Journal of Consulting and Clinical Psychology 2015, Vol. 83, No. 3, 524 –533

© 2015 American Psychological Association 0022-006X/15/$12.00 http://dx.doi.org/10.1037/a0039055

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Predicting Suicide Attempts by Time-Varying Frequency of Nonsuicidal Self-Injury Among Chinese Community Adolescents Jianing You

Min-Pei Lin

South China Normal University

National Taiwan Normal University

Objective: This study predicted suicide attempts (SA) by time-varying frequency of nonsuicidal selfinjury (NSSI) beyond the contributions of their shared risk factors and examined gender differences in this association. This study also tested for a moderating effect of NSSI in the relationship between suicide ideation (SI) and SA. Method: A large number of Chinese adolescents participated in this study (the exact number of participants varied from 3,623 to 6,911 in different analyses). They completed questionnaires assessing NSSI, SA, SI, borderline personality disorder features, depressive symptoms, and self-criticism 3 times at 6-month intervals. Generalized estimating equations were used to test the hypotheses. Results: In females, but not in males, NSSI was significantly associated with future SA after controlling for the effects of shared risk factors. With the same frequency of SI, the frequency of NSSI also enhanced the risk for future SA. Conclusions: This study established a longitudinal association between NSSI and SA, which could not be fully accounted for by their shared risk factors, in Chinese female community adolescents. Moreover, female adolescents who frequently engage in NSSI may gain the capability for attempting suicide.

What is the public health significance of this article? This study suggests that nonsuicidal self-injury is an important risk factor for future suicide attempts among female adolescents. Suicide prevention programs for female adolescents should take into account the identification and treatment of nonsuicidal self-injury.

Keywords: nonsuicidal self-injury, suicide attempt, suicide ideation, longitudinal association, adolescents

The lifetime prevalence estimates for NSSI among adolescents vary from 5.5% to 30.7%, the 12-month prevalence rates vary from 7.5% to 37.2%, and the 6-month prevalence rates vary from 13.9% to 16.3% (Muehlenkamp, Claes, Havertape, & Plener, 2012). Although the prevalence estimates of NSSI vary greatly because of the different assessment methods used across studies (i.e., singleand multiple-item checklists or open-ended questions), previous research collectively suggests that this behavior has become relatively common among community adolescents. NSSI is a costly health problem. It can incur economic (e.g., health care expenses), social (e.g., relationships damaged or lost because of self-injury) and personal costs (e.g., shame or embarrassment based on others’ reactions to the behavior). Apart from these significant negative consequences, NSSI has also been found to be associated with suicide attempts (SA; Hamza, Stewart, & Willoughby, 2012). Understanding this association may be of great clinical significance for suicide prevention. The primary objective of the present study was to examine the predictive utility over time of NSSI for SA among Chinese community adolescents.

Nonsuicidal self-injury (NSSI) is the deliberate, direct, and socially unacceptable destruction of body tissue without conscious suicidal intent (Nock, 2010). This behavior has become a major public health concern in adolescents (Jacobson & Gould, 2007).

This article was published Online First March 16, 2015. Jianing You, Center for Studies of Psychological Application & School of Psychology, South China Normal University; Min-Pei Lin, Department of Educational Psychology and Counseling, National Taiwan Normal University. This study was funded by the National Natural Science Foundation of China (Grant 31300874), the National Social Science Foundation of China (Grant 14ZDB159), and the Ministry of Science and Technology in Taiwan (Grant NSC 102-2511-S-003-016 -MY3). This study was also supported by Key Laboratory of Mental Health and Cognitive Science of Guangdong Province and Research Center for Crisis Intervention and Psychological Service of Guangdong Province, South China Normal University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data used in this study were drawn from a large scale longitudinal project examining borderline personality features and NSSI among adolescents in Hong Kong. This project was led by Dr. Freedom Leung from the Department of Psychology, The Chinese University of Hong Kong. We greatly appreciate Dr. Leung’s generous sharing of this dataset. Correspondence concerning this article should be addressed to Jianing You, Center for Studies of Psychological Application & School of Psychology, South China Normal University, Guangzhou, China. E-mail: [email protected]

The Association Between NSSI and SA Previous studies examining the association between NSSI and SA were mostly cross-sectional. Generally, one of two approaches was used: correlational or group comparisons. On the one hand, correlational studies among psychiatric inpatients in the United States revealed that the presence and number of NSSI episodes 524

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

PREDICTING SUICIDE ATTEMPTS BY TIME-VARYING NSSI

were significantly and positively related to the presence and number of SA (Andover & Gibb, 2010), and a longer history of NSSI and the use of a greater number of NSSI methods were also associated with SA (Nock, Joiner, Gordon, Lloyd-Richardson, & Prinstein, 2006). For Chinese community adolescents, Tang et al. (2011) found that NSSI was concurrently associated with SA, and this association was significantly higher in participants not reporting suicide ideation (SI) than in those reporting SI in the past year. Additionally, Whitlock and Knox (2007) revealed that NSSI was strongly predictive of suicidality when demographic variables were controlled among a large sample of United States college students. Finally, Klonsky, May, and Glenn (2013) examined the relationship between NSSI and SA among four distinct United States samples: adolescent psychiatric patients, adolescent high school students, university undergraduates, and a random-digit dialing sample of adults. In all four samples, NSSI exhibited a robust relationship to SA. Moreover, NSSI maintained a significant association with SA after controlling for some known suicide risk factors, including depression, anxiety, impulsivity, and borderline personality disorder. On the other hand, studies using the group comparison method usually examined group differences in psychopathology symptoms across four groups: (a) participants not engaging in NSSI or SA, (b) participants engaging in NSSI only, (c) participants engaging in SA only, and (d) participants engaging in both NSSI and SA. Results showed that participants engaging in both NSSI and SA generally scored the highest on psychopathology symptom variables, such as depression, hopelessness, aggression, anxiety, impulsivity and SI; participants engaging in neither NSSI nor SA scored the lowest; and the scores of the participants engaging in NSSI only and those engaging in SA only were in the middle (Brausch & Gutierrez, 2010; Claes et al., 2010; Liang et al., 2014; Muehlenkamp & Gutierrez, 2007; Stanley, Gameroff, Michalsen, & Mann, 2001; Swahn et al., 2012; Whitlock & Knox, 2007). Regarding the specific differences between the NSSI-only and SA-only groups, mixed results were found. Some studies revealed that the SA-only group exhibited higher levels of depression, hopelessness, impulsivity, and SI (Claes et al., 2010; Liang et al., 2014) and less attraction to life (Whitlock & Knox, 2007) than the NSSI-only group, whereas other studies reported no group differences for depression, SI, and impulsivity (Muehlenkamp & Gutierrez, 2007; Swahn et al., 2012). Most of the above studies were conducted among populations in the United States. Only two studies were from different countries: the study of Claes et al. (2010) was conducted in Belgium, and the study of Liang et al. (2014) was conducted in China. Longitudinal studies examining the association between NSSI and SA have been accumulating in recent years. Five studies of this type have been located to date. Asarnow et al. (2011) and Wilkinson et al. (2011) revealed that a history of NSSI predicted an SA incident over a 24- and 28-week period in 334 and 164 adolescents with major depressive disorder in the United States, respectively. Asarnow et al. also found that an NSSI history was a stronger predictor of future SA than a history of SA. Among 399 community adolescents in the United States, Guan et al. (2012) examined NSSI as a time-invariant, prospective predictor for SA over a 2.5-year interval. They found that baseline NSSI was significantly and prospectively associated with elevated levels of SA. Whitlock et al. (2013) showed that a history of NSSI signif-

525

icantly predicted concurrent and later SA independent of shared risk factors in 1,466 United States college students. The study of Wichstrøm (2009), conducted with 2,924 Norwegian high school students, however, revealed no significant relation of baseline NSSI to SA 5 years later. This null finding may be because of the broad and vague definition of NSSI. In Wichstrøm’s study, participants who answered, “yes” to the question, “Have you taken an overdose of pills or otherwise tried to harm yourself on purpose?” and “no” to the question, “Have you ever tried to kill yourself?” comprised the NSSI group. This definition included indirect forms of NSSI and was different from that used in the present and other previous studies. Although previous studies established a preliminary association between NSSI and SA, they were limited in several regards; thus, avoiding these limitations was a focus of the present study. First, most of the past work used a cross-sectional design, which did not allow for the inference of the use of NSSI in predicting future SA. The few longitudinal studies were all conducted in Western countries (Asarnow et al., 2011; Guan et al., 2012; Wichstrøm, 2009; Whitlock et al., 2013; Wilkinson et al., 2011). Given that NSSI has become a global public health concern (Muehlenkamp et al., 2012), it is of great significance to examine whether suicidality could be predicted by NSSI in Eastern countries. Additionally, only two longitudinal research projects have examined the association between NSSI and SA in community adolescents, and these studies had mixed results (i.e., the Guan et al.’s study reported a significant relationship of NSSI and future SA, whereas Wichstrøm’s study revealed no significant findings). More research to establish the relationship of NSSI to future SA, especially among large samples of Eastern community adolescents, is needed. Second, the five existing longitudinal studies all used baseline NSSI as a predictor for SA over a follow-up period. This method examined the role of a static status of NSSI in predicting either a static or varying status of SA in the future. This approach sacrifices important information regarding the engagement in NSSI during the follow-up period. Moreover, it is possible that relatively large variations exist in the performance of NSSI within individuals across a long period. Thus, using the time-varying frequency of NSSI in predicting future SA affects the predictive utility of not only baseline NSSI but also changes in NSSI during the follow-up period. Third, few previous studies have examined gender differences in the association between NSSI and SA. To our knowledge, only two studies of this type exist to date, and they revealed mixed results. Specifically, Klonsky et al. (2013) reported that a concurrent association between NSSI and SA was higher for United States high school girls than for their male counterparts, whereas Guan et al. (2012) showed that gender did not moderate the relationship of baseline NSSI and later SA. Knowing the differential associations between NSSI and SA across genders may be essential for formulating different suicide prevention plans for boys and girls. Thus, it is important for future studies to clarify whether NSSI is linked to SA differently across genders. Fourth, although people generally agree that NSSI predicts future SA, the association could be because of other variables or shared risk factors. Previous longitudinal studies have controlled for some of these factors (e.g., demographic variables, history of mental illness, psychosocial variables, etc.) and revealed inconsistent results. Specifically, Wilkinson et al. (2011), Guan et al.

YOU AND LIN

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

526

(2012), and Whitlock et al. (2013) found that NSSI significantly increased the risk for future SA, whereas Wichstrøm (2009) reported a null finding. Moreover, some important shared risk factors have been largely ignored in longitudinal studies. For example, NSSI and SA are both diagnostic criteria for borderline personality disorder (BPD) (Diagnostic and Statistical Manual for Mental Disorders-Fifth Edition [DSM-5]; American Psychiatric Association, 2013). They may be manifested with other symptoms of BPD. Previous studies have empirically demonstrated the relation of both NSSI and SA to other BPD features, including impulsivity (e.g., Glenn & Klonsky, 2010; Gvion & Apter, 2011), unstable relationships (e.g., Phillips et al., 2002; You, Leung, Lai, & Fu, 2012), self-disturbances (i.e., identity disturbance and unstable sense of self; e.g., Yen et al., 2004; You et al., 2012). Thus, the association between NSSI and SA may result from the presence of these BPD features. Additionally, individuals who engage in NSSI and/or SA often report a greater degree of depression than those who do not engage in any self-harm behaviors (e.g., Brausch & Gutierrez, 2010; Muehlenkamp & Gutierrez, 2007; Whitlock & Knox, 2007). This suggests that NSSI and SA may be correlated because they are both indicators of extreme psychological distress. Another potential variable may be self-criticism because both NSSI and SA are considered to serve the self-punishment function (e.g., Klonsky, 2007; Kraft, Jobes, Lineberry, Conrad, & Kung, 2010) and are found to correlate with self-criticism (e.g., Fazaa & Page, 2003; Wedig & Nock, 2007). To better understand the unique association between NSSI and SA, future studies should account for these shared risk factors.

NSSI as a Moderator of the Relationship Between SI and SA Another potential explanation for the link between NSSI and SA is Joiner’s (2005) interpersonal theory of suicide. This theory suggests that to commit suicide, an individual should have not only SI but also the capability to execute a suicide plan. This capability may include feeling no fear or pain of suicidal behaviors or being willing to face this fear or pain as the means to an end. Frequent engagement in NSSI, according to Joiner, is one way to acquire this capability. This may be because (a) NSSI releases an endogenous opioid that is related to a reduction in the experience of pain (Bresin & Gordon, 2013) and (b) although NSSI causes physical pain, it is an effective method for escaping emotional pain or a desperate situation or for communicating the emotion and desperation. Based on the interpersonal theory of suicide, NSSI may moderate the association between SI and SA, such that with the same level of SI, those who engage in NSSI may have a higher likelihood of engaging in SA. This moderating effect has not been examined in previous studies, and it should be a focus of future research.

The Present Study The aims of the present study were threefold. The first was to examine the longitudinal predictive ability of NSSI to SA beyond the contributions of shared risk factors, that is, BPD features, depressive symptoms, and self-criticism, among Chinese community adolescents. The second was to examine gender differences in the association of NSSI and SA. The third aim was to test whether

NSSI moderated the association between SI and SA, as suggested by Joiners’ (2005) interpersonal theory of suicide. Based on the previous literature, we hypothesized that NSSI would be associated with a higher likelihood of future SA beyond the contributions of the shared risk factors. This association would be stronger in females than in males. Moreover, NSSI would significantly moderate the association between SI and SA such that, with the same level of SI, participants who engaged in NSSI with a higher frequency would have a higher likelihood of engaging in SA.

Method Participants Participants were recruited from eight coeducational high schools (five of the schools were Band 1 schools and the other three were Band 2 schools1) in Hong Kong and were surveyed three times. At the baseline assessment (Wave 1), 5,423 students (2,857 females) from Grades 7–10 participated. Because of the cooperation of school authorities, overall student participation rates in Grades 7–10 were close to 99% for those who were present on the day of assessment in all schools. Participants ages were between 12 and 18 years (M ⫽ 14.63 years, SD ⫽ 1.25) at baseline; 87.7% of the participants came from intact families, and 12.3% came from single-parent families. At the Wave 2 assessment 6 months later, 6,911 adolescents (52.6% females) were recruited from Grades 7–11, and 3,999 (55.4% females) were retained from the Wave 1 sample. The original Wave 1 sample was in Grades 8 –11 at Wave 2; the newly added participants were in Grade 7. At the Wave 3 assessment, 6 months after Wave 2, 6,831 adolescents (52.6% females) from the same grades as at Wave 2 participated. The participant retention rates were 73.7% and 66.4% for the first 2 waves and all 3 waves, respectively. In total, 1,369 participants had data for Wave 1 only, 747 had data for Wave 2 only, 935 had data for Wave 3 only, 431 had data for Waves 1 and 2 only, 23 had data for Waves 1 and 3 only, and 2,133 had data for Waves 2 and 3 only. Additionally, among the 1,015 adolescents who engaged in NSSI at Wave 1, 718 (70.7%) and 639 (63.0%) were followed at Waves 2 and 3, respectively. Among the 4,408 participants who did not report NSSI at Wave 1, 3,305 (75.0%) and 3,008 (68.2%) were successfully followed at Waves 2 and 3, respectively. Overall, we successfully followed 3,600 adolescents over the three waves. Attrition was mainly because of students transferring to other schools (94.1% and 93.0% of attrition cases in Waves 2 and 3, respectively) and being absent from school on the day of assessment (5.9% and 7.0% of the attrition cases in Waves 2 and 3, respectively). Differences in study variables were examined between adolescents who participated at Wave 1 only (Group 1), at both Wave 1 and Wave 2 (Group 2), at both Wave 2 and Wave 3 (Group 3), and at all three waves (Group 4). Groups 1, 2, and 4 did not differ on Wave 1 study variables. The Groups 2 and 4 and Groups 3 and 4 did not differ on Wave 2 study variables. 1 Secondary schools in Hong Kong are divided into three Bands. The Bands are ranked in order of academic prestige, with Band 1 being the most prestigious.

PREDICTING SUICIDE ATTEMPTS BY TIME-VARYING NSSI

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Procedure At Wave 1, students in Grades 7–10 from all participating schools were invited to participate in this study on a voluntary basis. We framed this research as “Mental Health of Adolescents” and obtained written informed consent from participants’ parents before the testing. At Waves 2 and 3, students in Grades 7–11 in all participating schools joined the study. During each assessment, the same set of questionnaires was group-administered in classrooms of 35– 42 students under the supervision of school personnel. At the beginning of each assessment, teachers were required to inform students that they could hand in the questionnaires whenever they wanted and that they did not need to finish all the items if they felt uncomfortable. A unique ID number for each student was created for data-matching purposes. Participants were assured of the strict confidentiality of the collected data; only research personnel had access to the questionnaires. All testing materials and procedures were approved by the Institutional Review Board.

Measures Nonsuicidal self-injury (NSSI). Seven NSSI behaviors, that is, self-cutting, burning, biting, punching, scratching skin, inserting objects into the nail or skin, and banging the head or other parts of the body against the wall, were assessed in the present study. These behaviors were selected because they were found to be relatively common among adolescents (Nock, 2010) and had been used in previous studies (You et al., 2012; You, Lin, Fu, & Leung, 2013). At the Wave 1 assessment, participants were asked, “In the past 12 months, have you engaged in the following behaviors to deliberately harm yourself, but without suicidal intent?” At the Wave 2 and 3 assessments, the period for assessing NSSI was the past 6 months because the time interval between each assessment wave was 6 months. The seven NSSI behavior items were rated on a 4-point scale, ranging from 1 ⫽ never, 2 ⫽ once or twice, 3 ⫽ three to five times, to 4 ⫽ six times or more. This scale has demonstrated sufficient concurrent and overtime validity via its relationship to other psychopathology measures (You et al., 2012). In the current study, this scale had a Cronbach’s ␣ of .79, .74, and .79 for the Wave 1, Wave 2, and Wave 3 data, respectively. SA and SI. A single item was used to assess participants’ suicide attempts and ideation, respectively. Participants were asked “Have you attempted suicide/had SI in the past 12 (at Wave 1 assessment) or 6 months (at Wave 2 and Wave 3 assessments)?” Responses were made on a 4-point scale, ranging from 1 ⫽ never, 2 ⫽ once or twice, 3 ⫽ three to five times, to 4 ⫽ six times or more. Because very few participants endorsed the suicide attempt item, we dichotomized this item: “0” represented no report of SA and “1” represented reporting at least one incidence of SA. (Lack of) Premeditation. The (lack of) Premeditation subscale of the UPPS Impulsive Behavior Scale (Whiteside & Lynam, 2001) was used to measure the cognitive facet of impulsivity. The UPPS Impulsive Behavior Scale has good factor structure (Miller, Flory, Lynam, & Leukefeld, 2003) and validity via its relations to gambling, drinking, and other impulsive behaviors (e.g., Cyders & Smith, 2008; Magid & Colder, 2007). The (lack of) Premeditation subscale has 11 items. Sample items include “I have a reserved and cautious attitude toward life” and “I usually think carefully before doing anything.” Responses were made on a 4-point scale (1 ⫽ strongly disagree to 4 ⫽ strongly agree). This scale had Cron-

527

bach’s ␣ values of .79, .84, and .84 for the Wave 1, 2, and 3 data, respectively. Negative urgency. The Negative Urgency subscale of the UPPS Impulsive Behavior Scale (Whiteside & Lynam, 2001) was used to measure the urgency facet of impulsivity. This scale has 12 items. Sample items include “I have trouble controlling my impulses” and “Sometimes I do things on impulse that I later regret.” Responses were made on a 4-point scale (1 ⫽ strongly disagree to 4 ⫽ strongly agree). This scale had Cronbach’s ␣s of .85, .85, and .89 for the Wave 1, 2, and 3 data, respectively. Unstable relationship. A 7-item scale extracted and modified from the DIB-R (Zanarini et al., 1989) was used to assess unstable relationships in this study. This scale has good concurrent validity and has been validated and used in previous studies with Chinese community adolescents (Leung & Leung, 2009; You et al., 2012). Sample items of this scale include “I either love or hate other people in an extreme way” and “My relationships with other people are very unstable.” Responses were made on a 4-point scale (1 ⫽ strongly disagree to 4 ⫽ strongly agree). This scale had Cronbach’s ␣ values of .85, .84, and .86 for the Wave 1, 2, and 3 data in this study, respectively. Unstable self-image. Four items extracted from the Rosenberg’s Stability of Self Scale (Alsaker & Olweus, 1986) were used to assess the construct of unstable self-image. Previous research revealed sufficient concurrent validity of this scale via its relations to other BPD measures (Leung & Leung, 2009; You et al., 2012). Items on this scale include “My self-evaluations are entirely different everyday,” “Compared to most people, my self-evaluation changes very quickly,” “Some days I have a very good opinion about myself, other days I have a very poor opinion about myself,” and “Sometimes I feel good one minute and then the next Minute I feel terrible.” Ratings were made on a 4-point scale (1 ⫽ strongly disagree to 4 ⫽ strongly agree). This scale had Cronbach’s ␣ values of .90, .90, and .91 for the Wave 1, 2, and 3 data, respectively. Depressive symptoms. We assessed depressive symptoms using the Chinese version of the 7-item Depression subscale of the short Depression Anxiety Stress Scale (DASS21; Taouk, Lovibond, & Laube, 2001). The DASS-21 has good convergent and discriminant validities (Antony, Bieling, Cox, Enns, & Swinson, 1998). Sample items on this scale include “I felt that life was meaningless,” “I couldn’t seem to experience any positive feelings at all,” and “I felt that I had nothing to look forward to.” Responses were made on a 4-point scale (0 ⫽ do not apply to me at all to 3 ⫽ applies to me very much or most of the time). The time-period assessed by the DASS-21 was the same as that of the NSSI scale in all three waves. The DASS-21 had Cronbach’s ␣s of .86, .87, and .86 for the Wave 1, Wave 2, and Wave 3 data, respectively. Self-criticism. The 9-item Self-Criticism Subscale of the Depressive Experiences Questionnaire (DEQ; Blatt, D’Afflitti, & Quinlan, 1976) was used to measure participants’ self-critical thinking style. This scale has sufficient internal consistency and satisfactory intercorrelations with the other two subscales, that is, Dependency and Efficacy of the DEQ (Zuroff, Quinlan, & Blatt, 1990). Sample items on the SCS include “I often find that I don’t live up to my own standards or ideals” and “There is a considerable difference between how I am now and how I would like to be.” Responses were made on a 7-point scale (1 ⫽ not at all like

528

YOU AND LIN

me to 7 ⫽ like me very much). This scale had Cronbach’s ␣ values of .82, .86, and .87 for the Wave 1, 2, and 3 data, respectively. Measures without Chinese versions (e.g., the Self-Criticism scale and the UPPS Impulsive Behavior Scale) were translated using the following procedure: (a) the first author translated the English versions into Chinese, (b) two English-Chinese bilinguals translated the Chinese versions back into English, and (c) discrepancies between the original versions and the back-translations were analyzed and adjustments were made to the Chinese versions as necessary.

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Data Analysis Data analyses were conducted in three steps. At Step 1, descriptive analyses were performed to examine the percentages of participants reporting SA, SI, and NSSI at all three waves. Adolescents who engaged in NSSI and those who did not were compared on all study variables using independent sample t tests (for continuous variables) and ␹2 statistics (for categorical variables). These analyses were conducted with all available data at each wave (i.e., 5,423 at Wave 1, 6,911 at Wave 2, and 6,831 at Wave 3). At Step 2, bivariate correlations between NSSI, SA, and SI across the three waves and gender differences in the correlations were examined with the available data in corresponding waves (e.g., the correlation between Wave 1 NSSI and Wave 2 SA were calculated for the participants with both Wave 1 and Wave 2 data). Univariate unconditional growth models were estimated to reveal the temporal stability of NSSI, SA, and SI with the three-wave follow-up participants. At Step 3, generalized estimating equations (GEE; Liang & Zeger, 1986; Zeger & Liang, 1986) were used to test our hypotheses. In this longitudinal study design, within-subject observations were correlated; thus, violating the assumption of independent observations found in classical regression models. Moreover, the outcome variable in this study (i.e., having engaged in SA or not) was a binary variable and did not fit into the methods for correlated continuous data (e.g., repeated measures analysis of variance [ANOVA] or random effects models). The GEE approach, compared with those methods, can account for correlated responses within a participant and produce unbiased regression estimates for binary response variables. Rather than provide parameter estimates that would allow prediction for individuals as in a regression, GEE estimates the average response over a population. In other words, for every one-unit increase in a predictor across the population, GEE tells us how much the average response would change (Zorn, 2001). In the present study, participants with both two and three data points (N ⫽ 6,187) were included in the GEE analyses.2 The dependent variable was the time-varying occurrence of SA, and the independent variable was the time-varying frequency of NSSI. To examine whether NSSI was associated with SA independent of shared risk factors, we included all the other time-varying study variables as covariates. Gender and age were also included as covariates because NSSI and SA differ in the prevalence rate and peak at different times across genders (Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, 2010; Jacobson & Gould, 2007). To examine the potential gender difference in the association between NSSI and SA, we also

performed the GEE analyses separately by gender. To test whether NSSI moderated the association between SI and SA, we included the interaction term of SI and NSSI in the model. Given the longitudinal nature of the current data, the autoregressive correlation structure was used. Missing data were handled using the pairwise deletion method. All analyses were conducted with SPSS version 20.0.

Results Descriptive Analyses Among all available participants at each wave, 2.6% (n ⫽ 141), 2.2% (n ⫽ 152), and 2.9% (n ⫽ 198) reported an attempted suicide at Wave 1, Wave 2, and Wave 3, respectively; 16.0% (n ⫽ 868), 13.9% (n ⫽ 961), and 13.9% (n ⫽ 950) of adolescents reported SI at the three waves, respectively; and 18.7% (n ⫽ 1,014), 12.6% (n ⫽ 757), and 9.2% (n ⫽ 628) reported having engaged in NSSI at the three waves, respectively. For each specific NSSI behavior, the average rate of occurrence across the three waves was 6.1% for cutting (8.5% and 3.5% for females and males, respectively), 5.8% for biting (7.1% and 4.3% for females and males, respectively), 5.1% for scratching (6.3% and 3.7% for females and males, respectively), 4.0% for banging the head or other parts of the body against the wall (4.6% and 3.8% for females and males, respectively), 3.1% for punching (2.4% and 3.8% for females and males, respectively), 2.4% for inserting sharp objects into the nail or skin (2.6% and 2.0% for females and males, respectively), and 1.0% for burning (0.8% and 1.1% for females and males, respectively). Additionally, ⬃60.4% (n ⫽ 613) of the Wave 1 self-injurers, 54.2% (n ⫽ 410) of the Wave 2 self-injurers, and 47.3% (n ⫽ 297) of the Wave 3 self-injurers reported engaging in only one type of NSSI, whereas 39.6% (n ⫽ 401), 45.8% (n ⫽ 347), and 52.7% (n ⫽ 331) of the Wave 1, Wave 2, and Wave 3 self-injurers reported using more than one NSSI behavior, respectively. Table 1 presents the descriptive statistics of all study variables separately for adolescents who engaged in NSSI and those who did not at each wave. Differences in study variables were examined. The results showed that a significantly higher percentage of selfinjurers reported suicide attempts than noninjurers at all waves. Moreover, adolescents who engaged in NSSI scored significantly higher than those who did not on all study variables for all three assessment waves. Table 2 presents the means and SDs for SA, SI, and NSSI, as well as the bivariate correlations between these variables at all three waves separately by gender. For both genders, the three variables exhibited small to moderate auto-correlations over the three waves. We also conducted unconditional growth models for the three variables to examine their temporal stability. For both genders, the frequency of NSSI decreased significantly (for females, mean of slope ⫽ ⫺.10, p ⬍ .001; for males, mean of slope ⫽ ⫺.11, p ⬍ .001). The frequency of SA (for females, mean of slope ⫽ .01, p ⬎ .05; for males, mean of slope ⫽ .00, p ⬎ .05) and SI (for females, mean of slope ⫽ .00, p ⬎ .05; for males, mean of slope ⫽ ⫺.02, p ⬎ .05) remained stable. The three variables 2 We also conducted GEE analyses with participants who completed all three waves of assessment, and the results were similar.

PREDICTING SUICIDE ATTEMPTS BY TIME-VARYING NSSI

529

Table 1 Descriptive Statistics for All Study Variables in Adolescents Who Engaged in NSSI and Those Who Did Not at Each Wave

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Wave 1

Wave 2

Wave 3

Variables

NSSI

No NSSI

t/␹2

NSSI

No NSSI

t/␹2

NSSI

No NSSI

t/␹2

SA SI DEP PRE NU UR US SC

110 (10.9%) 1.60 (0.84) 7.10 (5.12) 29.24 (4.82) 29.11 (5.67) 16.36 (4.40) 9.60 (3.32) 38.28 (8.89)

30 (0.7%) 1.13 (0.42) 3.46 (3.63) 30.43 (4.57) 25.39 (5.87) 12.72 (4.21) 7.38 (2.93) 32.97 (8.87)

328.48 25.58 26.10 ⫺7.27 18.03 24.34 20.97 16.92

88 (11.7%) 1.62 (0.86) 6.77 (5.07) 27.80 (5.39) 27.73 (6.34) 15.33 (4.30) 9.28 (3.26) 36.12 (9.63)

43 (0.8%) 1.12 (0.39) 3.13 (3.55) 29.42 (5.47) 23.11 (6.50) 12.06 (4.06) 7.18 (2.97) 29.23 (10.06)

359.88 27.52 24.35 ⫺7.51 18.01 20.31 17.83 17.25

99 (16.2%) 1.93 (1.32) 6.27 (4.79) 28.23 (5.20) 27.94 (6.22) 16.75 (4.22) 9.76 (3.18) 36.00 (9.87)

77 (1.3%) 1.14 (0.52) 2.52 (3.18) 29.88 (5.17) 23.59 (6.42) 13.05 (4.19) 7.50 (2.93) 28.83 (10.09)

485.13 29.44 26.25 ⫺7.50 15.77 20.68 17.98 16.93

Note. Values under the columns of each wave are descriptive statistics for each variable at the corresponding wave. Values in the SA row are the numbers of participants (percentages), and values in all other rows are means (SDs). NSSI ⫽ nonsuicidal self-injury; SA ⫽ suicide attempt; SI ⫽ suicide ideation; DEP ⫽ depressive symptoms; PRE ⫽ (lack of) premeditation; NU ⫽ negative urgency; UR ⫽ unstable relationship; US ⫽ unstable self-image; SC ⫽ self-criticism. All group differences are significant at .001 level.

frequency was still significant in predicting SA. As shown by the odds ratio, each 1-point increase in NSSI frequency increased the likelihood of SA by 49%. Moreover, the interaction between NSSI frequency and SI was also significant. With the same level of SI, a 1-point increase in NSSI frequency was associated with an 8% increase in the likelihood of SA. The GEE analyses were also performed separately by gender. The results are summarized in Table 3. For females, NSSI frequency was significant after controlling for the effects of other predictors. Each 1-point increase in NSSI frequency increased the likelihood of SA by 68% in females. The interaction between NSSI frequency and SI was also significant. With the same level of SI, a 1-point increase in NSSI frequency was associated with a 10% increase in the likelihood of SA. For males, however, both NSSI frequency and the NSSI frequency and SI interaction were not significant.

also showed small to moderate concurrent associations with each other. Moreover, the variables generally demonstrated positive and significant correlations with each other longitudinally. Gender differences in all correlation coefficients were also examined using Fisher’s z transformation (Cohen & Cohen, 1983), and the results are presented in Table 2. To reduce the enlarged Type I error because of multiple testing, the ␣ level was set to .001. The results showed that the intercorrelations between SA and NSSI variables at the three waves were significantly higher in females than in males. Additionally, the cross-sectional and over time correlations between SA and SI and between SA and NSSI were higher in females than in males.

Predicting Suicide Attempts by Time-Varying Frequency of NSSI We used GEE to predict the time-varying occurrence of SA during the study period by time-varying frequency of NSSI, SI, and their interaction after controlling for the effects of gender, age, and all the other time-varying study variables. The analysis was first performed with the whole sample, and results are summarized in Table 3. After controlling for all the other variables, NSSI

Discussion This study examined the association over time between NSSI and SA among a large sample of Chinese community adolescents. After controlling for some shared risk factors for NSSI and SA,

Table 2 Bivariate Correlations Between SA, SI, and NSSI at All Three Waves Separately by Gender Variable

1

2

3

4

5

6

7

8

9

M

SD

1. SA, Wave 1 2. SA, Wave 2 3. SA, Wave 3 4. SI, Wave 1 5. SI, Wave 2 6. SI, Wave 3 7. NSSI, Wave 1 8. NSSI, Wave 2 9. NSSI, Wave 3 M SD

— 0.17 0.11 0.39 0.20 0.19 0.20 0.13 0.10 1.03 0.21

0.38 — 0.25 0.15 0.36 0.27 0.11 0.16 0.11 1.02 0.14

0.18 0.37 — 0.07 0.14 0.55 0.11 0.06 0.11 1.03 0.31

0.43 0.26 0.20 — 0.45 0.37 0.27 0.15 0.12 1.16 0.52

0.23 0.42 0.29 0.47 — 0.43 0.18 0.29 0.19 1.13 0.44

0.18 0.26 0.49 0.42 0.53 — 0.18 0.18 0.22 1.14 0.53

0.23 0.23 0.23 0.37 0.29 0.26 — 0.31 0.26 7.40 1.30

0.22 0.25 0.26 0.25 0.35 0.33 0.44 — 0.30 7.23 0.97

0.22 0.25 0.30 0.27 0.31 0.42 0.38 0.48 — 7.16 0.85

1.03 1.04 1.05 1.24 1.22 1.25 7.56 7.35 7.32

0.24 0.26 0.29 0.56 0.63 0.67 1.61 1.17 1.39

z12 ⫽ 7.14, z19 ⫽ 3.86 z23 ⫽ 4.16, z24 ⫽ 3.59, z27 ⫽ 3.87, z29 ⫽ 4.53 z34 ⫽ 4.15, z35 ⫽ 4.93, z37 ⫽ 3.87, z38 ⫽ 6.44, z39 ⫽ 6.22 z68 ⫽ 5.03, z69 ⫽ 7.00 z78 ⫽ 4.74, z79 ⫽ 4.19 z89 ⫽ 6.67

Note. NSSI ⫽ nonsuicidal self-injury; SA ⫽ suicide attempt; SI ⫽ suicide ideation. All correlation coefficients larger than .10 were significant at .001 level. Data for females are presented above the diagonal, and data for males are presented below the diagonal. Significant gender differences (p ⬍ .001) were indicated in bold. The z statistics for gender difference are also presented, with the subscripts representing the two variables between which the correlation coefficients were compared.

YOU AND LIN

530

Table 3 Generalized Estimating Equation Results for the Whole Sample and Separately by Gender

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Whole sample Predictor

b

Odd ratio [95% CI]

Gender (female) Age Depressive symptoms (Lack of) premeditation Negative urgency Unstable relationship Unstable self-image Self-criticism SI NSSI frequency NSSI frequency ⫻ SI

0.21 ⫺0.00 0.07ⴱ 0.02 0.05ⴱ 0.05 ⫺0.01 0.04 2.04ⴱⴱ 0.40ⴱⴱ 0.08ⴱⴱ

1.24 [0.81, 1.89] 1.00 [0.85, 1.12] 1.08 [1.02, 1.14] 1.02 [0.98, 1.06] 1.05 [1.01, 1.09] 1.06 [0.99, 1.12] 0.99 [0.93, 1.06] 0.96 [0.93, 0.99] 7.66 [5.36, 10.94] 1.49 [1.34, 1.65] 1.08 [0.90, 0.96]

Females

Males

b

Odd ratio [95% CI]

b

Odd ratio [95% CI]

0.05 0.07 0.04 0.04 0.06 ⫺0.05 0.04 2.17ⴱⴱ 0.52ⴱⴱ 0.09ⴱⴱ

1.05 [0.85, 1.30] 1.07 [0.99, 1.15] 1.04 [0.99, 1.10] 1.04 [0.99, 1.09] 1.06 [0.99, 1.15] 0.95 [0.88, 1.03] 1.04 [1.00, 1.08] 8.79 [5.94, 13.00] 1.68 [1.48, 1.91] 1.10 [1.07, 1.13]

⫺0.07 0.10ⴱ 0.00 0.06 0.04 0.05 0.06ⴱ 1.49ⴱⴱ 0.10 0.00

0.94 [0.71, 1.24] 1.11 [1.02, 1.21] 1.00 [0.95, 1.06] 1.06 [1.00, 1.12] 1.04 [0.94, 1.14] 1.05 [0.93, 1.18] 1.06 [1.01, 1.11] 4.43 [1.93, 10.17] 1.11 [0.90, 1.37] 1.00 [0.91, 1.09]

Note. CI ⫽ confidence interval; NSSI ⫽ nonsuicidal self-injury; SI ⫽ suicide ideation. p ⬍ .01. ⴱⴱ p ⬍ .001.

ⴱ

their longitudinal association was established in females, but not in males. Moreover, NSSI frequency moderated the relation of SI to SA, providing partial support for Joiner’s (2005) interpersonal theory of suicide. The major finding of a significant longitudinal association between NSSI and SA in this study is consistent with those in previous studies (Asarnow et al., 2011; Guan et al., 2012; Whitlock et al., 2013; Wilkinson et al., 2011). Compared with past research, a unique contribution of the present study lies partly in the large sample of community adolescents. This may add more confidence to the potential predictive utility of NSSI for later SA. Moreover, unlike previous studies using baseline NSSI as a predictor for future SA, this study treated NSSI as a time-varying predictor for SA. Using the time-varying frequency of NSSI as the predictor allowed the use of information regarding not only baseline NSSI but also changes in NSSI during the follow-up period. Our findings suggest that an increase in NSSI frequency is linked to a higher likelihood of attempting suicide in the future. The engagement in NSSI varied greatly in this sample (i.e., the prevalence of NSSI decreased considerably from Wave 1 to Wave 3); therefore, using all the information over the three time points may have given a more accurate estimate of the longitudinal association between NSSI and SA. The significant longitudinal relationship of NSSI and SA was beyond the contributions of other shared risk factors, including BPD features, depressive symptoms, and self-criticism. This result was consistent with those of Guan et al. (2012) and Klonsky et al. (2013) and suggests that NSSI and SA may be two distinct phenomena, not symptoms of other clinical disorders, such as major depression or BPD. This result may also support the proposal of two new diagnoses in the DSM-5 (American Psychiatric Association, 2013): NSSI Disorder and Suicidal Behavior Disorder. The present study also examined the moderating effect of NSSI on the relationship between SI and SA. This may be regarded as a direct test of Joiner’s (2005) interpersonal theory of suicide. Previous studies testing that theory examined only the main effects of NSSI and SI on SA rather than their interaction effect (e.g., Klonsky et al., 2013). The results from this study are consistent with Joiner’s theory, that is, adolescents with the same frequency

of SI but more frequent NSSI had a higher probability of actually attempting suicide than their counterparts with less frequent NSSI. According to Joiner, making an actual suicide attempt requires both the desire and capability for suicide. Suicide ideation is an indicator of the desire, whereas frequent engagement in NSSI may be an indicator of the capability. Those who have SI may not attempt suicide because of the fear of pain. This group may also possess adequate behavioral inhibition; thus, they are able to control the impulse to actually attempt suicide. Frequent engagement in NSSI may serve not only to habituate individuals to self-inflicted pain (Nock et al., 2006) but also to loosen the behavioral inhibitions through repeatedly engaging in self-harm behaviors, for example, self-cutting. Thus, frequently engaging in NSSI may increase the capability for suicide. It is also possible that repeated NSSI may become a habitual response toward intrapersonal and interpersonal distress, and SA may gradually become a method in the repertoire of coping strategies among repetitive self-injurers. The interaction effects of NSSI and SI on SA can also be considered from a different viewpoint. That is, SI may be seen as a moderator of the relationship between NSSI and SA. By definition, NSSI is conducted without the intent to die, while SA are conducted with a clear suicidal intent. NSSI may only escalate to SA among individuals who have SI. Thus, as a moderator of the NSSI-SA relationship, SI is different than the shared risk factors for NSSI and SA, for example, BPD features, depressive symptoms, and self-criticism, assessed in this study. Shared risk factors increase the risk for engaging in both NSSI and SA; moderators, such as SI, only increase the risk for SA among individuals with NSSI. Another contribution of this study was the examination of gender differences in the relationship between NSSI and SA: the association was significant in females but not in males. This finding is consistent with the results of a cross-sectional study (Klonsky et al., 2013). Under the framework of the interpersonal theory of suicide (Joiner, 2005), this result suggests that NSSI serves as a more important way of acquiring capability for suicide in females than in males. This may be because males, more so than females, acquire this capability through an array of means (Joiner,

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

PREDICTING SUICIDE ATTEMPTS BY TIME-VARYING NSSI

2005). Males have more opportunities than females in general to experience pain and provocation. They may have more exposure to physical fights, violent sports (e.g., boxing and football), and alcohol and substance abuse. To males, NSSI is just one of many ways of acquiring suicide capability, while NSSI is one of few ways of acquiring this capability for females. Thus, NSSI is more closely or more exclusively associated with SA in females. Another factor that may account for the stronger association between NSSI and SA in females is that females engage in NSSI more often for its negative reinforcement function (e.g., to relieve negative emotions) than males do (Laye-Gindhu & Schonert-Reichl, 2005; You, Lin, & Leung, 2013). As a result, in females, more so than in males, NSSI may, because of the opponent process, gradually lose its painful and fear-inducing properties and gain rewarding properties (Joiner, 2005). As this occurs, the capability for suicide increases more in females with NSSI than in their male counterparts. Apart from gender difference in the association between NSSI and SA, we also note that none of the covariates significantly predicted SA for females in the GEE models but both depressive symptoms and self-criticism (in addition to SI) did for males (see Table 3). These analyses might illustrate two different pathways to SA for males versus females. Future studies may delve into this area. Another finding of this study meriting attention is that the prevalence of NSSI dropped significantly from Wave 1 (18.7%) to Wave 2 (12.6%) and to Wave 3 (9.2%). This may be accounted for by four possible explanations. One is that at Wave 1, we assessed the 12-month prevalence of NSSI, whereas the 6-month prevalence was assessed at both Wave 2 and Wave 3. Another is that more participants without NSSI at Wave 1 were successfully followed at Wave 2 (75.0%) and Wave 3 (68.2%) compared with participants with NSSI at Wave 1 (70.7% and 63.0% were followed at Wave 2 and Wave 3, respectively). Third, because the prevalence of NSSI was relatively high at Wave 1, many participating schools may have felt alarmed and proceeded to take measures to reduce the NSSI rate, such as organizing NSSI-related workshops. School social workers and clinical psychologists may have spent more time identifying and helping adolescents with NSSI. A fourth possible explanation is that participants may stop reporting this behavior after repeated assessments. Repeated assessments can sometimes lead to respondent fatigue and lower rates of pathology even when no reductions would be expected. These all could contribute to the decreasing prevalence rates of NSSI in this study.

Limitations and Future Directions Compared with prior literature, this study had several merits, including a large sample of community adolescents, using a timevarying NSSI as a predictor for subsequent SA, controlling for the shared risk factors of NSSI and SA, using a direct test of the interpersonal theory of suicide, and examining gender difference in the NSSI-SA relationship. Despite these strengths, several limitations should be considered. First, despite the large sample size, all participants in this study were Chinese high school students. It is not clear that our findings can be generalized to adolescents in other countries or cultures, populations of different ages, or clinical samples. Additionally, we included only adolescents from Band 1 and Band 2 schools in Hong Kong, which may limit the

531

generalizability of our results to students with educational difficulties, such as found in Band 3 schools. Nevertheless, the rates of SA found in this study are comparable with that reported in a national survey in China (2.7%; Xing et al., 2010). This increases the confidence in the generalizability of our results. Second, this study had some measurement drawbacks. We used two single items to measure SI and SA. Single-item measures may be less preferable than multiple-item measures because they may be less reliable and more susceptible to extremity, acquiescent response styles and socially desirable responding. Participants might also interpret the questions differently than the researchers, and the single item prevented participants from checking their understanding with other items. Using single items to assess SI and SA, nevertheless, has also been used in other studies (e.g., Guan et al., 2012; Klonsky et al., 2013). This may be partly because the two constructs are unidimensional, which might ameliorate the concern regarding the reliability of single-item measures. Additionally, some measures used in this study (e.g., the UPPS Impulsive Behavior Scale and the self-criticism scale) have not been validated in the Chinese population. Future studies should be conducted to establish the validity of these measures in Chinese. Another concern related to the measurement was that the NSSI scale used in this study did not include a full list of NSSI behaviors, but only seven typical forms. Some minor forms of NSSI, such as skin picking and hair pulling, were not included in the present study as in previous studies (e.g., Andover & Gibb, 2010; Liang et al., 2014; Tang et al., 2011) because those behaviors are relatively common and regarded as less clinically significant (Yates, Tracy, & Luthar, 2008). Additionally, the minor forms of NSSI are less likely to induce pain than the forms assessed in this study, and thus, according to Joiner (2005), may be less influential in acquiring the ability for suicide. If this were the case, excluding these minor forms would not have exerted a great influence on our results. Third, this study did not assess the lifetime history of SA. Thus, the strong relationship between NSSI and SA could be spurious and accounted for by an earlier history of suicidal behavior. Additionally, although not consistent with Joiner’s theory, it is possible that past SA predicts future NSSI. For example, the associations between Wave 1 SA and Wave 3 NSSI in Table 2 (r ⫽ .22 and .10 in females and males, respectively, both ps ⬍ .001) were as strong as the associations between Wave 1 NSSI and Wave 3 SA (r ⫽ .23 and .11 in females and males, respectively, both ps ⬍ .001). This possibility should be examined in future studies. Finally, because of time and budget constraints, this study covered only a 2-year period. A thorough investigation of the developmental trajectory from NSSI to SA requires a much longer follow-up period. It should also be noted that despite the longitudinal nature of the design, this study was still investigating associations. Causal interpretation regarding the association between NSSI and SA cannot be drawn.

Clinical Implications Despite the limitations, this study has important implications for suicide prevention among school adolescents. Given that the longitudinal association between NSSI and SA is significant beyond the contributions of other known risk factors, the assessment of

YOU AND LIN

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

532

NSSI, along with other risk factors, should become a routine part of assessing suicide risk. Because the association between NSSI and SA appeared to be stronger in females than in males, NSSI in female adolescents should be given more attention. Additionally, clinicians or school counselors should assess not only the presence of NSSI but also the frequency of this behavior, as this study revealed that NSSI frequency is related to SA. Moreover, attention should be paid not only to the baseline level of NSSI but also to the increase in NSSI frequency, which may signal an increase in the probability of SA. This also indicates that prevention of the escalation from NSSI to SA could focus on decreasing the frequency of NSSI and ending the NSSI habit. Because NSSI is often performed impulsively with little or no forethought (Nock, 2010), mindfulness-based therapy focusing on observing the urge, tolerating the urge, and distracting one’s attention from the urge may be helpful in reducing NSSI frequency (Stratton, 2006).

Conclusion As a relatively common behavior in adolescents, NSSI has received much research attention in recent years. This study found that NSSI might be a robust risk factor for future SA, especially among female adolescents. This may be because frequent engagement in NSSI eventually increases one’s ability to attempt suicide. Understanding how NSSI increases this ability may be an important area for future research.

References Alsaker, F., & Olweus, D. (1986). Assessment of global negative selfevaluations and perceived stability of self in Norwegian preadolescents and adolescents. The Journal of Early Adolescence, 6, 269 –278. http:// dx.doi.org/10.1177/0272431686063005 American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing. Andover, M. S., & Gibb, B. E. (2010). Non-suicidal self-injury, attempted suicide, and suicidal intent among psychiatric inpatients. Psychiatry Research, 178, 101–105. http://dx.doi.org/10.1016/j.psychres.2010.03 .019 Antony, M. M., Bieling, P. J., Cox, B. J., Enns, M. W., & Swinson, R. P. (1998). Psychometric properties of the 42-item and 21-item versions of the Depression Anxiety Stress Scales in clinical groups and a community sample. Psychological Assessment, 10, 176 –181. http://dx.doi.org/ 10.1037/1040-3590.10.2.176 Asarnow, J. R., Porta, G., Spirito, A., Emslie, G., Clarke, G., Wagner, K. D., . . . Brent, D. A. (2011). Suicide attempts and nonsuicidal self-injury in the treatment of resistant depression in adolescents: Findings from the TORDIA study. Journal of the American Academy of Child & Adolescent Psychiatry, 50, 772–781. http://dx.doi.org/10.1016/ j.jaac.2011.04.003 Blatt, S. J., D’Afflitti, J. P., & Quinlan, D. M. (1976). The depressive experiences quesionnaire. New Haven, CT: Yale University. Brausch, A. M., & Gutierrez, P. M. (2010). Differences in non-suicidal self-injury and suicide attempts in adolescents. Journal of Youth and Adolescence, 39, 233–242. http://dx.doi.org/10.1007/s10964-0099482-0 Bresin, K., & Gordon, K. H. (2013). Endogenous opioids and nonsuicidal self-injury: A mechanism of affect regulation. Neuroscience and Biobehavioral Reviews, 37, 374 –383. http://dx.doi.org/10.1016/j.neubiorev .2013.01.020 Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. (2010). Web-based Injury Statistics Query and

Reporting System (WISQARS) [online]. Available from www.cdc.gov/ injury/wisqars/index.html Claes, L., Muehlenkamp, J., Vandereycken, W., Hamelinck, L., Martens, H., & Claes, S. (2010). Comparison of non-suicidal self-injurious behavior and suicide attempts in patients admitted to a psychiatric crisis unit. Personality and Individual Differences, 48, 83– 87. http://dx.doi .org/10.1016/j.paid.2009.09.001 Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum. Cyders, M. A., & Smith, G. T. (2008). Clarifying the role of personality dispositions in risk for increased gambling behavior. Personality and Individual Differences, 45, 503–508. http://dx.doi.org/10.1016/j.paid .2008.06.002 Fazaa, N., & Page, S. (2003). Dependency and self-criticism as predictors of suicidal behavior. Suicide and Life-Threatening Behavior, 33, 172– 185. http://dx.doi.org/10.1521/suli.33.2.172.22777 Glenn, C. R., & Klonsky, E. D. (2010). A multimethod analysis of impulsivity in nonsuicidal self-injury. Personality Disorders: Theory, Research, and Treatment, 1, 67–75. http://dx.doi.org/10.1037/a0017427 Guan, K., Fox, K. R., & Prinstein, M. J. (2012). Nonsuicidal self-injury as a time-invariant predictor of adolescent suicide ideation and attempts in a diverse community sample. Journal of Consulting and Clinical Psychology, 80, 842– 849. http://dx.doi.org/10.1037/a0029429 Gvion, Y., & Apter, A. (2011). Aggression, impulsivity, and suicide behavior: A review of the literature. Archives of Suicide Research, 15, 93–112. http://dx.doi.org/10.1080/13811118.2011.565265 Hamza, C. A., Stewart, S. L., & Willoughby, T. (2012). Examining the link between nonsuicidal self-injury and suicidal behavior: A review of the literature and an integrated model. Clinical Psychology Review, 32, 482– 495. http://dx.doi.org/10.1016/j.cpr.2012.05.003 Jacobson, C. M., & Gould, M. (2007). The epidemiology and phenomenology of non-suicidal self-injurious behavior among adolescents: A critical review of the literature. Archives of Suicide Research, 11, 129 – 147. http://dx.doi.org/10.1080/13811110701247602 Joiner, T. (2005). Why people die by suicide. Cambridge, MA: Harvard University Press. Klonsky, E. D. (2007). The functions of deliberate self-injury: A review of the evidence. Clinical Psychology Review, 27, 226 –239. http://dx.doi .org/10.1016/j.cpr.2006.08.002 Klonsky, E. D., May, A. M., & Glenn, C. R. (2013). The relationship between nonsuicidal self-injury and attempted suicide: Converging evidence from four samples. Journal of Abnormal Psychology, 122, 231– 237. http://dx.doi.org/10.1037/a0030278 Kraft, T. L., Jobes, D. A., Lineberry, T. W., Conrad, A., & Kung, S. (2010). Brief report: Why suicide? Perceptions of suicidal inpatients and reflections of clinical researchers. Archives of Suicide Research, 14, 375–382. http://dx.doi.org/10.1080/13811118.2010.524073 Laye-Gindhu, A., & Schonert-Reichl, K. A. (2005). Nonsuicidal self-harm among community adolescents: Understanding the “whats” and “whys” of self-harm. Journal of Youth and Adolescence, 34, 447– 457. http://dx .doi.org/10.1007/s10964-005-7262-z Leung, S. W., & Leung, F. (2009). Construct validity and prevalence rate of borderline personality disorder among Chinese adolescents. Journal of Personality Disorders, 23, 494 –513. http://dx.doi.org/10.1521/pedi .2009.23.5.494 Liang, K. Y., & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73, 13–22. http://dx.doi.org/ 10.1093/biomet/73.1.13 Liang, S., Yan, J., Zhang, T., Zhu, C., Situ, M., Du, N., . . . Huang, Y. (2014). Differences between non-suicidal self injury and suicide attempt in Chinese adolescents. Asian Journal of Psychiatry, 8, 76 – 83. http:// dx.doi.org/10.1016/j.ajp.2013.11.015 Magid, V., & Colder, C. R. (2007). The UPPS Impulsive Behavior Scale: Factor structure and associations with college drinking. Personality and

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

PREDICTING SUICIDE ATTEMPTS BY TIME-VARYING NSSI Individual Differences, 43, 1927–1937. http://dx.doi.org/10.1016/j.paid .2007.06.013 Miller, J., Flory, K., Lynam, D., & Leukefeld, C. (2003). A test of the four-factor model of impulsivity-related traits. Personality and Individual Differences, 34, 1403–1418. http://dx.doi.org/10.1016/S01918869(02)00122-8 Muehlenkamp, J. J., Claes, L., Havertape, L., & Plener, P. L. (2012). International prevalence of adolescent non-suicidal self-injury and deliberate self-harm. Child and Adolescent Psychiatry and Mental Health, 6, 10. http://dx.doi.org/10.1186/1753-2000-6-10 Muehlenkamp, J. J., & Gutierrez, P. M. (2007). Risk for suicide attempts among adolescents who engage in non-suicidal self-injury. Archives of Suicide Research, 11, 69 – 82. http://dx.doi.org/10.1080/ 13811110600992902 Nock, M. K. (2010). Self-injury. Annual Review of Clinical Psychology, 6, 339 –363. http://dx.doi.org/10.1146/annurev.clinpsy.121208.131258 Nock, M. K., Joiner, T. E., Jr., Gordon, K. H., Lloyd-Richardson, E., & Prinstein, M. J. (2006). Non-suicidal self-injury among adolescents: Diagnostic correlates and relation to suicide attempts. Psychiatry Research, 144, 65–72. http://dx.doi.org/10.1016/j.psychres.2006.05.010 Phillips, M. R., Yang, G., Zhang, Y., Wang, L., Ji, H., & Zhou, M. (2002). Risk factors for suicide in China: A national case-control psychological autopsy study. The Lancet, 360, 1728 –1736. http://dx.doi.org/10.1016/ S0140-6736(02)11681-3 Stanley, B., Gameroff, M. J., Michalsen, V., & Mann, J. J. (2001). Are suicide attempters who self-mutilate a unique population? The American Journal of Psychiatry, 158, 427– 432. http://dx.doi.org/10.1176/appi.ajp .158.3.427 Stratton, K. J. (2006). Mindfulness-based approaches to impulsive behaviors. The New School Psychology Bulletin, 4, 49 –71. Swahn, M. H., Ali, B., Bossarte, R. M., Van Dulmen, M., Crosby, A., Jones, A. C., & Schinka, K. C. (2012). Self-harm and suicide attempts among high-risk, urban youth in the U. S.: Shared and unique risk and protective factors. International Journal of Environmental Research and Public Health, 9, 178 –191. http://dx.doi.org/10.3390/ijerph9010178 Tang, J., Yu, Y., Wu, Y., Du, Y., Ma, Y., Zhu, H., . . . Liu, Z. (2011). Association between non-suicidal self-injuries and suicide attempts in Chinese adolescents and college students: A cross-section study. [Advance online publication]. PLoS ONE, 6, e17977. http://dx.doi.org/ 10.1371/journal.pone.0017977 Taouk, M., Lovibond, P. F., & Laube, R. (2001). Psychometric properties of a Chinese version of the short Depression Anxiety Stress Scales (DASS21). Sydney: New South Wales Transcultural Mental Health Centre, Cumberland Hospital. Wedig, M. M., & Nock, M. K. (2007). Parental expressed emotion and adolescent self-injury. Journal of the American Academy of Child & Adolescent Psychiatry, 46, 1171–1178. http://dx.doi.org/10.1097/chi .0b013e3180ca9aaf Whiteside, S. P., & Lynam, D. R. (2001). The Five Factor Model and impulsivity: Using a structural model of personality to understand impulsivity. Personality and Individual Differences, 30, 669 – 689. http:// dx.doi.org/10.1016/S0191-8869(00)00064-7 Whitlock, J., & Knox, K. L. (2007). The relationship between self-injurious behavior and suicide in a young adult population. Archives of Pediatrics & Adolescent Medicine, 161, 634 – 640. http://dx.doi.org/10.1001/ archpedi.161.7.634

533

Whitlock, J., Muehlenkamp, J., Eckenrode, J., Purington, A., Baral Abrams, G., Barreira, P., & Kress, V. (2013). Nonsuicidal self-injury as a gateway to suicide in young adults. Journal of Adolescent Health, 52, 486 – 492. http://dx.doi.org/10.1016/j.jadohealth.2012.09.010 Wichstrøm, L. (2009). Predictors of non-suicidal self-injury versus attempted suicide: Similar or different? Archives of Suicide Research, 13, 105–122. http://dx.doi.org/10.1080/13811110902834992 Wilkinson, P., Kelvin, R., Roberts, C., Dubicka, B., & Goodyer, I. (2011). Clinical and psychosocial predictors of suicide attempts and nonsuicidal self-injury in the Adolescent Depression Antidepressants and Psychotherapy Trial (ADAPT). The American Journal of Psychiatry, 168, 495–501. http://dx.doi.org/10.1176/appi.ajp.2010.10050718 Xing, X.-Y., Tao, F.-B., Wan, Y.-H., Xing, C., Qi, X.-Y., Hao, J.-H., . . . Huang, L. (2010). Family factors associated with suicide attempts among Chinese adolescent students: A national cross-sectional survey. Journal of Adolescent Health, 46, 592–599. http://dx.doi.org/10.1016/j .jadohealth.2009.12.006 Yates, T. M., Tracy, A. J., & Luthar, S. S. (2008). Nonsuicidal self-injury among “privileged” youths: Longitudinal and cross-sectional approaches to developmental process. Journal of Consulting and Clinical Psychology, 76, 52– 62. http://dx.doi.org/10.1037/0022-006X.76.1.52 Yen, S., Shea, M. T., Sanislow, C. A., Grilo, C. M., Skodol, A. E., Gunderson, J. G., . . . Morey, L. C. (2004). Borderline personality disorder criteria associated with prospectively observed suicidal behavior. The American Journal of Psychiatry, 161, 1296 –1298. http://dx.doi .org/10.1176/appi.ajp.161.7.1296 You, J., Leung, F., Lai, C. M., & Fu, K. (2012). The associations between non-suicidal self-injury and borderline personality disorder features among Chinese adolescents. Journal of Personality Disorders, 26, 226 – 237. http://dx.doi.org/10.1521/pedi.2012.26.2.226 You, J., Lin, M. P., Fu, K., & Leung, F. (2013). The best friend and friendship group influence on adolescent nonsuicidal self-injury. Journal of Abnormal Child Psychology, 41, 993–1004. http://dx.doi.org/ 10.1007/s10802-013-9734-z You, J., Lin, M. P., & Leung, F. (2013). Functions of nonsuicidal selfinjury among Chinese community adolescents. Journal of Adolescence, 36, 737–745. http://dx.doi.org/10.1016/j.adolescence.2013.05.007 Zanarini, M. C., Gunderson, J. G., Frankenburg, F. R., & Chauncey, D. L. (1989). The revised Diagnostic Interview for Borderlines: Discriminating BPD from other Axis II disorders. Journal of Personality Disorders, 3, 10 –18. http://dx.doi.org/10.1521/pedi.1989.3.1.10 Zeger, S. L., & Liang, K. Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42, 121–130. http://dx.doi.org/ 10.2307/2531248 Zorn, C. J. W. (2001). Generalized estimating equation models for correlated data: A review with applications. American Journal of Political Science, 45, 470 – 490. http://dx.doi.org/10.2307/2669353 Zuroff, D. C., Quinlan, D. M., & Blatt, S. J. (1990). Psychometric properties of the Depressive Experiences Questionnaire in a college population. Journal of Personality Assessment, 55, 65–72. http://dx.doi.org/ 10.1207/s15327752jpa5501&2_7

Received February 20, 2013 Revision received December 23, 2014 Accepted January 30, 2015 䡲