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. 2020 Jun 18;15(6):e0234850. doi: 10.1371/journal.pone.0234850

Evaluation of health-related quality of life using EQ-5D in China during the COVID-19 pandemic

Weiwei Ping 1,*, Jianzhong Zheng 1, Xiaohong Niu 2, Chongzheng Guo 1, Jinfang Zhang 2, Hui Yang 1, Yan Shi 1
Editor: Amir H Pakpour3
PMCID: PMC7302485  PMID: 32555642

Abstract

Objective

Since December 2019, an increasing number of cases of the 2019 novel coronavirus disease (COVID-19) infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been identified in Wuhan, Hubei Province, China. Now, more cases have been reported in 200 other countries and regions. The pandemic disease not only affects physical health who suffered it, but also affects the mental health of the general population. This study aims to know about the impact of the COVID-19 epidemic on the health-related quality of life (HRQOL) of living using EQ-5D in general population in China.

Methods

An online-based survey was developed and participants were recruited via social media. The questionnaires included demographic and socioeconomic data, health status, the condition epidemic situation and EQ-5D scale. The relationships of all factors and the scores of EQ-5D were analyzed. Logistic regression model were used to the five health dimensions.

Results

The respondents obtained a mean EQ-5D index score of 0.949 and a mean VAS score of 85.52.The most frequently reported problem were pain/discomfort (19.0%) and anxiety/depression (17.6%). Logistic regression models showed that the risk of pain/discomfort and anxiety/depression among people with aging, with chronic disease, lower income, epidemic effects, worry about get COVID-19 raised significantly.

Conclusion

The article provides important evidence on HRQOL during the COVID-19 pandemic. The risk of pain/discomfort and anxiety/depression in general population in China raised significantly with aging, with chronic disease, lower income, epidemic effects, worried about get COVID-19 during the COVID-19 pandemic. The results from each categorical data can be used for future healthcare measures among general population.

Introduction

Since December 2019, an increasing number of cases of the 2019 novel coronavirus disease (COVID-19) infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been identified in Wuhan, Hubei Province, China. Until April 9, 2020, the rapid spread of the virus had caused 83249 cases and 3344 deaths in China [1,2].More cases have been reported in 200 other countries and regions, including the USA, Italy, Spain, France, Germany and so on [3]. As a consequence, since the beginning of the COVID-19 epidemic in China, to minimize the risk of infection, the Chinese government, health agency and medias recommended people decreased go out and travel, wear a face mask at outside and wash hands frequently after outside by mobile phone short note, TV, We-chat and community education [4].The Chinese government began to provide social distancing advice minimizing the risk of the virus. It is necessary to know about the impact of the COVID-19 epidemic on the health-related quality of life (HRQOL) of living in China.

In recent years, health-related quality of life (HRQOL) has pay worldwide attention, and several multidimensional health status classifications have been increasingly used to describe and evaluate HRQOL in China [5,6]. Healthy China 2030 that is an outline for “the Healthy China 2030” initiative, has been announced in 2016, aims to promote life expectancy and improve HRQOL in all Chinese people [7].

Some scales have been widely developed to measure HRQOL. Generic instrument of HRQOL, for example: the World Health Organization Quality of Life (WHOQOL—BREF) [8], can be used to compare HRQOL across various diseases/conditions and can be use for different populations to assess the impact of various interventions on QOL [9]. Condition-specific instrument of HRQOL (e.g.EORTCQLQ2-C30), can only be used to compare HRQOL specific diseases/conditions for specific populations (e.g. cancer or diabetes) [10]. On the other hand, the quality of life should be measured on a utility scale on which 1.0 corresponds to full health and 0.0 corresponds to death. Therefore, HRQOL scales can be classified into psychometric instrument (e.g. SF-36) and utility instrument (e.g. the Health Utilities Index) [11,12]. The EQ-5D is a simple but widely used instrument based on characteristic that can be used to measure generic population based on utility [13]. Internationally, studies using the EuroQol (EQ-5D) survey have demonstrated lower scores in older individuals compared with younger [14,15], lower scores in women than in men, lower scores in individuals of lower socioeconomic status compared with of higher socioeconomic status [16,17].

EQ-5D has been translated into Chinese, its validity and reliability validity were evaluated by [18, 19], and the EQ-5D instrument is a valid measure for Chinese HRQOL. Its population value set has been development using a time trade-off (TTO) approach in 2014 [13] and VAS approach in 2015 [20].Recently, some studies have been used the instrument to measure HRQOL in China [21].We conducted questionnaires using EQ-5D to evaluate Health-Related Quality of Life during the COVID-19 pandemic in Changzhi city, Shanxi province, China.

Methods

Respondents

Changzhi city is a small city located in east-south of Shanxi province in north China. Changzhi city covers an area of 13864km2, with a population of 3.468 million people. During the period of COVID-19 epidemic, there were 8 definite case and 42 suspected cases in the Changzhi city. The survey was conducted from March 2 to March 10 after receiving ethical approval from the Ethics Committee of Changzhi Medical College. An online-based survey was developed and participants were recruited via social media (e.g. We-chat). A statement about informed consent was included with the questionnaire, and returning the questionnaire was considered to constitute provision of informed consent. At March 10, 1500 questionnaire were returned by respondents. Of these, 215 respondents were not residents that lived in Changzhi city according to the location.146 were deemed unusable due to using time is less than 100 seconds. The left 1139 were deemed usable.

Survey questionnaire

The survey questionnaires included the following information.

  1. The demographic and socioeconomic data of the respondents. The demographic variables included age and sex, we categorized age into six groups (<18, 18–29, 30–39, 40–49, 50–59, and 60+ years).The socioeconomic variables included marital status, employment status, educational level and income level in the local. The categorization of these variables was showed in Table 1.

  2. Health status: The health status variables included chronic condition and Health-related behaviors. A chronic condition was defined as a chronic condition by a doctor, for whom either the symptoms persisted or relevant medical treatment continued over the past six months. Chronic conditions covered 12 major medical chronic conditions: hypertension, heart disease (including coronary heart disease and other heart condition), stroke, hyperlipidemia, liver disease, diabetes mellitus and other endocrine disease, respiratory disease, urinary and reproductive disease, musculoskeletal disease, gastrointestinal disease, dermal diseases, and dental caries or other dental diseases. Respondents answered “yes” if they had one or more chronic condition. Respondents were classified by the number of chronic disease as no, with one chronic disease, with two chronic diseases, with three or more chronic disease. Health-related behaviors included regular exercise(moderate or vigorous exercise for >30min,≧3times/week), sufficient sleep(7-8h/day).

  3. The condition epidemic situation: worry about got COVID-19 (respondents were classified by 5 degree); whether influenced by pandemic at the aspects of social activity, usual activity, sleeping, diet, and exercise. Respondents answered “yes” if they had one or more aspects effects. Respondents were classified by the number of chronic disease as no and yes regardless of affected on one or more aspects.

  4. EQ-5D scale: the Chinese version of EQ-5D was included in the questionnaire. It is a self-completed instrument for describing and valuing quality of health states defined by the EQ-5D index. It measures five dimensions of health: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression, as well as overall health rated on a VAS. Each dimension has three levels, corresponding to “no problem”, “some problem”, and “extreme problem”, allowing for 35 (i.e., 243) possible health combinations. The VAS scores ranged from 0(worst health) to (best health). HRQOL results measured by the EQ-5D were converted to an index score using the China value set which ranges from -0.149 to 1.00 [13]. A negative value represents a health status worse than being dead, 0 represents being dead and 1 represents the state of full health.

Table 1. Characteristics of respondents and EQ-5D index and visual analogue (VAS) scores.

EQ-5D-3L Index EQ-5D-3L VAS
N (%) mean(SD) p value mean(SD) p value
Total 1139 100 0.949(0.102) 85.52(19.373)
Sex
Male 460 40.4 0.947 (0.108) 0.482 86.89(18.459) 0.050
Female 679 59.6 0.951 (0.098) 84.60(19.930)
Age (year)
<18 36 3.2 0.963 (0.074) 0.001 95.94(5.560) 0.047
18–29 271 23.8 0.975 (0.063) 85.12(23.034)
30–39 322 28.3 0.963 (0.090) 85.45(19.622)
40–49 276 24.2 0.953 (0.084) 85.20(18.377)
50–59 158 13.9 0.898 (0.150) 85.35(14.899)
60+ 76 6.7 0.889 (0.141) 83.84(19.070)
Marital status
Married 869 76.2 0.967 (0.107) 0.130 85.63(17.848) 0.005
Unmarried 233 20.5 0.962 (0.081) 85.64(22.180)
Divorced/widowed 38 3.3 0.944 (0.087) 75.47(29.784)
Employment status
Employed 693 60.8 0.957 (0.088) 0.001 84.48(20.527) 0.018
Retired 106 9.3 0.886 (0.152) 84.38(17.501)
Unemployed 340 29.9 0.954 (0.102) 88.01(19.373)
Chronic disease condition
No chronic disease 671 58.9 0.979 (0.053) 0.001 89.44(17.184) 0.001
With one chronic disease 248 21.8 0.936 (0.112) 81.44(19.802)
With two chronic disease 112 9.8 0.916 (0.101) 81.57(20.683)
With three or more chronic disease 108 9.5 0.828 (0.175) 74.63(22.864)
Education level
Primary school and Below 203 17.8 0.948(0.085) 0.803 84.73(20.466) 0.752
Junior middle school 256 22.5 0.946(0.109) 85.56(21.021)
Senior middle school 346 30.4 0.954(0.098) 86.39(18.326)
University and above 334 29.3 0.944(0.112) 84.77(18.802)
Family income(in the local)
Low 58 5.1 0.945(0.133) 0.148 81.10(11.274) 0.006
Lower 614 53.9 0.951(0.099) 86.67(18.209)
Middle 318 27.9 0.952(0.106) 83.66(21.571)
Higher 90 7.9 0.925(0.091) 81.20(21.389)
High 59 5.2 0.964(0.072) 84.75(19.907)
Worry about got COVID-19
Very high 32 2.8 0.868(0.220) 0.001 82.19(19.144) 0.001
High 102 9.0 0.918(0.124) 73.63(25.020)
Low 484 42.5 0.948(0.089) 85.66(17.074)
Very low 521 45.7 0.962(0.095) 87.93(19.311)
Epidemic effects
Yes 660 57.9 0.936(0.116) 0.001 83.21(21.439) 0.001
No 479 42.1 0.968(0.074) 88.71(15.567)
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SD: standard deviation. p-value: p values come from t test or ANOVA or Mann-Whitney U test or Kruskall-Wallis test.

Data analysis

Statistical analyses were carried out using the Statistical Product and Service Solutions (SPSS) sofeware23.0. Mean and standard deviations were calculated for continuous variables, frequencies and percentages for categorical variables. The relationships of all factors and the scores of EQ-5D were analyzed with t-test, analysis of variance (ANOVA), and nonparametric statistics (Mann-Whitney U test or Kruskall-Wallis test). The percentage of people in each dimension was calculated and x2-test were performed to examine the statistical significance of the difference between groups in the percentage of reported problems. Fisher`s exact test was used when exact theory frequency less than 1. Logistic regression model were used to the five health dimensions as dependent variables (0 = no problem, 1 = some/extreme problem). Statistical significance was set at 0.05 using two-side tests.

The permission of the study was obtained from the Ethics Committee of Changzhi Medical College on March, 2020.

Results

Characteristics of respondents

The respondents had a mean age of 38.3 years (SD: 12.5 years; range12-78years). 40.4% were men and all of them were Han ethnicity. The mean years of formal education were 11.5 years. 60.8%were employed by full time, 41.1% respondents reported diagnosed with one or more chronic diseases over the past six months. 11.8% worried got the COVID-19, and 57.9% respondents had been affected by epidemic disease on social activity, usual activity, sleeping, diet, and exercise on one or more field (Table 1).

EQ-5D results

The respondents obtained a mean EQ-5D index score of 0.949 (SD: 0.102) and a mean VAS score of 85.52(19.37). Highest possible EQ-5D index score reported at 71.9% respondents, highest possible VAS score at 24.2%. Older age (p<0.001), Unemployed (p<0.001), with chronic disease (p<0.001), low family income (p<0.05), worry about got COVID-19 (p<0.001), and have epidemic effects (p<0.001) were associated with lower EQ-5D index score. The VAS score obtained consistent results as those of EQ-5D index score.

The most frequently reported problem were pain/discomfort (19.0%), followed by anxiety/depression (17.6%), self-care (1.1%) was the least frequently reported problem. Men were more likely to report problem in mobility (6.1%) than women (2.4%). Compared with less 18 years respondents, other age respondents were more likely to report problem in five dimension of EQ-5D, and above 60 years group respondents reported the most problem in mobility (13.2%), usual activities (7.9%), pain/discomfort (52.6%), and anxiety/depression (23.7%).Unemployed respondents reported the most problem in self-care (1.9%), usual activities(11.3%), pain/discomfort(49.1%), and anxiety/depression (26.4%) than employed respondents. Compared with no chronic disease respondents, with chronic disease (one or more) respondents were more likely to report problem in five dimension of EQ-5D.Those who very worry about got COVID-19 were more likely to report problem in mobility (12.5%), self-care (6.3%), usual activities(18.7%), pain/discomfort (43.7%), and anxiety/depression (37.5%) than those who no worry. Those who reported had been affected by epidemic reported the more problem in mobility (5.2%), usual activities (3.0%), and anxiety/depression (22.4%) than those who reported had not been effected by epidemic (Table 2).

Table 2. Percentage of reported any problem in 5 dimensions of EQ-5D.

Mobility Self-care Usual Activities Pain/discomfort Anxiety/depression
No Some or extreme p value No Some or extreme p value No Some or extreme p value No Some or extreme p value No%/N Some or extreme p value
Total 96.1 3.9 98.9 1.1 98.1 1.9 81.0 19.0 82.4 17.6
Sex
Male 93.9 6.1 0.001 98.3 1.7 0.062 97.4 2.6 0.172 81.7 18.3 0.685 83.0 17.0 0.660
Female 97.6 2.4 99.4 0.6 98.5 1.5 80.6 19.4 82.0 18.0
Age(yea)
<18 100.0 0.0 0.001 94.4 5.6 0.001△ 100.0 0.0 0.001△ 100 0.0 0.001 77.8 22.2 0.008
18–29 98.5 1.5 99.3 0.7 100.0 0.0 95.6 4.4 87.5 12.5
30–39 98.1 1.9 99.4 0.6 100.0 0.0 87.0 13.0 85.1 14.9
40–49 97.1 2.9 100.0 0.0 98.6 1.4 79.7 20.3 81.2 18.8
50–59 89.9 10.1 96.2 3.8 92.4 7.6 58.2 41.8 74.7 25.3
60+ 86.8 13.2 100.0 0.0 92.1 7.9 47.4 52.6 76.3 23.7
Marital status
Unmarried 97.4 2.6 0.203 98.3 1.7 0.530 100.0 0.0 0.020 92.3 7.7 0.001 83.7 16.3 0.065
Married 95.6 4.4 99.1 0.9 97.5 2.5 77.9 22.1 82.7 17.3
Divorced/widowed 100.0 0.0 100.0 0.0 100.0 0.0 84.2 15.8 68.4 31.6
Employment status
Employed 97.1 2.9 0.001 99.4 0.6 0.145 99.1 0.9 0.001 83.8 16.2 0.001 84.1 15.9 0.007
Retired 97.1 2.9 98.2 1.8 98.8 1.2 84.7 15.3 81.8 18.2
Unemployed 86.8 13.2 98.1 1.9 88.7 11.3 50.9 49.1 73.6 26.4
Chronic disease condition
No chronic disease 99.4 0.6 0.001 99.7 0.3 0.001 100.0 0.0 0.001 93.4 6.6 0.001 89.9 10.1 0.001
With one chronic disease 94.4 5.6 98.4 1.6 97.6 2.4 75.8 24.2 79.8 20.2
With two chronic disease 96.4 3.6 100.0 0.0 96.4 3.6 62.5 37.5 71.4 28.6
With three and more chronic disease 79.6 20.4 94.4 5.6 88.9 11.1 35.2 64.8 53.7 46.3
Education level
Primary school and Below 98.0 2.0 0.031 99.0 1.0 0.719 99.0 1.0 0.002 79.3 20.7 0.495 81.3 18.7 0.719
Junior middle school 93.8 6.2 98.4 1.6 95.3 4.7 82.0 18.0 82.0 18.0
Senior middle school 97.7 2.3 98.8 1.2 98.3 1.7 83.2 16.8 81.5 18.5
University and above 95.2 4.8 99.4 0.6 99.4 0.6 79.0 21.0 84.4 15.6
Family income level(in the local)
Low 93.1 6.9 0.066 100.0 0.0 0.377△ 96.6 3.4 0.596 79.3 20.7 0.002 86.2 13.8 0.003
Lower 96.7 3.3 99.0 1.0 98.4 1.6 79.8 20.2 84.7 15.3
Middle 96.9 3.1 98.7 1.3 97.5 2.5 85.5 14.5 80.5 19.5
Higher 91.1 8.9 100.0 0.0 97.8 2.2 68.9 31.1 68.9 31.1
High 96.6 3.4 96.6 3.4 100.0 0.0 89.8 10.2 86.4 13.6
Worry about get COVID-19
Very high 87.5 12.5 0.001 93.8 6.2 0.019 81.3 18.7 0.001 56.3 43.7 0.001 62.5 37.5 0.001
High 90.2 9.8 100.0 0.0 98.0 2.0 68.6 31.4 72.5 27.5
Low 97.5 2.5 98.8 1.2 98.3 1.7 78.1 21.9 80.2 19.8
Very low 96.5 3.5 99.2 0.8 98.8 1.2 87.7 12.3 87.7 12.3
Epidemic effects
Yes 94.8 5.2 0.008 99.2 0.8 0.538 97.0 3.0 0.002 76.1 23.9 0.001 77.6 22.4 0.001
no 97.9 2.1 98.8 1.2 99.6 0.4 87.9 22.1 89.1 10.9
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△: p values is the probability of Fish`s exact test. Bold values are statistically significant.

Logistic regression analysis

Each dimension of EQ-5D have been dichotomized, and have been used dependent variable. Sex, age, marital status, employment status, chronic disease condition, education level, family income level in the local, worry about get COVID-19, epidemic effects have been included as independent variables, multivariate logistic regression models were conducted, only those variables which exerted a significant relationship with any dimension from EQ-5D were reported in Table 3. The results showed that sex (OR = 2.621, 95%CI:1.333–5.150), age (OR = 2.028, 95%CI:1.458–2.820), marital (OR = 0.172,95%CI:0.052–0.570), with chronic disease (OR = 2.428, 95%CI:1.790–3.293), and epidemic effects(OR = 2.451, 95%CI:1.133–5.306) showed a significant relationship in mobility dimension; marital (OR = 0.166, 95%CI:0.039–0.707), employ(OR = 0.166, 95%CI:0.039–0.707) and with chronic disease(OR = 1.964, 95%CI:1.022–3.773) showed a significant relationship in self-care dimension; age(OR = 2.719, 95%CI:1.706–4.333), employ(OR = 2.084, 95%CI:1.044–4.158), with chronic disease (OR = 2.365, 95%CI:1.459–3.835), worry about got COVID-19 (OR = 0.487, 95%CI:0.280–0.847), and epidemic effects(OR = 9.156, 95%CI:1.825–45.276) showed a significant relationship in usual activities dimension. Age(OR = 2.182,95%CI:1.825–2.610), marital(OR = 0.487, 95%CI:0.277–0.855), with chronic disease (OR = 2.312, 95%CI:1.951–2.740),worry about got COVID-19 (OR = 1.201, 95%CI:0.520–0.836), and epidemic effects (OR = 2.375,95%CI:1.607–3.511) showed a significant relationship in pain/discomfort dimension. With chronic disease (OR = 1.843, 95%CI: 1.590–2.137), family income level in the local (OR = 1.259, 95%CI: 1.061–1.494), worry about got COVID-19(OR = 0.724, 95%CI: 0.590–0.889), and epidemic effects (OR = 2.076, 95%CI: 1.455–2.963) showed a significant relationship in anxiety/depression dimension.

Table 3. Multivariate logistic regression analysis results on the relationships between 5 dimensions of EQ-5D and influence factors.

Dimensions of EQ-5D Influence factors B SE p value Odds ratio 95%CI
Mobility Sex 0.963 0.345 0.005 2.621 1.333–5.150
Age 0.707 0.168 0.001 2.028 1.458–2.820
Marital -1.758 0.610 0.004 0.172 0.052–0.570
With chronic disease 0.887 0.156 0.001 2.428 1.790–3.293
Epidemic effects 0.897 0.394 0.023 2.451 1.133–5.306
Self-care Marital -1.798 0.740 0.015 0.166 0.039–0.707
Employ 0.675 0.333 0.043 1.964 1.022–3.773
With chronic disease 1.167 0.299 0.000 3.212 1.787–5.774
Usual Activities Sex 0.888 0.508 0.080 2.431 0.898–6.578
Age 1.000 0.238 0.001 2.719 1.706–4.333
Employ 0.734 0.352 0.037 2.084 1.044–4.158
With chronic disease 0.861 0.247 0.001 2.365 1.459–3.835
Worry about get COVID-19 -0.720 0.283 0.011 0.487 0.280–0.847
Epidemic effects 2.214 0.815 0.007 9.156 1.852–45.276
Pain/discomfort Age 0.780 0.091 0.001 2.182 1.825–2.610
Marital -0.720 0.288 0.012 0.487 0.277–0.855
With chronic disease 0.838 0.087 0.001 2.312 1.951–2.740
Worry about get COVID-19 -0.416 0.121 0.001 0.660 0.520–0.836
Epidemic effects 0.865 0.199 0.001 2.375 1.607–3.511
Anxiety/depression With chronic disease 0.611 0.075 0.001 1.843 1.590–2.137
Family income 0.231 0.087 0.008 1.259 1.061–1.494
Worry about get COVID-19 -0.322 0.105 0.002 0.724 0.590–0.889
Epidemic effects 0.731 0.181 0.001 2.076 1.455–2.963
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Discussion

The present study aimed to assess the HRQOL in Chinese population during the COVID-19 pandemic using the EQ-5D scale, the mean score for EQ-5D score and VAS scale were 0.949 (0.102) and 85.52(19.373) respectively. The results similar to the Singapore population score (0.95) that measured in general population used the UK time trade-off values [21], but higher than the score reported in the USA (0.87) in 2010 [22], Demark (0.889) in 2009 [23], Sri Lanka (0.85) in 2014 [24], and Japan (0.877) in 2011 [25]. The mean VAS score of our study (85.52) is higher than the national average (80.12) of in Heilongjiang of China and Taiwan population score (74.5) [26, 27]. The results revealed that the HRQOL in Chinese population during the COVID-19 pandemic perhaps had not been changed on the whole. As noted by the Sun et al [20], there are several possible reasons why the Chinese population obtained the higher score. Firstly, people in different countries may refer to the levels of health differently due to cultural differences. Secondly, the health status as well as the age and sex structure is different across countries. Thirdly, at the Changzhi city, the epidemic condition that only 8 definite cases had not affected largely life for mostly people.

Our study showed that the mean EQ-5D index score decreased with increasing age. Older people were more likely to reported problem in all of the five domains than younger people. Those who are of 50 and older dropped to a level of below average. The association between age and EQ-5D index score remained significant even after adjusting for all socio-demographic variables. The study showed that with chronic disease is the most significant variable, the most reported problems in all of the five domains, and the mean EQ-5D index score decreased rapidly with increasing the number of chronic disease. Aging had been a great challenge in China, 65 years and older has reached 12.5% in China in 2019 [28]. So, more people lived with disease for a long time, in particular, chronic non-communicable disease (NCD). Some study using EQ-5D also reported that QOL was lower among individuals with diabetes, gastrointestinal disease, hypertension, heart disease, and so on [29,30]. Our study showed that our respondents with three and above chronic diseases reported lower EQ-5D scores than other respondents, it similes to the Japan and Heilongjiang population results [25, 26]. The finding noted that aging people with three and above chronic diseases have lower QOL score in the period of epidemic, they should be pay close attention at the time.

Pain/discomfort was the most frequently reported problem in our study. The finding was consistent with the EQ-5D population studies from other countries. The proportion of our population reported pain/discomfort is similar to from Japan and Heilongjiang population results [25, 26], but lower than those reported from UK [31], Poland [32], Greece [33] and USA [22]. The bivariate analysis found that people who aging, unemployed, with one or more chronic disease, very worry about get COVID-19, with epidemic effects were more likely to reported problem in pain/discomfort domain. The logistic regression analysis showed similar trend in pain/discomfort domain. Based on earlier experiences of SARS, MERS [34,35] and the limited recent evidence about get COVID-19 [36,37], pain/discomfort was associated with older age, low educational level, clinical severity, depression, anxiety, low quality of life. It is important to realize about pain/discomfort in the population during the COVID-19 pandemic.

Anxiety/depression was the second frequently reported problem that its proportion is close to pain/discomfort. The bivariate analysis and logistic regression analysis showed with chronic disease, lower income, worry about got COVID-19, have epidemic effects were more likely reported lower scores in anxiety/depression domain. The result was lower than in China [26], Japan [25], and Singapore [21]. Liu`s [34] study about SARS in 2003 showed that perceived SARS-related risk level during the outbreak increased the odds of having a high level of depressive symptoms 3 years later. Kang `s study about COVID-19 in 2020 in Wuhan in China showed that medical and nursing staff had subthreshold mental health disturbances [36]. The number of people suffering from mental health impacts after a major event is often greater than the number of people who are physically injured, and mental health effects may last longer, therefore, it is necessary to pay attention to mental health, in particular, with chronic disease and lower income population.

Limitation

This study has several limitations. First, compared with face-to-face interviews, online-based self-reporting survey has certain limitations. Second, in this study, the overall ceiling effects of the EQ-5D index may occur when measuring the quality of life of Chinese sample. Third, it is a cross-sectional study that conducted survey from March2 to March 10 that the period that epidemic has weakened in China, changes in QOL dropped off with the extension of time. A randomized prospective study could better determine correlation and causation. Fourth, the epidemic condition affects all of Chinese people; it now appears that it affects even the entire world. China is so large and diverse in culture and social development, and suffered varying degrees effects during of epidemic in all of area, a larger sample size coming from different areas in China is needed to verify the results. Lastly, the measure on worry of COVID-19 developed by ourselves in our study, but psychological distress instruments on COVID-19 have been developed by Ahorsu and Taylor in the recent study [38,39]. So, it is necessary to use appropriate instruments on COVID-19 (e.g., the Fear of COVID-19 Scale and the COVID Stress Scales) to measure mental health in the future study.

Conclusion

The article provides important evidence on HRQOL during the COVID-19 pandemic. The risk of pain/discomfort and anxiety/depression in general population in China raised significantly with aging, with chronic disease, lower income, epidemic effects, worried about get COVID-19 during the COVID-19 pandemic. The results came from each categorical data can be used for future healthcare measures among general population.

Supporting information

S1 File. Questionnaires in Chinese.

(DOC)

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S2 File. Questionnaires in English.

(DOCX)

Click here for additional data file. (67.9KB, docx)
S1 Data

(SAV)

Click here for additional data file. (133.6KB, sav)

Acknowledgments

We thank all the participants who gave their time to make this project a reality.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The study was funded by the Soft Science Project of Shanxi of China (2017041040-4), the Soft Science Project of Shanxi of China (2018041033-1) to WP, and by the prevention and control COVID-19 project of Changzhi Medical College by XN (XG202004).

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Decision Letter 0

Amir H Pakpour

13 May 2020

PONE-D-20-12772

Evaluation of Health-Related Quality of Life Using EQ-5D in China During the COVID-19 Pandemic

PLOS ONE

Dear Mrs ping,

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PLOS ONE

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Reviewer #1: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I believe that the study entitled “Evaluation of Health-Related Quality of Life Using EQ-5D in China during the COVID-19 Pandemic” may bring something to the literature and help healthcare providers in caring general population. However, several parts of the manuscript need to be improved. Please see my specific comments below.

1. In Introduction, the sentence “The EuroQol (EQ-5D) is perhaps the most commonly used by researchers” is too strong. Please tone down the statement. If the authors want to keep this statement, please provide evidence. For example, how many papers have been using EQ-5D and how many papers have been using other quality of life instruments?

2. As the authors want to study in quality of life, they should firstly introduce different types of quality of life measures. From the aspect of “condition”, quality of life instruments can be classified into “condition-specific instrument” and “generic instrument”. From the aspect of “psychometrics”, quality of life instruments can be classified into “psychometric instrument” and “utility instrument”. The authors should clearly introduce these different types and clearly let the readers know that the EQ-5D is a generic instrument based on utility. Please refer to the following.

*References on “condition”

Lin, C.-Y., Lee, T.-Y., Sun, Z.-J., Yang, Y.-C., Wu, J.-S., & Ou, H.-t. (2017). Development of diabetes-specific quality of life module to be in conjunction with the World Health Organization Quality of Life Scale Brief Version (WHOQOL-BREF). Health & Quality of Life Outcomes, 15, 167.

Lin, C.-Y., Hwang, J.-S., Wang, W.-C., Lai, W.-W., Su, W.-C., Wu, T.-Y., Yao, G., & Wang, J.-D. (2019). Psychometric evaluation of the WHOQOL-BREF, Taiwan version, across five kinds of Taiwanese cancer survivors: Rasch analysis and confirmatory factor analysis. Journal of the Formosan Medical Association, 118(1), 215-222.

Lin, C.-Y. (2018). Comparing quality of life instruments: Sizing Them Up versus PedsQL and Kid-KINDL. Social Health & Behavior, 1, 42-47.

Pakpour, A. H., Chen, C.-Y., Lin, C.-Y., Strong, C., Tsai, M.-C., & Lin, Y.-C. (2019). The relationship between children's overweight and quality of life: A comparison of Sizing Me Up, PedsQL, and Kid-KINDL. International Journal of Clinical and Health Psychology, 19(1), 49-56.

*Reference on “psychometrics”

Lin, H. W., Li, C. I., Lin, F. J., Chang, J. Y., Gau, C. S., Luo, N., Pickard, A. S., Ramos Goñi, J. M., Tang, C. H., & Hsu, C. N. (2018). Valuation of the EQ-5D-5L in Taiwan. PloS One, 13(12), e0209344.

Lee, H. Y., Hung, M. C., Hu, F. C., Chang, Y. Y., Hsieh, C. L., & Wang, J. D. (2013). Estimating quality weights for EQ-5D (EuroQol-5 dimensions) health states with the time trade-off method in Taiwan. Journal of the Formosan Medical Association, 112(11), 699-706.

3. Regarding the 1500 surveys, how did the authors perform the random selection? Please provide the process. For example, did the authors obtain a list before sending out the surveys? If yes, how and where did the authors obtain the list? How was the representativeness of the list? Did the list include all the residents in Changzhi city?

4. In Results, the subheading of Regression analysis is misleading. Specifically, regression analysis gives an impression of “linear regression”. Therefore, the authors should explicitly mention “logistic regression” for the subheading.

5. Table 3. The heading of “CI 95 (Exp B)” is confusing. Please remove (Exp B). Also, it is unclear why there is a * sign before 1.798 for the marital variable. The title of Table 3 should clearly state whether this is multivariate logistic regression model or univariate logistic regression model.

6. Following the prior comment, it is unclear whether the authors conducted multivariate logistic regression or univariate logistic regression. This needs to be clear.

7. As the authors conducted a series of related analyses, they should adjust the p-values.

8. The authors should mention a limitation that, apart from the EQ-5D, all other measures used in the present study did not have been tested for psychometric properties. In particular, the measure on worry of COVID-19 seems to be developed by the authors themselves. Thus, the authors should acknowledge in the present study that there are available psychological distress instruments on COVID-19. Indeed, the authors found that anxiety/depression is the second frequently reported problem. Encouraging future studies using appropriate instruments on COVID-19 (e.g., the Fear of COVID-19 Scale and the COVID Stress Scales) is needed. Please see and cite the following references for discussion.

Ahorsu, D. K., Lin, C. Y., Imani, V., Saffari, M., Griffiths, M. D., & Pakpour, A. H. (2020). The Fear of COVID-19 Scale: Development and initial validation. International Journal of Mental Health and Addiction. Advance online publication. doi: 10.1007/s11469-020-00270-8.

Sakib, N., Mamun, M. A., Bhuiyan, A. K. M. I., Hossain, S., Mamun, F. A., Hosen, I., … Pakpour, A. H. (2020). Psychometric validation of the Bangla Fear of COVID-19 Scale: Confirmatory factor analysis and Rasch analysis. International Journal of Mental Health and Addiction. Advance online publication. doi: 10.1007/s11469-020-00289-x.

Satici, B., Gocet-Tekin, E., Deniz, M. E., & Satici, S. A. (2020). Adaptation of the Fear of COVID-19 Scale: Its association with psychological distress and life satisfaction in Turkey. International Journal of Mental Health Addiction. Advance online publication. doi: 10.1007/s11469-020-00294-0.

Soraci, P., Ferrari, A., Abbiati, F.A., Del Fante, E., De Pace, R., Urso A. Griffiths, M.D. (2020). Validation and psychometric evaluation of the Italian version of the Fear of COVID-19 Scale. International Journal of Mental Health and Addiction. Advance online publication. doi: 10.1007/s11469-020-00277-1.

Taylor, S., Landry, C., Paluszek, M., Fergus, T. A., Mckay, D., Asmundson, G. J. G. (2020). Development and initial validation of the COVID Stress Scales. Journal of Anxiety Disorders. Advance online publication. doi: 10.1016/j.janxdis.2020.102232.

9. Lastly, I think that the authors cannot claim to study the “impact of COVID-19 epidemic on quality of life” throughout the manuscript. The present study is a cross-sectional study and cannot provide such causality statement. Please revise all such statements.

**********

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Reviewer #1: No

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Decision Letter 1

Amir H Pakpour

4 Jun 2020

Evaluation of Health-Related Quality of Life Using EQ-5D in China During the COVID-19 Pandemic

PONE-D-20-12772R1

Dear Dr. ping,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Amir H. Pakpour, Ph.D.

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have satisfactory responded to my prior comments. I have no more comments and am glad to recommend publication in the present form. Congrats!

**********

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If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Acceptance letter

Amir H Pakpour

10 Jun 2020

PONE-D-20-12772R1

Evaluation of Health-Related Quality of Life Using EQ-5D in China During The COVID-19 Pandemic

Dear Dr. Ping:

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    Supplementary Materials

    S1 File. Questionnaires in Chinese.

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