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. Author manuscript; available in PMC: 2022 Dec 1.
Published in final edited form as: J Behav Med. 2021 Jun 9;44(6):811–821. doi: 10.1007/s10865-021-00234-w

Sleep and Neighborhood Socioeconomic Status: A micro longitudinal study of chronic low-back pain and pain-free individuals

Deanna D Rumble 1,*, Katie O’Neal 2, Demario S Overstreet 1, Terence M Penn 1, Pamela Jackson 3, Edwin N Aroke 3, Andrew M Sims 4, Annabel L King 1, Fariha N Hasan 1, Tammie L Quinn 1, D Leann Long 4, Robert E Sorge 1, Burel R Goodin 1
PMCID: PMC8595521  NIHMSID: NIHMS1716585  PMID: 34106368

Abstract

Individuals with chronic low back pain (cLBP) frequently report sleep disturbances. Living in a neighborhood characterized by low-socioeconomic status (SES) is associated with a variety of negative health outcomes, including poor sleep. Whether low-neighborhood SES exacerbates sleep disturbances of people with cLBP, relative to pain-free individuals, has not previously been observed. This study compared associations between neighborhood-level SES, pain-status (cLBP vs. pain-free), and daily sleep metrics in 117 adults (cLBP = 82, pain-free = 35). Neighborhood-level SES was gathered from Neighborhood Atlas, which provides a composite measurement of overall neighborhood deprivation (e.g. area deprivation index). Individuals completed home sleep monitoring for 7-consecutive days/nights. Neighborhood SES and pain-status were tested as predictors of actigraphic sleep variables (e.g., sleep efficiency). Analyses revealed neighborhood-level SES and neighborhood-level SES*pain-status interaction significantly impacted objective sleep quality. These findings provide initial support for the negative impact of low neighborhood-level SES and chronic pain on sleep quality.

Keywords: neighborhood area deprivation, socioeconomic status, sleep, chronic low back pain, sociocultural influences on health and illness

Introduction

The importance of sleep for everyday homeostasis of basic functioning impacting multiple physiological systems of the body has been long established. This homeostasis can be altered by a variety of factors—such as pain. Chronic low back pain (cLBP) is a growing health crisis, and back pain is one of the main causes of disability throughout the world (Stubbs et al., 2016). The occurrence of back pain and chronic lower back pain lead to higher cases of mental health ailments such as depression and anxiety (Asmundson & Katz, 2009; Goesling et al., 2013; Stubbs et al., 2016). Chronic pain has also shown to be related to more sleep disturbances and lower overall sleep quality (Lewandowski et al., 2010; O’Donoghue et al., 2009; Palermo et al., 2007). Investigators of several longitudinal studies in a variety of chronic pain disorders have reported that baseline sleep disruption predicts subsequent increases in pain severity, and that increased pain severity also predicts subsequent sleep disturbance (Edwards et al., 2008; Finan et al., 2013; Kundermann et al., 2004; Lautenbacher et al., 2006).

SES and Race

The literature describes differences and variability in quality of health based on a variety of demographic factors (e.g. race, socioeconomic status, etc.). Pain and sleep are both known to vary by race and socioeconomic status (Jehan et al., 2018; Kim et al., 2017; Meeus, 2018; Ostrom et al., 2017). Individuals with lower SES have been noted to have reduced sleep time and quality (Cauter & Spiegel, 1999; Goodin et al., 2010; Troxel et al., 2020; Whinnery et al., 2014) and increased pain intensity (Fuentes et al., 2007; C. R. Green & Hart-Johnson, 2012). Variables used to describe SES differ by race, in particular non-Hispanic white compared to non-Hispanic Black individuals, since increases in variables associated with SES (education, income, occupation, etc.) do not produce equivalent improvements in health metrics (K. O. Anderson et al., 2009; N. B. Anderson et al., 2004; C. N. Bell et al., 2020; Troxel et al., 2020; Williams et al., 2016). Neighborhood level deprivation may be more informative in delineating this relationship compared to individual SES factors as the characteristics of the neighborhood may influence aspects of an individual’s well-being– individuals living in urban poverty or disadvantaged neighborhoods may face increased exposure to factors that influence reduced quality of life (e.g. low-SES neighborhood environments may be more crowded, noisy, and less temperature regulated and perceived as less safe, all of which can impact sleep) (Fuentes et al., 2007; C. R. Green & Hart-Johnson, 2012; Maly & Vallerand, 2018; Poleshuck & Green, 2008; Ulirsch et al., 2014; University of Wisconsin School of Medicine and Public Health, n.d.). However, there is limited data on the impact of neighborhood characteristics exclusive to chronic pain status on sleep quality (Tomfohr-Madsen et al., 2020; Xiao & Hale, 2018). One way to quantify neighborhood characteristics is through the 2015 Area Deprivation Index (ADI) v.2.0. from Neighborhood Atlas (University of Wisconsin School of Medicine and Public Health, n.d.). ADI provides a composite measurement of overall neighborhood deprivation (e.g. area deprivation index) in the form of a national decile (NADI) comprised from 17 socioeconomic variables in the domains of education, income, employment, and housing quality. Neighborhood level characteristics have been related to socioeconomic and health disparities with a variety of health conditions (Stubbs et al., 2016) (e.g. asthma (Bacon et al., 2009; Keet et al., 2017), diabetes (Bilal et al., 2018; Chaikiat et al., 2012; Gaskin et al., 2014), cardiovascular health (Barber et al., 2016; Browning et al., 2012; Chaikiat et al., 2012)). To date intersectional effects of SES disadvantage at the neighborhood level and known risk factors (e.g. depression (Goesling et al., 2013; Stubbs et al., 2016), race (C. R. Green & Hart-Johnson, 2012; Whinnery et al., 2014), OSA risk (F. Chung et al., 2012; Taylor et al., 2019), cLBP (Alsaadi et al., 2011; Kelly et al., 2011)) and sleep have not been systematically studied.

Hypotheses

To address these gaps in knowledge, we hypothesized that the role of (1) neighborhood disadvantage, using the Area Deprivation Index (University of Wisconsin School of Medicine and Public Health, n.d.) National Decile (NADI), (2) race, and (3) chronic pain status (cLBP vs. pain-free) in adults, would be significantly related to sleep quality (e.g. objective actigraphy and subjective diary metrics) measurements. We anticipate that demonstrating an independent association between race, neighborhood disadvantage, pain status, and sleep metrics may help identify particularly vulnerable individuals towards whom sleep intervention strategies should be directed.

Materials and Methods

Study Overview

This study was part of a larger ongoing parent project investigating ethnic/racial and socioeconomic differences in cLBP severity and disability (Examining Racial And SocioEconomic Disparities in cLBP; ERASED; R01MD010441). The parent project employs a biopsychosocial conceptual rubric that examines biobehavioral, psychological, and sociocultural factors that may help explain differences in cLBP between non-Hispanic Black and non-Hispanic White adults. Participant data presented in this study were collected between November 2017 and January 2020. The procedures and experimental methods described below are limited to those involved in the current research study. Interested participants completed telephone-based screening to determine initial study eligibility; health history was also reviewed via electronic medical records to confirm eligibility. Eligible participants presented to the laboratory to complete questionnaires and in-person study protocols (described in greater detail below). They were then sent home with a wrist-worn actigraph and a standardized sleep diary to complete seven consecutive nights of sleep monitoring. Participants returned their actigraph and sleep diary following the seventh night of monitoring. This study was conducted in accordance with the cLBP research standards put forth by the Research Task Force of the NIH Pain Consortium (Deyo et al., 2014). It was reviewed and approved the Institutional Review Board at the University of Alabama at Birmingham (IRB-170119003) and carried out in a manner consistent with ethical research guidelines and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards; written informed consent was obtained.

Participants

Community-dwelling participants with cLBP and pain-free individuals were recruited via flyers posted at the University of Alabama at Birmingham Pain Treatment Clinic and surrounding community. A total of 190 participants were enrolled in this study (cLBP n = 130; pain-free n=60). However, national area deprivation index (NADI) could not be calculated for some participants due to either 1) participants not completing area level neighborhood information or 2) the information was not calculable by the Neighborhood Atlas (University of Wisconsin School of Medicine and Public Health, n.d.) (cLBP n=48, pain-free n = 25)—these participants were deleted list-wise. This resulted in a final sample size of 82 participants with cLBP and 35 pain-free individuals.

Procedures

Telephone Screening and Medical Record Review

A comprehensive list of inclusion and exclusion criteria for the parent project from which this study’s participants were sampled has previously been reported (Penn et al., 2020). Briefly, a telephone-based screening was completed for each participant. This was followed by review of electronic medical records to confirm cLBP status and that all inclusion criteria were met. Individuals were included in the study if low back pain had reportedly persisted for at least three consecutive months and was present for at least half the days in the past six months (Treede et al., 2015) or if they had no back pain for the pain-free individuals. The primary pain complaint had to be low back pain, and there must not have been any evidence of surgical intervention or accident/trauma within the past 12 months. Electronic medical records also provided information pertaining to other health comorbidities and current prescriptions for opioid analgesic medications.

Laboratory Session

Eligible participants initially provided sociodemographic information that included race/ethnicity, age, and sex/gender. Area-level data were obtained by geocoding the residential addresses of participants to US Census block groups—small geographic units that serve as proxies for neighborhoods. Individuals also completed measures of obstructive sleep apnea risk, depressive symptoms, and chronic pain severity prior to seven consecutive nights of sleep monitoring using wrist actigraphy in the home environment as standard pain and sleep measurements. All study participants underwent the same set of procedures.

National Area Deprivation Index.

The 2015 Area Deprivation Index v.2.0. (University of Wisconsin School of Medicine and Public Health, n.d.) is comprised from 17 socioeconomic variables in the domains of education, income, employment, and housing quality. Each participant was assigned a National Decile Area Deprivation Index (NADI) value according to the census block group (9-digit zip code) they resided in. NADI values range from 1-100 with higher scores indicating greater deprivation.

Graded Chronic Pain Scale (GCPS). (Dixon et al., 2007).

Descriptive pain intensity for cLBP individuals was assessed using the GCPS Characteristic Pain Intensity subscale. This 3-item subscale asks how participants rate their pain 1) right now, and in the past six months their 2) worst pain and 3) average pain (0 –no pain to 10 – pain as bad as could be). Responses are averaged and multiplied by 10 (range 0-100), with higher scores indicating greater characteristic pain.

Center for Epidemiological Studies-Depression Scale (CES-D). (Radloff, 1977).

Depressive symptoms were assessed using the CES-D. This 20-item measure assesses the frequency of experiencing depressive symptoms over the past week (0 – never or rarely to 3 – most of the time/all the time). Symptoms of depression measured by the CES-D include negative mood, guilt/worthlessness, helplessness/hopelessness, psychomotor retardation, loss of appetite, and sleep disturbance. This measure has been shown to be reliable and valid in general populations, including when used in chronic pain populations. Responses are summed (range 0 – 60), with higher scores indicating greater severity of depression.

The STOP-BANG Sleep Apnea Questionnaire. (Frances Chung et al., 2016).

Obstructive sleep apnea (OSA) risk was assessed using the STOP-BANG. The STOP-BANG comprises of eight yes/no questions, scored 0 = no/1 = yes (range 0–8). Major risk factors captured include snoring, daytime fatigue, and high blood pressure; peripheral observation of the participant not breathing while sleeping; a BMI >35 kg/m2, age > 50 years, neck circumference >16 in. in males or >15in. in females, and male gender (Frances Chung et al., 2016) are included in the assessment. Risk for OSA is determined to be high if the participant answers “yes” to five or more of the questions (F. Chung et al., 2012). None of the participants was excluded due to sleep apnea risk; rather, the intent was to quantify risk and include it as a covariate in data analysis. This was to control for the variance in sleep quality and depressive symptoms that was attributable to OSA risk.

Home Sleep Monitoring

Objective-quantitative sleep data was acquired using the Actiwatch2 (Respironics, Bend, OR), a watch-like actigraph. The Actiwatch2 is a solid-state accelerometer, or movement detector, designed to measure ambulatory activity sampling at 32 Hz. It was used to measure daily sleep-wake patterns and record body movement. The Actiwatch2 has good reliability and criterion validity (Gironda et al., 2007; Wood et al., 2008). Study participants were instructed to press a button (event marker) on the Actiwatch2 at bedtime and awakening in the morning. These events were also compared to the corresponding sleep diaries participants completed daily. With these materials, researchers used a protocol for actigraphy scoring and set sleep periods. Sleep-wake cycle patterns were examined from the actigraphy data using the Actiware Sleep version 6.0.8 in 30-s epochs. The algorithm is based on the amplitude and frequency of detected movements. The following parameters were used from the actigraphy data: total sleep time, sleep onset latency, wake after sleep onset time, and sleep efficiency. Total sleep time was defined as sleep (in minutes) from sleep onset to sleep offset. Sleep onset latency represents the length of time in minutes it took to transition from fully awake to asleep. Wake after sleep onset (WASO) was calculated by adding the number of minutes in which participants were awake from sleep onset to final awakening. Sleep efficiency is the ratio of estimated total sleep time divided by total time spent in bed (sleep efficiency = (total sleep time/time in bed) × 100, with values closer to 100 meaning the most efficient sleep.

Sleep diaries. (Carney et al., 2012).

Participants completed sleep diaries each day of actigraphic recording. They were asked questions regarding their sleep habits (e.g. “what time did you get into bed,” “how many times did you wake up not counting your final awakening,” etc.) was gathered from sleep diaries. Individuals were also asked to rate their subjective quality of sleep (1-very poor to 5-very good) and how rested or refreshed did you feel when you woke up for the day (1- not at all to 5-very well-rested).

Covariates

Demographic and psychosocial factors including age, sex, indicators of socioeconomic status, and depression have all been shown to contribute to the experience of pain (Bulls et al., 2015, 2017; Dorner et al., 2011; Goodin et al., 2014; Maly & Vallerand, 2018; Mogil & Bailey, 2010) and sleep (Beydoun et al., 2017; Bulls et al., 2017; Grandner et al., 2010; Madrid-Valero et al., 2017; Marco et al., 2012; Whinnery et al., 2014). Further, OSA tends to result in poor sleep quality (F. Chung et al., 2012; Roure et al., 2008). Accordingly, age and sex are accounted for in the OSA risk calculation and are therefore not included in the analyses. Risk for OSA (coded 0 = low / intermediate risk, 1 = high risk) and depressive symptoms were included in all analyses as covariates. Interaction effects were included initially for 1) race and NADI and 2) factors with significant differences between groups (pain status*NADI interaction, pain status* OSA Risk, and pain status*depression). Non-significant interaction effects and terms with the largest p-values were removed systematically to create the most parsimonious model.

Data Analysis

All data were analyzed using SPSS, version 25 (IBM, Chicago, IL, USA). Descriptive statistics were computed for the overall sample as well as separately by pain status (cLBP and pain-free) group; data are presented as percentages or as means and standard deviations (SD). T-tests were used to compare psychosocial, demographic, and pain data between groups. The strength and direction of associations among continuous variables were examined using a Pearson’s correlation analysis, while a point-biserial correlation was run to determine the relationship between dichotomous and continuous variables. A linear mixed model approach was performed using the SPSS MIXED module with a compound symmetry repeated covariance of time to analyze the daily sleep actigraphic data to assess the associations between sleep and pain status, while accounting for NADI and race, and adjusting for covariates (OSA-Risk and depressive symptoms). We first examined whether the covariates predicted sleep variables by entering all biopsychosocial variables simultaneously. Next, we developed a reduced, parsimonious model by removing parameters that either were not statistically significant or did not improve overall model fit. This statistical technique is advantageous because each participant is represented in the model so that participants with missing actigraphic data points in the study can be included in the same analysis without estimating missing values or violating assumptions (Holditch-Davis et al., 1998; Krueger & Tian, 2004). Although all of the quantifiable-objective sleep parameters were examined, it was anticipated that actigraphic sleep efficiency would be the most relevant given that its calculation is derived from the other parameters (e.g., total sleep time = time in bed—sleep onset latency—wake after sleep onset; sleep efficiency = (total sleep time/time in bed) × 100) (Ancoli-Israel et al., 2003; Schutte-Rodin et al., 2008).

Results

Participant Characteristics

Descriptive characteristics for the sample of participants are shown in Table 1.

TABLE 1:

Descriptive characteristics and clinical information for participants (N = 117)

cLBP n=82 Pain-free n=35
Demographic characteristics Mean (SD) or % Mean (SD) or %
Age (years) 46.3 (14.2) 40.5 (14.8)
Sex (% female) 51.2% 60.0%
Race (% African American) 57.3% 48.6%
National Area Deprivation Index (NADI) 63.6 (28.4) 44.6 (25.5)
 1 – 20 19.0% 28.9%
 21 – 40 14.3% 31.6%
 41 – 60 17.9% 21.1%
 61 – 80 22.6% 10.5%
 81 – 100 26.2% 7.9%
Clinical characteristics Mean (SD) or % Mean (SD) or %
Pain Severity (GCPS) 65.0 (19.9) 1.9 (6.9)
BMI (weight/height2) 31.1 (6.7) 28.9 (6.7)
Depressive symptoms (CES-D) 21.9 (10.7) 9.9 (5.7)
OSA Risk (% high risk) 31.7% 5.7%
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Note: BMI = body mass index; CES-D = Center for Epidemiological Studies – Depression Scale; OSA = obstructive sleep apnea

Group differences.

A Mann-Whitney test indicated that pain severity was greater for individuals with cLBP (Mdn = 66.7) than for pain-free individuals (Mdn = 0.0), U=4.5, p = .000, r = .79, depressive symptoms was greater for individuals with cLBP (Mdn = 21.0) than for pain-free individuals (Mdn = 9.0), U=526.0, p = .000, r = .54, and NADI was significantly greater for individuals with cLBP (Mdn = 68.5) than for pain-free individuals (Mdn = 39.0). Mean descriptive characteristics of sleep metrics are provided in Table 2.

TABLE 2:

Sleep Metrics of cLBP and Pain-free Individuals

cLBP Pain-free Group difference t
Sleep Metrics characteristics Mean (SD) Mean (SD)
Sleep efficiency (%) 79.4 (10.2) 81.3 (7.4) −0.9
WASO 50.6 (23.8) 36.2 (15.9) 3.3**
# minutes awake 35.4 (13.6) 31.1 (10.2) 1.7
Sleep Latency (minutes) 33.4 (35.7) 33.2 (28.8) 0.03
Time in bed (hh:mm) 08:43 (01:24) 08:06 (01:04) 2.3*
Total sleep time (hh:mm) 06:57 (1:21) 06:36 (0:58) 1.4
Quality of Sleep 3.12 (0.5) 3.7 (.6) −5.2**
Rested/Refreshed Sleep 2.7 (.6) 3.4 (.6) −5.3**
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Note. Means and standard deviations reported on unstandardized scores. Means calculated based on each participant’s score on each variable averaged across all interactions. Mean differences tested using independent sample t-test.

*

p<.05

**

p<.01

Correlation Analyses.

Correlational analyses are provided in Table 3. As expected, the sleep variables were significantly correlated with each other (all p values<.05). NADI was significantly associated with worse outcomes for the majority of the variables tested. Additionally, to examine how individual SES variables would be related to NADI, correlation analyses indicated that NADI significantly correlated with household income (r=.54), educational achievement (r=.45), and poverty status (r=.46) (all p values<.05).

TABLE 3:

Correlation Coefficients

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.
1. NADI -
2. CES-D .369 ** -
3. WASO x¯ .386 ** .269 ** -
4. Sleep efficiency x¯ −.386 * −.175 −.588 ** -
5. # minutes awake x¯ .236 * .108 .707 ** −.275 ** -
6. Sleep Latency x¯ .280 ** .102 .276 ** −.839 ** .025 -
7. Refreshed Sleep x¯ −.207 * −.497 ** −.257 ** .125 −.081 −.021 -
8. Sleep Quality x¯ −.259 ** −.492 ** −.296 ** .156 −.107 −.029 .807 ** -
9. Pain Status −.307 −.538 ** −.291 ** .092 −.154 −.003 .437 ** .431 ** -
10. Race −.363 ** −.067 −.105 .287 ** −.023 −.207 .110 .059 .091 -
11. OSA Risk .162 .256 ** .133 −.160 −.010 .052 −.271 * −.271 * −.251 ** −.089 -
12. Pain intensity
(cLBP only)		 .385 ** .356 ** −.011 −.206 .015 .217 * −.312 ** −.276 * - −.350 ** .189 -
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*

p<.05

**

p<.01

Actigraphy.

Using a forward selection procedure to identify a parsimonious model of the covariates and effects (CES-D, OSA Risk, Race, NADI, & Pain Status), the repeated-measures linear mixed model indicated that the main effect for NADI was significant for sleep efficiency (F = 20.43; P < . 0001) [CI: −.183, −.071], onset latency (F = 10.87; P = . 001) [CI: .136; .54], # minutes awake (F = 6.19; P = .014) [CI: .020; .179]. Sleep efficiency, latency, and # minutes awake was worse for individuals living in neighborhoods with increased area deprivation, regardless of pain status. The repeated-measures linear mixed model demonstrated a significant pain status*NADI interaction for WASO (F = 7.04, p = .009). Specifically, WASO was worse for individuals with cLBP living in neighborhoods with increased area deprivation, relative to pain-free controls. For total time spent in bed, the interaction effect of pain status*depression (F = 3.86, p = .024) indicated that individuals with cLBP and more depressive symptoms spent more time in bed. Total sleep time was not significant.

Sleep Diary.

The repeated-measures linear mixed model indicated that the main effects for depressive symptoms (F = 16.83, p = .000; CI: −.029; −.010) and pain status (F = 7.89, p = .006) were significant for sleep quality. There was also significant main effects for depressive symptoms (F = 20.19, p = 0.000017; CI: −.034; −.013) and pain status (F = 7.74, p = .006) in relation to feeling refreshed. These findings indicate that that individuals with more depressive symptoms and individuals with cLBP reported worse subjective sleep parameters.

Discussion

The purpose of this study was to investigate the relationship between NADI, race, pain status and sleep quality. Our study protocol allowed us to examine the associations among these variables on a micro-longitudinal scale of 7 days between individuals with cLBP and pain-free adults. We found that several aspects of objective sleep quality was influenced by a combination of NADI and group status; subjective sleep quality was influenced primarily by depressive symptoms and pain status, which has been demonstrated throughout the literature (Moser et al., 2015; Parmelee et al., 2015; Raniti et al., 2017).

Living in neighborhoods with high area deprivation

For most of our sleep actigraphy data (sleep efficiency, onset latency, # minutes awake), NADI was the primary predictor of sleep quality. This suggests that individuals who live in disadvantaged neighborhoods have worse sleep outcomes regardless of pain status. Living in geographically- concentrated low-income or disadvantaged communities has been noted to contribute to worse health outcomes overall (Galea et al., 2007; Lang et al., 2008; Winkleby et al., 2007), including chronic pain (Ulirsch et al., 2014) and poor sleep (Troxel et al., 2020). The significant effect of NADI*pain group status on WASO is suggestive of pain’s potential influence for wakefulness after sleep onset for cLBP individuals with high NADI. Conversely, at lower NADI, it appears the influence of pain status on sleep is significantly reduced. Also, the observed interaction effect and the positive relationship between NADI and pain intensity, suggest that cLBP vulnerability is amplified in socioeconomically vulnerable individuals. This interaction of more neighborhood area deprivation with cLBP suggests that the vulnerability of having a chronic pain condition is amplified when the neighborhood in which these individuals live is taken into account. Within socioeconomically disadvantaged neighborhoods, we often see more chronic pain characteristics in these individuals (Ulirsch et al., 2014), reduced access to resources such as primary care (S. Bell et al., 2013; Bissonnette et al., 2012), increased exposure to neighborhood stressors [e.g. crime, violence] (Browning et al., 2012; R. D. Green et al., 2013), and a decreased sense of safety (Brooks Holliday et al., 2019; De Jesus et al., 2010; Meyer et al., 2014; Troxel et al., 2020). While our findings suggest that NADI may be a potential contributor to reduced sleep quality, it did not appear to impact pain-free individuals to the same degree. We hypothesize that individuals from greater socioeconomically disadvantaged neighborhoods without pain, though they too have increased stressors, may not have developed the same amplified hyper-sensitivity from these stressors which may influence pain (Caceres & Burns, 1997; Feuerstein et al., 1985; Konecka & Sroczynska, 1990) and sleep (Kim et al., 2006; Sadeh, 1996; Slopen & Williams, 2014).

Why was race not a factor as predicted?

Both race and socioeconomic status influence pain and sleep (Goodin et al., 2010; Grandner et al., 2010; C. R. Green & Hart-Johnson, 2012; Whinnery et al., 2014) and it has been suggested that variables used to describe SES differ by race, since increases in variables associated with SES (education, income, occupation, etc.) do not produce equivalent improvements in health metrics (K. O. Anderson et al., 2009; N. B. Anderson et al., 2004; C. N. Bell et al., 2020; Troxel et al., 2020; Williams et al., 2016). Race was, however, significantly associated with neighborhood area deprivation—where non-Hispanic black individuals tended to live in more deprived neighborhoods compared to non-Hispanic white individuals. However, we did not see an effect for race within our models suggesting that controlling for other variables in the model may have diminished the potential effect of race. Differences in racial-ethnic backgrounds have been known to exist within the urban disadvantaged neighborhoods further perpetuating a cycle of isolation from other communities (E. Anderson, 2013; Peterson & Krivo, 2009; Tigges et al., 1998). Future studies further delineating this relationship should be examined.

Neighborhood level deprivation

SES has often been defined by individual level characteristics (e.g. education, income, employment status) (N. B. Anderson et al., 2004; Bacon et al., 2009; Whinnery et al., 2014; Williams et al., 2016). The literature today tends to be more focused on individual SES factors rather than on a larger comprehensive SES factor like NADI, which encompasses individual factors into one larger component. Compared to individual factors, there is less known about NADI. However, NADI encompasses 17 different variables related to SES (University of Wisconsin School of Medicine and Public Health, n.d.) which allows us to incorporate these typical individual measures into a more cohesive predictor with potentially better validity and reliability. Rather than attempting to use individual SES variables, observing NADI allows us to literally quantify the neighborhood where a person lays their head down at night, which may be a useful contextual tool when characterizing risk factors for poor sleep quality.

Implications

Part of the interest in observing disparities in sleep quality and pain associated with ADI, stems through wanting to know why individuals from certain communities are at greater risk for poorer health outcomes. Although there is plenty of evidence indicating the importance of sleep duration/quality in understanding some of these disparities based on individual level SES items (Cauter & Spiegel, 1999; Whinnery et al., 2014), a composite measure like the ADI can better reflect the multidimensional nature of a community’s SES. In essence, the location a person resides is of particular importance. From a recent scoping review on allostatic load and area deprivation index, there are implications that individuals who live in areas with greater deprivation may be at risk for physiological dysregulation and subsequent chronic illness because of the stressors for living in that environment (Ribeiro et al., 2018). Measures of this disadvantage may improve targeting of programs not only in improving sleep quality, but health outcomes in general. Measures of ADI are relatively accessible as most patients provide information regarding their address, but are rarely used clinically. Imagine a quick profile of a patient’s living environment provided as a number—being able to utilize a single number to quickly assess a patient’s living conditions could potentially save a provider time in assessing what type of neighborhood the patient lives in, and by proxy the environmental quality of life. Additionally, these types of conclusions would advocate that neighborhoods with greater deprivation should be improved (noise, safety, other stressors) to prevent poorer sleep quality and other poor health outcomes.

Limitations

This study is not without limitations. First, this was not a polysomnography diagnostic sleep study, which is considered the gold standard for the quantification of sleep—polysomnography can be highly invasive and inconvenient for a micro-longitudinal study whereas actigraphy is less invasive and convenient to the participant. These actigraphic data results serve more to inform future larger-scale studies where polysomnography may be utilized, than to provide us with definitive conclusions about sleep quality. Second, the distribution of NADI between groups were slightly skewed so that more individuals with cLBP reported higher NADI compared to pain-free individuals, however many of the individual SES variables (e.g. income, education) were similar between groups. Third, a limitation from this is that ADI was not able to be gathered from all participants as some participants did not provide geocoding information, or could not be calculated from Neighborhood Atlas. This likely reduced the power of the model by sample size, however ADI could still be seen to significantly contribute to sleep quality. Finally, there are a variety of other variables that influence both pain and sleep, such as utilization of opiates, alcohol, and recreational drug use, which is observed more frequently in cLBP than pain-free individuals, may impact sleep quality (Angarita et al., 2016) – however, ADI was the primary factor noted to impact sleep quality, not pain status from the data. As such pain management was out of the purview of this study. Experts in the field have provided commentaries and reviews that prescription opiate use is more common in non-Hispanic white individuals who can afford a prescription for said opiates (Hansen & Netherland, 2016), and illegal use is observed most commonly in lower SES environments (Pampel et al., 2010) which is typically observed as urban black in metropolitan areas (Beckett et al., 2006; James & Jordan, 2018; Riley, 1998) which is reflective of our recruitment location. This further reinforces how race may be stratified by SES and encourage a further commentary on how the war on drugs is more sympathetic to NHWhite individuals compared to minorities (Netherland & Hansen, 2016). Future studies further examining this may provide insight to this conundrum in the application of pain and sleep. Despite these limitations, this study had a number of important strengths. The sample was diverse with respect to race/ethnicity and individual SES. Along with the clinical heterogeneity of the level of pain in individuals with cLBP, this diversity may demonstrate the greater impact that NADI has. Finally, this study demonstrates the hypotheses generation promise of using NADI to predict variables associated with sleep quality and that neighborhood level disadvantage could influence quality of sleep in combination with or independent of other health conditions. Additional larger scale studies are necessary.

Funding:

This work was supported by Examining Racial And Socio Economic Disparities in cLBP; ERASED; R01MD010441.

Footnotes

Conflict of Interest: The authors declare that they have no conflict of interest.

Publisher's Disclaimer: This Author Accepted Manuscript is a PDF file of an unedited peer-reviewed manuscript that has been accepted for publication but has not been copyedited or corrected. The official version of record that is published in the journal is kept up to date and so may therefore differ from this version.

Ethics approval. It was reviewed and approved the Institutional Review Board at [Anon] (rRB-170119003) and carried out in a manner consistent with ethical research guidelines and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards; written informed consent was obtained from all individual participants included in the study.

All authors were fully involved in the study and preparation of the manuscript, each of the authors has read and concurs with the content in the final manuscript.

Written informed consent was obtained from all individual participants included in the study.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

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Associated Data

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Data Availability Statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

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