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General population normative values for the EORTC QLQ-C30 by age, sex, and health condition for the French general population



General population normative values for the widely used health-related quality of life (HRQoL) measure EORTC QLQ-C30 support the interpretation of trial results and HRQoL of patients in clinical practice. Here, we provide sex-, age- and health condition-specific normative values for the EORTC QLQ-C30 in the French general population.


French general population data was collected in an international EORTC project. Online panels with quota samples were used to recruit sex and age groups. Number and type of comorbidities were assessed. Descriptive statistics were used to calculate general population values for each QLQ-C30 scale, separately for sex, age, and presence of one- and more chronic health conditions. A multivariate linear regression model has been developed to allow estimating the effect of sex, age, and the presence for one- and more chronic health conditions on EORTC QLQ-C30 scores. Data was weighted according to United Nation statistics adjusting for the proportion of sex and age groups.


In total, 1001 French respondents were included in our analyses. The weighted mean age was 47.9 years, 514 (51.3%) participants were women, and 497 (52.2%) participants reported at least one health condition. Men reported statistically significant better scores for Emotional Functioning (+9.6 points, p = 0.006) and Fatigue (−7.8 point; p = 0.04); women reported better profiles for Role Functioning (+8.7 points; p = 0.008) and Financial Difficulty (−7.8 points, p = 0.011). According to the regression model, the sex effect was statistically significant in eight scales; the effect of increasing age had a statistically significant effect on seven of the 15 EORTC QLQ-C30 scales. The sex- and age effect varied in its direction across the various scales. The presence of health conditions showed a strong negative effect on all scales.


This is the first publication of detailed French normative values for the EORTC QLQ-C30. It aims to support the interpretation of HRQoL profiles in French cancer populations. The strong impact of health conditions on QLQ-C30 scores highlights the importance of considering the impact of comorbidities in cancer patients when interpreting HRQoL data.


The patient’s perspective and its standardized assessment via patient-reported outcome (PRO) measures are key aspects in the evaluation of cancer treatments. This is evident not only in the large number of well-validated PRO measures available but it is also reflected in international guidelines on how to incorporate PRO measures in clinical trials [1, 2] and daily practice [3]. The increasing use of PROs in daily clinical practice [4,5,6] and their widespread implementation as study endpoints in cancer clinical trials [7, 8] support this statement.

The European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core-30 (EORTC QLQ-C30) [9] is the most widely used PRO measure in cancer clinical trials [10, 11] and clinical practice [6]. To date, the EORTC QLQ-C30 has been translated into over 100 languages and its use is reported in more than 5000 publications indexed on PubMed alone. Various efforts have been made to improve interpretability of its 15 scales for the multidimensional assessment of health-related quality of life (HRQoL). Minimal important differences have been published to guide interpretation of differences between time points or patient groups [12, 13], the development of thresholds for clinical importance supports interpretation of scores from individual patients at a single-time point [14, 15], and the publication of general population normative data provides important comparative information for interpretation of scores from cancer patients [16,17,18,19,20].

Such normative data, obtained from the general population, provide a reference score against which the scores of individual patients or patient groups can be compared. Normative data typically provide reference values for the whole target population (e.g., general population in a specific country) and commonly for groups defined by age and sex. For the EORTC QLQ-C30, country-, sex-, and age-specific values have been reported previously, whereby the impacts of economic factors [21, 22] or comorbidities on the EORTC QLQ-C30 reference values have been highlighted in many publications [18, 22,23,24,25,26,27,28].

A large number of comorbidities is not only associated with worse prognosis regarding overall survival [29,30,31,32] but has also a strong negative impact on HRQoL [33, 34]. Notably, the treatment experience of patients with cancer and their HRQoL are likely influenced by comorbidities and not just the main cancer diagnosis [34]. Due to higher complication rates, lower treatment tolerability, or presence of polypharmacy, comorbidities contribute to worse health outcomes in general, and higher healthcare costs in patients with cancer [34,35,36]. Furthermore, patients with cancer and multimorbidity are more likely to receive modified treatment [32] or treatment without curative intent [34]. Approximately 40% of patients with cancer have at least one additional (chronic) condition whereas 15% have two or more conditions. The most common comorbidities among these patients include cardiovascular diseases, obesity, metabolic illness, mental health problems, and musculoskeletal conditions [37]. Thus, general population normative data that display the influence of comorbidities on HRQoL can provide valuable complementary information for clinicians and researchers.

In this article, we present detailed normative data for the EORTC QLQ-C30 for the French general population relying on data from a previous project [16]. Adding to this previous publication, we present detailed results for the French population with normative data for groups defined by sex, age, and the presence of health conditions. To the best of our knowledge, this is the first manuscript reporting detailed EORTC QLQ-C30 normative data for the French general population by sex and age, thus enhancing the interpretability of EORTC QLQ-C30 data for French cancer patients. These normative data may be used in daily clinical practice [38]; in cancer clinical trials [39] assessing French patients; or for benchmarking in French hospitals.


EORTC QLQ-C30 questionnaire

The EORTC QLQ-C30 [9] is a standardized and well-validated [40] HRQoL questionnaire for patients with cancer. The EORTC QLQ-C30 consists of 30 items that assess five functioning dimensions (physical, social, role, emotional, and cognitive), nine symptoms (fatigue, pain, nausea/vomiting, dyspnoea, sleep disturbances, appetite loss, diarrhoea, constipation, and financial difficulties), and Global health status/Quality of life (QOL). The recall period for all but the physical functioning scale is one week (no recall period for physical functioning) [9]. Each item is scored on a 4-point ordinal scale except the two last items on a 1 to 7 scale and summarized into the 15 HRQoL dimensions according to the EORTC QLQ-C30 scoring manual [41]. High scores (range 0–100) on the symptom scales indicate a high symptom burden whereas high scores for the functioning and Global health status/QOL scales indicate high HRQoL. The EORTC QLQ-C30 Summary Score aggregates the information gathered from all individual scales (with the exception of Global health status/QOL and Financial Impact) into one singular overall result [42, 43]. Please note that for the Summary Score the scoring direction of the symptom scales was reversed, so that a high Summary Score corresponds to high overall HRQoL.

Data collection

The data in this manuscript stem from a multinational EORTC project assessing the general population of 13 European countries, Canada, and the United States. Data collection was carried out by the panel research company GfK SE ( that contacted their panel members for participation in this study. Participants completed a total of 86 items of the EORTC item banks [44], including the 30 items of the EORTC QLQ-C30, and answered questions assessing sociodemographic characteristics as well as presence of doctor-diagnosed chronic conditions [16, 45]. The selection of chronic conditions was based on their prevalence in the community (i.e., chronic pain, heart disease, cancer, depression, chronic obstructive pulmonary disease, arthritis, diabetes, asthma, anxiety disorder, obesity, drug/alcohol use disorder), including a free-text option for respondents to add any further chronic condition(s) not included in the list. This analysis relies on the responses from French participants, with quota sampling to obtain 100 patients for each of the groups defined by age (18–39, 40–49, 50–59, 60–69 and ≥70 years) and sex. Data was collected in March and April of 2017 and only complete data sets were eligible for the analysis. The panel research company GfK SE claims that the response rate of internet panels is between 75 and 90% [16]. No further information on response rates and drop-out rates was made available.

Statistical analysis

We weighted the collected data to match the world population distribution statistics as published by the United Nations (UN) in 2017 [46], which were the most recent statistics available at the time of data collection. Weights were calculated to adjust for under-and overrepresentation of quotas in the sample and ranged from 0.647 (for men ≥ 70 years) to 3.576 (for men in the age group 18–39 years). Sample characteristics are provided for weighted and unweighted data. Relying on the weighted data, general population normative values are presented as mean and standard deviation (SD), for groups defined according to sex, age (18–39, 40–49, 50–59, 60–69 and ≥70 years), and existing health conditions (none vs. one and more). Ceiling and floor effects were calculated for the EORTC QLQ-C30 scales, displaying the percentage of participants obtaining the highest or the lowest possible score, respectively. Additionally, we established a multivariate linear regression model estimating the effect of sex, of age (continuous variable with linear and quadratic term), the age by sex- interaction, and the presence of comorbidities (none, one or more) for each EORTC QLQ-C30 scale. The multivariate regression model aims to allow a precise calculation of normative values for the French population and to supplement the descriptive normative data tables. The selection of covariates was consistent with previously applied methods [18,19,20], whereby the full model (block entry) retained all covariates in the model. To allow for a non-linear association between age and the QLQ-C30 scores, the regression model included a quadratic age term as well as a linear age term. IBM SPSS version 21 was used for the statistical analysis.


In the unweighted sample of 1001 French respondents, 499 participants (49.9%) were women, and the mean age was 53.6 (SD 14.7) years. Applying weights based on UN statistics [46] increased the proportion of women to 51.3% and decreased the mean age to 47.9 (SD 17.0) years. In the weighted sample, 42.7% reported having a postgraduate degree, 66.0% reported being married or in a steady relationship, and 44.0% were working full-time. Having one or more health conditions was reported by 52.2% of participants. The statistical weights applied to the data from individual participants ranged from 0.647 to 3.576. See Table 1 for unweighted and weighted sample characteristics.

Table 1 Sample characteristics (N = 1001)

In Table 2, normative data are presented for the total sample and for specific age groups. For the overall sample, mean scores on the functioning scales were 89.1 (SD 15.9) for Physical Functioning, 90.5 (SD 20.8) for Social Functioning, 87.8 (SD 22.4) for Role Functioning, 76.7 (SD 24.3) for Emotional Functioning, and 86.7 (SD 19.5) for Cognitive Functioning. For the functioning scales, the highest mean difference between age groups was found for Emotional Functioning in the age group 18–39 years as compared with the group aged 70+ years (mean difference of +11.4 points, indicating better functioning status for the older group) and Social Functioning in the age group 60–69 years as compared with those aged 70+ years (mean difference of +11.0 points, indicating better functioning status for the older group). For the symptom scales, the highest mean difference between age groups was found for Fatigue in the age group 18–39 years compared with age 60–69 years (mean difference of −16.7 points, indicating lower symptom burden for the older group) and for Insomnia in the age group 50–59 years compared with age 70+ years (mean difference of −8.7 points, indicating lower symptom burden for the older group). The highest mean value for the QLQ-C30 Summary Score (88.2 points; SD 12.3) was found for the age group 60–69 years, a finding that was mirrored by the highest Global health status/QOL score (69.8 points; SD 17.7) in this age group. The results of sex- and age group-specific analysis is provided in Supplementary Table S1.

Table 2 EORTC QLQ-C30 reference values for the general population of France (weighted data)

In the French general population, the EORTC QLQ-C30 Summary Score had a ceiling effect of 9.0%, indicating that nearly one in ten participants reported the highest possible functioning levels and no symptoms for the scales included in this score over the recall period of 1 week. On the functioning scales, ceiling effects were most pronounced for Social, Role, and Cognitive Functioning, with 75.4%, 68.6%, and 54.9% of participants respectively reporting no impairment. On the symptom scales, floor effects (i.e., a lack of symptoms) were most pronounced in the scales Nausea/Vomiting (87.2% of participants reporting no problem), Financial Difficulties (86.6%), Diarrhoea (83.5%), and Appetite Loss (82.5%). For further information on floor and ceiling effects see Table 3.

Table 3 Floor and ceiling effects in the EORTC QLQ-C30 scales (weighted data)

The multivariable linear regression model (Table 4) revealed the influence of sex, age, and self-reported health conditions on the EORTC QLQ-C30 scales. Adjusted R2 for the model ranged from 0.027 for Diarrhoea to 0.215 for Global health status/QOL. The influence of sex varied across scales. Whilst male sex was significantly associated with worse scores on the Role Functioning scale (−8.64 points, p = 0.001), the opposite was observed for the Emotional Functioning (+12.35 points, p ≤ 0.001) and Cognitive Functioning (+4.90 points, p = 0.042) scales. Additionally, men reported lower Insomnia (−13.38, p ≤ 0.001) and lower Fatigue (−7.79 points, p = 0.012) than women, but higher scores for Financial difficulties (+6.68 points, p = 0.006) and Diarrhoea (+5.43 points, p = 0.025). Increasing age was associated with better HRQoL in the French population. The single or quadratic age terms were significantly associated with lower scores for Fatigue, Dyspnoea, Insomnia or Financial Difficulties. Additionally, the single or quadratic age term was significantly associated with higher scores for Social Functioning and Global health status/QOL. Finally, we found a strong effect for the presence of self-reported comorbidities on the EORTC QLQ-C30 scales and Summary Score. The effect of reporting one or more health conditions was associated with an increase of up to +21.78 points (p ≥ 0.001) for Pain and +19.44 points (p ≤ 0.001) for Fatigue on the symptom scales. Furthermore, on the functioning scales, the presence of health conditions was associated with lower scores, e.g., −17.76 points (p < 0.001) for Role Functioning, and −15.06 points (p < 0.001) for Emotional Functioning. A similar pattern of lower scores was observed for the Global health status/QOL scale and the Summary Score. Supplementary Table S2 displays the EORTC QLQ-C30 mean values for sex and age groups, with or without a self-reported health condition. Participants with self-reported health conditions had worse HRQoL than participants without self-reported health conditions across all HRQoL domains. Table S3 displays the incremental impact of an increasing number of comorbidities on EORTC QLQ-C30 scale scores.

Table 4 Regression models for the EORTC QLQ-C30 values in general population of France

For illustration purposes, the calculation of the predicted Global health status/QOL score for a 55-year-old French man with one or more health conditions based on the regression model is as follows:

$$\eqalign{{\text{Global health status/QOL }}\left( {{\text{predicted}}} \right)\, & {\text{ = }}76.78 + {\text{ sex}}\,*\,6.12{\text{ }} + {\text{ }}\left( {{\text{age}} - 18} \right){\text{ }}*{\text{ }} - 0.32{\text{ }} + {\text{ }}{\left( {{\text{age}} - 18} \right)^2} \cr & \quad *{\text{ }}0.01{\text{ }} + {\text{ }}\left( {{\text{age }} - {\text{ }}18} \right){\text{ }}*{\text{ sex }}*{\text{ }} - 0.09{\text{ }} + {\text{ health condition }}*{\text{ }} - 18.41 \cr}$$
$$\eqalign{{\text{Global health status/QOL }}\left( {{\text{predicted}}} \right){\text{ }}&= {\text{ }}76.78{\text{ }} + {\text{ }}1*{\text{ }}6.12{\text{ }} + {\text{ }}\left( {55 - 18} \right){\text{ }}*{\text{ }} - 0.32{\text{ }} + {\text{ }}{\left( {55 - 18} \right)^2} \cr &\quad *{\text{ }}0.01{\text{ }} + {\text{ }}\left( {55 - 18} \right){\text{ }}*{\text{ }}1{\text{ }}*{\text{ }} - 0.09{\text{ }} + {\text{ }}1{\text{ }}*{\text{ }} - 18.41{\text{ }} = {\text{ }}63.01 \cr}$$


In this study, we estimated general population normative data for the EORTC QLQ-C30 in the French general population. We present normative data separately for groups defined by sex, age, and presence of one or more chronic health conditions to support the interpretation of EORTC QLQ-C30 data in cancer research and clinical practice. To the best of our knowledge, this is the first study to present detailed normative data for this measure for France. In addition to descriptive general population normative data, the established regression models allow for ad hoc estimations of normative values as reference for cancer patient groups with specific sociodemographic and clinical characteristics.

In line with previous research, we found large differences in HRQoL between individuals with and without health conditions [17, 23], thus highlighting the detrimental impact of comorbidities on HRQoL. Additionally, our analysis provides a detailed insight into the impact of sex, age and comorbidities on HRQoL by stratifying for these factors. The impact of sex and age varied across the EORTC QLQ-C30 scales but was consistently much less pronounced than the impact of health conditions. Unlike in the descriptive (unadjusted) tables, in the multivariable regression models significantly better scores were found for men compared with women for Emotional Functioning, Cognitive Functioning, Insomnia and Fatigue whereas the opposite was true for Role Functioning and Financial Difficulties and Diarrhoea. In these models, Social Functioning, Global health status/QOL, Fatigue, Dyspnoea, Insomnia, and Financial Difficulties were significantly improved with increasing age. Interestingly, neither age nor sex showed a statistically significant association with the QLQ-C30 Summary Score in the multivariable regression model. In the Italian general population, older age was linked to improved HRQoL across several domains [19]. In contrast, the mixed sex and age patterns across the EORTC QLQ-C30 scales in this study were in line with results from the Austrian [18], German [17] and Spanish [20] general populations. The large impact of health conditions on HRQoL results has been previously reported for the general population [47,48,49]. Further, the impact of comorbidities was found in cancer patients and was reported for numerous populations, such as cancer survivors [47], elderly patients with cancer [48], patients with colorectal cancer [50], or patients with chronic myeloid leukaemia [51]. Most studies have simply focused on the impact of having a comorbidity and have not investigated in detail the impact of the type or number of comorbidities. In the Supplementary Table S3 we provide an additional regression analysis, which displays the incremental impact of the increasing number of comorbidities on HRQoL score. Similar to this finding, Park et al. showed that a higher number of comorbidities in breast cancer survivors was linked to lower PROMIS physical and mental HRQoL scores [52].

Such findings suggest the possible usefulness of adjusting for the presence of health conditions in patients with cancer when interpreting HRQoL data and comparing study populations. This may be particularly important when comparing data from effectiveness and efficacy trials, as populations that are eligible for trials can be highly selective and may exclude patients with comorbidities. When investigating country specific differences or general population norms Italian men almost exclusively reported better HRQoL scores compared to women [15], while Spanish women reported better HRQoL on several scales compared to Spanish men [20] which is similar to the pattern observed in the French population. Further, older-age was positively associated with HRQoL in Italy [19], whereby a German sample reported mixed age effect on EORTC QLQ-C30 scales [17]. While the impact of age also varies across various scales in the French population, higher age appears to be positively associated with better HRQoL, as indicated by the summary score. These country differences clearly show how crucial it is to present a detailed analysis of national norm data for each respective country included in the European Norm Data study, as these specific country differences might be missed if only overall data are shown. The French national general population norm data for the EORTC QLQ-C30 provide a very useful reference for the interpretation of HRQoL data as reported by French cancer patients.

General population norm data have been used to support the interpretation of HRQoL data from cancer clinical trials, including trials in patients with melanoma [53], multiple myeloma [54], and endometrial cancer [55]. Besides the interpretation of trial results, normative data have also been used to contextualise registry data, such as in the PROFILES registry [56] or the EORTC reference values dataset [26].

However, because patients with a large number of comorbidities are frequently excluded from randomized controlled trials [34], the evaluation of optimal treatment choices for this patient population remains limited, and the results of clinical trials that exclude this patient cohort may substantially overestimate the HRQoL experienced by patients. Knowledge about the impact of health conditions may help to translate findings from trial populations into daily clinical practice where patients with multimorbidity are much more common than in a trial setting. In clinical practice, patients with comorbidities may be subjected to deviation from treatment protocols or modification of potentially curative treatment owing to a lack of knowledge on how comorbidities interfere with cancer treatment [34]. Additionally, the presence of comorbidities may lead to concomitant treatments and polypharmacy in clinical practice [57], whereby differences in HRQoL results may also be influenced by varying toxicity profiles and side effects. This leads to the argument that real-world data with correctly interpreted HRQoL data are important for patients with pre-existing health issues.

Although there are various measures of comorbidity available (e.g., Adult Comorbidity Evaluation-27, Charlson Comorbidity Index, Chronic Disease Score, Cumulative Illness Rating Scale, Index of Coexisting Disease) [58], here, we relied on an ad-hoc assessment of self-reported comorbidities. The literature reports that sum scores of comorbidities result in loss of information. It is acknowledged that there is no gold standard in assessing comorbidities, but this is dependent on the context of the study [58]. The World Health Organisations’ EPIC study group recently investigated the aetiology and determinants of multimorbidity, which may inform the optimal assessment of comorbidities in patients with cancer [59].

A limitation of our study is the sampling procedure used when collecting responses of the general population. The panel research company GfK SE aims to provide representativeness of the general population; however, in the original study [16], although a high level of congruence between the study data and official population statistics was reported, highly educated individuals were overrepresented in the sample [60]. In a previous publication using this European dataset [45], higher education was found to be associated with higher HRQoL, although the impact was classified as small (the effect size eta2 was below 0.015 across all domains) [45]. Comorbidity data was collected via participant self-reported doctor-diagnosed chronic condition, whereby health conditions such as diabetes [61] and depression [62] appear to be slightly underrepresented in the current sample. The assessment of health conditions was aimed at covering common conditions with a possibly strong impact on HRQoL. However, it did not follow standardized assessment of comorbidities, such as using the Charlson Comorbidity Index [63], even though this may not be a suitable tool because it was designed to predict mortality rather than HRQoL impairment. Furthermore, the sampling procedure did not include strata for pre-existing health conditions, therefore the representativeness of this self-reported clinical variable is limited.


To the best of our knowledge, this is the first study to provide French general population normative data for the EORTC QLQ-C30. The availability of general population normative data is useful to support the interpretation of HRQoL scores among French patients with cancer in clinical studies and clinical practice. Whilst the effects of sex and age varied across EORTC QLQ-C30 scales in the French population, there was a strong negative impact associated with the presence of comorbidities. These findings should be taken into consideration when interpreting HRQoL in patients with cancer. In conclusion, the present study presents new EORTC QLQ-C30 norm data from the French general population that can be used for comparative purposes with data obtained from French patients with cancer.

Data availability

The participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research supporting data is not available.


  1. Calvert M, Kyte D, Mercieca-Bebber R, Slade A, Chan AW, King MT, Hunn A, Bottomley A, Regnault A, Chan AW, Ells C, O’Connor D, Revicki D, Patrick D, Altman D, Basch E, Velikova G, Price G, Draper H, Blazeby J, Scott J, Coast J, Norquist J, Brown J, Haywood K, Johnson LL, Campbell L, Frank L, von Hildebrand M, Brundage M, Palmer M, Kluetz P, Stephens R, Golub RM, Mitchell S, Groves T (2018) Guidelines for inclusion of patient-reported outcomes in clinical trial protocols: the SPIRIT-PRO extension. JAMA 319:483–494

    Article  PubMed  Google Scholar 

  2. Calvert M, Blazeby J, Altman DG, Revicki DA, Moher D, Brundage MD, CONSORT PRO Group (2013) Reporting of patient-reported outcomes in randomized trials: the CONSORT PRO extension. JAMA 309:814–822

    Article  Google Scholar 

  3. EORTC Quality of Life Group (2016) Manual for the use of EORTC measures in daily clinical practice. Brussels

  4. LeBlanc TW, Abernethy AP (2017) Patient-reported outcomes in cancer care - hearing the patient voice at greater volume. Nat Rev Clin Oncol 14:763–772

    Article  PubMed  Google Scholar 

  5. Austin E, LeRouge C, Hartzler AL, Segal C, Lavallee DC (2020) Capturing the patient voice: implementing patient-reported outcomes across the health system. Qual Life Res 29:347–355

    Article  PubMed  Google Scholar 

  6. Howell D, Molloy S, Wilkinson K, Green E, Orchard K, Wang K, Liberty J (2015) Patient-reported outcomes in routine cancer clinical practice: a scoping review of use, impact on health outcomes, and implementation factors. Ann Oncol 26:1846–1858.

    Article  CAS  PubMed  Google Scholar 

  7. Blazeby JM, Avery K, Sprangers M, Pikhart H, Fayers P, Donovan J (2006) Health-related quality of life measurement in randomized clinical trials in surgical oncology. J Clin Oncol 24:3178–3186

    Article  PubMed  Google Scholar 

  8. Cella D, Grünwald V, Nathan P, Doan J, Dastani H, Taylor F, Bennett B, DeRosa M, Berry S, Broglio K, Berghorn E, Motzer RJ (2016) Quality of life in patients with advanced renal cell carcinoma given nivolumab versus everolimus in CheckMate 025: a randomised, open-label, phase 3 trial. Lancet Oncol 17:994–1003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Aaronson NK, Ahmedzai S, Bergman B, Bullinger M, Cull A, Duez NJ, et al (1993) The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst 85:365–376

    Article  CAS  PubMed  Google Scholar 

  10. Giesinger JM, Efficace F, Aaronson NK, Calvert M, Kyte D, Cottone F, et al (2021) Past and current practice of patient-reported outcome measurement in randomized cancer clinical trials: a systematic review. Value Health 24(8)

  11. Smith AB, Cocks K, Parry D, Taylor M (2014) Reporting of health-related quality of life (HRQOL) data in oncology trials: a comparison of the European Organization for Research and Treatment of Cancer Quality of Life (EORTC QLQ-C30) and the Functional Assessment of Cancer Therapy-General (FACT-G). Qual Life Res 23:971–976

    Article  PubMed  Google Scholar 

  12. Musoro JZ, Bottomley A, Coens C, Eggermont AM, King MT, Cocks K, et al (2018) Interpreting European Organisation for Research and Treatment for Cancer Quality of life Questionnaire core 30 scores as minimally importantly different for patients with malignant melanoma. Eur J Cancer 104:169–181.

    Article  PubMed  Google Scholar 

  13. Musoro JZ, Coens C, Greimel E, King MT, Sprangers MAG, Nordin A, van Dorst EBL, Groenvold M, Cocks K, Velikova G, Flechtner HH, Bottomley A, EORTC Gynecological and Quality of Life Groups (2020) Minimally important differences for interpreting European Organisation for Research and Treatment of Cancer (EORTC) Quality of life Questionnaire core 30 scores in patients with ovarian cancer. Gynecol Oncol 159:515–521

    Article  Google Scholar 

  14. Giesinger JM, Loth FLC, Aaronson NK, Arraras JI, Caocci G, Efficace F, et al (2020) Thresholds for clinical importance were established to improve interpretation of the EORTC QLQ-C30 in clinical practice and research. J Clin Epidemiol 118:1–8.

    Article  PubMed  Google Scholar 

  15. Pilz MJ, Aaronson NK, Arraras JI, Caocci G, Efficace F, Groenvold M, Holzner B, van Leeuwen M, Loth FLC, Petersen MA, Ramage J, Tomaszewski KA, Young T, Giesinger JM (2021) Evaluating the thresholds for clinical importance of the EORTC QLQ-C15-PAL in patients receiving palliative treatment. J Palliat Med 24:397–404

    Article  PubMed  Google Scholar 

  16. Nolte S, Liegl G, Petersen MA, Aaronson NK, Costantini A, Fayers PM, et al (2019) General population normative data for the EORTC QLQ-C30 health-related quality of life questionnaire based on 15,386 persons across 13 European countries, Canada and the Unites States. Eur J Cancer 107:153–163.

    Article  CAS  PubMed  Google Scholar 

  17. Nolte S, Waldmann A, Liegl G, Petersen MA, Groenvold M, Rose M (2020) Updated EORTC QLQ-C30 general population norm data for Germany. Eur J Cancer 137:161–170.

    Article  PubMed  Google Scholar 

  18. Lehmann J, Giesinger JM, Nolte S, Sztankay M, Wintner LM, Liegl G, et al (2020) Normative data for the EORTC QLQ-C30 from the Austrian general population. Health Qual Life Outcomes 18(1):275

  19. Pilz MJ, Gamper EM, Efficace F, Arraras JI, Nolte S, Liegl G, Rose M, Giesinger JM, EORTC Quality of Life Group (2022) EORTC QLQ-C30 general population normative data for Italy by sex, age and health condition: an analysis of 1,036 individuals. BMC Public Health 22:1040

    Article  Google Scholar 

  20. Arraras JI, Nolte S, Liegl G, Rose M, Manterola A, Illarramendi JJ, Zarandona U, Rico M, Teiejria L, Asin G, Hernandez I, Barrado M, Vera R, Efficace F, Giesinger JM, EORTC Quality of Life Group (2021) General Spanish population normative data analysis for the EORTC QLQ-C30 by sex, age, and health condition. Health Qual Life Outcomes 19:208

    Article  Google Scholar 

  21. Velenik V, Secerov-Ermenc A, But-Hadzic J, Zadnik V (2017) Health-related quality of life assessed by the EORTC QLQ-C30 questionnaire in the general slovenian population. Radiol Oncol 51:342–350.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Michelson H, Bolund C, Nilsson B, Brandberg Y (2000) Health-related quality of life measured by the EORTC QLQ-C30–reference values from a large sample of Swedish population. Acta Oncol 39:477–484.

    Article  CAS  PubMed  Google Scholar 

  23. Juul T, Petersen MA, Holzner B, Laurberg S, Christensen P, Grønvold M (2014) Danish population-based reference data for the EORTC QLQ-C30: associations with gender, age and morbidity. Qual Life Res 23:2183–2193.

    Article  PubMed  Google Scholar 

  24. van de Poll-franse LV, Mols F, Gundy CM, Creutzberg CL, Nout RA, Verdonck-de Leeuw IM, et al (2011) Normative data for the EORTC QLQ-C30 and EORTC-sexuality items in the general Dutch population. Eur J Cancer 47:667–675.

    Article  PubMed  Google Scholar 

  25. Ficko SL, Pejsa V, Zadnik V (2019) Health-related quality of life in Croatian general population and multiple myeloma patients assessed by the EORTC QLQ-C30 and EORTC QLQ-MY20 questionnaires. Radiol Oncol 53:337–347.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Mierzynska J, Taye M, Pe M, Coens C, Martinelli F, Pogoda K, et al (2020) Reference values for the EORTC QLQ-C30 in early and metastatic breast cancer. Eur J Cancer 125:69–82.

    Article  PubMed  Google Scholar 

  27. Yun YH, Kim SH, Lee KM, Park SM, Kim YM (2007) Age, sex, and comorbidities were considered in comparing reference data for health-related quality of life in the general and cancer populations. J Clin Epidemiol 60:1164–1175.

    Article  PubMed  Google Scholar 

  28. Mercieca-Bebber R, Costa DS, Norman R, Janda M, Smith DP, Grimison P, et al (2019) The EORTC quality of life questionnaire for cancer patients (QLQ-C30): Australian general population reference values. Med J Aust 210:499–506.

    Article  PubMed  Google Scholar 

  29. Morishima T, Matsumoto Y, Koeda N, Shimada H, Maruhama T, Matsuki D, Nakata K, Ito Y, Tabuchi T, Miyashiro I (2019) Impact of comorbidities on survival in gastric, colorectal, and lung cancer patients. J Clin Epidemiol 29:110–115

    Google Scholar 

  30. Mao JJ, Armstrong K, Bowman MA, Xie SX, Kadakia R, Farrar JT (2007) Symptom burden among cancer survivors: impact of age and comorbidity. J Am Board Fam Med 20:434–443

    Article  PubMed  Google Scholar 

  31. Kiderlen M, de Glas NA, Bastiaannet E, van de Water W, de Craen AJ, Guicherit OR, Merkus JW, Extermann M, van de Velde CJ, Liefers GJ (2014) Impact of comorbidity on outcome of older breast cancer patients: a FOCUS cohort study. Breast Cancer Res Treat 145:185–192

    Article  PubMed  Google Scholar 

  32. Piccirillo JF, Tierney RM, Costas I, Grove L, EL Jr S (2004) Prognostic importance of comorbidity in a hospital-based cancer registry. JAMA 291:2441–2447

    Article  CAS  PubMed  Google Scholar 

  33. Pate A, Lowery J, Kilbourn K, Blatchford PJ, McNulty M, Risendal B (2020) Quality of life and the negative impact of comorbidities in long-term colorectal cancer survivors: a population-based comparison. J Cancer Surviv 14:653–659

    Article  PubMed  Google Scholar 

  34. Sarfati D, Koczwara B, Jackson C (2016) The impact of comorbidity on cancer and its treatment. CA Cancer J Clin 66:337–350

    Article  PubMed  Google Scholar 

  35. Nilsson J, Berglund A, Bergström S, Bergqvist M, Lambe M (2017) The role of comorbidity in the management and prognosis in non-small cell lung cancer: a population-based study. Acta Oncol 56:949–956

    Article  PubMed  Google Scholar 

  36. Rim SH, Gp Jr G, Yabroff KR, McGraw KA, Ekwueme DU (2016) The impact of chronic conditions on the economic burden of cancer survivorship: a systematic review. Expert Rev Pharmacoecon Outcomes Res 16:579–589

    Article  PubMed  PubMed Central  Google Scholar 

  37. Edwards BK, Noone AM, Mariotto AB, Simard EP, Boscoe FP, Henley SJ, Jemal A, Cho H, Anderson RN, Kohler BA, Eheman CR, Ward EM (2014) Annual Report to the Nation on the status of cancer, 1975-2010, featuring prevalence of comorbidity and impact on survival among persons with lung, colorectal, breast, or prostate cancer. Cancer 120:1290–1314

    Article  PubMed  Google Scholar 

  38. Ludwig H, Bailey AL, Marongiu A, Khela K, Milligan G, Carlson KB, et al (2022) Patient-reported pain severity and health-related quality of life in patients with multiple myeloma in real world clinical practice. Cancer Rep (Hoboken) 5:e1429.

    Article  PubMed  Google Scholar 

  39. Reijneveld JC, Machingura A, Coens C, Taphoorn MJB, Taal W, Clement PM, et al (2023) Health-related quality-of-life results from the randomised phase II TAVAREC trial on temozolomide with or without bevacizumab in 1p/19q intact first-recurrence World Health Organization grade 2 and 3 glioma (European Organization for Research and Treatment of Cancer 26091). Eur J Cancer 190:112946.

    Article  CAS  PubMed  Google Scholar 

  40. Fayers P, Bottomley A (2002) Quality of life research within the EORTC— the EORTC QLQ-C30. Eur J Cancer 38:125–133

    Article  Google Scholar 

  41. Fayers PM, Aaronson NK, Bjordal K, Groenvold M, Curran D, Bottomley A (2001) The EORTC QLQ-C30 scoring manual, 3rd edn. European Organisation for research and treatment of cancer, Brussels

    Google Scholar 

  42. Giesinger JM, Kieffer JM, Fayers PM, Groenvold M, Petersen MA, Scott NW, et al (2016) Replication and validation of higher order models demonstrated that a summary score for the EORTC QLQ-C30 is robust. J Clin Epidemiol 69:79–88.

    Article  PubMed  Google Scholar 

  43. Efficace F, Cottone F, Sommer K, Kieffer J, et al (2019) Validation of the European Organisation for Research and treatment of cancer quality of life questionnaire core 30 summary score in patients with hematologic malignancies. Value Health 22:1303–1310

    Article  PubMed  Google Scholar 

  44. Petersen MA, Aaronson NK, Arraras JI, Chie WC, Conroy T, Costantini A, Dirven L, Fayers P, Gamper EM, Giesinger JM, Habets EJJ, Hammerlid E, Helbostad J, Hjermstad MJ, Holzner B, Johnson C, Kemmler G, King MT, Kaasa S, Loge JH, Reijneveld JC, Singer S, Taphoorn MJB, Thamsborg LH, Tomaszewski KA, Velikova G, Verdonck-de Leeuw IM, Young T, Groenvold M, European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Group (2018) The EORTC CAT Core-the computer adaptive version of the EORTC QLQ-C30 questionnaire. Eur J Cancer 100:8–16

    Article  Google Scholar 

  45. Liegl G, Petersen MA, Groenvold M, Aaronson NK, Costantini A, Fayers PM, Holzner B, Johnson CD, Kemmler G, Tomaszewski KA, Waldmann A, Young TE, Rose M, Nolte S, EORTC Quality of Life Group (2019) Establishing the European Norm for the health-related quality of life domains of the computer-adaptive test EORTC CAT Core. Eur J Cancer 107:133–141

    Article  Google Scholar 

  46. United Nations Department of Economic and Social Affairs Population Division World population prospects: the 2017 revision

  47. Subramaniam S, Kong YC, Chinna K, Kimman M, Ho YZ, Saat N, Malik RA, Taib NA, Abdullah MM, Lim GC, Tamin NI, Woo YL, Chang KM, Goh PP, Yip CH, Bhoo-Pathy N (2018) Health-related quality of life and psychological distress among cancer survivors in a middle-income country. Psychooncology 27:2172–2179

    Article  PubMed  Google Scholar 

  48. Schmidt H, Nordhausen T, Boese S, Vordermark D, Wheelwright S, Wienke A, Johnson CD (2018) Factors influencing global health related quality of life in elderly cancer patients: results of a secondary data analysis. Geriatrics 3(1):5

  49. Götze H, Taubenheim S, Dietz A, Lordick F, Mehnert A (2018) Comorbid conditions and health-related quality of life in long-term cancer survivors-associations with demographic and medical characteristics. J Cancer Surviv 12:712–720

    Article  PubMed  Google Scholar 

  50. Cummings A, Grimmett C, Calman L, Patel M, Permyakova NV, Winter J, Corner J, Din A, Fenlon D, Richardson A, Smith PW, Members of CREW Study Advisory Committee, Foster C (2018) Comorbidities are associated with poorer quality of life and functioning and worse symptoms in the 5 years following colorectal cancer surgery: results from the ColoREctal Well-being (CREW) cohort study. Psychooncology 27:2427–2434

    Article  PubMed Central  Google Scholar 

  51. Efficace F, Rosti G, Breccia M, Cottone F, Giesinger JM, Stagno F, et al (2016) The impact of comorbidity on health-related quality of life in elderly patients with chronic myeloid leukemia. Ann Hematol 95:211–219

    Article  CAS  PubMed  Google Scholar 

  52. Park J, Rodriguez JL, O’Brien KM, Nichols HB, Hodgson ME, Weinberg CR, Sandler DP (2021) Health-related quality of life outcomes among breast cancer survivors. Cancer 127:1114–1125

    Article  CAS  PubMed  Google Scholar 

  53. Bottomley A, Coens C, Mierzynska J, Blank CU, Mandalà M, Long GV, Atkinson VG, Dalle S, Haydon AM, Meshcheryakov A, Khattak A, Carlino MS, Sandhu S, Puig S, Ascierto PA, Larkin J, Lorigan PC, Rutkowski P, Schadendorf D, Koornstra R, Hernandez-Aya L, Di Giacomo AM, van den Eertwegh AJM, Grob JJ, Gutzmer R, Jamal R, van Akkooi ACJ, Krepler C, Ibrahim N, Marreaud S, Kicinski M, Suciu S, Robert C, Eggermont AMM, EORTC Melanoma Group (2021) Adjuvant pembrolizumab versus placebo in resected stage III melanoma (EORTC 1325-MG/KEYNOTE-054): health-related quality-of-life results from a double-blind, randomised, controlled, phase 3 trial. Lancet Oncol 22:655–664

    Article  Google Scholar 

  54. Roussel M, Moreau P, Hebraud B, Laribi K, Jaccard A, Dib M, Slama B, Dorvaux V, Royer B, Frenzel L, Zweegman S, Klein SK, Broijl A, Jie KS, Wang J, Vanquickelberghe V, de Boer C, Kampfenkel T, Gries KS, Fastenau J, Sonneveld P (2020) Bortezomib, thalidomide, and dexamethasone with or without daratumumab for transplantation-eligible patients with newly diagnosed multiple myeloma (CASSIOPEIA): health-related quality of life outcomes of a randomised, open-label, phase 3 trial. Lancet Haematol 7:e874–e883

    Article  PubMed  Google Scholar 

  55. Post CCB, de Boer SM, Powell ME, Mileshkin L, Katsaros D, Bessette P, Haie-Meder C, Ottevanger NPB, Ledermann JA, Khaw P, D’Amico R, Fyles A, Baron MH, Kitchener HC, Nijman HW, Lutgens LCHW, Brooks S, Jürgenliemk-Schulz IM, Feeney A, Goss G, Fossati R, Ghatage P, Leary A, Do V, Lissoni AA, McCormack M, Nout RA, Verhoeven-Adema KW, Vthbm S, Putter H, Creutzberg CL (2021) Long-term toxicity and health-related quality of life after adjuvant chemoradiation therapy or radiation therapy alone for high-risk endometrial cancer in the randomized PORTEC-3 trial. Int J Radiat Oncol Biol Phys 109:975–986

    Article  PubMed  Google Scholar 

  56. Oertelt-Prigione S, de Rooij BH, Mols F, Oerlemans S, Husson O, Schoormans D, Haanen JB, van de Poll-franse LV (2021) Sex-differences in symptoms and functioning in >5000 cancer survivors: results from the PROFILES registry. Eur J Cancer 156:24–34

    Article  PubMed  Google Scholar 

  57. Shrestha S, Shrestha S, Khanal S (2019) Polypharmacy in elderly cancer patients: challenges and the way clinical pharmacists can contribute in resource-limited settings. Aging Med 2:42–49

    Article  Google Scholar 

  58. Koczwara B (ed) (2016) Cancer and chronic conditions: how do we measure comorbidity? Springer

  59. Freisling H, Viallon V, Lennon H, Bagnardi V, Ricci C, Butterworth AS, Sweeting M, Muller D, Romieu I, Bazelle P, Kvaskoff M, Arveux P, Severi G, Bamia C, Kühn T, Kaaks R, Bergmann M, Boeing H, Tjønneland A, Olsen A, Overvad K, Dahm CC, Menéndez V, Agudo A, Sánchez MJ, Amiano P, Santiuste C, Gurrea AB, Tong TYN, Schmidt JA, Tzoulaki I, Tsilidis KK, Ward H, Palli D, Agnoli C, Tumino R, Ricceri F, Panico S, Picavet HSJ, Bakker M, Monninkhof E, Nilsson P, Manjer J, Rolandsson O, Thysell E, Weiderpass E, Jenab M, Riboli E, Vineis P, Danesh J, Wareham NJ, Gunter MJ, Ferrari P (2020) Lifestyle factors and risk of multimorbidity of cancer and cardiometabolic diseases: a multinational cohort study. BMC Med 18

  60. OECD - Social Policy Division - Directorate of Employment, Labour and Social Affairs (2019) OECD family database: CO3.1: educational attainment by gender. Accessed 6 Feb 2023

  61. International Diabetes Federation (2021) Diabetes Atlas: diabetes prevalence (% of population ages 20 to 79) – France. Accessed 2 May 2023

  62. Fond G, Lancon C, Auquier P, Boyer L (2019) Prévalence de la dépression majeure en France en population générale et en populations spécifiques de 2000 à 2018: une revue systématique de la littérature [Prevalence of major depression in France in the general population and in specific populations from 2000 to 2018: a systematic review of the literature]. Presse Med 48:365–375

    Article  PubMed  Google Scholar 

  63. Charlson M, Szatrowski TP, Peterson J, Gold J (1997) Validation of a combined comorbidity index. J Clin Epidemiol 47:1245–1251

    Article  Google Scholar 

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The general population norm data collection was funded by the European Organisation for Research and Treatment of Cancer Quality of Life Group (grant number 001 2015), awarded to SN, and is using one or more of the EORTC quality of life instruments that are available via licensing from the EORTC Headquarters. The EORTC QLG business model involves charges for commercial companies using any of their instruments. Academic use of EORTC instruments is free of charge. If you are interested in using any of the EORTC quality of life instruments, you may contact

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Conceptualization: SN, GL. Acquisition of data: SN, GL. Analysis and interpretation of data: MJP, FLCL, AA, JMG. Drafting of the manuscript: MJP, FLCL, AMMT, EMG. Critical revision of the manuscript: SN, GL, AA. Statistical analysis: MJP, FLCL, AMMT, JMG. Provision of study materials or patients: SN, GL. Obtaining funding: SN. Administrative, technical, or logistic support: EMG, JMG. Supervision: JMG. All authors have read and approved the manuscript.

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Correspondence to Johannes M. Giesinger.

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No ethics approval was sought as the study is based on panel data. According to the NHS Health Research Authority and the European Pharmaceutical Market Research Association (EphMRA), panel research does not require ethical approval if ethical guidelines are followed. The survey was distributed via the GfK SE (member of EphMRA) and obtained informed consent by each participant before the study. All data were collected anonymously and identifcation of the respondents through the authors is impossible. All methods were carried out in accordance with relevant guidelines and regulations.

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Pilz, M., Loth, F., Nolte, S. et al. General population normative values for the EORTC QLQ-C30 by age, sex, and health condition for the French general population. J Patient Rep Outcomes 8, 48 (2024).

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