Spatial Relative Risk Patterns of Autism Spectrum Disorders in Utah
- 306 Downloads
Heightened areas of spatial relative risk for autism spectrum disorders (ASD), or ASD hotspots, in Utah were identified using adaptive kernel density functions. Children ages four, six, and eight with ASD from multiple birth cohorts were identified by the Utah Registry of Autism and Developmental Disabilities. Each ASD case was gender-matched to 20 birth cohort controls. Demographic and socioeconomic characteristics of children born inside versus outside ASD hotspots were compared. ASD hotspots were found in the surveillance area for all but one birth cohort and age group sample; maximum relative risk in these hotspots ranged from 1.8 to 3.0. Associations were found between higher socioeconomic status and birth residence in an ASD hotspot in five out of six birth cohort and age group samples.
KeywordsAscertainment age Autism spectrum disorders Diagnostic age Maternal residential birth address Socioeconomic status Spatial analysis Race/ethnicity
Autism surveillance data was obtained through Centers for Disease Control and Prevention Cooperative Agreement UR3DD000685-03. Research analysis was supported by the Utah Registry of Autism and Developmental Disabilities, the National Institute of Mental Health of the National Institutes of Health under Award Number R01MH094400, and University of Utah Department of Psychiatry funds. Thank you to Drs. Harper Randall, Paul Carbone, Marc Babitz, Eric Fombonne, Barry Nangle and Sam LeFevre for feedback on earlier versions of this manuscript. Brian Robison provided editorial assistance. We are extremely grateful to our health and education data sources for their data contributions.
Conflict of interest
The authors have no conflicts of interest to disclose.
Approval to conduct this research was granted by the University of Utah and Utah Department of Health’s Institutional Review Boards. This manuscript does not contain clinical studies or patient data.
- Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, 57(1), 289–300.Google Scholar
- Besag, J., & Newell, J. (1991). The detection of clusters in rare diseases. Journal of the Royal Statistical Society. Series A, 154(Part 1), 143–155.Google Scholar
- Bilder, D. A., Bakian, A. V., Viskochil, J., Clark, E. A. S., Botts, E. B., Smith, K. R., et al. (2013). Maternal prenatal weight gain and autism spectrum disorders. Pediatrics. doi: 10.1542/peds.2013-1188.
- Centers for Disease Control and Prevention, Autism and Developmental Disabilities Monitoring Network Year 2008 Principal Investigators (CDC). (2012). Prevalence of autism spectrum disorders-Autism and Developmental Disabilities Monitoring Network, 14 sites, United States, 2008. MMWR Surveillance Summary, 61(3), 1–19.Google Scholar
- Centers for Disease Control and Prevention, Autism and Developmental Disabilities Monitoring Network Year 2008 Principal Investigators (CDC). (2014). Prevalence of autism spectrum disorder among children aged 8 years-Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2010. MMWR Surveillance Summary, 63(2), 1–21.Google Scholar
- Davies, T. M., Hazelton, M. L., Marshall, J. C. (2011). Sparr: Analyzing spatial relative risk using fixed and adaptive kernel density estimation in R. Journal of Statistical Software, 39(1), 1–14.Google Scholar
- Downey, D. J., & Timberlake, M. F. (2006). Diversity in deseret: Race/ethnic segregation and inequality in Utah. In C. D. Zick & K. S. Smith (Eds.), Utah in the new millennium: A demographic perspective (pp. 203–215). Salt Lake City, UT: University of Utah Press.Google Scholar
- Gatrell, A. C. (2002). Geographies of health: An introduction. Oxford, UK: Wiley-Blackwell.Google Scholar
- Hastie, T., & Tibshirani, R. (1990). Generalized additive models. New York: Chapman and Hall.Google Scholar
- Hertz-Picciotto, I., Croen, L. A., Hansen, R., Jones, C. R., van de Water, J., & Pessah, I. N. (2006). The CHARGE study: An epidemiologic investigation of genetic and environmental factors contributing to autism. Environmental Health Perspectives, 114(7), 1119–1125.CrossRefPubMedCentralPubMedGoogle Scholar
- Hoffman, K., Kalbrenner, A. E., Vieira, V. M., Daniels, J. L. (2012). The spatial distribution of known predictors of autism spectrum disorders impacts geographic variability in prevalence in central North Carolina. Environmental Health, 11, 80. doi: 10.1186/1476-069X-11-80.
- Hoffman, K., Vieira, V. M., Daniels, J. L. (2013). Brief report: Diminishing geographic variability in autism spectrum disorders over time? Journal of Autism and Developmental Disorders. doi: 10.1007/s10803-013-1907-7.
- Liptak, G. S., Benzoni, L. B., Mruzek, D. W., Nolan, K. W., Thingvoll, M. A., Wade, C. M., et al. (2008). Disparities in diagnosis and access to health services for children with autism: Data from the National Survey of Children’s Health. Journal of Developmental and Behavioral Pediatrics, 29(3), 152–160.CrossRefPubMedGoogle Scholar
- Mazumdar, S., Winter, A., Liu, K., & Bearman, P. (2012). Spatial clusters of autism births and diagnoses point to contextual drivers of increased prevalence. Social Science & Medicine. doi: 10.1016/j.socscimed.2012.11.032.
- Pinborough-Zimmerman, J., Bakian, A. V., Fombonne, E., Bilder, D., Taylor, J., & McMahon, W. M. (2012). Changes in the administrative prevalence of autism spectrum disorders: Contribution of special education and health from 2002-2008. Journal of Autism and Developmental Disabilities, 42(4), 521–530.CrossRefGoogle Scholar
- R Development Core Team. (2012). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. Downloaded from http://www.R-project.org.
- Rai, D., Lewis, G., Lundberg, M., Araya, R., Svensson, A., Dalman, C., et al. (2012). Parental socioeconomic status and risk of offspring autism spectrum disorders in a Swedish population-based study. Journal of the American Academy of Child and Adolescent Psychiatry, 51(5), 467–476.CrossRefPubMedGoogle Scholar
- Ritvo, E. R., Breeman, B. J., Pingree, C., Mason-Brothers, A., Jorde, L., Jenson, W. R., et al. (1989). The UCLA-University of Utah epidemiologic survey of autism: Prevalence. American Journal of Psychiatry, 146(2), 194–199.Google Scholar
- Roberts, E. M., Gross, R., Weiser, M., Bresnahan, M., Silverman, J., & Wolff, C. (2007). Maternal residence near agricultural pesticide applications and autism spectrum disorders among children in the California Central Valley. Environmental Health Perspectives, 115(1), 1482–1489.PubMedCentralPubMedGoogle Scholar
- SAS Institute. (2008). SAS version 9.2 Cary, North Carolina, USA: SAS Institute.Google Scholar
- Scott, D. J., & Terrell, G. R. (1986). Biased and unbiased cross-validation in density estimation. Technical Report # 23, Department of Statistics, Stanford University, CA.Google Scholar
- Shattuck, P. T., Durkin, M., Maenner, M., Newschaffer, C., Mandell, D. S., Wiggins, L., et al. (2009). Timing of identification among children with an autism spectrum disorder: Findings from a population-based surveillance study. Journal of the American Academy of Child and Adolescent Psychiatry, 48(5), 474–483.CrossRefPubMedCentralPubMedGoogle Scholar
- Volk, H. E., Lurmann, F., Penfold, B., Hertz-Picciotto, I., & McConnell, R. (2013). Traffic-related air pollution, particulate matter, and autism. Archives of General Psychiatry, 70(1), 71–77.Google Scholar
- Webster, T., Vieira, V., Weinberg, J., & Aschengrau, A. (2006). Method for mapping population-based case-control studies: An application using generalized additive models. International Journal of Health Geography, 5, 26. doi: 10.1186/1476-072X-5-26.
- Windham, G. C., Anderson, M. C., Croen, L. A., Smith, K. S., Collins, J., & Grether, J. K. (2011). Birth prevalence of autism spectrum disorders in the San Francisco Bay area by demographic and ascertainment source characteristics. Journal of Autism and Developmental Disabilities, 41(10), 1362–1372.CrossRefGoogle Scholar
- Zerbo, O., Iosif, A., Walker, C., Ozonoff, S., Hansen, R. L., & Hertz-Picciotto, I. (2013). Is maternal influenza or fever during pregnancy associated with autism or developmental delays? Results from the CHARGE (Childhood Autism Risks from Genetics and Environment) Study. Journal of Autism and Developmental Disorders, 43, 25–33.CrossRefPubMedCentralPubMedGoogle Scholar