Prevalence and Predictors of Somatization in Peruvian Undergraduate Students during the COVID-19 Pandemic.

The COVID-19 pandemic had a strong impact on mental health. Multiple studies report the alarming prevalence of depression, anxiety, and stress-related conditions due to the lockdown measures. Nevertheless, somatization has been an overlooked topic in current literature despite its strong relationship with most mental health conditions. The aim of this study was to describe the prevalence of somatic symptoms and their associated factors in a sample of 3218 undergraduate students from Lima, Peru. A cross-sectional design was carried out. The prevalence of somatic symptoms was measured with the PHQ-15. As predictors of somatic symptom severity, we included psychopathological (depression, anxiety, and stress), psychological (perceived social support, resilience, satisfaction with life, and academic self-efficacy), and sociodemographic (e.g., age, sex, employment status, relationship status, daily hours of sleep) variables. A generalized linear model from a binomial family and a logit link function were applied based on a Factor Score Regression approach, with half of the sample presenting moderate-to-severe somatic symptoms. Anxiety was the strongest predictor of somatic symptom severity, followed by academic self-efficacy. Significant differences were found regarding sex, relationship status, daily hours of sleep and COVID-19 risk-related variables. In conclusion, interventions on reducing anxiety and promoting academic self-efficacy may have a stronger impact on somatic symptom severity and should focus on more vulnerable specific demographic groups such as females.

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.