Normative data for the EORTC QLQ-C30 from the Austrian general population

Patient-reported outcomes (PROs) are the gold standard of evaluating the impact of cancer and its treatment from the patient’s perspective. The importance of PROs is reflected in their widespread use as study endpoints in cancer clinical trials [1, 2] and in the steadily increasing integration of routine PRO assessments in daily clinical practice [3, 4]. Regulatory authorities such as the United States Food and Drug Administration [5] and the European Medicines Agency [6] have published guidance documents to foster the collection of high-quality PRO data in clinical trials. Guidelines from international associations such as the European Organisation for Research and Treatment of Cancer (EORTC) [7] and the International Society for Quality of Life Research [8] deal with the integration of PRO measures into clinical routine, and provide information on how to collect, process, and use PRO data. In the present study, we calculate reference values based on data assessed with EORTC QLQ-C30 [9], a questionnaire that is among the most widely used PRO measures in cancer clinical trials [10‐13]. This questionnaire covers important functional health domains (e.g., physical and emotional functioning) and key cancer symptoms such as fatigue, pain, and nausea.

In the literature, different methods have been proposed to support the interpretation of results from PRO measures, analyzing not only individual patients and patient groups at a single time point, but also group differences and changes over time. One approach is to use minimally important differences to assess or compare changes in PRO results at the group or patient level [14]. To interpret scores obtained at a single time point, thresholds for clinical importance can be used [15]. Another important approach to interpret PRO data are normative data, if comparisons are to be made with, for example, a specific disease group or the general population on an individual- or group-level. When using normative data, it is important to note that these can vary considerably between countries [16‐18]. Focusing on country-specific values allows for more precise interpretation, which becomes even more accurate when regression models that take age and sex distributions into account are used to generate PRO predictions.

The EORTC Quality of Life Group conducted a large-scale international panel study that collected normative data for the EORTC QLQ-C30 for 13 European countries, Canada, and the United States [19]. While the EORTC QLQ-C30 has widely been used in studies in Austria (e.g., [20‐22]), no normative studies have so far been published. In the current study, we present age- and sex-specific normative data for the 15 health domains of the EORTC QLQ-C30 from the Austrian general population. In addition, we provide a regression model defined by age, sex and the presence of health conditions to calculate normative data for specific groups.

In this study, we present age- and sex-specific normative data for the EORTC QLQ-C30 from a sample of the Austrian general population. Men and women differed in several domains, most notably insomnia, fatigue, and emotional functioning. In general, men reported a higher functioning (except for social functioning) and less symptom burden (except for diarrhea) than women did. Older participants (≥70 years) reported higher symptom burden (e.g., pain and dyspnea) and lower physical and role functioning compared to younger participants. We observed both ceiling effects (for functioning scales) and floor effects (for symptom scales) for some scales of the EORTC QLQ-C30. However, floor and ceiling effects were not unexpected, considering that we administered a cancer-specific questionnaire to a general population sample.

A potential limitation of panel studies is that this method of data collection may be prone to under-representing specific groups of individuals (e.g., those who are older or less educated). Therefore, it should be considered whether the assessment of general population data collected via online surveys are truly representative. A comparison of our data with Statistics Austria’s 2017 report suggests that the data obtained from the online survey are representative in terms of most basic individual characteristics (age, sex, and marital status) [30]. For example, regarding the relationship status in our sample, 63.9% of participants stated to be in a long-term relationship. Statistic Austria reported a similar rate with 67.1% of Austrian adults being in a long-term relationship. The unemployment rate of the Austrian sample was 2.8% while the unemployment rate in the 2017 report of the Statistic Austria institute was 5.3% for individuals older than 20 years. Furthermore, the prevalence rates for common health conditions found in our data match other data on the Austrian population: The prevalence of self-reported chronic pain in our sample (21.8%) is close to the prevalence of chronic pain in Austria [30] as diagnosed by a doctor, which is 24.4% for chronic back pain and 18.5% for chronic neck pain. The percentage of participants with diabetes in our sample (6.3%) is in line with the 5–7% prevalence rate of diagnosed diabetes among adults estimated in the latest Austrian diabetes report published by the Austrian Health Ministry [31]. For cancer, the prevalence rate was 2.8%, which is only slightly lower than the 4.1% prevalence rate found in the latest Statistics Austria cancer report [32]. Notable differences of our data to the Statistics Austria data [30] were observed only regarding the level of education. More than one-third of individuals taking part in the online survey (35%) reported at least a university-level education while in the Statistic Austria data only 15% of the sample report a university or comparable degree. In our publication of international normative data for the EORTC CAT CORE (based on the international dataset), the relationship between education and HRQoL scales was investigated in depth [26]. Higher education (some post-compulsory vs. less than post-compulsory education) was linked to more favorable HRQoL scores. However, differences were of low practical relevance as indicated by the small effect sizes (all eta² ≤ .015).

A strength of our study is the consistent data collection mode used by Nolte et al. [19], which allows comparison with other European normative data. Comparisons of HRQoL ratings across country borders can be made, providing insight into international differences [28, 33]. At the national level, our study offers two different approaches for interpreting EORTC QLQ-C30 data. First, the normative data allow interpretation and comparison for different age and sex groups. Second, the regression model permits the calculation of expected EORTC QLQ-C30 normative scores using sex, age, and the presence of health conditions. This regression model can be used to generate more precise predictions than those made through comparing different age and sex groups using normative data.

Compared with other score interpretation approaches, normative data, in particular when relying on regression models, offer the advantage of not reducing the amount of information. For example, in an alternative approach, thresholds for clinical importance are used to condense the information into severity categories (e.g., clinically important vs. not clinically important), which can ease interpretation but also decreases the amount of information conveyed. The data we present in this study can be used to compare HRQoL among patients with cancer and HRQoL in the general population, matched by sex and age group. Such comparisons can be useful at any stage of the cancer journey, as patients’ HRQoL is likely to be compromised at the time of diagnosis [34], and they can be used to determine whether cancer survivors return to population levels or whether problems and impairments persist [35, 36]. Ideally, such comparisons use country-specific normative data, which most accurately reflect the average level of HRQoL within a particular national context. Of course, normative data need a certain amount of currentness to be relevant. For the years to come, this publication can serve as a reference, but data should be updated in due time.

Several observed results merit in depth discussion. The sex differences observed across various domains (especially physical functioning) are in line with other normative studies on the EORTC QLQ-C30 that have reported differences by sex. In studies in Germany [18, 24], Slovenia [33], Denmark [34], Sweden [37], and the Netherlands [38], men tended to report higher functioning and lower symptom burden on most scales than women did. A similar pattern can be observed in the presented Austrian normative data, though both point differences and effect sizes were rather small. Our data allow for better interpretation of these differences among patients with cancer through the comparison of the magnitude of impairment in both groups of individuals (patients with cancer and members of the Austrian general population). As has been discussed previously [39], sex differences among patients with cancer do not necessarily reflect a sex-specific impact of disease or treatment; rather, these differences may reflect more general factors including sex-specific response styles that also affect the general population, as we observed in the present sample.

Regarding age, a European sample [19], as well as single-country studies in Denmark, Sweden, and Slovenia [33, 34, 37] report similar deterioration with lower physical and role functioning in older people (≥60 or ≥ 70 years) compared to younger people (< 60 or < 70 years) as found in our sample. Those studies also found emotional functioning to be independent of age [33, 34] or to even increase with age (≥60 years) [19, 37], showing a similar pattern of emotional functioning and age as observed in our sample. However, our sample differed from two study samples in Germany, which did not show an increase in emotional functioning with age and reported much higher disparity between age groups on the global health status scale, with a difference of up to 23 points (oldest vs. youngest group) in favor of younger people [18, 24].

Compared with the European sample reported by Nolte et al. [19], fewer respondents in our sample reported health conditions or diseases (36.6% in the European sample vs. 52.6% in our sample reported having no health conditions), and our sample showed higher functioning and lower symptom burden on most scales. According to the 2018 EU Statistics on Income and Living Conditions, the Austrian life expectancy is slightly above the EU average (81.3 years vs 80.6 years) and 71.7% of the Austrian adult population report a good or very good perceived health which ranks higher than most other European countries measured [40]. Both of these findings support our result of generally high HRQoL in terms of fewer health conditions and high functioning among the Austrian general population.

The normative data for the EORTC QLQ-C30 generated in this study will ease and foster a more meaningful interpretation of scores obtained from patients with cancer and cancer survivors, allowing comparisons of patient-level and group-level data with the sex- and age-matched general population sample from Austria. Using our regression model, precise predictions for individuals based on sex, age, and presence of health conditions can be generated.