Age of diagnosis for autism: individual and community factors across 10 birth cohorts

Abstract

Background: The incidence of autism rose dramatically between 1992 and 2001, while the age at which children were first diagnosed declined. During this period the size and composition of the autism caseload has changed, but little is known about whether the factors associated with the timing of diagnosis may also have shifted. Using a multilevel analysis strategy, the individual and community-level factors associated with age of diagnosis were modelled across 10 birth cohorts of California children.

Methods: Linked birth and administrative records on 17,185 children with diagnoses of autistic disorder born in California between 1992 and 2001 and enrolled with the California Department of Developmental Services (DDS) were analysed. Information on cases, their parents and their residential location were extracted from birth and DDS records. Zip codes of residence were matched to census data to create community-level measures. Multilevel linear models were estimated for each birth cohort, with individual-level effects for sex, race, parental characteristics, poverty status, birth order and symptom expression. At the community level measures of educational and economic composition, local autism prevalence and the presence of a child psychiatrist were included.

Results: Children with highly educated parents are diagnosed earlier, and this effect has strengthened over time. There is a persistent gap in the age of diagnosis between high and low socioeconomic status (SES) children that has shrunk but not disappeared over time.

Conclusion: Routine screening for autism in early childhood for all children, particularly those of low SES, is necessary to eliminate disparities in early intervention.

Figure 1

Median age at autism diagnosis by birth cohort, with 95% CI for median. All cohorts are composed of those children born in a calendar year and diagnosed with autism without comorbid mental retardation before the age of 9 years but after 12 months.

Figure 2

Coefficient estimates with 95% CI by birth cohort for selected variables. Effect sizes can be interpreted as the number of months earlier a diagnosis occurs, on average, given a 1 SD change in the independent variable, ceteris paribus.

Figure 3

(A) Predicted age of diagnosis displayed for high and low socioeconomic status (SES) individuals. Predicted ages are calculated based on a mean of 10 000 posterior draws for coefficient fixed effects, for hypothetical individuals at the mean (or modal category) on all variables except those relevant to SES. The ‘high’ SES child is also male, white non-hispanic, has parental education and age at the 90th percentile, was born to a non-immigrant mother, was not born on Medi-Cal, and lives in a zip code at the 90th percentile for property value and education. The ‘low’ SES child is also male, but is of Hispanic origin, has parental education and age at the 10th percentile, was born on Medi-Cal to a mother born outside the USA, and lives in a zip code at the 10th percentile for property value and education. (B) The SES gap. Displays, in months, the relative advantage in early diagnosis for a high SES child compared with a low SES child.