Search

Objective: Preventing suicide in US youth is of paramount concern, with rates increasing over 50% between 2007 and 2018. Statistical modeling using electronic health records may help identify at-risk youth before a suicide attempt. While electronic health records contain diagnostic information, which are known risk factors, they generally lack or poorly document social determinants (e.g., social support), which are also known risk factors. If statistical models are built incorporating not only diagnostic records, but also social determinants measures, additional at-risk youth may be identified before a suicide attempt. Methods: Suicide attempts were predicted in hospitalized patients, ages 10–24, from the State of Connecticut's Hospital Inpatient Discharge Database (HIDD; N = 38943). Predictors included demographic information, diagnosis codes, and using a data fusion framework, social determinants features transferred or fused from an external source of survey data, The National Longitudinal Study of Adolescent to Adult Health (Add Health). Social determinant information for each HIDD patient was generated by averaging values from their most similar Add Health individuals (e.g., top 10), based upon matching shared features between datasets (e.g., Pearson's r). Attempts were then modelled using an elastic net logistic regression with both HIDD features and fused Add Health features. Results: The model including fused social determinants outperformed the conventional model (AUC = 0.83 v. 0.82). Sensitivity and positive predictive values at 90 and 95% specificity were almost 10% higher when including fused features (e.g., sensitivity at 90% specificity = 0.48 v. 0.44). Among social determinants variables, the perception that their mother cares and being non-religious appeared particularly important to performance improvement. Discussion: This proof-of-concept study showed that incorporating social determinants measures from an external survey database could improve prediction of youth suicide risk from clinical data using a data fusion framework. While social determinant data directly from patients might be ideal, estimating these characteristics via data fusion avoids the task of data collection, which is generally time-consuming, expensive, and suffers from non-compliance. [ABSTRACT FROM AUTHOR]

Background: Suicides and opioid overdose deaths are among the most pressing public health concerns in the US. However direct evidence for the association between opioid use and suicidal behavior is limited. The objective of this article is to examine the association between frequency and dose of prescription opioid use and subsequent suicide attempts. Methods and findings: This retrospective cohort study analyzed 4 years of statewide medical claims data from the Connecticut All-Payer Claims Database. Commercially insured adult patients in Connecticut (n = 842,773) who had any medical claims beginning in January 2012 were followed through December 2015. The primary outcome was suicide attempt identified using International Classification of Diseases (ICD 9) diagnosis codes. Primary predictor variables included frequency of opioid use, which was defined as the number of months with claims for prescription opioids per year, and strength of opioid dose, which was standardized using morphine milligram equivalent (MME) units. We also controlled for psychiatric and medical comorbidities using ICD 9 codes. We used Cox proportional hazards regression to examine the association between frequency, dose, and suicide attempts, adjusting for medical and psychiatric comorbid conditions. Interactions among measures of opioid use and comorbid conditions were analyzed. In this cohort study with follow-up time up to 4 years (range = 2–48 months, median = 46 months), the hazard ratios (HR) from the time-to-event analysis indicated that patients prescribed opioid medications for at least 6 months during the past year and at 20–50 MME levels or higher had 4.44 (95% CI: [3.71, 5.32]) to 7.23 (95% CI: [6.22, 8.41]) times the risk of attempted suicide compared to those not prescribed opioids. Risk of suicide attempt was sharply elevated among patients with psychiatric conditions other than anxiety who were prescribed more frequent and higher opioid doses. In contrast, more frequent and higher doses of prescription opioids were associated with lower risk of suicide attempts among patients with medical conditions necessitating pain management. This study is limited by its exclusive focus on commercially insured patients and does not include patients covered by public insurance. It is also limited to patients' receipt of prescription opioids and does not take into account opioids obtained through other means, nor does it include measures of actual patient opioid use. Conclusions: This analysis provides evidence of a complex relationship among prescription opioids, mental health, pain and other medical comorbidities, and suicide risk. Findings indicate the need for proactive suicide surveillance among individuals diagnosed with affective or psychotic disorders who are receiving frequent and high doses of opioids. However, appropriate opioid treatment may have significant value in reducing suicide risk for those without psychiatric comorbidities. [ABSTRACT FROM AUTHOR]

In this paper we propose a statistical modeling framework that contributes to advancing methods for modeling insurance policy premium in the actuarial literature. Specification of separate frequency and severity models, accounting for territorial risk and performing accurate inference, are some of the challenges an actuary faces while modeling policy premium. Policy premiums are characterized to follow a semi-continuous probability distribution, featuring a non-zero probability at zero along with a positive continuous support. Interpretability is a concern when quantifying unobserved risks premiums face from spatial variation. Commonly used strategies in the literature are known to successfully quantify this risk, but do not necessarily produce interpretable estimates. Resorting to frequency-severity models leaves the actuary indecisive about the specification of covariates and spatial effects. The novelty of our proposed approach lies in the development of a parsimonious and interpretable zero-adjusted modeling framework that allows for joint estimation of the effect of policy and individual characteristics on the mean premium and dispersion, while quantifying spatial variability in the mean model. The developed methods are applied to a database featuring premiums arising from the collision coverage in insurance policies for motor vehicles within the state of Connecticut, USA, for the year 2008. [ABSTRACT FROM AUTHOR]