BackgroundMeasurement of health-related quality of life (HRQoL) in dementia is difficult. At some point people with dementia become unable to meaningfully assess their own HRQoL. At such a point in time researchers need to rely on other types of information such as observation or assessments from informal caregivers (proxies). However, caregiver assessments may be biased by several mechanisms. The current study explores whether caregivers project part of their own HRQoL in their assessments of patient HRQoL.MethodsThe participants in the current study were 175 pairs, consisting of community-dwelling persons with dementia and their caregivers. The EQ-5D, the EQ-VAS and the QoL-AD were administered to collect HRQoL measurements from patients and caregivers at baseline, 6 months and 12 months. Two linear mixed models were used to investigate factors that bias proxy ratings, one with the EQ-VAS as dependent variable, and one with the EQ-5D utility as dependent variable. The independent variables were caregiver age, caregiver sex and caregiver QoL-AD items.ResultsThe linear mixed model with EQ-VAS as dependent variable indicated that 3 caregiver characteristics, namely caregiver age, money (caregiver’s financial situation) and valuation of life as a whole were significant predictors of the patient-by-proxy VAS scores. The linear mixed model with utility value as the dependent variable showed that caregiver age and valuation of the ability to do things for fun were significant predictors of the patient-by-proxy EQ-5D utility values.ConclusionsThe current study was a first step in identifying factors that bias patient-by-proxy HRQoL assessments. It was discovered that caregivers project part of their own HRQoL onto patients when assessing patient HRQoL. This implies that patient-by-proxy HRQoL values should be interpreted with caution and not be used as a direct substitute for patient self-assessment, even when patients are no longer able meaningfully assess themselves.
Introduction In several studies, the chimeric antigen receptor T‐cell therapy tisagenlecleucel demonstrated encouraging rates of remission and lasting survival benefits in pediatric patients with relapsed/refractory (r/r) acute lymphoblastic leukemia (ALL). We assessed the cost‐effectiveness of tisagenlecleucel (list price: 320 000 EUR) among these patients when compared to clofarabine monotherapy (Clo‐M), clofarabine combination therapy (Clo‐C), and blinatumomab (Blina) from both a healthcare and a societal perspective. We also assessed future medical and future non‐medical consumption costs. Methods A three‐state partitioned survival model was used to simulate a cohort of pediatric patients (12 years of age) through different disease states until the end of life (lifetime horizon). Relevant outcomes were life years, quality‐adjusted life years (QALYs), healthcare costs, societal costs, and the incremental cost‐effectiveness ratio (ICER). Uncertainty was explored through deterministic and probabilistic sensitivity analyses as well as through several scenario analyzes. Results Total discounted costs for tisagenlecleucel were 552 679 EUR from a societal perspective, which was much higher than the total discounted costs from a healthcare perspective (ie, 409 563 EUR). Total discounted societal costs for the comparator regimens ranged between 160 803 EUR for Clo‐M and 267 259 EUR for Blina. Highest QALYs were estimated for tisagenlecleucel (11.26), followed by Blina (2.25), Clo‐C (1.70) and Clo‐M (0.74). Discounted societal ICERs of tisagenlecleucel ranged between 31 682 EUR/QALY for Blina and 37 531 EUR/QALY for Clo‐C and were considered cost‐effective with a willingness‐to‐pay (WTP) threshold of 80 000 EUR/QALY. None of the scenarios exceeded this threshold, and more than 98% of the iterations in the probabilistic sensitivity analysis were cost‐effective. Discussion At the current price and WTP threshold, tisagenlecleucel is cost‐effective from both a healthcare and a societal perspective. Nevertheless, long‐term effectiveness data are needed to validate the several assumptions that were necessary for this model.
Interest is rising in measuring subjective health outcomes, such as treatment outcomes that are not directly quantifiable (functional disability, symptoms, complaints, side effects and healthrelated quality of life). Health economists in particular have applied probabilistic choice models in the area of health evaluation. They increasingly use discrete choice models based on random utility theory to derive values for healthcare goods or services. Recent attempts have been made to use discrete choice models as an alternative method to derive values for health states. In this article, various probabilistic choice models are described according to their underlying theory. A historical overview traces their development and applications in diverse fields. The discussion highlights some theoretical and technical aspects of the choice models and their similarity and dissimilarity. The objective of the article is to elucidate the position of each model and their applications for health-state valuation.
Statistical considerations allow using all Qualidem items in all dementia stages. Future research should determine balance of statistical- versus conceptual-based reasoning in this academic debate.
BackgroundIndex measures for health-related quality of life (HRQoL) quantify the desirability (utility) of a certain health state. The commonly used generic index measure, e.g. EuroQol: EQ-5D, may underestimate relevant areas of specific diseases, resulting in lower validity. Disease-specific index measures on the other hand combine disease-specificity and quantification of perceived quality on several health domains of a certain disease into one single figure. These instruments have been developed for several diseases, but a dementia-specific HRQoL index instrument was not yet available. Facing the increasing individual and societal burden of dementia, specific HRQoL values with metric characteristics are especially useful because they will provide vital information for health outcome research and economic evaluations.Aims of the studyTo develop and validate the prototype of a dementia-specific HRQoL index measure: Dementia Quality of life Instrument (DQI), as the first step towards valuation of the dementia health state.MethodsFor development of the DQI we created a conceptual framework based on a review of the literature, qualitative interviews with people with dementia and their carers, expert opinion and team discussion. To assess validity we undertook a survey under 241 dementia professionals. Measurements consisted of ranking (1–5) and rating (1–10) of 5 dementia-specific DQI domains (memory, orientation, independence, social activities and mood) and simultaneously rating of 9 DQI-derived health states on a visual analogue scale (VAS). We also performed a cross-sectional study in a large sample of people with very mild to moderate dementia and their caregivers (N = 145) to assess feasibility and concurrent validity. In addition, caregivers valued 10 DQI and 10 EQ-5D + C derived health states of the patient simultaneously on the same VAS. Setting: outpatient clinics, nursing homes and patient residences.ResultsAll professionals judged the selected DQI domains to be relevant. Differences in ranking and rating behaviors were small. Mood was ranked (≥3.3) and rated (≥8.2) as most, orientation as least important (rank ≤2.6, value 7.5) health domain for dementia. For the validation part of this study the completion rates for all domains were above 98% for patients and 100% for caregivers on patients. A priori hypothesized DQI versus QOL-AD correlations that were significant in both patients and caregivers were: memory/memory, orientation/memory, independence/physical health, social activities/energy and mood/mood. Patient/caregiver inter-rater agreement was low (K < 0.2) for memory/independence, fair (K 0.2-0.4) for orientation/mood, and moderate (K 0.4-0.6) for social activities. Concurrent validity of the DQI with the EQ-5D + C was moderate. The fact that most of the correlations between the domains of these two instruments were low (≤0.40) showed that both instruments measure different elements of health status. As expected, modest correlations (≥0.40) were observed between corresponding domains of the two in...
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