Neighborhood Disadvantage and Autism Spectrum Disorder in a Population With Health Insurance

Abstract

Importance: Family socioeconomic status has been associated with autism spectrum disorder (ASD) diagnoses. Less is known regarding the role of neighborhood disadvantage in the United States, particularly when children have similar access to health insurance.

Objective: To evaluate the association between neighborhood disadvantage and the diagnosis of ASD and potential effect modification by maternal and child demographic characteristics.

Design, setting, and participants: This cohort study examined a retrospective birth cohort from Kaiser Permanente Southern California (KPSC), an integrated health care system. Children born in 2001 to 2014 at KPSC were followed up through KPSC membership records. Electronic medical records were used to obtain an ASD diagnosis up to December 31, 2019, or the last follow-up. Data were analyzed from February 2022 to September 2023.

Exposure: Socioeconomic disadvantage at the neighborhood level, an index derived from 7 US census tract characteristics using principal component analysis.

Main outcomes and measures: Clinical ASD diagnosis based on electronic medical records. Associations between neighborhood disadvantage and ASD diagnosis were determined by hazard ratios (HRs) from Cox regression models adjusted for birth year, child sex, maternal age at delivery, parity, severe prepregnancy health conditions, maternal race and ethnicity, and maternal education. Effect modification by maternal race and ethnicity, maternal education, and child sex was assessed.

Results: Among 318 372 mothers with singleton deliveries during the study period, 6357 children had ASD diagnoses during follow-up; their median age at diagnosis was 3.53 years (IQR, 2.57-5.34 years). Neighborhood disadvantage was associated with a higher likelihood of ASD diagnosis (HR, 1.07; 95% CI, 1.02-1.11, per IQR = 2.70 increase). Children of mothers from minoritized racial and ethnic groups (African American or Black, Asian or Pacific Islander, Hispanic or Latinx groups) had increased likelihood of ASD diagnosis compared with children of White mothers. There was an interaction between maternal race and ethnicity and neighborhood disadvantage (difference in log-likelihood = 21.88; P < .001 for interaction under χ24); neighborhood disadvantage was only associated with ASD among children of White mothers (HR, 1.17; 95% CI, 1.09-1.26, per IQR = 2.00 increase). Maternal education and child sex did not significantly modify the neighborhood-ASD association.

Conclusions and relevance: In this study, children residing in more disadvantaged neighborhoods at birth had higher likelihood of ASD diagnosis among a population with health insurance. Future research is warranted to investigate the mechanisms behind the neighborhood-related disparities in ASD diagnosis, alongside efforts to provide resources for early intervention and family support in communities with a higher likelihood of ASD.

Figure.. Associations Between Neighborhood Disadvantage, Maternal Race and Ethnicity, and Autism Spectrum Disorder (ASD) Diagnosis

A, Main association between neighborhood disadvantage (per IQR = 2.70 increase) and ASD in the entire study population from the fully adjusted model 2. B, Main association between maternal race and ethnicity and ASD in the entire study population from the fully adjusted model 2. C, Association between neighborhood disadvantage and ASD in analyses fully stratified by maternal race and ethnicity scaled to the stratum-specific IQR, adjusted for birth year, maternal age, parity, history of maternal comorbidity, medical center, maternal education, and child sex at birth. IQRs for African American or Black, Asian or Pacific Islander, Hispanic or Latinx, White, and other race and ethnicity were 2.71, 2.25, 2.45, 2.00, and 2.36, respectively. Similar results were found when the coefficient for each race and ethnicity group was extracted from the interaction model (eTable 8 in Supplement 1). The category other race and ethnicity includes patients who identified as other or multiple races. Census tracts were modeled as random intercepts. The interpretation of the coefficient was an effect corresponding to the range of exposure of the middle 50% of the population.