Beyond Income: Multidimensional Economic Insecurity and Healthcare Access Among Autistic Children

Background and Purpose

Economic insecurity among families of autistic children is multifaceted and not fully captured by income-based measures. Research often relies on single indicators of socioeconomic status, limiting understanding of how material hardship, financial burden, and access to economic resources jointly shape family circumstances. Conceptualizing economic insecurity as a multidimensional construct may better capture heterogeneity in family circumstances and its implications for healthcare access. Latent class analysis (LCA) allows for identification of subgroups with shared patterns of economic risk. This study identified multidimensional economic profiles among autistic children and examined how these profiles are associated with healthcare access.

Methods

Data were drawn from the 2022–2023 National Survey of Children’s Health (NSCH), a nationally representative survey of U.S. children ages 0–17. The analytic sample included children ages 3–17 with a current parent-reported autism diagnosis. Economic insecurity was operationalized across three domains: material hardship, family financial burden, and economic resources, using eight binary indicators. Latent class analysis was conducted to identify distinct economic profiles, with model selection guided by AIC, BIC, likelihood ratio tests, and entropy. A three-step approach examined predictors of class membership, including demographic characteristics and disability-related factors. Associations between class membership and healthcare access outcomes, including access to a personal doctor or nurse, telehealth use, unmet healthcare need, and difficulty obtaining referrals, were examined using weighted logistic regression models.

Results

Five distinct economic profiles were identified. Class 1 (4.8%), “privately strained, medically burdened,” reflected high medical costs and employment disruption with limited public assistance. Class 2 (46.8%), “economically stable,” showed minimal hardship or burden. Class 3 (13.3%), “low-income, resource-supported,” reflected poverty with access to public supports. Class 4 (12.6%), “severe economic insecurity,” showed high hardship and poverty with insufficient supports. Class 5 (22.4%), “healthcare cost-burdened,” reflected high out-of-pocket costs despite otherwise stable conditions.

Economic profiles were significantly associated with healthcare access outcomes. Compared to the economically stable class, children in the privately strained group had substantially higher unmet healthcare needs (35.8% vs. 8.2%, p < .001) and greater difficulty obtaining referrals. Children in the severe insecurity class also demonstrated elevated unmet need (21.7%, p < .05). In contrast, the low-income, resource-supported and cost-burdened groups showed lower unmet need (~10%). Telehealth use was higher among economically burdened groups, particularly the privately strained (44.5%) and cost-burdened (55.0%) classes. Importantly, the highest unmet need was observed among families experiencing financial strain without access to public assistance.

Conclusions and Implications

Findings demonstrate that economic insecurity among families of autistic children is multidimensional and cannot be adequately captured by income alone. Distinct profiles reveal multiple pathways of risk, including poverty-related hardship, healthcare cost burden, and unsupported financial strain. Critically, families experiencing financial burden without access to public assistance exhibited the greatest unmet healthcare need, highlighting gaps in current policy structures. Results support the use of multidimensional screening approaches in clinical and service settings and underscore the need for targeted interventions addressing specific forms of economic risk. These findings have direct implications for improving equity in healthcare access among autistic children.