Ramos VCA

Language: Spanish
References: 13
Page: 318-322
PDF size: 149.46 Kb.

ABSTRACT

Background The current SARS-CoV-2 pandemic accounts for more than one million infections, and has caused the death, so far, more than 300,000 people. In addition, it has had a great psychological impact in the Latin American context.
Objective to describe the dynamic characteristics of mental health in Peruvian adults during the COVID-19 quarantine.
Methods descriptive study, which explored the dynamic characteristics graphically, using a mental health instrument applied (online) to 763 Peruvian adults. A network analysis was applied using the qgrap packages and the LASSO graph.
Results higher network centrality measures were observed in the items related to negative symptoms in mental health, such as item 9 (feeling of unhappiness and depression) and item 10 (loss of confidence); as well as the greater positive connection between items 3 (decision making and trust) and 4 (meaning of life); and the highest negative relationship between components 1 (concentration) and 5 (tension and anguish).
Conclusion the individuals participating in the study mainly manifested feelings of unhappiness, depression and loss of confidence, among other negative symptoms. The study of the impact of the COVID-19 pandemic on mental health acquires greater precision and scientific basis when applying methods such as network analysis.

REFERENCES

1. Qiu J, Shen B, Zhao M, Xie B, Xu Y. A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: implications and policy recommendations General Psychiatry. 2020;33(2):e100213.

2. Galea S, Merchant RM, Lurie N. The mental health consequences of COVID-19 and physical distancing: the need for prevention and early intervention. JAMA Intern Med. 2020;180(6):817-8.

3. Huang Y, Zhao N. Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 outbreak in China: a web-based cross-sectional survey. Psychiatry Res. 2020;288:112954.

4. Roberton T, Carter ED, Chou VB, Stegmuller AR, Jackson BD, Tam Y, et al. Early estimates of the indirect effects of the COVID-19 pandemic on maternal and child mortality in low-income and middle-income countries: a modelling study. The Lancet Global Health. 2020;7(8):e901-8.

5. Urzúa A, Caqueo A, Bargsted M, Irarrázaval M. ¿Afecta la forma de puntuación la estructura factorial del GHQ-12? Estudio exploratorio en estudiantes iberoamericanos. Cad Saúde Pública. 2015;31:1305-12.

6. Epskamp S., Fried, EI. A tutorial on regularized partial correlation networks. Psychol. Methods. 2018;23(4):617-34.

7. Bringmann LF, Elmer T, Epskamp S, Krause RW, Schoch D, Wichers M, et al. What do centrality measures measure in psychological networks? J Abnorm Psychol. 2019;128(8):892-903.

8. Böhnke JR, Croudace TJ. Calibrating well-being, quality of life and common mental disorder items: psychometric epidemiology in public mental health research. Br J Psychiatry. 2016;209(2):162-8.

9. Li LZ, Wang S. Prevalence and predictors of general psychiatric disorders and loneliness during COVID-19 in the United Kingdom. Psychiatry Res. 2020;291:113267.

10. Ramos CA. Estructura de red del impacto y la percepción de amenaza de la COVID-19. Rev Cubana Enferm[Internet]. 2020[citado 23/08/2020];36:[aprox. 5p]. Disponible en: http://www.revenfermeria.sld.cu/index.php/enf/article/view/4136/661

11. Ramos CA. Miedo a la COVID-19 en adultos peruanos mediante análisis de red. Rev Cubana Enferm[Internet]. 2020[citado 23/08/2020];36:[aprox. 4p]. Disponible en: http://www.revenfermeria.sld.cu/index.php/enf/article/view/4082/664

12. Ramos C, Baños J, Ogundokun R. Network structure of depressive symptoms in Peruvian adults with arterial hypertension. F1000Research. 2021;10:19.

13. Van Zyl CJJ. A network analysis of the General Health Questionnaire. J health psycho. 2021;26(2):249-59.