RESUMO
OBJECTIVE: To develop and evaluate a novel Opioid Safety Clinic (OSC) initiative to enhance adherence to guidelines on the assessment and monitoring of patients prescribed chronic opioid therapy (COT). PATIENTS AND METHODS: The OSC was developed at an urban Federally Qualified Health Center to provide guideline-concordant care for COT, standardize workflows, and efficiently use clinic staff. We evaluated the OSC using a matched cohort study. Five hundred thirty-nine patients participated in the clinic between July 1, 2014, and March 31, 2016. Of these, 472 clinic participants were matched to 472 nonparticipants by sex and age on the date of the OSC visit. The OSC was evaluated by its completion rates of standardized pain assessments, urine toxicology, and naloxone dispensings. We conducted logistic regression comparing OSC participants to OSC nonparticipants. RESULTS: A total of 539 patients attended an OSC visit, representing approximately 53% of patients in the chronic opioid registry. The OSC participants were more likely than nonparticipants to have completed a pain assessment (adjusted odds ratio [aOR], 169.8; 95% CI, 98.3-293.5), completed a urine toxicology (aOR, 46.1; 95% CI, 30.4-69.9), or had naloxone dispensed (aOR, 2.8; 95% CI, 1.9-4.3) over 12 months of follow-up. CONCLUSION: The OSC model improved adherence to guideline-concordant COT in primary care. Future research is needed to assess the impact of these interventions on pain, quality of life, and adverse events from opioid analgesics.
RESUMO
Conditional Poisson models have been used to analyze vaccine safety data from self-controlled case series (SCCS) design. In this paper, we derived the likelihood function of fixed effects models in analyzing SCCS data and showed that the likelihoods from fixed effects models and conditional Poisson models were proportional. Thus, the maximum likelihood estimates (MLEs) of time-varying variables including vaccination effect from fixed effects model and conditional Poisson model were equal. We performed a simulation study to compare empirical type I errors, means and standard errors of vaccination effect coefficient, and empirical powers among conditional Poisson models, fixed effects models, and generalized estimating equations (GEE), which has been commonly used for analyzing longitudinal data. Simulation study showed that both fixed effect models and conditional Poisson models generated the same estimates and standard errors for time-varying variables while GEE approach produced different results for some data sets. We also analyzed SCCS data from a vaccine safety study examining the association between measles mumps-rubella (MMR) vaccination and idiopathic thrombocytopenic purpura (ITP). In analyzing MMR-ITP data, likelihood-based statistical tests were employed to test the impact of time-invariant variable on vaccination effect. In addition a complex semi-parametric model was fitted by simply treating unique event days as indicator variables in the fixed effects model. We conclude that theoretically fixed effects models provide identical MLEs as conditional Poisson models. Because fixed effect models are likelihood based, they have potentials to address methodological issues in vaccine safety studies such as how to identify optimal risk window and how to analyze SCCS data with misclassification of adverse events.