Abstract
Objective:
The coronavirus disease 2019 (COVID-19) pandemic created a sudden shift in the social lives with important negative impacts on the mental health. The current article aims to understand how the pandemic may have differentially impacted the mental health of adolescents and young adults with recent (1 year or less) and past (> 1 year) exposure to self-directed violence (SDV).
Method:
Data were collected online from 990 youth and young adults, aged 13–23 years between November 27, 2020 and December 11, 2020.
Results:
Participants who had recently been exposed to SDV reported being more impacted by the pandemic and had poorer mental health indicators. Participants with past SDV exposure who engaged in a high number of prosocial activities (e.g., talking with friends) were less likely to report depressive symptoms (β = −.13, p=.01) than similarly engaged non-exposed participants; the same was true for recently exposed participants (β = −.14, p=.02).
Conclusions:
Findings highlight that the effects of the COVID-19 pandemic on the mental health of young people are compounded by exposure to mental health concerns of people in their network.
Keywords: self-directed violence, suicide, prosocial activity, COVID-19, mental health
In response to the global coronavirus disease 2019 (COVID-19) pandemic, a number of public health policies were put into place to help reduce the spread of the virus, including social distancing, home quarantine, and school closures (Hartley & Perencevich, 2020; Komaroff & Belhouchet, 2020; Pan et al., 2020; Viner et al., 2020). These policies created a sudden and dramatic shift in the social lives of youth with important negative impacts on the mental health of teens and young adults, as seen in a growing body of research (de Miranda et al., 2020; Guessoum et al., 2020; Magson et al., 2021; Racine et al., 2020). Further, COVID-19 and related public health policies may be amplifying existing mental health disparities for youth exposed to self-directed violence (SDV; i.e., suicide attempts, suicide ideation, non-suicidal self-injury). Indeed, before to COVID-19, data documents the toll of exposure to SDV on depressive symptomatology, suicidal ideation, and attempts (Gould et al., 2018; Maple et al., 2017; Mitchell et al., 2019).
Social integration is a protective factor against suicide among youth (Sheftall et al., 2013; Wyman et al., 2019). We need to better understand how youth, especially at-risk youth like those exposed to SDV, stayed socially connected and with what effects on their well-being. Extant data during COVID-19 suggest complex relationships; some findings indicate positive relationships between helping others and higher daily positive affect (Sin et al., 2021) while other research found greater engagement in prosocial activities was related to more symptoms of anxiety (Alvis et al., 2020).
The current article aims to understand how the COVID-19 pandemic may be differentially impacting the mental health of youth with recent (1 year or less) and past (> 1 year) exposure to SDV. Data explores whether high engagement in prosocial activities moderates or buffers any relationships between recency of SDV exposure and mental health.
Method
Sample
The Exploring Your YOU-niverse Study is one in a series of independent national surveys of adolescents and young adults. This most recent one was designed to understand exposure to SDV. The protocol was reviewed and approved by Pearl Institutional Review Board. A sample of 1,031 youth and young adults (aged 13–23 years) was recruited between November 27, 2020, and December 11, 2020. The analytic sample for the current article were those who completed at least 90% of the COVID questions (n=990). Table 1 provides details of the demographic characteristics of the sample by recency of exposure to SDV.
Table 1.
Participant Demographic Characteristics by Recency of SDV Exposure
| All participants (N=990) n (%) |
No SDV exposure (n=165) n (%) |
SDV exposure >1 year ago (past) (n=371) n (%) |
SDV exposure < 1 year ago (recent) (n=454) n (%) |
P value |
|
|---|---|---|---|---|---|
| Age (M, SE) | 17.1 (2·75) | 16.5 (.20) | 17.7 (.15)a | 16.8 (.12)b | <.001 |
| Race | |||||
| White | 750 (75.8) | 109 (66.1) | 278 (74.9)a | 363 (80.0)a | .002 |
| Black | 87 (8.8) | 19 (11.5) | 37 (10.0) | 31 (6.8) | .11 |
| Asian | 91 (9.2) | 28 (17.0) | 31 (8.4)a | 32 (7.1)a | .001 |
| Native American | 25 (2.5) | 3 (1.8) | 9 (2.4) | 13 (2.9) | .75 |
| Mixed race | 103 (10.4) | 12 (7.3) | 40 (10.8) | 51 (11.2) | .35 |
| Hispanic/Latino ethnicity | 177 (17.9) | 29 (17.6) | 73 (19.7) | 75 (16.5) | .50 |
| Sexual minority | |||||
| No | 438 (44.2) | 107 (64.9) | 164 (44.2)a | 167 (36.8)a,b | <.001 |
| Yes | 552 (55.8) | 58 (35.1) | 207 (55.8) | 287 (63.2) | |
| Gender | |||||
| Male | 421 (42.8) | 89 (54.6) | 161 (43.5)a | 171 (37.9)a,b | <.001 |
| Female | 391 (39.7) | 65 (39.9) | 152 (41.1) | 174 (38.6) | |
| Gender minority | 172 (27.5) | 9 (5.5) | 57 (15.4) | 106 (23.5) | |
| Family income | |||||
| Lower than average | 208 (21.0) | 15 (9.1) | 80 (21.6)a | 113 (24.9)a | <.001 |
| Similar to average | 504 (50.9) | 95 (57.6) | 184 (49.6) | 225 (49.6) | |
| Higher than average | 203 (20.5) | 35 (21.2) | 75 (20.2) | 93 (20.5) | |
| Not sure | 75 (7.6) | 20 (12.1) | 32 (8.6) | 23 (5.1) | |
| Status in school | |||||
| Middle school (6–8 grade) | 153 (15.5) | 33 (20.0) | 36 (9.7)a | 84 (18.5)b | <.001 |
| High school (9–12 grade) | 557 (56.3) | 96 (58.2) | 200 (53.9) | 261 (57.5) | |
| High school graduate (not enrolled) | 58 (5.9) | 7 (4.2) | 30 (8.1) | 21 (4.6) | |
| Dropped out | 21 (2.1) | 2 (1.2) | 8 (2.2) | 11 (2.4) | |
| Higher education (trade or college) | 201 (20.3) | 27 (16.4) | 97 (26.1) | 77 (17.0) |
Note. SDV = self-directed violence.
Significantly different from no SDV exposure;
Significantly different from past SDV exposure.
Procedure
Participants were recruited through study ads on Facebook and Instagram. Survey aims were not mentioned in ads to reduce self-selection bias based upon interest in a particular topic. Those interested clicked on the online ad, which linked them to a secure survey website. This first page provided a study description and screening questions to determine eligibility. Those who were eligible (i.e., 13–23 years of age, living in the United States, English speaking), were then asked to read an assent form and to indicate their willingness to participate in the survey before continuing with the main survey. A waiver of parental permission was granted because requiring parental consent could potentially place youth in situations where their sexual experiences and/or sexual attraction could be unintentionally disclosed to their parents. Appropriate mechanisms were in place to protect participants, such as localized referrals to mental health supports via a clinical member of the research team.
Participants were given a $5 incentive as an Amazon gift code for completing the survey. Ineligible youth were directed to a web page that included links to general resources for youth. To promote a diverse sample, demographic quotas were utilized.
Measures
Exposure to self-directed violence (SDV) was measured with items used in prior research (Turner & Butler, 2003; Turner et al., 2006). Participants were asked about three different types of exposure to SDV: suicide/suicide attempt, suicide ideation, and non-suicidal self-injury (NSSI). Specifically, participants were asked:
“Has someone close to you ever tried to kill him or herself on purpose (like by shooting or cutting him or herself, or taking too many pills or drugs)?”
“Now thinking of situations where someone was thinking about, considering, or planning to kill themselves. Has someone close to you ever thought about killing themselves but did not make an attempt?”
“Now thinking of situations where someone was hurting themselves on purpose without wanting to die, like cutting or burning. Has someone close to you ever hurt themselves on purpose without wanting to die, as far as you know?”
For each type of SDV exposure we asked a series of follow-up questions designed for the current study to gather more specific details of the event. One of these items queried how long ago the SDV occurred. Responses options were less than 1 year ago, 1–3 years ago, 4–5 years ago, 6–10 years ago, and more than 10 years ago. Across each of the three SDV types examined, participants were classified into the following SDV exposure groups based on recency: (a) no SDV exposure (n=165), (b) past SDV exposure (> 1 year ago/not sure) (n=371), and (c) recent SDV exposure (1 year ago or less) (n=454).
COVID impact questions.
Participants were asked, in the past 3 months, how much the pandemic impacted: (a) mental health, (b) physical health, and (c) schoolwork / work. Response options ranged from 1 = not at all to 3 = a lot. Missing data was low (0.5% or less) and replaced with the item mean.
COVID prosocial activities.
Participants were asked how many times they had done nine different prosocial activities in the past 2 weeks (0 times, 1–2 times, 3–9 times, 10–19 times, 20–39 times, 40+ times). Missing data (2% or less) was replaced by the item mean. Reliability was acceptable (α = .75). For each prosocial activity queried, we created a dichotomous variable indicating any engagement. These items then were summed to create a total prosocial activity count. A variable indicating high prosocial activity was created to reflect those who had a count score at one standard deviation above the mean or higher.
Depressive symptoms.
We used the Modified Depression Scale (Dunn et al., 2012) to assess symptoms of depression. Participants were asked to report the frequency of six symptoms in the past month. We derived total scores by summing the 5-point Likert scale items (range = 5–25). Reliability for the scale was acceptable (x = .79). Missing data ranged from .48% to 2.04% and replaced with the item mean.
Subjective well-being was measured using seven items and assesses general life satisfaction (Hamby et al., 2018). Response options range from 1 = not true about me to 4 = mostly true about me. Reliability for the entire scale in the current study was excellent (x = .89). Items were summed to create a total scale score (range = 6–25). Missing data was no more than 5% and replaced with the item mean.
Demographic characteristics.
Age was a continuous variable ranging from 13–23 years. Self-reported household income comprised three answer choices: lower than average, about average, and higher than average. For multivariate analyses, those who indicated their family income was “lower than average” were compared to all other youth. Youth reported their race (coded as multiple variables: White vs Other; Black vs. Other; Mixed race vs. Other) and ethnicity (Hispanic vs. Other). Response options for gender were coded as male, female, and gender minority. Response options for sexual identity were coded as sexual minority (gay, lesbian, bisexual, questioning, queer, pansexual, asexual, other, and unsure) versus heterosexual.
Statistical Analysis
First, we made comparisons between recency of SDV exposure (none, > 1 year ago, 1 year ago or less) across participant demographic characteristics using chi-square cross tabulations for categorical variables and analysis of variance (ANOVA) for continuous variables. Next, we conducted bivariate analyses examining self-reported impact of COVID-19 and more generalized mental health across the three SDV exposure groups. We then explored bivariate differences for a variety of types of prosocial activities undertaken during COVID-19 by SDV exposure group, including comparisons in the total number of types of prosocial activity and engagement in a high number of activities. We conducted two linear regression analyses (outcomes: depressive symptoms, subjective well-being) to examine the main effects of recency of SDV exposure and high prosocial activity as well as the interaction of these two constructs on our outcomes of interest. Predictive margins were obtained for each of the levels in the interaction of these variables to further decipher the interaction effects. Participant demographic characteristics and a measure indicating self-reported honesty when completing the survey were included in all multivariate analyses.
Results
Recency of Exposure to Self-Directed Violence by Participant Characteristics
Almost half (45.9%) of youth in this sample had been exposed to SDV in the past year (recent); another 37.5% had exposure more than one year ago (past). Differences in recency of SDV exposure by demographic characteristics and mental health indicators are shown in Table 1.
COVID-19 Indicators by Recency of SDV Exposure
Youth who had recently been exposed to SDV reported being more impacted by the pandemic, particularly in the area of mental health (Table 2). Differences were noted between those recently exposed and nonexposed, as well as between recent and past exposed participants in terms of mental health impact and impact on schoolwork or work. Recently exposed youth reported more physical health impact compared to nonexposed participants. Similar patterns were noted for past month depressive symptomatology and subjective well-being except here we also found significant differences between the nonexposed and past exposed groups.
Table 2.
Bivariate Relationships Between COVID Impact by Recency of SDV Exposure
|
All participants (N=990) n (%) |
No SDV exposure (n=165) n (%) |
SDV exposure > 1 year ago (past) (n=371) n (%) |
SDV exposure < 1 year ago (recent) (n=454) n (%) |
P value |
|
|---|---|---|---|---|---|
| Past 3 months impact of pandemic on: | |||||
| Mental health (%) | |||||
| Not at all | 93 (9.4) | 25 (15.1) | 42 (11.3) | 26 (5.7)a,b | <.001 |
| A bit | 334 (33.7) | 66 (40.0) | 131 (35.3) | 137 (30.2) | |
| A lot | 563 (56.9) | 74 (44.9) | 198 (53.4) | 291 (64.1) | |
| Physical health (%) | |||||
| Not at all | 212 (21.4) | 45 (27.3) | 85 (22.9) | 82 (18.1)a | .06 |
| A bit | 489 (49.4) | 83 (50.3) | 177 (47.7) | 229 (50.4) | |
| A lot | 289 (29.2) | 37 (22.4) | 109 (29.4) | 143 (31.5) | |
| School work / work (%) | |||||
| Not at all | 70 (7.1) | 16 (9.7) | 32 (8.6) | 22 (4.9)a,b | .001 |
| A bit | 233 (23.5) | 50 (30.3) | 95 (25.6) | 88 (19.4) | |
| A lot | 687 (69.4) | 99 (60.0) | 244 (65.8) | 344 (75.8) | |
| Past month depressive symptomatology (M, SE) | 16.8 (4.03) | 14.7 (.33) | 16.7 (.19)a | 17.7 (.18)a,b | <.001 |
| Subjective well-being (M, SE) | 17.3 (4.96) | 19.3 (.35) | 17..7 (.24)a | 16.3 (.24)a,b | <.001 |
Note. COVID = Coronavirus Disease; SDV = self-directed violence
Significantly different from no SDV exposure;
Significantly different from past SDV exposure.
Prosocial Activity During the COVID-19 Pandemic by Recency of SDV Exposure
Many participants engaged in a variety of different types of prosocial activities in the 2 weeks prior to the survey (see Table 3). Many participants actively reached out to friends, family, and neighbors who they thought might be having a hard time because of the pandemic. Participants who were never exposed to SDV reported the highest number of prosocial activities. Significantly fewer types of prosocial activities were reported by past exposed youth compared to nonexposed youth; recently exposed youth were not significantly different from the other groups. Details of participant demographic characteristics by report of high prosocial activity (versus to vs. less) is provided in online supplemental materials Table S1.
Table 3.
Prosocial Activity During COVID by Recency of SDV Exposure
|
All participants (N=990) n (%) |
No SDV exposure (n=165) n (%) |
SDV exposure > 1 year ago (past) (n=371) n (%) |
SDV exposure < 1 year ago (recent) (n=454) n (%) |
P value |
|
|---|---|---|---|---|---|
| Social integration in past 2 weeks | |||||
| I have talked to friends virtually (like Facetime, Zoom) | 787 (79.5) | 127 (77.0) | 290 (78.2) | 370 (81.5) | .34 |
| I have spent time with people, like friends or family, face to face outside that I do not live with in-person |
732 (73.9) | 134 (81.2) | 271 (73.1) | 327 (72.0) | .06 |
| I have spent time with people, like friends or family, face to face inside that I do not live with in person |
725 (73.2) | 130 (78.8) | 266 (71.7) | 329 (72.5) | .20 |
| I have talked to family virtually (like Facetime, Zoom) | 582 (58.8) | 106 (64.2) | 217 (58.5) | 259 (57.1) | .27 |
| Helping behaviors in past 2 weeks | |||||
| I have reached out to friends who I think may be having a hard time because of the pandemic |
677 (68.4) | 103 (62.4) | 232 (62.5) | 342 (75.3)a,b | <.001 |
| I have reached out to family who I think may be having a hard time because of the pandemic |
416 (42.0) | 80 (48.5) | 141 (38.0) | 195 (42.9) | .07 |
| I have spent time helping a child, sibling or younger family member with online school |
514 (51.9) | 97 (58.8) | 178 (48.0) | 239 (52.6) | .06 |
| I have helped neighbors who I think may be having a hard time because of the pandemic |
202 (20.4) | 41 (24.9) | 73 (19.7) | 88 (19.4) | .30 |
| I have volunteered for programs to help other people deal with the impact of the pandemic |
165 (16.7) | 38 (23.0) | 60 (16.2) | 67 (14.8)a | .05 |
| Count of prosocial activities (M, SE) | 4.85 (2.07) | 5.19 (.17) | 4.66 (.11)a | 4.88 (.09) | .03 |
| High prosocial activities n (%) | 228 (23.0) | 54 (32.7) | 75 (20.2)a | 99 (21.8)a | .005 |
Note. SDV = self-directed violence
Significantly different from no SDV exposure;
Significantly different from past SDV exposure.
The Impact of Recency of SDV Exposure on Mental Health in the Context of High Prosocial Activity
We found main effects between both recent and past SDV exposure with past month depressive symptomatology in comparison to youth with no SDV exposure (see Table 4). A main effect of high prosocial activity with more depressive symptomatology was also identified. Main effects of recent and past SDV exposure on subjective well-being were noted; no significant main effect was found prosocial activity with subjective well-being.
Table 4.
The Intersection of Recency of SDV Exposure and Prosocial Activity During COVID-19
| Past month depressive symptoms |
Subjective well-being |
|||
|---|---|---|---|---|
| β (SE) | P value | β (SE) | P value | |
| Main effects | ||||
| SDV Exposure | ||||
| None (ref) | ·· | ·· | ·· | ·· |
| Past exposure (>1 year ago) | .26 (.43) | <.001 | -.12 (.53) | .02 |
| Recent exposure (1 year ago or less) | .36 (.42) | <.001 | -.26 (.52) | <.001 |
| High prosocial activity during COVID-19 | .13 (.63) | .04 | .02 (.78) | .72 |
| Interaction effects | ||||
| SDV Exposure X Prosocial Activity | ||||
| No SDV Exposure + High Prosocial (ref) | ·· | ·· | ·· | ·· |
| Past SDV Exposure + High Prosocial | -.13 (.80) | .01 | .05 (.98) | .38 |
| Recent SDV Exposure + High Prosocial | -.14 (.76) | .02 | .11 (.94) | .06 |
| Demographic characteristics | ||||
| Young adult vs adolescent | -.08 (.27) | .008 | .05 (.33) | .09 |
| Sexual minority | .15 (.27) | <.001 | -.12 (.33) | <.001 |
| Gender minority | .14 (.37) | <.001 | -.12 (.46) | .001 |
| Female gender | .10 (.27) | .002 | -.02 (.33) | .62 |
| Hispanic or Latino ethnicity | .04 (.34) | .24 | -.01 (.42) | .84 |
| White race | -.04 (.32) | .19 | .05 (.40) | .11 |
| Black race | .04 (.48) | .25 | .02 (.59) | .51 |
| Mixed race | -.02 (.44) | .60 | -.01 (.54) | .66 |
| Low income | .09 (.30) | .003 | -.12 (.37) | <.001 |
Note. All models adjust for self-reported honesty in answering survey questions.
COVID-19 = coronavirus disease 2019; Ref = reference category; SDV = self-directed violence
Participants with past SDV exposure who engaged in a high number of prosocial activities were less likely to report depressive symptoms than similarly engaged nonexposed participants; the same was true for recently exposed participants. For the recent SDV exposed group, high prosocial activities resulted in marginal improvement to subjective well-being scores compared to similarly engaged nonexposed participants. Adolescents, sexual minority youth, gender minority youth, cisgender females, and those residing in low income households were similarly likely to report elevated depressive symptomatology. Sexual and gender minority participants as well as those living in low income households had less subjective well-being.
Predictive Margins for the Interaction Between Recency of SDV Exposure and High Prosocial Activity
Table 5 depicts the predictive margins for the interaction of recency of SDV and high prosocial activity on the two mental health outcomes, contrasting all possible combinations. The expected depressive symptom score was lowest for participants with no SDV exposure who had engaged in less prosocial activity (M = 14.7, SE = .37) and highest for those who had recent SDV exposure and less prosocial activity (M = 17.6, SE = .20). When examining expected depressive symptom scores within SDV exposure group by prosocial activity, we found that, among both exposed groups the expected depressive symptom score was lower when the participant had engaged in high prosocial activity. For example, among recently exposed youth, the expected depressive symptom score was a mean of 17.0 (SE = .38) for those who had engaged in high prosocial activity and the mean was 17.6 (SE = .20) for those who had less prosocial activity. Among the non-SDV exposed group, we found the opposite: Higher depressive symptom scores were noted for the high prosocial activity group (M = 16.0, SE = .52) compared to the less prosocial activity group (M = 14.7, SE = .37), although average expected depressive symptom scores were lowest for this group overall. Similar patterns were seen with subjective well-being with high prosocial activity in the context of past or recent SDV exposure. See online supplemental materials Figures S1 & S2 for graphic depictions of these interactions.
Table 5.
Predictive Margins of Mental Health Based on Regression Models
|
Construct |
95% CI | ||||
|---|---|---|---|---|---|
|
Margin |
Standard error |
P value |
Lower |
Upper |
|
| Depressive symptoms | |||||
| No SDV exposure/less prosocial | 14.7 | .37 | <.001 | 14.0 | 15.4 |
| No SDV exposure/high prosocial | 16.0 | .52 | <.001 | 14.9 | 17.0 |
| Past SDV exposure/less prosocial | 16.9 | .22 | <.001 | 16.5 | 17.3 |
| Past SDV exposure/high prosocial | 16.2 | .44 | <.001 | 15.4 | 17.1 |
| Recent SDV exposure/less prosocial | 17.6 | .20 | <.001 | 17.2 | 18.0 |
| Recent SDV exposure/high prosocial | 17.0 | .38 | <.001 | 16.3 | 17.8 |
| Subjective well-being | |||||
| No SDV exposure/less prosocial | 18.6 | .45 | <.001 | 17.7 | 19.5 |
| No SDV exposure/high prosocial | 18.9 | .64 | <.001 | 17.6 | 20.2 |
| Past SDV exposure/less prosocial | 17.4 | .27 | <.001 | 16.8 | 17.9 |
| Past SDV exposure/high prosocial | 18.5 | .55 | <.001 | 17.4 | 19.6 |
| Recent SDV exposure/less prosocial | 16.1 | .25 | <.001 | 15.6 | 16.6 |
| Recent SDV exposure/high prosocial | 18.1 | .47 | <.001 | 17.2 | 19.1 |
Note. SDV = self-directed violence; CI = confidence interval
Discussion
Almost half of youth in this study reported SDV exposure within the past year; roughly during the time of COVID-19 public health policies requiring stay-at-home orders, social distancing and remote learning. Recent exposure (compared to past exposure) was more common among adolescents than young adults, supporting suicide trend data indicating increases in suicide among this population (Miron et al., 2019).
Consistent with research that SDV exposure is associated with personal emotional distress (Bottomley et al., 2018; Mitchell et al., 2019), participants in the current study who report SDV exposure were more likely than those not exposed to say COVID-19 had impacted their mental health, physical health and schoolwork or work. This was particularly true for youth with recent SDV exposure than more distal exposure. The current study highlights that negative effects of the COVID-19 pandemic on the mental health of young people may be compounded by exposure to mental health concerns of people in their network. Prevention and intervention strategies should focus on youth not only as individuals who may need to be connected to mental health resources for themselves, but to support and help them process effects of more vicarious SDV exposure.
Although prosocial activity itself did not substantially influence recent depressive symptomatology, interesting patterns emerged in the context of SDV exposure that supports the buffering hypothesis about the role of social integration and support as a protective factor. Compared with participants with no SDV exposure, those with both past and recent SDV exposure were less likely to report depressive symptoms if they reported engaging in a high number of prosocial activities - a significant moderating effect. This is consistent with resilience research that highlights the benefits of interpersonal strengths as well as how helping others may contribute to purpose and meaning making (Hamby et al., 2018).
Unexpectedly, while non-exposed participants had the best mental health overall, for this group high prosocial activity was associated with slightly higher depressive symptoms. Given the cross-sectional nature of the data it is difficult to interpret the direction of these effects. This may be an indicator of distressed youth reaching out to others and using prosocial activities to help themselves, creating connections in some ways by helping others. It may be that youth without the stress of SDV in their lives and who are very socially integrated are experiencing some vicarious stress as a result of reaching out to help others and this stress outweighs any social connection benefits. For youth bearing the burden of SDV exposure, the social integration and sense of purpose from helping others may outweigh any added stress burden experienced from helping others. Further research could better unpack how social connections and outreach to others are being used and how that variation might explain varied impacts of support and outreach. These findings are consistent with research findings indicating that the role of social connections is complex (Mitchell et al., 2021).
Limitations
Although our measure of the recency of SDV exposure was coded to roughly parallel events that may have occurred during the COVID-19 pandemic, we did not specifically ask whether exposure occurred during this time. It is also possible that the impact participants are reporting on is not necessarily negative as this was not specified in the questions. The cross-sectional nature of this study limits inferences about temporal associations and direction of effects. Although the sample is national it is not representative. We also need future studies to look at a range of outcome measures.
Implications and Conclusion
As COVID vaccine distribution continues it is critical to consider how such an extensive period of time with limited social engagement, loss of critical developmental milestones, and family safety concerns will impact how teens navigate life as they knew it prior to COVID-19. Certain populations of youth, including those exposed to SDV, may find this transition particularly challenging and may benefit from strategies that remind them that they can seek resources and services not only if they are concerned about themselves, but also if they are concerned about others. Teens are also quite resilient, so it will be critical to understand how some teens are able to draw on their strengths to navigate this emergence; current findings suggest some viable and effective prosocial activities which improved well-being and reduced mental health impact in the context of SDV exposure.
Supplementary Material
Clinical Impact Statement:
The effects of the COVID-19 pandemic on the mental health of young people is more extensive than initially believed. Not only has the pandemic impacted individual mental health and well-being, it is compounded by exposure to mental health concerns of people in their network. Prevention and intervention strategies should focus on young people not only as individuals who may need to be connected to mental health resources for themselves, but to support and help them process effects of more vicarious self-directed violence exposure. Encouraging youth to create positive community connections is a modifiable protective factor.
Acknowledgments
This work is supported by NIH grant R01 HD083072b; research funds from the University of New Hampshire; and research funds from Rutgers University. The research presented in this paper is that of the authors and does not reflect the official policy of the NIH. This research was conducted with the approval of the Pearl IRB [approved 11/18/2020, Study ID: 19-CIPH-101].
Footnotes
None of the authors have conflicts of interest to disclose. No financial disclosures were reported by the authors of this paper.
References
- Alvis L, Shook N, & Oosterhoff B (2020). Adolescents’ prosocial experiences during the covid-19 pandemic: Associations with mental health and community attachments. PsyArXiv Preprints 10.31234/OSF.IO/2S73N. [DOI] [PMC free article] [PubMed]
- Bottomley JS, Abrutyn S, Smigelsky MA, & Neimeyer RA (2018). Mental health symptomatology and exposure to non-fatal suicidal behavior: Factors that predict vulnerability and resilience among college students. Archives of Suicide Research, 22(4), 596–614. DOI: 10.1080/13811118.2017.1387632. [DOI] [PubMed] [Google Scholar]
- Dunn EC, Johnson RM, & Green JG (2012). The Modified Depression Scale (MDS): A brief, no-cost assessment tool to estimate the level of depressive symptoms in students and schools. School Ment Health, 4(1), 34–45. DOI: 10.1007/s12310-011-9066-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gould MS, Lake AM, Kleinman M, Galfalvy H, Chowdhury S, & Madnick A (2018). Exposure to suicide in high schools: Impact on serious suicidal ideation/behavior, depression, maladaptive coping strategies, and attitudes toward help-seeking. International Journal of Environmental Research and Public Health, 15(3), 455. DOI: 10.3390/ijerph15030455. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Guessoum SB, Lachal J, Radjack R, Carretier E, Minassian S, Benoit L, & Moro MR (2020). Adolescent psychiatric disorders during the COVID-19 pandemic and lockdown. Psychiatry Res, 113264. DOI: 10.1016/j.psychres.2020.113264. [DOI] [PMC free article] [PubMed]
- Hamby S, Grych J, & Banyard V (2018). Resilience portfolios and poly-strengths: Identifying protective factors associated with thriving after adversity. Psychol Violence, 8(2), 172–183. 10.1037/vio0000135. [DOI] [Google Scholar]
- Hartley DM, & Perencevich EN (2020). Public health interventions for COVID-19: Emerging evidence and implications for an evolving public health crisis. JAMA, 323(19), 1908–1909. DOI: 10.1001/jama.2020.5910. [DOI] [PubMed] [Google Scholar]
- Komaroff M, & Belhouchet AA (2020). Public health policies and global COVID-19 outbreak. European Journal of Public Health, 30(Supplement_5), ckaa165. 428. doi: 10.1093/eurpub/ckaa165.428. [DOI] [Google Scholar]
- Magson NR, Freeman JY, Rapee RM, Richardson CE, Oar EL, & Fardouly J (2021). Risk and protective factors for prospective changes in adolescent mental health during the COVID-19 pandemic. J Youth Adolesc, 50(1), 44–57. DOI: 10.1007/s10964-020-01332-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Maple M, Cerel J, Sanford R, Pearce T, & Jordan J (2017). Is exposure to suicide beyond kin associated with risk for suicidal behavior? A systematic review of the evidence. Suicide and Life‐ Threatening Behavior, 47(4), 461–474. DOI: 10.1111/sltb.12308. [DOI] [PubMed] [Google Scholar]
- Marques de Miranda D, da Silva Athanasio B, Sena Oliveira AC, & Simoes-E-Silva AC (2020). How is COVID-19 pandemic impacting mental health of children and adolescents? International Journal of Disaster Risk Reduction. 51, 101845. 10.1016/j.ijdrr.2020.101845 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Miron O, Yu K-H, Wilf-Miron R, & Kohane IS (2019). Suicide rates among adolescents and young adults in the United States, 2000–2017. JAMA, 321(23), 2362–2364. DOI: 10.1001/jama.2019.5054. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mitchell KJ, Banyard V, Goodman KL, Strøm IF, & Ybarra ML (2021). Exposure to suicidal behavior and social support among sexual-and gender-minority youth. Pediatrics, 147(4). DOI: 10.1542/peds.2020-033134. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mitchell KJ, Turner HA, & Jones LM (2019). Youth exposure to suicide attempts: relative impact on personal trauma symptoms. American Journal of Preventive Medicine, 56(1), 109–115. DOI: 10.1016/j.amepre.2018.09.008. [DOI] [PubMed] [Google Scholar]
- Pan A, Liu L, Wang C, Guo H, Hao X, Wang Q, Huang J, He N, Yu H, & Lin X (2020). Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China. JAMA, 323(19), 1915–1923. DOI: 10.1001/jama.2020.6130. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Racine N, Cooke JE, Eirich R, Korczak DJ, McArthur B, & Madigan S (2020). Child and adolescent mental illness during COVID-19: A rapid review. Psychiatry Res, 292, 113307. 10.1016/j.psychres.2020.113307. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sheftall AH, Mathias CW, Furr RM, & Dougherty DM (2013). Adolescent attachment security, family functioning, and suicide attempts. Attachment & Human Development, 15(4), 368–383. DOI: 10.1080/14616734.2013.782649. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sin NL, Klaiber P, Wen JH, & DeLongis A (2021). Helping amid the pandemic: Daily affective and social implications of COVID-19-related prosocial activities. The Gerontologist, 61(1), 59–70. DOI: 10.1093/geront/gnaa140. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Turner HA, & Butler MJ (2003). Direct and indirect effects of childhood adversity on depressive symptoms in young adults. J Youth Adolesc, 32(2), 89–103. 10.1023/A:1021853600645. [DOI] [Google Scholar]
- Turner HA, Finkelhor D, & Ormrod R (2006). The effect of lifetime victimization on the mental health of children and adolescents. Soc Sci Med, 62(1), 13–27. DOI: 10.1016/j.socscimed.2005.05.030. [DOI] [PubMed] [Google Scholar]
- Viner RM, Russell SJ, Croker H, Packer J, Ward J, Stansfield C, Mytton O, Bonell C, & Booy R (2020). School closure and management practices during coronavirus outbreaks including COVID-19: A rapid systematic review. The Lancet Child & Adolescent Health, 4(5), 397–404. DOI: 10.1016/S2352-4642(20)30095-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wyman PA, Pickering TA, Pisani AR, Rulison K, Schmeelk-Cone K, Hartley C, Gould M, Caine ED, LoMurray M, Brown CH, & Valente TW (2019). Peer-adult network structure and suicide attempts in 38 high schools: implications for network-informed suicide prevention. Journal of Child Psychology and Psychiatry, 60(10), 1065–1075. DOI: 10.1111/jcpp.13102. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.