bluuebunny/crossref_metadata_2025_split huggingface dataset:
0244 Time Poverty as a Potential Pathway of Sleep Disparities Among Four Disability Populations
Authors: Garrison Michelle 
Published: April 2024

Abstract

Introduction
Past research has found that increased sleep disparities in populations experiencing disability, and this has often been attributed to increases in pain, stress, and comorbid conditions. However, it is also possible that the excess time burdens imposed by life with disability may also play a mediating role, especially in those with other risk factors for time poverty.

Methods
The Behavioral Risk Factor Surveillance System (BRFSS) is an annual cross-sectional survey sampled to be representative of the non-institutionalized US adult population. Using the 2022 data for 18-65 year-olds, we conducted survey weighted linear regression models to evaluate the impact of four disability categories (deafness, blindness, mobility impairment, and difficulties with concentrating, remembering, or making decisions) on sleep duration in cumulative models 1) adjusting for age category and sex, 2) adding poor health and financial stress, and 3) stratifying by risk for time poverty (risk index based on employment, children in the home, caregiving load, poor access to reliable transportation, and rural location).

Results
The analytic sample included 153,040 adults, with 24.4% endorsing at least one of the four disabilities, with prevalence ranging from 4.4% (deaf or hard of hearing) to 14.0% (difficulty with concentrating, remembering, or making decisions). All four disability categories were associated with decreased sleep duration (such as a 20.2 minute deficit, 95%CI 16.7-23.7 in those with mobility impairment), which remained significant after adjusting for poor health and financial stress. The sleep deficits associated with disability categories were consistently about twice as large among those in the top decile of the time poverty risk index as in the rest of the sample.

Conclusion
All disability categories measured by BRFSS are associated with sleep disparities, and these are exacerbated among those with other risk factors for time poverty, consistent with the time burden hypothesis, as those with multiple risk factors for time poverty would have less flexibility to allocate additional time without sacrificing sleep. Especially given that those experiencing disabilities are under-represented in much of the broader sleep literature, as are those with time poverty, sleep disparities in these populations are a crucial area for more robust exploration.

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0245 Treatment for Obstructive Sleep Apnea by Area Socioeconomic Deprivation in Six Million Adults
Authors: Greider Montana , Fernandez Chris , Rusk Sam , Nygate Yoav , Richardson Dana , Hutchinson Brian , Bartholow Tim , Watson Nathaniel , Wickwire Emerson 
Published: April 2024

Abstract

Introduction
Area socioeconomic deprivation, as measured by the Area Deprivation Index (ADI), is associated with numerous adverse health and economic outcomes (e.g., cardiovascular events, hospital readmissions, Alzheimer’s disease). This composite score is based on 17 health disparities indicators (e.g., income, education, employment, housing quality) used to rank relative disadvantage across communities, and is a widely utilized key social determinant of health and a validated marker of health risk. The purpose of this study is to determine the association between the ADI and OSA treatment initiation.

Methods
Our data source was the All-Payer Claims Database (APCD) for the Wisconsin Health Information Organization (WHIO). The APCD includes claims data (e.g., healthcare visits, procedures, pharmacy information) from health insurers, employers, and Medicaid. Inclusion criteria included continuous enrollment coverage for a minimum of 12-months prior to the date of OSA diagnosis (based on ICD code G47.33) and a diagnostic sleep test (based on CPT codes). ADI was measured at state and national levels. OSA treatment initiation was defined using Healthcare-Common-Procedure Coding System (HCPCS) codes. Rates of OSA treatment initiation were compared between individuals with OSA living in the highest and lowest ADI quantiles (e.g., the areas of greatest and least socioeconomic deprivation) using ordinary least squares (OLS) regression analysis to evaluate the directionality and significance of their association.

Results
Of N=6,026,463 participants in the overall sample, N=154,821 underwent OSA diagnostic testing, and N=43,601 were subsequently diagnosed with OSA. OSA treatment initiation was significantly, negatively associated with area socioeconomic deprivation based on National-ADI (Slope: -0.0011, p&lt; 0.0050) and State-ADI (Slope: -0.0016, p&lt; 0.0050). The highest rates of OSA treatment were observed in areas of greatest socioeconomic advantage (&gt;70% in ADIs 15-25). Conversely the lowest OSA treatment initiation rates were observed in areas of greatest socioeconomic disadvantage (&lt; 50% in ADIs 90-100), reflecting a 20% difference in the likelihood of OSA treatment between the highest and lowest levels of socioeconomic disparity.

Conclusion
Area socioeconomic deprivation measured by ADI is associated with significantly reduced OSA treatment initiation. Future research should seek to increase access to OSA care in areas of socioeconomic disadvantage to reduce sleep health disparities and achieve health equity.

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0247 Impact of Area Socioeconomic Deprivation and Demographic Variables on Machine Learning Models for OSA Treatment
Authors: Greider Montana , Fernandez Chris , Rusk Sam , Nygate Yoav , Richardson Dana , Hutchinson Brian , Bartholow Tim , Arguelles Jessica , Klimper Matthew , Hwang Dennis , Watson Nathaniel , Wickwire Emerson 
Published: April 2024

Abstract

Introduction
Ensuring equitable performance of sleep-related machine learning (ML) models is vital for public health and health equity. This research sought to determine the impact of sociodemographic and health disparities factors on the performance and characteristics of ML models designed to predict OSA treatment initiation.

Methods
Our data source was the All-Payer Claims Database (APCD) for the Wisconsin Health Information Organization (WHIO). Inclusion criteria included continuous insurance coverage for &gt;12 months prior and &gt;30 months after OSA diagnosis (defined by OCD code G47.33), and having undergone OSA diagnostic testing (defined by CPT codes). OSA treatment was defined based on durable medical equipment charges for PAP machines and supplies. Sociodemographic variables were extracted from APCD and included race, gender, age, and area socioeconomic deprivation (Area Deprivation Index; ADI). The ADI is a validated marker of health risk based on 17 health disparities factors ranking relative disadvantage across communities. Random Forest ML models were trained to predict OSA treatment initiation using sociodemographic variables and a medication history including 39,712 unique medications codified across 94 medication categories.

Results
Of N=6,026,463 subjects in the ACPD, n=154,821 underwent OSA diagnostic testing, and n=43,601 were diagnosed with OSA. 10-Fold Cross-Validation training-testing was applied to estimate sensitivity-specificity of ML models for predicting treatment initiation. Receiver operating characteristic curve area-under-the-curve (ROC-AUC) analyses were used to compare relative differences in predictive power of each variable. In ROC-AUC analysis of individual variables, the power for predicting OSA treatment were observed in relative rank order by age (0.568), race (0.547), ADI national-level (0.545), ADI state-level (0.544), gender (0.524), and medication history (0.516). In ROC-AUC analysis of combination variables, the highest ML model performance was observed in the combination of only three-variables (age-gender-race, 0.600), while the combination of all-variables showed an ROC-AUC value of (0.594), and the resulting performance difference was not statistically significant based on comparison of the ROC-AUC measures.

Conclusion
Demographic and health disparity factors may play an important role in future development of predictive AI/ML models. Population sleep health data represents an important resource to identify and bridge care gaps, reduce sleep health disparities, and achieve health equity.

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0251 Racial Residential Segregation and Outdoor Artificial Light at Night: Potential Contributors to Sleep Disparities
Authors: Gaston Symielle , Wilkerson Jesse , MacNell Nathaniel , Jackson W Braxton , Jackson Chandra 
Published: April 2024

Abstract

Introduction
Racial/ethnic residential segregation (RRS) likely contributes to sleep health disparities. For instance, nighttime lighting for surveillance and policing efforts that vary by neighborhood racial composition likely contributes to differences in exposure to artificial light at night (ALAN), which disrupts melatonin production and sleep. Yet, the RRS-ALAN-sleep pathway is understudied.

Methods
To investigate RRS and ALAN as mediators of racial/ethnic differences in sleep among US adults, we geographically linked National Health Interview Survey (2011-2017) data to 2012 American Community Survey 5-year population estimates and outdoor ALAN data from the 2010-2011 US Defense Meteorological Satellite Program’s Operational Linescan System. The local Getis-Ord Gi* was used to categorize RRS (low [z-score&lt; 0], medium [0&lt;=z-score&lt;=1.96], high [z-score&gt;1.96]). ALAN was divided into quintiles. Short sleep duration (&lt; 7-hours) and poor sleep quality were self-reported. We used adjusted, survey-weighted robust Poisson regression to estimate prevalence ratios (PRs) and 95% confidence intervals (CIs); tested for effect modification by race/ethnicity; and assessed mediation using the Sobel’s test.

Results
Among 126,539 adults (mean±SE age=46±0.1 years), RRS prevalence was 35% low, 43% medium, and 22% high. RRS and poor sleep were higher among minoritized adults (non-Hispanic Black (NHB), Mexican Latino, non-Mexican Latino, and Asian); ALAN was higher among non-Hispanic White (NHW) adults. High RRS was associated with a lower prevalence of ALAN (Q5 vs. Q1: PR=0.59 [95% CI:0.44-0.80]), short sleep (PR=0.94 [0.91-0.97]), and poor sleep quality (e.g., PR-trouble falling asleep =0.94 [0.90-0.98]), with no differences by race/ethnicity. ALAN was not associated with short sleep (sleep quality data unavailable). RRS explained differences in short sleep between both NHB and Mexican vs. NHW adults (e.g., NHB: PR-total effect=1.30 [1.26-1.33]; PR-direct effect=1.39 [1.34-1.45]; PR-indirect effect=0.93 [0.89-0.98]) before but not after adjustment for ALAN (PR-indirect effect=0.97 [0.87-1.08]).

Conclusion
RRS was associated with lower ALAN and non-poor sleep. RRS attenuated sleep duration disparities among NHB and Mexican adults only prior to adjustment for ALAN. RRS and ALAN may be contrasting modifiable contributors to sleep disparities among US adults.

Support (if any)
The findings/conclusions in this research are those of the authors and do not necessarily represent the views of the Research Data Center, National Center for Health Statistics, or CDC.
0252 Association Between Sleep Environment and Insomnia Among Hispanics in Florida
Authors: Palencia-Lamela Israel , Coppello Matthew , Carasco Mary , Garchitorena Laurent , Grandner Michael A , Chung Debbie , Jean-Louis Girardin , Seixas Azizi 
Published: April 2024

Abstract

Introduction
Insomnia prevalence among Hispanics is a significant public health concern, underscoring the need to uncover novel predictors of this sleep disorder. While prior studies have highlighted the high incidence of insomnia within this demographic, there is an urgent need to explore less-explored factors contributing to its development. Specifically, the impact of the sleep environment on insomnia symptoms in the Hispanic population. This study aims to bridge this gap by investigating the relationship between sleep environment quality and insomnia severity among Hispanics in Florida, offering fresh insights into the predictors of insomnia within this group.

Methods
Analyses include data from a Hispanic population in Florida (N= 221), ages 18-87 years that were enrolled in the NIH-funded heart health study, DORMIR. Participants completed several surveys on behavioral, clinical, social, and environmental determinants of sleep. For the current study, we measured insomnia symptoms via the Insomnia Severity Index (ISI) and the self-perceived quality of sleep environment was measured via the Assessment of Sleep Environment (ASE) survey. ISI measures presence and severity of insomnia symptoms and ASE asks respondents to rate their physical sleep environment by answering questions about the amount of light, temperature, and noise among other factors. Greater ISI scores indicate insomnia severity and greater ASE scores indicate worst sleep environment. To test whether sleep environment is associated with insomnia symptoms, we performed descriptive and inferential statistical analyses to characterize the sample as well as to determine the association between sleep environment and insomnia symptoms.

Results
Our population comprised 41% male (n=64) and 59% female (n=157). The mean ASE score of our total sample was 22.4±7.00 and the mean ISI score was 8.82±5.29. The regression model indicated that ASE was independently associated with ISI (F(1,219=6.09, B-0.12, p=0.0114), after controlling for sociodemographic, behavioral, and clinical variables.

Conclusion
Our results indicate that poorer sleep environments are associated with greater insomnia symptoms. These findings can be used to increase awareness of the benefits of monitoring the quality of sleep environments and associated risk of experiencing insomnia. These findings also elucidate the importance of limiting nighttime disturbances to improve quality of sleep.

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5R01HL152453
0253 Sleep Disturbances and Short Sleep Duration Among Rural and Non-Rural Populations
Authors: Leete Jacqueline , Graves Janessa 
Published: April 2024

Abstract

Introduction
Rural populations experience a higher prevalence of insomnia and insufficient sleep compared to urban populations. The purpose of this study was to compare self-reported sleep disturbances and objective sleep duration measured using consumer-wearable devices among individuals living in rural versus non-rural communities.

Methods
Participants in this analysis were from the National Institutes of Health (NIH) All of Us (AOU) Research Program, a diverse US health database. We assessed rurality using an item on a self-report questionnaire measuring geographic isolation, dichotomized as rural or non-rural. Sleep disturbance symptoms were measured using item 3 on the Patient Health Questionaire-9, which assesses trouble falling or staying asleep or sleeping too much in the past 2 weeks. Objective total sleep time (TST) was measured using Fitbit devices. Participants with &lt; 14 days of wearable data were excluded from TST analyses.

Results
Chi-squared analysis revealed that AOU participants living in rural communities were more likely to report sleep disturbance symptoms at least several nights per week (75.4%, n=1,552, ◻2(1) =165.6, p&lt;.001) compared to individuals living in non-rural communities (59.2%, n=59,778). Linear regression analysis revealed that rurality was significantly associated with increased self-reported sleep disturbances (rural M=1.44, SD=1.12; non-rural M=.92, SD=.97; b=.424, SE=.025, F(3,59812)=1187, p&lt;.001, adjusted R2=.056) and decreased objective average TST (rural M=351 min, SD=73, n=356; non-rural M=363 min, SD=75, n=11,760; b=-15.769, SE=3.980, F(3,12112)=75.93, p&lt;.001, adjusted R2=.018), when controlling for age and sex.

Conclusion
Our findings align with previous research that individuals in rural communities typically experience poorer sleep than those living in non-rural communities. These results suggest a need for targeted sleep interventions for individuals living in geographically isolated areas of the US. Moreover, these findings imply that future research examining geographic sleep health disparities is warranted.

Support (if any)
The AOU Research Program is supported by the NIH and the contributions of its participants.
0254 Acculturative Stress Is Associated with Sleep-Related Impairment Among a Sample of Rural and Urban Latinos/as
Authors: Carrasco Mary , Coppello Matthew , Garchitorena Laurent , Scaramutti Carolina , Palencia Israel , Grandner Michael , Osorio Ricardo , Thawani Sujata , Ramos Alberto , Blanc Judite , Chung Debbie , Jean-Louis Girardin , Seixas Azizi 
Published: April 2024

Abstract

Introduction
Latinos/as in the United States face distinctive sociocultural stressors rooted in their marginalized status, immigration experiences, and challenges adapting to a new culture. These stressors, including acculturative stress, contribute to elevated stress levels that can have detrimental effects on sleep health and may engender sleep-related impairment. While the association between stress and sleep difficulties is well-documented, the impact of acculturative stress on Latinos/as sleep health remains an understudied area. This study aims to illuminate the relationship between acculturative stress and sleep, highlighting a critical aspect of sleep health within this demographic.

Methods
This work utilized data from DORMIR, an NIH-funded study investigating multi-level determinants of sleep and heart health outcomes among urban and rural Latinos/as. Participants in New York and Florida completed surveys capturing biological, clinical, behavioral, psychosocial, and environmental determinants of health, including the Sleep-Related Impairment (SRI) and the Multidimensional Acculturation Stress Inventory (MASI) measures. We hypothesized that acculturative stress will be associated with sleep impairment outcomes and differentially across two locations. Linear regression analyses were performed to determine if acculturative stress predicted sleep-related impairments differentially in different geographical regions.

Results
Florida’s sample (n=241, 35.6±14.7 years, 71% female) differed from New York’s sample (n=112, 33.6±12.6 years, 61% female). For Florida participants, the average SRI score was 8.32±3.53 and the MASI score was 76.7±7.56. For New York participants, the average SRI was 9.35±3.50 and the MASI score was 87.6±25.1. Acculturative stress was found to be significantly associated with sleep-related impairment in Florida [F(1,239) = 8.95, p=0.003] and New York [F(1,110) = 11.8, p=0.008]. Regression analysis indicates greater acculturative stress predicted higher sleep related impairment in Florida (β1=0.08) and New York (β1=0.04), albeit a greater magnitude was observed in Florida.

Conclusion
Higher levels of acculturative stress predicted greater sleep-related impairments among Latinos/as in Florida and New York. With the recent influx of immigrants at the U.S southern border, this study illustrates the importance of addressing unique stressors like acculturative stress and its effect on sleep health among these populations. Further studies are needed focusing on reducing acculturation stress as a meaningful intervention for improving sleep health among Latinos/as in the United States.

Support (if any)
R01HL152453
0255 Associations Between Coping with Discrimination, Sleep, and Mental Health Among Black Sexual Minority Men
Authors: Dong Lu , Bogart Laura , Klein David , Mutchler Matt , Lawrence Sean , Pantalone David 
Published: April 2024

Abstract

Introduction
Black sexual minority men (SMM) suffer disproportionately from health inequities, including high rates of HIV, as well as poor mental and physical health. In addition to intersectional stigma and discrimination, sleep health, an understudied factor, may contribute to these health inequities. We examined the associations between coping with discrimination, sleep disturbances, and mental and behavioral health outcomes among Black SMM.

Methods
We recruited 191 Black SMM participants both in person at community-based events and organizations and online. Sleep disturbance, depression, anxiety, alcohol use, and quality of life were measured using PROMIS-Sleep Disturbance, Physical Health Questionnaire, Generalized Anxiety Disorder, Alcohol Use Disorders Identification Test, and PROMIS global health items. Experience of and coping with discrimination were measured using validated questionnaires. Regression and mediation analysis were conducted, controlling for age.

Results
One-quarter of the sample reported elevated levels of sleep disturbance. Experiences of racial discrimination were marginally associated with greater sleep disturbances (b = 0.59, p = 0.07). Effective coping (e.g., getting emotional support, thinking about steps to take) (b = -2.62, p = 0.03) and higher coping self-efficacy (b = -2.00, p &lt; 0.001) were associated with fewer sleep disturbances. Less effective coping (e.g., self-blame, denial) was associated with more sleep disturbances (b = 4.70, p = 0.009). Greater sleep disturbance was associated with higher ratings of depression (b = 0.04, p &lt; 0.001), anxiety (b = 0.03, p &lt; 0.001) and lower ratings of quality of life (b = -0.04, p &lt; 0.001), and marginally with greater alcohol use disturbances (b = 0.12, p = 0.06). Indirect paths via sleep disturbance were significant for the effects of effective coping or ineffective coping on depression, anxiety, alcohol use (for ineffective coping only), and quality of life.

Conclusion
Effective coping strategies appeared to mitigate sleep disturbances and other health challenges, underscoring the need for culturally congruent interventions that address both sleep and mental health to improve overall wellbeing in this population. Additionally, it is imperative to actively address intersectional stigma and discrimination. Mediation analysis was based on cross-sectional data and should be interpreted with caution.

Support (if any)
This work is supported by NIMHD R01MD014722 (LMB)