Changes in Postpartum Opioid Prescribing After Implementation of State Opioid Prescribing Limits.

Importance: In response to the growing opioid crisis, states implemented opioid prescribing limits to reduce exposure to opioid analgesics. Research in other clinical contexts has found that these limits are relatively ineffective at changing opioid analgesic prescribing.

Objective: To examine the association of state-level opioid prescribing limits with opioid prescribing within the 30-day postpartum period, as disaggregated by type of delivery (vaginal vs cesarean) and opioid naivete.

Antidepressant prescribing inequalities in people with comorbid depression and type 2 diabetes: A UK primary care electronic health record study.

Aims: To compare the likelihood of being prescribed an antidepressant in depressed individuals with and without type 2 diabetes.

Methods: We performed a matched cohort study using primary care record data from the UK Clinical Practice Research Datalink. We used multivariable logistic regression to compare antidepressant prescribing during the first five years of starting oral antidiabetic medication to a comparison group without type 2 diabetes, matched based on GP practice, age and sex.

Impact of chronic pelvic pain on quality of life in diverse young adults.

Purpose: To examine the relationship between quality of life (QoL) and chronic pelvic pain (CPP), including an evaluation of whether differences exist between reported races and coping mechanisms used.

Methods: We used a cross-sectional survey design and analyzed data using descriptive and inferential statistics. We administered two surveys: the World Health Organization Quality of Life-BREF (26 items) and the Impact of Female Chronic Pelvic Pain Questionnaire (8 items).

Teaching Data Science through an Interactive, Hands-On Workshop with Clinically Relevant Case Studies.

Background:  In this case report, we describe the development of an innovative workshop to bridge the gap in data science education for practicing clinicians (and particularly nurses). In the workshop, we emphasize the core concepts of machine learning and predictive modeling to increase understanding among clinicians.

Objectives:  Addressing the limited exposure of health care providers to leverage and critique data science methods, this interactive workshop aims to provide clinicians with foundational knowledge in data science, enabling them to contribute effectively to teams focused on improving care quality.

Integrating Diversity, Equity, Inclusion, and Accessibility into a Data Storytelling Model for Health Informatics Education.

Background:  Health informatics education is pivotal in integrating diversity, equity, inclusion, and accessibility (DEIA) principles into curricula and leveraging data with equity considerations. Integrating clinically driven data with other datasets is crucial to comprehensive understanding of patient care demographics, experiences, and outcomes to create equity-minded data storytelling. Publicly available Healthy People 2030 (HP2030) resources complement academic electronic health records, supporting tailored learning activities in informatics education to enhance educational utility through a DEIA lens.

Vaccination with a Lawsonia intracellularis subunit water in oil emulsion vaccine mitigated some disease parameters but failed to affect shedding.

Lawsonia intracellularis is the causative agent of ileitis in swine that manifests as slower weight gain, mild or hemorrhagic diarrhea and/or death in severe cases. As an economically important swine pathogen, development of effective vaccines is important to the swine industry. In developing a subunit vaccine with three recombinant antigens - FliC, GroEL and YopN - we wanted to identify a formulation that would produce robust immune responses that reduce disease parameters associated with Lawsonia intracellularis infection.

A Bayesian Interrupted Time Series framework for evaluating policy change on mental well-being: An application to England's welfare reform.

Factors contributing to social inequalities are associated with negative mental health outcomes and disparities in mental well-being. We propose a Bayesian hierarchical controlled interrupted time series to evaluate the impact of policies on population well-being whilst accounting for spatial and temporal patterns. Using data from the UKs Household Longitudinal Study, we apply this framework to evaluate the impact of the UKs welfare reform implemented in the 2010s on the mental health of the participants, measured using the GHQ-12 index.

Factors Associated with Acute Telemental Health Consultations in Older Veterans.

Introduction: The United States Veterans Health Administration is a leader in the use of telemental health (TMH) to enhance access to mental healthcare amidst a nationwide shortage of mental health professionals. The Tennessee Valley Veterans Affairs (VA) Health System piloted TMH in its emergency department (ED) and urgent care clinic (UCC) in 2019, with full 24/7 availability beginning March 1, 2020. Following implementation, preliminary data demonstrated that veterans ≥65 years old were less likely to receive TMH than younger patients.

Immune response induced by a Streptococcus suis multi-serotype autogenous vaccine used in sows to protect post-weaned piglets.

Streptococcus suis is a bacterial pathogen that causes important economic losses to the swine industry worldwide. Since there are no current commercial vaccines, the use of autogenous vaccines applied to gilts/sows to enhance transfer of passive immunity is an attractive alternative to protect weaned piglets. However, there is no universal standardization in the production of autogenous vaccines and the vaccine formulation may be highly different among licenced manufacturing laboratories.

Antidepressant treatment and mortality in people with comorbid depression and type 2 diabetes: UK electronic health record study.

Background: Depression is associated with higher rates of premature mortality in people with physical comorbidities, such as type 2 diabetes. Conceptually, the successful treatment of depression in people with type 2 diabetes could prevent premature mortality.

Aims: To investigate the association between antidepressant prescribing and the rates of all-cause and cause-specific (endocrine, cardiovascular, respiratory, cancer, unnatural) mortality in individuals with comorbid depression and type 2 diabetes.

Use of noisy labels as weak learners to identify incompletely ascertainable outcomes: A Feasibility study with opioid-induced respiratory depression.

Objective: Assigning outcome labels to large observational data sets in a timely and accurate manner, particularly when outcomes are rare or not directly ascertainable, remains a significant challenge within biomedical informatics. We examined whether noisy labels generated from subject matter experts' heuristics using heterogenous data types within a data programming paradigm could provide outcomes labels to a large, observational data set. We chose the clinical condition of opioid-induced respiratory depression for our use case because it is rare, has no administrative codes to easily identify the condition, and typically requires at least some unstructured text to ascertain its presence.

The effect of immigration policy reform on mental health in people from minoritised ethnic groups in England: an interrupted time series analysis of longitudinal data from the UK Household Longitudinal Study cohort.

Background: In 2012, the UK Government announced a series of immigration policy reforms known as the hostile environment policy, culminating in the Windrush scandal. We aimed to investigate the effect of the hostile environment policy on mental health for people from minoritised ethnic backgrounds. We hypothesised that people from Black Caribbean backgrounds would have worse mental health relative to people from White ethnic backgrounds after the Immigration Act 2014 and the Windrush scandal media coverage in 2017, since they were particularly targeted.

Use of Noisy Labels as Weak Learners to Identify Incompletely Ascertainable Outcomes: A Feasibility Study with Opioid-Induced Respiratory Depression.

Objective: Assigning outcome labels to large observational data sets in a timely and accurate manner, particularly when outcomes are rare or not directly ascertainable, remains a significant challenge within biomedical informatics. We examined whether noisy labels generated from subject matter experts' heuristics using heterogenous data types within a data programming paradigm could provide outcomes labels to a large, observational data set. We chose the clinical condition of opioid-induced respiratory depression for our use case because it is rare, has no administrative codes to easily identify the condition, and typically requires at least some unstructured text to ascertain its presence.

Mental Imagery to Reduce Alcohol-related harm in patients with alcohol use disorder and alcohol-related liver damaGE: the MIRAGE randomised pilot trial results.

Objective: The healthcare burden of alcohol-related liver disease (ARLD) is increasing. ARLD and alcohol use disorder (AUD) is best managed by reduction or cessation of alcohol use, but effective treatments are lacking. We tested whether people with ARLD and AUD admitted to hospital could be recruited to and retained in a trial of Functional Imagery Training (FIT), a psychological therapy that uses mental imagery to reduce alcohol craving.

Developing a Quality Improvement Implementation Taxonomy for Organizational Employee Wellness Initiatives.

Background: Standardized taxonomies (STs) facilitate knowledge representation and semantic interoperability within health care provision and research. However, a gap exists in capturing knowledge representation to classify, quantify, qualify, and codify the intersection of evidence and quality improvement (QI) implementation. This interprofessional case report leverages a novel semantic and ontological approach to bridge this gap.

The association between antidepressant treatment and rates of insulin initiation in comorbid depression and type 2 diabetes: A UK electronic health record nested case-control study.

Aims: To investigate the association between antidepressant prescribing and the rate of insulin initiation in type 2 diabetes.

Methods: Using UK primary care records we completed a nested-case control study in a individuals with comorbid depression and type 2 diabetes. Cases were defined as individuals initiating insulin, controls were individuals remaining on oral antidiabetic medication.

Developing a Quality Improvement Implementation Taxonomy for Organizational Employee Wellness Initiatives.

Background: Standardized taxonomies (STs) facilitate knowledge representation and semantic interoperability within health care provision and research. However, a gap exists in capturing knowledge representation to classify, quantify, qualify, and codify the intersection of evidence and quality improvement (QI) implementation. This interprofessional case report leverages a novel semantic and ontological approach to bridge this gap.

Inpatient nurses' preferences and decisions with risk information visualization.

Objective: We examined the influence of 4 different risk information formats on inpatient nurses' preferences and decisions with an acute clinical deterioration decision-support system.

Materials And Methods: We conducted a comparative usability evaluation in which participants provided responses to multiple user interface options in a simulated setting. We collected qualitative data using think aloud methods.

Detecting Problematic Opioid Use in the Electronic Health Record: Automation of the Addiction Behaviors Checklist in a Chronic Pain Population.

Importance: Individuals whose chronic pain is managed with opioids are at high risk of developing an opioid use disorder. Large data sets, such as electronic health records, are required for conducting studies that assist with identification and management of problematic opioid use.

Objective: Determine whether regular expressions, a highly interpretable natural language processing technique, could automate a validated clinical tool (Addiction Behaviors Checklist) to expedite the identification of problematic opioid use in the electronic health record.

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature.

Objectives: The goal of this work was to provide a review of the implementation of data science-driven applications focused on structural or outcome-related nurse-sensitive indicators in the literature in 2021. By conducting this review, we aim to inform readers of trends in the nursing indicators being addressed, the patient populations and settings of focus, and lessons and challenges identified during the implementation of these tools.

Methods: We conducted a rigorous descriptive review of the literature to identify relevant research published in 2021.

Polypharmacy and antidepressant acceptability in comorbid depression and type 2 diabetes: a cohort study using UK primary care data.

Background: Polypharmacy may increase the risk of drug interactions, side effects, and poor adherence; however, the impact of polypharmacy on antidepressant acceptability in individuals with type 2 diabetes (T2DM) is unknown.

Aim: To investigate the association between number of prescribed medications and early antidepressant discontinuation in adults with T2DM.

Design And Setting: Cohort study using UK primary care data from the Clinical Practice Research Datalink between 1 January 2000 and 31 December 2018.

The effectiveness of a primary care-based collaborative care model to improve quality of life in people with severe mental illness: PARTNERS2 cluster randomised controlled trial.

Background: Individuals living with severe mental illness can have significant emotional, physical and social challenges. Collaborative care combines clinical and organisational components.

Aims: We tested whether a primary care-based collaborative care model (PARTNERS) would improve quality of life for people with diagnoses of schizophrenia, bipolar disorder or other psychoses, compared with usual care.

Fine-tuning of ultrathin carbon shells coated on metal nanoparticles: carbon etching and defect healing effects.

Carbon-incorporated metal nanoparticles are heat-treated in various gas atmospheres to create carbon shells with different porosity. A highly porous carbon shell is formed due to the carbon etching effect by H, and the defect healing effect by CO is discovered through additional CO gas treatment of the H/N gas-treated sample.

Telemental health in emergency care settings: A qualitative analysis of considerations for sustainability and spread.

Objectives: Following rapid uptake of telehealth during the COVID-19 pandemic, we examined barriers and facilitators for sustainability and spread of telemental health video (TMH-V) as policies regarding precautions from the pandemic waned.

Methods: We conducted a qualitative study using semistructured interviews and observations guided by RE-AIM. We asked four groups, local clinicians, facility leadership, Veterans, and external partners, about barriers and facilitators impacting patient willingness to engage in TMH-V (reach), quality of care (effectiveness), barriers and facilitators impacting provider uptake (adoption), possible adaptations to TMH-V (implementation), and possibilities for long-term use of TMH-V (maintenance).