Quantifying the adverse effects of long COVID on individuals’ health after infection: A Propensity Score Matching design study

doi:10.21203/rs.3.rs-3327670/v1

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Quantifying the adverse effects of long COVID on individuals’ health after infection: A Propensity Score Matching design study

https://doi.org/10.21203/rs.3.rs-3327670/v1

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This study assessed the prevalence and influencing factors of long COVID, and measured the difference in health status between long COVID and non-long COVID cases. A total of 3165 COVID-19 patients (53.11% female) completed the questionnaire and provided data on socio-demographic characteristics, lifestyles and health status. The total score of lifestyle assignment ranged from 5 to 21 points. Health status was assessed through self-reported, and the score ranged from 0 to 100 points. The criteria of “persistent symptoms or health problems after 4 weeks (28 days) of COVID-19 infection” issued by the US Centers for Disease Control and Prevention was used to assess whether the individuals had long COVID. The answers revealed that 9.73% of patients were identified long COVID. The health status scores of the long COVID cases 74.79 (95% CI: 72.75 ~ 76.82) was lower than that of the non-long COVID cases 81.06 (95% CI: 80.50 ~ 81.61). Never focused on mental decompression was a common risk factor for the health status of long and non-long COVID cases (β=-25.40, 95% CI: -38.82~-11.98; β=-4.86, 95% CI: -7.51~-2.21). The total lifestyle scores were identified as a mediating factor long COVID infection on individuals’ health, with a mediating effect of 0.2249 (95%CI: 0.0774 ~ 0.4096). In conclusion, the proportion of long COVID cases was low, but they were significantly worse off in health. This study proved that healthy lifestyles including mental decompression should be considered as the core strategy of primary prevention when the epidemic of COVID-19 is still at a low level.

Health sciences/Health care/Public health

Health sciences/Health care/Quality of life

long COVID

lifestyle

self-reported health status

propensity score matching

effect of mediation

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel virus, has caused a worldwide pandemic of coronavirus disease 2019 (COVID-19). Although the World Health Organization has declared that “COVID-19 no longer constitutes a Public health Emergency of International concern.” ¹, but this does not mean that COVID-19 infection is over. COVID-19 is still circulating at a low level, and data suggest that by April 2023, approximately 764 million people worldwide had contracted the COVID-19 virus ². Most infected people can return to their pre-infection life after recovery ³. However, one study has shown that COVID-19 infection can cause sequelae in the immune system, blood system, lung system, cardiovascular system, gastrointestinal system, musculoskeletal system, nervous system, and other aspects ⁴. These sequelae may persist after infection and may recur after cure ⁵. This disease, which manifests as persistent symptoms that affect some people after infection, is known as long COVID and affects people's health ⁶.

Currently, there is no universally known definition of long COVID. In this study, the definition of long COVID as defined by the Centers for Disease Control and Prevention of the United States of America was adopted: the condition in which an individual has persistent symptoms or health problems for at least 4 weeks (28 days) after COVID-19 infection ⁷. Persistent symptoms include a range of physical and mental (psychological) symptoms ⁸. including fatigue ⁹, headache and joint pain ¹⁰, respiratory symptoms ¹¹, cognitive impairment and impaired mental health ¹². The persistent effects of these symptoms can lead to poorer health and reduced quality of life ¹³. One study shows that at least 65 million people are infected with long COVID-19 worldwide ¹⁴, but due to lack of surveillance and reporting, the actual number of infected people may be much higher than reported. Previous studies have revealed risk factors for long COVID-19, such as being female ^15,16, the number of symptoms during the infection ¹⁷, education level, smoking ^18,19, sleep ^20,21, and psychological factors ²².

In February 2023, China announced the implementation of “Class B epidemic and B management” control measures for COVID-19 to reduce the negative impact of large-scale lockdown ²³. As a result of this policy, the risk of COVID-19 infection has increased nationwide, and acute cases have been treated in a timely and effective manner. However, the health status of long COVID cases also requires urgent attention. The purpose of this study is to explore the health status and influencing factors of individuals with and without long COVID after the social distancing strategy has ended in China, and to explore the effects of long COVID infection on the health of individuals and the regulatory effect of lifestyle to develop targeted healthy lifestyle guidance and policy recommendations to improve the health status of long COVID cases.

Procedures and study participants

A nationwide survey was conducted from February 1 to 8, 2023, using the stratified random sampling method. The selection of representative sample areas included Changzhou in the eastern region, Zhengzhou in the central region, Xining in the western region, and Mudanjiang in the northeastern region of China mainland. Within each city, more than two rural areas and two urban areas were randomly selected. A cross-sectional online survey was conducted among adults aged 18 years and above in each household to obtain a sample of 5780 eligible participants. In this study, a total of 3165 patients infected with COVID-19 were enrolled. The sample exclusion criteria were as follows: minors aged less than 18 years, participants with incomplete information, and individuals who were not residents of mainland China. We performed 1:1 nearest neighbor matching between long COVID cases and non-long COVID cases, and a total of 281 pairs of samples were matched. The participant flow chart is shown in Fig. 1.

Assessments

In the questionnaire, participants were asked about the post-COVID-19 symptoms they experienced after testing negative for COVID-19. Additionally, they were asked to indicate the duration of these symptoms in terms of the number of days. Participants who self-reported symptoms lasting for more than 4 weeks (28 days) were categorized as long COVID-19 cases based on the measurement criteria used in the study. The health status of participants was assessed using a questionnaire that asked them to rate their current health status on a scale from 0 to 100. Higher scores on the scale indicated a better health status.

We used 13 covariates (sociodemographic factors and health factors), including gender (male, female), age (18–29, 30–39, 40–49, 50–59, ≥ 60), marital status (married, other), education level (high school or less, bachelor's degree or more), religious affiliation (atheist, others), nationality (Han, others), annual household income (RMB) (< 20000, 20,000–49,999, 50000-99,999, 100000-199,999, ≥ 200000), BMI (< 18.50, 18.50-24.99, 25.00-29.99, ≥ 30.00), smoking (smoking, quit never), drinking (drinking, quit, never), wearing masks (always, often, sometimes, seldom, never), sufficient sleeping (always, often, sometimes, seldom, never), and mental decompression (always, often, sometimes, seldom, never).

To verify the mediating effect of smoking, drinking, wearing masks, sufficient sleeping, and mental decompression on the health status of individuals affected by long COVID, we assigned scores to each aspect. Smoking status was scored as 1 point for current smokers, 2 points for those who had quit smoking, and 3 points for individuals who had never smoked. Similarly, drinking status was scored as 1 point for current drinkers, 2 points for ex-drinkers, and 3 points for those who had never consumed alcohol. The frequency of wearing masks, sufficient sleeping, and mental decompression were evaluated and assigned scores based on the following scale: always = 5 points, often = 4 points, sometimes = 3 points, seldom = 2 points, and never = 1 point. The overall scores for these factors ranged from 5 to 21, reflecting the cumulative assessment of participants' behaviors in these areas.

Statistical analysis

The rank-sum test of two independent samples and the Kruskal-Wallis test were used to analyze the differences in health status scores between the long COVID cases and non-long COVID cases. The frequencies and proportions of categorical variables were calculated, along with their corresponding health status scores while continuous variables were categorized into quartiles, and the frequencies and proportions within each quartile were reported along with the respective health status scores. The factors influencing the health status score were analyzed by using linear regression models. A bias-corrected non-parametric percentile bootstrap method was used to verify the mediating effect of lifestyle on the impact of long COVID-19 on the health status of patients. To minimize the potential influence of confounding factors, propensity score matching was employed. Covariate selection was carried out using probit regression, and the propensity score was calculated through probit regression as well. Subsequently, 1:1 nearest neighbor matching was conducted between the longCOVID-19 and non-long COVID-19 populations among the 3,165 individuals, ensuring a balanced comparison between the two groups while addressing potential biases. All analyses were performed by using STATA17.0 and SPSS27.0 statistical software, and P < 0.05 was considered statistically significant. This study was approved by the Life Science Ethics Committee of Zhengzhou University (approval number: 2021-01-12-05). In addition to this, this study can establish that all methods were performed in accordance with the relevant guidelines and regulations and all participants provided written informed consent.

Characteristics, lifestyle and health status scores of long COVID cases and non-long COVID cases

The basic characteristics, lifestyle, and health status scores of the participants are presented in Table 1. A total of 3165 COVID-19 patients were included in this study, of which 308 (9.73%, 95% CI: 8.70 ~ 10.76) were long COVID cases and 2857 (90.27%, 95% CI: 89.24 ~ 91.30) were non-long COVID cases. The characteristics, lifestyle, and health status scores of patients with and without long COVID-19 are shown in Table 1. The overall health status score of COVID-19 patients was 80.45 (95% CI: 79.91 ~ 80.99) The health status score of long COVID cases was 74.79 (95% CI: 72.75 ~ 76.82), which was lower than that of non-long COVID cases 81.06 (95% CI: 80.50 ~ 81.61). The findings show that among long-COVID cases, lower education was associated with a health status score of 68.33 (95% CI: 62.87 ~ 73.8), while higher annual income level of 50,000–99,999 yuan was associated with a health status score of 68.46 (95% CI: 63.73 ~ 73.19), never felt sufficient sleep was associated with a health status score of 60.50 (95% CI: 20.16 ~ 100.84), and those who never paid attention to mental decompression was associated with a health status score of 61.05 (95% CI:51.66 ~ 70.44).

Table 1

The characteristics, lifestyle and health status scores of the study participants.
Variables	Total %	Health status scores (95%CI)	P value ^a	Long COVID			Non-long COVID
Variables	Total %	Health status scores (95%CI)	P value ^a	(N = 308) %	Health status scores (95%CI)	P value ^b	(N = 2857) %	Health status scores (95%CI)	P value ^c
Total	3165(100)	80.45(79.91 ~ 80.99)		308(9.73)	74.79(72.75 ~ 76.82)		2857(90.27)	81.06(80.50 ~ 81.61)
Sex			0.413			0.697			0.300
Male	1484 (46.89)	80.30(79.51 ~ 81.08)		123 (39.94)	74.19(70.94 ~ 77.43)		1361 (47.64)	80.85(80.05 ~ 81.65)
Female	1681 (53.11)	80.58(79.83 ~ 81.33)		185 (60.06)	75.18(72.57 ~ 77.8)		1496 (52.36)	81.25(80.47 ~ 82.02)
Age			0.0001			0.253			0.0001
18–29	357(11.28)	85.29(83.67 ~ 86.92)		19(6.17)	77.73(67.70 ~ 87.77)		338(11.83)	85.71(84.10 ~ 87.33)
30–39	890(28.12)	81.13(80.06 ~ 82.19)		94(30.52)	73.37(69.56 ~ 77.18)		796(27.86)	82.05(80.97 ~ 83.12)
40–49	627(19.81)	81.18(79.95 ~ 82.42)		78(25.32)	77.79(73.97 ~ 81.60)		549(19.22)	81.66(80.33 ~ 82.96)
50–59	671(21.20)	80.03(79.02 ~ 81.05)		65(21.10)	74.08(70.07 ~ 78.09)		606(21.21)	80.67(79.65 ~ 81.70)
≥ 60	620(19.59)	76.39(75.14 ~ 77.64)		52(16.88)	72.63(67.45 ~ 77.81)		568(19.88)	76.73(75.45 ~ 78.01)
Education level			0.0001			0.017			<0.001
High school or below	1182 (37.35)	79.06(78.13 ~ 79.98)		66 (21.43)	68.33(62.87 ~ 73.8)		1116 (39.06)	79.69(78.78 ~ 80.6)
Bachelor or above	1983 (62.65)	81.27(80.61 ~ 81.95)		242 (78.57)	76.55(74.48 ~ 78.62)		1741 (60.94)	81.94(81.24 ~ 82.63)
Marital status			<0.001			0.380			<0.001
Married	283(89.42)	80.15(79.59 ~ 80.72)		28(91.23)	71.37(62.52 ~ 80.23)		2549(89.22)	83.96(82.21 ~ 85.70)
Other	335(10.58)	82.94(81.16 ~ 84.73)		27(8.77)	75.11(73.05 ~ 77.18)		308(10.78)	80.71(80.12 ~ 81.29)
Ethnic			0.732			0.730			0.842
Han	3094(97.76)	80.30(76.01 ~ 84.58)		303(98.38)	74.75(72.70-76.81)		2791(97.69)	81.07(80.51 ~ 81.63)
Minority	71(2.24)	80.45(79.91 ~ 81.00)		5(1.62)	76.8(60.02 ~ 93.58)		66(2.31)	80.56(76.10 ~ 85.02)
Religion			0.008			0.354			0.011
Atheist	303(95.96)	75.74(72.30 ~ 79.18)		29(96.75)	75.07(73.05–77.09)		2739(95.87)	81.25(80.69 ~ 81.81)
Others	128(4.04)	80.65(80.10 ~ 81.19)		10(3.24)	66.4(48.82 ~ 83.98)		118(4.13)	76.53(73.12 ~ 79.95)
Annual household income (CNY)			0.185			0.034			0.404
<20000	246(7.78)	77.94(75.63 ~ 80.25)		18(5.84)	76.72(70.17 ~ 83.28)		228(7.98)	78.04(75.60 ~ 80.48)
20000–49999	510(16.11)	80.13(78.69 ~ 81.57)		35(11.36)	74.6(68.77 ~ 80.43)		475(16.63)	80.54(79.06 ~ 82.02)
50000–99999	725(22.91)	79.29(78.05 ~ 80.53)		78(25.32)	68.46(63.73 ~ 73.19)		647(22.65)	80.60(79.36 ~ 81.83)
100000–199999	847(26.76)	81.31(80.32 ~ 82.29)		85(27.60)	77.01(72.93 ~ 81.09)		762(26.67)	81.78(80.80 ~ 82.77)
≥ 200000	837(26.45)	81.51(80.61 ~ 82.42)		92(29.87)	77.78(74.94 ~ 80.62)		745(26.08)	81.97(81.08 ~ 82.92)
BMI			0.098			0.206			0.281
<18.50	105(3.32)	77.90(74.12 ~ 81.69)		15(4.87)	74.6(62.67–86.53)		90(3.15)	78.46(74.49 ~ 82.42)
18.50-24.99	199(63.13)	81.01(80.35 ~ 81.66)		19(63.96)	75.87(73.47 ~ 78.27)		1801(63.04)	81.57(80.90 ~ 82.24)
25.00-29.99	897(28.34)	80.02(78.98 ~ 81.06)		73(23.70)	74.95(71.04 ~ 78.85)		824(28.84)	80.46(79.39 ~ 81.54)
≥ 30	165 (0.05)	77.67(74.92 ~ 80.43)		23(0.07)	65.13(55.19 ~ 75.07)		142(4.97)	79.70(77.07 ~ 82.34)
Wearing masks			0.0001			0.118			0.0001
Always	120(38.17)	82.16(81.25 ~ 83.06)		15(49.35)	76.52 (73.67 ~ 79.37)		1056(36.96)	82.97(82.03 ~ 83.91)
Often	151(47.93)	79.87(79.11 ~ 80.63)		12(40.91)	74.33(71.39 ~ 77.26)		1391(48.69)	80.38(79.60 ~ 81.16)
Seldom	440(13.90)	77.74(76.30 ~ 79.17)		30(9.74)	67.93(59.46 ~ 76.40)		410(14.35)	78.46(77.07 ~ 79.84)
Smoking			0.069			0.300			0.116
Smoke	623(19.68)	80.21(78.97 ~ 81.46)		43(13.96)	68.40(61.16 ~ 75.63)		580(20.30)	81.09(79.89 ~ 82.28)
Quit	199(6.29)	78.15(75.89 ~ 80.41)		28(9.09)	74.10(67.69 ~ 80.52)		171(5.99)	78.81(76.40 ~ 81.21)
Never	134(42.43)	80.71(80.08 ~ 81.33)		23(76.95)	76.03(73.88 ~ 78.17)		2106(73.71)	81.23(80.58 ~ 81.88)
Drinking			0.008			0.091			0.016
Drink	857(27.08)	80.26(79.22 ~ 81.30)		76(24.68)	76.82(72.26 ~ 81.37)		781(27.34)	80.60(79.54 ~ 81.65)
Quit	173(5.47)	76.14(73.35 ~ 78.92)		23(7.47)	67.26(59.21 ~ 75.31)		150(5.25)	77.50(74.58 ~ 80.42)
Never	213(67.46)	80.87(80.23 ~ 81.52)		20(67.86)	74.88(72.55 ~ 77.20)		1926(67.41)	81.52(80.86 ~ 82.19)
Sufficient sleeping			0.0001			0.0016			0.0001
Always	681(21.52)	84.74(83.62 ~ 85.86)		46(14.94)	81.00(76.55 ~ 85.45)		635(22.23)	85.01(83.85 ~ 86.16)
Often	155(49.04)	81.24(80.52 ~ 81.96)		11(36.04)	76.93(73.64 ~ 80.21)		1441(50.44)	81.57(80.84 ~ 82.30)
Sometimes	727 (22.97)	77.43(76.28 ~ 78.57)		10(34.09)	73.89(70.83 ~ 76.94)		622 (21.77)	78.02(69.96 ~ 75.49)
Seldom	196(6.2)	71.14(68.48 ~ 73.80)		44(14.29)	65.68(58.83 ~ 72.53)		152(5.32)	72.72(69.96 ~ 75.49)
Never	9(0.28)	66.33(52.08 ~ 80.59)		2(0.65)	60.5(20.16 ~ 100.84)		7(0.25)	68.00(51.77 ~ 84.23)
Mental decompression			0.0001			0.0001			0.0001
Always	315(0.10)	87.13(85.45 ~ 88.81)		10(3.25)	92.6(86.99 ~ 98.21)		305(10.68)	86.95(85.23 ~ 88.68)
Often	850(0.27)	82.40(81.47 ~ 83.34)		51(16.56)	80.80(77.02 ~ 84.58)		799(27.97)	82.51(81.55 ~ 83.47)
Sometimes	1014 (32.04)	79.39(78.46 ~ 80.31)		13(42.21)	75.48(72.71 ~ 78.24)		884(30.94)	79.96(78.99 ~ 80.94)
Seldom	728(23.00)	78.02(76.84 ~ 79.19)		97(31.49)	71.69(67.64 ~ 75.74)		631(22.09)	78.99(77.80 ~ 80.18)
Never	258(8.15)	76.86(74.62 ~ 79.11)		20(6.49)	61.05(51.66 ~ 70.44)		238(8.33)	78.19(75.96 ~ 80.42)

Among non-long COVID cases, the elderly (76.73, 95% CI: 75.45 ~ 78.01), lower education level (79.69, 95% CI: 78.78 ~ 80.6), non-married (80.71, 95% CI: 80.12 ~ 81.29), those who with religious belief (80.56, 95% CI: 76.10 ~ 85.02༉had the lowest health status score. Those who never drank alcohol (81.52, 95% CI: 80.86 ~ 82.19) and those who always wore masks (82.97, 95% CI: 82.03 ~ 83.91) had the highest health status scores. In addition, people who always slept enough (81.00, 95% CI: 76.55 ~ 85.45; 85.01, 95% CI: 83.85 ~ 86.16) and those who always had mental decompression (92.6, 95% CI: 86.99 ~ 98.21; 86.95, 95% CI: 85.23 ~ 88.68) had higher health status scores.

Influencing factors of health status of long COVID and non-long COVID cases.

After accounting for confounding factors, the results of the linear regression model revealed that religious belief, annual household income, education level, wearing masks, smoking, drinking, and mental decompression (all P < 0.05) were significantly associated with the health status of long COVID, and the influencing factors of the health status score of non-long COVID cases were age, religious belief, BMI, annual household income, sufficient sleep and mental decompression (all P < 0.05), as shown in Fig. 2.( see Table S1 in the Supplementary Material)

Atheism was associated with a higher health status score among long COVID cases (β = 11.69, 95% CI: 0.25 ~ 23.13), while those with a bachelor’s degree or higher had a better health status score (β = 7.44, 95% CI: 1.14 ~ 13.74). People who seldom used masks had lower health scores (β = 7.33, 95% CI: -14.29~-0.37). Among non-long COVID cases, the health status score of the elderly(≥ 60 years old) was lower (β=-10.30, 95% CI: -12.91~-7.70),and the health status score of atheism was higher (β = 4.12, 95% CI: 1.09 ~ 7.14). People with normal BMI (18.5–25.0) had higher health status score (β = 4.03, 95% CI: 0.9 ~ 7.15), and those who never felt sufficient sleep had lower health status score (β=-15.8, 95% CI: -26.72~-4.88).

Moreover, among long COVID cases, those who had quit smoking (β = 10.92, 95% CI%: 4.23 ~ 17.62) and those who had never smoked (β = 12.83, 95% CI: 3.83 ~ 21.83) had higher health status scores than smokers. Ex-drinkers (β=-14.28, 95% CI: -22.68~-5.88)and never-drinkers (β=-5.56, 95% CI: -11.01~-0.10) had lower health status scores than drinkers, however, there was no statistically significant difference in non-long COVID cases (all P > 0.05). In both groups, those who never paid attention to mental decompression had the lowest health status score (β=-25.40, 95% CI: -38.82~-11.98;β=-4.86, 95% CI: -7.51~-2.21), which represented the worst health status.

Mediating effect analysis of lifestyle scores.

As shown in Fig. 3, long COVID significantly positively predicted health status scores (c = 6.2734, P < 0.001) and negatively predicted lifestyle scores (a=-0.3542, P < 0.05). When both long COVID and lifestyle were considered in the model, the 95% CI did not include 0, suggesting that the mediating effect of lifestyle was confirmed. Furthermore, the results indicated that there was a significant correlation between long COVID and health status scores (c'=6.0486, P < 0.001), as well as between lifestyle and health status scores (b=-0.6349, P < 0.001). This suggests that lifestyle partially mediated the relationship between long COVID-19 and health status scores, with a mediating effect estimated to be 0.2249 (95% CI: 0.077 ~ 0.410). (see Table S2 in the Supplementary Material).

Results of propensity score matching.

A total of 281 pairs of 562 individuals were matched from the population sample of 3165. The covariates included education level, wearing masks, and sufficient sleeping (see Table S3 in the Supplementary Material). Based on the matched data, the differences in the health status scores between long and non-long COVID cases are shown in Fig. 4. After PSM matching, the health status score of long COVID cases 73.43 (95% CI: 72.29 ~ 76.57) was still lower than that of non-long COVID cases 79.03 (95% CI: 77.22 ~ 80.83). Before and after matching, the difference in the health status score between the long COVID cases and non-long COVID cases was 5.59, as shown in Fig. 4.

Our results showed that the incidence of what we termed long COVID was 9.73%. The overall health status score of the patients was 80.45, and the health status scores of long COVID cases and non-long COVID cases were 74.49 and 81.06, respectively. After propensity score matching, long COVID cases still had lower health status scores than non-long COVID cases, with a difference of 5.59. We found that the lifestyle of COVID-19 patients, including wearing masks, smoking, drinking, sufficient sleeping and mental decompression, was also associated with their health status.

One study showed that the prevalence of long COVID was between 10% and 15% ²⁴, and a previous study from China showed that 8.89% of infected individuals self-reported long COVID symptoms ²⁵, which is basically consistent with the results of our study (9.73%). However, other studies have reported different prevalence rates. For example, a study from Italy showed that 87.4% of people who recovered from COVID-19 reported at least one persistent symptom (such as asthma and fatigue) ⁹, a study from the United States showed that 14.7% of patients had varying degrees of prolonged COVID-19 symptoms ²⁶, and a study from the Faroe Islands showed that 39% of patients developed long COVID symptoms ²⁷. This difference in research results is caused by a variety of factors, mainly because the definition of long COVID has not been formally determined, and the definitions and standards adopted by different studies are inconsistent. In addition, the different treatment statuses received by the hospitalized population and the non-hospitalized population ²⁸ and the difference in vaccination ²⁹ also lead to the inconsistent results. Moreover, our questionnaires were self-reported by infected persons, who tended to report the symptoms that boded them most, and were not professionally validated by health care providers. The patients may also attribute symptoms to their illness rather than their infection, and recall bias can occur when reporting the number of days. Therefore, it can be speculated that our survey results are conservative and may be at the lower limit of prevalence. However, we do not believe that this discrepancy seriously undermines the value of our findings, because most persistent symptoms are subjective, such as fatigue, and are difficult to verify objectively, even when assessed by a health care professional.

This study suggests that the effects of long COVID on the health status of infected individuals are common and persistent, which is consistent with the findings of a previous study ³⁰. The health status of COVID-19 patients differs significantly by having long COVID, with long COVID cases having worse health status, which is similar to the findings of previous studies ^18,31,32. The main reason is that the long-term persistent symptom cluster caused by long COVID includes respiratory sequelae, mental health disorders, cardiovascular diseases, gastrointestinal diseases, discomfort, fatigue, musculoskeletal pain and anemia ³³, which disrupt almost all aspects of daily life of infected people. In the absence of effective treatment, the persistence of sequelae reduces the quality of life of COVID-19 patients and negatively impacts their health status ³⁴. Another reason for the worse health status of long COVID cases may be that compared with acutely infected patients, long COVID cases are not prioritized as a group that does not need to be hospitalized for dynamic monitoring, resulting in their lack of close monitoring or guidance during the recovery process ³⁵. Moreover, given the huge scale of the COVID-19 epidemic, even if the proportion of long COVID cases is very low, it will cause a significant burden of long-term illness among COVID-19 patients ³⁶.

In this study, we found that the health status of long COVID cases was associated with smoking, drinking, and mental decompression. Compared with smokers, ex-smokers and never-smokers had better health status, which is consistent with a previous finding ³⁵. This may be because smoking is a potential risk factor for long COVID ³⁷, which can worsen the health status of the infected person. Since SARS-CoV-2 is mainly transmitted from person to person through respiratory droplets ³⁸, smoking can increase the burden on the respiratory system and lungs and the symptoms of virus infection, weaken the immune and cardiovascular systems of infected people, increase their susceptibility to various health complications ¹⁸, and have a negative impact on the health status of infected individuals.

Among long COVID cases, ex-drinkers and those who never drank did not show better health than drinkers. We consider that the most important reason for this result may be due to the infection itself because such an association was not found in non-long COVID cases. Moreover, alcohol has both advantages and disadvantages for human health. Although studies have shown that heavy drinking or frequent alcohol abuse is positively correlated with many diseases including cardiovascular and cerebrovascular diseases, which is not conducive to personal health ^39,40, there is also evidence that compared with moderate drinkers, non-drinkers have worse health conditions ⁴¹.

The health status of non-long COVID cases is related to sufficient sleeping, and less adequate sleep was associated with poorer health status, consistent with a previous study ⁴². This may be due to physical discomfort caused by symptoms during the acute infection phase ⁴³ and the negative effects of restrictive measures including “social distancing strategy” to contain the spread of infection.

We did not find sex differences in the health status of the COVID-19 patients, but we found that people who never paid attention to mental decompression had the worst health status, regardless of whether they were long COVID cases or not. This is consistent with the results of a previous study ²². This may be due to the disruptive social changes caused during the COVID-19 epidemic, which exacerbated the mental stress of individuals, and the increase in public restrictions during the epidemic, the pressure of hospital medical care, the fear of infecting others, and the stigma increased the psychological stress of COVID-19 patients, which had a considerable negative impact on their health status ^44,45.

Although any one of these factors can reduce health status individually, the combination of all these factors is most likely to have an additive effect ⁴⁶, suggesting a link between lifestyle and the health status of patients. A healthy lifestyle has a positive effect on the health of patients, and a healthier lifestyle, especially mental health support for individuals who recover from COVID-19, can help regulate the harm of long COVID to the health of individuals ⁴⁷.

This study has several strengths. First, this is the first national survey after the “Class B epidemic and B management” policy was adjusted in mainland China. The timeliness of the findings helps to deepen the understanding of the long-term harm of COVID-19 infection. Given the significant mediating effect, healthy lifestyles should be considered a cost-effective approach to cope with the continued low level of the COVID-19 epidemic. Second, the PSM method was used to control for confounding factors and ensure the reliability and robustness of the absolute difference in health status between long COVID cases and non-long COVID cases in this study, which was better than previous similar studies.

This study has several limitations. First, COVID-19 infection and health status scores were based on self-reports, which caused recollection and subjectivity bias. Second, the factors affecting the health status of COVID-19 patients are complex, which means that in addition to the positive mediating effect of lifestyle identified in this study, there are other unknown covariates to be uncovered. Finally, although PSM was used in this research to avoid the influence of some confounding variables, the confounding effect of unknown covariates was difficult to eliminate.

The proportion of long COVID cases was relatively low in all infected individuals. Education level, wearing masks, smoking, drinking and mental decompression were influencing factors. The health status of long COVID cases is significantly worse, and it is necessary to strengthen the follow-up of this population. Given the positive moderating role of healthy lifestyles in improving the health status of long COVID cases, healthy lifestyles including mental decompression should still be considered the most active prevention strategy at the current low epidemic level of COVID-19.

Acknowledgements

All authors thank all participants involved in the study.

Author contributions statement

Yudong Miao, Wenyong Dong, Ruizhe Ren, Zhanlei Shen designed the concept.

Yudong Miao, Zhanlei Shen, Wenyong Dong, Jianping Hu managed the data. Qiuping Zhao, Rongmei Liu, XueZhou provide financial support. Zhanlei Shen Ruizhe Ren, Yi Li, Wanliang Zhang, Dongfang Zhu, Jingbao Zhang, Junwen Bai, Dan Guo designed the method. Zhanlei Shen, Dongfang Zhu directed the software. Ruizhe Ren, Wenyong Dong wrote the manuscript. Yudong Miao, Clifford Silver Tarimo participated in article review and editing. All authors reviewed the manuscript.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding

This work was supported by the Major Program of National Fund of Philosophy and Social Science of China [Grant No.21BGL222]; the Health Medical Collaborative Innovation Program of Zhengzhou [Grant No.20XTZX05015]

Data availability statement

The data underlying this article will be shared on reasonable request to the corresponding author.

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No competing interests reported.

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Quantifying the adverse effects of long COVID on individuals’ health after infection: A Propensity Score Matching design study

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Characteristics, lifestyle and health status scores of long COVID cases and non-long COVID cases

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