Sydney D Pettygrove

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• Shaw, K. A., Williams, S., Hughes, M. M., Warren, Z., Bakian, A. V., Durkin, M. S., Esler, A., Hall-Lande, J., Salinas, A., Vehorn, A., Andrews, J. G., Baroud, T., Bilder, D. A., Dimian, A., Galindo, M., Hudson, A., Hallas, L., Lopez, M., Pokoski, O., , Pettygrove, S., et al. (2023). Statewide county-level autism spectrum disorder prevalence estimates - seven U.S. states, 2018. Annals of epidemiology.
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  Autism spectrum disorder (ASD) prevalence information is necessary for identifying community needs such as addressing disparities in identification and services.
• Okusanya, B., Nweke, C., Gerald, L. B., Pettygrove, S., Taren, D., & Ehiri, J. (2022). Are prevention of mother-to-child HIV transmission service providers acquainted with national guideline recommendations? A cross-sectional study of primary health care centers in Lagos, Nigeria. BMC health services research, 22(1), 769.
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  Implementation of interventions for the prevention of mother-to-child transmission (PMTCT) of HIV in low- and middle-income countries, faces several barriers including health systems challenges such as health providers' knowledge and use of recommended guidelines. This study assessed PMTCT providers' knowledge of national PMTCT guideline recommendations in Lagos, Nigeria.
• Taren, D., Pettygrove, S., Okusanya, B., Mantina, N., Kimaru, L. J., Gerald, L. B., & Ehiri, J. (2022). Interventions to increase early infant diagnosis of HIV infection: A systematic review and meta-analysis.. PloS one, 17(2), e0258863. doi:10.1371/journal.pone.0258863
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  Early infant diagnosis (EID) of HIV infection increases antiretroviral therapy initiation, which reduces pediatric HIV-related morbidity and mortality. This review aims to critically appraise the effects of interventions to increase uptake of early infant diagnosis..This is a systematic review and meta-analysis of interventions to increase the EID of HIV infection. We searched PubMed, EMBASE, CINAHL, and PsycINFO to identify eligible studies from inception of these databases to June 18, 2020. EID Uptake at 4-8 weeks of age was primary outcome assessed by the review. We conducted meta-analysis, using data from reports of included studies. The measure of the effect of dichotomous data was odds ratios (OR), with a 95% confidence interval. The grading of recommendations assessment, development, and evaluation (GRADE) approach was used to assess quality of evidence..The review was not limited by time of publication or setting in which the studies conducted..HIV-exposed infants were participants..Database search and review of reference lists yielded 923 unique titles, out of which 16 studies involving 13,822 HIV exposed infants (HEI) were eligible for inclusion in the review. Included studies were published between 2014 and 2019 from Kenya, Nigeria, Uganda, South Africa, Zambia, and India. Of the 16 included studies, nine (experimental) and seven (observational) studies included had low to moderate risk of bias. The studies evaluated eHealth services (n = 6), service improvement (n = 4), service integration (n = 2), behavioral interventions (n = 3), and male partner involvement (n = 1). Overall, there was no evidence that any of the evaluated interventions, including eHealth, health systems improvements, integration of EID, conditional cash transfer, mother-to-mother support, or partner (male) involvement, was effective in increasing uptake of EID at 4-8 weeks of age. There was also no evidence that any intervention was effective in increasing HIV-infected infants' identification at 4-8 weeks of age..There is limited evidence to support the hypothesis that interventions implemented to increase uptake of EID were effective at 4-8 weeks of life. Further research is required to identify effective interventions that increase early infant diagnosis of HIV at 4-8 weeks of age..(CRD42020191738).
• Maenner, M. J., Shaw, K. A., Bakian, A. V., Bilder, D. A., Durkin, M. S., Esler, A., Furnier, S. M., Hallas, L., Hall-Lande, J., Hudson, A., & others, . (2021). Prevalence and characteristics of autism spectrum disorder among children aged 8 years?autism and developmental disabilities monitoring network, 11 sites, United States, 2018. MMWR Surveillance Summaries, 70(11), 1.
• Maenner, M. J., Shaw, K. A., Bakian, A. V., Bilder, D. A., Durkin, M. S., Esler, A., Furnier, S. M., Hallas, L., Hall-Lande, J., Hudson, A., Hughes, M. M., Patrick, M., Pierce, K., Poynter, J. N., Salinas, A., Shenouda, J., Vehorn, A., Warren, Z., Constantino, J. N., , DiRienzo, M., et al. (2021). Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2018. Morbidity and mortality weekly report. Surveillance summaries (Washington, D.C. : 2002), 70(11), 1-16.
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  Autism spectrum disorder (ASD).
• Shaw, K. A., Maenner, M. J., Bakian, A. V., Bilder, D. A., Durkin, M. S., Furnier, S. M., Hughes, M. M., Patrick, M., Pierce, K., Salinas, A., & others, . (2021). Early identification of autism spectrum disorder among children aged 4 years?Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018. MMWR Surveillance Summaries, 70(10), 1.
• Shaw, K. A., Maenner, M. J., Bakian, A. V., Bilder, D. A., Durkin, M. S., Furnier, S. M., Hughes, M. M., Patrick, M., Pierce, K., Salinas, A., Shenouda, J., Vehorn, A., Warren, Z., Zahorodny, W., Constantino, J. N., DiRienzo, M., Esler, A., Fitzgerald, R. T., Grzybowski, A., , Hudson, A., et al. (2021). Early Identification of Autism Spectrum Disorder Among Children Aged 4 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2018. Morbidity and mortality weekly report. Surveillance summaries (Washington, D.C. : 2002), 70(10), 1-14.
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  Autism spectrum disorder (ASD).
• Shaw, K. A., McArthur, D., Hughes, M. M., Bakian, A. V., Lee, L. C., Pettygrove, S., & Maenner, M. J. (2021). Progress and Disparities in Early Identification of Autism Spectrum Disorder: Autism and Developmental Disabilities Monitoring Network, 2002-2016. Journal of the American Academy of Child and Adolescent Psychiatry.
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  Early identification can improve outcomes for children with autism spectrum disorder (ASD). We sought to assess changes in early ASD identification over time and by co-occurring intellectual disability (ID) and race/ethnicity.
• Pettygrove, S., Zahorodny, W., Wiggins, L. D., White, T., Washington, A., Shenouda, J., Shaw, K. A., Salinas, A., Rosenberg, C. R., Pettygrove, S., Maenner, M. J., Fitzgerald, R. T., Durkin, M. S., Dietz, P. M., Daniels, J. L., Constantino, J. N., Christensen, D. L., Baio, J., & Andrews, J. G. (2020). Early Identification of Autism Spectrum Disorder Among Children Aged 4 Years - Early Autism and Developmental Disabilities Monitoring Network, Six Sites, United States, 2016.. Morbidity and mortality weekly report. Surveillance summaries (Washington, D.C. : 2002), 69(3), 1-11. doi:10.15585/mmwr.ss6903a1
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  Autism spectrum disorder (ASD)..2016..The Early Autism and Developmental Disabilities Monitoring (Early ADDM) Network, a subset of the overall ADDM Network, is an active surveillance program that estimates ASD prevalence and monitors early identification of ASD among children aged 4 years. Children included in surveillance year 2016 were born in 2012 and had a parent or guardian who lived in the surveillance area in Arizona, Colorado, Missouri, New Jersey, North Carolina, or Wisconsin, at any time during 2016. Children were identified from records of community sources including general pediatric health clinics, special education programs, and early intervention programs. Data from comprehensive evaluations performed by community professionals were abstracted and reviewed by trained clinicians using a standardized ASD surveillance case definition with criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5)..In 2016, the overall ASD prevalence was 15.6 per 1,000 (one in 64) children aged 4 years for Early ADDM Network sites. Prevalence varied from 8.8 per 1,000 in Missouri to 25.3 per 1,000 in New Jersey. At every site, prevalence was higher among boys than among girls, with an overall male-to-female prevalence ratio of 3.5 (95% confidence interval [CI] = 3.1-4.1). Prevalence of ASD between non-Hispanic white (white) and non-Hispanic black (black) children was similar at each site (overall prevalence ratio: 0.9; 95% CI = 0.8-1.1). The prevalence of ASD using DSM-5 criteria was lower than the prevalence using Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) criteria at one of four sites that used criteria from both editions. Among sites where ≥60% of children aged 4 years had information about intellectual disability (intelligence quotient ≤70 or examiner's statement of intellectual disability documented in an evaluation), 53% of children with ASD had co-occurring intellectual disability. Of all children aged 4 years with ASD, 84% had a first evaluation at age ≤36 months and 71% of children who met the surveillance case definition had a previous ASD diagnosis from a community provider. Median age at first evaluation and diagnosis for this age group was 26 months and 33 months, respectively. Cumulative incidence of autism diagnoses received by age 48 months was higher for children aged 4 years than for those aged 8 years identified in Early ADDM Network surveillance areas in 2016..In 2016, the overall prevalence of ASD in the Early ADDM Network using DSM-5 criteria (15.6 per 1,000 children aged 4 years) was higher than the 2014 estimate using DSM-5 criteria (14.1 per 1,000). Children born in 2012 had a higher cumulative incidence of ASD diagnoses by age 48 months compared with children born in 2008, which indicates more early identification of ASD in the younger group. The disparity in ASD prevalence has decreased between white and black children. Prevalence of co-occurring intellectual disability was higher than in 2014, suggesting children with intellectual disability continue to be identified at younger ages. More children received evaluations by age 36 months in 2016 than in 2014, which is consistent with Healthy People 2020 goals. Median age at earliest ASD diagnosis has not changed considerably since 2014..More children aged 4 years with ASD are being evaluated by age 36 months and diagnosed by age 48 months, but there is still room for improvement in early identification. Timely evaluation of children by community providers as soon as developmental concerns have been identified might result in earlier ASD diagnoses, earlier receipt of evidence-based interventions, and improved developmental outcomes.
• Pettygrove, S., Zahorodny, W., Wiggins, L. D., White, T., Washington, A., Warren, Z., Vehorn, A., Shenouda, J., Shaw, K. A., Schwenk, Y., Salinas, A., Rosenberg, C. R., Poynter, J. N., Pettygrove, S., Patrick, M. E., Maenner, M. J., Lopez, M., Lee, L. C., Huston, M., , Hudson, A., et al. (2020). Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2016.. Morbidity and mortality weekly report. Surveillance summaries (Washington, D.C. : 2002), 69(4), 1-12. doi:10.15585/mmwr.ss6904a1
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  Autism spectrum disorder (ASD)..2016..The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance program that provides estimates of the prevalence of ASD among children aged 8 years whose parents or guardians live in 11 ADDM Network sites in the United States (Arizona, Arkansas, Colorado, Georgia, Maryland, Minnesota, Missouri, New Jersey, North Carolina, Tennessee, and Wisconsin). Surveillance is conducted in two phases. The first phase involves review and abstraction of comprehensive evaluations that were completed by medical and educational service providers in the community. In the second phase, experienced clinicians who systematically review all abstracted information determine ASD case status. The case definition is based on ASD criteria described in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition..For 2016, across all 11 sites, ASD prevalence was 18.5 per 1,000 (one in 54) children aged 8 years, and ASD was 4.3 times as prevalent among boys as among girls. ASD prevalence varied by site, ranging from 13.1 (Colorado) to 31.4 (New Jersey). Prevalence estimates were approximately identical for non-Hispanic white (white), non-Hispanic black (black), and Asian/Pacific Islander children (18.5, 18.3, and 17.9, respectively) but lower for Hispanic children (15.4). Among children with ASD for whom data on intellectual or cognitive functioning were available, 33% were classified as having intellectual disability (intelligence quotient [IQ] ≤70); this percentage was higher among girls than boys (39% versus 32%) and among black and Hispanic than white children (47%, 36%, and 27%, respectively) [corrected]. Black children with ASD were less likely to have a first evaluation by age 36 months than were white children with ASD (40% versus 45%). The overall median age at earliest known ASD diagnosis (51 months) was similar by sex and racial and ethnic groups; however, black children with IQ ≤70 had a later median age at ASD diagnosis than white children with IQ ≤70 (48 months versus 42 months)..The prevalence of ASD varied considerably across sites and was higher than previous estimates since 2014. Although no overall difference in ASD prevalence between black and white children aged 8 years was observed, the disparities for black children persisted in early evaluation and diagnosis of ASD. Hispanic children also continue to be identified as having ASD less frequently than white or black children..These findings highlight the variability in the evaluation and detection of ASD across communities and between sociodemographic groups. Continued efforts are needed for early and equitable identification of ASD and timely enrollment in services.
• Catherine Rice, ., Joyce Nicholas, ., Jon Baio, ., Sydney Pettygrove, ., Li-Ching Lee, ., Kim Van Naarden Braun, ., Nancy Doernberg, ., Chris Cunniff, ., Craig Newschaffer, ., F. John Meaney, ., Jane Charles, ., Anita Washington, ., Lydia King, ., Maria Kolotos, ., Kristen Mancilla, ., Cynthia A. Mervis, ., Laura Carpenter, ., & Marshalyn Yeargin-Allsopp, . (2010). Changes in autism spectrum disorder prevalence in 4 areas of the United States. Disability and Health Journal, 3(3), 186-201.
• Christensen, D. L., Maenner, M. J., Bilder, D., Constantino, J. N., Daniels, J., Durkin, M. S., Fitzgerald, R. T., Kurzius-Spencer, M., Pettygrove, S. D., Robinson, C., Shenouda, J., White, T., Zahorodny, W., Pazol, K., & Dietz, P. (2019). Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 4 Years - Early Autism and Developmental Disabilities Monitoring Network, Seven Sites, United States, 2010, 2012, and 2014. Morbidity and mortality weekly report. Surveillance summaries (Washington, D.C. : 2002), 68(2), 1-19.
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  Autism spectrum disorder (ASD) is estimated to affect up to 3% of children in the United States. Public health surveillance for ASD among children aged 4 years provides information about trends in prevalence, characteristics of children with ASD, and progress made toward decreasing the age of identification of ASD so that evidence-based interventions can begin as early as possible.
• George L. Carlo, ., Kelly G. Sund, ., Nora L. Lee, ., Maureen R. Jablinske, ., & Sydney D. Pettygrove, . (1992). The health scientist survey: Identifying consensus on assessing human health risk. Environment International, 18(4), 331-339.
• Kim Van Naarden Braun, ., Sydney Pettygrove, ., Julie Daniels, ., Lisa Miller, ., Joyce Nicholas, ., Jon Baio, ., Laura Schieve, ., Russell S Kirby, ., Anita Washington, ., Sally Brocksen, ., Hossein Rahbar, ., & Catherine Rice, . (2007). Evaluation of a methodology for a collaborative multiple source surveillance network for autism spectrum disorders--Autism and Developmental Disabilities Monitoring Network, 14 sites, United States, 2002.. MMWR. Surveillance summaries : Morbidity and mortality weekly report. Surveillance summaries / CDC, 56(1), 29-40.
• Laura A. Schieve, ., Catherine Rice, ., Owen Devine, ., Matthew J. Maenner, ., Li-Ching Lee, ., Robert Fitzgerald, ., Martha S. Wingate, ., Diana Schendel, ., Sydney Pettygrove, ., Kim van Naarden Braun, ., & Maureen Durkin, . (2011). Have Secular Changes in Perinatal Risk Factors Contributed to the Recent Autism Prevalence Increase? Development and Application of a Mathematical Assessment Model. Annals of Epidemiology, 21(12), 930-945.
• Pettygrove, S. D., Kurzius-Spencer, M., Christensen, D. L., Maenner, M. J., Bilder, D., Constantino, J. N., Daniels, J., Durkin, M. S., Fitzgerald, R. T., Robinson, C., Shenouda, J., White, T., Zahorodny, W., Pazol, K., & Dietz, P. (2019). Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 4 Years — Early Autism and Developmental Disabilities Monitoring Network, Seven Sites, United States, 2010, 2012, and 2014. MMWR. Surveillance Summaries, 68(2), 1-19. doi:10.15585/mmwr.ss6802a1
• Ralph Renger, ., Adriana Cimetta, ., Sydney Pettygrove, ., & Seumas Rogan, . (2002). Geographic information systems (GIS) as an evaluation tool. American Journal of Evaluation, 23(4), 469-479.
• Andrews, J. G., Galindo, M. K., Meaney, F. J., Benavides, A., Mayate, L., Fox, D., Pettygrove, S., O'Leary, L., & Cunniff, C. (2018). Recognition of clinical characteristics for population-based surveillance of fetal alcohol syndrome. BIRTH DEFECTS RESEARCH, 110(10), 851-862.
• Baio, J., Wiggins, L., Christensen, D. L., Maenner, M. J., Daniels, J., Warren, Z., Kurzius-Spencer, M., Zahorodny, W., Robinson Rosenberg, C., White, T., Durkin, M. S., Imm, P., Nikolaou, L., Yeargin-Allsopp, M., Lee, L. C., Harrington, R., Lopez, M., Fitzgerald, R. T., Hewitt, A., , Pettygrove, S., et al. (2018). Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2014. Morbidity and mortality weekly report. Surveillance summaries (Washington, D.C. : 2002), 67(6), 1-23.
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  Autism spectrum disorder (ASD).
• Baio, J., Wiggins, L., Christensen, D. L., Maenner, M. J., Daniels, J., Warren, Z., Kurzius-Spencer, M., Zahorodny, W., Robinson, R. C., White, T., Durkin, M. S., Imm, P., Nikolaou, L., Yeargin-Allsopp, M., Lee, L. C., Harrington, R., Lopez, M., Fitzgerald, R. T., Hewitt, A., , Pettygrove, S., et al. (2018). Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2014 (vol 67, pg 1, 2018). MMWR-MORBIDITY AND MORTALITY WEEKLY REPORT, 67(19), 564-564.
• Gu, Y., Leroy, G., Pettygrove, S., Galindo, M. K., & Kurzius-Spencer, M. (2018). Optimizing Corpus Creation for Training Word Embedding in Low Resource Domains: A Case Study in Autism Spectrum Disorder (ASD). AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018, 508-517.
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  Automating the extraction of behavioral criteria indicative of Autism Spectrum Disorder (ASD) in electronic health records (EHRs) can contribute significantly to the effort to monitor the condition. Word embedding algorithms such as Word2Vec can encode semantic meanings of words in vectors and assist in automated vocabulary discovery from EHRs. However, text available for training word embeddings for ASD is miniscule compared to the billions of tokens typically used. We evaluate the importance of corpus specificity versus size and hypothesize that for specific domains small corpora can generate excellent word embeddings. We custom-built 6 ASD-themed corpora (N=4482), using ASD EHRs and abstracts from PubMed (N=39K) and PsychInfo (N=69K) and evaluated them. We were able to generate the most useful 200-dimension embeddings based on the small ASD EHR data. Due to diversity in its vocabulary, the abstract-based embeddings generated fewer related terms and saw minimal improvement when the size of the corpus increased.
• Kurzius-Spencer, M., Pettygrove, S., Christensen, D., Pedersen, A. L., Cunniff, C., Meaney, F. J., Soke, G. N., Harrington, R. A., Durkin, M., & Rice, S. (2018). Behavioral problems in children with autism spectrum disorder with and without co-occurring intellectual disability. RESEARCH IN AUTISM SPECTRUM DISORDERS, 56, 61-71.
• Kurzius-Spencer,, M., Aurora, A., Kelly Galindo, M., Pettygrove, S. D., Gu, Y., & Leroy, G. A. (2018). Automated Extraction of Diagnostic Criteria from Electronic Health Recordes for Autism Spectrum Disorders: Development, Evaluation and Case Study. Journal of Medical Internet Research (JMIR).
• Leroy, G., Gu, Y., Pettygrove, S., Galindo, M. K., Arora, A., & Kurzius-Spencer, M. (2018). Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application. JOURNAL OF MEDICAL INTERNET RESEARCH, 20(11).
• Leroy, G., Gu, Y., Pettygrove, S., Galindo, M. K., Arora, A., & Kurzius-Spencer, M. (2018). Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application. Journal of medical Internet research, 20(11), e10497.
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  Electronic health records (EHRs) bring many opportunities for information utilization. One such use is the surveillance conducted by the Centers for Disease Control and Prevention to track cases of autism spectrum disorder (ASD). This process currently comprises manual collection and review of EHRs of 4- and 8-year old children in 11 US states for the presence of ASD criteria. The work is time-consuming and expensive.
• Parikh, C., Kurzius-Spencer, M., Mastergeorge, A. M., & Pettygrove, S. (2018). Characterizing Health Disparities in the Age of Autism Diagnosis in a Study of 8-Year-Old Children. Journal of autism and developmental disorders, 48(7), 2396-2407.
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  The diagnosis of autism spectrum disorder (ASD) is often delayed from the time of noted concerns to the actual diagnosis. The current study used child- and family-level factors to identify homogeneous classes in a surveillance-based sample (n = 2303) of 8-year-old children with ASD. Using latent class analysis, a 5-class model emerged and the class memberships were examined in relation to the child's median age at ASD diagnosis. Class 3, with known language delays and a high advantage socioeconomically had the lowest age of ASD diagnosis (46.74 months) in comparison to Classes 1 (64.99 months), 4 (58.14 months), and 5 (69.78 months) in this sample. Findings demonstrate sociodemographic and developmental disparities related to the age at ASD diagnosis.
• Pettygrove, S., Meaney, F. J., Andrews, J. G., Galindo, M. K., Benavides, A., Mayate, L., Fox, D., O'Leary, L., & Cunniff, C. (2018). Recognition of clinical characteristics for population-based surveillance of fetal alcohol syndrome. Birth Defects Research, 110(10), 851-862. doi:10.1002/bdr2.1203
• Pettygrove, S., Zahorodny, W., Yeargin-allsopp, M., Wingate, M. S., Wells, C., Schulz, E., Robinson, C., Lee, L., Kurzius-spencer, M., Fitzgerald, R. T., Durkin, M. S., Daniels, J., Constantino, J. N., Christensen, D. L., Charles, J., Braun, K. V., Bilder, D., & Baio, J. (2018). Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012.. Morbidity and mortality weekly report. Surveillance summaries (Washington, D.C. : 2002), 65(13), 1-23. doi:10.15585/mmwr.ss6513a1
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  Autism spectrum disorder (ASD)..2012..The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance system that provides estimates of the prevalence and characteristics of ASD among children aged 8 years whose parents or guardians reside in 11 ADDM Network sites in the United States (Arkansas, Arizona, Colorado, Georgia, Maryland, Missouri, New Jersey, North Carolina, South Carolina, Utah, and Wisconsin). Surveillance to determine ASD case status is conducted in two phases. The first phase consists of screening and abstracting comprehensive evaluations performed by professional service providers in the community. Data sources identified for record review are categorized as either 1) education source type, including developmental evaluations to determine eligibility for special education services or 2) health care source type, including diagnostic and developmental evaluations. The second phase involves the review of all abstracted evaluations by trained clinicians to determine ASD surveillance case status. A child meets the surveillance case definition for ASD if one or more comprehensive evaluations of that child completed by a qualified professional describes behaviors that are consistent with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision diagnostic criteria for any of the following conditions: autistic disorder, pervasive developmental disorder-not otherwise specified (including atypical autism), or Asperger disorder. This report provides ASD prevalence estimates for children aged 8 years living in catchment areas of the ADDM Network sites in 2012, overall and stratified by sex, race/ethnicity, and the type of source records (education and health records versus health records only). In addition, this report describes the proportion of children with ASD with a score consistent with intellectual disability on a standardized intellectual ability test, the age at which the earliest known comprehensive evaluation was performed, the proportion of children with a previous ASD diagnosis, the specific type of ASD diagnosis, and any special education eligibility classification..For 2012, the combined estimated prevalence of ASD among the 11 ADDM Network sites was 14.5 per 1,000 (one in 69) children aged 8 years. Estimated prevalence was significantly higher among boys aged 8 years (23.4 per 1,000) than among girls aged 8 years (5.2 per 1,000). Estimated ASD prevalence was significantly higher among non-Hispanic white children aged 8 years (15.3 per 1,000) compared with non-Hispanic black children (13.1 per 1,000), and Hispanic (10.2 per 1,000) children aged 8 years. Estimated prevalence varied widely among the 11 ADDM Network sites, ranging from 8.2 per 1,000 children aged 8 years (in the area of the Maryland site where only health care records were reviewed) to 24.6 per 1,000 children aged 8 years (in New Jersey, where both education and health care records were reviewed). Estimated prevalence was higher in surveillance sites where education records and health records were reviewed compared with sites where health records only were reviewed (17.1 per 1,000 and 10.4 per 1,000 children aged 8 years, respectively; p
• Dickerson, A. S., Rahbar, M. H., Pearson, D. A., Kirby, R. S., Bakian, A. V., Bilder, D. A., Harrington, R. A., Pettygrove, S., Zahorodny, W. M., Moye, L. A., Wingate, M. S., & Durkin, M. S. (2017). Autism spectrum disorder reporting in lower socioeconomic neighborhoods.. Autism : the international journal of research and practice, 21(4), 470-480. doi:10.1177/1362361316650091
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  Utilizing surveillance data from five sites participating in the Autism and Developmental Disabilities Monitoring Network, we investigated contributions of surveillance subject and census tract population sociodemographic characteristics on variation in autism spectrum disorder ascertainment and prevalence estimates from 2000 to 2008 using ordinal hierarchical models for 2489 tracts. Multivariable analyses showed a significant increase in ascertainment of autism spectrum disorder cases through both school and health sources, the optimal ascertainment scenario, for cases with college-educated mothers (adjusted odds ratio = 1.06, 95% confidence interval = 1.02-1.09). Results from our examination of sociodemographic factors of tract populations from which cases were drawn also showed that after controlling for other covariates, statistical significance remained for associations between optimal ascertainment and percentage of Hispanic residents (adjusted odds ratio = 0.93, 95% confidence interval = 0.88-0.99) and percentage of residents with at least a bachelor's degree (adjusted odds ratio = 1.06, 95% confidence interval = 1.01-1.11). We identified sociodemographic factors associated with autism spectrum disorder prevalence estimates including race, ethnicity, education, and income. Determining which specific factors influence disparities is complicated; however, it appears that even in the presence of education, racial and ethnic disparities are still apparent. These results suggest disparities in access to autism spectrum disorder assessments and special education for autism spectrum disorder among ethnic groups may impact subsequent surveillance.
• Gunn, J. K., Pettygrove, S., Pettygrove, S., Osuji, A., Ogidi, A. G., Obiefune, M. C., Musei, N., Jacobs, E. T., Gunn, J. K., Ezeanolue, E. E., Ezeanolue, C. O., Ernst, K. C., & Ehiri, J. E. (2017). Prevalence of Caesarean sections in Enugu, southeast Nigeria: Analysis of data from the Healthy Beginning Initiative.. PloS one, 12(3), e0174369. doi:10.1371/journal.pone.0174369
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  In order to meet the Sustainable Development Goal to decrease maternal mortality, increased access to obstetric interventions such as Caesarean sections (CS) is of critical importance. As a result of women's limited access to routine and emergency obstetric services in Nigeria, the country is a major contributor to the global burden of maternal mortality. In this analysis, we aim to establish rates of CS and determine socioeconomic or medical risk factors associated with having a CS in Enugu, southeast Nigeria..Data for this study originated from the Healthy Beginning Initiative study. Participant characteristics were obtained from 2300 women at baseline via a semi-structured questionnaire. Only women between the ages of 17-45 who had singleton deliveries were retained for this analysis. Post-delivery questionnaires were used to ascertain mode-of-delivery. Crude and adjusted logistic regressions with Caesarean as the main outcome are presented..In this sample, 7.22% women had a CS. Compared to women who lived in an urban setting, those who lived in a rural setting had a significant reduction in the odds of having a CS (aOR: 0.58; 0.38-0.89). Significantly higher odds of having a CS were seen among those with high peripheral malaria parasitemia compared to those with low parasitemia (aOR: 1.54; 1.04-2.28)..This study revealed that contrary to the increasing trend in use of CS in low-income countries, women in this region of Nigeria had limited access to this intervention. Increasing age and socioeconomic proxies for income and access to care (e.g., having a tertiary-level education, full-time employment, and urban residence) were shown to be key determinants of access to CS. Further research is needed to ascertain the obstetric conditions under which women in this region receive CS, and to further elucidate the role of socioeconomic factors in accessing CS.
• Kurzius-Spencer, M., Pettygrove, S. D., Gu, Y., & Leroy, G. A. (2017). Automated Pattern Extraction for Recognizing DSM Diagnostic Criteria for Autism Spectrum Disorder in Mental Health EHR. In: Frasincar F., Ittoo A., Nguyen L., Métais E. (eds). Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science., 10260.
• Pettygrove, S., Meaney, F. J., Kurzius-spencer, M., Cunniff, C., Pettygrove, S., Pedersen, A. L., Meaney, F. J., Lu, Z., Lee, L. C., Kurzius-spencer, M., Durkin, M. S., Cunniff, C., & Andrews, J. (2017). DSM Criteria that Best Differentiate Intellectual Disability from Autism Spectrum Disorder.. Child psychiatry and human development, 48(4), 537-545. doi:10.1007/s10578-016-0681-0
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  Clinical characteristics of autism spectrum disorder (ASD) and intellectual disability (ID) overlap, creating potential for diagnostic confusion. Diagnostic and statistical manual of mental disorders (DSM) criteria that best differentiate children with ID and some ASD features from those with comorbid ID and ASD were identified. Records-based surveillance of ASD among 8-year-old children across 14 US populations ascertained 2816 children with ID, with or without ASD. Area under the curve (AUC) was conducted to determine discriminatory power of DSM criteria. AUC analyses indicated that restricted interests or repetitive behaviors best differentiated between the two groups. A subset of 6 criteria focused on social interactions and stereotyped behaviors was most effective at differentiating the two groups (AUC of 0.923), while communication-related criteria were least discriminatory. Matching children with appropriate treatments requires differentiation between ID and ASD. Shifting to DSM-5 may improve differentiation with decreased emphasis on language-related behaviors.
• Christensen, D. L., Baio, J., Van Naarden Braun, K., Bilder, D., Charles, J., Constantino, J. N., Daniels, J., Durkin, M. S., Fitzgerald, R. T., Kurzius-Spencer, M., Lee, L. C., Pettygrove, S., Robinson, C., Schulz, E., Wells, C., Wingate, M. S., Zahorodny, W., Yeargin-Allsopp, M., & , C. f. (2016). Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years--Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012. Morbidity and mortality weekly report. Surveillance summaries (Washington, D.C. : 2002), 65(3), 1-23.
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  Autism spectrum disorder (ASD).
• Christensen, D. L., Bilder, D. A., Zahorodny, W., Pettygrove, S., Durkin, M. S., Fitzgerald, R. T., Rice, C., Kurzius-Spencer, M., Baio, J., & Yeargin-Allsopp, M. (2016). Prevalence and Characteristics of Autism Spectrum Disorder Among 4-Year-Old Children in the Autism and Developmental Disabilities Monitoring Network. Journal of developmental and behavioral pediatrics : JDBP, 37(1), 1-8.
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  Early identification of children with autism spectrum disorder (ASD) facilitates timely access to intervention services. Yet, few population-based data exist on ASD identification among preschool-aged children. The authors aimed to describe ASD prevalence and characteristics among 4-year-old children in 5 of 11 sites participating in the 2010 Autism and Developmental Disabilities Monitoring Network.
• Dickerson, A. S., Rahbar, M. H., Bakian, A. V., Bilder, D. A., Harrington, R. A., Pettygrove, S., Kirby, R. S., Durkin, M. S., Han, I., Moyé, L. A., Pearson, D. A., Wingate, M. S., & Zahorodny, W. M. (2016). Autism spectrum disorder prevalence and associations with air concentrations of lead, mercury, and arsenic. Environmental monitoring and assessment, 188(7), 407.
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  Lead, mercury, and arsenic are neurotoxicants with known effects on neurodevelopment. Autism spectrum disorder (ASD) is a neurodevelopmental disorder apparent by early childhood. Using data on 4486 children with ASD residing in 2489 census tracts in five sites of the Centers for Disease Control and Prevention's Autism and Developmental Disabilities Monitoring (ADDM) Network, we used multi-level negative binomial models to investigate if ambient lead, mercury, and arsenic concentrations, as measured by the US Environmental Protection Agency National-Scale Air Toxics Assessment (EPA-NATA), were associated with ASD prevalence. In unadjusted analyses, ambient metal concentrations were negatively associated with ASD prevalence. After adjusting for confounding factors, tracts with air concentrations of lead in the highest quartile had significantly higher ASD prevalence than tracts with lead concentrations in the lowest quartile (prevalence ratio (PR) = 1.36; 95 '% CI: 1.18, 1.57). In addition, tracts with mercury concentrations above the 75th percentile (>1.7 ng/m(3)) and arsenic concentrations below the 75th percentile (≤0.13 ng/m(3)) had a significantly higher ASD prevalence (adjusted RR = 1.20; 95 % CI: 1.03, 1.40) compared to tracts with arsenic, lead, and mercury concentrations below the 75th percentile. Our results suggest a possible association between ambient lead concentrations and ASD prevalence and demonstrate that exposure to multiple metals may have synergistic effects on ASD prevalence.
• Dickerson, A. S., Rahbar, M. H., Pearson, D. A., Kirby, R. S., Bakian, A. V., Bilder, D. A., Harrington, R. A., Pettygrove, S., Zahorodny, W. M., Moyé, L. A., Durkin, M., & Slay Wingate, M. (2016). Autism spectrum disorder reporting in lower socioeconomic neighborhoods. Autism : the international journal of research and practice.
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  Utilizing surveillance data from five sites participating in the Autism and Developmental Disabilities Monitoring Network, we investigated contributions of surveillance subject and census tract population sociodemographic characteristics on variation in autism spectrum disorder ascertainment and prevalence estimates from 2000 to 2008 using ordinal hierarchical models for 2489 tracts. Multivariable analyses showed a significant increase in ascertainment of autism spectrum disorder cases through both school and health sources, the optimal ascertainment scenario, for cases with college-educated mothers (adjusted odds ratio = 1.06, 95% confidence interval = 1.02-1.09). Results from our examination of sociodemographic factors of tract populations from which cases were drawn also showed that after controlling for other covariates, statistical significance remained for associations between optimal ascertainment and percentage of Hispanic residents (adjusted odds ratio = 0.93, 95% confidence interval = 0.88-0.99) and percentage of residents with at least a bachelor's degree (adjusted odds ratio = 1.06, 95% confidence interval = 1.01-1.11). We identified sociodemographic factors associated with autism spectrum disorder prevalence estimates including race, ethnicity, education, and income. Determining which specific factors influence disparities is complicated; however, it appears that even in the presence of education, racial and ethnic disparities are still apparent. These results suggest disparities in access to autism spectrum disorder assessments and special education for autism spectrum disorder among ethnic groups may impact subsequent surveillance.
• Kurzius-Spencer, M., Pettygrove, S. D., Rice, S. A., Meaney, F. J., Pedersen, A., Gotschall, K., Durkin, M., Christensen, D., Wu, Y. T., Harrington, R., Soke, G. N., & Cunniff, C. (2016). DSM criteria that best differentiate intellectual disability from autism spectrum disorder. Child Psychiatry & Human Development.
• Pedersen, A. L., Pettygrove, S., Lu, Z., Andrews, J., Meaney, F. J., Kurzius-Spencer, M., Lee, L. C., Durkin, M. S., & Cunniff, C. (2016). DSM Criteria that Best Differentiate Intellectual Disability from Autism Spectrum Disorder. Child psychiatry and human development.
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  Clinical characteristics of autism spectrum disorder (ASD) and intellectual disability (ID) overlap, creating potential for diagnostic confusion. Diagnostic and statistical manual of mental disorders (DSM) criteria that best differentiate children with ID and some ASD features from those with comorbid ID and ASD were identified. Records-based surveillance of ASD among 8-year-old children across 14 US populations ascertained 2816 children with ID, with or without ASD. Area under the curve (AUC) was conducted to determine discriminatory power of DSM criteria. AUC analyses indicated that restricted interests or repetitive behaviors best differentiated between the two groups. A subset of 6 criteria focused on social interactions and stereotyped behaviors was most effective at differentiating the two groups (AUC of 0.923), while communication-related criteria were least discriminatory. Matching children with appropriate treatments requires differentiation between ID and ASD. Shifting to DSM-5 may improve differentiation with decreased emphasis on language-related behaviors.
• Pettygrove, S., Kurzius-Spencer, M., Charles, J., Christensen, D. L., Baio, J., Braun, K. V., Bilder, D., Constantino, J. N., Daniels, J., Durkin, M. S., Fitzgerald, R. T., Lee, L., Robinson, C., Schulz, E., Wells, C., Wingate, M. S., Zahorodny, W., & Yeargin-Allsopp, M. (2016). Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012. MMWR. Surveillance Summaries, 65(3), 1-23. doi:10.15585/mmwr.ss6503a1
• Dickerson, A. S., Rahbar, M. H., Han, I., Bakian, A. V., Bilder, D. A., Harrington, R. A., Pettygrove, S., Durkin, M., Kirby, R. S., Wingate, M. S., Tian, L. H., Zahorodny, W. M., Pearson, D. A., Moyé, L. A., & Baio, J. (2015). Autism spectrum disorder prevalence and proximity to industrial facilities releasing arsenic, lead or mercury. The Science of the total environment, 536, 245-251.
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  Prenatal and perinatal exposures to air pollutants have been shown to adversely affect birth outcomes in offspring and may contribute to prevalence of autism spectrum disorder (ASD). For this ecologic study, we evaluated the association between ASD prevalence, at the census tract level, and proximity of tract centroids to the closest industrial facilities releasing arsenic, lead or mercury during the 1990s. We used 2000 to 2008 surveillance data from five sites of the Autism and Developmental Disabilities Monitoring (ADDM) network and 2000 census data to estimate prevalence. Multi-level negative binomial regression models were used to test associations between ASD prevalence and proximity to industrial facilities in existence from 1991 to 1999 according to the US Environmental Protection Agency Toxics Release Inventory (USEPA-TRI). Data for 2489 census tracts showed that after adjustment for demographic and socio-economic area-based characteristics, ASD prevalence was higher in census tracts located in the closest 10th percentile compared of distance to those in the furthest 50th percentile (adjusted RR=1.27, 95% CI: (1.00, 1.61), P=0.049). The findings observed in this study are suggestive of the association between urban residential proximity to industrial facilities emitting air pollutants and higher ASD prevalence.
• Durkin, M. S., Bilder, D. A., Pettygrove, S., & Zahorodny, W. (2015). The validity and usefulness of public health surveillance of autism spectrum disorder. Autism : the international journal of research and practice, 19(1), 118-9.
• Fox, D. J., Pettygrove, S., Cunniff, C., O'Leary, L. A., Gilboa, S. M., Bertrand, J., Druschel, C. M., Breen, A., Robinson, L., Ortiz, L., Frías, J. L., Ruttenber, M., Klumb, D., Meaney, F. J., & , C. f. (2015). Fetal alcohol syndrome among children aged 7-9 years - Arizona, Colorado, and New York, 2010. MMWR. Morbidity and mortality weekly report, 64(3), 54-7.
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  Fetal alcohol syndrome (FAS) is a serious birth defect and developmental disorder caused by in utero exposure to alcohol. Assessment of the public health burden of FAS through surveillance has proven difficult; there is wide variation in reported prevalence depending on the study population and surveillance method. Generally, records-based birth prevalence studies report estimates of 0.2-1.5 per 1,000 live births, whereas studies that use in-person, expert assessment of school-aged children in a community report estimates of 6-9 per 1,000 population. The Fetal Alcohol Syndrome Surveillance Network II addressed some of the challenges in records-based ascertainment by assessing a period prevalence of FAS among children aged 7‒9 years in Arizona, Colorado, and New York. The prevalence across sites ranged from 0.3 to 0.8 per 1,000 children. Prevalence of FAS was highest among American Indian/Alaska Native children and lowest among Hispanic children. These estimates continue to be much lower than those obtained from studies using in-person, expert assessment. Factors that might contribute to this discrepancy include 1) inadequate recognition of the physical and behavioral characteristics of FAS by clinical care providers; 2) insufficient documentation of those characteristics in the medical record; and 3) failure to consider prenatal alcohol exposure with diagnoses of behavioral and learning problems. Addressing these factors through training of medical and allied health providers can lead to practice changes, ultimately increasing recognition and documentation of the characteristics of FAS.
• Gunn, J. K., Pettygrove, S., Pettygrove, S., Ogidi, A. G., Obiefune, M. C., Kohler, L. N., Jacobs, E. T., Haenchen, S. D., Gunn, J. K., Ezeanolue, E. E., Ezeanolue, C. O., Ernst, K. C., & Ehiri, J. E. (2015). Population-based prevalence of malaria among pregnant women in Enugu State, Nigeria: the Healthy Beginning Initiative.. Malaria journal, 14(1), 438. doi:10.1186/s12936-015-0975-x
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  Malaria adversely affects pregnant women and their fetuses or neonates. Estimates of the malaria burden in pregnant women based on health facilities often do not present a true picture of the problem due to the low proportion of women delivering at these facilities in malaria-endemic regions..Data for this study were obtained from the Healthy Beginning Initiative using community-based sampling. Self-identified pregnant women between the ages of 17-45 years were recruited from churches in Enugu State, Nigeria. Malaria parasitaemia was classified as high and low based on the malaria plus system..Of the 2069 pregnant women for whom malaria parasitaemia levels were recorded, over 99 % tested positive for malaria parasitaemia, 62 % showed low parasitaemia and 38 % high parasitaemia. After controlling for confounding variables, odds for high parasitaemia were lower among those who had more people in the household (for every one person increase in a household, OR = 0.94, 95 % CI 0.89-0.99)..Results of this study are consistent with hospital-based estimates of malaria during pregnancy in southeastern Nigeria. Based on the high prevalence of malaria parasitaemia in this sample, education on best practices to prevent malaria during pregnancy, and resources in support of these practices are urgently needed.
• O'Leary, L. A., Ortiz, L., Montgomery, A., Fox, D. J., Cunniff, C., Ruttenber, M., Breen, A., Pettygrove, S., Klumb, D., Druschel, C., Frías, J. L., Robinson, L. K., Bertrand, J., Ferrara, K., Kelly, M., Gilboa, S. M., Meaney, F. J., & , F. (2015). Methods for surveillance of fetal alcohol syndrome: The Fetal Alcohol Syndrome Surveillance Network II (FASSNetII) - Arizona, Colorado, New York, 2009 - 2014. Birth defects research. Part A, Clinical and molecular teratology, 103(3), 196-202.
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  Surveillance of fetal alcohol syndrome (FAS) is important for monitoring the effects of prenatal alcohol exposure and describing the public health burden of this preventable disorder. Building on the infrastructure of the Fetal Alcohol Syndrome Surveillance Network (FASSNet, 1997-2002), in 2009 the Centers for Disease Control and Prevention awarded 5-year cooperative agreements to three states, Arizona, Colorado, and New York, to conduct population-based surveillance of FAS. The Fetal Alcohol Syndrome Surveillance Network II (FASSNetII, 2009-2014) developed a surveillance case definition based on three clinical criteria: characteristic facial features, central nervous system abnormalities, and growth deficiency. FASSNetII modified the FASSNet methods in three important ways: (1) estimation of a period prevalence rather than birth prevalence; (2) surveillance of FAS among school-age children (ages 7-9 years) to better document the central nervous system abnormalities that are not apparent at birth or during infancy; and (3) implementation of an expert clinical review of abstracted data for probable and confirmed cases classified through a computerized algorithm. FASSNetII abstracted data from multiple sources including birth records, medical records from child development centers or other specialty clinics, and administrative databases such as hospital discharge and Medicaid. One challenge of FASSNetII was its limited access to non-medical records. The FAS prevalence that could be estimated was that of the population identified through an encounter with the healthcare system. Clinical and public health programs that identify children affected by FAS provide critical information for targeting preventive, medical and educational services in this vulnerable population.
• Pettygrove, S., Durkin, M. S., Bilder, D. A., & Zahorodny, W. (2014). The validity and usefulness of public health surveillance of autism spectrum disorder. Autism, 19(1), 118-119. doi:10.1177/1362361314548732
• Pettygrove, S., Meaney, F. J., Cunniff, C., Sheehan, D. W., Price, E. T., Powis, Z., Pettygrove, S., Pandya, S., Ouyang, L., Meaney, F. J., Lu, Z., Fox, D. J., Cunniff, C., Apkon, S. D., & Andrews, J. G. (2014). Sibling concordance for clinical features of Duchenne and Becker muscular dystrophies.. Muscle & nerve, 49(6), 814-21. doi:10.1002/mus.24078
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  The correlation of markers of disease severity among brothers with Duchenne or Becker muscular dystrophy has implications for clinical guidance and clinical trials..Sibling pairs with Duchenne or Becker muscular dystrophy (n = 60) were compared for ages when they reached clinical milestones of disease progression, including ceased ambulation, scoliosis of ≥ 20°, and development of cardiomyopathy..The median age at which younger brothers reached each milestone, compared with their older brothers ranged from 25 months younger for development of cardiomyopathy to 2 months older for ceased ambulation. For each additional month of ambulation by the older brother, the hazard of ceased ambulation by the younger brother decreased by 4%..The ages when siblings reach clinical milestones of disease vary widely between siblings. However, the time to ceased ambulation for older brothers predicts the time to ceased ambulation for their younger brothers.
• Wingate, M., Kirby, R. S., Pettygrove, S., Cunniff, C., Schulz, E., Ghosh, T., Robinson, C., Lee, L., Landa, R., Constantino, J., Fitzgerald, R., Zahorodny, W., Daniels, J., Nicholas, J., Charles, J., McMahon, W., Bilder, D., Durkin, M., Baio, J., , Christensen, D., et al. (2014). Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2010. MMWR SURVEILLANCE SUMMARIES, 63(2).
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  Problem/Condition: Autism spectrum disorder (ASD).
• Pettygrove, S., Lu, Z., Andrews, J. G., Meaney, F. J., Sheehan, D. W., Price, E. T., Fox, D. J., Pandya, S., Ouyang, L., Apkon, S. D., Powis, Z., & Cunniff, C. (2013). Sibling concordance for clinical features of Duchenne and Becker muscular dystrophies. Muscle & nerve.
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  Introduction: The correlation of markers of disease severity among brothers with Duchenne or Becker muscular dystrophy has implications for clinical guidance and clinical trials. Methods: Sibling pairs with Duchenne or Becker muscular dystrophy (n=60) were compared for ages when they reached clinical milestones of disease progression, including ceased ambulation, scoliosis of ≥ 20°, and development of cardiomyopathy. Results: The median age at which younger brothers reached each milestone, compared with their older brothers ranged from 25 months younger for development of cardiomyopathy to 2 months older for ceased ambulation. For each additional month of ambulation by the older brother, the hazard of ceased ambulation by the younger brother decreased by 4%. Conclusions: The ages when siblings reach clinical milestones of disease vary widely between siblings. However, the time to ceased ambulation for older brothers predicts the time to ceased ambulation for their younger brothers. © 2013 Wiley Periodicals, Inc.
• Sydney Pettygrove, ., Judith Pinborough-Zimmerman, ., F. John Meaney, ., Kim Van Naarden Braun, ., Joyce Nicholas, ., Lisa Miller, ., Judith Miller, ., & Catherine Rice, . (2013). Predictors of ascertainment of autism spectrum disorders across nine us communities. Journal of Autism and Developmental Disorders, 43(8), 1867-1879.
• Amy E. Kalkbrenner, ., Joe M. Braun, ., Maureen S. Durkin, ., Matthew J. Maenner, ., Christopher Cunniff, ., Li-Ching Lee, ., Sydney Pettygrove, ., Joyce S. Nicholas, ., & Julie L. Daniels, . (2012). Maternal smoking during pregnancy and the prevalence of autism spectrum disorders, using data from the autism and developmental disabilities monitoring network. Environmental Health Perspectives, 120(7), 1042-1048.
• Anita Pedersen, ., Sydney Pettygrove, ., F. John Meaney, ., Kristen Mancilla, ., Kathy Gotschall, ., Daniel B. Kessler, ., Theresa A. Grebe, ., & Christopher Cunniff, . (2012). Prevalence of autism spectrum disorders in hispanic and non-hispanic white children. Pediatrics, 129(3), e629-e635.
• Pettygrove, S., Kalkbrenner, A. E., Braun, J. M., Durkin, M. S., Maenner, M. J., Cunniff, C., Lee, L., Nicholas, J. S., & Daniels, J. L. (2012). Maternal Smoking during Pregnancy and the Prevalence of Autism Spectrum Disorders, Using Data from the Autism and Developmental Disabilities Monitoring Network. Environmental Health Perspectives, 120(7), 1042-1048. doi:10.1289/ehp.1104556
• Pettygrove, S., Pedersen, A., Meaney, F. J., Mancilla, K., Gotschall, K., Kessler, D. B., Grebe, T. A., & Cunniff, C. (2012). Prevalence of Autism Spectrum Disorders in Hispanic and Non-Hispanic White Children. Pediatrics, 129(3), e629-e635. doi:10.1542/peds.2011-1145
• Williams, B. L., Florez, Y., & Pettygrove, S. (2001). Inter- and intra-ethnic variation in water intake, contact, and source estimates among Tucson residents: Implications for exposure analysis. Journal of exposure analysis and environmental epidemiology, 11(6), 510-21.
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  Water-related exposures among Hispanics, particularly among Mexican Americans, are relatively unknown. Exposure and risk assessment is further complicated by the absence of good time-activity data (e.g., water intake) among this population. This study attempts to provide some insight concerning water-related exposure parameters among Hispanics. Determining the extent to which non-Hispanic whites and Hispanics living in the Tucson metropolitan area differ with respect to direct water intake and source patterns is the primary purpose of this investigation. Using random digit dialing, researchers conducted a cross-sectional telephone population survey of 1183 Tucson residents. Significant ethnic variation was observed in water intake patterns among this sample, particularly in terms of source. Hispanics reported much higher rates of bottled water consumption than did non-Hispanic whites. Ethnic variation in exposure parameters such as that observed in this study increases the potential for measurement error in exposure analysis. Erroneous assumptions that exposure estimates (i.e., water intake source) are generalizable across various ethnic groups may lead to both overestimation and underestimation of contaminant exposure.
• Carlo, G. L., Lee, N. L., Sund, K. G., & Pettygrove, S. D. (1992). The interplay of science, values, and experiences among scientists asked to evaluate the hazards of dioxin, radon, and environmental tobacco smoke. Risk analysis : an official publication of the Society for Risk Analysis, 12(1), 37-43.
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  To investigate the extent to which personal values and experiences among scientists might affect their assessment of risks from dioxin, radon, and environmental tobacco smoke (ETS), we conducted an experiment through a telephone survey of 1461 epidemiologists, toxicologists, physicians, and general scientists. Each participant was read a vignette designed to reflect the mainstream scientific thinking on one of the three substances. For half of the participants (group A) the substance was named. For the other half (group B), the substance was not named but was identified only as Substance X, Y, or Z. Knowing the name of the substance had little effect on the scientists' evaluation of dioxin, although those who knew the substance to be dioxin were more likely to rate the substance as a serious environmental health hazard (51% vs. 42%, p = 0.062). For radon, those who knew the substance by name were significantly more likely to consider it an environmental health hazard than were those who knew it as substance Z (91% vs. 78%, p less than 0.001). Participants who knew they were being asked about ETS rather than substance X were significantly more likely to consider the substance an environmental health hazard (88% vs. 66%, p less than 0.001), to consider the substance a serious environmental health hazard (70% vs. 33%, p less than 0.001), to believe that background exposure required public health intervention (85% vs. 41%, p less than 0.001), and to believe that above-background exposure required public health intervention (90% vs. 74%, p less than 0.001). These findings suggest that values and experiences may be influencing health risk assessments for these substances, and indicate the need for more study of this phenomenon.

Proceedings Publications

• Kurzius-Spencer, M., Kelly Galindo, M., Pettygrove, S. D., Leroy, G. A., & Gu, Y. (2018, November). Optimizing Corpus Creation for Training Word Embedding in Low Resource Domains: A Case Study in Autism Spectrum Disorder (ASD). In AMIA Fall Symposium.
• Leroy, G., Pettygrove, S., Kurzius-spencer, M., Pettygrove, S., Leroy, G., Kurzius-spencer, M., & Gu, Y. (2017). Automated lexicon and feature construction using word embedding and clustering for classification of asd diagnoses using EHR. In International Conference on Applications of Natural Language to Information Systems, 34-37.
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  Using electronic health records of children evaluated for Autism Spectrum Disorders, we are developing a decision support system for automated diagnostic criteria extraction and case classification. We manually created 92 lexicons which we tested as features for classification and compared with features created automatically using word embedding. The expert annotations used for manual lexicon creation provided seed terms that were expanded with the 15 most similar terms (Word2Vec). The resulting 2,200 terms were clustered in 92 clusters parallel to the manually created lexicons. We compared both sets of features to classify case status with a FF\BP neural network (NN) and C5.0 decision tree. For manually created lexicons, classification accuracy was 76.92% for the NN and 84.60% for C5.0. For the automatically created lexicons, accuracy was 79.78% for the NN and 86.81% for C5.0. Automated lexicon creation required a much shorter development time and brought similarly high quality outcomes.

Presentations

• Pettygrove, S. D. (2020, January 15). Autism Spectrum Disorders: Changes over time. WebinarSpectrum Magazine.
• Pettygrove, S. D. (2019, 07-16-2019). Understanding How We Know 1 in 71 Children in Arizona Have ASD. Evidence Based Practices in Disability Disciplines of the Combined IHD 2019 Evidence for Success Disability Conference. Phoenix, AZ: Arizona Assistive Technology Center (AzTAP) and the Institute for Human Development at NAU.
• Pettygrove, S. D. (2019, 08-09-2019). ASD Surveillance; Once and Future Surveillance System. Steward Autism Internal Action Committee Meeting. Phoenix, AZ: AZ Department of Economic Security Steward Autism Internal Action Committee.
• Pettygrove, S. D., Cutshaw, C. A., Pope, B., & Anbar, J. (2018, November). Racial and ethnic disparities in patterns of practice and recognition of Autism Spectrum Disorder. 2018 American Public Health Association Annual Conference. San Diego, CA.

Poster Presentations

• Pettygrove, S. D., Schalewski, L., Glider, P., Barnett, M. A., Haynes, P. L., Cutshaw, C. A., & Anbar, J. (2021, May). Use and Intention to Use Mental Health Services Among College Students with Autism Spectrum Disorder.. International Society for Autism Research annual meeting. virtual.
• Pettygrove, S. D., Cutshaw, C. A., Pope, B., & Anbar, J. (2018, April). Sex disparities in patterns of practice and recognition of Autism from surveillance years 2000 – 2010.. 2018 Public Health Research Poster Forum, University of Arizona, Tucson, AZ,. Tucson, AZ.
• Parikh, C., Andrews, J. G., Rice, S. A., Mastergeorge, A., Pettygrove, S. D., & Kurzius-Spencer, M. (2017, April). Characterizing Health Disparities in the Age of Autism Diagnosis in a Study of 8-Year-Old Children. Society for Research on Child Development Biennial Meeting. Austin, Texas: Society for Research on Child Development.
• Leroy, G. A., Kurzius-Spencer, M., & Pettygrove, S. D. (2014, Novemberr). Using Natural Language Processing for Autism Trigger Extraction. AMIA Annual Symposium. Washington DC.