Getting our ducks in a row: The need for data utility comparisons of healthcare systems data for clinical trials.

BACKGROUND: Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. "Data Utility Comparison Studies" (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS. METHODS-AND-RESULTS: Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at "patient-level" or "trial-level", depending on the item of interest and trial status. DISCUSSION: DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them.

Observational retrospective study calculating health service costs of patients receiving surgery for chronic rhinosinusitis in England, using linked patient-level primary and secondary care electronic data.

OBJECTIVES: Chronic rhinosinusitis (CRS) symptoms are experienced by an estimated 11% of UK adults, and symptoms have major impacts on quality of life. Data from UK and elsewhere suggest high economic burden of CRS, but detailed cost information and economic analyses regarding surgical pathway are lacking. This paper estimates healthcare costs for patients receiving surgery for CRS in England. DESIGN: Observational retrospective study examining cost of healthcare of patients receiving CRS surgery. SETTING: Linked electronic health records from the Clinical Practice Research Datalink, Hospital Episode Statistics and Office for National Statistics databases in England. PARTICIPANTS: A phenotyping algorithm using medical ontology terms identified 'definite' CRS cases who received CRS surgery. Patients were registered with a general practice in England. Data covered the period 1997-2016. A cohort of 13 462 patients had received surgery for CRS, with 9056 (67%) having confirmed nasal polyps. OUTCOME MEASURES: Information was extracted on numbers and types of primary care prescriptions and consultations, and inpatient and outpatient hospital investigations and procedures. Resource use was costed using published sources. RESULTS: Total National Health Service costs in CRS surgery patients were £2173 over 1 year including surgery. Total costs per person-quarter were £1983 in the quarter containing surgery, mostly comprising surgical inpatient care costs (£1902), and around £60 per person-quarter in the 2 years before and after surgery, of which half were outpatient costs. Outpatient and primary care costs were low compared with the peak in inpatient costs at surgery. The highest outpatient expenditure was on CT scans, peaking in the quarter preceding surgery. CONCLUSIONS: We present the first study of costs to the English healthcare system for patients receiving surgery for CRS. The total aggregate costs provide a further impetus for trials to evaluate the relative benefit of surgical intervention.

Flexible Bayesian longitudinal models for cost-effectiveness analyses with informative missing data.

Cost-effectiveness analyses (CEA) are recommended to include sensitivity analyses which make a range of contextually plausible assumptions about missing data. However, with longitudinal data on, for example, patients' health-related quality of life (HRQoL), the missingness patterns can be complicated because data are often missing both at specific timepoints (interim missingness) and following loss to follow-up. Methods to handle these complex missing data patterns have not been developed for CEA, and must recognize that data may be missing not at random, while accommodating both the correlation between costs and health outcomes and the non-normal distribution of these endpoints. We develop flexible Bayesian longitudinal models that allow the impact of interim missingness and loss to follow-up to be disentangled. This modeling framework enables studies to undertake sensitivity analyses according to various contextually plausible missing data mechanisms, jointly model costs and outcomes using appropriate distributions, and recognize the correlation among these endpoints over time. We exemplify these models in the REFLUX study in which 52% of participants had HRQoL data missing for at least one timepoint over the 5-year follow-up period. We provide guidance for sensitivity analyses and accompanying code to help future studies handle these complex forms of missing data.

Medications for chronic obstructive pulmonary disease: a historical non-interventional cohort study with validation against RCT results.

BACKGROUND: Chronic obstructive pulmonary disease treatment is informed by randomised controlled trial results, but it is unclear if these findings apply to people excluded from these trials. We used data from the TORCH (TOwards a Revolution in COPD Health) randomised controlled trial to validate non-interventional methods for assessing the clinical effectiveness of chronic obstructive pulmonary disease treatment in the UK Clinical Practice Research Datalink, before applying these methods to the analysis of people who would have been excluded from TORCH. OBJECTIVES: To validate the use of non-interventional Clinical Practice Research Datalink data and methods for estimating chronic obstructive pulmonary disease treatment effects against trial results, and, using validated methods, to determine treatment effects in people who would have been excluded from the TORCH trial. DESIGN: A historical non-interventional cohort design, including validation against randomised controlled trial results. SETTING: The UK Clinical Practice Research Datalink. PARTICIPANTS: People aged ≥ 18 years with chronic obstructive pulmonary disease registered in Clinical Practice Research Datalink GOLD between January 2000 and January 2017. For objective 1, we prepared a cohort that was analogous to the TORCH trial cohort by applying TORCH trial inclusion/exclusion criteria followed by individual matching to TORCH trial participants. For objectives 2 and 3, we prepared cohorts that were analogous to the TORCH trial that, nevertheless, would not have been eligible for the TORCH trial because of age, asthma, comorbidity or mild disease. INTERVENTIONS: The long-acting beta-2 agonist and inhaled corticosteroid combination product Seretide (GlaxoSmithKline plc) [i.e. fluticasone propionate plus salmeterol (FP-SAL)] compared with (1) no FP-SAL exposure or (2) exposure to salmeterol (i.e. the long-acting beta-2 agonist) only. MAIN OUTCOME MEASURES: Exacerbations, mortality, pneumonia and time to treatment change. RESULTS: For objective 1, the exacerbation rate ratio was comparable to that in the TORCH trial for FP-SAL compared with salmeterol (0.85, 95% confidence interval 0.74 to 0.97, vs. TORCH trial 0.88, 95% confidence interval 0.81 to 0.95), but not for FP-SAL compared with no FP-SAL (1.30, 95% confidence interval 1.19 to 1.42, vs. TORCH trial 0.75, 95% confidence interval 0.69 to 0.81). Active comparator results were also consistent with the TORCH trial for mortality (hazard ratio 0.93, 95% confidence interval 0.65 to 1.32, vs. TORCH trial hazard ratio 0.93, 95% confidence interval 0.77 to 1.13) and pneumonia (risk ratio 1.39, 95% confidence interval 1.04 to 1.87, vs. TORCH trial risk ratio 1.47, 95% confidence interval 1.25 to 1.73). For objectives 2 and 3, active comparator results were consistent with the TORCH trial for exacerbations, with the exception of people with milder chronic obstructive pulmonary disease, in whom we observed a stronger protective association (risk ratio 0.56, 95% confidence interval 0.46 to 0.70, vs. TORCH trial risk ratio 0.85, 95% confidence interval 0.74 to 0.97). For the analysis of mortality, we saw a lack of association with being prescribed FP-SAL (vs. being prescribed salmeterol), with the exception of those with prior asthma, for whom we observed an increase in mortality (hazard ratio 1.49, 95% confidence interval 1.21 to 1.85, vs. TORCH trial-analogous HR 0.93, 95% confidence interval 0.64 to 1.32). CONCLUSIONS: Routinely collected electronic health record data can be used to successfully measure chronic obstructive pulmonary disease treatment effects when comparing two treatments, but not for comparisons between active treatment and no treatment. Analyses involving patients who would have been excluded from trials mostly suggests that treatment effects for FP-SAL are similar to trial effects, although further work is needed to characterise a small increased risk of death in those with concomitant asthma. LIMITATIONS: Some of our analyses had small numbers. FUTURE WORK: The differences in treatment effects that we found should be investigated further in other data sets. Currently recommended chronic obstructive pulmonary disease inhaled combination therapy (other than FP-SAL) should also be investigated using these methods. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 51. See the NIHR Journals Library website for further project information.

Chronic obstructive pulmonary disease affects 3 million people in the UK and is characterised by breathing difficulties that get worse over time, with sudden acute symptoms (exacerbations), possibly requiring hospitalisation. The evidence for use of medicines for treating chronic obstructive pulmonary disease comes from randomised controlled trial results. Randomised controlled trials generally include younger people with severe disease who do not have any other illnesses apart from chronic obstructive pulmonary disease, meaning that the effectiveness of these trials in all people with chronic obstructive pulmonary disease is unknown. Very large databases of anonymous electronic health records captured during NHS consultations can be used to study patients excluded from trials. However, confidence in results from studies using these data can be low because of fears of unaccounted bias, as patients are not randomised to treatment. In this project, we selected a group of patients from a very large electronic health record database called the Clinical Practice Research Datalink who were very similar to participants in a well-known large chronic obstructive pulmonary disease randomised controlled trial [the TORCH (TOwards a Revolution in COPD Health) trial]. When we analysed data from these patients, we found very similar results to the TORCH trial in relation to the reduction of exacerbations, development of pneumonia and time until death, when comparing one chronic obstructive pulmonary disease treatment with another. Having shown that our methods could be trusted to produce valid results when comparing one chronic obstructive pulmonary disease treatment with another, we then went on to analyse patients in the Clinical Practice Research Datalink who would have been excluded from the TORCH trial for the following reasons: aged > 80 years, having asthma as well as chronic obstructive pulmonary disease, or having only mild chronic obstructive pulmonary disease. For exacerbations, we found that, for people with milder chronic obstructive pulmonary disease, one of the treatments we studied seemed to work better than in the trial. For the analysis of mortality, we found that, for people with asthma as well as chronic obstructive pulmonary disease, one of the treatments seemed not to work so well, with more people dying. Future studies are needed in different populations (such as in a database from another country) to confirm these results.

Use of the Behavior Assessment Tool in 18 Pilot Residency Programs.

BACKGROUND: The purpose of this study was to determine the feasibility and evaluate the effectiveness of the American Board of Orthopaedic Surgery Behavior Tool (ABOSBT) for measuring professionalism. METHODS: Through collaboration between the American Board of Orthopaedic Surgery and American Orthopaedic Association's Council of Residency Directors, 18 residency programs piloted the use of the ABOSBT. Residents requested assessments from faculty at the end of their clinical rotations, and a 360° request was performed near the end of the academic year. Program Directors (PDs) rated individual resident professionalism (based on historical observation) at the outset of the study, for comparison to the ABOSBT results. RESULTS: Nine thousand eight hundred ninety-two evaluations were completed using the ABOSBT for 449 different residents by 1,012 evaluators. 97.6% of all evaluations were scored level 4 or 5 (high levels of professional behavior) across all of the 5 domains. In total, 2.4% of all evaluations scored level 3 or below reflecting poorer performance. Of 431 residents, the ABOSBT identified 26 of 32 residents who were low performers (2 or more < level 3 scores in a domain) and who also scored "below expectations" by the PD at the start of the pilot project (81% sensitivity and 57% specificity), including 13 of these residents scoring poorly in all 5 domains. Evaluators found the ABOSBT was easy to use (96%) and that it was an effective tool to assess resident professional behavior (81%). CONCLUSIONS: The ABOSBT was able to identify 2.4% low score evaluations (

Reference-based multiple imputation for missing data sensitivity analyses in trial-based cost-effectiveness analysis.

Missing data are a common issue in cost-effectiveness analysis (CEA) alongside randomised trials and are often addressed assuming the data are 'missing at random'. However, this assumption is often questionable, and sensitivity analyses are required to assess the implications of departures from missing at random. Reference-based multiple imputation provides an attractive approach for conducting such sensitivity analyses, because missing data assumptions are framed in an intuitive way by making reference to other trial arms. For example, a plausible not at random mechanism in a placebo-controlled trial would be to assume that participants in the experimental arm who dropped out stop taking their treatment and have similar outcomes to those in the placebo arm. Drawing on the increasing use of this approach in other areas, this paper aims to extend and illustrate the reference-based multiple imputation approach in CEA. It introduces the principles of reference-based imputation and proposes an extension to the CEA context. The method is illustrated in the CEA of the CoBalT trial evaluating cognitive behavioural therapy for treatment-resistant depression. Stata code is provided. We find that reference-based multiple imputation provides a relevant and accessible framework for assessing the robustness of CEA conclusions to different missing data assumptions.

Unintended Consequences of mHealth Interactive Voice Messages Promoting Contraceptive Use After Menstrual Regulation in Bangladesh: Intimate Partner Violence Results From a Randomized Controlled Trial.

BACKGROUND: Mobile phones for health (mHealth) hold promise for delivering behavioral interventions. We evaluated the effect of automated interactive voice messages promoting contraceptive use with a focus on long-acting reversible contraceptives (LARCs) among women in Bangladesh who had undergone menstrual regulation (MR), a procedure to "regulate the menstrual cycle when menstruation is absent for a short duration." METHODS: We recruited MR clients from 41 public- and private-sector clinics immediately after MR. Eligibility criteria included having a personal mobile phone and consenting to receive messages about family planning by phone. We randomized participants remotely to an intervention group that received at least 11 voice messages about contraception over 4 months or to a control group (no messages). The primary outcome was LARC use at 4 months. Adverse events measured included experience of intimate partner violence (IPV). Researchers recruiting participants and 1 analyst were blinded to allocation groups. All analyses were intention to treat. The trial is registered with ClinicalTrials.gov (NCT02579785). RESULTS: Between December 2015 and March 2016, 485 women were allocated to the intervention group and 484 to the control group. We completed follow-up on 389 intervention and 383 control participants. Forty-eight (12%) participants in the intervention group and 59 (15%) in the control group reported using a LARC method at 4 months (adjusted odds ratio [aOR] using multiple imputation=0.95; 95% confidence interval [CI]=0.49 to 1.83; P=.22). Reported physical IPV was higher in the intervention group: 42 (11%) intervention versus 25 (7%) control (aOR=1.97; 95% CI=1.12 to 3.46; P=.03) when measured using a closed question naming acts of violence. No violence was reported in response to an open question about effects of being in the study. CONCLUSIONS: The intervention did not increase LARC use but had an unintended consequence of increasing self-reported IPV. Researchers and health program designers should consider possible negative impacts when designing and evaluating mHealth and other reproductive health interventions. IPV must be measured using closed questions naming acts of violence.

Correction to: Sensitivity Analysis for Not-at-Random Missing Data in Trial-Based Cost-Effectiveness Analysis: A Tutorial.

The Open Access license, which previously read.

A Bayesian framework for health economic evaluation in studies with missing data.

Health economics studies with missing data are increasingly using approaches such as multiple imputation that assume that the data are "missing at random." This assumption is often questionable, as-even given the observed data-the probability that data are missing may reflect the true, unobserved outcomes, such as the patients' true health status. In these cases, methodological guidelines recommend sensitivity analyses to recognise data may be "missing not at random" (MNAR), and call for the development of practical, accessible approaches for exploring the robustness of conclusions to MNAR assumptions. Little attention has been paid to the problem that data may be MNAR in health economics in general and in cost-effectiveness analyses (CEA) in particular. In this paper, we propose a Bayesian framework for CEA where outcome or cost data are missing. Our framework includes a practical, accessible approach to sensitivity analysis that allows the analyst to draw on expert opinion. We illustrate the framework in a CEA comparing an endovascular strategy with open repair for patients with ruptured abdominal aortic aneurysm, and provide software tools to implement this approach.

Estimation of TETRA radio use in the Airwave Health Monitoring Study of the British police forces.

BACKGROUND: The Airwave Health Monitoring Study aims to investigate the possible long-term health effects of Terrestrial Trunked Radio (TETRA) use among the police forces in Great Britain. Here, we investigate whether objective data from the network operator could be used to correct for misreporting in self-reported data and expand the radio usage availability in our cohort. METHODS: We estimated average monthly usage of personal radio in the 12 months prior to enrolment from a missing value imputation model and evaluated its performance against objective and self-reported data. Factors associated with TETRA radio usage variables were investigated using Chi-square tests and analysis of variance. RESULTS: The imputed data were better correlated with objective than self-reported usage (Spearman correlation coefficient = 0.72 vs. 0. 52 and kappa 0.56 [95% confidence interval 0.55, 0.56] vs. 0.46 [0.45, 0.47]), although the imputation model tended to under-estimate use for higher users. Participants with higher personal radio usage were more likely to be younger, men vs. women and officer vs. staff. The median average monthly usage level for the entire cohort was estimated to be 29.3 min (95% CI: [7.2, 66.6]). CONCLUSION: The availability of objective personal radio records for a large proportion of users allowed us to develop a robust imputation model and hence obtain personal radio usage estimates for ~50,000 participants. This substantially reduced exposure misclassification compared to using self-reported data and will allow us to carry out analyses of TETRA usage for the entire cohort in future work.

Sensitivity Analysis for Not-at-Random Missing Data in Trial-Based Cost-Effectiveness Analysis: A Tutorial.

Cost-effectiveness analyses (CEA) of randomised controlled trials are a key source of information for health care decision makers. Missing data are, however, a common issue that can seriously undermine their validity. A major concern is that the chance of data being missing may be directly linked to the unobserved value itself [missing not at random (MNAR)]. For example, patients with poorer health may be less likely to complete quality-of-life questionnaires. However, the extent to which this occurs cannot be ascertained from the data at hand. Guidelines recommend conducting sensitivity analyses to assess the robustness of conclusions to plausible MNAR assumptions, but this is rarely done in practice, possibly because of a lack of practical guidance. This tutorial aims to address this by presenting an accessible framework and practical guidance for conducting sensitivity analysis for MNAR data in trial-based CEA. We review some of the methods for conducting sensitivity analysis, but focus on one particularly accessible approach, where the data are multiply-imputed and then modified to reflect plausible MNAR scenarios. We illustrate the implementation of this approach on a weight-loss trial, providing the software code. We then explore further issues around its use in practice.

Missing data in trial-based cost-effectiveness analysis: An incomplete journey.

Cost-effectiveness analyses (CEA) conducted alongside randomised trials provide key evidence for informing healthcare decision making, but missing data pose substantive challenges. Recently, there have been a number of developments in methods and guidelines addressing missing data in trials. However, it is unclear whether these developments have permeated CEA practice. This paper critically reviews the extent of and methods used to address missing data in recently published trial-based CEA. Issues of the Health Technology Assessment journal from 2013 to 2015 were searched. Fifty-two eligible studies were identified. Missing data were very common; the median proportion of trial participants with complete cost-effectiveness data was 63% (interquartile range: 47%-81%). The most common approach for the primary analysis was to restrict analysis to those with complete data (43%), followed by multiple imputation (30%). Half of the studies conducted some sort of sensitivity analyses, but only 2 (4%) considered possible departures from the missing-at-random assumption. Further improvements are needed to address missing data in cost-effectiveness analyses conducted alongside randomised trials. These should focus on limiting the extent of missing data, choosing an appropriate method for the primary analysis that is valid under contextually plausible assumptions, and conducting sensitivity analyses to departures from the missing-at-random assumption.

Qualitative Assessment and Quantitative Analysis of the Long Head of the Biceps Tendon in Relation to the Pectoralis Major Tendon Humeral Insertion: An Anatomic Study.

PURPOSE: To qualitatively assess and to quantitatively analyze the long head of the biceps tendon (LHBT) in the region of the pectoralis major (PM). METHODS: From 11 fresh cadaveric donors, 20 cadaveric shoulders without operative scars were dissected-mean age, 76.9 years (range, 61 to 93 years); male, 75%; left-sided, 55%; mean height, 67.8 inches (range, 61 to 71 inches); mean weight, 148.1 pounds (range, 106 to 176 pounds). Two specimens with discovered ruptures were excluded. The LHBT circumference was measured at the anterior edge of supraspinatus, suprapectorally, midpectorally, and subpectorally. The muscle was then removed from the LHBT and the circumference was again measured at the supra-, mid-, and subpectoral levels. These data were used to calculate the area of the tendon. All measurements were performed by 2 independent observers. Statistical analysis was performed to assess reliability of data and the difference between serial measurements. RESULTS: The mean calculated percentage tendon decreased from 86.7% at the superior edge of the PM to 49.8% at the midpoint of the PM and to 17.5% at the inferior edge of the PM. CONCLUSIONS: Distal to the PM, the LHBT was composed of a small percentage of tendon to muscle, which may have implications for the mechanical strength of fixation of tenodesis. The anatomic location of the musculotendinous junction of the LHBT began proximal to the superior edge of the PM tendon, which implies that restoration of anatomic tensioning may require a more proximal docking site than previously described. Tenodesis performed between the midpoint of the PM insertion and more distal points involves a significant portion of muscle, which may not be optimal. CLINICAL RELEVANCE: Tenodesis performed between the midpoint of the PM insertion and more distal points involves a significant portion of muscle, which may affect the mechanical strength or optimal choice of fixation location.

Multilevel models for cost-effectiveness analyses that use cluster randomised trial data: An approach to model choice.

Multilevel models provide a flexible modelling framework for cost-effectiveness analyses that use cluster randomised trial data. However, there is a lack of guidance on how to choose the most appropriate multilevel models. This paper illustrates an approach for deciding what level of model complexity is warranted; in particular how best to accommodate complex variance-covariance structures, right-skewed costs and missing data. Our proposed models differ according to whether or not they allow individual-level variances and correlations to differ across treatment arms or clusters and by the assumed cost distribution (Normal, Gamma, Inverse Gaussian). The models are fitted by Markov chain Monte Carlo methods. Our approach to model choice is based on four main criteria: the characteristics of the data, model pre-specification informed by the previous literature, diagnostic plots and assessment of model appropriateness. This is illustrated by re-analysing a previous cost-effectiveness analysis that uses data from a cluster randomised trial. We find that the most useful criterion for model choice was the deviance information criterion, which distinguishes amongst models with alternative variance-covariance structures, as well as between those with different cost distributions. This strategy for model choice can help cost-effectiveness analyses provide reliable inferences for policy-making when using cluster trials, including those with missing data.

Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation.

The Cox proportional hazards model is frequently used in medical statistics. The standard methods for fitting this model rely on the assumption of independent censoring. Although this is sometimes plausible, we often wish to explore how robust our inferences are as this untestable assumption is relaxed. We describe how this can be carried out in a way that makes the assumptions accessible to all those involved in a research project. Estimation proceeds via multiple imputation, where censored failure times are imputed under user-specified departures from independent censoring. A novel aspect of our method is the use of bootstrapping to generate proper imputations from the Cox model. We illustrate our approach using data from an HIV-prevention trial and discuss how it can be readily adapted and applied in other settings.

Analysis of longitudinal trials with protocol deviation: a framework for relevant, accessible assumptions, and inference via multiple imputation.

Protocol deviations, for example, due to early withdrawal and noncompliance, are unavoidable in clinical trials. Such deviations often result in missing data. Additional assumptions are then needed for the analysis, and these cannot be definitively verified from the data at hand. Thus, as recognized by recent regulatory guidelines and reports, clarity about these assumptions and their implications is vital for both the primary analysis and framing relevant sensitivity analysis. This article focuses on clinical trials with longitudinal quantitative outcome data. For the target population, we define two estimands, the de jure estimand, "does the treatment work under the best case scenario," and the de facto estimand, "what would be the effect seen in practice." We then carefully define the concept of a deviation from the protocol relevant to the estimand, or for short a deviation. Each patient's postrandomization data can then be divided into predeviation data and postdeviation data. We set out an accessible framework for contextually appropriate assumptions relevant to de facto and de jure estimands, that is, assumptions about the joint distribution of pre- and postdeviation data relevant to the clinical question at hand. We then show how, under these assumptions, multiple imputation provides a practical approach to estimation and inference. We illustrate with data from a longitudinal clinical trial in patients with chronic asthma.

Variation in use of reverse total shoulder arthroplasty across hospitals.

BACKGROUND: The opportunity for variation exists in the choice between anatomic and reverse total shoulder arthroplasty. Quality improvement methods seek to reduce variation. We used supply-chain data to characterize variation in the selection of anatomic versus reverse total shoulder arthroplasty across hospitals and to analyze the effect of hospital volume on this variation. METHODS: Mendenhall Associates, Inc (Ann Arbor, MI, USA) provided us with a database of hospital supply-chain data from orthopaedic surgical cases. This study included hospitals in which at least one total shoulder arthroplasty was performed. We calculated, for each hospital, the percentages of each type of prosthesis implanted and examined the distribution of these percentages across all hospitals. We also divided the sample of hospitals into tertiles, by volume of total shoulder arthroplasties performed, and examined the distributions of percentage reverse shoulder arthroplasty performed in each tertile. RESULTS: Across all hospitals, we saw wide variation in the volume of total shoulder arthroplasties and the percentage of reverse shoulder arthroplasties performed. Hospitals with lower total shoulder arthroplasty volumes exhibited greater variation in the percentages of each type of total shoulder arthroplasty performed. Higher volume hospitals exhibited smaller variation. CONCLUSIONS: This study revealed wide variation in the selection of anatomic and reverse total shoulder arthroplasty across all hospitals and an inverse relationship between hospital volume and variation. This variation signals uncertainty about the best application of each device and that there is need for improvement in the consistency of treatment of patients with shoulder disease. LEVEL OF EVIDENCE: Level II, cost-effectiveness study, economic and decision analysis.

Orthopaedic surgeons frequently underestimate the cost of orthopaedic implants.

BACKGROUND: A poor understanding of cost among healthcare providers may contribute to high healthcare expenditures. Currently, it is unclear whether and how much surgeons know about the costs of implantable medical devices (IMDs). QUESTIONS/PURPOSES: We (1) determined the level of comfort with orthopaedic IMD costs among orthopaedic residents and attending surgeons, (2) quantified how accurately surgeons understand the costs of orthopaedic IMDs, and (3) identified which constructs yield the most accurate cost estimations among residents and attending surgeons. METHODS: A questionnaire was presented to 60 residents and 37 attending orthopaedic surgeons from two large academic medical centers. Respondents estimated the cost of 13 commonly used orthopaedic devices. Fifty-one surgeons participated (36 residents, 15 attending surgeons), for an overall response rate of 53%. Cost estimates were compared against the actual material costs, and we recorded the percentage error for each estimate. RESULTS: More than ½ of the respondents rated their knowledge of IMD cost as poor. The mean percentage error in estimation for all respondents was 69% (range, 29%-289%). Overall, 67% of responses were underestimations and 33% were overestimations. Residents demonstrated a mean percentage error of 73% (range, 29%-289%) while attending surgeons had a mean percentage error of 59% (range, 49%-79%). Residents and attending surgeons demonstrated differences in accuracy within groups and between groups based on the IMD being estimated. CONCLUSIONS: We found the knowledge of orthopaedic IMD costs among the orthopaedic residents and attending surgeons surveyed was poor. Further investigation of how physicians conceptualize material costs will be important to healthcare cost control.

Relationship between implant use, operative time, and costs associated with distal biceps tendon reattachment.

The suture anchor and transosseous drill hole techniques for reattachment of the distal biceps tendon to the radius have been found to have similar clinical and biomechanical outcomes. However, a comparison of the cost effectiveness of these techniques is lacking. The purpose of this study was to determine whether the use of suture anchors decreases operative time enough to offset the additional cost of the implants. The records of all patients undergoing a distal biceps tendon reattachment were reviewed to determine the method of fixation, operative time, and associated surgical costs. Two surgeons used a technique of fixing the tendon directly to the bone (transosseous group), whereas 3 surgeons used suture anchors. Given the standard nature of the surgical procedure (other than the fixation technique), only the costs that differed between the 2 groups were included. Surgical center costs were obtained from the local outpatient surgical center in 2011 US dollars. Five surgeons treated 70 men (mean age, 45.9±9.2 years). Mean time from injury to surgery was 14 days. Mean operative times for the transosseous and suture anchor groups were 97.6±14.9 and 95.8±25.8 minutes, respectively (P=.74). Two anchors were used in 79% of the anchor cases. The use of anchors cost $474.33 more per patient. However, this value is sensitive to the cost of the individual anchors, intersurgeon variation in operative time, and per-minute value of saved operative time. No operative time was saved with the use of suture anchors. This cost comparison framework can be used to evaluate the balance in surgical resource use due to implant cost vs savings in operative time.