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.

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.

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.

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.

Unmet Social Needs and Patterns of Hair Cortisol Concentration in Mother-Child Dyads.

Background: Mothers and their children demonstrate dyadic synchrony of hypothalamic-pituitary-adrenal (HPA) axis function, likely influenced by shared genetic or environmental factors. Although evidence has shown that chronic stress exposure has physiologic consequences for individuals-including on the HPA axis-minimal research has explored how unmet social needs such as food and housing instability may be associated with chronic stress and HPA axis synchrony in mother-child dyads. Methods: We conducted a secondary analysis of data from 364 mother-child dyads with low-income recruited during a randomized trial conducted in an urban pediatric clinic. We used latent profile analysis (LPA) to identify subgroups based on naturally occurring patterns of within-dyad hair cortisol concentration (HCC). A logistic regression model predicted dyadic HCC profile membership as a function of summative count of survey-reported unmet social needs, controlling for demographic and health covariates. Results: LPA of HCC data from dyads revealed a 2-profile model as the best fit. Comparisons of log HCC for mothers and children in each profile group resulted in significantly "higher dyadic HCC" versus "lower dyadic HCC" profiles (median log HCC for mothers: 4.64 vs 1.58; children: 5.92 vs 2.79, respectively; P < .001). In the fully adjusted model, each one-unit increase in number of unmet social needs predicted significantly higher odds of membership in the higher dyadic HCC profile when compared to the lower dyadic HCC profile (odds ratio = 1.13; 95% confidence interval [1.04-1.23]; P = .01). Conclusion: Mother-child dyads experience synchronous patterns of physiologic stress, and an increasing number of unmet social needs is associated with a profile of higher dyadic HCC. Interventions aimed at decreasing family-level unmet social needs or maternal stress are, therefore, likely to affect pediatric stress and related health inequities; efforts to address pediatric stress similarly may affect maternal stress and related health inequities. Future research should explore the measures and methods needed to understand the impact of unmet social needs and stress on family dyads.

Structural Racism Operationalized via Adverse Social Events in a Single-Center Neonatal Intensive Care Unit.

OBJECTIVE: To evaluate structural racism in the neonatal intensive care unit (NICU) by determining if differences in adverse social events occur by racialized groups. STUDY DESIGN: Retrospective cohort study of 3290 infants hospitalized in a single center NICU between 2017 and 2019 in the Racial and Ethnic Justice in Outcomes in Neonatal Intensive Care (REJOICE) study. Demographics and adverse social events including infant urine toxicology screening, child protective services (CPS) referrals, behavioral contracts, and security emergency response calls were collected from electronic medical records. Logistic regression models were fit to test the association of race/ethnicity and adverse social events, adjusting for length of stay. Racial/ethnic groups were compared with a White referent group. RESULTS: There were 205 families (6.2%) that experienced an adverse social event. Black families were more likely to have experienced a CPS referral and a urine toxicology screen (OR, 3.6; 95% CI, 2.2-6.1 and OR, 2.2; 95% CI, 1.4-3.5). American Indian and Alaskan Native families were also more likely to experience CPS referrals and urine toxicology screens (OR, 15.8; 95% CI, 6.9-36.0 an OR, 7.6; 95% CI, 3.4-17.2). Black families were more likely to experience behavioral contracts and security emergency response calls. Latinx families had a similar risk of adverse events, and Asian families were less likely to experience adverse events. CONCLUSIONS: We found racial inequities in adverse social events in a single-center NICU. Investigation of generalizability is necessary to develop widespread strategies to address institutional and societal structural racism and to prevent adverse social events.

The association between prescription drug monitoring programs and controlled substance prescribing: a cross-sectional study using data from 2019 National Electronic Health Records Survey.

OBJECTIVE: The use of controlled medications such as opioids, stimulants, anabolic steroids, depressants, and hallucinogens has led to an increase in addiction, overdose, and death. Given the high attributes of abuse and dependency, prescription drug monitoring programs (PDMPs) were introduced in the United States as a state-level intervention. MATERIALS AND METHODS: Using cross-sectional data from the 2019 National Electronic Health Records Survey, we assessed the association between PDMP usage and reduced or eliminated controlled substance prescribing as well as the association between PDMP usage and changing a controlled substance prescription to a nonopioid pharmacologic therapy or nonpharmacologic therapy. We applied survey weights to produce physician-level estimates from the survey sample. RESULTS: Adjusting for physician age, sex, type of medical degree, specialty, and ease of PDMP, we found that physicians who reported "often" PDMP usage had 2.34 times the odds of reducing or eliminating controlled substance prescriptions compared to physicians who reported never using the PDMP (95% confidence interval [CI] 1.12-4.90). Adjusting for physician age, sex, type of doctor, and specialty, we found that physicians who reported "often" use of the PDMP had 3.65 times the odd of changing controlled substance prescriptions to a nonopioid pharmacologic therapy or nonpharmacologic therapy (95% CI: 1.61-8.26). DISCUSSION: These results support the continued use, investment, and expansion of PDMPs as an effective intervention for reducing controlled substance prescription and changing to nonopioid/pharmacologic therapy. CONCLUSION: Overall, frequent usage of PDMPs was significantly associated with reducing, eliminating, or changing controlled substance prescription patterns.

Socioeconomic Factors Are Associated With Severity of Hospitalization in Pediatric Lupus: An Analysis of the 2016 Kids' Inpatient Database.

OBJECTIVE: Health disparities in adult lupus, including higher disease severity and activity among those in poverty, have been identified. Similar associations in pediatric lupus have not been clearly established. This study was undertaken to investigate the relationship of income level and other socioeconomic factors with length of stay (LOS) in the hospital and severe lupus features using the 2016 Kids' Inpatient Database (KID). METHODS: Lupus hospitalizations were identified in children ages 2-20 years in the 2016 KID using International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes (M32). Univariate and multivariate negative binomial regression analyses were used to analyze the association of income level, race and ethnicity, and insurance status with LOS in the hospital. Univariate and multivariate logistic regression analyses were used to analyze the association of the same predictors with the presence of severe lupus features, defined using ICD-10 codes associated with lupus sequelae (e.g., lupus nephritis). RESULTS: A total of 3,367 unweighted (4,650 weighted) lupus hospitalizations were identified. Income level was found to be a statistically significant predictor of increased LOS in the hospital for those in the lowest income quartile (adjusted incidence rate ratio 1.12 [95% confidence interval (95% CI) 1.02-1.23]). Black race, "other" race, and public insurance were also associated with severe lupus features (adjusted odds ratio [ORadj ] 1.51 [95% CI 1.11-2.06]; ORadj 1.61 [95% CI 1.01-2.55]; and ORadj 1.51 [95% CI 1.17-2.55], respectively). CONCLUSION: Using a nationally representative data set, income level was found to be a statistically significant predictor of LOS in the hospital among those with the lowest reported income, highlighting a potential target population for intervention. Additionally, Black race and public insurance were associated with severe lupus features.

Characterizing the relative frequency of clinician engagement with structured social determinants of health data.

OBJECTIVE: Electronic health records (EHRs) are increasingly used to capture social determinants of health (SDH) data, though there are few published studies of clinicians' engagement with captured data and whether engagement influences health and healthcare utilization. We compared the relative frequency of clinician engagement with discrete SDH data to the frequency of engagement with other common types of medical history information using data from inpatient hospitalizations. MATERIALS AND METHODS: We created measures of data engagement capturing instances of data documentation (data added/updated) or review (review of data that were previously documented) during a hospitalization. We applied these measures to four domains of EHR data, (medical, family, behavioral, and SDH) and explored associations between data engagement and hospital readmission risk. RESULTS: SDH data engagement was associated with lower readmission risk. Yet, there were lower levels of SDH data engagement (8.37% of hospitalizations) than medical (12.48%), behavioral (17.77%), and family (14.42%) history data engagement. In hospitalizations where data were available from prior hospitalizations/outpatient encounters, a larger proportion of hospitalizations had SDH data engagement than other domains (72.60%). DISCUSSION: The goal of SDH data collection is to drive interventions to reduce social risk. Data on when and how clinical teams engage with SDH data should be used to inform informatics initiatives to address health and healthcare disparities. CONCLUSION: Overall levels of SDH data engagement were lower than those of common medical, behavioral, and family history data, suggesting opportunities to enhance clinician SDH data engagement to support social services referrals and quality measurement efforts.

Social Risk Factor Documentation in Emergency Departments.

STUDY OBJECTIVE: Social Z codes are International Classification of Diseases, Tenth Revision, Clinical Modification codes that provide one way of documenting social risk factors in electronic health records. Despite the utility and availability of these codes, no study has examined social Z code documentation prevalence in emergency department (ED) settings. METHODS: In this descriptive, cross-sectional study of all ED visits included in the 2018 Nationwide Emergency Department Sample, we estimated the prevalence of social Z code documentation and used logistic regression to examine the association between documentation and patient and hospital characteristics. RESULTS: Of more than 35.8 million adult and pediatric ED visits, there was a 1.21% weighted prevalence (n=452,499) of at least 1 documented social Z code. Social Z codes were significantly more likely to be documented in ED visits among patients aged 35 to 64 compared to patients aged 18 to 34 (18.6/1000 [16.9 to 20.4] versus 12.7/1000 [11.5 to 14.0], odds ratio (OR) 1.47 [1.42 to 1.53]), male patients (16.6/1000 [15.1 to 18.2] versus female 8.5/1000 [7.8 to 9.2], OR 1.97 [1.89 to 2.06]), patients with Medicaid compared to patients with private insurance (15.9/1000 [14.4 to 17.6] versus (6.6/1000 [6.0 to 7.2], OR 2.45 [1.30 to 1.63]), and patients who had a Charlson Comorbidity Index≥1 compared to those with a Charlson Comorbidity Index of 0 (ranges 15.0 to 16.6/1000 versus 10.6/1000 [9.6 to 11.7], ORs ranging 1.43 to 1.58). ED visits with a primary diagnosis of mental, behavioral, and neurodevelopmental illness had the strongest positive association with social Z code documentation (85.6/1000 [78.4 to 93.4], OR 10.75 [9.88 to 11.70]) compared to ED visits without this primary diagnosis. CONCLUSION: We found a very low prevalence of social Z code documentation in ED visits nationwide. More systematic social Z code documentation could support targeted social interventions, social risk payment adjustments, and future policy reforms.

Physician awareness of social determinants of health documentation capability in the electronic health record.

Healthcare organizations are increasing social determinants of health (SDH) screening and documentation in the electronic health record (EHR). Physicians may use SDH data for medical decision-making and to provide referrals to social care resources. Physicians must be aware of these data to use them, however, and little is known about physicians' awareness of EHR-based SDH documentation or documentation capabilities. We therefore leveraged national physician survey data to measure level of awareness and variation by physician, practice, and EHR characteristics to inform practice- and policy-based efforts to drive medical-social care integration. We identify higher levels of social needs documentation awareness among physicians practicing in community health centers, those participating in payment models with social care initiatives, and those aware of other advanced EHR functionalities. Findings indicate that there are opportunities to improve physician education and training around new EHR-based SDH functionalities.

Assessment of structured data elements for social risk factors.

OBJECTIVES: Computable social risk factor phenotypes derived from routinely collected structured electronic health record (EHR) or health information exchange (HIE) data may represent a feasible and robust approach to measuring social factors. This study convened an expert panel to identify and assess the quality of individual EHR and HIE structured data elements that could be used as components in future computable social risk factor phenotypes. STUDY DESIGN: Technical expert panel. METHODS: A 2-round Delphi technique included 17 experts with an in-depth knowledge of available EHR and/or HIE data. The first-round identification sessions followed a nominal group approach to generate candidate data elements that may relate to socioeconomics, cultural context, social relationships, and community context. In the second-round survey, panelists rated each data element according to overall data quality and likelihood of systematic differences in quality across populations (ie, bias). RESULTS: Panelists identified a total of 89 structured data elements. About half of the data elements (n = 45) were related to socioeconomic characteristics. The panelists identified a diverse set of data elements. Elements used in reimbursement-related processes were generally rated as higher quality. Panelists noted that several data elements may be subject to implicit bias or reflect biased systems of care, which may limit their utility in measuring social factors. CONCLUSIONS: Routinely collected structured data within EHR and HIE systems may reflect patient social risk factors. Identifying and assessing available data elements serves as a foundational step toward developing future computable social factor phenotypes.

Documentation and review of social determinants of health data in the EHR: measures and associated insights.

OBJECTIVE: Electronic Health Records (EHRs) increasingly include designated fields to capture social determinants of health (SDOH). We developed measures to characterize their use, and use of other SDOH data types, to optimize SDOH data integration. MATERIALS AND METHODS: We developed 3 measures that accommodate different EHR data types on an encounter or patient-year basis. We implemented these measures-documented during encounter (DDE) captures documentation occurring during the encounter; documented by discharge (DBD) includes DDE plus documentation occurring any time prior to admission; and reviewed during encounter (RDE) captures whether anyone reviewed documented data-for the newly available structured SDOH fields and 4 other comparator SDOH data types (problem list, inpatient nursing question, social history free text, and social work notes) on a hospital encounter basis (with patient-year metrics in the Supplementary Appendix). Our sample included all patients (n = 27 127) with at least one hospitalization at UCSF Health (a large, urban, tertiary medical center) over a 1-year period. RESULTS: We observed substantial variation in the use of different SDOH EHR data types. Notably, social history question fields (newly added at study period start) were rarely used (DDE: 0.03% of encounters, DBD: 0.26%, RDE: 0.03%). Free-text patient social history fields had higher use (DDE: 12.1%, DBD: 49.0%, RDE: 14.4%). DISCUSSION: Our measures of real-world SDOH data use can guide current efforts to capture and leverage these data. For our institution, measures revealed substantial variation across data types, suggesting the need to engage in efforts such as EHR-user education and targeted workflow integration. CONCLUSION: Measures revealed opportunities to optimize SDOH data documentation and review.

Impact of a National Quality Collaborative on Pediatric Asthma Care Quality by Insurance Status.

OBJECTIVE: To assess whether disparities in asthma care and outcomes based on insurance type existed before a national quality improvement (QI) collaborative, and to determine the effects of the collaborative on these disparities. METHODS: Secondary analysis of data from Pathways for Improving Pediatric Asthma Care (PIPA), a national collaborative to standardize emergency department (ED) and inpatient asthma management. PIPA included children aged 2 to 17 with a diagnosis of asthma. Disparities were examined based on insurance status (public vs private). Outcomes included guideline adherence and health care utilization measures, assessed for 12 months before and 15 months after the start of PIPA. RESULTS: We analyzed 19,204 ED visits and 11,119 hospitalizations from 89 sites. At baseline, children with public insurance were more likely than those with private insurance to receive early administration of corticosteroids (52.3% vs 48.9%, P= .01). However, they were more likely to be admitted (20.0% vs 19.4%, P = .01), have longer inpatient length of stay (31 vs 29 hours, P = .01), and have a readmission/ED revisit within 30 days (7.4% vs 5.6%, P = .02). We assessed the effects of PIPA on these disparities by insurance status and found no significant changes across 6 guideline adherence and 4 health care utilization measures. CONCLUSION: At baseline, children with public insurance had higher asthma health care utilization than those with private insurance, despite receiving more evidence-based care. The PIPA collaborative did not affect pre-existing disparities in asthma outcomes. Future research should identify effective strategies for leveraging QI to better address disparities.

Racial and ethnic disparities in outcomes through 1 year of life in infants born prematurely: a population based study in California.

OBJECTIVES: To investigate racial/ethnic differences in rehospitalization and mortality rates among premature infants over the first year of life. STUDY DESIGN: A retrospective cohort study of infants born in California from 2011 to 2017 (n = 3,448,707) abstracted from a California Office of Statewide Health Planning and Development database. Unadjusted Kaplan-Meier tables and logistic regression controlling for health and sociodemographic characteristics were used to predict outcomes by race/ethnicity. RESULTS: Compared to White infants, Hispanic and Black early preterm infants were more likely to be readmitted; Black late/moderate preterm (LMPT) infants were more likely to be readmitted and to die after discharge; Hispanic and Black early preterm infants with BPD were more likely to be readmitted; Black LMPT infants with RDS were more likely to be readmitted and die after discharge. CONCLUSIONS: Racial/ethnic disparities in readmission and mortality rates exist for premature infants across several co-morbidities. Future studies are needed to improve equitability of outcomes.

A call for social informatics.

As evidence of the associations between social factors and health outcomes continues to mount, capturing and acting on social determinants of health (SDOH) in clinical settings has never been more relevant. Many professional medical organizations have endorsed screening for SDOH, and the U.S. Office of the National Coordinator for Health Information Technology has recommended increased capacity of health information technology to integrate and support use of SDOH data in clinical settings. As these efforts begin their translation to practice, a new subfield of health informatics is emerging, focused on the application of information technologies to capture and apply social data in conjunction with health data to advance individual and population health. Developing this dedicated subfield of informatics-which we term social informatics-is important to drive research that informs how to approach the unique data, interoperability, execution, and ethical challenges involved in integrating social and medical care.