https://orcid.org/0000-0003-0540-4963
Figures
Abstract
Student suicide ideation increased globally during the SARS-CoV-2 pandemic. There is scarce literature addressing suicide ideation during global health crises. Therefore, this study investigates prevalence and predictors of suicide ideation during India’s COVID-19 Second Wave. We also examine the 3-Step Theory’s assertion that both pain and hopelessness are necessary to have suicidal thoughts. Sample recruitment was through an online anonymous questionnaire. Inclusion criteria included students aged 18 or older, living in India during the time of the study (N = 535). Data collection was through the online questionnaire. Three categories of variables were investigated as potential predictors of suicide ideation: Sociodemographic (e.g., age, gender, education, economic status), COVID-19-specific (e.g., online classes, fear of virus contagion, vaccination status) and Clinical (e.g., sad mood, fear, loneliness, hopelessness). Data analysis (using SPSS-26) included descriptive statistics for describing data characteristics, Spearman Rho Correlation (assess the strength and direction of association between variables), and Binary Logistic Regression to help identify predictors of suicide ideation. Ordinal variables were measured using Likert scales with some recoded into binomial variables for the Regression analysis. Clinical variables predicted suicidal ideation, including fear of failure (OR = 4.17, 95% CI:2.51–6.94; p < .001), sleep disturbance (OR = 3.04, CI:1.67–5.52; p < .001), loneliness (OR = 2.77, CI:1.21–6.32; p < .01), sadness (O = 2.89, CI:1.59–5.32), and loss of interest (OR = 2.60, CI:1.37–4.93). Suicidal thoughts were reported by 48.7% of the student-participants. The Three-Step Theory was partially supported, as students feeling psychological pain but not hopelessness still reported suicidal ideation. Anticipating future global health crises, policy-supported mental health mitigation strategies are critically needed for youth, designed to reduce suicidal ideation, enhance resiliency, and to foster mental skills. These should enable them to successfully manage unexpected life challenges.
Citation: Frey LM, Venugopal D, Dev VS (2024) Prevalence and predictors of suicide ideation among university and high-school students during India’s 2nd wave of the COVID-19 pandemic. PLoS ONE 19(12): e0311403. https://doi.org/10.1371/journal.pone.0311403
Editor: Yadeta Alemayehu, Mattu University, ETHIOPIA
Received: October 20, 2023; Accepted: September 18, 2024; Published: December 5, 2024
Copyright: © 2024 Frey et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: SI, Suicide Ideation; 3ST, Three-Step Theory of suicide behaviors; NCRB, National Crime Record Bureau; OR, Odds Ratios
Introduction
The SARS-CoV-2 pandemic had a strong influence on suicidal behaviors in many countries. Suicide ideation typically precedes an act of suicide, which is among the most tragic causes of death among youth. A recognized global health crisis [1,2], suicide is the fourth leading cause of mortality among those aged 15 to 29 [3], marking this age group as a vulnerable population. The World Health Organization (WHO) reports that 800,000 to one million self-inflicted global deaths occur annually. Yet many researchers consider this to be an underestimate due to underreporting [4], misattributing suicides as unintentional injuries and “other causes” [5], concealing suicides due to family stigma [6–8], and because only 15% of WHO’s 194 member states verify vital data registration [4,8]. For example, one study reported that 87% of deaths labeled as “undetermined” had evidence of suicidal intent [9]. Underreporting self-inflicted deaths distorts information that would otherwise be used to inform policy, research, treatment interventions, and preventative actions.
Suicide ideation (SI) is a predictor of suicide attempts [10–13]. It has a crucial role in understanding the process of a person transitioning from suicidal thoughts to a plan, to action [10,11,14,15]. Research reports that 33% of those with SI will make an attempt [16,17]. For example, a 13-year study found that those with SI at baseline were more likely to have made attempts at the follow-up assessment [18]. This helpful knowledge emphasizes the importance of studying suicide ideation because historically, it has been difficult to identify consistent indicators that predict suicidal behaviors [19–22].
This study examines suicidal ideation among school and university-attending youth in India during the SARS-CoV-2 pandemic. This is an important undertaking for at least four reasons. First, there is a notable increase in SI onset between the ages of 18 and 35 [16], a sensitive age period that warrants further study. Yet, even young children experience suicide ideation as early as age 5 [23,24]. Second, during the first year of SI onset, the risk of transitioning to a plan or attempt is at its highest [16,23] underscoring the importance of recognizing, evaluating, and acting upon suicide ideation quickly. The third reason for studying youth SI is that the COVID-19 pandemic triggered numerous stressful situations around the world. These circumstances are important to understand as they led to a rise in suicidal behaviors, particularly among youth and marginalized communities. Fourth, there is a dearth of information regarding suicide behaviors that occur during large-scale, unprecedented disease outbreaks. For these reasons we assessed student suicide ideation during India’s COVID-19 2nd Wave. To create effective solutions in anticipation of future pandemics and other emergencies, it can be helpful to understand the factors that increased SI among youth during the pandemic.
From here, section 2 provides a brief overview of prevalence rates of suicide mortality in India, and the context of India’s second wave of the pandemic. Section 3 introduces the Three-Step Theory of suicide behaviors, and the objectives of the study. Section 4 outlines the methodology. Our findings are reported in Section 5, which is followed by the Discussion (Section 6).
Suicide mortality in India
Suicide mortality is high in India compared to other countries. Between 1990 and 2016, suicides in India increased by 40% [25,26]. Student suicide rates steadily increased during the last three decades. Between 1995 and 1999 the reported total number was 27,359. This figure spiked to 48,537 between 2015 and 2019. In 2019 the one-year student suicide rate, 10,335, was higher than any other in the prior 25 years [27]. Despite these somber figures, experts question the accuracy of India’s suicide prevalence reported by the National Crime Record Bureau (NCRB) [27]. For example, in 2016 there were 131,008 NCRB-reported deaths by suicide [27] compared to 230,314 reported by a Global Burden of Disease study in India [26], a difference of 54.97%. Yet the suicide reports were even higher during the pandemic. We now address the COVID-19 pandemic and its influence on suicide ideation.
The contextual situation during India’s 2nd wave
Internationally, government responses to curb COVID-19 viral transmission included multiple constraints and directives: Mass lockdowns, restrictions of large gatherings and mobility, and public safety health awareness to prevent viral contraction (handwashing, mask-wearing) [28]. India’s response to the pandemic was one of the world’s most restrictive as measured by the Oxford Coronavirus Government Response Tracker “Stringency Index” [29]. The index is a composite of nine restrictions: public gatherings; international travel; closures of: jobs, schools, public transport; canceled public events; restrictions on internal movement and public information campaigns; and stay-at-home orders [29].
A very high stringency index suggests greater restrictions upon a populace as noted below. Fig 1 indicates that among the top six countries with the most reported COVID-19 cases in 2022, only India’s stringency index reached the 100% mark (Y-axis) [30]. Brazil, France, and Germany hovered between a score of 60 to 80, whereas the US held to a level of 65, then reduced steadily through the year to a low index between 5 and 30 (blue line). Japan, by contrast, never reached the score of 60, and mostly hovered around an index of 40 to 50.
(Fig 1 courtesy of Hale et al., 2021 [29]: Oxford COVID-19 Government Response Tracker).
Did a country’s stringency index level influence percentages of COVID-19 cases? The US has one-fourth the population of India but reported 60 million more COVID-19 cases. At first glance this suggests that the more stringent lockdowns in India mitigated the spread of COVID-19. However, alternate explanations were suggested by scientists and journalists who provided evidence of the country underestimating both COVID-19 cases and mortality rates by up to 30 times higher than what was reported [31,32]. Overall, stringency measures attempted to reduce a rapidly advancing lethal virus. But it was at the economic expense of citizens who lost income due to the closure of non-essential businesses, a bitter choice with risks and repercussions both ways [33]. Education was also heavily impacted, as we now demonstrate.
Education constraints.
Education expands youth employment opportunities which can lead to economic security. School attendance provides opportunities for youth connections, socialization, and creative learning endeavors with teachers and friends. Overall, approximately 1.6 billion students from 190 countries [34] were affected by the pandemic. In India, the mutating, persistent COVID-19 virus and lockdowns forced nearly 300 million students to rely on online platforms to continue their education [35]. This was a humongous task requiring massive efforts to rapidly integrate educational technology into institutions [36].
Millions of people were deprived of their usual income due to lockdowns, leaving innumerable families without the resources to provide computers or smartphones to their children for online classes. Moreover, only 45.0% of India’s population had internet access as of January 2021 [37]. These factors magnified the educational disparities between the economically stable and those more marginalized, particularly in rural and poor regions. Consequently, millions of youth had no digital access, and reports of student suicides haunted the nation [38,39]. Reasons for the suicides included a fear of losing access to education, inability to take exams, fear of exam failure, and poor networks that interrupted class participation and learning [40].
Additionally, for many children problems also erupted on the home front. Families are theoretically safe harbors for their members. But during the pandemic many women and children were reportedly subjected to violence and maltreatment [41,42], which can substantially affect a child’s capacity to learn [43].
India’s 2nd wave.
Even as all of the above-mentioned educational problems were happening, in early 2021 the country was more confident, having successfully contained the virus during the initial year of the pandemic (2020). This was partly due to good practices and outcomes achieved in the state of Kerala [44] which other states then adopted.
However, as other countries had also done, in early 2021 the lockdowns were prematurely relaxed and the government opened the doors to unrestricted public events: marriages, huge election rallies, public festivals, and other social functions [45]. This led to the tragic second wave of the pandemic. The sudden removal of social restrictions led to massive surges of COVID-19 infections, up to 300,000 daily. This unanticipated astronomical number of cases led to national acute shortages of hospital beds, oxygen, ventilators, other essential equipment, and antiviral drugs. In spite of a multitude of courageous, dedicated medical staff and personnel, the shortage of beds led to many ill patients dying outside of hospitals while waiting for treatment [46]. Independent researchers such as the Center for Global Development reported millions of deaths beyond those officially disclosed [47]. There were insufficient crematorium resources to keep pace with the innumerable deaths, necessitating vast numbers of mass outdoor cremation sites [48]. Grief and fear gripped the nation. Mental distress soared.
The accumulation of the above-mentioned factors contributed to increases in suicides and declines in mental wellbeing. Mental health problems increased by 35% during the protracted pandemic [49]. In 2020 alone a 10% increase in self-inflicted deaths contributed to an average of 400 suicides daily (146,000 annually), with student suicides being among the highest [50]. Alarmingly, this figure again rose by 7.1% the following year to 164,033 [51]. Even prior to COVID-19 the United Nations pressed all nations to reduce suicide mortality by 33% by the year 2030 [52,53]. This is an ambitious yet crucial goal to mobilize further understanding of the underlying causes of this heartbreaking problem [8]. Experts warned that if India’s recent alarming suicide surge continued, the country would have a “zero probability” of meeting the United Nations goal [26]. Given these multiple national challenges during the pandemic, and the scarcity of data on suicide behaviors during large-scale, protracted disease outbreaks, we assessed student suicide ideation during India’s COVID-19 2nd Wave. From here we introduce the 3-Step Theory (3ST) of suicide behaviors, the objectives of our study, and the study methods.
Theory of suicidal ideation
Although said to be preventable, suicide remains shrouded with complexities after a century of study. Recent theories propose meaningful steps between the transition from suicide thoughts to suicidal actions. One of these is the Three-Step Theory (3ST) [16] which has three progressive steps. These are: 1) suicide ideation, 2) presence or absence of connectedness, and 3) dispositional capacity to commit lethal self-harm. Step 1, suicide ideation (SI), is our focus. The 3ST claims that SI is influenced by the paired experience of pain (psychological and/or physical) and hopelessness. The authors, Klonsky and May [17], suggest that pain alone is not likely to lead to suicide ideation. But if the person also experiences hopelessness, then the risk of experiencing SI increases. Klonsky and May [17] suggest that heightened SI can be mitigated by connectedness (step 2) which they define as an active interest, hobby, or supportive people. Yet in the absence of connectedness suicide ideation is at risk of escalating. This, in turn, elevates the risk of a person considering and/or making a suicide plan and attempt. A suicide attempt is particularly more likely by those whose temperament is unintimidated by the act of committing suicide (step 3).
Our study aims to determine predictors of SI during a tumultuous time in India, and to examine the first step of the 3ST: Whether psychological pain and hopelessness coexist and determine reported SI among participants, or whether SI can occur without one or both of these two variables being present. Therefore, in this study we examine potential predictors of suicide ideation, and we also test the 3ST theory with regard to paired pain and hopelessness as a predictor of SI.
Study objectives
Given the highly unusual global circumstances of the COVID-19 pandemic and related national and international restrictions, we sought to determine possible pandemic-related variables and research-supported variables that could be predictive indicators for youth SI. We also tested the 3ST theory regarding paired pain and hopelessness.
Objective 1.
What are the sociodemographic, COVID-19-related, and research-supported ‘Clinical’ predictors of SI among this sample of students? Is there a model related to these variables that contributes to our understanding of SI among students during the pandemic?
We hypothesized that specific COVID-19 related variables and Clinical variables would predict suicide ideation (e.g., economic vulnerability, viral fear, loneliness). We also hypothesized that loneliness would be among the strongest predictors given the social isolation induced by necessary enforced lockdowns.
Objective 2.
Do the identified predictors support the first step of the 3ST of suicidal behaviors in terms of combined hopelessness and pain? Drawing from 3ST theory of suicide, we questioned whether combined pain and hopelessness would co-exist among our sample. We also questioned whether these were necessary to experience suicidal thoughts, or whether SI can occur without these two variables being present.
Methods
Ethics
The procedures involved in this study comply with, and are consistent with, ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. All procedures involving human subjects/patients were approved by University Institutional Ethics Committee (Approval # IEC-AIMS-2021-CW141). As the study includes an online questionnaire, participants were provided with the Informed Consent at the beginning of the questionnaire. Prior to the survey questions, participants were provided with consent selections to confirm their willingness to participate in the study, followed by a question to affirm their age as 18 or older.
Participants
Convenience sampling was conducted through an online, digital survey link sent to the email database of a university in southern India. The link recipients included staff and students who were invited to share the link across social groups with encouragement to include university students. Inclusion criteria included students residing in India during the study period, aged 18 or older. Those who were not students, not living in India during the time of the study, and not aged 18 or older were excluded from this study. This resulted in 536 qualified participants: 246 (46%) students from high school, and 290 (36.9%) students in higher education studies (bachelor’s through doctoral degree studies 17.2%). Participants were aged between 18 and 49 years (M = 21.53, SD = 3.43) of which 61% (n = 327) were female and 39% were male (n = 209).
Research design
This is a cross-sectional, exploratory, anonymous, web-based digital questionnaire study. The researchers aimed to identify factors that influenced suicide ideation among students during India’s 2nd Wave of the COVID-19 pandemic. To this end, a questionnaire was developed to capture specific variables.
Measures
Independent variables.
We created three categories of independent variables: 1) Sociodemographic, 2) COVID-19-specific variables, and 3) Clinical variables. The sociodemographic variables included age, gender, education, rural or urban residence, exercise, economic status, and marital status. The COVID-19 variables assessed online class challenges, concerns about viral contagion, fear of the 1st and 2nd waves of the pandemic, family challenges, vaccination status, and future expectations. The Clinical variables included psychological factors that can influence suicide ideation, including sad mood, fear, sleep disturbance, hopelessness, feeling like a failure, loss of interest, and loneliness.
The sociodemographic variables were ranked on a nominal scale (e.g., marital status) or ordinal scale (e.g., economic status). COVID-19 and Clinical variables were assessed using ordinal scales (Likert scales, e.g., 1 = not at all, 5 = very much). For the Binary Logistic Regression analysis, we recoded the suicide ideation variable, and relevant independent Clinical variables to binary variables.
Clinical variables selected.
We included the following variables in our study because research has demonstrated their influence on suicide behaviors.
Sleep disturbance. Students with sleep disturbance have significantly higher levels of suicide ideation than those without [54–57]. Insomnia is independently and significantly associated with higher intensity of SI over and above other core depression symptoms. Sleep disturbance can also indicate the need for a further SI evaluation [54]. For these reasons we included sleep disturbance as a possible predictor of SI in our study.
Sad mood and hopelessness. Maladaptive sad moods can be long-standing and can influence suicidal thoughts [55,56]. Both high and low-income countries report adolescent sadness to be alarmingly pervasive. For example, two national surveys in Vietnam conducted in 2003 and 2009 (combined N > 10,000 adolescents) found an increase in adolescent sadness from 34% to 37.34% respectively, and in suicide behaviors from 5.28% to 12.21% respectively [57]. Likewise, independent studies conducted in south Korea and Malaysia found strong associations between sadness and suicidal behaviors [58,59]. In high-income countries, 31.5% to 35.8% of adolescents reported persistent sadness and hopelessness, and 18% seriously considered suicide in the prior 12 months [60,61]. Students with suicidal ideation also report higher levels of hopelessness [62,63], therefore we included these variables in our study.
Loneliness. Whereas social support can be a protective factor for suicide behaviors, loneliness is a determinant [64], contributing to suicidal thoughts and attempts [64–66]. Experiencing both depression and loneliness increases the risk for suicide ideation and attempts [66–73]. As students were separated from friends and classmates for protracted periods of time during the pandemic, loneliness is an included variable in our study as a possible contributor to SI.
Economic stress. Economic crises are associated with elevated suicide ideation, attempts, and deaths [74–76]. The pandemic stay-at-home orders led to suicides related to job loss and financial crisis exacerbated by fear of infection, loneliness, anxiety, and depression [77–80]. Of 153,000 suicides in India during 2020, one fourth occurred among daily wage workers who typically receive less pay [81]. Tragically, during the pandemic 272 family suicidal deaths occurred in India related to economic crisis and loss of a home [82]. For these reasons we included economic stress as a viable variable in our study. We now introduce our dependent variable for this study.
Dependent variable. Suicide ideation is the dependent variable in this study. We define it according to the 9-item Patient Health Questionnaire (PHQ-9): thoughts of self-harm; or thoughts that one would be better if dead. We further define this form of SI as ‘passive’ in line with Franklin [83], in that it implies a degree of attempt that is not explicitly communicated, as opposed to ‘active’ SI that implies an explicitly expressed intent. We recoded SI into a binary variable for the Logistic Binary Logistic Regression analysis.
Data analysis
Statistical analysis was conducted with SPSS, version 26. Descriptive statistics assisted in summarizing the characteristics of the data (means, standard deviations, percentages, and distribution of data visually through charts and graphs). Spearman Rank-Order Correlation Coefficient indicated the strength and direction of association between specific COVID-19 ordinal variables to help us understand their impact upon students during the 2nd wave. It also revealed the association between these COVID-19 variables and suicide ideation. The Chi-Square of Independence examined the strength of association between SI and categorical variables (age groups, gender). The Binary Logistic Regression was performed initially to identify individual variables that were significantly associated with the suicide ideation outcome variable (SI). Those individual variables that were significantly related to SI were then applied to a Binary Logistic Regression model to determine how much each of these contributed to, or predicted, the presence of suicide ideation among the student sample.
Results
Objective 1
What are the primary socio-demographic, covid-related, and Clinical predictors of SI in our sample of students?
Five-hundred thirty-six students participated in this study. The mean age was 21.53 (N = 536; SD = 3.43, range = 18–49) of which 61% were female and 39% male. Suicide ideation point prevalence was 48.7%. Prevalence of SI among the females was 49.8% (n = 327), and within the sample of males it was 46.8% (n = 209). SI was higher among those completing 12th grade (52.4%) than those with university education (45.5%). Most respondents were single (91.8%), living in their family home (81.7%), and most self-identified as financially poor (55.6%). Table 1 provides frequencies of socio-demographic and COVID-19 specific variables.
Seven percent of the students lost a loved one to COVID-19, and 69.2% knew of someone who had COVID-19. More than 90% were concerned about contracting COVID-19.
Regarding age and gender, suicide ideation among those aged 21 and below, and those 22 and above was 51.9% and 44.1% respectively. Chi-square for independence tests (with Yates’ Continuity Correction) indicated no significant association between suicide ideation and these two age groups: X2(1, n = 536) = .29, p = .09, phi = .08, and no significant association between SI and gender: X2(1, n = 536) = .34, p = .56, phi = -.03.
To better understand the relationships between age, gender, and SI we created further divisions (Fig 2): a) aged 18 to 19, b) 20 to 21, c) 22 to 24, and d) 25 and above. The outcomes were striking with SI spiking and converging across gender among those aged 20 to 21, then dropping dramatically to 5.4% among those aged 25 and older. Females aged 22 to 24 had the highest rate of SI (39.9%).
Alarmingly, as Fig 3 shows below, 85.6% of respondents had not received any vaccine more than one year into the pandemic. Of these, 49% reported suicide ideation. Nearly 80% of the participants reported moderate to severe fear related directly to the pandemic (Fig 3). Further, most experienced “very-to-extreme” concern during India’s 1st wave and 2nd wave of COVID-19.
Correlates of suicide ideation.
We used the Spearman Rank Order correlation to assess the relationship between COVID-19 risk factors and suicide ideation as is shown in Table 2 (n = 536). As hypothesized, “suicide Ideation” was positively and moderately associated with “More fear since COVID” (r = .29, p < .0001) and with “Loneliness” (r = .20, p < .0001) but was not significantly related to the other variables in the table.
We then examined relationships between several pandemic-related variables using Spearman’s rank-order correlation. A strong association emerged between “More fear since COVID” and a “Fear of contracting the virus.” Similarly, there were moderately strong associations between “2nd Wave Worry” and the following variables: “More fear since the pandemic,” “Fear of contracting the virus,” and “Fear of the 1st Wave” all of which suggest that fear and worry were consistently strong in the minds of the participants during the 2nd Wave. These associations are in bold in the table.
As we might anticipate, “Future expectations” was significantly and positively associated with “Economic security” suggesting that those who had reliable financial resources were more likely to feel positive about the future. On the other hand, “Future expectations” was negatively and significantly associated with “More fear since the pandemic,” and “More fear of the 2nd Wave” (both with small effect sizes) which could suggest that those with unreliable income experienced more fear as the virus persisted.
Individual binary logistic regression.
Variables were recoded into binary variables for the Individual Binary Logistic Regression. This test was helpful in examining the predictive power of individual COVID-related variables (e.g., fear of covid, vaccine status, online classes) and individual Clinical values (sad mood, sleep disturbance, and others) on suicidal thoughts. Results indicated that most sociodemographic and pandemic-specific variables were not predictors of SI, including online classes, nor were the effect sizes strong. For example, of those participants (70%) who reported no family challenges, 46.9% experienced suicide ideation, whereas those with family challenges also reported SI (52.5%). As shown in Table 3 below, the difference between the two groups was not significant and the effect size was very small (r = .05, p = .278). Reducing or stopping exercise routine increased the odds of experiencing SI (Odds Ratio/OR = 1.59), but the effect size was small (r = .110, p < .05). The variable of “more fear” since COVID-19 was not statistically associated with SI (CI = 98–3.38, p = .07) in the individual odds ratio (OR). Hence, variables that were either unrelated to SI, or that had only weak associations to SI were not included in our final model using Binary Logistic Regression.
Logistic regression model.
The strongest individual ORs relative to SI were sad mood, feeling like a failure, loneliness, sleep disturbance, loss of interest (apathy) as indicated at the bottom of Table 3. We applied these five independent variables in a Binary Logistic Regression. The full model was statistically significant, X2 (5, N = 536) = 183.1, p < .0001, indicating that it was able to distinguish between respondents who reported and did not report suicide ideation. The model explained between 28.9% (Cox & Snell R2) and 38.6% (Nagelkerke R2) of the variance of suicide ideation, and correctly classified 75.7% of cases. As shown in Table 4, all five of the independent variables made a unique statistically significant contribution to the model. The strongest predictor was fear of failure, which increased the odds of experiencing suicidal thoughts 4.2-fold. The other model variables were also strong predictors of SI as follows:
- Sleep disturbance: 3-fold increase in the odds having SI
- Persistent sad mood: 2.9-fold increase in the odds having SI
- Loneliness: 2.8-fold increase in the odds having SI
- Loss of interest: 2.6 times the odds having SI
Objective 2
Do these predictors help to support the first step of the 3-Step Theory of suicide ideation in terms of combined hopelessness and pain?
Recall that the 3ST asserts that suicide ideation is likely to occur if one experiences both pain and hopelessness. In terms of pain, students reported pandemic-related sad mood, loneliness, fear of the pandemic and of contracting the virus, feeling like a failure, sleep disturbance, and fear and worry, all of which fall under 3ST’s pain category. Additionally, sad mood (psychological pain) was significantly associated with suicide ideation in the individual OR (Table 3: r = .413, p < .0001). However, hopelessness, reported by 64.7% of the respondents, was not significantly associated with SI (Table 3).
We explored this more deeply and found that out of 536 participants, 35.3% (N = 189) had no sense of hopelessness, but many of these reported high levels of pandemic-related fear. Of those with no hopelessness, 16.4% (N = 88) experienced suicide ideation, and their reported fear was very high with 94.3% expressing concern about India’s 1st Wave of the pandemic, and 95.5% reporting concern (somewhat to extreme) about India’s 2nd Wave. Further, 94.3% feared contracting COVID-19, and 95.5% felt more fear since the arrival of the pandemic. Overall, a modest subset of students who experienced psychological pain but who did not experience hopelessness reported SI. Fear appeared to be the dominant factor affecting them. Hence, Step 1 of the 3ST was not fully supported by our study, although most of the participants with SI also felt hopelessness.
Discussion
Suicide ideation levels were inordinately high, 48.7%, among students within India during the pandemic’s 2nd wave. Participants struggled with COVID-19-related variables that convey worry and fear, including online classes, low vaccine status, economic vulnerability, and fear of viral contagion. Our supposition that the variables: “fear of the virus” and “economic hardship” would predict suicide ideation was not statistically supported. This is likely because even those who were economically secure reported suicidal thoughts. In our study the odds of having suicide ideation were predicted by sleep disturbance, sad mood, loneliness, apathy, and feelings of failure. The findings are important given the high level of SI reported by students.
Why was SI so high among students in this study? There are several possibilities. First, 68% of the participants experienced a sense of failure, the strongest predictor of suicide ideation in this study. During the pandemic, school closure, perceived stress, and fear of viral infection all contributed to scholastic stress [84]. Studies reported that students faced online class obstacles. These included unreliable electricity, accessibility, networks, and lack of digital skills. This led to social isolation, boredom, frustration, poorer academic performance, and lethargy [85]. This confluence of frustrations could understandably lead to a perceived sense of failure.
Second, positive, supportive peer and family relationships among youth are associated with declines in SI [86], but the pandemic lockdowns prohibited in-person gatherings. Loneliness was reported by nearly all students (90%) in this study across gender, suggesting inadequate or absent supportive connections. Loneliness, a public health concern, can predict suicidal thoughts, mental and physical illnesses, and death [87,88]. We had predicted that loneliness would be among the top predictors of SI, and it was, indeed, a strong predictor, but not the strongest. Still, loneliness was strongly associated with SI.
Finally, a completely unprecedented change in lifestyle was, by necessity, imposed upon the public by the unparalleled convergence of atypical negative life events: enforced quarantines, lockdowns, financial stress, school closures, loss of work, social isolation, and fear of a silent, potentially fatal virus quietly racing around the world. These events, along with witnessing millions becoming ill and dying, potentially intensified to a crescendo of exceptional levels of stress, worry, fear, and suicide ideation among the students. Humanity was not prepared for such a catastrophic world event. Yet we have no excuse not to be prepared for the next such event.
Implications for research and practice
Ultimately, we must take a step back and reevaluate the current mental health of young people worldwide considering the high prevalence of SI among youth during the Second Wave: Why are thoughts of suicide plaguing so many young people? What coping mechanisms are they using to deal with tense circumstances, negative thoughts, and challenging feelings? Do they have someone they can confide their pleasures and sorrows in who is positive and safe? What is the nature of their relationships with peers, teachers, and family members?
The rise in suicide ideation suggests that today’s youth have not internalized strong, healthy coping mechanisms for dealing with life’s inevitable challenges. When constructive techniques are lacking, self-criticism can lead to suicidal ideation and other poor decisions such self-harm, excessive internet use, or substance use. Our findings suggest the importance of developing policy-supported robust, positive interventions that can strengthen our youth psychologically and reduce suicidal thoughts and behaviors. Interventions should increase their resiliency when facing unpredictable life events, including future disasters. Positive, supportive parental involvement buffers youth from suicidal thoughts, hence parents should be included in such initiatives. Additionally, community mental health centers, schools, medical offices, and hospital emergency departments should train personnel in standardized suicide behaviors assessments [89] along with overall mental health assessments.
Uniqueness of this study
This study is unique as it captures an unprecedented global historical period during which socioeconomic, behavioral, and other restrictions were government mandated. The irony of these intended safeguards is that they precipitated varying degrees of stress depending upon one’s context. The authors are not aware of another historical period during which prevalence rates of suicide ideation were so high among a general student population. In 2008 a study in India reported a “high” student SI rate of 15.8%, which was high at that time [90]. Now 16 years later, it is exceptionally high at 48.7%.
What is clear about studies examining suicide ideation and potential predictors is that this relationship is often multifaceted and complex. At times SI is contextually influenced (e.g., pandemic, economic recessions, family adversities), and at times more psychologically initiated (e.g., feeling like a failure). We see these two categories as mutually interdependent.
Limitations
This study did not include variables such as a personal or family history of suicidal attempts/deaths, or excessive online use, all of which can influence SI. Given the unique scenario of India’s 2nd wave, the cross-sectional design, the online, non-randomized convenience sampling, and self-report data (which researchers debate regarding reliability), results are not generalizable to a larger and/or different population. Overall, there are certainly important lessons to be learned from the findings in this study conducted during an unprecedented, threatening, world pandemic.
Acknowledgments
The authors express sincere gratitude to our Chancellor, MA Devi, whose guidance, compassion, and wisdom influenced this research. We also sincerely thank the participants of the study.
References
- 1. Brahmbhatt K, Kurtz BP, Afzal KI, Giles LL, Kowal ED, Johnson KP, et al. Suicide risk screening in pediatric hospitals: Clinical pathways to address a global health crisis. Psychosomatics. 2019;60(1):1–9. pmid:30384966
- 2. Shorey S, Ng ED, Wong CH. Global prevalence of depression and elevated depressive symptoms among adolescents: A systematic review and meta‐analysis. Br J Clin Psychol. 2022;61(2):287–305. pmid:34569066
- 3.
World Health Organization [internet]. Geneva: Suicide: Key Facts.2023. [updated 2023 Aug 28; cited 2024 Jul 29]. Available from:https://www.who.int/news-room/fact-sheets/detail/suicide.
- 4. Philip S., Isaac T. G., & Sharda A. Suicide prevention strategy in India. Correspondence. The Lancet Psychiatry. 2022;9(3):192.
- 5. Tripathi A., Nadkarni A., Pathare S. Suicide set to become leading cause of death in Indian women. And NRCB won’t tell you that. ThePrint [Internet]. 2022 March 8 [cited 2024 Jul 29]. Available from: https://theprint.in/pageturner/excerpt/suicide-set-to-become-leading-cause-of-death-in-Indian-women-and-nrcb-wont-tell-you-that/863443/.
- 6. Timmermans S. Suicide determination and the professional authority of medical examiners. Am Sociol Rev. 2005;70(2):311–33.
- 7. Mishara BL, Weisstub DN. The legal status of suicide: A global review. Int J Law Psychiatry. 2016;44:54–74. pmid:26375452
- 8. Droogers M, Jansen D, Lindert J, Saboga-Nunes L, Rudén M, Guichardon M, et al. Health-related Sustainable Development Goals: countdown on alcohol use, smoking prevalence, child overweight and suicide mortality. Eur J Public Health. 2020;30:0–3. pmid:32391905
- 9. Ohberg A, Lonnqvist J. Suicides hidden among undetermined deaths. Acta Psychiatr Scand. 1998;98(3):214–8. pmid:9761408
- 10. Rossom RC, Coleman KJ, Ahmedani BK, Beck A, Johnson E, Oliver M. et al. Suicidal ideation reported on the PHQ9 and risk of suicidal behavior across age groups. J Affect Disord. 2017;215:77–84. pmid:28319695
- 11. Briñol P, Petty RE, Stavraki M, Lamprinakos G, Wagner B, Díaz D. Affective and cognitive validation of thoughts: An appraisal perspective on anger, disgust, surprise, and awe. J Pers Soc Psychol. 2018;114(5):693. pmid:29672103
- 12. Miranda R, Ortin A, Scott M, Shaffer D. Characteristics of suicidal ideation that predict the transition to future suicide attempts in adolescents. J Child Psychol Psychiatry. 2014;55(11):1288–96. pmid:24827817
- 13. Large M, Corderoy A, McHugh C. Is suicidal behaviour a stronger predictor of later suicide than suicidal ideation? A systematic review and meta-analysis. Aust N Z J Psychiatr. 2021;55(3):254–67. pmid:32579030
- 14. Grahek I, Shenhav A, Musslick S, Krebs RM, Koster EH. Motivation and cognitive control in depression. Neurosci Biobehav Rev. 2019;102:371–81. pmid:31047891
- 15. Klonsky ED, Qiu T, Saffer BY. Recent advances in differentiating suicide attempters from suicide ideators. Curr Opin Psychiatry. 2017;30(1):15–20. pmid:27798483
- 16. Nock MK, Borges G, Bromet EJ, Alonso J, Angermeyer M, Beautrais A, et al. Cross-national prevalence and risk factors for suicidal ideation, plans and attempts. Br J Psychiatry. 2008;192(2):98–105. pmid:18245022
- 17. Klonsky ED, May AM, Saffer BY. Suicide, suicide attempts, and suicidal ideation. Annu Rev Clin Psychol. 2016;12:307–30. pmid:26772209
- 18. Kuo WH, Gallo JJ, Tien AY. Incidence of suicide ideation and attempts in adults: the 13-year follow-up of a community sample in Baltimore, Maryland. Psychol Med. 2001;31(7):1181–91. pmid:11681544
- 19. Rich AR, Bonner RL. Concurrent validity of a stress–vulnerability model of suicidal ideation and behavior: a follow‐up study. Suicide Life Threat Behav. 1987;17(4):265–70. pmid:3424397
- 20. Skala K, Kapusta ND, Schlaff G, Unseld M, Erfurth A, Lesch OM, et al. Suicidal ideation and temperament: an investigation among college students. J Affect Disord. 2012;141(2–3):399–405. pmid:22475473
- 21. Lamis DA, Ballard ED, May AM, Dvorak RD. Depressive symptoms and suicidal ideation in college students: The mediating and moderating roles of hopelessness, alcohol problems, and social support. J Clin Psychol. 2016;72(9):919–32. pmid:27008096
- 22. Jobes D. A., & Joiner T. E. Reflections on suicidal ideation [Editorial]. Crisis. 2019;40(4):227–230.
- 23. Borges G, Benjet C, Medina-Mora ME, Orozco R, Nock M. Suicide ideation, plan, and attempt in the Mexican adolescent mental health survey. J Am Acad Child Adolesc Psychiatry. 2008;47(1):41–52. pmid:18174824
- 24. World Population by Country (Live). World Population Review. Retrieved July 26, 2024, from https://worldpopulationreview.com/country-rankings/suicide- rate-by-country.
- 25. Naghavi M. Global, regional, and national burden of suicide mortality 1990 to 2016: systematic analysis for the Global Burden of Disease Study 2016. bmj. 2019;364. pmid:31339847
- 26. Dandona R, Kumar GA, Dhaliwal RS, Naghavi M, Vos T, Shukla DK, et al. Gender differentials and state variations in suicide deaths in India: the Global Burden of Disease Study 1990–2016. Lancet Public Health. 2018;3(10):e478–89. pmid:30219340
- 27.
National Crime Records Bureau [Internet]. India: Chapter II: Suicides in India. [2023 Dec 03; cited 2024 Jul 29]. Available from: https://www.ncrb.gov.in/accidental-deaths-suicides-in-india-adsi.html.
- 28. Kishore K, Jaswal V, Pandey AK, Verma M, Koushal V. Utility of the Comprehensive Health and Stringency Indexes in Evaluating Government Responses for Containing the Spread of COVID-19 in India: Ecological Time-Series Study. JMIR Pub Health Surveill. 2023;9:e38371. pmid:36395334
- 29. Hale T, Angrist N, Goldszmidt R, Kira B, Petherick A, Phillips T, et al. A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nat Hum Behav. 2021;5(4):529–38. pmid:33686204
- 30. Ritchie H, Mathieu E, Rodés-Guirao L, Appel C, Giattino C, Ortiz-Ospina E, et al. Coronavirus pandemic (COVID-19). Our world in data. 2020. Available from: https://ourworldindata.org/coronavirus.
- 31. Yeung J. As Covid sweeps India, experts say cases and deaths are going unreported. CNN News [Internet]. 2021 Apr 28 [cited 2024 Jul 29]. Available from: https://docs.google.com/document/d/1V4xfg-X9Goo-HfViFarzifDz_ J6GLIQH/edit.
- 32. Guilmoto CZ. An alternative estimation of the death toll of the Covid-19 pandemic in India. PLoS One. 2022;17(2):e0263187. pmid:35171925
- 33. Cross M, Ng SK, Scuffham P. Trading health for wealth: the effect of COVID-19 response stringency. Int J Environ Res Public Health. 2020;17(23):8725. pmid:33255383
- 34.
United Nations [internet]. New York: Policy Brief: Education during COVID-19 and Beyond. [2020 Aug 1; cited 2024 Jul 29]. Available from: https://www.un.org/en/un-chronicle/education-during-covid-19-and-beyond-commentary-secretary-general%E2%80%99s-policy-brief.
- 35.
UNESCO [internet]. New York. Education: from disruption to recovery. [updated 2023; cited 2024 Jul 2]. Available from: https://docs.google.com/document/d/1QjhG-NgVRRDuoL_i5ToQ0s0jqYWjBZeS/edit.
- 36. Menon S, Suresh M. Enablers of technology agility in higher education. The Int J Inform Learn Tech. 2022;39(2):166–96.
- 37.
Hootsuite & We Are Social [internet]. India. Digital 2021. [updated 2024; cited 2024 Jul 29]. Available from: https://datareportal.com/reports/ digital-2021-india.
- 38. Nanisetti S. Student suicides go up: More students ended their lives in 2020 than 2019. The Hindu [Internet]. 2021 Nov 27 [cited 2024 Jul 29]. Available from: https://www.thehindu.com/news/cities/ Hyderabad/student-suicides-go-up/ article37729564.ece.
- 39. Khadse PA, Ghosh S, Murthy P, Girimaji SC. Student suicides in the context of online education during COVID-19 pandemic in India: analysis of media reports. Indian J Psychol Med. 2022;44(1):91–4. pmid:35509658
- 40.
Murali VS, Maiorano D. Education during the COVID-19 Pandemic in India. Institute of South Asian Studies. 2021.
- 41. Campbell AM. An increasing risk of family violence during the Covid-19 pandemic: Strengthening community collaborations to save lives. Forensic Sci Int: reports. 2020;2:100089. pmid:38620174
- 42. Humphreys KL, Myint MT, Zeanah CH. Increased risk for family violence during the COVID-19 pandemic. Pediatrics. 2020;146(1).
- 43. Fry D, Fang X, Elliott S, Casey T, Zheng X, Li J, et al. The relationships between violence in childhood and educational outcomes: A global systematic review and meta-analysis. Child Abuse Negl. 2018;75:6–28. pmid:28711191
- 44. Menon JC, Rakesh PS, John D, Thachathodiyl R, Banerjee A. What was right about Kerala’s response to the COVID-19 pandemic?. BMJ Global Health. 2020;5(7):e003212. pmid:32718951
- 45. Samarasekera U. India grapples with the second wave of COVID-19. The Lancet Microbe. 2021;2(6):e238.
- 46. Frayer L. Why Is India Running Out of Oxygen? National Public Radio [Internet]. 2021 May 5 [cited 2024 Jul 29]. Available from: https://www.npr.org/sections/goatsandsoda/2021/05/05 /989461528/why-is-india-running-out-of-oxygen.
- 47.
Anand A, Sandefur J, Subramanian A. Three new estimates of India’s all-cause excess mortality during the COVID-19 pandemic. Washington, DC: Center for Global Development; 2021,Jul 20.
- 48. Yeung J, Ward C. As India’s crematoriums overflow with COVID victims, pyres burn through the night. CNN Cable News Network [Internet]. 2021 May 1 [cited 2024 Jul 29]. Available from: https://edition.cnn.com/2021/04/29/india/india-covid-deaths-crematoriums-intl-hnk-dst/index.html.
- 49. Santomauro DF, Herrera AM, Shadid J, Zheng P, Ashbaugh C, Pigott DM, et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. The Lancet. 2021;398(10312):1700–12. pmid:34634250
- 50. National Crime Records Bureau [Internet]. Distribution of Mass/Family Suicides during 2020. [updated 2024 Jul 29; cited 2024 Jul 29]. Available from: https://ncrb.gov.in/sites/default/files/adsi_reports_previous_year/Table%202. 14%20state-ut-city.pdf.
- 51. Singh N. India lost more people to suicide than to Coronavirus in 2020, shows NCRB Data. NEWS 18 [Internet]. 2021 Nov 1 [cited 2024 Jul 29]. Available from: https://www.news18.com/news/india/india-lost-more-people-to-suicide-than-c.
- 52. Izutsu T, Tsutsumi A, Minas H, Thornicroft G, Patel V, Ito A. Mental health and wellbeing in the Sustainable Development Goals. The Lancet Psychiatry. 2015;2(12):1052–4. pmid:26613844
- 53. Cratsley K, Mackey TK. Health policy brief: Global mental health and the United Nations’ sustainable development goals. Fam Syst Health. 2018;36(2):225. pmid:29369647
- 54. Tham SG, Ibrahim S, Hunt IM, Kapur N, Gooding P. Examining the mechanisms by which adverse life events affect having a history of self-harm, and the protective effect of social support. J Affect Disord. 2020;263:621–8. pmid:31744741
- 55. McCall WV, Blocker JN, D’Agostino R Jr, Kimball J, Boggs N, Lasater B, et al. Insomnia severity is an indicator of suicidal ideation during a depression Clinical trial. Sleep med. 2010;11(9):822–7. pmid:20478741
- 56. Bernert RA, Hom MA, Iwata NG, Joiner TE. Objectively assessed sleep variability as an acute warning sign of suicidal ideation in a longitudinal evaluation of young adults at high suicide risk. J Clin Psychiatry. 2017;78(6):19738.
- 57. Becker SP, Dvorsky MR, Holdaway AS, Luebbe AM. Sleep problems and suicidal behaviors in college students. J Psychiatr Res. 2018;99:122–8. pmid:29448236
- 58. Batterham PJ, Werner-Seidler A, Calear AL, McCallum S, Gulliver A. Specific aspects of sleep disturbance associated with suicidal thoughts and attempts. Journal of J Affect Disord. 2021;282:574–9. pmid:33440302
- 59. Winer ES, Drapeau CW, Veilleux JC, Nadorff MR. The association between anhedonia, suicidal ideation, and suicide attempts in a large student sample. Arch Suicide Res. 2016;20(2):265–72. pmid:26214573
- 60. Ducasse D, Loas G, Dassa D, Gramaglia C, Zeppegno P, Guillaume S, et al. Anhedonia is associated with suicidal ideation independently of depression: A meta‐analysis. Depress and anxiety. 2018;35(5):382–92. pmid:29232491
- 61. Kovacs M, George CJ. Maladaptive mood repair predicts suicidal behaviors among young adults with depression histories. J Affect Disord. 2020;265:558–66). pmid:31787418
- 62. Pilkington P, Younan R, Bishop A. Early maladaptive schemas, suicidal ideation, and self-harm: A meta-analytic review. Journal of affective disorders reports. 2021;3:100051.
- 63. Le MT, Nguyen HT, Tran TD, Fisher JR. Experience of low mood and suicidal behaviors among adolescents in Vietnam: findings from two national population-based surveys. J Adolesc Health. 2012;51(4):339–48. pmid:22999834
- 64. Woo HG, Park S, Yon H, Lee SW, Koyanagi A, Jacob L, et al. National Trends in Sadness, Suicidality, and COVID-19 Pandemic–Related Risk Factors Among South Korean Adolescents From 2005 to 2021. JAMA network open. 2023;6(5):e2314838. pmid:37223902
- 65. Chen PC, Lee LK, Wong KC, Kaur J. Factors relating to adolescent suicidal behavior: a cross-sectional Malaysian school survey. J Adolesc Health. 2005;37(4):337–e11. pmid:16182146
- 66. Michael SL, Lowry R, Merlo C, Cooper AC, Hyde ET, McKeon R. Physical activity, sedentary, and dietary behaviors associated with indicators of mental health and suicide risk. Prev Med. 2020;19:101153. pmid:32670781
- 67. Trimble DG, Chandran A. Associations between sad feelings and suicide behaviors in the 2019 youth risk behavior survey: A call for action. Front Pediatr. 2021;9:694819. pmid:34595142
- 68. Konick LC, Gutierrez PM. Testing a model of suicide ideation in college students. Suicide Life Threat Behav. 2005;35(2):181–92. pmid:15843335
- 69. Ribeiro JD, Huang X, Fox KR, Franklin JC. Depression and hopelessness as risk factors for suicide ideation, attempts and death: meta-analysis of longitudinal studies. Br J Psychiatry. 2018;212(5):279–86. pmid:29587888
- 70. Chang EC, Chang OD, Martos T, Sallay V, Lee J, Stam KR, et al. Family support as a moderator of the relationship between loneliness and suicide risk in college students: Having a supportive family matters! The Fam J. 2017;25(3):257–63.
- 71. McClelland H, Evans JJ, Nowland R, Ferguson E, O’Connor RC. Loneliness as a predictor of suicidal ideation and behaviour: a systematic review and meta-analysis of prospective studies. J Affect Disord. 2020;274:880–96. pmid:32664029
- 72. Ebesutani C, Fierstein M, Viana AG, Trent L, Young J, Sprung M. The role of loneliness in the relationship between anxiety and depression in Clinical and school‐based youth. Psychol Sch. 2015;52(3):223–34.
- 73. Killgore WD, Cloonan SA, Taylor EC, Lucas DA, Dailey NS. Loneliness during the first half-year of COVID-19 Lockdowns. Psychiatry Res. 2020;294:113551. pmid:33171416
- 74. Chang SS, Stuckler D, Yip P, Gunnell D. Impact of 2008 global economic crisis on suicide: time trend study in 54 countries. Bmj. 2013;347. pmid:24046155
- 75. Barr B, Taylor-Robinson D, Scott-Samuel A, McKee M, Stuckler D. Suicides associated with the 2008–10 economic recession in England: time trend analysis. Bmj. 2012;345:e5142. pmid:22893569
- 76. Reeves A, McKee M, Stuckler D. Economic suicides in the great recession in Europe and North America. Br J Psychiatry. 2014;205(3):246–7. pmid:24925987
- 77. Elbogen EB, Lanier M, Blakey SM, Wagner HR, Tsai J. Suicidal ideation and thoughts of self‐harm during the COVID‐19 pandemic: The role of COVID‐19‐ related stress, social isolation, and financial strain. Depress Anxiety. 2021;38(7):739–48. pmid:33949747
- 78. Sher L. The impact of the COVID-19 pandemic on suicide rates. QJM: An Int J Med. 2020;113(10):707–12. pmid:32539153
- 79. Dsouza DD, Quadros S, Hyderabadwala ZJ, Mamun MA. Aggregated COVID-19 suicide incidences in India: Fear of COVID-19 infection is the prominent causative factor. Psychiatry Res. 2020;290:113145. pmid:32544650
- 80. Stevenson C, Wakefield JR. Financial distress and suicidal behaviour during COVID-19: Family identification attenuates the negative relationship between COVID-related financial distress and mental Ill-health. J Health Psychol. 2021;26(14):2665–75. pmid:34259082
- 81. Khan ST, Bhat MA, Saeed W. Financial Strain, Covid-19 Anxiety and Suicidal Ideation Among Wage Workers During the Pandemic Crisis 2019. Vikalpa. 2023;48(4):269–82.
- 82. Sarkar S. Why families died by suicide following Covid-19. Cases of mass suicides and familicides have been reported with shocking regularity. The Week [Internet]. 2022 December 4 [cited 2024 Jul 29]. Available from: https://www.theweek.in/theweek/specials /2022/11/25/rise-in-suicides-due -to-covid19-pandemic.html.
- 83. Franklin JC, Ribeiro JD, Fox KR, Bentley KH, Kleiman EM, Huang X, et al. Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychol Bull. 2017;143(2):187. pmid:27841450
- 84. Yang C, Chen A, Chen Y. College students’ stress and health in the COVID-19 pandemic: The role of academic workload, separation from school, and fears of contagion. PLoS One. 2021;16(2): e0246676. pmid:33566824
- 85. Atlam ES, Ewis A, Abd El-Raouf MM, Ghoneim O, Gad I. A new approach in identifying the psychological impact of COVID-19 on university student’s academic performance. Alexandria Engineering Journal. 2022;61(7):5223–33.
- 86. Arango A, Cole-Lewis Y, Lindsay R, Yeguez CE, Clark M, King C. The protective role of connectedness on depression and suicidal ideation among bully victimized youth. J Clin Child Adolesc Psychol. 2018;22. pmid:29565682
- 87. McClelland H, Evans JJ, Nowland R, Ferguson E, O’Connor RC. Loneliness as a predictor of suicidal ideation and behaviour: a systematic review and meta-analysis of prospective studies. J Affect Disord.2020;274:880–96. pmid:32664029
- 88. Rico-Uribe LA, Caballero FF, Martín-María N, Cabello M, Ayuso-Mateos JL, Miret M. Association of loneliness with all-cause mortality-: A meta-analysis. PloS one. 2018;13(1): e0190033. pmid:29300743
- 89. King CA, O’mara RM, Hayward CN, Cunningham RM. Adolescent suicide risk screening in the emergency department. Acad Emerg Med. 2009;16(11):1234–41. pmid:19845554
- 90. Sharma R, Grover VL, Chaturvedi S. Suicidal behavior amongst adolescent students in south Delhi. Indian J Psychiatry. 2008;50(1):30. pmid:19771304