Early developmental milestone clusters of autistic children based on electronic health records.

Autistic children vary in symptoms, co-morbidities, and response to interventions. This study aimed to identify clusters of autistic children with a distinct pattern of attaining early developmental milestones (EDMs). The clustering of 5836 autistic children was based on the attainment of 43 gross motor, fine motor, language, and social developmental milestones during the first 3 years of life as recorded in baby wellness visits. K-means cluster analysis detected four EDM clusters: mild (n = 1686); moderate (n = 1691); severe (n = 2265); and global (n = 194). The most prominent cluster differences were in the language domain. The global cluster showed earlier and greater developmental delay across domains, unique early gross motor delays, and more were born preterm via cesarean section. The severe cluster had poor language development prominently in the second year of life, and later fine motor delays. Moderate cluster had mainly language delays in the third year of life. The mild cluster mostly passed milestones. EDM clusters differed demographically, with higher socioeconomic status in mild cluster and lowest in global cluster. However, the severe cluster had more immigrant and non-Jewish mothers followed by the moderate cluster. The rates of parental concerns and provider developmental referrals were significantly higher in the global, followed by the severe, moderate, and mild EDM clusters. Autistic children's language and motor delay in the first 3 years can be grouped by common magnitude and onset profiles as distinct groups that may link to specific etiologies (like prematurity or genetics) and specific intervention programs. Early autism screening should be tailored to these different developmental profiles.

Predicting autism traits from baby wellness records: A machine learning approach.

LAY ABSTRACT: Timely identification of autism spectrum conditions is a necessity to enable children to receive the most benefit from early interventions. Emerging technological advancements provide avenues for detecting subtle, early indicators of autism from routinely collected health information. This study tested a model that provides a likelihood score for autism diagnosis from baby wellness visit records collected during the first 2 years of life. It included records of 591,989 non-autistic children and 12,846 children with autism. The model identified two-thirds of the autism spectrum condition group (boys 63% and girls 66%). Sex-specific models had several predictive features in common. These included language development, fine motor skills, and social milestones from visits at 12-24 months, mother's age, and lower initial growth but higher last growth measurements. Parental concerns about development or hearing impairment were other predictors. The models differed in other growth measurements and birth parameters. These models can support the detection of early signs of autism in girls and boys by using information routinely recorded during the first 2 years of life.

A Prediction Model of Autism Spectrum Diagnosis from Well-Baby Electronic Data Using Machine Learning.

Early detection of autism spectrum disorder (ASD) is crucial for timely intervention, yet diagnosis typically occurs after age three. This study aimed to develop a machine learning model to predict ASD diagnosis using infants' electronic health records obtained through a national screening program and evaluate its accuracy. A retrospective cohort study analyzed health records of 780,610 children, including 1163 with ASD diagnoses. Data encompassed birth parameters, growth metrics, developmental milestones, and familial and post-natal variables from routine wellness visits within the first two years. Using a gradient boosting model with 3-fold cross-validation, 100 parameters predicted ASD diagnosis with an average area under the ROC curve of 0.86 (SD < 0.002). Feature importance was quantified using the Shapley Additive explanation tool. The model identified a high-risk group with a 4.3-fold higher ASD incidence (0.006) compared to the cohort (0.001). Key predictors included failing six milestones in language, social, and fine motor domains during the second year, male gender, parental developmental concerns, non-nursing, older maternal age, lower gestational age, and atypical growth percentiles. Machine learning algorithms capitalizing on preventative care electronic health records can facilitate ASD screening considering complex relations between familial and birth factors, post-natal growth, developmental parameters, and parent concern.

The motivation and mental health of sex workers.

Commercial sex work presents specific mental health concerns. We aimed to study motivation for sex work and mental health issues in a sample of such women. We contacted 55 consenting women through organized brothels and interviewed them using the Farley questionnaire and screening items for posttraumatic stress disorder (PTSD) and depression. Eighty-two percent of the women had arrived illegally and had been "trafficked." All but 2 were engaged voluntarily in sex work. Seventeen percent met criteria for PTSD, and 19% were likely to be clinically depressed. We present representative case histories. Availability of mental health treatment for workers in the sex industry could improve compliance with HIV prevention programs and enlarge options for women to leave the sex industry. We observed that stereotypes of sex workers as either always having histories of childhood abuse or as being always "happy hookers" were incorrect.