Agreement and measurement properties of the interviewer-administered, self-completed and proxy‐reported versions of the Tigrinya EQ-5D-Y-5L in Ethiopia

Agreement and measurement properties of the interviewer-administered, self-completed and proxy‐reported versions of the Tigrinya EQ-5D-Y-5L in Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Agreement and measurement properties of the interviewer-administered, self-completed and proxy‐reported versions of the Tigrinya EQ-5D-Y-5L in Ethiopia Abraham Welie, Elly Stolk, Janine Verstraete, Laura Duncan, Mike Herdman, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9066454/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To assess measurement properties of self-complete (SC), interviewer-administered (IA), and proxy-report versions of EQ-5D‐Y-5L and to assess the degree of agreement in response between these versions. Methods SC, IA, and proxy-report were administered to children and adolescents with and without health conditions on two occasions, separated by either a 10-day interval (to assess test-retest reliability) or a one-month interval (to assess responsiveness). The SC and IA versions were administered in randomized order. Agreement and test–retest reliability assessed using Gwet’s agreement coefficient (Gwet’s AC) and intraclass correlation coefficient (ICC). Feasibility, known-group validity, and responsiveness were assessed using missing values, Kruskal-Wallis test, and standardized effect size (SES), respectively. Results A total of 644 child/parent dyads participated. Missing values were higher for SC, particularly among younger children aged 8–12 years (4.65%-11.63%). Agreement between EQ-5D‐Y‐5L versions ranged from moderate to almost perfect, with excellent test-retest reliability across all dimensions and EQ-VAS. The EQ-5D-Y-5L level sum score (LSS) significantly differentiated between known disease groups across all versions (SC: χ² = 285; IA: χ² = 273; Proxy-report: χ² = 232; all p < 0.01). Both SC and IA versions were responsive to change in health with a moderate SES in “worsened” and “improved” groups. Conclusion The substantial agreement and similar measurement properties between SC and IA versions suggest that IA could be used as an alternative approach for data collection when self-complete is not an option. Although the proxy-report version had acceptable measurement properties, they were inferior to the SC and IA versions. Agreement EQ-5D-Y-5L Ethiopia measurement property Tigrinya Figures Figure 1 Figure 2 Figure 3 Introduction Health Technology Assessment (HTA) is a multidisciplinary process used to systematically assess the value of health technologies to inform health-care decision-making (1). It is important for ensuring efficient and equitable use of scarce resources, making it critical in low-middle income countries (LMICs) like Ethiopia, where budgets are constrained, and there is a large burden of communicable disease and an increasing burden of non-communicable disease (2). Ethiopia has implemented both community-based health insurance and social health insurance as part of its health financing strategy to improve access to healthcare with the aim of resource allocation based on the principles of HTA (3). Institutionalizing HTA requires adequate organizational resources (including human, financial, and informational) with relevant guidelines and legal frameworks, stakeholder collaboration, and support. It also necessitates mechanisms to both measure health-related quality of life (HRQoL) and to derive the value sets that accompany preference-weighted measures (PWMs) of HRQoL and that are an essential component of cost-utility analyses (4). In the context of LMICs, the presence of local value sets could facilitate economic evaluation and improve the quality of regulatory, coverage, and reimbursement policy decisions in private and public healthcare sectors (5). Ethiopia was one of the first African countries to develop a value set for EQ-5D-5L (6); it has been used in cost-utility analysis in local HTA reporting to inform health decision-making in Ethiopia. In recent years, there has been growing recognition of the importance of having available PWMs, which can be used to reliably and accurately measure and value children’s and adolescents’ health (7). The health concepts that may be relevant in children and the health state values that might be derived from PWMs for use in CUA could differ from those of adults. This has led to significant progress in the development of instruments and methods for measuring and valuing child health and growing interest in comparing the impact of using child versus adult health-state preferences in cost-utility analyses of pediatric health technologies and resource allocation decisions (8,9). One indication of this is the increasing number of developed health state measurement instruments of children/adolescents (10–12). One health state measurement instrument that is widely used to measure and value children’s HRQoL is EQ-5D-Y (13), a PWM (14) that is designed to be self-completed (SC) by children and adolescents aged 8–15 years. The EuroQol Group has recently launched a new version of the EQ-5D-Y, EQ-5D-Y-5L, which has increased the number of levels of severity in each dimension to five from the original three (15). Both the original 3-level version of EQ-5D-Y and the new EQ-5D-Y-5L are designed to be self-completed (SC), but an interviewer-administered (IA) version can be used if limited literacy or other barriers prevent SC of the questionnaire. When children/adolescents are mentally or physically incapable of reporting their own HRQoL, or if they are too young to do so, proxy reports can be used as an alternative source of information on the child’s HRQoL (16). EQ-5D-Y-5L data can be collected in different approaches, including SC (paper and pencil and digital versions) and IA (face-to-face, telephone, and computer-assisted personal interview or by asking a proxy to report the child’s health (proxy report)) (15–16). If we are to compare EQ-5D-Y-5L results obtained using different data collection approaches or to be able to pool data obtained using different approaches, evidence is needed on the extent to which they produce similar responses. It is also important to know whether using different approaches affects the instrument’s measurement properties. To date, no study has investigated how different approaches to data collection affect responses on EQ-5D-Y-5L or whether they affect psychometric performance. Therefore, the objective of this study was to assess the measurement properties of the SC, IA, and proxy-report versions of EQ-5D‐Y-5L and to assess the degree of agreement in response between them. Methods Study design and study participants A repeated measure using the EQ-5D-Y-5L Tigrinya version of SC, IA, and proxy report was conducted. Children and adolescents aged 8 to 18 years, both with and without health conditions, along with their caregivers or legal guardians, were recruited from Mekelle City, Tigray, Ethiopia. For the group with health conditions, we included children and adolescents with human immunodeficiency virus (HIV), congestive heart failure (CHF), type-1 diabetes mellitus (T1DM), acute injury, and exacerbated asthma. These disease conditions have a significant impact on HRQoL (17–19), and it was anticipated that respondents would make use of response options over the full range of dimensions and severity levels of EQ-5D-Y-5L. Data was collected between 30/07/2024 and 15/03/2025. To calculate the required sample size, we used effect size data from existing research that employed the same agreement, test-retest, responsiveness, and known group-validity statistical tests as we planned in our study (20–22). Specifically, we calculated sample size for Gwet’s AC, two-way mixed-effects model, absolute ICC, SES, and the Wilcoxon signed-rank test using the mean effect size scores between and in each data collection method, which we also planned to use in our study. At an α of 0.05 and power of 0.90, we estimated the required sample size for each objective using R software. We estimated that a total sample of 183 children and adolescents would be required for the general population school group and the disease groups, respectively. To be conservative, we took the maximum sample size from both the general population school group and the disease groups for all statistical tests and aimed to recruit at least 200 participants for the general population school group and 100 for each disease group. Study setting and sampling Study participants were recruited from public schools and the public hospital, the Ayder Comprehensive Specialized Hospital (ACSH), in Mekelle city, Tigray, Ethiopia. ACSH is the largest teaching hospital under the administration of Mekelle University in Ethiopia. Children and adolescents with CHF, HIV, and T1DM were recruited from their corresponding clinics. Those with acute injury and asthmatic exacerbation were recruited from emergency and pediatric medical wards and from the chest clinic and physiotherapy units of ACSH, respectively. A combination of sampling methods was used. Within selected schools, a supervisor used simple random sampling to select five classes from both elementary and high schools. Within each class (n = 40–60), 20 students between the ages of 8 and 18 were randomly selected. Children and adolescents with disease conditions attending the respective clinics were recruited consecutively from the clinic waiting list, followed by randomization of MoA (SC vs. IA) as they awaited their physician visit. Data collection procedure Ethical approval was secured from the Ethics Review Committee of the College of Health Sciences, Mekelle University, Ethiopia (MU-IRB2035/2024), and prior permission was sought from the different clinical units that would participate. Written informed consent and assent were obtained from all study participants to confirm their willingness to participate after explaining the purpose of the study. The study was conducted in accordance with the Declaration of Helsinki (23). Participants were invited to complete the EQ-5D-Y-5L SC and IA versions on the day of recruitment, and further appointments were made to collect test-retest and responsiveness data. On the day of data collection, children and adolescents were invited to sit in a private consultation room to participate in the study. Individuals with chronic conditions were recruited at routine outpatient visits at respective clinics, and those severely ill were from inpatient wards. For individuals with an acute injury requiring acute medical treatment and those with an asthma exacerbation admitted to a medical ward, the questionnaires were administered at the bedside after they had been admitted and clinically stabilized. Participants completed both a pen and paper SC version of EQ-5D-Y-5L and an IA version on the same day. SC and IA versions were assigned in random order. SC and IA MoA were separated by an age-appropriate demanding cognitive algebra task to distract memory between SC and IA versions. Four trained interviewers conducted the interviews using the EQ-5D-Y-5L IA version. Proxy report of the EQ-5D-Y-5L completed by the children’s corresponding caregiver/legal guardian. Each SC, IA, and proxy-reported EQ-5D-Y-5L included the visual analogue scale (VAS), scaled from 0 (the worst health you can imagine) to 100 (the best health you can imagine). In addition to the EQ-5D-Y-5L, demographic information and disease-specific clinical data were collected. The supervisor has visited the respective schools and handed out informed consent forms to participants to take home and return signed by parents/legal guardians or caregivers. The supervisor further allowed parents/caregivers to schedule an appointment for proxy completion of the research packs on the same day as their child. Test-retest reliability and responsiveness for SC, IA, and proxy reports of EQ-5D-Y-5L were assessed after ten days and one month among participants who reported no change and a change in health status, respectively, using the global rating scale (GRS). GRS was used as a health classifier to determine participant changes in global health retrospectively, by which patients have improved, worsened, or remained unchanged between visits for analysis of responsiveness in a subgroup of children and adolescents. All participants were provided with a question, ‘‘How would you rate your overall health now compared to the first visit?” with a seven-point Likert scale ranging from − 3 to 3 corresponding to the “much worse” to “much better” options with 0 for “no change”. Preference, acceptability, and feasibility of different approaches to data collection Preference, acceptability, and feasibility questions were included to explore which version (SC vs IA) of EQ-5D-Y-5L is more suitable for the 8-18-year-old group, and some questions were relevant to the proxy report version. The questions included: Considering the two versions (SC vs IA) of the EQ-5D-Y-5L, which one did you prefer and find easiest to use? Overall, was the questionnaire easy to understand? (SC and proxy-report) (not at all easy, not very easy, fairly easy, very easy) How difficult did you find reading and understanding the questions on this survey? (SC and proxy report) (no difficulty at all, some difficulty, moderate difficulty, a lot of difficulty) How much difficulty did you have understanding the questions on this survey (IA) (no difficulty at all, some difficulty, moderate difficulty, a lot of difficulty) How did you find assistance from an interviewer? (IA) (not at all helpful, not very helpful, fairly helpful, very helpful) Quality control To ensure data quality and minimize interviewer effects, a comprehensive quality control process was applied during the data collection period. This included standardized training, a consistent interview protocol, regulated participant load, time-managed interviews, and ongoing supervision with feedback. All interviewers received standardized training covering HRQoL concepts, EQ-5D-Y-5L instruments, and data collection approaches. Training also focused on interview techniques and proper data recording procedures. Interviewers followed detailed, standardized scripts when administering the EQ-5D-Y-5L IA version. The protocol included instructions on how to assist participants struggling to choose a response, how to maintain neutrality, and how to accurately record answers. To reduce interviewer fatigue and maintain data quality, each interviewer conducted a maximum of seven interviews per day, with scheduled breaks. Supervisors conducted periodic observations of interviews and provided feedback to interviewers to address any deviations from the protocol. In addition, collected data was assessed for patterns or response distribution that could indicate interviewer effects. Data Analysis Statistical analysis was conducted using Stata Version 16.0 SE. The EQ-5D-Y-5L responses and descriptive data were summarized in terms of frequency of responses. The EQ-5D-Y-5L LSS was scored by summing up the levels reported across the 5 dimensions, with participants who had missing responses excluded from the LSS calculation. Each dimension has 5 levels, scored 1 (no problems) to 5 (unable/extreme problems). The total score ranges from 5 (best health) to 25 (worst health), with higher scores indicating poorer health. Feasibility and response distribution were evaluated by assessing the percentage of patients with missing values and the proportion of patients with the maximum rating (11111 –no problems in any dimension), respectively, for each data collection approach. The proportion of respondents reporting 11111 was compared between versions (SC vs. IA, SC vs. proxy report, IA vs. proxy report) using the chi-square test and by calculating the absolute reduction in proportion score. We also calculated the absolute difference in ceiling between SC, IA, and proxy reports in individuals with health conditions. Feasibility was further assessed by calculating the percentage of responses from preference between versions (SC vs IA), acceptability, and difficulty of EQ-5D-Y-5L questions. Test–retest reliability for each version was assessed by Gwet’s agreement coefficient (Gwet’s AC) with 95% CIs for the EQ-5D-Y-5L dimensions, and intraclass correlation coefficients (ICC) with 95% CIs were calculated using a two-way mixed-effects model (absolute agreement) for LSS and EQ-VAS. ICCs were interpreted as 0.7 was considered acceptable (25). Agreement between each pair of EQ-5D-Y-5L versions was assessed by comparing Gwet’s AC and the percent of agreement for the EQ-5D-Y-5L dimensions and ICC for the LSS at baseline. Gwet’s AC1 of 0.8 as almost perfect agreement based on Landis & Koch criteria (26,27). ICCs are labeled as ≤ 0.40 poor to fair agreement, 0.41–0.60 moderate agreement, 0.61–0.80 good agreement, and 0.81–1.00 excellent agreement (28). Known-group validity was assessed by determining the degree to which the EQ-5D-Y-5L LSS met hypotheses about expected scoring patterns in previously defined groups (acute and chronically ill patients and those without health problems) using the Kruskal-Wallis test. A priori hypotheses about the HRQoL differences were specified according to previous studies (29). We hypothesized that individuals with acute or chronic health conditions would report a relatively lower HRQoL or more problems due to their health challenges and may perceive themselves as having high problems compared to those without acute or chronic health conditions. Based on previous research demonstrating lower HRQoL among children with chronic conditions and acute injury compared with healthy individuals, we hypothesized that healthy children and adolescents would report fewer problems on the LAM, UA, P/D, and WSU dimensions than those with chronic or acute health conditions (30,31). Furthermore, consistent with evidence indicating greater short-term physical impairment after injury, we hypothesized that children and adolescents with acute injuries would report greater limitations on the Mob dimension than those with chronic health conditions (32). Furthermore, we expected that greater clinical severity based on objective clinical indicators such as higher Ross classification for congestive heart failure (Classes I–IV), reduced forced expiratory volume percent predicted (FEV₁ % predicted) in exacerbated asthma ( 80%), and increasing need for assistive devices following acute injury (none, crutches, walker, wheelchair/buggy) would be associated with worse HRQoL. Post-hoc pairwise comparisons with Bonferroni correction were conducted to determine which specific health conditions or groups/clinical indicators differed significantly. Three groups were defined using results on the GRS: “worse” (-3 to -1), “unchanged” (0), and “improved” (1 to 3). Responsiveness was evaluated using a paired t-test and standardized effect size (SES) (difference of mean/baseline SD) to assess the magnitude of change in mean LSS of EQ-5D-Y-5L between baseline and follow-up assessments. Standardized effect size was interpreted as insignificant/trivial for SES, 0.2 and < 0.5; and large for 0.8 (33). Independent t-tests were carried out to compare the mean change in EQ-5D-Y-5L LSS between different categories on the GRS (“improved” vs. “unchanged”; “unchanged” vs. “worsened”, etc) of SC, IA, and proxy-report. Results presented using mean difference and 95% CI, Cohen’s d, and area under the curve (AUC) were used as the measure of responsiveness, with AUC > 0.7 considered adequate responsiveness. The statistical significance level was set at p < 0.05. Results Respondent characteristics Data from 644 paired children/adolescents and proxy respondents were analyzed; 56.06% of the children/adolescents were male. The mean age was 13.22 years (SD = 2.83) for children/adolescents and 43.2 years (SD = 7.98) for proxies. About one-third of the study participants (31.37%) were school children/adolescents (Table 1 ). It was not possible to collect data on the completion times of all versions. Table 1 Background characteristics of the respondents Characteristics Children/adolescents Proxies Sample size 644 644 Mean age (SD) 13.22 (2.83) 43.23 (7.98) Age category, n (%) 8–12 258(40.06) 13–18 386(59.94) Gender, n (%) Male 361(56.06) 257(40.00) Female 283(43.94) 287(44.50) Missing 0(0.00) 100(15.50) Residence, n (%) Rural 135(20.96) 122(19.00) Semi-urban 44(6.83) 42(6.50) Urban 465(72.20) 382(59.30) Missing 0(0.00) 98(15.20) School level, n (%) No education 8(1.24) 96(15.00) Grade 1–4 211(32.76) 60(9.30) Grade 5–8 380(59.01) 140(21.70) Grade 9–12 41(6.37) 118(18.30) College and above 0(0.00) 92(14.30) Missing 4(0.62) 138(21.40) Religion, n (%) Christian 593(92.08) 576(89.50) Muslim 51(7.92) 40(6.20) Missing 0(0.00) 28(4.30) Health conditions Acute Injury (AI) 109(16.93) Congestive Heart Failure 104(16.15) Exacerbation Asthma 29(4.50) HIV/AIDS 92(14.29) Type 1 Diabetes Mellitus 108(16.77) School child/adolescent 202(31.37) Order of administration Self-complete first 326(50.6) Interviewer administered first 318(49.4) Reported health problems and performance of EQ-5D-Y-5L versions Table 2 and Fig. 1 show the proportion and distribution of respondents reporting problems on EQ-5D-Y-5L dimensions for SC, IA, and proxy reports. In all versions, the highest proportions of health problems (level 2 to 5) were reported in the “WSU” dimension: SC (47.20%), IA (47.52%), and proxy-report (54.04%). The most severe problems (Level 5) were rarely reported in any dimension in any version. Mean EQ-VAS scores were 78.84 (SD = 20.13) (SC), 79.48 (SD = 18.51) (IA), and 79.04 (SD = 18.26) (proxy-report). Table 2 Percentage of reported health problems of EQ-5D-Y-5L versions Dimension Level SC(N = 644) IA(N = 644) Proxy(N = 644) Mobility (walking about), n (%) 1 430 (66.77) 436 (67.70) 390 (60.56) 2 55 (8.54) 71 (11.02) 66 (10.25) 3 77 (11.96) 72 (11.18) 83 (12.89) 4 48 (7.45) 41 (6.37) 56 (8.70) 5 21 (3.26) 21 (3.26) 15 (2.33) Missing 13 (2.02) 3 (0.47) 34 (5.28) Looking after myself, n (%) 1 450 (69.88) 453 (70.34) 437 (67.86) 2 38 (5.90) 55 (8.54) 44 (6.83) 3 62 (9.63) 69 (10.71) 58 (9.01) 4 52 (8.07) 49 (7.61) 55 (8.54) 5 17 (2.64) 15 (2.33) 15 (2.33) Missing 25 (3.88) 3 (0.47) 35 (5.43) Doing usual activities, n (%) 1 383 (59.47) 395 (61.34) 361 (56.06) 2 61 (9.47) 78 (12.11) 81 (12.58) 3 80 (12.42) 89 (13.82) 81 (12.58) 4 56 (8.70) 55 (8.54) 68 (10.56) 5 27 (4.19) 24 (3.73) 18 (2.80) Missing 37 (5.75) 3 (0.47) 35 (5.43) Having pain/discomfort, n (%) 1 347 (53.88) 360 (55.90) 319 (49.53) 2 98 (15.22) 112 (17.39) 131 (20.34) 3 106 (16.46) 105 (16.30) 94 (14.60) 4 40 (6.21) 46 (7.14) 46 (7.14) 5 24 (3.73) 16 (2.48) 20 (3.11) Missing 29 (4.50) 5 (0.78) 34 (5.28) Feelingworried/sad/unhappy, n (%) 1 340 (52.80) 338 (52.48) 296 (45.96) 2 134 (20.81) 155 (24.07) 138 (21.43) 3 88 (13.66) 93 (14.44) 110 (17.08) 4 38 (5.90) 35 (5.43) 39 (6.06) 5 20 (3.11) 18 (2.80) 27 (4.19) Missing 24 (3.73) 5 (0.78) 34 (5.28) VAS Mean 78.84 79.48 79.04 SD 20.13 18.51 18.26 Minimum 20 20 20 Maximum 100 100 100 Median 80 80 80 Missing 25 5 38 LSS Mean 8.45 8.67 8.12 SD 4.99 4.72 4.96 Minimum 5 5 5 Maximum 24 25 25 In general, when aggregating across all populations, the proportion of reported problems was slightly higher using proxy reports (33.54%) of respondents reporting no problems on any dimension (11111) compared to SC (36.95%) and IA (34.78%) (χ² = 4.48, p = 0.04, χ² = 0.65, p = 0.48, respectively) (Table 3 )). The same pattern was observed in those with a health condition. Respondents reported no problems on any dimension (11111) using proxy reports (19.23%), compared with SC (22.62%) and IA (21.26%); the difference was not statistically significant) (χ² = 2.78, p = 0.12, and χ² = 1.05, p = 0.36, respectively) (Table 4 ). Table 3 Overall absolute difference in ceiling across all children and adolescents Dimension Level SC (N = 644) IA (N = 644) Proxy (N = 644) SC vs IA SC vs Proxy IA vs proxy Δ X 2 (p-value) Δ X 2 (p-value) Δ X 2 (p-value) Mob, n (%) 1 430(66.77) 436(67.70) 390(60.56) -6(-0.93) 1.06(0.39) 40(6.21) 14.04(< 0.001) 46(7.14) 18.24(< 0.001) LAM, n (%) 1 450(69.88) 453(70.34) 437(67.86) -3(-0.46) 0.18(0.77) 13(2.02) 1.74(0.22) 16(2.48) 2.51(0.14) UA, n (%) 1 383(59.47) 395(61.34) 361(56.06) -12(-1.87) 2.57(0.14) 22(3.41) 4.17(0.05) 34(5.28) 9.17(0.003) P/D, n (%) 1 347(53.88) 347(53.88) 319(49.53) 0(0.00) 0.00(1.00) 28(4.35) 5.16(0.028) 28(4.35) 181(< 0.001) WSU, n (%) 1 340(52.80) 338(52.48) 296(45.96) 2(0.32) 0.06(0.90) 44(6.84) 12.57(< 0.001) 42(6.52) 11.31(0.001) Total ceiling, n (%) 11111 238(36.95) 224(34.78) 216(33.54) 14(2.17) 6.53(0.01) 22(3.41) 4.48(< 0.04) 8(1.24) 0.65(< 0.48) Table 4 Absolute difference in ceiling effect in children and adolescents with health conditions Dimension Level SC N = 442 IA N = 442 Proxy N = 442 SC vs IA SC vs Proxy IA vs proxy Δ X 2 (p-value) Δ X 2 (p-value) Δ X 2 (p-value) Mob, n (%) 1 241 (54.50) 244 (55.20) 222(50.23) -3(-0.7) 0.39 (0.68) 19 (4.27) 4.46 (0.044) 22 (4.97) 5.90 (0.02) LAM, n (%) 1 259 (58.60) 252 (57.01) 257(58.14) 7 (1.59 1.26 (0.33) 2 (0.46) 0.05 (0.91) -5 (-1.13) 0.31 (0.66) UA, n (%) 1 207 (46.83) 206 (46.61) 193 (43.67) 1 (0.22) 0.02 (1.00) 14 (3.16) 2.13 (0.18) 13(2.94) 1.82 (0.21) P/D, n (%) 1 173 (39.14) 173 (39.14) 155(35.07) 0(0.00) 0.00 (1.00) 18 (4.07) 2.66 (0.12) 18(4.07) 2.66 (0.12) WSU, n (%) 1 178 (40.27) 173 (39.14) 148 (33.48) 6 (1.13) 0.53 (0.56) 30(6.79) 7.63 (0.007) 25 (5.66) 5.43 (0.02) Total ceiling, n (%) 11111 100 (22.62) 94 (21.26) 85 (19.23) 6 (1.67) 1.8 (0.26) 15 (3.39) 2.78 (0.12) 9 (2.03) 1.05 (0.36) Levels of missing data for EQ-5D-Y-5L dimensions were slightly higher using the SC (0.26%-11.63%) compared to the IA version (0.00%-1.94%), particularly among younger children aged 8–12 years (4.65%-11.63%) (Table 5 and Fig. 1 ). Table 5 Missing values by age category in dimensions of SC and IA versions MoA Age group Mob n (%) LAM n (%) UA n (%) PD n (%) WSU n (%) SC 8–12 (N = 258) 12 (4.65) 20 (7.75) 30 (11.63) 25 (9.69) 21 (8.14) 13–18 (N = 386) 1 (0.26) 5 (1.30) 7 (1.81) 4 (1.04) 3 (0.78) p-value < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 IA 8–12 (N = 258) 3 (1.16) 3 (1.16) 3 (1.16) 4 (1.55) 5 (1.94) 13–18 (N = 386) 0 (0.00) 0 (0.00) 0 (0.00) 1 (0.26) 0 (0.00) p-value 0.034 0.034 0.034 0.067 0.006 Interviewer effect Slight differences were observed in the distributions of EQ-5D-Y-5L LSS between interviewers (Fig. 2 ). These variations may indicate the presence of interviewer effects that influenced how participants interpreted or responded to the EQ-5D-Y-5L health-state descriptors. Agreement between data collection methods (SC vs IA, SC vs proxy, and IA vs proxy) Table 6 presents the agreement of health responses between data collection approaches (SC vs IA, SC vs proxy, and IA vs proxy) by dimensions, LSS, and EQ-VAS. Gwet’s AC1 and perfect agreement showed moderate to almost perfect agreement across all dimensions, ranging from 0.51 to 0.89 and 59% to 90%, respectively. The ICC for LSS and EQ-VAS was high for SC and IA (0.93–0.95), followed by IA and proxy-report (0.70–0.79). Table 6 Agreement statistics between EQ-5D-Y-5L versions. Dimension Gwet’s AC1 (95%) Percent agreement (%) (95%) SC and IA SC and Proxy IA and Proxy SC and IA SC and Proxy IA and Proxy Mob 0.89 (0.86, 0.91) 0.75(0.70, 0.79) 0.75(0.71, 0.79) 90(88, 92) 78(74, 82) 78(74, 82) LAM 0.87(0.84, 0.91) 0.79(0.75, 0.82) 0.79(0.75, 0.83) 89(86, 92) 81(77, 84) 81(78, 85) UA 0.84(0.81, 0.88) 0.67(0.62, 0.72) 0.67(0.62, 0.71) 86(83, 90) 72(68, 76) 72(68, 76) P/D 0.71(0.67, 0.76) 0.54(0.49, 0.59) 0.58(0.53, 0.62) 76(72, 79) 61(57, 66) 64(60, 68) WSU 0.74(0.70, 0.78) 0.51(0.46, 0.56) 0.51(0.46, 0.56) 78(75, 82) 59(55, 63) 59(55, 63) ICC value (95% CI) SC and IA SC and Proxy IA and Proxy LSS(N = 588) 0.95(0.94–0.95) 0.80(0.77,0.82) 0.79(0.76, 0.82) EQ-VAS 0.93(0.92, 0.94) 0.68(0.63, 0.72) 0.70(0.66, 0.74) All results were significant at 0.01 level, CI confidence interval A Gwet’s AC1 of 0.8 as almost perfect agreement Test-retest reliability Test-retest reliability for SC, IA, and proxy reports of EQ-5D-Y-5L dimensions between baseline and follow-up ranged from moderate to excellent (0.61–0.89) among those who reported unchanged health status. The highest reliability was observed for the "LAM" dimension (Gwet’s AC1 = 0.89), whereas the lowest was observed for the "WSU" dimension (Gwet’s AC1 = 0.61), both in the proxy-report version. EQ-VAS demonstrated excellent test-retest reliability, with ICC values of 0.93, 0.96, and 0.92 for SC, IA, and proxy-report, respectively (Table 7 ). Table 7 Test–retest reliability of each EQ-5D-Y-5L version Gwet’s AC2 (95% CI) Dimension SC (n = 343) IA (n = 343) Proxy-report (n = 339) Mob 0.84 (0.79, 0.88) 0.81 (0.76, 0.85) 0.88 (0.84, 0.92) LAM 0.86 (0.82, 0.90) 0.82 (0.78, 0.87) 0.89 (0.85, 0.93) UA 0.84 (0.80, 0.88) 0.81 (0.77, 0.86) 0.83 (0.78, 0.88) P/D 0.66 (0.61, 0.71) 0.69 (0.64, 0.75) 0.69 (0.64, 0.75) WSU 0.65 (0.60, 0.70) 0.72(0.67, 0.77) 0.61 (0.56, 0.66) ICC value (95%CI) SC IA IA EQ-VAS 0.93 (0.91, 0.94) 0.96 (0.95, 0.97) 0.92 (0.89, 0.96) Known group validity Table 8 shows that the EQ-5D-Y-5L LSS of all versions were able to differentiate between known disease groups (SC: χ² = 285, p < 0.01; IA: χ² = 273, p < 0.01; Proxy-report: χ² = 232; p < 0.01). The highest (i.e., poorest HRQoL) LSS scores were observed among participants with acute conditions, followed by those with chronic conditions. Post hoc analysis showed a significant difference between school participants and those with chronic or acute conditions (χ² > 93, p = 0.001). Children/adolescents with chronic or acute health conditions reported significantly more problems across all EQ-5D-Y-5L dimensions (Mob, LAM, UA, P/D, and WSU) compared with school children/adolescents (χ² > 103, P < 0.001). Consistently, EQ-VAS scores were significantly lower among clinical groups, particularly those with CHF, acute injury, and asthma exacerbation, indicating poorer perceived health status, whereas schoolchildren/adolescents reported the highest EQ-VAS scores (Tables 9 and 10 ). These patterns were consistent across both SC and IA versions. Table 8 Known group validity for EQ-5D-Y-5L LSS of SC and IA versions SC LSS IA LSS SC vs IA Sample size (n) Mean (SD) Mean (SD) Paired Difference (Mean ± SD) Health conditions School child/adolescent 189 5.62(1.75) 5.66(1.67) -0.04(0.08) Congestive Heart Failure 97 10.74(5.18) 10.70(4.96) 0.04(0.22) Type 1 Diabetes 102 7.42(3.49) 7.57(3.58) -0.10(-0.09) Acute Injury (AI) 104 14.64(5.09) 14.42(5.06) 0.22(0.03) HIV AIDS 89 7.13(2.68) 7.28(2.52) -0.15(0.16) Exacerbation Asthma 27 12.48(2.68) 12.44(2.53) 0.04(0.15) Chi-squared with ties 285.42 273.50 12.68 p value < 0.001* < 0.001* 0.03* Clinical indicators of disease conditions Ross classification for CHF Sample size 92 Class 1 5 7 (3.39) 6.80 (3.03) 0.2(0.8) Class 2 8 7.62 (3.20) 8.87 (4.32) -1.25(1.12) Class 3 37 10.45 (5.43) 9.94 (4.78) 0.51(0.65) Class 4 42 12.02 (4.90) 12.21 (4.86) -0.19(0.04) Chi-squared with ties 9.02 9.99 5.06 p -value 0.02* 0.01* 0.16 FEV PP for exacerbated asthma 27 60–79 8 12.50 (3.77) 12.25(3.69) 0.25(0.08) 80 2 11.00 (2.82) 12.50 (2.70) -1.5(0.12) Chi-squared with ties 0.80 0.32 0.16 p -value 0.66 0.84 0.92 Assistive device for Acute Injury 98 None 50 13.70 (5.17) 13.06 (5.58) 0.64() Single or double crutches 3 21.66 (2.51) 22.00 (2.00) -0.34(0.51) Walker 15 13.26 (5.44) 12.86 (4.38) 0.4(1.06) Wheelchair/buggy 30 16.63 (3.59) 16.70 (3.35) -0.07(0.24) Chi-squared with ties 12.54 17.45 5.30 p -value < 0.001* < 0.001* 0.15 *Kruskal-Wallis rank test, where a p < 0.05 was considered statistically significant Table 9 Known group validity on EQ-5D-Y-5L SC dimensions and EQ-VAS Dimensions Health conditions School child/adolescent Congestive Heart Failure Type 1 Diabetes Acute Injury (AI) HIV AIDS Exacerbation Asthma Mob n 189 97 102 104 88 27 Mean rank 41442 37802 28044 47513 21971 7755 Chi-square 157 p -value < 0.001 LAM n 188 97 102 103 87 27 Mean rank 41362 30901 25861 50228 22770 11587 Chi-square 185 p- value < 0.001 UA n 188 96 101 100 87 27 Mean rank 39175 36188 25703 46301 20630 11701 Chi-square 194 p -value < 0.001 P/D n 188 96 102 102 89 26 Mean rank 37318 32885 28644 47425 23760 12610 Chi-square 195 P -value < 0.001 WSU n 188 96 102 102 89 27 Mean rank 39690 33380 29966 38381 28274 13018 Chi-square 105 P- value < 0.001 EQ-VAS mean(sd) 90.82(15.40) 64.07(21.42) 86.70(14.40) 66.00(14.88) 77.77(19.05) 62.42(12.92) n 189 97 102 101 88 26 Mean rank 79438 18071 37308 17957 25441 3889 Chi-square 215 P -value < 0.001 Table 10 Known group validity on EQ-5D-Y-5L IA dimensions and EQ-VAS Dimensions Health conditions School child/adolescent Congestive Heart Failure Type 1 Diabetes Acute Injury (AI) HIV AIDS Exacerbation Asthma Mob n 189 97 102 104 89 27 Mean rank 41476 37416 28351 48008 22042 7842 Chi-square 160 p -value < 0.001 LAM n 189 97 102 104 89 27 Mean rank 40707 31602 27236 50620 23037 11932 Chi-square 189 p- value < 0.001 UA n 188 97 102 104 89 27 Mean rank 38578 38021 26112 48183 22421 11820 Chi-square 194 p -value < 0.001 P/D n 189 97 102 104 89 27 Mean rank 38846 34419 28058 46497 24210 13105 Chi-square 171 P -value < 0.001 WSU n 189 97 102 104 89 27 Mean rank 39916 33199 31077 38958 28954 13031 Chi-square 103 P- value < 0.001 EQ-VAS mean(sd) 91.72(13.05) 65.75(19.18) 86.68(13.03) 66.85(13.44) 79.15(16.22) 64.07(11.68) n 189 96 102 104 89 27 Mean rank 81562 17698 37034 18155 26012 4066 Chi-square 233 P -value < 0.001 Both SC and IA versions yielded LSS that differed significantly across Ross classes, with the highest LSS (i.e., poorest HRQoL) observed in class IV (most severe stage of CHF) (SC: χ² = 9.02, p = 0.02; IA: χ² = 9.99, p = 0.01). Significant differences were also observed according to assistive mobility device use among participants with acute injury, with those requiring wheelchairs, walkers, or crutches reporting poorer HRQoL (SC: χ² = 12.54, p = 0.005; IA: χ² = 17.45, p = 0.001). Responsiveness Among 478 study participants who completed the follow-up assessment, 301 (63.00%) reported no change in global health based on the self-reported health classifier, whereas 146 (30.50%) and 31 (6.50%) reported improved and worsened health status, respectively, compared to baseline. Table 11 shows the LSS mean change and SES of SC, IA, and proxy-report scores by GRS. The mean EQ-5D-Y-5L LSS of SC, IA, and proxy-report versions in the improved group at 1-month follow-up was 10.51, 9.97, and 10.88 with significant mean changes of 2.85, 3.16, and 1.49 (p < 0.001). Table 11 Mean Change and SES of EQ-5D-Y-5L LSS by degree of Global Rating of Change Scale Measure Baseline (Mean_ SD) Follow-up (Mean_ SD) Paired Difference (Mean_ SD) p-value SES (95% CI) Global rating of change Worsened group (n = 31) SC LSS 8.16(3.39) 10.67(4.21) 2.52(2.45) < 0.001 0.74(0.48,1.01) IA LSS 8.22(3.77) 9.93(4.11) 1.71(1.93) < 0.001 0.36(0.21,0.50) Proxy LSS 9.22(4.08) 10.81(4.99) 1.58(2.62) 0.002 0.33(0.13,0.53) Unchanged group (n = 301) SC LSS 7.28(3.84) 7.18(3.59) -0.10(1.00) 0.10 -0.02(-0.05,0.004) IA LSS 7.28(3.70) 7.16(3.58) -0.12(0.06) 0.05 -0.03(-0.05,0.00) Proxy LSS 7.61(4.12) 7.60(3.95) 0.01(0.07) 0.92 -0.01(-0.027,0.030) Improved group (n = 146) SC LSS 13.36(4.71) 10.51(3.72) -2.85(2.07) < 0.001 -0.61(-0.68, -0.53) IA LSS 13.14(4.70) 9.97(3.58) -3.16(2.14) < 0.001 -0.66(-0.73, -0.58) Proxy LSS 12.34(4.81) 10.88(4.30) -1.49(1.98) < 0.001 -0.34(-0.38, -0.24) EQ-5D-Y-5L, EuroQol Five-Dimensions Five-level youth version, CI, confidence interval; RS, Responsiveness Statistic; Scale; SES, Standardized Effect Size; SRM, Standardized Response Mean. Level sum scores were calculated based on five-level EQ-5D level score. The magnitude and direction of these changes were consistent with the distribution of GRS responses. SC and IA versions were responsive to change in health with a moderate SES (SC: 0.74, − 0.61; IA: 0.36, − 0.66), while proxy-reported data indicated more modest responsiveness (SES: 0.33, − 0.34) for the “worsened” and “improved” groups respectively. The differences in mean change of LSS at follow-up by GRS is presented in Table 12 . Comparing patients with “unchanged” health and “worsened” health in the SC, IA, proxy-report versions, “unchanged” health patients had higher LSS with mean differences of 2.61, 1.84, and 1.57, respectively. Comparing patients with “unchanged” health and “improved” in the SC, IA, and proxy-report versions, “improved” health patients reported higher LSS with positive mean differences of 2.75, 3.04, and 1.50 respectively. The SC version showed the highest responsiveness in discriminating improved from worsened groups (Cohen’s d = 2.51, AUC = 0.98), followed by IA (d = 2.32, AUC = 0.97), whereas proxy-report showed lower responsiveness (d = 1.46, AUC = 0.82). Table 12 Difference in Mean Change at Follow-up by degree of Global Rating of Change Scale Health status Change in LSS SC Change in LSS IA Change in LSS proxy Mean Difference (95% CI) Cohen’s d AUC (95% CI) Mean Difference (95% CI) Cohen’s d AUC (95% CI) Mean Difference (95% CI) Cohen’s d AUC (95% CI) Unchanged vs.Worsened 2.61(2.16,3.06) 2.17 0.89(0.79,0.98) 1.84(1.39,2.29) 1.51 0.73(0.59,0.87) 1.57(1.05,2.10) 1.12 0.16(0.11, 0.20) Improved vs. Unchanged 2.75(2.47, 3.04) 1.91 0.04(0.02,0.07) 3.04(2.74, 3.34) 1.99 0.02(0.01,0.04) 1.50(1.20, 1.80) 0.99 0.16(0.11, 0.20) Improved vs. Worsened 5.36(4.53,6.20) 2.51 0.98(0.97,0.99) 4.87(4.05,5.69) 2.32 0.97(0.95, 0.99) 3.10(2.25,3.89) 1.46 0.82(0.74, 0.90) Improved/Unchanged vs. Worsened 3.51(2.79,4.23) 1.75 0.92(0.85,0.98) 2.83(2.07,3.58) 1.36 0.81(0.71, 0.91) 2.06(1.43,2.70) 1.18 0.67(0.56, ,0.78) Improved vs.worsen /unchanged 2.99(2.68,3.32) 1.88 0.88(0.84,0.92) 3.21(2.89,3.52) 1.34 0.89(0.85,0.93) 1.65(1.32,1.97) 1.46 0.69(0.64,0.75) EQ-5D-Y-5L, EuroQol Five-Dimensions Five-level youth version, CI, confidence interval; RS, Responsiveness Statistic; Scale; SES, Standardized Effect Size. Level sum scores were calculated based on the five-level EQ-5D level score. Preference, feasibility, and acceptability of EQ-5D-Y-5L SC and IA Preference between IA and SC versions was similar, with 47% preferring the SC version (Fig. 3). Most of the respondents reported the SC version as "very easy" or "fairly easy" (82.42%) to understand. A high proportion reported no difficulty in reading and understanding both the SC (68.10%) and IA (77.30%) versions, whereas the level of no difficulty in understanding was lower in the proxy report (61.24%) (Table 13 ). Table 13 Acceptability for SC, IA, and proxy-report versions How did you find the questionnaire to understand (SC) Very easy 151(45.76) Fairly easy 121(36.66) Not very easy 27(8.18) Not at all easy 31(9.39) How much difficulty did you have reading and understanding (SC) No difficulty at all 225(68.81) Some difficulty 75(22.94) Moderate difficulty 20(6.12) A lot of difficulty 7(2.14) How much difficulty did you have understanding the questions on this survey (IA) No difficulty at all 252(77.30) Some difficulty 54(16.56) Moderate difficulty 16(4.91) A lot of difficultly 4(1.23) How did you find assistance from an interviewer? (IA) Very helpful 41(12.42) Fairly helpful 233(70.61) Not very helpful 25(7.58) Not at all helpful 31(9.39) How did you find the questionnaire to understand (Proxy-report) Very easy 229(40.53) Fairly easy 168(29.73) Not very easy 135(23.89) Not at all easy 33(5.84) How much difficulty did you have reading and understanding (Proxy-report) No difficulty at all 346(61.24) Some difficulty 126(22.30) Moderate difficulty 64(11.33) A lot of difficulty 29(5.13) Discussion This study compared the measurement properties of the SC, IA, and proxy-report versions of EQ-5D‐Y-5L and the degree of agreement in health response between them. High agreement in mean LSS was observed between the SC, IA, and proxy-report versions, although psychometric performance was somewhat superior in the SC and IA than in the proxy report version in this study sample. Agreement between SC and IA was higher than that observed between either SC and proxy report or IA and proxy report, particularly in the “WSU” and “P/D” dimensions, where agreement ranged from moderate to substantial. Proxy reports exhibited lower ceiling effects than SC and IA versions, suggesting that proxies tend to report more health problems than children/adolescents self-reporting their own health. These findings are consistent with previous literature indicating that proxy respondents may have difficulty accurately capturing subjective aspects of health, particularly psychosocial domains (10). A meta-analytic review concluded that extensive evidence indicates that SC and IA versions of health responses of EQ-5D-Y and PedsQL questionnaires were comparable with high correlations between different data collection approaches (23, 30). This agreement result supports the case for IA as an alternative to SC for self-reported health status in children and adolescents, particularly in settings where different issues limit the use of SC. A slight difference in missing values was observed between the SC and IA versions. This finding is similar to previous studies by Amien et al. (2022) and Rajmil et al. (2014), which reported higher levels of missing data in SC and paper versions compared with IA and digital versions (22,34). The interviewer-administered version provides a number of advantages and limitations relative to the SC version. Although the presence of an interviewer may reduce missing or incomplete responses, it may also increase personnel time and resource requirements for data collection. The SC and IA versions performed closely as well in terms of psychometric properties as the proxy report. Test-retest reliability of the EQ-5D-Y-5L was acceptable across all dimensions, as well as for LSS and EQ-VAS, based on Gwet’s AC and ICC values. Reliability was generally higher for SC and IA versions compared with proxy reports. The Gwet’s AC values were lower on “P/D” and “WSU” dimensions. This finding implies proxies may not fully understand the aspects that are covered by “P/D” and “WSU” dimensions, which may result in random variation in health responses over time. Previous research on the EQ-5D-Y has similarly reported high missing values and more variation in score and lower reliability in the “P/D” and “WSU” dimensions (35). Moreover, a systematic review evaluated agreement between self- and proxy-reported HRQoL using generic preference-based measures and found that agreement was typically lower for psychosocial dimensions and higher for physical domains (10). It is unclear whether these differences are attributed to the differences in perspective between children/adolescents and proxies or limitations in proxy understanding of the child's internal health experience. Differences may also be due to differences in how the EQ-5D-Y-5L dimension descriptors and severity labels are interpreted by children/adolescents and proxies. Both SC and IA were able to discriminate EQ-5D-Y-5L LSS between groups of health conditions, with a marked difference between patients with chronic or acute health conditions and healthy school children/adolescents. The difference was particularly marked in patients with acute injury and CHF. Patients with acute injury and CHF experience functional limitations, pain/discomfort, and emotional and psychological distress, which can be associated with the nature and severity of the diseases (36,37). Both SC and IA versions had comparable strength of responsiveness to change in health status over one month, with the largest noted SES (SC: − 0.61; IA: − 0.66) for the “worsened” in health group. The mean difference resulted from the change of LSS of each version and was significant. It is suggestive that all versions of EQ-5D-Y-5L are capable of capturing both positive and negative changes in the improved and worsened health groups. This responsiveness result was supported by the ability of each version in discriminating change in LSS across health groups (“worsened”, “improved”, “no changes”). It was expected that those who reported worse or improved health scores would have a greater change in LSS than those who reported no change in health (38). The strength of this study was that all participants completed both (SC and IA) versions with repeated measures and also that both versions were randomly administered. This study also has some limitations. Firstly, the small sample size (n = 31) of participants reporting worsened health in the responsiveness analysis. Second, the findings may not be generalizable to other settings due to sociodemographic differences and the single-center design, although representativeness is not a requirement for psychometric studies. As this was the first study evaluating the measurement properties and agreement in response between the data collection approaches (SC, IA, and proxy-report) for EQ-5D‐Y-5L among children/adolescent, there is limited literature for comparison. Both SC and IA versions have been completed on a single day, which may have caused some recall bias. However, studies conducted in children and adolescents by administering the questionnaires on the same day with distracting activities before starting the second data collection method showed acceptable psychometric results (21,22,34). The 1-month follow-up period may have been too short to capture clinically meaningful changes in HRQoL outcomes, particularly for patients recovering from acute injuries or asthma exacerbations, as symptom resolution and return to usual activities may require more time. It is recommended that future work investigate changes in EQ-5D-Y-5L scores across SC, IA, and proxy reports over longer follow-up periods. Conclusion The substantial agreement and similar measurement properties between SC and IA approaches to data collection using EQ-5D-Y-5L suggest that IA could be used as an alternative MoA when SC is not an option and that the two approaches will likely produce data that can be compared and/or pooled. Although the proxy-report version had acceptable measurement properties, they were inferior to the SC and IA versions and should be used only when a self-report is not possible. Declarations Funding: This project was financial supported by EuroQol Research Foundation, The Netherlands (340- RA). Acknowledgements: We would like to acknowledge the EuroQol Research Foundation for funding this study (340- RA) and Elias Fitsum for his assistance with data collection. Data Availability: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Authors’ contributions: AW, FX, and ES conceived the idea; AG, FX, ES, JV, and MK designed the study, acquired the funding, and AW, FX, ES and, LD provided detailed information regarding study protocol and data collection processes in Ethiopia. AG prepared the draft manuscript. All authors reviewed the analysis, interpretation of the results, and the final manuscript. Declaration of Interest: Elly Stolk, Feng Xie, Mike Herdman, Abraham Welie and Janine Verstraete are a member of the EuroQol Research Foundation. 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Health Qual Life Outcomes. 2015 Dec;13(1):72. doi:10.1186/s12955-015-0271-z Amien R, Scott D, Verstraete J. Performance of the EQ-5D-Y Interviewer Administered Version in Young Children. Children. 2022 Jan 10;9(1):93. doi:10.3390/children9010093 World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA. 2013 Nov 27;310(20):2191. doi:10.1001/jama.2013.281053 Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess. 1994 Dec;6(4):284–90. doi:10.1037/1040-3590.6.4.284 Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med. 2016 Jun;15(2):155–63. doi:10.1016/j.jcm.2016.02.012 Gwet KL. Computing inter‐rater reliability and its variance in the presence of high agreement. Br J Math Stat Psychol. 2008 May;61(1):29–48. doi:10.1348/000711006X126600 Wongpakaran N, Wongpakaran T, Wedding D, Gwet KL. A comparison of Cohen’s Kappa and Gwet’s AC1 when calculating inter-rater reliability coefficients: a study conducted with personality disorder samples. BMC Med Res Methodol. 2013 Dec;13(1):61. doi:10.1186/1471-2288-13-61 McGraw KO, Wong SP. Forming inferences about some intraclass correlation coefficients. Psychol Methods. 1996 Mar;1(1):30–46. doi:10.1037/1082-989X.1.1.30 Huguet A, Miró J. Development and Psychometric Evaluation of a Catalan Self- and Interviewer-Administered Version of the Pediatric Quality of Life Inventory TM Version 4.0. J Pediatr Psychol. 2008 Jan 1;33(1):63–79. doi:10.1093/jpepsy/jsm040 Varni JW, Limbers CA, Burwinkle TM. How young can children reliably and validly self-report their health-related quality of life?: An analysis of 8,591 children across age subgroups with the PedsQL TM 4.0 Generic Core Scales. Health Qual Life Outcomes. 2007 Dec;5(1):1. doi:10.1186/1477-7525-5-1 Scott D, Ferguson GD, Jelsma J. The use of the EQ-5D-Y health related quality of life outcome measure in children in the Western Cape, South Africa: psychometric properties, feasibility and usefulness - a longitudinal, analytical study. Health Qual Life Outcomes. 2017 Jan;15(1):12. doi:10.1186/s12955-017-0590-3 Martin-Herz SP, Zatzick DF, McMahon RJ. Health-Related Quality of Life in Children and Adolescents Following Traumatic Injury: A Review. Clin Child Fam Psychol Rev. 2012 Sep;15(3):192–214. doi:10.1007/s10567-012-0115-x Cohen J. Statistical power analysis for the behavioral sciences. 2. ed., reprint. New York, NY: Psychology Press; 2009. 567 p. Rajmil L, Robles N, Rodriguez-Arjona D, Azuara M, Codina F, Raat H, et al. Comparison of the Web-Based and Digital Questionnaires of the Spanish and Catalan Versions of the KIDSCREEN-52. Eapen V, editor. PLoS ONE. 2014 Dec 5;9(12):e114527. doi:10.1371/journal.pone.0114527 Golicki D, Młyńczak K. Measurement Properties of the EQ-5D-Y: A Systematic Review. Value Health. 2022 Nov;25(11):1910–21. doi:10.1016/j.jval.2022.05.013 Alpert CM, Smith MA, Hummel SL, Hummel EK. Symptom burden in heart failure: assessment, impact on outcomes, and management. Heart Fail Rev. 2017 Jan;22(1):25–39. doi:10.1007/s10741-016-9581-4 Impact of Injuries Study Group, Kendrick D, O’Brien C, Christie N, Coupland C, Quinn C, et al. The impact of injuries study. multicentre study assessing physical, psychological, social and occupational functioning post injury - a protocol. BMC Public Health. 2011 Dec;11(1):963. doi:10.1186/1471-2458-11-963 Wong CKH, Cheung PWH, Luo N, Lin J, Cheung JPY. Responsiveness of EQ-5D Youth version 5-level (EQ-5D-5L-Y) and 3-level (EQ-5D-3L-Y) in Patients With Idiopathic Scoliosis. Spine. 2019 Nov 1;44(21):1507–14. doi:10.1097/BRS.0000000000003116 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9066454","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610319565,"identity":"29d312f8-1c8c-4366-944e-c9771794ee1e","order_by":0,"name":"Abraham Welie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYFAC5oYDDAw2JGlhBGlJg7APEKsFSBwmQYvB8cbGAx9qzkfzTzv8+POHinsM/O0ENBqcOdhwcMax27kzbqeZSRw4U8wgcSYBvxazG4kNh3nYbudukE4wYzjYlsBgwEBIy/2HDYf//DsH1JL++cPBf0At/A8I2cLYcJix7QBQS46BxMEGoBYJArbYn0lsONjblwz0S06ZxJljCTwSNwjYItl++PCHH9/scvtnp2/+UFGTIMffT8AWDMBDovpRMApGwSgYBdgAACP6T4cprguuAAAAAElFTkSuQmCC","orcid":"","institution":"McMaster University","correspondingAuthor":true,"prefix":"","firstName":"Abraham","middleName":"","lastName":"Welie","suffix":""},{"id":610319566,"identity":"b9d1b26a-a2a9-4c10-966c-ff5dd195633a","order_by":1,"name":"Elly Stolk","email":"","orcid":"","institution":"EuroQol Research Foundation","correspondingAuthor":false,"prefix":"","firstName":"Elly","middleName":"","lastName":"Stolk","suffix":""},{"id":610319567,"identity":"0fb997fa-aad5-4ec7-b6d1-6bf27804653b","order_by":2,"name":"Janine Verstraete","email":"","orcid":"","institution":"University of Cape Town","correspondingAuthor":false,"prefix":"","firstName":"Janine","middleName":"","lastName":"Verstraete","suffix":""},{"id":610319571,"identity":"1ae3503a-d490-4a65-9aae-7202db5c19e9","order_by":3,"name":"Laura Duncan","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Duncan","suffix":""},{"id":610319574,"identity":"45048f7c-a3a8-4fd7-8b29-46d5d58a1984","order_by":4,"name":"Mike Herdman","email":"","orcid":"","institution":"National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"Mike","middleName":"","lastName":"Herdman","suffix":""},{"id":610319578,"identity":"f8199551-69c4-4958-a354-4bd0e7f8f614","order_by":5,"name":"Feng Xie","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2026-03-08 20:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9066454/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9066454/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105363343,"identity":"52232643-6b79-4b64-a96e-eaf22ade0020","added_by":"auto","created_at":"2026-03-25 08:15:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35304,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of EQ-5D-Y-5L levels by dimensions and data collection approach\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9066454/v1/473d256b26f066a92c6830b8.png"},{"id":105363342,"identity":"2b7f5e9e-a772-4a74-bdef-41ade6ff6864","added_by":"auto","created_at":"2026-03-25 08:15:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17721,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePreferences between EQ-5D-Y-5L SC and IA versions\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9066454/v1/c6470b633e863cae0ea0853d.png"},{"id":105363341,"identity":"24e2843f-c2ee-452a-a68b-93aeec4f0a41","added_by":"auto","created_at":"2026-03-25 08:15:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4493,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"fig.png","url":"https://assets-eu.researchsquare.com/files/rs-9066454/v1/1f5c259a5e2b559059ecd0a5.png"},{"id":105565624,"identity":"077bcb15-18ea-44ad-9637-79b523abd847","added_by":"auto","created_at":"2026-03-27 12:53:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1876168,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9066454/v1/7c58d6e4-0eed-4c82-872f-3fdf80426c9c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Agreement and measurement properties of the interviewer-administered, self-completed and proxy‐reported versions of the Tigrinya EQ-5D-Y-5L in Ethiopia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHealth Technology Assessment (HTA) is a multidisciplinary process used to systematically assess the value of health technologies to inform health-care decision-making (1). It is important for ensuring efficient and equitable use of scarce resources, making it critical in low-middle income countries (LMICs) like Ethiopia, where budgets are constrained, and there is a large burden of communicable disease and an increasing burden of non-communicable disease (2). Ethiopia has implemented both community-based health insurance and social health insurance as part of its health financing strategy to improve access to healthcare with the aim of resource allocation based on the principles of HTA (3). Institutionalizing HTA requires adequate organizational resources (including human, financial, and informational) with relevant guidelines and legal frameworks, stakeholder collaboration, and support. It also necessitates mechanisms to both measure health-related quality of life (HRQoL) and to derive the value sets that accompany preference-weighted measures (PWMs) of HRQoL and that are an essential component of cost-utility analyses (4).\u003c/p\u003e \u003cp\u003eIn the context of LMICs, the presence of local value sets could facilitate economic evaluation and improve the quality of regulatory, coverage, and reimbursement policy decisions in private and public healthcare sectors (5). Ethiopia was one of the first African countries to develop a value set for EQ-5D-5L (6); it has been used in cost-utility analysis in local HTA reporting to inform health decision-making in Ethiopia.\u003c/p\u003e \u003cp\u003eIn recent years, there has been growing recognition of the importance of having available PWMs, which can be used to reliably and accurately measure and value children\u0026rsquo;s and adolescents\u0026rsquo; health (7). The health concepts that may be relevant in children and the health state values that might be derived from PWMs for use in CUA could differ from those of adults. This has led to significant progress in the development of instruments and methods for measuring and valuing child health and growing interest in comparing the impact of using child versus adult health-state preferences in cost-utility analyses of pediatric health technologies and resource allocation decisions (8,9). One indication of this is the increasing number of developed health state measurement instruments of children/adolescents (10\u0026ndash;12).\u003c/p\u003e \u003cp\u003eOne health state measurement instrument that is widely used to measure and value children\u0026rsquo;s HRQoL is EQ-5D-Y (13), a PWM (14) that is designed to be self-completed (SC) by children and adolescents aged 8\u0026ndash;15 years. The EuroQol Group has recently launched a new version of the EQ-5D-Y, EQ-5D-Y-5L, which has increased the number of levels of severity in each dimension to five from the original three (15). Both the original 3-level version of EQ-5D-Y and the new EQ-5D-Y-5L are designed to be self-completed (SC), but an interviewer-administered (IA) version can be used if limited literacy or other barriers prevent SC of the questionnaire. When children/adolescents are mentally or physically incapable of reporting their own HRQoL, or if they are too young to do so, proxy reports can be used as an alternative source of information on the child\u0026rsquo;s HRQoL (16).\u003c/p\u003e \u003cp\u003eEQ-5D-Y-5L data can be collected in different approaches, including SC (paper and pencil and digital versions) and IA (face-to-face, telephone, and computer-assisted personal interview or by asking a proxy to report the child\u0026rsquo;s health (proxy report)) (15\u0026ndash;16). If we are to compare EQ-5D-Y-5L results obtained using different data collection approaches or to be able to pool data obtained using different approaches, evidence is needed on the extent to which they produce similar responses. It is also important to know whether using different approaches affects the instrument\u0026rsquo;s measurement properties. To date, no study has investigated how different approaches to data collection affect responses on EQ-5D-Y-5L or whether they affect psychometric performance. Therefore, the objective of this study was to assess the measurement properties of the SC, IA, and proxy-report versions of EQ-5D‐Y-5L and to assess the degree of agreement in response between them.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and study participants\u003c/h2\u003e \u003cp\u003eA repeated measure using the EQ-5D-Y-5L Tigrinya version of SC, IA, and proxy report was conducted. Children and adolescents aged 8 to 18 years, both with and without health conditions, along with their caregivers or legal guardians, were recruited from Mekelle City, Tigray, Ethiopia. For the group with health conditions, we included children and adolescents with human immunodeficiency virus (HIV), congestive heart failure (CHF), type-1 diabetes mellitus (T1DM), acute injury, and exacerbated asthma. These disease conditions have a significant impact on HRQoL (17\u0026ndash;19), and it was anticipated that respondents would make use of response options over the full range of dimensions and severity levels of EQ-5D-Y-5L. Data was collected between 30/07/2024 and 15/03/2025.\u003c/p\u003e \u003cp\u003e To calculate the required sample size, we used effect size data from existing research that employed the same agreement, test-retest, responsiveness, and known group-validity statistical tests as we planned in our study (20\u0026ndash;22). Specifically, we calculated sample size for Gwet\u0026rsquo;s AC, two-way mixed-effects model, absolute ICC, SES, and the Wilcoxon signed-rank test using the mean effect size scores between and in each data collection method, which we also planned to use in our study. At an α of 0.05 and power of 0.90, we estimated the required sample size for each objective using R software. We estimated that a total sample of 183 children and adolescents would be required for the general population school group and the disease groups, respectively. To be conservative, we took the maximum sample size from both the general population school group and the disease groups for all statistical tests and aimed to recruit at least 200 participants for the general population school group and 100 for each disease group.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy setting and sampling\u003c/h3\u003e\n\u003cp\u003e Study participants were recruited from public schools and the public hospital, the Ayder Comprehensive Specialized Hospital (ACSH), in Mekelle city, Tigray, Ethiopia. ACSH is the largest teaching hospital under the administration of Mekelle University in Ethiopia. Children and adolescents with CHF, HIV, and T1DM were recruited from their corresponding clinics. Those with acute injury and asthmatic exacerbation were recruited from emergency and pediatric medical wards and from the chest clinic and physiotherapy units of ACSH, respectively. A combination of sampling methods was used. Within selected schools, a supervisor used simple random sampling to select five classes from both elementary and high schools. Within each class (n\u0026thinsp;=\u0026thinsp;40\u0026ndash;60), 20 students between the ages of 8 and 18 were randomly selected. Children and adolescents with disease conditions attending the respective clinics were recruited consecutively from the clinic waiting list, followed by randomization of MoA (SC vs. IA) as they awaited their physician visit.\u003c/p\u003e\n\u003ch3\u003eData collection procedure\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e was secured from the Ethics Review Committee of the College of Health Sciences, Mekelle University, Ethiopia (MU-IRB2035/2024), and prior permission was sought from the different clinical units that would participate. Written informed consent and assent were obtained from all study participants to confirm their willingness to participate after explaining the purpose of the study. The study was conducted in accordance with the Declaration of Helsinki (23).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eParticipants were invited to complete the EQ-5D-Y-5L SC and IA versions on the day of recruitment, and further appointments were made to collect test-retest and responsiveness data. On the day of data collection, children and adolescents were invited to sit in a private consultation room to participate in the study. Individuals with chronic conditions were recruited at routine outpatient visits at respective clinics, and those severely ill were from inpatient wards. For individuals with an acute injury requiring acute medical treatment and those with an asthma exacerbation admitted to a medical ward, the questionnaires were administered at the bedside after they had been admitted and clinically stabilized.\u003c/p\u003e \u003cp\u003eParticipants completed both a pen and paper SC version of EQ-5D-Y-5L and an IA version on the same day. SC and IA versions were assigned in random order. SC and IA MoA were separated by an age-appropriate demanding cognitive algebra task to distract memory between SC and IA versions. Four trained interviewers conducted the interviews using the EQ-5D-Y-5L IA version. Proxy report of the EQ-5D-Y-5L completed by the children\u0026rsquo;s corresponding caregiver/legal guardian. Each SC, IA, and proxy-reported EQ-5D-Y-5L included the visual analogue scale (VAS), scaled from 0 (the worst health you can imagine) to 100 (the best health you can imagine). In addition to the EQ-5D-Y-5L, demographic information and disease-specific clinical data were collected. The supervisor has visited the respective schools and handed out informed consent forms to participants to take home and return signed by parents/legal guardians or caregivers. The supervisor further allowed parents/caregivers to schedule an appointment for proxy completion of the research packs on the same day as their child. Test-retest reliability and responsiveness for SC, IA, and proxy reports of EQ-5D-Y-5L were assessed after ten days and one month among participants who reported no change and a change in health status, respectively, using the global rating scale (GRS).\u003c/p\u003e \u003cp\u003eGRS was used as a health classifier to determine participant changes in global health retrospectively, by which patients have improved, worsened, or remained unchanged between visits for analysis of responsiveness in a subgroup of children and adolescents. All participants were provided with a question, \u0026lsquo;\u0026lsquo;How would you rate your overall health now compared to the first visit?\u0026rdquo; with a seven-point Likert scale ranging from \u0026minus;\u0026thinsp;3 to 3 corresponding to the \u0026ldquo;much worse\u0026rdquo; to \u0026ldquo;much better\u0026rdquo; options with 0 for \u0026ldquo;no change\u0026rdquo;.\u003c/p\u003e\n\u003ch3\u003ePreference, acceptability, and feasibility of different approaches to data collection\u003c/h3\u003e\n\u003cp\u003ePreference, acceptability, and feasibility questions were included to explore which version (SC vs IA) of EQ-5D-Y-5L is more suitable for the 8-18-year-old group, and some questions were relevant to the proxy report version. The questions included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eConsidering the two versions (SC vs IA) of the EQ-5D-Y-5L, which one did you prefer and find easiest to use?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOverall, was the questionnaire easy to understand? (SC and proxy-report) (not at all easy, not very easy, fairly easy, very easy)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHow difficult did you find reading and understanding the questions on this survey? (SC and proxy report) (no difficulty at all, some difficulty, moderate difficulty, a lot of difficulty)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHow much difficulty did you have understanding the questions on this survey (IA) (no difficulty at all, some difficulty, moderate difficulty, a lot of difficulty)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHow did you find assistance from an interviewer? (IA) (not at all helpful, not very helpful, fairly helpful, very helpful)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eQuality control\u003c/h3\u003e\n\u003cp\u003eTo ensure data quality and minimize interviewer effects, a comprehensive quality control process was applied during the data collection period. This included standardized training, a consistent interview protocol, regulated participant load, time-managed interviews, and ongoing supervision with feedback. All interviewers received standardized training covering HRQoL concepts, EQ-5D-Y-5L instruments, and data collection approaches. Training also focused on interview techniques and proper data recording procedures. Interviewers followed detailed, standardized scripts when administering the EQ-5D-Y-5L IA version. The protocol included instructions on how to assist participants struggling to choose a response, how to maintain neutrality, and how to accurately record answers. To reduce interviewer fatigue and maintain data quality, each interviewer conducted a maximum of seven interviews per day, with scheduled breaks. Supervisors conducted periodic observations of interviews and provided feedback to interviewers to address any deviations from the protocol. In addition, collected data was assessed for patterns or response distribution that could indicate interviewer effects.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted using Stata Version 16.0 SE. The EQ-5D-Y-5L responses and descriptive data were summarized in terms of frequency of responses. The EQ-5D-Y-5L LSS was scored by summing up the levels reported across the 5 dimensions, with participants who had missing responses excluded from the LSS calculation. Each dimension has 5 levels, scored 1 (no problems) to 5 (unable/extreme problems). The total score ranges from 5 (best health) to 25 (worst health), with higher scores indicating poorer health. Feasibility and response distribution were evaluated by assessing the percentage of patients with missing values and the proportion of patients with the maximum rating (11111 \u0026ndash;no problems in any dimension), respectively, for each data collection approach. The proportion of respondents reporting 11111 was compared between versions (SC vs. IA, SC vs. proxy report, IA vs. proxy report) using the chi-square test and by calculating the absolute reduction in proportion score. We also calculated the absolute difference in ceiling between SC, IA, and proxy reports in individuals with health conditions. Feasibility was further assessed by calculating the percentage of responses from preference between versions (SC vs IA), acceptability, and difficulty of EQ-5D-Y-5L questions.\u003c/p\u003e \u003cp\u003e Test\u0026ndash;retest reliability for each version was assessed by Gwet\u0026rsquo;s agreement coefficient (Gwet\u0026rsquo;s AC) with 95% CIs for the EQ-5D-Y-5L dimensions, and intraclass correlation coefficients (ICC) with 95% CIs were calculated using a two-way mixed-effects model (absolute agreement) for LSS and EQ-VAS. ICCs were interpreted as \u0026lt;\u0026thinsp;0.40 (poor), 0.40\u0026ndash;0.59 (fair), 0.60\u0026ndash;0.74 (good), and \u0026ge;\u0026thinsp;0.75 (excellent) (24). An ICC\u0026thinsp;\u0026gt;\u0026thinsp;0.7 was considered acceptable (25).\u003c/p\u003e \u003cp\u003eAgreement between each pair of EQ-5D-Y-5L versions was assessed by comparing Gwet\u0026rsquo;s AC and the percent of agreement for the EQ-5D-Y-5L dimensions and ICC for the LSS at baseline. Gwet\u0026rsquo;s AC1 of \u0026lt;\u0026thinsp;0.2 was interpreted as slight agreement; 0.21\u0026ndash;0.4 as fair; 0.41\u0026ndash;0.6 as moderate; 0.61\u0026ndash;0.8 as substantial; and \u0026gt;\u0026thinsp;0.8 as almost perfect agreement based on Landis \u0026amp; Koch criteria (26,27). ICCs are labeled as \u0026le;\u0026thinsp;0.40 poor to fair agreement, 0.41\u0026ndash;0.60 moderate agreement, 0.61\u0026ndash;0.80 good agreement, and 0.81\u0026ndash;1.00 excellent agreement (28).\u003c/p\u003e \u003cp\u003eKnown-group validity was assessed by determining the degree to which the EQ-5D-Y-5L LSS met hypotheses about expected scoring patterns in previously defined groups (acute and chronically ill patients and those without health problems) using the Kruskal-Wallis test. A priori hypotheses about the HRQoL differences were specified according to previous studies (29). We hypothesized that individuals with acute or chronic health conditions would report a relatively lower HRQoL or more problems due to their health challenges and may perceive themselves as having high problems compared to those without acute or chronic health conditions. Based on previous research demonstrating lower HRQoL among children with chronic conditions and acute injury compared with healthy individuals, we hypothesized that healthy children and adolescents would report fewer problems on the LAM, UA, P/D, and WSU dimensions than those with chronic or acute health conditions (30,31). Furthermore, consistent with evidence indicating greater short-term physical impairment after injury, we hypothesized that children and adolescents with acute injuries would report greater limitations on the Mob dimension than those with chronic health conditions (32).\u003c/p\u003e \u003cp\u003eFurthermore, we expected that greater clinical severity based on objective clinical indicators such as higher Ross classification for congestive heart failure (Classes I\u0026ndash;IV), reduced forced expiratory volume percent predicted (FEV₁ % predicted) in exacerbated asthma (\u0026lt;\u0026thinsp;60%, 60\u0026ndash;79%, \u0026gt;\u0026thinsp;80%), and increasing need for assistive devices following acute injury (none, crutches, walker, wheelchair/buggy) would be associated with worse HRQoL. Post-hoc pairwise comparisons with Bonferroni correction were conducted to determine which specific health conditions or groups/clinical indicators differed significantly.\u003c/p\u003e \u003cp\u003eThree groups were defined using results on the GRS: \u0026ldquo;worse\u0026rdquo; (-3 to -1), \u0026ldquo;unchanged\u0026rdquo; (0), and \u0026ldquo;improved\u0026rdquo; (1 to 3). Responsiveness was evaluated using a paired t-test and standardized effect size (SES) (difference of mean/baseline SD) to assess the magnitude of change in mean LSS of EQ-5D-Y-5L between baseline and follow-up assessments. Standardized effect size was interpreted as insignificant/trivial for SES, \u0026lt; 0.2; small for \u0026gt;\u0026thinsp;0.2 and \u0026lt;\u0026thinsp;0.5; and large for 0.8 (33). Independent t-tests were carried out to compare the mean change in EQ-5D-Y-5L LSS between different categories on the GRS (\u0026ldquo;improved\u0026rdquo; vs. \u0026ldquo;unchanged\u0026rdquo;; \u0026ldquo;unchanged\u0026rdquo; vs. \u0026ldquo;worsened\u0026rdquo;, etc) of SC, IA, and proxy-report. Results presented using mean difference and 95% CI, Cohen\u0026rsquo;s d, and area under the curve (AUC) were used as the measure of responsiveness, with AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7 considered adequate responsiveness. The statistical significance level was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eRespondent characteristics\u003c/h2\u003e \u003cp\u003eData from 644 paired children/adolescents and proxy respondents were analyzed; 56.06% of the children/adolescents were male. The mean age was 13.22 years (SD\u0026thinsp;=\u0026thinsp;2.83) for children/adolescents and 43.2 years (SD\u0026thinsp;=\u0026thinsp;7.98) for proxies. About one-third of the study participants (31.37%) were school children/adolescents (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It was not possible to collect data on the completion times of all versions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBackground characteristics of the respondents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChildren/adolescents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProxies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e644\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e644\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.22 (2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.23 (7.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAge category, n (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258(40.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e386(59.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e361(56.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e257(40.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283(43.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e287(44.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100(15.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eResidence, n (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135(20.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122(19.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemi-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(6.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42(6.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e465(72.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e382(59.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98(15.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool level, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96(15.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 1\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211(32.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60(9.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 5\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e380(59.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140(21.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 9\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(6.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118(18.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92(14.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138(21.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eReligion, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e593(92.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e576(89.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51(7.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(6.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(4.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHealth conditions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute Injury (AI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109(16.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive Heart Failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104(16.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExacerbation Asthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(4.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV/AIDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92(14.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 1 Diabetes Mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108(16.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool child/adolescent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202(31.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrder of administration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-complete first\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e326(50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterviewer administered first\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e318(49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eReported health problems and performance of EQ-5D-Y-5L versions\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show the proportion and distribution of respondents reporting problems on EQ-5D-Y-5L dimensions for SC, IA, and proxy reports. In all versions, the highest proportions of health problems (level 2 to 5) were reported in the \u0026ldquo;WSU\u0026rdquo; dimension: SC (47.20%), IA (47.52%), and proxy-report (54.04%). The most severe problems (Level 5) were rarely reported in any dimension in any version. Mean EQ-VAS scores were 78.84 (SD\u0026thinsp;=\u0026thinsp;20.13) (SC), 79.48 (SD\u0026thinsp;=\u0026thinsp;18.51) (IA), and 79.04 (SD\u0026thinsp;=\u0026thinsp;18.26) (proxy-report).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentage of reported health problems of EQ-5D-Y-5L versions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSC(N\u0026thinsp;=\u0026thinsp;644)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIA(N\u0026thinsp;=\u0026thinsp;644)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProxy(N\u0026thinsp;=\u0026thinsp;644)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMobility (walking about), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e430 (66.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e436 (67.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e390 (60.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (8.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (11.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (10.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (11.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (11.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83 (12.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (7.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (6.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56 (8.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (2.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (5.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLooking after myself, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e450 (69.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e453 (70.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e437 (67.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (5.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (8.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (6.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (9.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (10.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58 (9.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (8.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (7.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55 (8.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (2.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (3.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (5.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoing usual activities, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e383 (59.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e395 (61.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e361 (56.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (9.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (12.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81 (12.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (12.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (13.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81 (12.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (8.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (8.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68 (10.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (4.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (3.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (2.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (5.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (5.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaving pain/discomfort, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e347 (53.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e360 (55.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e319 (49.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (15.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (17.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131 (20.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (16.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105 (16.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94 (14.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (6.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (7.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (7.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (3.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (3.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (4.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (5.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeelingworried/sad/unhappy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e340 (52.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e338 (52.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e296 (45.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134 (20.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155 (24.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138 (21.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (13.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (14.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110 (17.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (5.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (5.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (6.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (3.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (4.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (3.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (5.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn general, when aggregating across all populations, the proportion of reported problems was slightly higher using proxy reports (33.54%) of respondents reporting no problems on any dimension (11111) compared to SC (36.95%) and IA (34.78%) (χ\u0026sup2; = 4.48, p\u0026thinsp;=\u0026thinsp;0.04, χ\u0026sup2; = 0.65, p\u0026thinsp;=\u0026thinsp;0.48, respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)). The same pattern was observed in those with a health condition. Respondents reported no problems on any dimension (11111) using proxy reports (19.23%), compared with SC (22.62%) and IA (21.26%); the difference was not statistically significant) (χ\u0026sup2; = 2.78, p\u0026thinsp;=\u0026thinsp;0.12, and χ\u0026sup2; = 1.05, p\u0026thinsp;=\u0026thinsp;0.36, respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverall absolute difference in ceiling across all children and adolescents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;644)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIA\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;644)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProxy\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;644)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSC vs IA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSC vs Proxy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eIA vs proxy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eΔ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e(p-value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eΔ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e(p-value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eΔ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e(p-value)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMob, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e430(66.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e436(67.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e390(60.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6(-0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.06(0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e40(6.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.04(\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e46(7.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e18.24(\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e450(69.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e453(70.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e437(67.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3(-0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.18(0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13(2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.74(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e16(2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.51(0.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e383(59.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e395(61.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e361(56.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-12(-1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.57(0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22(3.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.17(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e34(5.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9.17(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP/D, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e347(53.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e347(53.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e319(49.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00(1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28(4.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.16(0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e28(4.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e181(\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWSU, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e340(52.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e338(52.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e296(45.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06(0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e44(6.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12.57(\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e42(6.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11.31(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal ceiling, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e238(36.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e224(34.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e216(33.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.53(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22(3.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.48(\u0026lt;\u0026thinsp;0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8(1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.65(\u0026lt;\u0026thinsp;0.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAbsolute difference in ceiling effect in children and adolescents with health conditions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;442\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIA\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;442\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProxy\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;442\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSC vs IA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSC vs Proxy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eIA vs proxy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eΔ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e(p-value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eΔ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e(p-value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eΔ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e(p-value)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMob, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e241 (54.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e244 (55.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e222(50.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3(-0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.39 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19 (4.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.46 (0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e22 (4.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.90 (0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e259 (58.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e252 (57.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e257(58.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.26 (0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2 (0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.05 (0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-5 (-1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.31 (0.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e207 (46.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e206 (46.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e193 (43.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02 (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14 (3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.13 (0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13(2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.82 (0.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP/D, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e173 (39.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e173 (39.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e155(35.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00 (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18 (4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.66 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e18(4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.66 (0.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWSU, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178 (40.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e173 (39.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e148 (33.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.53 (0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30(6.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.63 (0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e25 (5.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.43 (0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal ceiling, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100 (22.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94 (21.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85 (19.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.8 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15 (3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.78 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9 (2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.05 (0.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLevels of missing data for EQ-5D-Y-5L dimensions were slightly higher using the SC (0.26%-11.63%) compared to the IA version (0.00%-1.94%), particularly among younger children aged 8\u0026ndash;12 years (4.65%-11.63%) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMissing values by age category in dimensions of SC and IA versions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMob n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLAM n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUA n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePD n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWSU n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u0026ndash;12 (N\u0026thinsp;=\u0026thinsp;258)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (4.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20 (7.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30 (11.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25 (9.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21 (8.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u0026ndash;18 (N\u0026thinsp;=\u0026thinsp;386)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4 (1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3 (0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u0026ndash;12 (N\u0026thinsp;=\u0026thinsp;258)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4 (1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5 (1.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u0026ndash;18 (N\u0026thinsp;=\u0026thinsp;386)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInterviewer effect\u003c/h2\u003e \u003cp\u003eSlight differences were observed in the distributions of EQ-5D-Y-5L LSS between interviewers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These variations may indicate the presence of interviewer effects that influenced how participants interpreted or responded to the EQ-5D-Y-5L health-state descriptors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAgreement between data collection methods (SC vs IA, SC vs proxy, and IA vs proxy)\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the agreement of health responses between data collection approaches (SC vs IA, SC vs proxy, and IA vs proxy) by dimensions, LSS, and EQ-VAS. Gwet\u0026rsquo;s AC1 and perfect agreement showed moderate to almost perfect agreement across all dimensions, ranging from 0.51 to 0.89 and 59% to 90%, respectively. The ICC for LSS and EQ-VAS was high for SC and IA (0.93\u0026ndash;0.95), followed by IA and proxy-report (0.70\u0026ndash;0.79).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAgreement statistics between EQ-5D-Y-5L versions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGwet\u0026rsquo;s AC1 (95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePercent agreement (%) (95%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSC and IA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSC and Proxy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIA and Proxy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSC and IA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSC and Proxy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIA and Proxy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMob\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89 (0.86, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75(0.70, 0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75(0.71, 0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90(88, 92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78(74, 82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78(74, 82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87(0.84, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79(0.75, 0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79(0.75, 0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89(86, 92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81(77, 84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81(78, 85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84(0.81, 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67(0.62, 0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67(0.62, 0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86(83, 90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72(68, 76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72(68, 76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP/D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71(0.67, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54(0.49, 0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58(0.53, 0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76(72, 79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61(57, 66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64(60, 68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWSU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74(0.70, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51(0.46, 0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51(0.46, 0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78(75, 82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59(55, 63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59(55, 63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eICC value (95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSC and IA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSC and Proxy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eIA and Proxy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSS(N\u0026thinsp;=\u0026thinsp;588)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.95(0.94\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.80(0.77,0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.79(0.76, 0.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEQ-VAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.93(0.92, 0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.68(0.63, 0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.70(0.66, 0.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAll results were significant at 0.01 level, CI confidence interval\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eA Gwet\u0026rsquo;s AC1 of \u0026lt;\u0026thinsp;0.2 was interpreted as slight agreement; 0.21\u0026ndash;0.4 as fair; 0.41\u0026ndash;0.6 as moderate; 0.61\u0026ndash;0.8 as substantial and \u0026gt;\u0026thinsp;0.8 as almost perfect agreement\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTest-retest reliability\u003c/h2\u003e \u003cp\u003eTest-retest reliability for SC, IA, and proxy reports of EQ-5D-Y-5L dimensions between baseline and follow-up ranged from moderate to excellent (0.61\u0026ndash;0.89) among those who reported unchanged health status. The highest reliability was observed for the \"LAM\" dimension (Gwet\u0026rsquo;s AC1\u0026thinsp;=\u0026thinsp;0.89), whereas the lowest was observed for the \"WSU\" dimension (Gwet\u0026rsquo;s AC1\u0026thinsp;=\u0026thinsp;0.61), both in the proxy-report version. EQ-VAS demonstrated excellent test-retest reliability, with ICC values of 0.93, 0.96, and 0.92 for SC, IA, and proxy-report, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTest\u0026ndash;retest reliability of each EQ-5D-Y-5L version\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eGwet\u0026rsquo;s AC2 (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSC (n\u0026thinsp;=\u0026thinsp;343)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eIA (n\u0026thinsp;=\u0026thinsp;343)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProxy-report (n\u0026thinsp;=\u0026thinsp;339)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMob\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84 (0.79, 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.81 (0.76, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88 (0.84, 0.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86 (0.82, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.82 (0.78, 0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89 (0.85, 0.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84 (0.80, 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.81 (0.77, 0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83 (0.78, 0.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP/D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66 (0.61, 0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.69 (0.64, 0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69 (0.64, 0.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWSU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65 (0.60, 0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.72(0.67, 0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61 (0.56, 0.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eICC value (95%CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eIA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEQ-VAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.91, 0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.95, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.92 (0.89, 0.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eKnown group validity\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows that the EQ-5D-Y-5L LSS of all versions were able to differentiate between known disease groups (SC: χ\u0026sup2; = 285, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; IA: χ\u0026sup2; = 273, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Proxy-report: χ\u0026sup2; = 232; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The highest (i.e., poorest HRQoL) LSS scores were observed among participants with acute conditions, followed by those with chronic conditions. Post hoc analysis showed a significant difference between school participants and those with chronic or acute conditions (χ\u0026sup2; \u0026gt; 93, p\u0026thinsp;=\u0026thinsp;0.001). Children/adolescents with chronic or acute health conditions reported significantly more problems across all EQ-5D-Y-5L dimensions (Mob, LAM, UA, P/D, and WSU) compared with school children/adolescents (χ\u0026sup2; \u0026gt; 103, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Consistently, EQ-VAS scores were significantly lower among clinical groups, particularly those with CHF, acute injury, and asthma exacerbation, indicating poorer perceived health status, whereas schoolchildren/adolescents reported the highest EQ-VAS scores (Tables\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). These patterns were consistent across both SC and IA versions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKnown group validity for EQ-5D-Y-5L LSS of SC and IA versions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSC LSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIA LSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSC vs IA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample size (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePaired Difference (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHealth conditions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool child/adolescent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.62(1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.66(1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.04(0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive Heart Failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.74(5.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.70(4.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04(0.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 1 Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.42(3.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.57(3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.10(-0.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute Injury (AI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.64(5.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.42(5.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22(0.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV AIDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.13(2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.28(2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.15(0.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExacerbation Asthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.48(2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.44(2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04(0.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChi-squared with ties\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e273.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eClinical indicators of disease conditions\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoss classification for CHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSample size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.80 (3.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2(0.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.62 (3.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.87 (4.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.25(1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.45 (5.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.94 (4.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51(0.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.02 (4.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.21 (4.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.19(0.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChi-squared with ties\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV PP for exacerbated asthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.50 (3.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.25(3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25(0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.64(2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.52(2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1(0.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.00 (2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.50 (2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.5(0.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChi-squared with ties\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssistive device for Acute Injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.70 (5.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.06 (5.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64()\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle or double crutches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.66 (2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.00 (2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.34(0.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWalker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.26 (5.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.86 (4.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4(1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheelchair/buggy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.63 (3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.70 (3.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.07(0.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChi-squared with ties\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e*Kruskal-Wallis rank test, where a p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKnown group validity on EQ-5D-Y-5L SC dimensions and EQ-VAS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimensions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eHealth conditions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSchool child/adolescent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCongestive Heart Failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eType 1 Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAcute Injury (AI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHIV AIDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExacerbation Asthma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMob\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP/D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWSU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEQ-VAS mean(sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.82(15.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.07(21.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.70(14.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.00(14.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.77(19.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62.42(12.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKnown group validity on EQ-5D-Y-5L IA dimensions and EQ-VAS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimensions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eHealth conditions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSchool child/adolescent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCongestive Heart Failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eType 1 Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAcute Injury (AI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHIV AIDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExacerbation Asthma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMob\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP/D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWSU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEQ-VAS mean(sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.72(13.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.75(19.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.68(13.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.85(13.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79.15(16.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.07(11.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBoth SC and IA versions yielded LSS that differed significantly across Ross classes, with the highest LSS (i.e., poorest HRQoL) observed in class IV (most severe stage of CHF) (SC: χ\u0026sup2; = 9.02, p\u0026thinsp;=\u0026thinsp;0.02; IA: χ\u0026sup2; = 9.99, p\u0026thinsp;=\u0026thinsp;0.01). Significant differences were also observed according to assistive mobility device use among participants with acute injury, with those requiring wheelchairs, walkers, or crutches reporting poorer HRQoL (SC: χ\u0026sup2; = 12.54, p\u0026thinsp;=\u0026thinsp;0.005; IA: χ\u0026sup2; = 17.45, p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eResponsiveness\u003c/h2\u003e \u003cp\u003eAmong 478 study participants who completed the follow-up assessment, 301 (63.00%) reported no change in global health based on the self-reported health classifier, whereas 146 (30.50%) and 31 (6.50%) reported improved and worsened health status, respectively, compared to baseline. Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows the LSS mean change and SES of SC, IA, and proxy-report scores by GRS. The mean EQ-5D-Y-5L LSS of SC, IA, and proxy-report versions in the improved group at 1-month follow-up was 10.51, 9.97, and 10.88 with significant mean changes of 2.85, 3.16, and 1.49 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean Change and SES of EQ-5D-Y-5L LSS by degree of Global Rating of Change Scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u0026nbsp;(Mean_ SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFollow-up\u003c/p\u003e \u003cp\u003e(Mean_ SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePaired\u0026nbsp;Difference\u003c/p\u003e \u003cp\u003e(Mean_ SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSES (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eGlobal rating of change\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eWorsened group (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSC LSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.16(3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.67(4.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.52(2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74(0.48,1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIA LSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.22(3.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.93(4.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.71(1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36(0.21,0.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProxy LSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.22(4.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.81(4.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58(2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33(0.13,0.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eUnchanged group (n\u0026thinsp;=\u0026thinsp;301)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSC LSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.28(3.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.18(3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.10(1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.02(-0.05,0.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIA LSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.28(3.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.16(3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.12(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.03(-0.05,0.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProxy LSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.61(4.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.60(3.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01(-0.027,0.030)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eImproved group (n\u0026thinsp;=\u0026thinsp;146)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSC LSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.36(4.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.51(3.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.85(2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.61(-0.68, -0.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIA LSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.14(4.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.97(3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.16(2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.66(-0.73, -0.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProxy LSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.34(4.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.88(4.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.49(1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.34(-0.38, -0.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eEQ-5D-Y-5L, EuroQol Five-Dimensions Five-level youth version, CI, confidence interval; RS, Responsiveness Statistic; Scale; SES, Standardized Effect Size; SRM, Standardized Response Mean.\u003c/p\u003e \u003cp\u003eLevel sum scores were calculated based on five-level EQ-5D level score.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe magnitude and direction of these changes were consistent with the distribution of GRS responses. SC and IA versions were responsive to change in health with a moderate SES (SC: 0.74, \u0026minus;\u0026thinsp;0.61; IA: 0.36, \u0026minus;\u0026thinsp;0.66), while proxy-reported data indicated more modest responsiveness (SES: 0.33, \u0026minus;\u0026thinsp;0.34) for the \u0026ldquo;worsened\u0026rdquo; and \u0026ldquo;improved\u0026rdquo; groups respectively.\u003c/p\u003e \u003cp\u003eThe differences in mean change of LSS at follow-up by GRS is presented in Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e. Comparing patients with \u0026ldquo;unchanged\u0026rdquo; health and \u0026ldquo;worsened\u0026rdquo; health in the SC, IA, proxy-report versions, \u0026ldquo;unchanged\u0026rdquo; health patients had higher LSS with mean differences of 2.61, 1.84, and 1.57, respectively. Comparing patients with \u0026ldquo;unchanged\u0026rdquo; health and \u0026ldquo;improved\u0026rdquo; in the SC, IA, and proxy-report versions, \u0026ldquo;improved\u0026rdquo; health patients reported higher LSS with positive mean differences of 2.75, 3.04, and 1.50 respectively. The SC version showed the highest responsiveness in discriminating improved from worsened groups (Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;2.51, AUC\u0026thinsp;=\u0026thinsp;0.98), followed by IA (d\u0026thinsp;=\u0026thinsp;2.32, AUC\u0026thinsp;=\u0026thinsp;0.97), whereas proxy-report showed lower responsiveness (d\u0026thinsp;=\u0026thinsp;1.46, AUC\u0026thinsp;=\u0026thinsp;0.82).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifference in Mean Change at Follow-up by degree of Global Rating of Change Scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHealth status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eChange in LSS SC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eChange in LSS IA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eChange in LSS proxy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Difference\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohen\u0026rsquo;s d\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Difference\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCohen\u0026rsquo;s d\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean Difference\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCohen\u0026rsquo;s d\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnchanged vs.Worsened\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.61(2.16,3.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89(0.79,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.84(1.39,2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.73(0.59,0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.57(1.05,2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16(0.11, 0.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved vs. Unchanged\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.75(2.47, 3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04(0.02,0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.04(2.74, 3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02(0.01,0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.50(1.20, 1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16(0.11, 0.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved vs. Worsened\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.36(4.53,6.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98(0.97,0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.87(4.05,5.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97(0.95, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.10(2.25,3.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.82(0.74, 0.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved/Unchanged vs. Worsened\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.51(2.79,4.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92(0.85,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.83(2.07,3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.81(0.71, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.06(1.43,2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.67(0.56, ,0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved vs.worsen /unchanged\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.99(2.68,3.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88(0.84,0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.21(2.89,3.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89(0.85,0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.65(1.32,1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.69(0.64,0.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eEQ-5D-Y-5L, EuroQol Five-Dimensions Five-level youth version, CI, confidence interval; RS, Responsiveness Statistic; Scale; SES, Standardized Effect Size. Level sum scores were calculated based on the five-level EQ-5D level score.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePreference, feasibility, and acceptability of EQ-5D-Y-5L SC and IA\u003c/h2\u003e \u003cp\u003ePreference between IA and SC versions was similar, with 47% preferring the SC version (Fig.\u0026nbsp;3). Most of the respondents reported the SC version as \"very easy\" or \"fairly easy\" (82.42%) to understand. A high proportion reported no difficulty in reading and understanding both the SC (68.10%) and IA (77.30%) versions, whereas the level of no difficulty in understanding was lower in the proxy report (61.24%) (Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAcceptability for SC, IA, and proxy-report versions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHow did you find the questionnaire to understand (SC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery easy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151(45.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFairly easy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121(36.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot very easy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(8.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot at all easy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(9.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHow much difficulty did you have reading and understanding (SC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo difficulty at all\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225(68.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSome difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75(22.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(6.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA lot of difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(2.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHow much difficulty did you have\u0026nbsp;understanding\u0026nbsp;the questions on this survey (IA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo difficulty at all\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e252(77.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSome difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54(16.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(4.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA lot of difficultly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(1.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHow did you find assistance from an interviewer? (IA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery helpful\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(12.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFairly helpful\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233(70.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot very helpful\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(7.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot at all helpful\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(9.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHow did you find the questionnaire to understand (Proxy-report)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery easy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e229(40.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFairly easy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168(29.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot very easy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135(23.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot at all easy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(5.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHow much difficulty did you have reading and understanding (Proxy-report)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo difficulty at all\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e346(61.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSome difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126(22.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64(11.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA lot of difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(5.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study compared the measurement properties of the SC, IA, and proxy-report versions of EQ-5D‐Y-5L and the degree of agreement in health response between them. High agreement in mean LSS was observed between the SC, IA, and proxy-report versions, although psychometric performance was somewhat superior in the SC and IA than in the proxy report version in this study sample. Agreement between SC and IA was higher than that observed between either SC and proxy report or IA and proxy report, particularly in the \u0026ldquo;WSU\u0026rdquo; and \u0026ldquo;P/D\u0026rdquo; dimensions, where agreement ranged from moderate to substantial. Proxy reports exhibited lower ceiling effects than SC and IA versions, suggesting that proxies tend to report more health problems than children/adolescents self-reporting their own health. These findings are consistent with previous literature indicating that proxy respondents may have difficulty accurately capturing subjective aspects of health, particularly psychosocial domains (10). A meta-analytic review concluded that extensive evidence indicates that SC and IA versions of health responses of EQ-5D-Y and PedsQL questionnaires were comparable with high correlations between different data collection approaches (23, 30). This agreement result supports the case for IA as an alternative to SC for self-reported health status in children and adolescents, particularly in settings where different issues limit the use of SC.\u003c/p\u003e \u003cp\u003eA slight difference in missing values was observed between the SC and IA versions. This finding is similar to previous studies by Amien et al. (2022) and Rajmil et al. (2014), which reported higher levels of missing data in SC and paper versions compared with IA and digital versions (22,34). The interviewer-administered version provides a number of advantages and limitations relative to the SC version. Although the presence of an interviewer may reduce missing or incomplete responses, it may also increase personnel time and resource requirements for data collection.\u003c/p\u003e \u003cp\u003eThe SC and IA versions performed closely as well in terms of psychometric properties as the proxy report. Test-retest reliability of the EQ-5D-Y-5L was acceptable across all dimensions, as well as for LSS and EQ-VAS, based on Gwet\u0026rsquo;s AC and ICC values. Reliability was generally higher for SC and IA versions compared with proxy reports. The Gwet\u0026rsquo;s AC values were lower on \u0026ldquo;P/D\u0026rdquo; and \u0026ldquo;WSU\u0026rdquo; dimensions. This finding implies proxies may not fully understand the aspects that are covered by \u0026ldquo;P/D\u0026rdquo; and \u0026ldquo;WSU\u0026rdquo; dimensions, which may result in random variation in health responses over time. Previous research on the EQ-5D-Y has similarly reported high missing values and more variation in score and lower reliability in the \u0026ldquo;P/D\u0026rdquo; and \u0026ldquo;WSU\u0026rdquo; dimensions (35). Moreover, a systematic review evaluated agreement between self- and proxy-reported HRQoL using generic preference-based measures and found that agreement was typically lower for psychosocial dimensions and higher for physical domains (10). It is unclear whether these differences are attributed to the differences in perspective between children/adolescents and proxies or limitations in proxy understanding of the child's internal health experience. Differences may also be due to differences in how the EQ-5D-Y-5L dimension descriptors and severity labels are interpreted by children/adolescents and proxies.\u003c/p\u003e \u003cp\u003eBoth SC and IA were able to discriminate EQ-5D-Y-5L LSS between groups of health conditions, with a marked difference between patients with chronic or acute health conditions and healthy school children/adolescents. The difference was particularly marked in patients with acute injury and CHF. Patients with acute injury and CHF experience functional limitations, pain/discomfort, and emotional and psychological distress, which can be associated with the nature and severity of the diseases (36,37).\u003c/p\u003e \u003cp\u003eBoth SC and IA versions had comparable strength of responsiveness to change in health status over one month, with the largest noted SES (SC: \u0026minus;\u0026thinsp;0.61; IA: \u0026minus;\u0026thinsp;0.66) for the \u0026ldquo;worsened\u0026rdquo; in health group. The mean difference resulted from the change of LSS of each version and was significant. It is suggestive that all versions of EQ-5D-Y-5L are capable of capturing both positive and negative changes in the improved and worsened health groups. This responsiveness result was supported by the ability of each version in discriminating change in LSS across health groups (\u0026ldquo;worsened\u0026rdquo;, \u0026ldquo;improved\u0026rdquo;, \u0026ldquo;no changes\u0026rdquo;). It was expected that those who reported worse or improved health scores would have a greater change in LSS than those who reported no change in health (38).\u003c/p\u003e \u003cp\u003eThe strength of this study was that all participants completed both (SC and IA) versions with repeated measures and also that both versions were randomly administered. This study also has some limitations. Firstly, the small sample size (n\u0026thinsp;=\u0026thinsp;31) of participants reporting worsened health in the responsiveness analysis. Second, the findings may not be generalizable to other settings due to sociodemographic differences and the single-center design, although representativeness is not a requirement for psychometric studies. As this was the first study evaluating the measurement properties and agreement in response between the data collection approaches (SC, IA, and proxy-report) for EQ-5D‐Y-5L among children/adolescent, there is limited literature for comparison. Both SC and IA versions have been completed on a single day, which may have caused some recall bias. However, studies conducted in children and adolescents by administering the questionnaires on the same day with distracting activities before starting the second data collection method showed acceptable psychometric results (21,22,34). The 1-month follow-up period may have been too short to capture clinically meaningful changes in HRQoL outcomes, particularly for patients recovering from acute injuries or asthma exacerbations, as symptom resolution and return to usual activities may require more time. It is recommended that future work investigate changes in EQ-5D-Y-5L scores across SC, IA, and proxy reports over longer follow-up periods.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe substantial agreement and similar measurement properties between SC and IA approaches to data collection using EQ-5D-Y-5L suggest that IA could be used as an alternative MoA when SC is not an option and that the two approaches will likely produce data that can be compared and/or pooled. Although the proxy-report version had acceptable measurement properties, they were inferior to the SC and IA versions and should be used only when a self-report is not possible.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis project was financial supported by EuroQol Research Foundation, The Netherlands (340- RA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe would like to acknowledge the EuroQol Research Foundation for funding this study (340- RA) and Elias Fitsum for his assistance with data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAW, FX, and ES conceived the idea; AG, FX, ES, JV, and MK designed the study, acquired the funding, and AW, FX, ES and, LD provided detailed information regarding study protocol and data collection processes in Ethiopia. AG prepared the draft manuscript. All authors reviewed the analysis, interpretation of the results, and the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDeclaration of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eElly Stolk, Feng Xie, Mike Herdman, Abraham Welie and Janine Verstraete are a member of the EuroQol Research Foundation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical considerations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was secured from Ethics Review Committee of College of Health Sciences, Mekelle University, Ethiopia (MU-IRB2035/2023) and prior permission was sought from Ayder Comprehensive Specialized Hospital of diabetic clinic, infectious disease clinic, respiratory disease clinic, children/adolescent medical ward, physiotherapy unit, Mekelle, Ethiopia. Written informed consent and assent consent was obtained from all study participants to confirm their willingness for participation after explaining the purpose of study. The study was conducted in accordance with the declaration of Helsinki.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eO\u0026rsquo;Rourke B, Oortwijn W, Schuller T, the International Joint Task Group. The new definition of health technology assessment: A milestone in international collaboration. Int J Technol Assess Health Care. 2020 Jun;36(3):187\u0026ndash;90. doi:10.1017/S0266462320000215\u003c/li\u003e\n\u003cli\u003eGouda HN, Charlson F, Sorsdahl K, Ahmadzada S, Ferrari AJ, Erskine H, et al. Burden of non-communicable diseases in sub-Saharan Africa, 1990\u0026ndash;2017: results from the Global Burden of Disease Study 2017. Lancet Glob Health. 2019 Oct;7(10):e1375\u0026ndash;87. doi:10.1016/S2214-109X(19)30374-2\u003c/li\u003e\n\u003cli\u003eErku DA, Desalegn A, Mekonnen TM, Dessie E, Wolde FB, Ababulgu SA, et al. Towards institutionalizing HTA in Ethiopia: using a political economy analysis to explore stakeholder perspectives and assessing capacity needs. Int J Technol Assess Health Care. 2025;41(1):e24. doi:10.1017/S0266462325000170\u003c/li\u003e\n\u003cli\u003eMbau R, Vassall A, Gilson L, Barasa E. Factors Influencing the Institutionalization of Health Technology Assessment: A Scoping Literature Review. Health Syst Reform. 2023 Dec 31;9(3):2360315. doi:10.1080/23288604.2024.2360315\u003c/li\u003e\n\u003cli\u003ePitt C, Vassall A, Teerawattananon Y, Griffiths UK, Guinness L, Walker D, et al. Foreword: Health Economic Evaluations in Low‐ and Middle‐income Countries: Methodological Issues and Challenges for Priority Setting. Health Econ. 2016 Feb;25(S1):1\u0026ndash;5. doi:10.1002/hec.3319\u003c/li\u003e\n\u003cli\u003eWelie AG, Gebretekle GB, Stolk E, Mukuria C, Krahn MD, Enquoselassie F, et al. Valuing Health State: An EQ-5D-5L Value Set for Ethiopians. Value Health Reg Issues. 2019;22:7\u0026ndash;14. doi:10.1016/j.vhri.2019.08.475\u003c/li\u003e\n\u003cli\u003eJones R, Mulhern B, McGregor K, Yip S, O\u0026rsquo;Loughlin R, Devlin N, et al. Psychometric Performance of HRQoL Measures: An Australian Paediatric Multi-Instrument Comparison Study Protocol (P-MIC). Children. 2021 Aug 20;8(8):714. doi:10.3390/children8080714\u003c/li\u003e\n\u003cli\u003eDewilde S, Janssen MF, Lloyd AJ, Shah K. Exploration of the Reasons Why Health State Valuation Differs for Children Compared With Adults: A Mixed Methods Approach. Value Health. 2022 Jul;25(7):1185\u0026ndash;95. doi:10.1016/j.jval.2021.11.1377\u003c/li\u003e\n\u003cli\u003eCanadian Agency for Drugs and Technologies in Health (CADTH). 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A systematic review of the application of preference-based instruments in self and proxy reporting of quality of life in older people. Soc Sci Med. 2022 Jul;305:115046. doi:10.1016/j.socscimed.2022.115046\u003c/li\u003e\n\u003cli\u003eKreimeier S, Greiner W. EQ-5D-Y as a Health-Related Quality of Life Instrument for Children and Adolescents: The Instrument\u0026rsquo;s Characteristics, Development, Current Use, and Challenges of Developing Its Value Set. Value Health. 2019 Jan;22(1):31\u0026ndash;7. doi:10.1016/j.jval.2018.11.001\u003c/li\u003e\n\u003cli\u003eWille N, Badia X, Bonsel G, Burstro K, Ravens-sieberer LSU. Development of the EQ-5D-Y : a child-friendly version of the EQ-5D. Qual Life Res. 2010;19(6):875\u0026ndash;86. doi:10.1007/s11136-010-9648-y\u003c/li\u003e\n\u003cli\u003eKreimeier S, \u0026Aring;str\u0026ouml;m M, Burstr\u0026ouml;m K, Egmar AC, Gusi N, Herdman M, et al. EQ-5D-Y-5L: developing a revised EQ-5D-Y with increased response categories. 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Children. 2022 Jan 10;9(1):93. doi:10.3390/children9010093\u003c/li\u003e\n\u003cli\u003eWorld Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA. 2013 Nov 27;310(20):2191. doi:10.1001/jama.2013.281053\u003c/li\u003e\n\u003cli\u003eCicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess. 1994 Dec;6(4):284\u0026ndash;90. doi:10.1037/1040-3590.6.4.284\u003c/li\u003e\n\u003cli\u003eKoo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med. 2016 Jun;15(2):155\u0026ndash;63. doi:10.1016/j.jcm.2016.02.012\u003c/li\u003e\n\u003cli\u003eGwet KL. Computing inter‐rater reliability and its variance in the presence of high agreement. 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The impact of injuries study. multicentre study assessing physical, psychological, social and occupational functioning post injury - a protocol. BMC Public Health. 2011 Dec;11(1):963. doi:10.1186/1471-2458-11-963\u003c/li\u003e\n\u003cli\u003eWong CKH, Cheung PWH, Luo N, Lin J, Cheung JPY. Responsiveness of EQ-5D Youth version 5-level (EQ-5D-5L-Y) and 3-level (EQ-5D-3L-Y) in Patients With Idiopathic Scoliosis. Spine. 2019 Nov 1;44(21):1507\u0026ndash;14. doi:10.1097/BRS.0000000000003116\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Agreement, EQ-5D-Y-5L, Ethiopia, measurement property, Tigrinya","lastPublishedDoi":"10.21203/rs.3.rs-9066454/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9066454/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo assess measurement properties of self-complete (SC), interviewer-administered (IA), and proxy-report versions of EQ-5D‐Y-5L and to assess the degree of agreement in response between these versions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSC, IA, and proxy-report were administered to children and adolescents with and without health conditions on two occasions, separated by either a 10-day interval (to assess test-retest reliability) or a one-month interval (to assess responsiveness). The SC and IA versions were administered in randomized order. Agreement and test\u0026ndash;retest reliability assessed using Gwet\u0026rsquo;s agreement coefficient (Gwet\u0026rsquo;s AC) and intraclass correlation coefficient (ICC). Feasibility, known-group validity, and responsiveness were assessed using missing values, Kruskal-Wallis test, and standardized effect size (SES), respectively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 644 child/parent dyads participated. Missing values were higher for SC, particularly among younger children aged 8\u0026ndash;12 years (4.65%-11.63%). Agreement between EQ-5D‐Y‐5L versions ranged from moderate to almost perfect, with excellent test-retest reliability across all dimensions and EQ-VAS. The EQ-5D-Y-5L level sum score (LSS) significantly differentiated between known disease groups across all versions (SC: χ\u0026sup2; = 285; IA: χ\u0026sup2; = 273; Proxy-report: χ\u0026sup2; = 232; all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Both SC and IA versions were responsive to change in health with a moderate SES in \u0026ldquo;worsened\u0026rdquo; and \u0026ldquo;improved\u0026rdquo; groups.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe substantial agreement and similar measurement properties between SC and IA versions suggest that IA could be used as an alternative approach for data collection when self-complete is not an option. Although the proxy-report version had acceptable measurement properties, they were inferior to the SC and IA versions.\u003c/p\u003e","manuscriptTitle":"Agreement and measurement properties of the interviewer-administered, self-completed and proxy‐reported versions of the Tigrinya EQ-5D-Y-5L in Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 08:15:49","doi":"10.21203/rs.3.rs-9066454/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"64bb2a4d-bc48-43dc-82bb-d401aaa402d3","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T07:39:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 08:15:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9066454","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9066454","identity":"rs-9066454","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}