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Japanese Population Norms of EQ-5D-5L and Health Utilities Index Mark 3: Disutility Catalog by Disease and Symptom in Community Settings

Open AccessPublished:April 21, 2021DOI:https://doi.org/10.1016/j.jval.2021.03.010

      Highlights

      • Population norms of preference-based measures (PBMs) have been established in some countries and are important for cost-effectiveness analysis and interpretation of PBM values. No comparative data exist for population norms with regard to the EQ-5D-5L and Health Utilities Index Mark 3.
      • We established the Japanese population norms of the EQ-5D-5L and Health Utilities Index Mark 3 using data from a large sample. Age, sex, household income, and education level had a significant influence on health state index values. Disutility associated with diseases and symptoms was also estimated, revealing that Parkinson disease, dementia, stroke, and depression were associated with a large disutility.
      • These findings can contribute to a more reliable analysis of economic evaluations and may help clarify the characteristics of the 2 PBMs, which in turn will lead to an appropriate interpretation of cost-effectiveness analysis to facilitate healthcare decision making.

      Abstract

      Objectives

      This study aimed to establish the Japanese population norms of the EQ-5D-5L and Health Utilities Index Mark 3 (HUI3) and estimate the disutility associated with diseases and symptoms.

      Methods

      We performed a door-to-door survey of the general population by random sampling. The planned sample size was 10 000 residents (age ≥16 years) of 334 districts in Japan. In addition to the EQ-5D-5L and HUI3 questionnaires, questions regarding demographic factors and self-reported main diseases and symptoms were asked. The EQ-5D-5L and HUI3 responses were converted to index values on the basis of Japanese value sets. Summary values by age and sex were calculated to obtain Japanese normative values. A multiple linear model was used to examine relationships between these values and diseases and symptoms.

      Results

      We collected 10 183 responses from 334 districts. The mean EQ-5D-5L index values were 0.821 (male) and 0.774 (female) in the age group of 80 to 89 years, which were lower compared with 0.978 (male) and 0.967 (female) in the age group of 16 to 19 years. Similar trends were observed for the HUI3 values. Age, sex, household income, and education level had a significant influence on the values of both instruments. When measured with the EQ-5D-5L, Parkinson disease, dementia, and stroke were associated with the largest disutility (>0.2), and the disutility for depression was approximately 0.18. In contrast, the HUI3 disutility values for Parkinson disease and dementia were approximately 0.4.

      Conclusions

      This study established the Japanese population norms of the EQ-5D-5L and HUI3, which can be used in healthcare decision making and contribute to a more reliable analysis of economic evaluations.

      Keywords

      Background

      Preference-based measures (PBMs), such as the EQ-5D, Health Utilities Index Mark 3 (HUI3), and Short-Form 6 Dimensions (SF-6D), are used to calculate quality-adjusted life-years (QALYs) in cost-effectiveness analysis. Responses to these questionnaires are converted to quality-of-life values (referred to as utilities, index values, or quality-of-life scores) anchored to 0 (death) and 1 (full health) on the basis of predetermined value sets. Therefore, the same health state assessed with the same PBM may result in different values across countries and regions.
      In some countries, the population norms of PBMs have been established, which help interpret PBM data and can be used as values without diseases or symptoms in cost-effectiveness analysis. The population norms of the EQ-5D-5L have been reported in Germany,
      • Hinz A.
      • Kohlmann T.
      • Stöbel-Richter Y.
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      • Brähler E.
      The quality of life questionnaire EQ-5D-5L: psychometric properties and normative values for the general German population.
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      • Grochtdreis T.
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      • König H.H.
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      Health-related quality of life measured with the EQ-5D-5L: estimation of normative index values based on a representative German population sample and value set.
      Poland,
      • Golicki D.
      • Niewada M.
      EQ-5D-5L polish population norms.
      Spain,
      • Garcia-Gordillo M.A.
      • Adsuar J.C.
      • Olivares P.R.
      Normative values of EQ-5D-5L: in a Spanish representative population sample from Spanish Health Survey, 2011.
      ,
      • Hernandez G.
      • Garin O.
      • Pardo Y.
      • et al.
      Validity of the EQ-5D-5L and reference norms for the Spanish population.
      South Korea,
      • Kim T.H.
      • Jo M.W.
      • Lee S.I.
      • Kim S.H.
      • Chung S.M.
      Psychometric properties of the EQ-5D-5L in the general population of South Korea.
      South Australia,
      • McCaffrey N.
      • Kaambwa B.
      • Currow D.C.
      • Ratcliffe J.
      Health-related quality of life measured using the EQ-5D-5L: South Australian population norms.
      Indonesia,
      • Purba F.D.
      • Hunfeld J.A.M.
      • Iskandarsyah A.
      • et al.
      Quality of life of the Indonesian general population: test–retest reliability and population norms of the EQ-5D-5L and WHOQOL-BREF [published correction appears in PLoS One. 2018;13(8):e0203091].
      Ireland,
      • Hobbins A.
      • Barry L.
      • Kelleher D.
      • O’Neill C.
      The health of the residents of Ireland: population norms for Ireland based on the EQ-5D-5L descriptive system - a cross sectional study.
      China,
      • Yang Z.
      • Busschbach J.
      • Liu G.
      • Luo N.
      EQ-5D-5L norms for the urban Chinese population in China.
      Trinidad and Tobago,
      • Bailey H.
      • Janssen M.F.
      • La Foucade A.
      • Kind P.
      EQ-5D-5L population norms and health inequalities for Trinidad and Tobago.
      Hong Kong,
      • Wong E.L.
      • Cheung A.W.
      • Wong A.Y.
      • Xu R.H.
      • Ramos-Goñi J.M.
      • Rivero-Arias O.
      Normative profile of health-related quality of life for Hong Kong general population using preference-based instrument EQ-5D-5L.
      Quebec,
      • Poder T.G.
      • Carrier N.
      • Kouakou C.R.C.
      Quebec health-related quality-of-life population norms using the EQ-5D-5L: decomposition by sociodemographic data and health problems.
      Bulgaria,
      • Encheva M.
      • Djambazov S.
      • Vekov T.
      • Golicki D.
      EQ-5D-5L Bulgarian population norms.
      the United States,

      Jiang R, Janssen MFB, Pickard AS. US population norms for the EQ-5D-5L and comparison of norms from face-to-face and online samples. 2021;30(3):803-816.

      Slovenia,
      • Prevolnik Rupel V.
      • Ogorevc M.
      EQ-5D-5L Slovenian population norms.
      and Japan
      • Shiroiwa T.
      • Fukuda T.
      • Ikeda S.
      • et al.
      Japanese population norms for preference-based measures: EQ-5D-3L, EQ-5D-5L, and SF-6D.
      (some reports only provide EQ-5D-5L responses without index values). The population norms of the HUI3 have also been established in Canada.
      • Guertin J.R.
      • Feeny D.
      • Tarride J.É.
      Age- and sex-specific Canadian utility norms, based on the 2013-2014 Canadian Community Health Survey.
      ,
      • Guertin J.R.
      • Humphries B.
      • Feeny D.
      • Tarride J.
      Health Utilities Index Mark 3 scores for major chronic conditions: population norms for Canada based on the 2013 and 2014 Canadian Community Health Survey.
      One way to collect general population norm data is to include PBMs in national surveys for government statistics. Nevertheless, some countries, including Japan, do not permit this practice. Therefore, we conducted an original survey to collect data necessary for constructing the population norms of the EQ-5D-5L and HUI3 in Japan. This study had the 3 major objectives. First, we aimed to establish the Japanese population norms of the EQ-5D-5L and HUI3. Normative data of the HUI3 in the general Japanese population are still lacking. As for the EQ-5D-5L, our group has reported population norms in 2013,
      • Shiroiwa T.
      • Fukuda T.
      • Ikeda S.
      • et al.
      Japanese population norms for preference-based measures: EQ-5D-3L, EQ-5D-5L, and SF-6D.
      but these values need to be updated. The sample size was limited in the previous survey, and with this large sample survey we expect to improve the generalizability of the results. Second, we set out to simultaneously collect data on respondents’ self-reported diseases and symptoms to estimate the disutilities associated with various diseases and symptoms. These data can be useful for QALY calculations in economic evaluations. The final objective was to clarify the characteristics of the EQ-5D-5L and HUI3 by comparing their normative values. In fact, this is the first study to compare EQ-5D-5L norms with those of the HUI3.

      Methods

      Instruments

      In this cross-sectional study, we measured respondents’ health state with the EQ-5D-5L and HUI3. The EQ-5D-5L
      • Herdman M.
      • Gudex C.
      • Lloyd A.
      • et al.
      Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L).
      comprises 5 dimensions (“mobility,” “self-care,” “usual activities,” “pain・discomfort,” and “anxiety・depression”), with each dimension having 5 levels. The EQ-5D-5L was introduced to improve the sensitivity of the original EQ-5D (EQ-5D-3L), which has 5 dimensions with 3 levels. The Japanese value set for the EQ-5D-5L, which has been developed on the basis of the societal preferences of the general population,
      • Shiroiwa T.
      • Ikeda S.
      • Noto S.
      • et al.
      Comparison of value set based on DCE and/or TTO data: scoring for EQ-5D-5L health states in Japan.
      can be used to convert responses to index values.
      The HUI3 classification system
      • Feeny D.
      • Furlong W.
      • Torrance G.W.
      • et al.
      Multiattribute and single-attribute utility functions for the health utilities index mark 3 system.
      consists of 8 attributes (“vision,” “hearing,” “speech,” “ambulation,” “dexterity,” “emotion,” “cognition,” and “pain”), which are mapped from answers to the HUI questionnaire comprising 15 items. The HUI was developed on the basis of the multiattribute utility theory, and a multiplicative utility function is used to convert responses to scores. The Japanese HUI3 multiattribute utility function has recently been developed.
      • Noto S.
      • Shiroiwa T.
      • Kobayashi M.
      • Murata T.
      • Ikeda S.
      • Fukuda T.
      Development of a multiplicative, multi-attribute utility function and eight single-attribute utility functions for the health utilities index mark 3 in Japan.
      Japanese is the official language of Japan and is almost exclusively spoken in the country. Therefore, only Japanese versions of the EQ-5D-5L and HUI3 were used in this survey.
      We also included in our survey some questions from the National Livelihood Survey conducted annually by the Japanese Ministry of Health, Labour, and Welfare. These questions ask respondents whether they have any diseases for which they consult a doctor and whether they have any subjective symptoms. Those who answer “yes” must then select their main diseases and symptoms from among a list of 40 symptoms (eg, fever, sluggishness, sleeplessness) and diseases (eg, diabetes, obesity, hyperlipidemia).

      Sampling Method

      For the present survey, a total of 10 000 respondents (age ≥16 years) from 334 districts in Japan were selected by random sampling. The 334 districts (cho-me in Japanese) were selected using the following methods. First, the number of districts in 8 regions (Hokkaido, Tohoku, Kanto, Chubu, Kinki, Chugoku, Shikoku, and Kyushu) was determined in proportion to the population of each region. Then, for each region, the number of districts belonging to each stratum (i.e., prefecture [47 prefectures in Japan] × category of size of municipality [approximately 1700 municipalities in Japan]) was calculated on the basis of the population of the stratum. The surveyed districts were randomly selected according to the allocated number of districts in each stratum. Respondents were also randomly sampled from each selected district, stratified by sex and age. Therefore, for each district, the planned sample size was 1 (men aged 16–19 years and women aged 16–19 years) and 2 (men aged 20–29 years to 80–89 years and women aged 20–29 years to 80–89 years), totaling 30 respondents. Individuals in hospitals and nursing homes were excluded.
      The Basic Resident Register was used to select respondents living in each district in a random manner. In Japan, each municipality has its own Basic Resident Register data, which include information, such as name, sex, address, and date of birth. We obtained permission from each municipality to use these data in this study.
      A door-to-door survey was conducted between November 2019 and January 2020 (before the coronavirus disease outbreak in Japan). Investigators visited the registered addresses and distributed the questionnaire forms. If informed consent was obtained, they then collected the forms after a few days and checked for missing data. These visits continued until the planned number of responses was collected for each district. The investigators obtained informed consent from all respondents. This sampling method was the same as that used in our previous survey (conducted between January 2013 and March 2013),
      • Prevolnik Rupel V.
      • Ogorevc M.
      EQ-5D-5L Slovenian population norms.
      except that the sample size (1000 vs 10 000) and the number of districts (100 vs 334) were larger.

      Statistical Analysis

      Summary index values were calculated for background factors. Responses to the EQ-5D-5L and HUI3 were converted to index values using Japanese value sets.
      • Shiroiwa T.
      • Ikeda S.
      • Noto S.
      • et al.
      Comparison of value set based on DCE and/or TTO data: scoring for EQ-5D-5L health states in Japan.
      ,
      • Noto S.
      • Shiroiwa T.
      • Kobayashi M.
      • Murata T.
      • Ikeda S.
      • Fukuda T.
      Development of a multiplicative, multi-attribute utility function and eight single-attribute utility functions for the health utilities index mark 3 in Japan.
      The percentage of individuals who reported no problem or any problem was similarly computed. The interclass correlation coefficient (ICC) was used to compare the EQ-5D-5L and HUI3 values.
      A multiple linear model was used to examine relationships between the EQ-5D-5L and HUI3 values and sociodemographic factors. Age, sex, region of residence, household income, employment status, education level, and marital status were included in the model. Next, to estimate the size of the disutility associated with diseases and symptoms, a model that included age category, sex, and main disease or symptom was constructed to calculate estimated regression coefficients. Considering that the utility distribution was censored at 1, the Tobit model was also applied.
      • Sullivan P.W.
      • Lawrence W.F.
      • Ghushchyan V.
      A national catalog of preference-based scores for chronic conditions in the United States.
      ,
      • Pullenayegum E.M.
      • Tarride J.E.
      • Xie F.
      • Goeree R.
      • Gerstein H.C.
      • O’Reilly D.
      Analysis of health utility data when some subjects attain the upper bound of 1: are Tobit and CLAD models appropriate?.
      Spearman correlation coefficients were used to address the problem of multicollinearity between independent variables.
      All statistical analyses were performed using SAS 9.4, SAS Institute Inc., Cary, NC, USA. This study was approved by the ethics committee of the National Institute of Public Health.

      Results

      Response Rate and Demographic Factors

      We collected 10 183 responses from 334 districts. The overall response rate was 34.1%, and the response rates in younger age groups tended to be lower than those in older age groups (16–19 years, 33.4%; 20–29 years, 26.5%; 30–39 years, 31.8%; 40–49 years, 32.4%; 50–59 years, 33.2%; 60–69 years, 38.2%; 70–79 years, 40.4%; and 80–89 years, 39.6%). The region with the highest response rate was Kyushu (45.4%) and that with the lowest response rate was Kanto (29.3%). Generally, response rates in larger cities tended to be lower.
      Respondent demographic factors are shown in Table 1. The proportion of the national population in each region as of October 2019 as estimated by the Japanese government was 4.2% in Hokkaido, 6.9% in Tohoku, 34.4% in Kanto, 16.8% in Chubu, 17.7% in Kinki, 5.8% in Chugoku, 2.9% in Shikoku, and 11.3% in Kyushu. The sampled respondents were representative of the entire Japanese population.
      Table 1Demographic factors.
      CategoryN%
      Age, y
      16-197437.3
      20-29127912.6
      30-39126512.4
      40-49129212.7
      50-59130212.8
      60-69137013.5
      70-79143314.1
      80-89149914.7
      Sex
      Male505749.7
      Female512650.3
      Region of residence
      Hokkaido4214.1
      Tohoku7267.1
      Kanto346434.0
      Chubu169716.7
      Kinki185118.2
      Chugoku5835.7
      Shikoku2962.9
      Kyushu114511.2
      Household income (¥10 000)
      <1003414.8
      100-2006429.1
      200-400171324.2
      400-600173824.6
      600-1000174324.6
      1000-15006669.4
      1500-20001542.2
      >2000821.2
      Employment status
      Full-time worker351534.5
      Part-time worker137013.5
      Self-employed or manager8538.4
      Homemaker9369.2
      Retiree, etc.228622.5
      Other122312.0
      Education level
      Elementary or junior high school143514.1
      High school391738.5
      College176717.4
      University or graduate217621.4
      Current student and other8888.7
      Marital status
      Married604859.4
      Single278127.3
      Divorced/bereaved135413.3
      Because we sampled respondents principally by the same number among the age and sex categories (except for age 16–19 years), it was natural that the characteristics of demographic factors were inconsistent with those of the entire Japanese population. When comparing the data shown in Table 1 with those of the Japanese general population, the percentage of young individuals in our sample was larger than that in the entire Japanese population. According to the National Livelihood Survey by the Ministry of Health, Labour, and Welfare, the average household income in 2018 was ¥5.5 million ($52 000; $1 = ¥106, as of September 2020), with a median of ¥4.3 million ($41 000). The median household income among our respondents ranged from ¥4 million to ¥6 million. According to the 2019 Labour Force Survey, part-time workers accounted for 22.6% of all workers (23.8% in our sample), 19.9% of Japanese individuals graduated from a university or graduate school in 2010, and the government predicted that 26.6% and 17.8% of Japanese men and women, respectively, were unmarried at the age of 50 years (known as “lifetime unmarried people”) in 2020.
      Cabinet Office
      Annual report on the declining birthrate 2019.
      The characteristics of our study population did not differ largely from these figures.

      Population Norms of the EQ-5D-5L and HUI3

      The EQ-5D-5L and HUI3 summary index values, stratified by categories of age and sex, are shown in Table 2. Both values tended to be higher in younger individuals, and the percentage of respondents in full health had the same tendency. In the age group of 16 to 19 years, the mean EQ-5D-5L values were 0.978 (male) and 0.967 (female), and the mean HUI3 values were 0.892 (male) and 0.888 (female). The higher the age, the lower the values: in the age group of 80 to 89 years, the mean EQ-5D-5L values were 0.821 (male) and 0.774 (female), and the mean HUI3 values were 0.714 (male) and 0.679 (female). EuroQol–visual analog scale scores and responses to each item are presented in the Appendix in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.03.010.
      Table 2Population norms of EQ-5D-5L and HUI3.
      PBMAge, ySexNMeanSDMedian% of full health
      EQ-5D-5L16-19Male3620.9780.0591.00085.9
      Female3810.9670.0731.00080.6
      20-29Male6370.9510.0911.00071.1
      Female6420.9530.0881.00072.1
      30-39Male6330.9530.0911.00071.6
      Female6320.9440.1041.00069.2
      40-49Male6400.9470.0941.00068.0
      Female6520.9450.0901.00065.6
      50-59Male6150.9310.1091.00060.2
      Female6870.9250.1061.00056.0
      60-69Male6820.9300.1201.00060.9
      Female6880.9270.1041.00055.7
      70-79Male7230.8890.1540.89546.8
      Female7100.8760.1570.89542.3
      80-89Male7650.8210.2030.88936.2
      Female7340.7740.2200.83126.8
      HUI316-19Male3560.8920.1510.94027.9
      Female3690.8880.1570.94023.6
      20-29Male6190.8550.1870.91222.6
      Female6320.8730.1580.93019.2
      30-39Male6170.8560.1750.91217.9
      Female6170.8750.1570.93017.6
      40-49Male6290.8710.1580.93019.7
      Female6400.8840.1470.93119.9
      50-59Male5940.8440.1660.9029.6
      Female6630.8630.1530.91210.3
      60-69Male6630.8370.1850.9028.1
      Female6690.8700.1430.91210.0
      70-79Male6640.8070.1990.8756.1
      Female6690.8180.1940.8758.5
      80-89Male6740.7140.2540.8134.3
      Female6450.6790.2710.7385.6
      HUI3 indicates Health Utilities Index Mark 3; PBM, preference-based measure.
      The EQ-5D-5L values were higher than HUI3 values in all age and sex categories. The percentage of respondents in full health was also higher when measured with the EQ-5D-5L. For example, 85.9% (male) and 80.6% (female) of the respondents aged 16 to 19 years reported a full health state on the EQ-5D-5L compared with 27.9% (male) and 23.6% (female) on the HUI3. When the linear regression model without an intercept was applied using the EQ-5D-5L and HUI3 values of the same respondent, the following estimated equation was obtained: “EQ-5D-5L” = 1.07 × “HUI3.” The HUI3 values were lower than the EQ-5D-5L values in 83.2% of our respondents. The ICC between the HUI3 and EQ-5D-5L values was 0.53.
      As shown in Figure 1, the EQ-5D-5L values were higher than the HUI3 values in all age and sex categories. The EQ-5D-5L values obtained in the present survey were comparable to those obtained in our previous survey in 2013. The largest difference between the present and previous values was observed in the age group of 60 to 69 years; nevertheless, the difference was only 0.024, which is smaller than the minimal clinically important difference for the EQ-5D-5L.
      • Henry E.B.
      • Barry L.E.
      • Hobbins A.P.
      • McClure N.S.
      • O’Neill C.
      Estimation of an instrument-defined minimally important difference in EQ-5D-5L index scores based on scoring algorithms derived using the EQ-VT version 2 valuation protocols.
      Figure thumbnail gr1
      Figure 1Comparison of population norms between the EQ-5D-5L and HUI3.
      HUI3 indicates Health Utilities Index Mark 3.

      Relationships Between EQ-5D-5L and HUI3 Values and Demographic Factors

      Table 3 shows the relationships between the EQ-5D-5L and HUI3 values and demographic factors. Model 1 included only age category, sex, household income, and education level, all of which had an influence on the EQ-5D-5L values in the previous survey. Model 2 included all demographic factors. Because the absolute values of Spearman correlation coefficients among demographic factors were low (<0.3), the influence of multicollinearity was not considered in this study. The results of analyses using both models revealed age and sex to be statistically significant factors. The effect size of sex was small, but the direction of the effect was different between the 2 instruments. Household income was also a significant factor for both instruments. Individuals with a higher education level tended to have a better health state. Region of residence did not have a significant influence on health state, and no difference by area was observed. The results obtained using the Tobit model are shown in the Appendix in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.03.010.
      Table 3Relationship between values by both PBM and demographic factor.
      VariableModel 1Model 2
      EQ-5D-5LHUI3EQ-5D-5LHUI3
      CoefficientP-valueCoefficientP-valueCoefficientP-valueCoefficientP-value
      Intercept0.906<.0010.709<.0010.948<.0010.791<.001
      Age, y
      16-19
      20-29−0.027.023−0.007.703−0.036.003−0.021.227
      30-39−0.033.010−0.006.728−0.050<.001−0.043.0207
      40-49−0.039.002−0.002.899−0.057<.001−0.042.0242
      50-59−0.056<.001−0.029.118−0.073<.001−0.069<.001
      60-69−0.047<.001−0.011.554−0.056<.001−0.044.020
      70-79−0.080<.001−0.029.118−0.072<.001−0.046.019
      80-89−0.148<.001−0.108<.001−0.123<.001−0.108<.001
      Sex
      Male
      Female−0.008.0130.023<.001−0.011.0010.018.001
      Household income (¥10 000)
      <100
      100-2000.017.04170.058<.0010.012.1410.050<.001
      200-4000.039<.0010.084<.0010.027<.0010.065<.001
      400-6000.050<.0010.099<.0010.032<.0010.071<.001
      600-10000.055<.0010.117<.0010.034<.0010.085<.001
      1000-15000.057<.0010.136<.0010.036<.0010.104<.001
      1500-20000.056<.0010.135<.0010.034.0090.101<.001
      >20000.071<.0010.136<.0010.047.0030.103<.001
      Education level
      Elementary or junior high
      High school0.026<.0010.041.04120.022<.0010.037<.001
      College0.030<.0010.048.04790.027<.0010.045<.001
      University or graduate0.036<.0010.065.06530.033<.0010.062<.001
      Other0.012.2970.059.05850.037.0050.091<.001
      Region of residence
      Hokkaido
      Tohoku0.008.349−0.001.915
      Kanto0.003.7340.003.757
      Chubu0.005.505−0.001.916
      Kinki0.002.7730.004.745
      Chugoku−0.007.465−0.014.301
      Shikoku0.004.7360.003.864
      Kyushu0.014.0930.023.047
      Employment status
      Full-time worker
      Part-time worker0.006.2540.006.384
      Self-employed or manager−0.002.763−0.003.723
      Homemaker−0.008.223−0.010.291
      Retiree−0.064<.001−0.070<.001
      Other−0.033.002−0.036.004
      Marital status
      Married
      Single−0.016.001−0.050<.001
      Divorced/bereaved−0.017.001−0.022.002
      R20.1430.1680.1090.132
      Coefficient >0.05 and significant
      Coefficient <0.05 and significant
      HUI3 indicates Health Utilities Index Mark 3; PBM, preference-based measure.

      Disutility Associated with Diseases and Symptoms

      Table 4 shows the relationships between the EQ-5D-5L and HUI3 values and the respondents’ self-reported main diseases. For the EQ-5D-5L, Parkinson disease, dementia, and stroke were associated with the largest disutility (>0.2), and depression was associated with a disutility of approximately 0.18. For the HUI3, Parkinson disease and dementia were associated with the largest disutility (approximately 0.4). Respondents with stroke and depression had estimated disutilities of >0.2. The pattern of disutility was similar between the EQ-5D-5L and HUI3, but differences in values were considerable for some diseases (eg, dementia and ear disease). Some diseases did not cause a significant decrease in the EQ-5D-5L and HUI3 values (eg, thyroid disease, hyperlipidemia, hypertension, asthma, tooth diseases, and gout).
      Table 4Disutility associated with disease.
      VariableNEQ-5D-5LHUI3
      CoefficientP-valueCoefficientP-value
      Intercept0.989<.0010.897<.001
      Age, y
      16-19743
      20-291279−0.018.001−0.023.006
      30-391265−0.019.001−0.018.025
      40-491292−0.018.001−0.004.670
      50-591302−0.028<.001−0.021.013
      60-691370−0.021<.001−0.013.121
      70-791433−0.057<.001−0.042<.001
      80-891499−0.129<.001−0.145<.001
      Sex
      Male5057
      Female5126−0.011<.0010.011.003
      Main disease
      No disease5693
      Diabetes341−0.046<.001−0.055<.001
      Obesity8−0.034.4170.019.780
      Hyperlipidemia135−0.002.816−0.004.782
      Thyroid disease45−0.006.722−0.046.097
      Depression140−0.184<.001−0.282<.001
      Dementia27−0.222<.001−0.426<.001
      Parkinson disease15−0.352<.001−0.421<.001
      Other neuropathic diseases49−0.211<.001−0.232<.001
      Eye diseases158−0.049<.001−0.101<.001
      Ear diseases33−0.033.114−0.108.001
      Hypertension838−0.005.275−0.006.388
      Stroke92−0.265<.001−0.293<.001
      Angina or myocardial infarction131−0.073<.001−0.081<.001
      Other cardiovascular diseases142−0.054<.001−0.068<.001
      Cold18−0.083.004−0.040.368
      Allergic rhinitis68−0.027.069−0.049.029
      COPD11−0.114.002−0.075.161
      Asthma85−0.025.0603−0.035.086
      Other respiratory diseases72−0.121<.001−0.116<.001
      Gastroduodenal diseases90−0.053<.001−0.040.040
      Liver or gallbladder diseases53−0.067<.001−0.072.004
      Other gastrointestinal diseases87−0.087<.001−0.085<.001
      Tooth diseases214−0.013.129−0.021.091
      Atopic dermatitis78−0.031.025−0.049.014
      Other dermatologic diseases91−0.043.001−0.037.050
      Gout37−0.012.556−0.022.460
      Rheumatoid arthritis63−0.103<.001−0.096<.001
      Arthrosis194−0.157<.001−0.101<.001
      Shoulder pain108−0.051<.001−0.043.014
      Back pain323−0.117<.001−0.100<.001
      Osteoporosis58−0.034.034−0.054.025
      Kidney diseases83−0.079<.001−0.122<.001
      Benign prostatic hyperplasia89−0.029.024−0.054.008
      Menopausal problem7−0.048.286−0.007.925
      Bone fracture45−0.124<.001−0.125<.001
      Injury without bone fracture and burn injury38−0.134<.001−0.075.009
      Anemia or blood diseases24−0.079.001−0.121.001
      Malignant neoplasm101−0.084<.001−0.103<.001
      Pregnant or postpartum disorders13−0.058.0820.019.695
      Infertility8−0.077.071−0.118.059
      Other273−0.080<.001−0.101<.001
      Unknown5−0.093.082−0.053.550
      R20.2760.180
      Coefficient >0.1 and significant
      0.05< Coefficient <0.1 and significant
      Coefficient <0.05 and significant
      COPD indicates chronic obstructive pulmonary disease; HUI3, Health Utilities Index Mark 3.
      Table 5 shows the relationships between the EQ-5D-5L and HUI3 values and the respondents’ self-reported main symptoms. Almost all symptoms listed in Table 5 were associated with disutilities significantly larger than 0, with effect sizes exceeding 0.05. Symptoms that had the most significant influence on disutilities included “limb motion problems” (0.300), “chest pain” (0.212), and “acraturesis” (0.211) for the EQ-5D-5L and “limb motion problems” (0.328), “forgetfulness” (0.301), and “hearing impairment” (0.268) for the HUI3.
      Table 5Disutility associated with symptoms.
      VariableNEQ-5D-5LHUI3
      CoefficientP-valueCoefficientP-value
      Intercept0.996<.0010.908<.001
      Age, y
      16-19743
      20-291279−0.015.005−0.019.020
      30-391265−0.011.040−0.011.183
      40-491292−0.010.0640.005.495
      50-591302−0.023<.001−0.015.056
      60-691370−0.022<.001−0.016.040
      70-791433−0.059<.001−0.049<.001
      80-891499−0.130<.001−0.147<.001
      Sex
      Male5057
      Female5126−0.009<.0010.012<.001
      Main symptom
      No symptoms6310
      Fever31−0.078.002−0.122<.001
      Sluggishness109−0.155<.001−0.192<.001
      Sleeplessness74−0.165<.001−0.206<.001
      Irritability58−0.140<.001−0.259<.001
      Forgetfulness42−0.147<.001−0.301<.001
      Headache147−0.093<.001−0.125<.001
      Dizziness53−0.098<.001−0.075.003
      Blurred vision55−0.056.004−0.116<.001
      Visual impairment59−0.113<.001−0.238<.001
      Buzzing73−0.052.001−0.085<.001
      Hearing impairment45−0.093<.001−0.268<.001
      Palpitation50−0.094<.001−0.127<.001
      Breathlessness53−0.159<.001−0.160<.001
      Chest pain33−0.212<.001−0.223<.001
      Cough and/or sputum162−0.066<.001−0.070<.001
      Nasal congestion or mucus139−0.032.001−0.065<.001
      Wheezing27−0.101<.001−0.142<.001
      Indigestion or heartburn84−0.071<.001−0.102<.001
      Diarrhea55−0.066<.001−0.118<.001
      Constipation89−0.071<.001−0.077<.001
      Lack of appetite10−0.136<.001−0.087.129
      Abdominal pain or stomachache52−0.110<.0001−0.143<.001
      Pain due to hemorrhoids20−0.060.0120−0.063.104
      Dental pain52−0.055<.001−0.137<.001
      Swelling or bleeding from the gums37−0.038.049−0.067.025
      Trouble biting21−0.081.001−0.106.005
      Rash (eg, urticaria, blotch)52−0.052.001−0.065.008
      Itching (eg, eczema, tinea pedis)91−0.067<.001−0.087<.001
      Stiff shoulders388−0.050<.001−0.058<.001
      Back pain637−0.112<.001−0.101<.001
      Arthritic pain291−0.154<.001−0.130<.001
      Limb motion problems104−0.300<.001−0.328<.001
      Numbness of limbs109−0.185<.001−0.174<.001
      Coldness of limbs53−0.093<.001−0.093<.001
      Swelling or heaviness of legs36−0.171<.001−0.144<.001
      Difficulty in urinating33−0.096<.001−0.177<.001
      Frequent urination124−0.055<.001−0.104<.001
      Acraturesis30−0.211<.001−0.154<.001
      Menstrual disorder39−0.049.008−0.118<.001
      Bone fracture, sprain, or abarticulation71−0.120<.001−0.161<.001
      Incisura or burn20−0.083.001−0.141<.001
      Other165−0.134<.001−0.096<.001
      R20.3280.215
      Coefficient >0.1 and significant
      0.05< Coefficient <0.1 and significant
      Coefficient <0.05 and significant
      HUI3 indicates Health Utilities Index Mark 3.

      Discussion

      We conducted a large random sample survey to establish the Japanese population norms of the EQ-5D-5L and HUI3. We also constructed a catalog of disutility values by disease and symptom for use in QALY calculations. For example, the EQ-5D-5L value of women aged 50 to 59 years with depression can be calculated thus: 0.989 (intercept) – 0.028 (age) – 0.011 (sex) – 0.184 (disease) = 0.766. The updated values were comparable to and consistent with those of our previous survey in 2013, suggesting that our survey method is reproducible and reliable. The established population norms are based on responses from the general population living at home (those who were hospitalized or living in nursing homes were excluded) and are thus considered “community-based” values that do not reflect acute or severe health states. In addition, to estimate the size of the disutility, we only used the main disease or symptom reported by the respondents. Interactions between multiple diseases were not considered.
      In the present study, the ICC between the EQ-5D-5L and HUI3 values was 0.53. In our previous survey, the ICCs were 0.802 between the EQ-5D-3L and EQ-5D-5L, 0.249 between the EQ-5D-3L and SF-6D, and 0.234 between the EQ-5D-5L and SF-6D.
      • Prevolnik Rupel V.
      • Ogorevc M.
      EQ-5D-5L Slovenian population norms.
      The ICC between the EQ-5D-5L and HUI3 was larger than that between the EQ-5D-5L and SF-6D, suggesting that the EQ-5D-5L index values are more comparable to those of HUI3 than to those of SF-6D. Nevertheless, it should be noted that the EQ-5D-5L values were generally higher than the HUI3 values (an average of approximately 7% higher).
      Although the EQ-5D-5L dimensions (“mobility,” “self-care,” “usual activities,” “pain・discomfort,” and “anxiety・depression”) and HUI3 attributes (“ambulation,” “dexterity,” “emotion,” and “pain”) share similar items, the HUI3 is more sensitive to the influence of dementia and ear/eye disease (which includes the related items “vision,” “hearing,” and “cognition”). In fact, the symptoms “forgetfulness” (0.301) and “hearing impairment” (0.268) have large disutility coefficients. Only the HUI3 has the “speech” item (speech-related diseases and symptoms were excluded in the present survey). Owing to these differences (forgetfulness and hearing impairment), either the EQ-5D-5L and HUI3 are sensitive to certain diseases and symptoms. The mixed use of measurements using both instruments may thus result in an arbitrary analysis, which could be misleading with regard to favorable analysis conclusions.
      With regard to the relationships of the 2 instruments with demographic factors, education level and household income had a significant influence. For the EQ-5D-5L, similar trends have been reported in Spain,
      • Garcia-Gordillo M.A.
      • Adsuar J.C.
      • Olivares P.R.
      Normative values of EQ-5D-5L: in a Spanish representative population sample from Spanish Health Survey, 2011.
      South Australia,
      • McCaffrey N.
      • Kaambwa B.
      • Currow D.C.
      • Ratcliffe J.
      Health-related quality of life measured using the EQ-5D-5L: South Australian population norms.
      and Quebec.
      • Poder T.G.
      • Carrier N.
      • Kouakou C.R.C.
      Quebec health-related quality-of-life population norms using the EQ-5D-5L: decomposition by sociodemographic data and health problems.
      In addition, the EQ-5D-5L index values are slightly higher in men than in women, regardless of country. The effect size of sex is approximately 0.01 in Japan, and similar values have been reported in other countries (0.019 in Trinidad and Tobago,
      • Bailey H.
      • Janssen M.F.
      • La Foucade A.
      • Kind P.
      EQ-5D-5L population norms and health inequalities for Trinidad and Tobago.
      0.005 in Hong Kong,
      • Wong E.L.
      • Cheung A.W.
      • Wong A.Y.
      • Xu R.H.
      • Ramos-Goñi J.M.
      • Rivero-Arias O.
      Normative profile of health-related quality of life for Hong Kong general population using preference-based instrument EQ-5D-5L.
      0.01 in Quebec,
      • Poder T.G.
      • Carrier N.
      • Kouakou C.R.C.
      Quebec health-related quality-of-life population norms using the EQ-5D-5L: decomposition by sociodemographic data and health problems.
      0.008 in Bulgaria,
      • Encheva M.
      • Djambazov S.
      • Vekov T.
      • Golicki D.
      EQ-5D-5L Bulgarian population norms.
      and 0.005 in Slovenia
      • Prevolnik Rupel V.
      • Ogorevc M.
      EQ-5D-5L Slovenian population norms.
      ), except in Spain (slightly higher at 0.064
      • Garcia-Gordillo M.A.
      • Adsuar J.C.
      • Olivares P.R.
      Normative values of EQ-5D-5L: in a Spanish representative population sample from Spanish Health Survey, 2011.
      ). Although this sex-based difference is commonly observed in many countries, the underlying reason is unclear.
      This study has a limitation. Although our survey was based on a rigid random sampling method, the response rate was lower than that of the previous survey. The lower response rate may be due to changes in Japan’s social environment in the past decade. Japanese individuals now pay more attention to security and personal information than before because their importance has been stressed by the government and police from the perspective of preventing crime. Thus, people are more reluctant to participate in surveys, especially when a survey involves unfamiliar researchers visiting their homes and asking questions regarding their personal information. Indeed, some respondents suspected our survey to be fraudulent, and in 1 case, the police were called. In addition, younger individuals have increasingly variable lifestyles today, and it is difficult to hand out questionnaires in person (because researchers cannot be certain when they are home). Under these circumstances, the nonresponse rate for national surveys, to which Japanese citizens are required by law to respond, has increased greatly from 1.7% in 2000 to 13.1% in 2015. This issue represents a common problem in the current Japanese government statistics. Nonetheless, we believe that our sampling method resulted in no serious bias in terms of background factors. In a future study, nevertheless, the sampling method must be reevaluated to ensure a sufficiently high response rate for updating normative values.

      Conclusion

      We established the Japanese population norms of the EQ-5D-5L and HUI3 on the basis of a large random sample survey. In addition, disutility by disease and symptom was also estimated. Our findings can be used in healthcare decision making and may contribute to a more reliable analysis of economic evaluations and clarification of the characteristics of the EQ-5D-5L and HUI3, which will help in selecting suitable PBMs and offering appropriate interpretations.

      Article and Author Information

      Author Contributions: Concept and design: Shiroiwa, Noto, Fukuda
      Acquisition of data: Noto
      Analysis and interpretation of data: Shiroiwa, Noto, Fukuda
      Drafting of the manuscript: Shiroiwa
      Critical revision of the paper for important intellectual content: Noto, Fukuda
      Statistical analysis: Shiroiwa
      Supervision: Fukuda
      Conflict of Interest Disclosures: The authors reported no conflicts of interest.
      Funding/Support: This work was supported by the research budget of the National Institute of Public Health.
      Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

      Supplemental Materials

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