METHODS: A total of 1028 confirmed cases of COVID-19 from Africa with definite survival outcomes were identified retrospectively from an open-access individual-level worldwide COVID-19 database. The live version of the dataset is available at https://github.com/beoutbreakprepared/nCoV2019 . Multivariable logistic regression was conducted to determine the risk factors that independently predict mortality among patients with COVID-19 in Africa.
RESULTS: Of the 1028 cases included in study, 432 (42.0%) were females with a median (interquartile range, IQR) age of 50 (24) years. Older age (adjusted odds ratio {aOR} 1.06; [95% confidence intervals {95% CI}, 1.04-1.08]), presence of chronic disease (aOR 9.63; [95% CI, 3.84-24.15]), travel history (aOR 2.44; [95% CI, 1.26-4.72]), as well as locations of Central Africa (aOR 0.14; [95% CI, 0.03-0.72]) and West Africa (aOR 0.12; [95% CI, 0.04-0.32]) were identified as the independent risk factors significantly associated with increased mortality among the patients with COVID-19.
CONCLUSIONS: The COVID-19 pandemic is evolving gradually in Africa. Among patients with COVID-19 in Africa, older age, presence of chronic disease, travel history, and the locations of Central Africa and West Africa were associated with increased mortality. A regional response should prioritize strategies that will protect these populations. Also, conducting a further in-depth study could provide more insights into additional factors predictive of mortality in COVID-19 patients.
OBJECTIVES: To measure the level of COVID-19 information overload (COVIO) and assess the association between COVIO and sociodemographic characteristics among the general public.
METHODS: A cross-sectional online survey was conducted between April and May 2020 using a modified Cancer Information Overload scale. The survey was developed and posted on four social media platforms. The data were only collected from those who consented to participate. COVIO score was classified into high vs. low using the asymmetrical distribution as a guide and conducted a binary logistic regression to examine the factors associated with COVIO.
RESULTS: A total number of 584 respondents participated in this study. The mean COVIO score of the respondents was 19.4 (± 4.0). Sources and frequency of receiving COVID-19 information were found to be significant predictors of COVIO. Participants who received information via the broadcast media were more likely to have high COVIO than those who received information via the social media (adjusted odds ratio ([aOR],14.599; 95% confidence interval [CI], 1.608-132.559; p = 0.017). Also, participants who received COVID-19 information every minute (aOR, 3.892; 95% CI, 1.124-13.480; p = 0.032) were more likely to have high COVIO than those who received information every week.
CONCLUSION: The source of information and the frequency of receiving COVID-19 information were significantly associated with COVIO. The COVID-19 information is often conflicting, leading to confusion and overload of information in the general population. This can have unfavorable effects on the measures taken to control the transmission and management of COVID-19 infection.
METHODS: Our literature search of peer-reviewed English language primary source articles published between 1991 and 2018 was conducted across six databases (Embase, PubMed, Web of Sciences, CINAHL, PsychINFO, Academic Search Complete) and Google Scholar, yielding 3844 articles. After duplicate removal, we independently screened 3413 studies to determine whether they met inclusion criteria. Seventy-six studies were identified for inclusion in this review. Data were extracted on study characteristics, content, and findings.
FINDINGS: Seventy-six studies met the inclusion criteria. The most represented subgroups were Chinese (n = 74), Japanese (n = 60), and Filipino (n = 60), while Indonesian (n = 1), Malaysian (n = 1), and Burmese (n = 1) were included in only one or two studies. Several Asian American subgroups listed in the 2010 U.S. Census were not represented in any of the studies. Overall, the most studied health conditions were cancer (n = 29), diabetes (n = 13), maternal and infant health (n = 10), and cardiovascular disease (n = 9). Studies showed that health outcomes varied greatly across subgroups.
CONCLUSIONS: More research is required to focus on smaller-sized subgroup populations to obtain accurate results and address health disparities for all groups.