Publications

STUDY OBJECTIVE: The COVID-19 pandemic disrupted multiple aspects of the health care system, including the diagnosis and control of chronic conditions. This study aimed to quantify pandemic-related changes in the rates of clinical events among patients with atrial fibrillation (AF).

DESIGN/SETTING/PARTICIPANTS: In this retrospective cohort study, we identified individuals with established AF at any time before 2019 using de-identified Optum's Clinformatics® Data Mart, and followed them from 3/18/2019 to death, or disenrollment, or the end of the study (09/30/2021).

MAIN OUTCOME: Rates of clinical event, including all-cause hospitalization, ischemic stroke, and bleeding. We constructed interrupted time series to test changes in outcomes after the onset of the COVID-19 pandemic (3/11/2020, date of pandemic declaration). We then identified the first month after the start of the pandemic in which outcomes returned to pre-pandemic levels.

RESULTS: A total of 561,758 patients, with a mean age of 77 ± 9.9 years, were included in the study. The monthly incidence rate of all-cause hospitalization decreased from 2.8 % in the period immediately before the pandemic declaration to 1.7 % in the period immediately after, with p-value for level change<0.001. The rate of new ischemic stroke diagnoses decreased from 0.28 % in the period immediately before pandemic declaration to 0.20 % in the period immediately after, and the rate of major bleeding diagnoses from 0.81 % to 0.59 %, both p-values for level change<0.01. The incidence rate of ischemic stroke and bleeding events returned to pre-pandemic levels in October and November 2020, respectively.

CONCLUSIONS: The COVID-19 pandemic was associated with a decrease in health care visits for ischemic stroke and bleeding in a nationwide cohort of patients with established AF.

BACKGROUND: Integrating advanced machine-learning (ML) algorithms into clinical practice is challenging and requires interdisciplinary collaboration to develop transparent, interpretable, and ethically sound clinical decision support (CDS) tools. We aimed to design a ML-driven CDS tool to predict opioid overdose risk and gather feedback for its integration into the University of Florida Health (UFHealth) electronic health record (EHR) system.

METHODS: We used user-centered design methods to integrate the ML algorithm into the EHR system. The backend and UI design sub-teams collaborated closely, both informed by user feedback sessions. We conducted seven user feedback sessions with five UF Health primary care physicians (PCPs) to explore aspects of CDS tools, including workflow, risk display, and risk mitigation strategies. After customizing the tool based on PCPs' feedback, we held two rounds of one-on-one usability testing sessions with 8 additional PCPs to gather feedback on prototype alerts. These sessions informed iterative UI design and backend processes, including alert frequency and reappearance circumstances.

RESULTS: The backend process development identified needs and requirements from our team, information technology, UFHealth, and PCPs. Thirteen PCPs (male = 62%, White = 85%) participated across 7 user feedback sessions and 8 usability testing sessions. During the user feedback sessions, PCPs (n = 5) identified flaws such as the term "high risk" of overdose potentially leading to unintended consequences (e.g., immediate addiction services referrals), offered suggestions, and expressed trust in the tool. In the first usability testing session, PCPs (n = 4) emphasized the need for natural risk presentation (e.g., 1 in 200) and suggested displaying the alert multiple times yearly for at-risk patients. Another 4 PCPs in the second usability testing session valued the UFHealth-specific alert for managing new or unfamiliar patients, expressed concerns about PCPs' workload when prescribing to high-risk patients, and recommended incorporating the details page into training sessions to enhance usability.

CONCLUSIONS: The final backend process for our CDS alert aligns with PCP needs and UFHealth standards. Integrating feedback from PCPs in the early development phase of our ML-driven CDS tool helped identify barriers and facilitators in the CDS integration process. This collaborative approach yielded a refined prototype aimed at minimizing unintended consequences and enhancing usability.

IMPORTANCE: Drug shortages are a persistent public health issue that increased during the COVID-19 pandemic. Both the US and Canada follow similar regulatory standards and require reporting of drug-related supply chain issues that may result in shortages. However, it is unknown what proportion are associated with meaningful shortages (defined by a significant decrease in drug supply) and whether differences exist between Canada and the US.

OBJECTIVE: To compare how frequently reports of drug-related supply chain issues in the US vs Canada were associated with drug shortages.

DESIGN, SETTING, AND PARTICIPANTS: Longitudinal cross-sectional study conducted from January 2023 to March 2024 using drug-related reports of supply chain issues from 2017 to 2021 that were less than 180 days apart in Canada and the US. Shortages were assessed using data from the IQVIA Multinational Integrated Data Analysis database, comprising 89% of US and 100% of Canadian drug purchases.

EXPOSURE: Country (Canada vs US), timing of report issuance (before vs after the COVID-19 pandemic), and characteristics of the supply chain prior to the reports of drug-related supply chain issues (including World Health Organization essential medicine status, Health Canada tier 3 medicine [moderate risk classification], whether there was sole-source manufacturing of the drug, the formulation, the price per unit, ≥20 years since drug approval, and the number of therapeutic alternatives).

MAIN OUTCOMES AND MEASURES: A drug shortage (a decrease of ≥33% in monthly purchased standardized drug units) within 12 months, relative to the average units purchased during the 6 months prior to the report of supply chain issues to a US or Canadian reporting system.

RESULTS: Among the 104 drug-related reports of supply chain issues in both countries, 49.0% (95% CI, 39.3%-59.7%) were associated with drug shortages in the US vs 34.0% (95% CI, 25.0%-45.0%) in Canada (adjusted hazard ratio [HR], 0.53 [95% CI, 0.36-0.79]). The lower risk of drug shortages in Canada vs the US was consistent before the COVID-19 pandemic (adjusted HR, 0.47 [95% CI, 0.30-0.75]) and after the pandemic (adjusted HR, 0.31 [95% CI, 0.15-0.66]). After combining reports of supply chain issues in both countries, the shortage risk was double for sole-sourced drugs (adjusted HR, 2.58 [95% CI, 1.57-4.24]) and nearly half for Canadian tier 3 medicines (moderate risk) (adjusted HR, 0.56 [95% CI, 0.32-0.98]).

CONCLUSIONS AND RELEVANCE: Drug-related reports of supply chain issues were 40% less likely to result in meaningful drug shortages in Canada compared with the US. These findings highlight the need for international cooperation between countries to curb the effects of drug shortages and improve resiliency of the supply chain for drugs.

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Twenty-one U.S. states have passed recreational cannabis laws as of November 2022. Cannabis may be a substitute for prescription opioids in the treatment of chronic pain. Previous studies have assessed recreational cannabis laws' effects on opioid prescriptions financed by specific private or public payers or dispensed to a unique endpoint. Our study adds to the literature in three important ways: by (1) examining these laws' impacts on prescription opioid dispensing across all payers and endpoints, (2) adjusting for important opioid-related policies such as opioid prescribing limits, and (3) modeling opioids separately by type. We implement two-way fixed-effects regressions and leverage variation from eleven U.S. states that adopted a recreational cannabis law (RCL) between 2010 and 2019. We find that RCLs lead to a reduction in codeine dispensed at retail pharmacies. Among prescription opioids, codeine is particularly likely to be used non-medically. Thus, the finding that RCLs appear to reduce codeine dispensing is potentially promising from a public health perspective.

OBJECTIVE: Weight loss achieved with standard doses of glucagon-like peptide-1 (GLP-1) agonists among real-world patients with type 2 diabetes has not been determined. This study sought to describe the percent change in body weight 72 weeks after starting a GLP-1 agonist.

METHODS: A retrospective cohort study of nonpregnant adults who were first dispensed a GLP-1 agonist between 2011 and 2018 was conducted using electronic health record data from patients receiving care at a large health system. Linear mixed models were used, with a person-level random intercept controlling for baseline variables associated with missing weight data to estimate percent body weight change during follow-up.

RESULTS: The cohort included 2405 patients (mean [SD] age 48 [10] years, 53% female), with a mean BMI of 37 (8) kg/m2 and a mean baseline weight of 238 (54) lb. Mean percent weight loss significantly increased from 1.1% (95% CI: 0.6%-1.6%) 8 weeks after GLP-1-agonist dispensing to 2.2% (95% CI: 1.7%-2.6%) 72 weeks after GLP-1-agonist dispensing (p value for quadratic trend < 0.001). One-third of patients lost ≥5% body weight at 72 weeks.

CONCLUSIONS: In this real-world study of more than 2400 patients with overweight or obesity and type 2 diabetes, starting a GLP-1 agonist at standard glycemic control doses was associated with modest weight loss through 72 weeks.

Social determinants of health (SDoH) impact health and wellness. The link between SDoH and adverse health outcomes, including symptom occurrence and severity, may be explained by an individual's physiologic response to one or more SDoH. One potential mechanism underlying this physiologic response linking SDoH and symptoms is the dynamic epigenome. The purpose of this scoping review of the literature was to examine differential susceptibility for symptoms by identifying and summarizing research linking SDoH and symptoms through epigenomic mechanisms. PubMed was searched to identify empirical research where at least one SDoH was an independent or dependent variable, at least one symptom was investigated, and the investigation included an epigenomic measure. Of the 484 articles initially retrieved, after thorough vetting, 41 articles met eligibility. The most studied symptom was depressive symptoms followed by anxiety, cognitive function, sleep dysfunction, and pain. The most frequently studied SDoH were: 1) stress, particularly early life stress and acculturative stress; and 2) trauma, predominantly childhood trauma. DNA methylation and telomere length were the most studied epigenomic measures. Four genes (SLC6A4, BDNF, NR3C1, OXTR) had evidence from multiple studies and across methodological approaches linking SDoH to symptoms. This review supports the inclusion of epigenomic approaches to better understand the link between SDoH and symptoms and provides evidence that SDoH impact telomere length and the methylation of genes involved in neurotransmitter signaling, neuronal survival, behavior, inflammation and stress response.

BACKGROUND: The COVID-19 pandemic drove significant disruptions in access to substance use disorder (SUD) treatment and harm reduction services. Healthcare delivery via telemedicine has increasingly become the norm, rendering access to a phone essential for engagement in care.

METHODS: Adult patients with SUD who lacked phones (n = 181) received a free, pre-paid phone during encounters with inpatient and outpatient SUD programs. We evaluated changes in healthcare engagement including completed in-person and telemedicine outpatient visits and telephone encounters 30 days before and after phone receipt. We used descriptive statistics, where appropriate, and paired t-tests to assess the change in healthcare engagement measures.

RESULTS: Patients were predominantly male (64%) and white (62%) with high rates of homelessness (81%) and opioid use disorder (89%). When comparing 30 days before to 30 days after phone receipt, there was a significant increased change in number of telemedicine visits by 0.3 (95% CL [0.1,0.4], p < 0.001) and telephone encounters by 0.2 (95% CL [0.1,0.3], p = 0.004). There was no statistically significant change in in-person outpatient visits observed.

CONCLUSIONS: Pre-paid phone distribution to patients with SUD was associated with an increased healthcare engagement including telemedicine visits and encounters.