Project description:There is a very high suicide rate in the year after psychiatric hospital discharge. Intensive postdischarge case management programs can address this problem but are not cost-effective for all patients. This issue can be addressed by developing a risk model to predict which inpatients might need such a program. We developed such a model for the 391,018 short-term psychiatric hospital admissions of US veterans in Veterans Health Administration (VHA) hospitals 2010-2013. Records were linked with the National Death Index to determine suicide within 12 months of hospital discharge (n=771). The Super Learner ensemble machine learning method was used to predict these suicides for time horizon between 1 week and 12 months after discharge in a 70% training sample. Accuracy was validated in the remaining 30% holdout sample. Predictors included VHA administrative variables and small area geocode data linked to patient home addresses. The models had AUC=.79-.82 for time horizons between 1 week and 6 months and AUC=.74 for 12 months. An analysis of operating characteristics showed that 22.4%-32.2% of patients who died by suicide would have been reached if intensive case management was provided to the 5% of patients with highest predicted suicide risk. Positive predictive value (PPV) at this higher threshold ranged from 1.2% over 12 months to 3.8% per case manager year over 1 week. Focusing on the low end of the risk spectrum, the 40% of patients classified as having lowest risk account for 0%-9.7% of suicides across time horizons. Variable importance analysis shows that 51.1% of model performance is due to psychopathological risk factors accounted, 26.2% to social determinants of health, 14.8% to prior history of suicidal behaviors, and 6.6% to physical disorders. The paper closes with a discussion of next steps in refining the model and prospects for developing a parallel precision treatment model.
Project description:Hypertension is increasing in children and warrants disease surveillance. We therefore sought to evaluate the validity of case definitions to identify pediatric hypertension in administrative healthcare data. Cases of hypertension in children 3-18 years of age were identified utilizing blood pressures recorded in the Manitoba Primary Care Research Network (MaPCReN) electronic medical record from 2014 to 2016. Prevalence of hypertension and associated clinical characteristics were determined. We then evaluated the validity of 18 case definitions combining outpatient physician visits (ICD9CM codes), hospital claims (ICD9CM/ICD10 codes) and antihypertensive use within 1-3 years of data housed at the Manitoba Centre for Health Policy. The MaPCReN database identified 241 children with hypertension and 4090 without (prevalence = 5.6%). The sensitivity of algorithms ranged between 0.18 and 0.51 and the specificity between 0.98 and 1.00. Pharmaceutical use increased the sensitivity of algorithms significantly. The algorithms with the highest sensitivity and area under the ROC curve were 1 or more hospitalization OR 1 or more physician claim OR 1 or more pharmaceutical record. Evaluating 2 years of data is recommended. Administrative data alone reflects diagnosis of hypertension with high specificity, but underestimate the true prevalence of this disease. Alternative data sources are therefore required for disease surveillance.
Project description:The Paycheck Protection Program (PPP), a principal element of the fiscal stimulus enacted by Congress in response to the COVID-19 economic shock, was intended to assist small businesses to maintain employment and wages during the crisis, as well as cover other expenses. We use high-frequency administrative payroll data from ADP—one of the world’s largest payroll processing firms—to estimate the causal effect of the PPP on the evolution of employment at PPP-eligible firms relative to PPP-ineligible firms, where eligibility is determined by industry-specific firm-size cutoffs. Our estimates indicate that the PPP boosted employment at eligible firms by between 2 percent to 5 percent at its peak effect around mid-May 2020. The boost to employment waned thereafter and ranged from no effect to a 3 percent boost at the end of 2020. Our estimates imply that employers retained an additional 3.6 million jobs as of mid-May 2020, and 1.4 million jobs at the end of 2020, as a consequence of PPP. The estimated cost per year of employment retained was
Project description:Fifty five Armed Forces personnel detected to be seropositives for human immunodeficiency virus were the subjects of the study. After baseline clinical evaluation, laboratory investigations and Centre for Disease Control classification, through a semistructured interview, their sexual orientation, behaviour and psychiatric morbidity were assessed. Sixtynine percent had another sexually transmitted disease as comorbidity. Heterosexual contact was responsible for the infection in 54 out of 55 subjects. Seven patients were freshly diagnosed to have psychiatric illness.
Project description:This paper presents a novel method for automatically recognizing symptom severity by using natural language processing of psychiatric evaluation records to extract features that are processed by machine learning techniques to assign a severity score to each record evaluated in the 2016 RDoC for Psychiatry Challenge from CEGS/N-GRID. The natural language processing techniques focused on (a) discerning the discourse information expressed in questions and answers; (b) identifying medical concepts that relate to mental disorders; and (c) accounting for the role of negation. The machine learning techniques rely on the assumptions that (1) the severity of a patient's positive valence symptoms exists on a latent continuous spectrum and (2) all the patient's answers and narratives documented in the psychological evaluation records are informed by the patient's latent severity score along this spectrum. These assumptions motivated our two-step machine learning framework for automatically recognizing psychological symptom severity. In the first step, the latent continuous severity score is inferred from each record; in the second step, the severity score is mapped to one of the four discrete severity levels used in the CEGS/N-GRID challenge. We evaluated three methods for inferring the latent severity score associated with each record: (i) pointwise ridge regression; (ii) pairwise comparison-based classification; and (iii) a hybrid approach combining pointwise regression and the pairwise classifier. The second step was implemented using a tree of cascading support vector machine (SVM) classifiers. While the official evaluation results indicate that all three methods are promising, the hybrid approach not only outperformed the pairwise and pointwise methods, but also produced the second highest performance of all submissions to the CEGS/N-GRID challenge with a normalized MAE score of 84.093% (where higher numbers indicate better performance). These evaluation results enabled us to observe that, for this task, considering pairwise information can produce more accurate severity scores than pointwise regression - an approach widely used in other systems for assigning severity scores. Moreover, our analysis indicates that using a cascading SVM tree outperforms traditional SVM classification methods for the purpose of determining discrete severity levels.
Project description:IntroductionEntrustable professional activities (EPAs) were developed as a way to ensure adequate skills of the medical school graduate. While the 12 EPAs apply to all medical specialties, EPA 1, "Gather a history and perform a physical examination," applies most explicitly to psychiatry through the performance of a mental status exam. Although proficiency in performing a psychiatric interview and mental status exam evolves throughout a psychiatrist's professional life, basic proficiency is essential in order to function as a psychiatry intern. We developed a tool for assessing the mental status exams conducted by future psychiatry residents.MethodsOur tool contains both a video of a psychiatrist interviewing a patient and a mental status exam rating sheet that can be used when students present a mental status exam orally or in writing. We incorporated feedback from psychiatry educators at an annual meeting of the Association for Medical Student Educators in Psychiatry, followed by the reiteration of the video and the rubric. Subsequently, the rubric was verified on the performance of a cohort of 13 third- and fourth-year medical students from three institutions.ResultsIn their mental status exam presentations, students covered all the items measured by the rubric. There was a significant difference between the third- and fourth-year medical students in describing the cognitive exam.DiscussionOverall, our tool offers an opportunity to standardize mental status presentations by senior medical students who wish to specialize in psychiatry.
Project description:Stress cardiomyopathy or Tako Tsubo cardiomyopathy is a cardiac pathology evoking acute coronary syndrome characterized by electrocardiographic signs, cardiac enzyme elevation and no obstructive coronary lesions. It generally affects postmenopausal women and it usually occurs after periods of intense stress. Disease onset is widely variable, ranging from anginal pain (most common) to cardiogenic shock. Exact pathophysiological mechanism continues to be debated. Various hypotheses have been posited. Abrupt elevation of adrenaline levels appears to be the most credible. In particular, there is no consensus on treatment and prevention. Questions may then be asked about the existence of an underlying psychiatric pathology or a personality predisposition and, therefore, about the role of the psychiatrist in the management of this condition.
Project description:BACKGROUND:The World Health Organization is in the process of developing an international administrative classification for health called the International Classification of Health Interventions (ICHI). The purpose of ICHI is to provide a tool for supporting intervention reporting and analysis at a global level for policy development and beyond. Nurses represent the largest resource carrying out clinical interventions in any health system. With the shift in nursing care from hospital to community settings in many countries, it is important to ensure that community nursing interventions are present in any international health information system. Thus, an investigation into the extent to which community nursing interventions were covered in ICHI was needed. OBJECTIVE:The objectives of this study were to examine the extent to which International Classification for Nursing Practice (ICNP) community nursing interventions were represented in the ICHI administrative classification system, to identify themes related to gaps in coverage, and to support continued advancements in understanding the complexities of knowledge representation in standardized clinical terminologies and classifications. METHODS:This descriptive study used a content mapping approach in 2 phases in 2018. A total of 187 nursing intervention codes were extracted from the ICNP Community Nursing Catalogue and mapped to ICHI. In phase 1, 2 coders completed independent mapping activities. In phase 2, the 2 coders compared each list and discussed concept matches until consensus on ICNP-ICHI match and on mapping relationship was reached. RESULTS:The initial percentage agreement between the 2 coders was 47% (n=88), but reached 100% with consensus processes. After consensus was reached, 151 (81%) of the community nursing interventions resulted in an ICHI match. A total of 36 (19%) of community nursing interventions had no match to ICHI content. A total of 100 (53%) community nursing interventions resulted in a broader ICHI code, 9 (5%) resulted in a narrower ICHI code, and 42 (23%) were considered equivalent. ICNP concepts that were not represented in ICHI were thematically grouped into the categories family and caregivers, death and dying, and case management. CONCLUSIONS:Overall, the content mapping yielded similar results to other content mapping studies in nursing. However, it also found areas of missing concept coverage, difficulties with interterminology mapping, and further need to develop mapping methods.
Project description:Different psychiatric disorders as well as exposure to adverse life events have individually been associated with multiple age-related diseases and mortality. Age acceleration in different epigenetic clocks can serve as biomarker for such risk and could help to disentangle the interplay of psychiatric comorbidity and early adversity on age-related diseases and mortality. We evaluated five epigenetic clocks (Horvath, Hannum, PhenoAge, GrimAge and DunedinPoAm) in a transdiagnostic psychiatric sample using epigenome-wide DNA methylation data from peripheral blood of 429 subjects from two studies at the Max Planck Institute of Psychiatry. Burden of psychiatric disease, represented by a weighted score, was significantly associated with biological age acceleration as measured by GrimAge and DunedinPoAm (R2-adj. 0.22 and 0.33 for GrimAge and DunedinPoAm, respectively), but not the other investigated clocks. The relation of burden of psychiatric disease appeared independent of differences in socioeconomic status and medication. Our findings indicate that increased burden of psychiatric disease may associate with accelerated biological aging. This highlights the importance of medical management of patients with multiple psychiatric comorbidities and the potential usefulness of specific epigenetic clocks for early detection of risk and targeted intervention to reduce mortality in psychiatric patients.