Back

Risk factors and protective factors of mental health during COVID-19 outbreak and lockdown in adult Indian population- A cross-sectional study

Saikarthik, J.; Saraswathi, I.; Siva, T.

2020-07-03 public and global health
10.1101/2020.06.13.20130153 medRxiv
Show abstract

BackgroundThe novel Corona virus has derailed the entire world and various steps have been taken by the health authorities to tackle this pandemic. Nationwide lockdown has been imposed to control the spread of COVID-19 outbreak in India, which could have psychological impact on the population. AimOur study aims to study the effect of the COVID-19 outbreak & subsequent lockdown on mental health status of adult Indian population along with identifying the high-risk groups. MethodologyAn online survey was conducted during 3rd phase of lockdown gathering details about sociodemographic variables, practice of precautionary measures, awareness and concerns regarding COVID-19 and mental health status of the participants through DASS21 questionnaire from 873 adults. ResultsThe prevalence of depression, anxiety and stress were 18.56%, 25.66%, and 21.99% respectively including higher number of participants with mild depression (15.1%) and stress (14.5%) and moderate anxiety (16.3%). Female gender, age <25 years, unemployment, self-business, employed in private sector, lack of formal education, larger household size, parenthood (>2 kids) were associated with increased likelihood of negative mental health. Confidence in physicians ability to diagnose COVID-19 infection, decreased self-perceived likelihood of contracting COVID-19, lesser frequency of checking for information on COVID-19 and satisfaction of information received were protective against negative mental health. ConclusionThis landmark study identified the protective and risk factors of mental health during COVID-19 pandemic, to help authorities and mental health workers to strategize and deliver interventional methods to maintain psychosocial wellbeing of the population.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.