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Associations between COVID-19 incidence, weight status, and social participation restrictions in the U.S.: evidence from the national population, cross-sectional study

BMC Public Health volume 24, Article number: 1066 (2024) Cite this article

Abstract

Background

To explore the associations between coronavirus infection incidence and weight status and social participation restrictions among community-dwelling adults in the United States.

Methods

We analyzed data from the 2021 National Health Interview Survey (NHIS), which included a representative sample of 29,394 individuals (Coronavirus disease 2019 (COVID-19): 3,205) and a weighted total of 252,461,316 individuals (COVID-19: 31,697,404), considering the complex sampling design used in the survey.

Results

Age, race/ethnicity, education level, family income index, body mass index (BMI), and smoking status were significantly associated with COVID-19 infection. Weight status was significantly correlated with social participation restrictions and strongly associated with COVID-19 infection, particularly among individuals who were overweight or obese.

Conclusion

Weight status was shown to be associated not only with social participation restrictions but also with COVID-19 infection among U.S. adults. Understanding the complex interplay between weight status, social participation, and COVID-19 is crucial for developing effective preventive measures and promoting overall well-being in the community population.

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Background

The novel coronavirus has rapidly spread to millions of individuals worldwide, resulting in significant morbidity and mortality [1]. The COVID-19 pandemic has brought about a profound transformation in global health. To address the escalating transmission rates, authorities at the federal, state, and local levels in the U.S. have implemented targeted public health interventions with the aim of controlling the spread of the virus effectively [2]. The Centers for Disease Control and Prevention estimates that the impact of COVID-19 on the United States corresponds to approximately 1 in every 1.32 COVID-19-related deaths, highlighting the significant burden imposed by the disease [2].

The potential relationship between weight and COVID-19 incidence has been acknowledged by the scientific community and healthcare professionals. There is a strong association between obesity and a significant increase in the incidence of illness and death related to COVID-19 in individuals [3]. Obesity is also a major public health concern in the United States, imposing an annual cost of approximately $173 billion on the healthcare system [4]. In the United States, approximately 30.2% of adult hospitalizations due to COVID-19 were directly attributed to obesity (body mass index ≥30 kg/m2) [5]. Additionally, countries with higher BMI levels exhibit elevated rates of both COVID-19 incidence and mortality [6]. Specifically, a higher body mass index is linked to an increased risk of COVID-19-related hospitalization and death in the United States [7].

Social participation refers to engaging in activities that involve interacting with others in a society or community [8], and it has been linked to various health outcomes, including mortality, morbidity, and quality of life [8]. However, the implementation of social distancing measures during the pandemic, necessary for individual health and safety, poses a challenge to social participation. Consequently, individuals may choose to stay at home and refrain from going out, leading to a decrease in participation in social activities This trend raises new concerns about the complex relationship between health and social interaction. Specifically, previous findings indicated an interplay between social participation and obesity. For example, a systematic literature review provided evidence of the association between social participation and obesity [9]. Additionally, there was a noticeable increase in sedentary behavior and excessive eating during the lockdown, leading to an imbalance in energy intake and subsequent weight gain [10]. These findings underscore the critical need to comprehend the dynamic between weight status and social participation during the pandemic.

Globally, having a high BMI (metabolic risk) was associated with death from diabetes and disability-adjusted life years [11]. However, previous studies have faced limitations in identifying unmeasured variables that could affect metabolic control due to data constraints [12, 13]. Furthermore, previous studies have relied primarily on medical records, which has led to a limited understanding of weight status, social participation restrictions, and COVID-19 infection among individuals in the community.

The objective of this study was to investigate the incidence of COVID-19 and its association with weight status and social participation restrictions in adults within the U.S. community utilizing data from the 2021 National Health Interview Survey. Specifically, our aims were to analyze the prevalence of COVID-19 incidence, explore the relationship between COVID-19 incidence, weight status, and social participation restrictions, and identify potential risk factors associated with its occurrence. By investigating these factors within a population-based framework, our study contributes to a better understanding of the broader transmission of COVID-19 and its implications for future public health interventions.

Methods

Study design and subjects

For our study, we utilized data from the National Health Interview Survey, a long-standing survey implemented since 1957 to monitor the health of the nation. The 2021 NHIS data, covering COVID-19 status measurements from January to December 2021, formed the foundation of our analysis [14]. To prioritize safety during face-to-face interview, the survey implemented several protocols such as wearing masks, practicing social distancing, conducting interviews outdoors, refraining from entering respondent’s homes, and prohibiting respondents from touching Census Bureau equipment or materials [15]. Out of the 29,394 participants aged ≥18 who were asked about their positive COVID-19 diagnosis based on the test, 3,205 tested positive for COVID-19. Our study conducted a secondary data analysis using publicly available de-identified NHIS data. As a result, no Institutional Review Board approval was necessary [16, 17].

Outcome variable: COVID-19 incidence

Participants who answered “yes” to the questions “Have you ever been tested for coronavirus or COVID-19?”, “Did the test confirm that you had coronavirus or COVID-19?”, and “yes” to this question were classified as confirmed COVID-19 cases [14].

Exposure variables: social participation restrictions, weight status, smoking status, diabetes status, hypertension status, and history of heart attack

We investigated social participation restrictions, weight status, smoking status, diabetes status, hypertension status, and history of heart attack. Individuals who indicated having “a lot of difficulty” or being “can’t do at all” in response to the following question were categorized as having social participation restrictions: “Because of a physical, mental, or emotional condition, do you have difficulty participating in social activities such as visiting friends, attending clubs and meetings, or going to parties?” [18]. Weight status was measured using BMI. The NRS-2002 is used to evaluate overweight and obesity and is calculated by dividing the participant’s weight in kilograms by the square of their height in meters. Individuals with a BMI of 25 kg/m2 or higher were categorized as overweight, while those with a BMI of 30 kg/m2 or higher were classified as obese. Participants were asked a series of questions about their smoking habits and status. The questions included the following: (1) “Do you smoke now?”, (2) “Do you smoke every day, some days, or not at all?”, and (3) “Have you ever smoked 100 cigarettes in your lifetime?” Based on their responses, participants were categorized as follows: those who reported currently smoking, smoking daily, or smoking occasionally during the survey were classified as current smokers; individuals who had smoked 100 cigarettes in the past but were not currently smoking were classified as former smokers; and participants who had never smoked were classified as never smokers [19]. The presence of diabetes, hypertension, or heart attack was determined based on the participants’ response to the question "Have you ever been told by a doctor or health professional that you had hypertension, diabetes or heart attack?" Participants who answered affirmatively to any of these conditions were classified accordingly.

Covariates

To address potential confounding factors, we controlled for several covariates, including age (categorized as 18-59 and 60 years or older), sex (male and female), race/ethnicity (classified as white only, black/African American only, American Indian/Alaska native only, Asian only and the other race/ethnicity group), educational attainment (divided into four categories: (a) less than high school education (<12th grade or a general educational development program), (b) high school graduate, (c) associate degree (some college, associate degree, occupational, technical, or vocational program), and (d) bachelor’s degree or higher (bachelor’s or master’s degree, or doctoral degree)), and family income index [20].

Statistical analysis

We estimated the actual number of reported COVID-19 cases as well as the weighted number of cases. To examine differences in participant characteristics, we conducted Pearson’s chi-square test. Spearman’s correlation analysis was then employed to evaluate the correlations between COVID-19 infection incidence and weight and social participation restrictions. Finally, we used logistic regression models to estimate adjusted odds ratios (ORs) of predictors of COVID-19. All the statistical analyses were conducted using SPSS version 28.0, and a significance level of 0.05 (two-tailed) was applied.

Results

Table 1 shows that 3205 patients were classified as COVID-19, corresponding to an estimated total of 31697404 COVID-19 patients. There were significant differences in the characteristics of the COVID-19 patients based on age, race/ethnicity, level of education, family income index, BMI, and smoking status. Individuals aged 18-59 exhibited a higher percentage of COVID-19 cases compared to those aged 60 or above, with a significant association found between age group and COVID-19 infection. Similarly, significant associations were observed between race/ethnicity, marital status, education level, and family income index with COVID-19 infection, excluding sex. While social participation restrictions did not show significant association with COVID-19, BMI and smoking status did. In particular, overweight (BMI 25<=BMI<30/m2) and obese (BMI>=30 kg/m2) individuals had significantly greater COVID-19 incidence than did their corresponding counterparts.

Table 1 COVID-19 cases according to participant characteristics
Full size table

Table 2 displays the correlations between COVID-19 infection incidence and weight status and social participation restrictions. There was a strong correlation between COVID-19 incidence and weight status (r=0.056, p=<.001). In addition, weight status was significantly correlated with social participation restrictions (r=0.012, p=.043). However, there was no significant correlation between COVID-19 infections and social participation restrictions.

Table 2 Correlations between COVID-19 incidence, weight status and social participation restrictions
Full size table

Table 3 shows that the odds analysis revealed significant associations between exposure factors and COVID-19 infection risk. In terms of BMI, individuals classified as overweight exhibited a significantly higher likelihood of COVID-19 infection, with obese individuals showing and even greater risk. In addition, smoking was significantly associated with COVID-19 infection risk. However, health conditions such as DM, hypertension, and heart attack did not exhibit statistically significant associations with COVID-19 infection risk.

Table 3 Factors associated with COVID-19 infection
Full size table

Discussion

This study aimed to explore the associations between COVID-19 incidence, weight status, and social participation restrictions in the U.S. The main results showed that weight status was associated with social participation restrictions and COVID-19 infection. Specifically, individuals who were overweight or obese were found to be more susceptible to coronavirus infection, as evidenced by national data in the U.S. As the first study to examine the association of these variables using a nationally representative dataset, the findings of this study have important implications for public health interventions and strategies.

The study revealed a significant association between weight status and social participation restrictions in U.S. adults. Individuals with a higher BMI were more likely to experience limitations in their engagement in social activities. This association can be attributed to the social stigma faced by individuals with larger body sizes in the U.S. [21], which can lead to social isolation [22]. Moreover, overweight or obesity can hinder individuals’ participation in various social activities, such as joining local sports teams or engaging in physical activities in public spaces [23]. The COVID-19 pandemic has further exacerbated these social restrictions through lockdown measures [24]. In addition, obesity plays a role in creating differences in overall and healthy life expectancy for the U.S. population [25]. To promote a healthy post-COVID life, it is necessary to implement measures that facilitate social engagement for individuals who are overweight or obese.

In addition, the study emphasized the association between weight and COVID-19 infection, which was supported by public data. Individuals with a higher body weight index were shown to be at an elevated risk of contracting COVID-19. These findings are consistent with emerging evidence suggesting that obesity is a significant risk factor for severe outcomes related to COVID-19 [5, 7]. During the pandemic, obesity has been shown to be an independent risk factor for increased hospitalization and mortality compared to a normal BMI. Researchers have explained that the pathophysiological understanding of this association involves the inflammatory state associated with obesity [26]. Another concern is that individuals with obesity often have comorbidities [27], which further increase their vulnerability to COVID-19 [28]. Additionally, high BMI and obesity have been identified as risk factors for the development of long-term COVID-19 [29, 30], which refers to persistent symptoms following the acute phase of the infection. Given these findings, addressing obesity as a public health priority and implementing targeted preventive measures for high-risk populations are crucial.

One notable finding of our study is the lack of significant association between social participation restriction and COVID-19 infection in our regression and correlation models. While this may seem counterintuitive, it underscores the complexity of factors influencing COVID-19 transmission [31]. Social participation restrictions, although implemented as preventive measures, may not directly correlate with infection rates due to the presence of cofounding variables or other mediating factors that were not fully accounted for in our analysis. Future research should explore additional factors that may mediate or moderate these relationships using additional data sources, which may provide further insights into the relationship between social participation restriction and COVID-19 infection. In addition, while the finding that smokers were less likely to be infected by COVID-19 than never-smokers is noteworthy, it should be interpreted within the broader context of the detrimental effects of smoking on overall health [32]. Moreover, there are studies that have highlighted the ongoing debate regarding the relationship between smoking and vulnerability to COVID-19 [33]. Therefore, further research is needed to understand the mechanisms underlying this observed association and its implications for public health policy and practice.

Strengths and limitations

This study contributes to our understanding of weight status, social participation restrictions and COVID-19 infection in several significant ways. First, this study provides a national estimate of the burden of COVID-19 among adults residing in the U.S. in 2021. Second, the study revealed a noteworthy association between weight status and the risk of COVID-19, indicating that effectively managing weight status may reduce the risk of COVID-19. These findings emphasize the importance of developing interventions and policies at the community level, targeting high-risk populations, to address weight and its impact on COVID-19 incidence. Third, the study sheds light on the relationship between weight status and social participation restrictions, emphasizing the need to raise awareness about weight bias and promote education and empathy to reduce social stigmatization toward individuals with larger body sizes. Additionally, this study underscores the significance of recognizing the potential psychological impact of social participation restrictions and providing resources for mental health support.

This study has certain limitations that should be acknowledged. First, the use of self-reported data for the variables introduces the possibility of recall bias and measurement error. Second, the cross-sectional design of the study prevents the establishment of causality or determination of the temporal sequence of events. Conducting longitudinal studies could provide more robust evidence regarding the observed associations. Finally, although the study claims to use nationally representative data, it is important to consider the generalizability of the findings beyond the U.S. population. The relationships between weight status, social participation restrictions, and COVID-19 infection may vary according to culture, socioeconomic status, and healthcare context in other countries. Despite these limitations, the findings of this study, based on nationally representative data, contribute valuable new evidence regarding the association between weight status, social participation restrictions, and COVID-19 incidence. The use of a representative population survey helps to avoid the risk of collider bias [34]. Unlike previous studies that focused on individuals admitted to hospitals with COVID-19 symptoms, which may introduce sampling bias, this study benefits from the use of national survey data, providing more reliable evidence.

Conclusion

This study offers important insights into the association between COVID-19 incidence, weight status, and social participation restrictions in the U.S. The findings demonstrated that weight status is linked to both social participation restrictions and COVID-19 infection, with overweight and obesity being particularly associated with increased susceptibility to the virus. This finding emphasizes the need to address the impact of weight status on social engagement and COVID-19 infection and advocates for public health interventions targeting weight management and the reduction of social stigmatization. These findings may have significant implications for public health strategies.

Availability of data and materials

All data generated or analyzed during this study is publicly-available dataset. Data was available in https://www.cdc.gov/nchs/nhis/2021nhis.htm website.

Abbreviations

COVID-19:

Coronavirus disease 2019

BMI:

Body Mass Index

NHIS:

National Health Interview Survey

DM:

Diabetes mellitus

References

  1. World Health Organization. Coronavirus disease (COVID-19) – World Health Organization. 2020 [Cited 2023 Jun 20]. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019.

  2. Centers for Disease Control and Prevention. Centers for Disease Control and Prevention. 2020 [Cited 2023 Jun 20]. Coronavirus Disease 2019 (COVID-19). Available from: https://www.cdc.gov/coronavirus/2019-ncov/index.html.

  3. Popkin BM, Du S, Green WD, Beck MA, Algaith T, Herbst CH, et al. Individuals with obesity and COVID-19: a global perspective on the epidemiology and biological relationships. Obes Rev. 2020;21(11):e13128.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ward ZJ, Bleich SN, Long MW, Gortmaker SL. Association of body mass index with health care expenditures in the United States by age and sex. PLOS One. 2021;16(3):e0247307.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. O’Hearn M, Liu J, Cudhea F, Micha R, Mozaffarian D. Coronavirus Disease 2019 Hospitalizations Attributable to Cardiometabolic Conditions in the United States: a comparative risk assessment analysis. J Am Heart Assoc. 2021;10(5):e019259.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Sarmadi M, Ahmadi-Soleimani SM, Fararouei M, Dianatinasab M. COVID-19, body mass index and cholesterol: an ecological study using global data. BMC Public Health. 2021;21(1):1712.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kompaniyets L. Body Mass Index and Risk for COVID-19–Related Hospitalization, Intensive Care Unit Admission, Invasive Mechanical Ventilation, and Death — United States, March–December 2020. MMWR Morb Mortal Wkly Rep. 2021;70. Cited 2023 Jun 20. Available from: https://www.cdc.gov/mmwr/volumes/70/wr/mm7010e4.htm.

  8. Levasseur M, Richard L, Gauvin L, Raymond E. Inventory and analysis of definitions of social participation found in the aging literature: proposed taxonomy of social activities. Soc Sci Med 1982. 2010;71(12):2141–9.

    Google Scholar 

  9. Lee XYB, Yusof NW, Pillai NK, Yap CG, Jahan NK. Review paper: association between social network and obesity among adult population. Open J Endocr Metab Dis. 2022;12(2):20–46.

    Article  Google Scholar 

  10. Khan MA, Moverley Smith JE. “Covibesity”, a new pandemic. Obes Med. 2020;19:100282.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Lin X, Xu Y, Pan X, Xu J, Ding Y, Sun X, et al. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Sci Rep. 2020;10(1):14790.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Biamonte E, Pegoraro F, Carrone F, Facchi I, Favacchio G, Lania AG, et al. Weight change and glycemic control in type 2 diabetes patients during COVID-19 pandemic: the lockdown effect. Endocrine. 2021;72(3):604–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Djuric O, Ottone M, Vicentini M, Venturelli F, Pezzarossi A, Manicardi V, et al. Diabetes and COVID-19 testing, positivity, and mortality: a population-wide study in Northern Italy. Diabetes Res Clin Pract. 2022;1(191):110051.

    Article  Google Scholar 

  14. NHIS. 2021 National Health Interview Survey Description. 2021. Cited 2023 Jan 23. Available from: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2021/srvydesc-508.pdf.

  15. Blumberg SJ, Parker JD, Moyer BC. National Health Interview Survey, COVID-19, and online data collection platforms: adaptations, tradeoffs, and new directions. Am J Public Health. 2021;111(12):2167–75.

    Article  PubMed  PubMed Central  Google Scholar 

  16. NBER. National Bureau of Economic Research. GUIDANCE: Data Sets Not Requiring IRB Review. Cited 2023 Jan 23. Available from: https://www.nber.org/centers/human-subjects-protection-and-institutional-review-board-irb/guidance-data-sets-not-requiring-irb-review.

  17. NCHS. National Health Interview Survey 2021. Cited 2023 Jan 23. Availablefrom: https://www.cdc.gov/nchs/nhis/index.htm.

  18. Theis KA, Murphy L, Hootman JM, Wilkie R. Social participation restriction among U.S. adults with arthritis: A population-based study using the International Classification of Functioning, Disability, and Health (ICF). Arthritis Care Res. 2013;65(7):1059–69.

    Article  CAS  Google Scholar 

  19. NHIS. NHIS - Adult Tobacco Use - Smoking Status Recodes. 2021. Cited 2023 Jun 21. Available from: https://www.cdc.gov/nchs/nhis/tobacco/tobacco_recodes.htm.

  20. NHIS. Supplemental Imputed Family Income/Personal Earnings Files. 2002. Cited 2023 Jan 26. Available from: https://www.cdc.gov/nchs/nhis/2002supplementalimputedincome.htm.

  21. Fulton M, Dadana S, Srinivasan VN. Obesity, Stigma, and Discrimination. In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2024. Cited 2024 Mar 26. Available from: http://www.ncbi.nlm.nih.gov/books/NBK554571/.

  22. Brewis A, Trainer S, Han S, Wutich A. Publically misfitting: extreme weight and the everyday production and reinforcement of felt stigma. Med Anthropol Q. 2017;31(2):257–76.

    Article  PubMed  Google Scholar 

  23. Thedinga HK, Zehl R, Thiel A. Weight stigma experiences and self-exclusion from sport and exercise settings among people with obesity. BMC Public Health. 2021;21(1):565.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Jia P. A changed research landscape of youth’s obesogenic behaviours and environments in the post-COVID-19 era. Obes Rev. 2021;22(S1):e13162.

    Article  CAS  PubMed  Google Scholar 

  25. Frisco ML, Van Hook J, Hummer RA. Would the elimination of obesity and smoking reduce U.S. racial/ethnic/nativity disparities in total and healthy life expectancy? SSM Popul Health. 2019;7:100374.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Manolis AS, Manolis AA, Manolis TA, Apostolaki NE, Melita H. COVID-19 infection and body weight: a deleterious liaison in a J-curve relationship. Obes Res Clin Pract. 2021;15(6):523–35.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Valerio A, Nisoli E, Rossi AP, Pellegrini M, Todesco T, El Ghoch M. Obesity and Higher Risk for Severe Complications of Covid-19: What to do when the two pandemics meet. J Popul Ther Clin Pharmacol J Ther Popul Pharmacol Clin. 2020;27(S Pt 1):e31–6.

  28. Aghili SMM, Ebrahimpur M, Arjmand B, Shadman Z, Pejman Sani M, Qorbani M, et al. Obesity in COVID-19 era, implications for mechanisms, comorbidities, and prognosis: a review and meta-analysis. Int J Obes. 2021;45(5):998–1016.

    Article  CAS  Google Scholar 

  29. Fernández-de-Las-Peñas C, Torres-Macho J, Elvira-Martínez CM, Molina-Trigueros LJ, Sebastián-Viana T, Hernández-Barrera V. Obesity is associated with a greater number of long-term post-COVID symptoms and poor sleep quality: a multicentre case-control study. Int J Clin Pract. 2021;75(12):e14917.

    Article  PubMed  Google Scholar 

  30. Vimercati L, De Maria L, Quarato M, Caputi A, Gesualdo L, Migliore G, et al. Association between Long COVID and Overweight/Obesity. J Clin Med. 2021;10(18):4143.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Meyerowitz EA, Richterman A, Gandhi RT, Sax PE. Transmission of SARS-CoV-2: a review of viral, host, and environmental factors. Ann Intern Med. 2021;174(1):69–79.

    Article  PubMed  Google Scholar 

  32. Haddad C, Malhab SB, Sacre H, Salameh P. Smoking and COVID-19: A Scoping Review. Tob Use Insights. 2021;14. Cited 2024 Mar 26. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890709/.

  33. Usman MS, Siddiqi TJ, Khan MS, Patel UK, Shahid I, Ahmed J, et al. Is there a smoker’s paradox in COVID-19? BMJ Evid-Based Med. 2021;26(6):279–84.

    Article  PubMed  Google Scholar 

  34. Griffith GJ, Morris TT, Tudball MJ, Herbert A, Mancano G, Pike L, et al. Collider bias undermines our understanding of COVID-19 disease risk and severity. Nat Commun. 2020;11(1):5749.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. School of Nursing, California State University Long Beach, Long Beach, USA

    SuJung Jung

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SJ: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Supervision. All authors read and approved the final manuscript.

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Correspondence to SuJung Jung.

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Our study conducted a secondary data analysis using publicly available de-identified NHIS data. As a result, no Institutional Review Board approval was necessary.

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The authors declare no competing interests.

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Jung, S. Associations between COVID-19 incidence, weight status, and social participation restrictions in the U.S.: evidence from the national population, cross-sectional study. BMC Public Health 24, 1066 (2024). https://doi.org/10.1186/s12889-024-18566-y

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