COVID-19 and psychological distress: Lessons for India

<h4>Purpose</h4> The COVID-19 pandemic has undoubtedly altered the routine of life and caused unanticipated changes resulting in severe psychological responses and mental health crisis. The study aimed to identify psycho-social factors that predicted distress among Indian population duri...

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Main Authors: Vaijayanthee Anand, Luv Verma, Aekta Aggarwal, Priyadarshini Nanjundappa, Himanshu Rai
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336880/?tool=EBI
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spelling doaj-84f37c9d821f4041992fed9afa2f06352021-08-08T04:31:45ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01168COVID-19 and psychological distress: Lessons for IndiaVaijayanthee AnandLuv VermaAekta AggarwalPriyadarshini NanjundappaHimanshu Rai<h4>Purpose</h4> The COVID-19 pandemic has undoubtedly altered the routine of life and caused unanticipated changes resulting in severe psychological responses and mental health crisis. The study aimed to identify psycho-social factors that predicted distress among Indian population during the spread of novel Coronavirus. <h4>Method</h4> An online survey was conducted to assess the predictors of distress. A global logistic regression model was built, by identifying significant factors from individual logistic regression models built on various groups of independent variables. The prediction capability of the model was compared with the random forest classifier. <h4>Results</h4> The respondents (N = 1060) who are more likely to be distressed, are in the age group of 21-35 years, are females (OR = 1.425), those working on site (OR = 1.592), have pre-existing medical conditions (OR = 1.682), do not have health insurance policy covering COVID-19 (OR = 1.884), have perceived seriousness of COVID-19 (OR = 1.239), have lack of trust in government (OR = 1.246) and whose basic needs’ fulfillment are unsatisfactory (OR = 1.592). The ones who are less likely to be distressed, have higher social support and psychological capital. Random forest classifier correctly classified 2.3% and 17.1% of people under lower and higher distress respectively, with respect to logistic regression. <h4>Conclusions</h4> This study confirms the prevalence of high distress experienced by Indians at the time of COVID-19 and provides pragmatic implications for psychological health at macro and micro levels during an epidemiological crisis.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336880/?tool=EBI
collection DOAJ
language English
format Article
sources DOAJ
author Vaijayanthee Anand
Luv Verma
Aekta Aggarwal
Priyadarshini Nanjundappa
Himanshu Rai
spellingShingle Vaijayanthee Anand
Luv Verma
Aekta Aggarwal
Priyadarshini Nanjundappa
Himanshu Rai
COVID-19 and psychological distress: Lessons for India
PLoS ONE
author_facet Vaijayanthee Anand
Luv Verma
Aekta Aggarwal
Priyadarshini Nanjundappa
Himanshu Rai
author_sort Vaijayanthee Anand
title COVID-19 and psychological distress: Lessons for India
title_short COVID-19 and psychological distress: Lessons for India
title_full COVID-19 and psychological distress: Lessons for India
title_fullStr COVID-19 and psychological distress: Lessons for India
title_full_unstemmed COVID-19 and psychological distress: Lessons for India
title_sort covid-19 and psychological distress: lessons for india
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2021-01-01
description <h4>Purpose</h4> The COVID-19 pandemic has undoubtedly altered the routine of life and caused unanticipated changes resulting in severe psychological responses and mental health crisis. The study aimed to identify psycho-social factors that predicted distress among Indian population during the spread of novel Coronavirus. <h4>Method</h4> An online survey was conducted to assess the predictors of distress. A global logistic regression model was built, by identifying significant factors from individual logistic regression models built on various groups of independent variables. The prediction capability of the model was compared with the random forest classifier. <h4>Results</h4> The respondents (N = 1060) who are more likely to be distressed, are in the age group of 21-35 years, are females (OR = 1.425), those working on site (OR = 1.592), have pre-existing medical conditions (OR = 1.682), do not have health insurance policy covering COVID-19 (OR = 1.884), have perceived seriousness of COVID-19 (OR = 1.239), have lack of trust in government (OR = 1.246) and whose basic needs’ fulfillment are unsatisfactory (OR = 1.592). The ones who are less likely to be distressed, have higher social support and psychological capital. Random forest classifier correctly classified 2.3% and 17.1% of people under lower and higher distress respectively, with respect to logistic regression. <h4>Conclusions</h4> This study confirms the prevalence of high distress experienced by Indians at the time of COVID-19 and provides pragmatic implications for psychological health at macro and micro levels during an epidemiological crisis.
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336880/?tool=EBI
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