scholarly journals Bidirectional Associations Between Depressive and Anxiety Symptoms and Loneliness During the COVID-19 Pandemic: Dynamic Panel Models With Fixed Effects

2021 ◽  
Vol 12 ◽  
Author(s):  
Cillian P. McDowell ◽  
Jacob D. Meyer ◽  
Daniel W. Russell ◽  
Cassandra Sue Brower ◽  
Jeni Lansing ◽  
...  
Keyword(s):  
Mental Health ◽  
Depressive Symptoms ◽  
Fixed Effects ◽  
Online Survey ◽  
Retention Rate ◽  
Anxiety Symptoms ◽  
Dynamic Panel ◽  
Panel Models ◽  
Bidirectional Associations ◽  
Dynamic Panel Models

Background: Understanding the direction and magnitude of mental health-loneliness associations across time is important to understand how best to prevent and treat mental health and loneliness. This study used weekly data collected over 8 weeks throughout the COVID-19 pandemic to expand previous findings and using dynamic panel models with fixed effects which account for all time-invariant confounding and reverse causation.Methods: Prospective data on a convenience and snowball sample from all 50 US states and the District of Colombia (n = 2,361 with ≥2 responses, 63.8% female; 76% retention rate) were collected weekly via online survey at nine consecutive timepoints (April 3–June 3, 2020). Anxiety and depressive symptoms and loneliness were assessed at each timepoint and participants reported the COVID-19 containment strategies they were following. Dynamic panel models with fixed effects examined bidirectional associations between anxiety and depressive symptoms and loneliness, and associations of COVID-19 containment strategies with these outcomes.Results: Depressive symptoms were associated with small increases in both anxiety symptoms (β = 0.065, 95% CI = 0.022–0.109; p = 0.004) and loneliness (β = 0.019, 0.008–0.030; p = 0.001) at the subsequent timepoint. Anxiety symptoms were associated with a small subsequent increase in loneliness (β = 0.014, 0.003–0.025; p = 0.015) but not depressive symptoms (β = 0.025, −0.020–0.070; p = 0.281). Loneliness was strongly associated with subsequent increases in both depressive (β = 0.309, 0.159–0.459; p < 0.001) and anxiety (β = 0.301, 0.165–0.436; p < 0.001) symptoms. Compared to social distancing, adhering to stay-at-home orders or quarantining were not associated with anxiety and depressive symptoms or loneliness (both p ≥ 0.095).Conclusions: High loneliness may be a key risk factor for the development of future anxiety or depressive symptoms, underscoring the need to combat or prevent loneliness both throughout and beyond the COVID-19 pandemic. COVID-19 containment strategies were not associated with mental health, indicating that other factors may explain previous reports of mental health deterioration throughout the pandemic.

scholarly journals Mental health difficulties of adults with COVID-19-like symptoms in Bangladesh: a case control-study

2020 ◽  
Author(s):  
Musammet Rasheda Begum ◽  
Shafiqul Islam Khan ◽  
Hasan Al Banna ◽  
Satyajit Kundu ◽  
Munnaf Hossen ◽  
...  
Keyword(s):  
Mental Health ◽  
Logistic Regression ◽  
Depressive Symptoms ◽  
Online Survey ◽  
Case Control Study ◽  
Case Control ◽  
Anxiety Symptoms ◽  
Multivariate Logistic Regression ◽  
Mental Health Difficulties ◽  
Control Study

IntroductionThe rapid spread of SARS-CoV-2 coupled with inefficient testing capacities in Bangladesh has resulted in a number of deaths from COVID-19-like symptoms that have no official test results. Insufficient test sites and healthcare facilities catered to COVID-19 has led to feelings of fear and frustration in those who are sick. This study was the first study which explored the mental health of adults with the most common COVID-19-like symptoms in Bangladesh.MethodsThis retrospective case control study gathered data via an online survey to explore the mental health of Bangladeshi adults with symptoms akin to COVID-19. Level of stress, anxiety symptoms, and depressive symptoms were measured with the DASS-21. Chi-square tests and multivariate logistic regression was performed to examine the association of variables. ResultsThe prevalence rates of anxiety symptoms, and depressive symptoms of overall population were 26.9% and 52.0% respectively and 55.6% reported mild to extremely severe levels of stress. Multivariate logistic regression determined that respondents with COVID-19-like symptoms (case) reported higher odds for stress level (AOR: 2.043; CI: 1.51-2.76), anxiety symptoms (AOR: 2.770; CI: 2.04-3.77) and depressive symptoms (AOR: 1.482; CI: 1.12-1.96) than asymptomatic respondents (control). ConclusionPatients with symptoms like those of COVID-19 should be prioritized in the healthcare setting in order to reduce mental health difficulties throughout the pandemic.

scholarly journals Does job promotion affect men’s and women’s health differently? Dynamic panel models with fixed effects

2016 ◽  
pp. dyw310 ◽  
Author(s):  
Anna Nyberg ◽  
Paraskevi Peristera ◽  
Hugo Westerlund ◽  
Gunn Johansson ◽  
Linda L Magnusson Hanson
Keyword(s):  
Women’S Health ◽  
Women's Health ◽  
Fixed Effects ◽  
Dynamic Panel ◽  
Panel Models ◽  
Job Promotion ◽  
Dynamic Panel Models

scholarly journals Capital adjustment cost and inconsistency in income-based dynamic panel models with fixed effects

2019 ◽  
Vol 20 (4) ◽  
pp. e1002-e1018 ◽  
Author(s):  
Parantap Basu ◽  
Yoseph Getachew ◽  
Keshab Bhattarai
Keyword(s):  
Fixed Effects ◽  
Cost Of Capital ◽  
Moving Average ◽  
Adjustment Cost ◽  
Dynamic Panel ◽  
Panel Models ◽  
Capital Adjustment ◽  
Long Run ◽  
Fixed Effects Estimator ◽  
Dynamic Panel Models

Abstract After the seminal work of Nickell (1981), a vast literature demonstrates the inconsistency of ‘conditional convergence’ estimator in income-based dynamic panel models with fixed effects when the time horizon (T) is short but the sample of countries (N) is large. Less attention is given to the economic root of inconsistency of the fixed effects estimator when T is also large. Using a variant of the Ramsey growth model with long-run adjustment cost of capital, we demonstrate that the fixed effects estimator of such models could be inconsistent when T is large. This inconsistency arises because of the long-run adjustment cost of capital which gives rise to a negative moving average coefficient in the error term. Income convergence will be thus overestimated. We theoretically characterize the order of this inconsistency. Our Monte Carlo simulation demonstrates that the size of the bias is substantial and it is greater in economies with higher capital adjustment costs. We show that the use of instrumental variables that take into account the presence of the negative moving average term in the error will overcome this bias.

BIAS REDUCTION FOR DYNAMIC NONLINEAR PANEL MODELS WITH FIXED EFFECTS

2011 ◽  
Vol 27 (6) ◽  
pp. 1152-1191 ◽  
Author(s):  
Jinyong Hahn ◽  
Guido Kuersteiner
Keyword(s):  
Monte Carlo ◽  
Nonlinear Dynamic ◽  
Fixed Effects ◽  
Bias Reduction ◽  
Small Sample ◽  
Dynamic Panel ◽  
Panel Models ◽  
Fixed Effects Estimator ◽  
Dynamic Panel Models

The fixed effects estimator of panel models can be severely biased because of well-known incidental parameter problems. It is shown that this bias can be reduced in nonlinear dynamic panel models. We consider asymptotics wherenandTgrow at the same rate as an approximation that facilitates comparison of bias properties. Under these asymptotics, the bias-corrected estimators we propose are centered at the truth, whereas fixed effects estimators are not. We discuss several examples and provide Monte Carlo evidence for the small sample performance of our procedure.

scholarly journals Socio-demographic correlates of depressive and anxiety symptoms among Jamaicans during the COVID-19 pandemic: A Rapid Online Survey

2021 ◽  
Author(s):  
Gabrielle Mitchell
Keyword(s):  
Mental Health ◽  
Depressive Symptoms ◽  
Educational Level ◽  
Online Survey ◽  
Anxiety Symptoms ◽  
Cross Sectional ◽  
Lower Educational Level ◽  
Younger Age ◽  
Demographic Correlates ◽  
The Government

Objectives This study sought to investigate the prevalence and socio-demographic correlates of depressive and anxiety symptoms among Jamaicans during the ongoing COVID-19 pandemic. Methods This was a cross-sectional, population-based online survey. Persons 18 years and older, who reside in Jamaica were targeted via the Facebook social media platform. Those interested in participating were routed to a separate survey platform. The questionnaire captured data related to socio-demographics, knowledge of COVID-19, and beliefs and attitudes about COVID-19. Mental health issues including depressive and anxiety symptoms were assessed with questionnaire items from the Patient Health Questionnaire (PHQ-2) and the Generalized Anxiety Disorder (GAD-2). Results There were 485 participants in the study (90.8% female and 9.2% male). Almost all (97.7%) of participants felt that COVID-19 had greatly affected their lives. The prevalence of notable depressive symptoms and anxiety symptoms was 54.7% and 49.2% respectively. Binary logistic regression found significant associations between younger age, lower educational level, unemployment and significant depressive symptoms. While younger age and lower educational level were associated with significant anxiety symptoms. Conclusion The findings suggest the need for the government to develop targeted mental health initiatives for young adults, the under- or unemployed and those with lower levels of education.

Identification without assuming mean stationarity: Quasi ML estimation of dynamic panel models with endogenous regressors

2020 ◽  
Author(s):  
Hugo Kruiniger
Keyword(s):  
Fixed Effects ◽  
Control Function ◽  
Limited Information ◽  
Dynamic Panel ◽  
Ml Estimation ◽  
Panel Models ◽  
Endogenous Regressors ◽  
Model Residuals ◽  
Stationarity Conditions ◽  
Dynamic Panel Models

Abstract Linear GMM estimators for dynamic panel models with predetermined or endogenous regressors suffer from a weak instruments problem when the data are highly persistent. In this paper we propose new random and fixed effects Limited Information Quasi ML estimators (LIQMLEs) for such models. We also discuss LIQMLEs for models that contain time-varying individual effects. Unlike System GMM estimators, the LIQMLEs do not require mean stationarity conditions for consistency. Such conditions often do not hold for the models we consider. Our LIQMLEs are based on a two-step control function approach that includes the first stage model residuals for a predetermined or endogenous regressor in the outcome equation. The LIMLEs are more precise than non-linear GMM estimators that are based on the original outcome equation. The LIQMLEs also compare favourably to various alternative (Q)MLEs in terms of precision, robustness and/or ease of computation.

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