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Chemotherapy side effects are often reported in clinical trials; however, there is little evidence about their incidence in routine clinical care. The objective of this study was to describe the frequency and severity of patient-reported chemotherapy side effects in routine care across treatment centres in Australia. We conducted a prospective cohort study of individuals with breast, lung or colorectal cancer undergoing chemotherapy. Side effects were identified by patient self-report. The frequency, prevalence and incidence rates of side effects were calculated by cancer type and grade, and cumulative incidence curves for each side effect computed. Frequencies of side effects were compared between demographic subgroups using chi-squared statistics. Side effect data were available for 449 eligible individuals, who had a median follow-up of 5.64 months. 86% of participants reported at least one side effect during the study period and 27% reported a grade IV side effect, most commonly fatigue or dyspnoea. Fatigue was the most common side effect overall (85%), followed by diarrhoea (74%) and constipation (74%). Prevalence and incidence rates were similar across side effects and cancer types. Age was the only demographic factor associated with the incidence of side effects, with older people less likely to report side effects. This research has produced the first Australian estimates of self-reported incidence of chemotherapy side effects in routine clinical care. Chemotherapy side effects in routine care are common, continue throughout chemotherapy and can be serious. This work confirms the importance of observational data in providing clinical practice-relevant information to decision-makers.

Background Discrete-choice experiments (DCEs) are used in the development of preference-based measure (PBM) value sets. There is considerable variation in the methodological approaches used to elicit preferences. Objective Our objective was to carry out a structured review of DCE methods used for health state valuation. Methods PubMed was searched until 31 May 2018 for published literature using DCEs for health state valuation. Search terms to describe DCEs, the process of valuation and preference-based instruments were developed. English language papers with any study population were included if they used DCEs to develop or directly inform the production of value sets for generic or condition-specific PBMs. Assessment of paper quality was guided by the recently developed Checklist for Reporting Valuation Studies. Data were extracted under six categories: general study information, choice task and study design, type of designed experiment, modelling and analysis methods, results and discussion. Results The literature search identified 1132 published papers, and 63 papers were included in the review. Paper quality was generally high. The study design and choice task formats varied considerably, and a wide range of modelling methods were employed to estimate value sets. Conclusions This review of DCE methods used for developing value sets suggests some recurring limitations, areas of consensus and areas where further research is required. Methodological diversity means that the values should be seen as experimental, and users should understand the features of the value sets produced before applying them in decision making.

Purpose The EORTC QLU-C10D is a new multi-attribute utility instrument derived from the EORTC QLQ-C30, a widely used cancer-specific quality of life questionnaire. It covers ten dimensions: physical, role, social, emotional functioning, pain, fatigue, sleep, appetite, nausea, and bowel problems. To allow national health attitudes to be reflected, country-specific valuations are being performed by collaboration of the Multi-Attribute Utility Cancer (MAUCa) Consortium and the EORTC. The purpose of this paper is to provide German value sets (utility weights) for the QLU-C10D. Methods Valuations were run in a web-based setting in two general population samples of approximately 2000 adults in total. As the German version of the QLQ-C30 is presently undergoing a revision of the wording of one response category, valuations for both the current and the new version were performed (Germany 1 and 2). Utilities were elicited using a discrete choice experiment (DCE). Data were analyzed by conditional logistic regression and mixed logits. Results Completion rates were 88.3% (1002/1135) and 90.4% (1016/1124) for Germany 1 and Germany 2 valuations, respectively. Dimensions with the largest impact on utility weights were, in this order: physical functioning, pain, role functioning, social functioning and nausea (same ordering for both German versions). Several violations of the logical ordering of levels were observed for Germany 1; this was largely improved for Germany 2. Conclusion This study established German utility weights for the cancer-specific utility instrument QLU-C10D.

Background The EQ-5D-5L has recently been developed to improve the sensitivity of the widely used three-level version. Valuation studies are required before the use of this new instrument can be adopted. The use of discrete choice experiments (DCEs) in this area is a promising area of research. Purpose To test the plausibility and acceptability of estimating an Australian algorithm for the newly developed five-level version of the EQ-5D using a DCE. Methods A choice experiment was designed, consisting of 200 choice sets blocked such that each respondent answered 10 choice sets. Each choice set presented two health state–duration combinations, and an immediate death option. The experiment was implemented in an online Australian-representative sample. A random-effects probit model was estimated. To explore the feasibility of the approach, an indicative algorithm was developed. The algorithm is transformed to a 0 to 1 scale suitable for use to estimate quality-adjusted life-year weights for use in economic evaluation. Results A total of 973 respondents undertook the choice experiment. Respondents were slightly younger and better educated than the general Australian population. Of the 973 respondents, 932 (95.8 %) completed all ten choice sets, and a further 12 completed some of the choice sets. In choice sets in which one health state–duration combination dominated another, the dominant option was selected on 89.5 % of occasions. The mean and median completion times were 17.9 and 9.4 min, respectively, exhibiting a highly skewed distribution. The estimation results are broadly consistent with the monotonic nature of the EQ-5D-5L. Utility is increasing in life expectancy (i.e., respondents tend to prefer health profiles with longer life expectancy), and mainly decreases in higher levels in each dimension of the instrument. A high proportion of respondents found the task clear and relatively easy to complete. Conclusions DCEs are a feasible approach to the estimation of utility weights for more complex multi-attribute utility instruments such as the EQ-5D-5L.

There is demand from women for alternatives to giving birth in a standard hospital setting however access to these services is limited. This systematic review examines the literature relating to the economic evaluations of birth setting for women at low risk of complications. Searches of the literature to identify economic evaluations of different birth settings of the following electronic databases: MEDLINE, CINAHL, EconLit, Business Source Complete and Maternity and Infant care. Relevant English language publications were chosen using keywords and MeSH terms between 1995 and 2015. Inclusion criteria included studies focussing on the comparison of birth setting. Data were extracted with respect to study design, perspective, PICO principles, and resource use and cost data. Eleven studies were included from Australia, Canada, the Netherlands, Norway, the USA, and the UK. Four studies compared costs between homebirth and the hospital setting and the remaining seven focussed on the cost of birth centre care and the hospital setting. Six studies used a cost-effectiveness analysis and the remaining five studies used cost analysis and cost comparison methods. Eight of the 11 studies found a cost saving in the alternative settings. Two found no difference in the cost of the alternative settings and one found an increase in birth centre care. There are few studies that compare the cost of birth setting. The variation in the results may be attributable to the cost data collection processes, difference in health systems and differences in which costs were included. A better understanding of the cost of birth setting is needed to inform policy makers and service providers.

Current measurement systems focus mostly on health, and not on multiple constructs of quality of life outcomes (for example health and social outcomes) together. This means we don’t capture all that is of value to those receiving treatments, and to society more broadly. Recent research has explored how to extend the quality adjusted life year (QALY) beyond a narrow focus on health-related quality of life (HRQoL) aiming to improve the allocation of scarce health and social care resources. Measures of different constructs, including the EuroQol-Health and Wellbeing (EQ-HWB), and different versions of the Adult Social Care Outcomes Toolkit (ASCOT) and ICEpop CAPability (ICECAP), have been developed. Another approach to extending the health focused QALY is to combine existing descriptive systems with different foci into a single instrument. This has the advantage of using available information and allowing trade-offs between the domains of the descriptive systems to be made explicit. The aim of this paper is to propose a framework to guide this approach and outline the methodological process for generating broader descriptive systems. The first section of the paper explains the framework for combining existing instruments and discusses advantages and disadvantages. Advantages include increasing measurement sensitivity to the wider combined quality of life (QoL) impacts of many interventions and using value sets encompassing preferences that are based on trade-offs across diverse constructs. This enables values informed by impacts on broader QoL with relevance across diverse populations, to be used. Disadvantages include theoretical limitations linked to the constructs of QoL included, and practical difficulties combining instruments. The second section of the paper describes the methodological process for generating combined descriptive systems. This includes how to identify which constructs of QoL could be included, and a description of the mixed methods work required to generate a descriptive system that is psychometrically valid, and appropriate for valuation. Combining constructs of QoL from existing instruments offers a promising way to extend the QALY that differs to developing instruments de novo. Future research can use the framework outlined to develop combined instruments and explore the feasibility and wider applicability of the approach, and the use of the instruments generated in resource allocation decision making.

Purpose EQ-5D and PROMIS-29 are both concise, generic measures of patient-reported outcomes accompanied by preference weights that allow the estimation of quality-adjusted life years (QALYs). Both instruments are candidates for use in economic evaluation. However, they have different features in terms of the domains selected to measure respondents’ self-perceived health and the characteristics of (and methods used to obtain) the preference weights. It is important to understand the relationship between the instruments and the implications of choosing either for the evidence used in decision-making. This literature review aimed to synthesise existing evidence on the relationship between PROMIS-29 (and measures based on it, such as PROMIS-29+2) and EQ-5D (both EQ-5D-3L and EQ-5D-5L). Methods A literature review was conducted in PubMed and Web of Science to identify studies investigating the relationship between PROMIS-29 and EQ-5D-based instruments. Results The literature search identified 95 unique studies, of which nine studies met the inclusion criteria, i.e. compared both instruments. Six studies examined the relationship between PROMIS-29 and EQ-5D-5L. Three main types of relationship have been examined in the nine studies: (a) comparing PROMIS-29 and EQ-5D as descriptive systems; (b) mapping PROMIS-29 domains to EQ-5D utilities; and (c) comparing and transforming PROMIS-29 utilities to EQ-5D utilities. Conclusion This review has highlighted the lack of evidence regarding the relationship between PROMIS-29 and EQ-5D. The impact of choosing either instrument on the evidence used in cost-effectiveness analysis is currently unclear. Further research is needed to understand the relationship between the two instruments.

PurposeUp to 40% of cancer patients treated with neurotoxic chemotherapies experience chemotherapy-induced peripheral neuropathy (CIPN). Currently, there is no gold standard assessment tool for CIPN and there is little information in the literature on patient preferences for such assessments. This study aims to address this gap by identifying the features of a CIPN assessment tool that cancer patients value.MethodsAn online discrete choice experiment (DCE) survey of neurotoxic chemotherapy–treated patients was implemented. Respondents completed 8 choice questions each. In each choice question, they chose between two hypothetical CIPN assessment tools, each described by six attributes: impact on quality of life; level of nerve damage detected; questionnaire length; physical tests involved; impact on clinic time; impact on care.ResultsThe survey was completed by 117 respondents who had a range of cancers of which breast cancer was the most common. Respondents favoured an assessment tool that includes a physical test and that asks about impact on quality of life. Respondents were strongly opposed to clinicians, alone, deciding how the results of a CIPN assessment might influence their care especially their chemotherapy treatment. They were concerned about small changes in their CIPN, independent of clinical relevance. Respondents were willing to add half an hour to the usual clinic time to accommodate the CIPN assessment.ConclusionThe findings of this DCE will assist clinicians in choosing an assessment tool for CIPN that is satisfactory to both clinician and patient.

Background The EORTC QLU-C10D is a preference-based measure derived from the EORTC QLQ-C30. For use in economic evaluations, country-specific value sets are needed. This study aimed to generate an EORTC QLU-C10 value set for Spain. Methods A sample of the Spanish general population completed an online discrete choice experiment. An attribute-balanced incomplete block design was used to select 960 choice tasks, with a total of 1920 health states. Each participant was randomly assigned 16 choice sets without replacement. Data were modelled using generalized estimating equations and mixed logistic regressions. Results A total of 1625 panel members were invited to participate, 1010 of whom were included in the study. Dimension decrements were generally monotonic with larger disutilities at increased severity levels. Dimensions associated with larger decrements were physical functioning and pain, while the dimension with the smallest decrement was sleep disturbances. The PITS state (i.e. worst attainable health) for the Spanish population is − 0.043. Conclusions This study generated the first Spanish value set for the QLU-C10D. This can facilitate cost-utility analyses when applied to data collected with the EORTC QLQ-C30.

Introduction The application of artificial intelligence (AI) in radiation therapy holds promise for addressing challenges, such as healthcare staff shortages, increased efficiency and treatment planning variations. Increased AI adoption has the potential to standardise treatment protocols, enhance quality, improve patient outcomes, and reduce costs. However, drawbacks include impacts on employment and algorithmic biases, making it crucial to navigate trade‐offs. A discrete choice experiment (DCE) was undertaken to examine the AI‐related characteristics radiation oncology professionals think are most important for adoption in radiation therapy treatment planning. Methods Radiation oncology professionals completed an online discrete choice experiment to express their preferences about AI systems for radiation therapy planning which were described by five attributes, each with 2–4 levels: accuracy, automation, exploratory ability, compatibility with other systems and impact on workload. The survey also included questions about attitudes to AI. Choices were modelled using mixed logit regression. Results The survey was completed by 82 respondents. The results showed they preferred AI systems that offer the largest time saving, and that provide explanations of the AI reasoning (both in‐depth and basic). They also favoured systems that provide improved contouring precision compared with manual systems. Respondents emphasised the importance of AI systems being cost‐effective, while also recognising AI's impact on professional roles, responsibilities, and service delivery. Conclusions This study provides important information about radiation oncology professionals' priorities for AI in treatment planning. The findings from this study can be used to inform future research on economic evaluations and management perspectives of AI‐driven technologies in radiation therapy. We conducted a discrete choice experiment (DCE) to explore the preferences of radiation oncology professionals regarding which key features of AI systems might impact future adoption in clinical settings. Our findings suggest that the respondents preferred AI systems that offer the largest time saving, and that provide explanations of the AI reasoning (both in‐depth and basic) and systems that provide improved contouring precision compared with manual systems. Respondents emphasised the importance of AI systems being cost‐effective, while also recognising AI's impact on professional roles, responsibilities, and service delivery. This study offers valuable insights into radiation oncology professionals' priorities regarding the integration of AI in treatment planning, ensuring its potential value is maximised.