BackgroundThe characteristics of patients who suffer from noncancer pain and opioid-induced constipation are not well understood.MethodsCross-sectional patient survey and chart review data from the baseline assessment of an ongoing longitudinal study in the USA, Canada, Germany, and the UK were evaluated via descriptive statistics. Participants had confirmation of daily opioid therapy ≥30 mg for ≥4 weeks and self-reported opioid-induced constipation. Response to laxatives was defined by classifying participants into categories of laxative use and evaluating the prevalence of inadequate response to one laxative agent and two or more agents from at least two different laxative classes. Outcomes included the Patient Assessment of Constipation-Symptoms, Work Productivity and Activity Impairment Questionnaire-Specific Health Problem, EuroQOL 5 Dimensions, and Global Assessment of Treatment Benefit, Satisfaction, and Willingness to Continue.ResultsPatients reported a mean of 1.4 bowel movements not preceded by laxatives and 3.7 bowel movements with laxative use per week; 83% wanted at least one bowel movement per day. Most commonly reported on Patient Assessment of Constipation-Symptoms were straining/squeezing to pass bowel movements (83%), bowel movements too hard (75%), flatulence (69%), and bloating (69%). Eighty-four percent were taking natural or behavioral therapies; 60% were taking at least one over-the-counter laxative; and 19% were taking at least one prescription laxative. Prevalence of inadequate response to one laxative agent was 94%; inadequate response to two or more agents from at least two different laxative classes was 27%. Mean Work Productivity and Activity Impairment Questionnaire-Specific Health Problem values for percent work time missed, percent impairment while working, and percent activity impairment were 9%, 32% (equivalent of 14 hours of lost productivity per week), and 38%. Mean EuroQOL 5 Dimensions index and visual analog scale scores were 0.49 and 50.6, respectively. Forty-four percent reported being satisfied with their treatment for constipation.ConclusionPatients treated with opioids for noncancer pain commonly endure constipation symptoms that limit their work productivity and overall health-related quality of life while adhering to treatments that provide little relief. Further research is needed to identify more efficacious constipation therapies for this patient population.

OIC among noncancer pain patients is a persistent and significant condition with varying utilization and response to laxatives thus increasing the ongoing burden of chronic pain. © 2014 Wiley Periodicals, Inc.

The covariate-balancing propensity score (CBPS) extends logistic regression to simultaneously optimize covariate balance and treatment prediction. Although the CBPS has been shown to perform well in certain settings, its performance has not been evaluated in settings specific to pharmacoepidemiology and large database research. In this study, we use both simulations and empirical data to compare the performance of the CBPS with logistic regression and boosted classification and regression trees. We simulated various degrees of model misspecification to evaluate the robustness of each propensity score (PS) estimation method. We then applied these methods to compare the effect of initiating glucagonlike peptide-1 agonists versus sulfonylureas on cardiovascular events and all-cause mortality in the US Medicare population in 2007-2009. In simulations, the CBPS was generally more robust in terms of balancing covariates and reducing bias compared with misspecified logistic PS models and boosted classification and regression trees. All PS estimation methods performed similarly in the empirical example. For settings common to pharmacoepidemiology, logistic regression with balance checks to assess model specification is a valid method for PS estimation, but it can require refitting multiple models until covariate balance is achieved. The CBPS is a promising method to improve the robustness of PS models.

Purpose It is often preferable to simplify the estimation of treatment effects on multiple outcomes by using a single propensity score (PS) model. Variable selection in PS models impacts the efficiency and validity of treatment effects. However, the impact of different variable selection strategies on the estimated treatment effects in settings involving multiple outcomes is not well understood. The authors use simulations to evaluate the impact of different variable selection strategies on the bias and precision of effect estimates to provide insight into the performance of various PS models in settings with multiple outcomes. Methods Simulated studies consisted of dichotomous treatment, two Poisson outcomes, and eight standard-normal covariates. Covariates were selected for the PS models based on their effects on treatment, a specific outcome, or both outcomes. The PSs were implemented using stratification, matching, and weighting (IPTW). Results PS models including only covariates affecting a specific outcome (outcome-specific models) resulted in the most efficient effect estimates. The PS model that only included covariates affecting either outcome (generic-outcome model) performed best among the models that simultaneously controlled measured confounding for both outcomes. Similar patterns were observed over the range of parameter values assessed and all PS implementation methods. Conclusions A single, generic-outcome model performed well compared with separate outcome-specific models in most scenarios considered. The results emphasize the benefit of using prior knowledge to identify covariates that affect the outcome when constructing PS models and support the potential to use a single, generic-outcome PS model when multiple outcomes are being examined.

The importance and severity of OIC are perceived differently by patients and their HCPs, a discordance that complicates pain management and demonstrates a need for greater communication. These disparate perceptions indicate a need for clinical education and coordination of care by HCPs to improve understanding and proactively manage OIC in patients with chronic noncancer pain.