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Friday, August 21, 2015

Screening and Diagnosis: Age Cohort, Poverty, Race, and Family

In The Politics of AutismI discuss disparities in screening and diagnosis. At The Journal of Autism and Developmental Disorders,  Natacha D. Emerson, Holly E. R. Morrell1, Cameron Neece have an article titled "Predictors of Age of Diagnosis for Children with Autism Spectrum Disorder: The Role of a Consistent Source of Medical Care, Race, and Condition Severity."
Birth cohort was an important predictor of ASD diagnosis. Children born in or after 2006 (the year AAP guidelines were published) were diagnosed on average 35 months earlier than those born between 1994 and 2005. Compared to the 2014 CDC report, which reported an average age of diagnosis at 53 months for children born pre-AAP implementation, this suggests a noteworthy improvement in the diagnostic process (CDC 2014). However, while this may indicate that nationwide implementation of ASD screening guidelines improved screening and precipitated diagnosis, children in the later cohort were also significantly younger. Given that children born in or after 2006 were so young, they may represent the optimally diagnosed. As a result, interpretation of this effect should be made cautiously. Moreover, research on the success of AAP guideline implementation has produced mixed evidence. While most primary care practices increased their structured screening of ASD, referral of children who screened positive was inconsistent. In a study examining outcomes of the AAP screening guidelines, King et al. (2010) found that more than one-third of children who failed the screen were not referred to specialty care. Moreover, doctors that did refer patients to specialists found that many families failed to follow-up with recommended referrals (King et al. 2010). Consequently, conclusions drawn from the considerable effect size of the cohort predictor may not be solely attributable to AAP guideline implementation. Instead, we can speculate that increases in parental and physician awareness of ASD have also contributed to earlier identification and diagnosis seeking (Fountain et al. 2011; Hertz-Picciotto and Delwiche 2009). We can also postulate that age differences contributed to the magnitude of the measured effect.
Two other covariates significantly predicted age of ASD diagnosis. First, being a younger sibling was associated with earlier diagnosis. Parents’ familiarity with early warning signs of ASD likely explains this effect. Second, poverty level predicted diagnostic age, such that more impoverished children were diagnosed later than wealthier children. As suggested by the literature, families of lower SES are less likely to have a regular source of care and to receive routine medical supervision (DeVoe et al. 2007, 2008) even after controlling for health insurance (Fiscella et al. 2002). Moreover, families of lower SES may encounter additional economic and environmental barriers following referral to specialty care, given an increased likelihood of financial difficulties to afford medical copayments, time off work, and childcare for other family members (Kuhlthau et al. 2004). Consequently, the main effect of poverty level may reflect both pre- and postconsultation disparities. In contrast, while we expected that parental education would have an impact on diagnostic age, the current sample was better educated than that of the average United States citizen, which may explain the lack of anticipated findings.
In terms of interaction effects, the relationship between having a CSC [consistent source of care] and age of initial diagnosis depended on race. While having a CSC prompted earlier ASD diagnosis in Caucasian children, it delayed diagnosis for African American children. This racial disparity in children who have a CSC suggests two possibilities: race-based differences in practitioner and/or parent behavior.
  • DeVoe, J. E., Baez, A., Angier, H., Krois, L., Edlund, C., & Carney, P. A. (2007). Insurance ? access = health care: Typology of barriers to health care access for low-income families. The Annals of Family Medicine, 5(6), 511–518. 
  • DeVoe, J. E., Petering, R., & Krois, L. (2008). A usual source of care: Supplement or substitute for health insurance among low-income children? Medical Care, 46(10), 1041–1048. 
  • Fiscella, K., Franks, P., Doescher, M. P., & Saver, B. G. (2002). Disparities in health care by race, ethnicity, and language among the insured: Findings from a national sample. Medical Care, 40(1), 52–59 
  • Fountain, C., King, M. D., & Bearman, P. S. (2011). Age of diagnosis for autism: Individual and community factors across 10 birth cohorts. Journal of Epidemiology and Community Health, 65(6), 503–510.Hertz-Picciotto, I., & Delwiche, L. (2009). The rise in autism and the role of age at diagnosis. Epidemiology, 20, 84–90. 
  • King, T. M., Tandon, S. D., Macias, M. M., Healy, J. A., Duncan, P. M., Swigonski, N. L., et al. (2010). Implementing developmental screening and referrals: Lessons learned from a national project. Pediatrics, 125(2), 350–360. 
  • Kuhlthau, K., Nyman, R. M., Ferris, T. G., Beal, A. C., & Perrin, J. M. (2004). Correlates of use of specialty care. Pediatrics, 113(3), e249–e255.