These authors contributed equally to this work.
Depression and resilience are different psychological outcomes caused by experiencing traumatic events. We aimed to 1) explore heterogeneity patterns of co-occurrence between depression symptoms and resilience among children and adolescents exposed to an earthquake and 2) assess covariates (trauma exposure, sex, age, ethnicity, and sleep quality) in identifying the best fitting solution.
Latent profile analysis (LPA) was used to examine patterns of self-reported depression and resilience in an epidemiological sample of 2,887 Chinese youth survivors 1 year after the Lushan earthquake.
A suitable 3-class model were identified, which are mild depression/high resilience (65.0%), severe depression/high resilience (22.1%), and severe depression/low resilience (12.9%). Trauma exposure, demographic characteristics and sleep state can be used to identify the different latent classes.
Our results contribute to understanding the heterogeneous coexisting patterns of depression and resilience and provide suggestions for identifying high-risk youth survivors and offering effective interventions.
According to the United Nations International Children Fund, there were about 535 million children and adolescents exposed to traumatic events worldwide in 2016 alone [
Besides negative psychological outcomes (e.g., depression), traumatic events also can result in some positive psychological changes, such as resilience [
Thus, what is the relationship between those two different psychological outcomes caused by experiencing traumatic events, depression as well as resilience? Previous studies have found that resilience can reduce the risk of depression among individuals with adverse childhood experience [
We conducted an LPA study in an epidemiological sample of Chinese youth earthquake survivors. The first aim was to identify the patterns of depression and resilience among children and adolescents exposed to an earthquake; It is well demonstrated that the severity of trauma exposure [
Students were recruited from 21 primary and secondary schools located in Lushan County, Sichuan Province, where was hit by the 2013 Lushan Earthquake (
Data collection was conducted at 1 year after the Lushan earthquake. We used a paper-and-pencil questionnaire and conducted the assessment by well-trained researchers with master’s degree in psychology in each class to supervise participants when they filled the questionnaire. Included participants were the students who consented to participate in the survey. Excluded individuals were those who refused to participate or hadn’t the ability to complete the investigation. In total, 2,917 children and adolescents agreed to participate. Due to that 33 students hadn’t completed the assessments, we finally included 2,887 students in our analysis. The response rate for the assessments was 98.97% (2,887/2,917). Details about sampling strategy showed in
We selected Baoxing County for two reasons: 1) there were 21 primary and secondary schools in this region, satisfying the needs for a large sample; and 2) the directors of the Education Bureau and teachers of these schools paid close attention to the mental health of students. They were willing to sustain this survey. The research protocol was approved by the Ethics Committee of the University of Sichuan and the Education Bureau of Baoxing Country in China and was consistent with the latest version of Helsinki Declaration [IRB approval number: 2017 (103)] and was consistent with the latest vision of Helsinki Declaration. In the present survey, we only attained informed consent from participants and teachers. Because in China, if the local education institution (e.g., the Education Bureau of Baoxing Country) supports a survey as a mental health service to the students, parental consent is not required [
Students completed the following self-report questionnaires in their own classroom: sociodemographic characteristics, Patient Health Questionnaire-9 (PHQ-9), Child and Youth Resilience Measure (CYRM), and two self-constructed questionnaires for trauma-exposure and sleep [
A self-constructed questionnaire included 6 items used to assess trauma exposure severity: 1) Did your immediate family members injured in the earthquake? 2) Did your immediate family members die in the earthquake? 3) Was your house damaged in the disaster? 4) Was there anyone with you after the earthquake? 5) Did the earthquake cause severe property damage to your family? 6) Did you sleep in a tent rather than your own home last night? All answers were coded as binary variables (1 “Yes” or 0 “No”), and we calculated the sum score (range from 0 to 6) to reflect the severity of trauma exposure. The total score of the scale was used to assess the severity of the earthquake trauma exposure.
A self-constructed sleep questionnaire was used to assess insomnia and 3 items were included: 1) difficulty falling asleep, 2) difficulty staying asleep, and 3) early waking. All questions and answers were coded as binary variables (yes or no). If the student’s answers are all “yes,” then this student is considered to have insomnia symptoms.
The self-report symptom scale of the PHQ-9 is a widely used screening tool for depression according to the Diagnostic and Statistical Manual of Mental Disorder-IV (DSM-IV) criteria [
The CYRM-12 is a self-report instrument to measure youth resilience. The specific items are 1) I have people I look up to. 2) Getting an education is important to me. 3) My parent(s)/caregivers(s) know a lot about me. 4) I try to finish what I start. 5) I solve problems without harming myself or others. 6) I know where to go in my community to get help. 7) I feel I belong(ed) to at my school. 8) My family will stand by me during difficult times. 9) My friends stand by me during difficult times. 10) I am treated fairly in my community. 11) I have opportunities to develop skills that will be useful later in life. 12) I enjoy my cultural and family traditions. Items are rated on a 5-point scale from 1=does not describe me at all to 5=describes me a lot. Higher scores suggest higher levels of resilience [
Statistical analyses were accomplished by Mplus 7.0 (Linda Muthén & Bengt Muthén, Los Angeles, CA, USA). The number of missing responses on each item of PHQ-9 were 10, 14, 13, 14, 23, 14, 23, 14, 14, 12, and 17 respectively. As for CYRM-12, they were 55, 55, 58, 70, 61, 65, 61, 54, 61, 63, 66, and 63, respectively. Missing values were estimated with maximum likelihood procedures. Considering the difference in scale range between PHQ-9 and CYRM-12, all items’ scores were converted into standardized Z-scores. The 3-step LPA (R3STEP) approach was conducted via maximum likelihood estimation [
The evaluation indexes of LPA model fitting degree are Akaike Information Criteria (AIC) values, Bayesian Information Criteria (BIC) values, adjusted BIC (aBIC), Entropy, Lo–Mendell–Rubin likelihood ratio test (LMR LRT) and Bootstrap-based Likelihood Ratio Test (BLRT) [
A total of 2,887 children and adolescents completed the assessment (1,541 female; 2,375 Han-people and 247 meet the criteria of insomnia) (
Fit indicators of the different LPA models are shown in
The 3-class model was characterized by severe depression/low resilience (12.90%), severe depression/high resilience (22.11%), mild depression/high resilience (65.00%). The 3-class model’s profile plot of depression and resilience was shown in
The final step of the 3-step LPA results was presented in
Our study identified 3 latent classes of depression and resilience (severe depression/low resilience, mild depression/high resilience, and severe depression/high resilience) among Chinese youth earthquake survivors, in line with Spahni’s founding [
In presented study, a great proportion of individuals were in the mild depression/high resilience group, which was in accordance with findings of Wenchuan earthquake’s youth sample [
An interesting phenomenon of this study also sheds light on the controversial relationship between depression and resilience. Negative relationship between those two constructs is shown in the mild depression/high resilience and severe depression/low resilience subgroups. While, as for the severe depression/high resilience subgroup, depression and resilience levels were reflective of each other. It’s presented a positive association between depression and resilience. These findings indicate complicated reactions among youth survivors, and individual’s heterogeneity should be considered.
The higher level of trauma exposure was related to the higher probability of being classified into the latent class with a high level of depression (severe depression/high resilience and severe depression/low resilience). A link had been found between trauma exposure and depression, such that individuals who witness the disaster, death/injuries of family members and damage to one’s home are being more prone to depression [
The role of demographic characteristics was also explored. Compared to their counterparts, male and younger survivors were more likely to enter mild depression/high resilience. Female and male’s post-traumatic reactions are markedly different. For example, greater levels of self-esteem and mastery, more common among the male than the female, are positively associated with problem-focused coping, which could reduce the negative impacts of stress [
The roles of sleep characteristics were also clarified. The results showed that insomnia individuals were likely to enter the severe depression/low resilience subgroup. Seelig’s study found that poor sleep is an important factor and related to the construct of resilience [
The current study also provided potential clinical implications. First, based on the person-centered method LPA, our study promotes the understanding of different psychological reactions (depression vs. resilience) as well as individual heterogeneity among youth survivors. Most individuals were in the mild depression/high resilience group, suggesting that resilience may buffer the influence of trauma experience on depression. Thus, in future, we can provide many types of online psychological services for youth survivors to promote their psychological resilience. Second, in our study, we found some important moderating factors (e.g. sleep disturbance) that can influence the relationship between resilience and depression, and indicated more attention should be paid to older insomnia female individuals. As posited by previous research, the presentation of mental disorders are influenced by culture and social milieu [
Several limitations of our study should be mentioned. First, the findings of our study are based on the self-report scales (e.g., PHQ-9) reported rather than the clinic structure interviewed. And the design of our study was just a cross-sectional study. High quality prospective studies are needed. Second, our research targets on the sample of Chinese youth-earthquake survivors after 1-year exposure, which limits the generalization of the findings. Further studies should examine the profile of depression and resilience in various types of traumatic events among different time points to test the robustness of the model. Third, we only focused on depression in this paper. Other important mental health outcomes (e.g., PTSD, generalized anxiety disorder) are relevant to the trauma events and should not be ignored in further study. Forth, whether resilience is a personality trait or dynamic process is controversial in different studies. In future research, we can evaluate positive psychological through the post-traumatic growth (PTG).
The online-only Data Supplement is available with this article at
None.
The authors have no potential conflicts of interest to disclose.
Conceptualization: Jun Zhang. Data curation: Yue Wang. Formal analysis: Fenfen Ge, Mentong Wan. Methodology: Jun Zhang. Software: Yue Wang. Validation: Jun Zhang. Writing—original draft: Yue Wang, Fenfen Ge. Writing—review & editing: Fenfen Ge, Mentong Wan, Jun Zhang.
Geographical location of the 2013 Lushan earthquake (red dot), China.
Flow diagram of the participants.
Z-scores of the PHQ-9 and CYRM-12 for the 3-class model symptom profiles. Class-1 severe depression/low resilience; Class-2 mild depression/high resilience; Class-3 severe depression/high resilience. PHQ: Patient Health Questionnaire, CYRM: Child and Youth Resilience Measure.
Descriptive information of youth survivors
Characteristics | Mean±SD/N (%) |
---|---|
Age | 12.83±2.59 |
Sex | |
Female | 1,541 (53.38) |
Male | 1,346 (46.62) |
Ethnicity | |
Han-people | 2,375 (82.26) |
Not Han-people | 512 (17.73) |
Trauma-related experience | |
Did your immediate family members injure in the earthquake? (Yes) | 218 (7.55) |
Did your immediate family members die in the earthquake? (Yes) | 174 (6.03) |
Was your house damaged in the disaster? (Yes) | 1,995 (69.10) |
Did the earthquake cause severe property damage to your family? (Yes) | 1,981 (68.62) |
Did you sleep in a tent rather than your own home last night? (Yes) | 528 (18.29) |
Was there anyone with you after the earthquake? (Yes) | 2,678 (92.76) |
Total score | 2.62±0.77 |
Sleep | |
Difficulty falling asleep | 727 (24.93) |
Difficulty staying asleep | 609 (21.09) |
Early waking | 1,425 (49.36) |
Insomnia | 247 (8.56) |
Depression (PHQ-9) | |
No depression (0–4) | 1,538 (53.27) |
Minimal depression (5–9) | 625 (21.65) |
Mild depression (10–14) | 414 (14.34) |
Moderate depression (15–19) | 210 (7.27) |
Severe depression (20–27) | 100 (3.46) |
Total score | 6.05±6.25 |
Resilience (CYRM-12) | |
Total score | 36.9±10.86 |
SD: standard deviation, N: number, PHQ-9: Patient Health Questionnaire, CYRM-12: Child and Youth Resilience Measure
Fit indices for LPA
Latent | k | AIC | BIC | aBIC | Entropy | LMR | BLRT | Class probability (%) |
---|---|---|---|---|---|---|---|---|
1c | 42 | 172,114.973 | 172,365.628 | 172,232.179 | - | - | - | 100 |
2c | 64 | 159,975.23 | 160,357.180 | 160,153.829 | 0.944 | <0.0001 | <0.0001 | 23.668/76.332 |
3c | 86 | 154,435.765 | 154,949.011 | 154,675.758 | 0.934 | 0.0353 | 0.0365 | 12.895/22.109/64.996 |
4c | 108 | 150,525.599 | 151,170.140 | 150,826.985 | 0.945 | 0.0164 | 0.0168 | 4.815/13.821/22.238/59.127 |
5c | 130 | 148,274.685 | 149,050.522 | 148,637.465 | 0.928 | 0.0054 | 0.0057 | 2.840/4.780/19.120/21.060/52.200 |
AIC: Akaike Information Criteria, BIC: Bayesian Information Criteria, aBIC: adjusted BIC, LMR LRT: Lo–Mendell–Rubin likelihood ratio test, BLRT: Bootstrap-based Likelihood Ratio Test
Factors associated with the latent class groups
Reference Class 2 |
Reference Class 1 |
|||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Class 1 |
Class 3 |
Class 3 |
||||||||||
B | SE | OR | p | B | SE | OR | p | B | SE | OR | p | |
Sex | -0.283 | 0.119 | 0.754 | 0.018 | 0.019 | 0.100 | 1.019 | 0.852 | 0.301 | 0.141 | 1.351 | 0.033 |
Age | 0.065 | 0.022 | 1.067 | 0.002 | 0.166 | 0.020 | 1.181 | <0.001 | 0.098 | 0.026 | 1.103 | <0.001 |
Ethnicity | -0.777 | 0.201 | 0.460 | <0.001 | 0.176 | 0.123 | 1.192 | 0.152 | 0.954 | 0.221 | 2.596 | <0.001 |
Sleep | 1.538 | 0.183 | 4.655 | <0.001 | 1.358 | 0.168 | 3.888 | <0.001 | -0.180 | 0.186 | 0.835 | 0.333 |
Trauma exposure | 0.078 | 0.007 | 1.081 | 0.312 | 0.272 | 0.067 | 1.313 | <0.001 | 0.194 | 0.092 | 1.214 | 0.034 |
Class 1: severe depression/low resilience, Class 2: mild depression/high resilience, Class 3: severe depression/high resilience. Sex was code: 0 female, 1 male. Ethnicity was code: 0 Han-people, 1 not Han-people. Sleep: 0 non-insomnia, 1 insomnia