Comparing modeling strategies combining changes in multiple serum tumor biomarkers for early prediction of immunotherapy non-response in non-small cell lung cancer.

BACKGROUND: Patients treated with immune checkpoint inhibitors (ICI) are at risk of adverse events (AEs) even though not all patients will benefit. Serum tumor markers (STMs) are known to reflect tumor activity and might therefore be useful to predict response, guide treatment decisions and thereby prevent AEs. OBJECTIVE: This study aims to compare a range of prediction methods to predict non-response using multiple sequentially measured STMs. METHODS: Nine prediction models were compared to predict treatment non-response at 6-months (n = 412) using bi-weekly CYFRA, CEA, CA-125, NSE, and SCC measurements determined in the first 6-weeks of therapy. All methods were applied to six different biomarker combinations including two to five STMs. Model performance was assessed based on sensitivity, while model training aimed at 95% specificity to ensure a low false-positive rate. RESULTS: In the validation cohort, boosting provided the highest sensitivity at a fixed specificity across most STM combinations (12.9% -59.4%). Boosting applied to CYFRA and CEA achieved the highest sensitivity on the validation data while maintaining a specificity >95%. CONCLUSIONS: Non-response in NSCLC patients treated with ICIs can be predicted with a specificity >95% by combining multiple sequentially measured STMs in a prediction model. Clinical use is subject to further external validation.

Predicting the Population Health Economic Impact of Current and New Cancer Treatments for Colorectal Cancer: A Data-Driven Whole Disease Simulation Model for Predicting the Number of Patients with Colorectal Cancer by Stage and Treatment Line in Australia.

OBJECTIVES: Effective healthcare planning, resource allocation, and budgeting require accurate predictions of the number of patients needing treatment at specific cancer stages and treatment lines. The Predicting the Population Health Economic Impact of Current and New Cancer Treatments (PRIMCAT) for Colorectal Cancer (CRC) simulation model (PRIMCAT-CRC) was developed to meet this requirement for all CRC stages and relevant molecular profiles in Australia. METHODS: Real-world data were used to estimate treatment utilization and time-to-event distributions. This populated a discrete-event simulation, projecting the number of patients receiving treatment across all disease stages and treatment lines for CRC and forecasting the number of patients likely to utilize future treatments. Illustrative analyses were undertaken, estimating treatments across disease stages and treatment lines over a 5-year period (2022-2026). We demonstrated the model's applicability through a case study introducing pembrolizumab as a first-line treatment for mismatch-repair-deficient stage IV. RESULTS: Clinical registry data from 7163 patients informed the model. The model forecasts 15 738 incident and 2821 prevalent cases requiring treatment in 2022, rising to 15 921 and 2871, respectively, by 2026. Projections show that over 2022 to 2026, there will be a total of 116 752 treatments initiated, with 43% intended for stage IV disease. The introduction of pembrolizumab is projected for 706 patients annually, totaling 3530 individuals starting treatment with pembrolizumab over the forecasted period, without significantly altering downstream utilization of subsequent treatments. CONCLUSIONS: PRIMCAT-CRC is a versatile tool that can be used to estimate the eligible patient populations for novel cancer therapies, thereby reducing uncertainty for policymakers in decisions to publicly reimburse new treatments.

The effect of time before diagnosis and treatment on colorectal cancer outcomes: systematic review and dose-response meta-analysis.

BACKGROUND: This systematic review and meta-analysis aimed to evaluate existing evidence on the relationship between diagnostic and treatment intervals and outcomes for colorectal cancer. METHODS: Four databases were searched for English language articles assessing the role of time before initial treatment in colorectal cancer on any outcome, including stage and survival. Two reviewers independently screened articles for inclusion and data were synthesised narratively. A dose-response meta-analysis was performed to examine the association between treatment interval and survival. RESULTS: One hundred and thirty papers were included in the systematic review, eight were included in the meta-analysis. Forty-five different intervals were considered in the time from first symptom to treatment. The most common finding was of no association between the length of intervals on any outcome. The dose-response meta-analysis showed a U-shaped association between the treatment interval and overall survival with the nadir at 45 days. CONCLUSION: The review found inconsistent, but mostly a lack of, association between interval length and colorectal cancer outcomes, but study design and quality were heterogeneous. Meta-analysis suggests survival becomes increasingly poorer for those commencing treatment more than 45 days after diagnosis. REGISTRATION: This review was registered, and the protocol is available, in PROSPERO, the international database of systematic reviews, with the registration ID CRD42021255864.

Pharmacological treatment patterns in patients with juvenile idiopathic arthritis in the Netherlands: a real-world data analysis.

OBJECTIVE: To investigate medication prescription patterns among children with JIA, including duration, sequence and reasons for medication discontinuation. METHODS: This study is a single-centre, retrospective analysis of prospective data from the electronic medical records of JIA patients receiving systemic therapy aged 0-18 years between 1 April 2011 and 31 March 2019. Patient characteristics (age, gender, JIA subtype) and medication prescriptions were extracted and analysed using descriptive statistics, Sankey diagrams and Kaplan-Meier survival methods. RESULTS: Over a median of 4.2 years follow-up, the 20 different medicines analysed were prescribed as monotherapy (n = 15) or combination therapy (n = 48 unique combinations) among 236 patients. In non-systemic JIA, synthetic DMARDs were prescribed to almost all patients (99.5%), and always included MTX. In contrast, 43.9% of non-systemic JIA patients received a biologic DMARD (mostly adalimumab or etanercept), ranging from 30.9% for oligoarticular persistent ANA-positive JIA, to 90.9% for polyarticular RF-positive JIA. Among systemic JIA, 91.7% received a biologic DMARD (always including anakinra). When analysing medication prescriptions according to their class, 32.6% involved combination therapy. In 56.8% of patients, subsequent treatment lines were initiated after unsuccessful first-line treatment, resulting in 68 unique sequences. Remission was the most common reason for DMARD discontinuation (44.7%), followed by adverse events (28.9%) and ineffectiveness (22.1%). CONCLUSION: This paper reveals the complexity of pharmacological treatment in JIA, as indicated by: the variety of mono- and combination therapies prescribed, substantial variation in medication prescriptions between subtypes, most patients receiving two or more treatment lines, and the large number of unique treatment sequences.

Willingness to participate in combination screening for lung cancer, chronic obstructive pulmonary disease and cardiovascular disease in four European countries.

OBJECTIVES: Lung cancer screening (LCS), using low-dose computed tomography (LDCT), can be more efficient by simultaneously screening for chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD), the Big-3 diseases. This study aimed to determine the willingness to participate in (combinations of) Big-3 screening in four European countries and the relative importance of amendable participation barriers. METHODS: An online cross-sectional survey aimed at (former) smokers aged 50-75 years elicited the willingness of individuals to participate in Big-3 screening and used analytical hierarchy processing (AHP) to determine the importance of participation barriers. RESULTS: Respondents were from France (n = 391), Germany (n = 338), Italy (n = 399), and the Netherlands (n = 342), and consisted of 51.2% men. The willingness to participate in screening was marginally influenced by the diseases screened for (maximum difference of 3.1%, for Big-3 screening (73.4%) vs. lung cancer and COPD screening (70.3%)) and by country (maximum difference of 3.7%, between France (68.5%) and the Netherlands (72.3%)). The largest effect on willingness to participate was personal perceived risk of lung cancer. The most important barriers were the missed cases during screening (weight 0.19) and frequency of screening (weight 0.14), while diseases screened for (weight 0.11) ranked low. CONCLUSIONS: The difference in willingness to participate in LCS showed marginal increase with inclusion of more diseases and limited variation between countries. A marginal increase in participation might result in a marginal additional benefit of Big-3 screening. The amendable participation barriers are similar to previous studies, and the new criterion, diseases screened for, is relatively unimportant. CLINICAL RELEVANCE STATEMENT: Adding diseases to combination screening modestly improves participation, driven by personal perceived risk. These findings guide program design and campaigns for lung cancer and Big-3 screening. Benefits of Big-3 screening lie in long-term health and economic impact, not participation increase. KEY POINTS: • It is unknown whether or how combination screening might affect participation. • The addition of chronic obstructive pulmonary disease and cardiovascular disease to lung cancer screening resulted in a marginal increase in willingness to participate. • The primary determinant influencing individuals' engagement in such programs is their personal perceived risk of the disease.

Developing and validating a multi-criteria decision analytic tool to assess the value of cancer clinical trials: evaluating cancer clinical trial value.

BACKGROUND: Demonstrating safety and efficacy of new medical treatments requires clinical trials but clinical trials are costly and may not provide value proportionate to their costs. As most health systems have limited resources, it is therefore important to identify the trials with the highest value. Tools exist to assess elements of a clinical trial such as statistical validity but are not wholistic in their valuation of a clinical trial. This study aims to develop a measure of clinical trials value and provide an online tool for clinical trial prioritisation. METHODS: A search of the academic and grey literature and stakeholder consultation was undertaken to identify a set of criteria to aid clinical trial valuation using multi-criteria decision analysis. Swing weighting and ranking exercises were used to calculate appropriate weights of each of the included criteria and to estimate the partial-value function for each underlying metric. The set of criteria and their respective weights were applied to the results of six different clinical trials to calculate their value. RESULTS: Seven criteria were identified: 'unmet need', 'size of target population', 'eligible participants can access the trial', 'patient outcomes', 'total trial cost', 'academic impact' and 'use of trial results'. The survey had 80 complete sets of responses (51% response rate). A trial designed to address an 'Unmet Need' was most commonly ranked as the most important with a weight of 24.4%, followed by trials demonstrating improved 'Patient Outcomes' with a weight of 21.2%. The value calculated for each trial allowed for their clear delineation and thus a final value ranking for each of the six trials. CONCLUSION: We confirmed that the use of the decision tool for valuing clinical trials is feasible and that the results are face valid based on the evaluation of six trials. A proof-of-concept applying this tool to a larger set of trials with an external validation is currently underway.

Health economic evidence for adjuvant chemotherapy in stage II and III colon cancer: a systematic review.

OBJECTIVE: The aims of this study was to appraise the health economic evidence for adjuvant chemotherapy (AC) strategies in stage II and III colon cancer (CC) and identify gaps in the available evidence that might inform further research. METHOD: A systematic review of published economic evaluations was undertaken. Four databases were searched and full-text publications in English were screened for inclusion. A narrative synthesis was performed to summarise the evidence. RESULTS: Thirty-eight studies were identified and stratified by cancer stage and AC strategy. The majority (89%) were full economic evaluations considering both health outcomes, usually measured as quality-adjusted life years (QALYs), and costs. AC was found to be cost-effective compared to no AC for both stage II and III CC. Oral and oxaliplatin-based AC was cost-effective for stage III. Three months of CAPOX was cost-effective compared to 6-month in high-risk stage II and stage III CC. Preliminary evidence suggests that biomarker approaches to AC selection in stage II can reduce costs and improve health outcomes. Notably, assessment of QALYs were predominantly reliant on a small number of non-contemporary health-utility studies. Only 32% of studies considered societal costs such as travel and time off work. CONCLUSIONS: Published economic evaluations consistently supported the use of AC in stage II and III colon cancer. Biomarker-driven approaches to patient selection have great potential to be cost-effective, but more robust clinical and economic evidence is warranted. Patient surveys embedded into clinical trials may address critical knowledge gaps regarding accurate assessment of QALYs and societal costs in the modern era.

An evidence-base for the implementation of hospital-based palliative care programs in routine cancer practice: A systematic review.

BACKGROUND: Despite global support, there remain gaps in the integration of early palliative care into cancer care. The methods of implementation whereby evidence of benefits of palliative care is translated into practice deserve attention. AIM: To identify implementation frameworks utilised in integrated palliative care in hospital-based oncology services and to describe the associated enablers and barriers to service integration. DESIGN: Systematic review with a narrative synthesis including qualitative, mixed methods, pre-post and quasi experimental designs following the guidance by the Centre for Reviews and Dissemination (PROSPERO registration CRD42021252092). DATA SOURCES: Six databases searched in 2021: EMBASE, EMCARE, APA PsycINFO, CINAHL, Cochrane Library and Ovid MEDLINE searched in 2023. Included were qualitative or quantitative studies, in English language, involving adults >18 years, and implementing hospital-based palliative care into cancer care. Critical appraisal tools were used to assess the quality and rigour. RESULTS: Seven of the 16 studies explicitly cited the use of frameworks including those based on RE-AIM, Medical Research Council evaluation of complex interventions and WHO constructs of health service evaluation. Enablers included an existing supportive culture, clear introduction to the programme across services, adequate funding, human resources and identification of advocates. Barriers included a lack of communication with the patients, caregivers, physicians and palliative care team about programme goals, stigma around the term 'palliative', a lack of robust training, or awareness of guidelines and undefined staff roles. CONCLUSIONS: Implementation science frameworks provide a method to underpin programme development and evaluation as palliative care is integrated within the oncology setting.

Heterogeneity in how women value risk-stratified breast screening.

PURPOSE: Risk-stratified screening has potential to improve the cost effectiveness of national breast cancer screening programs. This study aimed to inform a socially acceptable and equitable implementation framework by determining what influences a woman's decision to accept a personalized breast cancer risk assessment and what the relative impact of these key determinants is. METHODS: Multicriteria decision analysis was used to elicit the relative weights for 8 criteria that women reported influenced their decision. Preference heterogeneity was explored through cluster analysis. RESULTS: The 2 criteria valued most by the 347 participants related to program access, "Mode of invitation" and "Testing process". Both criteria significantly influenced participation (P < .001). A total of 73% preferred communication by letter/online. Almost all women preferred a multidisease risk assessment with potential for a familial high-risk result. Four preference-based subgroups were identified. Membership to the largest subgroup was predicted by lower educational attainment, and women in this subgroup were concerned with program access. Higher relative perceived breast cancer risk predicted membership to the smallest subgroup that was focused on test parameters, namely "Scope of test" and "Test specificity". CONCLUSION: Overall, Australian women would accept a personalized multidisease risk assessment, but when aligning with their preferences, it will necessitate a focus on program access and the development of online communication frameworks.

Mapping disadvantage: identifying inequities in functional outcomes for prostate cancer survivors based on geography.

BACKGROUND: Prostate cancer is the most common internal malignancy in Australian men, and although most patients have good survival outcomes, treatment toxicities can impair function, leading to diminished quality of life for prostate cancer survivors. Socioeconomic disadvantage and geographical remoteness have been shown to be related to worse oncologic outcomes, and it is expected that they would similarly influence functional outcomes in prostate cancer. METHODS: Using data from the Victorian Prostate Cancer Outcomes Registry (n = 10,924), we investigated functional outcomes as measured by the Expanded Prostate Cancer Index Composite-26 (EPIC-26) following prostate cancer treatment, focusing on associations with socioeconomic status and geographical remoteness and controlling for clinicopathologic characteristics. A single composite score was developed from the five separate EPIC-26 domains for use in geo-mapping. RESULTS: A total of 7690 patients had complete EPIC-26 data, allowing mapping hotspots of poor function using our composite score. These hotspots were observed to relate to areas of socioeconomic disadvantage. Significant heterogeneity in outcomes was seen in urban areas, with hotspots of good and poor function. Both socioeconomic disadvantage and geographical remoteness were found to predict for worse functional outcomes, although only the former is significant on multivariate analysis. CONCLUSIONS: Geo-mapping of functional outcomes in prostate cancer has the potential to guide health care service provision and planning. A nuanced policy approach is required so as not to miss disadvantaged patients who live in urban areas. We have demonstrated the potential of geo-mapping to visualise population-level outcomes, potentially allowing targeted interventions to address inequities in quality of care.

Health economic evidence for the use of molecular biomarker tests in hematological malignancies: A systematic review.

OBJECTIVES: Molecular biomarker tests can inform the clinical management of genomic heterogeneous hematological malignancies, yet their availability in routine care largely depends on the supporting health economic evidence. This study aims to systematically review the economic evidence for recent molecular biomarker tests in hematological malignancies. METHODS: We conducted a systematic search in five electronic databases for studies published between January 2010 and October 2020. Publications were independently screened by two reviewers. Clinical study characteristics, economic methodology, and results were extracted, and reporting quality was assessed. RESULTS: Fourteen studies were identified, of which half (n = 7; 50%) were full economic evaluations examining both health and economic outcomes. Studies were predominantly conducted in a first-line treatment setting (n = 7; 50%) and adopted a non-lifetime time horizon to measure health outcomes and costs (n = 7; 50%). Five studies reported that companion diagnostics for associated therapies were likely cost-effective for acute myeloid leukemia, chronic myeloid leukemia, diffuse large B-cell lymphoma, and multiple myeloma. Four studies suggested molecular biomarker tests for treatment monitoring in chronic myeloid leukemia were likely cost-saving. CONCLUSIONS: Although there is initial confirmation of the promising health economic results, the present research for molecular biomarker tests in hematological malignancies is sparse with many applications of technological advances yet to be evaluated.

Can we increase efficiency of CT lung cancer screening by combining with CVD and COPD screening? Results of an early economic evaluation.

OBJECTIVES: Estimating the maximum acceptable cost (MAC) per screened individual for low-dose computed tomography (LDCT) lung cancer (LC) screening, and determining the effect of additionally screening for chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), or both on the MAC. METHODS: A model-based early health technology assessment (HTA) was conducted to estimate whether a new intervention could be cost-effective by calculating the MAC at a willingness-to-pay (WTP) of €20k/quality-adjusted life-year (QALY) and €80k/QALY, for a population of current and former smokers, aged 50-75 years in The Netherlands. The MAC was estimated based on incremental QALYs gained from a stage shift assuming screened individuals are detected in earlier disease stages. Data were obtained from literature and publicly available statistics and validated with experts. RESULTS: The MAC per individual for implementing LC screening at a WTP of €20k/QALY was €113. If COPD, CVD, or both were included in screening, the MAC increased to €230, €895, or €971 respectively. Scenario analyses assessed whether screening-specific disease high-risk populations would improve cost-effectiveness, showing that high-risk CVD populations were more likely to improve economic viability compared to COPD. CONCLUSIONS: The economic viability of combined screening is substantially larger than for LC screening alone, primarily due to benefits from CVD screening, and is dependent on the target screening population, which is key to optimise the screening program. The total cost of breast and cervical cancer screening is lower (€420) than the MAC of Big-3, indicating that Big-3 screening may be acceptable from a health economic perspective. KEY POINTS: • Once-off combined low-dose CT screening for lung cancer, COPD, and CVD in individuals aged 50-75 years is potentially cost-effective if screening would cost less than €971 per screened individual. • Multi-disease screening requires detailed insight into the co-occurrence of these diseases to identify the optimal target screening population. • With the same target screening population and WTP, lung cancer-only screening should cost less than €113 per screened individual to be cost-effective.

Comparing Modeling Approaches for Discrete Event Simulations With Competing Risks Based on Censored Individual Patient Data: A Simulation Study and Illustration in Colorectal Cancer.

OBJECTIVES: This study aimed to provide detailed guidance on modeling approaches for implementing competing events in discrete event simulations based on censored individual patient data (IPD). METHODS: The event-specific distributions (ESDs) approach sampled times from event-specific time-to-event distributions and simulated the first event to occur. The unimodal distribution and regression approach sampled a time from a combined unimodal time-to-event distribution, representing all events, and used a (multinomial) logistic regression model to select the event to be simulated. A simulation study assessed performance in terms of relative absolute event incidence difference and relative entropy of time-to-event distributions for different types and levels of right censoring, numbers of events, distribution overlap, and sample sizes. Differences in cost-effectiveness estimates were illustrated in a colorectal cancer case study. RESULTS: Increased levels of censoring negatively affected the modeling approaches' performance. A lower number of competing events and higher overlap of distributions improved performance. When IPD were censored at random times, ESD performed best. When censoring occurred owing to a maximum follow-up time for 2 events, ESD performed better for a low level of censoring (ie, 10%). For 3 or 4 competing events, ESD better represented the probabilities of events, whereas unimodal distribution and regression better represented the time to events. Differences in cost-effectiveness estimates, both compared with no censoring and between approaches, increased with increasing censoring levels. CONCLUSIONS: Modelers should be aware of the different modeling approaches available and that selection between approaches may be informed by data characteristics. Performing and reporting extensive validation efforts remains essential to ensure IPD are appropriately represented.

Machine Learning Methods in Health Economics and Outcomes Research-The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force.

Advances in machine learning (ML) and artificial intelligence offer tremendous potential benefits to patients. Predictive analytics using ML are already widely used in healthcare operations and care delivery, but how can ML be used for health economics and outcomes research (HEOR)? To answer this question, ISPOR established an emerging good practices task force for the application of ML in HEOR. The task force identified 5 methodological areas where ML could enhance HEOR: (1) cohort selection, identifying samples with greater specificity with respect to inclusion criteria; (2) identification of independent predictors and covariates of health outcomes; (3) predictive analytics of health outcomes, including those that are high cost or life threatening; (4) causal inference through methods, such as targeted maximum likelihood estimation or double-debiased estimation-helping to produce reliable evidence more quickly; and (5) application of ML to the development of economic models to reduce structural, parameter, and sampling uncertainty in cost-effectiveness analysis. Overall, ML facilitates HEOR through the meaningful and efficient analysis of big data. Nevertheless, a lack of transparency on how ML methods deliver solutions to feature selection and predictive analytics, especially in unsupervised circumstances, increases risk to providers and other decision makers in using ML results. To examine whether ML offers a useful and transparent solution to healthcare analytics, the task force developed the PALISADE Checklist. It is a guide for balancing the many potential applications of ML with the need for transparency in methods development and findings.

Developing a dynamic simulation model to support the nationwide implementation of whole genome sequencing in lung cancer.

BACKGROUND: This study shows how dynamic simulation modeling can be applied in the context of the nationwide implementation of Whole Genome Sequencing (WGS) for non-small cell lung cancer (NSCLC) to inform organizational decisions regarding the use of complex and disruptive health technologies and how these decisions affect their potential value. METHODS: Using the case of the nationwide implementation of WGS into clinical practice in lung cancer in the Dutch healthcare system, we developed a simulation model to show that including service delivery features across the diagnostic pathway can provide essential insight into the affordability and accessibility of care at the systems level. The model was implemented as a hybrid Agent-Based Model and Discrete-Event Simulation model in AnyLogic and included 78 hospital agents, 7 molecular tumor board agents, 1 WGS facility agent, and 5313 patient agents each year in simulation time. RESULTS: The model included patient and provider heterogeneity, including referral patterns, capacity constraints, and diagnostic workflows. Patient preference and adoption by healthcare professionals were also modeled. The model was used to analyze a scenario in which only academic hospitals have implemented WGS. To prevent delays in the diagnostic pathway, the capacity to sequence at least 1600 biopsies yearly should be present. There is a two-fold increase in mean diagnostic pathway duration between no patients referred or all patients referred for further diagnostics. CONCLUSIONS: The systems model can complement conventional health economic evaluations to investigate how the organization of the workflow can influence the actual use and impact of WGS. Insufficient capacity to provide WGS and referral patterns can substantially impact the duration of the diagnostic pathway and thus should be considered in the implementation of WGS.

Do Health Technology Assessment organisations consider manufacturers' costs in relation to drug price? A study of reimbursement reports.

INTRODUCTION: Drug reimbursement decisions are often made based on a price set by the manufacturer. In some cases, this price leads to public and scientific debates about whether its level can be justified in relation to its costs, including those related to research and development (R&D) and manufacturing. Such considerations could enter the decision process in collectively financed health care systems. This paper investigates whether manufacturers' costs in relation to drug prices, or profit margins, are explicitly mentioned and considered by health technology assessment (HTA) organisations. METHOD: An analysis of reimbursement reports for cancer drugs was performed. All relevant Dutch HTA-reports, published between 2017 and 2019, were selected and matched with HTA-reports from three other jurisdictions (England, Canada, Australia). Information was extracted. Additionally, reimbursement reports for three cases of expensive non-oncolytic orphan drugs prominent in pricing debates in the Netherlands were investigated in depth to examine consideration of profit margins. RESULTS: A total of 66 HTA-reports concerning 15 cancer drugs were included. None of these reports contained information on manufacturer's costs or profit margins. Some reports contained general considerations of the HTA organisation which related prices to manufacturers' costs: six contained a statement on the lack of price setting transparency, one mentioned recouping R&D costs as a potential argument to justify a high price. For the case studies, 21 HTA-reports were selected. One contained a cost-based price justification provided by the manufacturer. None of the other reports contained information on manufacturer's costs or profit margins. Six reports contained a discussion about lack of transparency. Reports from two jurisdictions contained invitations to justify high prices by demonstrating high costs. CONCLUSION: Despite the attention given to manufacturers' costs in relation to price in public debates and in the literature, this issue does not seem to get explicit systematic consideration in the reimbursement reports of expensive drugs.

Disparities in quality of life, social distress and employment outcomes in Australian cancer survivors.

PURPOSE: To examine how socio-demographic, comorbidities and information needs influence quality of life (QoL) outcomes of survivors of breast, colorectal, or prostate cancer, non-Hodgkin lymphoma or melanoma. METHODS: Cross-sectional postal survey with eligible participants identified through a population-based cancer registry. QoL outcomes were assessed by EQ-5D-5L, social difficulties index (SDI) and, for those employed at diagnosis, current employment. Regression analyses explored associations between outcome variables and cancer type, age, time since diagnosis, residential location, socio-economic disadvantage, comorbidities and unmet information needs. Mediation analyses examined whether comorbidities and information needs explained relationships between outcome variables and socio-economic disadvantage. RESULTS: 2115 survivors participated. Mean EQ-5D-5L scores (mean = 0.84) were similar to population averages and SDI scores were low for the entire sample (mean = 3.80). In multivariate analyses, being aged over 80, greater socio-economic disadvantage, comorbidities and unmet information needs decreased EQ-5D-5L scores. Higher SDI scores were associated with socio-economic disadvantage, comorbidities and unmet information needs. Not being employed was associated with being aged over 50, more comorbidities and socio-economic disadvantage. Comorbidities but not information needs partially mediated the impact of socio-economic disadvantage on EQ-5D-5L and SDI accounting for 17% and 14% of the total effect of socio-economic disadvantage respectively. Neither comorbidities nor information needs mediated the association between socio-economic disadvantage and employment outcomes. CONCLUSIONS: To improve quality of life, survivorship care should be better tailored to address the needs of individuals given their overall health and impact of comorbidities, their age and type of cancer and not simply time since diagnosis.

The societal impact of implementing an at-home blood sampling device for chronic care patients: patient preferences and cost impact.

BACKGROUND: Diabetes mellitus, cardiovascular diseases, chronic kidney disease, and thyroid diseases are chronic diseases that require regular monitoring through blood tests. This paper first investigates the experiences of chronic care patients with venipuncture and their expectations of an at-home blood-sampling device, and then assesses the impact on societal costs of implementing such a device in current practice. METHODS: An online survey was distributed among chronic care patients to gain insight into their experience of blood sampling in current practice, and their expectations of an at-home blood-sampling device. The survey results were used as input parameters in a patient-level monte carlo analysis developed to represent a hypothetical cohort of Dutch chronically ill patients to investigate the impact on societal costs compared to usual care. RESULTS: In total, 1311 patients participated in the survey, of which 31% experience the time spent on the phlebotomy appointment as a burden. Of all respondents, 71% prefer to use an at-home blood-sampling device to monitor their chronic disease. The cost analysis indicated that implementing an at-home blood-sampling device increases the cost of phlebotomy itself by €27.25 per patient per year, but it reduces the overall societal costs by €24.86 per patient per year, mainly due to limiting productivity loss. CONCLUSIONS: Patients consider an at-home blood-sampling device to be more user-friendly than venous phlebotomy on location. Long waiting times and crowded locations can be avoided by using an at-home blood-sampling device. Implementing such a device is likely cost-saving as it is expected to reduce societal costs.

Evaluating the resource implications of different service delivery models for offering additional genomic findings.

PURPOSE: To evaluate the resource implications of different delivery models for the provision of additional findings (AF) in genomics from a health-care purchaser perspective. METHODS: Data from the Additional Findings study were used to develop and validate a discrete event simulation model that represented the pathway of delivering AF. Resource implications were estimated by microcosting the consultations, sample verifications, bioinformatics, curation, and multidisciplinary case review meetings. A proof-of-concept model was used to generate costing, and then the simulation model was varied to assess the impact of an automated analysis pipeline, use of telehealth consultation, full automation with electronic decision support, and prioritizing case review for cases with pathogenic variants. RESULTS: For the proof-of-concept delivery model, the average total cost to report AF was US$430 per patient irrespective of result pathogenicity (95% confidence interval [CI] US$375-US$489). However, the cost of per AF diagnosis was US$4349 (95% CI US$3794-US$4953). Alternative approaches to genetic counseling (telehealth, decision support materials) and to multidisciplinary case review (pathogenic AF cases only) lowered the total per patient cost of AF analysis and reporting by 41-51%. CONCLUSION: Resources required to provide AF can be reduced substantially by implementing alternative approaches to counseling and multidisciplinary case review.

Whole genome sequencing in oncology: using scenario drafting to explore future developments.

BACKGROUND: In oncology, Whole Genome Sequencing (WGS) is not yet widely implemented due to uncertainties such as the required infrastructure and expertise, costs and reimbursements, and unknown pan-cancer clinical utility. Therefore, this study aimed to investigate possible future developments facilitating or impeding the use of WGS as a molecular diagnostic in oncology through scenario drafting. METHODS: A four-step process was adopted for scenario drafting. First, the literature was searched for barriers and facilitators related to the implementation of WGS. Second, they were prioritized by international experts, and third, combined into coherent scenarios. Fourth, the scenarios were implemented in an online survey and their likelihood of taking place within 5 years was elicited from another group of experts. Based on the minimum, maximum, and most likely (mode) parameters, individual Program Evaluation and Review Technique (PERT) probability density functions were determined. Subsequently, individual opinions were aggregated by performing unweighted linear pooling, from which summary statistics were extracted and reported. RESULTS: Sixty-two unique barriers and facilitators were extracted from 70 articles. Price, clinical utility, and turnaround time of WGS were ranked as the most important aspects. Nine scenarios were developed and scored on likelihood by 18 experts. The scenario about introducing WGS as a clinical diagnostic with a lower price, shorter turnaround time, and improved degree of actionability, scored the highest likelihood (median: 68.3%). Scenarios with low likelihoods and strong consensus were about better treatment responses to more actionable targets (26.1%), and the effect of centralizing WGS (24.1%). CONCLUSIONS: Based on current expert opinions, the implementation of WGS as a clinical diagnostic in oncology is heavily dependent on the price, clinical utility (both in terms of identifying actionable targets as in adding sufficient value in subsequent treatment), and turnaround time. These aspects and the optimal way of service provision are the main drivers for the implementation of WGS and should be focused on in further research. More knowledge regarding these factors is needed to inform strategic decision making regarding the implementation of WGS, which warrants support from all relevant stakeholders.