Estimating the magnitude and healthcare costs of melanoma in situ and thin invasive melanoma overdiagnosis in Australia.

BACKGROUND: Research suggests a high proportion of melanoma in situ (MIS) may be overdiagnosed, potentially contributing to overtreatment, patient harm and inflated costs for individuals and healthcare systems. However, Australia-wide estimates of the magnitude of melanoma overdiagnosis are potentially outdated and there has been no estimation of the cost to the healthcare system. OBJECTIVE: To estimate the magnitude and cost of overdiagnosed MIS and thin invasive melanomas in Australia. METHODS: Using two different methods for calculating lifetime risk, we used routinely collected, national-level data to estimate overdiagnosed MIS and thin invasive melanomas (stage IA) in Australia in 2017 and 2021, separately for men and women. We multiplied the number of overdiagnosed melanomas by the estimated annual cost of a MIS or thin invasive melanoma to quantify the financial burden of melanoma overdiagnosis to the Australian healthcare system in the year following diagnosis. RESULTS: We estimated that between 67-70% of MIS were overdiagnosed in 2017, rising to 71-76% in 2021, contributing to between 19,829 (95%CI: 19,553-20,105) and 20,811 (95%CI: 20,528-21,094) overdiagnosed MIS. In 2021, the estimated costs in Australia ranged between $17.7 million (95%CI: $17.4-17.9 million) and $18.6 million (95%CI: $18.3-18.8 million). We estimated that 22-29% of thin invasive melanomas were overdiagnosed in 2017, rising to 28-34% in 2021, contributing to between 2,831 (95%CI: 2,726-2,935) and 3,168 (95%CI: 3,058-3,279) overdiagnosed thin invasive melanomas. In 2021, the estimated costs from thin invasive melanoma overdiagnoses ranged between $2.5 million (95%CI: $2.4-2.6 million) and $2.8 million (95%CI: $2.7-2.9 million). CONCLUSIONS: Melanoma overdiagnosis is a growing clinical and public health problem in Australia, producing significant economic costs in the year following overdiagnosis. Limiting melanoma overdiagnosis may prevent unnecessary healthcare resource use and improve financial sustainability within the Australian healthcare system.

Methods to Include Environmental Impacts in Health Economic Evaluations and Health Technology Assessments: A Scoping Review.

OBJECTIVES: The environmental impacts of healthcare are important factors that should be considered during health technology assessments. This study aims to summarize the evidence that exists about methods to include environmental impacts in health economic evaluations and health technology assessments. METHODS: We identified records for screening using an existing scoping review and a systematic search of academic databases and gray literature up to September 2023. We screened the identified records for eligibility and extracted data using a narrative synthesis approach. The review was conducted following the JBI Manual for Evidence Synthesis and reported according to the Preferred Reporting Items for Systematic Reviews and Meta Analyses Extension for Scoping Reviews checklist. RESULTS: We identified 2898 records and assessed the full text of 114, of which 54 were included in this review. Ten methods were identified to include environmental impacts in health economic evaluations and health technology assessments. Methods included converting environmental impacts to dollars or disability-adjusted life years and including them in a cost-effectiveness, cost-utility, or cost-benefit analysis, calculating an incremental carbon footprint effectiveness ratio or incremental carbon footprint cost ratio, incorporating impacts as one criteria of a multi-criteria decision analysis, and freely considering impacts during health technology assessment deliberation processes. CONCLUSIONS: Methods to include environmental impacts in health economic evaluations and health technology assessments exist but have not been tested for widespread use by health technology assessment agencies. Further research and implementation work is needed to determine which method can best aid decision makers to choose low environmental impact healthcare interventions.

Outcome-based analytical performance specifications: current status and future challenges.

Analytical performance specifications (APS) based on outcomes refer to how 'good' the analytical performance of a test needs to be to do more good than harm to the patient. Analytical performance of a measurand affects its clinical performance. Without first setting clinical performance requirements, it is difficult to define how good analytically the test needs to be to meet medical needs. As testing is indirectly linked to health outcomes through clinical decisions on patient management, often simulation-based studies are used to assess the impact of analytical performance on the probability of clinical outcomes which is then translated to Model 1b APS according to the Milan consensus. This paper discusses the related key definitions, concepts and considerations that should assist in finding the most appropriate methods for deriving Model 1b APS. We review the advantages and limitations of published methods and discuss the criteria for transferability of Model 1b APS to different settings. We consider that the definition of the clinically acceptable misclassification rate is central to Model 1b APS. We provide some examples and guidance on a more systematic approach for first defining the clinical performance requirements for tests and we also highlight a few ideas to tackle the future challenges associated with providing outcome-based APS for laboratory testing.

Applying the Milan models to setting analytical performance specifications - considering all the information.

Analytical performance specifications (APS) are used for decisions about the required analytical quality of pathology tests to meet clinical needs. The Milan models, based on clinical outcome, biological variation, or state of the art, were developed to provide a framework for setting APS. An approach has been proposed to assign each measurand to one of the models based on a defined clinical use, physiological control, or an absence of quality information about these factors. In this paper we propose that in addition to such assignment, available information from all models should be considered using a risk-based approach that considers the purpose and role of the actual test in a clinical pathway and its impact on medical decisions and clinical outcomes in addition to biological variation and the state-of-the-art. Consideration of APS already in use and the use of results in calculations may also need to be considered to determine the most appropriate APS for use in a specific setting.

Breast Cancer Stage and Size Detected with Film versus Digital Mammography in New South Wales, Australia: A Population-Based Study Using Routinely Collected Data.

BACKGROUND: Digital mammography has replaced film mammography in breast-screening programs globally, including Australia. This led to an increase in the rate of detection, but whether there was increased detection of clinically important cancers is uncertain. METHODS: In this population-wide retrospective cohort study in New South Wales, Australia spanning 2004 to 2016 and including 4,631,656 screens, there were 22,965 cancers in women screened with film (n = 11,040) or digital mammography (n = 11,925). We examined the change in tumor characteristics overall and how these rates changed over time, accounting for changes in background rates using an interrupted time-series. Comparisons were made with unscreened women (n = 26,326) during this time. RESULTS: We found increased detection of in situ cancer (3.36 per 10,000 screens), localized invasive, and smaller-sized breast cancers attributable to the change in mammography technology, whereas screen-detected intermediate-sized and metastatic breast cancers decreased. Rates of early-stage and intermediate-sized interval cancers increased, and late-stage (-1.62 per 10,000 screens) and large interval cancers decreased. In unscreened women, there were small increases in the temporal trends of cancers across all stages. CONCLUSIONS: At least some of the increased detection of smaller early-stage cancers may have translated into a reduction in larger and late-stage cancers, indicating beneficial detection of cancers that would have otherwise progressed. However, the increased detection of smaller early-stage and small cancers may also have increased over-diagnosis of lesions that would otherwise have not caused harm. IMPACT: Robust evaluation of potential benefits and harms is needed after changes to screening programs. See related In the Spotlight, p. 638.

Communicating the Imperfect Diagnostic Accuracy of COVID-19 Rapid Antigen Self-Tests: An Online Randomized Experiment.

OBJECTIVE: To investigate the potential impacts of optimizing coronavirus disease 2019 (COVID-19) rapid antigen test (RAT) self-testing diagnostic accuracy information. DESIGN: Online randomized experiment using hypothetical scenarios: in scenarios 1 to 3 (RAT result positive), the posttest probability was considered to be very high (likely true positives), and in scenarios 4 and 5 (RAT result negative), the posttest probability was considered to be moderately high (likely false negatives). SETTING: December 12 to 22, 2022, during the mixed-variant Omicron wave in Australia. PARTICIPANTS: Australian adults. Intervention: diagnostic accuracy of a COVID-19 self-RAT presented in a health literacy-sensitive way; usual care: diagnostic accuracy information provided by the manufacturer; control: no diagnostic accuracy information. MAIN OUTCOME MEASURE: Intention to self-isolate. RESULTS: A total of 226 participants were randomized (control n = 75, usual care n = 76, intervention n = 75). More participants in the intervention group correctly interpreted the meaning of the diagnostic accuracy information (P = 0.08 for understanding sensitivity, P < 0.001 for understanding specificity). The proportion who would self-isolate was similar across scenarios 1 to 3 (likely true positives). The proportion was higher in the intervention group than in the control for scenarios 4 and 5 (likely false negatives). These differences were not statistically significant. The largest potential effect was seen in scenario 5 (dinner party with confirmed cases, the person has symptoms, negative self-RAT result), with 63% of the intervention group and 49% of the control group indicating they would self-isolate (absolute difference 13.3%, 95% confidence interval: -2% to 30%, P = 0.10). CONCLUSION: Health literacy sensitive formatting supported participant understanding and recall of diagnostic accuracy information. This may increase community intentions to self-isolate when there is a likely false-negative self-RAT result. Trial registration: Australia New Zealand Clinical Trial Registry (ACTRN12622001517763). HIGHLIGHTS: Community-based diagnostic accuracy studies of COVID-19 self-RATs indicate substantially lower sensitivity (and higher risk of false-negative results) than the manufacturer-supplied information on most government public Web sites.This online randomized study found that a health literacy-sensitive presentation of the imperfect diagnostic accuracy COVID-19 self-RATs supported participant understanding and recall of diagnostic accuracy information.Health literacy-sensitive presentation may increase community intentions to self-isolate after a negative test result where the posttest probability is still moderately high (i.e., likely false-negative result).To prevent the onward spread of infection, efforts to improve communication about the high risk of false-negative results from COVID-19 self-RATs are urgently needed.

Using the Behaviour Change Wheel to identify barriers and targeted strategies to improve adherence in randomised clinical trials: The example of MEL-SELF trial of patient-led surveillance for melanoma.

BACKGROUND: Adherence to self-management interventions is critical in both clinical settings and trials to ensure maximal effectiveness. This study reports how the Behaviour Change Wheel may be used to assess barriers to self-management behaviours and develop strategies to maximise adherence in a trial setting (the MEL-SELF trial of patient-led melanoma surveillance). METHODS: The Behaviour Change Wheel was applied by (i) using the Capability, Opportunity, Motivation-Behaviour (COMB) model informed by empirical and review data to identify adherence barriers, (ii) mapping identified barriers to corresponding intervention functions, and (iii) identifying appropriate behaviour change techniques and developing potential solutions using the APEASE (Affordability, Practicability, Effectiveness and cost-effectiveness, Acceptability, Side-effects and safety, Equity) criteria. RESULTS: The target adherence behaviour was defined as conducting a thorough skin self-examination and submitting images for teledermatology review. Key barriers identified included: non-engaged skin check partners, inadequate planning, time constraints, low self-efficacy, and technological difficulties. Participants' motivation was positively influenced by perceived health benefits and negatively impacted by emotional states such as anxiety and depression. We identified the following feasible interventions to support adherence: education, training, environmental restructuring, enablement, persuasion, and incentivisation. Proposed solutions included action planning, calendar scheduling, alternative dermatoscopes, optimised communication, educational resources in various formats to boost self-efficacy and motivation and optimised reminders (which will be evaluated in a Study Within A Trial (SWAT)). CONCLUSION: The Behaviour Change Wheel may be used to improve adherence in clinical trials by identifying barriers to self-management behaviours and guiding development of targeted strategies.

Addressing missing outcome data in randomised controlled trials: A methodological scoping review.

BACKGROUND: Missing outcome data is common in trials, and robust methods to address this are needed. Most trial reports currently use methods applicable under a missing completely at random assumption (MCAR), although this strong assumption can often be inappropriate. OBJECTIVE: To identify and summarise current literature on the analytical methods for handling missing outcome data in randomised controlled trials (RCTs), emphasising methods appropriate for data missing at random (MAR) or missing not at random (MNAR). STUDY DESIGN AND SETTING: We conducted a methodological scoping review and identified papers through searching four databases (MEDLINE, Embase, CENTRAL, and CINAHL) from January 2015 to March 2023. We also performed forward and backward citation searching. Eligible papers discussed methods or frameworks for handling missing outcome data in RCTs or simulation studies with an RCT design. RESULTS: From 1878 records screened, our search identified 101 eligible papers. 90 (89%) papers described specific methods for addressing missing outcome data and 11 (11%) described frameworks for overall methodological approach. Of the 90 methods papers, 30 (33%) described methods under the MAR assumption, 48 (53%) explored methods under the MNAR assumption and 11 (12%) discussed methods under a hybrid of MAR and MNAR assumptions. Control-based methods under the MNAR assumption were the most common method explored, followed by multiple imputation under the MAR assumption. CONCLUSION: This review provides guidance on available analytic approaches for handling missing outcome data, particularly under the MNAR assumption. These findings may support trialists in using appropriate methods to address missing outcome data.

Systematic review and meta-analysis of prognostic characteristics for breast cancers in populations with digital vs film mammography indicate the transition may have increased both early detection and overdiagnosis.

OBJECTIVES: Film mammography has been replaced by digital mammography in breast screening programs globally. This led to a small increase in the rate of detection, but whether the detection of clinically important cancers increased is uncertain. We aimed to assess the impact on tumor characteristics of screen-detected and interval breast cancers. STUDY DESIGN AND SETTING: We searched seven databases from inception to October 08, 2023, for publications comparing film and digital mammography within the same population of asymptomatic women at population (average) risk of breast cancer. We recorded reported tumor characteristics and assessed risk of bias using the Risk Of Bias In Non-randomised Studies - of Interventions tool. We synthesized results using meta-analyses of random effects. RESULTS: Eighteen studies were included in the analysis from 8 countries, including 11,592,225 screening examinations (8,117,781 film; 3,474,444 digital). There were no differences in tumor size, morphology, grade, node status, receptor status, or stage in the pooled differences for screen-detected and interval invasive cancer tumor characteristics. There were statistically significant increases in screen-detected ductal carcinoma in situ (DCIS) across all grades: 0.05 (0.00-0.11), 0.14 (0.05-0.22), and 0.19 (0.05-0.33) per 1000 screens for low, intermediate, and high-grade DCIS, respectively. There were similar (non-statistically significant) increases in screen-detected invasive cancer across all grades. CONCLUSION: The increased detection of all grades of DCIS and invasive cancer may indicate both increased early detection of more aggressive disease and increased overdiagnosis.