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. Author manuscript; available in PMC: 2024 Mar 4.
Published in final edited form as: J Affect Disord. 2021 Jul 21;294:430–440. doi: 10.1016/j.jad.2021.07.088

Temporal sequences of suicidal and nonsuicidal self-injurious thoughts and behaviors among inpatient and community-residing military veterans

Jaclyn C Kearns a,b, Sarah L Brown b,c, Ian Cero d, Kaitlyn R Gorman b,e, Matthew K Nock f, Terence M Keane b,g,h, Brian P Marx b,g,h,*
PMCID: PMC10910846  NIHMSID: NIHMS1968649  PMID: 34320450

Abstract

Background:

Suicidal and nonsuicidal self-injurious thoughts and behaviors (SITBs) are major health concerns among military veterans yet little is known about the temporal relations among these outcomes. This study examined the temporal relations between suicidal and nonsuicidal SITBs among higher-risk veterans. Specifically, we identified when SITBs emerged and evaluated the role of nonsuicidal self-injury (NSSI) in the medical lethality of suicide attempts (SA), relative risk, and survival time of suicidal SITBs (i.e., suicide ideation [SI], suicide plan, SA).

Method:

Cross-sectional data were collected from two samples examining suicide risk among veterans receiving inpatient psychiatric care (n = 157) and community-residing veterans with current depression and/or past month SI (n = 200). Participants completed an interview to assess SITBs.

Results:

SITBs emerged between ages 14-28 years with behaviors emerging, on average, earlier among inpatient veterans. The time lag between SITBs was not significantly different between groups. Inpatient veterans had a significantly shorter time lag from SI to SA. NSSI history predicted an increase in relative risk for all suicidal SITBs and shorter survival time. There was no association between NSSI history and medical lethality of the most serious SA for both groups.

Limitations:

Limitations included use of cross-sectional, retrospective self-report with age-of-onset endorsed in years and not all SITBs were assessed (e.g., passive SI).

Conclusions:

Veterans with a NSSI history are at high risk for suicidal SITBs and have a shorter survival time. Results showed thoughts (i.e., NSSI thoughts, SI) emerged before behavior (i.e., NSSI, SA) and NSSI emerged before SA.

Keywords: Suicide, Nonsuicidal self-injury, Military, Veterans, Suicide attempt

In the past two decades, suicide prevalence among United States (US) military veterans has increased and exceeded that observed in the general population with a mortality rate 1.5 times greater than the non-veteran adults (Department of Veterans Affairs [VA], 2019; Ramchand et al., 2011), making it a national priority for the VA. Lifetime prevalence of nonsuicidal self-injury (NSSI) among veterans–approximately 8% to 30.3% (Bryan et al., 2015; Bryan et al., 2015; Villatte et al., 2015)–closely parallels and, in some instances, surpasses the prevalence of NSSI among non-veterans (Klonsky, 2011; Swannell et al., 2014). Although many studies have examined suicidal thoughts and behaviors among veterans (e.g., Kang et al., 2015; Lee et al., 2018), less is known about NSSI thoughts and behaviors in this group. Similarly, we know little about the temporal relations between suicidal and nonsuicidal self-injurious thoughts and behaviors (SITBs) among veterans. Additional research may provide valuable insights for risk assessment, prevention, and intervention efforts among veterans.

Suicidal SITBs include thoughts and actions with some desire to die. These include suicide ideation (SI; i.e., thoughts of killing oneself; Silverman et al., 2007), suicide plans (SP; i.e., selection of method, that can lead to self-injury, with some intent to use it; Silverman et al., 2007), and suicide attempts (SA; i.e., non-fatal self-directed injury with at least some intent to die; Silverman et al., 2007). Nonsuicidal SITBs include thinking about engaging in NSSI (‘NSSI thoughts’) or engaging in NSSI behavior (‘NSSI’) (Nock, 2010). Although suicidal and nonsuicidal SITBs are distinct in their characteristics (e.g., age of onset; Glenn et al., 2017) and presence of suicidal intent (i.e., lack of suicidal intent in NSSI; Muehlenkamp, 2005), research indicates that these phenomena are related (e.g., Franklin et al., 2011; Joiner, 2007; Whitlock et al., 2013). NSSI and SAs co-occur (Jacobson et al., 2008; Klonsky et al., 2013) and share risk factors (e.g., depression; Fox et al., 2015; Franklin et al., 2017). Further, evidence demonstrates that prior NSSI is a robust predictor of a SA (Franklin et al., 2017). Taken together, research suggests a link between suicidal and nonsuicidal SITBs yet the temporal relations between these phenomena remain unclear, particularly among veterans–a group at high-risk for suicide.

Research on the temporal relations and development of SITBs has focused primarily on suicidal SITBs among non-veterans. Findings suggest that SI emerges in the year before a SA (Nock et al., 2008), but little is known about when SITBs emerge in relation to one another among veterans. To date, two studies examined the temporal relations of suicidal and nonsuicidal SITBs. Glenn et al. (2017) examined age-of-onset and time lag of suicidal and nonsuicidal SITBs among adolescents and found that NSSI thoughts and SI had the earliest age-of-onset and, temporally, emerged before NSSI and SAs. In one of the only studies to explore these relations among service members and veterans, Bryan and colleagues (2015) examined temporal relations between SI, SA, and NSSI. They found that SI emerged before NSSI and, among those with an NSSI history (i.e., past engagement in NSSI), NSSI emerged before a SA. Further, the average length of time to transition from SI to SA was longer for individuals who had an NSSI history compared with individuals with no NSSI history. These studies suggest NSSI thoughts and SI precede NSSI and SA (Bryan et al., 2015; Glenn et al., 2017) and that NSSI history is associated with an increased likelihood of suicidal SITBs (Bryan et al., 2015), and a slower transition to SA (Bryan et al., 2015; Glenn et al., 2017).

The study by Bryan et al. (2015) was an important step in improving our understanding of the temporal relations between suicidal and nonsuicidal SITBs among veterans. It has notable strengths in its examination of time lag and age-of-onset, but was limited by its inclusion of a nonclinical sample attending college, reliance on single-item measurements of SITBs that may lead to misclassification (Millner et al., 2015), and limited assessment of SITBs. The study did not examine SA medical lethality or the role of NSSI in relation to risk for suicidal SITBs, both of which may provide insight into the relations among these phenomena.

1. Present study

To address these limitations, we examined suicidal and nonsuicidal SITBs separately among two samples of veterans–a moderate-risk community sample and a high-risk acute psychiatric inpatient sample.2 Using two different samples allows us to examine the degree to which associations of interest generalize across samples ranging in clinical severity and suicide risk. Notably, this study will enhance our understanding of SITB development among veterans who have received acute inpatient psychiatric care–a group at high-risk for suicide following discharge (Britton et al., 2017). We identified age-of-onset, temporal sequence, and time lag between suicidal and nonsuicidal SITBs to understand: 1) when SITBs emerge; 2) which veterans are more likely to have a more medically serious SA; and 3) what role NSSI history has in lifetime relative risk and survival time of suicidal SITBs.

We hypothesized, in both samples, nonsuicidal SITBs would have an earlier age-of-onset relative to suicidal SITBs. Consistent with Glenn et al. (2017), NSSI thoughts and SI would have an earlier age-of-onset compared to NSSI and SAs and, temporally, NSSI would precede a SA. Consistent with Bryan et al. (2015), we hypothesized that veterans with an NSSI history would have a significantly longer time lag from SI to SA compared with veterans with no NSSI history. We hypothesized, based on clinical severity, the inpatient sample would display significantly shorter time lags. Second, consistent with acquired capability (e.g., NSSI results in decreased fear of death; Van Orden et al., 2010), we hypothesized that veterans with an NSSI history will have made a SA with greater medical lethality compared with veterans with no NSSI history in both samples. Third, veterans with an NSSI history would have greater lifetime relative risk of experiencing suicidal SITBs (i.e., SI, SP, SA) and significantly shorter lifetime survival time across all suicidal SITBs (i.e., shorter time to SITB event since birth) compared with veterans with no NSSI history.

2. Method

Cross-sectional data were drawn from two independent samples examining veteran suicide risk. Study procedures and participants will be described by sample. Measures will be described together as both samples completed the same self-report and interview assessments.

2.1. Sample 1: veterans admitted to inpatient psychiatric care

2.1.1. Procedure

Eligible participants were admitted to a VA Medical Center inpatient psychiatric unit for current SI or SA, ≥ 18 years of age, and fluent in English. In consultation with the attending psychiatrist, we excluded individuals who were severely cognitively impaired (cognitive screener; Callahan et al., 2002). Participants completed self-report questionnaires and an interview during their inpatient psychiatric stay. Study procedures were approved by the VA Institutional Review Board and the Human Research Protection Office of the US Army Medical Research and Materiel Command.

2.1.2. Participants

Participants were 157 veterans recruited from a VA inpatient psychiatric hospital in the northeastern US as part of a larger study focused on identifying suicide risk factors (Millner et al., 2019). See Table 1 for demographic characteristics.

Table 1.

Demographic, military, and clinical characteristics by sample.

Sample 1:
Inpatient (n =
157)		 Sample 2:
Community-
Residing (n =
200)		 Group Comparison
Statistical
Test1 		p
Male, % (n) 70.8 (109) 89.5 (179) χ2 = 20.10 <.001
Age, M (SD) 39.31 (23.51) 43.44 (14.53) t = −.950 .343
Race/Ethnicity, % (n) χ2 = 4.89 .428
Caucasian 74.5 (117) 71.0 (142)
Black 14.6 (23) 20.5 (41)
Asian - 1.0 (2)
Native American - 1.0 (2)
Multi-racial 3.2 (5) 3.0 (6)
Hispanic 3.8 (6) 3.5 (7)
Highest Education, % (n)
High school/GED 26.1 (40) 18.0 (36) χ2 = 8.59 .126
Technical School 9.1 (14) 6.0 (12)
Some College 43.8 (67) 41.5 (83)
College Graduate 13.1 (20) 20.5 (41)
Some Graduate School 3.3 (5) 5.0 (10)
Advanced Degree 4.6 (7) 9.0 (18)
Age entered warzone, M (SD) 22.61 (6.03) 23.04 (5.21) t = −1.23 .220
Military Branch, % (n) χ2 = 3.81 .431
Army 51.3 (78) 48.2 (94)
Air Force 15.8 (24) 10.8 (21)
Navy 15.1 (23) 20.0 (39)
Marines 16.4 (25) 16.9 (33)
Other 1.3 (2) 4.1 (7)
Type of Military Duty, % (n) χ2 = 1.48 .477
Active 74.3 (113) 70.2 (139)
Guard/Reserves 8.6 (13) 12.6 (25)
Both 17.1 (26) 16.7 (33)
Combat Exposure, % (n) χ2 = 2.87 .238
Yes 45.4 (69) 45.2 (89)
No 49.3 (75) 45.2 (89)
Unsure 5.3 (8) 9.6 (19)
Deployment to a warzone/draw hazard pay, % (n) 56.6 (86) 58.4 (115) χ2 = .113 .736
Yes 56.6 (86) 58.4 (115)
No 43.4 (66) 41.6 (82)
Symptom severity, M (SD)
PHQ-9 16.62 (7.00) 12.16 (6.38) t = 7.52 .003
BSS 16.43 (6.5) 11.97 (5.23) t = 4.15 <.001
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Note. BSS = Beck Scale for Suicide Ideation; PHQ-9 = Patient Health Questionnaire – 9.

1

Mean group differences were examined using independent samples t-tests and categorial group differences were examined using Pearson chi-square (χ2) tests.

2.2. Sample 2: community-residing veterans

2.2.1. Procedures

Eligible participants were individuals from the community surrounding the local VA Medical Center where Sample 1 was recruited, ≥ 18 years of age, fluent in English, provided documentation of military service/veteran status, and endorsed at least one primary depressive symptoms (depressed mood, anhedonia) in the past two weeks and/or past month SI. We excluded individuals with severe cognitive impairment (congitive screener; Callahan et al., 2002). Participants completed self-report assessments and an interview in a university-based research laboratory. Study procedures were approved by the University’s Institutional Review Board and the Human Research Protection Office of the US Army Medical Research and Materiel Command.

2.2.2. Participants

Participants were 200 veterans recruited from the surrounding community of a large city in the northeastern US as part of a larger study focused on identifying suicide risk factors. Approximately 59.5% (n = 119) were currently in psychiatric treatment at the time of enrollment. See Table 1 for demographic characteristics.

2.3. Measures

2.3.1. Self-injurious thoughts and behaviors interview

The Self-Injurious Thoughts and Behaviors Interview (SITBI; Nock et al., 2007) is an interview that assesses a range of characteristics related to suicidal and nonsuicidal SITBs. The presence and age-of-onset for the following constructs were the focus of analyses: NSSI thoughts, NSSI behaviors, suicide ideation (SI), suicide plan (SP), and suicide attempt (SA; not including aborted/interrupted SA).3 Trained interviewers administered the SITBI.

2.3.2. Medical lethality of most severe suicide attempt

Trained interviewers used the Lethality of Suicide Attempt Rating Scale-II (Berman et al., 2003) to assess medical lethality of the most severe SA. This scale, intended for use by non-medical professionals, generates a severity score of 0 (no damage) to 10 (death) and is designed to allow for comparison across SA methods.

2.3.3. Symptom severity

The Patient Health Questionnaire-9 (PHQ-9; Kroenke and Spitzer, 2002) and Beck Scale for Suicide Ideation (BSS; Beck et al., 1988) provided an overview of current symptom severity for depression and suicide intensity, respectively. Cronbach’s alpha for PHQ-9 (Inpatient: α = .90; Community: α =.88) and BSS scores (Inpatient: α = .84; Community: α =.80) were good.

3. Data analytic plan

We performed analyses in SPSS 25. We examined the presence of suicidal and nonsuicidal SITBs separately for the samples and then compared across samples using Pearson chi-square tests and Cramer’s phi for effect size. We examined SITB age-of-onset for each sample and then compared across samples using non-parametric Mann-Whitney U tests and r for effect size. For age-of-onset, outliers (M ± 3 SD) were examined and retained if they did not change the pattern of results. To examine the time lag between SITBs, consistent with Glenn et al. (2017), we calculated difference scores using age-of-onset for each SITB; these were calculated with and without outliers (M ± 3 SD). For example, to calculate the time lag between SI and SA, we subtracted SI age-of-onset from SA age-of-onset. As an example interpretation, a positive value would indicate that the onset of SI occurred before SA, whereas a negative value would indicate the onset of SI occurred after SA. We analyzed time lags in each sample using independent samples t-tests and Cohen’s d for effect size. To determine whether NSSI history was related to the medical lethality of the most medically serious SA, we analyzed each sample using independent samples t-tests and Cohen’s d for effect size.

Using data from each sample, we conducted survival analysis models with person year (i.e., number of years participants were at risk of the suicidal SITB outcome since birth to time of assessment) as the unit of analysis. We treated each year in the life of each respondent as a separate observation, with years prior to the onset of the outcome (e.g., SI) coded 0 and the year of onset coded 1. For respondents who never experienced the outcome, we included all person-years up to the age at assessment. We used cox regression analyses to determine relative risk of experiencing the suicidal SITB outcome based on NSSI history (no NSSI history coded as 0, NSSI history coded as 1). The primary assumption of this analysis is that the model(s) passes the test of proportional hazards (Flynn, 2012); sample size and expected effect size are similar to prior military veteran studies using this approach (e.g., Bryan et al., 2015). We used Kaplan Meier analyses to determine median (i.e., time point at which the cumulative survival drops below 50%) and mean survival times for all of the suicidal SITB outcomes in each sample. We conducted log rank tests to determine if there were differences in the survival distribution for suicidal SITBs for veterans with and without an NSSI history.

4. Results

4.1. Descriptive analyses

Table 2 provides SITBs characteristics.

Table 2.

Lifetime prevalence, age-of-onset, and characteristics of SITB in inpatient and community samples.

Prevalence Sample 1: Inpatient (n = 157)
%, (n)		 Sample 2: Community-Residing (n = 200)
%, (n)		 Group Comparison
Statistical Test (χ2)1 		P ES (ϕ)
NSSI Thoughts 19.9 (31) 22.5 (45) 0.36 0.548 0.032
NSSI 21.2 (33) 17.1 (34) 0.94 0.331 0.052
Suicide Ideation 69.2 (108) 59.5 (119) 3.59 0.058 0.100
Suicide Plan 47.4 (74) 36.5 (73) 4.32 0.038 0.110
SA 34.6 (54) 20.0 (40) 9.63 0.002 0.165
Age-of-Onset (Years) M (SD); Mdn (Range) M (SD); Mdn (Range) Statistical Test 2 P ES (r)
NSSI Thoughts 14.97 (22.27); 16 (5-45) 21.79 (11.59); 18 (8-55) 593.00 0.338 −0.11
NSSI 18.15 (8.79); 16 (3-45) 20.87 (13.46); 18 (6-88) 589.00 0.431 −0.09
Suicide Ideation 24.56 (12.23); 22 (4-58) 22.66 (11.74); 20 (4-63) 5784.50 0.194 −0.08
Suicide Plan 28.20 (13.36); 25 (5-61) 26.94 (13.36); 25 (4-62) 2527.50 0.501 −0.05
SA 26.46 (11.43); 23.50 (7-58) 28.57 (14.66); 25 (4-65) 1056.50 0.567 −0.06
Most Medically Serious SA Statistical Test 3 t df P d
LSARS-II, M (SD) 4.15 (1.81) 5.0 (3.22) −1.35 68 0.179 0.32
Age at SA, M (SD) 33.87 (11.18) 33.89 (14.25) −.008 89 0.994 0.01
Method, % (n)4
Medication 72.2 (39) -
Multiple Methods 7.4 (4) -
Cutting 7.4 (4) -
Jumping 5.6 (3) -
Suffocation 3.7 (2) -
Firearm 1.9 (1) -
Drowning 1.9 (1) -
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Note. ES = effect size; LSARS-II: Lethality of Suicide Attempt Rating Scale-II; NSSI = nonsuicidal self-injury; SA = suicide attempt; SITB = self-injurious thoughts and behaviors.

1

Group differences were examined using Pearson chi-square tests and Cramer’s phi coefficient (ϕ) was used for examination of effect size.

2

Group differences in age-of-onset were examined using Mann-Whitney U tests and r was used for examination of effect size.

3

Group differences concerning the most medically serious suicide attempt were examined using independent samples t-tests and Cohen’s d was used for examination of effect size.

4

Method used in the most medically serious suicide attempt was not collected for the community sample.

4.1.1. Sample 1: veterans admitted to inpatient psychiatric care

Regarding history of suicidal SITBs, there was no statistically significant difference between men and women in SA history (χ2 [1, N = 126] = 1.31, p = .252). There was a statistically significant difference between men and women in SI (χ2 [1, N = 154] = 6.71, p = .010) and SP history (χ2 [1, N = 154] = 9.46, p = .002), such that 63.03% (n = 69) of men and 84.44% (n = 38) of women reported a SI history and 39.5% (n = 43) of men and 66.67% (n = 30) of women reported a SP history.

4.1.2. Sample 2: community-residing veterans

Regarding suicidal SITBs, there was no significant difference between men and women in SP (χ2 [1, N = 200] = .102, p = .750) or SA history (χ2 [1, N = 200] = .023, p = .879); however, there was a statistically significant difference between men and women in SI history (χ2 [1, N = 200] = 4.46, p = .035), such that 62.01% (n = 111) of men and 38.10% (n = 8) women reported an SI history.

4.2. Age-of-onset of suicidal and nonsuicidal SITBs

Both samples reported all SITBs began between ages 14-28. SA had an earlier age-of-onset than SP in the inpatient sample (SP: age 28; SA: age 26); however, this pattern was not observed among the community-residing sample (SP: age 26; SA: age 28). In both samples, nonsuicidal SITBs preceded all suicidal SITBs; however, the age-of-onset was not statistically significant across samples (ps > .05).

4.3. Time lag of suicidal and nonsuicidal SITBs

We examined the time lag between SITBs using difference scores that were calculated using the age-of-onset of each SITB (Table 3). With one exception (i.e., SI to SA in the inpatient sample), the time lag between SITBs was not significantly different across the two samples. On average, the time lag between suicidal SITBs was shorter in the inpatient sample compared with the community-residing sample.

Table 3.

Time lag (in years) between one type of SITB to another type of SITB in inpatient and community samples.

Time lag between SITBs1 Sample 1: Inpatient (n = 157) Sample 2: Community-Residing (n = 200) Group Comparison2
n M (SD) Range (years) n M (SD) Range (years) t df p Adj p d
NSSI thoughts to NSSI
With outliers 28 0.57 (1.45) 0 to 6 31 −0.19 (3.70) −15 to 7 1.02 57 0.311 0.734 0.27
Without outliers 27 0.37 (1.01) 0 to 4 30 0.33 (2.53) −10 to 7 0.13 55 0.893 0.963 0.03
NSSI thoughts to SI
With outliers 28 0.71 (11.44) −23 to 32 36 −0.33 (8.66) −27 to 25 0.38 62 0.699 0.904 0.09
Without outliers 28 0.71 (11.44) −23 to 32 35 0.42 (7.47) −20 to 25 0.09 61 0.929 0.963 0.02
NSSI thoughts to SP
With outliers 22 5.09 (11.53) −12 to 34 27 4.11 (11.75) −27 to 42 0.29 47 0.771 0.917 0.08
Without outliers 22 5.09 (11.53) −12 to 34 26 2.65 (9.16) −27 to 25 0.25 46 0.419 0.734 0.23
NSSI thoughts to SA
With outliers 19 3.73 (13.46) −23 to 34 17 5.05 (16.40) −27 to 55 0.92 34 0.792 0.917 0.08
Without outliers 19 3.73 (13.46) −23 to 34 16 1.93 (10.51) −27 to 16 0.43 33 0.434 0.734 0.14
NSSI to SI
With outliers 29 0.68 (11.06) −23 to 28 37 −1.18 (7.48) −21 to 25 0.82 64 0.415 0.734 0.18
Without outliers 29 0.68 (11.06) −23 to 28 36 −1.91 (6.12) −21 to 12 1.20 63 0.233 0.734 0.29
NSSI to SP
With outliers 22 5.13 (10.90) −12 to 30 29 5.00 (10.10) −13 to 42 0.04 49 0.963 0.963 0.01
Without outliers 22 5.13 (10.90) −12 to 30 28 3.67 (7.30) −13 to 25 0.56 48 0.575 0.878 0.15
NSSI to SA
With outliers 19 4.26 (13.27) −23 to 30 20 7.50 (11.15) −2 to 42 −0.82 37 0.414 0.734 0.26
Without outliers 19 4.26 (13.27) −23 to 30 19 5.68 (7.86) − 2 to 25 −0.40 36 0.690 0.904 0.12
SI to SP
With outliers 74 4.22 (8.61) −24 to 39 72 5.90 (10.62) −33 to 50 −1.03 144 0.301 0.734 0.17
Without outliers 73 3.76 (7.64) −24 to 27 69 5.34 (7.11) −8 to 28 −1.27 140 0.205 0.734 0.20
SI to SA
With outliers 54 3.38 (7.18) −6 to 44 42 7.07 (10.29) −7 to 40 −2.06 94 0.042 0.462 0.41
Without outliers 53 2.62 (4.51) −6 to 20 41 6.28 (8.99) −7 to 30 −2.56 92 0.012 0.264 0.51
SP to SA
With outliers 45 0.44 (8.53) −15 to 43 35 3.25 (9.75) −11 to 39 −1.37 78 0.174 0.734 0.30
Without outliers 44 −0.52 (5.61) −15 to 13 34 2.20 (7.63) −11 to 27 −1.82 76 0.071 0.513 0.40
SI to Most Lethal
With outliers 55 10.60 (12.44) −6 to 48 38 12.00 (12.76) 0 to 42 −0.52 91 0.599 0.878 0.11
Without outliers 54 9.99 (11.44) −6 to 44 38 12.00 (12.76) 0 to 42 −0.82 90 0.413 0.734 0.17
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Note. NSSI = nonsuicidal self-injury; SA = suicide attempt; SI = suicide ideation; SP = suicide plan.

1

Time lags were calculated by subtracting the first SITB’s age-of-onset of the from the second SITB’s age-of-onset (e.g., age-of-onset for SI minus age-of-onset for SA). A negative value indicates that the second SITB had an earlier age-of-onset than the first SITB. For example, to calculate the SI to SA time lag for a participant with an SI age-of-onset of 12 years old and an SA age-of-onset of 20 years old, we would subtract 12 years old (SI) from 20 years old (SA) to get a value of 8 years. This positive value indicates that the onset of SI occurred before the onset of SA. Consistent with Glenn et al. (2017), outliers are defined as M ± 3 SD.

2

Independent samples t-tests were used for group comparison and Cohen’s d was used for examination of effect size. Adjusted p-values, using the standard false discovery rate procedures (Benjamini and Hochberg, 1995), are also reported in this table.

We examined the time lag of SI to SA based on presence of NSSI history. Among veteran inpatients, those with an NSSI history reported a shorter transition from SI to SA compared with veteran with No-NSSI history (3.2 vs. 3.4 years on average, respectively). In the community sample, those with an NSSI history reported a shorter transition from SI to SI compared with veteran with No-NSSI history (6.4 vs. 7.6 years on average, respectively). In sum, veterans with an NSSI history, had a shorter transition, on average, from SI to SA compared with veterans with No-NSSI history.

After adjustment for multiple comparisons when the results of hypothesis testing have statistically dependent outcomes (Sjölander and Vansteelandt, 2019) using the standard false discovery rate procedures (Benjamini and Hochberg, 1995), the significant time-lag results were no longer significant.

4.4. NSSI history and medical lethality of the most medically serious SA

In the inpatient sample, there was no significant difference in medical lethality in the most serious SA between the No-NSSI history group (M = 4.41, SE = 0.33) and the NSSI history group (M = 3.70, SE = 0.34). In the community-residing sample, there was no significant difference in medical lethality in the most serious SA between the No-NSSI history group (M = 5.18, SE = 1.07) and the NSSI history group (M = 4.50, SE = 1.50).

5. Survival analyses

5.1. Cox regression results

5.1.1. Suicide ideation

In the inpatient sample, NSSI history significantly predicted lifetime survival time (χ2 [1, N = 154] = 15.95, p < .001), such that the NSSI history group was 2.65 times (p < .001, 95% CI = 1.53-3.65) more likely to experience SI than the No-NSSI history group. In the community-residing sample, NSSI history significantly predicted lifetime survival time (χ2 [1, N = 199] = 52.77, p < .001), such that the NSSI history group was 4.26 times (p < .001, 95% CI = 2.79-6.51) more likely to experience SI than the No-NSSI history group.

5.1.2. Suicide plan

In the inpatient sample, NSSI history significantly predicted lifetime survival time (χ2 [1, N = 155] = 13.24, p < .001), such that the NSSI history group was 2.50 (p < .001, 95% CI = 1.50-4.16) times more likely to experience a SP than the No-NSSI history group. In the community-residing sample, NSSI history significantly predicted lifetime survival time (χ2 [1, N = 199] = 60.80, p < .001), such that the NSSI history group was 6.05 times (p < .001, 95% CI = 3.63 10.10) more likely to experience a SP than the No-NSSI history group.

5.1.3. Suicide attempt

In the inpatient sample, NSSI history significantly predicted lifetime survival time (χ2 [1, N = 155] = 18.12, p < .001), such that the NSSI history group was 3.19 times (p < .001, 95% CI = 1.82-5.60) more likely to experience a SA than the No-NSSI history group. In the community-residing sample, NSSI history significantly predicted lifetime survival time (χ2 [1, N = 199] = 42.69, p < .001), such that the NSSI history group was 6.60 times (p < .001, 95% CI = 3.45-12.64) more likely to experience a SA than the No-NSSI history group.

5.2. Kaplan Meier results

5.2.1. Suicide ideation

Lifetime survival distributions for SI are presented in Fig. 1. Lifetime survival distributions for the two groups (NSSI history vs. No-NSSI history) were significantly different, χ2(1) = 62.408, p < .001, adjusting for sample. In the inpatient sample, the median lifetime survival time was 22 years for the NSSI history group and 27 years for the No-NSSI history group. The mean lifetime survival time was 18.67 years and 45.58 years for the NSSI history and No-NSSI history group, respectively. In the community-residing sample, the median lifetime survival time was 16 years for the NSSI history group and 43 years for the No-NSSI history group. The mean lifetime survival time was 23.38 years and 39.48 years for the NSSI history and No-NSSI history group, respectively.

Fig. 1.

Fig. 1.

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Survival curves for time to suicide ideation according to history of nonsuicidal self-injury.

5.2.2. Suicide plan

Lifetime survival distributions for SP are presented in Fig. 2. Lifetime survival distributions for the two groups (NSSI history vs. No-NSSI history) were statistically significantly different, χ2(1) = 61.655, p < .001, adjusting for sample. In the inpatient sample, the median lifetime survival time was 30 years for the NSSI history group and 55 years for the No-NSSI history group. The mean lifetime survival time was 27.66 years and 59.84 years for the NSSI history and No-NSSI history group, respectively. In the community-residing sample, the median lifetime survival time was 23 years for the NSSI history group and 76 years for the No-NSSI history group. The mean lifetime survival time was 32.48 years and 50.92 years for the NSSI history and No-NSSI history group, respectively.

Fig. 2.

Fig. 2.

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Survival curves for time to suicide plan according to history of nonsuicidal self-injury.

5.2.3. Suicide attempt

Lifetime survival distributions for SA are presented in Fig. 3. Lifetime survival distributions for the two groups (NSSI history vs. No-NSSI history) were statistically significantly different, χ2(1) = 54.418, p < .001, adjusting for sample. In the inpatient sample, the median lifetime survival time was 28 years for the NSSI history group but could not be determined for the No-NSSI history group.4 The mean lifetime survival time was 39 years and 72.12 years for the NSSI history and No-NSSI history group, respectively. In the community-residing sample, the median lifetime survival time was 43 years for the NSSI history group but could not be determined for the No-NSSI history group. The mean lifetime survival time was 35.07 years and 59.02 years for the NSSI history and No-NSSI history group, respectively.

Fig. 3.

Fig. 3.

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Survival curves for time to suicide attempt according to history of nonsuicidal self-injury.

6. Discussion

The findings from this study improve our understanding of suicidal and nonsuicidal SITBs among inpatient (high-risk) and community-residing (moderate-risk) veterans by identifying when SITBs emerge and their temporal development. We identified an important subgroup of veterans–those with an NSSI history–and showed that those veterans are not only at an increased lifetime relative risk for all suicidal SITBs, but also have a shorter lifetime survival time for all suicidal SITBs compared to veterans without an NSSI history.

Suicidal and nonsuicidal SITBs emerged, on average, between ages 14-28 in both samples with suicidal and nonsuicidal behavior beginning several years earlier in the inpatient sample relative to the community-residing sample. Nonsuicidal SITBs had an earlier age-of-onset compared to suicidal SITBs in both samples, which is consistent with active duty military (Turner et al., 2019) and non-veteran samples (Glenn et al., 2017). Suicidal SITBs began in early adulthood (ages 22-28)–much later than what is observed in the general population (Nock et al., 2008). For the most part, thoughts (NSSI thoughts, SI) had an earlier age-of-onset than behaviors (NSSI, SA); however, in the community-residing sample, NSSI thoughts emerged after NSSI. This discrepancy may be related to impulsiveness or negative urgency associated with NSSI (e.g., Bresin et al., 2013), but given this finding is inconsistent with literature on NSSI development (Glenn et al., 2017), it is likely an artifact. Finally, NSSI had an earlier age-of-onset than SA. This pattern suggests the temporal ordering of SITBs begins with NSSI thoughts and/or NSSI and is then followed by SI and SA. However, the inclusion of SP complicates this temporal ordering. SA precedes SP in the inpatient sample whereas SP precedes SA in the community-residing sample. This difference suggests that the inpatient sample may be engaging in more “impulsive” SAs (see Millner et al., 2017 for a disussion on SPs and Rimkeviciene and De Leo, 2015 for a review on impulsive SAs) or, alternatively, current research lacks measures sensitive enough to examine SPs (Anestis et al., 2014). More nuanced examinations of SPs–an inconsistently defined construct (Millner et al., 2017)–and replication is needed. In sum, the majority of these findings align with age-of-onset research among veterans (Bryan et al., 2015) and adolescents (Glenn et al., 2017).

The time lag of SITBs was not significantly different across samples, indicating, overall, the time and pattern between SITB transitions is relatively similar among inpatient and community-residing veterans. Generally, veterans reported thinking about suicide for 4-5 years before developing their first SP and it took <1 year-3 years to transition from SP to SA. The time lag between SI and SA and between SP and SA is longer than what is observed among non-veterans (Nock et al., 2008). The reasons for this difference are unclear but may be related to characteristics of being in a military environment which is comparatively more controlled than non-military environments. Taken together with our age-of-onset results, there are differences in when SITBs begin, but the time it takes to transition between SITBs is relatively similar.

We examined the role of NSSI history in relation to risk for suicidal SITBs, their temporal development, and time lag between types of SITBs. For both samples, an NSSI history was associated with an increased lifetime relative risk for all suicidal SITBs, shorter lifetime survival time, and shorter time lag between SITBs. Age-of onset was earlier in veterans with a NSSI history compared with those without a NSSI history for both samples. The findings from time lag analyses, specifically the role of NSSI in the SI to SA time lag, provide clarity for the temporal development of suicidal and nonsuicidal SITBs. For veterans with an NSSI history, the transition from SI to SA was, on average, shorter compared to veterans with no NSSI history, lending support to NSSI as a marker of suicide risk for inpatient and community-residing veterans. One explanation may be through acquired capability in which NSSI results in habituation to the fear and pain of death. Recent work with military service members and veterans supports this hypothesis, demonstrating that the association between NSSI and SA history was only significant for individuals with high levels of acquired capability (Chu et al., 2018). Our time lag results contrast with the Bryan and colleagues (2015) study which found that a history of NSSI slowed the transition from SI to SA. This discrepancy may be the result of differences in clinical severity and underscores the need to understand the full spectrum of clinical severity in veterans and how NSSI’s role may change as function of that clinical severity. Overall, our results converge with studies in active duty personnel (Turner et al., 2019), veterans (Bryan and Bryan, 2014; Bryan et al., 2015; Bryan et al., 2015), and general population (e.g., Klonsky, 2011) which indicate that NSSI plays an important role in relative risk for SAs.

Our hypothesis that NSSI increases medical lethality of the most serious SA was not supported. Veterans with an NSSI history did not have a SA with greater medical lethality compared with veterans with no NSSI history. This finding was surprising given that NSSI is theoretically posited to increase an individual’s acquired capability and should facilitate more medically serious SAs through increased pain tolerance and decreased fear of death (Van Orden et al., 2010). Although there is research on the relation between NSSI and subjective lethal intent (subjective SA lethality; e.g., Andover and Gibb, 2010), to our knowledge, research on whether NSSI increases the objective medical lethality of SAs is absent from literature. Subjective medical lethality was not measured in this study and cannot be examined. Future studies may want to examine the role of NSSI in both objective and subjective medical lethality of a SA.

Our results have important clinical implications for risk assessment and prevention efforts among veterans. These findings provide clinicians with a general sense of the temporal timing and patterns of transition between suicidal and nonsuicidal SITBs for two higher-risk groups of veterans. Findings suggest that NSSI may be an important factor to routinely incorporate into suicide risk assessments, while being mindful of potentially stigmatizing or gendered language that can decrease reporting in men (e.g., Berger et al., 2012). Veterans who screen positive for NSSI may be at elevated risk for suicide and may benefit from effective interventions targeted towards SITBs (e.g., Cognitive Behavioral Therapy for Suicide Prevention; Stanley et al., 2009). Contrary to prior work, our time lag results suggest that NSSI does not slow the transition from SI to SA for higher-risk groups of veterans and clinical decisions based on previous findings may be misguided. Our time lag results suggest that clinicians should be quick to intervene when working with veterans with an NSSI history, ideally within a year of onset for inpatient veterans before SI emerges. The transition from NSSI to SA occurs, on average, 4-7 years from the onset of NSSI. Although this longer time period may be less helpful for treatment, it may represent a period of heightened awareness for clinical monitoring. Veterans who screen positive for NSSI, but have not attempted suicide, may benefit from preventative care to reduce the likelihood of transitioning to a SA. Early intervention or increased targeted VA outreach (Tsai et al., 2020) may be a crucial step in preventing veteran suicide.

7. Limitations and future directions

Study results should be interpreted within the context of their limitations. First, the study is cross-sectional and the onset of SITBs was retrospectively self-reported in years, not in specific dates or months. This decreased specificity hinders our ability to understand the precise timing of when SITBs emerged and to provide more specific periods for intervention. The results provide a general timeline for the development of SITBs in veterans that can be used to narrow down assessment periods for future research that utilizes more intensive retrospective (e.g., timeline followback interview; Bagge et al., 2013) or prospective measurement (e.g., ecological momentary assessment; Kleiman et al., 2017). Second, this study did not assess for age-of-onset of all SITBs, such as passive SI (i.e., thoughts of wishing one were dead) or aborted and interrupted SAs–all of which may have important implications for understanding SITB trajectories. Suicide death was also not examined and understanding this outcome in relation to other non-fatal SITBs is needed, as meta-analytic evidence suggests differential associations with risk factors (Franklin et al., 2017). Third, this study examined the time course of SITBs, not mechanisms that may underlie the transition between SITBs, why NSSI influences suicide trajectories and trajectory lengths, or why veterans have a higher suicide death rate relative to non-veterans. Future studies could build upon our findings by identifying veteran-specific (e.g., enlistment age/timing, combat; Nichter et al., 2020; Smith et al., 2020) and time-varying factors (e.g., housing instability; Blosnich et al., 2020) that can be used to improve prediction (Glenn and Nock, 2014). Lastly, this study was conducted with high-risk inpatient and moderate risk community-residing samples (mostly male) and results may not generalize to lower-risk groups (e.g., outpatient veterans without recent SI), suggesting a need for additional research on the spectrum of risk and the role of gender. Further, the high-risk inpatient veterans received psychiatric care at a VA facility and may differ from high-risk veterans not receiving VA care. Since not all veterans are enrolled in VA care (Landes et al., 2018), future studies may benefit from seeking out additional sources outside of VA and increase enrollment of female veterans to better understand SITBs among these groups.

8. Conclusion

Study results diverge from recent temporal research in veterans, indicating that a NSSI history may shorten the transition from SI to SA in inpatient and community-residing veterans. NSSI history was associated with shorter lifetime survival time and an increased lifetime relative risk for all suicidal SITBs. This study enhanced our understanding of relative risk, age-of-onset, and temporal development of suicidal and non-suicidal SITBs among inpatient and community-residing veterans and highlights critical assessment points based on NSSI history.

Acknowledgements

Authors have no known conflict of interest to disclose. This research was funded by the Military Suicide Research Consortium (MSRC), an effort supported by the Office of the Assistant Security of Defense for Health Affairs (Award W81XWH-10-2-0181; MKN, TMK, BPM). Opinions, interpretations, conclusions, and recommendations are those of the authors and not necessarily endorsed by MSRC or the Department of Defense.

Sarah L. Brown is now at the Department of Psychiatry, University of Pittsburgh. Her work was supported, in part, by a grant from the National Institute for Mental Health (Award R01 MH115922).

Role of the funding source

This research was funded by the Military Suicide Research Consortium (MSRC), an effort supported by the Office of the Assistant Security of Defense for Health Affairs (Award W81XWH-10-2-0181; MKN, TMK, BPM). Opinions, interpretations, conclusions, and recommendations are those of the authors and not necessarily endorsed by MSRC or the Department of Defense. Sarah L. Brown’s work was supported, in part, by a grant from the National Institute for Mental Health (Award R01 MH115922).

Footnotes

Declaration of Competing Interest

Authors have no known conflict of interest to disclose.

2

Data for the two samples were collected simultaneously at two sites (see Procedure sections for additional details).

3

All modules of the SITBI were administered. SITBI administration time varied, depending on the number of SITBs endorsed by the interviewee. Example items from the SITBI include: (a) SI: “Have you had thoughts of killing yourself?” (response provided as yes or no), (b) SI age-of-onset: “How old were you the first time you had thoughts of killing yourself?” (response provided in years), (c) SA: “Have you ever made an actual attempt to kill yourself in which you had at least some intent to die?” (response provided as yes or no), (d) SA age-of-onset: “How old were you the first time you made a suicide attempt?” (response provided in years)

4

Median survival times for some subgroups were unable to be determined because the event had not occurred for more than half of that subgroup.

References

  1. Affairs, D.o.V., 2019. National Veteran Suicide Prevention Annual Report. https://www.mentalhealth.va.gov/docs/data-sheets/20.
  2. Andover MS, Gibb BE, 2010. Non-suicidal self-injury, attempted suicide, and suicidal intent among psychiatric inpatients. Psychiatry Res. 178 (1), 101–105. 10.1016/j.psychres.2010.03.019. [DOI] [PubMed] [Google Scholar]
  3. Anestis MD, Soberay KA, Gutierrez PM, Hernández TD, Joiner TE, 2014. Reconsidering the link between impulsivity and suicidal behavior. Pers. Soc. Psychol. Rev 18 (4), 366–386. 10.1177/1088868314535988. [DOI] [PubMed] [Google Scholar]
  4. Bagge CL, Lee H-J, Schumacher JA, Gratz KL, Krull JL, Holloman G Jr,, 2013. Alcohol as an acute risk factor for recent suicide attempts: A case-crossover analysis. J. Stud. Alcohol Drugs 74 (4), 552–558. 10.15288/jsad.2013.74.552. [DOI] [PubMed] [Google Scholar]
  5. Beck AT, Steer RA, Ranieri WF, 1988. Scale for suicide ideation: Psychometric properties of a self-report version. J. Clin. Psychol 44 (4), 499–505. . [DOI] [PubMed] [Google Scholar]
  6. Benjamini Y, Hochberg Y, 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Ser. A 57 (1), 289–300. 10.1111/j.2517-6161.1995.tb02031.x. [DOI] [Google Scholar]
  7. Berger JL, Addis ME, Reilly ED, Syzdek MR, Green JD, 2012. Effects of gender, diagnostic labels, and causal theories on willingness to report symptoms of depression. J. Soc. Clin. Psychol 31 (5), 439–457. 10.1521/jscp.2012.31.5.439. [DOI] [Google Scholar]
  8. Berman AL, Shepherd G, Silverman MM, 2003. The LSARS-II: Lethality of suicide attempt rating scale—updated. Suicide Life Threat. Behav. 33 (3), 261–276. 10.1521/suli.33.3.261.23211. [DOI] [PubMed] [Google Scholar]
  9. Blosnich JR, Monteith LL, Holliday R, Brenner LA, Montgomery AE, 2020. Differences in methods of suicide among veterans experiencing housing instability, 2013–2016. Psychiatry Res. 288, 112947 10.1016/j.psychres.2020.112947. [DOI] [PubMed] [Google Scholar]
  10. Bresin K, Carter DL, Gordon KH, 2013. The relationship between trait impulsivity, negative affective states, and urge for nonsuicidal self-injury: a daily diary study. Psychiatry Res. 205 (3), 227–231. 10.1016/j.psychres.2012.09.033 [DOI] [PubMed] [Google Scholar]
  11. Britton PC, Bohnert KM, Ilgen MA, Kane C, Stephens B, Pigeon WR, 2017. Suicide mortality among male veterans discharged from Veterans Health Administration acute psychiatric units from 2005 to 2010. Soc. Psychiatry Psychiatr. Epidemiol. 52 (9), 1081–1087. 10.1007/s00127-017-1377-x. [DOI] [PubMed] [Google Scholar]
  12. Bryan CJ, Bryan AO, 2014. Nonsuicidal self-injury among a sample of united states military personnel and veterans enrolled in college classes. J. Clin. Psychol. 70 (9), 874–885. 10.1002/jclp.22075. [DOI] [PubMed] [Google Scholar]
  13. Bryan CJ, Bryan AO, May AM, Klonsky ED, 2015. Trajectories of suicide ideation, nonsuicidal self-injury, and suicide attempts in a nonclinical sample of military personnel and veterans. Suicide Life Threat. Behav 45 (3), 315–325. 10.1111/sltb.12127. [DOI] [PubMed] [Google Scholar]
  14. Bryan CJ, Rudd MD, Wertenberger E, Young-McCaughon S, Peterson A, 2015. Nonsuicidal self-injury as a prospective predictor of suicide attempts in a clinical sample of military personnel. Compr. Psychiatry 59, 1–7. 10.1016/j.comppsych.2014.07.009. [DOI] [PubMed] [Google Scholar]
  15. Callahan CM, Unverzagt FW, Hui SL, Perkins AJ, Hendrie HC, 2002. Six-item screener to identify cognitive impairment among potential subjects for clinical research. Med. Care 771–781. 10.1097/00005650-200209000-00007. [DOI] [PubMed] [Google Scholar]
  16. Chu C, Hom MA, Stanley IH, Gai AR, Nock MK, Gutierrez PM, Joiner TE, 2018. Non-suicidal self-injury and suicidal thoughts and behaviors: A study of the explanatory roles of the interpersonal theory variables among military service members and veterans. J. Consult. Clin. Psychol 86 (1), 56–68. 10.1037/ccp0000262. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Flynn R, 2012. Survival analysis. J. Clin. Nurs 21 (19pt20), 2789–2797. 10.1111/j.1365-2702.2011.04023.x. [DOI] [PubMed] [Google Scholar]
  18. Fox KR, Franklin JC, Ribeiro JD, Kleiman EM, Bentley KH, Nock MK, 2015. Meta-analysis of risk factors for nonsuicidal self-injury. Clin. Psychol. Rev 42, 156–167. 10.1016/j.cpr.2015.09.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Franklin JC, Hessel ET, Prinstein MJ, 2011. Clarifying the role of pain tolerance in suicidal capability. Psychiatry Res. 189 (3), 362–367. 10.1016/j.psychres.2011.08.001. [DOI] [PubMed] [Google Scholar]
  20. Franklin JC, Ribeiro JD, Fox KR, Bentley KH, Kleiman EM, Huang X, Musacchio KM, Jaroszewski AC, Chang BP, Nock MK, 2017. Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychol. Bull 143 (2), 187–232. 10.1037/bul0000084. [DOI] [PubMed] [Google Scholar]
  21. Glenn CR, Lanzillo EC, Esposito EC, Santee AC, Nock MK, Auerbach RP, 2017. Examining the course of suicidal and nonsuicidal self-injurious thoughts and behaviors in outpatient and inpatient adolescents. J. Abnorm. Child Psychol 45 (5), 971–983. 10.1007/s10802-016-0214-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Glenn CR, Nock MK, 2014. Improving the short-term prediction of suicidal behavior. Am. J. Prev. Med 47 (3), S176–S180. 10.1016/j.amepre.2014.06.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Jacobson CM, Muehlenkamp JJ, Miller AL, Turner JB, 2008. Psychiatric impairment among adolescents engaging in different types of deliberate self-harm. J. Clin. Child Adolescent Psychol 37 (2), 363–375. 10.1080/15374410801955771. [DOI] [PubMed] [Google Scholar]
  24. Joiner T, 2007. Why people die by suicide. Harvard University Press. 10.2307/j.ctvjghv2f. [DOI] [Google Scholar]
  25. Kang HK, Bullman TA, Smolenski DJ, Skopp NA, Gahm GA, Reger MA, 2015. Suicide risk among 1.3 million veterans who were on active duty during the Iraq and Afghanistan wars. Ann. Epidemiol 25 (2), 96–100. 10.1016/j.annepidem.2014.11.020. [DOI] [PubMed] [Google Scholar]
  26. Kleiman EM, Turner BJ, Fedor S, Beale EE, Huffman JC, Nock MK, 2017. Examination of real-time fluctuations in suicidal ideation and its risk factors: Results from two ecological momentary assessment studies. J. Abnorm. Psychol. 126 (6), 726. 10.1037/abn0000273. [DOI] [PubMed] [Google Scholar]
  27. Klonsky ED, 2011. Non-suicidal self-injury in United States adults: Prevalence, sociodemographics, topography and functions. Psychol. Med 41 (9), 1981–1986. 10.1017/s0033291710002497. [DOI] [PubMed] [Google Scholar]
  28. Klonsky ED, May AM, Glenn CR, 2013. The relationship between nonsuicidal self-injury and attempted suicide: converging evidence from four samples. J. Abnorm. Psychol 122 (1), 231–237. 10.1037/a0030278. [DOI] [PubMed] [Google Scholar]
  29. Kroenke K, Spitzer RL, 2002. The PHQ-9: a new depression diagnostic and severity measure. Psychiatric Ann. 32 (9), 509–515. 10.3928/0048-5713-20020901-06. [DOI] [Google Scholar]
  30. Landes SD, London AS, Wilmoth JM, 2018. Mortality among veterans and non-veterans: does type of health care coverage matter? Population Res. Policy Rev 37 (4), 517–537. 10.1007/s11113-018-9468-2. [DOI] [Google Scholar]
  31. Lee DJ, Kearns JC, Wisco BE, Green JD, Gradus JL, Sloan DM, Nock MK, Rosen RC, Keane TM, Marx BP, 2018. A longitudinal study of risk factors for suicide attempts among operation enduring freedom and operation Iraqi freedom veterans. Depress. Anxiety 35 (7), 609–618. 10.1002/da.22736. [DOI] [PubMed] [Google Scholar]
  32. Millner AJ, den Ouden HE, Gershman SJ, Glenn CR, Kearns JC, Bornstein AM, Marx BP, Keane TM, Nock MK, 2019. Suicidal thoughts and behaviors are associated with an increased decision-making bias for active responses to escape aversive states. J. Abnorm. Psychol 128 (2), 106–118. 10.1037/abn0000395. [DOI] [PubMed] [Google Scholar]
  33. Millner AJ, Lee MD, Nock MK, 2015. Single-item measurement of suicidal behaviors: Validity and consequences of misclassification. PLoS One 10 (10), e0141606. 10.1371/journal.pone.0141606. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Millner AJ, Lee MD, Nock MK, 2017. Describing and measuring the pathway to suicide attempts: a preliminary study. Suicide Life Threat. Behav 47 (3), 353–369. 10.1111/sltb.12284. [DOI] [PubMed] [Google Scholar]
  35. Muehlenkamp JJ, 2005. Self-injurious behavior as a separate clinical syndrome. Am. J. Orthopsychiatry 75 (2), 324–333. 10.1037/0002-9432.75.2.324. [DOI] [PubMed] [Google Scholar]
  36. Nichter B, Hill M, Norman S, Haller M, Pietrzak RH, 2020. Impact of specific combat experiences on suicidal ideation and suicide attempt in US military veterans: Results from the National Health and Resilience in Veterans Study. J. Psychiatr. Res 130, 231–239. 10.1016/j.jpsychires.2020.07.041. [DOI] [PubMed] [Google Scholar]
  37. Nock MK, 2010. Self-injury. Annual review of clinical psychology 6, 339–363. 10.1146/annurev.clinpsy.121208.131258. [DOI] [PubMed] [Google Scholar]
  38. Nock MK, Borges G, Bromet EJ, Alonso J, Angermeyer M, Beautrais A, Bruffaerts R, Chiu WT, De Girolamo G, Gluzman S, 2008. Cross-national prevalence and risk factors for suicidal ideation, plans and attempts. Br. J. Psychiatry 192 (2), 98–105. 10.1192/bjp.bp.107.040113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Nock MK, Holmberg EB, Photos VI, Michel BD, 2007. Self-injurious thoughts and behaviors interview: development, reliability, and validity in an adolescent sample. Psychol. Assess 19 (3), 309–317. 10.1037/1040-3590.19.3.309. [DOI] [PubMed] [Google Scholar]
  40. Ramchand R, Acosta J, Burns RM, Jaycox LH, Pernin CG, 2011. The war within: Preventing suicide in the US military. Rand Health Quarterly, 10.1037/e534112011-001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Rimkeviciene J, De Leo D, 2015. Impulsive suicide attempts: A systematic literature review of definitions, characteristics and risk factors. J. Affect. Disord 171, 93–104. 10.1016/j.jad.2014.08.044. [DOI] [PubMed] [Google Scholar]
  42. Silverman MM, Berman AL, Sanddal ND, O’Carroll PW, Joiner TE Jr., 2007. Rebuilding the tower of Babel: a revised nomenclature for the study of suicide and suicidal behaviors. Part 1: Background, rationale, and methodology. Suicide Life Threat. Behav 37 (3), 248–263. 10.1521/suli.2007.37.3.248. [DOI] [PubMed] [Google Scholar]
  43. Sjölander A, Vansteelandt S, 2019. Frequentist versus Bayesian approaches to multiple testing. Eur. J. Epidemio 34 (9), 809–821. 10.1007/s10654-019-00517-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Smith JD, Schaughency KC, Christopher P, Watkins EY, Anke KM, 2020. Time to suicide and suicide attempt among army enlisted soldiers’ first year of service. Military Behav. Health 1–8. 10.1080/21635781.2020.1860169. [DOI] [Google Scholar]
  45. Stanley B, Brown G, Brent DA, Wells K, Poling K, Curry J, Kennard BD, Wagner A, Cwik MF, Klomek AB, 2009. Cognitive-behavioral therapy for suicide prevention (CBT-SP): treatment model, feasibility, and acceptability. J. Am. Acad. Child Adolesc. Psychiatry 48 (10), 1005–1013. 10.1097/chi.0b013e3181b5dbfe. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Swannell SV, Martin GE, Page A, Hasking P, St John NJ, 2014. Prevalence of nonsuicidal self-injury in nonclinical samples: systematic review, meta-analysis and meta-regression. Suicide Life Threat. Behav 44 (3), 273–303. 10.1111/sltb.12070. [DOI] [PubMed] [Google Scholar]
  47. Tsai J, Snitkin M, Trevisan L, Kraus SW, Pietrzak RH, 2020. Awareness of suicide prevention programs among US military veterans. Admin. Policy Mental Health Mental Health Serv. Res 47 (1), 115–125. 10.1007/s10488-019-00975-6. [DOI] [PubMed] [Google Scholar]
  48. Turner BJ, Kleiman EM, Nock MK, 2019. Non-suicidal self-injury prevalence, course, and association with suicidal thoughts and behaviors in two large, representative samples of US Army soldiers. Psychol. Med 49 (9), 1470–1480. 10.1017/s0033291718002015. [DOI] [PubMed] [Google Scholar]
  49. Van Orden KA, Witte TK, Cukrowicz KC, Braithwaite SR, Selby EA Jr, Joiner T,E, 2010. The interpersonal theory of suicide. Psychol. Rev 117 (2), 575–600. 10.1037/a0018697. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Villatte JL, O’Connor SS, Leitner R, Kerbrat AH, Johnson LL, Gutierrez PM, 2015. Suicide attempt characteristics among veterans and active-duty service members receiving mental health services: a pooled data analysis. Military Behav. Health 3 (4), 316–327. 10.1080/21635781.2015.1093981. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Whitlock J, Muehlenkamp J, Eckenrode J, Purington A, Abrams GB, Barreira P, Kress V, 2013. Nonsuicidal self-injury as a gateway to suicide in young adults. J. Adolesc. Health 52 (4), 486–492. 10.1016/j.jadohealth.2012.09.010. [DOI] [PubMed] [Google Scholar]

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