Structured and unstructured social risk factor documentation in the electronic health record underestimates patients' self-reported risks.

OBJECTIVES: National attention has focused on increasing clinicians' responsiveness to the social determinants of health, for example, food security. A key step toward designing responsive interventions includes ensuring that information about patients' social circumstances is captured in the electronic health record (EHR). While prior work has assessed levels of EHR "social risk" documentation, the extent to which documentation represents the true prevalence of social risk is unknown. While no gold standard exists to definitively characterize social risks in clinical populations, here we used the best available proxy: social risks reported by patient survey. MATERIALS AND METHODS: We compared survey results to respondents' EHR social risk documentation (clinical free-text notes and International Statistical Classification of Diseases and Related Health Problems [ICD-10] codes). RESULTS: Surveys indicated much higher rates of social risk (8.2%-40.9%) than found in structured (0%-2.0%) or unstructured (0%-0.2%) documentation. DISCUSSION: Ideally, new care standards that include incentives to screen for social risk will increase the use of documentation tools and clinical teams' awareness of and interventions related to social adversity, while balancing potential screening and documentation burden on clinicians and patients. CONCLUSION: EHR documentation of social risk factors currently underestimates their prevalence.

Revising the Logic Model Behind Health Care's Social Care Investments.

Policy Points This article summarizes recent evidence on how increased awareness of patients' social conditions in the health care sector may influence health and health care utilization outcomes. Using this evidence, we propose a more expansive logic model to explain the impacts of social care programs and inform future social care program investments and evaluations.

Associations between Social Adversity and Biomarkers of Inflammation, Stress, and Aging in Children.

BACKGROUND: Prior work has found relationships between childhood social adversity and biomarkers of stress, but knowledge gaps remain. To help address these gaps, we explored associations between social adversity and biomarkers of inflammation (interleukin-1ß [IL-1ß], IL-6, IL-8, tumor necrosis factor-alpha [TNF-α], and salivary cytokine hierarchical "clusters" based on the three interleukins), neuroendocrine function (cortisol, cortisone, dehydroepiandrosterone, testosterone, and progesterone), neuromodulation (N-arachidonoylethanolamine, stearoylethanolamine, oleoylethanolamide, and palmitoylethanolamide), and epigenetic aging (Pediatric-Buccal-Epigenetic clock). METHODS: We collected biomarker samples of children ages 0-17 recruited from an acute care pediatrics clinic and examined their associations with caregiver-endorsed education, income, social risk factors, and cumulative adversity. We calculated regression-adjusted means for each biomarker and compared associations with social factors using Wald tests. We used logistic regression to predict being in the highest cytokine cluster based on social predictors. RESULTS: Our final sample included 537 children but varied based on each biomarker. Cumulative social adversity was significantly associated with having higher levels of all inflammatory markers and with cortisol, displaying a U-shaped distribution. There were no significant relationships between cumulative social adversity and cortisone, neuromodulation biomarkers or epigenetic aging. CONCLUSION: Our findings support prior work suggesting that social stress exposures contribute to increased inflammation in children. IMPACT: Our study is one of the largest studies examining associations between childhood social adversity and biomarkers of inflammation, neuroendocrine function, neuromodulation, and epigenetic aging. It is one of the largest studies to link childhood social adversity to biomarkers of inflammation, and the first of which we are aware to link cumulative social adversity to cytokine clusters. It is also one of the largest studies to examine associations between steroids and epigenetic aging among children, and one of the only studies of which we are aware to examine associations between social adversity and endocannabinoids among children. CLINICAL TRIAL REGISTRATION: NCT02746393.

Screening for Financial Hardship: Comparing Patient Survey Responses Using Two Different Screening Tools.

BACKGROUND: Healthcare delivery organizations are increasingly screening patients for social risks using tools that vary in content and length. OBJECTIVES: To compare two screening tools both containing questions related to financial hardship. DESIGN: Cross-sectional survey. PARTICIPANTS: Convenience sample of adult patients (n = 471) in three primary care clinics. MAIN MEASURES: Participants randomly assigned to self-complete either: (1) a screening tool developed by the Centers for Medicare & Medicaid Services (CMS) consisting of six questions on financial hardship (housing stability, housing quality, food security, transportation security, utilities security); or (2) social and behavioral risk measures recommended by the National Academy of Medicine (NAM), including one question on financial hardship (financial strain). We compared patient acceptability of screening, positive screening rates for financial hardship, patient interest in assistance, and self-rated health. RESULTS: Ninety-one percent of eligible/interested patients completed the relevant survey questions to be included in the study (N = 471/516). Patient acceptability was high for both tools, though more participants reported screening was appropriate when answering the CMS versus NAM questions (87% vs. 79%, p = 0.02). Of respondents completing the CMS tool, 57% (132/232) reported at least one type of financial hardship; on the NAM survey, 52% (125/239) reported financial hardship (p = 0.36). Nearly twice as many respondents indicated interest in assistance related to financial hardship after completing items on the CMS tool than on the NAM question (39% vs. 21%, p < 0.01). CONCLUSIONS: Patients reported high acceptability of both social risk assessment tools. While rates of positive screens for financial hardship were similar across the two measures, more patients indicated interest in assistance after answering questions about financial hardship on the CMS tool. This might be because the screening questions on the CMS tool help patients to appreciate the types of assistance related to financial hardship that may be available after screening. Future research should assess the validity and comparative validity of individual measures and measure sets. Tool selection should be based on setting and population served, screening goals, and resources available.

Caregiver Perceptions of Social Risk Screening in Pediatric Liver Transplantation: From the Multicenter SOCIAL-Tx Study.

BACKGROUND: The social determinants of health contribute to adverse post-liver transplant outcomes. Identifying unmet social risks may enable transplant teams to improve long-term outcomes for at-risk children. However, providers may feel uncomfortable asking about household-level social risks in the posttransplant period because they might make their patients/families uncomfortable. METHODS: We conducted a mixed-methods analysis of caregiver participants (ie, parents/guardians of pediatric liver transplant recipients) in the Social and Contextual Impact on Children Undergoing Liver Transplantation study to assess their perceptions of provider-based social risk screening. Participants (N = 109) completed a 20-min social determinants of health questionnaire that included questions on the acceptability of being asked intimate social risk questions. A subset of participants (N = 37) engaged in an in-depth qualitative interview to share their perceptions of social risk screening. RESULTS: Of 109 participants across 9 US transplant centers, 60% reported financial strain and 30% reported at least 1 material economic hardship (eg, food insecurity, housing instability). Overall, 65% of respondents reported it very or somewhat appropriate and 25% reported being neutral to being screened for social risks in a liver transplant setting. In qualitative analyses, participants reported trust in the providers and a clear understanding of the intention of the screening as prerequisites for liver transplant teams to perform social risk screening. CONCLUSIONS: Only a small minority of caregivers found social risk screening unacceptable. Pediatric liver transplant programs should implement routine social risk screening and prioritize the patient and family voices when establishing a screening program to ensure successful implementation.

Hospital Characteristics Associated With Social Needs Activities in the US.

This study assesses what hospital characteristics, including hospital participation in payment and delivery reform, are associated with activities related to health-related social needs.

Advancing health equity through social care interventions.

OBJECTIVE: To use evidence on addressing racism in social care intervention research to create a framework for advancing health equity for all populations with marginalized social identities (e.g., race, gender, and sexual orientation). Such groups have disproportionate social needs (e.g., food insecurity) and negative social determinants of health (SDOH; e.g., poverty). We recommend how the Agency for Healthcare Research and Quality (AHRQ) could advance health equity for marginalized populations through social care research and care delivery. DATA SOURCES AND STUDY SETTING: This commentary is informed by a literature review of social care interventions that were affiliated with healthcare systems; input from health equity researchers, policymakers, and community leaders attending the AHRQ Health Equity Summit; and consensus of the authors. PRINCIPAL FINDINGS: We recommend that AHRQ: (1) create an ecosystem that values research on SDOH and the effectiveness and implementation of social care interventions in the healthcare sector; (2) work with other federal agencies to (a) develop position statements with actionable recommendations about racism and other systems that perpetuate marginalization based on social identity and (b) develop aligned, complementary approaches to research and care delivery that address social marginalization; (3) advance both inclusive care delivery and inclusive research teams; (4) advance understanding of racism as a social determinant of health and effective strategies to mitigate its adverse impact on health; (5) advance the creation and scaling of effective strategies for addressing SDOH in healthcare systems, particularly in co-creation with community partners; and (6) require social care intervention researchers to use methods that advance our understanding of social health equity. CONCLUSIONS: AHRQ, as a federal agency, could help advance health equity using a range of strategies, including using the agency's levers to ensure AHRQ stakeholders examine and address the unique experiences of socially marginalized populations in SDOH and social care intervention research.

"Wanting the Best for Our Folks"-A Mixed Methods Analysis of Community Health Center Social Risk Screening Initiatives.

BACKGROUND: Many community health centers (CHC) are scaling social risk screening in response to growing awareness of the influence of social adversity on health outcomes and concurrent incentives for social risk data collection. We studied the implementation of social risk screening in Texas CHCs to inform best practices and understand equity implications. METHODS: Convergent mixed methods of 3 data sources. Using interviews and surveys with CHC providers and staff, we explored social risk screening practices to identify barriers and facilitators; we used electronic health record (EHR) data to assess screening reach and disparities in screening. RESULTS: Across 4 urban/suburban Texas CHCs, we conducted 27 interviews (15 providers/12 staff) and collected 97 provider surveys; 2 CHCs provided EHR data on 18,672 patients screened during the study period. Data revealed 2 cross-cutting themes: 1) there was broad support for social risk screening/care integration that was rooted in CHCs' mission and positionalities, and 2) barriers to social risk screening efforts were largely a result of limited time and staffing. Though EHR data showed screens per month and screens/encounters increased peri-pandemic (4.1% of encounters in 8/2019 to 46.1% in 2/2021), there were significant differences in screening rates by patient race/ethnicity and preferred language (P < .001). In surveys, 90.0% of surveyed providers reported incorporating social risk screening into patient conversations; 28.6% were unaware their clinic had an embedded screening tool. CONCLUSIONS: Study CHCs were in the early stages of standardizing social risk screening. Differences in screening reach by patient demographics raise concerns that social screening initiatives, which often serve as a path to resource/service connection, might exacerbate disparities. Overcoming barriers to reach, sustainability, and equity requires supports targeted to program design/development, workforce capacity, and quality improvement.

Social and Medical Care Integration Practices Among Children's Hospitals.

OBJECTIVES: In response to evidence linking social risk factors and adverse health outcomes, new incentives have emerged for hospitals to screen for adverse social determinants of health (SDOH). However, little information is available about the current state of social risk-related care practices among children's hospitals. To address outstanding knowledge gaps, we sought to describe social risk-related care practices among a national sample of children's hospitals. METHODS: We analyzed responses to the 2020 American Hospital Association Annual Survey. Among children's hospitals, we calculated the prevalence of screening for social needs, strategies to address social risks/needs, partnerships with community-based organizations to address social risks/needs at the individual and community level, and rates of impact assessments of how social risk-related interventions affect outcomes. We also used χ2 tests to compare results by hospital characteristics. We weighted results to adjust for nonresponse. RESULTS: The sample included 82 children's hospitals. A total of 79.6% screened for and 96.0% had strategies to address at least 1 social risk factor, although rates varied by SDOH domain. Children's hospitals more commonly partnered with community-based organizations to address patient-level social risks than to participate in community-level initiatives. A total of 39.2% of hospitals assessed SDOH intervention effectiveness. Differences in social risk-related care practices commonly varied by hospital ownership and Medicaid population but not by region. CONCLUSIONS: We found wide variability in social risk-related care practices among children's hospitals based on the risk domain and hospital characteristics. Findings can be used to monitor whether social risk-related care practices change in the setting of new incentives.

Evaluating the comparability of patient-level social risk data extracted from electronic health records: A systematic scoping review.

Objective: To evaluate how and from where social risk data are extracted from EHRs for research purposes, and how observed differences may impact study generalizability. Methods: Systematic scoping review of peer-reviewed literature that used patient-level EHR data to assess 1 ± 6 social risk domains: housing, transportation, food, utilities, safety, social support/isolation. Results: 111/9022 identified articles met inclusion criteria. By domain, social support/isolation was most often included (N = 68/111), predominantly defined by marital/partner status (N = 48/68) and extracted from structured sociodemographic data (N = 45/48). Housing risk was defined primarily by homelessness (N = 39/49). Structured housing data was extracted most from billing codes and screening tools (N = 15/30, 13/30, respectively). Across domains, data were predominantly sourced from structured fields (N = 89/111) versus unstructured free text (N = 32/111). Conclusion: We identified wide variability in how social domains are defined and extracted from EHRs for research. More consistency, particularly in how domains are operationalized, would enable greater insights across studies.

Community Health Center Staff Perspectives on Financial Payments for Social Care.

Policy Points State and federal payers are actively considering strategies to increase the adoption of social risk screening and interventions in health care settings, including through the use of financial incentives. Activities related to social care in Oregon community health centers (CHCs) provided a unique opportunity to explore whether and how fee-for-service payments for social risk screening and navigation influence CHC activities. CHC staff, clinicians, and administrative leaders were often unaware of existing financial payments for social risk screening and navigation services. As currently designed, fee-for-service payments are unlikely to strongly influence CHC social care practices. CONTEXT: A growing crop of national policies has emerged to encourage health care delivery systems to ask about and try to address patients' social risks, e.g., food, housing, and transportation insecurity, in care delivery contexts. In this study, we explored how community health center (CHC) staff perceive the current and potential influence of fee-for-service payments on clinical teams' engagement in these activities. METHODS: We interviewed 42 clinicians, frontline staff, and administrative leaders from 12 Oregon CHC clinical sites about their social care initiatives, including about the role of existing or anticipated financial payments intended to promote social risk screening and referrals to social services. Data were analyzed using both inductive and deductive thematic analysis approaches. FINDINGS: We grouped findings into three categories: participants' awareness of existing or anticipated financial incentives, uses for incentive dollars, and perceived impact of financial incentives on social care activities in clinical practices. Lack of awareness of existing incentives meant these incentives were not perceived to influence the behaviors of staff responsible for conducting screening and providing referrals. Current or anticipated meaningful uses for incentive dollars included paying for social care staff, providing social services, and supporting additional fundraising efforts. Frontline staff reported that the strongest motivator for clinic social care practices was the ability to provide responsive social services. Clinic leaders/managers noted that for financial incentives to substantively change CHC practices would require payments sizable enough to expand the social care workforce as well. CONCLUSIONS: Small fee-for-service payments to CHCs for social risk screening and navigation services are unlikely to markedly influence CHC social care practices. Refining the design of financial incentives-e.g., by increasing clinical teams' awareness of incentives, linking screening to well-funded social services, and changing incentive amounts to support social care staffing needs-may increase the uptake of social care practices in CHCs.

Social Determinants of Health Documentation in Structured and Unstructured Clinical Data of Patients With Diabetes: Comparative Analysis.

Background: Electronic health records (EHRs) have yet to fully capture social determinants of health (SDOH) due to challenges such as nonexistent or inconsistent data capture tools across clinics, lack of time, and the burden of extra steps for the clinician. However, patient clinical notes (unstructured data) may be a better source of patient-related SDOH information. Objective: It is unclear how accurately EHR data reflect patients' lived experience of SDOH. The manual process of retrieving SDOH information from clinical notes is time-consuming and not feasible. We leveraged two high-throughput tools to identify SDOH mappings to structured and unstructured patient data: PatientExploreR and Electronic Medical Record Search Engine (EMERSE). Methods: We included adult patients (≥18 years of age) receiving primary care for their diabetes at the University of California, San Francisco (UCSF), from January 1, 2018, to December 31, 2019. We used expert raters to develop a corpus using SDOH in the compendium as a knowledge base as targets for the natural language processing (NLP) text string mapping to find string stems, roots, and syntactic similarities in the clinical notes of patients with diabetes. We applied advanced built-in EMERSE NLP query parsers implemented with JavaCC. Results: We included 4283 adult patients receiving primary care for diabetes at UCSF. Our study revealed that SDOH may be more significant in the lives of patients with diabetes than is evident from structured data recorded on EHRs. With the application of EMERSE NLP rules, we uncovered additional information from patient clinical notes on problems related to social connectionsisolation, employment, financial insecurity, housing insecurity, food insecurity, education, and stress. Conclusions: We discovered more patient information related to SDOH in unstructured data than in structured data. The application of this technique and further investment in similar user-friendly tools and infrastructure to extract SDOH information from unstructured data may help to identify the range of social conditions that influence patients' disease experiences and inform clinical decision-making.

Assessing Implementation of Social Screening Within US Health Care Settings: A Systematic Scoping Review.

PURPOSE: Though a growing crop of health care reforms aims to encourage health care-based social screening, no literature has synthesized existing social screening implementation research to inform screening practice and policymaking. METHODS: Systematic scoping review of peer-reviewed literature on social screening implementation published 1/1/2011-2/17/2022. We applied a 2-concept search (health care-based screening; social risk factors) to PubMed and Embase. Studies had to explore the implementation of health care-based multi-domain social screening and describe 1+ outcome related to the reach, adoption, implementation, and/or maintenance of screening. Two reviewers extracted data related to key study elements, including sample, setting, and implementation outcomes. RESULTS: Forty-two articles met inclusion criteria. Reach (n = 7): We found differences in screening rates by patient race/ethnicity; findings varied across studies. Patients who preferred Spanish had lower screening rates than English-preferring patients. Adoption (n = 13): Workforce education and dedicated quality improvement projects increased screening adoption. Implementation (n = 32): Time was the most cited barrier to screening; administration time differed by tool/workforce/modality. Use of standardized screening tools/workflows improved screening integration. Use of community health workers and/or technology improved risk disclosure and facilitated screening in resource-limited settings. Maintenance (n = 1): Only 1 study reported on maintenance; results showed a drop in screening over 21 months. CONCLUSIONS: Critical evidence gaps in social screening implementation persist. These include gaps in knowledge about effective strategies for integrating social screening into clinical workflows and ways to maximize screening equity. Future research should leverage the rapidly increasing number of screening initiatives to elevate and scale best practices.

Essential not Supplemental: Medicare Advantage Members' Use of Non-Emergency Medical Transportation (NEMT).

BACKGROUND: Over five million people in the USA miss or delay medical care because of a lack of transportation. Transportation barriers are especially relevant to Medicare Advantage (MA) health plan enrollees, who are more likely to live with multiple chronic conditions and experience mobility challenges. Non-Emergency Medical Transportation (NEMT) helps to address transportation gaps by providing rides to and from routine medical care (for example, medical appointments, laboratory tests, and pharmacy visits) and has been added as a supplemental benefit to some MA health plans. OBJECTIVE: We aimed to characterize MA enrollees' experiences with supplemental NEMT benefits. DESIGN: Qualitative interviews focused on participants' experiences with existing NEMT benefits, transportation, and mobility. PARTICIPANTS: Twenty-one MA enrollees who used their MA NEMT benefit in 2019 and who remained eligible for ongoing transportation benefits through 2021. APPROACH: Using purposive sampling from a list of eligible participants, we recruited individuals who used their MA NEMT benefit in 2019 and who remained eligible for benefit-covered transportation services through 2021. KEY RESULTS: Participants considered NEMT an essential service, particularly because these services helped them decrease social isolation, reduce financial insecurity, and manage their own medical needs. Navigating logistical challenges associated with arranging NEMT services required participants to commit considerable time and energy and limited the effectiveness and reliability of NEMT. CONCLUSION: Participants described NEMT as a valued service essential to their ability to access health care. They suggested ways to increase service flexibility and reliability that could inform future NEMT policy and practice. As health systems and payers learn how to best address social risks, particularly as the US population ages, our findings underscore the importance of NEMT services and highlight opportunities to advance comprehensive transportation solutions for MA participants.

Assessing Area-Level Deprivation as a Proxy for Individual-Level Social Risks.

INTRODUCTION: Concerns about the opportunity costs of social screening initiatives have led some healthcare organizations to consider using social deprivation indices (area-level social risks) as proxies for self-reported needs (individual-level social risks). Yet, little is known about the effectiveness of such substitutions across different populations. METHODS: This analysis explores how well the highest quartile (cold spot) of three different area-level social risk measures-the Social Deprivation Index, Area Deprivation Index, and Neighborhood Stress Score-corresponds with six individual-level social risks and three risk combinations among a national sample of Medicare Advantage members (N=77,503). Data were derived from area-level measures and cross-sectional survey data collected between October 2019 and February 2020. Agreement between individual and individual-level social risks, sensitivity values, specificity values, positive predictive values, and negative predictive values was calculated for all measures in summer/fall 2022. RESULTS: Agreement between area and individual-level social risks ranged from 53% to 77%. Sensitivity for each risk and risk category never exceeded 42%; specificity values ranged from 62% to 87%. Positive predictive values ranged from 8% to 70%, and negative predictive values ranged from 48% to 93%. There were modest performance discrepancies across area-level measures. CONCLUSIONS: These findings provide additional evidence that area-level deprivation indices may be inconsistent indicators of individual-level social risks, supporting policy efforts to promote individual-level social screening programs in healthcare settings.

Implementation Support for a Social Risk Screening and Referral Process in Community Health Centers.

Evidence is needed about how to effectively support health care providers in implementing screening for social risks (adverse social determinants of health) and providing related referrals meant to address identified social risks. This need is greatest in underresourced care settings. The authors tested whether an implementation support intervention (6 months of technical assistance and coaching study clinics through a five-step implementation process) improved adoption of social risk activities in community health centers (CHCs). Thirty-one CHC clinics were block-randomized to six wedges that occurred sequentially. Over the 45-month study period from March 2018 to December 2021, data were collected for 6 or more months preintervention, the 6-month intervention period, and 6 or more months postintervention. The authors calculated clinic-level monthly rates of social risk screening results that were entered at in-person encounters and rates of social risk-related referrals. Secondary analyses measured impacts on diabetes-related outcomes. Intervention impact was assessed by comparing clinic performance based on whether they had versus had not yet received the intervention in the preintervention period compared with the intervention and postintervention periods. In assessing the results, the authors note that five clinics withdrew from the study for various bandwidth-related reasons. Of the remaining 26, a total of 19 fully or partially completed all 5 implementation steps, and 7 fully or partially completed at least the first 3 steps. Social risk screening was 2.45 times (95% confidence interval [CI], 1.32-4.39) higher during the intervention period compared with the preintervention period; this impact was not sustained postintervention (rate ratio, 2.16; 95% CI, 0.64-7.27). No significant difference was seen in social risk referral rates during the intervention or postintervention periods. The intervention was associated with greater blood pressure control among patients with diabetes and lower rates of diabetes biomarker screening postintervention. All results must be interpreted considering that the Covid-19 pandemic began midway through the trial, which affected care delivery generally and patients at CHCs particularly. Finally, the study results show that adaptive implementation support was effective at temporarily increasing social risk screening. It is possible that the intervention did not adequately address barriers to sustained implementation or that 6 months was not long enough to cement this change. Underresourced clinics may struggle to participate in support activities over longer periods without adequate resources, even if lengthier support is needed. As policies start requiring documentation of social risk activities, safety-net clinics may be unable to meet these requirements without adequate financial and coaching/technical support.

Addressing Health-Related Social Needs Via Community Resources: Lessons From Accountable Health Communities.

The Center for Medicare and Medicaid Innovation launched the Accountable Health Communities (AHC) Model in 2017 to assess whether identifying and addressing Medicare and Medicaid beneficiaries' health-related social needs reduced health care use and spending. We surveyed a subset of AHC Model beneficiaries with one or more health-related social needs and two or more emergency department visits in the prior twelve months to assess their use of community services and whether their needs were resolved. Survey findings indicated that navigation-connecting eligible patients with community services-did not significantly increase the rate of community service provider connections or the rate of needs resolution, relative to a randomized control group. Findings from interviews with AHC Model staff, community service providers, and beneficiaries identified challenges connecting beneficiaries to community services. When connections were made, resources often were insufficient to resolve beneficiaries' needs. For navigation to be successful, investments in additional resources to assist beneficiaries in their communities may be required.

Household Social Needs, Emotional Functioning, and Stress in Low-Income Latinx Children and their Mothers.

Latinx families may be particularly vulnerable to emotional dysfunction, due to higher rates of economic hardship and complex social influences in this population. Little is known about the impact of environmental stressors such as unmet social needs and maternal stress on the emotional health of Latinx children from low-income families. We conducted secondary analyses using survey and biomarker data from 432 Latinx children and mothers collected in a separate study. We used binomial and multinomial logistic regression to test if household social needs, or maternal perceived stress or hair cortisol concentration (HCC), predicted child measures of emotional functioning or child HCC, independent of relevant sociodemographic factors. Approximately 40% of children in the sample had symptoms consistent with emotional dysfunction, and over 37% of households reported five or more social needs. High perceived maternal stress predicted higher odds of child emotional dysfunction (OR = 2.15; 95% CI [1.14, 4.04]; p = 0.01), and high maternal HCC was positively associated with high child HCC (OR = 10.60; 95% CI [4.20, 26.74]; p < 0.01). Most individual household social needs, as well as the level of household social need, were not independently associated with child emotional dysfunction or child HCC. Our findings begin to define a framework for understanding emotional health, stress, and resilience when caring for Latinx children and mothers living with high levels of social need, and the need for integrated mental health and social needs screening and interventions in settings that serve this population.