tetano
Editor, Senior Moderator
BMC Med Res Methodol
. 2022 Jan 14;22(1):15.
doi: 10.1186/s12874-021-01411-w.
COVID-19 impact on mental health
Jingyu Cui[SUP] 1 [/SUP], Jingwei Lu[SUP] 1 [/SUP], Yijia Weng[SUP] 1 [/SUP], Grace Y Yi[SUP] 2 3 [/SUP], Wenqing He[SUP] 1 [/SUP]
Affiliations
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic has posed a significant influence on public mental health. Current efforts focus on alleviating the impacts of the disease on public health and the economy, with the psychological effects due to COVID-19 relatively ignored. In this research, we are interested in exploring the quantitative characterization of the pandemic impact on public mental health by studying an online survey dataset of the United States.
Methods: The analyses are conducted based on a large scale of online mental health-related survey study in the United States, conducted over 12 consecutive weeks from April 23, 2020 to July 21, 2020. We are interested in examining the risk factors that have a significant impact on mental health as well as in their estimated effects over time. We employ the multiple imputation by chained equations (MICE) method to deal with missing values and take logistic regression with the least absolute shrinkage and selection operator (Lasso) method to identify risk factors for mental health.
Results: Our analysis shows that risk predictors for an individual to experience mental health issues include the pandemic situation of the State where the individual resides, age, gender, race, marital status, health conditions, the number of household members, employment status, the level of confidence of the future food affordability, availability of health insurance, mortgage status, and the information of kids enrolling in school. The effects of most of the predictors seem to change over time though the degree varies for different risk factors. The effects of risk factors, such as States and gender show noticeable change over time, whereas the factor age exhibits seemingly unchanged effects over time.
Conclusions: The analysis results unveil evidence-based findings to identify the groups who are psychologically vulnerable to the COVID-19 pandemic. This study provides helpful evidence for assisting healthcare providers and policymakers to take steps for mitigating the pandemic effects on public mental health, especially in boosting public health care, improving public confidence in future food conditions, and creating more job opportunities.
Trial registration: This article does not report the results of a health care intervention on human participants.
Keywords: COVID-19; Lasso; logistic regression; mental health; missing data; multiple imputation; survey data.
. 2022 Jan 14;22(1):15.
doi: 10.1186/s12874-021-01411-w.
COVID-19 impact on mental health
Jingyu Cui[SUP] 1 [/SUP], Jingwei Lu[SUP] 1 [/SUP], Yijia Weng[SUP] 1 [/SUP], Grace Y Yi[SUP] 2 3 [/SUP], Wenqing He[SUP] 1 [/SUP]
Affiliations
- PMID: 35026998
- DOI: 10.1186/s12874-021-01411-w
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic has posed a significant influence on public mental health. Current efforts focus on alleviating the impacts of the disease on public health and the economy, with the psychological effects due to COVID-19 relatively ignored. In this research, we are interested in exploring the quantitative characterization of the pandemic impact on public mental health by studying an online survey dataset of the United States.
Methods: The analyses are conducted based on a large scale of online mental health-related survey study in the United States, conducted over 12 consecutive weeks from April 23, 2020 to July 21, 2020. We are interested in examining the risk factors that have a significant impact on mental health as well as in their estimated effects over time. We employ the multiple imputation by chained equations (MICE) method to deal with missing values and take logistic regression with the least absolute shrinkage and selection operator (Lasso) method to identify risk factors for mental health.
Results: Our analysis shows that risk predictors for an individual to experience mental health issues include the pandemic situation of the State where the individual resides, age, gender, race, marital status, health conditions, the number of household members, employment status, the level of confidence of the future food affordability, availability of health insurance, mortgage status, and the information of kids enrolling in school. The effects of most of the predictors seem to change over time though the degree varies for different risk factors. The effects of risk factors, such as States and gender show noticeable change over time, whereas the factor age exhibits seemingly unchanged effects over time.
Conclusions: The analysis results unveil evidence-based findings to identify the groups who are psychologically vulnerable to the COVID-19 pandemic. This study provides helpful evidence for assisting healthcare providers and policymakers to take steps for mitigating the pandemic effects on public mental health, especially in boosting public health care, improving public confidence in future food conditions, and creating more job opportunities.
Trial registration: This article does not report the results of a health care intervention on human participants.
Keywords: COVID-19; Lasso; logistic regression; mental health; missing data; multiple imputation; survey data.