Disparities in Prevalence of COVID-19, Long COVID, Disease Severity, and Mental Health Outcomes among US Adults by Industrial Sector of Employment, September-November 2022

2.1. Data

We used pooled data from three consecutive samples of the US Census Bureau's Household Pulse Survey (HPS) conducted during September, October, and November 2022. The three independent HPS samples that included information on COVID-19 diagnosis, long COVID, and industry of employment among adults aged ≥18 years were: Week 49, September 14-28; Week 50, October 5-17; and Week 51, November 2-14, 2022.²⁷ The HPS is a national sample household survey in which data on socioeconomic, demographic, physical and mental health, food insecurity, childcare, and healthcare characteristics, including health insurance coverage, COVID-19 diagnosis and vaccination, and access to telehealth during the COVID-19 pandemic are collected in near real-time via email and internet.The survey was developed as a rapid response survey in order to track the social and economic impacts of the COVID-19 pandemic on American households on a weekly or bi-weekly basis in partnership with several federal statistical agencies.^28,29 Information collected in the survey is based on self-reports by respondents aged 18 years and older.The HPS uses a systematic sample design and is representative of the civilian non-institutionalized population of the US. Substantive and methodological details of the survey are available elsewhere.^27-29

2.2. Measurement of Five Outcome Variables: COVID-19 Diagnosis, Long COVID, COVID Disease Severity, Serious Depression, and Serious Anxiety

We used five COVID-related binary outcome variables. The first outcome variable, the COVID-19 diagnosis, was derived from the question: “Have you ever tested (using a rapid point-of-care test, self-test, or laboratory test) positive for COVID-19 or been told by a doctor or other healthcare provider that you have or had COVID-19?” ²⁸ Adults responding “yes” were defined as having been diagnosed with COVID-19 and those with “no” responses were considered as not having the disease diagnosis.

The second outcome variable, Long COVID, was based on the question “Did you have any symptoms lasting 3 months or longer that you did not have prior to having coronavirus or COVID-19? Long-term symptoms may include tiredness or fatigue, difficulty of thinking, concentrating, forgetfulness, or memory problems (sometimes referred to as “brain fog" difficulty of breathing or shortness of breath, joint or muscle pain, fast-beating or pounding heart (also known as heart palpitations), chest pain, dizziness on standing, menstrual changes, changes to taste/smell, or inability to exercise.” COVID patients responding “yes” were defined as having Long COVID and those with “no” responses were considered not to have Long COVID.

The third outcome variable, disease severity (i.e., severe COVID symptoms), was based on the question “How would you describe your coronavirus symptoms when they were at their worst?” Respondents who reported severe symptoms were defined as having severe COVID symptoms, and those reporting, no, mild or moderate symptoms as not having severe symptoms.

The fourth outcome variable, depression, was derived from the question "Over the last 7 days, how often have you been bothered by... feeling down, depressed, or hopeless? Would you say not at all, several days, more than half the days, or nearly every day?" Respondents who reported feeling down, depressed, or hopeless nearly every day of the past week were defined as having serious depression.

The fifth outcome variable, anxiety, was derived from the question “Over the last 7 days, how often have you been bothered by the following problems. feeling nervous, anxious, or on edge? Would you say not at all, several days, more than half the days, or nearly every day?" Respondents who reported feeling nervous, anxious, or on edge nearly every day of the past week were defined as having serious anxiety.

The pooled sample size from September- December 2022 HPS was 148,813 adults aged ≥18 years for whom information on the outcome variables and industry was available. ²⁷

2.3. Measurement of Industrial Sector and Covariates

The independent variable of interest was the respondent's business or industry of employment in the past seven days at the time of the survey. ²⁷ The industrial sector of employment variable consisted of 23 categories, including agriculture, mining, utilities, construction, manufacturing, wholesale trade, retail trade, transportation and warehousing, information technology, finance and insurance, real estate, professional, scientific, and technical services, management of companies, administrative and support services, waste management, educational services, healthcare, social assistance, arts and entertainment, accommodation and food services, public administration, other services, and a category for non-employed individuals, as shown in Tables 1 and 2.

Based on previous research and the social determinants of health framework, we selected the following covariates for the five outcome variables: age, race/ethnicity, gender, LGBT status, marital status, region of residence, educational attainment, household income, housing tenure, and health insurance status.^{6,9,10,21,27,30} The covariates were measured as shown in Tables 1 and 2.

2.4. Statistical Methods

Multivariable logistic regression was used to model disparities in COVID-related outcomes by industrial sector after controlling for socioeconomic, demographic, and health characteristics. The chi-square statistic was used to test the overall association between each covariate and vaccination prevalence, whereas the two-sample t-test was used to test the difference in prevalence between any two groups or geographic areas. To account for the complex sample design of the HPS, SUDAAN software was used to conduct all statistical analyses, including the logistic modeling procedure RLOGIST.³¹ Adjusted prevalence estimates for the five outcome variables were derived by the logistic model at the mean values of the covariates.

3.1. Disparities in COVID-19 Prevalence by Industry

During September-November 2022, an estimated 117 million or 48.7% of US adults aged ≥18 years reported having been diagnosed with COVID-19 (Table 1). COVID-19 prevalence ranged from 46.7% for workers in the agricultural sector to 58.0% for workers in the healthcare sector. Adjusted for covariates, compared to workers in the agricultural sector, workers in wholesale trade, finance and insurance, educational services, healthcare, social assistance, and accommodation and food services had 30-39% higher odds of being diagnosed with COVID-19 (Table 1).

3.2. Disparities in Long COVID Prevalence by Industry of Employment

During September-November 2022, an estimated 34.3 million or 29.4% of US adults aged ≥18 years with COVID-19 infection reported experiencing Long COVID symptoms (Table 1). This amounts to 14.3% (or 1 in 7) of all US adults developing Long COVID at some point (data not shown in Tables). Among those who reported experiencing Long COVID, 16.8 million or 49.2% reported having Long COVID symptoms currently (data not shown in Tables).

Long COVID prevalence ranged from 20.4% for workers in the wholesale trade sector to 38.5% for workers in the social assistance sector. Adjusted for covariates, workers in social assistance, real estate, utilities, construction, and healthcare sectors had, respectively, 102%, 81%, 67%, 66%, and 48% higher odds of developing Long COVID than workers in wholesale trade (Table 1). Among those who experienced Long COVID, workers in retail trade, educational services, and healthcare industries had 58-61% higher odds of having Long COVID symptoms currently than workers in the accommodation and food services sector (data not shown in Tables).

3.3. Disparities in COVID-19 Severity by Industry of Employment

During September-November 2022, an estimated 14.7 million or 12.6% of US adults aged ≥18 years with COVID-19 infection reported experiencing severe COVID symptoms.The prevalence of severe COVID symptoms ranged from 6.8% for workers in public administration to 14.9% for workers in the social assistance sector. Adjusted for covariates, workers in retail trade, finance and insurance, real estate, professional and technical services, management of companies, healthcare, and social assistance sectors had 63-105% higher odds of experiencing severe COVID symptoms than workers in public administration (Table 1).

3.4. Disparities in Mental Health among COVID Patients (Survivors) by Industry

During September-November 2022, an estimated 10.7 million or 10.7% of adults with COVID infection (i.e., COVID-19 survivors) reported serious depression, and 18 million or 17.8% reported serious anxiety (Table 2). The prevalence of serious depression among COVID survivors ranged from 5.5% for workers in utilities to 14.8% for workers in the accommodation and food services sector. Adjusted for covariates, workers in agriculture, retail trade, finance and insurance, professional and technical services, educational services, healthcare, arts and entertainment, and accommodation and food services sectors had 78-126% higher odds of serious depression developing than workers in utilities (Table 2).

The prevalence of serious anxiety among COVID survivors ranged from 10.3% for workers in utilities to 24.6% for workers in accommodation and food services. Adjusted for covariates, workers in agriculture, retail trade, transportation and warehousing, finance and insurance, professional and technical services, management of companies, administrative and support services, educational services, healthcare, social assistance, arts and entertainment, and accommodation and food services sectors had 71-130% higher odds of serious depression developing than workers in utilities (Table 2).

3.4. Limitations

This study has some limitations. First, response bias, especially related to health and healthcare outcomes, can be an issue when using self-reported data in a sample survey like the HPS. Second, HPS does not collect information on many factors that could help explain disparities in mental health problems and disease severity among workers with COVID-19 or Long COVID in various job sectors during the pandemic, including job insecurity, work-related stress, autonomy, control, and decision-making in the workplace, social and physical isolation, lack of social support or connectedness, loss of self-identity and sense of control, unhealthy diet, lack of physical inactivity, smoking, alcohol, and other substance use, and comorbidities.²¹ Although comorbidities such as hypertension, smoking, diabetes, obesity, and other pre-existing conditions are important risk factors for COVID-19 infection, disease severity, and mental health problems, they may not fully explain disparities in Long COVID reported here.^16,25,35 In fact, Long COVID can develop among people who have had no or mild symptoms, although people with more severe COVID symptoms are at the greatest risk of developing Long COVID and long-term impairments.^16,25

Third, data on specific COVID symptoms are not available in HPS. Moreover, the reported number of COVID-19 infections reported here is probably underestimated, as many individuals with positive home-based self-test results do not report infections unless they seek care.¹¹

Fourth, HPS is a cross-sectional survey; causality cannot be inferred especially for the association of industrial sector with COVID-19 infection, Long COVID, disease severity, and mental health impact. However, reverse causality is less of an issue, since employment in different industrial sectors is less likely to change during the course of the pandemic, and mental health was measured at the time of or the week preceding the survey.²¹

Fifth, the respondents in HPS are more likely to be women and non-Hispanic Whites and have higher education when compared, for example, with another major national sample survey such as the American Community Survey.³⁶ This might have resulted in an underestimate of the magnitude of disparities in COVID-related outcomes, especially among disadvantaged workers in certain industries. However, we addressed disproportionate sampling of demographic characteristics by using survey weights, which rake the demographics of the interviewed persons to education attainment/sex/age distributions and ethnicity/race/sex/age population distributions.²⁹

Compliance with Ethical Standards