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Open Access Publications from the University of California

Open Access Policy Deposits

This series is automatically populated with publications deposited by UC Irvine Department of Emergency Medicine researchers in accordance with the University of California’s open access policies. For more information see Open Access Policy Deposits and the UC Publication Management System.

Cover page of Epidemiology of substance and opium use among adult residents of Tehran; a comprehensive report from Tehran cohort study (TeCS).

Epidemiology of substance and opium use among adult residents of Tehran; a comprehensive report from Tehran cohort study (TeCS).

(2024)

BACKGROUND: The prevalence and burden of substance and opium use have increased worldwide over the past decades. In light of rapid population changes in Tehran, we aimed to evaluate the prevalence of opium and other substance use among adult residents in Tehran, Iran. METHOD: From March 2016 to March 2019, we utilized data from 8 296 participants in the Tehran Cohort Study recruitment phase (TeCS). We calculated the age-sex-weighted prevalence of substance use and the geographic distribution of substance use in Tehran. We also used logistic regression analysis to determine possible determinants of opium use. RESULT: We analyzed data from 8 259 eligible participants with complete substance use data and the average age of participants was 53.7 ± 12.75 years. The prevalence of substance use was 5.6% (95% confidence interval [CI]: 4.6- 7.1%). Substance use was more common in males than females (Prevalence: 10.5% [95% CI: 8.6- 12.6%] vs. 0.5% [95% CI: 0.2- 1.2%], respectively). The age-sex weighted prevalence of substance use was 5.4% (95% CI: 4.6-7.1%). Moreover, opium was the most frequently used substance by 95.8% of substance users. Additionally, we found that male gender (Odds ratio [OR]: 12.1, P < 0.001), alcohol intake (OR: 1.3, P = 0.016), and smoking (OR: 8.5, P < 0.001) were independently associated with opium use. CONCLUSIONS: We found that the prevalence of substance use in Tehran was 5.6%, and opium was the most frequently used substance. In addition, male gender, lower levels of education, alcohol, and tobacco consumption are the main risk factors for substance use in Tehran. Healthcare providers and policymakers can utilize our results to implement preventive strategies to minimize substance use in Tehran.

Cover page of 9-1-1 Activations from Ambulatory Care Centers: A Sicker Pediatric Population.

9-1-1 Activations from Ambulatory Care Centers: A Sicker Pediatric Population.

(2023)

BACKGROUND: Pediatric patients transferred by Emergency Medical Services (EMS) from urgent care (UC) and office-based physician practices to the emergency department (ED) following activation of the 9-1-1 EMS system are an under-studied population with scarce literature regarding outcomes for these children. The objectives of this study were to describe this population, explore EMS level-of-care transport decisions, and examine ED outcomes. METHODS: This was a retrospective review of patients zero to <15 years of age transported by EMS from UC and office-based physician practices to the ED of two pediatric receiving centers from January 2017 through December 2019. Variables included reason for transfer, level of transport, EMS interventions and medications, ED medications/labs/imaging ordered in the first hour, ED procedures, ED disposition, and demographics. Data were analyzed with descriptive statistics, X test, point biserial correlation, two-sample z test, Mann-Whitney U test, and 2-way ANOVA. RESULTS: A total of 450 EMS transports were included in this study: 382 Advanced Life Support (ALS) runs and 68 Basic Life Support (BLS) runs. The median patient age was 2.66 years, 60.9% were male, and 60.7% had private insurance. Overall, 48.9% of patients were transported from an office-based physician practice and 25.1% were transported from UC. Almost one-half (48.7%) of ALS patients received an EMS intervention or medication, as did 4.41% of BLS patients. Respiratory distress was the most common reason for transport (46.9%). Supplemental oxygen was the most common EMS intervention and albuterol was the most administered EMS medication. There was no significant association between level of transport and ED disposition (P = .23). The in-patient admission rate for transported patients was significantly higher than the general ED admission rate (P <.001). CONCLUSION: This study demonstrates that pediatric patients transferred via EMS after activation of the 9-1-1 system from UC and medical offices are more acutely ill than the general pediatric ED population and are likely sicker than the general pediatric EMS population. Paramedics appear to be making appropriate level-of-care transport decisions.

Cover page of The 2023 Model Core Content of Disaster Medicine.

The 2023 Model Core Content of Disaster Medicine.

(2023)

Introduction

Disaster Medicine (DM) is the clinical specialty whose expertise includes the care and management of patients and populations outside conventional care protocols. While traditional standards of care assume the availability of adequate resources, DM practitioners operate in situations where resources are not adequate, necessitating a modification in practice. While prior academic efforts have succeeded in developing a list of core disaster competencies for emergency medicine residency programs, international fellowships, and affiliated health care providers, no official standardized curriculum or consensus has yet been published to date for DM fellowship programs based in the United States.

Study objective

The objective of this work is to define the core curriculum for DM physician fellowships in the United States, drawing consensus among existing DM fellowship directors.

Methods

A panel of DM experts was created from the members of the Council of Disaster Medicine Fellowship Directors. This council is an independent group of DM fellowship directors in the United States that have met annually at the American College of Emergency Physicians (ACEP)'s Scientific Assembly for the last eight years with meeting support from the Disaster Preparedness and Response Committee. Using a modified Delphi technique, the panel members revised and expanded on the existing Society of Academic Emergency Medicine (SAEM) DM fellowship curriculum, with the final draft being ratified by an anonymous vote. Multiple publications were reviewed during the process to ensure all potential topics were identified.

Results

The results of this effort produced the foundational curriculum, the 2023 Model Core Content of Disaster Medicine.

Conclusion

Members from the Council of Disaster Medicine Fellowship Directors have developed the 2023 Model Core Content for Disaster Medicine in the United States. This living document defines the foundational curriculum for DM fellowships, providing the basis of a standardized experience, contributing to the development of a board-certified subspecialty, and informing fellowship directors and DM practitioners of content and topics that may appear on future certification examinations.

Cover page of A Mobile Health Application Using Geolocation for Behavioral Activity Tracking.

A Mobile Health Application Using Geolocation for Behavioral Activity Tracking.

(2023)

The increasing popularity of mHealth presents an opportunity for collecting rich datasets using mobile phone applications (apps). Our health-monitoring mobile application uses motion detection to track an individuals physical activity and location. The data collected are used to improve health outcomes, such as reducing the risk of chronic diseases and promoting healthier lifestyles through analyzing physical activity patterns. Using smartphone motion detection sensors and GPS receivers, we implemented an energy-efficient tracking algorithm that captures user locations whenever they are in motion. To ensure security and efficiency in data collection and storage, encryption algorithms are used with serverless and scalable cloud storage design. The database schema is designed around Mobile Advertising ID (MAID) as a unique identifier for each device, allowing for accurate tracking and high data quality. Our application uses Googles Activity Recognition Application Programming Interface (API) on Android OS or geofencing and motion sensors on iOS to track most smartphones available. In addition, our app leverages blockchain and traditional payments to streamline the compensations and has an intuitive user interface to encourage participation in research. The mobile tracking app was tested for 20 days on an iPhone 14 Pro Max, finding that it accurately captured location during movement and promptly resumed tracking after inactivity periods, while consuming a low percentage of battery life while running in the background.

Cover page of Surgeon-specific factors have a larger impact on decision-making for the management of proximal humerus fractures than patient-specific factors: a prospective cohort study.

Surgeon-specific factors have a larger impact on decision-making for the management of proximal humerus fractures than patient-specific factors: a prospective cohort study.

(2023)

Background

There is significant variability both in how proximal humerus fractures (PHFs) are treated and the ensuing patient outcomes. The purpose of this study was to investigate which surgeon- and patient-specific factors contribute to decision-making in the treatment of adult PHFs. We hypothesized that orthopedic sub-specialty training creates inherent bias and plays an important role in management algorithms for PHFs.

Methods

We performed a prospective cohort investigation in 2 groups of surgeons-traumatologists (N = 25) and shoulder & elbow/sports surgeons (SES) (N = 26)-and asked them to provide treatment recommendations for 30 distinct clinical cases with standardized radiographic and clinical data. This is a population-based sample of surgeons who take trauma call and treat PHFs with different sub-specializations and practice settings including academic, hospital-employed, and private. Surgeons characterized based on subspecialty (trauma vs. SES), experience level (>10 vs. ≤10-years), and employment type (hospital- vs. non-hospital-employed). Chi-square analyses, logistic mixed-effects modeling, and relative importance analysis were used to evaluate the data.

Results

Of the patient-specific factors, we found that the management of PHFs is largely dependent on initial radiographs obtained. Traumatologists were more likely to offer open reduction internal fixation (ORIF) and less likely to offer arthroplasty: 69% ORIF (traumatologists) vs. 51% ORIF (SES, P < .001), 8% arthroplasty (traumatologists) vs. 17% (SES, P < .001). Traumatologists were less likely to change from operative (either ORIF or arthroplasty) to non-operative management compared to SES surgeons when presented with additional patient demographic data. Surgeon-specific factors contributed to more than one-half of the variability in decision-making of PHF management while patient-specific factors contributed to about one-third of the variability in decision-making.

Conclusions

As physicians strive to advance the treatment for PHFs and optimize patient outcomes, our findings highlight the complex overlap between surgeon-, fracture-, and patient-specific factors in the final decision-making process.

Cover page of Using a Linear Probe Ultrasound for the Detection of First-Trimester Pregnancies in the Emergency Department.

Using a Linear Probe Ultrasound for the Detection of First-Trimester Pregnancies in the Emergency Department.

(2023)

Linear probe point-of-care ultrasound (LPUS) presents a less invasive alternative for identifying intrauterine pregnancies (IUPs) compared to usual practice (transabdominal (TAUS) or transvaginal (TVUS) ultrasound). TAUS and TVUS can be invasive or produce lower-resolution images than LPUS. The purpose of this study is to determine whether a linear probe alone can identify first-trimester IUPs. A convenience sample of 21 patients were enrolled at the University of California Irvine ED during a 7-month period. The inclusion criteria were English- or Spanish-speaking women (≥18 years) in their first trimester of pregnancy (≤12 weeks pregnant) with a body mass index (BMI) of <35. The exclusion criteria were psychiatric, incarcerated, or cognitively impaired patients. An ED physician performed LPUS and ordered a confirmatory ultrasound. The 21 patients enrolled had a mean age of 28.6 ± 6.60 years, BMI of 26.6 ± 5.03, and gestational age of 7.4 ± 2.69 weeks. Considering the 95% confidence interval, we are 97.5% confident that the sensitivity and specificity of LPUS to identify IUPs does not exceed 67.1% and 93.2%, respectively. Our pilot data did not demonstrate that LPUS can independently visualize IUPs in first-trimester patients.

Cover page of Using machine learning to develop a clinical prediction model for SSRI-associated bleeding: a feasibility study.

Using machine learning to develop a clinical prediction model for SSRI-associated bleeding: a feasibility study.

(2023)

Introduction

Adverse drug events (ADEs) are associated with poor outcomes and increased costs but may be prevented with prediction tools. With the National Institute of Health All of Us (AoU) database, we employed machine learning (ML) to predict selective serotonin reuptake inhibitor (SSRI)-associated bleeding.

Methods

The AoU program, beginning in 05/2018, continues to recruit ≥ 18 years old individuals across the United States. Participants completed surveys and consented to contribute electronic health record (EHR) for research. Using the EHR, we determined participants who were exposed to SSRIs (citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, vortioxetine). Features (n = 88) were selected with clinicians' input and comprised sociodemographic, lifestyle, comorbidities, and medication use information. We identified bleeding events with validated EHR algorithms and applied logistic regression, decision tree, random forest, and extreme gradient boost to predict bleeding during SSRI exposure. We assessed model performance with area under the receiver operating characteristic curve statistic (AUC) and defined clinically significant features as resulting in > 0.01 decline in AUC after removal from the model, in three of four ML models.

Results

There were 10,362 participants exposed to SSRIs, with 9.6% experiencing a bleeding event during SSRI exposure. For each SSRI, performance across all four ML models was relatively consistent. AUCs from the best models ranged 0.632-0.698. Clinically significant features included health literacy for escitalopram, and bleeding history and socioeconomic status for all SSRIs.

Conclusions

We demonstrated feasibility of predicting ADEs using ML. Incorporating genomic features and drug interactions with deep learning models may improve ADE prediction.

Cover page of The role of perceived health in retention disparity: A HIV-testing-related behavioral intervention among African American and Latinx men who have sex with men in the United States

The role of perceived health in retention disparity: A HIV-testing-related behavioral intervention among African American and Latinx men who have sex with men in the United States

(2023)

Retention in healthcare and health behavior remains a critical issue, contributing to inequitable distribution of intervention benefits. In diseases such as HIV, where half of the new infections occur among racial and sexual minorities, it is important that interventions do not enlarge pre-existing health disparities. To effectively combat this public health issue, it is crucial that we quantify the magnitude of racial/ethnic disparity in retention. Further, there is a need to identify mediating factors to this relationship to inform equitable intervention design. In the present study, we assess the racial/ethnic disparity in retention in a peer-led online behavioral intervention to increase HIV self-testing behavior and identify explanatory factors. The research used data collected from the Harnessing Online Peer Education (HOPE) HIV Study that included 899 primarily African American and Latinx men who have sex with men (MSM) in the United States. Results show that African American participants had higher lost-to-follow-up rates at 12-week follow-up compared to Latinx participants (11.1% and 5.8% respectively, Odds Ratio = 2.18, 95% confidence interval: 1.12 - 4.11, p = 0.02), which is substantially mediated by participants' self-rated health score (14.1% of the variation in the African American v.s. Latinx difference in lost-follow-up, p = 0.006). Thus, how MSM perceive their health may play an important role in their retention in HIV-related behavioral intervention programs and its racial/ethnic disparity.

Cover page of Blockchain-enabled immutable, distributed, and highly available clinical research activity logging system for federated COVID-19 data analysis from multiple institutions

Blockchain-enabled immutable, distributed, and highly available clinical research activity logging system for federated COVID-19 data analysis from multiple institutions

(2023)

Objective

We aimed to develop a distributed, immutable, and highly available cross-cloud blockchain system to facilitate federated data analysis activities among multiple institutions.

Materials and methods

We preprocessed 9166 COVID-19 Structured Query Language (SQL) code, summary statistics, and user activity logs, from the GitHub repository of the Reliable Response Data Discovery for COVID-19 (R2D2) Consortium. The repository collected local summary statistics from participating institutions and aggregated the global result to a COVID-19-related clinical query, previously posted by clinicians on a website. We developed both on-chain and off-chain components to store/query these activity logs and their associated queries/results on a blockchain for immutability, transparency, and high availability of research communication. We measured run-time efficiency of contract deployment, network transactions, and confirmed the accuracy of recorded logs compared to a centralized baseline solution.

Results

The smart contract deployment took 4.5 s on an average. The time to record an activity log on blockchain was slightly over 2 s, versus 5-9 s for baseline. For querying, each query took on an average less than 0.4 s on blockchain, versus around 2.1 s for baseline.

Discussion

The low deployment, recording, and querying times confirm the feasibility of our cross-cloud, blockchain-based federated data analysis system. We have yet to evaluate the system on a larger network with multiple nodes per cloud, to consider how to accommodate a surge in activities, and to investigate methods to lower querying time as the blockchain grows.

Conclusion

Blockchain technology can be used to support federated data analysis among multiple institutions.

Cover page of Facilitators of and Barriers to Integrating Digital Mental Health Into County Mental Health Services: Qualitative Interview Analyses.

Facilitators of and Barriers to Integrating Digital Mental Health Into County Mental Health Services: Qualitative Interview Analyses.

(2023)

Background

Digital mental health interventions (DMHIs) represent a promising solution to address the growing unmet mental health needs and increase access to care. Integrating DMHIs into clinical and community settings is challenging and complex. Frameworks that explore a wide range of factors, such as the Exploration, Preparation, Implementation, Sustainment (EPIS) framework, can be useful for examining multilevel factors related to DMHI implementation efforts.

Objective

This paper aimed to identify the barriers to, facilitators of, and best practice recommendations for implementing DMHIs across similar organizational settings, according to the EPIS domains of inner context, outer context, innovation factors, and bridging factors.

Methods

This study stems from a large state-funded project in which 6 county behavioral health departments in California explored the use of DMHIs as part of county mental health services. Our team conducted interviews with clinical staff, peer support specialists, county leaders, project leaders, and clinic leaders using a semistructured interview guide. The development of the semistructured interview guide was informed by expert input regarding relevant inner context, outer context, innovation factors, and bridging factors in the exploration, preparation, and implementation phases of the EPIS framework. We followed a recursive 6-step process to conduct qualitative analyses using inductive and deductive components guided by the EPIS framework.

Results

On the basis of 69 interviews, we identified 3 main themes that aligned with the EPIS framework: readiness of individuals, readiness of innovations, and readiness of organizations and systems. Individual-level readiness referred to the extent to which clients had the necessary technological tools (eg, smartphones) and knowledge (digital literacy) to support the DMHI. Innovation-level readiness pertained to the accessibility, usefulness, safety, and fit of the DMHI. Organization- and system-level readiness concerned the extent to which providers and leadership collectively held positive views about DMHIs as well as the extent to which infrastructure (eg, staffing and payment model) was appropriate.

Conclusions

The successful implementation of DMHIs requires readiness at the individual, innovation, and organization and system levels. To improve individual-level readiness, we recommend equitable device distribution and digital literacy training. To improve innovation readiness, we recommend making DMHIs easier to use and introduce, clinically useful, and safe and adapting them to fit into the existing client needs and clinical workflow. To improve organization- and system-level readiness, we recommend supporting providers and local behavioral health departments with adequate technology and training and exploring potential system transformations (eg, integrated care model). Conceptualizing DMHIs as services allows the consideration of both the innovation characteristics of DMHIs (eg, efficacy, safety, and clinical usefulness) and the ecosystem around DMHIs, such as individual and organizational characteristics (inner context), purveyors and intermediaries (bridging factor), client characteristics (outer context), as well as the fit between the innovation and implementation settings (innovation factor).