Recovery from DSM-IV post-traumatic stress disorder in the WHO World Mental Health surveys.

BACKGROUND: Research on post-traumatic stress disorder (PTSD) course finds a substantial proportion of cases remit within 6 months, a majority within 2 years, and a substantial minority persists for many years. Results are inconsistent about pre-trauma predictors. METHODS: The WHO World Mental Health surveys assessed lifetime DSM-IV PTSD presence-course after one randomly-selected trauma, allowing retrospective estimates of PTSD duration. Prior traumas, childhood adversities (CAs), and other lifetime DSM-IV mental disorders were examined as predictors using discrete-time person-month survival analysis among the 1575 respondents with lifetime PTSD. RESULTS: 20%, 27%, and 50% of cases recovered within 3, 6, and 24 months and 77% within 10 years (the longest duration allowing stable estimates). Time-related recall bias was found largely for recoveries after 24 months. Recovery was weakly related to most trauma types other than very low [odds-ratio (OR) 0.2-0.3] early-recovery (within 24 months) associated with purposefully injuring/torturing/killing and witnessing atrocities and very low later-recovery (25+ months) associated with being kidnapped. The significant ORs for prior traumas, CAs, and mental disorders were generally inconsistent between early- and later-recovery models. Cross-validated versions of final models nonetheless discriminated significantly between the 50% of respondents with highest and lowest predicted probabilities of both early-recovery (66-55% v. 43%) and later-recovery (75-68% v. 39%). CONCLUSIONS: We found PTSD recovery trajectories similar to those in previous studies. The weak associations of pre-trauma factors with recovery, also consistent with previous studies, presumably are due to stronger influences of post-trauma factors.

The associations of earlier trauma exposures and history of mental disorders with PTSD after subsequent traumas.

Although earlier trauma exposure is known to predict posttraumatic stress disorder (PTSD) after subsequent traumas, it is unclear whether this association is limited to cases where the earlier trauma led to PTSD. Resolution of this uncertainty has important implications for research on pretrauma vulnerability to PTSD. We examined this issue in the World Health Organization (WHO) World Mental Health (WMH) Surveys with 34 676 respondents who reported lifetime trauma exposure. One lifetime trauma was selected randomly for each respondent. DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th Edition) PTSD due to that trauma was assessed. We reported in a previous paper that four earlier traumas involving interpersonal violence significantly predicted PTSD after subsequent random traumas (odds ratio (OR)=1.3-2.5). We also assessed 14 lifetime DSM-IV mood, anxiety, disruptive behavior and substance disorders before random traumas. We show in the current report that only prior anxiety disorders significantly predicted PTSD in a multivariate model (OR=1.5-4.3) and that these disorders interacted significantly with three of the earlier traumas (witnessing atrocities, physical violence victimization and rape). History of witnessing atrocities significantly predicted PTSD after subsequent random traumas only among respondents with prior PTSD (OR=5.6). Histories of physical violence victimization (OR=1.5) and rape after age 17 years (OR=17.6) significantly predicted only among respondents with no history of prior anxiety disorders. Although only preliminary due to reliance on retrospective reports, these results suggest that history of anxiety disorders and history of a limited number of earlier traumas might usefully be targeted in future prospective studies as distinct foci of research on individual differences in vulnerability to PTSD after subsequent traumas.

Post-traumatic stress disorder associated with sexual assault among women in the WHO World Mental Health Surveys.

BACKGROUND: Sexual assault is a global concern with post-traumatic stress disorder (PTSD), one of the common sequelae. Early intervention can help prevent PTSD, making identification of those at high risk for the disorder a priority. Lack of representative sampling of both sexual assault survivors and sexual assaults in prior studies might have reduced the ability to develop accurate prediction models for early identification of high-risk sexual assault survivors. METHODS: Data come from 12 face-to-face, cross-sectional surveys of community-dwelling adults conducted in 11 countries. Analysis was based on the data from the 411 women from these surveys for whom sexual assault was the randomly selected lifetime traumatic event (TE). Seven classes of predictors were assessed: socio-demographics, characteristics of the assault, the respondent's retrospective perception that she could have prevented the assault, other prior lifetime TEs, exposure to childhood family adversities and prior mental disorders. RESULTS: Prevalence of Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) PTSD associated with randomly selected sexual assaults was 20.2%. PTSD was more common for repeated than single-occurrence victimization and positively associated with prior TEs and childhood adversities. Respondent's perception that she could have prevented the assault interacted with history of mental disorder such that it reduced odds of PTSD, but only among women without prior disorders (odds ratio 0.2, 95% confidence interval 0.1-0.9). The final model estimated that 40.3% of women with PTSD would be found among the 10% with the highest predicted risk. CONCLUSIONS: Whether counterfactual preventability cognitions are adaptive may depend on mental health history. Predictive modelling may be useful in targeting high-risk women for preventive interventions.

Using self-report surveys at the beginning of service to develop multi-outcome risk models for new soldiers in the U.S. Army.

BACKGROUND: The U.S. Army uses universal preventives interventions for several negative outcomes (e.g. suicide, violence, sexual assault) with especially high risks in the early years of service. More intensive interventions exist, but would be cost-effective only if targeted at high-risk soldiers. We report results of efforts to develop models for such targeting from self-report surveys administered at the beginning of Army service. METHODS: 21 832 new soldiers completed a self-administered questionnaire (SAQ) in 2011-2012 and consented to link administrative data to SAQ responses. Penalized regression models were developed for 12 administratively-recorded outcomes occurring by December 2013: suicide attempt, mental hospitalization, positive drug test, traumatic brain injury (TBI), other severe injury, several types of violence perpetration and victimization, demotion, and attrition. RESULTS: The best-performing models were for TBI (AUC = 0.80), major physical violence perpetration (AUC = 0.78), sexual assault perpetration (AUC = 0.78), and suicide attempt (AUC = 0.74). Although predicted risk scores were significantly correlated across outcomes, prediction was not improved by including risk scores for other outcomes in models. Of particular note: 40.5% of suicide attempts occurred among the 10% of new soldiers with highest predicted risk, 57.2% of male sexual assault perpetrations among the 15% with highest predicted risk, and 35.5% of female sexual assault victimizations among the 10% with highest predicted risk. CONCLUSIONS: Data collected at the beginning of service in self-report surveys could be used to develop risk models that define small proportions of new soldiers accounting for high proportions of negative outcomes over the first few years of service.

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.

BACKGROUNDS: Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. METHOD: We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. RESULTS: Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. CONCLUSIONS: Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

First onset of suicidal thoughts and behaviours in college.

BACKGROUND: College students are a worldwide increasing group of young people at risk for suicidal thoughts and behaviours (STB). However, no previous studies have prospectively investigated the first onset of STB during the college period. METHODS: Using longitudinal data from the Leuven College Surveys, 2337 (response rate [RR]=66.6%) incoming freshmen provided baseline data on STB, parental psychopathology, childhood-adolescent traumatic experiences, 12-month risk for mental disorders, and 12-month stressful experiences. A total of 1253 baseline respondents provided data on 12-month STB in a two-year annual follow-up survey (conditional RR=53.6%; college dropout adjusted conditional RR=70.2%). RESULTS: One-year incidence of first-onset STB was 4.8-6.4%. Effect sizes of the included risk factors varied considerably whether viewed from individual-level (ORs=1.91-17.58) or population-level perspective (PARPs=3.4-34.3%). Dating violence prior to the age of 17, physical abuse prior to the age of 17, and 12-month betrayal by someone else than the partner were most strong predictors for first-onset suicidal ideation (ORs=4.23-12.25; PARPs=8.7-27.1%) and plans (ORs=6.57-17.58; PARPs=15.2-34.3%). Multivariate prediction (AUC=0.84-0.91) revealed that 50.7-65.7% of first-onset STB cases were concentrated in the 10% at highest predicted risk. LIMITATIONS: As this is a first investigation of STB onset in college, future studies should use validation samples to test the accuracy of our multivariate prediction model. CONCLUSIONS: The first onset of STB in college appears to be higher than in the general population. Screening at college entrance is a promising strategy to identify those students at highest prospective risk, enabling the cost-efficient clinical assessment of young adults in college.

Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

The 2013 US Veterans Administration/Department of Defense Clinical Practice Guidelines (VA/DoD CPG) require comprehensive suicide risk assessments for VA/DoD patients with mental disorders but provide minimal guidance on how to carry out these assessments. Given that clinician-based assessments are not known to be strong predictors of suicide, we investigated whether a precision medicine model using administrative data after outpatient mental health specialty visits could be developed to predict suicides among outpatients. We focused on male nondeployed Regular US Army soldiers because they account for the vast majority of such suicides. Four machine learning classifiers (naive Bayes, random forests, support vector regression and elastic net penalized regression) were explored. Of the Army suicides in 2004-2009, 41.5% occurred among 12.0% of soldiers seen as outpatient by mental health specialists, with risk especially high within 26 weeks of visits. An elastic net classifier with 10-14 predictors optimized sensitivity (45.6% of suicide deaths occurring after the 15% of visits with highest predicted risk). Good model stability was found for a model using 2004-2007 data to predict 2008-2009 suicides, although stability decreased in a model using 2008-2009 data to predict 2010-2012 suicides. The 5% of visits with highest risk included only 0.1% of soldiers (1047.1 suicides/100 000 person-years in the 5 weeks after the visit). This is a high enough concentration of risk to have implications for targeting preventive interventions. An even better model might be developed in the future by including the enriched information on clinician-evaluated suicide risk mandated by the VA/DoD CPG to be recorded.

Mental disorders among college students in the World Health Organization World Mental Health Surveys.

BACKGROUND: Although mental disorders are significant predictors of educational attainment throughout the entire educational career, most research on mental disorders among students has focused on the primary and secondary school years. METHOD: The World Health Organization World Mental Health Surveys were used to examine the associations of mental disorders with college entry and attrition by comparing college students (n = 1572) and non-students in the same age range (18-22 years; n = 4178), including non-students who recently left college without graduating (n = 702) based on surveys in 21 countries (four low/lower-middle income, five upper-middle-income, one lower-middle or upper-middle at the times of two different surveys, and 11 high income). Lifetime and 12-month prevalence and age-of-onset of DSM-IV anxiety, mood, behavioral and substance disorders were assessed with the Composite International Diagnostic Interview (CIDI). RESULTS: One-fifth (20.3%) of college students had 12-month DSM-IV/CIDI disorders; 83.1% of these cases had pre-matriculation onsets. Disorders with pre-matriculation onsets were more important than those with post-matriculation onsets in predicting subsequent college attrition, with substance disorders and, among women, major depression the most important such disorders. Only 16.4% of students with 12-month disorders received any 12-month healthcare treatment for their mental disorders. CONCLUSIONS: Mental disorders are common among college students, have onsets that mostly occur prior to college entry, in the case of pre-matriculation disorders are associated with college attrition, and are typically untreated. Detection and effective treatment of these disorders early in the college career might reduce attrition and improve educational and psychosocial functioning.

Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.

Occupational differences in US Army suicide rates.

BACKGROUND: Civilian suicide rates vary by occupation in ways related to occupational stress exposure. Comparable military research finds suicide rates elevated in combat arms occupations. However, no research has evaluated variation in this pattern by deployment history, the indicator of occupation stress widely considered responsible for the recent rise in the military suicide rate. METHOD: The joint associations of Army occupation and deployment history in predicting suicides were analysed in an administrative dataset for the 729 337 male enlisted Regular Army soldiers in the US Army between 2004 and 2009. RESULTS: There were 496 suicides over the study period (22.4/100 000 person-years). Only two occupational categories, both in combat arms, had significantly elevated suicide rates: infantrymen (37.2/100 000 person-years) and combat engineers (38.2/100 000 person-years). However, the suicide rates in these two categories were significantly lower when currently deployed (30.6/100 000 person-years) than never deployed or previously deployed (41.2-39.1/100 000 person-years), whereas the suicide rate of other soldiers was significantly higher when currently deployed and previously deployed (20.2-22.4/100 000 person-years) than never deployed (14.5/100 000 person-years), resulting in the adjusted suicide rate of infantrymen and combat engineers being most elevated when never deployed [odds ratio (OR) 2.9, 95% confidence interval (CI) 2.1-4.1], less so when previously deployed (OR 1.6, 95% CI 1.1-2.1), and not at all when currently deployed (OR 1.2, 95% CI 0.8-1.8). Adjustment for a differential 'healthy warrior effect' cannot explain this variation in the relative suicide rates of never-deployed infantrymen and combat engineers by deployment status. CONCLUSIONS: Efforts are needed to elucidate the causal mechanisms underlying this interaction to guide preventive interventions for soldiers at high suicide risk.

Understanding the elevated suicide risk of female soldiers during deployments.

BACKGROUND: The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) has found that the proportional elevation in the US Army enlisted soldier suicide rate during deployment (compared with the never-deployed or previously deployed) is significantly higher among women than men, raising the possibility of gender differences in the adverse psychological effects of deployment. METHOD: Person-month survival models based on a consolidated administrative database for active duty enlisted Regular Army soldiers in 2004-2009 (n = 975,057) were used to characterize the gender × deployment interaction predicting suicide. Four explanatory hypotheses were explored involving the proportion of females in each soldier's occupation, the proportion of same-gender soldiers in each soldier's unit, whether the soldier reported sexual assault victimization in the previous 12 months, and the soldier's pre-deployment history of treated mental/behavioral disorders. RESULTS: The suicide rate of currently deployed women (14.0/100,000 person-years) was 3.1-3.5 times the rates of other (i.e. never-deployed/previously deployed) women. The suicide rate of currently deployed men (22.6/100,000 person-years) was 0.9-1.2 times the rates of other men. The adjusted (for time trends, sociodemographics, and Army career variables) female:male odds ratio comparing the suicide rates of currently deployed v. other women v. men was 2.8 (95% confidence interval 1.1-6.8), became 2.4 after excluding soldiers with Direct Combat Arms occupations, and remained elevated (in the range 1.9-2.8) after adjusting for the hypothesized explanatory variables. CONCLUSIONS: These results are valuable in excluding otherwise plausible hypotheses for the elevated suicide rate of deployed women and point to the importance of expanding future research on the psychological challenges of deployment for women.

Sociodemographic and career history predictors of suicide mortality in the United States Army 2004-2009.

BACKGROUND: The US Army suicide rate has increased sharply in recent years. Identifying significant predictors of Army suicides in Army and Department of Defense (DoD) administrative records might help focus prevention efforts and guide intervention content. Previous studies of administrative data, although documenting significant predictors, were based on limited samples and models. A career history perspective is used here to develop more textured models. METHOD: The analysis was carried out as part of the Historical Administrative Data Study (HADS) of the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). De-identified data were combined across numerous Army and DoD administrative data systems for all Regular Army soldiers on active duty in 2004-2009. Multivariate associations of sociodemographics and Army career variables with suicide were examined in subgroups defined by time in service, rank and deployment history. RESULTS: Several novel results were found that could have intervention implications. The most notable of these were significantly elevated suicide rates (69.6-80.0 suicides per 100 000 person-years compared with 18.5 suicides per 100 000 person-years in the total Army) among enlisted soldiers deployed either during their first year of service or with less than expected (based on time in service) junior enlisted rank; a substantially greater rise in suicide among women than men during deployment; and a protective effect of marriage against suicide only during deployment. CONCLUSIONS: A career history approach produces several actionable insights missed in less textured analyses of administrative data predictors. Expansion of analyses to a richer set of predictors might help refine understanding of intervention implications.

The effects of temporally secondary co-morbid mental disorders on the associations of DSM-IV ADHD with adverse outcomes in the US National Comorbidity Survey Replication Adolescent Supplement (NCS-A).

BACKGROUND: Although DSM-IV attention deficit hyperactivity disorder (ADHD) is known to be associated with numerous adverse outcomes, uncertainties exist about how much these associations are mediated temporally by secondary co-morbid disorders. METHOD: The US National Comorbidity Survey Replication Adolescent Supplement (NCS-A), a national survey of adolescents aged 13-17 years (n = 6483 adolescent-parent pairs), assessed DSM-IV disorders with the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI). Statistical decomposition was used to compare direct effects of ADHD with indirect effects of ADHD through temporally secondary mental disorders (anxiety, mood, disruptive behavior, substance disorders) in predicting poor educational performance (suspension, repeating a grade, below-average grades), suicidality (ideation, plans, attempts) and parent perceptions of adolescent functioning (physical and mental health, interference with role functioning and distress due to emotional problems). RESULTS: ADHD had significant gross associations with all outcomes. Direct effects of ADHD explained most (51.9-67.6%) of these associations with repeating a grade in school, perceived physical and mental health (only girls), interference with role functioning and distress, and significant components (34.5-44.6%) of the associations with school suspension and perceived mental health (only boys). Indirect effects of ADHD on educational outcomes were predominantly through disruptive behavior disorders (26.9-52.5%) whereas indirect effects on suicidality were predominantly through mood disorders (42.8-59.1%). Indirect effects on most other outcomes were through both mood (19.8-31.2%) and disruptive behavior (20.1-24.5%) disorders, with anxiety and substance disorders less consistently important. Most associations were comparable for girls and boys. CONCLUSIONS: Interventions aimed at reducing the adverse effects of ADHD might profitably target prevention or treatment of temporally secondary co-morbid disorders.

Lifetime co-morbidity of DSM-IV disorders in the US National Comorbidity Survey Replication Adolescent Supplement (NCS-A).

BACKGROUND: Research on the structure of co-morbidity among common mental disorders has largely focused on current prevalence rather than on the development of co-morbidity. This report presents preliminary results of the latter type of analysis based on the US National Comorbidity Survey Replication Adolescent Supplement (NCS-A). METHOD: A national survey was carried out of adolescent mental disorders. DSM-IV diagnoses were based on the Composite International Diagnostic Interview (CIDI) administered to adolescents and questionnaires self-administered to parents. Factor analysis examined co-morbidity among 15 lifetime DSM-IV disorders. Discrete-time survival analysis was used to predict first onset of each disorder from information about prior history of the other 14 disorders. RESULTS: Factor analysis found four factors representing fear, distress, behavior and substance disorders. Associations of temporally primary disorders with the subsequent onset of other disorders, dated using retrospective age-of-onset (AOO) reports, were almost entirely positive. Within-class associations (e.g. distress disorders predicting subsequent onset of other distress disorders) were more consistently significant (63.2%) than between-class associations (33.0%). Strength of associations decreased as co-morbidity among disorders increased. The percentage of lifetime disorders explained (in a predictive rather than a causal sense) by temporally prior disorders was in the range 3.7-6.9% for earliest-onset disorders [specific phobia and attention deficit hyperactivity disorder (ADHD)] and much higher (23.1-64.3%) for later-onset disorders. Fear disorders were the strongest predictors of most other subsequent disorders. CONCLUSIONS: Adolescent mental disorders are highly co-morbid. The strong associations of temporally primary fear disorders with many other later-onset disorders suggest that fear disorders might be promising targets for early interventions.

Childhood adversities and adult psychopathology in the National Comorbidity Survey Replication (NCS-R) III: associations with functional impairment related to DSM-IV disorders.

BACKGROUND: Despite evidence that childhood adversities (CAs) are associated with increased risk of mental disorders, little is known about their associations with disorder-related impairment. We report the associations between CAs and functional impairment associated with 12-month DSM-IV disorders in a national sample. METHOD: We used data from the US National Comorbidity Survey Replication (NCS-R). Respondents completed diagnostic interviews that assessed 12-month DSM-IV disorder prevalence and impairment. Associations of 12 retrospectively reported CAs with impairment among cases (n=2242) were assessed using multiple regression analysis. Impairment measures included a dichotomous measure of classification in the severe range of impairment on the Sheehan Disability Scale (SDS) and a measure of self-reported number of days out of role due to emotional problems in the past 12 months. RESULTS: CAs were positively and significantly associated with impairment. Predictive effects of CAs on the SDS were particularly pronounced for anxiety disorders and were significant in predicting increased days out of role associated with mood, anxiety and disruptive behavior disorders. Predictive effects persisted throughout the life course and were not accounted for by disorder co-morbidity. CAs associated with maladaptive family functioning (MFF; parental mental illness, substance disorder, criminality, family violence, abuse, neglect) were more consistently associated with impairment than other CAs. The joint effects of co-morbid MFF CAs were significantly subadditive. Simulations suggest that CAs account for 19.6% of severely impairing disorders and 17.4% of days out of role. CONCLUSIONS: CAs predict greater disorder-related impairment, highlighting the ongoing clinical significance of CAs at every stage of the life course.

Multiple imputation in a large-scale complex survey: a practical guide.

The Cancer Care Outcomes Research and Surveillance (CanCORS) Consortium is a multisite, multimode, multiwave study of the quality and patterns of care delivered to population-based cohorts of newly diagnosed patients with lung and colorectal cancer. As is typical in observational studies, missing data are a serious concern for CanCORS, following complicated patterns that impose severe challenges to the consortium investigators. Despite the popularity of multiple imputation of missing data, its acceptance and application still lag in large-scale studies with complicated data sets such as CanCORS. We use sequential regression multiple imputation, implemented in public-available software, to deal with non-response in the CanCORS surveys and construct a centralised completed database that can be easily used by investigators from multiple sites. Our work illustrates the feasibility of multiple imputation in a large-scale multiobjective survey, showing its capacity to handle complex missing data. We present the implementation process in detail as an example for practitioners and discuss some of the challenging issues which need further research.

Education and race-ethnicity differences in the lifetime risk of alcohol dependence.

OBJECTIVES: While lower socioeconomic status (SES) is related to higher risk for alcohol dependence, minority race-ethnicity is often associated with lower risk. This study attempts to clarify the nature and extent of social inequalities in alcohol dependence by investigating the effects of SES and race-ethnicity on the development of alcohol dependence following first alcohol use. DESIGN: Cross-sectional analysis of data from the National Epidemiologic Survey on Alcohol and Related Conditions (n = 43,093). Survival analysis was used to model alcohol dependence onset according to education, race-ethnicity and their interaction. SETTING: United States, 2001-2. RESULTS: Compared with non-Hispanic white people, age-adjusted and sex-adjusted risks of alcohol dependence were lower among black people (odds ratio (OR) = 0.70, 95% confidence interval (CI) = 0.63 to 0.78), Asians (OR = 0.65, CI = 0.49 to 0.86) and Hispanics (OR = 0.68, CI = 0.58 to 0.79) and higher among American Indians (OR = 1.37, CI = 1.09 to 1.73). Individuals without a college degree had higher risks of alcohol dependence than individuals with a college degree or more; however, the magnitude of risk varied significantly by race-ethnicity (chi(2) for the interaction between education and race-ethnicity = 19.7, df = 10, p = 0.03); odds ratios for less than a college degree were 1.12, 1.46, 2.24, 2.35 and 10.99 among Hispanics, white people, black people, Asians, and American Indians, respectively. There was no association between education and alcohol dependence among Hispanics. CONCLUSIONS: Race-ethnicity differences in the magnitude of the association between education and alcohol dependence suggest that aspects of racial-ethnic group membership mitigate or exacerbate the effects of social adversity.

Cross-national prevalence and correlates of adult attention-deficit hyperactivity disorder.

BACKGROUND: Little is known about the epidemiology of adult attention-deficit hyperactivity disorder (ADHD). AIMS: To estimate the prevalence and correlates of DSM-IV adult ADHD in the World Health Organization World Mental Health Survey Initiative. METHOD: An ADHD screen was administered to respondents aged 18-44 years in ten countries in the Americas, Europe and the Middle East (n=11422). Masked clinical reappraisal interviews were administered to 154 US respondents to calibrate the screen. Multiple imputation was used to estimate prevalence and correlates based on the assumption of cross-national calibration comparability. RESULTS: Estimates of ADHD prevalence averaged 3.4% (range 1.2-7.3%), with lower prevalence in lower-income countries (1.9%) compared with higher-income countries (4.2%). Adult ADHD often co-occurs with other DSM-IV disorders and is associated with considerable role disability. Few cases are treated for ADHD, but in many cases treatment is given for comorbid disorders. CONCLUSIONS: Adult ADHD should be considered more seriously in future epidemiological and clinical studies than is currently the case.

Recalibration methods to enhance information on prevalence rates from large mental health surveys.

Comparisons between self-report and clinical psychiatric measures have revealed considerable disagreement. It is unsafe to consider these measures as directly equivalent, so it would be valuable to have a reliable recalibration of one measure in terms of the other. We evaluated multiple imputation incorporating a Bayesian approach, and a fully Bayesian method, to recalibrate diagnoses from a self-report survey interview in terms of those from a clinical interview with data from a two-phase national household survey for a practical application, and artificial data for simulation studies. The most important factors in obtaining a precise and accurate 'clinical' prevalence estimate from self-report data were (a) good agreement between the two diagnostic measures and (b) a sufficiently large set of calibration data with diagnoses based on both kinds of interview from the same group of subjects. From the case study, calibration data on 612 subjects were sufficient to yield estimates of the total prevalence of anxiety, depression or neurosis with a precision in the region of +/-2%. The limitations of the calibration method demonstrate the need to increase agreement between survey and reference measures by improving lay interviews and their diagnostic algorithms.