Dataset Information

Pediatrician identification of Latino children at risk for autism spectrum disorder.

ABSTRACT:

Background and objectives

Latino-white disparities in age at autism spectrum disorder (ASD) diagnosis may be modified by primary care pediatrician (PCP) practices and beliefs. The objectives of this study were to assess ASD and developmental screening practices, attitudes toward ASD identification in Latino children, and barriers to ASD identification for Latino children, in a sample of 267 California PCPs.

Methods

In mail-based PCP survey, we assessed rates of bilingual general developmental and ASD screening, perceptions of parent ASD knowledge in Latino and white families, reports of difficulty assessing for ASDs in Latino and white children, and perceptions of barriers to early ASD identification for Latinos.

Results

Although 81% of PCPs offered some form of developmental screening, 29% of PCPs offered Spanish ASD screening per American Academy of Pediatrics guidelines, and only 10% offered both Spanish general developmental and Spanish ASD screening per American Academy of Pediatrics guidelines. Most PCPs thought that Latino (English and Spanish primary family language) parents were less knowledgeable about ASDs than white parents. PCPs had more difficulty assessing ASD risk for Latino children with Spanish primary family language than for white children, even when the PCP conducted recommended ASD screening or had >25% Latino patients. The most frequent barrier to ASD identification in Latinos was access to developmental specialists.

Conclusions

Multiple factors in the primary care setting may contribute to delayed ASD identification for Latinos. Promoting language-appropriate screening, disseminating culturally appropriate ASD materials to Latino families, improving the specialist workforce, and providing PCP support in screening and referral of Latino children may be important ways to reduce racial and ethnic differences in care.

SUBMITTER: Zuckerman KE

PROVIDER: S-EPMC3876760 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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Publications

Pediatrician identification of Latino children at risk for autism spectrum disorder.

Zuckerman Katharine E KE Mattox Kimber K Donelan Karen K Batbayar Oyundari O Baghaee Anita A Bethell Christina C

Pediatrics 20130819 3

<h4>Background and objectives</h4>Latino-white disparities in age at autism spectrum disorder (ASD) diagnosis may be modified by primary care pediatrician (PCP) practices and beliefs. The objectives of this study were to assess ASD and developmental screening practices, attitudes toward ASD identification in Latino children, and barriers to ASD identification for Latino children, in a sample of 267 California PCPs.<h4>Methods</h4>In mail-based PCP survey, we assessed rates of bilingual general d ...[more]

PMID: 23958770

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Project description:Importance Sleep disorders are one of the most frequent comorbidities in children with autism spectrum disorder (ASD). However, the link between neurodevelopmental effects in ASD children with their underlying sleep microarchitecture is not well understood. An improved understanding of etiology of sleep difficulties and identification of sleep-associated biomarkers for children with ASD can improve the accuracy of clinical diagnosis. Objectives To investigate whether machine learning models can identify biomarkers for children with ASD based on sleep EEG recordings. Design, setting, and participants Sleep polysomnogram data were obtained from the Nationwide Children’ Health (NCH) Sleep DataBank. Children (ages: 8–16 yrs) with 149 autism and 197 age-matched controls without neurodevelopmental diagnosis were selected for analysis. An additional independent age-matched control group (n = 79) selected from the Childhood Adenotonsillectomy Trial (CHAT) was also used to validate the models. Furthermore, an independent smaller NCH cohort of younger infants and toddlers (age: 0.5–3 yr.; 38 autism and 75 controls) was used for additional validation. Main outcomes and measures We computed periodic and non-periodic characteristics from sleep EEG recordings: sleep stages, spectral power, sleep spindle characteristics, and aperiodic signals. Machine learning models including the Logistic Regression (LR) classifier, Support Vector Machine (SVM), and Random Forest (RF) model were trained using these features. We determined the autism class based on the prediction score of the classifier. The area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the model performance. Results In the NCH study, RF outperformed two other models with a 10-fold cross-validated median AUC of 0.95 (interquartile range [IQR], [0.93, 0.98]). The LR and SVM models performed comparably across multiple metrics, with median AUC 0.80 [0.78, 0.85] and 0.83 [0.79, 0.87], respectively. In the CHAT study, three tested models have comparable AUC results: LR: 0.83 [0.76, 0.92], SVM: 0.87 [0.75, 1.00], and RF: 0.85 [0.75, 1.00]. Sleep spindle density, amplitude, spindle-slow oscillation (SSO) coupling, aperiodic signal’s spectral slope and intercept, as well as the percentage of REM sleep were found to be key discriminative features in the predictive models. Conclusion and relevance Our results suggest that integration of EEG feature engineering and machine learning can identify sleep-based biomarkers for ASD children and produce good generalization in independent validation datasets. Microstructural EEG alterations may help reveal underlying pathophysiological mechanisms of autism that alter sleep quality and behaviors. Machine learning analysis may reveal new insight into the etiology and treatment of sleep difficulties in autism.

Dataset Information

Pediatrician identification of Latino children at risk for autism spectrum disorder.

Background and objectives

Methods

Results

Conclusions

Publications

Pediatrician identification of Latino children at risk for autism spectrum disorder.

Similar Datasets

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