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Abstract

Concerns have been raised globally regarding the long-term effects of the novel coronavirus disease 2019 (COVID-19). This study aimed to investigate the impact of long COVID on the health of patients recovering from acute COVID-19 in China. We conducted a cross-sectional questionnaire survey from 1 February to 9 March 2023. Propensity score matching (PSM) was used to understand the differences in health utility values between individuals with and without long COVID. Factors associated with health-related quality of life (HRQoL) were determined using a multiple linear regression model. A chi-square test was used to compare differences between the 2 groups for each dimension of the EuroQoL-5 Dimension-5 Level (EQ-5D-5L) scale. In total, 307 participants were included in the analysis, of which 40.39% exhibited at least 1 persistent symptom. The common symptoms of long COVID were fatigue/weakness, coughing, memory decline, poor concentration, and phlegm in the throat. Most patients with long COVID reported mild effects from their symptoms. After propensity score matching, the long-COVID group had lower health utility scores than the non-long-COVID group (0.94 vs 0.97). In the multivariable linear regression analysis, persistent symptoms and low annual household income were associated with lower health utility values (P < .05). Anxiety/depression and pain/discomfort were the major problems experienced by the participants with long COVID. Long-COVID symptoms following acute COVID-19 infection have a serious impact on health-related quality of life. Therefore, it is necessary to implement interventions to improve patient health after the recovery from acute COVID-19.

Keywords

COVID-19 persistent symptoms long COVID health-related quality of life China

What do we already know about this topic?

The COVID-19 pandemic in December 2019 has severely affected the lives of people around the world. Studies on the health-related quality of life (HRQoL) of long-COVID are few in China. Therefore, this study aimed to investigate whether long COVID has an impact on HRQoL.

How does your research contribute to the field?

Persistent symptoms and low annual household income were associated with lower health utility values. Therefore, interventions should be designed and implemented to improve the quality of life of COVID-19 recovering patients, especially those with persistent symptoms and low-income patients who require a comprehensive evaluation.

What are your research’s implications toward theory, practice, or policy?

The results contribute to the evidence for the impact of COVID-19 on the quality of life of recovered patients. The improvement of HRQoL in such patients is one of the ultimate goals of healthcare practices together with the need to provide the optimal long-term patient care.

Introduction

Since the start of the COVID-19 pandemic in December 2019, the disease has severely affected the lives of people around the world.¹ Acute coronavirus infection produces symptoms of acute respiratory infection as well as a variety of clinical syndromes, such as thrombosis, cardiac complications (including myocarditis) and neurological complications.² Although most patients recover fully from an acute coronavirus infection within a few weeks, some patients may experience persistent symptoms, which are called long COVID.³ The National Institute for Health and Care Excellence (NICE) in the UK has defined long COVID as “signs and symptoms that develop during or following an infection consistent with COVID-19 and persist for more than 4 weeks and cannot be explained by an alternative diagnosis.”⁴

Common symptoms in patients with long COVID include fatigue, cough, dyspnea, chest pain, headache, sleep disturbances, smell disturbances, and taste disturbances.^5
-8 Lemhöfer et al⁹ surveyed 1027 patients with mild to moderate SARS-CoV-2 infection in Germany, of whom 61.9% reported persistent symptoms for more than 3 months after infection. The top 5 most frequent persistent symptoms were fatigue/weakness (37.5%), pain (28.2%), dyspnea (26.0%), sleep disturbance (26.0%), and fear/anxiety (24.9%). Sigfrid et al¹⁰ surveyed 327 hospitalized patients with new coronavirus infections in the UK; 93% of participants had persistent symptoms, of which fatigue (83%) was the most prevalent, followed by dyspnea (54%). The diversity and length of symptoms and the wide impact on the population may cause health hazards, to the extent of leading to a decline in health-related quality of life (HRQoL).¹⁰ HRQoL¹¹ is a multidimensional concept that reflects a patient’s physical, psychological, social, and emotional health. It can be used to quantify mental and physical health so that healthcare workers can assess the impact of the disease and recognize areas of improvement to alleviate patient symptoms.

Tsuzuki et al¹² used the EuroQol-5 Dimension 3-Level (EQ-5D-3L) to measure HRQoL in 457 patients in April 2020. These patients were assessed at least 8 weeks after the COVID-19 infection. The study found that patients with long COVID had significantly lower EQ-5D-3L index values than patients without long COVID (0.85 vs 0.96). Malesevic et al¹³ used 2 scales, the EuroQol-5 Dimension 5-Level (EQ-5D-5L) and MOS 36-item Short-Form Health Survey (SF-36) to assess 112 long-COVID patients who had been infected with COVID-19 for at least 3 months. The EQ-5D-5L scores showed that most patients reported limitations in performing daily activities with pain/discomfort and anxiety. The SF-36 revealed lower physical health scores in patients with long COVID compared to those of the general Swiss population prior to the COVID-19 pandemic (38.5 vs 50).

Studies on the HRQoL of long-COVID patients have been conducted in some countries but there are few relevant studies in China. There have been some studies^14,15 in China on the long-term health effects of COVID-19 on patients discharged from hospitals. However, there are few studies on the long-term health effects and health utility values of long COVID in the general population. Therefore, this study aimed to investigate the symptoms and HRQoL of the general population with long COVID and whether long COVID impacts HRQoL.

Materials and Methods

Study Design and Population

This study was a cross-sectional survey, conducted from 1 February to 9 March 2023. Participants were recruited from Harbin Medical University, Heilongjiang Province, and the investigators surveyed students and their students’ families and friends amongst others. This study investigated the non-hospitalized acute COVID-19 population with an onset of >4 weeks according to the NICE definition⁴ (“signs and symptoms that develop during or following an infection consistent with COVID-19 and persist for >4 weeks and cannot be explained by an alternative diagnosis”) of the time criterion for long COVID. These patients were diagnosed positive using RT-PCR nasal swab or antigen test. The population was subsequently categorized into long-COVID and non-long-COVID groups according to the presence or absence of persistent symptoms. Data was collected through a questionnaire. After the participants provided informed consent, the investigators explained the questionnaire and it was subsequently completed face-to-face on site, or telephonically if absent. The questionnaires were entered through EpiData 3.1.

Measures

The questionnaire was divided into 3 main sections: demographic characteristics, symptoms, and severity of long COVID, and HRQoL. Demographic characteristics included age, sex, place of residence, educational level, annual household income, marital status, health insurance, smoking, drinking, physical exercise, and chronic diseases.

Following the National Institutes of Health report¹⁶ and some relevant studies^17,18 on long COVID, the symptoms of long COVID that we listed in the questionnaire were fatigue, weakness, cough, memory loss, poor concentration, phlegm in the throat, shortness of breath/difficulty breathing, irregular menstruation, hair loss, drowsiness, chest pain/tightness, sleep disorders, headache, loss of taste or distorted sense of taste, loss of smell or distorted sense of smell, loss of appetite, runny/stuffy nose, myalgia, palpitations, diarrhea, tinnitus, nausea and vomiting, and night sweats. The severity of long COVID was measured as slight, mild, moderate, severe, or very severe.

The presence of chronic diseases and the severity of long-COVID symptoms were obtained by self-report. To ensure the credibility of the questionnaire, the investigators were trained postgraduates at medical colleges. At the time of the survey, the investigators conducted detailed counseling on chronic diseases, long-COVID symptoms and their severity to improve the accuracy of the respondents’ answers.

HRQoL was assessed using the EQ-5D-5L.¹⁹ The EQ-5D-5L was developed by the EuroQol Group and is considered one of the best tools for measuring HRQoL. The EQ-5D-5L descriptive system comprises five dimensions—mobility, self-care, usual activities, pain/discomfort, and anxiety/depression—each of which has 5 levels (no problem, slight problem, moderate problem, severe problem, and unable to do). The Chinese version²⁰ of the EQ-5D-5L has been validated with satisfactory reliability and validity. The EQ-5D-5L has a corresponding Chinese utility point system that converts EQ-5D-5L values to health utility values.²¹

Statistical Analysis

The sample size was calculated based on the following formula: $n = {(\frac{Z_{1 - α / 2}}{ε})}^{2} p (1 - p)$ , where n is the total estimated sample size; $Z_{1 - α / 2}$ is the value representing the desired CI in which the confidence level selected was at 95%, with a critical value of 1.96; The relative error (ε) is 0.06; From a review, the P-value represents the incidence of long-COVID in the general population²² (P = 30%). The estimated sample size needed was 268 subjects (after considering an additional 20% sample loss). The study relied on the university to recruit investigators. This was done using snowball sampling until the required sample size was met. A total of 307 respondents were finally included (more than 20% of the required sample size).

Frequencies and percentages were used to describe the categorical variables, including demographic characteristics, symptoms of long COVID, number of those with long COVID, and severity of long COVID. To decrease the bias introduced by differences in the characteristics (sex, age, place of residence, etc.) of the participants in the 2 groups (with and without long COVID), we used propensity score matching (PSM) techniques. We then examined whether the matching procedure could balance the covariates in the long-COVID group and non-long-COVID group using the chi-square test. After PSM, the t-test was used if the EQ-5D index scores followed a normal distribution, and the Mann-Whitney U test was selected when the data followed a non-normal distribution or did not meet the requirements for parametric tests. We performed multivariate linear regression analysis to investigate the factors influencing HRQoL. The percentage of participants in each dimension (no problems or some/extreme problems) of the EQ-5D-5L was calculated, and a chi-square test or Fisher’s exact test was performed to examine the statistical significance of the difference between the groups in the percentage of reported problems. Significance was set at a two-sided P-value <.05. Data analysis was performed using SPSS software version 26.0.

Results

Characteristics of Participants

In this study, 314 patients were investigated, and 7 invalid questionnaires excluded, resulting in a questionnaire validity rate of 97.78%. Of the 307 participants included in the final analysis, 124 (40.39%) had long COVID, whereas 183 (59.61%) did not. 213 (69.38%) were women. Approximately three-quarters of participants were <30 years old (73.62%), 75.24% lived in a town or city, 69.71% were unmarried, and 73.62% engaged in some form of exercise. Over 40% of the participants had received education at the level of college/university (43.97%) and 41.37% had an annual household income of 80 000 to 150 000 yuan. Nearly two-thirds of participants paid for basic medical insurance for urban and rural residents (57.33%), 89.90% were non-smokers, and 48.21% were non-drinkers, 14.66% were suffering from chronic diseases. There was a significant difference in the “age,” “marital status,” “health insurance,” “physical exercise,” and “chronic disease” covariates between 2 groups (P < .05) (Table 1).

Table 1.

Characteristics of Participants.

Characteristics	Total (N = 307)	Long COVID (N = 124)	Non-long COVID (N = 183)	P
Sex				.079
Male	94 (30.62)	31 (25.00)	63 (34.43)
Female	213 (69.38)	93 (75.00)	120 (65.57)
Age(years)
<30	226 (73.62)	78 (62.90)	148 (80.87)	<.001
≥30	81 (26.38)	46 (37.10)	35 (19.13)
Place of residence				.206
Urban	231 (75.24)	98 (79.03)	133 (72.68)
Rural	76 (24.76)	26 (20.97)	50 (27.32)
Education level				.199
High school and below	57 (18.57)	29 (23.39)	28 (15.30)
College/university	135 (43.97)	52 (41.94)	83 (45.36)
Postgraduate and above	115 (37.46)	43 (34.68)	72 (39.34)
Annual household income (yuan)				.574
<80 000	68 (22.15)	30 (24.19)	38 (20.77)
80 000-150 000	127 (41.37)	47 (37.90)	80 (43.72)
≥150 000	112 (36.48)	47 (37.90)	65 (35.52)
Marital status				.006
Married	88 (28.66)	48 (38.71)	40 (21.86)
Unmarried	214 (69.71)	74 (59.68)	140 (76.50)
Others	5 (1.63)	2 (1.61)	3 (1.64)
Health insurance				.033
UEBMI	104 (33.88)	51 (41.13)	53 (28.96)
URBMI	176 (57.33)	60 (48.39)	116 (63.39)
Others	27 (8.79)	13 (10.78)	14 (7.65)
Smoking				.841
No	276 (89.90)	112 (90.32)	164 (96.17)
Yes	31 (10.10)	12 (10.38)	19 (9.68)
Drinking
No	148 (48.21)	59 (47.58)	89 (48.63)	.856
Yes	159 (51.79)	65 (52.42)	94 (51.37)
Physical exercise
No	81 (26.38)	44 (35.48)	37 (20.22)	.003
Yes	226 (73.62)	80 (64.52)	146 (79.78)
Chronic diseases				.025
No	262 (85.34)	99 (79.84)	163 (89.07)
Yes	45 (14.66)	25 (20.16)	20 (10.93)

UEBMI = basic medical insurance for urban employee; URBMI = basic medical insurance for urban and rural residents.

Type, Severity, and Number of Long COVID Symptoms

Overall, 22 symptoms were reported by the participants. The most common symptoms of long COVID were fatigue/weakness (83 [27.04%]), with 5 (6.02%) people reporting a low impact, 64 (77.11%) reporting a mild impact, and 14 (16.87%) reporting a moderate impact. This was followed by coughing (42 [13.68%]), memory decline (21 [6.84%]), poor concentration (20 [6.51%]), phlegm in throat (16 [5.21%]), shortness of breath/difficulty breathing (16 [5.21%]), irregular menstruation (16 [5.21%]), hair loss, drowsiness (14 [4.56%]), and chest pain/tightness (14 [4.56%]). Most patients with long COVID reported mild effects from their symptoms. None reported severe impacts (Figure 1).

Figure 1.

Distribution of symptoms of long COVID.

Of the 307 participants, 36 (11.73%) reported 1 symptom, 31 (10.10%) reported 2 symptoms, 19 (6.19%) reported 3 symptoms, and 38 (12.38%) reported 4 symptoms (Table 2). After PSM, 86.29% (107) of the 124 participants with long COVID were included in the long-COVID group, and 89.07% (163) of the 183 participants without long COVID were included in the non-long-COVID group. There were no statistically significant differences in any of the demographic characteristics (P > .05) (Table 3).

Table 2.

Numbers of Symptoms of Long-COVID.

Numbers of symptoms	N (%)
1	36 (11.73)
2	31 (10.10)
3	19 (6.19)
≥4	38 (12.38)

Table 3.

Characteristics of Participants After Propensity Score Matching.

Characteristics	Total (N = 270)	Long COVID (N = 107)	Non-long COVID (N = 163)	P
Sex				.424
Male	78 (28.89)	28 (26.17)	50 (30.67)
Female	192 (71.11)	79 (73.83)	113 (69.33)
Age (years)				.052
<30	206 (76.30)	75 (70.09)	131 (80.37)
≥30	64 (23.70)	32 (29.91)	32 (19.63)
Place of residence				.776
Urban	207 (76.67)	83 (77.57)	124 (76.07)
Rural	63 (23.33)	24 (22.43)	39 (23.93)
Education level
High school and below	48 (17.78)	23 (21.50)	25 (15.34)	.430
College/university	112 (41.48)	42 (39.25)	70 (42.94)
Postgraduate and above	110 (40.74)	42 (39.25)	68 (41.72)
Annual household income (yuan)				.482
<80 000	62 (22.96)	28 (26.17)	34 (20.86)
80 000-150 000	114 (42.22)	41 (38.32)	73 (44.79)
≥150 000	94 (34.81)	38 (35.51)	56 (34.36)
Marital status				.220
Married	72 (26.67)	34 (31.78)	38 (23.31)
Unmarried	194 (71.85)	71 (66.36)	123 (75.46)
Others	4 (1.48)	2 (1.87)	2 (1.23)
Health insurance				.385
UEBMI	89 (32.96)	39 (36.45)	50 (30.67)
URBMI	155 (57.41)	56 (52.34)	99 (60.74)
Others	26 (9.63)	12 (11.21)	14 (8.59)
Smoking				.693
No	247 (91.48)	97 (90.65)	150 (92.02)
Yes	23 (8.52)	10 (9.35)	13 (7.98)
Drinking				.897
No	130 (48.15)	51 (47.66)	79 (48.47)
Yes	140 (51.85)	56 (52.34)	84 (51.53)
Physical exercise				.099
No	64 (23.70)	31 (28.97)	33 (20.25)
Yes	206 (76.30)	76 (71.03)	130 (79.75)
Chronic diseases				.430
No	235 (87.04)	91 (85.05)	144 (88.34)
Yes	35 (12.96)	16 (14.95)	19 (11.66)

Comparison of Index Values of EQ-5D-5L Across Participant Demographics

The EQ-5D index scores did not follow a normal distribution after propensity matching when comparing the long-COVID and non-long-COVID groups. In this study, the Mann-Whitney U test was used to compare the EQ-5D index scores between the 2 groups. The results showed that the EQ-5D index score of the long-COVID group was 0.94 ± 0.07, and the EQ-5D index score of the non-long-COVID group was 0.97 ± 0.05. Moreover, the difference between the 2 groups was statistically significant (P < .05) (Table 4).

Table 4.

The Index Values of the EQ-5D-5L Across Participants.

Characteristics	EQ-5D-5L index values	P
Long-COVID (n = 107)	0.94 ± 0.07	<.001
Non long-COVID (n = 163)	0.97 ± 0.05

Multivariable Regression Analysis

Annual household income and long COVID were significant factors influencing the health utility value (P < .05). The presence of long COVID negatively affected health utility. Compared with participants without long COVID, participants with long COVID had a lower health utility value by 0.225. Furthermore, an annual household income of 150 000 yuan or more had a positive effect on the health utility value. Participants with annual incomes greater than 150 000 yuan had a 0.164 higher health utility value than those with annual incomes less than 80 000 yuan. No other variables were associated with health utility value (Table 5).

Table 5.

Multivariable Regression Analysis for Factors Associated With the Index Values of EQ-5D-5L Among Participants (N = 270).

Characteristics	β	P-value	95% CI
Sex
Female (ref)	—	—	—
Male	.073	.323	–0.010 to 0.030
Age
<30 (ref)	—	—	—
≥30	.045	.720	–0.030 to 0.043
Place of residence
Rural (ref)	—	—	—
Urban	.090	.151	–0.005 to 0.032
Education level
High school and below (ref)	—	—	—
College/university	.119	.406	–0.021 to 0.051
Postgraduate and above	.068	.648	–0.029 to 0.047
Annual household income (yuan)
<80 000 (ref)	—	—	—
80 000-150 000	.104	.179	–0.006 to 0.033
≥150 000	.164	.039	0.001-0.042
Marital status
Married (ref)	—	—	—
Unmarried	–.010	.925	–0.030 to 0.028
Others	–.087	.168	–0.110 to 0.019
Health insurance
UEBMI (ref)	—	—	—
URBMI	.084	.247	–0.007 to 0.029
Others	.057	.398	–0.016 to 0.041
Smoking
Yes (ref)	—	—	—
No	.140	.044	0.001-0.062
Drinking
Yes (ref)	—	—	—
No	–.011	.865	–0.018 to 0.015
Physical exercise
Yes (ref)	—	—	—
No	.023	.704	–0.014 to 0.021
Chronic diseases
Yes	–.005	.938	–0.026 to 0.024
No (ref)	—	—	—
With long-COVID
Yes	–.225	<.001	–0.044. to –0.014
No (ref)	—	—	—

CI = confidence interval; ref = reference category.

Comparison of the Two Groups Under Different Dimensions of the EQ-5D-5L

The symptom with the highest percentage reported by patients was anxiety/depression (29.63%), followed by pain/discomfort (18.52%). Approximately 5.93% participants had mobility problems, and 5.19% had problems with their usual activities. The self-care dimension had the lowest rate, with 2.22% of respondents reporting problems at any level. There were no significant differences in mobility, self-care, or usual activities between those with and without long COVID across the 5 EQ-5D-5L dimensions. However, assessments of pain/discomfort and anxiety/depression in the group with long COVID showed that more individuals in that group were affected by these related symptoms than in the group without long COVID (P < .05) (Table 6).

Table 6.

Distribution and Comparison of the Proportion of Patients Reporting EQ-5D-5L Domain Impairment.

Domain	Long COVID (N = 107)	Non-long COVID (N = 163)	Total (N = 270)	P
Mobility				.382
No problem	99 (92.52)	155 (95.09)	254 (94.07)
Having any problem	8 (7.48)	8 (4.91)	16 (5.93)
Self-care				.459
No problem	106 (99.07)	158 (96.93)	264 (97.78)
Having any problem	1 (0.93)	5 (3.07)	6 (2.22)
Usual activities				.385
No problem	103 (96.26)	153 (93.87)	256 (94.81)
Having any problem	4 (3.74)	10 (6.13)	14 (5.19)
Pain/discomfort				<.001
No problem	71 (66.36)	149 (91.41)	220 (81.48)
Having any problem	36 (33.64)	14 (8.59)	50 (18.52)
Anxiety/depression				.005
No problem	65 (60.75)	125 (76.69)	190 (70.37)
Having any problem	42 (39.25)	38 (23.31)	80 (29.63)

Discussion

Current research on long COVID focuses more on the hospitalized population than the general population. Few studies have measured health utility values in the general population with long-COVID in China. This study focused on long-COVID symptoms, symptom severity, and HRQoL of the general population after acute COVID-19 infection in China. The major findings of this study indicate that people with long COVID exhibit lower HRQoL.

In our study it was found that 40.39% of the respondents exhibited at least 1 persistent symptom after recovery from acute COVID-19 infection. This percentage is higher than that reported by Perlis et al²³ (14.8%). The reasons for this variation may be due to differences in time to recovery from acute COVID-19 infection at time of investigation. The persistent symptoms of COVID-19 tend to improve over time.²⁴ The Perlis study took place 6 months after recovery from acute COVID-19 infection, whereas this study took place 1 to 2 months after recovery. In our study, the most common symptoms of long COVID were fatigue/weakness, cough, memory decline, poor concentration, and phlegm in the throat. These findings are consistent with those of previous studies,^3,25 in which fatigue was the most common long-COVID symptom, as well as the figures from the UK Office for National Statistics.²⁶

Regarding the severity of long-term long-COVID symptoms, all participants indicated that their symptoms were of moderate or lower severity, with no symptoms above severe. This may be explained by the fact that 73.62% of the respondents were under 30 years of age. Some studies have shown that older adults have more severe long-COVID symptoms owing to the presence of underlying medical conditions and lower levels of physical activity.^27,28

The study shows that, there was a higher proportion of older patients (56.79%) with long-COVID, consistent with the results of 1 study.²⁹ This may³⁰ be owing to older individuals being more susceptible to long-COVID because of their declining physical abilities. The presence of chronic disease was associated with having long COVID. Some studies^31,32 have similarly shown that chronic kidney disease, hypertension and diabetes are risk factors for developing long-COVID. Compared to non-exercisers, those who are exercising have a lower prevalence of long COVID (35.40%), and the recent data from US college athletes show³³ a much lower prevalence of long-term symptoms. Therefore, we should focus on priority groups and adopt preventive measures at the early stages of the disease for older individuals and those who have chronic diseases. Individuals who already have long-COVID can seek care from a healthcare provider to develop a personal healthcare management plan to help improve their symptoms. Furthermore, it is important to encourage individuals to exercise.

COVID-19 has long-term effects on patients, not only in terms of symptoms but also HRQoL.³⁴ To compare the differences in HRQoL between patients with and without long COVID, we adopted PSM for the relevant demographic variables to ensure homogeneity between the 2 populations. The results of the study showed that after PSM, the health utility value of 0.94 for the long-COVID group was lower than the health utility value of 0.97 for the non-long-COVID group (P < .05). A study on the health utility values of a Korean population with long COVID showed the same results as those of the present study.³⁵ However, both scores were significantly higher than that reported for Japan of 0.85.¹² The variation in health utility values may be due to different survey populations. A survey population of patients hospitalized during the acute infection phase will show more severe symptoms of long COVID, which may lead to a lower HRQoL. Furthermore, HRQoL may be affected by age. One study showed that older age was associated with a higher risk of reporting long COVID.³⁶ Our research indicated that over 70% of our participants were under 30 years of age. Thus, hospitalized patients during acute infections and patients of higher age maybe the reasons for higher health utility values in other studies than in our study.

The results of the multiple linear regression showed that those with long-COVID symptoms exhibited lower health utility values. Moreover, participants with annual household incomes of more than 150 000 yuan had significantly higher health utility values than those with annual household incomes of less than 80 000 yuan, which may be attributed to better access to healthcare and preventive services in the higher-income group.³⁷

This study also analyzed the EQ-5D-5L for each dimension, and the proportions of people with problems with pain (33.64% vs 8.59%) and anxiety (39.25% vs 23.31%) were significantly higher in the long-COVID group than in the non-long-COVID group. Huynh et al³⁸ obtained the same result as this study. Pain is a common symptom following acute COVID-19, having a negative impact on HRQoL and increasing the incidence of anxiety.³⁹ There may be several reasons for this anxiety. First, the patient may be unwell due to the presence of symptoms and be overly worried about their health. Second, there may be a fear that the symptoms will worsen and last for an unpredictable period. Third, the patient’s work, life, and studies may face a brief period of stagnation owing to the presence of the symptoms. The coupling of pain and anxiety ultimately leads to a reduction in HRQoL. Therefore, pain and mental health should be important concerns, and it is important to provide the necessary care and psychological counseling to patients with long COVID with a view to improving their HRQoL.

There are some limitations to our study. First, the study was not randomized for the long-COVID and non-long-COVID groups, and the 2 populations were poorly comparable. However, the study did use PSM to eliminate the effect of confounding sociodemographic variables on the results for the 2 populations. Second, the sample size is not large enough so that prevalence was compromised. Third, the severity of long-COVID symptoms and prevalence of chronic diseases was self-reported, and lacks a certain degree of objectivity. However, to ensure the questionnaire’s credibility, the investigators were trained postgraduates at medical colleges. At the time of the survey, the investigators conducted detailed counseling on the severity of long-COVID symptoms to improve the accuracy of the individuals’ responses.

Conclusion

This study showed that 40.39% of respondents had long COVID symptoms after acute COVID-19 infection. Most patients with long COVID reported mild effects from their symptoms. The long-COVID group had lower health utility scores than the non-long-COVID group (0.94 vs 0.97). Persistent symptoms and low annual household income were associated with lower health utility values. Therefore, interventions should be designed and implemented to improve the quality of life of COVID-19 recovering patients, especially those with persistent symptoms and low-income patients who require a comprehensive evaluation.

Supplemental Material

sj-docx-1-inq-10.1177_00469580241246461 – Supplemental material for Impact of Long COVID on Health-Related Quality of Life Among Patients After Acute COVID-19 Infection: A Cross-Sectional Study

Supplemental material, sj-docx-1-inq-10.1177_00469580241246461 for Impact of Long COVID on Health-Related Quality of Life Among Patients After Acute COVID-19 Infection: A Cross-Sectional Study by Chengyao Sun, Ziwei Liu, Sixuan Li, Yuqi Wang and Guoxiang Liu in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Footnotes

We thank all the investigators at Harbin Medical University for their time and effort in this study.

Authors’ Contributions

Conceptualization,Chengyao Sun;methodology,Ziwei Liu;formal analysis,Sixuan Li and Yuqi Wang;data curation Sixuan Li and Yuqi Wang;writing—original draft preparation,Chengyao Sun and Ziwei Liu;writing—review and editing,Chengyao Sun,Ziwei Liu and Sixuan Li . All authors have read and agreed to the published version of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: The work was funded under a grant from Heilongjiang Province Postdoctoral Science Foundation (LBHZ-22211) and Project funding for the “New Era Longjiang Excellent Master’s and Doctoral Dissertations” in 2022(LJYXL2022-091). The funders of this study did not play any role in the study conception,designed,guided,data collection,data analysis,data interpretation,or writing of the report.

Ethical Approval

Ethical approval to hold and analyze the dataset was obtained from the Institutional Research Board of Harbin Medical University (Approval number: HMUIRB2023023).

ORCID iD

Guoxiang Liu

Supplemental Material

Supplemental material for this article is available online.

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Supplementary Material

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