In this article, we have reported a detailed analysis of normative data for the EORTC QLQ-C30 in the Spanish general population. While we observed age- and sex-specific differences, the most important aspect with a substantial negative impact on all EORTC QLQ-C30 domains was the presence of a health condition. Scores in the QLQ-C30 for the overall sample were generally high, in line with the scores from the international study’s global sample . Comparing the results from this analysis against the global sample published previously , differences between Spanish data and the global sample were trivial or small. Regarding summary score, Spain ranked 6th among the 13 European countries analysed in the international study, with only two countries outperforming Spain by more than 1 point (Austria + 3.2 points, Netherlands + 3.9 points). The summary score in our study aligns with that of a study from Croatia  but is much lower (7 points) than in the study from The Netherlands .
Fayers  has suggested possible reasons for these differences among countries including health habits and cultural effects: communities may perceive their HRQoL differently due to variations in expectations. Other reasons could involve selection bias or differences in the interview systems , although this is not likely in the overall sample as the selection process was standardised across the different countries.
Our EORTC QLQ-C30 scores were aligned with those in the EORTC Reference Values manual for the general population . Further, similar to our results, small differences by sex for emotional functioning and fatigue  were also found in the main general population study , other studies performed in Europe [1, 6, 17–19, 23, 26], and various other countries [25, 37]. Contrary to ours, however, most of those studies found differences in various HRQoL domains. One exception was a study performed in Denmark, where differences in other scale items were found (small differences, as in ours). The Danish authors indicate that differences by sex among various countries could be related to health and lifestyle differences .
Our HRQoL results are in keeping with an Australian study that showed that older adults have higher overall HRQoL (highest scores for 11 QLQ-C30 domains . Contrary to our data, some other studies have reported substantially lower HRQoL in older participants [1, 4–6, 16, 23]; in others, age effects were weak [22, 26]. Nevertheless, some differences we found with sex and increasing age are aligned with results of the main general population study  and other QLQ-C30 studies [1, 6, 17] as well as the reference values study of the EuroQol-5D-5L for Spain .
Our higher item/scale scores for older adults could be related to people being better at adapting to situations as they age . Also, older adults in Spain tend to have good health and life expectancies among the highest in Europe: 86.1 years for women; 81.6 years for men . Our results could also reflect the fact that patients > 80 years old were underrepresented in our sample (1.3 % of participants), and a decline in HRQoL could be expected at this age [1, 5, 21].
Other QLQ-C30 studies have indicated declines in HRQoL in people with chronic health conditions [1, 5, 18, 21, 23]. Thus, the results of this and other studies highlight the importance of accounting for this variable in HRQoL studies of both cancer patients and the general population. In view of this finding, HRQoL of patients with cancer may be impacted more by comorbidities than by late-stage treatment effects [6, 23, 41]. The presence of other health conditions could be one reason some studies have found lower HRQoL in older adults .
As mentioned above, the use of normative data is only one way to facilitate interpretation of PRO scores. Unlike the concept of MIDs that support interpretations of PRO score differences between groups or time points, normative data is primarily applicable for interpreting cross-sectional data from individual patients or patient groups. In this regard, normative data provides a different perspective than thresholds (cut-offs). Thresholds allow for categorisation of patients according to clinically relevant criteria ; they can also be linked to clinical actions and allow calculations of prevalence rates. However, they provide almost no detailed information of severity levels. PRO scores using normative data maintains the level of information conveyed by scores, adding further information by linking them to normative populations. Normative data can be integrated into the scoring of a PRO instrument itself, as usually done by calculating T-scores , but they can also be a key component of graphical result presentations , such as heat maps or reference lines in graphical charts. A key consideration when using normative data is the selection of the reference population. We consider general population data the most appropriate comparator when interpreting PRO scores of cancer survivors, or when estimates of pre-disease levels of symptoms or functional health are required. For populations of patients undergoing active anti-cancer treatment, it may be more appropriate to rely on reference data from cancer patient populations that share essential disease and treatment characteristics.
This study has several limitations. It would have been interesting to include a higher number of people older than 80 years to study the effect of aging on HRQoL in this group.
However, the authors of the main general population study  have indicated obtaining a larger sample of this hard-to-reach group was outside the scope of their study as it would have substantially increased the budget for GfK which was financially no viable
Also, our sample was relatively highly educated. This plus the lack of elderly persons could be a consequence of conducting the surveys online. The effect of comorbidity on HRQoL has been studied in organising participants into just two groups based on the presence/absence of comorbidities. It might be interesting to have a future study in which comorbidities can be studied in more detail.
In conclusion, Spanish normative data presented in this article will enhance outcome interpretation in future studies, by providing benchmark data against which study findings from the EORTC QLQ-C30 could be compared. Our results highlight that age, sex and comorbid health conditions must be considered when comparing HRQoL data from the general population with that of cancer patients [24, 36]. Easier interpretation of scores from PRO instruments is key to fostering their wider use in clinical research and daily practice.