The impact of sexual scripts in brand-generated cannabis social media posts on sex-related cannabis expectancies: Does body appreciation moderate effects?

INTRODUCTION: Cannabis messaging on digital media may include sexualised portrayals. We examined whether exposure to and perceptions of cannabis posts that included sexual objectification impacted two types of sex-related cannabis expectancies-sexual risk and sexual enhancement-and whether body appreciation moderated these relationships. METHODS: We conducted an online experiment with college students in Washington state. Participants viewed three brand-generated cannabis Instagram posts that either included sexually objectified women or recreational appeals (e.g., sitting by a firepit). We conducted regressions, using the PROCESS macro, to examine the hypothesized model and potential mediation and moderation. RESULTS: Exposure to sexualised advertisements was associated with increased perceptions of cannabis sex enhancement scripts (b = 0.34, p < 0.01), which was associated with increased cannabis sex enhancement expectancies (b = 0.34, p < 0.001) and decreased cannabis sexual risk expectancies (b = -0.16, p < 0.001); exposure to such advertisements were also associated with increased perceptions of cannabis sexual risk scripts (b = 0.61, p < 0.001), which was associated with increased cannabis sexual risk expectancies (b = 0.53, p < 0.001). Body appreciation was associated with increased cannabis sex enhancement expectancies (b = 0.13, p < 0.01) and moderated the relationship between exposure to sexualised ads and cannabis sex enhancement expectancies (b = -0.21, p < 0.01). DISCUSSION AND CONCLUSIONS: Practitioners may want to consider how to increase critical consumption of cannabis content on digital media. Researchers should consider the possible role of body appreciation as it relates to cannabis and sex enhancement expectancies.

Investigating the association between asthma and opioid use disorder with interactions of anxiety and depression among a national sample of the US population.

INTRODUCTION: Previous studies have not examined the association between asthma and opioid use disorder (OUD) in a comprehensive national sample of the U.S. population. This study aims to investigate such an association. METHODS: This is a matched retrospective cohort study, with a follow-up period of two years, utilizing longitudinal electronic medical records of a comprehensive national healthcare database in the U.S.-Cerner-Real World DataTM. Patients selected for analysis were ≥12 years old with a hospital encounter between January 2000 and June 2020. Adjusted risk ratios (aRRs) of incident OUD for those with asthma compared to those without asthma were calculated using a modified Poisson regressions with robust standard errors via the Huber-White sandwich estimator, and results were stratified by comorbid mental illnesses. RESULTS: Individuals with asthma had a greater risk of OUD compared to those without asthma (aRR = 2.12; 95% CI 2.03-2.23). When stratified by anxiety and depression status, individuals with asthma and no anxiety or depression had a greater risk of incident OUD compared to individuals with asthma and either anxiety, depression, or both. Additionally, individuals with asthma medication had 1.29 (95% CI: 1.24, 1.35) greater overall risk for incident OUD compared to those without medication. Independent of comorbid mental illnesses, individuals with asthma medication had greater risk for incident OUD compared to those without medication among individuals without severe/obstructive asthma. CONCLUSIONS: Individuals with asthma face a higher OUD risk compared to those without asthma. Comorbid mental illnesses modulate this risk. Caution is advised in opioid prescribing for asthma patients.

Exploring the Association Between Opioid Use Disorder and Alzheimer's Disease and Dementia Among a National Sample of the U.S. Population.

BACKGROUND: Past research suggests associations between heavy alcohol use and later life dementia. However, little is known about whether opioid use disorder (OUD) and dementia share this association, especially among age groups younger than 65 years old. OBJECTIVE: Examine the association between OUD and Alzheimer's disease (AD) and dementia. METHODS: Electronic health records between 2000 and 2021 for patients age 12 or older were identified in the Cerner Real-World database™. Patients with a prior diagnosis of dementia were excluded. Patients were followed for 1-10 years (grouped by one, three, five, and ten-year follow-up periods) in a matched retrospective cohort study. Cox proportional hazards regressions were used to estimate adjusted hazard ratios (aHRs) of incident AD/dementia stratified by age and follow-up group. RESULTS: A sample of 627,810 individuals with OUD were compared to 646,340 without OUD. Individuals with OUD exhibited 88% higher risk for developing AD/dementia compared to those without OUD (aHR = 1.88, 95% CI 1.74, 2.03) within 1 year follow-up and 211% (aHR = 3.11, 95% CI 2.63, 3.69) within 10 years follow-up. When stratifying by age, younger patients (age 12-44) had a greater disparity in odds of AD/dementia between OUD and non-OUD groups compared with patients older than 65 years. CONCLUSIONS: Additional research is needed to understand why an association exists between OUD and AD/dementia, especially among younger populations. The results suggest that cognitive functioning screening programs for younger people diagnosed with OUD may be useful for targeting early identification and intervention for AD/dementia in particularly high risk and marginalized populations.

A Mobile App to Support Self-management of Chronic Kidney Disease: Development Study.

BACKGROUND: Chronic kidney disease (CKD) is a common and costly condition that is usually accompanied by multiple comorbidities including type 2 diabetes, hypertension, and obesity. Proper management of CKD can delay or prevent kidney failure and help mitigate cardiovascular disease risk, which increases as kidney function declines. Smart device apps hold potential to enhance patient self-management of chronic conditions including CKD. OBJECTIVE: The objective of this study was to develop a mobile app to facilitate self-management of nondialysis-dependent CKD. METHODS: Our stakeholder team included 4 patients with stage 3-4 nondialysis-dependent CKD; a kidney transplant recipient; a caretaker; CKD care providers (pharmacists, a nurse, primary care physicians, a nephrologist, and a cardiologist); 2 health services and CKD researchers; a researcher in biomedical informatics, nutrition, and obesity; a system developer; and 2 programmers. Focus groups and in-person interviews with the patients and providers were conducted using a focus group and interview guide based on existing literature on CKD self-management and the mobile app quality criteria from the Mobile App Rating Scale. Qualitative analytic methods including the constant comparative method were used to analyze the focus group and interview data. RESULTS: Patients and providers identified and discussed a list of requirements and preferences regarding the content, features, and technical aspects of the mobile app, which are unique for CKD self-management. Requirements and preferences centered along themes of communication between patients and caregivers, partnership in care, self-care activities, adherence to treatment regimens, and self-care self-efficacy. These identified themes informed the features and content of our mobile app. The mobile app user can enter health data including blood pressure, weight, and blood glucose levels. Symptoms and their severity can also be entered, and users are prompted to contact a physician as indicated by the symptom and its severity. Next, mobile app users can select biweekly goals from a set of predetermined goals with the option to enter customized goals. The user can also keep a list of medications and track medication use. Our app includes feedback mechanisms where in-range values for health data are depicted in green and out-of-range values are depicted in red. We ensured that data entered by patients could be downloaded into a user-friendly report, which could be emailed or uploaded to an electronic health record. The mobile app also includes a mechanism that allows either group or individualized video chat meetings with a provider to facilitate either group support, education, or even virtual clinic visits. The CKD app also includes educational material on CKD and its symptoms. CONCLUSIONS: Patients with CKD and CKD care providers believe that a mobile app can enhance CKD self-management by facilitating patient-provider communication and enabling self-care activities including treatment adherence.

Using Concept Unique Identifiers to Filter Electronic Health Records for Delirium Cases.

Delirium, an acute mental status change associated with inattention, confusion, hypervigilance, or somnolence due to a medical cause, is considered a medical emergency. Unfortunately, screening and diagnosis of delirium in acute care are often inadequate. It is estimated that 60% of delirium cases are not identified, and in claims data, they are underreported. Using information technology, we investigated whether concept unique identifiers from the Unified Language Medical System Metathesaurus could be used as a method to filter electronic health records for possible delirium cases. This article provides the reader with an overview of delirium, the Unified Language Medical System Metathesaurus, and our method for retrospectively filtering electronic health records for delirium cases from our clinical research database. Using a retrospective observational approach, we randomly selected 150 electronic health records with narrative notes containing a delirium concept unique identifier. One hundred records were used for training and 50 were used for validation and interrater reliability. Our results validate electronic health record-selected concept unique identifiers and provide insights into their use. Refinement and application of this method on a larger scale can provide an initial filter for identifying patients with delirium from the electronic health record.

Development and application of a high throughput natural language processing architecture to convert all clinical documents in a clinical data warehouse into standardized medical vocabularies.

OBJECTIVE: Natural language processing (NLP) engines such as the clinical Text Analysis and Knowledge Extraction System are a solution for processing notes for research, but optimizing their performance for a clinical data warehouse remains a challenge. We aim to develop a high throughput NLP architecture using the clinical Text Analysis and Knowledge Extraction System and present a predictive model use case. MATERIALS AND METHODS: The CDW was comprised of 1 103 038 patients across 10 years. The architecture was constructed using the Hadoop data repository for source data and 3 large-scale symmetric processing servers for NLP. Each named entity mention in a clinical document was mapped to the Unified Medical Language System concept unique identifier (CUI). RESULTS: The NLP architecture processed 83 867 802 clinical documents in 13.33 days and produced 37 721 886 606 CUIs across 8 standardized medical vocabularies. Performance of the architecture exceeded 500 000 documents per hour across 30 parallel instances of the clinical Text Analysis and Knowledge Extraction System including 10 instances dedicated to documents greater than 20 000 bytes. In a use-case example for predicting 30-day hospital readmission, a CUI-based model had similar discrimination to n-grams with an area under the curve receiver operating characteristic of 0.75 (95% CI, 0.74-0.76). DISCUSSION AND CONCLUSION: Our health system's high throughput NLP architecture may serve as a benchmark for large-scale clinical research using a CUI-based approach.

Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation.

Objective: Alcohol misuse is present in over a quarter of trauma patients. Information in the clinical notes of the electronic health record of trauma patients may be used for phenotyping tasks with natural language processing (NLP) and supervised machine learning. The objective of this study is to train and validate an NLP classifier for identifying patients with alcohol misuse. Materials and Methods: An observational cohort of 1422 adult patients admitted to a trauma center between April 2013 and November 2016. Linguistic processing of clinical notes was performed using the clinical Text Analysis and Knowledge Extraction System. The primary analysis was the binary classification of alcohol misuse. The Alcohol Use Disorders Identification Test served as the reference standard. Results: The data corpus comprised 91 045 electronic health record notes and 16 091 features. In the final machine learning classifier, 16 features were selected from the first 24 hours of notes for identifying alcohol misuse. The classifier's performance in the validation cohort had an area under the receiver-operating characteristic curve of 0.78 (95% confidence interval [CI], 0.72 to 0.85). Sensitivity and specificity were at 56.0% (95% CI, 44.1% to 68.0%) and 88.9% (95% CI, 84.4% to 92.8%). The Hosmer-Lemeshow goodness-of-fit test demonstrates the classifier fits the data well (P = .17). A simpler rule-based keyword approach had a decrease in sensitivity when compared with the NLP classifier from 56.0% to 18.2%. Conclusions: The NLP classifier has adequate predictive validity for identifying alcohol misuse in trauma centers. External validation is needed before its application to augment screening.

A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning.

Acute Respiratory Distress Syndrome (ARDS) is a syndrome of respiratory failure that may be identified using text from radiology reports. The objective of this study was to determine whether natural language processing (NLP) with machine learning performs better than a traditional keyword model for ARDS identification. Linguistic pre-processing of reports was performed and text features were inputs to machine learning classifiers tuned using 10-fold cross-validation on 80% of the sample size and tested in the remaining 20%. A cohort of 533 patients was evaluated, with a data corpus of 9,255 radiology reports. The traditional model had an accuracy of 67.3% (95% CI: 58.3-76.3) with a positive predictive value (PPV) of 41.7% (95% CI: 27.7-55.6). The best NLP model had an accuracy of 83.0% (95% CI: 75.9-90.2) with a PPV of 71.4% (95% CI: 52.1-90.8). A computable phenotype for ARDS with NLP may identify more cases than the traditional model.

Patient-Centered Outcomes Research in Practice: The CAPriCORN Infrastructure.

CAPriCORN, the Chicago Area Patient Centered Outcomes Research Network, is one of the eleven PCORI-funded Clinical Data Research Networks. A collaboration of six academic medical centers, a Chicago public hospital, two VA hospitals and a network of federally qualified health centers, CAPriCORN addresses the needs of a diverse community and overlapping populations. To capture complete medical records without compromising patient privacy and confidentiality, the network created policies and mechanisms for patient consultation, central IRB approval, de-identification, de-duplication, and integration of patient data by study cohort, randomization and sampling, re-identification for consent by providers and patients, and communication with patients to elicit patient-reported outcomes through validated instruments. The paper describes these policies and mechanisms and discusses two case studies to prove the feasibility and effectiveness of the network.

CAPriCORN: Chicago Area Patient-Centered Outcomes Research Network.

The Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN) represents an unprecedented collaboration across diverse healthcare institutions including private, county, and state hospitals and health systems, a consortium of Federally Qualified Health Centers, and two Department of Veterans Affairs hospitals. CAPriCORN builds on the strengths of our institutions to develop a cross-cutting infrastructure for sustainable and patient-centered comparative effectiveness research in Chicago. Unique aspects include collaboration with the University HealthSystem Consortium to aggregate data across sites, a centralized communication center to integrate patient recruitment with the data infrastructure, and a centralized institutional review board to ensure a strong and efficient human subject protection program. With coordination by the Chicago Community Trust and the Illinois Medical District Commission, CAPriCORN will model how healthcare institutions can overcome barriers of data integration, marketplace competition, and care fragmentation to develop, test, and implement strategies to improve care for diverse populations and reduce health disparities.

Development of a tool to manage patient health records in support of burn injury research.

Data captured in electronic medical records (EMRs) and paper charts have enormous potential for clinical research and to improve the quality of health care; however, accessing, organizing, and analyzing these data pose significant challenges. To address these challenges, this article reports development of a web-based application that provides for local clinical data capture as well as integration of patient data directly from an institutional EMR. A web-based system was created using an existing institutional application development framework. The application consists of a local clinical data repository, processes that integrate data from an EMR, and programs that enable end-user access, manual data capture, and analysis. Data are maintained in a relational database at the patient level in a time- oriented manner and by clinical data type. The application and data repository have been used to integrate and analyze a broad range of clinical data of 637 patients with burn injury. Research findings have shown that in addition to tracking clinical outcomes, laboratory data provide the ability to risk stratify patient populations to target high-risk individuals for case management and interventions. This effort validates the utility of web-based applications to collect local clinical data and integrate clinical data directly from an institutional EMR. This approach leverages institutionally collected clinical information and provides the flexibility to incorporate disparate data and accommodate system modifications as needed. Although the current efforts have focused on a cohort of patients with burn injury, the approach and system design are extendable to other patient types.

An audience response system may influence student performance on anatomy examination questions.

This study integrated an in-house audience response system (ARS) in the human anatomy course over two years to determine whether students performed better on high-stakes examinations following exposure to similar interactive questions in a large lecture format. Questions in an interactive ARS format were presented in lectures via PowerPoint presentations. Students who chose to participate in the anonymous ARS sessions submitted answers via their personal wireless devices (e.g., laptops, smartphones, PDAs, etc). Students were surveyed for feedback. Student participation in ARS activities was greatest (65-80%) in the first lecture. The number of students who actively participated in ARS activities decreased over the next four sessions, and then slightly increased in the last two sessions. This trend was the same for both years. Use of the ARS did not dramatically enhance overall student performance on examination questions that dealt with content similar to content presented in the ARS sessions. However, students who scored in the lower quartile of the examination performed better on the examination questions after the ARS was implemented. Accordingly, our findings suggest that the effect of ARS to improve student performance on examinations was not uniform. The overall benefit of an ARS to enhance the lecture experience was confirmed by student surveys.

An analysis of lecture video utilization in undergraduate medical education: associations with performance in the courses.

BACKGROUND: Increasing numbers of medical schools are providing videos of lectures to their students. This study sought to analyze utilization of lecture videos by medical students in their basic science courses and to determine if student utilization was associated with performance on exams. METHODS: Streaming videos of lectures (n = 149) to first year and second year medical students (n = 284) were made available through a password-protected server. Server logs were analyzed over a 10-week period for both classes. For each lecture, the logs recorded time and location from which students accessed the file. A survey was administered at the end of the courses to obtain additional information about student use of the videos. RESULTS: There was a wide disparity in the level of use of lecture videos by medical students with the majority of students accessing the lecture videos sparingly (60% of the students viewed less than 10% of the available videos. The anonymous student survey revealed that students tended to view the videos by themselves from home during weekends and prior to exams. Students who accessed lecture videos more frequently had significantly (p < 0.002) lower exam scores. CONCLUSION: We conclude that videos of lectures are used by relatively few medical students and that individual use of videos is associated with the degree to which students are having difficulty with the subject matter.