Analysis of ‘COVID-19 Equity Metrics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/4a29e16f-3636-49a7-8ae1-f77bbf8b2ab3 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

CDPH strives to respond equitably to the COVID-19 pandemic and is therefore interested in how different communities are impacted. Collecting and reporting health equity data helps to identify health disparities and improve the state’s response. To that end, CDPH tracks cases, deaths, and testing by race and ethnicity as well as other social determinants of health, such as income, crowded housing, and access to health insurance.

During the response, CDPH used a health equity metric, defined as the positivity rate in the most disproportionately-impacted communities according to the Healthy Places Index. The purpose of this metric was to ensure California reopened its economy safely by reducing disease transmission in all communities. This metric is tracked and reported in comparison to statewide positivity rate. More information is available at https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/CaliforniaHealthEquityMetric.aspx.

Data completeness is also critical to addressing inequities. CDPH reports data completeness by race and ethnicity, sexual orientation, and gender identity to better understand missingness in the data.

Health equity data is updated weekly. Data may be suppressed based on county population or total counts.

For more information on California’s commitment to health equity, please see https://covid19.ca.gov/equity/

--- Original source retains full ownership of the source dataset ---

Without comprehensive examination of available literature on health disparities and minority health (HDMH), the field is left vulnerable to disproportionately focus on specific populations or conditions, curtailing our ability to fully advance health equity. Using scalable open-source methods, we conducted a computational scoping review of more than 200,000 articles to investigate major populations, conditions, and themes in the literature as well as notable gaps. We also compared trends in studied conditions to their relative prevalence in the general population using insurance claims (42 million Americans). HDMH publications represent 1% of articles in MEDLINE. Most studies are observational in nature, though randomized trial reporting has increased five-fold in the last twenty years. Half of all HDMH articles concentrate on only three disease groups (cancer, mental health, endocrine/metabolic disorders), while hearing, vision, and skin-related conditions are among the least well-represented despite substantial prevalence. To support further investigation, we also present HDMH Monitor, an interactive dashboard and repository generated from the HDMH bibliome.

Analysis of ‘Strategic Measure_ Client Referrals to Neighborhood Centers and Health Equity’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/5afc7cb2-94d1-41ca-88c9-f609f3bb70e0 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Data set description: Data demonstrates the percent of Austin Public Clients that receive a preventative health screening and are referred to a health care provided or community based resources and subsequently follow up on that referral.

This data set supports measure HE.B.5 of SD23.

Data Source is Austin Public Health Department Program Data.

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/n4an-tr8w

--- Original source retains full ownership of the source dataset ---

The Overview of Health Disparities analysis is a component of the Healthy People 2020 (HP2020) Final Review. The analysis included 611 objectives in HP2020. This file contains summary level information used for the evaluation of changes in disparities during HP2020, including calculations for the disparities measures and the disparities change categories for all objectives and population characteristics in the analysis. See Technical Notes for the Healthy People 2020 Overview of Health Disparities (https://www.cdc.gov/nchs/healthy_people/hp2020/health-disparities-technical-notes.htm) for additional information and criteria for objectives, data years, and population characteristics included in the analysis and statistical formulas and definitions for the disparities measures. Data for additional years during the HP2020 tracking period that are not included in the Overview of Health Disparities are available on the HP2020 website (https://www.healthypeople.gov/2020/). Note that “rate” as used may refer to a statistical rate expressed per unit population or a proportion, depending on how the HP2020 objective was defined.

The Overview of Health Disparities analysis is a component of the Healthy People 2020 (HP2020) Final Review. The analysis included 611 objectives in HP2020. See Technical Notes for the Healthy People 2020 Overview of Health Disparities (https://www.cdc.gov/nchs/healthy_people/hp2020/health-disparities.htm) for additional information and criteria for objectives, data years, and population characteristics included in the analysis and statistical formulas and definitions for the disparities measures. This file contains estimates and standard errors for the baseline and final years for individual population groups used in the Overview of Health Disparities analysis. The number and definitions of population groups varied across the HP2020 objectives and data sources used. These population groups are shown in the disparities file as originally reported by the data source, rather than the harmonized categories that were used for the HP2020 Progress by Population Group analysis (https://www.cdc.gov/nchs/healthy_people/hp2020/population-groups.htm). Additionally, for any given objective, the baseline and final years used for the disparities analysis do not necessarily correspond to the baseline and final years used to evaluate progress toward target attainment in the HP2020 Final Review Progress Table (https://www.cdc.gov/nchs/healthy_people/hp2020/progress-tables.htm) and Progress by Population Group analysis (https://www.cdc.gov/nchs/healthy_people/hp2020/population-groups.htm). These distinctions should be considered when merging the downloadable Progress Table or Progress by Population Group data files with the Overview of Health Disparities data files, or when integrative analyses that incorporate both disparities and progress data are conducted. Data for additional years during the HP2020 tracking period that are not included in the Overview of Health Disparities are available on the HP2020 website (https://www.healthypeople.gov/2020/).

Health equity is a rather complex issue. Social context and economical disparities, are known to be determining factors. Cultural and educational constrains however, are also important contributors to the establishment and development of health inequities. As an important starting point for a comprehensive discussion, a detailed analysis of the literature corpus is thus desirable: we need to recognize what has been done, under what circumstances, even what possible sources of bias exist in our current discussion on this relevant issue. By finding these trends and biases we will be better equipped to modulate them and find avenues that may lead us to a more integrated view of health inequity, potentially enhancing our capabilities to intervene to ameliorate it. In this study, we characterized at a large scale, the social and cultural determinants most frequently reported in current global research of health inequity and the interrelationships among them in different populations under diverse contexts. We used a data/literature mining approach to the current literature followed by a semantic network analysis of the interrelationships discovered. The analyzed structured corpus consisted in circa 950 articles categorized by means of the Medical Subheadings (MeSH) content-descriptor from 2014 to 2021. Further analyses involved systematic searches in the LILACS and DOAJ databases, as additional sources. The use of data analytics techniques allowed us to find a number of non-trivial connections, pointed out to existing biases and under-represented issues and let us discuss what are the most relevant concepts that are (and are not) being discussed in the context of Health Equity and Culture.

Analysis of ‘National Veteran Health Equity Report - FY13’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/08c042fa-4de7-4039-9bf1-fb03f39227f6 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The National Veteran Health Equity Report details patterns and provides comparative rates of health conditions for vulnerable Veteran groups. Specifically, this report is designed to provide basic comparative information on the sociodemographics, utilization patterns and rates of diagnosed health conditions among the groups over which the VHA Office of Health Equity (OHE) has responsibility with respect to monitoring, evaluating and acting on identified disparities in access, use, care, quality and outcomes. The report allows the VA, Veterans, and stakeholders to monitor the care vulnerable Veterans receive and set goals for improving their care.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Strategic Measure_Number and percentage of clients served through City’s health equity contracts who achieve intended healthy outcomes (e.g. healthy infant birth weight)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/037bbe19-e34c-4d6b-8637-c180a566e453 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Data set of Health Equity Social Service Contracts and clients who have a better health outcome. Data set displays rate, percentage and number. Data is from the PartnerGrants software used for Social Service Contracts. This data contains quarterly level data of clients served.

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/emj9-r2em

--- Original source retains full ownership of the source dataset ---

Abstract The issue of social inequalities is a subject of recurrent studies and remains relevant due to the growing trend of these inequalities over the years. This study proposes the creation of the Health Inequality Index (HII) composed of health indicators – Mean life span and Mean Potential Years of Life Lost (PYLL) – and socioeconomic indicators of income, schooling, and population living in poverty in the city of Natal – the State Capital of Rio Grande do Norte, Brazil. Therefore, a probabilistic linkage was made between mortality and socioeconomic databases in order to capture the census tracts of households with death records from 2007 to 2013. The authors used the Principal Component Factor Analysis to calculate the index. The Health Inequality Index showed areas with worse socioeconomic and health conditions located in the suburban areas of the city, with differences between and within the districts. The difference in the mean life span between the districts of Natal arrives at 25 years, and the worst district has mortality rates comparable to poor African countries. Public policymakers can use the index to prioritize actions aimed at reducing or eliminating health inequalities.

Social and spatial contexts affect health, and understanding nuances of context is key to informing successful interventions for health equity. Layering mixed methods and mixed scale data sources to visualize patterns of health outcomes facilitates analysis of both broad trends and person-level experiences across time and space. We used micro-scale citizen scientist-collected data from four Bay Area communities along with aggregate epidemiologic and population-level data sets to illustrate barriers to, and facilitators of, physical activity in low-income aging adults. These data integrations highlight the synergistic value added by combining data sources, and what might be missed by relying on either a micro- or macro-level data source alone. Mixed methods and granularity data integration can generate a deeper understanding of environmental context, which in turn can inform more relevant and attainable community, advocacy, and policy improvements.

Snapshot img

US Population Health Management (PHM) Market Size 2025-2029

The us population health management (phm) market size is forecast to increase by USD 6.04 billion at a CAGR of 7.4% between 2024 and 2029.

The Population Health Management (PHM) market in the US is experiencing significant growth, driven by the increasing adoption of healthcare IT solutions and analytics. These technologies enable healthcare providers to collect, analyze, and act on patient data to improve health outcomes and reduce costs. However, the high perceived costs associated with PHM solutions pose a challenge for some organizations, limiting their ability to fully implement and optimize these technologies. Despite this obstacle, the potential benefits of PHM, including improved patient care and population health, make it a strategic priority for many healthcare organizations. To capitalize on this opportunity, companies must focus on cost-effective solutions and innovative approaches to addressing the challenges of PHM implementation and optimization. By leveraging advanced analytics, cloud technologies, and strategic partnerships, organizations can overcome cost barriers and deliver better care to their patient populations.

What will be the size of the US Population Health Management (PHM) Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The Population Health Management (PHM) market in the US is experiencing significant advancements, integrating various elements to improve patient outcomes and reduce healthcare costs. Public health surveillance and data governance ensure accurate population health data, enabling healthcare leaders to identify health disparities and target interventions. Quality measures and health literacy initiatives promote transparency and patient activation, while data visualization and business intelligence facilitate data-driven decision-making. Behavioral health integration, substance abuse treatment, and mental health services address the growing need for holistic care, and outcome-based contracts incentivize providers to focus on patient outcomes. Health communication, community health workers, and patient portals enhance patient engagement, while wearable devices and mHealth technologies provide real-time data for personalized care plans. Precision medicine and predictive modeling leverage advanced analytics to tailor treatment approaches, and social service integration addresses the social determinants of health. Health data management, data storytelling, and healthcare innovation continue to drive market growth, transforming the industry and improving overall population health.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductSoftwareServicesDeploymentCloudOn-premisesEnd-userHealthcare providersHealthcare payersEmployers and government bodiesGeographyNorth AmericaUS

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

Population Health Management (PHM) software in the US gathers patient data from healthcare systems and utilizes advanced analytics tools, including data visualization and business intelligence, to predict health conditions and improve patient care. PHM software aims to enhance healthcare efficiency, reduce costs, and ensure quality patient care. By analyzing accurate patient data, PHM software enables the identification of community health risks, leading to proactive interventions and better health outcomes. The adoption of PHM software is on the rise in the US due to the growing emphasis on value-based care and the increasing prevalence of chronic diseases. Machine learning, artificial intelligence, and predictive analytics are integral components of PHM software, enabling healthcare payers to develop personalized care plans and improve care coordination. Data integration and interoperability facilitate seamless data sharing among various healthcare stakeholders, while data visualization tools help in making informed decisions. Public health agencies and healthcare providers leverage PHM software for population health research, disease management programs, and quality improvement initiatives. Cloud computing and data warehousing provide the necessary infrastructure for storing and managing large volumes of population health data. Healthcare regulations mandate the adoption of PHM software to ensure compliance with data privacy and security standards. PHM software also supports care management services, patient engagement platforms, and remote patient monitoring, empowering patients

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Population Health Management Market Size and Forecast 2025-2029

The population health management market size estimates the market to reach by USD 19.40 billion, at a CAGR of 10.7% between 2024 and 2029. North America is expected to account for 68% of the growth contribution to the global market during this period. In 2019 the software segment was valued at USD 16.04 billion and has demonstrated steady growth since then.

    Report Coverage


    Details




    Base year


    2024




    Historic period

    2019-2023



    Forecast period


    2025-2029



    Market structure
    Fragmented



    Market growth 2025-2029


    USD 19.40 billion






The market is experiencing significant growth, driven by the increasing adoption of healthcare IT and the rising focus on personalized medicine. Healthcare providers are recognizing the value of population health management platforms in improving patient outcomes and reducing costs. The implementation of these systems enables proactive care management, disease prevention, and population health analysis. However, the market faces challenges as well. The cost of installing population health management platforms can be a significant barrier for smaller healthcare organizations. Additionally, ensuring data security and interoperability across various systems remains a major concern.
Effective data management and integration are essential for population health management to deliver its full potential. Companies seeking to capitalize on market opportunities must address these challenges and provide cost-effective, secure, and interoperable solutions. By focusing on these areas, they can help healthcare providers optimize their population health management initiatives and improve patient care.

What will be the Size of the Population Health Management Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by advancements in technology and a growing focus on value-based care. Risk adjustment models, which help account for the variability in health risks among patient populations, are increasingly being adopted to improve care coordination and health outcome measures. For instance, a leading healthcare organization implemented risk stratification models, resulting in a 20% reduction in hospital readmissions. Remote patient monitoring, public health surveillance, and disease outbreak response are crucial applications of population health management. These technologies enable real-time health data collection, allowing for early intervention and improved health equity initiatives. Chronic disease management, a significant focus area, benefits from electronic health records, care coordination models, and health information exchange.

Value-based care programs, predictive modeling healthcare, and telehealth platforms are transforming the landscape of healthcare delivery. Healthcare data analytics, interoperability standards, and population health dashboards facilitate data-driven decision-making, enhancing health intervention efficacy. Behavioral health integration and preventive health services are gaining prominence, with health literacy programs and clinical decision support tools supporting personalized medicine strategies. The market is expected to grow at a robust rate, with industry growth estimates reaching 15% annually. This growth is fueled by the ongoing need for healthcare cost reduction, quality improvement initiatives, and the integration of technology into healthcare delivery.

How is this Population Health Management Industry segmented?

The population health management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Software
  Services


End-user

  Large enterprises
  SMEs


Delivery Mode

  On-Premise
  Cloud-Based
  Web-Based


End-Use

  Providers
  Payers
  Employer Groups
  Government Bodies


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

The market's software segment is experiencing significant growth and innovation, driven by various components that enhance healthcare organizations' capacity to manage and enhance the health outcomes of diverse populations. Population health management platforms aggregate and integrate data from multiple sources, includin

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Advanced Health Intelligence (AHI): Revolutionizing Healthcare through AI and Data Analytics?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

ObjectiveThe Brazilian remote rurality has been classified more reliably only recently, according to demographic density, proportion of urban population, and accessibility to urban centers. It comprises 5.8% of the municipalities, in nearly half of the states, with a population of 3,524,597 (1.85%). Remote rural localities (RRL) have reduced political/economic power, facing greater distances and barriers. Most health strategies are developed with the urban space in mind. We aim to understand how RRL are positioned concerning efficiency/effectiveness in health, compared to other urban-rural typologies of Brazilian localities, focusing on Primary Health Care (PHC), and its organizational models.MethodsWe evaluated the efficiency and effectiveness of the organizational models using the health production model, from 2010–2019, gradually deepening the immersion into the RRL reality. We analyzed the human and financial resources dimensions, emphasizing teams, the results of PHC actions, and health levels. We used the fixed effects model and data envelopment analysis, cross-sectioned by intersectional inequities. We compared the Brazilian states with and without RRL, Brazilian municipalities according to rural-urban typologies, and RRL clusters.ResultsBrazilian RRL states show superior resource/health efficiency through services utilization according to health needs. The remote rural typology demonstrated greater efficiency and effectiveness in health than the other typologies in the RRL states. The organizational models with the Family Health Strategy (FHS) teams and the Community Health Worker (CHW) visits played a key role, together with local per capita health expenditures and intergovernmental transfers. Thus, financial resources and health professionals are essential to achieve efficient/effective results in health services. Among the RRL, the Amazon region clusters stand out, denoting the importance of riverine and fluvial health teams, the proportion of diagnostic/treatment units in addition to the proportion of illiteracy and adolescent mothers along with the inequity of reaching high levels of schooling between gender/ethnicity.ConclusionHopefully, these elements might contribute to gains in efficiency and effectiveness, prioritizing the allocation of financial/human resources, mobile FHS teams, availability of local diagnosis/treatment, and basic sanitation. Finally, one should aim for equity of gender/ethnicity in income and education and, above all, of place, perceived in its entirety.

Mapping Layer Data Released: 06/15/2017, | Last Updated 04/20/2024Data Currency: This data is checked semi-annually from it's enterprise federal source fo 2010 CENSUS Data and will support mapping, analysis, data exports and the Open Geospatial Consortium (OGC) Application Programming Interface (API).Data Update Frequency: Twice, YearlyData Cycle | History (as required below)QA/QC Performed: December, 2024Next Scheduled Data QA/QC: July, 2024CDC PLACES (2010 CENSUS) FEATURE LAYERData Requester: Rhode Island Executive Office of Health and Human Service (OHHS) via Health Equity Institute (HEI).Data Requester: Rhode Island Department of Health, Maternal Child Health via Health Equity Institute (HEI).Data Request: Provide a database deliverable via download that contains both US CENSUS tracts and USPS Zip Code Tabulation Areas (ZCTA).HEALTH EQUITY INSTITUTE DATA CONNECT RI Using Modern GIS (Mapping)🡅 Click IT 🡅Facilitate transformative mapping visualizations that engage constituents and measure the impact of real-world solutions.Instructions to Join Your Data Provided Below STEP 1: Video (Pending)STEP 2: Video (Pending)STEP 3: Video (Pending)There are twenty-two U.S. CENSUS fields (download here) that you can join to your datasets. For additional insight, please contact the Center for Health Data and Analysis (CHDA) Rhode Island Department of Health (GIS) Mapping Department for assistance.Database Enhancement: This database contains two (2) additional data fields for consideration to be added to the existing 2020 State of Rhode Island Health Equity Map.Zip Code Tabulation Area (ZCTA)ZCTA/Tract Relationship (Singular ZCTAs per Tract, versus Multiple ZCTAs per Tract)Additional Information: While ZCTAs can be useful for certain qualitative purposes, such as broad or general high level analysis, they may not provide the level of granularity and accuracy required for in-depth demographic research which is required for policy mapping. ZCTAs can change frequently as the US Postal Service (USPS) adjusts postal routes and boundaries. These changes can lead to inconsistencies and challenges in tracking demographic trends and making accurate comparisons over time.RIDOH GIS encourages analysts to make the appropriate choice of using census based data, with their consistent boundaries readily available for suitability for spatial analysis when conducting detailed demographic research.Here are a few reasons why you might want to consider using census based data (tracts, block groups, and blocks) instead of ZCTAs:1. Inaccurate Representations: ZCTAs are not designed for statistical analysis or demographic research. They are created by the United States Postal Service (USPS) for efficient mail delivery and can often span multiple cities, counties, or even states. As a result, ZCTAs may not accurately represent the actual geographic boundaries or demographic characteristics of a specific area.2. Lack of Granularity: ZCTAs are typically larger than census tracts, which are smaller, more homogeneous geographic units defined by the U.S. Census Bureau. Census tracts are designed to be relatively consistent in terms of population size, allowing for more detailed analysis at a local level. ZCTAs, on the other hand, can vary significantly in terms of population size, making it challenging to draw precise conclusions about specific neighborhoods or communities.3. Data Availability and Compatibility: Census tracts are used by the U.S. Census Bureau to collect and report demographic data. Consequently, a wide range of demographic information, such as population counts, age distribution, income levels, and education levels, is readily available at the census tract level. In contrast, data specifically tailored to ZCTAs may be more limited, making it difficult to obtain comprehensive and consistent data for demographic analysis.4. Changes Over Time: Census tracts are relatively stable over time, allowing for consistent longitudinal analysis. ZCTAs, however, can change frequently as the USPS adjusts postal routes and boundaries. These changes can lead to inconsistencies and challenges in tracking demographic trends and making accurate comparisons over time.5. Spatial Analysis: Census tracts are designed to maintain a level of spatial proximity, adjacency, or connectedness of these data containers while providing consistency and continuity over time - making them useful for spatial analysis. Mapping. ZCTAs, on the other hand, may not exhibit the same level of spatial coherence due to their primary purpose being mail delivery efficiency rather than geographic representation.State Agencies - Contact RIDOH GIS - Learn More About Mapping Data Available at the Census Tract LevelRIDOH GIS releases this database with the caveats noted above and that the researcher can accurately align the ZCTAs with the corresponding census tracts. Careful consideration should be given to the comparability and compatibility of the data collected at different geographic levels to ensure valid and meaningful statistical conclusions. Data Dictionary: 2010 Decennial CensusOBJECT ID - the count of each census tract entity.GEOID (10) STATE,COUNTY,TRACT - Numeric US CENSUS Tract Description (2010) HEZ (10) - Health Equity Zone (2020)LOCATION (10) - Plain Language Census Tract Descriptor (2010)COUNTY (10) NAME - County Name (2010)STATE (10) NAME - State Name (2010)ZCTA (23) - Zip Code Tabulation Area - Numeric US CENSUS ZCTA Description (2023)ZCTA/TRACT CONTEXT - Number of ZCTAs (Singular/Multiple) that reside within a US CENSUS TractST (10) - Numeric US CENSUS Tract Description (2010) CO (10) - Numeric US CENSUS Tract Description (2010)ST (10) CO (10) - Numeric US CENSUS Tract Description (2010)TRACT (10) - Numeric US CENSUS Tract Description (2010)GEOID (10) - Numeric US CENSUS Tract Description (2010)TRIBAL TRACT (10) - Numeric US CENSUS Tract Description (2010)Additional Mapping DataThe user is provided authoritative Federal Information Processing Standards (FIPS) such as numeric descriptions of state, county and tract identification, in addition to shape and length measurements of each census tract for data joining purposes.STATE (10) - Federal Information Processing Standards (FIPS)COUNTY (10) - Federal Information Processing Standards (FIPS)STATE (10), COUNTY (10) - Federal Information Processing Standards (FIPS)TRACT (10) - Federal Information Processing Standards (FIPS)TRIBAL TRACT (10) - Federal Information Processing Standards (FIPS)ST ABBRV (10) - State AbbreviationShape_Length - Total length of the polygon's (census tract) perimeter, in the units used by the feature class' coordinate system.Shape_Area - Total area of the polygon's (census tract) in the units used by the feature class' coordinate system.Data Source: Series Information for 2020 Census 5-Digit ZIP Code Tabulation Area (ZCTA5) National TIGER/Line Shapefiles, Current Open Geospatial Consortium (OGC) Application Programming Interface (API) Census ZIP Code Tabulation Areas - OGC Features copy this link to embed it in OGC Compliant viewers. For more information, please visit: ZIP Code Tabulation Areas (ZCTAs)To Report Data Discrepancies Contact the Rhode Island Department of Health (RIDOH) GIS (mapping) OfficePlease Be Certain To --Provide a Brief Description of What the Discrepancy IsInclude Your, Name, Organization, Telephone NumberAttach the Complete .xlsx with the Discrepancy Highlighted

Campaign advertising media dataset for "Generating opposition to universal health care policies in the United States: an analysis of private health industry advertising on Meta platforms" (Chow, et al.).

HealthEquity, Inc. Business Operations, Opportunities, Challenges and Risk (SWOT, PESTLE and Porters Five Forces Analysis); Corporate and ESG Strategies; Competitive Intelligence; Financial KPI’s; Operational KPI’s; Recent Trends: “ Read More

To promote health equity, it is crucial to educate the next generation of healthcare workers about disparities early on during their education. We developed a virtual research program at a medical school in the United States of America with the goal of increasing the awareness of youth about the complexities of health inequities. The program was based on a near-peer mentorship where high school students were coached by medical student mentors under the oversight of an experienced faculty mentor. We evaluated the participants’ perspectives about the program using a mixed quantitative and qualitative method. Upon completion of the program, the participants were asked to complete a survey and rate their self-perceived knowledge, efficacy skills, and interest in addressing health disparities in the future. Additionally, the participants’ perspectives about the program were gathered using open-ended questions and analyzed using thematic analysis. Our preliminary findings indicate that the program enhanced the participant’s knowledge about the complexities of health disparities and their motivation to address them in the future. The near-peer mentorship model was valuable in the success of the program. The implications of enhancing intrinsic and extrinsic instincts through partnerships among educational settings, underserved communities, policymakers, and healthcare agencies are discussed. Methods An anonymous survey was distributed to high school and medical student coaches via REDCap hosted at our university. Some of the responses were based on a Likert Scale of 1-4, with 4 being the most positive outcome. In the manuscript, the average of Likert Scales was calculated for each question.

This data contains results of simulations from the existing and the modified algorithms used in the paper.