Cost-effectiveness of professional-mode flash glucose monitoring in general practice among adults with type 2 diabetes: Evidence from the GP-OSMOTIC trial.

AIM: To assess the cost-effectiveness of professional-mode flash glucose monitoring in adults with type 2 diabetes in general practice compared with usual clinical care. METHODS: An economic evaluation was conducted as a component of the GP-OSMOTIC trial, a pragmatic multicentre 12-month randomised controlled trial enrolling 299 adults with type 2 diabetes in Victoria, Australia. The economic evaluation was conducted from an Australian healthcare sector perspective with a lifetime horizon. Health-related quality of life (EQ-5D) and total healthcare costs were compared between the intervention and the usual care group within the trial period. The 'UKPDS Outcomes Model 2' was used to simulate post-trial lifetime costs, life expectancy and quality-adjusted life years (QALYs). RESULTS: No significant difference in health-related quality of life and costs was found between the two groups within the trial period. Professional-mode flash glucose monitoring yielded greater QALYs (0.03 [95% CI: 0.02, 0.04]) and a higher cost (A$3807 [95% CI: 3604, 4007]) compared with usual clinical care using a lifetime horizon under the trial-based monitoring frequency, considered not cost-effective (incremental cost-effectiveness ratio = A$120,228). The intervention becomes cost-effective if sensor price is reduced to lower than 50%, or monitoring frequency is decreased to once per year while maintaining the same treatment effect on HbA1c . CONCLUSIONS: Including professional-mode flash glucose monitoring every 3 months as part of a management plan for people with type 2 diabetes in general practice is not cost-effective, but could be if the sensor price or monitoring frequency can be reduced.

Mapping the Minnesota Living with Heart Failure Questionnaire (MLHFQ) onto the Assessment of Quality of Life 8D (AQoL-8D) utility scores.

PURPOSE: The Minnesota Living with Heart Failure Questionnaire (MLHFQ) is a widely used condition-specific measure of quality of life (QoL) in patients with heart failure. To use information from the MLHFQ in an economic evaluation, the MLHFQ must be mapped onto a preference-based measure of QoL. This study aims to develop a mapping algorithm between the MLHFQ and the Assessment of Quality of Life (AQoL) 8D utility instrument in patients with dilated cardiomyopathy (DCM). METHODS: MLHFQ and AQoL-8D data were collected on 61 Australian adults with idiopathic DCM or other non-hypertrophic cardiomyopathies. Three statistical methods were used as follows: ordinary least squares (OLS) regression, the robust MM estimator, and the generalised linear models (GLM). Each included a range of explanatory variables. Model performance was assessed using key goodness-of-fit measures, the mean absolute error (MAE), and the root-mean-square error (RMSE). RESULTS: The MLHFQ summary score and AQoL-8D utility scores were strongly correlated (r = - 0.83, p < 0.0001) and the two subscales of the MLHFQ were correlated with the eight dimensions of the AQoL-8D. Utility scores were predicted with acceptable precision based on responses to the MLHFQ physical, emotional, social, and other subscales. OLS and GLM performed similarly with MAE and RMSE ranging 0.086-0.106 and 0.114-0.130, respectively. CONCLUSION: The mapping algorithm developed in this study allows the derivation of AQoL-8D utilities from MLHFQ scores for use in cost-effectiveness analyses and most importantly, enables the economic evaluation of alternative heart failure therapy options when only the MLHFQ has been collected.

A cost-effectiveness model of genetic testing and periodical clinical screening for the evaluation of families with dilated cardiomyopathy.

PURPOSE: To assess the relative cost-effectiveness of cascade genetic testing in asymptomatic relatives of patients with dilated cardiomyopathy (DCM) compared with periodical clinical surveillance. METHODS: A decision-analytic model, combining a decision tree and a Markov model, was used to determine the lifetime costs and quality-adjusted life years (QALYs) for the two strategies. Deterministic and probabilistic sensitivity analyses were undertaken to assess the robustness of findings and to explore decision uncertainty. RESULTS: The incremental cost per additional QALY of cascade genetic testing prior to periodical clinical surveillance of first-degree relatives compared with periodical clinical surveillance alone was estimated at approximately AUD $6100. At established thresholds of cost-effectiveness, there is a 90% probability that cascade genetic testing is cost-effective. Extensive sensitivity analyses, including the addition of second-degree relatives, did not alter the conclusions drawn from the main analysis. CONCLUSION: Using cascade genetic testing to guide clinical surveillance of asymptomatic relatives of patients with DCM is very likely to be cost-effective. As the DCM pathogenic variant detection rate rises and new evidence for personalized treatment of at-risk individuals becomes available, the cost-effectiveness of cascade testing will further increase.

Cost-effectiveness of epileptic surgery compared with medical treatment in children with drug-resistant epilepsy.

BACKGROUND: Epilepsy surgery is an alternative to continued antiepileptic drugs (AEDs) in children with drug-resistant epilepsy (DRE). OBJECTIVE: The objective of the study was to measure, model, and compare the medical costs and impacts on health-related quality of life (HRQL) of epilepsy surgery versus continued medical treatment with AEDs in children with DRE. METHODS: A decision analytic model was created to estimate the cost-effectiveness of epilepsy surgery relative to continued medical treatment with AEDs. The model was based on costing and effectiveness data collected from 105 children with DRE who were operated on at the Royal Children's Hospital, Melbourne, Australia. The mean cost of conducting epilepsy surgery was AU$ 61,417 per person. Effectiveness of continued medical treatment was sourced from best available literature. In the absence of published utility values for pediatric patients with epilepsy and ethical approval to contact patients directly, HRQL was estimated by four clinicians using the Child Health Utility 9 Dimension (CHU9D). Outcome measures were seizure freedom and quality-adjusted life years (QALYs). RESULTS: The costs over 7.6 years of follow-up were AU$ 219,297 for the surgical treatment group compared with AU$ 170,583 for the medical treatment group. The incremental cost-effectiveness ratio (ICER) for surgically vs medical treatment was AU$ 76,538 per additional patient attaining seizure freedom and AU$ 75,541 per additional QALY gained. CONCLUSION: Epilepsy surgery resulted in a greater reduction of seizures and improvement in HRQL but was more expensive than continued medical treatment with AEDs. Including benefits outside of a healthcare perspective would likely lead to a more compelling cost-effective argument.

Economic evaluation of stepped care for the management of childhood anxiety disorders: Results from a randomised trial.

BACKGROUND: Stepped care has been promoted for the management of mental disorders; however, there is no empirical evidence to support the cost-effectiveness of this approach for the treatment of anxiety disorders in youth. METHOD: This economic evaluation was conducted within a randomised controlled trial comparing stepped care to a validated, manualised treatment in 281 young people, aged 7-17, with a diagnosed anxiety disorder. Intervention costs were determined from therapist records. Administrative data on medication and medical service use were used to determine additional health care costs during the study period. Parents also completed a resource use questionnaire to collect medications, services not captured in administrative data and parental lost productivity. Outcomes included participant-completed quality of life, Child Health Utility - nine-dimension and parent-completed Assessment of Quality of Life - eight-dimension to calculate quality-adjusted life years. Mean costs and quality-adjusted life years were compared between groups at 12-month follow-up. RESULTS: Intervention delivery costs were significantly less for stepped care from the societal perspective (mean difference -$198, 95% confidence interval -$353 to -$19). Total combined costs were less for stepped care from both societal (-$1334, 95% confidence interval -$2386 to $510) and health sector (-$563, 95% confidence interval -$1353 to $643) perspectives but did not differ significantly from the manualised treatment. Youth and parental quality-adjusted life years were not significantly different between groups. Sensitivity analysis indicated that the results were robust. CONCLUSION: For youth with anxiety, this three-step model provided comparable outcomes and total health sector costs to a validated face-to-face programme. However, it was less costly to deliver from a societal perspective, making it an attractive option for some parents. Future economic evaluations comparing various models of stepped care to treatment as usual are recommended.

Strategies to reduce diagnostic errors: a systematic review.

BACKGROUND: To evaluate the effectiveness of audit and communication strategies to reduce diagnostic errors made by clinicians. METHODS: MEDLINE complete, CINHAL complete, EMBASE, PSNet and Google Advanced. Electronic and manual search of articles on audit systems and communication strategies or interventions, searched for papers published between January 1990 and April 2017. We included studies with interventions implemented by clinicians in a clinical environment with real patients. RESULTS: A total of 2431 articles were screened of which 26 studies met inclusion criteria. Data extraction was conducted by two groups, each group comprising two independent reviewers. Articles were classified by communication (6) or audit strategies (20) to reduce diagnostic error in clinical settings. The most common interventions were delivered as technology-based systems n = 16 (62%) and within an acute care setting n = 15 (57%). Nine studies reported randomised controlled trials. Three RCT studies on communication interventions and 3 RCTs on audit strategies found the interventions to be effective in reducing diagnostic errors. CONCLUSION: Despite numerous studies on interventions targeting diagnostic errors, our analyses revealed limited evidence on interventions being practically used in clinical settings and a bias of studies originating from the US (n = 19, 73% of included studies). There is some evidence that trigger algorithms, including computer based and alert systems, may reduce delayed diagnosis and improve diagnostic accuracy. In trauma settings, strategies such as additional patient review (e.g. trauma teams) reduced missed diagnosis and in radiology departments review strategies such as team meetings and error documentation may reduce diagnostic error rates over time. TRIAL REGISTRATION: The systematic review was registered in the PROSPERO database under registration number CRD42017067056 .

Site-specific factors associated with clinical trial recruitment efficiency in general practice settings: a comparative descriptive analysis.

BACKGROUND: Recruitment of participants is crucial to the success of randomised control trials (RCTs) but can be challenging and expensive. Current research on trial efficiency is often focused at the patient-level with an emphasis on effective recruitment strategies. Less is known about selection of study sites to optimise recruitment. We examine site-level factors that are associated with patient recruitment and cost efficiency using data from an RCT conducted across 25 general practices (GP) in Victoria, Australia. METHODS: Data on number of participants screened, excluded, eligible, recruited, and randomised from each study site were extracted from a clinical trial. Details regarding site characteristics, recruitment practices, and staff time commitment were collected using a three-part survey. The key outcomes assessed were recruitment efficiency (ratio of screened to randomised), average time, and cost for each participant recruited and randomised. To identify practice-level factors associated with efficient recruitment and lower cost, outcomes were dichotomised (25th percentile vs others) and each practice-level factor assessed against the outcomes to determine its association. RESULTS: Across 25 GP study sites, 1968 participants were screened of which 299 (15.2%) were recruited and randomised. The mean recruitment efficiency was 7.2, varying from 1.4 to 19.8 across sites. The strongest factor associated with efficiency was assigning clinical staff to identify potential participants (57.14% vs. 22.2%). The more efficient sites were smaller practices and were more likely to be rural locations and in areas of lower socioeconomic status. The average time used for recruitment was 3.7 h (SD2.4) per patient randomised. The mean cost per patient randomised was $277 (SD161), and this varied from $74 to $797 across sites. The sites identified with the 25% lowest recruitment cost (n = 7) were more experienced in research participation and had high levels of nurse and/or administrative support. CONCLUSION: Despite the small sample size, this study quantified the time and cost used to recruit patients and provides helpful indications of site-level characteristics that can help improve feasibility and efficiency of conducting RCT in GP settings. Characteristics indicative of high levels of support for research and rural practices, which often tends to be overlooked, were observed to be more efficient in recruiting.

Feasibility, Validity and Differences in Adolescent and Adult EQ-5D-Y Health State Valuation in Australia and Spain: An Application of Best-Worst Scaling.

BACKGROUND: The measurement and valuation of health-related quality of life for and by young people are increasingly important, yet research on the impact of study perspective and validity of preferences obtained from young populations remains limited. OBJECTIVE: The objective of this study was to evaluate the feasibility and validity of collecting EQ-5D Youth version (EQ-5D-Y) preferences from adolescents, adults, and adults from a child perspective. METHODS: A profile case best-worst scaling (BWS) online survey was administered to representative Australian and Spanish adult (age ≥ 18 years) and child (age 11-17 years) samples. Adults were told to either answer from their own perspective or for a hypothetical 10-year-old child. Marginal best- and worst-choice frequencies, analysis of dominant choices, self-reported difficulty completing the tasks, and time to complete tasks were used to determine the validity of responses. RESULTS: In Australia, 2134 adults and 1010 adolescents completed the survey. In Spain, 2007 adults and 1000 adolescents completed it. Analysis of marginal choice frequencies and dominant choices indicated that the pattern of responses between adolescents and adults was similar. For Australian respondents, having no mobility problems was rated as best by adolescents, while adults rated having no pain and discomfort as 'best'. In Spain, both adults and adolescents rated no pain or discomfort as 'best'. Australian adolescents rated very worried, sad or unhappy as 'worst', while Spanish adolescents, Spanish adults and Australian adults rated a lot of pain and discomfort as 'worst'. CONCLUSIONS: Results suggest preferences from adolescents using direct BWS are valid. Our descriptive analysis also suggest that there are age-related and country-specific differences in elicitation values for the EQ-5D-Y.

Use of professional-mode flash glucose monitoring, at 3-month intervals, in adults with type 2 diabetes in general practice (GP-OSMOTIC): a pragmatic, open-label, 12-month, randomised controlled trial.

BACKGROUND: Continuous glucose monitoring, either real-time (personal) or retrospective (professional mode), can identify day-to-day glucose profiles to guide management decisions for people with type 2 diabetes. We aimed to examine the effects of professional-mode flash glucose monitoring, done at 3-month intervals, in adults with type 2 diabetes in general practice. METHODS: We did a pragmatic, two-arm, open label, 12-month, individually randomised controlled trial (GP-OSMOTIC) in 25 general practices in Victoria, Australia. Eligible participants were adults aged 18-80 years, with type 2 diabetes diagnosed for at least 1 year and HbA1c at least 5·5 mmol/mol (0·5%) above their target in the past month despite being prescribed at least two non-insulin glucose-lowering drugs, insulin, or both (with therapy stable for at least 4 months). We randomly assigned participants (1:1) to either use of a professional-mode flash glucose monitoring system or usual clinical care (control). All participants wore the flash glucose monitoring sensor at baseline, and electronic randomisation (using permuted block sizes of four and six, and stratified by clinic) was done after the sensor was attached. Masking of participants and treating clinicians to group allocation was not possible, but the study statistician was masked to allocation when analysing the data. At baseline, and 3, 6, 9, and 12 months, participants in the flash glucose monitoring group wore the professional-mode flash glucose monitoring sensor for 5-14 days before their general practice visit. The sensor recorded interstitial glucose concentrations every 15 min, but the glucose data were not available to the participant until their general practice visit, where the sensor output would be uploaded to a computer by the health professional and discussed. Control group participants wore the sensor at baseline and at 12 months for data analysis only, and had usual care visits every 3 months. The primary outcome was the between-group difference in mean HbA1c at 12 months. Secondary outcomes were the between-group differences in: mean percentage time in target glucose range (4-10 mmol/L), based on ambulatory glucose profile data at 12 months; mean diabetes-specific distress (assessed with the Problem Areas In Diabetes [PAID] scale) at 12 months; and mean HbA1c at 6 months. Analysis was done by intention to treat. This trial is registered at the Australian and New Zealand Clinical Trials Registry, ACTRN12616001372471. FINDINGS: Between Oct 4, 2016, and Nov 17, 2017, we randomly assigned 299 adults: 149 to flash glucose monitoring and 150 to usual care. At 6 months, HbA1c was lower in the flash glucose monitoring group than in the usual care group (difference -0·5%, 95% CI -0·8% to -0·3%; p=0·0001). However, at 12 months (primary outcome), there was no significant between-group difference in estimated mean HbA1c (8·2% [95% CI 8·0 to 8·4] for flash glucose monitoring vs 8·5% [8·3 to 8·7] for usual care; between-group difference -0·3%, 95% CI -0·5 to 0·01; [66 mmol/mol, 95% CI 64 to 68 vs 69 mmol/mol, 67 to 72; between-group difference -3·0, 95% CI -5·0 to 0·1]; p=0·059). Mean percentage time spent in target glucose range at 12 months was 7·9% (95% CI 2·3 to 13·5) higher in the flash glucose monitoring group than in the usual care group (p=0·0060). Diabetes-specific distress PAID scores were unchanged at 12 months (between-group difference -0·7, 95% CI -3·3 to 1·9; p=0·61). No episodes of severe hypoglycaemia or treatment-related deaths were reported. One participant died during the study from causes unrelated to the intervention (following complications post-myocardial infarction with multiple comorbidities). INTERPRETATION: Professional-mode flash glucose monitoring in adults with type 2 diabetes in general practice did not improve the primary outcome of HbA1c at 12 months or diabetes-specific distress compared with usual care, but did improve time in target glucose range at 12 months and HbA1c at 6 months. Our findings suggest that professional-mode flash glucose monitoring can be implemented in a pragmatic primary care environment. Although there was no change in HbA1c at 12 months, the improved time in target range might reflect the potential of the technology to support personalised clinical care by providing insights into glycaemic profiles for some people with type 2 diabetes. FUNDING: National Health and Medical Research Council of Australia, Sanofi Australia, and Abbott Diabetes Care.

Health-related quality of life: population epidemiology and concordance in Australian children aged 11-12 years and their parents.

OBJECTIVES: To describe the distribution of health-related quality of life (HRQL) in a national sample of Australian children aged 11-12 years and their parents, and examine associations within parent-child dyads. DESIGN: The Child Health CheckPoint, a population-based cross-sectional study nested between waves 6 and 7 of the Longitudinal Study of Australian Children (LSAC). SETTING: Assessment centres in seven Australian cities and eight regional towns, or home visit; February 2015 to March 2016. PARTICIPANTS: Of all participating CheckPoint families (n=1874), 1853 children (49.0% girls) and 1863 parents (87.7% mothers) with HRQL data were included (1786 pairs). OUTCOME MEASURES: HRQL was self-reported using preference-based (Child Health Utility 9Dimension, CHU9D) and non-preference-based (Pediatric Quality of Life, PedsQL V.4.0) measures for children and preference-based measures for parents (CHU9D; Assessment of Quality of Life 8 Dimension, AQoL-8D). Utility scores from preference-based measures were calculated using existing Australian algorithms to present a score on a 0-1 scale, where 1 represents full health. Parent-child concordance was assessed using Pearson's correlation coefficients and adjusted linear regression models. Survey weights and methods were applied to account for LSAC's complex sample design, stratification and clustering within postcodes. RESULTS: Children's means and SD were 0.81 (SD 0.16) for CHU9D and 78.3 (SD 13.03) for PedsQL. In adults, mean HRQL for AQoL-8D and CHU9D were 0.78 (SD 0.16) and 0.89 (SD 0.10), respectively. Mean HRQL was similar for boys and girls, but slightly higher for fathers than mothers. The Pearson correlation coefficient for parent-child CHU9D values was 0.13 (95% CI 0.09 to 0.18). Percentiles and concordance are presented for both samples for males and females separately and together. CONCLUSIONS: We provide Australian paediatric population values for HRQL measures, and the first national CHU9D values for mid-life adults. At age 11-12 years in this relatively healthy sample, parent-child concordance in HRQL was small.

Opioid use prior to elective surgery is strongly associated with persistent use following surgery: an analysis of 14 354 Medicare patients.

BACKGROUND: Persistent opioid use following total joint replacement (TJR) surgery is common; however, the association between pre-surgical opioid use and surgery type has not been established. The objective of this study was to determine the association between pre-surgery opioid use and persistent post-surgery opioid use in TJR patients compared to other elective surgical patients. METHODS: This is a retrospective cohort study, of univariate and multinomial logistic regression of linked, de-identified Medicare Benefits Schedule and Pharmaceutical Benefits Schedule data, adjusted for perioperative opioid use, age and sex. Oral morphine equivalents daily doses (OMEDD) were calculated and opioid use was categorized into three mutually exclusive categories for each observation window: low (0-5 OMEDD), moderate (5-10 OMEDD), high (10+ OMEDD). Persistent opioid use was defined as opioid use between 180 and 270 days after the date of surgery. RESULTS: Persistent opioid use was associated with older age, female gender and pre-surgery opioid use. There was no increased risk for persistent opioid use for TJR patients compared to other surgical patients. The intensity of pre-surgery opioid usage is strongly associated with persistent opioid use in all observed surgical patients. CONCLUSIONS: Our results suggest that many patients who use opioids prior to surgery will persist in their opioid use following surgery. No association was found between persistent opioid use and TJR surgery, but rather a risk reduction compared to other elective surgeries when associations with opioid use are controlled for. Primary care clinicians and surgeons should monitor the duration and dosage of perioperative opioid use.

Update on the General Practice Optimising Structured Monitoring to Improve Clinical Outcomes in Type 2 Diabetes (GP-OSMOTIC) trial: statistical analysis plan for a multi-centre randomised controlled trial.

BACKGROUND: General Practice Optimising Structured Monitoring to Improve Clinical Outcomes in Type 2 Diabetes (GP-OSMOTIC) is a multicentre, individually randomised controlled trial aiming to compare the use of intermittent retrospective continuous glucose monitoring (r-CGM) to usual care in patients with type 2 diabetes attending general practice. The study protocol was published in the British Medical Journal Open and described the principal features of the statistical methods that will be used to analyse the trial data. This paper provides greater detail on the statistical analysis plan, including background and justification for the statistical methods chosen, in accordance with SPIRIT guidelines. OBJECTIVE: To describe in detail the data management process and statistical methods that will be used to analyse the trial data. METHODS: An overview of the trial design and primary and secondary research questions are provided. Sample size assumptions and calculations are explained, and randomisation and data management processes are described in detail. The planned statistical analyses for primary and secondary outcomes and sub-group analyses are specified along with the intended table layouts for presentation of the results. CONCLUSION: In accordance with best practice, all analyses outlined in the document are based on the aims of the study and have been pre-specified prior to the completion of data collection and outcome analyses. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry, ACTRN12616001372471 . Registered on 3 August 2016.