html xmlns=”http://www.w3.org/1999/xhtml” xmlns:mml=”http://www.w3.org/1998/Math/MathML” xmlns:epub=”http://www.idpf.org/2007/ops”>
Chapter 7 Prevalence of physical violence in a forensic psychiatric hospital system during 2011–2013: patient assaults, staff assaults, and repeatedly violent patients
Introduction
Evidence has accumulated that shows that patients with a mental illness in a hospital setting have higher rates of violence in comparison to people with mental illness living in the community [1–3]. Investigations into patient violence in psychiatric hospitals have typically examined variables such as sex, age, ethnicity, and diagnosis. These investigations have typically found higher prevalence of violence among inpatients who are female [4–7], younger [8–10], and of ethnic minority status [11,12]. However, these findings have not been universal across all studies, as noted in the review by Bowers et al. [13] Their review found that of the 26 studies of psychiatric inpatients that specifically investigated the roles of age and aggression, 13 reported no significant relationship and 13 reported that aggressive patients were significantly younger.
Likewise, with regard to diagnosis, their review again found discrepancies; across 19 studies, nine reported no significant differences in diagnosis between the aggressor and non-aggressor groups, and only one study directly addressed the issue of personality disorder among aggressive and non-aggressive groups [9]. The presence and number of contradictory findings raises questions regarding methodological issues, such as the setting of the study (and subsequent generalizability to other settings), along with issues of statistical power related to the sample size of the study, which may have limited the ability of the investigators to find significance when the impact of a variable was small.
In the decade or more since many of these studies were conducted, there have been significant changes in the state psychiatric hospital system; these include a simultaneous reduction in hospital beds with an increase in the demand for beds by the criminal court system (i.e., forensic patients) [6,14]. Nationwide, as of 2012, expenditures by state psychiatric hospitals for forensic patients had grown to 36% of the total budget, with an additional 4.7% of expenditures dedicated for persons committed under sex offender commitment statutes. While several states now have a forensic population over 50% of the total inpatient population, perhaps nowhere has this impact been felt more than in the California State Hospital system, where shifts over the past decade have resulted in criminal-related, forensic inpatients comprising over 92% of the hospitalized patients.
The increasing number of forensic patients admitted to state hospitals creates a number of concerns, chief among these the concern of risk for violence. Because commitment to a state hospital in California requires an assessment of whether the patient can be safely treated in the community as an alternative to hospitalization, a patient can only be committed if the court finds that person too dangerous to treat in the community. Since the only distinguishing feature between those treated as outpatients or committed to a state hospital is that of dangerousness, in essence patients are hospitalized by courts primarily due to the issue of dangerousness and secondarily due to mental illness. Also considering the requirements of the commitment criteria in California, as the patients committed by the courts are presumed to be dangerous, they cannot be discharged solely by the treatment team’s recommendation; the court must evaluate any treatment team discharge recommendation and can choose to follow or not follow any such recommendation based on the relevant legal issue(s) brought up at the hearing or trial. This potentially can increase the length of stay of these patients, beyond what would reasonably be expected for simple treatment of their mental illness needs. In view of previous research findings that patients who were more violent in the community are more likely to be violent while hospitalized, and those patients diagnosed with schizophrenia with recent violence or law enforcement contact have increased violence risk, there are concerns that violence by forensic patients in state hospitals may be both quantitatively and qualitatively different from violence in other psychiatric facilities that do not treat forensic patients [2,4]. Owing to these issues, and a need to develop effective methodologies to decrease violence, we decided to enumerate both the prevalence of violent assaults, as well as investigate details of the assaults that may warrant further evaluation.
Previous studies that examined prevalence of inpatient violence in psychiatric facilities typically followed one of several common methodologies. Studies conducted before 2000 routinely used questionnaire-type surveys administered to staff, asking about previous violence – a technique methodologically subject to under-reporting [15,16]. Another methodology was to conduct a one-year “look-back” at the violence committed by all patients resident in the hospital, which could systematically overlook patients resident during any part of the year but discharged prior to the study initiation [6,16]. In one such study, it is estimated that potentially up to 25% of all patients resident at any point during the year were not included [6]. More recent studies have commonly followed inpatients for a prescribed length of time and had nursing staff fill out standardized aggression surveys immediately after aggressive/violent events [7]. An issue for some of these studies is that nursing time resources are needed, if aggression ratings forms are not a routine part of nursing duties, resulting in a more limited duration for the study period.
The present study endeavored to overcome these limitations encountered by past investigations by using a computerized violent incident reporting system that is routinely used by staff to record the occurrence of every violent incident. Use of other available patient databases enabled us to cross-reference patient information with the violent incident data, and determine who was and was not violent. Additionally, the use of these databases allowed us to track and record every patient and every violent incident for three years, allowing a sufficient time period to ensure a representative portrayal of violent incidents over time. To the best of our knowledge, this is the single largest study on violent assaults in a state psychiatric hospital system.
Methods
This study was reviewed and approved by the California Health and Human Services Agency Committee for the Protection of Human Subjects (CPHS), the IRB with oversight over all research with human subjects in the California Department of State Hospitals.
Description of setting
The California Department of State Hospitals (DSH) operates five different state hospitals across the state, with current populations ranging from 600 to 1500 patients at each facility. All facilities have a mix of patients, although one hospital is the designated Sexually Violent Predator (SVP) treatment facility. Typical housing unit size at each facility ranges from 35 to 70 patients, with the majority of units being single sex, although there are several co-ed dorms exclusively for the nonforensic patients. According to California law, forensically involved patients cannot be mixed with nonforensic patients; otherwise, patients of all forensic classes (while housed on units according to legal commitment code) typically mix during daytime group and leisure activities. Treatment modalities are also similar, with a similar range of individual and group treatments available to all patients, in addition to leisure and recreational activities on evenings and weekends.
Subjects
The study subjects consisted of the entire adult (age 18 and greater) patient population in residence at, or admitted to, all five California DSH hospitals between January 1, 2011, and December 31, 2013. The total number of subjects during the entire study period was N = 15,615 and included n = 2161 females and n = 13 454 males of various ethnicities, with a mean age of 42.17 years. (Table 7.1 lists the subject demo-graphics.) At the start of the study period (January 1, 2011) there were 5499 patients in residence at the hospitals; during the study period, 2887 of these patients discharged. During the course of the study period, 10,116 patients were admitted; of these, 7220 were discharged before the study period ended, and 2896 were admitted at various points during the three-year study period and remained until the end of the study (December 31, 2013), at which point 5508 patients were residing in the hospitals.
| Age at study start | |||||
|---|---|---|---|---|---|
| Ethnicity | Number | Mean | SD | Range | |
| Overall study | Total | 15,615 | 42.17 | 13.0 | 18.01–91.24 |
| Female | 2161 | 41.60 | 12.32 | 18.01–85.55 | |
| Male | 13,454 | 42.27 | 13.10 | 18.01–91.24 | |
| African-American | Total | 4525 | 41.81 | 12.61 | 18.01–88.76 |
| Female | 663 | 41.28 | 12.11 | 18.01–85.55 | |
| Male | 3862 | 41.90 | 12.69 | 18.08–88.76 | |
| Asian | Total | 471 | 42.29 | 12.93 | 19.02–87.49 |
| Female | 71 | 45.04 | 12.39 | 19.03–70.42 | |
| Male | 400 | 41.80 | 12.98 | 19.02–87.49 | |
| Hispanic | Total | 3549 | 38.12 | 12.56 | 18.01–90.15 |
| Female | 423 | 37.63 | 11.59 | 18.04–72.28 | |
| Male | 3126 | 38.18 | 12.68 | 18.01–90.15 | |
| Native American | Total | 117 | 39.75 | 12.12 | 20.38–69.91 |
| Female | 15 | 36.66 | 8.27 | 24.79–56.15 | |
| Male | 102 | 40.20 | 12.55 | 20.38–69.91 | |
| Other/unknown | Total | 244 | 38.64 | 11.92 | 18.32–79.86 |
| Female | 15 | 42.15 | 15.67 | 20.75–79.86 | |
| Male | 229 | 38.41 | 11.64 | 18.32–70.07 | |
| Pacific Islander | Total | 256 | 40.47 | 12.33 | 18.22–73.45 |
| Female | 34 | 37.67 | 13.13 | 18.22–67.14 | |
| Male | 222 | 40.89 | 12.18 | 18.43–73.45 | |
| White | Total | 6453 | 44.90 | 12.93 | 18.04–91.24 |
| Female | 940 | 43.56 | 12.27 | 18.08–81.22 | |
| Male | 5513 | 45.12 | 13.02 | 18.04–91.24 | |
The patients were grouped according to the overall “umbrella” legal commitment under which the exact legal code fell. (California has 39 different legal sections for holding patients in state psychiatric facilities, which can be collapsed into eight general categories.) Details and a description of the legal classes in the hospitals are shown in Box 7.1. Table 7.2 shows a summary of study subject demographics by general legal class.
Nonforensic commitment:
LPS: short for “Lanterman-Petris-Short,” i.e., nonforensically committed patients, typically patient conserved by county courts
Forensic commitments:
DJJ: Patients referred for treatment from the Division of Juvenile Justice system
IST: Patients found incompetent to stand trial
MDO: Mentally disordered offenders, i.e., parolees from the prison deemed too dangerous to allow to parole back to the community
MDSO: Mentally disordered sex offender, a since discontinued legal commitment that was a precursor to the present-day SVP commitment
NGI: Patients found not guilty by reason of insanity
PC2684: Mentally ill prisoners, i.e., prison inmates referred to DSH for treatment
SVP: Patients adjudicated under the Sexually Violent Predator law
| Age at study start | |||||
|---|---|---|---|---|---|
| Legal class | Number | Mean | SD | Range | |
| DJJ | Total | 30 | 20.52 | 1.87 | 18.01–24.21 |
| Female | 2 | 18.62 | 0.86 | 18.01–19.23 | |
| Male | 28 | 20.66 | 1.85 | 18.01–24.21 | |
| IST | Total | 7587 | 39.62 | 12.95 | 18.08–89.12 |
| Female | 1276 | 41.18 | 11.72 | 18.80–81.22 | |
| Male | 6311 | 39.30 | 13.16 | 18.08–89.12 | |
| LPS | Total | 974 | 41.46 | 13.94 | 18.04–88.03 |
| Female | 288 | 39.71 | 14.95 | 18.04–85.55 | |
| Male | 686 | 42.20 | 13.43 | 18.16–88.03 | |
| MDO | Total | 2272 | 42.63 | 10.97 | 19.63–81.34 |
| Female | 182 | 42.54 | 9.85 | 20.02–68.97 | |
| Male | 2090 | 42.64 | 11.07 | 19.63–81.34 | |
| MDSO | Total | 32 | 59.25 | 7.69 | 48.68–76.52 |
| Female | 0 | N/A | N/A | N/A | |
| Male | 32 | 59.25 | 7.69 | 48.68–76.52 | |
| NGI | Total | 1888 | 46.58 | 12.71 | 18.48–91.24 |
| Female | 275 | 45.97 | 13.07 | 19.80–77.40 | |
| Male | 1613 | 46.68 | 12.65 | 18.48–91.24 | |
| PC2684 | Total | 1784 | 41.86 | 11.71 | 19.03–83.63 |
| Female | 137 | 39.64 | 10.92 | 20.97–66.28 | |
| Male | 1647 | 42.04 | 11.75 | 19.03–83.63 | |
| SVP | Total | 1048 | 53.03 | 10.57 | 23.95–89.08 |
| Female | 1 | 51.31 | N/A | 51.31 | |
| Male | 1047 | 53.04 | 10.57 | 23.95–89.08 | |
See Box 7.1 for description of legal class abbreviations.
Data collection
Patient demographic and legal class information were collected from system databases that are routinely used for census tracking. Data on violent incidents were collected through a computerized incident management module of the patient treatment planning database.
Study variables
Sex, ethnicity, age, and legal commitment code were collected from the patient demographic information database. Information on patient Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) diagnosis was collected from the patient admission diagnostic fields contained in the patient information database [17]. Since patients commonly had multiple diagnoses, only the primary diagnosis indicated on Axis I and the primary diagnosis (if any) indicated on Axis II were used. Over 280 different DSM-IV-TR diagnoses were recorded for all the patients on admission; these various diagnoses were collapsed according to the DSM-IV-TR category or chapter title, with diagnoses of particular interest (such as schizophrenia, schizoaffective disorder, bipolar disorder, and psychotic disorder NOS/miscellaneous psychotic disorders) kept as separate categories.
Statistical analyses
Data preparation and analyses were performed with R version 3.1.1 [18]. Data files were provided by the centralized data management office of the California DSH for all patients who were in residence or admitted to the hospitals during the periods 2010–2014; from these were extracted the records of all patients resident or admitted to a hospital between January 1, 2011, and December 31, 2013, inclusive. Data files were also provided for all the records of physical assaults by patients during the period 2010–2014, which were again further refined to extract just the physical assaults recorded during the period between January 1, 2011, and December 31, 2013, inclusive.
The first level of data analysis consisted of a descriptive review of violence prevalence in the hospital system stratified by previously researched variables (sex, ethnicity, age, legal classification, DSM-IV-TR Axis I diagnosis and Axis II diagnosis), and calculated the prevalence of violence and approximate 95% confidence intervals (CI). Chi-squared tests were then performed to test the prevalence rates for significance. Last, a logistic regression main effects model was fitted to obtain the adjusted odds ratios (ORs) and 95% CI of violence for the different demographic and clinical diagnosis variables.
Description of aggression data
Physical violence during the study was defined as assaults directed against either another patient or a staff member, as defined in the California DSH policies (see Box 7.2). Analogous codes and definitions also existed for verbal aggression and property damage, but were not used in this study, as we examined only physical violence. There were a total of 11 302 unique recorded acts of physical violence against other patients during the study period, and a further 8482 unique recorded acts of physical violence directed against staff members. Of these total numbers, aggressors were identified in 10 958 assaults against patients, and in 8429 assaults against staff; these incidents in which aggressors were identified were used as the final count of violent assaults, as well as to determine an individual patient’s aggressor and victim status.
Aggressive Act to Another Patient – Physical: Hitting, pushing, kicking, or similar acts directed against another individual to cause potential or actual injury
Aggressive Act to Staff – Physical: Hitting, pushing, kicking, or similar acts directed against a staff person that could cause potential or actual injury
Aggressor: One who completes acts of hostility or assault; one who starts a hostile action or exhibits hostile behavior. An aggressive act must have occurred for there to be an aggressor
Victim: Recipient of an aggressive act
Results
Overview of violent incidents and patients
The total number of subjects in the study was N = 15 615. The number of unique patients having a single violent incident (whether patient assault or staff assault) was n = 4895, yielding an overall prevalence of violence during the study period of 31.35% (95% confidence interval (CI) 30.62%–32.08%). The number of patients having at least a single patient assault incident was n = 4075, yielding a violent patient assault prevalence of 26.10% (95% CI 25.54%–26.79%). The number of patients having at least a single staff assault incident was n = 2504, yielding a staff assault prevalence of 16.04% (95% CI 15.46%–16.62%). A simple tally showed that the top 156 aggressors (1% of the study population) were involved in 28.7% of all these violent assaults. When examining the patients still hospitalized at the conclusion of the study, those remaining (n = 5508) had an overall violence prevalence of 41.25% (95% CI 39.95%–42.55%), with a patient violence prevalence of 35.48% (95% CI 34.22%– 36.74%) and a staff violence prevalence of 22.97% (95% CI 21.86%–24.08%), which led us to investigate how violence impacted length of stay; these findings will be reported below.
Regarding severity of assaults, only data on patient injury severity were collected; these data showed that, for the most part, injuries suffered by patient victims were typically not severe, although one homicide did occur during the study period.
Sex differences
As shown in Table 7.3, there were no significant differences for patient assault, but there was a significant difference for staff assault [χ2 (1, N = 15 615) = 30.51, p < 0.001], with assaults committed by females more prevalent (20.08%, 95% CI 18.39%–21.77%) than males (15.38%, 95% CI 14.78%–16.00%). Examining the adjusted odds ratios (ORs) in Table 7.4 shows a similar relationship, with no significant difference in the adjusted odds between females and males for patient assault, but a significant difference (p < 0.001) in the odds for staff assault, with females having a higher odds (OR 1.256, 95% CI 1.104–1.423).
| Patient assaults | Staff assaults | ||||
|---|---|---|---|---|---|
| n | n (%) | 95% CI | n (%) | 95% CI | |
| Sex | |||||
| Females | 2161 | 597 (27.63) | (25.74, 29.51) | 434 (20.08) | (18.39, 21.77) |
| Males | 13,454 | 3478 (25.85) | (25.11, 26.59) | 2070 (15.38) | (14.78, 16.00) |
| Ethnicity | |||||
| African-American | 4525 | 1290 (28.51) | (27.19, 29.82) | 717 (15.84) | (14.78, 16.91) |
| Asian | 471 | 99 (21.02) | (17.34, 24.70) | 64 (13.59) | (10.49, 16.68) |
| Hispanic | 3549 | 1005 (28.32) | (26.84, 29.80) | 561 (15.81) | (14.61, 17.01) |
| Native American | 117 | 30 (25.64) | (17.73, 33.55) | 20 (17.09) | (10.27, 23.92) |
| Other/unknown | 244 | 63 (25.82) | (20.33, 31.31) | 36 (14.75) | (10.30, 19.20) |
| Pacific Islander | 256 | 72 (28.12) | (22.62, 33.63) | 42 (16.41) | (11.87, 20.94) |
| White | 6453 | 1516 (23.49) | (22.46, 24.53) | 1064 (16.49) | (15.58, 17.39) |
| Age group | |||||
| 18–29 | 3056 | 986 (32.26) | (30.61, 33.92) | 562 (18.39) | (17.02, 19.76) |
| 30–39 | 3721 | 1057 (28.41) | (26.96, 29.86) | 650 (17.47) | (16.25, 18.69) |
| 40–49 | 3724 | 873 (23.44) | (22.08, 24.80) | 519 (13.94) | (12.82, 15.05) |
| 50–59 | 3404 | 784 (23.03) | (21.62, 24.45) | 486 (14.28) | (13.10, 15.45) |
| 60–69 | 1363 | 305 (22.38) | (20.16, 24.59) | 232 (17.02) | (15.03, 19.02) |
| 70 + | 347 | 70 (20.17) | (15.95, 24.40) | 55 (15.85) | (12.01, 19.69) |
| Legal class | |||||
| DJJ | 30 | 15 (50.00) | (32.11, 67.89) | 11 (36.67) | (19.42, 53.91) |
| IST | 7587 | 1694 (22.33) | (21.39, 23.26) | 949 (12.51) | (11.76, 13.25) |
| LPS | 974 | 494 (50.72) | (47.58, 53.86) | 422 (43.33) | (40.21, 46.44) |
| MDO | 2272 | 698 (30.72) | (28.82, 32.62) | 423 (18.62) | (17.02, 20.22) |
| MDSO | 32 | 10 (31.25) | (15.19, 47.31) | 6 (18.75) | (5.23, 32.27) |
| NGI | 1888 | 559 (29.61) | (27.55, 31.67) | 290 (15.36) | (13.73, 16.99) |
| PC2684 | 1784 | 299 (16.76) | (15.03, 18.49) | 169 (9.47) | (8.11, 10.83) |
| SVP | 1048 | 306 (29.20) | (26.44, 31.95) | 234 (22.33) | (19.81, 24.85) |
See Box 7.1 for a full description of the legal class abbreviations.
Related posts:
Inpatient aggression in community hospitals
Augmentation of clozapine with amisulpride: an effective therapeutic strategy for violent treatment-resistant schizophrenia patients in a UK high-security hospital
The appropriateness of treating psychopathic disorders
Is impulsive violence an addiction? The Habit Hypothesis
Effectiveness of antipsychotic drugs against hostility in patients with schizophrenia in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study
Crime, violence, and behavioral health: collaborative community strategies for risk mitigation
Stay updated, free articles. Join our Telegram channel
Full access? Get Clinical Tree
