METHODS: To fill this gap, electronic databases including PubMed, Scopus, Science Direct, Sagepub, CINAHL, Psychology, and Behavioral Sciences Collection were searched for relevant studies. A total of 16 studies were included in the systematic review.
RESULTS: The analyses showed that the prevalence of depression, anxiety, and stress ranged from 14.3% to 81.7%, 8.0% to 81.7%, and 0.9% to 56.5% respectively. Adult populations demonstrated the highest prevalence of depression, whereas university students reported the highest prevalence of anxiety and stress. Several factors were associated with mental health conditions including age, gender, family income, and perception of COVID-19.
CONCLUSION: Differentials in mental health screening practices call for standardised screening practices. Mental health intervention should be targeted at high-risk populations with effective risk communication.
METHODS: The study included individuals from the Northern Finland Birth Cohort 1966 (NFBC1966) who had available data for adiposity measures (body mass index and waist-to-hip ratio), alexithymia (measured by the 20-Item Toronto Alexithymia Scale: TAS-20), depressive symptoms (measured by the 13-item depression subscale of Hopkins Symptom Checklist: HSCL-13) at age of 31 years (n = 4773) and 46 years (n = 4431). Pearson's (r) correlation, and multiple linear regression were used to investigate the relationships between alexithymia, depressive symptoms, and adiposity measures. The potential mediating role of depressive symptoms was examined via Hayes' procedure (PROCESS).
RESULTS: Positive correlations were confirmed between adiposity measures (BMI and WHR) and the TAS-20 score (and its subscale), but not between obesity and HSCL-13 score. The strongest correlation was between the DIF (difficulty identifying feelings) subscale of the TAS-20 and HSCL-13 at both time points (31 y: r(3013) = 0.41, p
METHODS: This study used data from the Global COVID-19 Index provided by PEMANDU Associates. The sample, representing 161 countries, comprised the number of confirmed cases, deaths, stringency indices, population density and GNI per capita (USD). Correlation matrices were computed to reveal the association between the variables at three time points: day-30, day-60 and day-90. Three separate principal component analyses were computed for similar time points, and several standardized plots were produced.
RESULTS: Confirmed cases and deaths due to COVID-19 showed positive but weak correlation with stringency and GNI per capita. Through principal component analysis, the first two principal components captured close to 70% of the variance of the data. The first component can be viewed as the severity of the COVID-19 surge in countries, whereas the second component largely corresponded to population density, followed by GNI per capita of countries. Multivariate visualization of the two dominating principal components provided a standardized comparison of the situation in the161 countries, performed on day-30, day-60 and day-90 since the first confirmed cases in countries worldwide.
CONCLUSION: Visualization of the global spread of COVID-19 showed the unequal severity of the pandemic across continents and over time. Distinct patterns in clusters of countries, which separated many European countries from those in Africa, suggested a contrast in terms of stringency measures and wealth of a country. The African continent appeared to fare better in terms of the COVID-19 pandemic and the burden of mortality in the first 90 days. A noticeable worsening trend was observed in several countries in the same relative time frame of the disease's first 90 days, especially in the United States of America.