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

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].

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].

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.

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].

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.

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.