RESUMO
Background: Health care workers (HCWs) are prone to stress and insomnia because of pandemic situations. Assessment of the actual burden of this stress and insomnia is essential to form preventive strategies. The study's objective was to find out the pooled prevalence of stress and insomnia among HCWs in India during the coronavirus disease (COVID-19) pandemic. Material and Methods: We conducted a systematic review and meta-analysis to determine the prevalence of stress and insomnia among HCWs during the COVID-19 pandemic in India. Cross-sectional studies conducted in India regarding stress and insomnia among HCWs were searched from PubMed, Scopus, Embase, and Google Scholar. These studies were published after the declaration of the COVID-19 pandemic till August 31, 2021. Articles were searched independently by both authors. Data were extracted in an Excel sheet and analyzed using the 'Meta' package of the 'R' software version 4.1.0. Result: A total of 23 and 16 studies were included in the final pooled analysis of stress and insomnia, respectively, following preferred reporting items for systematic review and meta-analysis guidelines. A random-effects model was used to determine the pooled prevalence of stress and insomnia. This study is registered in Prospero. The registration number is CRD42021253917. The total numbers of HCWs from India included were 8125 and 4974, respectively, for finding out the pooled prevalence of stress and insomnia. The pooled prevalence of stress and insomnia among HCWs is 43% [95% confidence interval (CI) 30-56%] and 35% (95% CI 28-44%), respectively. Two out of five and one in three Indian HCWs have stress and insomnia, respectively, during the COVID-19 pandemic. Conclusion: Human resource development should be prioritized to decrease the workload among HCWs. The findings from this study will be useful in preparing policy guidelines on mental health screening of HCWs during the pandemic.
RESUMO
BACKGROUND: COVID-19 is caused by SARS-CoV-2. The first case of COVID-19 was detected in Wuhan city of China in December 2019. Geographic information system (GIS) mapping is important for the surveillance of infectious diseases. OBJECTIVES: The objectives of the study are to map spatially total cases and case fatality rate of COVID-19 and to build a linear regression model for mortality based on socio-demographic variables. METHOLOGY: We plotted the epidemiological data of COVID-19 of Indian states as on 11th May 2021 using the Q-GIS software. We used socio-demographic variables as the predictors of COVID-19 mortality and developed a linear regression model. RESULTS: Adjusted R-squared in linear regression model based on socio-demographic variables for COVID-19 deaths is 0.82. CONCLUSIONS: There are spatial variations in COVID-19 cases and deaths.
