Age-Based Tracks: A Method to Tailor Autism Diagnostic Evaluation in Large-Scale Autism Specialty Centers.

This paper describes a proposed model of diagnostic evaluation for autism spectrum disorder (ASD) at a large-scale ASD specialty center. Our center has implemented age-based diagnostic tracks within an interdisciplinary team evaluation approach to assessing ASD. Data were collected as part of a program evaluation and included responses from provider surveys as well as patient medical record reviews. The results from 803 patients were included. The diagnostic outcomes, time for evaluation, and appropriateness of referral were analyzed in patients referred to the Younger (n = 155) and Older (n = 648) diagnostic tracks. In 92.8% of cases referred to the clinic's standard team evaluation model, the provider teams were able to make a diagnostic decision within the allotted evaluation time. The results from an additional diagnostic pathway, termed the Autism Psych Team (APT), within the older track were also presented. The intake providers had the option to triage older patients to this pathway when they anticipated that the patient might be diagnostically complex. Most patients (45.1%) triaged to the APT were referred due to psychiatric complexity. In 96% of APT cases, the APT providers felt the patient was an appropriate referral. Overall, these results suggest a method to efficiently triage patients to diagnostic models equipped to serve them within a high-volume ASD center.

Statistical Deconvolution for Inference of Infection Time Series.

Accurate measurement of daily infection incidence is crucial to epidemic response. However, delays in symptom onset, testing, and reporting obscure the dynamics of transmission, necessitating methods to remove the effects of stochastic delays from observed data. Existing estimators can be sensitive to model misspecification and censored observations; many analysts have instead used methods that exhibit strong bias. We develop an estimator with a regularization scheme to cope with stochastic delays, which we term the robust incidence deconvolution estimator. We compare the method to existing estimators in a simulation study, measuring accuracy in a variety of experimental conditions. We then use the method to study COVID-19 records in the United States, highlighting its stability in the face of misspecification and right censoring. To implement the robust incidence deconvolution estimator, we release incidental, a ready-to-use R implementation of our estimator that can aid ongoing efforts to monitor the COVID-19 pandemic.

Sustained Reduction in Urgent Care Antibiotic Prescribing During the Coronavirus Disease 2019 Pandemic: An Academic Medical Center's Experience.

We compared antibiotic prescribing before and during the -coronavirus disease 2019 (COVID-19) pandemic at 2 academic urgent care clinics and found a sustained decrease in prescribing driven by respiratory encounters and despite transitioning to telemedicine. Antibiotics were rarely prescribed during encounters for COVID-19 or COVID-19 symptoms. COVID-19 revealed opportunities for outpatient stewardship programs.

A Web-Based Risk-Reframing Intervention to Influence Early Childhood Educators' Attitudes and Supportive Behaviors Toward Outdoor Play: Protocol for the OutsidePlay Study Randomized Controlled Trial.

BACKGROUND: Early learning and childcare centers (ELCCs) can offer young children critical opportunities for quality outdoor play. There are multiple actual and perceived barriers to outdoor play at ELCCs, ranging from safety fears and lack of familiarity with supporting play outdoors to challenges around diverse perspectives on outdoor play among early childhood educators (ECEs), administrators, licensing officers, and parents. OBJECTIVE: Our study objective is to develop and evaluate a web-based intervention that influences ECEs' and ELCC administrators' perceptions and practices in support of children's outdoor play at ELCCs. METHODS: The development of the fully automated, open-access, web-based intervention was guided by the intervention mapping process. We first completed a needs assessment through focus groups of ECEs, ELCC administrators, and licensing officers. We identified key issues, needs, and challenges; opportunities to influence behavior change; and intervention outcomes and objectives. This enabled us to develop design objectives and identify features of the OutsidePlay web-based intervention that are central to addressing the issues, needs, and challenges of ECEs and ELCC administrators. We used social cognitive theory and behavior change techniques to select methods, applications, and technology to deliver the intervention. We will use a two-parallel-group randomized controlled trial (RCT) design to evaluate the efficacy of the intervention. We will recruit 324 ECEs and ELCC administrators through a variety of web-based means, including Facebook advertisements and mass emails through our partner networks. The RCT study will be a purely web-based trial where outcomes will be self-assessed through questionnaires. The RCT participants will be randomized into the intervention group or the control group. The control group participants will read the Position Statement on Active Outdoor Play. RESULTS: The primary outcome is increased tolerance of risk in children's play, as measured by the Teacher Tolerance of Risk in Play Scale. The secondary outcome is self-reported attainment of a self-developed behavior change goal. We will use mixed effects models to test the hypothesis that there will be a difference between the intervention and control groups with respect to tolerance of risk in children's play. Differences in goal attainment will be tested using logistic regression analysis. CONCLUSIONS: The OutsidePlay web-based intervention guides users through a personalized journey that is split into 3 chapters. An effective intervention that addresses the barriers to outdoor play in ELCC settings has the potential to improve children's access to outdoor play and support high-quality early childhood education. TRIAL REGISTRATION: ClinicalTrials.gov NCT04624932; https://clinicaltrials.gov/ct2/show/NCT04624932. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/31041.

The virtual COVID-19 classroom: surveying outcomes, individual differences, and technology use in college students

The COVID-19 pandemic caused many colleges to quickly shift to virtual learning, leading students to rely on technology to complete coursework while also experiencing new situations and stressors. The present study explored students’ technology use in their online course in conjunction with several student outcomes and individual difference measures. Ninety-six undergraduate students were surveyed about devices used and their perceptions of those devices. In addition, the survey measured students’ engagement, motivation, procrastination, perceived stress, and self-efficacy. It also asked students to report their current grade as well as how satisfied and isolated they felt in their course. Relationships emerged in predictable ways between course outcomes and individual difference measures. And though laptops were most used for coursework, more smartphone use related to lower feelings of isolation. Lower feelings of isolation then related to higher grades and less stress. Regression analyses confirmed that smartphone use explained unique variance in feelings of isolation, and further revealed that perceived stress consistently predicted all outcomes. From these results and complementary qualitative survey data, it seems that both laptops and smartphones hold importance for academics in the current context. Educators should further explore the role of device in students’ experience as well as consider this information when designing online courses.

It's complicated: characterizing the time-varying relationship between cell phone mobility and COVID-19 spread in the US.

Restricting in-person interactions is an important technique for limiting the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Although early research found strong associations between cell phone mobility and infection spread during the initial outbreaks in the United States, it is unclear whether this relationship persists across locations and time. We propose an interpretable statistical model to identify spatiotemporal variation in the association between mobility and infection rates. Using 1 year of US county-level data, we found that sharp drops in mobility often coincided with declining infection rates in the most populous counties in spring 2020. However, the association varied considerably in other locations and across time. Our findings are sensitive to model flexibility, as more restrictive models average over local effects and mask much of the spatiotemporal variation. We conclude that mobility does not appear to be a reliable leading indicator of infection rates, which may have important policy implications.