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Epidemiology
Original research
Interaction between economic status and healthy lifestyle in long COVID among Chinese older population: a cross-sectional study
  1. Yaping Wang1,2,
  2. Manchang Li3,
  3. Bingkun Zhang3,
  4. Yue Feng3,
  5. Yinghui Yu3,
  6. Ling Guo3,
  7. Min Du1,2,
  8. Wenxin Yan1,2,
  9. Qiao Liu1,2,
  10. Chenyuan Qin1,2,
  11. Jie Deng1,2,
  12. Chao Song3,
  13. Jue Liu1,2
  1. 1Department of Epidemiology and Biostatistics, Peking University, Beijing, China
  2. 2Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
  3. 3Anning First People's Hospital, Kunming University of Science and Technology, Kunming, China
  1. Correspondence to Professor Jue Liu; jueliu{at}bjmu.edu.cn; Professor Chao Song; chaosong_1{at}sina.com

Abstract

Objectives To estimate the interaction between economic status (ES) and healthy lifestyle in long COVID among Chinese older people infected with SARS-CoV-2.

Design A cross-sectional study based on the Peking University Health Cohort in Anning, Yunnan.

Setting All primary health institutions in Anning, Yunnan Province, China, from April to May 2023.

Participants A total of 4804 people aged 60 and older infected with SARS-CoV-2 were included in this study.

Primary and secondary outcome measures Long COVID was measured by participants’ self-reported symptoms using structured questionnaires. ES was measured by last-month personal income, and participants’ ES was defined as low if their income was below the per capita monthly income of local residents. Lifestyle score was equal to the number of healthy behaviours (including smoking, drinking, weight, exercise and diet) and grouped using the median score as the cut-off point. Univariate and multivariate logistic models were employed to estimate the association of ES with long COVID. Interaction between ES and lifestyle in long COVID was assessed by multiplicative interaction term.

Results We enrolled a total of 4804 participants infected with SARS-CoV-2, of whom 57.3% (2754 of 4804) had at least one long COVID symptom. Fatigue (1546, 56.1%), cough (1263, 45.9%) and muscle pain (880, 32.0%) were the top three common symptoms. Patients with low ES had a 48% (adjusted OR: 1.48; 95% CI 1.22, 1.82) increased risk of long COVID. A significant interaction was observed between ES and lifestyle (p value for interaction <0.001) in long COVID.

Conclusion The interaction between ES and healthy lifestyle in long COVID was prominent. Comprehensive strengthened economic support for patients recovering from COVID-19, especially for those with low healthy lifestyle, should be implemented to prevent and manage long COVID symptoms.

  • Aged
  • China
  • COVID-19
  • Behavior
  • Health economics

Data availability statement

Data are available upon reasonable request.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2023-082314

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    Strengths and limitations of this study

    • This study included a large sample of older people infected with SARS-CoV-2 at the community level in China.

    • The participants included in the study were ethnically diverse as Yunnan is a multiethnic province in China.

    • This study cannot neglect the recall bias associated with self-reported long COVID symptoms.

    • The cross-sectional design of this study cannot provide causal evidence and more studies are needed in the future.

    Introduction

    As of 7 November 2023, SARS-CoV-2 has contributed 771.82 million confirmed cases and 6.98 million confirmed deaths, although the actual number of infections may be significantly higher.1 During the SARS-CoV-2 Omicron variant outbreak, the number of confirmed cases in China increased sharply, particularly in November and December 2022.2 Although the mortality of prevalent SARS-CoV-2 variants is decreasing, it remains crucial to study the health outcomes following infection. According to WHO, long COVID, commonly known as post-COVID-19 condition, refers to symptoms that occur in people with a history of probable or confirmed SARS-CoV-2 infection 3 months from the onset and cannot be explained by other diagnoses.3 It has been reported that nearly 32% (95% CI 14%, 57%) of the infected population have developed long COVID.4 Different strains can lead to diverse symptoms, with common symptoms including fatigue, respiratory symptoms, memory problems or cognitive dysfunction, and sleep disorders, which can have significant adverse effects on health and lead to substantial medical costs.3 5 For example, people with long COVID have reported a higher prevalence of poor health-related quality of life (QoL)6 and are often unable to work in the same capacity as before the illness.7 As individuals can be reinfected with SARS-CoV-2 despite effective vaccination and because long-term effects may persist after recovery, it is necessary to estimate the prevalence of and understand the risk factors for long COVID to improve prevention and management strategies.

    Several studies have suggested that increasing age, female sex, hospital admission, severe COVID-19, ethnic minority, comorbidities, smoking and other demographic and clinical characteristics are associated with a greater risk of developing long COVID symptoms.8–10 In terms of age, the prevalence of long COVID was significantly higher in older patients compared with younger patients.10 11 Specifically, patients aged 45 years and older were more likely to experience fatigue, cognitive symptoms, abnormal breathing and pain.11 Additionally, older adults were more susceptible to severe COVID-19, which may generate challenges to health institutions in China due to the rapidly ageing population.8 Some studies have also reported that a lower socioeconomic status (SES) was associated with a higher risk of long COVID.9 12 13 Ferreira et al12 reported that lower SES (measured by estimated average household income per month) was significantly associated with increased dyspnoea, greater fatigue and worse functional status. Considering the connection between COVID-19 and long COVID, the mechanism underlying the adverse effects of lower SES on both COVID-19 and long COVID incidence and mortality could be similar, although related studies are scarce.14 15 In summary, numerous and complex symptoms of long COVID could cause increased disease burden and medical costs for older people, especially those of lower SES.

    A healthy lifestyle, which has been widely demonstrated to be associated with good health outcomes, could play a positive role in preventing adverse COVID-19 outcomes.16 However, current studies have focused only on the effects of one lifestyle factor, such as smoking, drinking, physical activity or diet, on COVID-19, with limited attention given to long COVID and with few studies conducted in China.17 18 Moreover, the interaction between ES and lifestyle factors in the context of long COVID remains largely unknown. Therefore, we conducted a cross-sectional study among an older Chinese population to estimate the effects of economic status (ES) and lifestyle and their interaction in long COVID.

    Materials and methods

    Study design and participants

    This was a cross-sectional study based on the Peking University Health Cohort in Anning, Yunnan (PKUHC-AN), which is registered with ClinicalTrials.gov (NCT05825651). This study was conducted from April to May 2023, at least 3 months from the last cluster of infection incidence in November and December 2022 in China. All participants were recruited from local primary medical institutions managed by Anning medical communities. In China, primary medical institutions are required to provide free physical examinations for all older adults living within their area of responsibility. Residents in China usually access basic healthcare services in primary healthcare facilities rather than private doctors/healthcare.19 Thus, by enrolling participants through primary healthcare facilities, we were able to recruit a sample that was representative of the local older people. At baseline, the PKUHC-AN planned to enrol approximately 10 000 older patients aged 60 years and above who attended a primary healthcare facility for physical examination or health services consecutively from April to May 2023.

    Participants were included if they were 60 years or older, had lived in the local area for at least 6 months, agreed to participate in our study and had no severe neurological disorders or other conditions disturbing normal communication. Each participant was interviewed by well-trained local doctors using a standardised structured questionnaire. The PKUHC-AN finally enrolled 11 527 participants who completed the questionnaire. We included 4804 eligible patients with probable or confirmed COVID-19 and excluded any participants who had missing data (n=569) or who had never had COVID-19 (n=6154). Using the guidelines produced by WHO,20 we used two definitions to identify confirmed COVID-19 cases. Under definition 1, confirmed COVID-19 cases were identified by real-time PCR tests for SARS-CoV-2 regardless of clinical symptoms or epidemiological criteria (such as contact with a probable or confirmed case or linkage to a COVID-19 cluster). Under definition 2, confirmed COVID-19 cases were those who had clinical symptoms or met the epidemiological criteria with a SARS-CoV-2 antigen detection rapid diagnostic test. Probable COVID-19 cases were defined as those who had clinical symptoms and were in contact with a probable or confirmed case or were linked to a COVID-19 cluster. The year and month of the last infection with SARS-CoV-2 were also recorded.

    Economic status

    After reviewing the literature, we chose personal income to evaluate the ES of participants. First, we obtained participants’ last-month income (including wage income, pensions and transfers) via a questionnaire. We then defined the ES of participants as low if their last-month income was inferior to the per capita monthly income of Yunnan residents; otherwise, the ES was defined as high.

    Long COVID symptoms

    In accordance with previous research,3 21 patients with long COVID in this study were those reported following symptoms in 3 months from the onset of the COVID-19 infection. Symptoms that were reviewed included the following: fever, fatigue, inability to perform exercise, sore throat, myalgia, neuralgia, headache, dyspnoea, cough, nasal obstruction, runny nose, dysphagia, dizziness, chest distress, abnormal sensations, tinnitus, blurred vision, diarrhoea, abdominal pain, constipation, loss of appetite, nausea, vomiting, loss of taste/smell, memory problems, rapid heart beat, sleep disorders, cognitive impairment, difficulty in concentration, anxiety, depression, rash, chest pain, hair loss and others. Specifically, these symptoms occurred after COVID-19 infection, did not appear before infection, could not be explained by an alternative diagnosis and lasted for at least 2 months. The 3-month period enabled us to rule out the usual recovery period from acute COVID-19 onset. We summed the number of these symptoms and defined participants as having long COVID if the number of symptoms was greater than or equal to 1.

    Healthy lifestyle

    We first measured healthy lifestyle by assigning a score based on certain factors, including smoking status, drinking status, weight, physical activity and diet. The score was determined by the number of healthy lifestyle factors (‘never smoke’, ‘never drink’, ‘have a normal weight with a body mass index in 18.5–23.9 kg/m2’, ‘follow a balanced diet’ and ‘exercise 6–7 times/week’). Second, we calculated the median score of all participants. Finally, we defined patients as having a low-level healthy lifestyle if their score was below the median score; otherwise, they were defined as having a high-level healthy lifestyle.

    Covariates

    Considering the complex indicators of ES and the influencing factors of long COVID, we included the following: (1) sociodemographic characteristics such as age (per 5 years), sex (female, male), marital status (married, widowed/other), education level (no formal education, primary school, middle school, high school and above), ethnicity (Han, minority), living condition (single, with spouse, other), residence (urban, rural) and social support (low, high); and (2) health-related characteristics including healthy lifestyle (low, high), presence of non-communicable disease (NCDs) (no, yes), inpatient care history (no, yes) and COVID-19 vaccination status (no, yes).

    Social support was evaluated by the Social Support Rating Scale (SSRS) developed by Xiao Shuiyuan according to China’s national conditions, which has high reliability and validity.22 This scale contains 10 items that can be grouped into three dimensions: objective support, subjective support and support availability. The total score is calculated as the sum of the 10 items.23 The cut-off point of the SSRS was inconclusive, so participants were identified as having low social support if their total score was less than the median score of all participants; otherwise, they were classified as having high social support. The data concerning NCDs were collected by local professional medical workers using the following question: Do you have any NCD diagnosed by doctors, including diabetes, hypertension and hyperlipidaemia; chronic respiratory diseases such as chronic obstructive pulmonary disease or asthma; cardiovascular diseases such as stroke or heart attack; chronic kidney diseases; cancer; or memory problems such as dementia?

    Data analysis

    For descriptive analyses, we used frequencies and percentages for categorical variables and mean (SD) for continuous variables. Pearson’s χ2 tests and t-tests were used to compare the distribution of characteristics among participants by ES (low vs high).

    Univariate and multivariate logistic models were used to estimate the ORs and 95% CIs of long COVID among patients with low ES compared with those with high ES. The multivariate model was adjusted for all covariates, including age, sex, education level, marital status, ethnicity, living status, residence, social support, healthy lifestyle, NCDs, inpatient care and vaccination status. We calculated the variance inflation factor of each covariate and found that all results were less than 3.0, indicating no multicollinearity.

    We also conducted three sensitivity analyses to examine the robustness of our findings. First, we replaced the categorical variable NCD (no, yes) with a continuous variable (representing the number of NCDs) in the final multivariate model. Second, we replaced the combined lifestyle indicators with individual factors: smoking status (never smoke, previously smoked or currently smoke), drinking status (never drink, drink less, drink moderately or drink heavily), weight (underweight, normal weight, overweight or obesity), physical activity (never, less than two times per week, two to five times per week, or six to seven times per week) and diet (balanced or unbalanced). Third, we used a Poisson generalised linear regression model to estimate the association between ES and the number of long COVID symptoms.

    Based on previous studies, our analysis first examined the mediating effects of lifestyle on the relationship between ES and long COVID. Furthermore, we stratified the analysis by healthy lifestyle level and explored the interaction between ES and healthy lifestyle using a multiplicative interaction term. Additionally, we considered sex, marital status, education level, ethnicity, residence and social support as stratifying factors because the ORs from univariate models including these variables were significant and are commonly used as indicators of ES.

    All analyses were conducted in R software (V.4.3.0; R Core Team, Vienna, Austria). A two-sided p value <0.05 was considered to be significant.

    Patient and public involvement

    Neither patients nor the public were involved in the design, conduct, reporting or dissemination plans of our research.

    Results

    Characteristics of the participants

    In total, we enrolled 10 958 eligible participants without missing data, of whom 4804 (43.8%) had a history of probable or confirmed SARS-CoV-2 infection. There were significant differences in participant characteristics between those with and without COVID-19 (online supplemental table S1). Among the 4804 patients with COVID-19, the mean (SD) age was 72.1 (6.4) years, 2754 (57.3%) were women, 3836 (79.9%) were married and 4269 (88.9%) were Han. A total of 58.6% (2814 of 4804) of these patients had a high-level healthy lifestyle, 3328 (69.3%) participants had at least one NCD, only 179 (3.7%) patients were hospitalised for COVID-19 and 4666 (97.1%) were vaccinated against COVID-19 (table 1).

    View this table:
    Table 1

    Distribution of participants’ characteristics by economic status

    In addition to healthy lifestyle and vaccination status, there were significant differences in demographic and health-related characteristics between participants with low ES and those with high ES (all p<0.05).

    Long COVID

    A total of 57.3% of the 4804 patients with COVID-19 exhibited at least one long COVID symptom. Fatigue (1546, 56.1%), cough (1263, 45.9%) and muscle pain (880, 32.0%) were the three most prevalent symptoms among people with long COVID.

    Both univariate and multivariate logistic models revealed that ES and lifestyle were significantly associated with long COVID (table 2). In univariate models, patients with low ES had a 35% (crude OR: 1.35; 95% CI 1.20, 1.52) greater risk of long COVID. In the multivariate model, the risk of long COVID was increased by 48% (adjusted OR (aOR): 1.48; 95% CI 1.22, 1.82) in patients with low ES compared with those with high ES. Compared with individuals with high-level healthy lifestyle, those with low-level healthy lifestyle had an OR of 1.19 (95% CI 1.06, 1.34) for long COVID symptoms in the univariate model and an OR of 1.26 (95% CI 1.11, 1.44) in the multivariate model.

    View this table:
    Table 2

    Association between economic status and long COVID in univariate and multivariate logistic regressions

    Analyses of mediating and interaction effects and sensitivity analyses

    Our analysis of the mediating effects revealed that the role of healthy lifestyle as a mediator was not significant (online supplemental table S2). Interaction effect analysis showed that the association between ES and long COVID was modified by healthy lifestyle (p value for interaction <0.001; figure 1). That is, the association between ES and long COVID was more pronounced among patients with a low-level healthy lifestyle (aOR: 1.86; 95% CI 1.36, 2.55) compared with those with a high-level healthy lifestyle (aOR: 1.28; 95% CI 0.99, 1.66) and across the entire study population. Additionally, male with low ES (aOR: 1.98; 95% CI 1.42, 2.76) were more likely to develop long COVID symptoms than female (aOR: 1.21; 95% CI 0.95, 1.56). The association between ES and long COVID was significant among minority patients (aOR: 3.38; 95% CI 1.87, 6.29) and those living in urban areas (aOR: 1.61; 95% CI 1.24, 2.10).

    Figure 1
    Figure 1

    Interaction between ES and healthy lifestyle in long COVID. *P <0.05. aOR, adjusted OR; ES, economic status.

    The results of the sensitivity analyses revealed that the association between ES and long COVID was robust under various conditions. Specifically, the association was consistent when we (1) replaced the categorical variable for NCDs with a continuous variable in the final model; (2) substituted single indicators of smoking status, drinking status, weight, physical activity and diet for the combined healthy lifestyle indicator; and (3) used a Poisson generalised linear regression model to assess the relationship between ES and the number of long COVID symptoms (table 3).

    View this table:
    Table 3

    Sensitivity analyses for the association of economic status with long COVID

    Discussion

    In this study, we found that 57.3% of older patients with a history of probable or confirmed SARS-CoV-2 infection developed long COVID in Yunnan, China. Low ES was associated with an increased risk of long COVID, and this association was significantly modified by healthy lifestyle. To prevent and manage long COVID, strengthened economic support should be implemented among patients with SARS-CoV-2, particularly those with a low-level healthy lifestyle.

    Our study revealed that more than half of older patients infected with SARS-CoV-2 during the Omicron variant pandemic in China experienced at least one long COVID symptom, with fatigue being the most common, consistent with findings from other studies.3 4 12 Globally, diverse symptoms and a high prevalence of long COVID have been reported across the spectrum of infection severity, affecting multiple organ systems.24 Commonly affected systems include the respiratory, cardiovascular, neurological, gastrointestinal and musculoskeletal systems. These symptoms are directly connected with impaired functional outcomes, which are frequently included in QoL assessments.25 Studies employing various tools to measure QoL have demonstrated that patients with long COVID often experience significant functional reductions or impairments in at least one dimension of QoL.26 27 These impairments can be exacerbated by pre-existing comorbidities, such as asthma, diabetes and mental health conditions, further impacting both physical and mental health.8 28 29 This increased healthcare demand presents major challenges for primary care institutions, increases the financial burden of diseases and exacerbates health inequalities.30 Additionally, individuals with long COVID may struggle to return to their preinfection work capabilities within a short time. Given the adverse health-related and economic impact of long COVID, along with the potential for unknown long-term effects, early prevention and detection is crucial.

    Our studies also revealed that low ES increased the risk of long COVID by 48%, consistent with results from other studies.12 31 Heller et al31 reported that patients with moderate or severe COVID-19 infection experienced a greater prevalence and number of long COVID symptoms among low-income groups compared with middle-income and high-income groups. Various factors can explain this association. First, lower income is associated with a higher risk of comorbidities, which are linked to more severe COVID-19 and an increased likelihood of developing long COVID.31 A systematic review showed that individuals in low SES groups (including those using income to measure SES) had a significantly higher prevalence of tobacco and alcohol use compared with those in high SES groups.32 Additionally, the review reported that the use of tobacco and alcohol is a known risk factor for multiple health outcomes, such as cardiovascular diseases and cancers.32 Another systematic review and meta-analysis found that low-income groups had a 49% increased risk for coronary artery diseases and a 24% higher risk for strokes compared with high-income groups.33 Second, lower-income populations may lack access to healthy and safe food, improved living conditions or affordable health services.34 35 One study reported that hospitalised patients often suffered from malnutrition and sarcopenia, which may further increase the prevalence of long COVID.36 Another predictive study revealed that income and medication purchase history were the strongest predictors of vaccination status.34 Therefore, to prevent and manage long COVID, targeted and improved economic protections and support measures should be implemented for people with COVID-19, particularly those with low SES.

    In this study, we found that a low-level healthy lifestyle could strengthen the association between ES and long COVID. Similarly, numerous studies have shown that unhealthy lifestyle, such as smoking, overweight or obesity, increases the likelihood of long COVID symptoms.8 9 12 Theoretically, both ES and lifestyle factors can influence the severity and presentation of long COVID symptoms, as these symptoms are directly associated with the number or severity of COVID-19 symptoms.12 31 37 Moreover, a series of lockdown policies and measures during the COVID-19 pandemic contributed to unhealthy lifestyle, such as reduced outdoor physical activity, longer sitting times, unbalanced diets and poorer sleep quality, which may exacerbate the occurrence of long COVID.38–40 Additionally, unhealthy lifestyle was associated with comorbidities, similar to low ES, further increasing the risk of long COVID symptoms.9 41–43 Consequently, to prevent the occurrence of long COVID, reduce its severity and promote recovery, it is essential to consider not only financial protection and economic support but also healthier lifestyle before, during and after COVID-19 infection.

    Consistent with other studies, our study revealed that, compared with females, males were less likely to experience long COVID symptoms.44 However, interaction analyses indicated that the association between low ES and long COVID was significant only in males. Some studies have suggested that female may have worse health outcomes but better resilience to cope with hardships, such as low income, compared with male.45 46 That is, under low ES conditions, female may be better able to adapt to the challenges associated with low income, which may protect them from long COVID.

    Our study has several limitations. First, recall bias could affect the accuracy of the time gap between COVID-19 infection and the onset of long COVID, although the study was conducted 3 months after the outbreak of the Omicron variant strain began in China. Second, long COVID symptoms were self-reported and lacked objective measurements, which may introduce reporting bias. Third, information on infection severity was missing among patients with probable COVID-19 infection compared with those with confirmed cases, which limited our ability to control for the impact of infection severity on long COVID outcomes. Fourth, to minimise recall bias, we only collected data on participants’ income from the past month, which may not accurately reflect their average income level during the period of SARS-CoV-2 infection.

    In conclusion, more than half of older patients with COVID-19 experienced at least one long COVID symptom. Both ES and lifestyle factors were associated with the occurrence of long COVID, with a significant interaction effect between ES and healthy lifestyle. To effectively prevent and manage long COVID symptoms, comprehensive economic support should be strengthened for patients recovering from COVID-19, particularly for those with a low-level healthy lifestyle.

    Data availability statement

    Data are available upon reasonable request.

    Ethics statements

    Patient consent for publication

    Ethics approval

    This study involves human participants and was approved by the Biomedical Ethics Review Committee of Peking University (ethical approval number: IRB00001052-21126). Participants gave informed consent to participate in the study before taking part.

    Acknowledgments

    We appreciate all the work from participants and investigators in the project.

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    Footnotes

    • Contributors JL conceptualised and supervised the study. YW, MD, WY, QL, CQ and JD designed the questionnaire and study protocol. YW, MD, WY, QL, CQ and JD collected the data. YW and MD developed the statistical model and the analysis strategy. YW wrote the first draft of the manuscript. CS, ML, BZ, YF, YY, LG and JL did critical reading and revised the manuscript. All authors read and approved the final manuscript before submission. JL is responsible for the overall content as guarantor.

    • Funding This study was supported by the National Natural Science Foundation of China (grant number 72122001) and Beijing Natural Science Foundation (grant number L222027).

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.