Causal Mediation of Neighborhood-Level Pediatric Hospitalization Inequities

Incorporating Parcel-Based Housing Conditions to Increase the Precision of Identifying Children With Elevated Blood Lead

CONTEXT

Area-level predictive models are commonly used to screen children for blood lead levels (BLLs) greater than the Center for Disease Control and Prevention (CDC) blood lead reference value (BLRV) of 3.5 µg/dL.

OBJECTIVES

To increase screening accuracy and precision by creating a parcel-level model incorporating housing characteristics to predict parcels where children are at high risk.

DESIGN

Residential addresses associated with child blood lead tests were linked to neighborhood- and parcel-level characteristics and proximity to lead sources. Regression forests were fit using different predictor combinations and compared using cross-validated accuracy and decile-based agreement across all residential parcels.

SETTING

Hamilton County, Ohio, United States.

PARTICIPANTS

Children less than 6 years of age with blood lead tests between January 2020 and April 2023.

MAIN OUTCOME MEASURE

Cross-validated model accuracy and decile-based agreement across residential parcels.

RESULTS

27,782 tests were matched to a residential parcel. Regression forests using Parcel + Source (70.8% AUC) and Neighborhood + Parcel + Source predictors (70.3% AUC) had the highest cross-validated accuracy for predicting BLLs >3.5 µg/dL. Parcel-level predictions revealed heterogeneity of risk across parcels within the same tract.

CONCLUSIONS

Parcel characteristics improved the accuracy of predicting locations of children with BLLs >3.5 µg/dL and can help identify children at high risk living in low-risk areas. A parcel-level identification of housing-based lead hazards could guide and support action to prevent pediatric lead exposure.

Seeking Optimal, Integrated Pediatric Medical and Social Care: A Qualitative, Peer Researcher-Driven Study

OBJECTIVE

To generate qualitative insights with patients, families, and community partners to catalyze nimble, aligned medical-social care responses.

METHODS

This study employed qualitative and co-design methods. We partnered with 4 peer researchers who interviewed individuals living in Greater Cincinnati with recent experience as pediatric patients or caregivers of pediatric patients. Interviews assessed ways in which health care, human services, patients, families, and communities do (or do not) collaborate to provide medical-social care. Interview transcripts were independently reviewed by an analytic team, inclusive of peer researchers, co-design experts, and pediatric clinicians and researchers. Findings were validated during community discussions, facilitating identification of themes and opportunity areas for intervention.

RESULTS

Peer researchers conducted 19 interviews (14 English and 5 Spanish). Interviewees included individuals ranging from young adults (recent patients) to older adults with lived experience as caregivers of pediatric patients. Most identified as minority race and ethnicity and lived in socioeconomically disadvantaged Cincinnati neighborhoods. Themes related to structural barriers included 1) services are difficult to navigate; 2) medical and social care are often reactive (or nonexistent) when they could be proactive; and 3) medical and social care could be more closely integrated. Themes related to human factors included 4) medical and social service institutions are often untrustworthy; 5) diversified care teams with shared lived experiences could improve care delivery; and 6) optimal care requires empathy, clear communication, and partnership.

CONCLUSIONS

Patients, families, and community members identified themes and opportunity areas for improving medical and social care delivery. Next steps include the implementation and evaluation of prototype interventions.

Neighborhood Socioeconomic Deprivation and Length of Stay in Children With Medical Complexity

OBJECTIVE

Children with medical complexity (CMC) often experience long hospital length of stay (LOS). Many families of CMC experience financial and social hardships, which impact arrangement of the home supports necessary for discharge. Understanding neighborhood context in which CMC live is one way to examine the effects of these hardships on LOS. We aimed to evaluate the association between neighborhood socioeconomic deprivation and hospital LOS in CMC.

METHODS

We conducted a single-center retrospective study including hospitalized children aged up to 21 years with 2 or more complex chronic conditions discharged from hospital medicine in 2016 to 2022. We excluded neonatal intensive care hospitalizations. We mapped home addresses to US census-tract data to calculate the primary exposure, the Brokamp neighborhood socioeconomic deprivation index. We used linear mixed models to examine the association between deprivation index and LOS (continuous days), adjusting for covariates (eg, patient clinical characteristics) and accounting for within patient clustering.

RESULTS

We included 4697 encounters from 2186 CMC. The median deprivation index was 0.33 (IQR, 0.25-0.42) and median LOS was 3.29 days (IQR, 1.86-6.91). In adjusted analysis, for each 0.1 increase in deprivation index, LOS increased 1.05-fold (95% CI, 1.03-1.08). Therefore, we expect a 9% increase in LOS for CMC living in more socioeconomically deprived neighborhoods (our cohort's 75th percentile deprivation index: 0.42) vs less deprived neighborhoods (our cohort's 25th percentile deprivation index: 0.25).

CONCLUSIONS

CMC from neighborhoods with greater socioeconomic deprivation experienced longer hospitalizations even when accounting for level of complexity and severity of illness. While the clinical effect is small individually, our results highlight systemic inequities.

Diagnostic disparities in inborn errors of immunity: From clinical suspicion to diagnosis

Background

Emerging evidence suggests that inborn errors of immunity (IEI) are underdiagnosed among underserved populations. However, there remains a lack of national studies evaluating diagnostic disparities in IEI.

Objective

We examined disparities in the timely IEI diagnosis and related health outcomes.

Methods

A retrospective analysis was performed of a US national claims database (years 2007 to 2021). Participants included patients diagnosed with an "unspecified immune deficiency" (uID) and presented with IEI-related symptoms, who later received an IEI diagnosis (n = 1429). We quantified the diagnostic interval from clinical suspicion (uID) to IEI diagnosis and examined its association with sociodemographic factors and related health outcomes.

Results

The median (interquartile range) diagnostic interval was 369 (126-808) days. Diagnostic interval was 14% longer among patients residing in predominantly non-White neighborhoods, compared with those in predominantly White neighborhoods (P = .04), despite having more severe IEI-related symptoms at uID diagnosis and significantly more health care encounters for pneumonia (incidence rate ratio, 2.24; 95% confidence interval, 1.40-3.70) and sepsis (incidence rate ratio, 2.15; 95% confidence interval, 1.21-3.99) in the year after uID diagnosis. Residence in neighborhoods with greater deprivation was also associated with more severe IEI-related symptoms and greater health care utilization in the year after uID diagnosis. Older age was associated with longer diagnostic interval (P < .001). Longer diagnostic interval was associated with a longer interval to receiving IgR therapy (hazard ratio, 0.64; 95% confidence interval, 0.49-0.83).

Conclusion

We observed significant racial and socioeconomic disparities in the timeliness of IEI diagnosis and IEI-related outcomes. Further studies are needed to address the underlying factors contributing to diagnostic inequity.

Advancing Equity in Rare Disease Research: Insights From the Undiagnosed Disease Network

Rare diseases affect 6%-8% of the population and present diagnostic challenges, particularly for historically marginalized ethnic and racial groups. The Undiagnosed Diseases Network (UDN) aims to enhance diagnosis rates and research participation among such minoritized groups. A retrospective review was conducted from 2015 to 2023, analyzing 2235 UDN participants to evaluate its progress toward this objective. Data on demographics, disease phenotypes, diagnostic outcomes, and socioeconomic factors were collected and statistical analyses assessed differences among ethnic and racial groups. This demonstrated that Hispanic and Black non-Hispanic groups were underrepresented, while White non-Hispanic participants were overrepresented in the UDN compared to the US population. Individuals whose primary language was not English were also significantly underrepresented. Diagnosis rates varied, with the highest rates among Asian non-Hispanic (39.5%) and Hispanic (35.3%) groups and the lowest rate in the White non-Hispanic group (26.8%) (p < 0.001). Binomial logistic regression found, however, that only participant age and disease phenotype predicted the likelihood of receiving a diagnosis (p < 0.001). Persistent ethnic and racial disparities in UDN participation appear to be associated with major differences in application rates. Under-enrollment of historically marginalized ethnic and racial groups may be due to economic hardships and language barriers. No differences in the diagnostic yield among ethnic and racial groups were observed after controlling for other factors. This work highlights the value of comprehensive genetic evaluations for addressing healthcare disparities and suggests priorities for advancing inclusion in rare disease research.