Min Yang

The Efficacy of Violence Prediction: A Meta-Analytic Comparison of Nine Risk Assessment Tools

Actuarial risk assessment tools are used extensively to predict future violence, but previous studies comparing their predictive accuracies have produced inconsistent findings as a result of various methodological issues. We conducted meta-analyses of the effect sizes of 9 commonly used risk assessment tools and their subscales to compare their predictive efficacies for violence. The effect sizes were extracted from 28 original reports published between 1999 and 2008, which assessed the predictive accuracy of more than one tool. We used a within-subject design to improve statistical power and multilevel regression models to disentangle random effects of variation between studies and tools and to adjust for study features. All 9 tools and their subscales predicted violence at about the same moderate level of predictive efficacy with the exception of Psychopathy Checklist--Revised (PCL-R) Factor 1, which predicted violence only at chance level among men. Approximately 25% of the total variance was due to differences between tools, whereas approximately 85% of heterogeneity between studies was explained by methodological features (age, length of follow-up, different types of violent outcome, sex, and sex-related interactions). Sex-differentiated efficacy was found for a small number of the tools. If the intention is only to predict future violence, then the 9 tools are essentially interchangeable; the selection of which tool to use in practice should depend on what other functions the tool can perform rather than on its efficacy in predicting violence. The moderate level of predictive accuracy of these tools suggests that they should not be used solely for some criminal justice decision making that requires a very high level of accuracy such as preventive detention.

Raised Incidence Rates of All Psychoses Among Migrant Groups Findings From the East London First Episode Psychosis Study

CONTEXTnCertain black and minority ethnic groups are at increased risk for psychoses. It is unknown whether risk for second- and later-generation black and minority ethnic groups in the United Kingdom is universally increased or varies by ethnicity, population structure, or diagnostic category.nnnOBJECTIVESnTo examine whether excess risk in black and minority ethnic groups varies by generation status and to determine whether this is explained solely by an excess of broadly defined schizophrenia.nnnDESIGNnPopulation-based epidemiological survey of first-onset psychoses during a 2-year study period.nnnSETTINGnThree inner-city boroughs in East London, England. Patients Four hundred eighty-four patients with first-episode psychosis aged 18 to 64 years.nnnMAIN OUTCOME MEASURESnNonaffective or affective psychoses according to the DSM-IV.nnnRESULTSnRaised incidence of both nonaffective and affective psychoses were found for all of the black and minority ethnic subgroups compared with white British individuals. The risk of nonaffective psychoses for first and second generations varied by ethnicity (likelihood ratio test, P = .06). Only black Caribbean second-generation individuals were at significantly greater risk compared with their first-generation counterparts (incidence rate ratio, 2.2; 95% confidence interval, 1.1-4.2) [corrected]. No significant differences between first and second generations were observed in other ethnic groups. Asian women but not men of both generations were at increased risk for psychoses compared with white British individuals. Patterns were broadly upheld for the affective psychoses.nnnCONCLUSIONSnBoth first- and second-generation immigrants were at elevated risk for both nonaffective and affective psychoses, but this varied by ethnicity. Our results suggest that given the same age structure, the risk of psychoses in first and second generations of the same ethnicity will be roughly equal. We suggest that socioenvironmental factors operate differentially by ethnicity but not generation status, even if the exact specification of these stressors differs across generations. Research should focus on differential rates of psychoses by ethnicity rather than between generations.

Psychoses, ethnicity and socio-economic status.

BACKGROUNDnConsistent observation of raised rates of psychoses among Black and minority ethnic (BME) groups may possibly be explained by their lower socio-economic status.nnnAIMSnTo test whether risk for psychoses remained elevated in BME populations compared with the White British, after adjustment for age, gender and current socio-economic status.nnnMETHODnPopulation-based study of first-episode DSM-IV psychotic disorders, in individuals aged 18-64 years, in East London over 2 years.nnnRESULTSnAll BME groups had elevated rates of a psychotic disorder after adjustment for age, gender and socio-economic status. For schizophrenia, risk was elevated for people of Black Caribbean (incidence rate ratios (IRR)=3.1, 95% CI 2.1-4.5) and Black African (IRR=2.6, 95% CI 1.8-3.8) origin, and for Pakistani (IRR=3.1, 95% CI 1.2-8.1) and Bangladeshi (IRR=2.3, 95% CI 1.1-4.7) women. Mixed White and Black Caribbean (IRR=7.7, 95% CI 3.2-18.8) and White Other (IRR=2.1, 95% CI 1.2-3.8) groups had elevated rates of affective psychoses (and other non-affective psychoses).nnnCONCLUSIONSnElevated rates of psychoses in BME groups could not be explained by socio-economic status, even though current socio-economic status may have overestimated the effect of this confounder given potential misclassification as a result of downward social drift in the prodromal phase of psychosis. Our findings extended to all BME groups and psychotic disorders, though heterogeneity remains.

Most items in structured risk assessment instruments do not predict violence

The predictive ability of static risk assessment instruments may be explained by a limited number of their items. This study examined the independent predictive accuracy of individual items in the Psychopathy Checklist-Revised (PCL-R), Violence Risk Appraisal Guide (VRAG) and Historical-Clinical-Risk Management-20 (HCR-20) for violent reconvictions following release among 1353 male prisoners in England and Wales. The study found most items in the three instruments were not independently predictive. Items not independently predictive were removed and all significant items in the original three instruments were combined, resulting in negligible gains in predictive accuracy for the VRAG and HCR-20, but a small improvement in the PCL-R. The study demonstrated that the predictive power of the PCL-R, VRAG and HCR-20 are based on a small number of their items. This may partly explain the ‘glass-ceiling’ effect beyond which further improvement cannot be achieved. Instruments lack outcome-specificity for violence, and independently predictive items include measures of general criminality.

Gang membership, violence, and psychiatric morbidity.

OBJECTIVEnGang members engage in many high-risk activities associated with psychiatric morbidity, particularly violence-related ones. The authors investigated associations between gang membership, violent behavior, psychiatric morbidity, and use of mental health services.nnnMETHODnThe authors conducted a cross-sectional survey of 4,664 men 18-34 years of age in Great Britain using random location sampling. The survey oversampled men from areas with high levels of violence and gang activities. Participants completed questionnaires covering gang membership, violence, use of mental health services, and psychiatric diagnoses measured using standardized screening instruments.nnnRESULTSnViolent men and gang members had higher prevalences of mental disorders and use of psychiatric services than nonviolent men, but a lower prevalence of depression. Violent ruminative thinking, violent victimization, and fear of further victimization accounted for the high levels of psychosis and anxiety disorders in gang members, and with service use in gang members and other violent men. Associations with antisocial personality disorder, substance misuse, and suicide attempts were explained by factors other than violence.nnnCONCLUSIONSnGang members show inordinately high levels of psychiatric morbidity, placing a heavy burden on mental health services. Traumatization and fear of further violence, exceptionally prevalent in gang members, are associated with service use. Gang membership should be routinely assessed in individuals presenting to health care services in areas with high levels of violence and gang activity. Health care professionals may have an important role in promoting desistence from gang activity.

Estimating Overweight Risk in Childhood From Predictors During Infancy

OBJECTIVE: The aim of this study was to develop and validate a risk score algorithm for childhood overweight based on a prediction model in infants. METHODS: Analysis was conducted by using the UK Millennium Cohort Study. The cohort was divided randomly by using 80% of the sample for derivation of the risk algorithm and 20% of the sample for validation. Stepwise logistic regression determined a prediction model for childhood overweight at 3 years defined by the International Obesity Task Force criteria. Predictive metrics R2, area under the receiver operating curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. RESULTS: Seven predictors were found to be significantly associated with overweight at 3 years in a mutually adjusted predictor model: gender, birth weight, weight gain, maternal prepregnancy BMI, paternal BMI, maternal smoking in pregnancy, and breastfeeding status. Risk scores ranged from 0 to 59 corresponding to a predicted risk from 4.1% to 73.8%. The model revealed moderately good predictive ability in both the derivation cohort (R2 = 0.92, AUROC = 0.721, sensitivity = 0.699, specificity = 0.679, PPV = 38%, NPV = 87%) and validation cohort (R2 = 0.84, AUROC = 0.755, sensitivity = 0.769, specificity = 0.665, PPV = 37%, NPV = 89%). CONCLUSIONS: Using a prediction algorithm to identify at-risk infants could reduce levels of child overweight and obesity by enabling health professionals to target prevention more effectively. Further research needs to evaluate the clinical validity, feasibility, and acceptability of communicating this risk.

The distribution of psychopathy among a household population: categorical or dimensional?

ObjectiveThe study aimed to examine the distribution of psychopathic traits in a representative household population to identify whether a transition point is reached on a continuum of psychopathy to indicate a ‘disease’ or categorical entity.MethodMixture Poisson distribution and epidemiological procedures were used to examine the distribution of the Psychopathy Checklist Screening Version (PCL:SV) score in a sample of 638 adults in households in Great Britain. Analysis aimed to identify a cut-off within the population using the distribution of continuous scores (mean and ½xa0SD) and validate a ‘probable’ psychopathy category using a scale of social and behavioural problems as an external validator.ResultsThe distribution of psychopathy within the population was quasicontinuous, represented by a mixture of three-Poisson distributions with differing demography and comorbid Axis I and II psychopathology. Independent calculation indicated a cut-score at 11.8 on the PCL:SV. There was an exponential rise of associated social and behavioural problems at a transition point of 11.3. The prevalence of ‘probable’ psychopathy was 3.6% (95% CIxa0=xa02.3–5.5%) above this level.ConclusionsThe findings suggest a transition from a non-clinical to clinical state of psychopathy which can be defined categorically using a cut-off on the PCL:SV. The cut-off approximates to that previously recommended for identification of a case using the instrument. Above this critical level, individuals are at exceptional risk of compulsory care or incarceration due to multiple social and behavioural problems. Psychopathy should be considered for future inclusion in DSM-V and successfully combines both categorical and dimensional approaches to diagnosis.

A randomized controlled trial of group cognitive behavioral therapy for Chinese breast cancer patients with major depression

Abstract Background: This study aims to evaluate the effects of Group Cognitive Behavioral Therapy (GCBT) in treating major depression in Chinese women with breast cancer. Methods: Sixty-two breast cancer patients diagnosed with major depression were randomly assigned to GCBT group (Nu2009=u200931) or a waiting list control group provided with an educational booklet (Nu2009=u200931). The primary outcome measure was the 17-Item Hamilton Depression Rating Scale (17-HAMD). The second outcome measures were Self-Rating Anxiety Scale, Functional Assessment of Cancer Therapy – Breast and Self-Esteem Scale (SES). Assessments were carried out at completion of the study and six-month afterwards. Results: Patients in the GCBT group had a significant reduction in the 17-HAMD mean score by 9 points (pu2009<u20090.001), more than any reduction among patients in the control group from baseline to the end of therapy and a significant 7 points (pu2009<u20090.001) more reduction from baseline to six-month follow-up. GCBT also yielded significantly greater improvement than the control group with regard to quality of life (QoL; pu2009<u20090.01) and self-esteem (pu2009<u20090.05). No significant differences were found between groups on improving anxiety (pu2009>u20090.05). Conclusion: The results of this trial suggest that GCBT is effective for treating major depression, as well as for improving QoL and self-esteem in breast cancer patients. Trial Registration: Chictr.org ChiCTR-TRC-11001689

Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China

Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model.

Preliminary studies of the ICD-11 classification of personality disorder in practice

OBJECTIVEnThis study aims to compare ICD-10 and putative ICD-11 classifications of personality disorder in different clinical populations.nnnDESIGNnProspective recording of ICD-10 and ICD-11 personality disorder classifications was carried out in (1) an anxious medical population, (2) an acute psychiatric in-patient population and (3) a retrospective recording of a mixed anxiety depression cohort in which all baseline data were scored from baseline information using the ICD-11 classification and compared with the original ICD-10 assessments.nnnMETHODnComparison of ICD-10 and ICD-11 prevalence of personality disorder in each population was carried out.nnnRESULTSnData from 722 patients were recorded. Using the ICD-10 criteria, the prevalence of generic personality disorder was 33.8% compared with 40.4% using the ICD-11 ones (χ2 u2009=u20096.7; Pu2009<u20090.01), with 103 (14.3%) discordant assessments. Using the severity definitions in ICD-11, 34.3% of patients had personality difficulty. Severity level varied greatly by population; severe personality disorder was five times more common in the inpatient group. The four domain traits originally denoted as qualifying severity in ICD-11, negative affective, dissocial, anankastic and detached, were linked to anxious, borderline, dissocial, anankastic and schizoid personality disorders in ICD-10. Many patients had pathology in two or more domains.nnnCONCLUSIONSnThe ICD-11 classification of personality disorder yields somewhat higher levels of personality dysfunction than ICD-10, possibly because the age range for the onset of diagnosis is now flexible. The range of severity levels make the classification more useful than ICD-10 in clinical practice as it identifies the greater pathology necessary for intervention.