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Patterns of posttraumatic stress disorder and posttraumatic growth among women after an earthquake: A latent profile analysis
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Patterns of posttraumatic stress disorder and posttraumatic growth among women after an earthquake: A latent profile analysis Yueyue Zhoua,b, Yiming Lianga,b, Huiqi Tongc, Zhengkui Liua,b,
⁎
a
CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, China Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China c Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA b
ARTICLE INFO
ABSTRACT
Keywords: PTSD PTG Latent profile analysis Trauma China
It is well known that women are more vulnerable than men to mental health problems following disasters. However, the patterns of posttraumatic stress disorder (PTSD) and posttraumatic growth (PTG) among women are unclear. This study was conducted to investigate the profiles of PTSD and PTG among women after an earthquake and determine the predictors of profile membership. A total of 1487 women (Mage = 40.66 years, SD = 10.39) completed questionnaires assessing PTSD, PTG, trauma exposure during an earthquake, trauma history, and demographic information. A three-step latent profile analysis was used. Five profiles were identified: mild PTSD/mild PTG (12.4% of the sample), moderate PTSD/moderate PTG (39.4%), high PTSD/moderate PTG (9.6%), mild PTSD/high PTG (17.5%) and high PTSD/high PTG (21.1%). Women who were older, had been injured, or felt horrible and those whose houses were severely damaged in the earthquake were more likely to be in profiles characterized by higher levels of PTSD and PTG, whereas women with lower education levels were more likely to be in profiles characterized by higher levels of PTSD but lower levels of PTG. The results of this study provide a foundation for providing psychological assistance for particular groups of women.
1. Introduction Traumatic events often have a serious negative impact on people’s physical and mental health. Among the impacts of traumatic events, posttraumatic stress disorder (PTSD) is one of the most concerning and common mental health problems (Liang et al., 2019; Lowell et al., 2018). However, researchers have found that traumatic events can also cause positive changes, such as posttraumatic growth (PTG; Tedeschi and Calhoun, 1996). PTG refers to positive psychological changes and growth beyond previous levels of functioning because of struggles with highly challenging life crises (Tedeschi and Calhoun, 1996, 2004). To provide more targeted suggestions for psychological assistance after disasters, researchers note the need to fully understand both the positive and negative impacts of traumatic events (Jin et al., 2014a). More importantly, individual differences in post-traumatic response (PTSD and PTG) need to be noted. That is, in different subgroups, the patterns of PTSD and PTG may be different. Only person-centred analyses can thoroughly investigate such heterogeneity (Peugh and Fan, 2013). Latent profile analysis (LPA), a person-centred approach, is a suitable method for determining the different patterns of PTSD and PTG
based on their indicators. To our knowledge, only a few studies have used LPA to investigate patterns of PTSD and PTG (Birkeland et al., 2015; Cao et al., 2018; Chen and Wu, 2017; Zhou et al., 2018). Although these studies were conducted using different age groups (adolescents and adults, including men and women), they usually found three profiles. Specifically, profile 1 was characterized by low PTSD and high PTG scores; profile 2 was characterized by low PTSD and low PTG scores; and profile 3 was characterized by high PTSD and high PTG scores. These studies are valuable. However, few studies have specifically investigated patterns of PTSD and PTG among women. In fact, women deserve special attention for the following two reasons. First, women’s posttraumatic responses (PTSD and PTG) may be different from those of other groups (adults in the general population and adolescents) and may show more complex patterns. Previous studies have consistently found that females are more vulnerable to stress/disaster and develop higher levels of PTSD than males (Cheng et al., 2018; Jin et al., 2014a; Marthoenis et al., 2019). Moreover, evidence from a meta-analysis showed that compared with males, females developed higher levels of PTG (Vishnevsky et al., 2010). Second, a female family member may be
⁎ Corresponding author at: Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Chaoyang District, Beijing 100101, China. E-mail address: [email protected] (Z. Liu).
https://doi.org/10.1016/j.ajp.2019.10.014 Received 31 August 2019; Received in revised form 8 October 2019; Accepted 8 October 2019 1876-2018/ © 2019 Elsevier B.V. All rights reserved.
Please cite this article as: Yueyue Zhou, et al., Asian Journal of Psychiatry, https://doi.org/10.1016/j.ajp.2019.10.014
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the most critical person for intervention when the whole family is traumatized. Especially in the context of Asian culture, a woman’s status after a disaster may affect the physical and mental health of all family members, particularly the woman’s children (Usami et al., 2019). It is unfortunate that no research has investigated the patterns of PTSD and PTG among women. To fill this gap, we used LPA (Peugh and Fan, 2013) to determine the different patterns of PTSD and PTG among women who have experienced an earthquake. Another important research issue is to determine the characteristics of women who are more likely to have a higher PTSD but lower PTG profile. Previous studies have found that in the general population, age, education level, and trauma exposure (such as injury, feeling horrible, experiencing damage to one’s home, and the deaths of relatives as a result of disasters) are potential predictive variables (Cao et al., 2018; Chen and Wu, 2017; Park et al., 2017; Zalta et al., 2017; Zhou et al., 2018). These findings have provided with valuable information for targeted interventions. However, there are several limitations of previous studies. First, no research has been conducted to examine this issue specifically in groups of women. In fact, the roles of these variables may be different in women than in the general population. Second, evidence regarding the roles of demographic variables and trauma exposure has been mixed (Cao et al., 2018; Jin et al., 2014b; Liu et al., 2017; Park et al., 2017; Solomon and Dekel, 2007; Wu et al., 2015; Xu and Liao, 2011; Zhou et al., 2018). Thus, more research is needed. Third, previous studies did not examine the impact of previous trauma history, which may also affect an individual’s reactions after a disaster (Cloitre et al., 2009; Marthoenis et al., 2019). According to the dose-response model (Johnson and Thompson, 2008), previous traumas have a cumulative effect on subsequent posttraumatic responses (Johnson and Thompson, 2008). That is, trauma history will strengthen an individual’s posttraumatic response when they encounter another traumatic event. Considering this important issue, we examined the role of trauma history, such as experiencing an armed robbery or domestic violence, of the participating women. The current study surveyed women affected by the Ya’an earthquake in China, which occurred on April 20, 2013 in Sichuan Province, and had a magnitude of 7.0. A total of 196 people were killed, 21 went missing and 11,470 were injured.
participants had a senior high school education or above. 2.2. Measures 2.2.1. Posttraumatic stress disorder PTSD was measured by the 17-item self-report PTSD ChecklistCivilian Version (PCL-C). The PCL-C was developed based on the diagnostic criteria for PTSD in the DSM-IV (Ruggiero et al., 2003). Three clusters of symptoms were measured: intrusion, avoidance and hyperarousal. The women were asked to recall reactions and feelings in the past month that had been caused by the earthquake. Items are rated on a 5-point scale ranging from 1 = not at all to 5 = extremely. Participants with a total score over 44 were considered to have PTSD symptoms (Li et al., 2010). The Chinese version of the PCL-C has been widely used and has demonstrated good reliability and validity in Chinese adults (Liang et al., 2019). In the present study, Cronbach’s alpha for the scale was 0.93. 2.2.2. Posttraumatic growth PTG was measured by the 21-item self-report Posttraumatic Growth Inventory (PTGI; Tedeschi and Calhoun, 1996). The PTGI has mainly been used to measure positive changes and growth after trauma and includes the following five parts: relating to others, new possibilities, personal strength, spiritual change and appreciation of life. The women were asked to rate items on a 6-point scale ranging from 0 = not at all to 5 = very much. The Chinese version of the PTGI has been widely used and has demonstrated good reliability and validity in Chinese adults (Cao et al., 2018). In the present study, Cronbach’s alpha for the scale was 0.89. 2.2.3. Predictive variables Demographic variables (i.e., age and education level), trauma exposure during the earthquake and trauma history were measured. Specifically, education level was dummy coded 1 for junior high school or below and 0 for senior high school or above. Trauma exposure during the earthquake included the following four aspects: injury during the earthquake, feelings of intense fear/helplessness/terror (feeling horrible), severe damage to one’s home, and the deaths of relatives. Finally, we used six items with yes/no response choices to assess whether the participants had ever experienced the following six relatively severe traumas in their lives: serious accidents, such as a serious traffic accident or an explosion; threats of violence, such as murder or armed robbery; family violence, such as fighting or kicking; outside violence, such as being beaten, cursed at or seriously injured; sexual violence, such as being the victim of rape; and serious or life-threatening diseases. The total score for traumatic events was calculated to represent the trauma history of each woman.
2. Method 2.1. Participants and procedures Our survey was conducted in April of 2014, one year after the Ya’an earthquake. All research procedures were approved by the Research Ethics Review Board of the authors’ institution. A convenience sampling method was used to recruit participants. We recruited subjects in places that women frequent, such as vegetable markets and leisure plazas. First, we explained the purpose and content of the survey to the subjects and promised to keep personal information confidential. The women voluntarily chose whether to participate in our survey, and those who were willing to participate in the study signed a written informed consent form. Participants who had difficulty reading questionnaires completed the survey with the help of research assistants. Approximately 70% of the women we met were willing to participate in our study. Possible reasons for not participating were various but mainly associated with a longer waiting time. When the women arrived in large numbers at a specific time, the lack of sufficient research assistants led to longer waiting times for some women, which led to the further loss of participants. A total of 1493 earthquake-affected women from four counties participated in the survey. Six of the women were excluded from the analysis due to missing data for all predictive variables. The final sample included 1487 women. The mean age of the women was 40.66 years (SD = 10.39). Regarding education level, 78.0% of the participants had a junior high school education or less, and 22.0% of the
2.3. Plan of analysis First, we evaluated the prevalence of PTSD as well as the occurrence of traumatic exposure and trauma history. Second, based on the three symptoms of PTSD and the five aspects of PTG, we used a three-step LPA method to identify potential profiles of PTSD and PTG among women. All indicators were converted to z-scores for ease of interpretation. In addition, we used robust maximum likelihood estimates to handle missing data and estimate the model. Analyses were conducted using Mplus Version 7.4 (Muthén & Muthén, 1998–2015). To determine the optimal number of profiles, a series of models were estimated, and multiple fit indices were used, including the Akaike information criterion (AIC), Bayesian information criterion (BIC), sample-size-adjusted BIC (a-BIC), Lo-Mendell-Rubin likelihood ratio test (LMR-LRT), bootstrapped likelihood ratio test (BLRT) and entropy (Nylund, Asparouhov, & Muthen, 2007). Generally, models with the lowest AIC, BIC, and a-BIC values are considered better solutions. The LMR-LRT test compares a model with k profiles to a 2
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comprehensively considered, and the 5-profile model was selected as the optimal model. As shown in Fig. 1, Profile 1 (12.4% of the sample) was characterized by low levels of PTSD and PTG and was labelled the mild PTSD/ mild PTG group. Profile 2 (39.4%) was characterized by medium levels of PTSD and PTG and was labelled the moderate PTSD/moderate PTG group. Profile 3 (9.6%) was characterized by a high level of PTSD and a medium level of PTG and was labelled the high PTSD/moderate PTG group. Profile 4 (17.5%) was characterized by a low level of PTSD and a high level of PTG and was labelled the mild PTSD/high PTG group. Finally, profile 5 (21.1%) was characterized by high levels of PTSD and PTG and was labelled the high PTSD/high PTG group. Parameter estimates and within-profile indicator means are presented in Table 3.
Table 1 Descriptive Statistics for Trauma Exposure and Trauma History (N = 1487).
Trauma exposure Injury in the earthquake Feeling horrible Severe house damage Deaths of relatives Trauma history (number of experiences) One Two Three Four Five Six
N
%
359 864 906 79
24.1 58.1 60.9 5.3
367 133 63 21 10 1
24.7 8.9 4.2 1.4 0.7 0.1
3.3. Variables that predicted profile membership model with a k – 1 profile. A significant p value on the LMR-LRT indicates that a model with k profiles has a better fit than a model with a k – 1 profile. Entropy shows classification accuracy. Entropy can range from 0 to 1.0, with higher values indicating that individuals were more precisely classified (ideally above .70). To increase the generalizability of our results, we also considered whether each profile consisted of at least 5% of the sample. Finally, it was also important to consider the substantive interpretability of the profiles (Nylund et al., 2007). At last, to test whether demographic variables, trauma exposure during the earthquake and trauma history differentially predicted profile membership, multinomial logistic regression analysis was conducted using the three-step method (Asparouhov and Muthén, 2014).
In the framework of the three-step LPA, the results of multinomial logistic regression analyses were obtained, and the results of betweenprofile comparisons are presented in Table 4. Overall, older women and those with a history of trauma were more likely to belong to profile groups with higher levels of PTSD and PTG. A lower education level was a risk factor. Women with lower education levels were more likely to belong to profile groups with higher levels of PTSD but lower levels of PTG than women with higher education levels. In addition, women who had been injured and felt horrible and those whose houses were severely damaged in the earthquake were more likely to belong to profile groups with higher levels of PTSD and PTG than women without those experiences; the deaths of relatives had very little ability to predict profile membership.
3. Results
4. Discussion
3.1. Descriptive analyses
4.1. Profiles of PTSD and PTG
Approximately 35.2% of the women met the diagnostic criteria for PTSD. The details of the women’s trauma exposure and trauma history are shown in Table 1.
This study was the first to examine profiles of PTSD and PTG in a sample of women. We identified five meaningful profiles of PTSD and PTG. This finding was inconsistent with previous studies, which found three profiles (i.e., low PTSD/high PTG, low PTSD/low PTG, and high PTSD/high PTG profiles) among adolescents and adults in the general population (Birkeland et al., 2015; Cao et al., 2018; Chen and Wu, 2017; Zhou et al., 2018). The two distinct profiles we found were moderate PTSD/moderate PTG and high PTSD/moderate PTG. The largest proportion of women (39.4%) belonged to the moderate PTSD/moderate PTG profile group. That is, most of the women had experienced moderate levels of PTSD and PTG after the earthquake. However, previous studies have found that the largest proportion of adults in the general population belonged to the mild PTSD/high PTG profile (Birkeland et al., 2015; Cao et al., 2018). These inconsistent findings indicated that women are more vulnerable to disaster and develop relatively severe PTSD symptoms and lower levels of PTG. These findings again emphasize the need to pay more attention to women after disasters. A considerable number of women (21.1%) belonged to the high PTSD/high PTG profile group. This ratio was close to that found in previous studies (Birkeland et al., 2015; Cao et al., 2018). In addition, some women belonged to the mild PTSD/high PTG (17.5%)
3.2. Profiles of PTSD and PTG To identify the most appropriate number of profiles, five models with two to six profiles were estimated. In the end, the 5-profile model was the optimal choice. As shown in Table 2, AIC, BIC, and a-BIC continued to decrease with the addition of profiles and therefore did not indicate the optimal profile. Entropy values fluctuated slightly with an increasing number of profiles. The 2-, 3- and 5-profile models showed higher classification accuracy than the other models. The LMR-LRT supported the 5-profile model, whereas the p value for the BLRT remained significant for all solutions. The generalizability of all models was favourable as the minimum proportion of participants in all profiles exceeded 5%. Finally, we found that compared with the 3- and 4-profile models, the 5-profile model contained profiles that were found to be meaningful and significantly different from the previous profiles. However, the 6-profile model simply subdivided one of the profiles in the 5-profile model into two similar small profiles. In brief, fit statistics and the substantive interpretability of the profiles were Table 2 Fit Statistics for the Latent Profile Analysis. Number of profiles
AIC
BIC
a-BIC
Entropy
LMR-LRT
BLRT
Proportions min
2 3 4 5 6
28318.04 27185.97 26786.08 26428.82 26116.60
28450.65 27366.32 27014.17 26704.66 26440.17
28371.23 27258.31 26877.58 26539.47 26246.39
0.83 0.79 0.77 0.79 0.78
< .001 < .001 < .05 < 0.01 .377
< < < < <
.34 .24 .17 .10 .08
Note. Boldface indicates the selected model. 3
.001 .001 .001 .001 .001
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Fig. 1. Latent profile indicator mean values for the five-profile solution. Table 3 Means and standard deviations of PTSD and PTG by latent profile membership. Mild PTSD / mild PTG (n = 185) PTSD Intrusion symptoms Avoidance symptoms Hyperarousal symptoms PTG Relating to others New possibilities Personal strength Spiritual change Appreciation of life
1.80 1.96 1.68 1.92 1.42 1.58 1.21 1.56 0.99 1.61
(0.48)a (0.69)a (0.51)a (0.63)a (0.49)a (0.70)a (0.68)a (0.71)a (0.89)a (0.84)a
Moderate PTSD / moderate PTG (n = 586) 2.13 2.40 1.78 2.36 2.74 3.12 2.17 3.11 1.55 3.06
(0.44)b (0.73)b (0.45)a (0.65)b (0.36)b (0.59)b (0.70)b (0.70)b (1.18)b (0.87)b
High PTSD / moderate PTG (n = 142) 3.63 4.03 3.11 3.99 2.42 2.83 1.68 2.71 1.32 2.93
(0.50)c (0.67)c (0.70)b (0.68)c (0.40)c (0.65)c (0.82)c (0.87)c (1.18)b (0.97)b
Mild PTSD / high PTG (n = 260) 1.63 1.84 1.42 1.77 3.73 4.02 3.40 4.24 2.46 3.75
(0.41)d (0.63)a (0.40)c (0.56)d (0.40)d (0.52)d (0.73)d (0.55)d (1.52)c (0.73)c
High PTSD / high PTG (n = 314) 3.36 3.95 2.67 3.72 3.63 3.90 3.14 3.96 2.77 3.97
(0.51)e (0.69)c (0.73)d (0.73)e (0.42)e (0.55)e (0.82)e (0.65)e (1.47)d (0.74)d
F
742.70*** 523.46*** 337.06*** 520.87*** 827.97*** 482.25*** 306.02*** 440.05*** 79.96*** 243.83***
Note. Standard deviations are in parentheses. Means in the same row with the same superscript letter are not significantly different at the p < .05 level. ***p < .001.
and mild PTSD/mild PTG (12.4%) profile groups. These two profiles were also common in previous studies of adolescents and adults in the general population (Birkeland et al., 2015; Cao et al., 2018; Chen and Wu, 2017; Zhou et al., 2018), indicating that these profiles are stable across populations and age groups. Finally, we found another unique profile, which was designated the high PTSD/moderate PTG profile. Although this profile accounted for only a small part of the population (9.6%), it is of great significance. To some extent, our findings challenged previous views, which held that when a person has a high level of PTSD, it must be accompanied by a higher level of PTG (Cao et al., 2018; Zhou et al., 2018).
(Priebe et al., 2009; Wang et al., 2011). Different aspects of trauma exposure have different predictive effects on profile membership. Women who had been injured and felt horrible and those whose houses were severely damaged in the earthquake were more likely to belong to profile groups with higher levels of PTSD and PTG. These findings were consistent with previous findings (Cao et al., 2018; Jin et al., 2014b) and with the theoretical view that distress and growth may develop simultaneously after exposure to a traumatic event (Tedeschi and Calhoun, 2004). It is well understood that the higher the degree of trauma exposure, the higher the level of PTSD (Jin et al., 2014b; Solomon and Dekel, 2007). At the same time, trauma exposure can trigger ruminant thinking (Vishnevsky et al., 2010) and may result in support from families, friends and the government, thus prompting PTG. Finally, we observed the counterintuitive finding that the deaths of relatives had very little ability to predict profiles. This finding was consistent with previous research (Marthoenis et al., 2019) also conducted in Asian cultures that found that injury rather than the death of a family member can predict PTSD in adolescents. Nevertheless, we need to be cautious about our results, which do not indicate that the deaths of relatives cannot predict profile membership. A possible explanation for our finding is that in our sample, the number of women who reported the deaths of relatives was relatively small, which resulted in low statistical test power. Finally, it is worth noting that individuals with histories of trauma had high levels of both PTSD and PTG. This finding is consistent with the dose-response model (Johnson and Thompson, 2008), which holds that trauma history plays an important role when people face additional traumatic events. Thus, it is necessary to consider the role of trauma history when examining the impact of later traumatic events.
4.2. Variables that predict profile membership This study also examined the roles of demographic variables, trauma exposure during the earthquake and trauma history. Consistent with some findings (Cao et al., 2018; Zhou et al., 2018) but inconsistent with other findings (Jin et al., 2014b; Xu and Liao, 2011), we found that older individuals were more likely to belong to profile groups with higher levels of PTSD and PTG. This may be because the older a person is, the more stable his or her personal world is. Highly traumatic events can break this balance and promote PTSD. Meanwhile, mature cognition helps people to better understand the resources around them and thus develop high PTG. In line with previous studies (Cao et al., 2018; Priebe et al., 2009; Wang et al., 2011), we found that women with lower education levels were more likely to belong to profile groups with higher levels of PTSD (Park et al., 2017; Wang et al., 2011; Zalta et al., 2017) but lower levels of PTG (e.g., Wu et al., 2015). One possible explanation is that women with lower education levels have fewer resources to cope with traumatic events 4
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Table 4 Results of the multinomial logistic regression for the effects of predictors on profile membership. Variables
Reference = Mild PTSD / mild PTG Moderate PTSD / moderate PTG
High PTSD / moderate PTG
Reference = Mild PTSD / high PTG
Mild PTSD / high PTG
High PTSD / high PTG
High PTSD / high PTG
OR
95% CI
OR
95% CI
OR
95% CI
OR
95% CI
OR
95% CI
Age Junior high school or below Trauma history Injury in the earthquake Feeling horrible
1.00 0.87
[0.98, 1.01] [0.54, 1.39]
1.02 1.14
[0.99, 1.05] [0.56, 2.31]
1.00 0.45**
[0.98, 1.03] [0.27, 0.75]
1.03** 1.11
[1.00, 1.05] [0.63, 1.96]
1.03** 2.48***
[1.01, 1.05] [1.54, 3.98]
0.85 0.90 1.92***
[0.70, 1.03] [0.57, 1.42] [1.32, 2.81]
1.36* 1.85* 8.47***
[1.08, 1.72] [1.03, 3.30] [4.52, 15.88]
0.65** 0.89 1.27
[0.50, 0.84] [0.52, 1.53] [0.82, 1.97]
1.26* 1.04 9.30***
1.95*** 1.16 7.30***
[1.54, 2.46] [0.73, 1.85] [4.68, 11.39]
Severe house damage Deaths of relatives
1.83** 0.60
[1.26, 2.66] [0.28, 1.32]
4.35*** 0.62
[2.44, 7.74] [0.21, 1.81]
2.22*** 0.61
[1.44, 3.42] [0.24, 1.58]
3.37*** 1.03
[1.02, 1.55] [0.62, 1.73] [5.73, 15.09] [2.16, 5.26] [0.45, 2.38]
1.52* 1.68
[1.01, 2.29] [0.72, 3.91]
Variables
Reference = Moderate PTSD / moderate PTG High PTSD/moderate PTG OR
Age Junior high school or below Trauma history Injury in the earthquake Feeling horrible Severe house damage Deaths of relatives
1.03* 1.30 1.61*** 2.05** 4.41*** 2.37** 1.03
95% CI [1.00, [0.71, [1.33, [1.30, [2.53, [1.44, [0.41,
1.05] 2.38] 1.94] 3.24] 7.69] 3.91] 2.59]
Mild PTSD/high PTG OR 1.00 0.52*** 0.77* 0.99 0.66* 1.21 1.02
High PTSD/high PTG
95% CI [0.99, [0.36, [0.61, [0.66, [0.48, [0.87, [0.46,
Reference = High PTSD / moderate PTG
OR
1.03] 0.74] 0.95] 1.49] 0.91] 1.68] 2.24]
1.04*** 1.28 1.49*** 1.15 4.84*** 1.84*** 1.71
95% CI [1.02, [0.84, [1.27, [0.80, [3.28, [1.31, [0.91,
1.05] 1.95] 1.74] 1.66] 7.12] 2.58] 3.21]
Mild PTSD/high PTG OR 0.98 0.40** 0.48*** 0.48 0.15*** 0.51* 0.98
95% CI [0.96, [0.21, [0.37, [0.28, [0.08, [0.29, [0.34,
1.01] 0.75] 0.62] 0.83] 0.27] 0.89] 2.89]
High PTSD/high PTG OR 1.01 0.98 0.93 0.56* 1.10 0.78 1.66
95% CI [0.99, [0.51, [0.78, [0.35, [0.59, [0.46, [0.68,
1.03] 1.88] 1.11] 0.90] 2.04] 1.32] 4.02]
Note. OR: odds ratio. CI: confidence interval. *p < .05; **p < .01; ***p < .001.
are heterogeneous and that psychological assistance and trauma intervention should vary from person to person. In addition, we found that PTSD and PTG always occur together, indicating that focusing only on PTSD symptoms may limit or slow recovery and mask the possibility of growth (Shakespeare-Finch and Lurie-Beck, 2014). More importantly, even individuals with high levels of PTG should not be ignored and should receive help to alleviate their PTSD symptoms (Liu et al., 2017). It is worth noting that PTG often occurs unconsciously. Interventions for PTG are not directed at the PTG itself but instead aim to help survivors establish useful, basic cognitive guidelines for living (Tedeschi and Calhoun, 2004). In addition, this study provides a useful direction for identifying high-risk groups. Women who are older, have a lower education level, have a history of trauma and were exposed to trauma during the disaster should be the focus of attention.
4.3. Limitations Our study also has several limitations. First, our results should be generalized with caution in the following two aspects. On the one hand, our research was carried out in an Asian country and should be extended to other cultures with caution (Marthoenis et al., 2019). In addition, because our survey was based on convenience sampling, only women who were willing to leave their homes could be included (selfselection). Women with more severe trauma exposure may not have been willing to go out one year after the earthquake. Second, we did not measure women’s social support, which may be a key predictor of profile membership (Cao et al., 2018; Liu et al., 2012). Future studies should examine the role of this important variable in samples of women. Third, the design of this study was cross-sectional, which precludes causal interpretations of the predictors of profile group membership. Nevertheless, almost all of the predictive variables related to the status of participants a year ago or earlier and therefore can be viewed as predictive. Finally, PTSD was assessed by self-reported questionnaires rather than more sensitive clinical interviews; thus, only the severity of PTSD symptoms was determined, and this information cannot be used for clinical diagnosis (Liang et al., 2019). Structured clinical interviews should be used in future research.
Financial disclosure This work was supported by the programs of Pioneer Initiative of the Chinese Academy of Sciences, Feature Institutes Program (TSS2015-06) and Consulting and Appraising Project of Chinese Academy of Sciences (Y7CX134003). Declaration of Competing Interest
4.4. Implications for practice
None.
The present study has important practical significance. First, our research shows that women’s post-traumatic response is indeed greater than that of other groups, and they are indeed vulnerable to the adverse effects of natural disasters. Given the susceptibility of this population, its members should be the top priority for psychological assistance after disasters. We also found that a considerable proportion of women had experienced more than one type of trauma in the past other than natural disasters, suggesting that the Chinese government should establish special institutions to protect women from harm and violence. Furthermore, this study confirms that women’s posttraumatic responses
Acknowledgment We thank the women for their participation. References Asparouhov, T., Muthén, B., 2014. Auxiliary Variables in Mixture Modeling: Using the BCH Method in Mplus to Estimate a Distal Outcome Model and an Arbitrary Second Model (Mplus Web Note No. 21). Muthén & Muthén, Los Angeles, CA. Birkeland, M.S., Hafstad, G.S., Blix, I., Heir, T., 2015. Latent classes of posttraumatic
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Patterns of posttraumatic stress disorder and posttraumatic growth among women after an earthquake: A latent profile analysis Yueyue Zhoua,b, Yiming Lianga,b, Huiqi Tongc, Zhengkui Liua,b,
⁎
a
CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, China Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China c Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA b
ARTICLE INFO
ABSTRACT
Keywords: PTSD PTG Latent profile analysis Trauma China
It is well known that women are more vulnerable than men to mental health problems following disasters. However, the patterns of posttraumatic stress disorder (PTSD) and posttraumatic growth (PTG) among women are unclear. This study was conducted to investigate the profiles of PTSD and PTG among women after an earthquake and determine the predictors of profile membership. A total of 1487 women (Mage = 40.66 years, SD = 10.39) completed questionnaires assessing PTSD, PTG, trauma exposure during an earthquake, trauma history, and demographic information. A three-step latent profile analysis was used. Five profiles were identified: mild PTSD/mild PTG (12.4% of the sample), moderate PTSD/moderate PTG (39.4%), high PTSD/moderate PTG (9.6%), mild PTSD/high PTG (17.5%) and high PTSD/high PTG (21.1%). Women who were older, had been injured, or felt horrible and those whose houses were severely damaged in the earthquake were more likely to be in profiles characterized by higher levels of PTSD and PTG, whereas women with lower education levels were more likely to be in profiles characterized by higher levels of PTSD but lower levels of PTG. The results of this study provide a foundation for providing psychological assistance for particular groups of women.
1. Introduction Traumatic events often have a serious negative impact on people’s physical and mental health. Among the impacts of traumatic events, posttraumatic stress disorder (PTSD) is one of the most concerning and common mental health problems (Liang et al., 2019; Lowell et al., 2018). However, researchers have found that traumatic events can also cause positive changes, such as posttraumatic growth (PTG; Tedeschi and Calhoun, 1996). PTG refers to positive psychological changes and growth beyond previous levels of functioning because of struggles with highly challenging life crises (Tedeschi and Calhoun, 1996, 2004). To provide more targeted suggestions for psychological assistance after disasters, researchers note the need to fully understand both the positive and negative impacts of traumatic events (Jin et al., 2014a). More importantly, individual differences in post-traumatic response (PTSD and PTG) need to be noted. That is, in different subgroups, the patterns of PTSD and PTG may be different. Only person-centred analyses can thoroughly investigate such heterogeneity (Peugh and Fan, 2013). Latent profile analysis (LPA), a person-centred approach, is a suitable method for determining the different patterns of PTSD and PTG
based on their indicators. To our knowledge, only a few studies have used LPA to investigate patterns of PTSD and PTG (Birkeland et al., 2015; Cao et al., 2018; Chen and Wu, 2017; Zhou et al., 2018). Although these studies were conducted using different age groups (adolescents and adults, including men and women), they usually found three profiles. Specifically, profile 1 was characterized by low PTSD and high PTG scores; profile 2 was characterized by low PTSD and low PTG scores; and profile 3 was characterized by high PTSD and high PTG scores. These studies are valuable. However, few studies have specifically investigated patterns of PTSD and PTG among women. In fact, women deserve special attention for the following two reasons. First, women’s posttraumatic responses (PTSD and PTG) may be different from those of other groups (adults in the general population and adolescents) and may show more complex patterns. Previous studies have consistently found that females are more vulnerable to stress/disaster and develop higher levels of PTSD than males (Cheng et al., 2018; Jin et al., 2014a; Marthoenis et al., 2019). Moreover, evidence from a meta-analysis showed that compared with males, females developed higher levels of PTG (Vishnevsky et al., 2010). Second, a female family member may be
⁎ Corresponding author at: Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Chaoyang District, Beijing 100101, China. E-mail address: [email protected] (Z. Liu).
https://doi.org/10.1016/j.ajp.2019.10.014 Received 31 August 2019; Received in revised form 8 October 2019; Accepted 8 October 2019 1876-2018/ © 2019 Elsevier B.V. All rights reserved.
Please cite this article as: Yueyue Zhou, et al., Asian Journal of Psychiatry, https://doi.org/10.1016/j.ajp.2019.10.014
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the most critical person for intervention when the whole family is traumatized. Especially in the context of Asian culture, a woman’s status after a disaster may affect the physical and mental health of all family members, particularly the woman’s children (Usami et al., 2019). It is unfortunate that no research has investigated the patterns of PTSD and PTG among women. To fill this gap, we used LPA (Peugh and Fan, 2013) to determine the different patterns of PTSD and PTG among women who have experienced an earthquake. Another important research issue is to determine the characteristics of women who are more likely to have a higher PTSD but lower PTG profile. Previous studies have found that in the general population, age, education level, and trauma exposure (such as injury, feeling horrible, experiencing damage to one’s home, and the deaths of relatives as a result of disasters) are potential predictive variables (Cao et al., 2018; Chen and Wu, 2017; Park et al., 2017; Zalta et al., 2017; Zhou et al., 2018). These findings have provided with valuable information for targeted interventions. However, there are several limitations of previous studies. First, no research has been conducted to examine this issue specifically in groups of women. In fact, the roles of these variables may be different in women than in the general population. Second, evidence regarding the roles of demographic variables and trauma exposure has been mixed (Cao et al., 2018; Jin et al., 2014b; Liu et al., 2017; Park et al., 2017; Solomon and Dekel, 2007; Wu et al., 2015; Xu and Liao, 2011; Zhou et al., 2018). Thus, more research is needed. Third, previous studies did not examine the impact of previous trauma history, which may also affect an individual’s reactions after a disaster (Cloitre et al., 2009; Marthoenis et al., 2019). According to the dose-response model (Johnson and Thompson, 2008), previous traumas have a cumulative effect on subsequent posttraumatic responses (Johnson and Thompson, 2008). That is, trauma history will strengthen an individual’s posttraumatic response when they encounter another traumatic event. Considering this important issue, we examined the role of trauma history, such as experiencing an armed robbery or domestic violence, of the participating women. The current study surveyed women affected by the Ya’an earthquake in China, which occurred on April 20, 2013 in Sichuan Province, and had a magnitude of 7.0. A total of 196 people were killed, 21 went missing and 11,470 were injured.
participants had a senior high school education or above. 2.2. Measures 2.2.1. Posttraumatic stress disorder PTSD was measured by the 17-item self-report PTSD ChecklistCivilian Version (PCL-C). The PCL-C was developed based on the diagnostic criteria for PTSD in the DSM-IV (Ruggiero et al., 2003). Three clusters of symptoms were measured: intrusion, avoidance and hyperarousal. The women were asked to recall reactions and feelings in the past month that had been caused by the earthquake. Items are rated on a 5-point scale ranging from 1 = not at all to 5 = extremely. Participants with a total score over 44 were considered to have PTSD symptoms (Li et al., 2010). The Chinese version of the PCL-C has been widely used and has demonstrated good reliability and validity in Chinese adults (Liang et al., 2019). In the present study, Cronbach’s alpha for the scale was 0.93. 2.2.2. Posttraumatic growth PTG was measured by the 21-item self-report Posttraumatic Growth Inventory (PTGI; Tedeschi and Calhoun, 1996). The PTGI has mainly been used to measure positive changes and growth after trauma and includes the following five parts: relating to others, new possibilities, personal strength, spiritual change and appreciation of life. The women were asked to rate items on a 6-point scale ranging from 0 = not at all to 5 = very much. The Chinese version of the PTGI has been widely used and has demonstrated good reliability and validity in Chinese adults (Cao et al., 2018). In the present study, Cronbach’s alpha for the scale was 0.89. 2.2.3. Predictive variables Demographic variables (i.e., age and education level), trauma exposure during the earthquake and trauma history were measured. Specifically, education level was dummy coded 1 for junior high school or below and 0 for senior high school or above. Trauma exposure during the earthquake included the following four aspects: injury during the earthquake, feelings of intense fear/helplessness/terror (feeling horrible), severe damage to one’s home, and the deaths of relatives. Finally, we used six items with yes/no response choices to assess whether the participants had ever experienced the following six relatively severe traumas in their lives: serious accidents, such as a serious traffic accident or an explosion; threats of violence, such as murder or armed robbery; family violence, such as fighting or kicking; outside violence, such as being beaten, cursed at or seriously injured; sexual violence, such as being the victim of rape; and serious or life-threatening diseases. The total score for traumatic events was calculated to represent the trauma history of each woman.
2. Method 2.1. Participants and procedures Our survey was conducted in April of 2014, one year after the Ya’an earthquake. All research procedures were approved by the Research Ethics Review Board of the authors’ institution. A convenience sampling method was used to recruit participants. We recruited subjects in places that women frequent, such as vegetable markets and leisure plazas. First, we explained the purpose and content of the survey to the subjects and promised to keep personal information confidential. The women voluntarily chose whether to participate in our survey, and those who were willing to participate in the study signed a written informed consent form. Participants who had difficulty reading questionnaires completed the survey with the help of research assistants. Approximately 70% of the women we met were willing to participate in our study. Possible reasons for not participating were various but mainly associated with a longer waiting time. When the women arrived in large numbers at a specific time, the lack of sufficient research assistants led to longer waiting times for some women, which led to the further loss of participants. A total of 1493 earthquake-affected women from four counties participated in the survey. Six of the women were excluded from the analysis due to missing data for all predictive variables. The final sample included 1487 women. The mean age of the women was 40.66 years (SD = 10.39). Regarding education level, 78.0% of the participants had a junior high school education or less, and 22.0% of the
2.3. Plan of analysis First, we evaluated the prevalence of PTSD as well as the occurrence of traumatic exposure and trauma history. Second, based on the three symptoms of PTSD and the five aspects of PTG, we used a three-step LPA method to identify potential profiles of PTSD and PTG among women. All indicators were converted to z-scores for ease of interpretation. In addition, we used robust maximum likelihood estimates to handle missing data and estimate the model. Analyses were conducted using Mplus Version 7.4 (Muthén & Muthén, 1998–2015). To determine the optimal number of profiles, a series of models were estimated, and multiple fit indices were used, including the Akaike information criterion (AIC), Bayesian information criterion (BIC), sample-size-adjusted BIC (a-BIC), Lo-Mendell-Rubin likelihood ratio test (LMR-LRT), bootstrapped likelihood ratio test (BLRT) and entropy (Nylund, Asparouhov, & Muthen, 2007). Generally, models with the lowest AIC, BIC, and a-BIC values are considered better solutions. The LMR-LRT test compares a model with k profiles to a 2
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comprehensively considered, and the 5-profile model was selected as the optimal model. As shown in Fig. 1, Profile 1 (12.4% of the sample) was characterized by low levels of PTSD and PTG and was labelled the mild PTSD/ mild PTG group. Profile 2 (39.4%) was characterized by medium levels of PTSD and PTG and was labelled the moderate PTSD/moderate PTG group. Profile 3 (9.6%) was characterized by a high level of PTSD and a medium level of PTG and was labelled the high PTSD/moderate PTG group. Profile 4 (17.5%) was characterized by a low level of PTSD and a high level of PTG and was labelled the mild PTSD/high PTG group. Finally, profile 5 (21.1%) was characterized by high levels of PTSD and PTG and was labelled the high PTSD/high PTG group. Parameter estimates and within-profile indicator means are presented in Table 3.
Table 1 Descriptive Statistics for Trauma Exposure and Trauma History (N = 1487).
Trauma exposure Injury in the earthquake Feeling horrible Severe house damage Deaths of relatives Trauma history (number of experiences) One Two Three Four Five Six
N
%
359 864 906 79
24.1 58.1 60.9 5.3
367 133 63 21 10 1
24.7 8.9 4.2 1.4 0.7 0.1
3.3. Variables that predicted profile membership model with a k – 1 profile. A significant p value on the LMR-LRT indicates that a model with k profiles has a better fit than a model with a k – 1 profile. Entropy shows classification accuracy. Entropy can range from 0 to 1.0, with higher values indicating that individuals were more precisely classified (ideally above .70). To increase the generalizability of our results, we also considered whether each profile consisted of at least 5% of the sample. Finally, it was also important to consider the substantive interpretability of the profiles (Nylund et al., 2007). At last, to test whether demographic variables, trauma exposure during the earthquake and trauma history differentially predicted profile membership, multinomial logistic regression analysis was conducted using the three-step method (Asparouhov and Muthén, 2014).
In the framework of the three-step LPA, the results of multinomial logistic regression analyses were obtained, and the results of betweenprofile comparisons are presented in Table 4. Overall, older women and those with a history of trauma were more likely to belong to profile groups with higher levels of PTSD and PTG. A lower education level was a risk factor. Women with lower education levels were more likely to belong to profile groups with higher levels of PTSD but lower levels of PTG than women with higher education levels. In addition, women who had been injured and felt horrible and those whose houses were severely damaged in the earthquake were more likely to belong to profile groups with higher levels of PTSD and PTG than women without those experiences; the deaths of relatives had very little ability to predict profile membership.
3. Results
4. Discussion
3.1. Descriptive analyses
4.1. Profiles of PTSD and PTG
Approximately 35.2% of the women met the diagnostic criteria for PTSD. The details of the women’s trauma exposure and trauma history are shown in Table 1.
This study was the first to examine profiles of PTSD and PTG in a sample of women. We identified five meaningful profiles of PTSD and PTG. This finding was inconsistent with previous studies, which found three profiles (i.e., low PTSD/high PTG, low PTSD/low PTG, and high PTSD/high PTG profiles) among adolescents and adults in the general population (Birkeland et al., 2015; Cao et al., 2018; Chen and Wu, 2017; Zhou et al., 2018). The two distinct profiles we found were moderate PTSD/moderate PTG and high PTSD/moderate PTG. The largest proportion of women (39.4%) belonged to the moderate PTSD/moderate PTG profile group. That is, most of the women had experienced moderate levels of PTSD and PTG after the earthquake. However, previous studies have found that the largest proportion of adults in the general population belonged to the mild PTSD/high PTG profile (Birkeland et al., 2015; Cao et al., 2018). These inconsistent findings indicated that women are more vulnerable to disaster and develop relatively severe PTSD symptoms and lower levels of PTG. These findings again emphasize the need to pay more attention to women after disasters. A considerable number of women (21.1%) belonged to the high PTSD/high PTG profile group. This ratio was close to that found in previous studies (Birkeland et al., 2015; Cao et al., 2018). In addition, some women belonged to the mild PTSD/high PTG (17.5%)
3.2. Profiles of PTSD and PTG To identify the most appropriate number of profiles, five models with two to six profiles were estimated. In the end, the 5-profile model was the optimal choice. As shown in Table 2, AIC, BIC, and a-BIC continued to decrease with the addition of profiles and therefore did not indicate the optimal profile. Entropy values fluctuated slightly with an increasing number of profiles. The 2-, 3- and 5-profile models showed higher classification accuracy than the other models. The LMR-LRT supported the 5-profile model, whereas the p value for the BLRT remained significant for all solutions. The generalizability of all models was favourable as the minimum proportion of participants in all profiles exceeded 5%. Finally, we found that compared with the 3- and 4-profile models, the 5-profile model contained profiles that were found to be meaningful and significantly different from the previous profiles. However, the 6-profile model simply subdivided one of the profiles in the 5-profile model into two similar small profiles. In brief, fit statistics and the substantive interpretability of the profiles were Table 2 Fit Statistics for the Latent Profile Analysis. Number of profiles
AIC
BIC
a-BIC
Entropy
LMR-LRT
BLRT
Proportions min
2 3 4 5 6
28318.04 27185.97 26786.08 26428.82 26116.60
28450.65 27366.32 27014.17 26704.66 26440.17
28371.23 27258.31 26877.58 26539.47 26246.39
0.83 0.79 0.77 0.79 0.78
< .001 < .001 < .05 < 0.01 .377
< < < < <
.34 .24 .17 .10 .08
Note. Boldface indicates the selected model. 3
.001 .001 .001 .001 .001
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Fig. 1. Latent profile indicator mean values for the five-profile solution. Table 3 Means and standard deviations of PTSD and PTG by latent profile membership. Mild PTSD / mild PTG (n = 185) PTSD Intrusion symptoms Avoidance symptoms Hyperarousal symptoms PTG Relating to others New possibilities Personal strength Spiritual change Appreciation of life
1.80 1.96 1.68 1.92 1.42 1.58 1.21 1.56 0.99 1.61
(0.48)a (0.69)a (0.51)a (0.63)a (0.49)a (0.70)a (0.68)a (0.71)a (0.89)a (0.84)a
Moderate PTSD / moderate PTG (n = 586) 2.13 2.40 1.78 2.36 2.74 3.12 2.17 3.11 1.55 3.06
(0.44)b (0.73)b (0.45)a (0.65)b (0.36)b (0.59)b (0.70)b (0.70)b (1.18)b (0.87)b
High PTSD / moderate PTG (n = 142) 3.63 4.03 3.11 3.99 2.42 2.83 1.68 2.71 1.32 2.93
(0.50)c (0.67)c (0.70)b (0.68)c (0.40)c (0.65)c (0.82)c (0.87)c (1.18)b (0.97)b
Mild PTSD / high PTG (n = 260) 1.63 1.84 1.42 1.77 3.73 4.02 3.40 4.24 2.46 3.75
(0.41)d (0.63)a (0.40)c (0.56)d (0.40)d (0.52)d (0.73)d (0.55)d (1.52)c (0.73)c
High PTSD / high PTG (n = 314) 3.36 3.95 2.67 3.72 3.63 3.90 3.14 3.96 2.77 3.97
(0.51)e (0.69)c (0.73)d (0.73)e (0.42)e (0.55)e (0.82)e (0.65)e (1.47)d (0.74)d
F
742.70*** 523.46*** 337.06*** 520.87*** 827.97*** 482.25*** 306.02*** 440.05*** 79.96*** 243.83***
Note. Standard deviations are in parentheses. Means in the same row with the same superscript letter are not significantly different at the p < .05 level. ***p < .001.
and mild PTSD/mild PTG (12.4%) profile groups. These two profiles were also common in previous studies of adolescents and adults in the general population (Birkeland et al., 2015; Cao et al., 2018; Chen and Wu, 2017; Zhou et al., 2018), indicating that these profiles are stable across populations and age groups. Finally, we found another unique profile, which was designated the high PTSD/moderate PTG profile. Although this profile accounted for only a small part of the population (9.6%), it is of great significance. To some extent, our findings challenged previous views, which held that when a person has a high level of PTSD, it must be accompanied by a higher level of PTG (Cao et al., 2018; Zhou et al., 2018).
(Priebe et al., 2009; Wang et al., 2011). Different aspects of trauma exposure have different predictive effects on profile membership. Women who had been injured and felt horrible and those whose houses were severely damaged in the earthquake were more likely to belong to profile groups with higher levels of PTSD and PTG. These findings were consistent with previous findings (Cao et al., 2018; Jin et al., 2014b) and with the theoretical view that distress and growth may develop simultaneously after exposure to a traumatic event (Tedeschi and Calhoun, 2004). It is well understood that the higher the degree of trauma exposure, the higher the level of PTSD (Jin et al., 2014b; Solomon and Dekel, 2007). At the same time, trauma exposure can trigger ruminant thinking (Vishnevsky et al., 2010) and may result in support from families, friends and the government, thus prompting PTG. Finally, we observed the counterintuitive finding that the deaths of relatives had very little ability to predict profiles. This finding was consistent with previous research (Marthoenis et al., 2019) also conducted in Asian cultures that found that injury rather than the death of a family member can predict PTSD in adolescents. Nevertheless, we need to be cautious about our results, which do not indicate that the deaths of relatives cannot predict profile membership. A possible explanation for our finding is that in our sample, the number of women who reported the deaths of relatives was relatively small, which resulted in low statistical test power. Finally, it is worth noting that individuals with histories of trauma had high levels of both PTSD and PTG. This finding is consistent with the dose-response model (Johnson and Thompson, 2008), which holds that trauma history plays an important role when people face additional traumatic events. Thus, it is necessary to consider the role of trauma history when examining the impact of later traumatic events.
4.2. Variables that predict profile membership This study also examined the roles of demographic variables, trauma exposure during the earthquake and trauma history. Consistent with some findings (Cao et al., 2018; Zhou et al., 2018) but inconsistent with other findings (Jin et al., 2014b; Xu and Liao, 2011), we found that older individuals were more likely to belong to profile groups with higher levels of PTSD and PTG. This may be because the older a person is, the more stable his or her personal world is. Highly traumatic events can break this balance and promote PTSD. Meanwhile, mature cognition helps people to better understand the resources around them and thus develop high PTG. In line with previous studies (Cao et al., 2018; Priebe et al., 2009; Wang et al., 2011), we found that women with lower education levels were more likely to belong to profile groups with higher levels of PTSD (Park et al., 2017; Wang et al., 2011; Zalta et al., 2017) but lower levels of PTG (e.g., Wu et al., 2015). One possible explanation is that women with lower education levels have fewer resources to cope with traumatic events 4
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Table 4 Results of the multinomial logistic regression for the effects of predictors on profile membership. Variables
Reference = Mild PTSD / mild PTG Moderate PTSD / moderate PTG
High PTSD / moderate PTG
Reference = Mild PTSD / high PTG
Mild PTSD / high PTG
High PTSD / high PTG
High PTSD / high PTG
OR
95% CI
OR
95% CI
OR
95% CI
OR
95% CI
OR
95% CI
Age Junior high school or below Trauma history Injury in the earthquake Feeling horrible
1.00 0.87
[0.98, 1.01] [0.54, 1.39]
1.02 1.14
[0.99, 1.05] [0.56, 2.31]
1.00 0.45**
[0.98, 1.03] [0.27, 0.75]
1.03** 1.11
[1.00, 1.05] [0.63, 1.96]
1.03** 2.48***
[1.01, 1.05] [1.54, 3.98]
0.85 0.90 1.92***
[0.70, 1.03] [0.57, 1.42] [1.32, 2.81]
1.36* 1.85* 8.47***
[1.08, 1.72] [1.03, 3.30] [4.52, 15.88]
0.65** 0.89 1.27
[0.50, 0.84] [0.52, 1.53] [0.82, 1.97]
1.26* 1.04 9.30***
1.95*** 1.16 7.30***
[1.54, 2.46] [0.73, 1.85] [4.68, 11.39]
Severe house damage Deaths of relatives
1.83** 0.60
[1.26, 2.66] [0.28, 1.32]
4.35*** 0.62
[2.44, 7.74] [0.21, 1.81]
2.22*** 0.61
[1.44, 3.42] [0.24, 1.58]
3.37*** 1.03
[1.02, 1.55] [0.62, 1.73] [5.73, 15.09] [2.16, 5.26] [0.45, 2.38]
1.52* 1.68
[1.01, 2.29] [0.72, 3.91]
Variables
Reference = Moderate PTSD / moderate PTG High PTSD/moderate PTG OR
Age Junior high school or below Trauma history Injury in the earthquake Feeling horrible Severe house damage Deaths of relatives
1.03* 1.30 1.61*** 2.05** 4.41*** 2.37** 1.03
95% CI [1.00, [0.71, [1.33, [1.30, [2.53, [1.44, [0.41,
1.05] 2.38] 1.94] 3.24] 7.69] 3.91] 2.59]
Mild PTSD/high PTG OR 1.00 0.52*** 0.77* 0.99 0.66* 1.21 1.02
High PTSD/high PTG
95% CI [0.99, [0.36, [0.61, [0.66, [0.48, [0.87, [0.46,
Reference = High PTSD / moderate PTG
OR
1.03] 0.74] 0.95] 1.49] 0.91] 1.68] 2.24]
1.04*** 1.28 1.49*** 1.15 4.84*** 1.84*** 1.71
95% CI [1.02, [0.84, [1.27, [0.80, [3.28, [1.31, [0.91,
1.05] 1.95] 1.74] 1.66] 7.12] 2.58] 3.21]
Mild PTSD/high PTG OR 0.98 0.40** 0.48*** 0.48 0.15*** 0.51* 0.98
95% CI [0.96, [0.21, [0.37, [0.28, [0.08, [0.29, [0.34,
1.01] 0.75] 0.62] 0.83] 0.27] 0.89] 2.89]
High PTSD/high PTG OR 1.01 0.98 0.93 0.56* 1.10 0.78 1.66
95% CI [0.99, [0.51, [0.78, [0.35, [0.59, [0.46, [0.68,
1.03] 1.88] 1.11] 0.90] 2.04] 1.32] 4.02]
Note. OR: odds ratio. CI: confidence interval. *p < .05; **p < .01; ***p < .001.
are heterogeneous and that psychological assistance and trauma intervention should vary from person to person. In addition, we found that PTSD and PTG always occur together, indicating that focusing only on PTSD symptoms may limit or slow recovery and mask the possibility of growth (Shakespeare-Finch and Lurie-Beck, 2014). More importantly, even individuals with high levels of PTG should not be ignored and should receive help to alleviate their PTSD symptoms (Liu et al., 2017). It is worth noting that PTG often occurs unconsciously. Interventions for PTG are not directed at the PTG itself but instead aim to help survivors establish useful, basic cognitive guidelines for living (Tedeschi and Calhoun, 2004). In addition, this study provides a useful direction for identifying high-risk groups. Women who are older, have a lower education level, have a history of trauma and were exposed to trauma during the disaster should be the focus of attention.
4.3. Limitations Our study also has several limitations. First, our results should be generalized with caution in the following two aspects. On the one hand, our research was carried out in an Asian country and should be extended to other cultures with caution (Marthoenis et al., 2019). In addition, because our survey was based on convenience sampling, only women who were willing to leave their homes could be included (selfselection). Women with more severe trauma exposure may not have been willing to go out one year after the earthquake. Second, we did not measure women’s social support, which may be a key predictor of profile membership (Cao et al., 2018; Liu et al., 2012). Future studies should examine the role of this important variable in samples of women. Third, the design of this study was cross-sectional, which precludes causal interpretations of the predictors of profile group membership. Nevertheless, almost all of the predictive variables related to the status of participants a year ago or earlier and therefore can be viewed as predictive. Finally, PTSD was assessed by self-reported questionnaires rather than more sensitive clinical interviews; thus, only the severity of PTSD symptoms was determined, and this information cannot be used for clinical diagnosis (Liang et al., 2019). Structured clinical interviews should be used in future research.
Financial disclosure This work was supported by the programs of Pioneer Initiative of the Chinese Academy of Sciences, Feature Institutes Program (TSS2015-06) and Consulting and Appraising Project of Chinese Academy of Sciences (Y7CX134003). Declaration of Competing Interest
4.4. Implications for practice
None.
The present study has important practical significance. First, our research shows that women’s post-traumatic response is indeed greater than that of other groups, and they are indeed vulnerable to the adverse effects of natural disasters. Given the susceptibility of this population, its members should be the top priority for psychological assistance after disasters. We also found that a considerable proportion of women had experienced more than one type of trauma in the past other than natural disasters, suggesting that the Chinese government should establish special institutions to protect women from harm and violence. Furthermore, this study confirms that women’s posttraumatic responses
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