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Understanding suicidal transitions in Australian adults: protocol for the LifeTrack prospective longitudinal cohort study

BMC Psychiatry volume 23, Article number: 821 (2023) Cite this article

Abstract

Background

The factors that influence transition from suicidal ideation to a suicide attempt or remission of suicidal thoughts are poorly understood. Despite an abundance of research on risk factors for suicidal ideation, no large-scale longitudinal population-based studies have specifically recruited people with suicidal ideation to examine the mechanisms underlying critical transitions to either suicide attempt or recovery from suicidal ideation. Without longitudinal data on the psychological, behavioural, and social determinants of suicide attempt and the remission of suicidal ideation, we are unlikely to see major gains in the prevention of suicide.

Aim

The LifeTrack Project is a population-based longitudinal cohort study that aims to identify key modifiable risk and protective factors that predict the transition from suicidal ideation to suicide attempt or remission of suicidal ideation. We will assess theory-informed risk and protective factors using validated and efficient measures to identify distinct trajectories reflecting changes in severity of suicidal ideation and transition to suicide attempt over three years.

Methods

A three-year prospective population-based longitudinal cohort study will be conducted with adults from the general Australian population who initially report suicidal ideation (n = 842). Eligibility criteria include recent suicidal ideation (past 30 days), aged 18 years or older, living in Australia and fluent in English. Those with a suicide attempt in past 30 days or who are unable to participate in a long-term study will be excluded. Participants will be asked to complete online assessments related to psychopathology, cognition, psychological factors, social factors, mental health treatment use, and environmental exposures at baseline and every six months during this three-year period. One week of daily measurement bursts (ecological momentary assessments) at yearly intervals will also capture short-term fluctuations in suicidal ideation, perceived burdensomeness, thwarted belongingness, capability for suicide, and distress.

Conclusion

This study is intended to identify potential targets for novel and tailored therapies for people experiencing suicidal ideation and improve targeting of suicide prevention programs. Even modest improvements in current treatments may lead to important reductions in suicide attempts and deaths.

Study Registration

Australian New Zealand Clinical Trials Registry identifier: ACTRN12623000433606.

Peer Review reports

Background

More than 700,000 people around the world die by suicide annually [1]. Despite reductions in global age-standardised suicide mortality rates, suicide is a leading cause of age-standardised years of life lost globally [2], and we continue to have limited knowledge of causal factors for the transition from suicidal ideation to suicidal behaviour [3, 4]. In Australia, more than 3,000 people die by suicide annually and suicide is the leading cause of death for those aged 15–44 years [5]. Rates of suicidal ideation (SI) are estimated to be 3.3% per year from national survey data, with 0.3% (i.e., ~ 75,000 Australians annually) reporting a suicide attempt (SA) [6]. Prevalence rates of self-reported SI and SA are even higher in representative Australian cohort studies [7]. Suicide attempts disproportionally occur in younger people, with devastating consequences for family and friends and broader societal, healthcare, and economic costs ($6.73B in Australia) [8].

Despite increased investment in suicide prevention, suicide rates have not declined over recent decades in many nations [6, 9]. This suggests that current clinical interventions for suicidality are not sufficiently effective, possibly because they are poorly targeted. While individuals are encouraged to seek support from general practitioners, hospitals, and mental health professionals, there are limited therapeutic interventions that directly target suicidal thinking and behaviour, with many focusing on reduction of depression symptoms. Suicidality and depression have considerable commonality, with SI representing one symptom of depression in Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5) and International Classification of Diseases 10th revision (ICD-10) criteria, and common underlying vulnerabilities [10]. However, Cognitive-Behaviour Therapy (CBT)-based interventions that reduce depression symptoms only have a small to medium effect on SI [11], and meta-analytic evidence shows that psychological and pharmacological treatments have only modest efficacy for reducing SI and SA [12]. There are also key differences between the trajectories of suicidality and depression, with remission in SI only partially explained by reductions in depressive symptoms [13]. Among individuals who report SI, suicidal behaviour is more closely tied to post-traumatic stress disorder (PTSD), obsessive-compulsive disorder (OCD), bipolar disorder and conduct disorder than depression [13, 14].

Public health approaches to suicide prevention suggest that multiple strategies are required to reduce suicidal behaviours [15]. However, limited knowledge about the effectiveness of current approaches to prevention and treatment means that a better understanding of the mechanisms underlying suicidal behaviour is needed. In particular, clinical treatments have limited efficacy for reducing suicidal thoughts and attempts. Focusing only on “high risk” patients in clinical settings will always miss a substantial proportion of people who attempt suicide in the community, many of whom are untreated [16]. Crisis services are not equipped to handle the large numbers of individuals who attempt suicide [17], and many people experiencing suicidal distress choose not to engage with health services [18, 19]. Clinical services also have limited resources to provide ongoing support for suicidal individuals. Suicide will only be fully understood and prevented through the inclusion of people in research who do not seek help. Both large scale public health approaches and improved clinical treatments that equip people with the tools to overcome SI and prevent SA are vital to preventing suicide in the community.

A more comprehensive understanding of the modifiable risk factors that predict the transition from SI to SA in adults, including those not in contact with clinical services, is necessary to understand the mechanisms underlying the transition from SI to SA and to inform new treatment and prevention approaches for reducing rates of SA. Theories of suicidal behaviour aim to identify these factors and the mechanisms by which they affect SI and SA. Three recent theories of suicidal behaviour with empirical support are the Interpersonal-Psychological Theory of Suicidal Behaviour (IPTS) [20], the Three-Step Theory (3-ST) [21] and Integrated Motivational-Volitional (IMV) theory [22]. Each theory acknowledges that understanding the key transition from SI to SA is vital for reducing suicide deaths. The IPTS posits that feelings of thwarted belongingness (not feeling accepted by others) and perceived burdensomeness (a feeling that one is a burden on others) drive the development of SI, but a third factor, the capability for suicide (reductions in fear and pain sensitivity sufficient to overcome self-preservation reflexes), is necessary for the transition to SA [23]. However, a systematic review of the theory found very few studies detecting a significant effect of capability for suicide on SA, and those that do find an effect have typically been cross-sectional retrospective studies with small effect sizes [24].

The IMV and 3-ST propose that a broader range of factors, including capability for suicide, access to means, psychopathology, and impulsivity all play key roles. More specifically, the IMV model of suicidal behaviour proposes that defeat and entrapment drive the development of SI and that volitional moderators, such as access to means, exposure to suicidal behaviour, capability for suicide, planning, impulsivity, mental imagery, and past suicidal behaviour, drive the transition from SI to SA [22]. Similarly, the 3-ST proposes that the progression from SI to SA is facilitated by dispositional factors (e.g., pain sensitivity, blood phobia), acquired factors (e.g., habituation to experiences of pain, injury, fear, and death) and practical factors (e.g., knowledge of, and access to, lethal means) [21].

Variability in risk factors may also be relevant to transition from SI to SA. Despite evidence of large short-term variability in suicidal ideation and proposed risk factors of suicidal ideation (e.g., hopelessness and burdensomeness) among psychiatric inpatients and people with a history of suicide attempt/s [25, 26], there is limited understanding of how this variability affects suicide risk over both the short- and long-term in the general population, and therefore a need for prospective burst measurement studies.

There is insufficient high-quality evidence for which factors are most influential in the transition from SI to SA, noting that there is likely to be considerable variability between different population groups. There are no existing population-based longitudinal studies that have adequately explored the role of multiple factors in predicting the transition from SI to SA. Existing studies have relied on retrospective reports of SI/SA [27, 28] or relied on small prospective samples (n < 70) [29, 30]. No previous study has been adequately powered to prospectively assess the roles of a comprehensive array of key risk factors for the transition to SA among adults who experience SI. There is also limited evidence around the factors that promote the remission of SI. Previous research suggests that protective factors such as social support and positive mental health are likely to influence a positive course of suicidal thinking [31, 32].

The LifeTrack Project will test the three contemporary theories of SA, as well as novel determinants, to identify the most prevalent pathways from SI to both SA and remission of SI, in the short-term and long-term.

Method

Aim

The LifeTrack Project aims to: (a) identify key risk and protective factors that predict the transition from SI to SA, (b) identify distinct trajectories of suicidal ideation severity, remission and/or transition to suicide attempt, and (c) identify subgroups of individuals with suicidal ideation who are most at risk for suicide attempt. We will assess theory-informed risk and protective factors related to psychopathology, cognition, psychological factors, social factors, treatment use, and environmental exposures at repeated intervals, using validated and efficient measures.

Aim 1: Prospectively identify key risk and protective factors that predict the transition from suicidal ideation to suicide attempt, or alternatively from suicidal ideation to remission from suicidal ideation.

  • H1: Transition to SA will be significantly predicted by the risk factors proposed by the three theoretical models (IMV, IPTS, 3-ST; see Table 1).

  • H2: Beyond the factors central to the three theoretical models, transition will be further predicted by a combination of psychological, cognitive, social, mental health, physical health, treatment, and demographic factors and adverse experiences.

  • H3: Factors associated with the transition from SI to SA will be different from those factors associated with persistent SI or remission of SI.

  • H4: Greater short-term variability in key constructs related to suicidal behaviour (SI, perceived burdensomeness, thwarted belongingness, and distress, measured in short-term bursts) will be positively associated with transition to SA.

Table 1 Assessment domains and instruments
Full size table

Aim 2: Identify distinct trajectories of SI severity, including remission and transition into SA, and the predictors of these classes of trajectories.

  • H5: Distinct trajectories (classes) of SI over time will be identifiable statistically, and these trajectories will have different levels of risk for transition to SA.

  • H6: Different trajectories across different SI severities will be predicted effectively by baseline factors and short-term variability in key constructs.

Aim 3: Identify subgroups of individuals most at risk of SA.

  • H7: Distinct clusters of individuals with differing risk profiles for transition to SA across the follow-up period will be identified.

Pilot work

In previous qualitative work, we examined factors associated with risk of SA [33, 34]. This research has informed the constructs included in the study that may influence the transition from SI to SA. Population-based quantitative research has also demonstrated preliminary evidence for the roles of a range of specific risk factors in the transition to SA. These include the roles of interpersonal factors [7, 24, 35, 36] and mental disorders including PTSD, OCD, and depression [10, 14, 37], relationship quality/breakdown [38, 39], and psychotic-like experiences [40]. This previous work ensures that the variables included in the study are the most appropriate and relevant for answering the research questions.

This study was funded by the Australian National Health and Medical Research Council (GNT2014841), approved by the Australian National University Human Research Ethics Committee (2022/851), and registered as a longitudinal cohort study with the Australian New Zealand Clinical Trials Registry (identifier: ACTRN12623000433606).

Design and measures

We will conduct a three-year prospective longitudinal cohort study of adults who report SI at baseline. The follow up period of three years has been chosen to enable sufficient time to establish long-term trajectories and capture relatively rare outcomes including SA. SA will be assessed based on endorsement of the relevant item from either the modified Youth Risk Behaviour Survey or the Suicidal Ideation Attributes Scale, or through report from a participant’s confidant. Table 1 shows a schedule of the survey measures at each assessment point. Measures have been chosen based on the best available scales for assessing the constructs of interest in epidemiological studies [41]. Timing of SA events will be reported by participants and/or confidants.

This study will be conducted entirely online. Participants will initially complete a screening survey to assess eligibility and obtain their contact details and the contact details of a confidant (to facilitate identification of SA or suicide death and welfare checks if required). Informed consent will be obtained from participants at this point. Two days after completing the screening survey, participants will be emailed the baseline assessment, with up to two weeks to complete the survey. Participants will be invited to complete follow-up assessments every six months for the duration of the study, with the final assessment point being three years after completion of the baseline survey. Follow-up assessments will include a subset of the baseline measures, including SI and SA, capturing frequency, severity and timing. The 6-, 18-, and 30-month assessments are estimated to take 15–20 min to complete while the 12-, 24-, and 36-month assessments will take 25–30 min as they cover more risk factors.

At yearly intervals (immediately after the baseline, one-year, and two-year assessments), participants will also be invited to complete one week of two-minute daily measurement bursts (ecological momentary assessments) to capture short-term fluctuations in current SI, perceived burdensomeness, capability for suicide, and distress. Links to the daily surveys will be sent to participants at 12am Australian Eastern Daylight Time or Australian Eastern Standard Time and will remain open for 24 h.

All surveys will be hosted using the online survey platform REDCap [42, 43]. All survey questions will be optional, except for questions that relate to eligibility or to the primary outcomes of the study (suicidal ideation and attempts). All outputs arising from the study will be reported consistent with STROBE guidelines.

Eligibility criteria

Eligibility criteria for this cohort study include:

(1) Recent or current suicidal ideation (past 30 days).

(2) No suicide attempt in past 30 days.

(3) Capacity to participate in a long-term study.

(4) Aged 18 years or older.

(5) Living in Australia.

(6) Fluent in English.

(7) Willing to provide contact details for self (email address and mobile number) and a confidant (email address).

(8) Access to a device (desktop, laptop, and/or smartphone) and internet connection.

All eligibility criteria will be self-reported by participants. All participants will be encouraged to engage (or continue engaging) with clinical services, and a clinical psychologist will be made available to maximise participant safety and facilitate referral to services, as outlined in the Ethical considerations section below. The clinician will not provide therapeutic services to participants. Participants who are not eligible according to the above criteria or do not consent will also be provided with feedback and information about support services.

Recruitment, sample size and follow-up

We will recruit from well-established community recruitment sources to maximise the coverage of the study and diversity of the sample: online, social media, primary care settings, and print media.

Many individuals who experience suicidal ideation or behaviours do not present to clinical services [44, 45]. However, previous research has shown that users of popular social media platforms (particularly Facebook and Instagram) who participate in mental health research report high prevalence of suicidal ideation [14]. Social media provides similarly representative samples to other population recruitment methods and is effective and appropriate for recruiting marginalised populations [46, 47]. Additional recruitment using print media advertising will expand the breadth of the sample to include those who do not interact with social media and to maximise ecological validity. Advertisements will be disseminated in locations where people with elevated risk of suicidal thoughts are likely to attend, including primary care clinics. These recruitment methods will result in samples that are diverse in age and reach marginalised populations (e.g., ethnic minorities and people living with mental illness) that are less likely to respond to traditional population recruitment methods [14, 46,47,48].

Participants will be compensated for their time with e-gift cards of between $25 and $50. The gift card amounts will depend on the length of the survey and the overall time commitment to date. Compensation for the later surveys will be slightly higher than compensation for the earlier surveys in acknowledgement of participants’ greater overall time commitment at the later stages of the research. The data will be screened for ineligible responses before participants are included in the study.

Participants will receive emailed invitations to participate in each six-monthly survey. Email reminders will be sent every four days if a participant has not yet completed the survey, up to a total of three possible reminders, with each follow-up survey staying open for 30 days to maximise completion rates. The study clinical psychologist will contact participants who do not respond to survey invitations via email and/or telephone. If a participant cannot be reached, the study clinical psychologist will contact their confidant to confirm the participant’s welfare and/or determine timing of SA events.

Participants will be considered to have withdrawn if they: (a) email or call the researchers with a withdrawal request, (b) request to withdraw when the clinical psychologist contacts them to follow up on survey completion, or (c) do not commence two consecutive main (i.e., non-EMA) surveys and do not respond to the clinician’s attempts to make contact. No further follow-up attempts will be made at that stage. De-identified data from withdrawn participants will be retained unless they request its deletion.

Power analysis

Our power calculation is based on detection of the effect of our explanatory variables on transition from SI to SA. We conservatively assume that at least 15% of the sample will attempt suicide at some stage during the follow-up period, based on research findings that up to 20% of people with SI will attempt suicide over 12 months [29, 30, 47]. To have 90% power to detect a moderate standardised effect of d = 0.5 (i.e., effect size at least half a standard deviation from zero) between those who do vs. do not attempt suicide, we require a sample of N = 374. To allow detection of interactions between multiple modifiable factors and have sufficiently narrow confidence intervals around estimates of population preventable fractions (PPF), we have inflated the target sample size by 35% (equivalent to a four-group comparison, rather than simply SA vs. no SA). Further assuming up to 40% attrition at 36 months, we will recruit a sample of N = 842 participants (374 × 1.35 ÷ 0.6). This sample will also be powered to detect up to five latent classes using growth mixture models and latent class analysis to identify subgroups within the sample based on trajectories of SI or baseline characteristics [49] and for machine learning analyses to identify novel combinations of risk factors [50].

Statistical methods

To identify factors most strongly associated with the transition from SI to SA and remission from SI (H1-H4, H7), statistical analyses will include Cox proportional hazards regression models (time to SA) and zero-inflated negative binomial mixed models (number of SA), accounting for lifetime SA, with suicide deaths treated as right-censored data. Effects will be converted to PPFs based on estimated hazard ratios, combined with prevalence rates taken from external representative data where available (e.g., [51]) or from the cohort. Growth mixture model analyses will classify subgroups of individuals based on their trajectories of SI severity (H5). We will test for both linear and quadratic trajectories using continuous Suicidal Ideation Attributes Scale severity scores. Latent class analyses will differentiate subgroups of individuals reporting SI at baseline (H7). Multinomial logistic regression analyses will then identify factors associated with each of the identified trajectories or latent classes (H6, H7). We will also explore H6 and H7 using machine learning algorithms [52] to identify novel interactions between factors, using a random split (development-validation) approach to classify participants on the basis of SA and on remission from SI.

Ethical considerations

Based on findings from multiple meta-analyses and systematic reviews [53, 54] and our own research [55], distress attributable to questions about suicide is very rare in community (and clinical) samples, with suicidal participants substantially more likely to report reduced distress and relief at being asked about their experiences, and with no evidence of iatrogenic effects. We have carefully considered the risks of research participation in exacerbating suicidal thinking, but the evidence strongly suggests that even repeated and intensive questions about suicide in high-risk groups are seen by participants as feasible and acceptable, and do not lead to increased distress even in clinical samples [53,54,55,56].

All individuals who participate in the study will receive a list of state and national mental health and suicide prevention resources at multiple timepoints throughout the study that they will be encouraged to utilise if they are experiencing distress or mental health concerns. These resources will include access to an online safety planning tool, an approach which has been shown to reduce suicidal behaviour [57].

A clinical psychologist involved in the project will assess risk when safety protocol criteria are met (e.g., if a recent suicide attempt is reported), or if a participant requests a callback, and refer participants to support services. Participants may telephone or email the research team to request support at any time throughout the study. Participant safety will also be supported by requiring participants to provide contact details for themselves (telephone and email address) and a confidant (email address), which will be used to ascertain participant safety if a participant does not respond to the surveys or reminder emails. Participants will be frequently reminded that they are free to withdraw from the study at any time without negative consequences, and to access treatments or services throughout the duration of the study.

Discussion

This study uses a population health approach to identify new psychological, social, or community targets that may address escalating suicidality. By identifying factors associated with the transition to SA, suicide prevention interventions can be better targeted to individuals in specific high-risk states and tailored to individual risk profiles. There is heterogeneity in trajectories of SI severity [10, 58] and likely to be considerable heterogeneity in the factors that are associated with transition to SA. Treatments that are tailored to individuals with SI, based on patterns of risk factors and trajectories of SI severity, are likely to be more effective. For example, there may be a cluster of people for whom ruminative and obsessive thinking might play an important role in maintenance of SI, leading to SA. For other clusters, interpersonal factors, sleep disturbance, emotional dysregulation, and/or impulsivity may be key precipitants of SA.

A key strength of this study is its 36-month follow-up period, which was chosen to allow sufficient time to establish long-term trajectories and capture relatively rare outcomes including SA, and the inclusion of burst measurements to facilitate a prospective examination of the effects of short-term variability in SI and its risk factors on SA. Other strengths of the protocol include the inclusion of a wide range of potentially relevant factors and the large sample size and appropriate statistical power.

There are limitations associated with online-only long-term studies, including risk of high attrition. The project will use principles of Eysenbach’s Law of Attrition [96] to mitigate this risk, including advantage (participant reimbursement), compatibility (enabling survey completion using only an internet browser), complexity (using brief screeners), and push factors (use of email reminders, with the option to add SMS reminders if necessary, and feedback on the study results).

Outcomes of this longitudinal study may inform new public health approaches to reduce suicide attempts in the community, with a focus on risk and protective factors. We will identify the factors most strongly associated with remission from suicidal ideation and behaviours, calculating population preventive fractions to assist in prioritising and targeting suicide prevention efforts by policymakers, community organisations, and service delivery organisations. By delivering public health interventions that are targeted to groups of people who are at greatest risk of SA, we can increase the efficiency and effectiveness of public health interventions for delivery in schools, workplaces, community groups, and through the internet.

Data availability

Not applicable.

Abbreviations

SI:

Suicidal ideation

SA:

Suicide attempt

CBT:

Cognitive Behavioural Therapy

IPTS:

Interpersonal-Psychological Theory of Suicidal behaviour

3-ST:

Three-Step Theory

IMV:

Integrated Motivational-Volitional

STROBE:

Strengthening the Reporting of Observational studies in Epidemiology guidelines

NHMRC:

National Health and Medical Research Council

References

  1. World Health Organization. : Suicide worldwide in 2019: global health estimates. In Global Health Estimates. Geneva; 2021.

  2. Naghavi M. Global, regional, and national burden of Suicide mortality 1990 to 2016: systematic analysis for the global burden of disease study 2016. BMJ. 2019;364:l94.

  3. O’Connor RC, Nock MK. The psychology of suicidal behaviour. Lancet Psychiatry. 2014;1(1):73–85.

    Article  PubMed  Google Scholar 

  4. Franklin JC, Ribeiro JD, Fox KR, Bentley KH, Kleiman EM, Huang X, et al. Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychol Bull. 2017;143(2):187–232.

    Article  PubMed  Google Scholar 

  5. Causes of Death., Australia [https://www.abs.gov.au/statistics/health/causes-death/causes-death-australia/2022].

  6. Australian Bureau of Statistics. National Study of Mental Health and Wellbeing. Canberra: ABS; 2020–2.

  7. Christensen H, Batterham PJ, Soubelet A, Mackinnon AJ. A test of the interpersonal theory of suicide in a large community-based cohort. J Affect Disord. 2013;144:225–34.

    Article  PubMed  Google Scholar 

  8. Kinchin I, Doran CM. The economic cost of suicide and non-fatal suicide behavior in the Australian workforce and the potential impact of a workplace suicide prevention strategy. Int J Environ Res Public Health. 2017;14:347.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Centers for Disease Control and Prevention. Suicide Data and Statistics. (2023). Accessed Nov 2023. https://www.cdc.gov/suicide/suicide-data-statistics.html.

  10. Batterham PJ, van Spijker BAJ, Mackinnon AJ, Calear AL, Wong Q, Christensen H. Consistency of trajectories of suicidal ideation and depression symptoms: evidence from a randomized controlled trial. Depress Anxiety. 2019;36:321–9.

    Article  PubMed  Google Scholar 

  11. Leavey K, Hawkins R. Is cognitive behavioural therapy effective in reducing suicidal ideation and behaviour when delivered face-to-face or via e-health? A systematic review and meta-analysis. Cogn Behav Ther. 2017;46(5):353–74.

    Article  PubMed  Google Scholar 

  12. D’Anci KE, Uhl S, Giradi G, Martin C. Treatments for the prevention and management of suicide: a systematic review. Ann Inter Med. 2019;171:334–42.

  13. Keilp JG, Ellis SP, Gorlyn M, Burke AK, Oquendo MA, Mann JJ, et al. Suicidal ideation declines with improvement in the subjective symptoms of major depression. J Affect Disord. 2018;227:65–70.

    Article  PubMed  Google Scholar 

  14. Batterham PJ, Calear AL, Christensen H, Carragher N, Sunderland M. Independent effects of mental disorders on suicidal behavior in the community. Suicide Life Threat Behav. 2018;48:512–21.

  15. Krysinska K, Batterham PJ, Tye M, Shand F, Calear AL, Cockayne N, et al. Best strategies for reducing the suicide rate in Australia. Aust N Z J Psychiatry. 2016;50(2):115–8.

    Article  Google Scholar 

  16. Franklin JC, Ribeiro JD, Fox KR, Bentley KH, Kleiman EM, Huang X, et al. Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychol Bullet. 2017; 143:187–232.

  17. Brent DA. Master Clinician review: saving holden caulfield: suicide prevention in children and adolescents. J Am Acad Child Adolesc Psychiatry. 2019;58(1):25–35.

    Article  PubMed  Google Scholar 

  18. Stene-Larsen K, Reneflot A. Contact with primary and mental health care prior to suicide: a systematic review of the literature from 2000 to 2017. Scand J Public Health. 2017;47(1):9–17.

    Article  PubMed  Google Scholar 

  19. Walby FA, Myhre MØ, Kildahl AT. Contact with mental health services prior to suicide: a systematic review and meta-analysis. Psychiatric Serv. 2018;69(7):751–9.

    Article  Google Scholar 

  20. Van Orden KA, Witte TK, Cukrowicz KC, Braithwaite SR, Selby EA, Joiner TE Jr. The interpersonal theory of suicide. Psychol Rev. 2010;117:575–600.

  21. Klonsky ED, May AM. The three-step theory (3ST): a new theory of suicide rooted in the ideation-to-action framework. Int J Cogn Therapy. 2015;8:114–29.

    Article  Google Scholar 

  22. O’Connor RC, Kirtley OJ. The integrated motivational–volitional model of suicidal behaviour. Philosophical Trans Royal Soc B: Biol Sci. 2018;373:20170268.

    Article  Google Scholar 

  23. Ribeiro JD, Joiner TE. The interpersonal-psychological theory of suicidal behavior: current status and future directions. J Clin Psychol. 2009;65:1291–9.

  24. Ma J, Batterham PJ, Calear AL, Han J. A systematic review of the predictions of the interpersonal–psychological theory of suicidal behavior. Clin Psychol Rev. 2016;46:34–45.

  25. Kleiman EM, Turner BJ, Fedor S, Beale EE, Huffman JC, Nock MK. Examination of real-time fluctuations in suicidal ideation and its risk factors: results from two ecological momentary assessment studies. J Abnorm Psychol. 2017;126:726.

    Article  PubMed  Google Scholar 

  26. Witte TK, Fitzpatrick KK, Warren KL, Schatschneider C, Schmidt NB. Naturalistic evaluation of suicidal ideation: variability and relation to attempt status. Behav Res Ther. 2006;44(7):1029–40.

    Article  PubMed  Google Scholar 

  27. Dhingra K, Boduszek D, O’Connor RC. Differentiating suicide attempters from suicide ideators using the integrated motivational–volitional model of suicidal behaviour. J Affect Disord. 2015;186:211–8.

    Article  PubMed  Google Scholar 

  28. Mars B, Heron J, Klonsky ED, Moran P, O’Connor RC, Tilling K, et al. What distinguishes adolescents with suicidal thoughts from those who have attempted suicide? A population-based birth cohort study. J Child Psychol Psychiatry. 2019;60:91–9.

    Article  PubMed  Google Scholar 

  29. Chan LF, Shamsul AS, Maniam T. Are predictors of future suicide attempts and the transition from suicidal ideation to suicide attempts shared or distinct: a 12-month prospective study among patients with depressive disorders. Psychiatry Res. 2014;220:867–73.

    Article  PubMed  Google Scholar 

  30. May AM, Klonsky ED, Klein DN. Predicting future suicide attempts among depressed suicide ideators: a 10-year longitudinal study. J Psychiatric Res. 2012;46:946–52.

  31. Teismann T, Forkmann T, Glaesmer H, Egeri L, Margraf J. Remission of suicidal thoughts: findings from a longitudinal epidemiological study. J Affect Disord. 2016;190:723–5.

    Article  PubMed  Google Scholar 

  32. Herzog S, Nichter B, Hill ML, Na PJ, Norman SB, Pietrzak RH. Factors associated with remission of suicidal ideation during the COVID-19 pandemic: a population-based, longitudinal study in US Military veterans. J Clin Psychiatry. 2022;83(4):41600.

    Article  Google Scholar 

  33. Batterham PJ, Poyser C, Gulliver A, Banfield M, Calear AL. Development and psychometric properties of the functioning and recovery scale: a new measure to assess psychosocial functioning after a suicide attempt. Suicide Life Threat Behav. 2020;50:1105–14.

  34. Larsen ME, Shand F, Morley K, Batterham PJ, Petrie K, Reda B, et al. A mobile text message intervention to reduce repeat suicidal episodes: design and development of reconnecting after a suicide attempt (RAFT). JMIR Mental Health. 2017;4:e7500.

  35. Batterham PJ, Walker J, Leach LS, Ma J, Calear AL, Christensen H. A longitudinal test of the predictions of the interpersonal-psychological theory of suicidal behaviour for passive and active suicidal ideation in a large community-based cohort. J Affect Disord. 2018;227:97–102.

    Article  PubMed  Google Scholar 

  36. Batterham PJ, Calear AL, van Spijker BA. The specificity of the interpersonal-psychological theory of suicidal behavior for identifying suicidal ideation in an online sample. Suicide Life Threat Behav. 2015;45:448–60.

  37. Batterham PJ, Christensen H, Calear AL. Anxiety symptoms as precursors of major depression and suicidal ideation. Depress Anxiety. 2013;30:908–16.

  38. Kazan D, Calear AL, Batterham PJ. The impact of intimate partner relationships on suicidal thoughts and behaviours: a systematic review. J Affect Disord. 2016;190:585–98.

    Article  PubMed  Google Scholar 

  39. Batterham PJ, Fairweather-Schmidt AK, Butterworth P, Calear AL, Mackinnon AJ, Christensen H. Temporal effects of separation on suicidal thoughts and behaviours. Social Sci Med. 2014;111:58–63.

    Article  Google Scholar 

  40. Hielscher E, DeVylder J, Saha S, Connell M, Scott J. Why are psychotic experiences associated with self-injurious thoughts and behaviours? A systematic review and critical appraisal of potential confounding and mediating factors. Psychol Med. 2018;48:1410–26.

  41. Batterham PJ, Ftanou M, Pirkis J, Brewer JL, Mackinnon AJ, Beautrais A, et al. A systematic review and evaluation of measures for suicidal ideation and behaviors in population-based research. Psychol Assess. 2015;27:501–12.

  42. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L, et al. The REDCap consortium: building an international community of software platform partners. J Biomedical Inf. 2019;95:103208.

    Article  Google Scholar 

  43. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomedical Inf. 2009;42:377–81.

    Article  Google Scholar 

  44. Johnston AK, Pirkis JE, Burgess PM. Suicidal thoughts and behaviours among Australian adults: findings from the 2007 National Survey of Mental Health and Wellbeing. Aust N Z J Psychiatry. 2009;43:635–43.

    Article  Google Scholar 

  45. Luoma JB, Martin CE, Pearson JL. Contact with mental health and primary care providers before suicide: a review of the evidence. Am J Psychiatry. 2002;159:909–16.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Thornton L, Batterham PJ, Fassnacht DB, Kay-Lambkin F, Calear AL, Hunt S. Recruiting for health, medical or psychosocial research using Facebook: systematic review. Intern Intervent. 2016;4:72–81.

  47. Van Spijker BA, Werner-Seidler A, Batterham PJ, Mackinnon A, Calear AL, Gosling JA, et al. Effectiveness of a web-based self-help program for suicidal thinking in an Australian community sample: randomized controlled trial. J Med Internet Res. 2018;20:e15.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Batterham PJ. Recruitment of mental health survey participants using internet advertising: content, characteristics and cost effectiveness. Int J Methods Psychiatric Res. 2014;23:184–91.

  49. Nylund KL, Asparouhov T, Muthén BO. Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct Equat Model Multidiscip J. 2007;14:535–69.

  50. Figueroa RL, Zeng-Treitler Q, Kandula S, Ngo LH. Predicting sample size required for classification performance. BMC Med Inform Decision Making. 2012;12:1–10.

  51. Franklin JC. Psychological primitives can make sense of biopsychosocial factor complexity in psychopathology. BMC Med. 2019;17:1–8.

  52. Lee Y, Ragguett R-M, Mansur RB, Boutilier JJ, Rosenblat JD, Trevizol A, et al. Applications of machine learning algorithms to predict therapeutic outcomes in depression: a meta-analysis and systematic review. J Affect Disord. 2018;241:519–32.

  53. Blades CA, Stritzke WG, Page AC, Brown JD. The benefits and risks of asking research participants about suicide: a meta-analysis of the impact of exposure to suicide-related content. Clin Psychol Rev. 2018;64:1–12.

    Article  PubMed  Google Scholar 

  54. Polihronis C, Cloutier P, Kaur J, Skinner R, Cappelli M. What’s the harm in asking? A systematic review and meta-analysis on the risks of asking about suicide-related behaviors and self-harm with quality appraisal. Archiv Suicide Res. 2022;26:325–47.

    Article  PubMed  Google Scholar 

  55. Batterham PJ, Calear AL, Carragher N, Sunderland M. Prevalence and predictors of distress associated with completion of an online survey assessing mental health and suicidality in the community. Psychiatry Res. 2018;262:348–50.

  56. Glenn CR, Kleiman EM, Kearns JC, Santee AC, Esposito EC, Conwell Y, et al. Feasibility and acceptability of ecological momentary assessment with high-risk suicidal adolescents following acute psychiatric care. J Clin Child Adolesc Psychol. 2022;51:32–48.

    Article  PubMed  Google Scholar 

  57. Stanley B, Brown GK. Safety planning intervention: a brief intervention to mitigate suicide risk. Cognitive Behav Prac. 2012;19:256–64.

  58. Madsen T, Van Spijker B, Karstoft K-I, Nordentoft M, Kerkhof AJ. Trajectories of suicidal ideation in people seeking web-based help for suicidality: secondary analysis of a Dutch randomized controlled trial. J Med Internet Res. 2016;18(6):e178.

    Article  PubMed  PubMed Central  Google Scholar 

  59. May A, Klonsky ED. Validity of suicidality items from the youth risk behavior survey in a high school sample. Assessment. 2011;18:379–81.

  60. Van Spijker BA, Batterham PJ, Calear AL, Farrer L, Christensen H, Reynolds J, et al. The suicidal ideation attributes scale (SIDAS): community-based validation study of a new scale for the measurement of suicidal ideation. Suicide Life Threat Behav. 2014;44:408–19.

    Article  PubMed  Google Scholar 

  61. Zanarini MC, Gunderson JG, Frankenburg FR, Chauncey DL. The revised diagnostic interview for borderlines: discriminating BPD from other axis II disorders. J Personal Disord. 1989;3:10–8.

    Article  Google Scholar 

  62. Rudd MD, Bryan CJ. The brief suicide cognitions scale: development and clinical application. Front Psychiatry. 2021;12:737393.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Van Orden KA, Cukrowicz KC, Witte TK, Joiner TE Jr. Thwarted belongingness and perceived burdensomeness: construct validity and psychometric properties of the interpersonal needs questionnaire. Psychol Assess. 2012;24:197–215.

  64. Holmes EP, Corrigan PW, Williams P, Canar J, Kubiak MA. Changing attitudes about schizophrenia. Schizophrenia Bullet. 1999;25:447–56.

  65. Ribeiro JD, Witte TK, Van Orden KA, Selby EA, Gordon KH, Bender TW, et al. Fearlessness about death: the psychometric properties and construct validity of the revision to the acquired capability for suicide scale. Psychol Assess. 2014;26:115–26.

  66. Meerwijk EL, Mikulincer M, Weiss SJ. Psychometric evaluation of the tolerance for mental pain scale in United States adults. Psychiatry Res. 2019;273:746–52.

  67. Schuster TL, Kessler RC, Aseltine RH Jr. Supportive interactions, negative interactions, and depressed mood. Am J Community Psychol. 1990;18:423–38.

    Article  CAS  PubMed  Google Scholar 

  68. Batterham PJ, Sunderland M, Carragher N, Calear AL, Mackinnon AJ, Slade T. The distress Questionnaire-5: population screener for psychological distress was more accurate than the K6/K10. J Clin Epidemiol. 2016;71:35–42.

    Article  PubMed  Google Scholar 

  69. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16:606–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Archiv Intern Med. 2006;166:1092–7.

  71. Sunderland M, Batterham PJ, Calear AL, Carragher N. The development and validation of static and adaptive screeners to measure the severity of panic disorder, social anxiety disorder, and obsessive compulsive disorder. Int J Methods Psychiatric Res. 2017;26:e1561.

    Article  Google Scholar 

  72. Prins A, Bovin MJ, Smolenski DJ, Marx BP, Kimerling R, Jenkins-Guarnieri MA, et al. The primary care PTSD screen for DSM-5 (PC-PTSD-5): development and evaluation within a veteran primary care sample. J Gen Intern Med. 2016;31:1206–11.

  73. Hielscher E, Connell M, Lawrence D, Zubrick SR, Hafekost J, Scott JG. Prevalence and correlates of psychotic experiences in a nationally representative sample of Australian adolescents. Aust N Z J Psychiatry. 2018;52:768–81.

    Article  Google Scholar 

  74. Yu L, Buysse DJ, Germain A, Moul DE, Stover A, Dodds NE, et al. Development of short forms from the PROMIS™ sleep disturbance and sleep-related impairment item banks. Behav Sleep Med. 2012;10:6–24.

    Article  Google Scholar 

  75. Kelly WE, Mathe JR. A brief self-report measure for frequent distressing nightmares: The Nightmare Experience Scale (NExS). Dreaming. 2019;29:180.

  76. World Health Organization. Wellbeing measures in primary health care/the DepCare Project: report on a WHO meeting: Stockholm, Sweden, 12–13 February 1998. World Health Organization. Regional Office for Europe; 1998.

  77. Fraser L, Burnell M, Salter LC, Fourkala E-O, Kalsi J, Ryan A, et al. Identifying hopelessness in population research: a validation study of two brief measures of hopelessness. BMJ Open. 2014;4:e005093.

  78. Monteiro RP, Coelho GLH, Hanel PH, de Medeiros ED, da Silva PDG. The efficient assessment of self-esteem: proposing the brief Rosenberg self-esteem scale. Appl Res Qual Life. 2022;17:931–47.

    Article  Google Scholar 

  79. Deeprose C, Holmes EA. An exploration of prospective imagery: the impact of future events scale. Behav Cogn Psychother. 2010;38:201–9.

    Article  PubMed  Google Scholar 

  80. Baker R, Thomas S, Thomas PW, Gower P, Santonastaso M, Whittlesea A. The emotional processing scale: scale refinement and abridgement (EPS-25). J Psychosom Res. 2010;68:83–8.

  81. Griffiths AW, Wood AM, Maltby J, Taylor PJ, Panagioti M, Tai S. The development of the short defeat and entrapment scale (SDES). Psychol Assess. 2015;27:1182–94.

  82. Ribeiro JD, Bender TW, Selby EA, Hames JL, Joiner TE. Development and validation of a brief self-report measure of agitation: the brief agitation measure. J Personality Assess. 2011;93:597–604.

  83. Wrosch C, Scheier MF, Miller GE, Schulz R, Carver CS. Adaptive self-regulation of unattainable goals: goal disengagement, goal reengagement, and subjective well-being. Personal Soc Psychol Bullet. 2003;29:1494–508.

  84. Schmidt NB, Richey JA, Fitzpatrick KK. Discomfort intolerance: development of a construct and measure relevant to panic disorder. J Anxiety Disord. 2006;20:263–80.

    Article  Google Scholar 

  85. Topper M, Emmelkamp PMG, Watkins E, Ehring T. Development and assessment of brief versions of the Penn state worry questionnaire and the ruminative response scale. Br J Clin Psychol. 2014;53:402–21.

    Article  PubMed  Google Scholar 

  86. Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA, Project ACQI. The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Archiv Intern Med. 1998;158:1789–95.

  87. Berman AH, Bergman H, Palmstierna T, Schlyter F. Evaluation of the drug use disorders identification test (DUDIT) in criminal justice and detoxification settings and in a Swedish population sample. Eur Add Res. 2004;11:22–31.

  88. Goodwin RD, Marusic A, Hoven CW. Suicide attempts in the United States: the role of physical illness. Soc Sci Med. 2003;56:1783–8.

  89. Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36): I. Conceptual framework and item selection. Med Care. 1992:473–83.

  90. Amtmann D, Cook KF, Jensen MP, Chen W-H, Choi S, Revicki D, et al. Development of a PROMIS item bank to measure pain interference. Pain. 2010;150:173–82.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Rickwood D, Deane FP, Wilson CJ, Ciarrochi J. Young people’s help-seeking for mental health problems. Aus EJ Advanc Mental Health. 2005;4:218–51.

  92. Herdman M, Gudex C, Lloyd A, Janssen M, Kind P, Parkin D, et al. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual Life Res. 2011;20:1727–36.

  93. Brugha TS, Cragg D. The list of threatening experiences: the reliability and validity of a brief life events questionnaire. Acta Psychiatrica Scandinavica. 1990;82:77–81.

    Article  CAS  PubMed  Google Scholar 

  94. Schirmer J. Carers in Regional Australia: 2016 Regional Wellbeing Survey Report. Canberra: Health Research Institute, University of Canberra; 2017.

    Google Scholar 

  95. Mundt JC, Marks IM, Shear MK, Greist JM. The work and social adjustment scale: a simple measure of impairment in functioning. Br J Psychiatry. 2002;180:461–4.

    Article  PubMed  Google Scholar 

  96. Eysenbach G. The law of attrition. J Med Internet Res. 2005; 7(1): e11. https://doi.org/10.2196/jmir.7.1.e11

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Acknowledgements

The authors would like to acknowledge the members of the LifeTrack Advisory Group for their feedback on the clinical protocol, recruitment materials, and survey question domains.

Funding

This study is funded through a peer-review process by the Australian National Health and Medical Research Council, GNT2014841. RB receives salary and research support from a NHMRC Emerging Leadership Investigator Grant (EL2; GNT2008073). HC is an Elizabeth Blackburn Fellow in Public Health GNT1155614. ALC is supported by an NHMRC Fellowship (1173146). AWS is supported by an NHMRC Fellowship (GNT1197074).

Author information

Authors and Affiliations

  1. Centre for Mental Health Research, The Australian National University, 63 Eggleston Road, Canberra, ACT, 2601, Australia

    Philip J Batterham, Monica Gendi, Alison L. Calear, Michelle Banfield, Cassandra Heffernan, Dominique Kazan & Louise M Farrer

  2. Black Dog Institute, University of New South Wales, Sydney, Australia

    Helen Christensen, Fiona Shand, Bridianne O’Dea, Mark Larsen, Aliza Werner-Seidler & Andrew J Mackinnon

  3. The Matilda Centre for Research in Mental health and Substance use, University of Sydney, Sydney, Australia

    Matthew Sunderland

  4. Melbourne School of Population and Global Health, Centre for Mental Health, The University of Melbourne, Melbourne, Australia

    Rohan Borschmann & Katherine M Boydell

  5. Department of Psychiatry, Medical Sciences Division, University of Oxford, Oxford, UK

    Rohan Borschmann

  6. Centre for Adolescent Health, Murdoch Children’s Research Institute, Melbourne, Australia

    Rohan Borschmann

  7. School of Population Health, Curtin University, Perth, Australia

    Rohan Borschmann

  8. Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia

    Mark Larsen

  9. School of Psychology, University of New South Wales, Sydney, Australia

    Aliza Werner-Seidler

  10. The Council of the Queensland Institute of Medical Research, Brisbane, Australia

    Emily Hielscher

  11. School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Australia

    Emily Hielscher

  12. Flourish Australia, Sydney, Australia

    Emily Hielscher

  13. Center for Global Health Equity, New York University Shanghai, Shanghai, China

    Jin Han

  14. National Centre for Epidemiology and Population Health, The Australian National University, Canberra, Australia

    Liana Leach

Authors
  1. Philip J Batterham
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  2. Monica Gendi
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  3. Helen Christensen
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  4. Alison L. Calear
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  6. Matthew Sunderland
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  7. Rohan Borschmann
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  8. Michelle Banfield
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  10. Mark Larsen
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  11. Cassandra Heffernan
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  12. Dominique Kazan
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  13. Aliza Werner-Seidler
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  14. Andrew J Mackinnon
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  15. Emily Hielscher
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  16. Jin Han
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  17. Katherine M Boydell
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  18. Liana Leach
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  19. Louise M Farrer
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Contributions

Lead investigator PJB conceptualised and designed the study, led the funding application and drafted the manuscript. MG contributed to the drafting of manuscript. HC, ALC, FS, MS, RB, MB, BO, ML, AWS, AJM, EH, JH, KMB, LL and LMF contributed to the funding application. All authors contributed to the study design and critically edited the manuscript.

Corresponding author

Correspondence to Philip J Batterham.

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Competing interests

The authors declare that they have no conflict of interest

Ethics approval and consent to participate

This study was approved by the Australian National University Human Research Ethics Committee (2022/851). Only participants who consent after reading the Information Statement will be recruited into the study.

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Not applicable.

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Batterham, P.J., Gendi, M., Christensen, H. et al. Understanding suicidal transitions in Australian adults: protocol for the LifeTrack prospective longitudinal cohort study. BMC Psychiatry 23, 821 (2023). https://doi.org/10.1186/s12888-023-05335-1

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Keywords

BMC Psychiatry

ISSN: 1471-244X

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