Directions
To complete this assignment, first review the Project Resources Document and select a category (e.g., higher education, business and finance, innovative technologies). Skim the articles in that category and pick an article to read in depth. Then identify topics within that article that you might want to work with for this course. The information under each prompt will guide you through this process.
For this assignment, you will also write a research question and state the purpose of your research. Feel free to use the research question template in the Supporting Materials section as a starting place; however, if you want to craft your research question without the template, you are welcome to do so.
Specifically, you must address the following:
Select a category in the Project Resources Document.
Review the categories in the Project Resources Document linked in the Supporting Materials section.
After selecting a category that interests you, note in your submission your category choice.
Then, review the articles related to your category.
Select one of the articles listed under your category.
Carefully read your chosen article.
As you read, consider some possible topic ideas related to your article that are debatable and could be used for a persuasive essay.
Cut and paste into your paper the corresponding citation in either MLA or APA format for the source you chose. You may choose if you would like to use MLA or APA documentation style this term. You will need to use the format you choose for this assignment for the duration of the course, including all assignments and the project.
Identify a debatable topic related to your chosen source. Consider the following in your response:
State the specific topic that you identified within your chosen source. This will become your focus for your persuasive essay.
State two perspectives on your topic and who would be interested in this topic.
Write a research question about your topic. These steps will help you write your research question:
Review the templates and/or the exemplar to help you draft your research question. While you may choose to formulate your research question without using the templates, your research question needs to aim to solve a problem and lead to a complex discussion.
Explain how your research question identifies a specific aspect of your issue.
Explain how your research question aims to solve a problem.
Identify the complex arguments and counterarguments that are potential answers to your research question. If you cannot find complex arguments and counterarguments, then you may need to reconsider your research question. If you are stuck, reach out to your instructor.
State the purpose of your research. Consider the following in your response:
Explain what you want to learn more about regarding your topic.
Discuss how research will help you understand the multiple voices within your debatable topic.
ARTICLE
The impact of the COVID-19 pandemic on mental health and functional outcomes in Veterans with psychosis or recent homelessness: A 15-month longitudinal study.
Citation metadata
Authors: Jonathan K. Wynn, Amanda McCleery, Derek M. Novacek, Eric A. Reavis, Damla Senturk and Catherine A. Sugar
Date: Aug. 24, 2022
From: PLoS ONE(Vol. 17, Issue 8)
Publisher: Public Library of Science
Document Type: Report
Length: 5,678 words
Content Level: (Level 5)
Lexile Measure:1510L
DOI: http://dx.doi.org.ezproxy.snhu.edu/10.1371/journal.pone.0273579
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Main content
Abstract:
Background
The COVID-19 pandemic has had unprecedented effects on mental health and community functioning. Negative effects related to disruption of individuals’ social connections may have been more severe for those who had tenuous social connections prior to the pandemic. Veterans who have recently experienced homelessness (RHV) or have a psychotic disorder (PSY) are considered particularly vulnerable because many had poor social connections prior to the pandemic.
Methods
We conducted a 15-month longitudinal study between May 2020 -July 2021 assessing clinical (e.g., depression, anxiety) and community (e.g., social functioning, work functioning) outcomes. Eighty-one PSY, 76 RHV, and 74 Veteran controls (CTL) were interviewed over 5 assessment periods. We assessed changes in mental health and community functioning trajectories relative to pre-pandemic retrospective ratings and examined group differences in these trajectories.
Results
All groups had significantly increased symptoms of depression, anxiety, and concerns with contamination at the onset of the pandemic. However, RHV and PSY showed faster returns to their baseline levels compared to CTL, who took nearly 15 months to return to baseline. With regards to functioning, both RHV and PSY, but not CTL, had significant improvements in family and social networks over time. Work functioning worsened over time only in PSY, and independent living increased over time in both RHV and PSY but not CTL.
Conclusions
These results reveal that vulnerable Veterans with access to VA mental health and case management services exhibited lower negative impacts of the COVID-19 pandemic on mental health and community functioning than expected.
Author(s): Jonathan K. Wynn 1,2,*, Amanda McCleery 3, Derek M. Novacek 1,2, Eric A. Reavis 1,2, Damla Senturk 2,4, Catherine A. Sugar 2,4, Jack Tsai 5,6,7, Michael F. Green 1,2
Introduction
The COVID-19 pandemic has affected most people throughout the world, either directly through contracting the SARS-CoV-2 virus or indirectly due to measures intended to slow the spread of the virus [1]. The initial stay-at-home orders andsocial distancing recommendations enacted by public health authorities in response to COVID-19 dramatically impacted people’s daily social interactions, work, and living situations [2-5]. These impacts are likely to have affected a range of mental health issues and community functioning [5]. The nature of these impacts, though, likely changed over time as the pandemic progressed. Measures intended to slow the spread were lifted, reinstated, and lifted again throughout most of the United States as the pandemic wore on. However, it is not clear if the negative psychosocial impacts of the pandemic after the initial shock of lockdowns and social distancing were short lived or sustained over time.
The pandemic and resulting public health measures have impacted mental health broadly in the general population in the U.S. and across the globe. Several studies have reported increased levels of depression, anxiety, loneliness, and suicidal ideation [6-12]. The pandemic also impacted other areas of mental health, for example with concerns about contamination and obsessive behaviors (such as hand washing) [13]. It has also been well documented that lockdowns and social distancing mandates have negatively affected community functioning, including reductions in number and frequency of social and family contacts, substantial job losses and reductions in pay at the onset of the pandemic, and loss of housing security [14].
Two populations within the U.S. Department of Veterans Affairs (VA) system who may have been particularly vulnerable to the mental health and community functioning impacts of the pandemic are those who have recently experienced homelessness (recently housed Veterans, RHV) or have a psychotic disorder (PSY). Veterans who are receiving services through the VA are an important group to examine because they have resources available, including case management and financial support, that are not uniformly available to other U.S. individuals with these conditions. However, recently housed Veterans and Veterans with PSY commonly lack strong social contacts and therefore might be particularly vulnerable to mental health and social impacts of the pandemic [15]. In a previously published paper from our group [16] that focused on the first several months of the pandemic (through June 2020), we assessed RHV, PSY, and control (CTL; no history of psychosis or chronic homelessness) Veterans. We found increased clinical symptoms (e.g., anxiety, depression, etc.) but relatively little change in social and functional outcomes across groups compared to a retrospective pre-pandemic measure. A recent study in a broad sample of U.S. Veterans showed little change in diagnoses of major depressive disorder or post-traumatic stress disorder due to the pandemic, though middle-aged Veterans showed an increase in generalized anxiety diagnoses [17]. In contrast to the detrimental effects of the pandemic, a national study of U.S. Veterans revealed that a substantial proportion of Veterans reported positive psychological changes and posttraumatic growth during the COVID-19 pandemic [18], which is consistent with pre-pandemic findings of high resilience in the Veteran population [19-21].
It is not clear if these conflicting findings, where some studies find detrimental effects whereas others find some positive effects, are due to the different samples (i.e., vulnerable Veterans vs. a broad Veteran sample) or the timeframe examined. Therefore, in the current study we examined the long-term impact of the COVID-19 pandemic (from the onset of the pandemic through July 2021) on clinical factors (e.g., anxiety, depression) and community integration factors (e.g., social networks, family networks, work) in these two vulnerable Veteran groups, as well as in control Veterans. The current paper extends our previous findings [16] by detailing changes in trajectories over a much longer time period (15 months) that was suited for a more sophisticated analytic approach, and to determine if being in a vulnerable group (i.e., RHV or PSY) impacted trajectories more or less than CTL.
Methods
Data collection and setting
Data collection occurred between May 2020 -July 2021. All data were collected remotely via telephone interviews from Veteran participants living in Los Angeles County. Data were collected longitudinally over five assessment periods during the pandemic: an initial period (“initial”) and four separate follow-ups (“Follow-Up 1”, “Follow-Up 2”, etc.). Each assessment period lasted approximately two-months. The initial period occurred between May-July 2020; Follow-Up 1 between August-October 2020; Follow-Up 2 between October-November 2020; Follow-Up 3 between January-February 2021; and Follow-Up 4 between April-July 2021. In addition to these assessments, at the initial visit participants were asked to provide ratings on all measures in reference to how they were in January 2020 (i.e., prior to the pandemic, which we refer to as “pre-COVID”). Thus, we relied on participants’ retrospective recall of their clinical and functional status to serve as a pre-COVID measure, which could potentially have caused recall bias due to assessing during the initial phase of the pandemic (i.e., in May 2020). The data from the initial and Follow-Up 1 assessments have previously been published [16], and further details on recruitment and measures assessed are available in that paper. The current paper reports for the first time on data from all four follow-up periods (including the first follow-up data that were previously published) but used a different analytical approach to examine changes over time in our key measures (generalized additive models; see Analytical Approach below). All recruitment and study procedures were approved by the VA Greater Los Angeles Institutional Review Board.
Recruitment and selection criteria
Selection criteria were intentionally broad for each group and relied on chart diagnoses (or lack thereof) obtained from the VA computerized patient record system (CPRS). All participants in each group consisted of Veterans. For PSY, participants required a psychotic disorderdiagnosis (other than substance-induced psychosis). For RHV, participants required a history of chronic homelessness and placement in housing within the past 12 months with a HUD-VASH voucher. Of the RHV, eight received a diagnosis for a psychotic disorder, which was permissible for this group. For CTL, exclusion criteria were no history of a psychotic disorder or evidence of homelessness based on review of CPRS. Thus, controls with other possible psychiatric disorders other than psychosis (e.g., depression, PTSD), substance use disorders, and no history of chronic homelessness, were eligible. Information on diagnosis (including mood disorder, PTSD, substance use disorder, and alcohol use disorder) for each group is provided in S1 Table.
Across the three groups, we identified 956 participants in the Los Angeles area who were potentially eligible and contacted them by phone. Demographic information on the three groups can be found in S1 Table. Following a short description of the study, participants provided informed consent if they agreed to partake in the study. The participant’s contact information was then provided to one of ten clinically trained interviewers who conducted all assessments via phone interview.
Clinical and functional measures
For clinical factors, we assessed depression (PHQ-9, range 0-27; [22]), anxiety (GAD-7, range 0-21; [23]), obsessive-compulsive thoughts related to germs and contamination (DOCS Category 1: Concerns about Germs and Contamination, range 0-20; [24]), and loneliness (ULS, range 0-60; [25]). For all clinical measures, higher scores indicate worse symptoms. For community functioning, we administered the Role Functioning Scale [26] which assesses four different domains: family networks, social networks, work, and independent living. Scores range from 1-7, with higher scores indicating better functioning.
In addition to the clinical and functional measures, we asked participants at each assessment period how many telephone or video telehealth contacts they made with VA healthcare providers in the prior month and examined the mean (standard error) number of visits across all time points.
Analytical approach
Our main analytical approach to examine fluctuations over time in the clinical and community functioning factors utilized varying coefficient models, which were implemented using the generalized additive models (GAM) structure via the mgcv package version 1.8-34 [27] implemented in R version 4.0.5 [28]. Because the time course of the pandemic has not behaved in a predictable manner (i.e., in a linear or even quadratic fashion), our analytical approach did not assume a rigid parametric form, such as that of a general linear model. Given that we were interested in the differential trajectories between groups in clinical and functional outcomes over time, we analyzed the data using a time varying coefficient model (VCM). With VCM, flexible, smoothed functions for the shape of the trajectories over time can be easily fit and both the linear and non-linear aspects of the data can be modeled without specifying a priori what the patterns should look like (e.g., linear, quadratic, etc.).
As we were interested in change in clinical and functional outcomes, we analyzed data in reference to the pre-COVID assessment. That is, we subtracted scores from pre-COVID measures from each respective assessment period to form a change score. While our follow-ups occurred across broad windows, there was wide variability during that window as to when an individual was assessed. To take advantage in the differences in timing, we used specific dates as predictors, rather than follow-up assessment number treated as a factor. We capitalized on the individual differences in the date of the assessment using the VCM approach by calculating how many days passed from the day of a participant’s interview for each assessment relative to the pre-COVID assessment period (for the sake of analyses and figures we set this date to March 1, 2020).
We fit a series of VCMs with each of the clinical or functional factors as the outcome (details of the VCM approach we adopted can be found in S2 File). Within each model, specific contrasts can be extracted from fitting the trajectories enabling us to simultaneously answer two separate questions: 1) does the level of the outcome vary over time within each group; and 2) does the level of the outcome vary differentially over time between groups? We report tests of significance both for time effects related to the pandemic (i.e., within group effects) and whether those effects differed by group (i.e., between group effects). For the statistical results of these analyses, we present the F – and p -values for the smooth terms for each of the three within group effects, along with the F – and p -values for the three between group effects, in Table 2, and describe the pattern of findings in the results.
We present the smoothed curves separately for each group as a function of time for each of the four main clinical outcomes (Fig 1) and the four main community functioning outcomes (Fig 2). We also present the mean difference between pairs of groups (CTL vs. PSY, CTL vs. RHV, and PSY vs. RHV), with 95% confidence intervals, in S1 and S2 Figs. However, the results of formal statistical tests for group differences, which use an omnibus approach, are presented in Table 2. This omnibus approach tests whether the entire time-varying group difference equals zero (hence, the tests are more conservative) and utilize the pointwise confidence intervals as visual guidance on identifying which time periods contributed to significant group differences (seen in S1 and S2 Figs).
Fig 1. Results for the GAM analyses of clincial outcomes. The smoothed curves represent the change over time in trajectories for controls (CTL; gray), recently housed Veterans (RHV; yellow), and Veterans with psychosis (PSY; blue). In all cases higher scores indicate higher symptoms. The panels show results for A) anxiety, B) depression, C) loneliness, and D) obsessive-compulsive traits. [see PDF for image]
Fig 2. Results for the GAM analyses of community integration outcomes. The smoothed curves represent the change over time in trajectories for controls (CTL; gray), recently housed Veterans (RHV; yellow), and Veterans with psychosis (PSY; blue). In all cases, higher scores indicate better functioning. The panels show results for A) family functioning, B) social functioning, C) work outcomes, and D) independent living. [see PDF for image]
Results
In comparing demographics, controls were significantly older and had more personal education than either the psychosis or homeless groups; however, there were no other group differences (including gender, parental education, ethnicity, or race). Results of a mixed model analysis examining the number of remote (telephone and/or video) telehealth visits revealed a significant main effect of Group, F2,231.98 = 5.64, p = 0.004, and Follow-Up Period, F4,753.12 = 7.99, p 2.60 x 10-6 ; the Group X Follow-Up Period interaction was not significant, F8,752.99 = 0.67, p = 0.714. For Group, there were significantly more visits (estimated marginal means [standard error]) in RHV, 2.54 (0.29), and PSY, 2.49 (0.27), compared to CTL, 1.34 (0.29), p’s PSY > CTL (Fig 2D, Table 2).
Discussion
The findings of this longitudinal study on the effects of the pandemic on clinical and community functioning outcomes in vulnerable Veterans showed significant group differences in trajectories for clinical factors as well as for functional factors. All three groups of Veterans showed large negative impacts on mental health outcomes, including depression, anxiety, and OCD-like behaviors, during the first months of the pandemic, relative to retrospective pre-pandemic self-ratings. However, the two vulnerable groups, RHV and PSY, showed faster improvements than CTL on several clinical measures; the CTL group only began to show recovery to pre-COVID levels in the latter part of the study (corresponding to April 2021, more than one year after the onset of the pandemic). Contrary to expectations, none of the Veteran groups showed worsening loneliness, with PSY showing reductions in loneliness over time relative to pre-COVID ratings. Regarding functional outcomes, both RHV and PSY showed improvements in both family and social functioning over time, only PSY showed worse work outcomes over time, and both RHV and PSY showed better independent living outcomes over time. CTL showed no significant changes in any functional outcomes throughout the study. Overall, the results paint a complex picture, showing that vulnerable Veterans were much more resilient to the negative impacts of the pandemic than expected, with the control group showing a more sustained negative impact on clinical outcomes that only began to recover late into the pandemic.
Despite our concerns about the negative mental health impact of the pandemic, the notable lack of greater negative impacts on mental health and community functioning in the vulnerable Veterans in the current study is consistent with a growing literature showing that mental health or community functioning, both in the Veteran [17-21] and general population [29,30], was not as severely impacted as expected. For example, a large general population cohort in the United Kingdom showed little or no change in mental health during the first six months of the pandemic [29]. A similar pattern across other countries, including the U.S., was seen in healthy older adults who did not show increases in negative mental health outcomes compared to younger populations [30]. Studies in Veterans [17,18], including our previous paper [16], suggested that Veterans may have good resilience to negative life events which may have impacted the findings. However, we did not examine resilience as a moderating effect in the current paper, and thus cannot explicitly determine if resilience reduced any potential negative impacts on the findings presented herein. While these studies do suggest that the negative impacts of the pandemic on mental health and community functioning were not as large as expected, those studies only covered the early months of the pandemic. Hence, we did not know if this pattern would continue the longer the pandemic wore on. The current study addresses this question directly and shows that the vulnerable groups of Veterans recovered to pre-pandemic levels (and in some cases showed improvements relative to baseline), and that controls (i.e., Veterans without psychosis or a history of chronic homelessness) showed extended elevations in symptomatology that eventually returned to baseline levels at the end of the 15-month study.
There are several potential reasons that might explain why the vulnerable Veterans did not exhibit increased negative impacts on mental health and community integration during the pandemic compared to control Veterans. Chief among these is that many of the vulnerable Veterans had access to and engaged with VA wrap-around services (including mental health and case management services) that were available to them remotely over the course of the study. In the month preceding each assessment, the two vulnerable groups self-reported nearly twice as many telehealth (phone or video) visits with a VA provider. A second possible explanation for our pattern of results is that our Veteran sample was older (mid-50s) and predominantly male (as expected at a VA), which may have been protective as negative mental health outcomes due to the pandemic appear to have been more severe in younger populations and in women, on average [31-33]. Finally, these two vulnerable groups may already be more accustomed to major disruptions, instability, and inconveniences in their lives, so the pandemic may not have seemed especially disruptive, relative to these other experiences.
There are some limitations that should be noted. First, the study relied on retrospective self-reports made by the participants, including the pre-pandemic rating, which could clearly be unreliable or biased based on when we began interviewing participants (May 2020). Second, nearly all participants were male and in the early to mid-fifties, making it impossible to examine if females or younger populations were more or less impacted by the pandemic. Finally, all participants were Veterans, making it difficult to determine if the results would generalize to a non-Veteran population. As mentioned above, the Veteran population has been found to have high resilience, likely due to their military training, past hardships they have overcome, and many medical, psychological, and housing resources provided by the Department of Veterans Affairs that are not readily available to those in the general population. Thus, these unique features of Veterans may differentiate them in many ways from the general non-Veteran population and could have impacted our results.
Despite these limitations, the current findings show that vulnerable Veterans experienced substantial resilience to the negative impacts of the pandemic on mental health and community integration outcomes. These results are consistent with a growing literature showing few negative impacts of the pandemic on mental health and community functioning outcomes in the general community. Our findings also suggest that making mental health counseling or access to community support services widely available in the general community, as they are within the VA, could help to buffer the population against negative mental health and community integration outcomes during future disasters or pandemics.
Supporting information
S1 Fig Between group differences in changes in clinical trajectories over time. Each column represents one clinical measure, and each row represents one between group comparison (top: controls vs. homeless; mid: controls vs. psychosis; bottom: homeless vs. psychosis). The red line represents the mean difference, and the dashed blue lines and gray shading represent the 95% confidence interval.
(TIF)
S2 Fig Between group differences in changes in functional trajectories over time. Each column represents one functional measure, and each row represents one between group comparison (top: controls vs. homeless; mid: controls vs. psychosis; bottom: homeless vs. psychosis). The red line represents the mean difference, and the dashed blue lines and gray shading represent the 95% confidence interval.
(TIF)
S3 Fig Bar chart of mean values by assessment period for clinical measures. (TIF)
S4 Fig Bar chart of mean values by assessment period for clinical measures. (TIF)
S1 Table Demographic information and clinical diagnoses by group. Demographic information and clinical diagnoses provided separately for each group based on baseline enrollment numbers. Values are either means (standard deviations) or percentages and indicated accordingly.
(DOCX)
S1 File Minimal data set. (CSV)
S2 File Supplemental methods. This file presents specific details for the VCM analyses, as well as supplemental figures.
(DOCX)
Acknowledgements:
We extend our gratitude to our recruiters and interviewers without whom this work would not have been possible: Lauren Catalano, PhD, Gerard De Vera, Arpi Hasratian, Julio Iglesias, Brian Ilagan, Mark McGee, Jessica McGovern, PhD, Ana Ceci Myers, Megan Olsen, and Michelle Torreliza. Finally, we thank our Veteran volunteers for taking the time to participate in this research.
The contents of this article do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.
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Author Roles:
Jonathan K. Wynn: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing
Amanda McCleery: Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing
Derek M. Novacek: Formal analysis, Writing – review & editing
Eric A. Reavis: Data curation, Investigation, Writing – original draft, Writing – review & editing
Damla Senturk: Formal analysis, Writing – review & editing
Catherine A. Sugar: Formal analysis, Methodology, Writing – review & editing
Jack Tsai: Conceptualization, Writing – review & editing
Michael F. Green: Conceptualization, Formal analysis, Funding acquisition, Investigation, Writing – original draft, Writing – review & editing
Author Affiliation:
1 Veterans Affairs Rehabilitation Research & Development Center for Enhancing Community Integration for Homeless Veterans, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California, United States of America, 2 Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California, United States of America,3 Department of Psychological and Brain Sciences, University of Iowa, Iowa City, Iowa, United States of America, 4 Department of Biostatistics, University of California, Los Angeles, Los Angeles, California, United States of America, 5 Veterans Affairs National Center on Homelessness Among Veterans, Washington, DC, United States of America, 6 School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, United States of America,7 Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
Corresponding Author: * E-mail:
Editor: Pedro Vieira da Silva Magalhaes, Universidade Federal do Rio Grande do Sul, BRAZIL
Article History:
Received Date: 2/15/2022
Accepted Date: 8/10/2022
Published Date: 8/24/2022
Copyright: ©
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Data Availability: All relevant data are within the paper and its Supporting information files.
Funding: This study was funded by the Research Enhancement Award Program to Enhance Community Integration in Homeless Veterans (MFG) Rehabilitation Research and Development grant D1875-F from the Department of Veteran Affairs, https://www.research.va.gov/, and by the VA National Center on Homelessness among Veterans (MFG), https://www.va.gov/homeless/nchav/index.asp. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
DOI: 10.1371/journal.pone.0273579
Copyright: COPYRIGHT 2022 Public Library of Science
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