Income and Household Material Hardship in Children With Medical Complexity

BACKGROUND AND OBJECTIVES

Household economic hardship negatively impacts child health but may not be adequately captured by income. We sought to determine the prevalence of household material hardship (HMH), a measure of household economic hardship, and to examine the relationship between household poverty and material hardship in a population of children with medical complexity.

METHODS

We conducted a cross-sectional survey study of parents of children with medical complexity receiving primary care at a tertiary children's hospital. Our main predictor was household income as a percentage of the federal poverty limit (FPL): <50% FPL, 51% to 100% FPL, and >100% FPL. Our outcome was HMH measured as food, housing, and energy insecurity. We performed logistic regression models to calculate adjusted odds ratios of having ≥1 HMH, adjusted for patient and clinical characteristics from surveys and the Pediatric Health Information System.

RESULTS

At least 1 material hardship was present in 40.9% of participants and 28.2% of the highest FPL group. Families with incomes <50% FPL and 51% to 100% FPL had ∼75% higher odds of having ≥1 material hardship compared with those with >100% FPL (<50% FPL: odds ratio 1.74 [95% confidence interval: 1.11-2.73], P = .02; 51% to 100% FPL: 1.73 [95% confidence interval: 1.09-2.73], P = .02).

CONCLUSIONS

Poverty underestimated household economic hardship. Although households with incomes <100% FPL had higher odds of having ≥1 material hardship, one-quarter of families in the highest FPL group also had ≥1 material hardship.

Accuracy of the Exeter Hospitalizations-Office Visits-Medical Conditions-Extra Care-Social Concerns Index for Identifying Children With Complex Chronic Medical Conditions in the Clinical Setting

OBJECTIVE

Our objective was to determine the accuracy of a point-of-care instrument, the Hospitalizations-Office Visits-Medical Conditions-Extra Care-Social Concerns (HOMES) instrument, in identifying patients with complex chronic conditions (CCCs) compared to an algorithm used to identify patients with CCCs within large administrative data sets.

METHODS

We compared the HOMES to Feudtner's CCCs classification system. Using administrative algorithms, we categorized primary care patients at a children's hospital into 3 categories: no chronic conditions, non-complex chronic conditions, and CCCs. We randomly selected 100 patients from each category. HOMES scoring was completed for each patient. We performed an optimal cut-point analysis on 80% of the sample to determine which total HOMES score best identified children with ≥1 CCC and ≥2 CCCs. Using the optimal cut points and the remaining 20% of the study population, we determined the odds and area under the curve (AUC) of having ≥1 CCC and ≥2 CCCs.

RESULTS

The median (interquartile range [IQR]) age was 4 (IQR: 0, 8). Using optimal cut points of ≥7 for ≥1 CCC and ≥11 for ≥2 CCCs, the odds of having ≥1 CCC was 19 times higher than lower scores (odds ratio [OR] 19.1 [95% confidence interval [CI]: 9.75, 37.5]) and of having ≥2 CCCs was 32 times higher (OR 32.3 [95% CI: 12.9, 50.6]). The AUCs were 0.76 for ≥1 CCC (sensitivity 0.82, specificity 0.80) and 0.74 for ≥2 CCCs (sensitivity 0.92, specificity 0.74).

CONCLUSIONS

The HOMES accurately identified patients with CCCs.