Assessing the social determinants of health care costs for Medicaid-enrolled adolescents in Washington State using administrative data

Cohort study examining social determinants of health and their association with mortality among hospitalised adults in New York and California

Background

Adults in the US face significant disparities in health as a result of the social determinants of health (SDOH). While the link between SDOH and mortality is well-established, their impact on outcomes after hospitalisation is less understood.

Methods

Among adults aged 18-84 years hospitalised in New York (NY) during the period of 2000-2009 and in California (CA) from during the period of 2000-2006, we examined the association between 1-year post-hospitalisation mortality and a community-level SDOH combined index (comprising six component domains) using Kaplan-Meier survival analysis and multivariable Cox proportional-hazard models to estimate the mortality HR (adjusted HR (aHR)) adjusted for age, gender, race, ethnicity and Charlson Comorbidity Index. We also studied subcohorts in NY and CA grouped by hospitalisation conditions (subgroups with chronic or acute disease).

Results

In NY, the overall 1-year mortality rate was 8.9% (9.7% for chronic diseases and 13.2% for acute diseases). In CA, the overall 1-year mortality rate was 8.3% (12.6% for chronic diseases and 15.8% for acute diseases). In both states, the 1-year risk of death was significantly lower for those in the best (Q4) SDOH (combined index) compared with the worst (Q1 is the reference category). In NY, the aHR was 0.964 (p<0.001 and 95% CI 0.950 to 0.978), while in CA, the aHR: 0.83 (p<0.001 and 95% CI 0.825 to 0.842). Similar patterns were observed for the disease cohorts in both states. The Economic and Education domains of SDOH showed stronger and more consistent associations with mortality risk compared with the domains of Neighbourhood, Food Access, Community and Social Context, and Healthcare.

Conclusions

This study demonstrates a significant association between worse SDOH and higher post-hospitalisation mortality. The findings emphasise the importance of community-level SDOH in patient care planning and discharge strategies to reduce health disparities.

Social Determinants of Health and Psychological Capital Among Youth Experiencing Homelessness

BACKGROUND

Social determinants of health affect health behaviors and outcomes. Youth experiencing homelessness suffer significant deprivation of resources such as inadequate housing, reduced education, poor health care, and decreased economic stability. Inner resources, such as psychological capital, may also be related to health behaviors and health outcomes.

OBJECTIVE

In this study, we sought to describe and explore associations among selected determinants of health and self-reported scores on indicators of psychological capital among youth experiencing homelessness.

METHODS

This cross-sectional secondary analysis was conducted with a randomized subsample of 148 youth. We calculated chi-square frequencies to describe the data, classical item analyses to evaluate responses, and correlation tests to examine significance of associations.

RESULTS

Youth in this sample demonstrated that they possess inner resources associated with determinants of health. Education, health care, and social support were significantly associated with attributes of psychological capital (hope, efficacy, resilience, optimism). Sexual minority groups had high representation in this subsample (25.7%), indicating a need for more study and equitable services for this population.

CONCLUSION

More research should be conducted to better understand the associations between determinants of health, psychological capital, and health behaviors among disadvantaged youth to advance health equity initiatives.

Leveraging Administrative Data to Better Understand and Address Child Maltreatment: A Scoping Review of Data Linkage Studies

BACKGROUND

This scoping review aimed to overview studies that used administrative data linkage in the context of child maltreatment to improve our understanding of the value that data linkage may confer for policy, practice, and research.

METHODS

We searched MEDLINE, Embase, PsycINFO, CINAHL, and ERIC electronic databases in June 2019 and May 2020 for studies that linked two or more datasets (at least one of which was administrative in nature) to study child maltreatment. We report findings with numerical and narrative summary.

RESULTS

We included 121 studies, mainly from the United States or Australia and published in the past decade. Data came primarily from social services and health sectors, and linkage processes and data quality were often not described in sufficient detail to align with current reporting guidelines. Most studies were descriptive in nature and research questions addressed fell under eight themes: descriptive epidemiology, risk factors, outcomes, intergenerational transmission, predictive modelling, intervention/service evaluation, multi-sector involvement, and methodological considerations/advancements.

CONCLUSIONS

Included studies demonstrated the wide variety of ways in which data linkage can contribute to the public health response to child maltreatment. However, how research using linked data can be translated into effective service development and monitoring, or targeting of interventions, is underexplored in terms of privacy protection, ethics and governance, data quality, and evidence of effectiveness.

Concordance in Adolescent and Caregiver Report of Social Determinants of Health

INTRODUCTION

Screening youth for negative social determinants of health is a widespread practice across healthcare settings in the U.S., with such systems almost exclusively relying on caregiver reports. Little work has sought to identify the social determinants of health adolescents identify as having the largest influence on their health and well-being or the extent to which adolescents agree with their caregiver. This study sought to (1) identify the most prevalent and influential negative social determinants of health, according to adolescent reports, and (2) assess concordance between adolescent and caregiver reports of social determinants of health.

METHODS

In Fall 2021, the study team conducted a cross-sectional, observational study within a predominately Latinx urban high school. The team invited all students and their caregivers to participate, resulting in 520 adolescent and 66 caregiver respondents (73% and 9% response rates, respectively). Data analyses occurred in 2022.

RESULTS

The most frequent adolescent-reported negative social determinants of health was stress (67%), followed by financial hardship (27%). The negative social determinant of health with the highest ranking of preventing adolescents from living their best and healthiest lives was depression, followed by isolation and stress. Concordance between caregiver and adolescent report of negative social determinants of health was very low across all negative social determinants of health (Krippendorf's α= -0.08 to 0.21). Caregivers under-reported adolescents' social and mental health needs, whereas adolescents under-reported material needs.

CONCLUSIONS

A hybrid informant approach may be a best practice for social determinants of health screening among adolescents, whereby caregivers and adolescents report material needs, and adolescents report social and mental health needs. Future work should evaluate the concordance between adolescent and caregiver social determinants of health reports in other settings.

An Algorithm Using Administrative Data to Measure Adverse Childhood Experiences (ADM-ACE)

OBJECTIVE

To develop an algorithm using administrative data to measure adverse childhood experiences (ADM-ACE) within routinely collected health insurance claims and enrollment data.

DATA SOURCES

We used claims and enrollment data from Tennessee's Medicaid program (TennCare) in 2018.

STUDY DESIGN

We studied five types of ACEs: maltreatment and peer violence, foster care and family disruption, maternal mental illness, maternal substance use disorder, and abuse of the mother. We used diagnosis and procedure codes, prescription drug fills, and enrollment files to develop the ADM-ACE, which we applied to measure the prevalence of ACEs and to examine prevalence by demographic characteristics among our sample of children in TennCare. We compared ADM-ACE prevalence to child welfare records and survey results from Tennessee.

DATA COLLECTION/EXTRACTION METHODS

Our study sample included children aged 0-17 years who were linked to their mothers if also enrolled in TennCare in 2018 (N=763,836 children).

PRINCIPAL FINDINGS

Approximately 19.2% of children in TennCare had indicators for ADM-ACEs. The prevalence of ACEs was higher among children who were younger (p<0.001), non-Hispanic White or Black (compared to Hispanic) (p<0.001), and children residing in rural vs. urban counties (p<0.001). The prevalence of maltreatment identified through the ADM-ACE (1.6%) falls between the percent of children in Tennessee who were reported to child welfare authorities and the percent for whom reports of maltreatment were substantiated. Comparison with survey reports from Tennessee parents suggests an advantage in measuring maternal mental illness with the ADM-ACE using health insurance claims data.

CONCLUSIONS

The ADM-ACE can be applied to health encounter data to study and monitor the prevalence of certain ACEs, their association with health conditions, and the effects of policies on reducing exposure to ACEs or improving health outcomes for children with ACEs.

When Families Do Not Request Help: Assessing a Social Determinants of Health Screening Tool in Practice

INTRODUCTION

Using pediatric social determinants of health screening data from a large medical system, we explored social needs dislosures and identified which needs were associated with resource connection requests.

METHOD

Data came from records of outpatient pediatric patients (0-18 years) seen between October 2018 and March 2020 (39,251 encounters). We assessed percent of encounters where families (1) indicated a social need, and (2) requested a resource connection. We conducted multivariable logistic regression to identify which needs were associated with resource connection requests.

RESULTS

Among all encounters, 8% indicated a need and 2% requested a resource connection. Among families indicating a need, needs associated with resource requests included: housing (odds ratio [OR], 3.49 [2.42-5.03]), employment (OR, 3.15 [2.21-4.50]), food (OR, 1.89 [1.41-2.52]), and transportation (OR, 1.82 [1.30-2.56]).

DISCUSSION

Families seldom requested resource connections to address social needs. Better understanding families' interests in receiving assistance is an important next step in pediatric social determinants of health screening system development.