Health-related quality of life and post-traumatic stress disorder in inpatients injured in the Ludian earthquake: a longitudinal study

On August 3, 2014, an earthquake (6.5 on the Richter scale) occurred in Yunnan Province of China, with 617 deaths, 112 missing persons, 3143 injuries, and 1,088,400 victims. The epicenter was located in Zhaotong City, a medium-sized city located southwest of China. The region has experienced serious natural disasters in recent years and was hit by the YiLang earthquake (5.7 on the Richter scale) in 2012, the Ludian earthquake (6.5 on the Richter scale) in 2014, and a flood in 2016.

This longitudinal study included injured inpatients in the Ludian earthquake and treated at the No. 1 People’s Hospital of Zhaotong City. Of approximately 600 injured patients, approximately 200 were discharged from the No. 1 People’s Hospital of Zhaotong City within a week, and 125 were transferred to other hospitals because of the severity of their injuries. In total, 174 patients aged between 10 and 75 years fulfilled the following inclusion criteria: (I) suffered physical injury caused by the Ludian earthquake, (II) treatmented in the hospital for longer than 1 month, (III) had no history of alcohol dependence or addictive drug use; and (IV) had no current pregnancy or breastfeeding; however, 101 injured patients did not meet the inclusion criteria for the study (shown in Fig. 1).

In total, 174 patients participated in the baseline assessment, and 163 and 147 participated in the first and second follow-up assessments, respectively. The initial assessment was conducted at the No. 1 People’s Hospital of Zhaotong between September and October 2014, approximately 1 month after the earthquake. The first follow-up assessment was conducted 3 months after the earthquake, between November and December 2014, and involved patient interviews at the hospital or resettlement sites, because most of patients had been discharged. Of the original 174 participants, 11 did not participate in the second assessment. The second follow-up assessment was conducted 18 months after the earthquake, in February 2016, in patients’ communities. Of the original 174 participants, 27 did not participate in the third assessment. A flowchart of the study is shown in Fig. 1.

The study was approved by the ethics committee at Sichuan University in Chengdou, China (2014) and was performed in accordance with the Declaration of Helsinki and its later amendments. All participants provided informed consent. Data were collected via face-to-face interviews conducted by four local psychiatrists and individuals who held master’s degrees in psychology from Sichuan university and were trained in group discussion and standardized data collection procedures. The four psychiatrists each held more than 5 years’ work experience and reviewed the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) diagnostic criteria for PTSD and the Clinician -Administered Posttraumatic Stress Disorder Scale (CAPS) prior to the interviews.

Participants’ demographic characteristics, including age, sex, ethnicity, educational level, marital status, occupation, earthquake-related disaster exposure, and experiences after the Ludian earthquake were measured via a self-report questionnaire, which included the following questions: (1) Was your house destroyed entirely in the earthquake? (2) Were you buried or did you witness the death of a family member in the earthquake? (3) Did a family member die in the earthquake? (4) Were you with your family members when the earthquake occurred? (5) Have you been diagnosed with any major disease such as hypertension, stroke, heart disease, diabetes, or tumor? (6) Did you experience any major life events, such as the death of a family member (child, spouse, or parent), serious injury, disability, or divorce, before the earthquake? All these questions were coded dichotomously as yes/no items (shown in Self-report Interview Questionnaire 1).

The PTSD Checklist–Civilian Version (PCL-C) was used to measure self-reported trauma-related stress 1 and 3 months after the disaster. The PCL-C is a clinical self-assessment diagnostic scale consisting of 17 items that correspond to the fourth edition of criteria the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) [25]. Responses are provided using a five-point Likert-type scale ranging from 1 (not at all) to 5 (extremely), with higher scores indicating more severe posttraumatic stress. Total symptom severity scores range from 17 to 85, and a cut-off point of 50 was recommended for the diagnosis of PTSD in Chinese sample [26, 27]. With a cut-off point of 50, sensitivity was 0.78, specificity was 0.86, and diagnostic efficiency was 0.83. The PCL-C scores were strongly correlated with the Clinician -Administered PTSD Scale (CAPS) scores. The correlation coefficient was 0.93, and diagnostic efficiency was 0.90 [28, 29]. The PCL-C has been used widely in various Chinese populations and has shown high internal consistency, with Cronbach’s α of 0.96 [30, 31].

Participants underwent a structured clinical interview via the CAPS, which was based on 1994 DSM-IV Diagnoses (American Psychiatric Association) [32], 18 months after the earthquake. The CAPS is recognized as one of the gold standards for the diagnosis of PTSD [33]. (The CAPS includes 30 items, with responses provided using a four-point Likert-type scale ranging from 1 (never) to 4 (routinely). The CAPS includes Criterion A (exposure to a traumatic event), Criteria B–D (core symptom clusters of re-experiencing, numbing, avoidance, and hyperarousal), Criterion E (chronology), Criterion F (functional impairment), and associated guilt and dissociation and assesses current and lifetime PTSD symptom status. The scale includes three subscales: re-experiencing, avoidance, and hyperarousal. Total scores range from 0 to 136, and patients with CAPS scores exceeding 65 are categorized as having PTSD, while those with CAPS scores exceeding 80 are categorized as having extreme PTSD [32]. The CAPS has shown good reliability and validity in Chinese samples [34, 35]. Moreover, Li et al. reported Cronbach’s αs between 0.80 and 0.90 and convergent validity of0.70 for the Chinese version of the CAPS [33, 35].

All assessments performed at 1 month (N = 174) and 3 months (N = 163) after the disaster involved the PCL-C, while those performed at 18 months (N = 147) after the disaster involved the CAPS, administered by four psychiatrists to confirm PTSD diagnosis. To evaluate investigator reliability, we selected 20 of the 147 cases at random for independent assessment by the four psychiatrists. Two of the 20 cases showed inconsistency in the diagnoses formed by the four psychiatrists. Therefore, we concluded that investigator reliability was 90%, and the diagnoses formed by the four psychiatrists showed good inter-rater reliability (W = .93, χ² = 32.45, P < .001). It reasonable to speculate that the clinical diagnosis of patients at 18-month are more valuable. Thus, the results of 18-month were reported in detail.

The Medical Outcomes Study 36-Item Short Form Health Survey (SF-36) is widely used in HRQoL research and has been validated and tested for reliability in some studies and used extensively to evaluate functional and QoL outcomes in injured individuals [6, 18]. The scale consists of two dimensions: the physical component summary and the mental component summary. The physical component summary contains four subscales: physical functioning (PF); role physical (RP), which pertains to role limitations because of physical health problems; bodily pain (BP); and general health (GH). The mental component summary contains four subscales: vitality (VT); social functioning (SF); role emotional (RE), which pertains to role limitations because of emotional problems; and mental health (MH) [36]. Subscales are scored from 0 to 100, and higher scores indicate greater HRQoL. The Chinese version of the SF-36 is used widely in China, and Cronbach’s αs for the scale have been reported between .70 and .80, with convergent validity ranging from .66 to .94 [37, 38].

Table 1 shows the participant data collected at 1 month, 3 months, and 18 months. Of the 174 patients assessed 1 month after the earthquake, 74 were men, 100 were women, 103 were married, 55 were unmarried, 16 were divorced, and 140 were Han, who is main ethnic group of the local area. The patients’ mean age (± sd) was 46 ± 18.4 years old, and their ages ranged from 10 to 88 years. The numbers of patients who had completed primary school, middle school, and high school and above were 56, 39, and 19, respectively, and 60 patients were illiterate. Most of the participants were peasants (n = 154). With respect to disaster exposure, 49 of 174 patients had experienced burial and bereavement, 24 patients were separated from their families, and 23 patients were affected by severe injury. The numbers of patients who reported a history of major diseases and life events were 27and 52, respectively.

As shown in Table 1 and Fig. 1, during follow-up, 11 (6%) and 27 (16%) of participants did not attend the 3-month and 18-month evaluations, respectively. We analyzed the participants’ circumstances before dropout, to clarify the effect of missing data and explore the causes of dropout. Specifically, we used t tests to compare PCL-C scores between the dropout and follow-up groups. Baseline PCL-C scores for participants who did not attend follow up at 3 months (n = 11) did not differ significantly from that of participants who attended follow-up assessment (n = 163; 41 ± 11.5 versus 40 ± 13.7, P > .05). In addition, of the participants who attended follow up at 3 months, PCL-C scores for those who did not attend follow up (n = 16) at 18 months did not differ significantly from that of participants who attended follow-up assessment (n = 147; 31 ± 14.9 versus 38 ± 13.7, P > .05). Of the participants who dropped out, 74% (20/27) were men and 26% (7/27) were women. Of the participants who dropped out, 7% (2/27) were seriously injured and 93% (25/27) were not seriously injured.

Moreover, to understand the reasons for dropout, we randomly selected eight of the 27 participants who had dropped out and interviewed them by telephone to ask about their recovery status and the reasons why they did not wish to participate in the assessments. Three main reasons were reported: some participants had left their settlements or homes to work in other cities; some participants lived far from the assessment of site, which was approximately 4 h’ walk away; and two participants had begun to receive financial and health support from other social organizations. Therefore, it is reasonable to speculate that there were valid reasons for attrition. Most men who dropped out had left to work in larger cities. Further, some participants lived in scattered areas and most lived high in the mountains, and it was difficult for them to attend assessments at community health centers. In addition, both follow-up interviews were conducted in autumn and winter; therefore, it was difficult for participants to attend. Moreover, the rehabilitation assessments might not have provided sufficient support to patients, and participants received more support from the government or other social organizations.

The prevalence rates for PTSD assessed by PCL-C at 1 month and 3 months after the earthquake were (40/174) 23%(95% CI: 17%, 29%)and (22/163)14%(95% CI: 7%, 16%)respectively. Further, the prevalence of PTSD was (10/147) 7%(95% CI: 3%, 10%)at 18 months, according the criteria of PTSD (CAPS Scale). Therefore, the overall prevalence of PTSD decreased gradually following the earthquake. In addition, we checked the individuals with PTSD at 3-month and 18-month. Seventeen of twenty-two individuals who had PTSD at the 3-month assessment were screened positive-PTSD at the 1-month and eight of twenty-two individuals were still diagnosed with PTSD at the 18-month. Meanwhile, seven and eight of ten individuals who had PTSD at the18-month assessment were screened positive-PTSD at the 1-month and 3-month, respectively.

The differences in demographic characteristics between the PTSD (n = 10) and non-PTSD (n = 137) groups 18 months after the disaster are summarized in Table 2. There were no significant differences between the two groups, according to age, gender, educational level, ethnicity, occupation, or marital status. In addition, the two groups did not differ significantly according to the consequences of disaster exposure, such as houses collapsing completely and separation from family, or the experience of major life events before the disaster. However, the proportions of participants who had experienced being buried 7/10 (70%) versus 30/137 (22%) (P = 0.003), being bereaved 7/10 (70%) versus 39/137 (29%) (P = 0.011), a history of major diseases 7/10 (70%) versus 16/137 (12%) (P < 0.001), and sustained severe injuries 7/10 (70%) versus32/137 (23%) (P = 0.004) in the PTSD group were significantly higher than those observed in the non-PTSD group. Moreover, the proportion of participants in the PTSD group who had undergone physical rehabilitation at 18 months was significantly lower than that observed in the non-PTSD group 6/10 (60%) versus 122/137 (89%) (P = 0.048).

The results of the logistic regression to identify factors that predicted PTSD were as follows (Table 3). All significant variables (P < .05) in the univariate analyses were included in the logistic regression model to identify the independent role of each predictor variable after adjustment, including bereavement, a history of major diseases, severe injury, experience of being buried and rehabilitation at 18 months. The results of the logistic regression analysis indicated that risk factors for PTSD included bereavement (OR = 29.26, CI: 3.125–274.046), a history of major diseases (OR = 15.92, CI: 1.850–136.934), and severe injury (OR = 0.039, CI: 0.005–0.309). The experience of being buried (OR = 1.323, CI: 0.123–14.20) and rehabilitation at 18 months (OR = 0.144, CI: 0.019, 1.115) were not risk factors for PTSD.

There were differences in health status between the participants (N = 147) and the general Chinese population (N = 4251). In our study, 46 and 54% of patients was for male and female and the average age (± sd) was 46 ± 17.6 years, old, while among the 4251 respondents of the general Chinese population, male and female respondents accounted for 51% (2154) and 49% (2097) and the average age (± sd) was 41 ± 17.3 years old, ranging from 14 ~ 99 years old [36, 39]. The two groups differed significantly in all eight subscales of the SF-36 are summarized in Table 4. The participants’ mean subscale scores were significantly lower relative to those observed in the general Chinese population (BP: 72.10 ± 17.38 versus 83.3 ± 19.7, P < .001; PF: 63.80 ± 31.75 versus 87.6 ± 16.8, P < .001; RP: 11.14 ± 30.19 versus 83.0 ± 20.7, P < .001; GH: 47.5 ± 15.23 versus 68.2 ± 19.4, P < .001; VT: 48.26 ± 11.49 versus 70.1 ± 16.8, P < .001; SF: 78.26 ± 19.42 versus 84.8 ± 16.6, P = .001; RE: 23.19 ± 42.15 versus 85.3 ± 17.7, P < .001; MH: 57.00 ± 8.93 versus 78.8 ± 15.4, P < .001). These data indicated that the health status of inpatients injured in the earthquake was poorer relative to that of the general Chinese population.

The results of the analysis of correlations between PTSD and the domains of HRQoL showed that CAPS scores were negatively correlated with scores for all eight SF-36 subscales including BP, PF, RP, GH, VT, SF, RE, and MH. The correlation coefficients ranged from −.26 to −.53, as shown in Table 5.

The study was subject to some limitations. First, although PCL-C scores are very strongly correlated with CAPS scores, differences between the results obtained via these diagnostic tools for PTSD remain. In the comprehensive assessment of PTSD, the CAPS is one of the gold standards for use in PTSD diagnosis. In the current study, we used the PCL-C 1 and 3 months after the earthquake and the CAPS 18 months after the earthquake, because the PCL-C involves self-assessment and saves time, while the CAPS is used by psychiatrists and takes approximately 33 ± 16 min to complete [33]. In the early post-disaster period, when responders are faced with heavy rescue tasks and a shortage of psychiatrists, the PCL-C is used as an evaluation tool to screen inpatients quickly. In addition, among the ten patients with PTSD at 18 months in our current study, seven and eight patients were positive PTSD at 1 month and 3 months, respectively, and only one patient was not screened for PTSD at 1 month and 3 months, respectively. This may indicate a relatively high degree of consistency between the PCL-C and the CAPS for assessment PTSD. Therefore, the above-mentioned limitation should be noted in future research and confirmed in future investigations. Second, self-report measures, such as the PCL and SF-36 used in the study, could involve potential information bias. In fact, response bias occurs in many areas of behavioral and medical research in which self-reported data are used [51]. Actually, the “response shift bias” phenomenon is a source of contamination in self-report measures, which can lead to inaccurate test ratings [52]. Moreover, our study sample size is relatively small. Therefore, the current findings should be confirmed in large samples in future studies. Third, another limitation was the high attrition rate in the follow-up research. One of the reasons for this was that some participants had moved to larger cities to work. Another reason was that it was difficult for participants to attend the assessment site in the community health center. Further, caution should be exercised when generalizing the results to other countries, different cultures, and different disaster scenarios. Finally, comparisons with Chinese general population data were limited to global mean testing since detailed data by gender and age categories or other confounders were not available. Therefore, although crude absolute SF-36 scale differences varied from 6 (social functioning) to 72 (role physical) and were all statistically significant (p ≤ 0.001) the present results should be considered with caution. Despite these limitations, this was the first study conducted in China to examine QoL and explore PTSD via follow-up research involving inpatients following a disaster. In addition, the findings could contribute to the design of evidence-based interventions to promote QoL and reduce the prevalence of PTSD in injured inpatients.