Department of Alcohol, Drug, and Mental Health Services

The study was conducted to explore what it is like for individuals and family members to live with obesity as a chronic illness. An interpretive phenomenological design was used to obtain and analyze interviews of 13 obese individuals and 5 of their family members. A convenience sample was used to recruit the subjects who participated in the audiotaped interviews. The interviews used open-ended questions. Audiotapes were transcribed and analyzed for identifying the major themes within each transcript, and patterns of meaning across narratives. The major themes and patterns were described through written essays and group discussions about the transcripts. The participants revealed frequent experiences of stigmatization and discrimination on the basis of their obesity. Those who are obese are reminded through their everyday encounters with family members, peers, healthcare providers, and strangers, that their being deviates from social norms, and that they are inferior to those who are not obese. Obese subjects experience a pattern of denigration and condemnation that is so pervasive as to constitute what Harvey has called civilized oppression. A discussion of the social construction of obesity and the elements of civilized oppression, as they are experienced by those who are obese, offers new insights into interpersonal relationships that can provide a foundation for more effective care of the obese population.

Electronic cigarettes (e-cigarettes)--devices that generate a nicotine vapor that can be inhaled by the user in a fashion that mimics the experience of smoking--are increasing in popularity, and many people seem to view them as reasonable alternatives to nicotine replacement therapy to help them refrain from smoking. Physicians should not encourage such a view. E-cigarettes are unregulated nicotine delivery systems that have never been subjected to any kind of testing of safety or of efficacy as nicotine replacement therapy. Moreover, for young people who have never smoked, these devices could potentially serve as a gateway drug.

OBJECTIVES: To illustrate the use of machine learning methods to search for heterogeneous effects of a target modifiable risk factor on suicide in observational studies. The illustration focuses on secondary analysis of a matched case-control study of vitamin D deficiency predicting subsequent suicide. METHODS: We describe a variety of machine learning methods to search for prescriptive predictors; that is, predictors of significant variation in the association between a target risk factor and subsequent suicide. In each case, the purpose is to evaluate the potential value of selective intervention on the target risk factor to prevent the outcome based on the provisional assumption that the target risk factor is causal. The approaches illustrated include risk modeling based on the super learner ensemble machine learning method, Least Absolute Shrinkage and Selection Operator (Lasso) penalized regression, and the causal forest algorithm. RESULTS: The logic of estimating heterogeneous intervention effects is exposited along with the illustration of some widely used methods for implementing this logic. CONCLUSIONS: In addition to describing best practices in using the machine learning methods considered here, we close with a discussion of broader design and analysis issues in planning an observational study to investigate heterogeneous effects of a modifiable risk factor.

Abstract Objectives: Electroconvulsive therapy (ECT) is an effective treatment of severe manifestations of mental illness. Since delay in initiation of ECT can have detrimental effects, prediction of the need for ECT could improve outcomes via more timely treatment initiation. Therefore, this study aimed to predict the need for ECT following admission to a psychiatric hospital. Methods: This study was based on electronic health record (EHR) data from routine clinical practice. Adult patients admitted to a hospital within the Psychiatric Services of the Central Denmark Region between January 2013 and November 2021 were included in the study. The outcome was initiation of ECT >7 days (to not include patients admitted for planned ECT) and ≤67 days after admission. The data was randomly split into an 85% training set and a 15% test set. On the 7 th day of the inpatient stay, machine learning models (extreme gradient boosting (XGBoost)) were trained to predict initiation of ECT and subsequently tested on the test set. Results: The cohort consisted of 41,610 patients with 164,961 admissions. In the held out test set, the trained model predicted ECT initiation with an area under the receiver operating characteristic curve of 0.94, 47% sensitivity, 98% specificity, positive predictive value (PPV) of 24% and negative predictive value (NPV) of 99%. The top predictors were the highest suicide assessment score and mean Brøset violence checklist score in the preceding three months. Conclusions: EHR data from routine clinical practice may be used to predict need for ECT. This may lead to more timely treatment initiation.