chronic diseases, aging populations, and rising healthcare expenditures globally are creating a fertile environment for continued investment in PHM software by providers of all sizes.

From a regional perspective, North America continues to dominate the Healthcare Provider Population Health Management Software market, accounting for the largest share in 2024. This leadership is attributed to the presence of advanced healthcare infrastructure, high EHR adoption rates, and supportive government policies promoting interoperability and quality reporting. Europe and Asia Pacific are also witnessing substantial growth, driven by increasing healthcare digitization, rising prevalence of chronic diseases, and expanding investments in health IT. Emerging markets in Latin America and the Middle East & Africa are gradually embracing PHM solutions, supported by ongoing healthcare reforms and international collaborations. Overall, the global market is characterized by dynamic regional trends, with each geography presenting unique opportunities and challenges for stakeholders.

The Health Examination Survey 2018-2019 of the CUORE Project is coordinated by the Department of Cardiovascular, Endocrine-metabolic Diseases and Aging of the Istituto Superiore di Sanità

The objectives of the survey, addressed to the general adult population (35-74 years), are to:

1. estimate the distribution of risk factors (arterial blood pressure, heart rate, weight, height, body mass index, waist and hips circumference);
2. study the distribution of life habits (physical activity, smoking habits, diet, alcohol consumption);
3. estimate the prevalence of conditions at risk (arterial hypertension, overweight and obesity, smoking habits);
4. evaluate the temporal trend of risk factors distribution (arterial blood pressure, heart rate, weight, height, body mass index, waist circumference and hips);
5. evaluate the temporal trend of life habits distribution (physical activity, smoking habits, diet, alcohol consumption);
6. evaluate the temporal trend of the prevalence of conditions at risk (arterial hypertension, overweight and obesity, smoking habits);
7. monitor national lifestyles campaigns, in particular estimate the average daily consumption of sodium, potassium and iodine per capita, as a result of the “Guadagnare Salute” program of the Ministry of Health;
8. evaluate physical performance, attention and memory in the elderly population (65-74 years);
9. study the degree of health literacy;

The survey is conducted in several Italian regions, between North, Central and South; in each region, a sample of 200 people is enrolled, stratified by gender and age group, randomly extracted from the general population residing in a selected municipality. For each age group (35-44, 45-54, 55-64, 65-74) and sex, 25 people are drawn.

The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts by state or county for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons as well as women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. The Gender measures in this release include the state-level earnings ratio, which compares the median earnings of full-time wage and salary workers identifying as male to the median earnings of full-time wage and salary workers identifying as female in a given state in a given year. To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable.

As part of Tempe Fire Medical Rescue, Patient Advocate Services (PAS) provides support and coordination of care for medically vulnerable Tempe residents, connecting patients with resources that address a wide range of social determinants of health. As EMS calls typically result in transport to an emergency room, and emergency rooms are rarely an appropriate place for the treatment of chronic illness, reductions in 911 calls are indicative of improved patient health, well-being, and safety. The performance measure page is available at 3.32 Patient Advocate Services. Additional InformationSource: Processed ImageTrend data Contact (author): Dan PettyContact E-Mail (author): daniel_petty@tempe.govContact (maintainer): Contact E-Mail (maintainer): Data Source Type: TablePreparation Method: For each enrolled patient, a tally of Emergency Medical Service 911 calls prior to enrollment with Patient Advocate Services is compared to their EMS 911 calls in the six months after enrollment is complete. Publish Frequency: AnnualPublish Method: ManualData Dictionary