San Antonio, Texas-based Zachry Industrial is an engineering and construction firm serving the refining, chemical, power, and pulp and paper industries. The Maintenance and Reliability Services Department at Zachry supports clients in the reliability, availability, and maintenance of their plant assets. Over the years, Zachry reliability engineers working in client process plants have observed that failure rates can abruptly increase due to unintended and unrecognized changes in repair quality, or operating or process conditions.
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The changed failure rates were slow to be recognized, often only after several additional failures had occurred. Zachry engineers then undertook to recognize failure rate trends at the very earliest time, so that negative failure trends could be immediately turned around. All new failures trigger an automatic analysis so that results with statistical significance are known prior to repair.
This permits the data analysis to influence inspection and repair plans. This immediate and selective intervention of reliability degradation allows elimination of failures that otherwise would occur. Conventional failure time analysis methods are slow to detect abrupt shifts in failure rates and require larger datasets than what are often available, so new methods were developed. One method uses Poisson distribution in reverse to identify failure times that do not fit the distribution. Probability values p-values quantify the likelihood that failure times are unusual relative to random variation.
These assets can range from large machines to small instruments, so there can be tens of thousands of individual assets within a manufacturing plant. While the total database is huge, each individual asset dataset can be extremely small. Confidence intervals show how wrong a value may be. Every maintenance action request for a particular asset triggers extraction of historical data for that asset.
How to Predict and Prevent Potential Risks
Using prior maintenance action dates, Poisson p-values are automatically generated. This textbook expectation is exactly the opposite of what is needed to identify the unusual special cause failures of interest, so the Poisson is used in reverse as a null hypothesis distribution. Low Poisson p-values suggest failure times are unlikely to be random variation from the textbook expectation; therefore, they are likely to be special cause failures that should be investigated.
This is done by forming probability distributions around the p-values. Of course this is physically impossible, but is easily done by computer simulation through RISK. Failure times dates are used to determine the time between failures TBF. The identification and management of risk for future violence has become an increasingly important component of psychiatric practice. In the UK, conducting risk assessments on psychiatric patients has become part of routine practice in general adult psychiatric settings and most NHS Trusts mandate the use of specific tools.
It is likely that this figure has since risen, but no recent audit data is available. In forensic settings, national guidance requires high and medium secure service providers to conduct a HCR History — Risk — Clinical on all patients. Again, no data is available regarding the compliance with this requirement, although given the inclusion of risk assessment in Commissioning for Quality and Innovation targets in these settings completion rates are likely to be high.
Despite this widespread implementation of risk assessment, driven largely by public concern, it remains uncertain which factors are associated with violence and how to best assess risk. To complicate matters further, risk assessment is not just a scientific or clinical endeavour, but carries a significant political dimension — which level of risk is acceptable even if it can be identified accurately and how to weigh the consequences of false positive and false negative when it is predicted that violent and aggressive behaviour will not occur, but it does assessments is ultimately for society as a whole to decide.
The review protocol summary, including the review questions and the eligibility criteria used for this chapter, can be found in Table 7 risk factors and Table 8 prediction instruments. A complete list of review questions can be found in Appendix 5 ; information about the search strategy can be found in Appendix 10 ; the full review protocols can be found in Appendix 9.
Clinical review protocol summary for the review of risk factors. Clinical review protocol summary for the review of prediction. The review of risk factors was restricted to prospective cohort studies that used multivariate models to look for independent risk factors. The review strategy primarily involved a meta-analysis of odds ratios for the risk of violence for each risk factor or antecedent.
Additionally, results from studies that examined the correlation between multiple factors and violence reported as R 2 or Beta are presented alongside the meta-analysis. Studies only presenting data from univariate analyses unadjusted results were excluded from the review. Additionally, sensitivity and specificity were plotted using a summary receiver operator characteristic ROC curve. It is the probability of an uncertain outcome occurring caused by a combination of factors risk factors that — if known — offer a chance to intervene to prevent the outcome from happening.
In addition to the likelihood of the negative event occurring, how soon it is likely to occur and the expected severity of the outcome are important considerations. Static risk factors are historical and do not change, such as family background, childhood abuse or seriousness of offending. Age and gender also fall within this category. Dynamic risk factors, on the other hand, are changeable and hence offer the opportunity for intervention. Examples include current symptoms, use of alcohol or illicit substances and compliance with treatment. Risk assessment involves the identification of risk factors and an estimation of the likelihood and nature of a negative outcome while risk management puts in place strategies to prevent these negative outcomes from occurring or to minimise their impact.
A large body of literature exists on risk factors for violence, including in individuals with mental disorders Bo et al. The largest of these Witt et al. The authors found that risk factors had been examined in these studies. In line with findings from other studies, criminal history was found to be the strongest static risk factor. While the factors identified by Witt and colleagues are based on a large body of evidence, it is of note that considerable heterogeneity exists in the samples studied with regards to the nature of the violence, the way in which the outcome was measured and the clinical settings involved.
Failings in the care provided to mentally ill individuals have been highlighted by a number of high profile cases of mentally ill patients committing serious acts of violence and subsequent inquiries into their care in the s 2. Since then, mental health practise in the UK has seen an increased focus on risk and guidance has been produced to aid the process of risk assessment and management Department of Health, ; Royal College of Psychiatrists, These documents stipulate that each patient's risk should be routinely assessed and identify a number of best practice recommendations.
The Department of Health best practice guidance outlines the following as key principles in risk assessment: This formulation should be discussed with the service user and a plan of action produced as to how to manage the risks identified. Tool-based assessments as outlined below should form part of a thorough and systematic overall clinical assessment. It is suggested that given the fluidity of risk, its assessment should not be a one-off activity but should be embedded in everyday practice and reviewed regularly.
For the purposes of this review, risk factors and antecedents were categorised using the psychosocial and clinical domains described by Witt and colleagues Amore Amore et al.
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Of these, all 13 were published in peer-reviewed journals between and In addition, studies failed to meet eligibility criteria for the guideline. Further information about both included and excluded studies can be found in Appendix Of those, 5 involved adult participants in an inpatient setting and 2 involved adult participants in a community setting.
Table 9 contains a summary of the study characteristics of these studies. Of the 6 studies not included in the analysis, 3 Ehmann , Kay , Kho reported no usable data, and 3 Oulis , Palmstierna , Yesavage reported statistics that made synthesis with the other studies very difficult. However, the latter 3 studies used very small samples ranging from 70 to and therefore the results from these studies are not included here as it was felt they would not be useful for making recommendations. Summary of study characteristics for the review of risk factors for violence and aggression in adults.
As can be seen in Table 10 , which shows the demographic and premorbid factors in the multivariate model for each study, only 2 factors age and gender were commonly included. Demographic and premorbid factors included in the multivariate model for each study. In 2 studies of adults in community settings Hodgins , UK , there was evidence that was inconsistent as to whether age was associated with the risk of violence in the community.
In 1 study of adults in an inpatient setting Watts , there was evidence that African ethnicity was associated with a reduced risk of violence, but the evidence was inconclusive as to whether African—Caribbean ethnicity was associated with a reduced risk. In 1 study of adults in community settings UK , there was evidence that non-white ethnicity was associated with an increased risk of violence. In a sub-sample of women, there was evidence that African—Caribbean ethnicity was associated with an increased risk of violence in the community.
In 1 study of adults in the community UK , there was inconclusive evidence as to the association between previous residence in supported accommodation and the risk of violence in the community. In 1 study of adults in community settings UK , there was evidence that history of being victimised was associated with an increased risk of violence but the association was inconclusive for history of homelessness, marital status and past special education.
In a sub-sample of women, there was evidence that unmet needs and history of being victimised were associated with an increased risk of violence in the community. In the inpatient setting, no criminal history factors were included in more than 1 study, and in the community setting, only 1 factor lifetime history of violence was included in both studies Table Criminal history factors included in the multivariate model for each study. In 1 study of adults in the community Hodgins , there was inconclusive evidence regarding whether the presence of a conduct disorder was associated with an increased risk of violence in the community.
In inpatient settings, in 1 study of adults Amore there was evidence that recent past month and lifetime history of physical aggression and recent verbal or against object aggression were associated with an increased risk of violence on the ward. However, the evidence was inconclusive as to whether a history lifetime of verbal or against object aggression was associated with the risk of violence.
In 1 study of inpatients Watts , there was evidence that violence in the 24 hours prior to admission was unlikely to be associated with violence on the ward. In 1 study of adults in community settings UK , there was evidence that a history of physical aggression was associated with increased risk of violence, and in the subsample of women, there was evidence that a conviction for non-violent offense was associated with an increased risk of violence in the community.
Psychopathological, positive symptom and negative symptom factors included in the multivariate model for each study. In 1 study of adults in inpatient wards Chang , there was evidence that later onset of a psychotic disorder was associated with an increased risk of violence on the ward. In 1 study of adult inpatients Amore , there was inconclusive evidence as to whether a mood disorder anxiety or depression was associated with an increased risk of violence on the ward.
In 1 study of adults in community settings Hodgins , there was inconclusive evidence as to whether the presence of anxiety was associated with an increased risk of violence in the community. In 2 studies of adults in inpatient settings Amore , Watts , 1 study was inconclusive, but the other found evidence that hostility-suspiciousness was associated with an increased risk of violence on the ward.
In 1 study of adults in inpatient wards Amore , there was inconclusive evidence as to whether a thought disturbance, the presence of tension or excitement or lethargy were associated with an increased risk of violence. In the inpatient setting, only 2 factors duration of hospitalisation and number of previous admissions were included in more than 1 study, and in the community setting, no factors were included in both studies Table Treatment-related factors included in the multivariate model for each study. In 2 studies of adult inpatients Chang , Cheung , there was evidence that duration of hospitalisation was not associated with an increased risk of violence on the ward.
In 1 study of adults in the community UK , there was inconclusive evidence as to whether longer duration of hospitalisation was associated with an increased risk of violence in the community.
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In contrast, referral by the doctor with regular responsibility for the service user was associated with a reduced risk. In the inpatient setting, no substance misuse factors were included, and in the community setting, recent drug use was the only factor and this was included in both studies Table Substance misuse factors included in the multivariate model for each study. In 2 studies of adults in community settings Hodgins , UK , there was evidence that indicated an association between recent past 6 or 12 months drug use and the risk of violence in the community.
In the inpatient setting, no suicidality factors were included, and in the community setting, previous attempted suicide was the only factor and this was included in only 1 study Table Suicidality factors included in the multivariate model for each study. One study of adults in the community UK examined previous attempted suicide as a potential risk factor for violence, but the evidence was inconclusive.
Identification of risk factors for violent and aggressive behaviour by mental health service users in health and community care settings may lead to better prediction of incidents of violence and aggression and has therefore potentially important resource implications.
However, this review question is not relevant for economic analysis. Prediction is the cornerstone of the assessment, mitigation and management of violence and aggression. The prediction of violence and aggression is challenging due to the diversity of clinical presentation and it is unlikely that a single broad predictive assessment tool could be valid and reliable in all circumstances where violence and aggression needs to be predicted. This is not surprising given that the prevalence of violence and aggression varies considerably in different clinical settings; the prevalence would vary markedly between the community, an inpatient psychiatric ward and a forensic setting.
Furthermore, the baseline prevalence of what one is trying to predict is important when considering the utility of the prediction tool.
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Fundamentally, the process of prediction requires 2 separate assessments. The application of the prediction tool constitutes the first assessment, and categorises the patient into a lower or higher risk of exhibiting the future behaviour one is interested in predicting. Further down the line, the second assessment concludes whether the patient did or did not exhibit the behaviour of interest. As an instrument, the prediction tool's statistical properties are relevant in assessing its clinical utility.
False positives when the prediction tool identifies that violence and aggression will occur, but it does not are especially troublesome in this respect, as they can lead to unnecessarily restrictive clinical interventions for the patient. False negatives when the prediction tool identifies that violence and aggression will not occur, but it does can have serious consequences for the patient, clinicians and potential victims of the violence or aggression.
In reality there is a balance between true and false predictions, which needs to be equated with the consequences thereof. Translating this process into the clinical or research setting is difficult. The majority of violence and aggression risk assessment tools prediction tools are not designed to be completed in minutes to allow for rapid screening, and, if they are designed to be completed expeditiously, they often incorporate a phase of retrospective monitoring of behaviour.
The behaviour of interest is violence and aggression, and there is a complex and often unclear relationship between the variables in risk assessment tools, the process of conducting a risk assessment, and the occurrence further down the line, of violence and aggression. An interesting example in this area is the idea that the mere process of conducting a risk assessment may change the probability of future violence and aggression, by either better structuring the ongoing clinical care of the patient or by changing their clinical pathway for example, to a more secure clinical setting Abderhalden et al.
With such obstacles to prediction of violence and aggression, the question is raised of whether accurate prediction is even possible. Yet in mental health and criminal justice settings, and increasingly in the wider health and social care setting, there is anecdotal evidence that violence and aggression is a major factor inhibiting the delivery of effective modern day services. Currently there is a genuine drive to achieve parity between mental and physical healthcare for patients in the health and social care system.
Given that violence and aggression is often associated with a clinical psychiatric emergency, 1 way to raise the profile of the management of violence and aggression may be to consider it to be on a par with more classical medical and surgical emergencies that clinicians encounter in the general hospital setting. In inpatient psychiatric settings, early detection and intervention with people at risk of behaving aggressively is crucial because once the aggression escalates, nurses are left with fewer and more coercive interventions such as sedation, restraint and seclusion Abderhalden et al.
In this sense, early detection has implications for a more therapeutic and safer patient and staff experience. Clinical experience and research has led to a plethora of identified violence and aggression risk variables static, dynamic, patient-related, environmental , which provide the predictive input for risk assessment tools. The utility of predictive risk assessment tools can only be as good as the robustness of the violence and aggression risk variables. In this guideline, the focus is on the evaluation of predictive risk assessment tools and their utility in the prediction of imminent violence and aggression.
Prediction instruments actuarial and structured clinical judgement can be used to assign service users to 2 groups: In this context, an actuarial assessment is a formal method to make this prediction based on an equation, a formula, a graph, or an actuarial table. Structured professional and clinical judgement involves the rating of specified risk factors that are well operationalised so their applicability can be coded reliably based on interview or other records.
Based on this, clinical judgement is used to come to a decision about risk, rather than using an established algorithm Heilbrun et al. In addition, the risk factors included in a prediction instrument can be static or dynamic changeable , and it is the latter that are thought to be important in predicting violence in the short-term Chu et al.
There is a long history of research demonstrating that unaided clinical prediction is not as accurate as structured or actuarial assessment Heilbrun et al. The behaviour being predicted could range from verbal threats to acts of aggression directed at objects or property to physical violence against other service users or staff. When evaluating prediction instruments, the following criteria were used to decide whether an instrument was eligible for inclusion in the review:. The qualities of a particular tool can be summarised in an ROC curve, which plots sensitivity expressed as a proportion against 1-specificity.
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See Chapter 3 for further information about the methodology used for this review. Abderhalden Abderhalden et al. All were published in peer-reviewed journals between and Of the 10 eligible studies, 6 Abderhalden , Abderhalden , Almvik , Chu a, McNiel , Yao included sufficient data to be included as evidence. As the reference standard, 3 studies Abderhalden , Abderhalden , Almvik used the SOAS-R or a modification of this to record all violent and aggressive incidents in the shift following the index test. Two studies Chu a, McNiel used the OAS, and violence data and preventive measures were concurrently collected from nursing records and case reports by 1 study Yao Data were available for 2 actuarial prediction instruments: See Table 16 for further information about each instrument.
Summary of characteristics for each included prediction instrument. All studies reported below had generally a low risk of bias except for the domain covering the reference standard, which was assessed by staff who also completed the instrument being investigated see Appendix 11 for further information. For comparison, 1 study of adults in an inpatient setting that evaluated unstructured clinical judgement is included here. When doctors and nurses independently agreed about the risk, the sensitivity was 0.
When doctors and nurses did not agree, the sensitivity was 0. Forest plot of sensitivity and specificity for instruments used to predict violence in the short-term. Summary ROC curve for the prediction of violence in the short-term. No studies assessing the cost effectiveness of prediction instruments for violent and aggressive behaviour by mental health service users in health and community care settings were identified by the systematic search of the economic literature.
Details on the methods used for the systematic review of the economic literature are described in Chapter 3.