Medical Conditions in the First Years of Life Associated with Future Diagnosis of ASD in Children

Thursday, May 11, 2017: 1:45 PM
Yerba Buena 3-6 (Marriott Marquis Hotel)
L. A. Croen1, S. E. Alexeeff2, V. Yau3, Y. Qian1, M. N. Davignon4, P. M. Crawford5, F. Lynch5 and R. L. Davis6, (1)Kaiser Permanente Division of Research, Oakland, CA, (2)Division of Research, Kaiser Permanente Northern California, Oakland, CA, (3)Kaiser Permanente, Berkeley, CA, (4)Kaiser Roseville Medical Center, Roseville, CA, (5)Center for Health Research, Kaiser Permanente Northwest, Portland, OR, (6)Department of Pediatrics, UTHSC, Memphis, TN
Background:  Many children with ASD have co-occurring medical conditions. However, large scale epidemiologic studies of health conditions emerging in the period preceding the first ASD diagnosis are lacking. Earlier ASD diagnosis and treatment can reduce the degree of impairment and improve function, thus identifying factors that precede a future ASD diagnosis could be immensely useful. If particular conditions are associated with future ASD diagnosis, ongoing surveillance of these conditions might aid in screening for ASD, enabling earlier identification and treatment.

Objectives:  To examine medical conditions diagnosed prior to the first ASD diagnosis, assessing their prevalence and associations with subsequent ASD diagnosis.

Methods:  The study population was drawn from the population of all children born from 2000-2009 and continuously enrolled in Kaiser Permanente (KP) in Northern California, Georgia, and the Northwest for the first two years of life. Data from electronic medical records (EMR) were reviewed through June 2012. We used a matched case-control design and included medical conditions documented prior to the first ASD diagnosis (N=3,911) or the matched age for controls (N=38,609). Over 1,000 ICD-9 codes were grouped into 79 medical conditions (e.g., constipation) within 19 domains (e.g., gastrointestinal). We fit conditional logistic regression models to estimate the odds ratio (OR) for the associations of medical conditions and subsequent ASD diagnosis. Adjusted models accounted for the matching by site and age and adjusting for sex, maternal race, maternal education, and household income. We also used the Conditional Inference Tree supervised clustering method to identify condition clusters associated with subsequent ASD risk.

Results: The average age of ASD diagnosis was 3.99 years. Of the 79 medical conditions tested, 38 were statistically significantly associated with subsequent diagnosis of ASD after adjusting for multiple testing. Developmental delay, mental health, and neurology conditions had the strongest associations with ASD diagnosis (ORs from 2.0 to 23.3). Within the developmental delay domain, language delays were the most frequently diagnosed among ASD cases during the pre-diagnostic period (49%) and most strongly associated with ASD risk (OR=23.3, 95% CI 21.4-25.4). Disruptive impulse conduct disorders (5.2%), attention-deficit/hyperactivity disorders (ADHD) (6.4%), and anxiety disorders (3.2%) were the most prevalent mental health conditions and had the strongest associations with ASD risk (ORs from 10-15). Moderately strong associations were observed for nutrition, genetic, ear nose and throat, and sleep disorder conditions (ORs from 2.1 to 3.2). Metabolic, musculoskeletal, ophthalmology, pulmonary, and many gastrointestinal conditions had weaker associations (ORs from 1.2 to 2.2). We identified several condition clusters associated with subsequent ASD diagnosis. Children with language delays in combination with mental health conditions had the highest risk of a subsequent ASD diagnosis. In the absence of developmental delay and mental health diagnoses, children with ophthalmology, ear nose and throat, and nutrition diagnoses were most likely to be subsequently diagnosed with ASD.

Conclusions: Children with ASD experience a higher prevalence of many medical conditions prior to their first ASD diagnosis. Using medical conditions as a predictive tool may speed the identification of children with ASD, leading to earlier intervention and improved outcomes.