An alternative network analysis in the exploration of mental health states, chronic conditions and COVID-19

Abstract

Network analysis is a graphical statistical technique that allows visualizing and intuitively interpreting the spectrum of various health conditions, being of clinical relevance in the current context of the COVID-19 pandemic. Given its limited dissemination in South America, we aimed at a narrative analysis of this network model during the pandemic. A narrative review of empirical studies published from May 2020 to July 2021 in the PubMed and ScienceDirect database was performed. We selected research that used partial correlation psychometric networks in participants assessed during the COVID-19 pandemic. This re-view reports 13 network studies that used mostly symptoms related to anxiety (7 studies), depression (6 studies) and stress (6 studies). The resulting information is grouped into 3 clusters (publications in psychiatry, psychological sciences, medicine and related journals). The presented re-view refers that this network analysis allows a new way of identifying important clinical aspects such as comorbidity, concurrence of symptoms and non-symptomatologic measures, groupings of symptoms with other variables of latent or observable nature that share a major common cause, the exploration of new holistic clinical hypotheses with epidemiological, psychological, biomedical and contextual variables of major current interest such as the comparison of causal association systems of multilevel variables in the psychobiological process, and their risk and protective factors in various time periods.

Keywords

• comorbidity
• covid-19
• diagnosis
• mental health
• methods
• signs and symptoms