Next Article in Journal
Sustainability of Cocoa (Theobroma cacao) Cultivation in the Mining District of Ponce Enríquez: A Trace Metal Approach
Next Article in Special Issue
Sleep Problems and New Occurrence of Chronic Conditions during the COVID-19 Pandemic in the UK
Previous Article in Journal
Technology Advancements and Employees’ Qualitative Job Insecurity in the Republic of Korea: Does Training Help? Employer-Provided vs. Self-Paid Training
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 19
Issue 21
10.3390/ijerph192114367
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Socioeconomic Deprivation, Sleep Duration, and Mental Health during the First Year of the COVID-19 Pandemic

by
Stephanie Griggs
1,*,
Christine Horvat Davey
1,
Quiana Howard
1,
Grant Pignatiello
1 and
Deepesh Duwadi
2
1
Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH 44106, USA
2
Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, OH 44106, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(21), 14367; https://doi.org/10.3390/ijerph192114367
Submission received: 20 October 2022 / Revised: 31 October 2022 / Accepted: 31 October 2022 / Published: 3 November 2022
(This article belongs to the Special Issue Impact of Sleep on Physical and Mental Health Following over 2 Years of the COVID-19 Pandemic)
Browse Figure
Review Reports Versions Notes

Abstract

:
The coronavirus disease 2019 (COVID-19) has had a rapid and sustained negative impact on sleep and mental health in the United States with disproportionate morbidity and mortality among socioeconomically deprived populations. We used multivariable and logistic regression to evaluate the associations among sleep duration, mental health, and socioeconomic deprivation (social deprivation index) in 14,676 Ohio residents from 1101 zip code tabulation areas from the 2020 Behavioral Risk Factor Surveillance System (BRFSS) survey. Higher socioeconomic deprivation was associated with shorter sleep and poorer mental health after adjusting for covariates (age, sex, race, education, income, and body mass index) in the multivariable linear regression models. Those in the highest socioeconomically deprived areas had 1.6 and 1.5 times higher odds of short sleep (duration < 6 h) and poor mental health (>14 poor mental health days), respectively, in the logistic regression models. Previous researchers have focused on limited socio-environmental factors such as crowding and income. We examined the role of a composite area based measure of socioeconomic deprivation in sleep duration and mental health during the first year of COVID-19. Our results suggest the need for a broader framework to understand the associations among socioeconomic deprivation, sleep duration, and mental health during a catastrophic event.

1. Introduction

The coronavirus disease 2019 (COVID-19) pandemic has underscored health disparities in the United States. For example, neighborhoods with lower median family income and higher unemployment rates have higher rates of COVID-19 transmission, hospitalization, and death [1,2,3,4]. There is an established relationship between poor health outcomes and socioeconomic deprivation, often assessed via the social deprivation index (SDI), a composite measure of the effects of poverty and deprivation based on income, education, employment, housing, household characteristics, transportation, and demographics [5]. The SDI permits the assessment of the individual components to understand the nuanced population effects or socio-economic variability in health outcomes of social determinants of health. The COVID-19 pandemic upended the delicate balance between sleep and mental health [6]. Evaluating the role of socioeconomic deprivation in sleep and mental health is important to (1) highlight inequality in highly overlooked domains of health behaviors, (2) understand how sleep and mental health are influenced, and (3) target higher-risk groups and areas for future interventions.
Social determinants of health are the social and economic conditions that impact a vast array of health and quality of life outcomes, including sleep and mental health [7]. Social determinants of health are classified into five main areas including healthcare access and quality, education, social and community context, neighborhood and built environment, and economic stability [7]. COVID-19 outcomes have been profoundly impacted by social determinants of health. For example, homelessness is linked to increased risk of COVID-19 transmission due to crowded living spaces and decreased access to testing facilities [8]. The effects of social determinants of health are far-reaching and have been magnified by the COVID-19 pandemic. Understanding the socioeconomic deprivation within the context of the COVID-19 pandemic can provide important insights into sleep and mental health outcomes.
The National Sleep Foundation recommends that adults sleep 7–9 h per night, and about a third of Americans report insufficient sleep (defined as <7 h) [9]. Sleep of insufficient quality or quantity is associated with higher body mass index (BMI), impaired body weight regulation (lower leptin and higher ghrelin levels), poorer insulin sensitivity, and lower daytime alertness and has been linked to multiple chronic conditions including overweight/obesity, type 2 diabetes, cardiovascular disease, stroke, dementia, and cancer [10,11,12,13,14]. During the COVID-19 pandemic years 2019 to 2021, sleep disturbances affected approximately 41% with higher rates during the lockdown compared to no lockdown, 42.49% versus 37.97% in a recent systematic review and meta-analysis of 250 studies (493,475 participants) across 49 countries [15]. However, there are few studies where the impact of the COVID-19 pandemic on sleep sufficiency has been examined. The COVID-19 impact has varied among populations with individuals reporting adequate sleep duration pre-pandemic and shorter sleep duration and increased sleep complaints during the pandemic [16,17,18]. The effect of sleep behavior on health outcomes has increasingly been studied with an emerging literature on social determinants of sleep [9,19,20,21]. For instance, lower socioeconomic status (individual level) and socioeconomic deprivation (area level) are associated with short or long sleep duration along with sleep disorders (e.g., sleep apnea) which all relate to increased mortality risk [22,23,24,25,26]. The COVID-19 pandemic has also magnified preexisting sleep disparities across individuals [6,27,28,29,30,31]. However, less is known about the association between neighborhood socioeconomic characteristics and the nuance of urban-rural classifications on sleep. Further, compared to those with sufficient sleep, individuals with insufficient sleep have nearly threefold increased odds of mental distress [32].
There is increasing concern about the impact of the pandemic on mental health [33,34]. Those with limited economic and social resources are at a higher risk for these impacts [35]. There were considerable increases in anxiety, depressive disorders, and suicidal ideation in mid-March–February 2021 (during the pandemic) compared to the same period in 2019 (pre-pandemic) in the United States [36]. In another national United States survey of 790,633 individuals, there was a significant increase in the percentage of adults with anxiety or depressive disorder symptoms (36.4% to 41.5%) during the past 7 days, as well as an increased need for mental health counseling or therapy during the past 4 weeks (from 9.2% to 11.7%) [37,38]. The higher need for mental health counseling persisted after the height of the COVID-19 pandemic in the United States with an increase in the percentage of adults needing treatment within the past 12 months (19.2% to 21.6%) [37,38].
The purpose of this study was to quantify levels of socioeconomic deprivation and examine associations among socioeconomic deprivation, sleep duration, and mental health while adjusting for covariates (e.g., age, sex at birth, racial identity, education, body mass index) during the first year of the COVID-19 pandemic. Specifically, through a cross-sectional investigation of Ohio residents from the Behavioral Risk Factor Surveillance System (BRFSS) in 2020, we tested the following hypotheses: (1) higher socioeconomic deprivation would be associated with shorter sleep and poorer mental health and (2) those in the highest socioeconomically deprived areas would have higher odds of short sleep (defined as <6 h per night) and poor mental health (defined as >14 days of poor mental health per month).

2. Materials and Methods

2.1. Data Sources and Study Population

The Ohio BRFSS 2020 data were used for this analysis. The BRFSS is a random-digit dialed state-based telephone survey about health practices, conditions, and risk among non-institutionalized adults ages 18 years and older conducted by the Ohio Department of Health and supported by the Centers for Disease Control and Prevention (CDC). Data are collected from a random probability sample representative of the 14 geographic regions in Ohio. The corresponding author’s institution, Case Western Reserve University approved the study (IRB # STUDY20221217). Case Western Reserve University has a federal-wide assurance.

2.2. Variables and Measures

Individual demographic variables of interest included age, sex at birth, race/ethnicity, and education. Covariates were selected based on a priori knowledge and included age, biological sex, and race/ethnicity, education, and body mass index. An area-level variable the Social Deprivation Index (SDI) was generated from the zip codes. The SDI is a composite measure of six domains collected in the American Community Survey: income (percent living in poverty), education (percent with less than 12 years of education), housing (percent living in rented housing unit and percent living in overcrowded housing unit), household characteristics (percent single-parent households with dependents <18 years), transportation (percent of households without a car), and employment (percent non-employed adults under 65 years of age) [5]. The SDI is an estimate of health care access and health outcomes within a rational primary care service area based on zip code tabulation (ZCTA). The SDI was updated with data from 5-year estimates from 2011–2015 [5]. We used ZCTA’s in this study based on generalized U.S. Postal Service Zip Codes [5].
Self-reported sleep duration was a single-item measure. Respondents were asked “on average, how many hours of sleep do you get in a 24 h period?”. Responses range from 1–24 h. We excluded implausible values for sleep duration (>18 h). The significance was not altered with the removal of implausible values. We used hours in the linear regression models. For the logistic regression models, we defined short sleep as ≤6 h consistent with other studies on sleep duration and the National Sleep Foundation Recommendation [39].
Mental health was derived from a single item measure. Respondents were asked “Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?”. We used poor mental health days from 0 to 30 in the linear regression models. For logistic regression, poor mental health was defined as 14 or more days of poor mental health in the past 30 days. This approach for defining poor mental health days has been documented in previous research [40,41].

2.3. Statistical Analysis

A quantitative descriptive approach was used to characterize socioeconomic deprivation, sleep duration, and mental health among the individuals in the study. Bivariate correlations, multivariable linear, and multivariable logistic regression models were used to examine the relationships among socioeconomic deprivation, sleep duration, and mental health.
To evaluate explanatory contributions of socioeconomic deprivation to sleep duration and mental health, we performed a series of multivariable linear regression models. To assess exposure in the logistic regression model, overall SDI was dichotomized into two groups (10% most socially deprived and the remaining 90% as a comparator group). This approach for determining risk in those most socially deprived, the highest 10% of SDI scores, has been documented in previous research [42]. We defined short sleep as ≤6 h and poor mental health as 14 days or more in the past 30 days consistent with prior research and national recommendations [39,40,41]. Statistical significance was set at p < 0.05.

3. Results

3.1. Sample Characteristics

A population of 14,676 individuals aged 18–99 (mean age 54.9 ± 18.1 years) residing across 1101 ZCTA’s were included in the current study. A little over half (54.3%) identified as female and the majority identified as Non-Hispanic White (86.9%), followed by 6.8% Non-Hispanic Black, 4.2% other race, and 2.1% Hispanic. Chronic conditions reported included asthma (13.9%), diabetes (14.9%), heart disease (6.6%), cancer (8.4%), chronic obstructive pulmonary disease (1.92%), and arthritis (1.67%). There was substantial variation in sleep duration and mental health across all ZCTA’s. The prevalence of short sleep (<6 h) was 11.9% and insufficient sleep (<7 h) was 34.7% which is comparable to other state data from BRFSS in previous years (2009) [21]. The mean sleep duration was 6.98 ± 1.50 h. Prevalence of poor mental health (>14 days) was 14.5% with a mean of 4.5 ± 8.7. We present demographic and clinical characteristics for the overall sample in Table 1.

3.2. Socioeconomic Deprivation and Sleep Duration

We found a higher prevalence of short sleep in areas with higher social deprivation indices (Figure 1). We examined the unadjusted association between SDI and sleep duration in the first set of linear regression models. The unadjusted association between SDI and sleep duration was statistically significant. Specifically, higher socioeconomic deprivation was associated with shorter sleep duration (β = −0.049, p < 0.001). We present the unstandardized coefficient regression coefficient (B), standard error (SE), standardized regression coefficient (β), and coefficient of determination (R2) for each model in Table 2.
In the second set of models, we examined socioeconomic deprivation and sleep duration after adjusting for covariates (age, sex at birth, racial identity, and education) using multivariable linear regression. The associations between SDI and sleep duration remained statistically significant after adjusting for covariates (p < 0.001) (Table 2).
In the unadjusted logistic regression models, individuals living in the most socially deprived areas had 1.6 times higher odds of short sleep (<6 h) (OR = 1.54, 95% confidence interval [1.34, 1.83]) than those living in less socially deprived areas (Table 3). These associations remained statistically significant after adjusting for covariates (short sleep: aOR = 1.33, 95% confidence interval [1.12, 1.57] (Table 3).

3.3. Socioeconomic Deprivation and Mental Health

We found a higher prevalence of poor mental health in areas with higher social deprivation indices. We examined the unadjusted association between SDI mental health in the first set of linear regression models. The unadjusted association between SDI and mental health was statistically significant (p < 0.001) (Table 2). Higher socioeconomic deprivation was associated with poorer mental health (β = 0.098, p < 0.001).
In the second set of models, we examined socioeconomic deprivation and mental health after adjusting for covariates (age, sex at birth, racial identity, and education) using multivariable linear regression. The associations between SDI and mental health remained statistically significant after adjusting for covariates (p < 0.001) (Table 2).
In the unadjusted logistic regression models, individuals living in the most socially deprived areas had 1.5 times higher odds of poor mental health (>14 days) (OR = 1.47, 95% confidence interval [1.26, 1.71] than those living in less socially deprived areas (Table 3). These associations remained statistically significant after adjusting for covariates (poor mental health: aOR = 1.18, 95% confidence interval [1.01, 1.39]).

4. Discussion

We investigated the associations among socioeconomic deprivation, sleep duration, and mental health status in a large representative sample of Ohio residents during the first year of COVID-19 pandemic. Individuals from the highest socioeconomically deprived areas had an almost 2-fold increase in odds of short sleep and poor mental health after adjusting for covariates (age, sex at birth, racial identity, education, and body mass index). These findings supplement the growing body of evidence on the longstanding sleep and mental health disparities that disproportionately affect those from socioeconomically disadvantaged areas. Sleep and mental health are dynamically interrelated and are essential to socioemotional development, physical health maintenance, and economic advancement. Thus, our findings highlight additional deleterious effects of structural inequities on individual-level outcomes and the disproportionate impact of COVID-19 on disadvantaged areas.
Close to one-third of the individuals in the study reported insufficient sleep (<7 h) and one-quarter short sleep (<6 h). This finding was consistent with other population-based studies of American adults sleeping less than 7–8 h per night [43,44]. Although sleep time remained unchanged from pre-pandemic, increases in short sleepers have been observed in other studies [45,46]. In an international online survey study (ECLB COVID-19), there was a modest increase in self-reported sleep duration as well as increases in poor sleep quality as measured by the Pittsburgh Sleep Quality Index (4.37 ± 2.71 before the lockdown and 5.32 ± 3.23 after the lockdown) [47]. The public health ramifications of insufficient sleep are under-recognized by society with a conservatively estimated economic burden of USD 107 billion [48]. Higher socioeconomic deprivation was associated with shorter sleep in the present study which is consistent with several studies where individual socioeconomic status was associated with shorter sleep duration or poorer sleep quality [49,50,51,52,53,54,55,56,57].
It is essential to recognize the negative ramifications of the COVID-19 pandemic on mental health to mitigate short- and long-term consequences. One in six (14.5%) individuals in the current study experienced poor mental health (>14 days) with a mean of 4.5 ± 8.7 days within a 30-day timeframe. These findings are consistent with other observational studies [33,58,59,60,61,62]. Large disease outbreaks are associated with increased mental health problems due to social disconnection through enforcement of stay-at-home orders, isolation, and unemployment [63]. Additionally, social disconnection either real or perceived is a primary risk factor for a suicidal attempt [64].
We found a higher prevalence of poor mental health in areas with higher social deprivation indices. This result is consistent with the literature where greater neighborhood deprivation was associated with worse mental health [65]. Communities with lower socio-economic status have endured the greatest mental and physical health burden from the pandemic compared to adults with a higher socioeconomic status [1,2,3,4]. These results indicate a critical need to understand the relationship between socioeconomic disadvantage and mental health particularly during a global health pandemic in order to mitigate negative short- and long-term consequences. Poor mental health can have profound ramifications including substance use disorders, drug overdose, and lost productivity [66].
Some important limitations should be considered when interpreting the results of the current study. First, self-reported sleep duration data were used; hence, estimates may be underestimated based on prior research comparing objective sleep duration with self-report [67,68]. People often over-estimate their sleep duration by reporting time in bed versus time asleep [68]. However, in large epidemiological studies, self-reported sleep data have been shown to be reliable [24]. Findings should be corroborated with objective measures of sleep duration to replicate the findings observed in the present study. Second, the use of a cross-sectional design prevents us from investigating changes over time and attributing causality to changes in sleep and mental health trends. Third, respondents may have under- or over-reported mental health symptom frequency leading to increased or decreased estimation of mental health symptom experience in our study. Fourth, our sample was overrepresented in Non-Hispanic White (86.9% vs. 77%), underrepresented in Non-Hispanic Black (6.8% vs. 12.5%), comparable to Asian (0.9% vs. 2.5%), and comparable to Hispanic (2.1% vs. 4.4%) when compared to published 2020 Ohio demographics (census.gov). Finally, there may be societal or cultural variations in how sleep duration and mental health symptoms are reported [69]. Nonetheless, the large sample size with demographic variability contributes to the literature and generalizability of the findings.
The poor sleep and health outcomes observed in Ohio communities with the worst socioeconomic deprivation might be a common phenomenon in the other parts of the country. Timely and geographically targeted public health interventions from local and national government and health care providers are needed to facilitate access to resources and care in historically underserved areas. In addition, public health initiatives aimed at addressing social determinants of health and developing a safety net system are required to mitigate the worsening effects of the pandemic on sleep and mental health.
We encourage replication of our findings in other areas of the United States and in other countries. Sleep disturbance is linked to the occurrence and worsening of mental health problems, which in turn may affect sleep duration and quality. Investigators should explore the bidirectional relationship between sleep and mental health. Additionally, investigators may consider examining populations at a comparable geographic level like ZCTA or to examine larger areas (e.g., state/country) for broader generalizable findings, or smaller clusters (e.g., neighborhoods) to understand conditional processes that can be used for prescriptive multilevel interventions targeting specific domains of socioeconomic deprivation. Our findings underscore the need for geographically targeted public health interventions that support the sleep and mental health of the most socioeconomically disadvantaged. Ultimately, this work can be used to support local, regional, or national policy initiatives to modify societal infrastructures that enable social inequities to perpetuate ongoing healthcare disparities.

5. Conclusions

The underlying mechanisms and trends between socioeconomic disadvantage, sleep duration, and mental health and whether these trends vary by sex, gender identity, or racial identity warrant further investigation. Socioeconomic deprivation characteristics may serve as novel targets to improve sleep and mental health in the United States population. In order to reduce these socioeconomic differences, key stakeholders and policymakers should consider the characteristics of the areas in which people live as well as the residents in those areas. The rising trend in short sleep and mental illness warrants further attention. Further, the current study highlights the need to address how catastrophic events disrupt sleep and mental health particularly in socioeconomically disadvantaged areas.

Author Contributions

According to the author guidelines, each of the authors named on the manuscript fit the criteria for authorship. S.G., principal investigator on the grants (R00NR018886), secured the funding, designed the study, acquired, analyzed, interpreted the data, and wrote the manuscript. C.H.D. contributed to study design, acquired, analyzed, interpreted the data, and wrote the manuscript. Q.H., D.D. and G.P. interpreted the findings and cowrote the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

The first author, S.G., is funded by the National Institute of Nursing Research (NINR) R00NR018886. The second author, C.H.D., is funded by the National Institute of Nursing Research (NINR) K23NR019744. Q.H. is funded by Frances Payne Bolton School of Nursing Legacy Fellowship Program. G.P. is funded by the National Center for Advancing Translational Sciences (NCATS) KL2TR002547. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NIH or other funders.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of CASE WESTERN RESERVE UNIVERSITY (IRB # STUDY20221217 on 26 September 2022).

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available upon request from https://odh.ohio.gov/know-our-programs/behavioral-risk-factor-surveillance-system/data-and-publications (accessed on 1 August 2022) through a data use agreement.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Abedi, V.; Olulana, O.; Avula, V.; Chaudhary, D.; Khan, A.; Shahjouei, S.; Li, J.; Zand, R. Racial, Economic, and Health Inequality and COVID-19 Infection in the United States. J. Racial Ethn. Health Disparities 2021, 8, 732–742. [Google Scholar] [CrossRef] [PubMed]
  2. Bhowmik, T.; Tirtha, S.D.; Iraganaboina, N.C.; Eluru, N. A comprehensive analysis of COVID-19 transmission and mortality rates at the county level in the United States considering socio-demographics, health indicators, mobility trends and health care infrastructure attributes. PLoS ONE 2021, 16, e0249133. [Google Scholar] [CrossRef] [PubMed]
  3. Jalali, A.M.; Khoury, S.G.; See, J.; Gulsvig, A.M.; Peterson, B.M.; Gunasekera, R.S.; Buzi, G.; Wilson, J.; Galbadage, T. Delayed interventions, low compliance, and health disparities amplified the early spread of COVID-19. medRxiv 2020. [Google Scholar] [CrossRef]
  4. Robertson, L.S. Predictors of COVID-19-confirmed cases and fatalities in 883 US counties with a population of 50,000 or more: Estimated effect of initial prevention policies. J. Urban Health 2021, 98, 205–210. [Google Scholar] [CrossRef]
  5. Butler, D.C.; Petterson, S.; Phillips, R.L.; Bazemore, A.W. Measures of social deprivation that predict health care access and need within a rational area of primary care service delivery. Health Serv. Res. 2013, 48, 539–559. [Google Scholar] [CrossRef] [Green Version]
  6. Alshumrani, R.; Qanash, S.; Aldobyany, A.; Alhejaili, F.; AlQassas, I.; Shabrawishi, M.; Alnashiwaty, O.; Badghaish, M.; Adnan, M.; Afeef, A.B.; et al. Sleep quality and mental health in coronavirus disease 2019 patients and general population during the pandemic. Ann. Thorac. Med. 2022, 17, 21–27. [Google Scholar] [CrossRef]
  7. Singu, S.; Acharya, A.; Challagundla, K.; Byrareddy, S.N. Impact of Social Determinants of Health on the Emerging COVID-19 Pandemic in the United States. Front. Public Health 2020, 8, 406. [Google Scholar] [CrossRef]
  8. Tsai, J.; Wilson, M. COVID-19: A potential public health problem for homeless populations. Lancet Public Health 2020, 5, e186–e187. [Google Scholar] [CrossRef]
  9. Grandner, M.A. Epidemiology of insufficient sleep and poor sleep quality. In Sleep and Health; Elsevier: Amsterdam, The Netherlands, 2019; pp. 11–20. [Google Scholar]
  10. Jacobson, A.; Ryan, C.; Cleary, P.; Waberski, B.; Weinger, K.; Musen, G.; Dahms, W. Biomedical risk factors for decreased cognitive functioning in type 1 diabetes: An 18 year follow-up of the Diabetes Control and Complications Trial (DCCT) cohort. Diabetologia 2011, 54, 245–255. [Google Scholar] [CrossRef] [Green Version]
  11. Knutson, K.L.; van Cauter, E. Associations between sleep loss and increased risk of obesity and diabetes. Ann. N. Y. Acad. Sci. 2008, 1129, 287–304. [Google Scholar] [CrossRef]
  12. Kohatsu, N.D.; Tsai, R.; Young, T.; VanGilder, R.; Burmeister, L.F.; Stromquist, A.M.; Merchant, J.A. Sleep duration and body mass index in a rural population. Arch. Intern. Med. 2006, 166, 1701–1705. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Leproult, R.; van Cauter, E. Role of sleep and sleep loss in hormonal release and metabolism. Pediatr. Neuroendocrinol. 2010, 17, 11–21. [Google Scholar]
  14. Van Cauter, E.; Holmbäck, U.; Knutson, K.; Leproult, R.; Miller, A.; Nedeltcheva, A.; Pannain, S.; Penev, P.; Tasali, E.; Spiegel, K. Impact of sleep and sleep loss on neuroendocrine and metabolic function. Horm. Res. Paediatr. 2007, 67, 2–9. [Google Scholar] [CrossRef] [PubMed]
  15. Jahrami, H.A.; Alhaj, O.A.; Humood, A.M.; Alenezi, A.F.; Fekih-Romdhane, F.; AlRasheed, M.M.; Saif, Z.Q.; Bragazzi, N.L.; Pandi-Perumal, S.R.; BaHammam, A.S.; et al. Sleep disturbances during the COVID-19 pandemic: A systematic review, meta-analysis, and meta-regression. Sleep Med. Rev. 2022, 62, 101591. [Google Scholar] [CrossRef]
  16. Kocevska, D.; Blanken, T.F.; van Someren, E.J.W.; Rösler, L. Sleep quality during the COVID-19 pandemic: Not one size fits all. Sleep Med. 2020, 76, 86–88. [Google Scholar] [CrossRef] [PubMed]
  17. Sinha, M.; Pande, B.; Sinha, R. Impact of COVID-19 lockdown on sleep-wake schedule and associated lifestyle related behavior: A national survey. J. Public Health Res. 2020, 9, 1826. [Google Scholar] [CrossRef] [PubMed]
  18. Tripathi, R.; Dhawan, A.; Rao, R.; Mishra, A.K.; Jain, R.; Sinha, S. Assessment of Subjective Sleep Problems in Men with Opioid Dependence Maintained on Buprenorphine. J. Addict. Med. 2020, 14, 132–138. [Google Scholar] [CrossRef]
  19. Grandner, M.A.; Patel, N.P.; Gehrman, P.R.; Xie, D.; Sha, D.; Weaver, T.; Gooneratne, N. Who gets the best sleep? Ethnic and socioeconomic factors related to sleep complaints. Sleep Med. 2010, 11, 470–478. [Google Scholar] [CrossRef] [Green Version]
  20. Grandner, M.A.; Seixas, A.; Shetty, S.; Shenoy, S. Sleep Duration and Diabetes Risk: Population Trends and Potential Mechanisms. Curr. Diabetes Rep. 2016, 16, 106. [Google Scholar] [CrossRef] [Green Version]
  21. Williams, N.J.; Grandner, M.A.; Wallace, D.M.; Cuffee, Y.; Airhihenbuwa, C.; Okuyemi, K.; Ogedegbe, G.; Jean-Louis, G. Social and behavioral predictors of insufficient sleep among African Americans and Caucasians. Sleep Med. 2016, 18, 103–107. [Google Scholar] [CrossRef] [Green Version]
  22. Grandner, M.A.; Patel, N.P. From sleep duration to mortality: Implications of meta-analysis and future directions. J. Sleep Res. 2009, 18, 145–147. [Google Scholar] [CrossRef] [PubMed]
  23. Grandner, M.A.; Hale, L.; Moore, M.; Patel, N.P. Mortality associated with short sleep duration: The evidence, the possible mechanisms, and the future. Sleep Med. Rev. 2010, 14, 191–203. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Krueger, P.M.; Friedman, E.M. Sleep duration in the United States: A cross-sectional population-based study. Am. J. Epidemiol. 2009, 169, 1052–1063. [Google Scholar] [CrossRef] [PubMed]
  25. Major, J.M.; Doubeni, C.A.; Freedman, N.D.; Park, Y.; Lian, M.; Hollenbeck, A.R.; Schatzkin, A.; Graubard, B.I.; Sinha, R. Neighborhood socioeconomic deprivation and mortality: NIH-AARP diet and health study. PLoS ONE 2010, 5, e15538. [Google Scholar] [CrossRef] [Green Version]
  26. Twaits, A.; Alwan, N.A. The association between area-based deprivation and change in body-mass index over time in primary school children: A population-based cohort study in Hampshire, UK. Int. J. Obes. 2020, 44, 628–636. [Google Scholar] [CrossRef] [Green Version]
  27. Willems, S.J.; Castells, M.C.; Baptist, A.P. The Magnification of Health Disparities during the COVID-19 Pandemic. J. Allergy Clin. Immunol. Pract. 2022, 10, 903–908. [Google Scholar] [CrossRef]
  28. Griggs, S.; Harper, A.; Pignatiello, G.; Hickman, R.L. “Feeling Anxious about Catching COVID”: Facilitators and Barriers of Sleep Health among Young Adults with Type 1 Diabetes. Behav. Sleep Med. 2022, 20, 357–367. [Google Scholar] [CrossRef]
  29. Arrona-Palacios, A.; Rebolledo-Mendez, G.; Escamilla, J.; Hosseini, S.; Duffy, J. Effects of COVID-19 lockdown on sleep duration, sleep quality and burnout in faculty members of higher education in Mexico. Ciênc. Saúde Coletiva 2022, 27, 2985–2993. [Google Scholar] [CrossRef]
  30. Wesley, K.L.; Cooper, E.H.; Brinton, J.T.; Meier, M.; Honaker, S.; Simon, S.L. A National Survey of U.S. Adolescent Sleep Duration, Timing, and Social Jetlag during the COVID-19 Pandemic. Behav. Sleep Med. 2022, 1–13. [Google Scholar] [CrossRef]
  31. Coelho, J.; Micoulaud-Franchi, J.A.; Wiet, A.S.; Nguyen, D.; Taillard, J.; Philip, P. Circadian misalignment is associated with Covid-19 infection. Sleep Med. 2022, 93, 71–74. [Google Scholar] [CrossRef]
  32. Blackwelder, A.; Hoskins, M.; Huber, L. Effect of Inadequate Sleep on Frequent Mental Distress. Prev. Chronic Dis. 2021, 18, E61. [Google Scholar] [CrossRef] [PubMed]
  33. Burhamah, W.; AlKhayyat, A.; Oroszlányová, M.; AlKenane, A.; Almansouri, A.; Behbehani, M.; Karimi, N.; Jafar, H.; AlSuwaidan, M. The psychological burden of the COVID-19 pandemic and associated lockdown measures: Experience from 4000 participants. J. Affect. Disord. 2020, 277, 977–985. [Google Scholar] [CrossRef] [PubMed]
  34. Planchuelo-Gómez, Á.; Odriozola-González, P.; Irurtia, M.J.; de Luis-García, R. Longitudinal evaluation of the psychological impact of the COVID-19 crisis in Spain. J. Affect. Disord. 2020, 277, 842–849. [Google Scholar] [CrossRef]
  35. Lee, H.; Singh, G.K. Monthly trends in self-reported health status and depression by race/ethnicity and socioeconomic status during the COVID-19 Pandemic, United States, April 2020–May 2021. Ann. Epidemiol. 2021, 63, 52–62. [Google Scholar] [CrossRef] [PubMed]
  36. World Health Organization. Mental Health and COVID-19: Early Evidence of the Pandemic’s Impact; World Health Organization: Geneva, Switzerland, 2022. [Google Scholar]
  37. Terlizzi, E.P.; Schiller, J.S. Mental Health Treatment among Adults Aged 18–44: United States, 2019–2021. NCHS Data Brief 2022, 1–8. Available online: https://www.cdc.gov/nchs/products/databriefs/db444.htm (accessed on 19 October 2022).
  38. Vahratian, A.; Blumberg, S.J.; Terlizzi, E.P.; Schiller, J.S. Symptoms of anxiety or depressive disorder and use of mental health care among adults during the COVID-19 pandemic—United States, August 2020–February 2021. Morb. Mortal. Wkly. Rep. 2021, 70, 490. [Google Scholar] [CrossRef]
  39. Fernandez-Mendoza, J.; He, F.; Calhoun, S.L.; Vgontzas, A.N.; Liao, D.; Bixler, E.O. Objective short sleep duration increases the risk of all-cause mortality associated with possible vascular cognitive impairment. Sleep Health 2020, 6, 71–78. [Google Scholar] [CrossRef]
  40. Asdigian, N.L.; Bear, U.R.; Beals, J.; Manson, S.M.; Kaufman, C.E. Mental health burden in a national sample of American Indian and Alaska Native adults: Differences between multiple-race and single-race subgroups. Soc. Psychiatry Psychiatr. Epidemiol. 2018, 53, 521–530. [Google Scholar] [CrossRef]
  41. Centers for Disease Control and Prevention. Self-reported frequent mental distress among adults—United States, 1993–2001. MMWR Morb. Mortal. Wkly. Rep. 2004, 53, 963–966. [Google Scholar]
  42. Basile Ibrahim, B.; Barcelona, V.; Condon, E.M.; Crusto, C.A.; Taylor, J.Y. The Association Between Neighborhood Social Vulnerability and Cardiovascular Health Risk Among Black/African American Women in the InterGEN Study. Nurs. Res. 2021, 70, S3–S12. [Google Scholar] [CrossRef]
  43. Jean-Louis, G.; Williams, N.J.; Sarpong, D.; Pandey, A.; Youngstedt, S.; Zizi, F.; Ogedegbe, G. Associations between inadequate sleep and obesity in the US adult population: Analysis of the national health interview survey (1977–2009). BMC Public Health 2014, 14, 290. [Google Scholar] [CrossRef] [PubMed]
  44. Knutson, K.L.; van Cauter, E.; Rathouz, P.J.; DeLeire, T.; Lauderdale, D.S. Trends in the prevalence of short sleepers in the USA: 1975–2006. Sleep 2010, 33, 37–45. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Neculicioiu, V.S.; Colosi, I.A.; Costache, C.; Sevastre-Berghian, A.; Clichici, S. Time to Sleep?—A Review of the Impact of the COVID-19 Pandemic on Sleep and Mental Health. Int. J. Environ. Res. Public Health 2022, 19, 3497. [Google Scholar] [CrossRef] [PubMed]
  46. Hisler, G.C.; Twenge, J.M. Sleep characteristics of U.S. adults before and during the COVID-19 pandemic. Soc. Sci. Med. 2021, 276, 113849. [Google Scholar] [CrossRef]
  47. Trabelsi, K.; Ammar, A.; Masmoudi, L.; Boukhris, O.; Chtourou, H.; Bouaziz, B.; Brach, M.; Bentlage, E.; How, D.; Ahmed, M.; et al. Globally altered sleep patterns and physical activity levels by confinement in 5056 individuals: ECLB COVID-19 international online survey. Biol. Sport 2021, 38, 495–506. [Google Scholar] [CrossRef] [PubMed]
  48. Institute of Medicine Committee on Sleep Medicine Research. The National Academies Collection: Reports funded by National Institutes of Health. In Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem; Colten, H.R., Altevogt, B.M., Eds.; National Academies Press: Washington, DC, USA, 2006. [Google Scholar]
  49. Hale, L. Who has time to sleep? J. Public Health 2005, 27, 205–211. [Google Scholar] [CrossRef]
  50. Lauderdale, D.S.; Knutson, K.L.; Yan, L.L.; Rathouz, P.J.; Hulley, S.B.; Sidney, S.; Liu, K. Objectively measured sleep characteristics among early-middle-aged adults: The CARDIA study. Am. J. Epidemiol. 2006, 164, 5–16. [Google Scholar] [CrossRef] [Green Version]
  51. Friedman, E.M.; Love, G.D.; Rosenkranz, M.A.; Urry, H.L.; Davidson, R.J.; Singer, B.H.; Ryff, C.D. Socioeconomic status predicts objective and subjective sleep quality in aging women. Psychosom. Med. 2007, 69, 682–691. [Google Scholar] [CrossRef] [Green Version]
  52. Adams, J. Socioeconomic position and sleep quantity in UK adults. J. Epidemiol. Community Health 2006, 60, 267–269. [Google Scholar] [CrossRef] [Green Version]
  53. Hale, L.; Do, D.P. Racial differences in self-reports of sleep duration in a population-based study. Sleep 2007, 30, 1096–1103. [Google Scholar] [CrossRef] [Green Version]
  54. Nunes, J.; Jean-Louis, G.; Zizi, F.; Casimir, G.J.; von Gizycki, H.; Brown, C.D.; McFarlane, S.I. Sleep duration among black and white Americans: Results of the National Health Interview Survey. J. Natl. Med. Assoc. 2008, 100, 317–322. [Google Scholar] [CrossRef]
  55. Patel, S.R. Social and demographic factors related to sleep duration. Sleep 2007, 30, 1077–1078. [Google Scholar] [CrossRef] [PubMed]
  56. Sickel, A.E.; Moore, P.J.; Adler, N.E.; Williams, D.R.; Jackson, J.S. The differential effects of sleep quality and quantity on the relationship between SES and health. Ann. N. Y. Acad. Sci. 1999, 896, 431–434. [Google Scholar] [CrossRef] [PubMed]
  57. Strine, T.W.; Chapman, D.P. Associations of frequent sleep insufficiency with health-related quality of life and health behaviors. Sleep Med. 2005, 6, 23–27. [Google Scholar] [CrossRef] [PubMed]
  58. Kapp, J.M.; Micheas, L.; Holmes, S.; Stormont, M.; Reinke, W.M. Prevalence of Poor Mental Health Days and Adverse Childhood Experience Reporting in U.S. Adults Before and After COVID-19. Community Ment. Health J. 2022. [Google Scholar] [CrossRef]
  59. Ransome, Y.; Luan, H.; Song, I.; Fiellin, D.A.; Galea, S. Association of Poor Mental-Health Days with COVID-19 Infection Rates in the U.S. Am. J. Prev. Med. 2022, 62, 326–332. [Google Scholar] [CrossRef]
  60. Wright, L.; Steptoe, A.; Fancourt, D. Are adversities and worries during the COVID-19 pandemic related to sleep quality? Longitudinal analyses of 46,000 UK adults. PLoS ONE 2021, 16, e0248919. [Google Scholar] [CrossRef]
  61. Diz-Ferreira, E.; Díaz-Vidal, P.; Da Cunha Soares Nicolau, M.L.; Criado-Santos, M.P.; Ayán, C.; Diz, J.C. Effect of confinement during COVID-19 outbreak on sleep quality in Galicia. Rev. Esp. Salud Pública 2021, 95. Available online: https://pesquisa.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/resource/pt/covidwho-1063742 (accessed on 19 October 2022).
  62. Zhang, Y.; Zhang, H.; Ma, X.; Di, Q. Mental health problems during the COVID-19 pandemics and the mitigation effects of exercise: A longitudinal study of college students in China. Int. J. Environ. Res. Public Health 2020, 17, 3722. [Google Scholar] [CrossRef]
  63. Ptacek, R.; Ptackova, H.; Martin, A.; Stefano, G.B. Psychiatric Manifestations of COVID-19 and Their Social Significance. Med. Sci. Monit. 2020, 26, e930340. [Google Scholar] [CrossRef]
  64. Necka, E.A.; Rowland, L.M.; Evans, J.D. Social Disconnection in Late Life Mental Illness—Commentary from the National Institute of Mental Health. Am. J. Geriatr. Psychiatry 2021, 29, 727–730. [Google Scholar] [CrossRef] [PubMed]
  65. Salvatore, M.A.; Grundy, E. Area deprivation, perceived neighbourhood cohesion and mental health at older ages: A cross lagged analysis of UK longitudinal data. Health Place 2021, 67, 102470. [Google Scholar] [CrossRef] [PubMed]
  66. Patel, V.; Chisholm, D.; Parikh, R.; Charlson, F.J.; Degenhardt, L.; Dua, T.; Ferrari, A.J.; Hyman, S.; Laxminarayan, R.; Levin, C.; et al. Addressing the burden of mental, neurological, and substance use disorders: Key messages from Disease Control Priorities, 3rd edition. Lancet 2016, 387, 1672–1685. [Google Scholar] [CrossRef] [Green Version]
  67. Griggs, S.; Conley, S.; Batten, J.; Grey, M. A systematic review and meta-analysis of behavioral sleep interventions for adolescents and emerging adults. Sleep Med. Rev. 2020, 54, 101356. [Google Scholar] [CrossRef] [PubMed]
  68. Lauderdale, D.S.; Knutson, K.L.; Yan, L.L.; Liu, K.; Rathouz, P.J. Sleep duration: How well do self-reports reflect objective measures? The CARDIA Sleep Study. Epidemiology 2008, 19, 838. [Google Scholar] [CrossRef] [Green Version]
  69. Paine, S.J.; Gander, P.H.; Harris, R.; Reid, P. Who reports insomnia? Relationships with age, sex, ethnicity, and socioeconomic deprivation. Sleep 2004, 27, 1163–1169. [Google Scholar] [CrossRef]
Ijerph 19 14367 g001 550
Figure 1. Short sleep and poor mental health prevalence % by social deprivation index quartiles.
Figure 1. Short sleep and poor mental health prevalence % by social deprivation index quartiles.
Ijerph 19 14367 g001
Table
Table 1. Demographic Characteristics of the Sample.
Table 1. Demographic Characteristics of the Sample.
VariableNumber (%) or Mean (SD)
Age54.9 (18.1)
Race/Ethnicity
            White
            African American/Black
            Asian
            American Indian/Alaskan Native
            Other
            Hispanic
12,754 (86.9)
992 (6.8)
139 (0.9)
96 (0.7)
385 (2.6)
310 (2.1)
Sex at Birth
            Female
            Male
7970 (54.3)
6706 (45.7)
Years of Education
            Never Attended or Only Kindergarten
            Elementary (Grades 1–8)
            Some Highschool (Grades 9–11)
            High School Graduate
            Some College (1–3 years)
            College Graduate (4 or more years)
6 (0)
165 (1.1)
732 (5)
4932 (33.6)
4086 (27.7)
4723 (32.2)
Annual Household Income
            Less than USD 10,000
            Less than USD 25,000
            Less than USD 50,000
            Less than USD 75,000
            USD 75,000 or more
495 (3.4)
2667 (18.5)
2965 (20.6)
1897 (13.2)
3666 (25.5)
Marital Status
            Married
            Divorced/Widowed/Separated/Never Married
7185 (49)
7335 (50)
Body Mass Index 29.1 (6.9)
Table
Table 2. Socioeconomic deprivation and covariates to sleep health and mental health outcomes (Linear Regression Models).
Table 2. Socioeconomic deprivation and covariates to sleep health and mental health outcomes (Linear Regression Models).
ModelIndependent Variable/sDependent Variable/sBSEβp ValueR2
Model 1 SDISleep Health−0.0030.001−0.049<0.0010.002
Mental Health0.0330.0030.098<0.0010.010
Model 2SDISleep Health−0.0020.001−0.037<0.0010.018
Mental Health0.0220.0030.064<0.0010.060
Note. Covariates adjusted for age, sex at birth, racial identity, education, income, and body mass index. SDI = social deprivation index. Model 1 is unadjusted. Model 2 is adjusted for covariates. B is the unstandardized coefficient regression coefficient. SE = standard error. β is the standardized regression coefficient. R2 = coefficient of determination shown for each model.
Table
Table 3. Socioeconomic deprivation, sleep health, and mental health (Logistic Regression Models). Residence in 10% most-deprived areas.
Table 3. Socioeconomic deprivation, sleep health, and mental health (Logistic Regression Models). Residence in 10% most-deprived areas.
Social Deprivation Index
OR95% CIp ValueaOR95% CIp Value
Short sleep 1.57[1.34, 1.83]<0.0011.33[1.12, 1.57]0.001
Poor mental health 1.47[1.26, 1.71]<0.0011.18[1.01, 1.39]0.043
Note: CI = confidence interval; OR = odds ratio; aOR = adjusted odds ratio; short sleep < 6 h; poor mental health > 14 poor mental health days. Covariates adjusted for: age, sex at birth, racial identity, education, income, and body mass index.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Griggs, S.; Horvat Davey, C.; Howard, Q.; Pignatiello, G.; Duwadi, D. Socioeconomic Deprivation, Sleep Duration, and Mental Health during the First Year of the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2022, 19, 14367. https://doi.org/10.3390/ijerph192114367

AMA Style

Griggs S, Horvat Davey C, Howard Q, Pignatiello G, Duwadi D. Socioeconomic Deprivation, Sleep Duration, and Mental Health during the First Year of the COVID-19 Pandemic. International Journal of Environmental Research and Public Health. 2022; 19(21):14367. https://doi.org/10.3390/ijerph192114367

Chicago/Turabian Style

Griggs, Stephanie, Christine Horvat Davey, Quiana Howard, Grant Pignatiello, and Deepesh Duwadi. 2022. "Socioeconomic Deprivation, Sleep Duration, and Mental Health during the First Year of the COVID-19 Pandemic" International Journal of Environmental Research and Public Health 19, no. 21: 14367. https://doi.org/10.3390/ijerph192114367

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop -