The basic self-harm incidence during the six-month follow-up was 29.1%, although this value was variable across prison and gender. The overall incidence rate recorded for males was 27.6%, which is more than double the self-harm incidence rate of 12.8% recorded among the general male prison population in 2017 (Ministry of Justice 2018a). This difference in rates would probably be expected, given the difference of study populations. For females, the overall incidence rate recorded during follow-up was 33.3%, which is not markedly higher than the self-harm incidence rate of 30% recorded among the general female prison population in 2017 (Ministry of Justice 2018a), suggesting that the ACCT population in female prisons may appear to be quite similar to the more general female prison population in terms of self-harm activity. It is speculated that, when compared to males, this closer similarity of female self-harm rates is due to a higher proportion of the total female prison population also falling into the corresponding ACCT population.

The primary aim of the study was to determine whether any pre-existing instruments could predict self-harm among an ACCT population. The AUC analysis that was carried out on the candidate instruments determined that none of these performed the task adequately enough to be considered a useful aid for prison staff to utilise as part of a standardised ACCT process. This finding has also been the case when using standardised measures to predict suicide following self-harm, where it has been warned that the use of these standardised scales, or an over-reliance on the identification of risk factors in clinical practice, may provide false reassurance that could be potentially dangerous (Chan et al. 2016).

With regard to the results obtained, it is acknowledged that a potential ‘risk paradox’ issue may also need to be considered: When an individual is identified as being at risk by one (or more) of the instruments that are being assessed, if risk is detected (especially in the case of self-harm risk), then generally something will be done in order to alleviate this risk in the individual. In turn, any element of risk reduction for a given individual may also reduce the probability of the final outcome occurring in the population of interest, thus interfering with any attempts to establish the predictive validity of the instruments that are being assessed. Although this issue may be present, in this instance it is unlikely to have had a major impact on the results as all study participants are from the prison-ACCT population, and are therefore already classified as being at an increased risk of self-harm.

A further potential limitation lies with the self-harm outcome data coming exclusively from prison records. This will likely lead to an under-ascertainment of self-harm events, as some self-harm remains self-managed and unreported. This has been previously observed (Borschmann et al. 2017), and it has been identified that self-harm may be more difficult than other clinical phenomena to measure accurately through medical records (Fliege et al. 2006). Although none of the pre-existing standardised instruments predicted the risk of self-harm in the ACCT population, an exploratory logistic regression revealed a set of items that may be useful when aggregated into a predictive algorithm, which could be used as a clinical decision aid to indicate risk of future self-harm. This risk factor approach has often been used to incorporate individual risk factors into composite scales to assess for the risk of suicide following self-harm (Chan et al. 2016), and these are commonly used in clinical practice, with a wide variety of scales being used across different healthcare settings (Quinlivan et al. 2014). In a prison setting, this approach has been used for the identification of inmates that carried out suicide (Blaauw et al. 2005). A similar approach has also been utilised in order to identify self-harm (self-injurious behaviour) in male prisoners (Lanes 2009) (Barton et al. 2014). These studies produced AUC values of 0.89 (Lanes 2009) and 0.91 (Barton et al. 2014), with 93% (Lanes 2009) and 87% (Barton et al. 2014) of cases correctly classified, both of which are superior to the values obtained in the present study. However, both of these studies used retrospective data to classify the difference between prisoners with and without a history of self-harm, whereas the current study used prospective data to classify whether self-harm occurred among an ACCT population during an active follow-up period.

An alternative option to assessing the predictive capacity of available data would be to utilise a machine learning approach, where it is possible to discover relevant structural and/or temporal patterns in complex data which are often hidden and inaccessible to the human expert (Holzinger 2016). Machine learning approaches can often outperform conventional statistical predictive modelling in predicting health outcomes (Song et al. 2004), although this is often at the expense of being able to derive an exclamatory, interpretable model (Tiffin and Paton 2018). Should a machine learning approach be adopted, it would be recommended that a human aspect should remain in any final decision-making process.

Some of the predictive items identified within the present study differ from those that have previously been reported as risk factors for self-harm. For example, one study focusing on female incarcerated adults reported shame, anger and child abuse as important (Milligan and Andrews 2005). Although child abuse was not addressed, shame was incorporated as a question in our study, but it did not appear to be predictive of future self-harm. Additionally a ‘cry of pain’ model (i.e. trauma of first weeks of imprisonment) has been presented as a predictor of early self-harm in a male prison population (Slade et al. 2012). This was successful at predicting self-harm (with a rate of 97.7%) but used eight separate questionnaires, which may be unfeasible for routine use in most prison settings where both the prison regime and high turnover of prisoners leads to significant time constraints. A further study identified several independent predictors for suicide including previous psychiatric service contact, history of self-harm, single cell occupation, remand status, and non-white ethnicity (Humber et al. 2013). In the present study, history of self-harm was predictive, but remand status and non-white ethnicity were not predictive of self-harm. Previous contact with a psychiatrist was predictive for males and females, but cell occupancy status was not determined.

Some of the items identified in the present study are particularly interesting. For example, the finding in the male sample that alcohol abuse works in a ‘protective’ manner is contrary to the existing evidence base in mainstream populations, where problematic alcohol use is recognised as a risk factor for self-harm (Ness et al. 2015). Although there are various possible explanations for these findings, it is recommended that these items are studied further within this setting.

An issue with all risk factor item sets that have been derived in this way, as is the case in the present study, is that although these item sets seem to work statistically, it is likely that the identified items involve an element of capitalisation on chance within the specific dataset that is used. Due to this restriction, it is vital that any of these risk factor items sets are revalidated prospectively. Another major issue with a lot of the scales that have been derived in this way are that they use solely retrospective data, and they are never further validated prospectively, meaning that along with the chance capitalisation, no process of causality can be assumed.

Additionally, the practical implementation of risk factor item sets may be limited for a number of reasons. The identified risk factors are often comparatively common in the populations of interest (Chan et al. 2016), meaning that an impractical amount of false negatives would be identified. Another issue with the item set identified in the present study is that many of the items are static in nature. These static items refer to background and lifetime information which cannot change once the item has been affirmed. For example, for the item ‘Have you ever cut yourself on purpose?’, then if this has been affirmed then this response is fixed as it cannot be ‘undone’. This impracticality has been previously highlighted (Völlm and Dolan 2009), where it has been identified that although these simple check lists may be useful to identify those at risk of self-harm upon prison reception, this risk is not static; therefore risk assessment has to be a continuous process and should not be restricted to reception screening.

If an actual incidence of self-harm has occurred in order to trigger initiation of the ACCT, it has been suggested that a comprehensive psychosocial assessment of the risks and needs that are specific to the individual should be central to the management of these people who have self-harmed (Chan et al. 2016). This may be a plausible approach following a self-harm event, or perhaps if a prisoner had been identified as being at high risk of self-harm, but considering the limited resources within the prison system, the use of comprehensive assessment instruments would not be feasible in day-to-day practise, especially when being used for early risk assessment at prison reception (Völlm and Dolan 2009).

The gender-specific predictive risk item sets identified in this study may be useful in this regard, as they offer the opportunity to classify three levels differing levels of risk that could be used at reception into prison. If the risk classification was medium or high, then a further in-depth assessment could be carried out, as has been previously recommended (Chan et al. 2016). Given the high negative predictive values, the predictive item sets appear to function better at screening out self-harm than screening it in. This could therefore be potentially useful to assist the ‘sign-off’ from an ACCT, if the clinician or ACCT team worker deemed it safe to do so. Although this is not the ideal intention, it could still help to save time and focus the limited resources that are available.

Despite an apparently limited predictive power, the implementation of a screening process that is specific to self-harm could certainly contribute to an increased awareness of self-harm and mental health issues amongst prison staff. It has been identified that 29% of prison staff have not received any ACCT training, and 82% have not received any training in mental health awareness (Ward and Bailey 2013). This is consistent with other reports of a lack of staff training and policy, along with an inconsistency in response to self-harm behaviour (Roe-Sepowitz 2006). Additionally, in over 20% of suicide cases, non-medical staff had documented signs of suicidality, but no referral or further action was taken (Fruehwald et al. 2003). This evidence leads to the critical point that an improvement in staff awareness and attitude, along with further training, are important factors which may help prevent self-harm and suicide in prisons (Hawton et al. 2014; Humber et al. 2011; Saunders et al. 2012). Although this staff awareness shortfall has been identified and is being addressed, it has been acknowledged that much work remains to be done (Forrester and Slade 2014).