Empirical Articles

Sleep and the Social Matrix: Determinants of Health Status Beyond Objective Social Status


Stacy A. Ogbeide*a, Christopher A. Neumannb

Abstract

Aim: The purpose of this study was to examine the relationship between subjective social status (SSS) and objective socioeconomic status (SES) on sleep status (sleep duration and daytime sleepiness).

Method: The study sample included 73 primary care patients from a free medical clinic in which low-income individuals are primarily treated. Subjective social status was measured using the MacArthur Scale of Subjective Social Status which uses a pictorial format (social ladder) in order to assess current social status. Socioeconomic status was measured by assessing highest level of education and current income level.

Results: Community SSS did not significantly predict sleep duration or daytime sleepiness. Additional regression analyses were conducted and it was found that an overall model of U.S. SSS and community SSS significantly predicted perceived stress. Community SSS was found to be significantly associated with perceived stress. Regression results also indicated that an overall model of U.S. SSS and community SSS significantly predicted perceived health status.

Conclusion: It may be beneficial for clinicians working with low-income primary care populations to include measures of SSS in addition to the traditional measures of SES for multidimensional patient care.

Keywords: socioeconomic status, social status, sleep

Psychology, Community & Health, 2015, Vol. 4(1), doi:10.5964/pch.v4i1.107

Received: 2014-05-22. Accepted: 2015-01-02. Published (VoR): 2015-03-31.

Handling Editors: Sofia von Humboldt, UIPES – Psychology & Health Research Unit, ISPA – Instituto Universitário, Lisbon, Portugal; Cristina Godinho, ISCTE – Lisbon University Institute, Lisbon, Portugal

*Corresponding author at: Department of Family and Community Medicine, Baylor College of Medicine, 3701 Kirby, Ste. 600, Houston, Texas, 77098, United States. E-mail: stacy.ogbeide@gmail.com

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Introduction [TOP]

Socioeconomic status (SES) has been linked to adverse health outcomes such as cardiovascular disease (CVD) morbidity and mortality (Ghaed & Gallo, 2007). The relationship between SES and health outcomes is complex. There are other factors that contribute to negative health outcomes such as “health care access, residential factors, physiological processes, psychosocial variables, and health behaviors,” but these factors do not completely explain the health disparities that are related to SES (Ghaed & Gallo, 2007, p. 668). Nevertheless, objective SES has been widely used as a proxy for education, occupation, and income as they relate to health.

Pathways that have been associated with SES that can lead to negative health outcomes are body fat assessed by body mass index (BMI), abdominal fat distribution assessed by waist-hip ratio (WHR), smoking, high-fat diet, alcohol consumption, and physical inactivity (Adler, Epel, Castellazzo, & Ickovics, 2000). Another pathway in which minimal research exists is the relationship between sleep duration, daytime sleepiness, and SES. It has been found that those who rate their sleep quality as poor and the presence of a sleep disorder experience poor health as well as impaired immune system functioning (Adler et al., 2000). Optimal sleep quality has been associated with higher income levels and better self-reported psychological and physical health.

There has been less research examining the relationship between objective SES, subjective social status, and health outcomes. Subjective social status (SSS) can be defined as reflecting on one’s “relative social standing and includes an individual’s impressions of current circumstances, educational and socioeconomic background, and future opportunities” (Ghaed & Gallo, 2007, p. 668). In order to assess SSS, past studies examined self-reported social class status (e.g., upper, middle, lower) and related those responses to psychological and physiological factors that have been traditionally related to objective SES research (Adler et al., 2000). A new scale was developed by Adler and colleagues: the MacArthur Scale of Subjective Social Status (Adler et al., 2000). This instrument consists of two 10-rung ladders in which one ladder represents how the participants would rank themselves in relation to others in their community and the other ladder is in relation to others in the United States using SES indicators such as income, occupation, and education level (Ghaed & Gallo, 2007).

It is important to assess SSS versus SES to predict health outcomes because of the suggestive evidence that higher subjective social status levels may be related to improved health (Adler et al., 2000). According to the Hierarchy-Health Hypothesis, past research has shown that SSS is a better predictor of health status because it takes into account social position and economics (Singh-Manoux, Marmot, & Adler, 2005). Also, in terms of income inequality and population health, self-perceptions of place in the social hierarchy can produce negative emotions that translate into poorer health through neuroendocrine mechanisms (Singh-Manoux et al., 2005).

Therefore, given the relationship between CVD and SES and SES and sleep, better understanding how SES and sleep are related is important. The purpose of the current study was to examine the relationship among SSS, objective SES, and sleep status (sleep duration and daytime sleepiness). In other words, the current study examined if subjective United States (U.S.; SSS and community SSS) or objective social status is a better predictor of sleep duration and daytime sleepiness. This topic is of importance due to the increasing evidence of the association between sleep and CVD as well as the established relationship between SES and health. Although much is known about these variables, there is less known about the link between SSS, SES and sleep status. The relationship between social status and health-related variables such as sleep status can be complex and not easily understood due to the multitude of factors that can impact health. Isolating SSS and SES can aid in a better understanding of these variables and their effect on sleep. Addressing the variables that are involved in predicting sleep status may lead to prevention efforts (in addition to the preventive strategies in place for reducing CVD risk factors) as well as a better understanding of health disparities.

In the current study, the CVD risk factors examined were psychosocial, behavioral, and physical risk factors. In terms of psychosocial risk factors, perceived stress levels were investigated. In terms of behavioral risk factors, physical activity levels were examined. Physical risk factors were examined using clinic BP and BMI. Lastly, sleep status was assessed by means of daytime sleepiness and sleep duration (weighted average of weekday and weekend sleep durations). Past research has correlated these risk factors to CVD morbidity and mortality (Ghaed & Gallo, 2007).

Sleep is beneficial for optimal physical functioning and is important due to its association with the development of CVD (Kotani et al., 2008). It would be important to know if SSS and SES have distinctive implications for sleep status due to these same variables having distinctive implications for CVD risk. Low SES has been associated with insufficient sleep and poor sleep quality due to the environmental conditions (e.g., crowded/unsafe living arrangements, noise levels, extreme temperatures) that are associated with low SES (Van Cauter & Spiegel, 1999). Results from a study by Van Cauter and Spiegel indicated that one week of partial sleep restriction in a healthy young adult population had negative effects on the cardiovascular system. The authors stated that if sleep loss was chronic, it could lead to long-term health problems, such as the development of insulin resistance, obesity, and hypertension. The link between chronic sleep restriction and metabolic changes could be an underlying factor in the association between social status and increased morbidity due to poor health. This information can contribute to the research that suggests that there is an effect of SSS on health outcomes beyond the impact of objective SES.

Hypotheses [TOP]

Hypothesis 1. U.S. SSS will be a stronger predictor of objective SES (education level) compared to community SSS (Ghaed & Gallo, 2007);

Hypothesis 2. U.S. SSS will be a stronger predictor to objective SES (income level) compared to community SSS (Ghaed & Gallo, 2007);

Hypothesis 3. Community SSS will be a predictor of the weighted average of sleep duration;

Hypothesis 4. Community SSS will be a predictor of daytime sleepiness.

Method [TOP]

Participants [TOP]

Seventy-three participants participated in the study, with the mean age of the study population 45.37 years (SD = 10.46). Approximately 34% of the participants were male and 66% were female. The racial demographics included African Americans (9.6%), American Indian/Alaska Natives (2.7%), Caucasians (75.3%), Hispanics (5.5%), and participants classified as Multiracial (6.8%). All of the participants were patients from a community health clinic located in the Midwest (USA) that provides medical and dental care to an uninsured population. No script was used to solicit participants. A recruitment flyer was placed near the clinic check-in area and on the research table. After reading the flyer, interested participants asked for an informed consent form and questionnaire packet.

Material [TOP]

The following variables were collected in this study: BMI, physical activity levels, self-reported health status, sleep quality, sleep duration, daytime sleepiness, perceived stress, objective SES, and SSS.

BMI — Body mass index was assessed using the height and weight measured at the clinic by a qualified health professional. Body mass index was calculated as weight (kilograms) divided by height (meters squared).

Blood pressure — Resting systolic blood pressure (SBP) and diastolic blood pressure (DBP) was assessed during the clinic visit by a qualified health professional using a standard sphygmomanometer and stethoscope. Hypertensive status was defined as SBP ≥ 140 mm Hg or DBP ≥ 90 mm Hg or current use of antihypertensive medication (Gottlieb et al., 2006).

Physical activity — Physical activity was measured using the short-form of the International Physical Activity Questionnaire (IPAQ). The purpose of the short-form IPAQ is to obtain an estimate of overall physical activity over the last seven days (Craig et al., 2003). The outcome measure was the total number of minutes spent sitting per day. The short-form of the IPAQ has been found to be valid and reliable in a previous study (alpha coefficient = .76; Craig et al., 2003).

Self-reported health status — Self-reported health status was assessed using a single-item in which the participants indicated their overall health status on a 1 to 5 scale: 1 = poor health, 2 = fair health, 3 = good health, 4 = very good health, 5 = excellent health. Self-reported health measures have been shown to predict mortality, even when physiological risk factors are controlled (Bierman, Bubolz, Fisher, & Wasson, 1999; Idler & Angel, 1990, as cited in Moore et al., 2002). Single-item health status estimates have been correlated with multi-item self-report health measures as well (Williams, Yu, Jackson, & Anderson, 1997, as cited in Moore et al. 2002).

Sleep quality — Sleep quality was assessed on a 1 to 5 scale (1 = poor, 2 = fair, 3 = good, 4 = very good, 5 = excellent; Moore et al., 2002).

Sleep duration — Sleep duration was assessed by obtaining information on weekday and weekend sleep duration. The following question was used to assess weekday sleep duration: “How many hours of sleep do you usually get each day on weekdays or workdays?” A similar question was used in order to assess weekend sleep duration. The outcome measure (daily sleep duration) was an integer value using the following calculation to obtain the weighted average: ([{usual weekday sleep duration} x 5] + [{usual weekend sleep duration} x 2])/7 (Gottlieb et al., 2006).

Daytime sleepiness — Excessive daytime sleepiness (EDS) was measured using the Epworth Sleepiness Scale (ESS). The ESS assesses overall daytime sleepiness by having participants rate the likelihood of falling asleep during eight different situations that occur during the daytime. Scores that are greater than 10 are considered to be EDS. The ESS is a valid and reliable tool and has been validated against objective measures assessed during polysomnography (alpha coefficient = .88; Johns, 1991; Johns, 1992).

Perceived stress — Perceived stress levels were measured using the 10-item Perceived Stress Scale (PSS; Cohen, Kamarck, & Mermelstein, 1983). The PSS is used to “…assess the degree to which a person appraises the situations in his or her life as stressful” (Brummett et al., 2004, p. 23). Past studies have demonstrated that PSS scores have been related to biological markers of stress and CVD risk factors, as well as being associated with health behaviors such as sleeping and physical activity (Brummett et al., 2004; Cohen & Williamson, 1988; Labbate et al., 1995; Malarkey, Pearl, Demers, Kiecolt-Glaser, & Glaser 1995). The PSS has been found to be valid and reliable (alpha coefficient = .78; Brummett et al., 2004; Cohen & Williamson, 1988). Higher scores on the PSS indicate a greater perception of stress.

Objective socioeconomic status (SES) — Two measures of SES were obtained: education and household income. The number of years of formal education completed by the participant (e.g., kindergarten = 1, 12th grade of high school = 13) indicated education. Household income was the participant’s household income for 2010. A categorical variable was be used for household income (e.g., 1 = Less than $5,000).

Subjective social status (SSS) — The MacArthur Scale of Subjective Social Status (1999) was used to assess SSS within the participant’s community and the U.S. The participants first viewed the 10-rung ladder associated with community social status which was accompanied with the following question:

Think of this ladder as representing where people stand in their communities. People define community in different ways; please define it in whatever way is most meaningful to you. At the top of the ladder are the people who have the highest standing in their community. At the bottom are the people who have the lowest standing in their community. Where would you place yourself on this ladder? (Ghaed & Gallo, 2007, p. 669)

The participants placed an “X” on the rung in which they associate their relative social status when compared to others in their community. The next 10-rung ladder represents the U.S. and is accompanied with the following question:

Think of this ladder as representing where people stand in the United States. At the top of the ladder are the people who are the best off – those who have the most money, the most education, and the most respected jobs. At the bottom are the people who are the worst off – who have the least money, the least education, and the least respected jobs or no job. The higher up you are on this ladder, the closer you are to the people at the very top and the lower you are, the closer you are to the people at the very bottom. Where would you place yourself on the ladder? (Ghaed & Gallo, 2007, p. 669)

The ladder scores were assessed by the number of rungs the participant used to represent their social status (lowest score = 1, highest score = 10).

Demographic information — The following demographic information was collected: age, gender, race, alcohol and tobacco use, and dietary habits.

Procedure [TOP]

The instruments took approximately 15 minutes to complete. Once participants returned the instrument packet, an identification number was assigned to their instrument packet as well as the participant data sheet. The primary investigator recorded the following information from the participant’s medical chart onto the participant data sheet: height, weight, SBP, and DBP. Participants who completed the instrument packet could choose to be entered into a drawing for a $25.00 gift card.

Statistical Analyses [TOP]

The principal investigator scored the questionnaires according to the manner that was defined by the developers of each instrument. Statistical Package for the Social Sciences (SPSS) version 17.0 was used to conduct the appropriate statistical analysis. Pearson correlations were used to assess the relationship between U.S. SSS and community SSS. A linear regression was used to examine if U.S. and community SSS (independent variables) were predictive of education and income (dependent variables). A series of multiple linear regression analyses were conducted in order to examine the relationship between sleep duration and EDS, in which the variables were measured on a continuous scale (the continuous variables also limit the degrees of freedom rather than using categorical data in which dummy codes would have to be created). Subjective Social Status was first regressed to sleep duration to assess predictive utility. The analysis was repeated by entering SSS, which was regressed on EDS to assess predictive ability. This statistical approach was similar to the Ghaed and Gallo (2007) study, which examined SSS, objective SES, and CVD risk in women. The level of significance was set at 0.05.

Results [TOP]

Socio-demographics can be found in Table 1. Information regarding health status and health behaviors can also be found in Table 1. Frequency of diagnoses can be found in Table 2.

Table 1

Study Population Characteristics

Characteristic n (%) M (SD)
Age (years) 45.37 (10.46)
Gender
Male 25 (34.2)
Female 48 (65.8)
Race/Ethnicity
African American/Black 7 (9.6)
AI/AN 2 (2.7)
Caucasian/White 55 (75.3)
Hispanic 4 (5.5)
Multiracial 5 (6.8)
Years of Education 12.19 (2.05)
2010 Household Income
Less than 5,000 31 (42.5)
5,001-10,000 15 (20.5)
10,001-15,000 12 (16.4)
15,001-20,000 4 (5.5)
20,001-25,000 4 (5.5)
25,001-30,000 2 (2.7)
30,001-35,000 3 (4.1)
35,001-40,000 2 (2.7)
Perceived Health Status 2.26 (.90)
Currently Smoke
Yes 41 (56.2)
No 32 (43.8)
# 8oz wine/week 1.18 (.69)
# 12oz beer/week 1.79 (1.69)
# 8oz mixed drink/week 1.07 (.48)
Weighted Weekly Sleep Duration 6.61 (1.87)
Perceived Sleep Quality 2.05 (.99)
SSS-Community 4.29 (2.58)
SSS-US 3.59 (2.23)
Time (min.) sitting/day 640.42 (399.42)
SBP 131.16 (15.24)
DBP 84.98 (10.79)
ESS 8.42 (5.63)
PSS 23.77 (7.80)
BMI 32.76 (9.70)

Note. AI/AN = American Indian/Alaska Native; Weighted Weekly Sleep Duration = ([{usual weekday sleep duration} x 5] + [{usual weekend sleep duration} x 2])/7 (Gottlieb, et al., 2006); SSS-US = Subjective Social Status – United States; SSS-Community = Subjective Social Status – Community; SBP = Systolic Blood Pressure; DBP = Diastolic Blood Pressure; ESS = Epworth Sleepiness Scale; PSS = Perceived Stress Scale; BMI = Body Mass Index.

Table 2

Frequency of Diagnoses in Study Population

Diagnosis n %
Anxiety 29 39.7
CVD 4 5.5
CAD 1 1.4
Depression 32 43.8
High Blood Pressure 30 41.1
High Cholesterol 10 13.7
Sleep Apnea 10 13.7
Stroke 1 1.4
Type I Diabetes 4 5.5
Type II Diabetes 10 13.7
No Diagnosis 17 23.3

Note. CVD = Cardiovascular Disease; CAD = Coronary Artery Disease.

Hypothesis 1: U.S. SSS will be a stronger predictor of objective SES (education level) compared to community SSS (Ghaed & Gallo, 2007). [TOP]

Multiple regression was conducted to determine if U.S. SSS and community SSS were predictors of objective SES (years of education). Regression results indicated that U.S. SSS and community SSS did not significantly predict objective SES (years of education), R2 = .000, R2adj = -.028, F(2, 70) = .011, p = .98. This model accounted for 0.00% of variance in objective SES (years of education). The variables in the regression that were not found to be significantly associated with objective SES (years of education) include U.S. SSS (b = -.010, p = .95) and community SSS (b = -.009, p = .95). Correlation coefficients between each predictor and the dependent variable are presented in Table 3.

Table 3

Summary of Correlations: Objective Versus Subjective SES (Years of Education)

Measure 1 2 3
1. Years of Education
2. SSS-US -.016
3. SSS-Community -.016 .651*

Note. SSS-US = Subjective Social Status – United States; SSS-Community = Subjective Social Status – Community

*p < .05.

Hypothesis 2: U.S. SSS will be a stronger predictor to objective SES (income level) compared to community SSS (Ghaed & Gallo, 2007). [TOP]

Multiple regression was conducted to determine if U.S. SSS and community SSS were predictors of objective SES (income level). Regression results indicated that U.S. SSS and community SSS did not significantly predict objective SES (income level), R2 = .045, R2adj = .018, F(2, 70) = 1.65, p = .20. This model accounted for 4.5% of variance in objective SES (income level). The variables in the regression that were not found to be significantly associated with objective SES (income level) include U.S. SSS (b = .27, p = .09) and community SSS (b = -.11, p = .47). Correlation coefficients between each predictor and the dependent variable are presented in Table 4.

Table 4

Summary of Correlations: Objective Versus Subjective SES (Income)

Measure 1 2 3
1. Income
2. SSS-US .194*
3. SSS-Community .062 .651*

Note. SSS-US = Subjective Social Status – United States; SSS-Community = Subjective Social Status – Community.

*p < .05.

Hypothesis 3: Community SSS will be a predictor of the weighted average of sleep duration. [TOP]

A linear regression was conducted to determine if community SSS was a predictor of the weighted average of sleep duration. Regression results indicated that community SSS did not significantly predict sleep duration, R2 = .016, R2adj = .002, F(1, 71) = 1.17, p = .28. This model accounted for 1.6% of variance in sleep duration. Community SSS was not found to be significantly associated with sleep duration (b = .13, p = .28). The correlation coefficient between the predictor and the dependent variable was .128.

Hypothesis 4: Community SSS will be a predictor of daytime sleepiness. [TOP]

A linear regression was conducted to determine if community SSS was a predictor of daytime sleepiness. Regression results indicated that community SSS did not significantly predict daytime sleepiness, R2 = .003, R2adj = -.011, F(1, 71) = .204, p = .65. This model accounted for .3% of variance in daytime sleepiness. Community SSS was not found to be significantly associated with daytime sleepiness (b = -.054, p = .65). The correlation coefficient between the predictor and the dependent variable was -.054.

Additional Analyses [TOP]

Additional multiple regressions were conducted to examine U.S. SSS, community SSS, and the following dependent variables: perceived stress, sleep quality, and perceived health status. In terms of perceived stress, regression results indicated that an overall model of U.S. SSS and community SSS significantly predicted perceived stress, R2 = .233, R2adj = .211, F(2, 70) = 10.61, p < .000. This model accounted for 23.3% of variance in perceived stress. The variable in the regression that was found to be significantly associated with perceived stress was community SSS (b = -.572, p < .000). Correlation coefficients between the predictor and the dependent variable are presented in Table 5.

Table 5

Summary of Correlations: Perceived Stress Versus SSS

Measure 1 2 3
1. PSS
2. SSS-Community -.466*
3. SSS-US -.210* .651*

Note. SSS-US = Subjective Social Status – United States; SSS-Community = Subjective Social Status – Community; PSS = Perceived Stress Scale.

*p < .05.

In terms of sleep quality, regression results indicated that an overall model of U.S. SSS and community SSS did not significantly predict sleep quality, R2 = .041, R2adj = .014, F(2, 70) = 1.50, p =.23. This model accounted for 4.1% of variance in sleep quality. It is important to note that the variable in the regression that was approaching significance with sleep quality was community SSS (b = .27, p = .09). Correlation coefficients between the predictor and the dependent variable are presented in Table 6.

Table 6

Summary of Correlations: Sleep Quality Versus SSS

Measure 1 2 3
1. Sleep Quality
2. SSS-Community .139
3. SSS-US -.021 .651*

Note. SSS-US = Subjective Social Status – United States; SSS-Community = Subjective Social Status – Community.

*p < .05.

In terms of perceived health status, regression results indicated that an overall model of U.S. SSS and community SSS significantly predicted perceived health status, R2 = .15, R2adj = .12, F(2, 70) = 6.07, p = .004. This model accounted for 15% of variance in perceived health status. The variable in the regression that was found to be significantly associated with perceived health status was community SSS (b = .49, p = .001). Correlation coefficients between the predictor and the dependent variable are presented in Table 7.

Table 7

Summary of Correlations: Perceived Health Status Versus SSS

Measure 1 2 3
1. Perceived Health Status
2. SSS-Community .351*
3. SSS-US .110 .651*

Note. SSS-US = Subjective Social Status – United States; SSS-Community = Subjective Social Status – Community

*p < .05.

Two additional one-way analyses of variance (ANOVA) were conducted to investigate sleep duration and daytime sleepiness differences in gender categories. ANOVA results did now show a significant difference for weighted average sleep duration (F(1,71) = .44, p = .51) or daytime sleepiness scores (F(1,71) = .90, p = .35). Results reveal that sleep duration and daytime sleepiness are not significantly different for men and women in this study.

Discussion [TOP]

This study examined a measure of subjective social status (SSS) and its association with traditional SES indicators (e.g., education, income) and its relation to sleep status in a low-income primary care population. As hypothesized, U.S. and community SSS were in association with each other. Previous research suggested that SSS was related to objective indicators of SES (Adler et al., 2000; Ghaed & Gallo, 2007; Moore, Adler, Williams, & Jackson, 2002; Ostrove, Adler, Kuppermann, & Washington, 2000; Singh-Manoux, Adler, & Marmot, 2003). The results of the current study indicated that community SSS was more related to income than years of education. Moreover, inconsistent with measure instructions, community SSS captured aspects of objective SES measures for the patients in this study.

Community SSS was not predictive of sleep duration or daytime sleepiness. Contrary to past research, the bivariate analysis also indicated no association between community SSS and sleep duration or community SSS and daytime sleepiness, suggesting that subjective perceptions of community standing did not have an impact of sleep duration (Ghaed & Gallo, 2007). This finding is consistent with previous studies which found “…little evidence that more deprived people obtain less sleep than more advantaged ones…” (Adams, 2006, p. 269). Arber, Bote, and Meadows (2009) found the contrary. In a study examining objective SES and sleep problems, those in the lower SES groups were susceptible to sleep problems and it was concluded that poor sleep may be a moderator through which low SES can be associated with poor health. The differences in results can also be due to how SES was measured (using SSS versus education, employment status, housing status, and household income).

Community SSS was predictive of lower levels of perceived stress, which is consistent with the previous research of Ghaed and Gallo (2007). This may suggest that subjective perceptions of community standing had an impact on perceived stress levels and that those who had lower perceptions of community SSS experienced higher levels of perceived stress. The bivariate analysis revealed an association between community and U.S. SSS and perceived stress. These findings suggest a possible path from community SSS to health status although the exact directions of the relationship are unclear (Ghaed & Gallo, 2007).

Moore, Adler, Williams, and Jackson (2002) examined objective SES and sleep and noted that “…sleep may play a role in translating SES into health…” (p. 341). Although sleep quality was moderately related to Community SSS in this study, notably, no prior study has examined community SSS and sleep quality within the same study. However, previous studies have examined specified health behaviors in relation to SSS and there is a substantial amount of research that supports the association between SSS and lifestyle factors (Adler et al., 1994; Ghaed & Gallo, 2007; Moore et al., 2002). Community SSS was also predictive of higher ratings of perceived health status, which is consistent with previous research (Ostrove, Adler, Kuppermann, & Washington, 2000; Singh-Manoux et al., 2003). These findings indicate that subjective status and health can be explained by perceived social position although the exact pathways remain unknown.

It is important to note that no difference was found in sleep duration or daytime sleepiness scores between men and women in this study. These results are inconsistent with past research examining gender differences in sleep status (Arber, Bote, & Meadows, 2009; Phillips & Mannino, 2005). This may be due to the mean age of the participants in the current study (M = 45.37, SD = 10.46). Although women are more likely to be diagnosed with sleep difficulties (e.g., insomnia) when compared to men, the difference increases with age and can often be attributed to biological changes due to menopause (Phillips & Mannino, 2005). Sleep complaints are more often seen in women during perimenopause and postmenopause and less likely seen in premenopausal women (Maartens et al., 2001 as cited in Phillips & Mannino, 2005). The women in the current study, due to the mean age (M = 44.71, SD = 10.60), most likely fall into the premenopausal category and may be less likely to report sleep difficulties. Lastly, it is important to highlight that menopause “…is not a strong predictor of specific sleep-disorder symptoms” (Phillips & Mannino, 2005, p. 281). Sleep difficulties in women during middle adulthood should not be largely related to menopause before examining for the possibility of an undiagnosed sleep disorder (Phillips & Mannino, 2005).

All of the findings lead to the primary focus of this study – why examine subjective social status in relation to indicators of health such as sleep, perceived stress, and perceived health status? Past research has found that subjective social status incorporates social, cultural, and economic aspects, which can make it a more reliable indicator of social class compared to the traditional measures of social class (Singh-Manoux et al., 2003). It is thought that U.S. SSS is most similar to objective SES measures and community SSS is related to “…personal assessments of social status, or an individual’s comprehensive understanding within a context of past, present, and future accomplishments” (Ghaed & Gallo, 2007, p. 673). This could explain why community SSS was a better predictor of psychosocial variables when compared to U.S. SSS.

How can this information regarding SSS and sleep be beneficial for patients? One approach that has been shown to be effective when working with low-income populations is the Empowerment Approach. This approach is defined as making connections between social and economic justice and individual suffering (Lee, 1996, as cited in Bushfield, 2006). This model uses concepts from empowerment, resilience, hardiness, and solution-focused theories to help individuals overcome and cope with adversity. Although there can be benefits to experiencing adversity, disadvantaged individuals may be less likely to cope effectively with adverse situations. In this paradigm, the practitioner would be able to highlight the strengths of the patient using “…a comprehensive assessment and holistic approach that acknowledges the interdependence and transactional nature of the person in his or her environment” (Bushfield, 2006, p. 63). The link between SSS and health can be complex due to the patient’s perception of where he or she stands in their community as well as in the U.S. Rather than attempting to combat patient perceptions, the practitioner acknowledges the powerlessness that comes from the patient’s experience (e.g., physical, psychological, social). This leads to the understanding that the empowerment process exists in the patient rather than the practitioner. Ultimately, this approach adopts a humanistic perspective that focuses on reflecting, encouraging, exploring, and planning in order to facilitate patient growth. Overall, the goal of this approach is to help the patient find meaning in their life and develop adequate coping skills in order to better cope with and make sense of life stressors (Bushfield, 2006).

This study adds to the growing literature regarding social status and determinants of health status beyond objective SES. Research suggests that SSS may have an impact on sleep status (particularly sleep quality) and may have implications for health risks such as cardiovascular disease. The results of this study indicate that it may be beneficial for clinicians working with low-income primary care populations to include measures of SSS in addition to the traditional measures of SES in order to obtain adequate information to improve patient care.

Limitations [TOP]

Some limitations of this study need to be acknowledged. First, the study was cross-sectional. This type of design can be problematic because this impacted the ability to determine the directionality of the associations between SSS and sleep status. While lower SSS could lead to poor sleep status, poor sleep status could also lead to lower SSS. Although longitudinal studies have been able to account for the link between social status and health, sleep has yet to be accounted for in this equation as either a factor or an indication of SES (Moore, Adler, Williams, & Jackson, 2002). Second, self-report data was collected regarding SSS, SES, and health status. Although self-report data is not unreliable, it can increase the chances of methodological error (Fox, Sexton, Hebel, & Thompson, 1989). Thus, underreporting or over reporting of health information may have taken place (Moore et al., 2002). Also, in terms of sleep status, using polysomnography may have aided in more precise measurements of sleep duration. Although more variable, psychological sleep measures are less costly to use (Moore et al., 2002).

Third, because this study focused on a low or no income cohort of primary care patients, it is possible that those with the most problematic sleep difficulties and poor health status choose to take part in the study. Fourth, the associations between the traditional indications of SES and SSS as well as between SSS and sleep status might be stronger in the general population than in a low income population of primary care patients. The associations might differ in a healthier population or in an affluent population. This may limit the ability to draw conclusions regarding the association between social status and sleep within the general population because of the use of this specific population (Adler et al., 2000). Fifth, which coincides with the previous limitation, no comparative group was used in the study. Testing the differences between different health populations (e.g., free clinic population versus a U. S. Medicaid population) would provide more information regarding differences in social perception and health. Lastly, in terms of ethnicity, the study population was predominately Caucasian. Examining ethnic differences could heighten the study’s generalizability.

Future Directions of Research [TOP]

Past research has found that poor sleep quality may be an intermediary pathway between low SES and health (Arber, Bote, & Meadows, 2009; Friedman et al., 2007; Hall et al., 1999; Patel, Malhotra, Gottlieb, White, & Hu, 2006; Van Cauter & Spiegel, 1999). The findings in the current study lend support to this link but in order to adequately examine this pathway, longitudinal research is needed to determine the magnitude and direction of these variables as well as between SSS, sleep, and health. Additionally, identifying elements that constitute sleep quality could lead to meaningful comparisons to SSS, SES, and health (Moore et al., 2002). For example, variables such as sleep latency, sleep stages, arousals, sleep satisfaction, and physiological indicators (e.g., glucose tolerance) can all reflect sleep quality. Future research should clarify the specific sleep variables being examined in order to identify which aspects of sleep are related to SSS, SES, and health status.

Funding [TOP]

The authors have no funding to report.

Competing Interests [TOP]

The authors have declared that no competing interests exist.

Acknowledgments [TOP]

The authors thank Dr. Stephanie Wood for technical assistance as well as assistance with the development of this research project.

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