Erratum: Assessment Methods for Problematic Eating Behaviors in Children and Adolescents With Autism Spectrum Disorder.

[This corrects the article on p. 57 in vol. 35, PMID: 38204745.].

Assessment Methods for Problematic Eating Behaviors in Children and Adolescents With Autism Spectrum Disorder.

Autism spectrum disorder (ASD) can be associated with eating problems. However, currently, there is a lack of established guidelines for assessing and addressing eating behaviors in individuals with ASD. This gap in research exists due to the challenges associated with using traditional assessment methods, which may lead to discrepancies in responses and unintentional potential biases from caregivers. In this review, we provided a comprehensive overview of various eating behaviors commonly observed in individuals with ASD. These behaviors include 1) food neophobia, 2) selective eating, 3) binge eating, 4) food avoidance, 5) chewing and swallowing problems, 6) pica, 7) rumination, 8) rituals, and 9) problematic behaviors. Furthermore, we provide a perspective of utilizing digital tools: 1) augmentative and alternative communication; 2) ecological momentary assessment; and 3) video analysis, behavioral analysis, and facial expression analysis. This review explores existing assessment methods and suggests novel assessment aiding together.

CETP and APOA2 polymorphisms are associated with weight loss and healthy eating behavior changes in response to digital lifestyle modifications.

Response to digital healthcare lifestyle modifications is highly divergent. This study aimed to examine the association between single nucleotide polymorphism (SNP) genotypes and clinical efficacy of a digital healthcare lifestyle modification. We genotyped 97 obesity-related SNPs from 45 participants aged 18-39 years, who underwent lifestyle modification via digital cognitive behavioral therapy for obesity for 8 weeks. Anthropometric, eating behavior phenotypes, and psychological measures were analyzed before and after the intervention to identify their clinical efficacy. CETP (rs9939224) SNP significantly predict "super-responders" with greater body mass index (BMI) reduction (p = 0.028; GG - 2.91%, GT - 9.94%), while APOA2 (rs5082) appeared to have some potential for predicting "poor-responders" with lower BMI reduction (p = 0.005; AA - 6.17%, AG + 2.05%, and GG + 5.11%). These SNPs was also associated with significant differences in eating behavior changes, healthy diet proportions, health diet diversity, emotional and restrained eating behavior changes. Furthermore, classification using gene-gene interactions between rs9939224 and rs5082 significantly predicted the best response, with a greater decrease in BMI (p = 0.038; - 11.45% for the best response group (CEPT GT/TT × APOA2 AA) vs. + 2.62% for the worst response group (CEPT GG × APOA2 AG/GG)). CETP and APOA2 SNPs can be used as candidate markers to predict the efficacy of digital healthcare lifestyle modifications based on genotype-based precision medicine.Trial registration: NCT03465306, ClinicalTrials.gov. Registered March, 2018.

Real-time, interactive website for US-county-level COVID-19 event risk assessment.

Large events and gatherings, particularly those taking place indoors, have been linked to multitransmission events that have accelerated the pandemic spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To provide real-time, geolocalized risk information, we developed an interactive online dashboard that estimates the risk that at least one individual with SARS-CoV-2 is present in gatherings of different sizes in the United States. The website combines documented case reports at the county level with ascertainment bias information obtained via population-wide serological surveys to estimate real-time circulating, per-capita infection rates. These rates are updated daily as a means to visualize the risk associated with gatherings, including county maps and state-level plots. The website provides data-driven information to help individuals and policy makers make prudent decisions (for example, increasing mask-wearing compliance and avoiding larger gatherings) that could help control the spread of SARS-CoV-2, particularly in hard-hit regions.

Real-time, interactive website for US-county level Covid-19 event risk assessment.

Large events and gatherings, particularly those taking place indoors, have been linked to multi-transmission events that have accelerated the pandemic spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To provide real-time, geo-localized risk information, we developed an interactive online dashboard that estimates the risk that at least one individual with SARS-CoV-2 is present in gatherings of different sizes in the United States. The website combines documented case reports at the county level with ascertainment bias information obtained via population-wide serological surveys to estimate real time circulating, per-capita infection rates. These rates are updated daily as a means to visualize the risk associated with gatherings, including county maps and state-level plots. The website provides data-driven information to help individuals and policy-makers make prudent decisions (e.g., increasing mask wearing compliance and avoiding larger gatherings) that could help control the spread of SARS-CoV-2, particularly in hard-hit regions.

Spread of COVID-19 through Georgia, USA. Near-term projections and impacts of social distancing via a metapopulation model.

Epidemiological forecasts of COVID-19 spread at the country and/or state level have helped shape public health interventions. However, such models leave a scale-gap between the spatial resolution of actionable information (i.e. the county or city level) and that of modeled viral spread. States and nations are not spatially homogeneous and different areas may vary in disease risk and severity. For example, COVID-19 has age-stratified risk. Similarly, ICU units, PPE and other vital equipment are not equally distributed within states. Here, we implement a county-level epidemiological framework to assess and forecast COVID-19 spread through Georgia, where 1,933 people have died from COVID-19 and 44,638 cases have been documented as of May 27, 2020. We find that county-level forecasts trained on heterogeneity due to clustered events can continue to predict epidemic spread over multi-week periods, potentially serving efforts to prepare medical resources, manage supply chains, and develop targeted public health interventions. We find that the premature removal of physical (social) distancing could lead to rapid increases in cases or the emergence of sustained plateaus of elevated fatalities.