Evaluating the risk of sleep disorders in subjects with a prior COVID-19 infection

Jaewhan Kim; Kenechukwu C Ben-Umeh; Rachel Weir; Karen Manotas; Kristi Kleinschmit; Aaron Fischer; Peter Weir; Fernando Wilson

doi:10.1371/journal.pone.0311929

. 2024 Oct 17;19(10):e0311929. doi: 10.1371/journal.pone.0311929

Evaluating the risk of sleep disorders in subjects with a prior COVID-19 infection

Jaewhan Kim ^1,^*, Kenechukwu C Ben-Umeh ², Rachel Weir ³, Karen Manotas ³, Kristi Kleinschmit ³, Aaron Fischer ⁴, Peter Weir ⁵, Fernando Wilson ⁶

Editor: Braja Gopal Patra⁷

PMCID: PMC11486372 PMID: 39418274

Abstract

Previous studies have reported a potential occurrence of sleep disorders in patients following a COVID-19 infection. However, these findings were based on surveys or retrospective studies with small sample sizes. This study examined if subjects with a previous COVID-19 infection in 2020 experienced sleep disorders in 2021. Using the 2019–2021 Utah All Payers Claims Database (APCD), adults (≥18 to 62 years old in 2019) covered by private insurance and Medicaid were identified. Sleep disorders were identified from the primary and secondary diagnosis in 2021. Baseline characteristics of subjects such as age, gender, race/ethnicity, type of insurance, and comorbid conditions were identified from the database. Entropy balancing was used to balance the baseline characteristics of subjects with and without a COVID-19 infection in 2020. Weighted logistic regression was used to identify significant factors that were associated with sleep disorders. A total of 413,958 subjects were included in the study. The average (SD) age was 38 (17) years old in 2019 and 58% were female. Among the subjects, about 39% had a COVID-19 infection in 2020. Those who had a COVID-19 infection in 2020 were 53% more likely to have a sleep disorder in 2021 (OR = 1.53; 95% Confidence Interval: 1.48–1.58). Sleep disorders could be one of long-term COVID-19 symptoms. More screening and observations for those who had a COVID-19 infection could be important to improve sleep related problems.

Introduction

Sleep disorders are a category of health concerns that impede the ability to sleep well without interruptions consistently over a span of time [1]. It is estimated that about 15–25% of the United States (US) population experience different sleep disorders and the most common manifestations include sleep apnea, insomnia, narcolepsy and restless leg syndrome [2–4]. Sleep disorders may be a result of various factors ranging from stressful life events to predisposing factors or medical disorders and are a growing public health concern in the United States [3, 5]. Occurrence of sleep disorders have been tied to several mental health disorders such as major depressive disorder, anxiety, post-traumatic stress disorder, as well as generalized stress [6].

The emergence of the novel coronavirus disease (COVID-19) which eventually morphed into a global pandemic impacted the world in many ways. The lockdown that ensued, the social isolation coupled with fear, and looming uncertainties and economic difficulties led to psychological effects and sleep disorders [7, 8]. There is growing concern that the effect of COVID-19 on the sleep cycle may not just be short term but also long term, which may be due to prolonged disruption of the circadian rhythm [9, 10]. Studies have shown sleep disorders to be a significant neuropsychiatric symptom of post-COVID-19 syndrome [11–13]. A study reported a potential association of sleep disturbances in patients 3–6 months after recovery from COVID-19 [12]. Despite the potential impact of COVID-19 infection and sleep disorder, most previous studies have explored sleep disorders with small sample sizes during the COVID-19 pandemic, but not in the long term after the pandemic with a state-wide large dataset. Hence, the aim of this study was to investigate the relationship between a previous COVID-19 infection and risk of sleep disorders using a population level data from 2019 through 2021.

Materials and methods

Data

The 2019–2021 Utah All Payers Claims Database (APCD) was used for the study. The APCD is known to cover over 70% of the Utah population covered by Medicaid, private insurance and Medicare Advantage [14, 15]. The APCD includes medical, pharmacy and dental claims of subjects. The database consists of two major files for research: enrollment file and claims file. The enrollment file provides insurance coverage start/end date, type of insurance (Medicaid, Medicare and private insurance), date of birth, gender, race/ethnicity, Medicaid/Medicare coverage, and medical/pharmacy/dental coverage indicator. The claims file includes service start/end date, type of claims (medical, pharmacy and dental), Current Procedural Terminology (CPT), International Classification of Diseases-10^th Revision (ICD-10) diagnosis code, place of service, bill type, provider taxonomy, reimbursement amounts, national drug code and drug name. The data were accessed for research purposes from August 1, 2022 through September 30, 2023. Authors had no access to information that could identify individual participants during or after data collection. This study received an exemption determination from Institutional Review Board (IRB) at the University of Utah (IRB 00151091).

Subjects

Adults (≥18 to 62 years old in 2019) who had continuous medical enrollment over 36 months (2019 through 2021) were included. Those who had a COVID-19 diagnosis in 2021 were excluded in the study. In addition, those who had sleep disorder diagnoses in 2019 and/or 2020 were excluded. Subjects who were dual eligible (i.e. Medicaid and Medicare) were excluded from the study as well.

Outcome

Sleep disorders were identified by using the primary and second diagnosis codes in 2021. The ICD-10 diagnosis codes for the sleep disorders (F51.XX, G47.XX, R063) were obtained from Agency for Healthcare Research and Quality (AHRQ) Clinical Classifications Software Refined (CCSR) [16].

Covariates

COVID-19 diagnosis (Yes/No) in 2020 was the independent variable in the study. The following codes were used to identify the COVID-19 infection (ICD-10 diagnosis codes: J1281, J1282, U071, U099, B948, B9729, Z8616, O985, Z20828; CPT: 86413, 86328, 86769, 87426, 87428, 87635, 87636, 87637, 87811, 87913, C9803) [16, 17]. Demographic information such as age in 2019 (18–30, 31–40, 41–50, and 51–62 years old), gender (male/female), race/ethnicity (non-Hispanic White, non-Hispanic African American, non-Hispanic Pacific Islander/Asian, Hispanic, Unknown), and Medicaid coverage (Yes/No) were included in the regression model. Individual physical and mental comorbid conditions were identified from 2019 using the diagnosis codes from Centers for Medicare and Medicaid (CMS) Chronic Conditions Warehouse and AHRQ CCSR [16, 18]. The comorbid conditions included depression, anxiety, cognitive disorder, personality disorder, schizophrenia, bipolar, eating disorder, attention-deficit/hyperactivity disorder (ADHD), diabetes, heart failure, cerebrovascular disease, opioid use disorder, thyroid disorder, obesity and Chronic Obstructive Pulmonary Disease (COPD)/asthma. These conditions were included because they were known to cause sleep difficulties [19–21].

Statistical approach

Mean, standard deviation, counts, and percentages were used to summarize the baseline characteristics of the subjects in 2019. To balance the different baseline characteristics of the two groups (i.e. with and without COVID-19 infection), Entropy Balancing (EB) with mean, variance and skewness was used to create weights for the subjects [22, 23]. The EB approach for a binary outcome is based on a maximum entropy reweighting scheme that assigns weights to each subject so that the control group (i.e. no COVID infection group in this study) is reweighted to match the controlled covariate moments (i.e. mean, standard deviation, and variance in this study) in the treatment group (i.e. COVID infection group in this study). Therefore, EB ensures that the two groups being compared are similar enough by reweighting the patient characteristics (i.e. covariates) [23–25]. To calculate a weight for each subject, all of the covariates that were listed above were included. Standardized differences in the variables were calculated to evaluate any differences in the variables between the two groups. Standardized differences less than 0.1 indicated no significant differences in the variables between the two groups. The standardized differences of all of the variables after EB was 0.00, indicating that the baseline variables were balanced well between the two groups. Following EB, T-tests for continuous variables and Chi-square tests for categorical variables were used to compare the baseline characteristics of the subjects in Table 1. Weighted logistic regression was used to identify which baseline variables were associated with sleep disorders in 2021. The weighted regression model controlled for the covariates that were included in the EB [26, 27]. E-value was used to estimate odds ratio that would invalidate the association between the COVID-19 infection and sleep disorder due to unmeasured confounding [28, 29].

Table 1. Summary statistics of subjects in 2019 after entropy balancing.

		COVID infection in 2020
Variable	Overall (n = 413,958; 100%)	No (n = 251,022; 60.64%)	Yes (n = 162,936; 39.36%)	p-value
	mean (SD)/ N(%)	mean (SD)/N(%)	mean (SD)/N(%)
Age (as continuous)	38.44 (12.90)	38.44 (14.28)	38.44 (11.38)	0.96
Age category				0.96
Age 18 to 30	130,438 (31.51%)	79,097 (31.51%)	51,341 (31.51%)
Age 31 to 40	102,579 (24.78%)	62,203 (24.78%)	40,376 (24.78%)
Age 41 to 50	91,609 (22.13%)	55,551 (22.13%)	36,058 (22.13%)
Age 51 to 62	89,332 (21.58%)	54,171 (21.58%)	35,162 (21.58%)
Female	240,054 (57.99%)	145,568 (57.99%)	94,487 (57.99%)	0.89
Race/Ethnicity				0.97
Non-Hispanic White	60,686 (14.66%)	36,825 (14.67%)	23,870 (14.65%)
Non-Hispanic Black	1,490 (0.36%)	904 (0.36%)	587 (0.36%)
Non-Hispanic Asian/Pacific Islander/American Indian	3,933 (0.95%)	2,385 (0.95%)	1,548 (0.95%)
Hispanic	9,935 (2.40%)	6,025 (2.40%)	3,910 (2.40%)
Unknown	337,914 (81.63%)	204,884 (81.62%)	133,021 (81.64%)
Medicaid coverage	34,441 (8.32%)	20,910 (8.33%)	13,540 (8.31%)	0.82
Comorbid condition
Depression	75,879 (18.33%)	46,012 (18.33%)	29,882 (18.34%)	0.96
Anxiety	62,466 (15.09%)	37,879 (15.09%)	24,587 (15.09%)	0.97
Cognitive disorder	14,861 (3.59%)	9,012 (3.59%)	5,849 (3.59%)	0.98
Personality disorder	1,242 (0.30%)	753 (0.30%)	489 (0.30%)	1.00
Schizophrenia	2,194 (0.53%)	1,330 (0.53%)	864 (0.53%)	0.92
Bipolar	7,037 (1.70%)	4,267 (1.70%)	2,770 (1.70%)	1.00
Eating disorder	911 (0.22%)	552 (0.22%)	358 (0.22%)	1.00
ADHD	13,371 (3.23%)	8,108 (3.23%)	5,263 (3.23%)	0.99
Diabetes	21,898 (5.29%)	13,279 (5.29%)	8,619 (5.29%)	0.99
Heart failure	952 (0.23%)	577 (0.23%)	375 (0.23%)	1.00
Cerebrovascular disease	1,780 (0.43%)	1,079 (0.43%)	701 (0.43%)	1.00
Opioid use disorder	4,802 (1.16%)	2,912 (1.16%)	1,890 (1.16%)	1.00
Thyroid disorder	22,933 (5.54%)	13,907 (5.54%)	9,043 (5.55%)	0.97
Obesity	70,456 (17.02%)	42,724 (17.02%)	27,732 (17.02%)	0.96
Asthma/COPD	11,384 (2.75%)	6,903 (2.75%)	4,481 (2.75%)	0.99
Outcome
Sleep disorders	15,689 (3.79%)	6,903 (2.94%)	8,293 (5.09%)	<0.01

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Abbreviations: SD, Standard deviation; COPD, Chronic obstructive pulmonary disease; ADHD, Attention-deficit hyperactivity disorder.

Another analysis was performed, categorizing COVID-19 infection severity into three groups: no infection, mild COVID-19 infection, and severe COVID-19 infection. Subjects with any emergency room (ER) visits or hospital admissions within 7 days before or after a COVID-19 infection diagnosis were categorized as having a severe COVID-19 infection, while those without such visits or hospital admissions were categorized as having a mild COVID-19 infection. The area under the receiver operating characteristic (ROC) curve (AUC) was used to measure the performance of the logistic regression models [30]. P-value less than 0.05 was defined as statistically significant. Stata version 18.0 was used for the analysis.

Results

A total of 1,069,177 subjects were continuously covered by insurance for 36 months. Among them, only 564,045 subjects met the age criteria (≥18 to 62 years old in 2019). Those who had a sleep disorder diagnosis in 2019 and 2020 (n = 34,994) or were dual eligible at any time (n = 26,487) were excluded. In addition, those with COVID-19 infection in 2021 (n = 88,606) were excluded. The total number of the subjects in the analysis was 413,958. Among the study subject, 39.36% (n = 162,936) had a COVID-19 infection in 2020 (Fig 1).

A total of 2.94% of the subjects who had no COVID-19 infection in 2020 and 5.09% of subjects who had COVID-19 infection had a sleep disorder that was newly diagnosed in 2021 (p<0.01). Average (SD) age was 38 (17) years old in 2019, and about 58% were female. About 18% had a depression diagnosis in 2019 and 17% had an obesity diagnosis in 2019 (Table 1).

Those who had a COVID-19 infection in 2020 were 53% more likely to have a sleep disorder in 2021 (OR = 1.53, p<0.01). As subjects got older, they were more likely to experience a sleep disorder by 39% (OR = 1.39, p<0.01 for 31–40 years old), by 81% (OR = 1.84, p<0.01 for 41–50 years old), and 107% (OR = 2.07, p<0.01 for 51–62 years old) as compared to the younger age group (18–30 years old in 2019). Females were less likely to have a sleep disorder than males by 18% (OR = 0.82, p<0.01). Subjects who had mental illnesses such as depression (OR = 1.73, p<0.01), anxiety (OR = 1.59, p<0.01), eating disorders (OR = 1.43, p = 0.02), ADHD (OR = 1.20, p<0.01) and cognitive disorder (OR = 1.43, p<0.01) were found to have a higher likelihood of experiencing a sleep disorder. Physical conditions such as heart failure (OR = 1.74, p<0.01), opioid use disorder (OR = 1.39, p<0.01), thyroid disorder (OR = 1.16, p<0.01), and obesity (OR = 2.84, p<0.01) were significantly associated with a higher risk of sleep disorder in 2021 (Table 2).

Table 2. Weighted logistic regression of association between a prior COVID-19 infection and risk of sleep disorders in all subjects in 2019 (n = 413,958).

Covariate	Odds ratio	p-value	95% confidence interval
COVID infection	1.53	<0.01	1.48	1.58
Age
Age 18 to 30	reference
Age 31 to 40	1.39	<0.01	1.32	1.46
Age 41 to 50	1.84	<0.01	1.75	1.93
Age 51 to 62	2.07	<0.01	1.97	2.18
Female	0.82	<0.01	0.80	0.85
Race/Ethnicity
Non-Hispanic White	reference
Non-Hispanic Black	0.78	0.08	0.60	1.03
Non-Hispanic Asian/Pacific Islander/American Indian	0.77	<0.01	0.64	0.92
Hispanic	0.81	<0.01	0.72	0.92
Unknown	0.86	<0.01	0.82	0.90
Medicaid coverage	0.94	0.08	0.88	1.01
Depression	1.73	<0.01	1.65	1.81
Anxiety	1.59	<0.01	1.52	1.67
Cognitive disorder	1.43	<0.01	1.28	1.-60
Personality disorder	1.34	0.03	1.03	1.74
Schizophrenia	0.98	0.79	0.82	1.16
Bipolar	1.05	0.36	0.95	1.17
Eating disorder	1.43	0.02	1.07	1.91
ADHD	1.20	<0.01	1.07	1.36
Diabetes	0.93	0.04	0.87	0.99
Heart failure	1.74	<0.01	1.39	2.17
Cerebrovascular disease	1.16	0.16	0.94	1.42
Opioid use disorder	1.39	<0.01	1.23	1.58
Thyroid disorder	1.16	<0.01	1.08	1.24
Obesity	2.84	<0.01	2.73	2.95
Asthma/COPD	1.33	<0.01	1.22	1.46

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Abbreviation: COPD, Chronic obstructive pulmonary disease; ADHD, Attention-deficit hyperactivity disorder.

E-value was 2.43, which was greater than any odds ratio of the COVID-19 infection variable in the regression. Because this odds ratio was bigger than any other odds ratios of the controlled variables in the regression, it may indicate that the regression model might not be impacted by unobservable confounders. The AUC following the logistic regressions were 0.72 for both Tables 2 and 3, indicating that there is a 72% chance of the models distinguishing between positive and negative cases.

Table 3. Association between COVID-19 severity and risk of sleep disorders.

Covariate	Odds ratio	p-value	95% confidence interval
Severity of COVID-19
No COVID infection	Reference
Mild COVID	1.25	<0.01	1.19	1.32
Severe COVID	1.64	<0.01	1.58	1.70
Age
Age 18 to 30	Reference
Age 31 to 40	1.39	<0.01	1.32	1.46
Age 41 to 50	1.82	<0.01	1.73	1.91
Age 51 to 62	2.03	<0.01	1.93	2.14
Female	0.81	<0.01	0.78	0.84
Race/Ethnicity
Non-Hispanic White	Reference
Non-Hispanic Black	0.78	0.07	0.59	1.02
Non-Hispanic Asian/Pacific Islander/American Indian	0.76	<0.01	0.64	0.92
Hispanic	0.81	<0.01	0.72	0.92
Unknown	0.88	<0.01	0.84	0.92
Medicaid coverage	0.94	0.06	0.88	1.00
Depression	1.72	<0.01	1.65	1.80
Anxiety	1.59	<0.01	1.52	1.67
Cognitive disorder	1.43	<0.01	1.27	1.59
Personality disorder	1.34	0.03	1.03	1.73
Schizophrenia	0.97	0.72	0.82	1.15
Bipolar	1.05	0.39	0.94	1.16
Eating disorder	1.42	0.02	1.06	1.90
ADHD	1.21	<0.01	1.07	1.36
Diabetes	0.93	0.02	0.87	0.99
Heart failure	1.72	<0.01	1.38	2.15
Cerebrovascular disease	1.14	0.19	0.93	1.40
Opioid use disorder	1.38	<0.01	1.22	1.56
Thyroid disorder	1.15	<0.01	1.08	1.23
Obesity	2.82	<0.01	2.71	2.93
Asthma/COPD	1.33	<0.01	1.22	1.45

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About 61% (n = 251,022) of subjects had no COVID-19 infection, 13% (n = 55,165) had mild COVID-19 infection, and 26% (n = 107,771) had severe COVID-19 infection. The analysis of COVID-19 infection severity categories showed that subjects with severe COVID-19 infection had a higher risk of experiencing sleep disorders than those with mild or no COVID-19 infection. Compared to subjects with no COVID-19 infection, those with mild COVID-19 infection were 25% more likely to have sleep disorders in the following year (OR = 1.25, p<0.01), while subjects with severe COVID-19 infection had a 64% higher likelihood of having sleep disorders (OR = 1.64, p<0.01). To compare those with mild infection to those with severe infection, we conducted a Wald test resulting in a statistically significant difference (F-value = 348, p<0.01) between the two groups. This indicated that those with severe infection experienced higher rates of sleep disorders than those with mild infection (Table 3).

Discussion

Our study utilizing the Utah All Payers Claims Database (APCD) showed that those who had COVID-19 infection in 2020 had over 50% higher chances of experiencing a sleep disorder in 2021 compared patients who did not have COVID-19 infection in 2020. Despite the widespread impact of the COVID-19 pandemic and the possibility of subsequent development of sleep disorders among millions, few previous studies have used robust population-level data to assess the long-term likelihood of developing sleep disorders following a COVID-19 infection. We use 3 years of comprehensive longitudinal data that capture patients covered by commercial insurance, Medicaid and Medicare advantage, enabling an in-depth analysis.

In a systematic review and meta-analysis that included 177 studies with a total of 345,270 participants across 39 countries, Alimoradi et al estimated the prevalence of sleep disorders during the COVID-19 pandemic and its relationship with psychological distress [30]. The study reported the prevalence of sleep disorders to be 57% among COVID-19 patients and 18% in the general population and the sleep problems were attributed to psychological distress such as depression and anxiety [31]. Similarly, Tasnim et al also reported an increased rate of sleep disorders during the pandemic [7]. Moreover, more studies have reported sleep disorders, among many other symptoms, as long-term effects associated with a prior COVID-19 infection [32–35]. This has been referred to as post-COVID-19 syndrome or long-COVID. Our results are consistent with the findings from these studies.

Premraj et al in a meta-analysis of over 10,000 patients in 18 studies reported a 31% prevalence of sleep disturbances as a neuropsychiatric post-COVID-19 symptom [12]. Surprisingly, these neuropsychiatric post-COVID-19 symptoms were significantly higher when accessed long-term (6 or more months after an infection) compared to mid-term (3 to 6 months) which may indicate that the symptoms are more likely to develop and not persist over time post-infection [12]. This is contrary to the results from another retrospective study that reported higher rates of sleep disorders at 6 months after an acute COVID-19 diagnosis compared to 2 years, showing declining post-COVID symptoms [33].

A cohort study that explored the long-term health consequences among patients who had been hospitalized for COVID-19 found that such patients reported having sleep disorders at a higher rate (26%) 6 months after an acute COVID-19 infection [31]. The severity of illness as well as being a woman were important risk factors for the long-term effects identified in this study [36]. Our study, however, shows being female as associated with a lower likelihood of having a sleep disorder in 2021. The study included only patients who had been discharged after hospitalization, hence it was not clear if the observed effect would have been different for patients who were not hospitalized. On one hand, Taquet et al in a retrospective study that estimated the incidence rates of neurological and psychiatric diagnoses in 236,379 patients 6 months after COVID-19 diagnosis reported lower rates of insomnia in patients who were not hospitalized when they had COVID-19 infection compared to patients who were hospitalized [32]. On the other hand, this was inconsistent with findings from another study that reported higher rates of sleep disorders among those non-hospitalized compared to those hospitalized when they had COVID-19 infection [12]. Patients with more severe infections were also found to have higher rates of both neurological and psychiatric outcomes months after diagnosis [32, 37].

The mechanism of the association between COVID-19 infection and sleep disorders is thought to be multifactorial and could encompass factors such as social isolation, ICU stay, direct effect of the viral infection, immunological response, cerebrovascular changes, medication use, among many others [37, 38]. In addition to infecting peripheral tissues linked to the central nervous system (CNS), the SARS-CoV-2 coronavirus also directly enters the brain by permeating the blood-brain barrier (BBB) [39, 40]. This neuroinvasion could potentially affect brain functions, including sleep regulation [40]. Furthermore, hyperinflammatory reactions where the concentrations of C-reactive proteins (CRP) and interleukin-6 (IL-6) are increased may also be implicated in the association between COVID-19 infection and psychiatric symptoms [41]. Specifically, there is a growing body of evidence that COVID-19 infection can lead to elevated levels of inflammatory mediators such as cytokines [42]. Certain cytokines, including IL-6, IL-1α, and tumor necrosis factor (TNF-α), exhibit notable circadian oscillations when they enter the brain, leading to sleep disorders [43, 44]. This relationship between inflammatory mediators and psychiatric symptoms has been previously demonstrated through the link between inflammation and depression [45]. Social isolation also plays a key role in sleep disorders. Sleep disorders were reported to increase during quarantine periods due to COVID-19 infection [46]. During COVID-19 lockdowns, insomnia, poor sleep maintenance, and reduced sleep quality increased for the general population in several countries. This has been attributed to the effect of confinement, worsened by the psychological effects of the disease and increased exposure to artificial lighting from electronic devices [6, 47, 48].

However, not all studies showed a positive association between COVID-19 infection and sleep disorders. A meta-analysis that investigated the evidence of long-term post-COVID symptoms among children and young people reported non-significant pooled risk difference in post-COVID cases compared to controls for insomnia [49]. It is particularly noteworthy that about half of the studies included in the meta-analysis had a high risk of bias and high heterogeneity which was a limitation.

Sleep disorders are recognized as important public health and economic concerns requiring prompt attention [50]. Thus, our study’s findings have various implications for healthcare providers and policymakers. This study contributes to the existing body of literature available to healthcare providers, highlighting the potential impact of COVID-19 infection on sleep. It may prompt providers to consider incorporating sleep-related questions into patient assessments, particularly considering that symptoms of sleep disorders may manifest in different ways and could be difficult to link specifically to the infection, especially in the absence of definitive diagnostics [51]. Additionally, healthcare providers can offer psychological support to patients who may feel unheard when complaining about their symptoms [51]. Healthcare providers could be instrumental in managing sleep disturbances, as well as in assessing patients’ progress through sleep pattern tracking and assisting patients in setting realistic goals for their recovery [52]. Cognitive behavioral therapy and progressive muscle relaxation are interventions that have been used to treat sleep disorders in patients with COVID-19 infection [53, 54]. In a randomized controlled trial, Liu et al reported that PMR reduced anxiety levels and improved sleep quality after 5 days [54]. The findings of this study were inconsistent with that of another study by Masih et al which showed no difference between intervention groups [55]. Other alternative therapies include short-term benzodiazepines and hypnosis [56].

Moreover, our study generates relevant scientific evidence that will better equip policymakers in their decision-making process [57]. Policies that could stem from our findings encompass launching public health campaigns aimed at raising awareness about the potential impact of COVID-19 on sleep and the importance of seeking timely medical attention for sleep-related issues. Other policies include establishing integrated care models that would ensure collaborative care and allocating funding for research and surveillance to sustain continued research into the relationship between COVID-19 and sleep disorders.

Even though our study used a state-wide dataset to identify COVID-19 infection and sleep disorders, there were some limitations to consider. First, some subjects who might have had COVID-19 infection might not have been seen by doctors. Thus, there could be an underreporting issue. Also, it is known that there were subjects who had COVID-19 infection with no symptoms. Second, the results of the study may not be generalizable to other states as the data used is specific to the state of Utah in the United States. In addition, these findings could not be generalizable to adolescents or the elderly population. Third, prescription medications such as antidepressants and medications for pain, hypertension and asthma that could interfere with sleep were not considered in the study. On the other hand, some antidepressant medications could be prescribed for sleep disorders, but these were not considered to identify patients with sleep disorder. Continuous positive airway pressure (CPAP) therapy is used for patients who have obstructive sleep apnea, but this therapy was not considered to identify sleep disorder in this study. Fourth, the reliability of the race/ethnicity variables may be compromised by the significant amount of missing information in the category. Therefore, the estimates obtained for race/ethnicity may be unreliable. Finally, while we controlled for observed variables that were potentially associated with the cause of sleep disorders, unmeasured confounders could potentially influence the results of the study, given the observational study design.

Conclusions

Utilizing a population database, this study has unveiled a potential correlation between COVID-19 infection and an increased likelihood of experiencing sleep disorders. Further investigations are essential to elucidate the potential mechanism underlying the connection between COVID-19 and sleep disturbances.

Supporting information

S1 Table. Standardized differences before and after entropy balancing.

Abbreviation: COPD, Chronic obstructive pulmonary disease; ADHD, Attention-deficit hyperactivity disorder.

(DOCX)

pone.0311929.s001.docx^{(23.2KB, docx)}

Data Availability

Data cannot be shared publicly due to regulations set by the Utah Department of Health and Human Services, and the Utah Resource for Genetic and Epidemiologic Research (RGE). Data are available upon approvals from the University of Utah Institutional Review Board (irb@utah.edu) and the Utah Resource for Genetic and Epidemiologic Research (rge@hsc.utah.edu).

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0311929.r001

Decision Letter 0

Braja Gopal Patra

4 Mar 2024

PONE-D-23-38335Evaluating the risk of sleep disorders in subjects with a prior COVID-19 infectionPLOS ONE

Dear Dr. Kim,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Additional Editor Comments:

This manuscript discusses sleep disorder prior to COVID-19 infection in the UTAH all payers claim database. It is an interesting study, however, there exist several concerns.

1. It will be great to have patient counts beside the patient percentages in tables and tests.

2. In Table 1, most of the % of the patients are same or different by 0.01% except for sleep disorder. Why is that?

3. There is a mention of 101% for 51-62 years old, is this a typo?

4. It will be great to have a flowchart for cohort selection.

5. In general, table 1 is very confusing. The values in the table look repetitive and it is not outlined how those values have been calculated. Apparently p values have been used to distinguish both categorical and continuous data, but it is not clear what statistical test has been used. Further, entropy balancing has not been cited.

6. Weighted logistic regression meaning ridge/lasso/something else?

7. Line 125: P-value written as E-value

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

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Reviewer #1: Yes

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: No

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Your paper "Evaluating the Risk of Sleep Disorders in Subjects with a Prior COVID-19 Infection" offers a noteworthy exploration into the long-term effects of COVID-19 on sleep health. The use of the Utah All Payers Claims Database gives your study a solid foundation, allowing for a comprehensive analysis of post-COVID-19 sleep disorders. Your choice of statistical methods, particularly weighted logistic regression and entropy balancing, effectively addresses potential confounding factors, strengthening the validity of your findings.

Despite these limitations, your research is an important contribution to understanding the aftermath of COVID-19. It paves the way for further studies, which I hope will expand on your work by incorporating a more diverse set of data and examining additional factors that might influence sleep health after COVID-19 infection. Your work is crucial in highlighting the need for ongoing research into the long-term health consequences of this global pandemic.

Reviewer #2: 1. A careful proofread is recommended to correct any minor typographical errors.

2. The authors should ensure data availability in line with PLOS's requirements. If there are restrictions, these should be specified.

3. A more in-depth discussion on the potential pathophysiological mechanisms linking COVID-19 to sleep disorders would enhance the paper's contribution to the existing body of knowledge.

4. The authors should clarify the severity of COVID-19 cases included in the study and discuss how this factor might influence the outcomes.

5. The paper would benefit from a limitations section that discusses the potential for unmeasured confounding, given the observational study design.

6. In the results section, the authors should discuss the practical implications of their findings for healthcare providers and public health policy.

**********

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Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2024 Oct 17;19(10):e0311929. doi: 10.1371/journal.pone.0311929.r002

Author response to Decision Letter 0

9 Apr 2024

April 05, 2024

Dear Dr. Patra and Reviewers,

We appreciate the opportunity to revise our submitted manuscript, Evaluating the risk of sleep disorders in subjects with a prior COVID-19 infection. Comments from the reviewers were very helpful in improving the article's focus and we believe the suggestions have made this paper much stronger. Below are the specific reviewer’s comments (in bold) followed by our responses. Newly added parts in the manuscript are highlighted in yellow. Thank you again for taking the time to consider our manuscript.

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Response: We double-checked the style requirements as well as the title, author, affiliations formatting guidelines. Everything is in good shape.

Before we proceed with your manuscript, please address the following prompts:

Response: Data are owned by Utah Department of Health and Human Services, thus requiring approvals from the University of Utah Institutional Review Board and the Utah Resource for Genetic and Epidemiologic Research. We made changes in the Data Availability in the manuscript as follows:

Data cannot be shared publicly due to regulations set by the Utah Department of Health and Human Services, and the Utah Resource for Genetic and Epidemiologic Research (RGE). However, data are available upon approvals from the University of Utah Institutional Review Board (irb@utah.edu) and the Utah Resource for Genetic and Epidemiologic Research (rge@hsc.utah.edu)

Additional Editor Comments:

This manuscript discusses sleep disorder prior to COVID-19 infection in the UTAH all payers claim database. It is an interesting study, however, there exist several concerns.

1. It will be great to have patient counts beside the patient percentages in tables and tests.

Response: Thank you for the comment. We have added patient counts in Table 1 so that patient counts in Table 1 can be informative for Tables 2-4.

Additionally, in the first sentence under the Statistical Approach, we have added “counts” as follows:

Mean, standard deviation, counts, and percentages were used to summarize the baseline characteristics of the subjects in 2019.

In Table 1, we have added one more row labeled “Outcome” just above the sleep disorders to clarify that the sleep disorders is the study outcome.

In Tables 2-4, we have corrected the mistakenly reported p-values of 0.00 to <0.01.

2. In Table 1, most of the % of the patients are same or different by 0.01% except for sleep disorder. Why is that?

Response: Thank you for the comment. We implemented Entropy Balancing (EB) as a method to balance observed variables between the two groups (i.e. subjects with and without a COVID-19 infection). EB is often used to address confounding in observational studies. After calculating weights for each individual based on EB, statistical insignificance of the variables in Table 1, except for the outcome variable (sleep disorder), indicated that the two groups at baseline (prior to COVID-19 infection) were well balanced. Therefore, potential selection bias was eliminated, and the regression aimed to determine if COVID-19 infection was the primary factor associated with the incidence of a sleep disorder. EB is similar to Inverse Probability Weighting (IPW), but it is known that EB is better than IPW in terms of the accuracy of balancing.

3. There is a mention of 101% for 51-62 years old, is this a typo?

Response: Thank you for the question. 101% is correct because the odds ratio of this age group (51-62) compared to the reference group (i.e. 18-30 years old). was 2.01

4. It will be great to have a flowchart for cohort selection.

Response: Thank you for the suggestion. We have added a flowchart for cohort selection as Figure 1 in the revision.

Figure 1. Flowchart for the cohort selection

5. In general, table 1 is very confusing. The values in the table look repetitive and it is not outlined how those values have been calculated. Apparently, p values have been used to distinguish both categorical and continuous data, but it is not clear what statistical test has been used. Further, entropy balancing has not been cited.

Response: The repetitive numbers in Table 1 were a result of balancing the observed variables through Entropy Balancing (EB) process. We aimed to demonstrate the elimination of potential selection bias and confounding associated with the observed variables. This crucial information indicates that selection bias and confounding might be mitigated through balancing the observed variables. In the Statistical Approach section, we described the statistical tests used for comparing the variables in Table 1 as follows:

Following EB, T-tests for continuous variables and Chi-square tests for categorical variables were used to compare the baseline characteristics of the subjects in Table 1.

To provide clarity on EB, we have added two additional references:

Hainmueller J. Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies. Political Analysis. 2012;20(1):25-46. doi:10.1093/pan/mpr025

Zhao, Qingyuan and Percival, Daniel. Entropy Balancing is Doubly Robust. Journal of Causal Inference, vol. 5, no. 1, 2017, pp. 20160010. https://doi.org/10.1515/jci-2016-0010

6. Weighted logistic regression meaning ridge/lasso/something else?

Response: The Entropy Balancing (EB) method generated weighting values for individuals in the analysis. These weights aim to balance the two groups across the control variables as presented in Table 1. It is necessary to incorporate these weights in the logistic regression, thereby conducting a weighted logistic regression.

7. Line 125: P-value written as E-value

Response: Thank you for verifying the information. The E-value is correct. The E-value has been used to assess the magnitude of potential unmeasured confounders for controlled variables, including the primary independent variables. We have added two references for the E-value as follows:

VanderWeele TJ, Ding P. Sensitivity Analysis in Observational Research: Introducing the E-Value. Ann Intern Med. 2017 Aug 15;167(4):268-274. doi: 10.7326/M16-2607. Epub 2017 Jul 11. PMID: 28693043.

Haneuse S, VanderWeele TJ, Arterburn D. Using the E-Value to Assess the Potential Effect of Unmeasured Confounding in Observational Studies. JAMA. 2019;321(6):602–603. doi:10.1001/jama.2018.21554

Reviewers' comments:

Reviewer's Responses to Questions

However, I would like to point out a couple of areas for potential improvement. The study's geographical focus on Utah might limit the generalizability of your results to broader populations. Additionally, the exclusion of non-prescription medication use and undiagnosed COVID-19 cases could impact the overall picture of sleep disorder prevalence in the post-COVID context. Despite these limitations, your research is an important contribution to understanding the aftermath of COVID-19. It paves the way for further studies, which I hope will expand on your work by incorporating a more diverse set of data and examining additional factors that might influence sleep health after COVID-19 infection. Your work is crucial in highlighting the need for ongoing research into the long-term health consequences of this global pandemic.

Response: Thank you for the comments. We agree with these points. While this manuscript offers several significant contributions, we recognize the importance of considering two limitations. The current data are specific to Utah, potentially limiting the generalizability of our results to other states. In addition, the data do not include non-prescription medications and undiagnosed COVID-19 cases, as they only provide information covered by insurance companies. We already included these limitations in the Discussion section and hope future studies address these limitations.

Second, the results of the study may not be generalizable to other states as the data used is specific to the state of Utah in the United States. In addition, these findings could not be generalizable to adolescents or the elderly population. Third, prescription medications such as antidepressants and medications for pain, hypertension and asthma that could interfere with sleep were not considered in the study. On the other hand, some antidepressant medications could be prescribed for sleep disorders, but these were not considered to identify patients with sleep disorder. Continuous positive airway pressure (CPAP) therapy is used for patients who have obstructive sleep apnea, but this therapy was not considered to identify sleep disorder in this study.

Reviewer #2: 1. A careful proofread is recommended to correct any minor typographical errors.

Response: Thank you for the comment. All authors went through the manuscript to identify and correct any typographical errors. Any changes made during the review were highlighted in the manuscript.

2. The authors should ensure data availability in line with PLOS's requirements. If there are restrictions, these should be specified.

Response: Thank you for the comment. Data are owned by Utah Department of Health and Human Services. Thus, approvals from the University of Utah Institutional Review Board and the Utah Resource for Genetic and Epidemiologic Research are required. We made changes in the Data Availability in the manuscript as follows:

Data cannot be shared publicly because of the regulations of Utah Department of Health and Human Services, and the Utah Resource for Genetic and Epidemiologic Research (RGE). Data are available after approvals from the University of Utah Institutional Review Board (irb@utah.edu) and the Utah Resource for Genetic and Epidemiologic Research (rge@hsc.utah.edu)

3. A more in-depth discussion on the potential pathophysiological mechanisms linking COVID-19 to sleep disorders would enhance the paper's contribution to the existing body of knowledge.

Response: Thank you for your comment. We agree with the need to expand this section of the discussion. We have added an in-depth discussion highlighting the potential mechanism linking COVID-19 to sleep disorders. We have also added relevant references. Here is the edited paragraph with the added discussion sentences and references highlighted:

The mechanism of the association between COVID-19 infection and sleep disorders is thought to be multifactorial and could encompass factors such as social isolation, ICU stay, direct effect of the viral infection, immunological response, cerebrovascular changes, medication use, among many others [32, 33]. In addition to infecting peripheral tissues linked to the central nervous system (CNS), the SARS-CoV-2 coronavirus also directly enters the brain by permeating the blood-brain barrier (BBB) [34, 35]. This neuroinvasion could potentially affect brain functions, including sleep regulation [35]. Furthermore, hyperinflammatory reactions where the concentrations of C-reactive proteins (CRP) and interleukin-6 (IL-6) are increased may also be implicated in the association between COVID-19 infection and psychiatric symptoms [36]. Specifically, there is a growing body of evidence that COVID-19 infection can lead to elevated levels of inflammatory mediators such as cytokines [37]. Certain cytokines, including IL-6, IL-1α, and tumor necrosis factor (TNF-α), exhibit notable circadian oscillations when they enter the brain, leading to sleep disorders [38, 39]. This relationship between inflammatory mediators and psychiatric symptoms has been previously demonstrated through the link between inflammation and depression [40]. Social isolation also plays a key role in sleep disorders. Sleep disorders were reported to increase during quarantine periods due to COVID-19 infection [41]. During COVID-19 lockdowns, insomnia, poor sleep maintenance, and reduced sleep quality increased for the general population in several countries. This has been attributed to the effect of confinement, worsened by the psychological effects of the disease and increased exposure to artificial lighting from electronic devices [6, 42, 43].

References

34. Burks SM, Rosas-Hernandez H, Alejandro Ramirez-Lee M, Cuevas E, Talpos JC. Can SARS-CoV-2 infect the central nervous system via the olfactory bulb or the blood-brain barrier? Brain Behav Immun. 2021;95:7-14.

35. Granholm AC. Long-Term Effects of SARS-CoV-2 in the Brain: Clinical Consequences and Molecular Mechanisms. J Clin Med. 2023;12(9).

37. Hojyo S, Uchida M, Tanaka K, Hasebe R, Tanaka Y, Murakami M, Hirano T. How COVID-19 induces cytokine storm with high mortality. Inflamm Regen. 2020;40:37.

38. Semyachkina-Glushkovskaya O, Mamedova A, Vinnik V, Klimova M, Saranceva E, Ageev V, et al. Brain Mechanisms of COVID-19-Sleep Disorders. Int J Mol Sci. 2021;22(13).

39. Agorastos A, Hauger RL, Barkauskas DA, Moeller-Bertram T, Clopton PL, Haji U, et al. Circadian rhythmicity, variability and correlation of interleukin-6 levels in plasma and cerebrospinal fluid of healthy men. Psychoneuroendocrinology. 2014;44:71-82.

40. Wohleb ES, Franklin T, Iwata M, Duman RS. Integrating neuroimmune systems in the neurobiology of depression. Nat Rev Neurosci. 2016;17(8):497-511. doi:10.1038/nrn.2016.69

41. Pilcher JJ, Dorsey LL, Galloway SM, Erikson DN. Social Isolation and Sleep: Manifestation During COVID-19 Quarantines. Front Psychol. 2021;12:810763.

42. Limongi F, Siviero P, Trevisan C, Noale M, Catalani F, Ceolin C, et al. Changes in sleep quality and sleep disturbances in the general population from before to during the COVID-19 lockdown: A systematic review and meta-analysis. Front Psychiatry. 2023;14:1166815.

43. Karkala A, Tzinas A, Kotoulas S, Zacharias A, Sourla E, Pataka A. Neuropsychiatric Outcomes and Sleep Dysfunction in COVID-19 Patients: Risk Factors and Mechanisms. Neuroimmunomodulation. 2023;30(1):237-49.

4. The authors should clarify the severity of COVID-19 cases included in the study and discuss how this factor might influence the outcomes.

Response: Thank you for the comment. We agree that the severity of COVID-19

Attachment

Submitted filename: Response to Reviewers.docx

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PLoS One. doi: 10.1371/journal.pone.0311929.r003

Decision Letter 1

Braja Gopal Patra

20 May 2024

PONE-D-23-38335R1Evaluating the risk of sleep disorders in subjects with a prior COVID-19 infectionPLOS ONE

Dear Dr. Kim,

Please submit your revised manuscript by Jul 04 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Braja Gopal Patra, Ph.D.

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

Reviewer #3: (No Response)

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: No

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4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

Reviewer #3: No

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

Reviewer #3: (No Response)

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6. Review Comments to the Author

Reviewer #2: All the comments are well addressed. The manuscript is now in publishable form. Other typos have also been addressed.

Reviewer #3: The authors studied the impact of covid on sleep disorders using the Utah all payers claims database. The results of the study can provide valuable insights to our understanding of covid which can benefit clinicians, public health workers, and patients. The study is well-designed, although I believe the analysis should be improved before the manuscript is ready for publication.

Elixhauser comorbidity index is for predicting in-hospital mortality and resource use. I’d assume most covid or sleep disorders patients would not require hospitalization, so can the authors justify why the included the Eixhauser comorbidity index? Also, some dx included in deriving the Eixhauser comorbidity index overlap with some covariates the authors included in the models (e.g., depression, obesity), so these variables were somehow adjusted twice in the models.

Entropy balancing is for casual inference. Although from the study design I can see the authors wanted to do some causal inference (dropped people with sleep disorders dx before 2021), but then they why did you balance the covariates and ALSO control for them in the logistic models?

I don’t see the point of separate models for private and medicaid beneficiaries (tables 3 and 4). The full model (table 2) already included insurance type (medicaid coverage indicator) as a main effect, which means you were assuming that the effects of all other covariates would be the same for medicaid and private beneficiaries, which seems to be a valid assumption since the two subgroup models (tables 3 and 4) had similar results. If you expect the effects of covariates to vary by insurance type, you should include interaction terms between medicaid coverage and all other covariates in the full model (table 2).

Discussion — It’s very hard to follow some of the literature summaries in the discussion section. E.g, in the second paragraph, the authors started of the paragraph saying their results were consistent with what had been published. But later in the paragraph they mentioned more studies reported “however, sleep disorders … as a long-term effect associated with prior COVID-19 infection”. From the summaries of the studies, It seems to me that the first few studies before the transition sentence (however…) were cross-sectional studies, and studies after the transition sentence were longitudinal studies looking at long term effects. But the starting sentences did not send off this message and were somehow misleading.

Some references were missing — authors mentioned xxx et al. did yyy, but there was no citation at the end. I highly recommend the authors carefully go through the draft again before resubmission. There are some broken sentences.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: Yes: Muskan Garg

Reviewer #3: No

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PLoS One. 2024 Oct 17;19(10):e0311929. doi: 10.1371/journal.pone.0311929.r004

Author response to Decision Letter 1

3 Jun 2024

May 25, 2024

Dear Dr. Patra and Reviewers,

We appreciate the opportunity to revise our manuscript, Evaluating the risk of sleep disorders in subjects with a prior COVID-19 infection. Below are the specific reviewer’s comments (in bold) followed by our responses. Newly added parts in the manuscript are highlighted in yellow. Thank you again for taking the time to consider our manuscript.

Reviewer #2: All the comments are well addressed. The manuscript is now in publishable form. Other typos have also been addressed.

Response: Thank you for taking the time to review the revised manuscript.

Response: Thank you for your comment. Studies have indicated that sleep disorders, including sleep apnea and insomnia, may be associated with an increased risk of mortality (Zolfaghari et al., 2024; Cappuccio et al., 2010; Lin et al., 2023). Additionally, the Centers for Disease Control and Prevention reported 848,943 deaths due to COVID-19 in the United States between 2020 and 2021 (source: https://www.cdc.gov/nchs/covid19/mortality-overview.htm). Therefore, we deemed it important to control for a comorbidity index such as Elixhauser.

The Elixhauser comorbidity index assigns weights to each condition and then sums these weights for each subject. Because this index considers multiple diseases dependently, we believe that controlling for individual comorbidities, even though they are included in the Elixhauser index calculation, would be beneficial for enhancing the overall goodness of fit of the regression.

In response to the comment above, we conducted a regression excluding the Elixhauser comorbidity index and only controlled for the individual comorbidities. The results, including odds ratios and p-values of the control variables, remained consistent with and without the inclusion of the Elixhauser index variable. Specifically, the odds ratio of the independent variable (i.e., COVID infection: OR=1.53, p<0.01 in Table 2) remained unchanged in both regressions. Subsequently, we updated the previous results with those from the regression excluding the Elixhauser index variable and made corresponding revisions to the Results section.

References

Zolfaghari S, Keil A, Pelletier A, Postuma RB. Sleep disorders and mortality: A prospective study in the Canadian longitudinal study on aging. Sleep Med. 2024 Feb;114:128-136. doi: 10.1016/j.sleep.2023.12.023

Cappuccio FP, D'Elia L, Strazzullo P, Miller MA. Sleep duration and all-cause mortality: a systematic review and meta-analysis of prospective studies. Sleep. 2010 May;33(5):585-92. doi: 10.1093/sleep/33.5.585.

Lin Y, Wu Y, Lin Q, et al. Objective Sleep Duration and All-Cause Mortality Among People With Obstructive Sleep Apnea. JAMA Netw Open. 2023;6(12):e2346085. doi:10.1001/jamanetworkopen.2023.46085

Centers for Disease Control and Prevention. COVID-19 Mortality Overview. Access on May 22, 2024 at https://www.cdc.gov/nchs/covid19/mortality-overview.htm.

Response: Thank you for the comment. Controlling the covariates that were used in the Entropy Balancing enhances the robustness of the estimation through doubly robust estimation. If either the Entropy Balancing model or the main regression model is correctly specified, the estimation remains consistent. This approach offers increased protection against model misspecification and enhances the reliability of causal inference in the study.

Response: Thank you for the comment. We wanted to explore potential differences in the main effect, specifically the odds ratio of the COVID-19 infection variable, as we considered subgroups based on insurance type (Medicaid only vs. private insurance only). Individuals covered by Medicaid may experience a more pronounced negative impact (OR=1.67, p<0.01) on sleep problems due to COVID-19 infection compared to those covered by private insurance (OR=1.51, p<0.01), although direct comparisons of odds ratios are not feasible (i.e. two regressions with two different number of subjects).

Having interaction terms between Medicaid and all other covariates in the full model has to control over 25 additional variables in Table 2. Also, comparing any differences in Medicaid vs. private insurance is not the main purpose of the paper. Thus, we would like to keep Table 2 as it is. Now, we removed Table 3 (i.e. regression results for those covered by private insurance) and 4 (i.e. regression results of those covered by Medicaid). S2 Table (Association between COVID-19 severity and risk of sleep disorders) has been moved to Table 3. In this model, we removed the Elixhauser index variable and reran the regression. Relevant texts were revised in the paper.

Response: Thank you for the comment. In the paragraph, we first tried to present studies that reported incidences of sleep disorders during the COVID-19 pandemic, followed by studies that reported sleep disorders as a long-term effect associated with prior COVID-19 infection. To address your concerns, we have restructured the flow of the paragraph to make it less confusing and easy to understand. We also changed “however” to “moreover” as the transition word since the studies reported incidences of sleep disorder both during and after COVID infection.

Response: The authors went through the manuscript and the references to make sure.

Thank you again for taking the time to review the manuscript!

Attachment

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PLoS One. doi: 10.1371/journal.pone.0311929.r005

Decision Letter 2

Braja Gopal Patra

7 Aug 2024

PONE-D-23-38335R2Evaluating the risk of sleep disorders in subjects with a prior COVID-19 infectionPLOS ONE

Dear Dr. Kim,

Please submit your revised manuscript by Sep 21 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Braja Gopal Patra, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Thank you for thoroughly addressing the reviewers' comments. Here are a few additional minor comments from the reviewers that need attention.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: All comments have been addressed

Reviewer #4: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #3: Yes

Reviewer #4: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: I Don't Know

Reviewer #4: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #3: No

Reviewer #4: No

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #3: Yes

Reviewer #4: Yes

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6. Review Comments to the Author

Reviewer #3: I appreciate the authors taking time to address all my comments. For the controlling for covariates in regression models after Entrophy Balancing part, I'd recommend the authors mention this in the "statistical approach" section that the weighted regression model also controlled for covariates, and cite a few published papers that have done so.

One other minor comment: P3, line 49, “sleep disorders may be a result of” — there was an additional “as” before “a result of” in the manuscript.

Reviewer #4: The paper studies the impact of covid -19 on sleep disorders.This is done by building a weighted logistic regression model taking patient characteristics including the occurrence of COVID-19 as independent variables and occurrence of sleep disorders as dependent variable.

Pros

1. The paper is detailed, well written and easy to understand.

2. the authors sufficiently addressed reviewer concerns

Cons

1. The AUC/ROC for logistic regression is not included

2. There is no description of the weighting scheme used by the logistic regression model.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #3: No

Reviewer #4: No

**********

PLoS One. 2024 Oct 17;19(10):e0311929. doi: 10.1371/journal.pone.0311929.r006

Author response to Decision Letter 2

15 Sep 2024

September 03, 2024

Dear Dr. Patra and Reviewers,

Reviewer #3: : I appreciate the authors taking time to address all my comments. For the controlling for covariates in regression models after Entrophy Balancing part, I'd recommend the authors mention this in the "statistical approach" section that the weighted regression model also controlled for covariates, and cite a few published papers that have done so.

Response: Thank you for the comment. We added the following sentence in the Statistical Approach section with two references:

The weighted regression model controlled for the covariates that were included in the EB.

References

Sudduth, J. D., et al. Preoperative opioid use and its association with postoperative complications. Journal of Substance Use. 2024: 1–7. https://doi.org/10.1080/14659891.2024.2351016.

Ricci C., et al. Appendiceal goblet cell carcinoma has marginal advantages from perioperative chemotherapy: a population-based study with an entropy balancing analysis. Langenbecks Arch Surg. 2023 Jan 25;408(1):65. doi: 10.1007/s00423-023-02791-x.

One other minor comment: P3, line 49, “sleep disorders may be a result of” — there was an additional “as” before “a result of” in the manuscript.

Response: Thank you for pointing it out. It was an error, and we have corrected it as follows:

“Sleep disorders may be a result of various factors ranging from stressful life events…”

Reviewer #4: The paper studies the impact of covid -19 on sleep disorders. This is done by building a weighted logistic regression model taking patient characteristics including the occurrence of COVID-19 as independent variables and occurrence of sleep disorders as dependent variable.

1. The AUC/ROC for logistic regression is not included.

Response: Thank you for the comment. The area under the ROC curve (AUC) for both Tables 2 and 3 is 0.72, indicating that there is a 72% chance that the model is able to distinguish between positive cases and negative cases.

ROC for Table 2:

ROC for Table 3:

According to Hosmer & Lemeshow (Applied logistic regression, 2013), our AUC/ROC=0.72 offers acceptable discrimination.

0.5 ≤ no discrimination

0.5-0.7 = poor discrimination

0.7-0.8 = Acceptable discrimination

0.8-0.9= Excellent discrimination

>0.9 = Outstanding discrimination

We added the following sentences in the Statistical Approach section:

The area under the receiver operating characteristic (ROC) curve (AUC) was used to measure the performance of the logistic regression models.

We added the following result in the Results section:

The AUC following the logistic regressions were 0.72 for both Tables 2 and 3, indicating that there is a 72% chance of the models distinguishing between positive and negative cases.

Reference

Hosmer Jr., D.W., Lemeshow, S. and Sturdivant, R.X. (2013) Applied Logistic Regression. 3rd Edition, John Wiley & Sons, Hoboken, NJ.

2. There is no description of the weighting scheme used by the logistic regression model.

Response: Thank you for the comment. We added the following parts in the Statistical Approach section:

The EB approach for a binary outcome is based on a maximum entropy reweighting scheme that assigns weights to each subject so that the control group (i.e. no COVID infection group in this study) is reweighted to match the controlled covariate moments (i.e. mean, standard deviation, and variance in this study) in the treatment group (i.e. COVID infection group in this study). Therefore, EB ensures that the two groups being compared are similar enough by reweighting the patient characteristics (i.e. covariates)

We have added three references for these parts:

Zhao, Qingyuan and Percival, Daniel. "Entropy Balancing is Doubly Robust" Journal of Causal Inference, vol. 5, no. 1, 2017, pp. 20160010. https://doi.org/10.1515/jci-2016-0010

Hainmueller, J., & Xu, Y. (2013). ebalance: A Stata Package for Entropy Balancing. Journal of Statistical Software, 54(7), 1–18. https://doi.org/10.18637/jss.v054.i07

Markoulidakis, A., Taiyari, K., Holmans, P. et al. A tutorial comparing different covariate balancing methods with an application evaluating the causal effects of substance use treatment programs for adolescents. Health Serv Outcomes Res Method 23, 115–148 (2023). https://doi.org/10.1007/s10742-022-00280-0

We appreciate your time to review the manuscript!

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PLoS One. doi: 10.1371/journal.pone.0311929.r007

Decision Letter 3

Braja Gopal Patra

27 Sep 2024

Evaluating the risk of sleep disorders in subjects with a prior COVID-19 infection

PONE-D-23-38335R3

Dear Dr. Kim,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Braja Gopal Patra, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The authors have addressed all reviewer comments.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0311929.r008

Acceptance letter

Braja Gopal Patra

7 Oct 2024

PONE-D-23-38335R3

PLOS ONE

Dear Dr. Kim,

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Standardized differences before and after entropy balancing.

Abbreviation: COPD, Chronic obstructive pulmonary disease; ADHD, Attention-deficit hyperactivity disorder.

(DOCX)

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Data Availability Statement

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PERMALINK

Evaluating the risk of sleep disorders in subjects with a prior COVID-19 infection

Jaewhan Kim

Kenechukwu C Ben-Umeh

Rachel Weir

Karen Manotas

Kristi Kleinschmit

Aaron Fischer

Peter Weir

Fernando Wilson

Roles

Abstract

Introduction

Materials and methods

Data

Subjects

Outcome

Covariates

Statistical approach

Table 1. Summary statistics of subjects in 2019 after entropy balancing.

Results

Fig 1. Flowchart for the cohort selection.

Table 2. Weighted logistic regression of association between a prior COVID-19 infection and risk of sleep disorders in all subjects in 2019 (n = 413,958).

Table 3. Association between COVID-19 severity and risk of sleep disorders.

Discussion

Conclusions

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Braja Gopal Patra

Roles

Author response to Decision Letter 0

Decision Letter 1

Braja Gopal Patra

Roles

Author response to Decision Letter 1

Decision Letter 2

Braja Gopal Patra

Roles

Author response to Decision Letter 2

Decision Letter 3

Braja Gopal Patra

Roles

Acceptance letter

Braja Gopal Patra

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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