Latent structure of intermittent explosive disorder in an epidemiological sample

Intermittent explosive disorder (IED) is characterized by distinct periods of impulsive aggression marked by assaultive acts or destruction of property. However, impulsive aggression is also a feature of other disorders, all of which are viewed in diagnostic nomenclature as qualitatively distinct from IED. This state of affairs is problematic for categorical models unless it is demonstrated empirically that IED-related impulsive aggression is qualitatively distinct from impulsive aggression observable in other axis I and II disorders. The current study addresses this question using taxometric methods to examine the latent structure of IED. Participants were respondents on the Collaborative Psychiatric Epidemiological Surveys, which obtained data on a range of disorders including intermittent explosive disorder (N = 20,013) and a range of psychological variables. Indicator variables used were drawn from the survey items and submitted to select taxometric methods (MAMBAC and MAXEIG) to determine the relative fits of a taxonic versus dimensional model. The results of taxometric analyses provided support for a taxonic, rather than dimensional, structure for IED symptoms in the epidemiological sample. Taxon group membership was associated with treatment seeking, family history of anger attacks, lower age of onset of anger attacks, and male biological sex, providing strong support for the validity of the IED taxon.     

Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis

Introduction  

Advances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome.     

The diet-heart situation

Even though some of the questions about the role of diet in heart disease are still unanswered, it is clear that obesity, diabetes, and hyperlipidemia increase the risk of heart disease. Physicians who would prevent arteriosclerosis in their patients are directing attention to dietary modification for young adults and children.     

Antiretroviral therapy drug adherence in Rwanda: Perspectives from patients and healthcare workers using a mixed-methods approach

Rwanda has achieved high enrollment into antiretroviral therapy (ART) programs but data on adherence after enrollment are not routinely collected. We used a mixed-methods approach (standardized questionnaires, pill counts, focus group discussions, and in-depth interviews) to determine levels of and barriers to ART adherence from the perspective of both patients and healthcare workers (HCW). Data were available from 213 patients throughout the first year on ART; 58 of them and 23 HCW participated in a qualitative sub-study. Self-reported adherence was high (96% of patients reporting more than 95% adherence), but adherence by pill count was significantly lower, especially in the first 3 months. In the standardized interviews, patients mostly reported that they “simply forgot” or “were away from home” as reasons for nonadherence. The qualitative research identified three interrelated constructs that appeared to negatively influence adherence: stigma, difficulty coming to terms with illness, and concealment of illness. Both standardized questionnaires and the qualitative research identified poverty, disruption to daily routines, factors related to regimen complexity and side effects, and service-related factors as barriers to adherence. We conclude that regular triangulation of different sources of adherence data is desirable to arrive at more realistic estimates. We propose that program monitoring and evaluation cycles incorporate more in-depth research to better understand concerns underlying reasons for nonadherence reported in routine monitoring.