research questions on social determinants of health

Social Determinants of Health

What are social determinants of health.

Social determinants of health (SDOH) are the conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks.

SDOH can be grouped into 5 domains:

Suggested citation

Healthy People 2030, U.S. Department of Health and Human Services, Office of Disease Prevention and Health Promotion. Retrieved [date graphic was accessed], from https://health.gov/healthypeople/objectives-and-data/social-determinants-health

Social determinants of health (SDOH) have a major impact on people’s health, well-being, and quality of life. Examples of SDOH include:

Safe housing, transportation, and neighborhoods
Racism, discrimination, and violence
Education, job opportunities, and income
Access to nutritious foods and physical activity opportunities
Polluted air and water
Language and literacy skills

SDOH also contribute to wide health disparities and inequities. For example, people who don't have access to grocery stores with healthy foods are less likely to have good nutrition. That raises their risk of health conditions like heart disease, diabetes, and obesity — and even lowers life expectancy relative to people who do have access to healthy foods.

Just promoting healthy choices won't eliminate these and other health disparities. Instead, public health organizations and their partners in sectors like education, transportation, and housing need to take action to improve the conditions in people's environments.

That's why Healthy People 2030 has an increased and overarching focus on SDOH.

How Does Healthy People 2030 Address SDOH?

One of Healthy People 2030’s 5 overarching goals is specifically related to SDOH: “Create social, physical, and economic environments that promote attaining the full potential for health and well-being for all.”

In line with this goal, Healthy People 2030 features many objectives related to SDOH. These objectives highlight the importance of "upstream" factors — usually unrelated to health care delivery — in improving health and reducing health disparities.

More than a dozen workgroups made up of subject matter experts with different backgrounds and areas of expertise developed these objectives. One of these groups, the Social Determinants of Health Workgroup , focuses solely on SDOH.

Explore Research Related to SDOH

Social determinants of health affect nearly everyone in one way or another. Our literature summaries provide a snapshot of the latest research related to specific SDOH.

View SDOH Infographics

Each SDOH infographic represents a single example from each of the 5 domains of the social determinants of health. You can download them, print them, and share them with your networks.

Learn How SDOH Affect Older Adults

SDOH have a big impact on our chances of staying healthy as we age. Healthy People’s actionable scenarios highlight ways professionals can support older adults’ health and well-being.

The Office of Disease Prevention and Health Promotion (ODPHP) cannot attest to the accuracy of a non-federal website.

Linking to a non-federal website does not constitute an endorsement by ODPHP or any of its employees of the sponsors or the information and products presented on the website.

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Social determinants of health: present status, unanswered questions, and future directions

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1 School of Health Policy and Management, York University, Toronto, Ontario. [email protected]
PMID: 17175840
DOI: 10.2190/3MW4-1EK3-DGRQ-2CRF

This article reviews the current status of theory and research concerning the social determinants of health. It provides an overview of current conceptualizations and evidence on the impact of various social determinants of health. The contributions of different disciplines--epidemiology, sociology, political economy, and the human rights perspective--to the field are acknowledged, but profound gaps persist in our understanding of the forces that drive the quality of various social determinants of health and why research is too infrequently translated into action. Many of these gaps in knowledge concern the political, economic, and social forces that make implementation of public policy agendas focused on strengthening the social determinants of health problematic. The author identifies the areas of inquiry needed to help translate knowledge into action.

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Introduction
Conclusions
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eMethods 1. Semi-Structured Interview Guide for Primary Care Team Members

eMethods 2. Discussion Questions for Patient Engagement Studio (PES) With Patient Stakeholders

eTable 1. Descriptive Statistics for Social Determinants of Health (SDOH) Screening Responses

eTable 2. Descriptive Statistics for Practices, Providers and Patients With Unrestricted Sample (N = 147 096)

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Rudisill AC , Eicken MG , Gupta D, et al. Patient and Care Team Perspectives on Social Determinants of Health Screening in Primary Care : A Qualitative Study . JAMA Netw Open. 2023;6(11):e2345444. doi:10.1001/jamanetworkopen.2023.45444

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Patient and Care Team Perspectives on Social Determinants of Health Screening in Primary Care : A Qualitative Study

1 Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Greenville
2 Department of Medicine, Prisma Health, Upstate, University of South Carolina School of Medicine Greenville, Greenville
3 Department of Epidemiology/Biostatistics, Arnold School of Public Health, University of South Carolina, Greenville
4 Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville
5 Addiction Medicine Center, Prisma Health, Greenville, South Carolina

Question Are patient and clinician factors associated with early implementation of social determinants of health (SDOH) screening in primary care, and what strategies can improve these efforts?

Findings In this qualitative study of 78 928 primary care visits from the inception of primary care–based SDOH screening, visits with a physician assistant, belonging to a racial minority group, and having noncommercial/nonprivate health insurance were associated with greater screening likelihood. Stakeholders suggest that patient-clinician rapport, practice champions, streamlined questions, and referral follow-up ability may improve screening implementation.

Meaning Results of this study suggest that primary care SDOH screening is feasible but limited by barriers that can be overcome with consideration of stakeholder feedback.

Importance Health systems in the US are increasingly screening for social determinants of health (SDOH). However, guidance incorporating stakeholder feedback is limited.

Objective To examine patient and care team experiences in early implementation of SDOH screening in primary care.

Design, Setting, and Participants This qualitative study included cross-sectional analysis of SDOH screenings during primary care visits from February 22 to May 10, 2022, primary care team member interviews from July 6, 2022, to March 8, 2023, and patient stakeholder engagement on June 30, 2022. The setting was a large southeastern US health care system. Eligible patients were aged 18 years or older with completed visits in primary care.

Exposure Screening for SDOH in primary care.

Main outcomes and Measures Multivariable logistic regression evaluated patient (eg, age, race and ethnicity) and care team characteristics (eg, practice type), and screening completeness. Interviews contextualized the quantitative analysis.

Results There were 78 928 visits in practices conducting any SDOH screening. The population with visits had a mean (SD) age of 57.6 (18.1) years; 48 086 (60.9%) were female, 12 569 (15.9%) Black, 60 578 (76.8%) White, and 3088 (3.9%) Hispanic. A total of 54 611 visits (69.2%) were with a doctor of medicine and 13 035 (16.5%) with a nurse practitioner. Most had no SDOH questions answered (75 298 [95.4%]) followed by all questions (2976 [3.77%]). Logistic regression analysis found that clinician type, patient race, and primary payer were associated with screening likelihood: for clinician type, nurse practitioner (odds ratio [OR], 0.13; 95% CI, 0.03-0.62; P = .01) and physician assistant (OR, 3.11; 95% CI, 1.19-8.10; P = .02); for patient race, Asian (OR, 1.69; 95% CI, 1.25-2.28; P = .001); Black (OR, 1.49; 95% CI, 1.10-2.01; P = .009); or 2 or more races (OR, 1.48; 95% CI, 1.12-1.94; P = .006); and for primary payer, Medicaid (OR, 0.62; 95% CI, 0.48-0.80; P < .001); managed care (OR, 1.17; 95% CI, 1.07-1.29; P = .001); uninsured or with Access Health (OR, 0.26; 95% CI, 0.10-0.67; P = .005), and Tricare (OR, 0.71; 95% CI, 0.55-0.92; P = .01). Interview themes included barriers (patient hesitancy, time and resources for screening and referrals, and number of questions/content overlap) and facilitators (communication, practice champions, and support for patient needs).

Conclusions and Relevance This qualitative study presents potential guidance regarding factors that could improve SDOH screening within busy clinical workflows.

Health systems in the US recognize the importance of social determinants of health (SDOH) in patient outcomes and care. The SDOH are economic and social conditions affecting health outcomes, 1 health care use, 2 and health inequities. 3 Health systems are increasingly engaging in SDOH screening. 4 Although such screening can potentially improve health outcomes and reduce health care use, 5 , 6 there is limited peer-reviewed evidence incorporating patient and clinician or care team characteristics and perspectives when describing early screening initiatives.

Given the personal nature and limited evidence guiding SDOH screening adoption, 7 - 9 it is critical to understand stakeholder perspectives. Prior research indicates that health care professionals recognize the importance of addressing patient SDOH needs and strive to adopt patient-centered approaches 10 but face ethical and time-related challenges. 8 , 11 , 12 Existing work reports greater SDOH screening uptake in primary care vs specialist visits and lower completion among patients requiring interpreters and patients with racial and ethnic minority status. 7 Studies on patient and caregiver perspectives have documented SDOH screening acceptability and preferences. 13 The role of practice and care team characteristics in screening uptake has not been assessed within a multistakeholder analysis.

To address this research gap, we conducted a qualitative study of a large southeastern US health care system's experiences during the early stages of SDOH screening in primary care. Quantitative analysis examined practice, care team, and patient characteristics and SDOH screening uptake. Qualitative analysis engaged team member feedback. Patient experts informed interview protocols and finding interpretation. Our goal was to identify barriers and facilitators to SDOH screening within primary care to inform future screening.

This qualitative study was classified as exempt by the Prisma Health institutional review board in accordance with 45 CFR §46. In February 2022, Prisma Health, South Carolina’s largest nonprofit health system with approximately 1.5 million unique patients annually, began screening adults for SDOH needs in primary care practices with the goal of annual screening. Practices had implementation flexibility and determined how and when to screen during the clinical workflow. Patients were screened using a 16-question electronic health record (EHR)–embedded survey (eTable 1 in Supplement 1 ). Questions were chosen using validated questionnaires and clinical input on system priorities and resource availability. Answers triggered automated input of community-based service information curated to patient SDOH needs and location into patient after-visit summaries using an EHR-compatible platform connecting patients to community-based organizations (NowPow; Unite Us). Practices provided the after-visit summaries to patients at visit end. Reporting follows the 21-item Standards for Reporting Qualitative Research ( SRQR ) reporting guideline.

The study population included patients aged 18 years or older with a visit in a family or internal medicine practice in the northwestern region of South Carolina from February 22 to May 10, 2022. Visits classified as future, cancelled, no show, or left without being seen were excluded. The last screen on a day was the patient final value, and the same patient could have multiple visits over the study period. In 2021, the northwestern region (4 counties) had 813 069 inhabitants, with 14.2% in poverty (11.4% nationally) and 13.9% uninsured (10.2% nationally). The population is 75.8% White, 14.6% Black, 6.5% Hispanic, 0.4% American Indian or Alaska Native, 1.6% Asian, and 0.1% Native Hawaiian or Other Pacific Islander. 14

The primary outcome was SDOH screening completion status. Visits with a response to at least 1 question were deemed partial screening while complete screening included responses to all questions. Our primary outcome compared visits with complete or partial screening (any screening) with no screening. Secondary outcomes compared visits with complete vs partial or no screening and visits with complete screening vs partial screening.

Potential explanatory variables included practice type (family or internal medicine), clinician qualification (medical doctor, doctor of osteopathic medicine, nurse practitioner, and physician assistant), patient demographic characteristics (age, sex, race and ethnicity [treated as classified in the electronic medical records as separate fields], preferred language, primary payer), and SDOH risk (calculated as the ratio of screener questions with positive responses to the total number of questions answered by patients). Race and ethnicity came from the EHR and thus were primarily patient self-reported. Race is reported as Asian, Black, White, 2 or more races, other race, patient refused, or unknown. Other race comprises American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, and other as reported in the EHR. Ethnicity is reported as in the EHR. We included SDOH risk to test whether patients with a need might be more likely to be screened (ie, care team members suspect a need or patients are more likely to answer questions).

Binary logistic regression was used to determine the odds of screening completion. Standard errors were clustered by practice to account for practice-specific differences. A 95% CI not including 1 indicated statistical significance. We tested for multicollinearity using variance inflation factors and omitted variable bias using the Ramsey Regression Equation Specification Error Test (RESET). Analysis was conducted using Stata/MP, version 11 (StataCorp LLC).

Six practices were categorized as higher-adopting facilities as they performed SDOH screening during at least 4.0% of visits over the study period. Two of these practices were excluded because of involvement in other SDOH-related studies. Lower-adopting practices performed at least 10 screenings but in less than 2.0% of visits. Four practices met this criterion, but 1 practice was excluded because of involvement in SDOH pilot efforts. Higher- and lower-adopting was defined by quantitative analysis. We excluded practices performing no or minimal screening because we wanted to learn from those practices with some screening familiarity and those screening at both higher and lower levels. These 7 practices were approached for interviews of primary care team members (ie, physicians, administrative staff, nursing staff, and allied health professionals). Six practices participated in a total of 9 interviews (at least 1 interviewee from each of these 6 practices). Interview findings contextualized the quantitative analysis.

Two trained medical students (E.K. and M.J.) conducted and recorded 9 semistructured interviews online between July 6, 2022, and March 8, 2023. The students had not met the interviewees or worked in these clinics prior to the interviews. Interview questions focused on potential barriers and facilitators to screening (eMethods 1 in Supplement 1 ). Oral consent was obtained prior to interviews. Interviews were transcribed verbatim by a speech-to-text service (rev.com). Interview recordings were accessible only to interviewers and the team member uploading for transcription. Interviewers asked questions aimed to not yield identifying information. Additionally, transcripts were kept either on secure file-sharing systems or on password-protected computers. Using a web application (Dedoose), transcripts were coded by 2 research team members (D.G. and M.M.) and analyzed using an inductive grounded theory approach, in which important concepts and themes are derived from close reading of the text, and similar concepts are grouped into conceptual categories (codes). No further interviews were necessary as theme saturation was achieved.

To ensure the research was relevant and ethical for patients and the broader community, we included a meeting with patient experts from the University of South Carolina Patient Engagement Studio (PES) in our research strategy. 15 - 17 The PES is built on guidance from the Patient-Centered Outcomes Research Institute and provides structured opportunities for research teams to engage with community-recruited patient experts. Patient expert refers to individuals or caregivers with substantial health system interaction due to their health conditions who are trained in communication, research methods, and team building.

The research team met with patient experts on June 30, 2022, prior to interviews with primary care practices. In accordance with standard PES processes, 18 patient experts were provided the health system SDOH screening tool as presession reading material. Discussion topics at that meeting included screening and referral processes (eMethods 2 in Supplement 1 ). Patient expert feedback was incorporated into the research process through practice interview topics and by incorporating what we heard from patient experts when discussing study results.

Over the study period, there were 147 096 practice visits, with 3630 (2.5%) involving complete (2976 [3.8%]) or partial (654 [0.8%]) SDOH screening. In the restricted sample, 22 of 58 practices (37.9%) performed any screening during the study period ( Table 1 ). Of the 78 928 visits (mean [SD] age of 57.6 [18.1] years; 48 086 [60.9%] were female, 12 569 [15.9%] Black, 60 578 [76.8%] White and 3088 [3.9%] Hispanic) in the restricted sample, 41 574 (52.7%) were in family medicine and 37 354 (47.3%) in internal medicine practices. Most visits were with medical doctors (54 611[69.2%]), followed by nurse practitioners (13 035 [16.5%]), doctors of osteopathic medicine (5877 [7.4%]), and physician assistants (2958 [3.8%]). On average, patients had a mean (SD) of 0.08 (0.13) (95% CI, 0.08-0.09) positive responses per SDOH question answered.

The SDOH screener responses in order of question appearance are given in eTable 1 in Supplement 1 . Earlier questions were more likely to be asked and answered. Overall, patient response refusal was low (≤3.3%). Descriptive statistics for the unrestricted sample (visits to all practices) are given in eTable 2 in Supplement 1 .

Table 2 displays regression results examining factors associated with any SDOH screening (complete or partial screening vs no screening) in the restricted (model 1) and unrestricted (model 2) practice samples. In model 1 (restricted), compared with visits with a medical doctor, visits with a physician assistant had 3.11 (95% CI, 1.19-8.10; P = .02) greater odds of any screening done, while visits with nurse practitioners had significantly lower odds (odds ratio [OR], 0.13; 95% 0.03-0.62; P = .01) of any screening done. Visits with patients identifying as Asian (OR, 1.69; 95% CI, 1.25-2.28; P = .001), Black (OR, 1.49; 95% CI, 1.10-2.01; P = .009), or 2 or more races (OR, 1.48; 95% CI, 1.12-1.94; P = .006) were more likely to have any screening compared with visits with patients identifying as White. With regard to primary payer, visits where patients had managed care had 1.17 (95% CI, 1.07-1.29; P = .001) greater odds of any screening compared to visits where patients had private or commercial payers. Visits where patients had Medicaid (OR, 0.62; 95% CI, 0.48-0.80; P < .001), were uninsured or had Access Health (OR, 0.26; 95% CI, 0.10-0.67; P = .005) or had Tricare (OR, 0.71; 95% CI, 0.55-0.92; P = .01) had lower odds of any screening. Practice type, patient age, sex, language, and ethnicity had no significant associations with screening likelihood. Results were consistent in model 2 (unrestricted) except for visits with physician assistants and uninsured patients, where the finding was not significant.

We also compared visits completing the entire screening questionnaire vs partial or no screening ( Table 3 ) for the restricted practice sample. In model 3, compared with visits with a medical doctor, visits with a physician assistant had 3.78 times (95% CI; 1.43-10.0; P = .007) greater odds of screening completion while visits with a nurse practitioner had lower screening completion odds (OR, 0.15; 95% CI, 0.03-0.75; P = .02). Visits where patients identified as Black had greater odds of screening completion (OR, 1.33; 95% CI, 1.01-1.74; P = .04) than visits where patients identified as White. Visits where patients had managed care had 1.15 (95% CI, 1.05-1.26; P = .002) times greater screening completion odds than visits where patients had private or commercial payers. However, screenings were less likely to be complete if patients had Medicaid (OR, 0.53; 95% CI, 0.40-0.72; P < .001), Tricare (OR, 0.76; 95% CI, 0.58-0.98; P = .04), or were uninsured or had Access Health (OR, 0.14; 95% CI, 0.05-0.40; P < .001). Results were consistent in model 4 comparing the odds of complete vs partial screening.

Model 5 extended model 4 to include patient SDOH risk from screening responses. Patient SDOH risk was not associated with screening completion (OR, 1.03; 95% CI, 0.56-1.88; P = .93). Results in model 5 are consistent with model 4.

All models had variance inflation factors of less than 10 indicating absence of multicollinearity. Models 4 and 5 had omitted variable bias.

We identified 7 themes regarding barriers and facilitators from health care team member interviews for implementing SDOH screening ( Table 4 ). Care team members reported patient reluctance in responding to screener questions. Hesitancy was attributed to perceptions about questions being intrusive or offensive. Interviewees reported patients reacting unfavorably to sensitive questions (eg, violence/abuse, financial strain). Time to administer the screener, interpret results, and address identified needs posed challenges with existing workloads.

Clinicians expressed concerns about potential patient response burden and overlap with routine care questions (eg, stress and Patient Health Questionnaire 2). Clinicians suggested streamlining the screener by combining multiple related questions and then tailoring subsequent questions based on patient initial responses.

Some clinicians felt inadequately trained in navigating the screening tool and expressed uncertainty about effective use of screening results. Many practices lacked social workers or resource navigators to connect patients with resources and follow up on referrals. Clinicians felt their attention diverted from the primary goal of medical care provision.

Care team members reported that screening facilitated patient care by uncovering socioeconomic issues not identified in routine care. Practices that informed patients about the screening purpose, assured them it would not affect care, and obtained verbal consent prior to screener administration perceived more successful uptake.

Some practices identified practice champions as being responsible for screening implementation and supporting patient needs. Some practices had a referral coordinator or social worker who connected patients to community-based resources and provided follow-up support. Clinicians reported they would benefit from training on how to best use screening.

Table 5 presents feedback from patient experts. Patient experts preferred that screening be done at annual appointments to allow for discussion time and in the examination room to ensure privacy. Patient experts emphasized rapport building between patients and care teams and providing information about the screening purpose. They expressed the importance of empathetic clinicians performing screening. Recommendations for rephrasing questions included expanding the partner violence or abuse questions (eTable 2 in Supplement 1 ) to include safety concerns related to family members, neighborhoods, and caretakers. Patient experts expressed concern about timely referral follow-up.

This qualitative study assessed factors associated with SDOH screening completion in primary care and explored patient and care team member perspectives on screening. We found that clinician type, patient race, and primary payer were linked to any screening but that practice type, patient age, sex, language, ethnicity and SDOH risk were not.

Completion rates differed in this study (3.8%) from previous research (58.7%) 7 also examining systemwide SDOH screening implementation. This may be related to study duration, timing (intra–COVID-19 pandemic vs pre–COVID-19 pandemic), or implementation (recommendation for all primary care patients vs preassigned screening). 7 Based on qualitative interviews, our study completion rates may be affected by the desire to receive more resources to support patient referrals.

Our findings suggest that primary care visits with nonphysician clinicians, such as physician assistants, may be favorable for SDOH screening. However, this result did not hold for nurse practitioners and deserves further research, as previous studies demonstrated nonphysician clinician confidence in addressing SDOH needs and greater community-based resource awareness. 19 Clinician type could be serving as a proxy for visit type as our data set did not include visit reason. Consistent with previous studies, 20 our interview-based findings suggest that clinicians faced an additional time burden from incorporating SDOH screening, which they perceived to affect care provision.

We found patients with managed care to be more likely to be screened, while those with Medicaid and those who were uninsured or had Access Health and Tricare were less likely. Medicare and Medicare Advantage had no effect relative to private or commercial payer status. Patients with Medicaid and uninsured or had Access Health may benefit most from screening; therefore this finding is critical for further implementation. Of note, these patients may have been screened via other programs at the health system thus, lack of screening in primary care is not necessarily reflective of screening otherwise.

A lack of association between screening and other patient characteristics (age, gender, language, ethnicity, SDOH risk) suggests that perhaps these characteristics are not associated with SDOH needs in the perceptions of those performing screening. These results differed from previous research that found members of racial and ethnic minority groups less likely to be screened, 7 thereby providing support for universal implementation across primary care practices as a potential mitigation against screening disparities. 7

In our quantitative analysis, questions appearing later in the screener were less likely to be completed. Interviews further explained this finding as questionnaire length and repetitive questions led to a greater perceived patient response burden by health care clinicians. Although there is no consensus on screener length, existing tools range from 6 to 23 questions. 21 Generally, short-form surveys are more acceptable to patients. 22 Notably, patients did not express the same concerns as clinicians about survey length or repetitiveness.

Interviews and patient expert feedback found that patient–care team communication is crucial for screener uptake. Sensitive questions about patient needs may lead to incomplete or untruthful responses if patients have privacy concerns, 10 , 23 feel embarrassed, or fear stigmatization. 24 Patient experts and health care team members emphasized rapport building and communicating the screening purpose to mitigate patient concerns and build trust. Future investigation should include assessment of standard phrasing to introduce the screener rationale and consideration of the best location and visit type for screening. Last, patient experts and care team members expressed concerns about referral follow-up, perceiving that care would benefit from an enhanced ability to follow up on referral outcomes.

Our study has a few limitations to be considered. First, findings are restricted to primary care practices within 1 health system in 1 region, limiting generalizability. However, this study is comprehensive by including all primary care practices in 1 region covered by a large health system that statewide serves approximately 25% of residents. 14 Second, we used a convenience sample of practice staff for our qualitative assessment. This restricted our examination of how qualitative themes differed based on practice characteristics. However, practice choice for interviews was based on screening implementation to intentionally capture those screening at higher and lower adoption rates. Third, our data set included whether a survey was taken on MyChart (Epic). No surveys were done on MyChart. Accordingly, we were unable to test screening modality association with screening completion. We also had no information on screening completion via telemedicine vs office visits and did not include this topic in our interview guide. In addition, we do not know at what rate patients refused to verbally consent to screener administration if a practice asked for such consent.

Although health systems face different challenges in implementing SDOH screening, identifying and addressing common barriers are critical for improved patient activation and care collaboration. Future research should focus on robust assessment of strategies to improve screening uptake.

Accepted for Publication: October 19, 2023.

Published: November 28, 2023. doi:10.1001/jamanetworkopen.2023.45444

Corresponding Author: A. Caroline Rudisill, PhD, MSc, Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, 300 E McBee Ave, Ste 401, Greenville, SC 29601 ( [email protected] ).

Author Contributions: Dr Rudisill and Ms Gupta had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Rudisill, Eicken, Macauda, Self, Thomas, Hartley.

Acquisition, analysis, or interpretation of data: Rudisill, Eicken, Gupta, Macauda, Self, Kennedy, Kao, Jeanty.

Drafting of the manuscript: Gupta, Kao, Hartley.

Critical review of the manuscript for important intellectual content: Rudisill, Eicken, Macauda, Self, Kennedy, Thomas, Jeanty.

Statistical analysis: Rudisill, Gupta, Self.

Obtained funding: Rudisill, Eicken.

Administrative, technical, or material support: Rudisill, Kennedy, Thomas, Kao, Jeanty.

Supervision: Rudisill, Eicken, Macauda.

Conflict of Interest Disclosures: Dr Rudisill reported grants from the Prisma Health Transformative Seed Grant Program during the conduct of the study and The Duke Endowment, Centers for Disease Control and Prevention, Viiv Healthcare, University of Michigan/National Institute on Aging/National Institutes of Health, South Carolina(SC)/NIA/NIH, SC Research Foundation (SCRF)/BlueCross/BlueShield Foundation of SC and National Heart, Lung, and Blood Institute/NIH. Dr Eicken reported grants from Prisma Health Transformative Seed Grant Program during the conduct of the study; grants from the Duke Endowment and grants from the Prisma Health Transformative Seed Grant Program outside the submitted work; Dr Eicken sits on the board of the Piedmont Health Foundation. Ms Gupta reported grants from Prisma Health during the conduct of the study; and support from the Duke Endowment. Dr Self reported grants from Prisma Health during the conduct of the study; personal fees from Companion Animal Parasite Council and personal fees from Merck outside the submitted work. Dr Kennedy reported grants from Prisma Health The Patient Engagement Studio received a portion of the grant to provide feedback during the conduct of the study; and has received 2 Eugene Washington Engagement Awards for capacity building with patients from the Patient-Centered Outcomes Research Institute in 2020 and in 2021. Ms Kao reported grants from Prisma Health Seed Grant during the conduct of the study. Ms Jeanty reported grants from Prisma Health Seed Grant Program during the conduct of the study. Mr Hartley reported grants from Prisma Health Seed Grant Program during the conduct of the study. No other disclosures were reported.

Funding/Support: This research was funded by the Prisma Health Research Seed Grant program.

Role of the Funder/Sponsor: The funding organization had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

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Screening patients to understand their social context is the gateway to addressing barriers and improving health.

DAVID T. O'GUREK, MD, FAAFP, AND CARLA HENKE, MD

Fam Pract Manag. 2018;25(3):7-12

Author disclosures: no relevant financial affiliations disclosed.

Decades ago, the historic Whitehall studies demonstrated the impact that social context can have on individuals' health and wellbeing. 1 , 2 Family physicians understand this well because they see firsthand how social needs (or “social determinants of health”) create access, adherence, or performance barriers, often impeding their efforts to provide evidence-based clinical care that improves overall health. 3 , 4 For example, a patient who lacks safe housing, reliable transportation, or adequate food resources may struggle with medication adherence or getting to visits on time.

The ecology of medical care 5 and the current financing system, which tend to focus on health care as opposed to health, may limit physicians' ability to address social context. Nevertheless, family physicians can take practical steps to address social determinants of health in their practices. This article will discuss the concerns and challenges related to screening for social determinants of health and offer several tools and recommendations.

Patients' social needs related to housing, food, safety, etc., can create significant obstacles to high-quality care and contribute to poor health.

Screening for social determinants of health without first equipping the practice to address identified needs would be ineffective and unethical.

Several brief screening tools can be effective in primary care practices as part of a workflow designed to address social needs with referrals to community-based resources.

TO SCREEN OR NOT TO SCREEN

Despite studies demonstrating the impact of socioeconomic factors on health, there is no evidence-based screening recommendation for social determinants of health from an organization such as the U.S. Preventive Services Task Force. Even without a formal recommendation, several policy statements support such screening, 6 , 7 and a current national initiative through the Centers for Medicare & Medicaid Services (CMS), the Accountable Health Communities Model, 8 may soon shed evidence on the impact of screening. Additionally, community health centers have been screening for social determinants of health and coordinating related services for years. Their experiences have suggested some best practices for developing “medical neighborhoods,” particularly in underserved and diverse communities.

Concerns about the limited research for screening for social determinants of health are understandable, but they reveal our implicit bias against information from sources other than randomized controlled trials (RCTs). 9 Although RCTs rely on standardization, consistency, and fidelity of the intervention, community-based research on community-level health interventions must rely on variation to deliver interventions in the field to tailor to community needs, often requiring longer study times and costlier studies. 10 Due to the challenge of controlling for multiple social variables, research in this field tends to be observational. For this reason, the Community Preventive Services Task Force, an independent, nonfederal panel of public health and prevention experts that provides evidence-based findings and recommendations, developed a guide for assessing evidence regarding health impacts of social interventions. 11

In addition to being aware of concerns about research, physicians should note that screening for social determinants is intrinsically different from traditional screening for medical problems. Both, however, require that screening occur in a setting where appropriate referral or linkage to resources to address an identified need can take place. To do otherwise would be ineffective and unethical. 12 Discovering a need and being ill-equipped to address that need creates potential harm for the patient, and frustration and burnout for the physician. To avoid these unintended consequences and make screening an invaluable part of the clinical process, practices need to ensure that screening is patient- and family-centered, integrated with referrals to community-based resources, comprehensive across all patient populations, and focused on leveraging the strengths of patients, families, and communities. 13

SCREENING TOOLS

There is no single preferred screening tool recommended for social determinants of health; however, the National Association of Community Health Centers and several other organizations use the Protocol for Responding to and Assessing Patients' Assets, Risks, and Experiences (PRAPARE). The PRAPARE tool collects demographic information and assesses a patient for a host of social needs including housing, employment, education, security, transportation, social integration, and stress with optional measures of incarceration history, domestic violence, and refugee status (15 core questions and 5 supplemental questions). The data can be directly uploaded into many electronic health records (EHRs) as structured data. It is generally administered by clinical or nonclinical staff at the time of the visit, but a paper version can be given to the patient to self-administer.

The American Academy of Family Physicians also offers a social determinants of health screening tool, available in short and long form in English and Spanish, as part of The EveryONE Project . The short form includes 11 questions about housing, food, transportation, utilities, personal safety, and the need for assistance. It can be self-administered or administered by clinical or nonclinical staff.

Additionally, CMS's Accountable Health Communities project developed a 10-question Health-Related Social Needs screening tool (the AHC-HRSN) that addresses housing instability, food insecurity, transportation needs, utility needs, and interpersonal safety. 14 This tool is meant to be self-administered. It draws on evidence from other validated assessments that address specific unmet social and material needs (see “ Screening tools for social determinants of health ”).


The Protocol for Responding to and Assessing Patients' Assets, Risks, and Experiences (PRAPARE)	15 core, 5 supplemental
The American Academy of Family Physicians Social Needs Screening Tool	11 (short form) 15 (long form)	Short: Long:
The Accountable Health Communities Health-Related Social Needs (AHC-HRSN) Screening Tool	10 core, 13 supplemental


Food insecurity	Hunger Vital Sign	Low-income families with young children
Food insecurity	U.S. Department of Agriculture U.S. Household Food Security Survey	Households with reported annual incomes below 185 percent of the federal poverty level
Housing instability	District of Columbia Department of Health & Human Services Temporary Assistance for Needy Families Comprehensive Assessment Housing Domain	Families at risk of or experiencing homelessness
Housing instability	National Center on Homelessness Among Veterans Homelessness Screening Clinical Reminder	Veteran population
Interpersonal safety	Hurt, Insulted, Threatened With Harm and Screamed Domestic Violence Screening Tool	Men and women
	Women Abuse Screening Tool – Short Form	Women
	Partner Violence Screen	Women
	Abuse Assessment Screen	Women
Utility needs	Children's Sentinel Nutrition Assessment Program	Families with children younger than 3 years old

WORKFLOW CONSIDERATIONS

Because physicians can become easily overwhelmed and stretched when asked to incorporate “just one more thing” to their daily practice flow, social determinants screening and follow up must not be the sole responsibility of the physician. Instead, it should be a team-based effort integrated into the practice's care management workflows. Large practices may have care coordinators, patient navigators, health coaches, or community health workers who can assist in streamlining and directing screening processes as well as coordination of care. In small practices, nurses, medical assistants, and other support staff will be critical.

In addition to deciding who on the care team will perform the screening, practices also need to decide how often the screening will occur, where the screening data will be stored, how results will be communicated to all care team members, how the patient's need will be prioritized, and how the follow-up strategy will be documented. For example, a practice may decide that the medical assistant is responsible for administering an annual screening after rooming the patient and entering the results in real-time as social history in the EHR for the physician to review. If a patient is currently experiencing housing instability, food insecurity, and domestic violence, the physician would decide which issue to address first, document the care plan and follow-up plan in the EHR, and instruct the medical assistant to handle the referral details with the patient.

Other workflow options would be to use nonclinical staff to conduct the screening either before or after the visit, or have patients complete a self-assessment while they wait.

Although having a standardized work-flow is important, that workflow may not always be sufficient; therefore, in screening for social determinants of health, clear communication among all team members is critical. Community of Hope, a community health center in the District of Columbia, has found that when a consistent framework for communication among care team members does not exist, either nothing is accomplished in regard to care management or duplicate and parallel processes occur, creating more work for the team and no change in the patient's health. The center uses a daily team huddle, appropriate routing of messages, and a process for consistent documentation within EHR notes, telephone encounters, and the problem list to improve communication and care coordination when it comes to addressing the social determinants of health.

For any workflow to succeed, practices will need to develop a list of referral resources to connect patients to needed services in the community, such as meal programs or utility assistance programs. While not comprehensive, the list of “ Community resources for addressing social determinants of health ” can assist practices in identifying resources. Practices can also develop partnerships with local agencies to address the needs most prevalent in their population and begin to build a medical neighborhood. (See also “ Caring for Seniors: How Community-Based Organizations Can Help ,” FPM, September/October 2014.)


211
Aunt Bertha
Cap4Kids

Feeding America
Supplemental Nutrition Assistance Program
Special Supplemental Nutrition Program for Women, Infants, and Children (WIC)

Public Housing and Voucher Program

Medical-Legal Partnerships

CODING AND PAYMENT

Physicians can include supplemental ICD-10 “Z” codes in the patient's diagnosis section and problem list, such as codes Z55–Z65, “Persons with potential health hazards related to socioeconomic and psychosocial circumstances.” Although Z codes are not generally reimbursable, including these codes in the medical record can help with population health, panel management, and quality improvement initiatives. 15 Data collected may also eventually factor into value-based payment systems that will reimburse family physicians for this critical work to improve health. The data can also be useful in developing innovative solutions and partnerships to address the social determinants that most directly affect a population. For example, Community of Hope created partnerships with a mobile farmer's market as well as a bike-share program to promote healthy eating and exercise not only to the health center patients but also to the community in which the clinic is located.

PULLING TOGETHER AND MOVING FORWARD

Family physicians have long understood the importance of social factors and their impact on the health of patients and communities. Transforming medical practice to have a larger impact on prevention and health as well as meeting the goals of national initiatives such as Healthy People 2020 will require screening for social determinants of health and development of coordinated care systems that meet social needs. The nation's community health centers have built a model for screening and care coordination; however, opportunities exist for developing best practices in other settings to improve the health of communities. While this process can be daunting, resources are available. Furthermore, screening and coordinating services to meet social needs is an opportunity to reduce physician and staff burnout related to the inertia of improving health in communities where social and policy barriers prevent us from doing so. This opportunity can also improve the system as a whole, encouraging payment reform that values the factors that most significantly affect health.

Marmot MG, Rose G, Shipley M, Hamilton PJ. Employment grade and coronary heart disease in British civil servants. J Epidemiol Community Health . 1978;32(4):244-249.

Marmot MG, Smith GD, Stansfeld S, et al.; Health inequalities among British civil servants: the Whitehall II study. Lancet . 1991;337(8754):1387-1393.

Lasser KE, Hummelstein DU, Woolhandler S. Access to care, health status, and health disparities in the United States and Canada: results of a cross-national population-based survey. Am J Public Health . 2006;96(7):1300-1307.

Schoen C, Doty MM. Inequities in access to medical care in five countries: findings from the 2001 Commonwealth Fund International Health Policy Survey. Health Policy . 2004;67(3):309-322.

Green LA, Fryer GE, Yawn BP, Lanier D, Dovey SM. The ecology of medical care revisited. N Engl J Med . 2001;344(26):2021-2025.

Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health Records, Board on Population Health and Public Health Practice, Institute of Medicine. Capturing Social and Behavioral Domains and Measures in Electronic Health Records . Washington, DC: National Academy of Medicine; 2014. http://www.nap.edu/catalog/18709/capturing-social-and-behavioral-domains-in-electronic-health-records-phase . Accessed March 8, 2018.

Council on Community Pediatrics, American Academy of Pediatrics; Poverty and child health in the United States. Pediatrics . 2016;137(4):e20160339.

Alley DE, Asomugha CN, Conway PH, Sanghavi DM. Accountable health communities – addressing social needs through Medicare and Medicaid. N Engl J Med . 2016;374(1):8-11.

Newman TB, Kohn MA. Evidence-Based Diagnosis . New York, NY: Cambridge University Press, 2009.

Braveman PA, Egerter SA, Woolf SH, Marks JS. When do we know enough to recommend action on the social determinants of health?. Am J Prev Med . 2011;40(1):S58-S66.

Briss PA, Brownson RC, Fielding JE, Zaza S. Developing and using the Guide to Community Preventive Services: lessons learned about evidence-based public health. Annu Rev Public Health . 2004;25:281-302.

Perrin EC. Ethical questions about screening. J Dev Behav Pediatr . 1998;19(5):350-352.

Garg A, Boynton-Jarrett R, Dworkin PH. Avoiding the unintended consequences of screening for social determinants of health. JAMA . 2016;316(8):813-814.

Billioux A, Verlander K, Anthony S, Alley D. Standardized Screening for Health-Related Social Needs in Clinical Settings: The Accountable Health Communities Screening Tool . Washington, DC: National Academy of Medicine; 2017. https://nam.edu/wp-content/uploads/2017/05/Standardized-Screening-for-Health-Related-Social-Needs-in-Clinical-Settings.pdf . Accessed March 8, 2018.

Gottlieb L, Tobey R, Cantor J, Hessler D, Adler NE. Integrating social and medical data to improve population health: opportunities and barriers. Health Aff (Millwood) . 2016;35(11):2116-2123.

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More in FPM

Social Determinants of Health

Social determinants of health (SDOH) are non-medical factors affecting health, like socioeconomic status, and geographic location.
Addressing SDOH can enhance health and lead to better outcomes.

Social determinants of health (SDOH) are non-medical factors that affect health outcomes. They include the conditions in which people are born, grow, work, live, and age. SDOH also include the broader forces and systems that shape everyday life conditions.

These forces and systems encompass economic policies, development agendas, social norms, social policies, racism, climate change, and political structures. CDC adapted this definition from the WHO 2022 definition of SDOH. Healthy People 2030 highlights SDOH in its key health indicators.

Image showing the 5 domains of the social determinants of health: 1) education access and quality, 2) health care and quality, 3) neighborhood and built environment, 4) social and community context and 5) economic stability

How SDOH Impacts Lives

A better understanding of incorporating SDOH into public health work and the 10 Essential Public Health Services strengthens public health capacity. Health equity ensures everyone has a fair chance to achieve optimal health. Along with the powerlessness that can arise from poverty and discrimination, limited access to the following can also occur:

Quality jobs
Safe environments

To advance health equity, SDOH factors, such as those above, should be addressed.

The following are a few ways in which the CDC's National Center for State, Tribal, Local, and Territorial Public Health Infrastructure and Workforce (Public Health Infrastructure Center) integrates SDOH into its activities.

Pathways to Population Health Equity

Pathways to Population Health Equity (P2PHE) provides tools for public health leaders to enhance population health, well-being, and equity. P2PHE includes a framework, roadmaps, compass, and various tools to help public health practitioners develop more prepared, resilient, and proactive systems. These tools and framework are adaptable to address the most pressing issues in any jurisdiction.

Community health assessment and improvement planning

CDC collaborates with various partners to offer technical support, resources, and training. This helps health departments and community partners in assessment and planning. For instance, the Public Health Infrastructure Center partners with the National Association of County and City Health Officials to promote the Mobilizing for Action through Planning and Partnerships (MAPP) planning framework. This framework integrates health equity in its guidance and practice.

Health department, hospitals, and others use certain tools and requirements to drive planning for community health assessment and health improvement. These tools and requirements emphasize data on health inequities, SDOH, and structural determinants of health in state and community planning efforts.

These collaborative processes involve diverse populations and multi-sector partners across jurisdictions. This results in plans that identify priorities and strategies to tackle health disparities.

Innovative funding strategies to support SDOH efforts

The Public Health Infrastructure Center works with partner organizations to explore health department use of innovative financing strategies . These strategies can be opportunities to address SDOH. Examples from partners include case stories about the following:

Braiding and layering funds to address housing and food insecurity .
Use of participatory budgeting to ensure community-driven funding decisions .

COVID-19 health equity grant

The National Initiative to Address COVID-19 Health Disparities Among Populations at High-Risk and Underserved, Including Racial and Ethnic Minority Populations and Rural Communities is a $2.5 billion grant supporting 108 state, territorial, local, and tribal health departments. It aims to reduce COVID-19 health disparities and promote health equity by enhancing capacity and services of the following health departments:

US territorial
Freely associated state

One key strategy is to engage partners and collaborators to advance health equity and address SDOH related to COVID-19's effect on higher-risk and underserved groups.

Public health accreditation

The CDC backs the national accreditation program , managed by the Public Health Accreditation Board (PHAB). The national standards, which are central to the accreditation program, emphasize health equity and SDOH across various requirements. This includes community health assessment, health improvement planning, health promotion, policy development, and internal training. Health departments are striving to meet these standards. Evidence from evaluation studies and accredited sites indicates that in doing so, they are enhancing multi-sectoral partnerships and adopting practices like:

Formulating health equity policies.
Setting up agency-wide health equity councils.

Public Health Gateway

CDC's National Center for State, Tribal, Local, and Territorial Public Health Infrastructure and Workforce helps drive public health forward and helps HDs deliver services to communities.

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Social Determinants of Health: A Review of Publicly Available Indices

Laurie Hinnant , Sara Hairgrove , Heather Kane , Jason Williams , and Jessica Duncan Cance .

In recent years, the number of publicly available tools and indices assessing social determinants of health (SDOH) has grown exponentially. While many of these indices have been developed to assist researchers and practitioners with identifying vulnerable communities, it is difficult to determine the most appropriate measure, index, or combination of indices to use given the research question of interest. This paper presents an overview of the most commonly included indices, highlights commonalities, and identifies some differences in what they measure. We also discuss challenges with using these measures, including the use of state level data to examine local level issues and how the use of atheoretical indices challenges the application of SDOH measurement. Findings are intended to provide researchers and practitioners with information about SDOH data available through these common indices to inform how they are applied based on the needs of their work.

Acknowledgments

This study was supported by Texas Targeted Opioid Response, a public health initiative operated by the Texas Health and Human Services Commission through federal funding from the Substance Abuse and Mental Health Services Administration (SAMHSA) grant award number 1H79TI081729. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation of the manuscript. The content of this study does not represent the official view of SAMHSA or the Texas Health and Human Services Commission.

Introduction

In recent years, social determinants of health (SDOH) research has rapidly gained traction. SDOH refer to “the conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks.” 1 These conditions are often grouped into domains: economic stability, education access and quality, health care access and quality, neighborhood and built environment, and social and community context. Each of these categories contains varied determinants, such as job opportunities, air pollution, and discrimination—all of which dramatically impact, and continually determine, one’s lifelong health and success. 1

Key Findings.

The late twentieth-century Whitehall Study, which examined mortality in relation to employment grades, originated the concept of SDOH. Since that study was conducted, the importance of SDOH have been recognized by the US Department of Health and Human Services, the Centers for Disease Control and Prevention (CDC), and the World Health Organization, among other key public health agencies. 2 , 3 Recent expansions in policy and broader community efforts toward equity have further spotlighted SDOH research.

To effectively improve the well-being and health outcomes of a population, researchers and practitioners must continue to emphasize health disparities investigation. Dozens of publicly available indices and tools, such as data visualization dashboards, have been developed to measure and describe SDOH. Several of these are commonly used by researchers and practitioners to identify communities of greatest need. Each index comprises different measures with varied levels of geographic granularity. The differences among the indices should be considered by researchers when selecting the appropriate one to use to measure population-level health determinants. 4 However, we lack a systematic assessment of indices or combination of indices to inform selection of indices for a particular study question of interest. This paper intends to provide researchers and practitioners a comparative overview of publicly available SDOH indices and considerations that should be made when selecting an index in line with the purpose of their work.

To identify common indices of SDOH, we started by holding brief discussions with public health experts to obtain information on indices of which they were aware and that are commonly used in the field. To supplement that information, we conducted a high-level environmental scan of publicly available indices. 5 We conducted Google searches, with keywords including social determinants of health , measurement / measures , index / indices , and data / data sources . Several organizations, including CDC 6 and the Rural Health Information Hub, have compiled lists of resources related to measurement of SDOH, and we also reviewed those. We identified a total of 16 indices that relate to understanding and measuring SDOH or their key components. We conducted a preliminary review of these indices to examine the specificity or breadth of SDOH measures included in each index (e.g., not just specific to one health outcome, included domains or measures that addressed several SDOH).

We selected six indices for in-depth review. They included publicly available data across a variety of common SDOH measures—health, environmental conditions, economics/socioeconomic status, crime and safety, and education, among others. We abstracted the indices, individual measures/items within the index, data sources, and frequency and recency of data into an Excel spreadsheet and then conducted a second abstraction to group common measures and data sources for comparison. Using this information, we selected five indices to include in this review. The Opioid and Health Indicators Database was dropped from this discussion because of its narrow focus on substance use and related health impacts.

Our review includes five indices commonly used in SDOH research and practice ( Table 1 ). Each index has a different sponsor and a different purpose. For example, the Social Vulnerability Index, sponsored by the CDC, focuses on identifying communities most vulnerable to disaster and least able to recover from such hardships; in contrast, the Opportunity Index, sponsored by the Forum for Youth Investment, emphasizes inequitable distribution of social advantage. Because of these differences in goals, the composition of each index also differs to align with its sponsor’s focus, even though all the indices provide a composite assessment of key elements of SDOH.

Summary of publicly available SDOH indices.

As Table 1 shows, each index varies in the overarching domains, or constructs, that compose it. Domains include socioeconomic status, housing, transportation, household composition, education, community, health, physical infrastructure, equity, and others. Adding to the complexity, for many indices, each domain also consists of sub-domains. Within sub-domains, the individual measures further vary. For example, the Healthiest Communities index includes the domain of Population Health, under which are five sub-domains (Access to Care, Health Behaviors, Health Conditions, Health Outcomes, and Mental Health), under which there are 16 individual measures.

Across the indices, significant variability appears in what constitutes a domain, what the associated sub-domains are (and whether sub-domains are used), and what measures are included under each domain/sub-domain. The economy domain illustrates this variability. The Social Vulnerability Index includes a Socioeconomic Status domain, which does not include a sub-domain but includes four measures—Below Poverty, Unemployed, Income, and No High School Diploma. The Opportunity Index includes an Economy domain, which also does not include a sub-domain but includes seven measures: Jobs, Wages, Poverty, Income Inequality, Access to Banking Services, Affordable Housing, and Broadband Internet Subscription. The Healthiest Communities index includes an Economy domain that includes three sub-domains and their related measures: Employment (measures: Average Weekly Wage, Labor Force Participation, and Unemployment Rate), Income (measures: Households Receiving Public Assistance Income, Median Household Income, Medical Debt in Collections, and Poverty Rate), and Opportunity (measures: Business Growth Rate, Job Diversity Index Score, and Jobs Within a 45-Minute Commute). Although some areas overlap, these similar domains include very different sub-domains and measures. In several cases, measures like affordable housing or broadband internet are included in one domain in one index and in different domains in other indices.

Comparison of example measures for economy/socioeconomic status domain of SDOH indices.

Variability extends to the measures and data that are used to construct those domains and (if applicable) sub-domains. In Table 2 , we provide examples of the many different measures and data sources used by these indices to examine the construct of Economy. For example, all five indices use a measure of Unemployment Rate. However, a deeper examination shows that these measures are drawn from different data sources. Two indices use the US Census’s American Community Survey, and three indices use the Bureau of Labor Statistics’ Local Area Unemployment Statistics. Similar examples can be seen in Table 3 when one considers the construct and measures of health. Three indices include a measure of adults with no health insurance coverage. Two of those use data from the US Census Bureau, and one uses data from the Behavioral Risk Factor Surveillance System.

Comparison of example measures for health domain of SDOH indices.

Examination of these indices shows that variability also exists in the geographic granularity of the data included. Many indices provide data at the state level and some other sub-state levels (e.g., county, county equivalent, census tract, ZIP code); however, not all data are available at the sub-state level. For example, the US Prosperity Index provides data at the state level but provides county-level data for only 1,196 counties in 12 selected states.

Another key consideration is the frequency and recency of when data are collected. Many publicly available data are not collected annually, or there is a delay between when data are collected and when they are available for analysis or when the most recent data are incorporated into an updated version of the indices examined. As a result, some data are several years old and of limited utility given rapidly changing community environments, especially since the COVID-19 pandemic. Many of the data available today predate the pandemic and may not capture the current state of key SDOH factors.

Finally, as discussed previously, each index has been constructed to meet the needs of the sponsor organization or agency. The domains, sub-domains, and measures have been carefully selected and crafted to capture the unique factors related to the outcome of interest, whether that be related to disaster recovery (Social Vulnerability Index), distribution of opportunity (Opportunity Index), patient-centered outcomes research (SDOH Database), community health (Healthiest Communities), or prosperity (US Prosperity Index). As such, the descriptive summaries from these indices have unique underlying statistical and analytical practices affecting the interpretation of these data. None of these indices has been designed to be a universal repository of SDOH measures that can facilitate in-depth modular, statistical analyses.

With the growing interest in SDOH, researchers and practitioners may seek publicly available resources for assessing SDOH for their analyses. However, the publicly available indices are not designed to be universal measures or compendia of measures that can be readily entered into statistical models. Each varies in its purpose and composition, which can affect analyses. Researchers and practitioners must carefully assess each index to ensure alignment with their study question and theoretical framework, to establish validity and reliability, to reach accurate conclusions, and to appropriately identify areas for intervention. As a starting point, researchers and practitioners should explore what data elements and samples are included, at what geographic levels, and at what time points.

The composition of an index has multiple analytic implications for statistical modeling, including impacts that skew the data, introduce collinearity, and generate error. A researcher or practitioner should examine the underlying measures to ensure that the planned outcome of interest is not already embedded in the index, which would skew results. For example, the Opportunity Index includes drug-related deaths and, therefore, should never be included as an independent variable in a statistical model with opioid-related deaths as an outcome.

The timing and periodicity of the data collection and availability for the underlying measures also can affect the analyses and limit the conclusions. Data can become outdated and cannot keep up with rapid changes in a community (especially over the past 2 years). Even when data collection is ongoing (e.g., American Community Survey, Behavioral Risk Factor Surveillance System), data may not be available for about 2 years after data collection takes place. Thus, data availability may constrain study conclusions. When designing a study or using data to inform decision making, researchers and practitioners should consider how the timing of the data collection and availability affects potential findings, and they should describe potential limitations arising from the timing of the data collection and availability.

Many of these indices have weighting and other analytic adjustments based on the purpose of the index. Although researchers and practitioners may download raw data elements, they must account for these adjustments when determining whether one can break out individual measures or sub-indices for other analyses. For example, the Healthiest Communities index differentially weights each domain when developing its community rankings and conclusions. Domains and their weighting are: Population Health (14.2%), Equity (12.23%), Economy (11.1%), Housing (9.5%), Food and Nutrition (8.38%), Environment (8.6%), Public Safety (8.5%), Community Vitality (7.6%), and Infrastructure (7.5%).

Several indices also use small area estimation to indirectly estimate data for a geographic area smaller than the original design accounted for (e.g., estimating at the county level when the survey was designed to gauge state-level prevalence). Inherent challenges in small area estimation may not account for local variation, 7 and validity of the estimates is dependent on both the measure and neighborhood factors. Likewise, when the indices are constructed, sometimes data collected at one level may be aggregated to another level (e.g., county-level data aggregated to the state level). This aggregation can affect analyses and conclusions by ignoring community-level variability, potentially reducing the association between the SDOH index score and the health outcome of interest. Therefore, when using specific measures or indices, researchers and practitioners should carefully review associated data collection documents, such as the data dictionary, sampling procedures, and analytic procedures.

Finally, populations who are underrepresented in data collection are often those who are most affected by health disparities. Incarcerated and homeless individuals are not represented well in large survey data collection efforts; thus, researchers and practitioners must attend to the study sample to draw study conclusions.

Limitations and Future Directions

Our review is based on accessible SDOH indices, which are those publicly available for download. We did not include indices available by purchase. In addition, the scope of this review was limited to a qualitative review of the indices.

The next step in this work is to compare the indices quantitatively with a common health outcome, such as drug overdose deaths or diabetes prevalence, to assess whether the indices have differential impact, as we would expect given the differences between the indices noted in this review.

As more researchers and practitioners plan to integrate SDOH in their studies, they may consider options from the many publicly available indices designed to assess the presence or level of social determinants across the domains of economic stability, education access and quality, health care access and quality, neighborhood and built environment, and social and community context within their communities of interest. However, these indices are not interchangeable and have significant differences across domains of interest, included measures, and geographic granularity. To ensure that findings can provide actionable conclusions, researchers and practitioners should use theory-informed criteria when selecting the appropriate index or indices to use in their work.

Laurie Hinnant , PhD, is a senior research public health analyst in the Child and Adolescent Research and Evaluation program in RTI International’s Community Health Research Division. https://orcid.org/0000-0003-0873-479X

Sara Hairgrove , BA, is a public health analyst in the Substance Use Prevention, Evaluation, and Research program in RTI International’s Community Health Research Division. https://orcid.org/0000-0002-6137-9849

Heather Kane , PhD, is program director of the Child and Adolescent Research and Evaluation program in RTI International’s Community Health Research Division. https://orcid.org/0000-0003-0994-6727

Jason Williams , PhD, is a research public health analyst in the Substance Use Prevention, Evaluation, and Research program in RTI International’s Community Health Research Division. https://orcid.org/0000-0002-3804-2594

Jessica Duncan Cance , MPH, PhD, is a senior research public health analyst in the Substance Use Prevention, Evaluation, and Research program in RTI International’s Community Health Research Division. https://orcid.org/0000-0002-2797-0037

RTI International is an independent, nonprofit research organization dedicated to improving the human condition. The RTI Press mission is to disseminate information about RTI research, analytic tools, and technical expertise to a national and international audience. RTI Press publications are peer-reviewed by at least two independent substantive experts and one or more Press editors.

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Hinnant, L., Hairgrove, S., Kane, H., Williams, J., and Cance, J. D. (2022). Social Determinants of Health: A Review of Publicly Available Indices . RTI Press Publication No. OP-0081-2212. Research Triangle Park, NC: RTI Press. https://doi.org/10.3768/rtipress.2022.op.0081.2212

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Published: 07 September 2024

Impact of systematic screening for social determinants of health in a level IV neonatal intensive care unit

Caitlin Hoffman 1 ,
Melissa Harris 2 ,
Krishna Acharya ORCID: orcid.org/0000-0002-6073-4669 1 ,
Margaret Malnory 1 ,
Susan Cohen ORCID: orcid.org/0000-0003-4444-2145 1 &
Joanne Lagatta ORCID: orcid.org/0000-0002-5147-2396 1

Journal of Perinatology ( 2024 ) Cite this article

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Health services
Paediatrics
Risk factors

To determine whether screening for social determinants of health (SDoH) in a level IV neonatal intensive care unit (NICU) could uncover additional family needs.

Secondary analysis of a prospective study in a level IV NICU. Participants filled out the Protocol for Responding to and Assessing Patients’ Assets, Risks and Experiences (PRAPARE) tool, which includes economic, housing, transportation, and safety questions. Questionnaires were completed via secure tablet; the research team notified social workers of reported needs. Illness and demographic characteristics were compared between families who did and did not report resource needs. Manual chart review assessed subsequent response to reported SDoH needs.

Of 319 respondents, 61(19%) reported resource needs. Of 61 families, 88% received repeat social work encounters to re-assess for resources; 59% received new resource referrals.

Conclusions

Systematic SDoH screening can identify needs throughout the NICU stay, even among families already connected to social work support.

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research questions on social determinants of health

The financial burden experienced by families of preterm infants after NICU discharge

“All these people saved her life, but she needs me too”: Understanding and responding to parental mental health in the NICU

Multilevel social factors and NICU quality of care in California

Data availability.

The datasets generated and analyzed are available from the corresponding author on reasonable request.

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Acknowledgements

The authors acknowledge Jill Winkler MSW, Megan Sheble, and Jonathan Leuthner for their contributions to this work.

This project is funded in part by the National Institutes of Health K23HL136525 (JL). The content is solely the responsibility of the author(s) and does not necessarily represent the official views of the NIH.

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Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA

Caitlin Hoffman, Krishna Acharya, Margaret Malnory, Susan Cohen & Joanne Lagatta

Institute for Health and Equity, Medical College of Wisconsin, Milwaukee, WI, USA

Melissa Harris

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C Hoffman: conceptualization, data collection, formal analysis, writing—original draft, visualization. M Harris: conceptualization, formal analysis, writing—review, and editing. KK Acharya: conceptualization, investigation, writing—review and editing. M Malnory: investigation, writing—review and editing, project administration. S Cohen: conceptualization, investigation, writing—review, and editing. J Lagatta: conceptualization, methodology, formal analysis, writing—review and editing, visualization, supervision, funding acquisition.

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Correspondence to Joanne Lagatta .

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Approval for this study was obtained from the IRB at Children’s Wisconsin. Written informed consent was obtained from parents on behalf of both parents and infants. All methods were performed in accordance with guidelines and regulations. No images were obtained.

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Hoffman, C., Harris, M., Acharya, K. et al. Impact of systematic screening for social determinants of health in a level IV neonatal intensive care unit. J Perinatol (2024). https://doi.org/10.1038/s41372-024-02096-x

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Published : 07 September 2024

DOI : https://doi.org/10.1038/s41372-024-02096-x

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