The COVID-19 pandemic: Challenges to prevent suicide in megacities

Unequal impact of the COVID-19 pandemic on mental health: Role of the neighborhood environment

The COVID-19 pandemic has taken a significant toll on people's mental wellbeing. Few studies have investigated how the neighborhood environment might help to moderate the mental health impact in a natural disaster context. We aim to investigate the unequal impact of the pandemic on mental health between different population groups, and the role of the neighborhood environment in alleviating this impact. We collected survey data (n=2,741) on mental health, neighborhood environment, and pandemic-related behaviors in Beijing metropolitan region between July 10 and 28, 2020, and then applied the partial proportional odds model. Overall, we found that the pandemic has disproportionately affected the lower-income people. The lower-income residents experienced a greater psychological impact than the higher-income residents. We further found that distance to an urban park was a key built environment variable that moderates mental health impact. Residents who lived near urban parks were 4.2 to 4.6% less likely to report an increase in negative emotions, and therefore are more resilient to the mental health impact. In addition to the built environment, a cohesive neighborhood environment may have also helped to mitigate the negative mental health impacts. These findings can inform planning policies that aim to promote healthy and resilient communities.

A Cross-Sectional Study on the Prevalence of Depressive Symptoms and Its Associated Sociodemographic Factors in Peru during the COVID-19 Pandemic

The present study aims to analyze the prevalence of depressive symptoms and its sociodemographic-associated factors in Peruvian adults. Data was extracted from a nation-wide representative survey in which depression symptoms were measured with the PHQ-9 and sociodemographic information was extracted from household data. Depression severity rates were estimated for each symptom, and responses were modeled through the Rating Scale Model to obtain a depression measure used as dependent variable on a Generalized Mixed Linear Model. The most frequent depression symptoms were emotional, such as discouragement, sad mood, hopelessness, and lack of pleasure when doing activities. Our model showed that, after controlling the effects of all the variables considered, the most relevant predictors were gender, education level, physiographic region, age, marital status, and number of coresidents. Higher depression levels were found in women, people who did not complete higher education, participants living in the Highlands, older adults, single participants, and people living alone. Thus, interventions to promote or prevent depression severity during similar situations as the pandemic should focus on specific sociodemographic groups and their particular needs.