July 2023 Sleep Case of the Month: Fighting for a Good Night’s Sleep

Meneena Bright MD

Sleep Medicine

Brigham and Women’s Hospital

Harvard Medical School

Boston, MA USA

A 31-year-old man presented for follow-up in the Sleep Medicine clinic. He has a past medical history of a seizure disorder and supraventricular tachycardia (SVT) and was referred after complaining of excessive daytime sleepiness. Current medications include citalopram and diltiazem. Four months prior, he was diagnosed with severe obstructive sleep apnea characterized by an apnea-hypopnea index (AHI) of 34 and an oxygen saturation nadir of 86%. The patient was initiated on continuous positive airway pressure (CPAP) therapy; however, he reported ongoing mask discomfort and difficulty with exhalation while wearing CPAP. Despite partial improvement in sleep fragmentation and daytime sleepiness, the patient reported sleeping for only 4 hours per night. The patient's sleep schedule was irregular, with bedtime ranging between 9:30 pm and 12 am, waking up at 5 am, and taking a daily nap for 4-5 hours in the morning during which he continued to use CPAP. Additionally, he described experiencing episodes of waking up from dreams, often screaming or throwing punches. A score of 15 on the Epworth Sleepiness Scale (ESS) indicated significant daytime sleepiness.

What should be done next? (Click on the correct answer to be directed to the second of five pages)

1. Begin oxygen at night
2. Begin self-titrating CPAP
3. Discontinue diltiazem
4. Neurology referral
5. Overnight laboratory polysomnography

Cite as: Bright M. July 2023 Sleep Case of the Month: Fighting for a Good Night’s Sleep. Southwest J Pulm Crit Care Sleep. 2023;27(1):1-3. doi: https://doi.org/10.13175/swjpccs027-23 PDF 

Associations Between Insomnia and Obstructive Sleep Apnea with Nutritional Intake After Involuntary Job Loss

Salma Batool-Anwar, MD, MPH¹

Patricia L. Haynes, MPH²

Aria Panchal³

Stuart F. Quan, MD^1,2

¹Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA; ²Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, ³University of Toronto, Canada

Abstract

Objectives: Involuntary job loss is a stressful life event that can result in changes in nutritional intake. Both insomnia and obstructive sleep apnea (OSA) also are associated with alterations in dietary intake, but the extent to which this occurs in those who have experienced involuntary job loss is unclear. This study assessed nutritional intake in recently unemployed persons with insomnia and obstructive sleep apnea in comparison to those without a sleep disorder.

Methods: Participants from the Assessing Daily Activity Patterns through Occupational Transitions (ADAPT) study were screened for sleep disorders using the Duke Structured Interview for Sleep Disorders. They were classified as having OSA, acute or chronic insomnia or no sleep disorder. Dietary data was collected using United States Department of Agriculture Multipass Dietary recall methodology. 

Results: A total of 113 participants had evaluable data and were included in this study. The cohort was comprised mainly of women (62%) and 24% were non-Hispanic white. Participants with OSA had a higher BMI compared with no sleep disorder (30.6 ± 9.1 vs 27.4 ± 7.1 kg/m², p≤0.001). Those with acute insomnia had significantly decreased consumption of total protein (61.5 ± 4.7 vs. 77.9 ± 4.9 g, p≤0.05) and total fat (60.0 ± 4.4 vs. 80.5 ± 4.6 g, p≤0.05). Among the participants with chronic insomnia, there was little overall difference in nutrient consumption compared to the no sleep disorder group although there were several gender specific differences. There were no overall differences between participants with OSA in comparison to no sleep disorder, but women consumed less total fat (89.0 ± 6.7 vs. 57.5 ± 8.0 g, p≤0.01). The Healthy Eating Index of all groups was below the average value of Americans. 

Conclusion: Unemployed persons compared to those with sleep disorders differ in their consumption of major nutrients; the dietary composition of those with acute insomnia exhibited the greatest divergence. Additionally, the overall nutritional intake of recently unemployed persons is poor.

Key Words: unemployment, insomnia, obstructive sleep apnea, nutrition, diet

Introduction

Sleep is a vital component of healthy living. According to the National Sleep Foundation and the American Academy of Sleep Medicine, 7-8 hours of regular sleep is essential for maintenance and restoration of metabolic homeostasis and to promote optimal health. Unfortunately increasing numbers of people across the globe suffer from sleep deprivation or sleep disorders (1).

Insomnia and obstructive sleep apnea (OSA) are considered health risks and have been linked with cardiovascular diseases (2), increased risk of accidents (3), loss of productivity, worsening metabolic profiles (4), and even premature mortality (5,6). Certain population subgroups comprising of night shift workers, minorities (racial/ethnic), those with fewer years of education or those belonging to lower socioeconomic class (SES) have been shown to be particularly susceptible to several of these adverse consequences.

A major determinant of SES is employment status. Unemployment can result from involuntary job loss, a stressful and disruptive life event. Moreover, joblessness is associated with insomnia symptoms (7), and we have demonstrated an association between recent job loss and OSA (8).

Both insomnia and OSA are associated with differences in nutritional intake in comparison to non-affected persons. Furthermore, we have demonstrated that the quality of nutritional intake is worse in recently unemployed persons (9). However, the impact of insomnia disorder or OSA on nutritional intake has not been evaluated in this population. The Assessing Daily Activity Patterns through Occupational Transitions (ADAPT) Study (10) is an ongoing longitudinal cohort study of individuals who have suffered involuntary job loss in the last 90 days with data collected on the effects of sleep and sleep disorders on nutritional intake and metabolic outcomes. To better understand the immediate health aftermath of job loss, we analyzed cross sectional data from the baseline assessment of the ADAPT study to assess the associations between two sleep disorders, insomnia disorder and OSA, and nutritional intake.

Methods

Participants

Study participants were part of the ADAPT Study, an 18-month longitudinal study that examined changes in sleep, social rhythms, and obesity following an involuntary job loss (10). The study protocol and recruitment strategy have been described in detail previously. Briefly, all individuals who applied for unemployment insurance (UI) in the greater Tucson, Arizona and surrounding areas between October 2015 and December 2018 received study recruitment flyers within their UI intake packets. Interested individuals contacted study staff and completed phone screens assessing exclusion criteria; potentially eligible individuals were then scheduled for in-person screening visits. Individuals were eligible if they had experienced an involuntary job loss within 90 days of study enrollment, had been with their employer for at least six months, were currently employed less than 5 hours per week and did not complete any night shift work within the last 30 days. During the in-person screening, participants provided written informed consent, as well as information about their demographics, employment and medical history. They also were screened for homelessness, existing physiological and mental health conditions, substance abuse, and major sleep diagnoses which could interfere with social rhythms and sleep patterns. Those who passed screening completed validated mental health and sleep diagnostic interviews.

Data used in this analysis originated from the study’s baseline visit. Of the 446 adults who provided written consent, 191 participants met eligibility criteria and completed a baseline assessment visit, including an at-home data collection period lasting two weeks. Participants were considered for the current analysis if there was an acceptable assessment of sleep and diet on their sleep diaries and dietary recalls respectively for analysis. However, 8 participants were excluded as outliers because their mean energy consumption (MEC) was significantly less than commonly reported norms (11, 12). In addition, data for all variables used in analyses were available for only 113 participants. Descriptive statistics for these participants who constitute the study sample are reported in Table 1.

Table 1. Demographic and Anthropometric Characteristics of the Study Population.*

*Data presented as mean ± SD or percentages as appropriate; ^ap<0.05; ^bp<0.01; ^cp<0.001 vs. No Sleep Disorder; ^**SES assessed as the sum of 4 component scores; education, Income, Employment, and Housing. Click here to open Table 1 in a separate, enlarged window.

Measures

Demographic and Anthropometric

Age, ethnicity and biological sex were collected during the initial interview. Height and weight were measured using a stadiometer and bioelectrical impedance analyzer via standardized protocols to calculate the body mass index (kg/m², BMI). Stressful life events were measured using the Life Events and Difficulties Schedule (LEDS -2) (13), a semi-structured interview and consensus panel rating system in which at least three raters provided contextual assessment of threat associated with different life events over the last three months. All raters were trained and required to achieve a kappa of 0.75 with a trained and reliable rater prior to participation in the rating meeting. Life events were considered severe if they conferred a high degree of threat or unpleasantness during both the immediate aftermath of an event and over the next 10 to 14 days. As in our previous (0=less than high school, 1=high school/some college, 2=bachelor’s degree, 4=postgraduate degree), Income (0=less than or equal to 100% of U.S. poverty line, 1=101-200% of poverty line, 2=201-400% of poverty line, 3=greater than 400% of poverty line), Employment (0=unemployed in last 6 months, 1=employed during last 6 months), and Housing (0=not a homeowner, 1=homeowner).

Diet Assessment

During the two-week, at-home baseline data collection period, participants completed up to three 24-hour dietary recalls administered by trained diet assessors at the Behavioral Measurements and Interventions Shared Resource of the University of Arizona Cancer Center utilizing the gold-standard United States Department of Agriculture Multi-pass Dietary recall (15) and the Nutrient Database System of the University of Minnesota for nutrient analysis (16). These interviews were supported by the Remote Food Photography Method (17), in which participants took pictures of all food and beverages prior to consumption, as well as after they had finished eating and drinking. Photos were used to review recall as a final verification of the multi-pass data. The diet recalls provided information on the types and quantity of food, including energy and nutrient values. At least 3 dietary recalls were completed by 172 participants (95.6% of the entire ADAPT cohort).

Sleep Phenotypes

The Duke Structured Interview for Sleep Disorders (DSISD) (18) was used to classify participants into 4 phenotypes: no sleep disorder (Control), obstructive sleep apnea (OSA), acute insomnia disorder and chronic insomnia disorder. The DSISD is a clinical semi-structured interview developed to assess sleep disorder symptoms and was updated to reflect international classification of sleep disorders (ICSD-3) criteria (19). It is divided into 4 modules respectively focused on insomnia disorders, excessive sleepiness conditions, circadian rhythm disorders and parasomnias. During the interview, participants are asked a series of questions related to possible sleep disturbances. Sections of the questionnaire are skipped if the participant endorses negative answers to screening questions. The DSISD has been validated for classifying persons for OSA (20) and insomnia (18). The DSISD was administered by research staff trained in sleep disorder diagnosis, who met reliability levels of 75% with a licensed clinician (PH).

Statistical Analysis

For baseline characteristics, mean (SD) for continuous variables and percentages for categorical variables were calculated. For multivariate models, estimated marginal means are displayed as mean (SD). The participants without any sleep diagnosis were classified as “No Sleep Disorder”. After classifying participants into sleep phenotypes, comparisons of dietary constituents between No Sleep Disorder and OSA, No Sleep Disorder and acute insomnia and No Sleep Disorder and chronic insomnia groups were performed using analysis of covariance. Models were constructed initially without and subsequently with gender stratification. Included covariates were age, BMI, socioeconomic index, presence of a severe life event as measured by the Life Events and Difficulties schedule. The more liberal rating of severity, short term threat vs long term threat, was used for the purpose of this analysis (long term threat rating of at least 2b). The current variable for severe life events was dichotomized (1 = at least one severe event in the last 3 months; 0 = no severe events in the last 3 months). The level of statistical significance for both models was set at 0.05, but comparisons between 0.05 and 0.10 are provided to illustrate a trend. All statistical analyses were done using STATA version 11 (StataCorp, LLC, College Station, TX, USA) or IBM SPSS version 28 (Armonk, NY).

Results

Table 1 demonstrates the demographic and anthropometric characteristics of the study cohort. Both insomnia groups and the OSA group were younger than controls without a sleep disorder. There was a higher proportion of women in all groups (51.6%, 76.5%, 60%, and 60.9% among participants without any sleep disorder, with acute insomnia, with chronic insomnia, and with OSA respectively). Participants with OSA had a higher BMI compared with no sleep disorder (30.6 ± 9.1 vs 27.4 ± 7.1 kg/m², p ≤ 0.001). Among the No Sleep Disorder group, 45.2% had college education compared with 20.0% among chronic insomnia, 41.2% in acute insomnia, and 39% among participants with OSA. The participants with acute insomnia had significantly less mean energy consumption in comparison to the control group without a sleep disorder (p<0.05). The proportion of participants who had experienced at least one severe life event was not significantly different amongst the groups.

Table 2 displays mean nutritional intakes for acute insomnia in comparison to controls without a sleep diagnosis stratified by gender.

Table 2. Mean Nutritional Intake--Acute Insomnia Compared to No Sleep Disorder Stratified by Gender.

Data are shown as mean ± standard deviation. Non gender and gender stratified models are adjusted for BMI, severity of stress, socioeconomic status, and age. Mean estimates are evaluated at the following values: BMI = 29.3, severity of stress = .58, socioeconomic status total = 2.25, Age = 42.6 years. Click here to view Table 2 in an enlarged, separate window.

Acute insomnia was characterized by consumption of lower amounts of total protein (61.5 ± 4.7 vs. 77.9 ± 4.9 g, p≤0.05) and total fat (60.0 ± 4.4 vs. 80.5 ± 4.6, p≤0.05). Reductions in total protein were primarily a result of decreased amounts of animal protein. Decreased total fat was accompanied by lower amounts of trans fatty acids and saturated fatty acids. However, these latter results were driven primarily by lower amounts among women (trans fatty acids: 1.3 ± 0.2 vs 2.5 ± 0.3 g, p≤0.05 among women in comparison to 1.9 ± 0.4 vs 1.6 ± 0.3 g, p=NS among men; saturated fatty acids: 19.4 ± 2.3 vs 32.5 ± 2.8 g, p < 0.01 among women in comparison to 25.5 ± 4.2 vs 22.7 ± 2.8 g, p=NS among men). Similarly, those with acute insomnia consumed less cholesterol compared to those without any sleep disorder (357.2 ± 34.6 vs. 241.6 ± 33.4 mg, p≤0.05). In contrast these results were significant only among men (242.5 ± 71.4 mg vs 417 ± 48.2 g, p<0.05 compared to 242.2 ± 38.9 mg vs 299.7 mg ± 47.4, p= NS among women.

Mean nutritional intakes for chronic insomnia are shown in Table 3.

Table 3: Mean Nutritional Intake--Chronic Insomnia Compared to No Sleep Disorder Stratified by Gender.

Data are shown as mean ± standard deviation. Non gender and gender stratified models are adjusted for BMI, severity of stress, socioeconomic status, and age. Mean estimates are evaluated at the following values: BMI = 29.7, severity of stress = .55, socioeconomic status = 2.29, Age = 41.4 years. Click here to view Table 3 in an enlarged, separate window.

There were no overall differences between chronic insomnia and no sleep disorder with the exception of cholesterol which was lower in chronic insomnia (348.7 ± 32.1 vs. 233.8 ± 36.3 mg, p≤0.05). However, after stratification by gender, men consumed more protein and cholesterol irrespective of sleep phenotype. In addition, there were several interactions; men with chronic insomnia consumed more fatty nutrients and women consumed less. Men with chronic insomnia also had greater intake of vegetable protein. (see Table 3 for numeric detail).

Table 4 provides the mean nutritional intake for participants with OSA in comparison to those with no sleep disorder.

Table 4. Mean Nutritional Intake—Obstructive Sleep Apnea (OSA) Compared to No Sleep Disorder Stratified by Gender.

Data are shown as mean ± standard deviation. Gender and Non gender stratified models are adjusted for BMI, severity of stress, socioeconomic status, and age. Mean estimates are evaluated at the following values: BMI = 30.96, severity of stress = .52, socioeconomic status total = 2.39, Age = 41.96 years. Click here to view Table 4 in an enlarged, separate window.

Overall, there were few differences between those with OSA and no sleep disorder. However, less cholesterol was consumed by OSA participants than no sleep disorder (351.7 ± 34.9 vs. 222.0 ± 41.0 mg, p≤0.05). Irrespective of sleep phenotype, women consumed less total protein and animal protein in comparison to men. In addition, women with OSA consumed less fatty nutrients (See Table 4 for numeric detail).

Tables 2, 3, and 4 also show the mean energy intake and Healthy Eating Index for no sleep disorder, acute and chronic insomnia and OSA participants. Participants with acute insomnia consumed fewer calories than controls (1508 ± 88 vs. 1836 ± 90, p≤0.05); this finding was principally observed in women. For chronic insomnia and OSA, there were no overall differences in comparison to controls. However, there was an interaction between phenotype and gender such that women with chronic insomnia had the lowest energy intake. In contrast to an ideal Healthy Eating Index of 100 and the value for the average American diet of 58, no sleep disorder and all three sleep phenotypes had lower values.

Discussion

In this paper, we determined the associations among nutrient intake, acute and chronic insomnia and OSA. We found that participants with acute insomnia had decreased intake of proteins and fats. Among participants with chronic insomnia and OSA, there were few overall differences in dietary intake compared with persons with no sleep disorder. However, for both chronic insomnia and OSA, intake of some nutrients was different from no sleep disorder after gender stratification and also between men and women.

There has been increased interest in diet and sleep quality in recent years; previous epidemiologic studies have demonstrated bidirectional associations between diet quality and sleep (21) (22) (23). The results of our study are consistent with the prior research demonstrating an association between dietary content with sleep quality. Increased sleep efficiency in the elderly has been linked with increased intake of tryptophan (24) which is thought to convert to serotonin, a precursor to melatonin, after crossing the blood brain barrier (25). Katagiri et al (22) demonstrated improved sleep quality as measured by Pittsburgh Sleep Quality Index among participants with high intake of fish and vegetables, whereas poor sleep quality was seen in relation to high consumption of confectionary and noodles.

We observed reductions in protein intake among those with acute insomnia. Previous research has suggested an association between sleep and protein intake. A number of neurotransmitters are known to affect sleep-wake cycle namely 5-HT, gamma aminobutyric acid (GABA), orexin, melanin-concentrating hormone, and histamine(26); dietary precursors can influence the synthesis and function of some of these neurotransmitters. Synthesis of 5-HT is dependent on its precursor availability, the amino acid L tryptophan (Trp)(27). Similar to our results, in a cross-sectional study of non-shift workers, researchers demonstrated an association between low protein intake and poor sleep quality particularly with sleep initiation problems. In contradistinction, in another study of middle-aged Japanese, high protein intake was associated with difficulty maintaining sleep (28). Gao et al also confirmed the differential association of individual insomnia symptoms on nutrition. Using the Health Professional Follow up Study (HPFS), the authors demonstrated difficulty maintaining sleep in relation to a greater energy intake along with an association between difficulty initiating sleep and lower overall diet quality score (29). Unlike our findings, the associations in these latter two studies were limited only to men and did not account for employment status. Although it is difficult to extract a definite conclusion from these studies, there appears to be an effect of protein intake on sleep characteristics.

There are several possible mechanisms that have been proposed to explain the association between diet quality and sleep disorders. One mechanism is that increased hunger and decreased satiety signals lead to orexigenic changes of hunger and fullness (30). Epidemiologic studies have demonstrated lower leptin and higher ghrelin levels among sleep deprived people (31, 32). Another explanation described is related to gastrointestinal discomfort from fullness making it difficult to fall or stay asleep (33). Differential impacts of these mechanisms in different study populations likely contribute to the variability in findings among studies.

Although an association between macroeconomic conditions and mortality and morbidity exists (34) (35), there is paucity of literature analyzing the effect of unemployment, dietary habits, and sleep disorders. Consistent with scant previous research, we observed poor diet quality in the face of involuntary job loss (36). The diet quality as assessed by the Healthy Eating Index (HEI) has been found to have an association with multiple chronic diseases outcomes (37). We found lower scores for HEI among women with chronic insomnia. Although we observed lower fat consumption in persons with acute insomnia, a change of macronutrient composition with an increase in high fat, high sugar, and low fruit and vegetable consumption has been described during stressful times (38)

Economic downturn has been linked with less intake of protein, saturated and total fats and more consumption of carbohydrates (36). Unemployment and financial instability have been shown to lead to unhealthy behaviors on one hand but on the other hand more time is available for healthy food preparation (39). To our knowledge, this study is unique as we examined the association between sleep disorders and diet quality in the setting of involuntary job loss.

We do acknowledge that the cross-sectional nature of this study is a limitation; therefore, we cannot determine causality. Another limitation is that the sample was comprised mainly of women and non- Hispanic whites. As previously described, employment opportunities vary by individual characteristics and particularly ethnicity, and therefore the results of this study may not be generalizable to other populations. Furthermore, persons with confirmed diagnoses of sleep disorders were excluded thus potentially limiting the population to those less impacted by any problems with their sleep. Although the DSISD has previously been validated for classifying OSA, we do acknowledge the limitation of using the structured interview as a surrogate for polysomnography.

In conclusion, the dietary intake of recently unemployed persons with insomnia or OSA is different than those without a sleep disorder. This may reflect the impact of an interaction between the effect of recent job loss and the presence of a sleep disorder on dietary habits. Future longitudinal studies of a racially and ethnically diverse population are needed to better understand the directionality/causality.

Acknowledgements

This work was supported by a grant from the National Heart, Lung and Blood Institute (HL117995).

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Cite as: Batool-Anwar S, Haynes PL, Panchal A, Quan SF. Associations Between Insomnia and Obstructive Sleep Apnea with Nutritional Intake After Involuntary Job Loss. Southwest J Pulm Crit Care Sleep. 2023;26(3):37-47. doi: https://doi.org/10.13175/swjpccs001-23 PDF

January 2023 Sleep Case of the Month: An Unexpected EEG Abnormality

David M. Baratz, MD¹

Ken Cooper, RPSGT²

¹Pulmonary Associates

Scottsdale, AZ USA

²Cobre Valley Regional Medical Center

Globe, AZ USA

A 46-year-old woman was referred because of snoring, observed apnea, and daytime hypersomnolence. Her Epworth Sleepiness Scale was 9 out of 24. She was slightly overweight but otherwise her physical examination was normal. An overnight polysomnography was requested but denied by her insurance company.

What should be done at this time? (Click on the correct answer to be directed to the second of six pages)

1. An at home sleep study
2. ENT referral
3. Reassurance
4. 1 and 3
   
5. All of the above

Cite as: Baratz DM, Cooper K. January 2023 Sleep Case of the Month: An Unexpected EEG Abnormality. Southwest J Pulm Crit Care Sleep. 2023;26(1):1-4. doi: https://doi.org/10.13175/swjpccs056-22 PDF

July 2022 Sleep Case of the Month: A Sleepy Scout

Christine S. Fukui MD

Honolulu, HI USA

History of Present Illness:

A 25-year-old African American man complaining of excessive daytime somnolence. He was a US Army Ranger scout who received a traumatic brain injury (TBI) from an improvised explosive device attack in Afghanistan which resulted in a loss of about ¼ of his visual field. He said he slept well at night and there was no history of snoring. There was no history of any parasomnias.

PMH, SH, FH:

Other than the traumatic brain injury there was no significant PMH. His most recent brain scan showed only the remnants of his brain injury which resulted in an intracerebral hemorrhage which was managed conservatively. He was single. He did not smoke and had only moderate alcohol intake. There was no significant FH of sleep apnea.

Physical Examination:

Other than the visual field loss his physical examination was unremarkable.

What should be done next? (Click on the correct answer to be directed to the second of five pages)

1. Brain MRI
2. Electroencephalogram (EEG)
3. PSG (polysomnography) sleep study
4. Repeat CT of head
5. All of the above

Cite as: Fukui CS. July 2022 Sleep Case of the Month: A Sleepy Scout. Southwest J Pulm Crit Care Sleep 2022;25(1):1-3. doi: https://doi.org/10.13175/swjpccs027-22 PDF

Assessing Depression and Suicidality Among Recently Unemployed Persons with Obstructive Sleep Apnea and Socioeconomic Inequality

Vijay Singh DO¹

Patricia L. Haynes PhD²

Stuart F. Quan MD^3,4

¹Burrell College of Osteopathic Medicine, Las Cruces, NM

²Department of Health Promotion Sciences, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ

³Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA

⁴Asthma and Airway Disease Research Center, University of Arizona, Tucson, AZ

Abstract

Background: Obstructive Sleep Apnea (OSA) is a common sleep-related breathing disorder that often is associated with several psychiatric conditions. Job loss is a stressful life event that can also affect mental health and socioeconomic status (SES). We investigated whether there was an association between the prevalence of OSA and several psychiatric conditions within a cohort of persons who recently became unemployed and whether SES was a contributing factor.

Methods: Data from 292 participants who completed the screening evaluation of the Assessing Daily Activity Patterns through occupational Transitions (ADAPT) Study were used to assess the association between the prevalence of OSA, and current and past depression, and past suicidality. A type III sleep home sleep monitor was used to identify the presence of OSA and assess its severity. Depression and suicidality were ascertained using the Mini-international neuropsychiatric interview. Years of education was used as a proxy for SES.

Results: There were no significant associations between severity of OSA, SES and current depression, past depression, and suicidality. Past suicidality was noted to be more common among those who were single/widowed (17.4%) or those who were divorced or separated (11.1%) (p=0.027). Current depression was more common among Hispanics in comparison to non-Hispanics. Furthermore, prevalence rates of both depression and past suicidality were higher than previous reports in general populations.

Conclusions: Within a cohort of individuals who experienced recent job loss, there was no association between OSA and depression or past suicidality. Prevalence rates of both depression and past suicidality were higher than previous reports in the general population.  

Introduction

Obstructive sleep apnea (OSA) is a highly prevalent medical condition characterized by recurrent episodes of partial or complete obstruction of the upper airways during sleep. Common symptoms are loud snoring, witnessed apneas, and daytime fatigue or sleepiness (1). It is associated with various mental health conditions such as depression, anxiety, and post-traumatic stress disorder. Importantly, recent studies with small sample sizes have shown a correlation between suicidal behavior and persons with OSA (2).

Additionally, OSA has also been shown to relate to lower socioeconomic status (SES) which can be impacted by job loss (3). However, the association of OSA, SES with mental health conditions in a population of individuals who have been recently unemployed has not adequately explored.

In this study, we evaluated the association between OSA and SES and a self-reported history of several common psychiatric disorders. We hypothesized that individuals with recent job loss and OSA would have an increased likelihood of having a history of self-reported psychiatric illness and that SES would be contributing factor.

Methods

Study participants were part of the ADAPT Study, an 18-month longitudinal study examining changes in sleep, social rhythms, and obesity following an involuntary job loss. The study protocol and recruitment strategy have been described in detail previously (4).  Briefly, all individuals who applied for unemployment insurance (UI) in the greater Tucson, Arizona and surrounding areas between October 2015 and December 2018 received study recruitment flyers within their UI intake packets. Interested individuals contacted study staff and completed phone screens assessing exclusion criteria; potentially eligible individuals were then scheduled for in-person screening visits. Individuals were eligible for overnight screening if they had experienced an involuntary job loss within 90 days of study enrollment, had been with their employer for at least six months, were currently employed less than five hours per week and did not complete any night shift work within the last 30 days. During the in-person screening, participants provided written informed consent, as well as information about their demographics, employment, and medical history. They also were screened for homelessness, existing physiological and mental health conditions, substance abuse, and major sleep diagnoses which could interfere with social rhythms and sleep patterns. An overnight at-home screening for sleep apnea was performed utilizing the ApneaLink Plus^TM (ResMed, San Diego, CA) to exclude moderate sleep apnea as a cause of sleep disruption. This study was approved by the University of Arizona Human Subjects Protection Program (#1401185772).

Data used in this analysis originated from the study’s screening visit. Of the 446 adults who provided written consent, 292 adult participants underwent overnight screening for obstructive sleep apnea (OSA) and completed the Mini-international neuropsychiatric interview (5). Participants were classified as having a current or past diagnosis of several mental health disorders including depression, mania, hypomania and suicidality on the basis of their responses to the structured screening interview. The screening survey also recorded information pertaining to socioeconomic status, educational attainment, race, and ethnicity. Severity of OSA was based on the Apnea-Hypopnea Index (AHI) from the home sleep study as follows: No OSA (AHI <5 /h), Mild OSA (AHI ≥5 and <15 /h), Moderate to Severe OSA (AHI ≥15 /h). Primary dichotomous endpoints included percent of people who had past or current episodes of depression, and past episodes of suicidality. Income in relation to the US Health and Human Services Poverty Line for Arizona was used as an indicator of SES based on data from the baseline survey.

Statistical Analysis. Mean and standard deviation for the variables being investigated were used to describe the baseline characteristics of the cohort. Comparison of the prevalence of various mental health conditions as a function of the presence of OSA was performed using a Pearson-Chi Square test. Comparisons of the prevalence of depression and suicidality to previously reported prevalence rates were completed using the binomial test. All statistical analyses were done using IBM SPSS version 28 (Armonk, NY).

Results

Table 1 describes the overall demographic and baseline characteristics for the participants who volunteered to be a part of the survey.

(Click here to view Table 1 in a separate, enlarged window) 

Most participants were White and 35.3% were Hispanic. The majority were women and were not married or had a live-in partner. A majority were living at or below the poverty line and two-thirds had a high school education or less. On average, the population being studied was obese. Moderate to severe OSA was observed in 48 participants (16.4%). Screening for mental health conditions showed that approximately one-third of the cohort had self-reported current as well as past depression. Both current and past depression rates are significantly higher (binomial test, p<0.001) than the 8.1% prevalence of depression reported from the 2013-2016 National Health and Nutrition Examination Survey (NHANES) (6). History of suicidality was present in approximately 11% of the sample. This rate is higher than the 4.2% prevalence of suicidal thoughts in Arizona reported by the National Survey on Drug Use and Health in 2008-2009 (7)(binomial test, p<0.001). The remaining health conditions were observed in very small numbers and were not analyzed further.

Table 2 shows relationships between severity of AHI and marital status, educational attainment, race, and socioeconomic status. No significant associations were observed.

(Click here to view Table 2 in a separate, enlarged window)

Shown in Table 3 are the associations of severity of OSA, social and demographic characteristics of the participants with current and past episodes of depression.

(Click here to view Table 3 in a separate, enlarged window)

There were no significant associations with respect to self-reported current or past depression with any demographic characteristics or severity of OSA.

The associations between severity of OSA and demographic characteristics with a history of self-reported past suicidality are shown in Table 4.

(Click here to view Table 4 in a separate, enlarged window)

Past suicidality was least likely to have occurred among those who were married or had a live-in partner (6.6%) in comparison to those who were single/widowed (17.4%) or those who were divorced or separated (11.1%) (p=0.027). No other significant relationships were found between these demographic factors as well as history of OSA.

Discussion

In this study of recently unemployed adults, we did not find any associations between OSA or SES with the prevalence of self-reported depression (past or current) or past suicidality. However, the prevalence rates for depression were higher in this cohort in comparison to previous reports in the general population particularly among Hispanics. History of suicidality was also higher than reported in the general Arizona population and was observed to be more common among those who were not married or living with a partner.

Although we were unable to find any associations between OSA and psychiatric conditions in our study, most but not all previous literature has found correlations between OSA and patients suffering from depression (8, 9). There are many similarities between OSA and depression including symptoms such as poor sleep, anhedonia, low energy, and lack of concentration (10). It is unclear why our results do not confirm previous findings of a close relationship. However, our cohort had relatively few participants with severe OSA, as compared to samples from sleep centers. It is possible that depressive symptoms are correlated with the AHI only in severe OSA (Lee 2015). Additionally, although most individuals in the study were female who are more likely to have major depression (11), the prevalence of OSA is higher in males (12). Thus, the ability to detect an association between depression and OSA may be low.  Furthermore, the prevalence of current (30.2% Male/34.3% Female) and past depression (32.5% Male/42.8% Female) was already high in our cohort of recently unemployed persons in comparison to estimates of the overall prevalence before the COVID-19 pandemic of ~8% (6, 13). Thus, it is possible that any additional impact of OSA would be difficult to detect.

Increased rates of depression have been previously reported among unemployed adults (14). We also found an elevated rate of depression among unemployed persons, but extend these findings to specifically those with recent job loss. Although our depression prevalence rate was higher than previously observed (14), this comparison should be interpreted cautiously given the dissimilarities in ascertainment instruments. It might be surmised that recent job loss is the most plausible explanation for high rates of current depression in this cohort, but it is unclear why the prevalence of past depression also is high. One explanation is that history of depression is a marker for subsequent unemployment as has been previously suggested (15). Irrespective of etiology, our results indicate that recently unemployed persons are at high risk for mental health issues.

Hispanics in our study had higher rates of current depression than non-Hispanics. In a review of depression in Hispanics in the United States, Lewis-Fernandez et al (16) noted that initial studies found that Hispanic populations have higher rates of depression than primarily white populations, but that this was not confirmed in later studies. Hispanics are a heterogenous ethnic group and are primarily of Mexican ancestry in Arizona. In their review, Lewis-Fernandez et al (16) noted that United States-born Mexican Americans had higher rates of depression than those born in Mexico. Although ability to speak English was an inclusion requirement for participation, we could not precisely determine the geographic origin of the Hispanics in this cohort. Thus, the explanation for our finding of higher depression rates in Hispanics remains unclear.

Our study did not find an association between OSA and past suicidality. In contrast, a recent large Danish cohort study of 48,168 patients with OSA found an increased risk of suicide as well as self-harm (17). It is likely that we were underpowered to detect any relationship, since only 33 participants reported past suicide. However, we did find a history of suicidality to be greater in those were single or widowed. This is consistent with previous studies demonstrating suicide rates are higher amongst those who are not married (18). Our data provide additional evidence showing that suicidality is more common among individuals who do not cohabitate. 

Similar to our findings related to the prevalence of depression, the past suicidality rate in our cohort was higher than reported in general populations (2, 7, 19). This is most likely related to higher rates of past depression in our cohort.

Lower SES has been associated with a higher occurrence of psychiatric co-morbidities. Patients with lower SES have been noted to have a greater likelihood of developing chronic mental health conditions, particularly depression (20). Studies of OSA and health disparities have demonstrated a higher prevalence of OSA among those who are disadvantaged (21). Although a majority of the cohort had incomes below the poverty line, this status may have been a recent event associated with job loss and therefore not sufficiently impactful to increase the prevalence of OSA.

Our study has several strengths. We assessed a moderate sized cohort consisting of 292 individuals, 126 of which were of the male gender. This allowed us to have a nearly balanced analysis of both male and female genders. Moreover, it is unique in that we explored associations of OSA and mental health among those who were recently unemployed. However, there are some limitations. In particular, our cohort does not reflect the ethnic distribution of the general US population. Approximately one-third of the cohort was Hispanic which is approximately twice the proportion in the US, but nevertheless reflects the ethnic composition of Tucson, AZ. In addition, the relative paucity of persons with severe OSA may have limited our power to detect significant associations.

In conclusion, in this study, we did not find any notable relationships between OSA, SES and depression and past suicidality in those who were recently unemployed although rates of both conditions were higher than previously reported in the general population. Further studies will need to be undertaken with a larger more diverse population to ascertain to confirm these findings. 

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Acknowledgements

This research was supported by the National Heart, Lung, and Blood Institute (#1R01HL117995-01A1; PI: Haynes).  

Cite as: Singh V, Haynes PL, Quan SF. Assessing Depression and Suicidality Among Recently Unemployed Persons with Obstructive Sleep Apnea and Socioeconomic Inequality. Southwest J Pulm Crit Care Sleep. 2022;24(5):81-88. doi: https://doi.org/10.13175/swjpccs020-22 PDF