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J Clin Transl Sci. 2024; 8(1): e60.
Published online 2024 Apr 5. doi:10.1017/cts.2024.506
PMCID: PMC11036426
PMID: 38655456
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Abstract
The Centers for Medicare & Medicaid Services have mandated that hospitals implementmeasures to screen social determinants of health (SDoH). We sought to report on availableSDoH screening tools. PubMed, Scopus, Web of Science, as well as the grey literature weresearched (1980 to November 2023). The included studies were US-based, written in English,and examined a screening tool to assess SDoH. Thirty studies were included in the analyticcohort. The number of questions in any given SDoH assessment tool varied considerably andranged from 5 to 50 (mean: 16.6). A total of 19 SDoH domains were examined. Housing(n = 23, 92%) and safety/violence (n = 21, 84%) werethe domains assessed most frequently. Food/nutrition (n = 17, 68%),income/financial (n = 16, 64%), transportation (n = 15,60%), family/social support (n = 14, 56%), utilities (n= 13, 52%), and education/literacy (n = 13, 52%) were also commonlyincluded domains in most screening tools. Eighteen studies proposed specific interventionsto address SDoH. SDoH screening tools are critical to identify various social needs andvulnerabilities to help develop interventions to address patient needs. Moreover, there ismarked heterogeneity of SDoH screening tools, as well as the significant variability inthe SDoH domains assessed by currently available screening tools.
Keywords: Health disparities, needs assessment, screening, social determinants of health, SDoH
Introduction
Social determinants of health (SDoH) are conditions in which individuals are born, reside,engage in employment, acquire knowledge, practice religion, enjoy recreational activities,and grow old [1]. Taken together, SDoH are awell-established classification of essential non-medical factors that directly or indirectlyimpact health outcomes [2,3]. These factors may impact access to health care and may be related toindividual behaviors as well as disease biology with important implications to anindividual’s health [2,3]. In addition, the COVID-19 pandemic highlighted how patients invulnerable socioeconomic contexts were at heightened risk of disease transmissibility,hospitalization, and mortality [4]. In response tothe exacerbation of longstanding health disparities during the pandemic, there has been anincreased interest in methods to identify and define SDoH [4,5]. By accessing data on SDoH, there isthe potential to implement policies and target interventions to address barriers to healthand healthcare delivery [6]. Importantly, resolvingunmet social needs that underpin SDoH represents an opportunity to meaningfully improvepopulation health, quality of life, and life expectancy, as well as patient outcomes [7].
Personal and systemic factors compromise a wide range of social determinants of health thatdrive health outcomes [8–12]. The World Health Organization (WHO) classifies SDoH into fivebroad domains: economic stability, education, social and community context, health careaccess and quality, and neighborhood and built environment [13]. In addition to these broad domains, additional dimensions include– but are not limited to – race and ethnicity, housing, food security, transportation,violence and safety, employment, health behaviors (i.e., substance use, physical activity,and dietary choices), mental health, disabilities, religion, immigration status, legalconcerns, gender, and sexual orientation.[14,15] For instance, substandard housing has beenassociated with a higher prevalence of respiratory, hematologic, and neurologic illness, aswell as childhood lead poisoning [16].
The COVID-19 pandemic exacerbated healthcare disparities [17,18], drawing attention to the need todevelop federal and community-based policies to improve health equity. The recently issuedUnited States Domestic Policy Counsel Playbook outlined recommendations for federal agenciesto improve policies around SDoH with an emphasis on identifying social metrics relevant tohealth outcomes [17]. The Playbook served as a callto stakeholders and agencies to develop actionable programmatic changes to quantify andimprove SDoH metrics. Proposed reforms are intended to occur at the federal and local levelsto support community organizations to institute patient-level screening. These broad changesalso seek to achieve a secondary goal: easing the substantial economic burden of healthexpenditures that occur due to pervasive health inequity [19,20]. Concurrent with theseinitiatives, the Centers for Medicare & Medicaid Services (CMS) have mandated thathospitals implement two new measures in 2024 to screen patients for SDoH: SDoH-1 orScreening for Social Drivers of Health and SDoH-2 or Screen Positive Rate for Social Driversof Health [21]. While mandating reporting of SDoHmeasures, CMS does not offer uniform data-capturing methods/approaches, instead givinghospitals flexibility/discretion in how SDoH characteristics are recorded.
Screening tools intended to capture data on SDoH can vary significantly with vastlydifferent domains, which may complicate how data are collected and used to develop communityinterventions to address health inequities [22,23]. Therefore, the objective of thecurrent study was to report on available SDoH screening tools in a systematic manner aimedat addressing disparities identified using these tools.
Methods
Search methods
The study adhered to the Preferred Reporting Items for Systematic Reviews andMeta-Analysis (PRISMA) guidelines. This systematic review protocol was registered withPROSPERO, an internationally recognized database for prospectively registered systematicreviews in the fields of health and social care [24]. A comprehensive search of the PubMed, Scopus, and Web of Science databasesfrom 1980 to November 2023 was performed using predetermined keywords. The search includeda mix of subject headings and keywords that related to different social determinantsscreening tools, as well as specific proposed SDoH addressing interventions (Table1). In addition to searching PubMed, Scopus, and Webof Science databases, a search of “grey literature” sources was also performed based onreferences of relevant studies, as well as an international clinical trials registryplatform to identify parallel and ongoing research. Inclusion criteria included: (a)written in the English language, (b) conducted in the United States, and (c) established ascreening tool to identify or address SDoH. Reviews and reports with no publiclyaccessible survey tool were excluded. Studies that fulfilled inclusion criteria reporteddata from 2007 to 2023, and each study provided an SDoH screening tool or an SDoHintervention. All reports initially identified from the database search were entered intoENDNOTE software for analysis.
Table 1.
Search strategy and keywords used for literature screening
| Database | Number |
|---|---|
| PubMed/Medline: ((“Recommended screeningtool”[Title] OR “recommended screening tool”[Title] OR Screen [Title] ORscreening [Title] OR Address [Title] OR Addressing [Title] OR tool [Title] ORtoolkit [Title] OR intervention [Title] OR interventional [Title]) OR(“Address” [Publication Type])) AND ((“Social Determinants of Health”[Mesh])OR (“Social determinants of health”[Title] OR “social determinants”[Title] ORSDoH [Title] OR “health-related social conditions”[Title])) | 806 |
| Scopus: (TITLE (“Recommended screening tool” OR“recommended screening tool” OR screen OR screening OR address OR addressingOR tool OR toolkit OR intervention OR interventional) AND TITLE (“Socialdeterminants of health” OR “social determinants” OR SDoH OR “health-relatedsocial conditions”)) | 639 |
| ISI Web of Science: “Recommended screening tool” OR“recommended screening tool” OR Screen OR screening OR Address OR AddressingOR tool OR toolkit OR intervention OR interventional (Title) AND “Socialdeterminants of health” OR “social determinants” OR SDoH OR “health-relatedsocial conditions” (Title) | 676 |
| https://trialsearch.who.int/ | 3 |
| Included investigations references | 9 |
Results
Study characteristics
The initial search identified 2,121 studies. After eliminating duplicate entries, a totalof 1,098 studies underwent primary screening. Following title and abstract screening,studies that did not address SDoH, or did not propose any publicly accessible SDoHscreening tools and/or interventions (n = 793), were excluded. Inaddition, non-primary studies (reviews, etc.) (n = 159) and studies thatwere not conducted in the USA (n = 87) were excluded. A total of 59studies were sought for full-text retrieval. Following a secondary review of these 59 fulltexts, 22 studies were deemed eligible for inclusion [13,25–45]. Following a manual search of the literature, as well as after snowballingthe citations of included studies, 8 additional articles were incorporated into the review[46–53]. As such, a total of 30 papers were included in the analytic cohort(Fig.1).
Screening tools characteristics
Table2 describes the SDoH screening toolcharacteristics of the 25 unique screening tools that were identified. Six screening toolswere administered to pediatric patients [27,29–31,37,47]. Onewas designed to assess pregnant patients [34],and the remaining tools (n = 18) were utilized for general screeningpurposes in clinical settings, such as hospitals or clinical offices [13,25,26,28,32,33,35,36,38–46,48–53] Sixtools were administered by healthcare professionals [13,25,30,32,46,49]; while 12 tools were completedby patients (or parents) either electronically or on paper [13,28,29,34,36,37,44,45,47,48,50,52]; six tools wereadministered verbally or were self-administered at the patient’s request [26,27,31,33,35,53]; Thenumber of questions in any given SDoH assessment tool varied considerably and ranged from5 in Health Leads (2018) and the North Carolina toolkit [36], as well as the Core 5 social risk tool [28] to 50 in the health system’s EPIC electronic health records screening tool[33]; overall, the mean number of questions inany given SDoH screening tool assessment was 16.6 (Table2).
Table 2.
SDoH screening tool characteristics of the 25 unique screening tools that wereidentified
| Number | Tool/ Study | Setting (type of application) | Who/how administered/number of questions |
|---|---|---|---|
| 1 | PRAPARE* [25] | Clinical settings | Physician or nurse practitioner, computer-based/ 12+ demographic |
| 2 | AHC HRSN * [27] | General Pediatrics Clinic | Self-administered or Verbal with outreachcoordinators/10 |
| 3 | de Ramirez et al [13] | Primary care offices | In-person questionnaire/22 |
| 4 | NLP-based method* [13] | Referral hospital system | Passive identification of SDoH through NLP /Non |
| 5 | WE CARE [26] | Urology Clinic | Self-administered or verbal /10 |
| 6 | Health Leads (2018) and the North Carolina [36] | Community-based MHC* | Paper patient intake/5 |
| 7 | Macias-Konstantopoulos et al [35] | Single academic medical center | Verbal or electronic /16 |
| 8 | SIPT* 34 | Pregnant patients | Paper patient intake /32 |
| 9 | Health system’s EPIC EHRs* (version 2019) [33] | Outpatient setting at a cancer center | Electronic (REDCap) + Verbal /50 |
| 10 | Gupta et al [32] | Patients (+ 18y) engaged in community health,inpatient, or ambulatory | Verbal /13 |
| 11 | Friedman et al [30] | Pediatric Resident Clinic | Asked by the physician during the visit /10 |
| 12 | Core 5 Social Risk Screening [28] | Clinical practice | Self-administered /5 |
| 13 | Sokol et al [37] | Outpatient pediatric patients | Self-administered /12 |
| 14 | I screen [31] | Pediatric emergency department | Self-administered or face-to-face with researchassistant/23 |
| 15 | AHC HRSN (modified with exploring perceivedacceptability of screening) [29] | Pediatric patients of primary care clinics andemergency departments | Self-administered using a tablet device/32 |
| 16 | Well Rx [50] | Clinical setting | Self-administered/11 |
| 17 | Health begins [49] | Clinic settings | Student, health care staff, or provider; Paper/29 |
| 18 | Health leads [48] | Clinical settings | Self-administered /9 |
| 19 | Help Steps [47] | Children’s Hospital + Public Health Commission | Web-based/ 12 |
| 20 | The Every ONE Project [46] | Clinical setting | Health care providers/ 14 |
| 21 | North Carolina Toolkit [51] | Clinical setting | Not mentioned/11 |
| 22 | IHELP* [45] | Clinical setting | Self-administered /13 |
| 23 | MASQ* [44] | Clinical setting | Self-administered /10 |
| 24 | Bright Future [52] | Clinical setting | Self-administered /45 |
| 25 | Montefiore’s Survey [53] | Clinical setting | Self-administered or verbal /10 |
SDoH = social determinants of health; PRAPARE = Protocol for Responding to andAssessing Patient Assets, Risks, and Experiences; AHC = Accountable HealthCommunities; HRSN = Health-Related Social Needs; NLP = natural language processing;MHC = Mobile Health Clinic; WE CARE = Welcome, Engage, Communicate, Ask, Reassure,Exit; SIPT = Social Determinants of Health in Pregnancy Tool; EHRs = electronichealth records; IHELP = Income, Housing, Education, Legal Status, Literacy, PersonalSafety; MASQ = Medical-legal Advocacy Screening Questionnaire.
Tools comprehensiveness
A total of 19 distinct SDoH domains were examined in the various screening tools(Fig.2). Various screening tools evaluateddifferent domains, ranging from four domains (21%) in Health Leads and the North Carolina[36], and Friedman et al. screening tools[30], to 11 (57.8%) within the natural languageprocessing (NLP) [13], Income, Housing,Education, Legal status, Literacy, Personal safety (IHELP) [45], and Medical-legal advocacy screening questionnaire (MASQ) [44] tools (Fig.3). The Well Rx tool [50] evaluated 10SDoH domains (52.6%), while the EPIC EHR [33],Health leads [48], EveryONE project [46], and Montefiore [53] tools evaluated 9 domains (47.3%). Eight SDoH domains (42.1%)were evaluated in Protocol for Responding to and Assessing Patient Assets, Risks, andExperiences (PARAPARE) [25], as well as the toolsproposed by de Ramirez et al., [13] Macias-Konstantopoulos et al., [35] Gupta et al., [32] Sokol et al. [37]The Iscreen [31]. Health Begins [49], Help Steps surveys [47]. Tools such as Accountable Health Communities Health-RelatedSocial Needs (AHC HRSN) [27], Welcome, Engage,Communicate, Ask, Reassure, Exit (WE CARE) [26],Social Determinants of Health in Pregnancy Tool (SIPT) [34], Core 5 social risk screening [28],Accountable Health Communities (modified) [29],North Carolina [51], and Bright Future [52] examined 5 domains of SDoH (26.3%). Fourscreening tools (16%) evaluated at least 10 (52.6%) different SDoH domains (Well Rx [50], NLP [13], IHELP [45], MASQ [44]), while the remaining screening tools(n = 21, 84%) evaluated fewer SDoH domains (Table3).
Relative number of SDoH domains assessed in the various screening tools. SDoH =social determinants of health; AHC = Accountable Health Communities; HRSN =Health-Related Social Needs; WE CARE = Welcome, Engage, Communicate, Ask, Reassure,Exit; SIPT = Social Determinants of Health in Pregnancy Tool; PRAPARE = Protocol forResponding to and Assessing Patient Assets, Risks, and Experiences; EHRs =electronic health records; NLP = natural language processing; IHELP = Income,Housing, Education, Legal Status, Literacy, Personal Safety; MASQ = Medical-legalAdvocacy Screening Questionnaire.
Table 3.
Domains assessed by each screening tool
| Domains Tools (studies) | Housing | Food/ Nutrition | Employment | Safety/Violence | Utility | Education/ Literacy | Transportation | Child/ elder care | Legal concern | Income/Financial | Family/social support | Moving/Transience | Race/ Ethnicity | Stress /Mental issues | Substance/ smoke/ alcohol | Disability | Physical activity | Neighborhood | HealthCare access/ Insurance |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PRAPARE* [25] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
| AHC HRSN* [27] | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||||||
| de Ramirez et al [13] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
| NLP-based method* [13] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||
| WE CARE* [26] | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||||||
| Health Leads and the North Carolina [36] | ✓ | ✓ | ✓ | ✓ | |||||||||||||||
| Macias-Konstantopoulos et al [35] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
| SIPT* [34] | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||||||
| Health system’s EPIC EHRs* [33] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
| Gupta et al [32] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
| Friedman et al [30] | ✓ | ✓ | ✓ | ✓ | |||||||||||||||
| Core 5 Social Risk Screening [28] | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||||||
| Sokol et al [37] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
| I screen [31] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
| AHC HRSN* (modified) [29] | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||||||
| Well Rx [50] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
| Health begins [49] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
| Health leads [48] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
| Help Steps [47] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
| The Every ONE [46] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
| North Carolina [51] | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||||||
| IHELP* [45] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||
| MASQ* [44] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||
| Bright Future [52] | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||||||
| Montefiore [53] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
PRAPARE = Protocol for Responding to and Assessing Patient Assets, Risks, andExperiences; AHC = Accountable Health Communities; HRSN = Health-Related SocialNeeds; NLP = Natural Language Processing; MHC = Mobile Health Clinic; WE CARE =Welcome, Engage, Communicate, Ask, Reassure, Exit; SIPT = Social Determinants ofHealth in Pregnancy Tool; EHRs = Electronic Health Records; IHELP = Income, Housing,Education, Legal status, Literacy, Personal Safety; MASQ = Medical-legal AdvocacyScreening Questionnaire.
SDoH domains
While no individual SDoH domain was assessed in every screening tool, housing(n = 23, 92%) and safety/violence (n = 21, 84%) werethe domains assessed most frequently examined (Fig.4). SDoH domains involving food/nutrition (n = 17, 68%),income/financial (n = 16, 64%), transportation (n = 15,60%), family/social support (n = 14, 56%), utilities (n= 13, 52%), and education/literacy (n = 13, 52%) were also commonlyincluded in most SDoH screening tools. Other SDoH domains that were commonly assessed inthe various screening tools included employment (n = 10, 40%),substance/smoke/alcohol use (n = 8, 32%), stress/mental issues(n = 6, 24%), child/elder care (n = 7, 28%), and legalconcerns (n = 7, 28%). In contrast, race/ethnicity (n =4, 16%), healthcare access/insurance (n = 4, 16%), moving/transience(n = 3, 12%), neighborhood (n = 3, 12%), disability(n = 1, 4.0%), and physical activity (n = 1, 4.0%)were the least commonly assessed domains among the different SDoH screening tools.
SDoH-based interventions
Of note, 18 studies not only screened SDoH but also proposed specific interventions aimedat addressing SDoH (Supplemental Table 1) [13,26–28,30,32–34,36–43,47,50]. Twelve screeningtools identified patient preferences toward receiving supplementary assistance relative tothe SDoH identified. If the response was affirmative, referral to relevant social workerswas made based on the positive domain that had been identified on screening [13,26–28,30,32–34,36,37,39,50].Interestingly, three separate studies proposed interventions grounded in sports [38], sleep health [40], and developing a national agenda aimed at homelessness and homelessindividuals to address SDoH [43]. Fleegleret al. had patients utilize a web-based application entitled Help Stepsto not only self-identify social needs but also identify community-based support for thoseneeds identified [47]. In a different study,Hassan et al. implemented a web-based tool for patients to assess SDoH domains, offeringfeedback and assistance in choosing appropriate agencies, and follow-up using phone calls[41]. In another study, Hatef etal.[42] developed an electronic healthrecord (EHR)-derived community health record that aggregated data at both the hospital andneighborhood level as a means to capture local community health data at the populationlevel, identify SDoH needs, and then link community-based resources to address patientneeds.
Discussion
SDoH represents a broad array of domains that can impact a patient’s lived experiences,including their overall well-being and health. Growing evidence has demonstrated thataddressing unmet health-related social needs such as hunger, exposure to violence,homelessness, and transportation can help improve well-being [54]. While health providers routinely use clinical assessmentalgorithms, tools to screen SDoH have not been as widely adopted or implemented [28]. Collection of such data may inform patienttreatment plans and referrals to community services [55]. When patients screen positive for particular social risks and social needs,targeted interventions may help address disparities and improve health equity. As such, CMShas mandated that hospitals screen patients for SDoH [21]. The means and methods to capture these data have not been well defined,however, with no single screening tool being universally adopted or available. The currentstudy was important as we performed a systematic review of various screening tools publishedin the literature to identify and target SDoH in the clinical setting. Of note, the variousscreening tools were heterogeneous in their use, application, scope of inquiry, and targeteddomains of SDoH. Many of the screening tools included a different number of SDoH domains, aswell as variable domain types. Specifically, the median number of domains evaluated in SDoHscreening tools was 8.0 (interquartile range, 9.0-5.0) with housing and safety/violencebeing the domains assessed most frequently (Fig.4).Food/nutrition, income/financial, transportation, family/social support, utilities, andeducation/literacy were also commonly included in many SDoH screening tools. While lessfrequent, some reports utilized the SDoH identified in the screening tools to inform sometype of intervention. For instance, sports-based interventions were proposed to improvepersonal physical and psychosocial health [38],while other studies proposed web-based applications and/or linking the EMR to communitydatabases to identify community-based support for those needs identified on screening [41,42,47].
Among the tools with explicitly defined criteria, the NLP [13], IHELP [45], and MASQ[44] screening tools were the most comprehensivein their approach as these tools included the highest number of SDoH domains. The NLPalgorithm system utilized the existing electronic medical record and identified keywords orphrases that suggested housing or financial needs (i.e., lack of permanent address); the NLPmodel performed with high accuracy. NLP combines computational linguistics with machine anddeep learning models [56]. In turn, large amountsof EMR text data can be processed to understand its meaning and identify different themesincluding the risk of adverse SDoH. Adverse social SDoH may include social risks associatedwith poor health (e.g., food insecurity), and individual preferences and prioritiesregarding seeking assistance to address the social needs (e.g., seeking food assistance)[57]. An NLP approach is limited, however, inthat it can only assess textual data that had been recorded in the EMR by healthcareproviders. In contrast, SDoH screening questionnaires provide an opportunity to querypatients specifically about different SDoH domains. The IHELP questionnaire focused onpediatric patients and queried SDoH domains such as income, housing/utilities, education,legal status/immigration, literacy, and personal safety [45]. In turn, data collected from this questionnaire may elicit specificenvironmental, legal, and psychosocial risk factors that can be utilized by social workersto address the needs of individual patients. For example, the use of the MASQ screening toolwas able to identify families of pediatric patients who required assistance with legalservices and help facilitate a referral [44].Therefore, the use of screening tools can pinpoint the different SDoH domains needed bypatients to allocate limited resources to serve that specific need.
Several tools such as the Health Leads and the North Carolina survey [36], as well as the screening tool proposed by Friedman etal., [30] focused on four domainsincluding housing, safety/violence, family/social support, and substance/smoke/alcoholmisuse. Other tools concentrated on screening for economic stability, education access andquality, health care access and quality, neighborhood and built environment, as well associal and community context [45,53]. Of note, IHELP was the only screening tool thataddressed all five main SDoH domains identified by WHO [13,45]. Housing and safety/violence werethe most frequently assessed domains among the screening tools. These SDoH themes highlighthow housing insecurity plays a significant role in health status as overcrowding, frequentrelocation, and housing expenses can negatively impact health [58,59]. In turn, helpingpatients secure housing can improve health through multiple mechanisms, including increasingpatient safety [43]. Exposure to unsafeenvironments can have long-term health consequences, including amplifying chronic diseasesand mental illnesses [60]. In addition, themes offood/nutrition, income/financial concerns, as well as transportation and education/literacywere other frequently evaluated domains across various assessment tools (Table3). Interestingly, although repeatedly associated withincreased risk of social vulnerability and adverse SDoH, race/ethnicity was often notincluded in screening tools – perhaps because these data are required already as part of the“meaningful use” of electronic health records [61].
Beyond proposing and implementing screening tools, several authors proposed interventionsto address adverse SDoH. Overall, a total of 18 interventions in addition to primary SDoHscreening were identified (Supplemental Table1). Most interventions consistedof referring individuals to social health workers, who were selected based on the specificSDoH identified through the screening process. For example, Fleegler et al. used the SDoHscreening tool to identify specific patient needs and then delivered assistance using aweb-based application, which recommended specific community-based agencies [47]. In a similar manner, Hassan et al. proposed adifferent web-based tool that provided patient feedback and assistance in choosingappropriate agencies based on the SDoH screening tool as well as performing follow-up usingtelephone calls [41]. Utilization of web-basedtools may serve to connect patients to resources based on needs identified through SDoHscreening. Web-based tools may need to be supplemented, however, with patient navigators,lay community health care workers, as well public health workers who can serve as a bridgebetween communities, health care systems, and state health departments.
One of the main strengths of this review is that no other study has performed a thoroughevaluation and comparison of available screening tools to address SDoH in the United Statesto date. Nevertheless, due to the heterogeneity of the tools and the diverse targetpopulations evaluated by each individual screening tool, future efforts should aim atdefining best practices in collecting SDoH, as well as identifying standardized means toreport SDoH in a timely manner. In addition, despite the available screening tools, futureefforts should aim at not only reporting but also addressing social needs and mitigatedisparities in access to high-quality care.
In conclusion, CMS has mandated evaluation of SDoH to identify medical and social barriersthat impede the health and well-being of patients [21]. SDoH and associated health disparities are important drivers of healthcareaccess and outcomes [62]. SDoH screening tools arecritical to identify various social needs and vulnerabilities so that patients can beconnected to effective interventions to address their needs [63]. The current systematic review demonstrated the heterogeneity ofcurrently available SDoH screening tools, as well as the variability in the SDoH domainsassessed. The use of technology via web-based screening platforms and the electronic medicalrecords is critical to capture patient SDoH, as well as potentially link individuals withcommunity resources. Patient navigators and public health community workers also play animportant role in connecting patients with resources.
Supporting information
Neshan et al. supplementary material:
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Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/cts.2024.506.
Author contributions
T.M.P.: Supervision, conceptualization, designation, data collection, analysis, andrevising the review; M.N.: original draft preparation, writing, reviewing, editing, datacollection, and analysis; N.F.: data collection, analysis, review, and editing themanuscript; V.P., D.I.T., and S.O.G.: review and editing of the manuscript; and T.M.P.:takes responsibility for the manuscript as a whole.
Competing interests
None.
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