Additional file 1: of Lost in the crowd? Using eye-tracking to investigate the effect of complexity on attribute non-attendance in discrete choice experiments

Supplementary material. (DOCX 592 kb

Economic Evaluation of Active Implementation versus Guideline Dissemination for Evidence-Based Care of Acute Low-Back Pain in a General Practice Setting

<div><p>Introduction</p><p>The development and publication of clinical practice guidelines for acute low-back pain has resulted in evidence-based recommendations that have the potential to improve the quality and safety of care for acute low-back pain. Development and dissemination of guidelines may not, however, be sufficient to produce improvements in clinical practice; further investment in active implementation of guideline recommendations may be required. Further research is required to quantify the trade-off between the additional upfront cost of active implementation of guideline recommendations for low-back pain and any resulting improvements in clinical practice.</p><p>Methods</p><p>Cost-effectiveness analysis alongside the IMPLEMENT trial from a health sector perspective to compare active implementation of guideline recommendations via the IMPLEMENT intervention (plus standard dissemination) against standard dissemination alone.</p><p>Results</p><p>The base-case analysis suggests that delivery of the IMPLEMENT intervention dominates standard dissemination (less costly and more effective), yielding savings of $135 per x-ray referral avoided (-$462.93/3.43). However, confidence intervals around point estimates for the primary outcome suggest that – irrespective of willingness to pay (WTP) – we cannot be at least 95% confident that the IMPLEMENT intervention differs in value from standard dissemination.</p><p>Conclusions</p><p>Our findings demonstrate that moving beyond development and dissemination to active implementation entails a significant additional upfront investment that may not be offset by health gains and/or reductions in health service utilization of sufficient magnitude to render active implementation cost-effective.</p></div

Schedule of measures for economic evaluation.

1<p>Primary outcome.</p>2<p>Medicare data: number of referrals for all lumbar spine and pelvis x-ray services by each included GP for a 12 month period after the intervention/control was delivered.</p>3<p>Medicare data: number of referrals for all lumbar spine and pelvis CT scan services by each included GP for a 12 month period after the intervention/control was delivered.</p>4<p>Medicare data: number of referrals for all lumbar spine and pelvis x-ray or CT scan services by each included GP for a 12 month period after the intervention/control was delivered.</p

Effect of the intervention on adherence as measured by the vignettes.

*<p>: p&lt;0.05; **: p&lt;0.01.</p><p><b>X-ray adherence</b> defined as GPs not referring for a lumbosacral plain x-ray.</p><p><b>Imaging adherence</b> for vignettes was defined as GPs not referring for any of following three diagnostic tests: lumbosacral plain x-ray, lumbar CT scan, lumbar MRI.</p><p><b>Activity adherence</b> defined as “Advise the patient to continue with their normal daily activities” regardless of other interventions selected (“Paracetamol”, “Non-steroidal anti-inflammatory drugs”, “Advise the patient to do specific back exercises”, “Advise the patient to do general exercises (e.g. walking)”,”Manual therapy”, “Referral to another health care provider”, “Other”).</p><p><b>Bed rest adherence</b> defined as either not recommending “Bed rest”, or recommending “Bed rest” for ≤ 2 days.</p>1<p>Adjusted Odds Ratio (OR)  =  Estimate of intervention effect adjusted for design strata and potential confounders (specified prior to undertaking the analysis). Adjusted OR estimated from models fitted using xtgee family(binomial) link(logit) vce(robust) yielding semi-robust standard errors.</p>2<p>Incremental effect  =  change in probability that simulated consult will be adherent to the key messages of the CPG due to exposure to the intervention after controlling for design strata and potential confounders (specified prior to undertaking the analysis). Here, incremental effects derived from model predicted values using method of recycled predictions <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075647#pone.0075647-Glick1" target="_blank">[24]</a>.</p>3<p>Standard errors derived from bootstrap using bsmultiv.do <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075647#pone.0075647-Glick1" target="_blank">[24]</a>.</p>4<p>Models adjusted for the following design strata and pre-specified potential confounders: GP age (years), years since GP graduated, self-reported special interest in LBP, number of GPs per practice, practice method of billing, rural/metro practice.</p>5<p>Models adjusted for the following design strata and pre-specified potential confounders: GP age (years), years since GP graduated, self-reported special interest in LBP, number of GPs per practice, practice method of billing, rural/metro practice, baseline measure of fear-avoidance beliefs.</p>6<p>No adjustment for stratification variables or potential confounders because of limited events of non-adherence.</p

Cost effectiveness acceptability curve for x-ray referral.

<p>Cost effectiveness acceptability curve for x-ray referral.</p

Mapping the Functional Independence Measure to a multi-attribute utility instrument for economic evaluations in rehabilitation: a secondary analysis of randomized controlled trial data

<p><b>Purpose:</b> To test whether the Functional Independence Measure (FIM) could be mapped to the EQ-5D-3L to give researchers a viable but “second-best” option for calculating quality-adjusted life-years (QALYs) and conducting a cost-utility analysis when only clinical outcomes have been collected.</p> <p><b>Materials and methods:</b> Secondary analysis of repeated measures data collected during a randomized controlled trial (<i>n</i> = 3506 observations) at two inpatient rehabilitation centres. Participants had a mean age of 74 (SD 13) years, 63% were women and 58% were admitted with an orthopaedic diagnosis. Ordinary least-squares regression and adjusted limited dependent variable mixture models were used to estimate regression-based mappings. Performance was evaluated based on mean absolute error and the proportion of errors in excess of the minimally important difference.</p> <p><b>Results:</b> In orthopaedic and neurological patients, high mean absolute errors (0.2 on the quality-adjusted life years scale) and a high proportion of errors (60%) in excess of the minimally important difference suggest that predicted EQ-5D-3L values provided a poor substitute for observed EQ-5D-3L values.</p> <p><b>Conclusions:</b> Regression-based mappings from the FIM to the EQ-5D-3L are error-prone and unsuitable for calculating QALYs in rehabilitation patients. Researchers and rehabilitation professionals should therefore include a multi-attribute utility instrument such as the EQ-5D as well as the FIM to evaluate the effect of rehabilitation interventions and in rehabilitation registries. This will provide additional information on health-related quality of life and support cost-utility analyses.Implications for rehabilitation</p><p>The Functional Independence Measure (FIM) cannot be used to calculate quality-adjusted life-years (QALYs) for cost-utility analyses.</p><p>Predicting QALYs from FIM data is a poor substitute for direct measurement of QALYs in orthopaedic or neurological rehabilitation populations.</p><p>Multi-attribute utility instruments (MAUIs) allow direct measurement of QALYs, as well as providing a patient-reported measure of clinical quality and outcomes in rehabilitation.</p><p>A MAUI should be included routinely in clinical practice by rehabilitation professionals as well as in rehabilitation trials and registries to track patient outcomes and improve clinical practice.</p><p></p> <p>The Functional Independence Measure (FIM) cannot be used to calculate quality-adjusted life-years (QALYs) for cost-utility analyses.</p> <p>Predicting QALYs from FIM data is a poor substitute for direct measurement of QALYs in orthopaedic or neurological rehabilitation populations.</p> <p>Multi-attribute utility instruments (MAUIs) allow direct measurement of QALYs, as well as providing a patient-reported measure of clinical quality and outcomes in rehabilitation.</p> <p>A MAUI should be included routinely in clinical practice by rehabilitation professionals as well as in rehabilitation trials and registries to track patient outcomes and improve clinical practice.</p

Additional file 1: Methods. of Sustainability in Health care by Allocating Resources Effectively (SHARE) 6: investigating methods to identify, prioritise, implement and evaluate disinvestment projects in a local healthcare setting

(PDF 353 kb

Effect of the intervention on adherence to the guideline for the behaviours x-ray referral, imaging referral, advice re activity and bed rest, as measured by the vignettes, using different effect metrics.

1<p>All models adjusted for design strata and confounders specified prior to undertaking the analysis. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065471#pone-0065471-t004" target="_blank">Table 4</a> for details of the design strata and confounders. The exception to this was “Bed rest adherence” which, due to limited events of non-adherence, was fitted with no adjustment for design strata and confounders.</p>2<p>Metrics (RR and RD) calculated from marginal probabilities <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065471#pone.0065471-Austin1" target="_blank">[62]</a>. Confidence intervals for the metric were bootstrapped in Stata <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065471#pone.0065471-Stata1" target="_blank">[65]</a> allowing for clustering of observations within general practices (using both the <i>cluster()</i> and <i>idcluster()</i> options). Bias corrected 95% confidence intervals were calculated from 1000 replicates.</p>3<p>CI limits could only be calculated from 612 bootstrapped replicates.</p

General practice and general practitioner (GP) baseline characteristics.

<p>SD: standard deviation; No.: number; IQR: Interquartile range [25^th percentile to 75^th percentile]; LBP: low back pain.</p>*<p>Bulk bill: the total payment for patient’s consultation is paid for by the Medicare system; Co-payment: Medicare system pays for part of the consultation and the patient pays for the remainder of the cost.</p>#<p>The Divisions of General Practice Program was funded by the Australian Government to provide services and support to general practice.</p

Planned outcomes from protocol [42] and outcomes actually measured.

1<p>Patient outcomes, and GP level outcomes measured at the patient level, were not collected because insufficient patient participants were recruited to the trial.</p