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Edition 48 –Changes in Income and Household Spending During Early Months of COVID-19 Pandemic Reveal Racial and Ethnic Disparities Among Older Adults


Edition 48 – COVID-19

Changes in Income and Household Spending During Early Months of COVID-19 Pandemic Reveal Racial and Ethnic Disparities Among Older Adults

By Melissa R. Holloway BS, Dr. Xueya Cai PhD, Dr. Adam Simning MD, Zijing Cheng MS, Dr. Yue Li PhD

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Citation

Holloway M. Cai X. Simning A. Cheng Z. Li Y. Changes in income and household spending during early months of COVID-19 pandemic reveal racial and ethnic disparities among older adults.. HPHR. 2021;48.  

Changes in Income and Household Spending During Early Months of COVID-19 Pandemic Reveal Racial and Ethnic Disparities Among Older Adults

Introduction

The COVID-19 pandemic has both elucidated and exacerbated racial and ethnic disparities in the United States.  Conspicuous early effects in healthcare access and direct morbidity and mortality due to COVID-19 disease revealed that, compared to White populations, Black and Hispanic communities suffered greater rates of mortality,1,2 especially among older adults.3  Preliminary findings also suggest that Black and Hispanic adults were more vulnerable to income instability and food insecurity during the pandemic.4  This study examined racial and ethnic disparities in household finances during the early period of the pandemic.  Using data from a nationally representative survey, we tested the hypotheses that Black and Hispanic older adults experienced reduced income and increased household spending in the first 3 months of the pandemic.

Methods

We used the University of Michigan Health and Retirement Study (HRS) COVID-19 September 2020 Supplement data fielded beginning on June 11, 2020.The institutional review board of the University of Rochester approved the study with a waiver of informed consent because of the use of publicly available, de-identified data.  The study cohort consisted of 3212 respondents aged 50 or over who reported on income and household spending changes during the pandemic.  After excluding respondents living in a nursing home (n=49), who selected “other” for race/ethnicity (n=153), and with inappropriate sampling weights due to non-responses (n=105), the sample consisted of 2905 respondents representing a weighted 93 266 929 community-dwelling older adults.

 

We controlled for variables that may affect the association of race and ethnicity with changes in financial resources (see Tables 1 and 2).  Bivariate analyses were performed using the Rao-Scott Chi-square tests for categorical variables and t-tests for continuous variables.  Separate multivariable logistic regression models were performed to test the independent associations of race and ethnicity and the odds of reduced income and increased household spending after adjustment for respondent characteristics.  All analyses were performed in SAS 9.4® and accounted for household and respondent level weights to make results nationally representative.6

Results

Table 1. Changes in Income and Household Spending During the First 3 Months of the COVID-19 Pandemic, by Characteristics of Survey Respondentsa

Feature

% Respondents

Incomeb

Household Spendingc

 

 

Increased or stayed the same

Decreased

P value

Decreased or stayed the same

Increased

P value

Race/Ethnicity

 

 

<.001

 

 

<.001

  White

77.5%

84.7%

15.3%

 

86.4%

13.6%

 

  Black

11.3%

85.8%

14.2%

 

74.1%

25.9%

 

  Hispanic

11.2%

66.0%

34.0%

 

74.1%

25.9%

 

Age Group

 

 

<.001

 

 

0.978

  50-64

42.0%

76.2%

23.8%

 

83.7%

16.3%

 

  65-74

34.8%

84.5%

15.5%

 

83.6%

16.4%

 

   ³75

23.2%

91.7%

8.3%

 

83.2%

16.8%

 

Gender

 

 

 

0.163

 

 

0.385

  Female

53.3%

84.0%

16.0%

 

82.8%

17.2%

 

  Non-female

46.7%

81.2%

18.8%

 

84.4%

15.6%

 

Education Level

 

 

0.067

 

 

<.001

  College

38.9%

80.7%

19.3%

 

87.9%

12.1%

 

  No college

61.1%

84.5%

15.5%

 

80.8%

19.2%

 

Marital Status

 

 

0.623

 

 

0.002

  Married

58.9%

82.2%

17.8%

 

85.9%

14.1%

 

  Single

41.1%

83.2%

16.8%

 

80.1%

19.9%

 

Insurance Status

 

 

0.211

 

 

<.001

  Medicaid

10.9%

79.4%

20.6%

 

74.2%

25.8%

 

  No Medicaid

89.1%

83.0%

17.0%

 

84.7%

15.3%

 

Self-Rated Health Score

 

 

 

<.001

 

 

<.001

  1 (Excellent)

7.8%

85.7%

14.3%

 

92.9%

7.1%

 

  2 (Very good)

32.7%

83.3%

16.7%

 

88.5%

11.5%

 

  3 (Good)

34.5%

80.6%

19.4%

 

82.6%

17.4%

 

  4 (Fair)

19.3%

82.4%

17.6%

 

75.2%

24.8%

 

  5 (Poor)

5.8%

87.2%

12.8%

 

76.1%

23.9%

 

 

Mean ± SD

Mean ± SD

Mean ± SD

 

Mean ± SD

Mean ± SD

 

IADL Scored

0.29±0.02

0.31±0.02

0.19±0.03

<.001

0.27±0.02

0.41±0.05

<.001

Depressive Symptomse

1.24±0.05

1.21±0.05

1.36±0.12

<.001

1.14±0.05

1.70±0.12

<.001

a Sample sizes vary slightly for different variables due to missing values; all analyses controlled for common medical conditions, including hypertension, diabetes, cancer, lung disease, heart disease, stroke history, psychiatric illness, and arthritis.

b Associated survey question: “Since the start of the coronavirus pandemic, has your income gone up or down or stayed about the same because of the pandemic?”

c Associated survey question: “Has your household spending gone up or down or stayed about the same?”

e Depressive symptoms were assessed as the sum of eight yes/no items from the Center for Epidemiologic Studies Depression scale (CES-D). The range of the score is 0 to 8 with higher score indicating more depressive symptoms.

 

 

Table 2. Multivariable Analysis of Changes in Income and Household Spending During the First 3 Months of the COVID-19 Pandemic, by Characteristics of Survey Respondentsa

Feature

Decreased Incomeb

Increased Household Spendingc

 

OR (95% CI)

P value

OR (95% CI)

P value

 

Race/Ethnicity

 

 

 

 

 

  White

Ref.

Ref.

Ref.

Ref.

 

  Black

0.83 (0.54-1.27)

0.3822

1.83 (1.28-2.61)

0.0009

 

  Hispanic

2.79 (1.84-4.23)

<.0001

2.10 (1.40-3.15)

0.0003

 

Age Group

 

 

 

 

 

  50-64

1.45 (1.02-2.07)

0.0408

0.91 (0.64-1.31)

0.6191

 

  65-74

Ref.

Ref.

Ref.

Ref.

 

  ³75

0.58 (0.38-0.89)

0.0132

0.91 (0.65-1.27)

0.5735

 

Female Gender

0.83 (0.60-1.13)

0.2363

0.92 (0.69-1.23)

0.5603

 

College Degree

1.34 (0.96-1.89)

0.0877

0.80 (0.58-1.12)

0.1985

 

Married

1.06 (0.76-1.47)

0.7531

0.78 (0.58-1.060

0.1092

 

Medicaid

1.06 (0.66-1.70)

0.8156

1.17 (0.79-1.74)

0.4418

 

Self-Rated Health Score

 

 

 

 

 

  1 (Excellent)

Ref.

Ref.

Ref.

Ref.

 

  2 (Very good)

1.28 (0.65-2.49)

0.4744

1.52 (0.88-1.25)

0.3652

 

  3 (Good)

1.48 (0.76-2.90)

0.2483

2.39 (0.97-5.90)

0.0585

 

  4 (Fair)

1.33 (0.60-3.52)

0.5450

3.07 (1.20-7.85)

0.0189

 

  5 (Poor)

1.05 (0.96-1.15)

0.5720

2.62 (0.94-7.30)

0.0661

 

IADL Scored (per each compromised)

0.84 (0.65-1.07)

0.1566

1.04 (0.87-1.24)

0.6759

 

Depressive Symptomse

(Each point higher on CES-D)

1.05 (0.96-1.15)

0.3373

1.04 (0.96-1.12)

0.3414

 

a Multivariable logistic regression adjusted for respondent characteristics listed in Table 1; all analyses controlled for common medical conditions, including hypertension, diabetes, cancer, lung disease, heart disease, stroke history, psychiatric illness, and arthritis.

b Associated survey question: “Since the start of the coronavirus pandemic, has your income gone up or down or stayed about the same because of the pandemic?”

c Associated survey question: “Has your household spending gone up or down or stayed about the same?”

e Depressive symptoms were assessed as the sum of eight yes/no items from the Center for Epidemiologic Studies Depression scale (CES-D). The range of the score is 0 to 8 with higher score indicating more depressive symptoms.

During the first 3 months of the COVID-19 outbreak, a weighted 17.2% of respondents experienced a decrease in income, while a weighted 16.4% of respondents experienced an increase in household spending.

 

Decreased income was reported by 15.3% of non-Hispanic White, 14.2% of non-Hispanic Black, and 34.0% of Hispanic respondents (Table 1).  In multivariable analysis (Table 2), demographic characteristics independently associated with decreased income were Hispanic (OR 2.79; 95% CI 1.84-4.23; p<.0001) vs White race/ethnicity, and age 50-64 (OR 1.45; 95% CI 1.02-2.07; p=0.0408) vs age 65 and over.

 

Increased household spending was reported by 13.6% of non-Hispanic White, 25.9% of Non-Hispanic Black, and 25.9% of Hispanic respondents (Table 1).  In multivariable analysis (Table 2), characteristics independently associated with increased household spending were Hispanic (OR 2.10; 95% CI 1.40-3.15; p=0.0003) and Black (OR 1.83; 95% CI 1.28-2.61; p=0.0009) vs White race/ethnicity, as well as Good (3) (OR 2.39; 95% CI 0.97-5.90; p=0.0585) and Fair (4) (OR 3.07; 95% CI 1.20-7.85; p=0.0189) vs Excellent (1) self-rated health scores.

Conclusion

Hispanic older adult respondents were more than twice as likely as Whites to experience decreased household income, and both Black and Hispanic older adult respondents were nearly twice as likely as Whites to experience increased household spending the first 3 months of the COVID-19 pandemic.

 

These findings suggest that the financial health of Black and Hispanic older adults was precarious at the pandemic onset.  The increased spending in both groups may have been driven by an increased need to switch to private transportation, losses in employment-based insurance, additional residents, or increases in energy insecurity or utility disconnection.7  These findings also suggest that Hispanic older adults were especially susceptible to loss of employment or supplemental income, reduced hours, or reduced pay, which may be partially due to discrepancies in job flexibility.8

 

Notably, worse self-rated health scores were associated with increased household spending, suggesting that necessary precautions and services may have been more financially burdensome for those with compromised health.  The association between decreased income and younger age is likely due to differences in employment, with older adults receiving more reliable income (e.g., social security payments).

 

Populations more susceptible to societal inequities and discrimination in housing, education, and labor markets may benefit from increased emergency funds and access to social services such as Medicaid, SNAP, and TANF during public health crises.  Poor short-term economic outcomes only corroborate the need for stronger federal public health emergency response infrastructure,9 and ongoing intervention in addressing nationwide inequity.  Our study was limited by inability to assess magnitude of decreased income and increased household spending.

Disclosure Statementt

The author(s) has no relevant financial disclosures or conflicts of interest.

References

1 Yu, Q., Salvador, C. E., Melani, I., Berg, M. K., Neblett, E. W., & Kitayama, S. (2021). Racial residential segregation and economic disparity jointly exacerbate COVID‐19 fatality in large American cities. Annals of the New York Academy of Sciences.

 

2 Strickland, O. L., Powell-Young, Y., Reyes-Miranda, C., Alzaghari, O., & Giger, J. N. (2020). African-Americans Have a Higher Propensity for Death from COVID-19: Rationale and Causation. Journal of National Black Nurses’ Association: JNBNA31(1), 1-12.

 

3 Garcia, M. A., Homan, P. A., García, C., & Brown, T. H. (2021). The color of COVID-19: Structural racism and the disproportionate impact of the pandemic on older Black and Latinx adults. The Journals of Gerontology: Series B76(3), e75-e80.

 

4 Lauren, B. N., Silver, E. R., Faye, A. S., Rogers, A. M., Baidal, J. A. W., Ozanne, E. M., & Hur, C. (2021). Predictors of households at risk for food insecurity in the United States during the COVID-19 pandemic. Public health nutrition, 1-19.

 

5 HRS Staff. (2020). 2020 HRS COVID-19 Project. Ann Arbor, MI: Survey Research Center, Institute for Social Research, University of Michigan. hrsdata.isr.umich.edu/data-products/2020-hrs-covid-19-project.

 

6 HRS Staff. (2019). Sampling Weights: Revised for Tracker 2.0 & Beyond. Ann Arbor, MI: Survey Research Center, Institute for Social Research, University of Michigan. https://hrs.isr.umich.edu/sites/default/files/biblio/wghtdoc_0.pdf

 

7 Memmott, T., Carley, S., Graff, M., & Konisky, D. M. (2021). Sociodemographic disparities in energy insecurity among low-income households before and during the COVID-19 pandemic. Nature Energy6(2), 186-193

 

8 Helppie‐McFall, B., & Hsu, J. W. (2020). Financial profiles of workers most vulnerable to coronavirus‐related earnings loss in the spring of 2020. Financial Planning Review3(4), e1102

 

9 Maani, N., & Galea, S. (2020). COVID‐19 and underinvestment in the public health infrastructure of the United States. The Milbank Quarterly98(2), 250

About the Authors

Melissa R. Holloway, BS

Ms. Melissa Holloway is a medical student at the University of Rochester School of Medicine and Dentistry. Her research interests include health services, health policy, and health justice.

Dr. Xueya Cai, PhD

Dr. Xueya Cai is a research associate professor in the Department of Biostatistics and Computational Biology at the University of Rochester. Her research interests include public health trends and health disparities. She received her formal training at State University of New York at Buffalo.

Dr. Adam Simning, MD, PhD

Dr. Adam Simning is an assistant professor in the Departments of Psychiatry and Public Health who completed his psychiatry residency in 2017 and geriatric psychiatry fellowship in 2018 at the University of Rochester. His research areas include patterns in care transition among older adults and associated interventions. Dr. Simning currently provides consultative clinical care to approximately 30 nursing homes through University of Rochester’s Skilled Nursing Facility Telepsychiatry Program.

Zijing Cheng, MS

Ms. Zijing Cheng is a masters-level researcher at the University of Rochester with interests in public health and health equity.

Dr. Yue Li, PhD

Dr. Yue Li is a tenured Professor and an experienced health services researcher at the University of Rochester with major focuses on Medicare policy and quality of hospital and long-term care. His current research includes: 1) Quality and outcome assessment for hospital and long-term care; 2) Impact of Medicare payment reforms such as bundled payments on health care quality; and 3) disparities in quality of health care.