Spanish General Population Normative Data Analysis for the EORTC QLQ-C30 and Relationships Between Sex, Age, and Health Conditions

Purpose General population normative data for the European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30 questionnaire facilitates interpretation of data assessed from cancer patients. This study aimed to present normative data of the Spanish general population. Methods/Patients Data were obtained from a prior larger study collecting EORTC QLQ-C30 norm data across 15 countries. Data were stratied by sex and age groups (18–39, 40–49, 50–59, 60–69 and >70 years). Sex and age distribution were weighted according to population distribution statistics. Sex- and age-specic normative values were analysed separately, as were participants with versus those without health conditions. Multiple linear regression was used to estimate the association of each of the EORTC QLQ-C30 scales with the determinants age, sex, sex-by-age interaction term, and health condition. Results 1165 Spanish individuals participated in the study. Differences were found by sex and age. The largest sex-related differences were seen in fatigue, emotional functioning, and global QoL (Quality of Life), favouring men. Largest age differences were seen in emotional functioning, insomnia, and pain, with middle-aged groups having the worst scores. Those >60 years scored better than those <60 years on all scales except for physical functioning. Participants with no health conditions scored better in all QLQ-C30 domains. The present study highlights differences in HRQoL between specic sex/age strata and especially between persons with and without a health condition in the Spanish general population. These factors must be considered when comparing general population HRQoL data with that of cancer patients.

For this study, data was collected via an online survey managed by GfK SE, a market research company experienced in online panels. The population sample was strati ed by sex and age, including 100 women and 100 men per pre-speci ed age stratum (18-39, 40-49, 50-59, 60-69 and ≥ 70 years). The sampling was also strati ed based on household size and geographic region, allowing for su cient sample sizes per group to establish normative values of age-and sex-speci c subgroups. However, strati cation resulted in a non-representative age-and sex-distribution; thus, post-hoc weighting of the data was required.
Weighting was done according to the sex and age distributions indicated in the United Nations o cial 2015 population distribution statistics report [31].
Sociodemographic data were collected, including sex, age, education, marital and employment status, and presence of self-reported health conditions, including cancer, via an online data form. Participants were asked to report only health conditions diagnosed by a doctor.
The questionnaire's Spanish version has been validated for use in Spain [33]. All questions are answered on a 4-point Likert-type scale, except for two global QoL items using a 7-point scale. The questionnaire is scored on a 0-00 metric according to the standard EORTC scoring algorithm [34]. The recently introduced QLQ-C30 Summary Score was calculated as the mean of the combined 13 QLQ-C30 scale scores (excluding nancial impact and Global QoL). Before calculating the mean, symptom scale scores are reversed, so that higher scores indicate lower symptom burden [11]. For the functioning scales, the global QoL scale and the summary score, a higher score indicates better health. For the symptom scales, a higher score indicates a higher level of symptom burden.

Statistical analyses
Normative values are given as means and standard deviations (SD) separately for women and men in ve age groups (18-39, 40-49, 50-59, 60-60, and 70 + years) and in combined sex and age groups. Furthermore, we calculated normative scores for participants with and without health conditions within each group.
As in prior studies [16,20,35], we also used multivariable linear regression to estimate the association of each of the QLQ-C30 scales (dependent variable) with age (linear and quadratic term), sex (0 = men, 1 = women), sex-by-age interaction term, and health condition (0 = none, 1 = one or more). Since all participants were 18 years or older, we used an age variable by counting the years above 18 to estimate regression coe cients (i.e. participant age minus 18).
The regression models predict normative scores for individuals or patient groups based on their sex, age and health conditions more precisely than the normative tables indicate. SPSS version 25.0 was used for all analyses.

Participant characteristics
In total, 1165 Spanish individuals participated in the study. The raw (unweighted) data set included 54.2% men (weighted, 48.6%); mean age was 54.3 (SD 14.7) years (weighted, 48.1 [SD 16.5] years). The applied weights for the individual participants ranged from 0.36 to 3.52.
In the weighted data, 91.8% % of the sample had at least post-compulsory education, 70.9% were married/in a steady relationship, 52.7% were working, and 61.6% presented one or more health conditions. Detailed sample characteristics are presented in Table 1.
Normative data for the general Spanish population Table 2 shows the EORTC QLQ-C30 reference values for the general population of Spain. The scores for the global sample in the functional scales ranged between 85.7 and 87.8, except for emotional functioning (77.1). Symptoms scores were > 20 points in fatigue, insomnia, and pain. The mean summary score was 84.8. For further details please see Table 2. Floor and ceiling effects for the EORTC QLQ-C30 scales (weighted data) are shown in Table 3.
Normative data by sex and age Table 4 shows descriptive statistics by sex. In the weighted descriptive data, the largest mean differences by sex were fatigue (men 21.6 versus women 26.5 points), emotional functioning (men 79.2 vs women 75.0 points), and global QoL (men 68.4 vs women 65.3 points), with better QoL in men. Mean differences for physical functioning, dyspnoea, nancial problems, and summary score were below 1 point (see tables 4 and 5).
In women, comparing age groups against the overall mean for women we found the ve largest differences for: insomnia + 7.1 points (women aged 50-59 years), emotional functioning + 7.0 (women aged > 70 years), nancial problems + 6.3 points (women aged 40-49 years), physical functioning − 5.9 points (women aged > 70 years), and pain + 5.7 (women aged 40-49 years). In men, the comparison of the age-group speci c mean against the overall mean in men showed the ve largest differences for: emotional functioning + 10.3 points, insomnia − 9.9 points, pain − 8.3 points, fatigue − 7.7 points (all in men aged > 70 years), and appetite loss + 6.6 points (men aged 18-39 years).
Normative data by sex and age, and health conditions In the total sample, the largest differences between participants with and without health conditions were found for pain (30.6 points vs 10. . In women the largest differences were found for pain (32.6 vs 9.6), global QoL (57.2 vs 78.6), pain (32.6 vs 9.6). All of these differences were in favour of participants without health conditions. For further details please see Table 5.

Regression models for prediction of normative scores
To predict scores for each of the QLQ-C30 scales for an individual or a group, we developed regression models based on age, sex (0 = men, 1 = women), and health conditions (0 = none, 1 = one or more). Details on the regression models are given in Supplementary Table S1

Discussion
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-speci c 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 [30]. Comparing the results from this analysis against the global sample published previously [14], 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 [26] but is much lower (7 points) than in the study from The Netherlands [1].
Fayers [36] 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 [22], 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 [12]. Further, similar to our results, small differences by sex for emotional functioning and fatigue [14] were also found in the main general population study [ [6,23,41]. The presence of other health conditions could be one reason some studies have found lower HRQoL in older adults [6].
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 [13]; 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 [42], but they can also be a key component of graphical result presentations [43], 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 [30] 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 nancially 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 ndings 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.