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The Electronic Journal of Information Systems in Developing Countries ; 2022.
Article in English | Web of Science | ID: covidwho-2121025


This study investigated the extent to which media richness, dialogic loop, and content type affect citizen engagement with local government social media information on the Covid-19 pandemic. Quantitative content analysis through scraping of Facebook posts by the local government was employed in this study. Effects of the determinant variables was tested using negative binomial regression. Results show that both media richness and dialogic loop have significant and positive effects on citizens' engagement. This means that the richer the media, and the more dialogic features present in a Facebook post, the higher the turnout of reactions, shares, and comments of such post. Content type, on the other hand, was found to have no significant effect, implying that the number of content categories a certain post belongs to does not influence engagement from citizens. The study focused only on the local government's pandemic information posted on Facebook. Local governments should continue utilizing social media in disseminating pandemic information, and in the process, consider maximum utilization of the social media features to generate more engagement from its citizens. This study is the first to determine the factors affecting citizen engagement with government social media during the Covid-19 pandemic in the Philippine context.

Journal of Clinical Outcomes Management ; 29(1):27-31, 2022.
Article in English | EMBASE | ID: covidwho-1884742


Background: Patient outcomes of COVID-19 have improved throughout the pandemic. However, because it is not known whether outcomes of COVID-19 in the type 1 diabetes (T1D) population improved over time, we investigated differences in COVID-19 outcomes for patients with T1D in the United States. Methods: We analyzed data collected via a registry of patients with T1D and COVID-19 from 56 sites between April 2020 and January 2021. We grouped cases into first surge (April 9, 2020, to July 31, 2020, n = 188) and late surge (August 1, 2020, to January 31, 2021, n = 410), and then compared outcomes between both groups using descriptive statistics and logistic regression models. Results: Adverse outcomes were more frequent during the first surge, including diabetic ketoacidosis (32% vs 15%, P< .001), severe hypoglycemia (4% vs 1%, P= .04), and hospitalization (52% vs 22%, P< .001). Patients in the first surge were older (28 [SD,18.8] years vs 18.0 [SD, 11.1] years, P< .001), had higher median hemoglobin A1c levels (9.3 [interquartile range {IQR}, 4.0] vs 8.4 (IQR, 2.8), P< .001), and were more likely to use public insurance (107 [57%] vs 154 [38%], P< .001). The odds of hospitalization for adults in the first surge were 5 times higher compared to the late surge (odds ratio, 5.01;95% CI, 2.11-12.63). Conclusion: Patients with T1D who presented with COVID-19 during the first surge had a higher proportion of adverse outcomes than those who presented in a later surge.