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Traumatic events are associated with increased risk of psychotic experiences, but it is unclear whether this association is explained by mental disorders prior to psychotic experience onset.
To investigate the associations between traumatic events and subsequent psychotic experience onset after adjusting for post-traumatic stress disorder and other mental disorders.
We assessed 29 traumatic event types and psychotic experiences from the World Mental Health surveys and examined the associations of traumatic events with subsequent psychotic experience onset with and without adjustments for mental disorders.
Respondents with any traumatic events had three times the odds of other respondents of subsequently developing psychotic experiences (OR=3.1, 95% CI 2.7–3.7), with variability in strength of association across traumatic event types. These associations persisted after adjustment for mental disorders.
Exposure to traumatic events predicts subsequent onset of psychotic experiences even after adjusting for comorbid mental disorders.
The treatment gap between the number of people with mental disorders and the number treated represents a major public health challenge. We examine this gap by socio-economic status (SES; indicated by family income and respondent education) and service sector in a cross-national analysis of community epidemiological survey data.
Data come from 16 753 respondents with 12-month DSM-IV disorders from community surveys in 25 countries in the WHO World Mental Health Survey Initiative. DSM-IV anxiety, mood, or substance disorders and treatment of these disorders were assessed with the WHO Composite International Diagnostic Interview (CIDI).
Only 13.7% of 12-month DSM-IV/CIDI cases in lower-middle-income countries, 22.0% in upper-middle-income countries, and 36.8% in high-income countries received treatment. Highest-SES respondents were somewhat more likely to receive treatment, but this was true mostly for specialty mental health treatment, where the association was positive with education (highest treatment among respondents with the highest education and a weak association of education with treatment among other respondents) but non-monotonic with income (somewhat lower treatment rates among middle-income respondents and equivalent among those with high and low incomes).
The modest, but nonetheless stronger, an association of education than income with treatment raises questions about a financial barriers interpretation of the inverse association of SES with treatment, although future within-country analyses that consider contextual factors might document other important specifications. While beyond the scope of this report, such an expanded analysis could have important implications for designing interventions aimed at increasing mental disorder treatment among socio-economically disadvantaged people.
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.
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.
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%).
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.
Exposure to multiple disasters, both natural and technological, is associated with extreme stress and long-term consequences for older adults that are not well understood. In this article, we address age differences in health-related quality of life in older disaster survivors exposed to the 2005 Hurricanes Katrina and Rita and the 2010 BP Deepwater Horizon oil spill and the role played by social engagement in influencing these differences.
Participants were noncoastal residents, current coastal residents, and current coastal fishers who were economically affected by the BP oil spill. Social engagement was estimated on the basis of disruptions in charitable work and social support after the 2005 hurricanes relative to a typical year before the storms. Criterion measures were participants’ responses to the SF-36 Health Survey which includes composite indexes of physical (PCS) and mental (MCS) health.
The results of logistic regressions indicated that age was inversely associated with SF-36 PCS scores. A reduction in perceived social support after Hurricane Katrina was also inversely associated with SF-36 MCS scores.
These results illuminate risk factors that impact well-being among older adults after multiple disasters. Implications of these data for psychological adjustment after multiple disasters are considered. (Disaster Med Public Health Preparedness. 2017;11:90–96)
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.
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).
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.
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.
Four tree ring-index site chronologies, representing standardised annual growth rates for spruce trees growing at high altitude sites in Colorado, have been employed as proxy data in a regression model for the annual variation of solar radio flux at 2800 MHz (F10·7) and the Catania sunspot area (Ac). These dendrochronological time series all exhibit significant power spectrum peaks at about 11 years and separately correlate with the annual values of Rz, F10·7 and Ac, as solar activity indicators. The two models constructed give the cyclic variation of F10·7 and Ac back to AD1673.
We aimed to explore how individually experienced disaster-related stressors and collectively experienced community-level damage influenced perceived need for mental health services in the aftermath of Hurricane Sandy.
In a cross-sectional study we analyzed 418 adults who lived in the most affected areas of New York City at the time of the storm. Participants indicated whether they perceived a need for mental health services since the storm and reported on their exposure to disaster-related stressors (eg, displacement, property damage). We located participants in communities (n=293 census tracts) and gathered community-level demographic data through the US Census and data on the number of damaged buildings in each community from the Federal Emergency Management Agency Modeling Task Force.
A total of 7.9% of participants reported mental health service need since the hurricane. Through multilevel binomial logistic regression analysis, we found a cross-level interaction (P=0.04) between individual-level exposure to disaster-related stressors and community-level building damage. Individual-level stressors were significantly predictive of individual service needs in communities with building damage (adjusted odds ratio: 2.56; 95% confidence interval: 1.58-4.16) and not in communities without damage.
Individuals who experienced individual stressors and who lived in more damaged communities were more likely to report need for services than were other persons after Hurricane Sandy. (Disaster Med Public Health Preparedness. 2016;10:428–435)
Initially some simple analytical properties based on the annual Zürich relative sunspot number are established for the 22-year Hale solar magnetic cycle. Since about AD1850, successive maximum sunspot numbers in a Hale cycle are highly correlated. Also, a regression model for the reconstruction of the 22-year Hale cycle has been formulated from proxy tree-ring data, obtained from spruce trees growing at a high altitude site in White River National Forest in Colorado. Over a considerable fraction of the past 300 years to AD1986, the ring-index time series power spectrum exhibits a strong 22-year periodicity, and more recently a significant spectral peak (at the 95% confidence level) at approximately 11 years. The model shows that the greatest variation in ‘amplitude’ in the magnetic cycle occurs over the early decades of the eighteenth century, when the sample size is small. Thereafter, a nearly constant amplitude is maintained until about AD1880 when a break occurs in both phase correspondence and amplitude, extending over the next three cycles. From AD1950 the signal recovers phase with the solar cycle, with reduced but increasing amplitude.
Ring-width time series obtained from Engelmann Spruce trees growing at high altitude sites in the Rocky Mountains, Colorado, exhibit dominant 11-year spectral periodicities. A significant linear cross-correlation also exists between these series and the Zurich series of annual sunspot numbers. A regression model based on these proxy data has been developed for the annual variation of the 11-year solar cycle. It is established that over the calibration period a very high percentage of the variance (40%) in growth patterns, contributed by a single source, can be explained by solar variation. The model correlates with the Zürich series of sunspot numbers at the 99% significance level post 1870 AD. However, over the total period 1700–1870 AD the comparison was found to be not statistically significant at lag 0. Some possible reasons for this are considered.
The management of patients with a glioma is challenging and best achieved by a team approach encompassing a combination of chemotherapy, radiotherapy, immunotherapy, and surgical excision in a specialist Cancer Center - the balance of treatment depending on the site and grade of tumor. Survival rates are improving and care of patients with or recovering from gliomas is increasingly handled in the community under the care of local physicians. This book provides an authoritative, multi-disciplinary summary of glioma biology, genetics, management and social issues, based on the world-leading program at the Duke University Preston Robert Tisch Brain Tumor Center, one of the world's largest and most successful Centers to offer brain cancer treatment and translational research. The text is written by specialists from this Center, giving it a consistent approach and style. This is an important educational resource for neurologists, neurosurgeons, oncologists, psychiatrists, neurohospitalists and ancillary members of neuro-oncology teams.
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.
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.
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.
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.
Although variation in the long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about co-morbid conditions. The current report presents results on this question.
Data came from 8261 respondents with lifetime DSM-IV MDD in the World Health Organization (WHO) World Mental Health (WMH) Surveys. Outcomes included four retrospectively reported measures of persistence/severity of course (years in episode; years in chronic episodes; hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously detected subtypes with information about prior co-morbidity to predict these outcomes.
Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found three clusters with consistently high, intermediate or low values. The high-risk cluster (32.4% of cases) accounted for 56.6–72.9% of high persistence, high chronicity, hospitalization and disability. This high-risk cluster had both higher sensitivity and likelihood ratio positive (LR+; relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of co-morbidity as predictors.
Although the results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence/severity, replication with prospective data is needed to confirm this preliminary conclusion.