prediction and network modelling of self-harm through
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See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/340512685
Prediction and network modelling of self-harm through daily self-report and
history of self-injury
Article in Psychological Medicine · April 2020
DOI: 10.1017/S0033291720000744
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PREDICTING INPATIENT SELF-HARM
Prediction and Network Modelling of Self-Harm Through Daily Self-Report and History of
Self-Injury
Michael J. Kyron1, Geoff R. Hooke
1,2, Andrew C. Page
1
1 School of Psychological Science, University of Western Australia.
2 Perth Clinic, West Perth, WA, Australia.
Correspondence concerning this article should be addressed to Michael Kyron, School of
Psychological Science, The University of Western Australia, Crawley, WA, Australia. Email:
Word Count: 4,500
PREDICTING INPATIENT SELF-HARM 1
Abstract
Background: Self-harm is a significant public health issue, and both our understanding and
ability to predict adverse outcomes are currently inadequate. The current study explores how
preventative efforts could be aided through short-term prediction and modelling of risk
factors for self-harm.
Methods: Patients (72% Female, Mage = 40.3 years) within an inpatient psychiatric facility
self-reported their psychological distress, interpersonal circumstances, and wish to live and
die on a daily basis during 3,690 unique admissions. Hierarchical logistic regressions
assessed whether daily changes in self-report and history of self-harm could predict self-
harm, with machine learning used to train and test the model. To assess interrelationships
between predictors, network and cross-lagged panel models were performed.
Results: Increases in a wish to die (β = 1.34) and psychological distress (β = 1.07) on a daily
basis were associated with increased rates of self-harm, while a wish to die on the day prior
(OR = 3.02) and a history of self-harm (OR = 3.02) was also associated with self-harm. The
model detected 77.7% of self-harm incidents (PPV = 26.6%, Specificity = 79.1%).
Psychological distress, wish to live and die, and interpersonal factors were reciprocally
related over the prior day.
Conclusions: Short-term fluctuations in self-reported mental health may provide an
indication of when an individual is at-risk of self-harm. Routine monitoring may provide
useful feedback to clinical staff to reduce risk of self-harm. Modifiable risk factors identified
in the current study may be targeted during interventions to minimize risk of self-harm.
Keywords: Non-suicidal self-injury, suicide, perceived burdensomeness, psychological
distress, wish to die.
PREDICTING INPATIENT SELF-HARM 2
Prediction and Network Modelling of Self-Harm Through Daily Self-Report and History of
Self-Injury
Self-harm encapsulates a set of behaviours which involve an individual deliberately
and directly causing harm to themselves (Nock et al., 2006). It is seen to be a behavioural
manifestation of heightened distress, and represents efforts to escape, regulate negative
emotional states, and communicate psychic pain (Taylor et al., 2018). Self-harm includes
incidents of non-suicidal self-injury (NSSI), which involves harm to one’s self without an
attempt to die; and suicidal behaviours, which involve intent to die. Despite differences
between these sets of behaviours, NSSI is a strong risk factor for future suicide attempts
(Muehlenkamp & Gutierrez, 2007; Ribeiro et al., 2016). Self-harm can be persistent, with an
individual capable of engaging in hundreds of self-injurious incidents and up to 16% of those
who self-harm have been estimated to continue the behaviour repeatedly (Hawton et al.,
2003; Muehlenkamp & Kerr, 2010). If left unchecked self-harm can also become persistent,
in some cases with incidents occurring for up to 10 years (Lilley et al., 2008).
Self-harm is a considerable burden to health care systems, and continues to be a
leading cause of death worldwide (Kinchin et al., 2017). Despite the attention it has received
in recent years, rates of death due to self-harm have risen in some nations (Australian Bureau
of Statistics, 2018; National Center for Health Statistics, 2018). There are two limitations of
research to date which serve as the basis for the current study: first, prediction of self-harm in
longitudinal research has been poor, which limits the ability to prevent incidents; and second,
there is a limited knowledge of evidence-based targets for interventions when risk of self-
harm may be evident. The current study looks to address both limitations through a short-
term longitudinal assessment of dynamic risk factors for self-harm in a clinical setting.
Self-harm is prevalent among general and psychiatric populations and is a leading
cause of preventable death internationally (James et al., 2012). It is estimated that over 90%
PREDICTING INPATIENT SELF-HARM 3
of those who self-harm meet the criteria for a mental health disorder (Haw et al., 2001).
Psychiatric populations therefore are a particularly high-risk population, which presents
difficulties in monitoring and preventing self-harm within psychiatric hospitals. The period
following discharge is also associated with heightened risk of suicide among inpatients,
which highlights the importance of directing preventive efforts towards targeted patients
(Gunnell et al., 2012). An advantage of an inpatient setting is that at-risk patients are already
within care, which provides an opportunity for targeted intervention.
The efficient allocation of clinical resources is hampered by limited methods to
prospectively predict who will self-harm and when it will occur, with risk assessments by
clinicians often being over-inclusive (Belsher et al., 2019; Kessler, 2019; Woodford et al.,
2019). Risk assessments using self-report measures are a cost-effective method to predict
adverse outcomes; however, there is a shortage of research that presents refined and effective
methods to predict self-harm. A recent meta-analysis by Large et al. (2018) assessed the
accuracy of risk categorization in predicting psychiatric inpatient suicide. They found the
positive predictive value of risk categorization over six cohort studies to be an unremarkable
0.43%, indicating that 1 out of 230 inpatients predicted to self-harm would do so before
discharge. Likewise, other clinical studies have noted poor ability to discriminate between
those who engage in NSSI or suicide attempts and those who do not when using isolated risk
assessments (Saunders et al., 2014; Waern et al., 2010), particular when history of prior self-
harm is not evident (Roaldset et al., 2012). Such disappointing findings over the past decade
have lead commentators to conclude that risk assessments are fraught (Large et al., 2011).
Basing clinical decisions on inaccurate risk assessments can profoundly impact clinical
resources and create restrictive and intrusive conditions for patients unlikely to self-harm
during their stay.
PREDICTING INPATIENT SELF-HARM 4
There are inherent difficulties in predicting self-harm due to variability in the daily
experiences of patients (Kleiman et al., 2017). Self-harm in many instances likely eventuates
due to predispositions to engage in self-harming behaviours (e.g., low distress tolerance, poor
emotional regulation) and day-to-day negative experiences or stressful triggers (Fliege et al.,
2009; Nock, 2009). Indeed, increases in interpersonal adversity, such as conflict with friends
and family, negative affect, and suicidal ideation have been shown to increase prior to self-
harm (Armey et al., 2011; Kashyap et al., 2015; Kyron et al., 2018; Muehlenkamp et al.,
2009). It is difficult to capture such variability through isolated measurement (Bryan & Rudd,
2016), and efforts to reduce self-harm within inpatient settings may benefit from routine
monitoring of dynamic risk factors over time.
Routine monitoring has become increasingly prominent within psychiatric settings,
providing valuable information regarding self-reported changes in mental health throughout
treatment (Boswell et al., 2015). However, its integration into clinical research to enhance
prediction of self-harm among psychiatric populations has been minimal (Czyz et al., 2018;
Plener et al., 2015). Early research suggests benefits in its implementation to predict increases
in risk of patient self-harm over short-term periods. A recent study by Kyron et al. (2018)
tracked inpatients’ suicidal thoughts and interpersonal circumstances on a daily basis, finding
short-term increases in all factors predicted a notable proportion of self-harm events. In
addition, a daily diary study of a small sample of individuals with a history of NSSI found
daily sadness to predict incidents of self-harm (Bresin et al., 2013). Short-term changes in a
combination of mood, interpersonal, and escape-related correlates of self-harm may therefore
be useful in the prediction of outcomes and assist in identifying patients with difficulties
coping with their current circumstances. This follows theoretical perspectives from the
Interpersonal Theory of Suicide (Joiner, 2005), which suggests that interpersonal adversity
generates significant psychological distress as to drive suicidal thoughts and self-injurious
PREDICTING INPATIENT SELF-HARM 5
behaviours. Specifically, it suggests two dynamic interpersonal constructs are central:
perceived burdensomeness, which reflects beliefs that an individual is a burden to those
around them; and thwarted belongingness, which encapsulates feelings of isolation and a
perceived lack of support from others. Interpersonal adversity has exhibited cyclical
relationships with symptoms of poor mental health over short-term periods, which may have
important implications for preventing positive feedback loops prior to self-harm (Kyron et al.,
2018; Rogers & Joiner, 2019).
The current study assesses the effectiveness of routine monitoring in predicting
incidents of self-harm within an inpatient setting. The study conducts daily assessments of
patients using a brief, but diverse daily index capturing psychological factors linked to
suicide and self-harm in prior research [i.e., interpersonal adversity, wish to live and die, and
psychological distress; (Bryan et al., 2016; O'Connor et al., 2012)]. Building on prior
ecological research, we expect: (1) changes in self-reported psychological distress and
interpersonal circumstances to predict incidents of self-harm; (2) a history of self-harm to be
associated with an increased risk of future self-harm; and (3) interpersonal, mood factors, and
a wish to live/die to be reciprocally related over time.
Method
Participants and Procedure
The current study was conducted as part of an ongoing assessment and treatment of
inpatients at a 100 bed psychiatric facility in Perth, Western Australia. Patients were
presented with the opportunity to self-report their mental health on a daily basis. Daily
response rates were high (78%) and questionnaires were completed at consistent times daily,
with an average of roughly 2.8 hours difference from day-to-day (SD = 1.55). Patients were
selected for inclusion in the study if they had a length of stay of five days or over to allow
sufficient time to collect self-report and self-harm data. In total, data from 3740 patients were
PREDICTING INPATIENT SELF-HARM 6
selected for the study. Patients were referred to the facility by their physician to receive
specialized treatment for a range of mental health issues regardless of a history of self-harm.
That is, patients were not referred to the facility solely due to self-injurious behaviours, and
the sample reflects a diverse range of psychopathology.
Information surrounding self-harm events was provided by clinical staff, who logged
reports regarding each incident as part of routine reporting undertaken for risk management
purposes within the facility. Staff outlined the nature of the incident, the time it occurred, the
outcome (i.e., transferred to external medical hospital, minor intervention), and perceived
intent (i.e., suicidal or non-suicidal). When a patient who self-harmed completed self-report
measures, they were completed in the hours prior to self-harm incidents in the majority of
cases (88.6%), with the remaining proportion occurring after an incident had occurred
(11.4%). Consent for the data to be used for research purposes was provided by patients at
admission, and all procedures were approved by the University of Western Australia’s
Human Research Ethics Committee.
Measures
Thwarted Belongingness. Belongingness was measured by summing two items, “In
the past 24 hours, I have felt that people care for me” and “In the past 24 hours, I have felt
close to others”. Both items aimed to capture sub-constructs of belongingness (i.e., perceived
support and loneliness, respectively) and have shown good predictive qualities in prior
research (Kyron et al., 2018; Kyron et al., 2019). Responses were reverse scored so that
higher scores indicated a greater sense of thwarted belongingness. Items were adapted from
the Interpersonal Needs Questionnaire [INQ; (Van Orden et al., 2012)], and were measured
on a 7-point Likert-type scale (1 = Not true for me at all, 7 = Very True for me). These items
were selected based on their strong factor loadings in clinical samples (Van Orden et al.,
2012) and had acceptable internal consistency in the current study ( = 0.78).
PREDICTING INPATIENT SELF-HARM 7
Perceived Burdensomeness. Two items in total were used to measure perceived
burdensomeness. One question assessed global feelings of burden, “In the past 24 hours, I
have felt like a burden”, and a second assessed perceptions of liability, “In the past 24 hours,
I have felt like my death would be a relief to people”. These items were taken from the INQ,
and were measured on a 7-point Likert-type scale (1 = Not true for me at all, 7 = Very True
for me). Item scores were combined, with higher scores representing higher perceived
burden. Both items had acceptable internal consistency in the current study ( = 0.74).
Psychological distress. Psychological distress was measured through the use of the
five item daily index (DI-5) developed by Dyer, Hooke, and Page (2014). The scale measures
feelings of anxiety, depression, worthlessness, suicidal ideation, and difficulty coping, with
each item measured on a 6-point Likert-type scale (0 = At no time, 5 = All the time). Higher
scores on the scale indicate higher psychological distress. The scale has shown strong
psychometric properties in prior research (Dyer et al., 2014), and good internal consistency in
the current study ( = 0.86).
Wish to live and wish to die. Single items were taken from the Scale for Suicide
Ideation (Beck et al., 1979) to measure patients’ wish to live and wish to die in the 24 hours
prior, measured on a four-point Likert-type scale (0 = None, 1 = Weak, 2 = Moderate, 3 =
Severe). Wish to live was reverse coded, with higher scores representing a lower wish to live.
There was a medium-strong correlation between the two variables in the current study (r =
0.64).
Analytical Approach
Missing responses. Although daily response rates were high, missing data is expected
in intense longitudinal research. In total, 13.7% of self-report data included in analyses were
missing. An analysis of these patients found no systematic difference between their
demographic and clinical characteristics when performing Little’s Test of Missing
PREDICTING INPATIENT SELF-HARM 8
Completely at Random (χ2 = 2.54, df = 4, p = 0.11; Little, 1988). Multiple imputation, using
fully conditional specification, was used to estimate missing responses on self-report
measures using SAS Version 9 software. Fully conditional specification has been shown to
produce low-bias estimates of missing data, accommodate for non-normality and non-
linearity, and also handle categorical and continuous missing data (van Buuren, 2007).
Analyses were performed on the ten imputed datasets, and regression coefficients and
prediction statistics were subsequently pooled. In total, 50 cases were excluded from analyses
due to no available self-report data, and were found not to differ in terms of age, length of
stay, sex, or primary diagnosis from the entire sample. Although the rate of missingness was
low, analysis results when excluding participants with missing responses have been reported
in the supplementary material, with no substantial differences identified.
Predicting self-harm from day-to-day. For patients who self-harmed, self-report
data were selected on the day of an incident (T2) and the day prior (T1). For the comparison
group (i.e., patients who did not self-harm), data were selected from the point they completed
self-report measures two days in a row (T1 and T2). Hierarchical logistic regressions were
performed to assess relationships between daily self-report, stable patient characteristics (i.e.,
prior self-harm, primary diagnosis), and self-harm. The first model assessed the predictive
utility of self-report at T1, while the second model added changes in self-report from T1 to
T2. This was to assess whether changes in circumstances could be used to alert staff of
potential increased risk of self-harm. The third and fourth models saw the inclusion of
demographic and clinical information, respectively. These sets of information were included
after self-report due to a desire to assess whether self-report measures on their own could
predict self-harm.
Training the model. Evaluating a model requires testing its parameters on a new
dataset. Often a model is trained or developed based on a large proportion of a dataset and
PREDICTING INPATIENT SELF-HARM 9
tested for predictive capabilities on the remaining portion. However, bias may be evident in
the data randomly selected for training and testing (Kuhn & Johnson, 2013). A solution is to
use k-fold cross-validation, which divides the dataset randomly into “k” approximately
equally-sized parts. The model is trained on k-1 parts, and tested on the remaining data. This
process is repeated until all parts have acted as a test set once. In the current study, repeated
cross validation was used, consistent with prior research (Kuhn & Johnson, 2013). This
procedure was performed using the caret package in R. The caret package was designed to
simplify the process of conducting supervised machine learning by identifying optimal model
hyperparameters to enhance prediction of outcomes. This reflects the benefits of machine
learning approaches over the standard use of regression, with an emphasis on maximising
prediction, rather than interpretability.
Evaluating model performance. A number of statistics were computed to assess
model fit at each step: sensitivity, the ability to detect a self-harm event; specificity, the
ability to determine whether a patient did not self-harm; positive predictive value, the ratio of
correctly identified self-harm incidents to falsely detected events; and negative predictive
value (NPV), the ratio of patients accurately predicted not to self-harm, and those falsely
predicted not to self-harm. Each statistic is a function of the probabilities of an event
produced by the model, and the cut-off used for classification of an event. Reductions in
probabilities tends to be associated with increases in sensitivity (which is of benefit to clinical
practice), although it comes at a cost of increased rates of false positives (Royston & Altman,
2010). After training the model on 70% on the total sample, optimal cut-off points for
balanced classification of events (i.e., high sensitivity and specificity) were defined on a
small validation portion of the total sample (10%). The model was then tested on a larger test
proportion (20%) using these pre-defined cut-off points. Standard logistic regression was also
PREDICTING INPATIENT SELF-HARM 10
performed to assess whether using machine learning to find optimal parameters impacted
prediction accuracy.
Temporal relationships between predictors. Cross-lagged panel models were
performed to assess associations between self-report measures across the two days of
assessment. The cross-panel model controls for stability in factors through the inclusion of
autoregressive relationships, and is regarded to be an effective method to assess “causal”
relationships between variables prone to change (Hamaker et al., 2015). Several panel models
were performed to assess how interpersonal factors were associated with aspects of mental
health (i.e., psychological distress, wish to live, and wish to die). An additional model
assessed relationships between psychological distress, wish to live, and wish to die. Separate
models were performed to assess how specific interpersonal factors readily targetable through
therapeutic interventions may affect change in other related mental health outcomes, with the
inclusion of correlated outcomes in the same model potentially acting to obscure associations
of interest. All models were performed using Mplus Version 8 software.
Cross-sectional network models were performed (using JASP software) to assess
simultaneous relationships between self-report measures and self-harm. A mixed graphical
model estimator was used to accommodate for categorical and continuous variables
(Haslbeck & Waldorp, 2015). Network models allow for patterns of covariance to be
examined under the assumption that factors can mutually influence each other (van Zyl,
2018). Least Absolute Shrinkage and Selection Operator regularization was used to shrink
small weights to zero to avoid issues of multiplicity, and represent partial correlations that
control for the influence of other factors when determining associations between two
variables. Nodes represented self-reported risk factors, while edges (lines) represent partial
correlation weights between two variables. Three statistics were calculated to assess
importance of variables to the network: degree, the extent to which a node is connected to all
PREDICTING INPATIENT SELF-HARM 11
other nodes; closeness, how well a node is indirectly connected to other nodes; and
betweenness, how important a node is to the average path between two nodes (Epskamp et
al., 2018). To assess stability of the model to sampling variability, 1000 bootstrap samples
were drawn.
Results
Sample Characteristics
Descriptive statistics of the sample have been reported in Table 1. Of note, patients in
the sample were predominantly female and not in a relationship, with the highest proportion
of patients having a primary diagnosis of an affective disorder. The majority of self-harm
events were not associated with an explicit intent to die as per staff reports. However, over
60% of all cases required a medical response from inpatient nursing staff (e.g., applying
dressing over wounds).
Prediction of Self-Harm using Daily Self-Report
Hierarchical logistic models were fit to the data to assess whether self-report measures
and patient characteristics could predict self-harm. Bivariate correlations between predictors
have been reported in Table 2. In Model 1, perceived burdensomeness (β = 1.07, 95% CI =
1.01 – 1.13, p = .015), psychological distress (β = 1.04, 95% CI = 1.00 – 1.08, p = .033), and
a wish to die on the first day of assessment (T1) significantly predicted next day self-harm
(Table 3). In particular, a strong wish to die was associated with higher odds of self-harm on
the next day (OR = 2.71, 95% CI = 1.17 – 6.29, p < .023). In Model 2, increases in
psychological distress (β = 1.08, 95% CI = 1.02 – 1.13, p = .003) and a wish to die (β = 1.38,
95% CI = 1.10 – 1.73, p < .001) from one day to the next (T2-T1) significantly predicted self-
harm. In the final model, younger (β = 0.95, 95% CI = 0.94 – 0.97, p < .001) and female
patients (OR = 1.42, 1.16 – 1.72, p < .001), and also those with a history of self-harm within
the facility (OR = 3.02, 95% CI = 2.22 – 4.10, p < .001), had significantly higher rates of
PREDICTING INPATIENT SELF-HARM 12
self-harm. Multicollinearity, as measured through variance inflation factor and tolerance
statistics, was found to be within acceptable limits (Hair et al., 2010).
Model Performance
Each step was associated with improvements in model performance (Table 3).
Specifically, area under the curve scores increased with each model, indicating an increased
ability to discriminate between patients who self-harm and those who do not. The model
detected the majority of self-harm incidents (77.7%), and correctly identified when 79.1% of
patients would not self-harm. While a PPV of 26.6% was modest, it indicated that the model
still produced a high number of false-positives. On the other hand, negative predictive value
was high (97.3%), suggesting that the model rarely incorrectly specified that a patient would
not self-harm. The final model showed marginally poorer predictive qualities when standard
logistic regression was performed (i.e., without using machine learning), with slightly lower
sensitivity (71.4%), specificity (77.8%). and positive predictive value (23.9%) statistics.
Relationships between Dynamic Variables and Self-Harm
Temporal relationships between predictors. Cross-lagged panel models identified
various reciprocal relationships between variables across the two days of assessment (Figure
1). Of note, perceived burdensomeness was reciprocally related to psychological distress,
wish to die, and wish to live, indicating they may influence each other day-to-day
(Supplementary Table 1). Thwarted belongingness, on the other hand, had unidirectional
relationships with psychological distress (B = 0.13, β = 0.06, p < .001) and a wish to die (B =
0.01, β = 0.04, p < .001), and a bidirectional relationship with wish to live. As expected,
psychological distress, wish to die, and wish to live were all reciprocally related from T1 to
T2.
A network model assessed associations between self-reported factors, measured at T2,
and self-harm (Figure 2; Panel A). As expected there were strong associations between a
PREDICTING INPATIENT SELF-HARM 13
wish to live, a wish to die, and psychological distress. Wish to die had the highest strength
(0.76), closeness (1.01), and betweenness (1.87) statistics, indicating strong direct and
indirect associations with other variables within the network (Supplementary Table 2). In
addition, only a wish to die (r = 0.08) and wish to live (r = 0.04) were associated with self-
harm in the model. Both burdensomeness (r = 0.20) and belongingness (r = 0.20) were
associated with a wish to live, while only burdensomeness had a notable associated with a
wish to die (r = 0.24). In addition, psychological distress was strongly associated with both
burdensomeness (r = 0.35) and a wish to die (r = 0.41). A second model included prior self-
harm, which was associated with current self-harm (r = 0.20) and a wish to die (r = 0.04)
(Figure 2; Panel B). Network patterns were found to be identical regardless of whether
individuals self-harmed or not, indicating consistent associations between variables.
Discussion
The current study explored the effectiveness of psychological factors and prior self-
harm in prospectively predicting self-harm over short-periods. A key finding was that
assessing a small index of items on a daily basis assisted in detecting a high number of self-
harm incidents, and suggests various risk factors may be important targets for interventions.
Specifically, daily increases in a wish to die and psychological distress were associated with
an increased risk of self-harm. These findings are consistent with those from Kyron et al.
(2018) and Bresin et al. (2013) who found daily changes in suicidal ideation and negative
mood significantly predicted self-harm. The current study synthesizes the findings from both
studies, and evaluates their effectiveness within a notably larger sample. These findings may
reflect difficulties in regulating emotions, with self-harm being a behavioural manifestation
(Anestis et al., 2014).
The current study found a wish to live and die, and psychological distress were
centrally related to self-harm and interpersonal adversity. A wish to die, like self-harm, may
PREDICTING INPATIENT SELF-HARM 14
represent a desire to escape from psychological pain, and therefore it is unsurprising that the
association between these variables was stronger in the current study (Baumeister, 1990). A
concurrent wish to live may represent an ambivalence between living and dying that may be
an important aspect to consider in future self-harm research (Bryan et al., 2016). Changes in a
wish to die and psychological distress were also significant proximal predictors of self-harm
incidents, which suggests their potential benefit to prediction. Interventions which increase
distress tolerance and problem solving may be an effective means to minimize risk of self-
harm amidst periods of heightened distress (Joiner et al., 2009).
Interpersonal factors were interrelated with changes in a wish to live and die, and
psychological distress from one day to the next in the current study. This is consistent with
research identifying reciprocal daily relationships between thwarted belongingness, perceived
burdensomeness, and suicidal ideation (Kyron et al., 2018; Rogers & Joiner, 2019). Thwarted
belongingness and perceived burdensomeness represent specific interpersonal cognitions
which can be targeted during interventions to minimize psychological distress and associated
escape related thoughts and behaviours (Joiner & Van Orden, 2008; Joiner et al., 2009). On
the other hand, targeting psychological distress directly may be difficult, as identifying
underlying causes may be require substantive exploration. Based on findings from the current
study, fostering positive interpersonal relationships may strengthen a wish to live, and also
reduce psychological distress and a wish to die linked to self-harm (Bryan et al., 2016).
A significantpredictor of self-harm in the current study was a history of self-harm
within the facility, consistent with prior research (Muehlenkamp & Gutierrez, 2007; Ribeiro
et al., 2016). Identifying prior self-harm may help in flagging patients with difficulties
regulating emotions when confronted with daily stressors. It is important to note that the
current study relied on a historical database of self-harm incidents within the facility, rather
than self-report. While this in part overcomes the issue of socially desirable responding
PREDICTING INPATIENT SELF-HARM 15
evident in self-report (Podlogar & Joiner, 2019), it prevents identification of self-harming
behaviour prior to initial admissions at the facility or between visits. Regardless, the findings
indicate that daily stressors and history of self-harm may assist in flagging at-risk patients.
A consistent problem with predictive models of self-harm is a low detection rate or a
high number of false positives. The current study was able to detect the majority of self-harm
incidents on the day they occurred. While the PPV in the current study was above that
identified in some prior studies, there was still a large amount of false positives. Within a
clinical setting, false positives represent a relatively small proportion of overall patients when
assessed daily. Patients falsely predicted to self-harm may none-the-less benefit from support,
and could also be at-risk of future self-harm. Preventative efforts within a clinical setting may
benefit from sensitive, rather than specific models in order to ensure that those at risk are
identified and receive immediate care. The costs of not detecting self-harm both inside and
outside of clinical settings (i.e., litigation, medical expenses) are significantly greater than the
cost of providing support to an individual who may not be at-risk of self-harm (Kessler,
2019). Daily monitoring provides insights into who and also when incidents may occur, and a
real time alert system may be of benefit to timely interventions (Whipple et al., 2003).
The current study assessed associations between primary diagnoses and risk of self-
harm. Compared to a primary diagnosis of an affective disorder, no significant associations
were identified between other diagnoses and future self-harm. An important caveat is that
information regarding comorbid diagnoses were not available in the current study, and may
be an important confounding factor. For instance, concurrent borderline personality and
anxiety disorder symptoms have been linked to increased rates of self-harm when compared
to either alone (Turner et al., 2015). Future research into prediction and understanding of self-
harm may benefit from the evaluation of comorbid diagnoses.
Limitations and Directions for Future Research
PREDICTING INPATIENT SELF-HARM 16
There are several limitations to the current research. Firstly, the use of brief measures
to assess cognitive and emotional risk factors may not have fully captured aspects of a
patient’s mental health, although they have shown to be strong related to more
comprehensive measures in prior research (Dyer et al., 2014). Second, there was some
inconsistency with regard to when a patient completed self-report questionnaires. The
majority of patients answered questionnaires at similar times each day, yet, differences in the
periods between assessments may have impacted results. Third, self-harm events occasionally
occurred before self-report measures were completed (11.4%). However, Armey et al. (2011)
found negative affect tended to remain heightened for several hours following self-harm,
which suggests the findings from the current study still hold merit. Fourth, it is difficult to
determine the intent behind some self-injurious behaviour, and reliance on nurse’s reports
may have led to the incorrect classification of non-suicidal self-injury. In addition, self-harm
may have gone undetected within the facility, which may understate predictive qualities of
the model. Where suicide attempts were explicitly made aware, however, the model was
effective in predicting each case.
The current study assessed a combination of proximal (i.e., daily self-reported
interpersonal adversity) and distal risk factors (i.e., history of self-harm) for self-harm.
However, other factors may prove useful in prediction, particular when there is limited
information available to clinicians upon preliminary admissions. For instance, distress
tolerance measured at admission may increase predictive capabilities by indicating patients
at-risk of relying on maladaptive self-harming behaviours as a means to regulate their
emotions (Anestis et al., 2013). This may capture information not acquired by clinical staff
during initial appointments, particular with patients hesitant to open up to their diagnosing
physician.
Conclusion
PREDICTING INPATIENT SELF-HARM 17
The current study suggests routine monitoring of patients may prove useful in
identifying short-term fluctuations in mood and interpersonal circumstances linked to an
increased risk of self-harm. Such information may not be available through isolated risk-
assessments, and could account for the inadequate capabilities of predictive models in prior
research. Further, keeping comprehensive records of self-harm incidents within psychiatric
facilities may help in predicting those at-risk in the future. These findings may be
generalizable to high-risk psychiatric settings that lend themselves to routine monitoring, and
should be replicated among the wider population. Despite promising signs, more research is
needed to aid in efforts to reduce the ever-present issue of self-harm.
Financial Support: This research was supported in part by an ARC Linkage Grant (LP
150100503) and the Young Lives Matter Foundation - UWA.
Conflicts of Interest: None.
PREDICTING INPATIENT SELF-HARM 18
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Table 1
Demographic characteristic of the total sample.
Total sample (N = 3,690)
Frequency or Mean Proportion or SD
Sex –
Male 1023 27.72%
Female 2667 72.28%
Marital Status –
Single 1832 49.65%
Divorced/Separated/Widowed 528 14.31%
De Factor/Married 1330 36.04%
Age 40.03 (SD = 16.96)
Length of Stay 18.82 (SD = 12.07)
Diagnosis (ICD Classification) –
Adult personality disorder 227 6.20%
Mood affective disorders 1982 52.97%
Behavioural disorder 25 0.86%
Neurotic, stress-related 818 21.95%
Organic 4 0.13%
Other 9 0.26%
Disorders of psychological development 4 0.11%
Schizophrenic 177 4.83%
Substance Disorder 434 11.67%
Information not available 11 0.39%
Type of Self-Harm –
Non-Suicidal Self-Injury 319 97.0%
Suicide Attempt 10 3.03%
Incident Risk* –
Low 112 34.04%
Moderate 171 51.98%
High 46 13.68%
Incident Outcome –
No Intervention 111 33.7%
Minor Intervention or Medical Assessment 193 58.7%
Enhanced Observations 13 4.0%
Transferred to medical facility or discharged early 12 3.6%
PREDICTING INPATIENT SELF-HARM 28
Note. SD = Standard Deviation. Incident Risk = likelihood of another incident and severity. * Incident risk as per Riskman software
guidelines is a judgement made by nursing staff based on the severity, intent, and number of self-harm incidents for an individual.
PREDICTING INPATIENT SELF-HARM 29
Table 2
Correlations between predictors and self-harm.
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. Mean (SD) Mix Max Skew Kurt
1. Self-Harm (current visit) 1 - - - - - - - - - - - - - - - -
2. Prior Self-Harm 0.27 1 - - - - - - - - - - - - - - -
Day Prior (T1)
3. Perceived Burdensomeness 0.15 0.19 1 - - - - - - - - - 8.34 (3.93) 2 14 -.18 -1.14
4. Thwarted Belongingness 0.10 0.13 0.49 1 - - - - - - - - 7.24 (3.21) 2 14 .33 -.81
5. Psychological Distress 0.16 0.21 0.71 0.44 1 - - - - - - - 13.85 (6.47) 0 25 -.25 -.83
6. Wish to Live (R) 0.14 0.19 0.61 0.45 0.60 1 - - - - - - 1.31 (0.93) 0 3 .01 -.99
7. Wish to Die 0.18 0.19 0.69 0.39 0.75 0.64 1 - - - - - 1.21 (1.11) 0 3 .36 -1.24
Change over 24 hours (T2-T1)
8. Perceived Burdensomeness 0.08** 0.03 ns -0.18 0.00 ns -0.03** -0.01 ns -0.01** 1 - - - - -0.50 (1.98) -12 10 -.47 3.38
9. Thwarted Belongingness 0.02 ns -0.02 ns -0.05 -0.34 -0.05 -0.04** -0.03 0.15 1 - - - 0.04 (2.32) -12 10 -.09 1.99
10. Psychological Distress 0.11 0.05 ns 0.02 ns 0.03* -0.18 0.05ns -0.01** 0.30 0.17 1 - - -1.38 (3.60) -20 19 -.40 2.35
11. Wish to Live (R) 0.07ns -0.01 ns -0.06 -0.03** -0.08 -0.33 -0.06 0.18 0.16 0.20 1 - -0.07 (0.69) -3 3 -.01 2.63
12. Wish to Die 0.09* 0.03ns -0.08 -0.03** -0.14 -0.08 -0.32 0.23 0.13 0.37 0.21 1 -.13 (0.63) -3 3 -.32 2.99
Note. (R) = Reverse coded wish to live score, with higher scores indicating a lower wish to live. Fifty cases excluded due to missing variables. Unless otherwise noted, correlation significant at p < .001. ns = non-significant at p = .05, * p < .05, ** p
< .01.
PREDICTING INPATIENT SELF-HARM 30
Table 3
Hierarchical logistic regressions predicting self-harm using daily self-report.
Standardized Regression Weights or Odds Ratios
Model 1
Self-Report (T1)
Model 2
With ∆Self-Report
Model 3
With Demographics
Model 4
With Prior Self-Harm & Diagnosis
Self-Report Measures at T1 –
Perceived Burdensomeness 1.07 (1.01 – 1.13)* 1.05 (0.96 – 1.08) 1.02 (0.96 – 1.09) 1.01 (0.95 – 1.09)
Thwarted Belongingness 1.01 (0.97 – 1.06) 0.99 (0.98 – 1.07) 0.99 (0.94 – 1.04) 0.99 (0.93 – 1.04)
Psychological Distress 1.04 (1.00 – 1.08)* 1.05 (1.00 – 1.08)* 1.04 (1.00 – 1.09)* 1.04 (0.99 – 1.08)
Wish to Live –
Strong 1 (ref) 1 (ref) 1 (ref) 1 (ref)
Moderate 1.49 (0.64 – 3.48) 1.52 (0.60 – 3.84) 1.36 (0.50 – 3.69) 1.27 (0.46 – 3.48)
Weak 1.47 (0.52 – 4.19) 1.47 (0.50 – 4.98) 1.26 (0.34 – 4.71) 1.14 (0.30 – 4.40)
None 1.77 (0.63 – 4.99) 1.71 (0.53 – 5.51) 1.22 (0.35 – 4.27) 0.98 (0.26 – 3.73)
Wish to Die –
None 1 (ref) 1 (ref) 1 (ref) 1 (ref)
Weak 2.26 (1.29 – 3.96)** 2.51 (1.42 – 4.41) ** 2.14 (1.91 – 3.82) * 2.06 (1.14 – 3.70) ***
Moderate 2.14 (1.02 – 4.51)* 2.57 (1.17 – 5.64) * 2.14 (0.94 – 4.88) 2.47 (0.91 – 4.96)
Strong 2.71 (1.17 – 6.29)* 3.73 (1.54 – 9.03) ** 3.17 (1.25 – 8.05)* 3.02 (1.13 – 8.08)***
Change over next 24 hours (T2-T1) –
Perceived Burdensomeness - 1.07 (0.96 – 1.19) 1.07 (0.97 – 1.19) 1.07 (0.97 – 1.19)
Thwarted Belongingness - 0.99 (0.92 – 1.08) 0.99 (0.90 – 1.09) 1.00 (0.91 – 1.10)
Psychological Distress - 1.08 (1.02 – 1.13)** 1.07 (1.01 – 1.11)* 1.07 (1.00 – 1.14)*
Wish to Live - 1.16 (0.92 – 1.46) 1.09 (0.86 – 1.39) 1.07 (0.84 – 1.36)
Wish to Die - 1.38 (1.10 – 1.73)*** 1.38 (1.08 – 1.76)* 1.34 (1.02 – 1.77)*
Age - - 0.95 (0.94 – 0.96)*** 0.95 (0.94 – 0.97)***
Sex –
Male - - 1 (ref) 1 (ref)
Female - - 1.55 (1.28 – 1.87)*** 1.42 (1.16 – 1.72)***
Marital Status –
Married/De Facto - - 1 (ref) 1 (ref)
Divorced/Separated - - 1.54 (0.81 – 2.94) 0.65 (0.34 – 1.22)
Single - - 1.70 (0.90 – 3.20) 0.87 (0.59 – 1.30)
Prior Self-Harm - - - 3.02 (2.22 – 4.10)***
Diagnosis –
Affective Disorder - - - 1(ref)
PREDICTING INPATIENT SELF-HARM 31
Neurotic Disorder - - - 1.20 (0.88 – 1.63)
Behavioural syndromes - - - 1.47 (0.44 – 4.91)
Personality Disorder - - - 1.02 (0.68 – 1.56)
Substance Use - - - 0.49 (0.22 – 1.08)
Schizophrenia - - - 0.97 (0.46 – 2.02)
Model Performance Statistics –
AUC 0.75 0.78 0.83 0.85
Sensitivity 66.8% 66.5% 76.8% 77.7%
Specificity 65.2% 70.3% 71.8% 79.1%
PPV 15.8% 17.6% 21.0% 26.6%
NPV 95.4% 95.6% 96.9% 97.3%
Note.* p < .05, ** p < .01, *** p < .001. Significant relationships are boldfaced. ∆ = Change over 24 hours. T1 = Day prior to a self-harm incident or the first
day of assessment of comparison group. PPV = Positive Predictive Value. NPV = Negative Predictive Value. AUC = Area Under Curve. Primary diagnoses
with insufficient cell sizes (i.e., less than 5) have not been included in the model. Model performance represents prediction on the test portion (20%) of the fully
sample. Predictive performance of logistic regression without machine learning: Sensitivity = 71.4%, Specificity = 77.8%, PPV = 23.9%, NPV = 96.5%, AUC =
0.84.
PREDICTING INPATIENT SELF-HARM 32
Figure 1. Cross-lagged panel model assessing associations between psychological risk factors across the two
days of assessment (T1 & T2). Grey bi-directional lines indicate reciprocal relationships between variables from
T1 to T2, while black dotted arrows indicate unidirectional relationships. Black curved arrows indicate
significant autoregressive correlations.
PREDICTING INPATIENT SELF-HARM 33
A
B
Figure 2. Two network models assessing cross-sectional relationships between risk factors at T2. Model A
assesses relationships between self-report measures and self-harm, while Model B includes prior self-harm.
Thicker and darker lines indicate stronger relationships.
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