Association of Human Mobility Restrictions and Race/Ethnicity-Based, Sex-Based, and Income-Based Factors With Inequities in Well-being During the COVID-19 Pandemic in the United States

Abstract

Importance: An accurate understanding of the distributional implications of public health policies is critical for ensuring equitable responses to the COVID-19 pandemic and future public health threats.

Objective: To identify and quantify the association of race/ethnicity-based, sex-based, and income-based inequities of state-specific lockdowns with 6 well-being dimensions in the United States.

Design, setting, and participants: This pooled, repeated cross-sectional study used data from 14 187 762 households who participated in phase 1 of the population-representative US 2020 Household Pulse Survey (HPS). Households were invited to participate by email, text message, and/or telephone as many as 3 times. Data were collected via an online questionnaire from April 23 to July 21, 2020, and participants lived in all 50 US states and the District of Columbia.

Exposures: Indicators of race/ethnicity, sex, and income and their intersections.

Main outcomes and measures: Unemployment; food insufficiency; mental health problems; no medical care received for health problems; default on last month's rent or mortgage; and class cancellations with no distance learning. Race/ethnicity, sex, income, and their intersections were used to measure distributional implications across historically marginalized populations; state-specific, time-varying population mobility was used to measure lockdown intensity. Logistic regression models with pooled repeated cross-sections were used to estimate risk of dichotomous outcomes by social group, adjusted for confounding variables.

Results: The 1 088 314 respondents (561 570 [51.6%; 95% CI, 51.4%-51.9%] women) were aged 18 to 88 years (mean [SD], 51.55 [15.74] years), and 826 039 (62.8%; 95% CI, 62.5%-63.1%) were non-Hispanic White individuals; 86 958 (12.5%; 95% CI, 12.4%-12.7%), African American individuals; 86 062 (15.2%; 95% CI, 15.0%-15.4%), Hispanic individuals; and 50 227 (5.6%; 95% CI, 5.5%-5.7%), Asian individuals. On average, every 10% reduction in mobility was associated with higher odds of unemployment (odds ratio [OR], 1.3; 95% CI, 1.2-1.4), food insufficiency (OR, 1.1; 95% CI, 1.1-1.2), mental health problems (OR, 1.04; 95% CI, 1.0-1.1), and class cancellations (OR, 1.1; 95% CI, 1.1-1.2). Across most dimensions compared with White men with high income, African American individuals with low income experienced the highest risks (eg, food insufficiency, men: OR, 3.3; 95% CI, 2.8-3.7; mental health problems, women: OR, 1.9; 95% CI, 1.8-2.1; medical care inaccessibility, women: OR, 1.7; 95% CI, 1.6-1.9; unemployment, men: OR, 2.8; 95% CI, 2.5-3.2; rent/mortgage defaults, men: OR, 5.7; 95% CI, 4.7-7.1). Other high-risk groups were Hispanic individuals (eg, unemployment, Hispanic men with low income: OR, 2.9; 95% CI, 2.5-3.4) and women with low income across all races/ethnicities (eg, medical care inaccessibility, non-Hispanic White women: OR, 1.8; 95% CI, 1.7-2.0).

Conclusions and relevance: In this cross-sectional study, African American and Hispanic individuals, women, and households with low income had higher odds of experiencing adverse outcomes associated with the COVID-19 pandemic and stay-at-home orders. Blanket public health policies ignoring existing distributions of risk to well-being may be associated with increased race/ethnicity-based, sex-based, and income-based inequities.

Figure 1.. Associations Between Changes in Mobility Restriction and in Outcomes

Solid lines represent probabilities from logistic regressions, holding all covariates at their sample means. Shaded areas represent 95% CIs. Mobility restriction represents within-state reductions in mobility based on week-to-week changes in mobility from normal levels. All models control for income, race/ethnicity, age, sex, education, marital status, numbers of individuals in the household, week of survey, state-level heterogeneity, and COVID-19 death and case rates. SE estimates are robust and clustered at the state level.

Figure 2.. Risk of Outcome by Income, Race/Ethnicity, and Sex

Dots are mutually adjusted point odds ratios, and whiskers represent 95% CIs. All models control for mobility, age, education, marital status, numbers of individuals in the household, week of survey, state-level heterogeneity, and COVID-19 death and case rates. SE estimates are robust and clustered at the state level. We provide more detail on the outcome descriptions in the legend for Figure 1. For African American, Hispanic, and Asian groups, the reference group was non-Hispanic White individuals. Low income (LI) was defined as households with income less than $35 000 per year; lower-middle income (LMI) are those with income between $35 000 and $75 000 per year. The reference group for low and lower-middle income groups is households with income greater than $75 000 per year.

Figure 3.. Intersectionality of Sex, Race/Ethnicity, and Income: Groups With Highest Risks of Outcomes

Dots are mutually adjusted odds ratios and whiskers represent 95% CIs. Models included 3-way interaction dummy variables for sex, race, and income groups. The reference group was non-Hispanic White men with high income (HI; ie, >$75 000/year). All models control for mobility, age, education, marital status, numbers of individuals in the household, week of survey, state-level heterogeneity, and COVID-19 death and case rates. SE estimates are robust and clustered at the state level. We provide more detail on the outcome descriptions in the legend for Figure 1. Low income (LI) was defined as households with income less than $35 000 per year; lower-middle income (LMI) are those with income between $35 000 and $75 000 per year.