The impact of the 2019 coronavirus pandemic (COVID-19) in the United States is unprecedented, with unknown implications for the autism community. We surveyed 3502 parents/caregivers of individuals ...with an autism spectrum disorder (ASD) enrolled in Simons Powering Autism Research for Knowledge (SPARK) and found that most individuals with ASD experienced significant, ongoing disruptions to therapies. While some services were adapted to telehealth format, most participants were not receiving such services at follow-up, and those who were reported minimal benefit. Children under age five had the most severely disrupted services and lowest reported benefit of telehealth adaptation. Caregivers also reported worsening ASD symptoms and moderate family distress. Strategies to support the ASD community should be immediately developed and implemented.
Background. Liver disease is common during human immunodeficiency virus (HIV) infection, but valid serum fibrosis markers are lacking. We hypothesize that HIV monoinfection and HIV/hepatitis C virus ...(HCV) coinfection is associated with an enhanced liver fibrosis (ELF) score higher than that for uninfected controls and examine whether this association is affected by factors other than liver injury. Methods. The association of HIV and HIV/HCV coinfection with the ELF score was evaluated using multivariable regression after controlling for transient elastography–measured liver stiffness and traditional and HIV-related factors in a cross-sectional analysis of 297 women. Results. HIV/HCV-coinfected and HIV-monoinfected women had higher median ELF scores than controls (9.6, 8.5, and 8.2, respectively). After adjustment for demographic, behavioral, and metabolic factors and for inflammatory markers, HIV/HCV co-infection remained associated with a 9% higher ELF score (95% confidence interval CI, 5%–13%), while the association of HIV monoinfection was substantially attenuated (1% higher ELF score; 95% CI, −2% to 4%). After further adjustment for liver stiffness, HIV/HCV coinfection remained associated with 6% higher levels (95% CI, 3%–10%). In HIV/HCV-coinfected and HIV-monoinfected women, higher liver stiffness values were associated with higher ELF scores, as were older age and a nadir CD4+ T-cell count of <200 cells/mm3. Conclusions. Our findings suggest that the ELF score can be used to assess liver fibrosis severity in HIV-infected women. However, higher ELF scores may reflect extrahepatic fibrosis in HIV-infected patients with a history of severe immunosuppression or advanced age.
Diverse large cohorts are necessary for dissecting subtypes of autism, and intellectual disability is one of the most robust endophenotypes for analysis. However, current cognitive assessment methods ...are not feasible at scale. We developed five commonly used machine learning models to predict cognitive impairment (FSIQ<80 and FSIQ<70) and FSIQ scores among 521 children with autism using parent‐reported online surveys in SPARK, and evaluated them in an independent set (n = 1346) with a missing data rate up to 70%. We assessed accuracy, sensitivity, and specificity by comparing predicted cognitive levels against clinical IQ data. The elastic‐net model has good performance (AUC = 0.876, sensitivity = 0.772, specificity = 0.803) using 129 predictive features to impute cognitive impairment (FSIQ<80). Top‐ranked predictive features included parent‐reported language and cognitive levels, age at autism diagnosis, and history of services. Prediction of FSIQ<70 and FSIQ scores also showed good performance. We show cognitive levels can be imputed with high accuracy for children with autism, using commonly collected parent‐reported data and standardized surveys. The current model offers a method for large‐scale autism studies seeking estimates of cognitive ability when standardized psychometric testing is not feasible.
Lay Summary
Children with autism who have more severe learning challenges or cognitive impairment have different needs that are important to consider in research studies. When children in our study were missing standardized cognitive testing scores, we were able to use machine learning with other information to correctly “guess” when they have cognitive impairment about 80% of the time. We can use this information in research in the future to develop more appropriate treatments for children with autism and cognitive impairment.
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