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Previous studies have shown various social determinants of health (SDOH) may have contributed to the disparities in COVID-19 incidence and mortality among minorities and underserved populations at county or zip code level.

This analysis was carried out at a granular spatial resolution of census tracts, to explore the spatial patterns and contextual SDOH associated with COVID-19 incidence from a Hispanic population mostly consisting of Mexican Americans living in Cameron County, TX on the border of US and Mexico. We performed age-stratified analysis to identify different contributing SDOH and quantify their effects by age groups.

We included all reported COVID-19 positive cases confirmed by reverse transcription-polymerase chain reaction (RT-PCR) testing between March 19th (first case reported) and December 16th, 2020 in Cameron County, TX. Confirmed COVID-19 cases were aggregated to weekly counts by census tracts. We adopted a Bayesian spatiotemporal Negative Binomial model to investigate the COVID-19 ioreover, age-stratified analyses identified different significant contributing factors, and varying magnitude of the "shelter-in-place" effect.

In our study, SDOH including social environment and local emergency measure were identified in relation to COVID-19 incidence risk at the census tract level in a highly disadvantaged population with limited health care access and high prevalence of chronic conditions. Results from our analysis provide key knowledge to design efficient testing strategies and assist local public health departments for COVID-19 control, mitigation and implementation of vaccine strategies.

Advanced prediction of the daily incidence of COVID-19 can aid policy making on the prevention of disease spread, which can profoundly affect people's livelihood. In previous studies, predictions were investigated for single or several countries and territories.

We aimed to develop models that can be applied for real-time prediction of COVID-19 activity in all individual countries and territories worldwide.

Data of the previous daily incidence and infoveillance data (search volume data via Google Trends) from 215 individual countries and territories were collected. A random forest regression algorithm was used to train models to predict the daily new confirmed cases 7 days ahead. Several methods were used to optimize the models, including clustering the countries and territories, selecting features according to the importance scores, performing multiple-step forecasting, and upgrading the models at regular intervals. The performance of the models was assessed using the mean absolute error (MAE), root me.

Child screen time (ST) has soared during the COVID-19 pandemic as lockdowns and restrictions have forced changes to regular family routines. It is important to investigate how families are navigating ST.

This study aimed to explore families' experiences of ST during the COVID-19 pandemic.

Virtual focus group sessions were conducted between December 2020 and February 2021 in English and Spanish. Transcripts were analyzed using reflexive thematic analysis.

In total, 48 parents (predominantly Hispanic) residing in California participated in 1 of 14 focus group sessions. Children were attending school remotely at the time of the study. A total of 6 themes and 1 subtheme were identified (1) total ST has increased; (2) children are too attached to screens; (3) ST has advantages and disadvantages but parents perceive ST as mostly negative; (4) parents and children have limited options; (5) ST restrictions (subtheme children react negatively when ST is restricted); and (6) parents are concerned that children are not getting enough exercise.

This study provides a cross-sectional insight into how family life has changed with regard to ST during the COVID-19 pandemic. Parents expressed concerns about total ST, the addictive nature of it, and lack of physical activity. It is important that future studies examine the long-term effects of heavy ST and preemptively introduce ways to redirect children's ST habits as the country attempts to establish a new normal.

This study provides a cross-sectional insight into how family life has changed with regard to ST during the COVID-19 pandemic. Parents expressed concerns about total ST, the addictive nature of it, and lack of physical activity. It is important that future studies examine the long-term effects of heavy ST and preemptively introduce ways to redirect children's ST habits as the country attempts to establish a new normal.

COVID-19 is impacting people worldwide and is currently a leading cause of death in many countries. Underlying factors, including Social Determinants of Health (SDoH), could contribute to these statistics. 3-Aminobenzamide supplier Our prior work has explored associations between SDoH and several adverse health outcomes (eg, asthma and obesity). Our findings reinforce the emerging consensus that SDoH factors should be considered when implementing intelligent public health surveillance solutions to inform public health policies and interventions.

This study sought to redefine the Healthy People 2030's SDoH taxonomy to accommodate the COVID-19 pandemic. Furthermore, we aim to provide a blueprint and implement a prototype for the Urban Population Health Observatory (UPHO), a web-based platform that integrates classified group-level SDoH indicators to individual- and aggregate-level population health data.

The process of building the UPHO involves collecting and integrating data from several sources, classifying the collected data ing urban population health.

UPHO serves as an apparatus for implementing effective interventions and can be adopted as a global platform for chronic and infectious diseases. The UPHO surveillance platform provides a novel approach and novel insights into immediate and long-term health policy responses to the COVID-19 pandemic and other future public health crises. The UPHO assists public health organizations and policymakers in their efforts in reducing health disparities, achieving health equity, and improving urban population health.

In 2019, a new coronavirus has emerged in China and was rapidly classified as a pandemic. Pregnant women with GDM are considered risk patients for a severe course of COVID-19. In the context of the COVID-19-pandemic and with a focus on women with GDM, there are serious concerns regarding adverse effects on maternal and neonatal outcomes. Effective treatments for GDM patients are therefore particularly important. Due to the contact restrictions and infection risks, digital approaches such as telemedicine are suitable. Against this background, it is important to focus on pregnant women with GDM in more detail in the context of COVID-19 and with a view to telemedical treatment options.

This systematic review aimes to summarize currently available evidence on maternal and offspring outcomes of pregnant women with GDM and COVID-19 (1) and to examine telemedical interventions to improve maternal glycemic control during COVID-19-pandemic (2).

Publications were systematically identified by searching the databases Cochrane Library, MEDLINE via PubMed, Web of Science Core Collection, EMBASE, and CINAHL for studies published until March 2021.