Psychotropic Prescriptions During the COVID-19 Pandemic Among U.S. Children and Adolescents Receiving Mental Health Services.

Objective: Increased mental health problems among children and adolescents during the COVID-19 pandemic may have impacted psychotropic medication use. This study describes trends in monthly psychotropic medications before and early in the COVID-19 pandemic among 2- to 17-year-old children and adolescents with mental health disorders. Methods: A cross-sectional study design using the 2019-2020 IQVIA™ prescription and medical commercial claims data to estimate the proportion of children and adolescents with any psychotropic prescription in the month out of all with any mental health-related medical or prescription services in the month and the year-over-year percent change. We assessed monthly proportions of youth who filled a psychotropic prescription overall and by psychotropic class, stratified by age and gender. Results: Of the 8,896,713 children and adolescents in the sample, 24.7% received psychotropic medication during the study period. The proportion of the cohort prescribed a psychotropic medication in a given month averaged 27%-28% from January 2019 to February 2020, peaked at 36.9% in April 2020, and gradually declined to 28.7% in September 2020. The largest year-over-year percent change was in April for antipsychotic (41.9%) and antidepressant (37.9%) medication, which remained higher in September 2020 compared to September 2019, particularly among ages 6 years or older and females. Conclusion: The proportion of youth with a psychotropic prescription increased at the onset of the COVID-19 pandemic, later returning to prepandemic levels. However, antipsychotics and antidepressants remained higher than prepandemic, highlighting the need to further understand the long-lasting effects of the pandemic on children and adolescents.

TNet: Transmission Network Inference Using Within-Host Strain Diversity and its Application to Geographical Tracking of COVID-19 Spread.

The inference of disease transmission networks is an important problem in epidemiology. One popular approach for building transmission networks is to reconstruct a phylogenetic tree using sequences from disease strains sampled from infected hosts and infer transmissions based on this tree. However, most existing phylogenetic approaches for transmission network inference are highly computationally intensive and cannot take within-host strain diversity into account. Here, we introduce a new phylogenetic approach for inferring transmission networks, TNet, that addresses these limitations. TNet uses multiple strain sequences from each sampled host to infer transmissions and is simpler and more accurate than existing approaches. Furthermore, TNet is highly scalable and able to distinguish between ambiguous and unambiguous transmission inferences. We evaluated TNet on a large collection of 560 simulated transmission networks of various sizes and diverse host, sequence, and transmission characteristics, as well as on 10 real transmission datasets with known transmission histories. Our results show that TNet outperforms two other recently developed methods, phyloscanner and SharpTNI, that also consider within-host strain diversity. We also applied TNet to a large collection of SARS-CoV-2 genomes sampled from infected individuals in many countries around the world, demonstrating how our inference framework can be adapted to accurately infer geographical transmission networks. TNet is freely available from https://compbio.engr.uconn.edu/software/TNet/.