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Postauthorization monitoring of vaccines in a large population may detect rare adverse events not identified in clinical trials such as Guillain-Barré syndrome (GBS), which has a background rate of 1 to 2 per 100 000 person-years. To describe cases and incidence of GBS following COVID-19 vaccination and assess the risk of GBS after vaccination for Ad.26.COV2.S (Janssen) and mRNA vaccines. This cohort study used surveillance data from the Vaccine Safety Datalink at 8 participating integrated health care systems in the United States. There were 10 158 003 participants aged at least 12 years. Data analysis was performed from November 2021 to February 2022. Ad.26.COV2.S, BNT162b2 (Pfizer-BioNTech), or mRNA-1273 (Moderna) COVID-19 vaccine, including mRNA vaccine doses 1 and 2, December 13, 2020, to November 13, 2021. GBS with symptom onset in the 1 to 84 days after vaccination, confirmed by medical record review and adjudication. Descriptive characteristics of confirmed cases, GBS incidence rates during postvaccination risk intervals after each type of vaccine compared with the background rate, rate ratios (RRs) comparing GBS incidence in the 1 to 21 vs 22 to 42 days postvaccination, and RRs directly comparing risk of GBS after Ad.26.COV2.S vs mRNA vaccination, using Poisson regression adjusted for age, sex, race and ethnicity, site, and calendar day. From December 13, 2020, through November 13, 2021, 15 120 073 doses of COVID-19 vaccines were administered to 7 894 989 individuals (mean [SE] age, 46.5 [0.02] years; 8 138 318 doses received [53.8%] by female individuals; 3 671 199 doses received [24.3%] by Hispanic or Latino individuals, 2 215 064 doses received [14.7%] by Asian individuals, 6 266 424 doses received [41.4%] by White individuals), including 483 053 Ad.26.COV2.S doses, 8 806 595 BNT162b2 doses, and 5 830 425 mRNA-1273 doses. Eleven cases of GBS after Ad.26.COV2.S were confirmed. The unadjusted incidence rate of GBS per 100 000 person-years in the 1 to 21 days after Ad.26.COV2.S was 32.4 (95% CI, 14.8-61.5), significantly higher than the background rate, and the adjusted RR in the 1 to 21 vs 22 to 42 days following Ad.26.COV2.S was 6.03 (95% CI, 0.79-147.79). Thirty-six cases of GBS after mRNA vaccines were confirmed. The unadjusted incidence rate per 100 000 person-years in the 1 to 21 days after mRNA vaccines was 1.3 (95% CI, 0.7-2.4) and the adjusted RR in the 1 to 21 vs 22 to 42 days following mRNA vaccines was 0.56 (95% CI, 0.21-1.48). In a head-to-head comparison of Ad.26.COV2.S vs mRNA vaccines, the adjusted RR was 20.56 (95% CI, 6.94-64.66). In this cohort study of COVID-19 vaccines, the incidence of GBS was elevated after receiving the Ad.26.COV2.S vaccine. Surveillance is ongoing.

BackgroundAlthough naloxone prevents opioid overdose deaths, few patients prescribed opioids receive naloxone, limiting its effectiveness in real-world settings. Barriers to naloxone prescribing include concerns that naloxone could increase risk behavior and limited time to provide necessary patient education.ObjectiveTo determine whether pharmacy-based naloxone co-dispensing affected opioid risk behavior. Secondary objectives were to assess if co-dispensing increased naloxone acquisition, increased patient knowledge about naloxone administration, and affected opioid dose and other substance use.DesignCluster randomized pragmatic trial of naloxone co-dispensing.SettingSafety-net health system in Denver, Colorado, between 2017 and 2020.ParticipantsSeven pharmacies were randomized. Pharmacy patients (N=768) receiving opioids were followed using automated data for 10 months. Pharmacy patients were also invited to complete surveys at baseline, 4 months, and 8 months; 325 survey participants were enrolled from November 15, 2017, to January 8, 2019.InterventionIntervention pharmacies implemented workflows to co-dispense naloxone while usual care pharmacies provided usual services.Main MeasuresSurvey instruments assessed opioid risk behavior; hazardous drinking; tobacco, cannabis, and other drug use; and knowledge. Naloxone dispensings and opioid dose were evaluated using pharmacy data among pharmacy patients and survey participants. Intention-to-treat analyses were conducted using generalized linear mixed models accounting for clustering at the pharmacy level.Key ResultsOpioid risk behavior did not differ by trial group (P=0.52; 8-month vs. baseline adjusted risk ratio [ARR] 1.07; 95% CI 0.78, 1.47). Compared with usual care pharmacies, naloxone dispensings were higher in intervention pharmacies (ARR 3.38; 95% CI 2.21, 5.15) and participant knowledge increased (P=0.02; 8-month vs. baseline adjusted mean difference 1.05; 95% CI 0.06, 2.04). There was no difference in other substance use by the trial group.ConclusionCo-dispensing naloxone with opioids effectively increased naloxone receipt and knowledge but did not increase self-reported risk behavior.Trial RegistrationRegistered at ClinicalTrials.gov; Identifier: NCT03337100

Attempts to discontinue opioid therapy to reduce the risk of overdose and adhere to prescribing guidelines may lead patients to be exposed to variability in opioid dosing. Such dose variability may increase the risk of opioid overdose even if therapy discontinuation is associated with a reduction in risk. To examine the association between opioid dose variability and opioid overdose. A nested case-control study was conducted in a large Colorado integrated health plan and delivery system from January 1, 2006, through June 30, 2018. Cohort members were individuals prescribed long-term opioid therapy. Dose variability was defined as the SD of the milligrams of morphine equivalents across each patient's follow-up and categorized based on the quintile distribution of the SD in the cohort (0-5.3, 5.4-9.1, 9.2-14.6, 14.7-27.2, and >27.2 mg of morphine equivalents). Opioid overdose cases were identified using International Classification of Diseases, Ninth Revision and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes. Each case patient with overdose was matched to up to 20 control patients using risk set sampling. Conditional logistic regression models were used to generate matched odds ratios and 95% CIs, adjusted for age, sex, race/ethnicity, drug or alcohol use disorder, tobacco use, benzodiazepine dispensings, medical comorbidities, mental health disorder, opioid dose, and opioid formulation. In a cohort of 14 898 patients (mean [SD] age, 56.3 [16.0] years; 8988 [60.3%] female) prescribed long-term opioid therapy, 228 case patients with incident opioid overdose were matched to 3547 control patients. The mean (SD) duration of opioid therapy was 36.7 (33.7) months in case patients and 33.0 (30.9) months in control patients. High-dose variability (SD >27.2 mg of morphine equivalents) was associated with a significantly increased risk of overdose compared with low-dose variability (matched odds ratio, 3.32; 95% CI, 1.63-6.77) independent of opioid dose. Variability in opioid dose may be a risk factor for opioid overdose, suggesting that practitioners should seek to minimize dose variability when managing long-term opioid therapy.

To develop and externally validate a prediction model for the 6-month risk of a severe hypoglycemic event among individuals with pharmacologically treated diabetes. The development cohort consisted of 31,674 Kaiser Permanente Colorado members with pharmacologically treated diabetes (2007–2015). The validation cohorts consisted of 38,764 Kaiser Permanente Northwest members and 12,035 HealthPartners members. Variables were chosen that would be available in electronic health records. We developed 16-variable and 6-variable models, using a Cox counting model process that allows for the inclusion of multiple 6-month observation periods per person. Across the three cohorts, there were 850,992 6-month observation periods, and 10,448 periods with at least one severe hypoglycemic event. The six-variable model contained age, diabetes type, HgbA1c, eGFR, history of a hypoglycemic event in the prior year, and insulin use. Both prediction models performed well, with good calibration and c-statistics of 0.84 and 0.81 for the 16-variable and 6-variable models, respectively. In the external validation cohorts, the c-statistics were 0.80–0.84. We developed and validated two prediction models for predicting the 6-month risk of hypoglycemia. The 16-variable model had slightly better performance than the 6-variable model, but in some practice settings, use of the simpler model may be preferred.

Optimal timing of initiating invasive mechanical ventilation (IMV) in coronavirus disease 2019 (COVID-19)-related respiratory failure is unclear. We hypothesized that a strategy of IMV as opposed to continuing high flow oxygen or non-invasive mechanical ventilation each day after reaching a high FiO2 threshold would be associated with worse in-hospital mortality. Using data from Kaiser Permanente Northern/Southern California's 36 medical centers, we identified patients with COVID-19-related acute respiratory failure who reached ≥80% FiO2 on high flow nasal cannula or non-invasive ventilation. Exposure was IMV initiation each day after reaching high FiO2 threshold (T0). We developed propensity scores with overlap weighting for receipt of IMV each day adjusting for confounders. We reported relative risk of inpatient death with 95% Confidence Interval. Of 28,035 hospitalizations representing 21,175 patient-days, 5758 patients were included (2793 received and 2965 did not receive IMV). Patients receiving IMV had higher unadjusted mortality (63.6% versus 18.2%, P < 0.0001). On each day after reaching T0 through day >10, the adjusted relative risk was higher for those receiving IMV compared to those not receiving IMV (Relative Risk>1). Initiation of IMV on each day after patients reach high FiO2 threshold was associated with higher inpatient mortality after adjusting for time-varying confounders. Remaining on high flow nasal cannula or non-invasive ventilation does not appear to be harmful compared to IMV. Prospective evaluation is needed. •It is unclear what the risks are of providing invasive mechanical ventilation versus non-invasive forms of respiratory support (high flow nasal cannula or non-invasive ventilation through CPAP/biPAP) for patients with COVID-19-associated respiratory failure. We sought to assess whether initiation of invasive mechanical ventilation in patients with COVID-19 pneumonia each day after reaching a high oxygen requirement (≥80% fractional inspired oxygen) was associated with increased risk of in-hospital mortality versus remaining on non-invasive respiratory support using propensity scores with overlap weighting.•On each day after reaching high oxygen requirement, the adjusted relative risk of in-hospital death was higher for those receiving invasive mechanical ventilation compared to those not receiving invasive mechanical ventilation after adjusting for time-varying confounders.•This result suggests that a strategy of providing non-invasive respiratory support after reaching a high FIO2 threshold versus invasive mechanical ventilation does not appear harmful. A strategy of invasive mechanical ventilation should be avoided if possible. However, unmeasured confounding could explain the findings. A prospective trial is needed.

BACKGROUND Information comparing characteristics of patients who do and do not pick up their prescriptions is sparse, in part because adherence measured using pharmacy claims databases does not include information on patients who never pick up their first prescription, that is, patients with primary non-adherence. Electronic health record medication order entry enhances the potential to identify patients with primary non-adherence, and in organizations with medication order entry and pharmacy information systems, orders can be linked to dispensings to identify primarily non-adherent patients. OBJECTIVE This study aims to use database information from an integrated system to compare patient, prescriber, and payment characteristics of patients with primary non-adherence and patients with ongoing dispensings of newly initiated medications for hypertension, diabetes, and/or hyperlipidemia. DESIGN This is a retrospective observational cohort study. PARTICIPANTS (OR PATIENTS OR SUBJECTS) Participants of this study include patients with a newly initiated order for an antihypertensive, antidiabetic, and/or antihyperlipidemic within an 18-month period. MAIN MEASURES Proportion of patients with primary non-adherence overall and by therapeutic class subgroup. Multivariable logistic regression modeling was used to investigate characteristics associated with primary non-adherence relative to ongoing dispensings. KEY RESULTS The proportion of primarily non-adherent patients varied by therapeutic class, including 7% of patients ordered an antihypertensive, 11% ordered an antidiabetic, 13% ordered an antihyperlipidemic, and 5% ordered medications from more than one of these therapeutic classes within the study period. Characteristics of patients with primary non-adherence varied across therapeutic classes, but these characteristics had poor ability to explain or predict primary non-adherence (models c-statistics = 0.61–0.63). CONCLUSIONS Primary non-adherence varies by therapeutic class. Healthcare delivery systems should pursue linking medication orders with dispensings to identify primarily non-adherent patients. We encourage conduct of research to determine interventions successful at decreasing primary non-adherence, as characteristics available from databases provide little assistance in predicting primary non-adherence.

BACKGROUND:Collaborations between clinical/operational leaders and researchers are advocated to develop “learning health systems,” but few practical examples are reported. OBJECTIVES:To describe collaborative efforts to reduce missed appointments through an interactive voice response and text message (IVR-T) intervention, and to develop and validate a prediction model to identify individuals at high risk of missing appointments. RESEARCH SUBJECTS AND DESIGN:Random assignment of 8804 adults with primary care appointments to a single IVR-T reminder or no reminder at an index clinic (IC) and 7497 at a replication clinic (RC) in an integrated health system in Denver, CO. MEASURES:Proportion of missed appointments; demographic, clinical, and appointment-specific predictors of missed appointments. RESULTS:Patients receiving IVR-T had a lower rate of missed appointments than those receiving no reminder at the IC (6.5% vs. 7.5%, relative risk=0.85, 95% confidence interval, 0.72–1.00) and RC (8.2% vs. 10.5%, relative risk=0.76, 95% confidence interval, 0.65–0.89). A 10-variable prediction model for missed appointments demonstrated excellent discrimination (C-statistic 0.90 at IC, 0.89 at RC) and calibration (P=0.99 for Osius and McCullagh tests). Patients in the 3 lowest-risk quartiles missed 0.4% and 0.4% of appointments at the IC and RC, respectively, whereas patients in the highest-risk quartile missed 24.1% and 28.9% of appointments, respectively. CONCLUSIONS:A single IVR-T call reduced missed appointments, whereas a locally validated prediction model accurately identified patients at high risk of missing appointments. These rigorous studies promoted dissemination of the intervention and prompted additional research questions from operational leaders.

IntroductionPublic health efforts to reduce opioid overdose fatalities include educating people at risk and expanding access to naloxone, a medication that reverses opioid-induced respiratory depression. People receiving long-term opioid therapy (LTOT) are at increased risk for overdose, yet naloxone uptake in this population remains low. The objective of this study was to determine if a targeted, digital health intervention changed patient risk behaviour, increased naloxone uptake and increased knowledge about opioid overdose prevention and naloxone.MethodsWe conducted a pragmatic randomised clinical trial among patients prescribed LTOT in a healthcare delivery system in Colorado. Participants were randomly assigned to receive an animated overdose prevention and naloxone educational video (intervention arm) or usual care (control arm). The 6 min video was designed to educate patients about opioid overdose and naloxone, increase overdose risk perception and prompt them to purchase naloxone from the pharmacy. Over an 8-month follow-up, opioid risk behaviour was assessed with the Opioid-Related Behaviours in Treatment survey instrument, and overdose and naloxone knowledge was measured with the Prescription Opioid Overdose Knowledge Scale after viewing the video at baseline. Naloxone dispensations were evaluated using pharmacy data over a 12-month period. Data were analysed with generalised linear mixed effects and log-binomial regression models.ResultsThere were 519 participants in the intervention arm and 485 participants in the usual care arm. Opioid risk behaviour did not differ between the study arms over time (study arm by time interaction p=0.93). There was no difference in naloxone uptake between the arms (risk ratio 1.13, 95% CI 0.77 to 1.66). Knowledge was significantly greater in the intervention arm compared with usual care (p<0.001).ConclusionsA targeted, digital health intervention video effectively increased knowledge about opioid overdose and naloxone, without increasing opioid risk behaviour. Naloxone uptake did not differ between the intervention and usual care arms.Trial registration numberNCT03337009.

This study measured geographic variation in depression hospitalizations and identified community-level risk factors. Depression hospitalizations were identified from the Statewide Inpatient Database. The dependent variable was specified as the indirectly standardized hospitalization rate. County-level data for 14 states were collected from federal agencies. The Bayesian spatial regression model included socio-demographic, economic, and health system characteristics as independent variables. There were 8.5 depression hospitalizations per 1,000 residents. 8.8% of counties had hospitalization rates 33% greater than the standardized rate. Significant risk factors included unemployment, poverty, physician supply, and hospital bed supply. Significant protective factors included rurality, economic dependence, and housing stress.