| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Psychiatric Bulletin | Advances in Psychiatric Treatment | All RCPsych Journals |
|
|
|
||||||||||
|
||||||||||||
Duke University Medical Center, Durham, North Carolina
University of Virginia, Charlottesville, Virginia, USA
Correspondence: Dr Eric Elbogen, Duke University Medical Center, DUMC 3071, Durham, NC 27710, USA. Tel: +1 919 682 8394; email: eric.elbogen{at}duke.edu
Declaration of interest None. Funding detailed in Acknowledgements.
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
|---|
Aims To explore the link between community violence and patients’beliefs about psychiatric treatment benefit.
Method A sample of 1011 adults receiving out-patienttreatment for a psychiatric disorder in the public mental health systems of five US states were interviewed.
Results Bivariate analyses revealed community violence was inversely related to treatment adherence, perceived treatment need and perceived treatment effectiveness. Multivariate analyses showed these three variables were associated with reduced odds of violent and other aggressive acts.
Conclusions The results suggest clinical consideration of patients’ perceptions of treatment benefit can help enhance violence risk assessment in psychiatric practice settings.
|
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
|---|
|
|
METHOD |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
|---|
At the Worcester, Tampa and San Francisco sites, potential participants were recruited sequentially in the waiting rooms of out-patient clinics of the community mental health centres. In Durham a list of potentially eligible individuals was created from management information system data, and patients were randomly selected to be approached for participation in the study. The Chicago site used both sampling methods, enrolling about half the sample using the waiting-room approach and the other half using the eligibility-list approach. Participants were enrolled after receiving a complete description of the study and providing written informed consent. All sites received approval from their respective institutional review boards. Refusal rates varied from 2% to 13% across sites. A single structured interview, lasting about 90 min, was administered in person by a trained lay interviewer. Participants were paid US$25 for the interview.
Sample characteristics
Consistent with the core paper from this study
(Monahan et al,
2005), we report the cross-site range of means and proportions for
these characteristics, i.e. the highest and lowest values across the five
sites. The mean age of participants ranged from 41.3 to 46.7 years. Between
24.6% and 41.1% of respondents reported having less than a high school
education and between 12.5% and 24.5% of respondents were married or
cohabiting. The proportion from Black and minority ethnic groups ranged from
28.5% to 64.0%, and the proportion of male participants ranged from 32.4% to
64.5%.
Regarding clinical characteristics, between 41.5% and 49.5% of respondents had a chart diagnosis of schizophrenia or another psychotic disorder, between 14.4% and 17.6% had a diagnosis of bipolar disorder and between 27.5% and 30.7% had major depression. Rates of substance abuse comorbidity ranged from 13.9% to 35.5% between sites, while mean scores on the Brief Psychiatric Rating Scale and the Global Assessment of Functioning (see below) ranged from 31 to 33 and 18 to 19 respectively across the sites. Between 30.2% and 38.2% of respondents indicated that they had not adhered to treatment during the past 6 months. Personality disorder diagnoses ranged from 13% to 26% across sites. Between 47.6% and 63.3% of respondents reported four or more lifetime hospitalisations. Finally, between 25.5% and 47.6% of respondents reported recognising the need for mental health treatment, and between 43.4% and 54.4% of respondents reported positive benefits from recent mental health treatment.
Measures
Violence and other aggressive acts
We used the MacArthur Community Violence Interview
(Monahan et al, 2000;
Steadman et al, 2000;
Monahan, 2002) to measure
violent and aggressive behaviour at three levels of severity:
This operationalisation of community violence corresponds to the concept of violence employed in the MacArthur Violence Risk Assessment Study and to other studies of violence among people with mental illness (Swanson et al, 2006).
Perceived treatment effectiveness
Commentators have noted two important and distinct dimensions of perceived
treatment benefit (Perkins,
2002). The first is perceived treatment effectiveness, which was
measured using the Consumer Satisfaction Questionnaire
(Ganju, 1999) assessed with
four items that were summed and dichotomised above the median; those
responding in the negative to two or more of these four questions served as
the reference group and were coded as 0. Items from this questionnaire
included, ‘As a direct result of the services I received, (a) I deal
more effectively with daily problems, (b) I am better able to control my life,
(c) I am getting along better with my family, and (d) my symptoms are not
bothering me as much’ (Ganju,
1999). Teague et al
(1997) describe the reliable
use of this scale to measure patients’ views of treatment
effectiveness.
Perceived treatment need
A second facet of perceived treatment benefit is a patient’s
perceptions of treatment need, which in this study was measured using
questions from the National Institute of Mental Health Epidemiologic Catchment
Area (ECA) study section on perceived barriers to care
(Blazer et al, 1985).
Participants were asked about reasons for not attending mental health
treatment care via three items that were summed and dichotomised above the
median; those responding in the affirmative to any of these three questions
served as the reference group and were coded as 0. The questions were,
‘You think that going for help probably wouldn’t do any
good’; ‘You think the [mental health] problem might get better by
itself’ and ‘You want to solve the [mental health] problem on your
own’. Research on violence and arrests in mental disorders confirm good
psychometric properties on employing the ECA study section on perceived
barriers to care in order to measure patients’ beliefs about the need
for psychiatric treatment (Elbogen et
al, 2006).
Demographic characteristics
Demographic variables included: age (reference group 44 years or younger),
education (reference group high school or beyond), married or cohabiting
(reference group single), ethnic status (reference group White) and gender
(reference group female).
Clinical factors
Psychiatric diagnosis was based on chart diagnoses at the mental health
centres. This analysis compares psychotic disorder with affective disorders as
well as the presence or absence of an Axis II personality disorder. The
anchored version of the Brief Psychiatric Rating Scale (BPRS;
Woerner et al, 1988)
was used to assess current psychiatric symptoms and the Global Assessment of
Functioning scale (GAF; Endicott et
al, 1976; American
Psychiatric Association, 1994) was used to score overall
functioning, with low scores indicating more severe functional impairment.
Treatment adherence was measured by the question, ‘In the past 6 months,
were there times when you thought you should go to a doctor or clinic for
mental health or alcohol or drug problems, but did not go?’ (0
nonadherent, 1 adherent). Age at onset of the disorder and the number of
lifetime hospitalisations were included in the model as well. All of these
factors were dichotomised above the median to capture non-linear
associations.
Substance misuse
Substance misuse was assessed with questions adapted from the CAGE
questionnaire (Allen et al,
1988). This consists of four questions asking whether people felt
they needed to cut down on their drinking, were annoyed by people complaining
about their drinking, felt guilty about drinking, and if they need an
eye-opener in the morning. These same four questions were also asked in
relation to drug use. For these analyses we combined alcohol and drug misuse
into a single dichotomous variable, coded 1 for one or more substance misuse
symptoms and 0 for no symptoms.
Statistical analysis
We used logistic regression to examine the associations between
participants’ demographic and clinical characteristics and the
likelihood of engaging in any physically assaultive behaviour, in addition to
other aggressive acts and violence in the past 6 months. For the purpose of
multivariable modelling, pooling the data across sites offered the advantage
of greater statistical power, but also posed two problems that required
adjustment in the analyses. First, we had to account for site effects and
site-by-covariate interactions associated with violence. To examine and
control for these site effects, we used Zelen’s test of the homogeneity
of odds ratios (Zelen, 1971;
StatXact, 2003: pp.
511–517). The Zelen statistic allowed us to test the null hypothesis
that the relative risk for the multiple measures of violence did not vary
across the five sites, but represented a sampling distribution from a common
population. If Zelen’s test showed the sites’ odds ratios for a
given variable were homogeneous, we then pooled the data for that variable and
calculated a common odds ratio across sites. The second problem was that
pooling the data could have distorted statistical inferences, insofar as the
observations within each site were not independent. Without an adjustment for
the clustered nature of the data, the standard errors around the pooled
estimates would have been understated, leading to overly liberal tests of
statistical significance. Accordingly, we used the same specialised
statistical software (StatXact,
2003) to adjust significance tests and confidence intervals around
the common (pooled) odds ratios.
For multivariable analysis we used a companion statistical package designed to conduct multivariable logistic regression with stratified data (LogXact, 2002: pp. 83–103). These techniques provided the appropriate correction of variance estimates, taking into account within-site correlation of observations. Specifically, the software uses the Cochran–Armitage method, as adapted by Rao & Scott (1992), to adjust the ‘effective sample size’ for design effects that occur with a clustered sample (LogXact, 2002: pp. 755, 774).
Finally, we dichotomised independent variables at the median to meet statistical assumptions of normal distribution and to allow a more informative classification of respondents in terms of the presence or absence of relevant characteristics of perceived treatment benefit that might be associated with violence. Along these lines, we appeal to the argument developed by Farrington & Loeber: ‘Dichotomized variables do not contain inherently less information than scales; it all depends on the relative number of variables of each type and on the accuracy of measurement’ (Farrington & Loeber, 2000: p. 107). These authors advocate dichotomising non-linear explanatory variables because this allows a risk- and protective-factor approach to the analysis, interpretation and presentation of data on crime and violence, which is consistent with the goals of our study.
|
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
|---|
|
We first examined bivariate associations between the three levels of violence and a range of salient demographic and clinical variables. Next, multivariable associations were tested using logistic regression procedures. The multivariable models were conducted in three stages: first, the domain of demographic characteristics was assessed; then the clinical characteristics were combined with the demographic characteristics; finally, the factors assessing perceived need for and benefits from recent treatment were added to the model along with the other two domains. All models also controlled for site and the clustering of observations within site.
Table 2 displays the results for the composite measure of any physically assaultive behaviour. In the demographic domain there was a significant and negative bivariate relationship between age and any physically assaultive behaviour (OR=0.52, P<0.001), whereas marital status (OR = 1.69, P<0.01) was positively related to any physically assaultive behaviour. In the clinical domain, significant and negative bivariate associations were present for treatment adherence (OR=0.31, P<0.001), psychotic diagnosis (OR=0.44, P<0.001) and GAF score (OR=0.67, P<0.05), whereas substance misuse (OR = 2.42, P<0.001), personality disorder (OR=1.59, P<0.01) and BPRS score (OR=1.90, P<0.001) were positively associated with any physically assaultive behaviour. Finally, both perceptions of the effectiveness of treatment (OR=0.48, P<0.001) and the need for treatment (OR=0.33, P<0.001) were negatively associated with any physically assaultive behaviour.
|
In the final multivariable model (stage 3), age was negatively associated with any physically assaultive behaviour (OR=0.58, P<0.01). Clinically, treatment adherence (OR=0.51, P<0.01) and having a psychotic diagnosis (OR=0.47, P<0.001) were negatively associated with the outcome, whereas substance misuse (OR = 1.97, P<0.001) was positively associated with the same. With respect to perceived treatment benefit, violence was negatively associated with both perceived treatment effectiveness (OR=0.69, P<0.05) and perceived treatment need (OR=0.59, P<0.01).
To assess the constancy of these findings across different levels of violence severity, we also modelled both serious violence and other aggressive acts. Bivariate associations were virtually identical to those described above for any physically assaultive behaviour. In the multivariable model assessing serious violence, age (OR=0.39, P<0.05), having a psychotic diagnosis (OR=0.44, P<0.05) and perceiving the need for treatment (OR=0.44, P<0.05) were negatively associated with violence. In the multivariable model assessing other aggressive acts, age (OR=0.62, P<0.05), treatment adherence (OR=0.53, P<0.01), having a psychotic diagnosis (OR=0.43, P<0.001) and substance misuse (OR=1.91, P<0.01) were significantly associated. Additionally, there was a significant and negative association between other aggressive acts and perceived treatment need (OR=0.45, P<0.01).
Figure 1 illustrates the odds of any physically assaultive behaviour as a function of level of treatment engagement, as measured by perceived treatment need, perceived treatment benefit and treatment adherence. With respect to violence risk, these findings show that in the absence of these three factors the predicted probability of any physically assaultive behaviour was 0.39. However, the presence or endorsement of these factors was associated with a greatly decreased probability of any physically assaultive behaviour (0.08). It should be noted that probabilities were calculated controlling for all other variables in the model; thus, even individuals in the A+B+C group may in fact possess characteristics increasing odds of violence (e.g. substance misuse or young age).
|
We additionally examined whether treatment engagement was related to
psychiatric diagnosis. We found that people with affective disorders were more
likely to report treatment non-adherence than people with psychotic disorders
(41% v. 28%; 
2=19.02, d.f.=1,
P<0.0001).
However, However, there was no relationship between treatment adherence and
personality disorder. Interestingly, although perceived treatment need was not
related to either Axis I or Axis II disorders, perceived treatment
effectiveness was significantly related to both: specifically, people with a
psychotic disorder (52%) were somewhat more likely to perceive their treatment
as effective than people with an affective disorder (45%;
2=4.9, d.f.=1, P=0.02), and people with a personality
disorder (39%) were less likely to perceive treatment as effective than people
without a personality disorder (51%; 2=7.46, d.f.=1,
P= 0.0063). Thus, although ‘perceived treatment need’ and
‘perceived treatment effectiveness’ are conceptually related, they
appear to tap into two distinct facets of treatment engagement.
|
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
|---|
Clinical implications
Given the cross-sectional nature of this study, there are several ways to
interpret the connection between perceived treatment need and violent
behaviour among people with mental disorder. First, one could conjecture that
people who do not perceive they need treatment are less likely to attend
treatment and take their medications. These individuals may instead
‘self-medicate’ by misusing alcohol or illicit drugs to stave off
psychiatric symptoms. Such lack of engagement in services may therefore lead
to relapse and increase the chances of violent behaviour. This interpretation
is consistent with sociocognitive theories of behaviour change, such as the
health belief model (Norman et
al, 2000), self-determination theory
(Ryan & Deci, 2000) and
the transtheoretical model (Prochaska
& DiClemente, 1983), which posit that perceptions about
treatment benefit predict treatment adherence. To the extent this causal
pathway exists, interventions that address perceived treatment need, such as
motivation interviewing (e.g. Ruesch &
Corrigan, 2002), may be warranted as means of managing – and
potentially reducing – violence risk among people with mental
disorders.
Another possibility is that violent behaviour might lead to a patient feeling less confident about the benefits of the treatment he or she may have been receiving. Specifically, if a person has been violent and then arrested or involuntarily detained in hospital, the often circuitous process of accessing services (even if the patient tries to do so) after incarceration or hospitalisation may colour people’s assessment of the value of these services or their effectiveness, compared with people who have not recently been violent.
This would be accentuated by more difficult access associated with public health insurance-related barriers and low income, certainly characteristic of a sample of patients in the public mental health system in the USA. Thus, violent behaviour may affect a patient’s attitudes about the benefits and needs for treatment, rather than the other way around.
However, there is a final interpretation of the data: the statistically significant association between perceived treatment need and violence may indicate that both of the aforementioned causal pathways are present, and are perhaps reinforcing one another. To illustrate, one could imagine a patient with a mental disorder who is violent and arrested and then has difficulty reconnecting to services in the community. Thus, this patient might very well become sceptical about the benefits of treatment, which in turn could lead to poor adherence to prescribed medications, substance misuse to self-medicate and increased psychiatric symptoms – each of which elevate the risks of violence. The findings in this study may thus indicate a cycle in which patients’ perceptions of treatment benefit and violence influence one another reciprocally.
Limitations
Although this study is a first step into exploring the link between
perceived treatment benefit and community violence among people with mental
disorders, it does have limitations that need to be considered. The overall
effect of mental disorder per se cannot be examined using these data,
since treatment for mental disorder was a requirement for study participation,
and no comparison group without treated mental illness was included. Despite
use of sample weighting and robust variance estimation techniques to improve
generalisability, it is difficult to define with precision the population with
treated major mental disorders to which our results should generalise. In
particular, the study surveyed patients connected with mental health services
in the USA, who may be different from patients with psychiatric disorders in
other countries.
Additionally, it should be noted that the study examines patients’ perceptions of treatment need, as opposed to whether patients’ need for treatment was actually met. If a patient has a need for treatment and the treatment is not provided, or is provided but is inappropriate, then one might anticipate that ‘unmet need’ would be positively associated with violence. Correspondingly, whereas 99% of patients in the sample were actively receiving pharmacological treatment, we did not measure the amount of psychosocial treatment obtained. Thus, future research needs to examine the interconnections between the quality and type of treatment provided, patients’ perceptions of treatment benefit, and violent behaviour.
Finally, our study relied only on self-report to obtain sensitive personal information about committing violent acts. Recent studies using composite indices of violence with multiple informants and record reviews have found higher rates of violence in psychiatric populations than those in our study (Steadman et al, 1998; Swanson et al, 1999). Further, it is possible that because our sample involved many patients over 40 years old (i.e. past the peak age of violent behaviour), violence rates might have been further influenced. This implies that our findings are probably conservative estimates of the true prevalence of violent behaviour in people with mental disorders.
Future research
The findings provide empirical support for the assertion that perceived
treatment need is associated with reduced levels of violence among patients
with mental disorders. Future research is needed to replicate findings, using
longitudinal data measuring violence from multiple sources. Systematic
examination of dynamic, malleable variables such as perceived treatment
benefit is needed in scientific literature (as well as in clinical practice)
because information on these variables can point to potential risk management
strategies. At the very least, the results from this survey of over a thousand
patients with mental disorders appear to support the clinical intuition that
treatment engagement is important to consider in the context of violence risk
assessment. Indeed, the findings also suggest that clinical consideration of
patients’ perceived need for treatment can help enhance violence risk
assessment in psychiatric practice settings.
|
|
ACKNOWLEDGMENTS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
|---|
|
|
REFERENCES |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
|---|
American Psychiatric Association (1994) Diagnostic and Statistical Manual of Mental Disorders (4th edn) (DSM–IV). Washington, DC: APA.
Blazer, D. G., George, L. K. & Landermau, R. (1985) Psychiatric disorders: a rural/urban comparison. Archives of General Psychiatry, 42, 651 –656.[Abstract]
Douglas, K. S. & Skeem, J. L. (2005) Violence risk assessment: getting specific about being dynamic. Psychology, Public Policy, and Law, 11, 347 –383.[CrossRef]
Douglas, K. S. & Webster, C. D. (1999) The
HCR–20 violence risk assessment scheme: concurrent validity in a sample
of incarcerated offenders. Criminal Justice and
Behavior, 26, 3
–19.
Douglas, K. S., Cox, D. N. & Webster, C. D. (1999) Violence risk assessment: science and practice. Legal and Criminological Psychology, 4, 149 –184.[CrossRef]
Elbogen, E. B., Mercado, C. C., Scalora, M. J., et al (2002) Perceived relevance of factors for violence risk assessment: a survey of clinicians. International Journal of Forensic Mental Health, 1, 37 –47.
Elbogen, E. B., Huss, M., Tomkins, A. J., et al (2005) Clinical decision-making about psychopathy and violence risk assessment in public sector mental health settings. Psychological Services, 2, 133 –141.[CrossRef]
Elbogen, E. B., Mustillo, S., Van Dorn, R., et al (2006) The impact of perceived need for treatment on risk of arrest and violence in severe mental illness. Criminal Justice and Behavior, in press.
Endicott, J., Spitzer, R., Fleiss, J., et al (1976) The global assessment scale: a procedure for measuring overall severity of psychiatric disturbances. Archives of General Psychiatry, 33, 766 –771.[Abstract]
Estroff, S. E., Swanson, J. W., Lachicotte, W., et al (1998) Risk reconsidered: targets of violence in the social networks of people with serious psychiatric disorders. Social Psychiatry and Psychiatric Epidemiology, 33, S95 –S101.[CrossRef][Medline]
Farrington, D. & Loeber, R. (2000) Some benefits of dichotomization in psychiatric and criminological research. Criminal Behaviour and Mental Health, 10, 100 –122.[CrossRef]
Ganju, V. (1999) The MHSIP Consumer Survey. Austin, TX: Texas Department of MHMR.
Harris, G. T., Rice, M. E. & Cormier, Cormier, C. A. (2002) Prospective replication of the Violence Risk Appraisal Guide in predicting violent recidivism among forensic patients. Law and Human Behavior, 26, 377 –394.[CrossRef][Medline]
Harris, G. T., Rice, M. E. & Camilleri, J. A. (2004) Applying a forensic actuarial assessment (the Violence Risk Appraisal Guide) to nonforensic patients. Journal of Interpersonal Violence, 19, 1063 –1074.[Abstract]
Heilbrun, K. (1997) Prediction versus management models relevant to risk assessment: the importance of legal decision-making context. Law and Human Behavior, 21, 347 –359.[CrossRef][Medline]
LogXact (2002) Software for Exact Logistic Regression. Cambridge, MA: Cytel Software Corporation.
Monahan, J. (2002) The MacArthur studies of violence risk. Criminal Behaviour and Mental Health, 12, S67 –S72.[CrossRef]
Monahan, J. & Steadman, H. J. (eds) (1994) Violence and Mental Disorder: Developments in Risk Assessment: Chicago: University of Chicago Press.
Monahan, J., Steadman, H. J., Robbins, P. C., et al
(2000) Developing a clinically useful actuarial tool for
assessing violence risk. British Journal of
Psychiatry, 176, 312
–319.
Monahan, J., Redlich, A. D., Swanson, J., et al
(2005) Use of leverage to improve adherence to psychiatric
treatment in the community. Psychiatric Services,
56, 37
–44.
Moran, P., Walsh, E., Tyrer, P., et al
(2003) Impact of comorbid personality disorder on violence in
psychosis: report from the UK700 trial. British Journal of
Psychiatry, 182, 129
–134.
Nicholls, T. L., Ogloff, J. R. P. & Douglas, K. S. (2004) Assessing risk for violence among male and female civil psychiatric patients: the HCR–20, PCL:SV, and VSC. Behavioral Sciences and the Law, 22, 127 –158.[CrossRef][Medline]
Norman, P., Abraham, C. & Conner, M. (eds) (2000) Understanding and Changing Health Behaviour: From Health Beliefs to Self-regulation. Amsterdam: Harwood.
Perkins, D. O. (2002) Predictors of noncompliance in patients with schizophrenia. Journal of Clinical Psychiatry, 63, 1121 –1128.
Prochaska, J. O. & DiClemente, C. C. (1983) Stages and processes of self-change of smoking: toward an integrative model of change. Journal of Consulting and Clinical Psychology, 51, 390 –395.[CrossRef][Medline]
Rao, J. & Scott, A. (1992) Asimplemethodfor for the analysis of clustered binary data. Biometrics, 48, 577 –585.[CrossRef][Medline]
Ruesch, N. & Corrigan, P. W. (2002) Motivational interviewing to improve insight and treatment adherence in schizophrenia. Psychiatric Rehabilitation Journal, 26, 23 –32.[Medline]
Ryan, R. M. & Deci, E. L. (2000) Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68 –78.[CrossRef][Medline]
Skeem, J. L. & Mulvey, E. P. (2001) Psychopathy and community violence among civil psychiatric patients: results from the MacArthur Violence Risk Assessment Study. Journal of Consulting and Clinical Psychology, 69, 358 –374.[CrossRef][Medline]
StatXact (2003) Statistical Software for Exact Nonparametric Inference. Cambridge, MA: Cytel Software Corporation.
Steadman, H. J., Monahan, J., Robbins, P. C., et al (1993) From dangerousness to risk assessment: implications for appropriate research strategies. In Mental Disorder and Crime (ed. S. Hodgins), pp 39–62. Thousand Oaks, CA: Sage.
Steadman, H. J., Mulvey, E. P., Monahan, J., et al
(1998) Violence by people discharged from acute psychiatric
inpatient facilities and by others in the same neighborhoods.
Archives of General Psychiatry,
55, 393
–401.
Steadman, H. J., Silver, E., Monahan, J., et al (2000) A classification tree approach to the development of actuarial violence risk assessment tools. Law and Human Behavior, 24, 83 –100.[CrossRef][Medline]
Strand, S., Belfrage, H., Fransson, G., et al (1999) Clinical and risk management factors in risk prediction of mentally disordered offenders – more important than historical data? A retrospective study of 40 mentally disordered offenders assessed with the HCR–20 violence risk assessment scheme. Legal and Criminological Psychology, 4, 67 –76.[CrossRef]
Swanson, J. W., Holzer, C. E., Ganju, V. K., et al
(1990) Violence and psychiatric disorder in the community:
evidence from the Epidemiological Catchment Area surveys. Hospital
and Community Psychiatry, 41, 761
–770.
Swanson, J. W., Swartz, M. S., Estroff, S. E., et al (1998) Psychiatric impairment, social contact, and violent behavior: evidence from a study of outpatient-committed persons with severe mental disorder. Social Psychiatry and Psychiatric Epidemiology, 33, S86 –S94.[CrossRef][Medline]
Swanson, J. W., Borum, R., Swartz, M. S., et al (1999) Violent behavior preceding hospitalization among persons with severe mental illness. Law and Human Behavior, 23, 185 –204.[CrossRef][Medline]
Swanson, J. W., Swartz, M. S. & Elbogen, E. B.
(2004a) Effectiveness of atypical antipsychotic
medications in reducing violent behavior among persons with schizophrenia in
community-based treatment. Schizophrenia Bulletin,
30, 3
–20.
Swanson, J. W., Swartz, M. S., Elbogen, E. B., et al (2004b) Reducing violence risk in persons with schizophrenia: olanzapine vs. risperidone. Journal of Clinical Psychiatry, 65, 1666 –1673.
Swanson, J. W., Swartz, M. S., Van Dorn, R. A., et al
(2006) A national study of violent behavior in persons with
schizophrenia. Archives of General Psychiatry,
63, 490
–499.
Swartz, M. S., Swanson, J. W., Hiday, V. A., et al
(1998a) Violence and severe mental illness: the
effects of substance abuse and nonadherence to medication. American
Journal of Psychiatry, 155, 226
–231.
Swartz, M. S., Swanson, J. W., Hiday, V. A., et al (1998b) Taking the wrong drugs: the role of substance abuse and medication noncompliance in violence among severely mentally ill individuals. Social Psychiatry and Psychiatric Epidemiology, 33, S75 –S80.[CrossRef][Medline]
Teague, G. B., Ganju, V., Hornik, J. A., et al
(1997) The MHSIP Mental Health Report Card: consumer-oriented
approach to monitoring the quality of mental health plans.
Evaluation Review, 21, 330
–341.
Walsh, E., Moran, P., Scott, C., et al
(2003) Prevalence of violent victimisation in severe mental
illness. British Journal of Psychiatry,
183, 233
–238.
Woerner, M. G., Mannuzza, S. & Kane, J. M. (1988) Anchoring the BPRS: an aid to improved reliability. Psychopharmacology Bulletin, 24, 112 –117.[Medline]
Zelen, M. (1971) The analysis of several 2
x 2 contingency tables. Biometrika,
58, 129
–137.
Received for publication October 5, 2005. Revision received April 3, 2006. Accepted for publication May 2, 2006.
Related articles in BJP:
This article has been cited by other articles:
|
|
 |
|
  E. F. Torrey, J. Stanley, J. Monahan, H. J. Steadman, and MacArthur Study Group The MacArthur Violence Risk Assessment Study Revisited: Two Views Ten Years After Its Initial Publication Psychiatr Serv, February 1, 2008; 59(2): 147 - 152. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Buchanan Risk of Violence by Psychiatric Patients: Beyond the "Actuarial Versus Clinical" Assessment Debate Psychiatr Serv, February 1, 2008; 59(2): 184 - 190. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 I. Kooyman, K. Dean, S. Harvey, and E. Walsh Outcomes of public concern in schizophrenia The British Journal of Psychiatry, August 1, 2007; 191(50): s29 - s36. [Abstract] [Full Text] [PDF]
|
|
Read all eLetters
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Psychiatric Bulletin | Advances in Psychiatric Treatment | All RCPsych Journals |
