Psychosocial Impact of COVID-19 Lockdown on Mental Wellbeing among 11 States of India: A Markov Modeling Approach
Author(s): Vishwak Reddy V, Satya Revanth Karri, Tabitha Jezreel, Shadaan Afeen, Praveen Khairkar
Objectives: Amid unprecedented health and socioeconomic crisis emanating from COVID-19 pandemic lockdown in India with effect from 25th March 2020 extending into its fourth phase is a matter of great concern to mental health professionals. The present study aims to evaluate psychological impact during current pandemic in difficult to reach, autonomous process of community spread of COVID-19 in partially observable system using respondent driven system with hidden Markov modeling approach.
Methods: The participants were asked to complete a demographic and clinical profile data form, psychological and behavioral changes in past 14 days, their stress levels, depression and anxiety was screened using standardized and validated DASS-21 Scale. Chi-square test, Mann Whitney U test and Pearson’s Correlation Coefficient was performed.
Results: A total of 891 people responded from 11 different states across the country and majority (90%) of them were from five South Indian states. We observed the prevalence of 22% of depression, with 15% anxiety and 27.5% with either of them. Young age, widow/unmarried marital status, moderate level of education, students, non-working status during lockdown, past history of psychiatric illnesses, presence of physical symptoms related to COVID-19, hypochondriacal thoughts, fear of contamination, social contagion, were found to be significantly associated (p<0.05) with reference to presence and/or severity of depression and anxiety.
Conclusion: Markov Modeling using respondent driven sampling is a innovative way of sampling method which can be used in difficult to reach out population in changing dynamic system. Findings of high prevalence of psychopathologies warrants appropriate planning and timely designing an intervention in coordination with mental health professionals to flatten curve in due course of time.