These datasets contain summarizing clusters and dimensions of place-based social determinant of health measures for the United States from AHRQ's Social Determinants of Health Database (https://www.ahrq.gov/sdoh/data-analytics/sdoh-data.html), along with the underlying SDOH data. Summary clusters and dimensions are available for both counties and Zip codes. The measures are taken from the 2019 and 2018 AHRQ SDOH datasets. Underlying SDOH measures are in the domains of social context, economic context, education, physical infrastructure, and healthcare context. The summary dimensions and cluster memberships for counties and Zip codes were generated using principal components analysis and hierarchical cluster analysis techniques to provide simple high-level representations of the SDOH context for counties and Zip codes.

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.

Recent developments include: March 2022:Azure Health Data Services aims to simplify the management and analysis of PHI, allowing healthcare organizations to gain insights and make informed decisions while maintaining data privacy and security. It provides tools such as Azure API for FHIR, Azure Cognitive Search, and Azure Machine Learning to help healthcare providers, researchers, and other stakeholders in the industry., November 2020:Change Healthcare's SDoH Analytics is a platform that leverages big data analytics to provide a deeper understanding of the impact of social determinants on health outcomes. This information can be used by healthcare organizations to improve patient care and reduce costs by addressing non-medical factors that contribute to negative health outcomes, such as poverty, lack of access to healthy food, and limited transportation options.. Notable trends are: Demand for Population Health Analytics to Boost Market Growth.

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.

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.
Request Free Sample

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

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..

Inequalities in the equipment and use of information and communications technology (ICT) in Spanish households can lead to users being unable to access certain information or to carry out certain procedures. Accessibility to ICT is considered a social determinant of health (SDOH) because it can generate inequalities in access to information and in managing access to health services. In the face of a chronic illness such as diabetes mellitus (DM)—for which a comprehensive approach is complex and its complications have a direct impact on current healthcare systems—all the resources that patients may have are welcome. We aimed to analyze hospitalizations and amputations as direct consequences of DM among the autonomous communities of Spain (ACS) in 2019, along with socioeconomic factors related to health, including inequalities in access to ICT between territories, as well as citizens' interest in online information searches about DM. We used different databases such as that of the Ministerio de Sanidad (Spain's health ministry), Ministerio de Asuntos Económicos y transformación (Ministry of Economic Affairs and Digital Transformation), Google Trends (GT), and the Instituto Nacional de Estadística (Spain's national institute of statistics). We examined the data with R software. We employed a geolocation approach and performed multivariate analysis (specifically factor analysis of mixed data [FAMD]) to evaluate the aggregate interest in health information related to DM in different regions of Spain grounded in online search behavior. The use of FAMD allowed us to adjust the techniques of principal component analysis (PCA) and multiple correspondence analysis (MCA) to detect differences between the direct consequences of DM, citizen's interest in this non-communicable disease, and socioeconomic factors and inequalities in access to ICT in aggregate form between the country's different ACS. The results show how SDOH, such as poverty and education level, are related to the ACS with the highest number of homes that cite the cost of connection or equipment as the reason for not having ICT at home. These regions also have a greater number of hospitalizations due to DM. Given that in Spain, there are certain differences in accessibility in terms of the cost to households, in the case of DM, we take this issue into account from the standpoint of an integral approach by health policies.

Florida data for the empirical analysis for CSCA manuscript that is submitted to IJGIS

Population Health Management Software and Services Market Outlook 2032

The global population health management software and services market size was USD 31.7 Billion in 2023 and is projected to reach USD 103.1 Billion by 2032, expanding at a CAGR of 14% during 2024–2032. The market is fueled by the increasing adoption of healthcare IT solutions post-pandemic and the growing emphasis on value-based care.



Growing emphasis on incorporating Social Determinants of Health (SDOH) data into population health management strategies propels the market. Healthcare organizations increasingly recognize the impact of social factors on patient health outcomes.

Integration of SDOH data enhances patient profiling and risk stratification, enabling tailored interventions. This trend reflects a holistic approach to patient care, emphasizing preventive measures and community-level interventions.

• 
  

  In August 2023, Aetna, part of CVS Health, granted USD 200,000 to Georgia's official health information exchange for a pilot aimed at enhancing the interoperability of SDOH. This investment will enable the Georgia Health Information Network (GaHIN) to showcase the benefits of a comprehensive referral system linking healthcare providers with community organizations, with Aetna pioneering its adoption.

  
  

Surging adoption of advanced analytics for predictive modeling marks a major trend in the market. Healthcare providers leverage these analytics to forecast patient health risks and outcomes accurately.



This capability supports proactive health management, optimizing resource allocation, and improving care delivery. Predictive modeling facilitates early intervention strategies, reducing emergency visits and hospitalizations, thereby, driving efficiency and effectiveness in healthcare services.

<span style="font-family:

The Gallup U.S. Daily Tracking poll was conducted between 2008 and 2017 to collect Americans' opinions and perceptions on political and economic current events. It included two parallel surveys, the U.S. Daily and the Gallup-Sharecare Well-Being Index. Gallup interviews approximately 1,000 U.S. adults every day, half of whom respond to the U.S. Daily survey and the other half respond to the Gallup-Sharecare Well-Being Index survey. The U.S. Daily survey includes information about political affiliation, presidential approval ratings, economic confidence, and religion. The Gallup-Sharecare Well-Being Index includes information on health insurance, exercise, dietary choices, and overall well-being.

ABSTRACT: Aims: To identify dietary patterns (DP) and to investigate their association with sociodemographic aspects. Methodology: A cross-sectional data analysis of a sub-sample from Phase 4 of the Pró-Saúde Longitudinal Study (2012-2013), constituting a total of 520 participants. DP were obtained by principal component analysis from a food frequency questionnaire. Association between DP and sociodemographic aspects was analyzed by adjusted logistic regression. Results: Four DP were identified: processed and ultraprocessed products; fresh food; meats and alcoholic beverages; and traditional Brazilian foods. There was a greater adherence chance to “processed and ultraprocessed products” pattern among adults ≥ 55 years and lower chance among men. The probability of adherence to “fresh food” pattern was directly associated to men, subjects with a high educational level and inversely associated to adults aged ≥ 60 years. There was a lower chance of “meats and alcoholic beverages” pattern among men and increased chance of adherence to “traditional Brazilian foods” pattern among whites, subjects with ≥ 60 years and low schooling. Conclusion: Sociodemographic factors were important determinants of DP, especially gender, schooling and age. Presence of a DP composed of processed and ultraprocessed products indicates the need for awareness strategies and supply limitation in this population, since it affects their health.

Neighborhood disadvantage and impact on access to pediatric kidney transplant. See full article in Kidney360

This repository contains all the data and code required to reproduce the results described in the article "Prostate Cancer Incidence and Social Determinants of Health in New York City: A Cancer Registry Analysis"

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.

This work is an implementation science study that examines different aspects of implementing a single intervention. The intervention consists of asking community health centers to implement an outreach strategy to screen patients for colorectal cancer and for social determinants of health in community health centers at the same contact point. These are both clinical targets that the CHCs feel that their patients need and want to offer at a higher rate. The intervention consists of outreach to patients in need of colorectal cancer screening (CRC) to offer fecal immunochemical test (FIT) screening and screening for social determinants of health (SDOH). In this implementation science study, the intervention is an evidence-based intervention being implemented in real-world clinical practice. The intervention is the outreach to offer FIT and SDOH, conducted by clinic staff. Both evidence-based screening activities-FIT and SDOH screening-are used in the practices included in the study but pairing them is intended to increase efficiency and patient-centeredness by addressing health related social needs that may impact patients’ ability to engage in cancer screening. The study aims to test the effect of implementing the intervention on clinical and process outcomes. Clinical outcomes are CRC screening and SDOH screening. Analysis of process outcomes includes measuring what organizational factors influence implementation.

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

Context

This database contains the data reported in the Annual Homeless Assessment Report to Congress (AHAR). It represents a point-In-time count (PIT) of homeless individuals, as well as a housing inventory count (HIC) conducted annually.

The data represent the most comprehensive national-level assessment of homelessness in America, including PIT and HIC estimates of homelessness, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth.

These data can be trended over time and correlated with other metrics of housing availability and affordability, in order to better understand the particular type of housing resources that may be needed from a social determinants of health perspective.

HUD captures these data annually through the Continuum of Care (CoC) program. CoC-level reporting data have been crosswalked to county levels for purposes of analysis of this dataset.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.sdoh_hud_pit_homelessness

Sample Query

What has been the change in the number of homeless veterans in the state of New York’s CoC Regions since 2012? Determine how the patterns of homeless veterans have changes across the state of New York

homeless_2018 AS ( SELECT Homeless_Veterans AS Vet18, CoC_Name
FROM bigquery-public-data.sdoh_hud_pit_homelessness.hud_pit_by_coc WHERE SUBSTR(CoC_Number,0,2) = "NY" AND Count_Year = 2018 ),

veterans_change AS ( SELECT homeless_2012.COC_Name, Vet12, Vet18, Vet18 - Vet12 AS VetChange FROM homeless_2018 JOIN homeless_2012 ON homeless_2018.CoC_Name = homeless_2012.CoC_Name )

SELECT * FROM veterans_change

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/).

Globally, 34 million children below 15 years have hearing loss (HL) and while research shows associations between social determinants of health and disability in general, research on the associations between these determinants and HL in children is limited. Therefore, this study sought to examine the association between social determinants of health and HL in children using the parental socioeconomic status, such as educational attainment level, employment status and income level, non-medical determinants of health (rurality, housing, type of toilet, availability of piped drinking water, and exposure to cigarette smoke) as proxy factors for social determinants of health in children. This was a secondary data analysis of a cross-sectional survey conducted with 517 children in South Africa. We conducted multivariable logistic regression to test for the association between HL and exposure variables such as non-medical determinants of health and parental socioeconomic status using Stata v18 for Macintosh. Odds ratios (OR) with 95% confidence intervals (CIs) were used to ascertain the odds of HL with exposure variables. One hundred and two participants (n = 102, 19.7%) had HL, including 57 (55.9%) females. Crude analysis showed increased odds of HL in females (OR:1.6; 95%CI: 1.0 – 2.5, P = 0.03) and children younger than9 years (OR: 2.0; 95%CI: 1.3 – 3.1, P = 0.003). After adjusting for age and sex, exposure to cigarette smoke (aOR: 4.0; 95%CI:2.4 – 6.4, P 

Title: HIV and Hepatitis C Among People Who Inject Drugs in Memphis, Tennessee: an Intersectional Risk Environment Analysis of the Social Determinants of Health Authors: Natalie Flath, Jack H. Marr, Lindey Sizemore, Latrice C. Pichon, and Meredith Brantley CEDEP Program: VH Product type: publication Conference, meeting, or publication accepted to: Journal of Racial and Ethnic Health Disparities