Overall, the AQL-5D discriminated across all levels of asthma control. The HUI-3, the EQ-5D, and the SF-6D differentiated between the highest and lowest levels of control but could not discriminate between the moderate levels.

ObjectiveTo evaluate the validity of cancer-specific and generic preference-based instruments to discriminate across different measures of cancer severities.MethodsPatients with breast (n = 66), colorectal (n = 57), and lung (n = 61) cancer completed the EORTC QLQ-C30 and the FACT-G, as well as three generic instruments: the EQ-5D, the SF-6D, and the HUI2/3. Disease severity was quantified using cancer stage, Eastern Cooperative Oncology Group Performance Status (ECOG-PS) score, and self-reported health status. Comparative analyses confirmed the multi-dimensional conceptualization of the instruments in terms of construct and convergent validity.ResultsIn general, the instruments were able to discriminate across severity measures. The instruments demonstrated moderate to strong correlation with each other (r = 0.37-0.73). Not all of the measures could discriminate between different groups of disease severity: the EQ-5D and SF-6D were less discriminative than the HUI2/3 and the cancer-specific instruments.ConclusionThe cancer-specific and generic preference-based instruments demonstrated to be valid in discriminating across levels of ECOG-PS scores and self-reported health states. However, the usefulness of the generic instruments may be limited if they are not able to detect small changes in health status within cancer patients. This raises concerns regarding the appropriateness of these instruments when comparing different cancer treatments within an economic evaluation framework.

There has been rapid implementation of virtual oncology appointments in response to the COVID-19 pandemic, particularly in its first wave. Our objective was to assess patterns and perspectives towards virtual oncology appointments during the pandemic among patients with cancer undergoing active treatment. We conducted an international Internet-based cross-sectional survey. Participants were eligible if they (1) were ≥18 years of age; (2) had been diagnosed with cancer (3) were currently undergoing cancer treatment, and (4) spoke English or French. Between 23 April 2020 and 9 June 2020, 381 individuals accessed the survey, with 212 actively undergoing treatment for cancer, including 27% with colorectal, 21% with breast, 7% with prostate and 7% with lung cancer. A total of 52% of respondents were from Canada and 35% were from the United States. Many participants (129, 62%) indicated having had a virtual oncology appointment during the COVID-19 pandemic and most were satisfied with their experience (83%). We found older participants (≥50 years; adjusted OR 0.22, 95% CI 0.06 to 0.85 compared to <50 years) and those with shortest duration of treatment (≤3 months; adjusted OR 0.06; 95% CI 0 to 0.69 compared to >12 months) were less likely to be satisfied with virtual oncology appointments. Virtual health platforms used differed across countries with higher telephone use in Canada (87%) and other countries (86%) as compared to the United States (54%; p-value < 0.05), where there was higher use of video conferencing. Altogether, our findings demonstrate favorable patient perspectives towards virtual oncology appointments experienced during the first wave of the COVID-19 pandemic.

ObjectiveTo help facilitate economic evaluations of oncology treatments, we mapped responses on cancer-specific instrument to generic preference-based measures.MethodsCancer patients (n = 367) completed one cancer-specific instrument, the FACT-G, and two preference-based measures, the EQ-5D and SF-6D. Responses were randomly divided to form development (n = 184) and cross-validation (n = 183) samples. Relationships between the instruments were estimated using ordinary least squares (OLS), generalized linear models (GLM), and censored least absolute deviations (CLAD) regression approaches. The performance of each model was assessed in terms of how well the responses to the cancer-specific instrument predicted EQ-5D and SF-6D utilities using mean absolute error (MAE) and root mean squared error (RMSE).ResultsPhysical, functional, and emotional well-being domain scores of the FACT-G best explained the EQ-5D and SF-6D. In terms of accuracy of prediction as measured in RMSE, the CLAD model performed best for the EQ-5D (RMSE = 0.095) whereas the GLM model performed best for the SF-6D (RMSE = 0.061). The GLM predicted SF-6D scores matched the observed values more closely than the CLAD and OLS.ConclusionOur results demonstrate that the estimation of both EQ-5D and SF-6D utility indices using the FACT-G responses can be achieved. The CLAD model for the EQ-5D and the GLM model for the SF-6D are recommended. Thus, it is possible to estimate quality-adjusted life years for economic evaluation from studies where only cancer-specific instrument have been administered.

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Objective. The EORTC QLQ-C30 is widely used for assessing quality of life in cancer. However, QLQ-C30 responses cannot be incorporated in cost-utility analysis because they are not based on general population’s preferences, or utilities. To overcome this limitation, the QLU-C10D, a cancer-specific utility algorithm, was derived from the QLQ-C30. The aim of this study was to obtain Canadian population utility weights for the QLU-C10D. Methods. Respondents from a Canadian research panel expressed their preferences for 16 choice sets in an online discrete choice experiment. Each choice set consisted of two health states described by the 10 QLU-C10D domains plus an attribute representing duration of survival. Using a conditional logit model, responses were converted into utility decrements by evaluating the marginal rate of substitution between each QLU-C10D domain level with respect to duration. Results. A total of 3,363 individuals were recruited. A total of 2,345 completed at least one choice set and 2,271 completed all choice sets. The largest utility decrements were associated with the worse levels of Physical Functioning (−0.24), Pain (−0.18), Role Functioning (−0.15), Emotional Functioning (−0.12), and Nausea (−0.12). The remaining domains and levels had decrements of −0.05 to −0.09. The utility of the worst possible health state was −0.15. Conclusion. Respondents from the general population were most concerned with generic health domains, but Nausea and Bowel Problems also had an impact on the individual’s utility. It is unclear as to whether cancer-specific domains will affect cost-utility analysis when evaluating cancer treatments; this will be tested in the next phase of the study.

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Asthma symptom control status corresponds well with both generic and disease-specific quality-of-life measures. The trade-off, however, between ease of use and predictive power should be reconsidered in developing simplified measures of control. Our results have direct relevance in informing decision-analytic models of asthma and deducing the effect of interventions on quality of life through their impact on asthma control.