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EQ‑5D‑Y-3L population norms for children and adolescents in Jiangsu, China

Health and Quality of Life Outcomes volume 22, Article number: 102 (2024) Cite this article

Abstract

Objective

This study aims to establish EQ-5D-Y-3L population norms in Jiangsu, China by conducting a large-scale cross-sectional survey.

Methods

Children and adolescents aged 9–17 from three cities of Jiangsu Province were selected by multistage stratified random sampling to complete the EQ-5D-Y-3L instrument independently. Population norms for Jiangsu, China were determined by calculating statistics based on age and gender. Logistic and Tobit regression models were employed to explain the relationship between HRQoL and factors such as sociodemographic characteristics/recent acute symptoms (experienced fever/cough/sore throat/diarrhea in the past two weeks).

Results

Three cities yielded 37,574 valid samples (a sample validity rate of 95.4%). The EQ-5D-Y-3L utility values (mean ± SD) were 0.964 ± 0.085 for males and 0.958 ± 0.077 for females. Males scored 85.94 ± 19.62 and females scored 84.83 ± 18.45 on the VAS (mean ± SD), while the percentages of respondents reporting full health ranged from 58.3 to 78.8%. The dimension in which most respondents reported having no problems was “feeling worried, sad, or unhappy” (23.0%). And the lowest HRQoL was shown in the 14-year-old age group. Gender, age, board at school, and BMI were found to have an association with HRQoL. In addition, recent acute symptoms also correlate with some aspects of HRQoL.

Conclusions

This study established EQ-5D-Y-3L population norms in Jiangsu, China for the first time. These norms will support resource allocation decision-making and be used as a reference for health evaluation studies.

Introduction

Health-related quality of life (HRQoL) measures an individual’s subjective well-being across physical, mental, social, and spiritual domains [1, 2]. Preference-based measures (PBMs) are common instruments used to measure HRQoL. PBMs comprise a preference-based value set generating utility values and a descriptive system measuring HRQoL [3]. The responses of the descriptive system reflect the different health statuses of individuals. By employing a value set to rate the health status, utility values on the full health-to-death (1 to 0) scale can be obtained. Weighting life years allows the utilization of the utility values in cost-effectiveness analyses to determine quality-adjusted life years (QALYs) [4]. Various pediatric PBMs have been developed, such as the AQoL-6D, EQ-5D-Y-3L, the CHU-9D, HUI, 15 D/16 D/17 D, and the QWB [5].

Population norms consist of standardized profiles or reference data for PBMs and provide an overview of the health status of the general population [6, 7]. However, in most countries, population norms for pediatric PBMs are lacking, which means that it is unknown what score represents “normal” HRQoL or health impairment. Population norms allow for the comparison of results obtained from a single child or a group of children with the scores in the normal population, as a “benchmark” for healthy populations with similarly relevant characteristics [8,9,10]. This comparison enables researchers to assess the burden of diseases in terms of HRQoL and to compare that burden to the normal population or other disease groups [11, 12]. Therefore, population norms can also provide valuable reference standards for economic assessment and medical decision-making [8, 13]. Currently, EQ-5D-Y-3L population norms have been established in several countries, but there are still few studies in China. The establishment of EQ-5D-Y-3L population norms based on Chinese children/adolescents not only enriches the regional diversity of the research field but also provides evidence for cross-regional comparisons [9, 14, 15].

EQ-5D-Y-3L is an instrument adapted from the adult version of EQ-5D-3L to assess the HRQoL of children and adolescents aged 8–15 years [16]. The official user guide suggests that using EQ-5D-Y-3L for adolescents aged 16–17 years may be preferable in studies that include children up to age 18, to maintain consistency and avoid using multiple versions of the EQ-5D [17]. EQ-5D-Y-3L has been modified in terms of linguistic wording, Visual Analog Scale (VAS) description, and questionnaire layout to accommodate the unique developmental and cognitive abilities of children and adolescents [18]. EQ-5D-Y-3L has been assessed for its feasibility, validity, and reliability in children and adolescents across various countries and regions globally [19, 20]. While the adult versions (3–5L) of the EQ-5D have established population norms in multiple countries [14, 21,22,23,24,25,26,27,28,29,30], including China [31, 32], the sociodemographic criteria for children and adolescents may differ. Currently, only Japan and Indonesia have established population norms for the EQ-5D-Y-3L [4, 33].

This study aims to establish the EQ-5D-Y-3L population norms in Jiangsu, China through a multi-stage stratified random sample survey. Besides, we also intend to explore the relationship between HRQoL and factors such as sociodemographic characteristics and recent acute symptoms.

Methods

EQ-5D-Y-3L

EQ-5D-Y-3L comprises a five-dimensional descriptive system and a visual analog scale (VAS) that prompts individuals to assess their present health states by themselves. The five dimensions are “mobility”, “looking after myself”, “doing usual activities”, “having pain or discomfort”, and “feeling worried, sad, or unhappy”. Each dimension offers three levels of response: “no problem”, “some problems”, and “a lot of problems“ [16]. A total of 35=243 possible health states are defined, which can be converted into EQ-5D-Y-3L utility values using the Chinese value set [34]. These utility values range from − 0.089 to 1.000, with higher values indicating better HRQoL. Respondents rate their overall health states on a Visual Analog Scale (VAS) from 0 (worst health imaginable) to 100 (best health imaginable). It has been noted in EQ-5D studies that VAS scores cannot be directly equated to utility values. For example, in the South Australian study [35], 42.8% of participants reported ‘no problems’ with the EQ-5D descriptive system, while only 7.2% reported VAS scores greater than 90. So, it is more comprehensive to combine utility values with VAS scores in evaluating HRQoL.

Survey and instrument

The survey was carried out alongside extensive monitoring in Jiangsu Province. Extensive monitoring refers to the health monitoring of children/adolescents based on the Jiangsu Province Common Childhood Diseases and Health Influencing Factors Monitoring Platform. The monitoring aims to identify major health problems and risk factors, provide reference for precise intervention, and promote the health of children and adolescents.

The survey instrument consists of a basic questionnaire and EQ-5D-Y-3L. The basic questionnaire has two sections, the sociodemographic characteristics section (gender, age, region, board at school, height, and weight) and the recent acute symptoms section (experienced fever/cough/sore throat/diarrhea in the past two weeks). EQ-5D-Y-3L was positioned after the sociodemographic characteristics section and before the recent acute symptoms section. The survey was conducted through an electronic questionnaire on a web-based platform, and all components except height and weight were completed independently by the children/adolescents on the computer in school. Height and weight data for children/adolescents were obtained from the school physical examination data for each semester, provided by the school health department.

Respondents

Inclusion criteria: students enrolled in the fourth grade of elementary school to the third grade of senior high school, with an age range of 9–17 years. Exclusion criteria: those hospitalized for medical reasons (at the time of the survey). The survey was carried out in the designated schools between September 2023 and November 2023. After obtaining the support of schools and the verbal informed consent of the children/adolescents and their guardians, the investigators, along with school staff, start the survey. The uniform training (including training on the content of the survey, on-site survey process, communication skills, and questionnaire platform operation skills, etc.) enables the researchers to introduce the content and process of the survey, explain and clarify children’s/adolescents’ confusion, and assist them in completing the survey. The survey data was synchronized and uploaded to a management platform. Quality controllers were employed within the platform to authenticate and validate the questionnaire data.

Sampling method

The study of population norms emphasizes the importance of sample size for representativeness and accuracy, rather than being solely determined by statistical considerations [4]. Therefore, multistage stratified random sampling was used in this survey (Fig. 1).

In stage 1, one prefecture-level city was randomly chosen from each of the southern, central, and northern regions of Jiangsu Province.

Moving to stage 2, two elementary schools, two middle schools, two senior high schools, and one vocational high school were randomly selected in the urban districts of the chosen prefecture-level cities, while two elementary schools, two middle schools, and two senior high schools were randomly selected in the suburban districts. (Urban and suburban districts are administrative districts set up according to geographical location. Suburban districts are administrative areas where rural areas exist. The survey covers the entire city, including both urban and rural areas.)

Finally, in stage 3, 80 individuals from each grade of grades 4–6 in elementary schools, grades 1–3 in middle schools, senior high schools, and vocational high schools in the sampled schools were randomly selected.

Fig. 1
figure 1

Flow chart of multistage stratified cluster sampling

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Statistical analysis

First of all, we conducted descriptive statistics on sociodemographic characteristics, BMI(Body Mass Index), and recent acute symptoms. Categorical data were presented as percentages, while continuous data were expressed as mean ± SD. The responses to the five dimensions of the EQ-5D-Y-3L descriptive system were then converted into utility values using the Chinese value set [34]. To establish population norms, we analyzed the utility values and VAS scores separately by age groups and gender, focusing on several key statistics: mean, SD, percentiles(25th/50th/75th), and the percentage of individuals reporting full health status (“11111”). Subsequently, descriptive statistical analyses were performed on the distribution of responses to the five dimensions of the EQ-5D-Y-3L descriptive system, stratified by age and gender, using n (%) to represent the data. Responses indicating " some problems” and " a lot of problems” in each dimension were combined into a single category of “having problems“ [36], and Chi-square tests were conducted to analyze whether there were differences in the proportion of reported “having problems” across age and gender groups.

Logistic regression models were established to assess the risk of reporting having problems in each dimension, with results expressed as OR (95% CI). The independent variables included sociodemographic characteristics, BMI (as a categorical variable), and recent acute symptoms. The dependent variables were the responses to each dimension (“no problems” vs. “having problems”). Given the presence of a ceiling effect in our data, we employed Tobit regression models, which are commonly used in economics to address issues of limited dependent variable ranges. In this study, two Tobit models were constructed for utility values and VAS scores, respectively. Model I and Model II were created with gender, age, board at school, BMI, and recent acute symptoms as independent variables. The dependent variable in Model I was utility values, while the dependent variable in Model II was VAS scores. All these regression models aim to explore the correlation between HRQoL and obtained factors. The significance level of the statistical analyses was set at 0.05. Subjects with missing values were defined as invalid. The valid sample size was divided by the total sample size to calculate the validity rate of the survey.

All data were analyzed using R4.3.3. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethical Review Committee of Nanjing Medical University (Approval number: (661/2021)).

Results

Sociodemographic characteristics

The sociodemographic characteristics of the respondents are detailed in Table 1. The total valid sample size was 37,574, with a validity rate of 95.4%.

Table 1 Sociodemographic characteristics of the respondents
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Population norms of EQ-5D-Y-3L

The EQ-5D-Y-3L population norms are presented in Tables 2 and 3, and Additional file 2, showcasing the utility values, VAS scores, and responses of five dimensions varying with gender and age. The mean utility values for all age groups and genders ranged from 0.945 to 0.970, while the mean VAS scores ranged from 80.00 to 91.21(Tables 2 and 3). The percentages of respondents reporting full health spanned from 58.3 to 78.8%, which means approximately 30.0–40.0% of children and adolescents reported experiencing at least one health problem. The utility values and VAS scores were notably lower in females after the age of 11 years, as shown in Figs. 2-(1) & (2). The VAS scores displayed a significant decreasing trend with age, as illustrated in Figs. 2-(2). As shown in Fig. 2-(3), the percentage of full health is lower for males than for females. The utility values, VAS scores, and percentage of full health all showed the lowest in the 14-year-old group. (Fig. 2)

The distribution of responses for all dimensions revealed that a majority of children and adolescents faced challenges in “having pain or discomfort” and “feeling worried, sad, or unhappy”, with only 85.0% and 77.0% reporting no problems. Conversely, over 95% of respondents indicated being in optimal health in terms of “mobility” and “looking after myself”. The proportion of having problems in the first three dimensions is greater for males than for females. However, the proportion of having problems in the last two dimensions is much greater than in the first three dimensions, and this proportion of females is larger than that of males (Additional file 2).

Table 2 Population norms of EQ-5D-Y-3L utility values
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Table 3 Population norms of EQ-5D-Y-3L VAS scores
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Fig. 2
figure 2

Population norms of EQ-5D-Y-3L by gender and age

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Relationships between HRQoL and Sociodemographic characteristics /Recent acute symptoms

The logistic regression results are shown in Table 4. In the first three dimensions, females were less likely to have problems than males, and older individuals were also less likely to have problems than younger individuals. Besides, individuals with a higher BMI, and without recent acute symptoms were more likely to have problems in the last three dimensions.

The results of the Tobit regression are presented in Table 5. Recent acute symptoms had no association with either utility values or VAS scores. However, gender, age, board at school, and BMI showed a correlation with both measures (P < 0.01).

Table 4 Results of logistic regression
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Table 5 Results of Tobit regression
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Discussion

This study established the EQ-5D-Y-3L population norms in Jiangsu, China for the first time. The lowest HRQoL was shown in the 14-year-old age group. Gender, age, board at school, and BMI have been shown to correlate to the HRQoL. Although recent acute symptoms did not have a relationship with utility values and VAS scores, it is associated with several dimensions of the EQ-5D-Y-3L. The results indicate that using EQ-5D-Y-3L for investigations is feasible, enabling clear interpretation of results and detection of a dose-response relationship.

Differences in health status between gender and age are common in adult studies [31, 32, 37]. Although some researchers claim that the health status of children/adolescents does not vary by gender and age [4, 38, 39], there are still other researches that show the same differences between gender and age as our study does [36, 39].

The study showed all dimensional responses fared well, especially for the first three dimensions where there were more than 94% of respondents had no problems. Only two dimensions, “having pain or discomfort” and “feeling worried, sad, or unhappy”, had a relatively higher percentage of respondents reporting having problems (15.0% and 23.0%). The utility values averaged 0.964 for males and 0.958 for females. VAS scores were 85.94 for males and 84.83 for females on average. The percentage of full health is 73.4.0% for males and 66.4% for females. Overall, the HRQoL for females was a little poorer than for males. Both the utility values and VAS scores tend to decrease with age, except for the 14-year-old group who have the lowest utility values than all age groups.

The proportions of individuals reporting “having problems” within the five dimensions of the EQ-5D-Y-3L instrument exhibit distinct gender differences, with these disparities being contingent upon age. Prior investigations have consistently demonstrated a more accelerated decline in HRQoL with advancing age among females compared to males [40, 41]. Notably, before the age of 11, the percentages of females reporting “having problems” across the five dimensions are either comparable to or lower than those reported by males. However, after the age of 11, this gender gap widens considerably, highlighting a significant divergence in HRQoL experiences between genders with increasing age. However, our study revealed an increased vulnerability among older individuals and females in experiencing challenges within the last two dimensions under investigation. Notably, these differences were found to be gender- and age-specific, highlighting a heightened prevalence of emotional issues among females. Another reason is that as women enter puberty, they face more significant hormonal changes, physical development, psychological development, social pressure, and differences in emotional processing. These factors interact with each other, increasing the risk of physiological and emotional problems for females at this stage [42]. We can find that the negative utility values of the last two dimensions are larger than the first three dimensions in the Chinese value set [34], which not only can explain the lower average utility values of females than males but also explains why the gap of utility values between female and males becomes obvious after age 11. This observation also underscores the importance of gender-specific considerations.

In the 14-16-year-old groups, we found respondents reported a larger proportion of having problems on “having pain or discomfort” and “feeling worried, sad, or unhappy” than other age groups. This result can also clarify why the percentage of full health and utility values is the lowest in the 14-year-old group. It may be because children and adolescents are entering adolescence and experiencing an important period of academic. Adolescence is known to be a challenging phase where substantial physical, psychological, and behavioral changes are experienced [43]. And there is a large population in China with a competitive work environment for adults, Chinese students are therefore also studying in a highly competitive academic environment [44]. In Jiangsu Province, respondents in this age group are in a critical period of further education, and entering high school almost determines whether they have the opportunity to go to university in the future. As a result, their academic stress may be greater than that of high school students during this period. It has been proved that excessive academic stress is related to many adverse consequences [45].

The percentage of full health is 40–60% in Japan [4] and 55.85% in Indonesia [33], which means that about half of the children and adolescents have health problems. Our findings of 58.3-78.8% are different from the previous two countries. Compared with the utility values of 0.90–0.95 for the Japanese, our results are higher. However, our utility values are slightly lower than the Indonesian (0.97), and the overall VAS score is also lower than the Indonesian (89.35). The VAS score is inherently a subjective score, related to the individuals’ perceptions and values about health [46]. It can explain other aspects of health not included in the EQ-5D-Y-3L descriptive system. And the differences in VAS scores in our study between the study populations and with other regions may only be explained by individual and regional characteristics at this time. “Having pain or discomfort” and “feeling worried, sad, or unhappy” were also the dimensions with high proportions of having problems in the Japanese [4]and Indonesian [33] norms, which are similar to our study. Nevertheless, both the Japanese and Indonesian studies have higher proportions of reporting having problems on all dimensions than our findings, yet interestingly, our average utility values are higher than those of Japan and lower than those of Indonesia. This difference is rooted in the different value sets and calculation methods in each country. The coefficients of the dimensions in Japan’s value set are very similar to China’s, and the calculation method follows the pattern of “1 minus the sum of the constant term and the coefficients of each dimension“ [34, 47]. However, since Japan has a higher proportion of reporting having problems on all dimensions than China, this directly leads to a lower average utility value than our results [4]. Looking at Indonesia’s value set [48], although the coefficients of each dimension are larger than those of China, and it is common sense to assume that its utility values should be lower than those of our results, the reality is quite different. This is because Indonesia uses a different calculation, which is 1 minus (sum of dimensional coefficients )^1.9013. The 1.9013th power of the calculation results in larger utility values instead. In the context of comparing EQ-5D-Y-3L data across diverse regions, our findings underscore the importance of not solely relying on a select portion of the data to discern disparities in HRQoL. Rather, a comprehensive evaluation that integrates the value set, calculation methods of utility values, and the dimensional responses specific to each region is imperative to accurately assess these differences.

A previous study of rural children/adolescents in China [49] showed approximately 22%, reported having problems on “having pain or discomfort” and “feeling worried, sad, or unhappy”. The proportion was larger than ours. The percentage of full health is 52.9% shows that the health status in rural areas is worse than our study population. There are two possible reasons for this. One is that the rural-related study was conducted in 2016–2017, which is a certain period from our study, and the socio-economic development during this period may have brought great improvements to rural life. The second is due to the significant differences between rural and urban areas in terms of the level of economic development, educational resources, healthcare conditions, social support networks, and cultural environments. The relative economic backwardness of rural areas may result in limited access to resources for children/adolescents in the areas of nutrition, education, and health care, which in turn affects their health and overall quality of life [50].

There was a difference in HRQoL between the diseased and the general population, with the diseased population having worse health, both in terms of dimensional responses and utility values [51, 52], which is an expected result. This may be because of the high health toll of the disease. Nonetheless, the data from this study may still provide a basis for disease populations that require a reference standard to clarify the impact of disease burden on HRQoL.

Besides, board at school and BMI were also significantly associated with HRQoL. Those who are overweight and obese are more likely to have problems with the last three dimensions. A study of primary school students found that overweight or obese students had lower HRQoL compared to healthy weight ones [53], which can support our study. Those who did not board at school were more likely to have problems with “having pain or discomfort”, and related to lower HRQoL. This is a factor that few studies have looked at before. Previous studies have shown that boarding life has a positive impact on student’s physical and psychological health. By promoting the development of self-awareness, independence, and autonomy, boarding helps students to become more mature and autonomous at the psychological and cognitive levels [54]. This ability to self-manage and live independently enables students to make healthier choices in their daily lives. Boarding schools usually have stricter schedules and diet plans. These rules not only help students maintain a good biological clock and eating habits but also effectively prevent health problems such as obesity. In contrast, non-boarding students may not enjoy such strict and scientific health management in the home environment, thus increasing the risk of obesity and other health problems [55]. Irregular schedules, unhealthy eating habits, obesity, and other health problems may adversely affect their physical health. As a result, non-boarding students may face a high risk of physical discomfort. And these problems may have a negative impact on their health-related quality of life.

In addition, children/adolescents without recent acute symptoms affected the last three dimensions but had no significant relationship with utility values and VAS scores. This may be explained by the fact that the children/adolescents experiencing symptoms received sufficient rest and care, which greatly relieves their mental and physical stress. It can be found that the effects of sociodemographic characteristics and recent acute symptoms on HRQoL are mainly focused on psychological and physiological perceptions. Although recent acute symptoms were associated with some aspects of health, they had no impact on the overall HRQoL of children and adolescents in Jiangsu. The strength of our study lies in the robust multi-stage stratified random sampling and reasonably large sample size. Most of the previous studies in this field, both in adults and children/adolescents, had sample sizes below 5,000 [8, 25, 30, 35, 56], whereas one of the strengths of our study is a sufficient and reasonable sample size to obtain reliable results. Sociodemographic characteristics and recent acute symptoms allowed us to explore the relationship of HRQoL with these factors. However, this study has some limitations. The survey was conducted in only one province in Eastern China, which suggests that it may not be representative of the whole country. The sample did not include children aged 8 years, thus the data on this age group is not available. In addition, we excluded hospitalized children/adolescents, which limited understanding of the HRQoL of individuals in poor-health environments.

Conclusion

This study presents the first EQ-5D-Y-3L population norms in Jiangsu, China. The overall utility values (mean ± SD) for males were 0.964 ± 0.085 and for females were 0.958 ± 0.077, with corresponding VAS scores (mean ± SD) of 85.94 ± 19.62 for males and 84.83 ± 18.45 for females. The percentages of respondents reporting full health ranged from 58.3 to 78.8% for all gender and age groups. The dimension in which most respondents reported having problems was “feeling worried, sad, or unhappy”. And the 14-year-old group showed the lowest HRQoL in the study. Gender, age, board at school, and BMI were found to have an association with all dimensions, utility values, and VAS scores. Additionally, recent acute symptoms also correlate with some of these dimensions. Overall, the study revealed the distribution of HRQoL among children and adolescents with different characteristics in Jiangsu, China. These findings can provide a reference that is valuable for the economic assessment of healthcare technologies for children and adolescents and contribute to enhancing our understanding of the health status and HRQoL of children and adolescents.

Data availability

The data that support the findings of this study are available from Nanjing Municipal Center for Disease Control and Prevention, Changzhou Municipal Center for Disease Control and Prevention and Huai’an Municipal Center for Disease Control and Prevention in China but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Nanjing Municipal Center for Disease Control and Prevention, Changzhou Municipal Center for Disease Control and Prevention and Huai’an Municipal Center for Disease Control and Prevention.

Abbreviations

HRQoL:

Health-related Quality of Life

PBMs:

Preference-based Measures

QALYs:

Quality-adjusted Life Years

VAS:

Visual Analog Scale

BMI:

Body Mass Index

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Acknowledgements

We would like to thank all the staff involved in this survey, for ensuring the successful completion of this study.

Funding

This work was supported by the National Natural Science Foundation of China [grant number 72074122].

Author information

Author notes

  1. Junyan Liang, Huibin Dong and Juan Yang have contributed equally to this work and share the first authorship.

Authors and Affiliations

  1. School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Jiangning District, Nanjing, 211166, China

    Junyan Liang, Xinpeng Xu, Qifeng Wu & Hua You

  2. Section of School Health, Changzhou Municipal Center for Disease Control and Prevention, Changzhou, 213022, China

    Huibin Dong

  3. Section of School Health, Huai’an Municipal Center for Disease Control and Prevention, Huai’an, 223001, China

    Juan Yang

  4. Section of School Health, Nanjing Municipal Center for Disease Control and Prevention, Nanjing, 210003, China

    Li Liu

  5. Institute of Healthy Jiangsu Development, Nanjing Medical University, Nanjing, 211166, China

    Hua You

Authors
  1. Junyan Liang
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Contributions

J.L. and H.Y. have designed an overall research and investigation plan.H.D., J.Y. and L.L. provide the survey sites and conducts surveys to obtain data. J.L., XX and Q.W. completed the data analysis work. The manuscript was written by J.L .and some modifications made under the guidance of H.Y.

Corresponding authors

Correspondence to Li Liu or Hua You.

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Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Ethical Review Committee of Nanjing Medical University (Ethics approval number: (661/2021)).

Consent to participate

Verbal informed consent was obtained from all individual participants and their legal guardians.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Liang, J., Dong, H., Yang, J. et al. EQ‑5D‑Y-3L population norms for children and adolescents in Jiangsu, China. Health Qual Life Outcomes 22, 102 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12955-024-02322-2

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Keywords

Health and Quality of Life Outcomes

ISSN: 1477-7525

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