Dataset Information

Associations among Neighborhood, Race, and Sleep Apnea Severity in Children. A Six-City Analysis.

ABSTRACT: Prior researchers found that individual-level environmental and social indicators did not explain the racial disparity in obstructive sleep apnea syndrome. Neighborhood socioeconomic variables, as well as risk factors for a range of adverse behavioral and health outcomes, may better explain this racial disparity and help identify modifiable intervention targets.To evaluate the associations of neighborhood socioeconomic variables with obstructive sleep apnea severity and to assess whether the neighborhood variables explain the association between race and obstructive sleep apnea severity.We performed a cross-sectional analysis of data of 774 children in six cities who participated in the Childhood Adenotonsillectomy Trial. The outcome variable was the apnea-hypopnea index (AHI). Neighborhood socioeconomic variables were obtained on the basis of the children's residential addresses and information in the American Community Survey. Regression models were used to assess the associations among neighborhood conditions, race, and AHI.Higher poverty rate and percentage of single-female-headed households were associated with higher AHI (P?=?0.008 and 0.002, respectively). African American race was associated with a 1.33 (1.08-1.64 95% confidence interval)-fold increase in AHI, adjusting for age and sex. After controlling for poverty rate or percentage of single-female-headed households with children, the association between race and AHI levels was no longer significant (P?=?0.15 and 0.26, respectively), and the magnitude of race association decreased 34 or 55%, suggesting that the association between race and AHI levels was largely explained by poverty rate or percentage of single-female-headed households with children.Neighborhood socioeconomic variables in comparison with individual-level socioeconomic indicators provides better explanations for the racial disparity in pediatric obstructive sleep apnea syndrome. Further research aimed at identifying factors that aggregate in disadvantaged neighborhoods and increase sleep apnea risk may suggest modifiable intervention targets. Clinical trial registered with clinicaltrials.gov (NCT00560859).

SUBMITTER: Wang R

PROVIDER: S-EPMC5291481 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Associations among Neighborhood, Race, and Sleep Apnea Severity in Children. A Six-City Analysis.

Wang Rui R Dong Yan Y Weng Jia J Kontos Emily Z EZ Chervin Ronald D RD Rosen Carol L CL Marcus Carole L CL Redline Susan S

Annals of the American Thoracic Society 20170101 1

<h4>Rationale</h4>Prior researchers found that individual-level environmental and social indicators did not explain the racial disparity in obstructive sleep apnea syndrome. Neighborhood socioeconomic variables, as well as risk factors for a range of adverse behavioral and health outcomes, may better explain this racial disparity and help identify modifiable intervention targets.<h4>Objectives</h4>To evaluate the associations of neighborhood socioeconomic variables with obstructive sleep apnea s ...[more]

PMID: 27768852

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Project description:BackgroundObstructive sleep apnea (OSA) increases risk for cognitive decline and Alzheimer's disease (AD). While the underlying mechanisms remain unclear, hypoxemia during OSA has been implicated in cognitive impairment. OSA during rapid eye movement (REM) sleep is usually more severe than in non-rapid eye movement (NREM) sleep, but the relative effect of oxyhemoglobin desaturation during REM versus NREM sleep on memory is not completely characterized. Here, we examined the impact of OSA, as well as the moderating effects of AD risk factors, on verbal memory in a sample of middle-aged and older adults with heightened AD risk.MethodsEighty-one adults (mean age:61.7±6.0 years, 62% females, 32% apolipoprotein E ε4 allele (APOE4) carriers, and 70% with parental history of AD) underwent clinical polysomnography including assessment of OSA. OSA features were derived in total, NREM, and REM sleep. REM-NREM ratios of OSA features were also calculated. Verbal memory was assessed with the Rey Auditory Verbal Learning Test (RAVLT). Multiple regression models evaluated the relationships between OSA features and RAVLT scores while adjusting for sex, age, time between assessments, education years, body mass index (BMI), and APOE4 status or parental history of AD. The significant main effects of OSA features on RAVLT performance and the moderating effects of AD risk factors (i.e., sex, age, APOE4 status, and parental history of AD) were examined.ResultsApnea-hypopnea index (AHI), respiratory disturbance index (RDI), and oxyhemoglobin desaturation index (ODI) during REM sleep were negatively associated with RAVLT total learning and long-delay recall. Further, greater REM-NREM ratios of AHI, RDI, and ODI (i.e., more events in REM than NREM) were related to worse total learning and recall. We found specifically that the negative association between REM ODI and total learning was driven by adults 60+ years old. In addition, the negative relationships between REM-NREM ODI ratio and total learning and REM-NREM RDI ratio and long-delay recall were driven by APOE4 carriers.ConclusionGreater OSA severity, particularly during REM sleep, negatively affects verbal memory, especially for people with greater AD risk. These findings underscore the potential importance of proactive screening and treatment of REM OSA even if overall AHI appears low.

Project description:BackgroundSleep Apnea Syndrome (SAS) is a common sleep-related breathing disorder, which affects about 4-7% males and 2-4% females all around the world. Different approaches have been adopted to diagnose SAS and measure its severity, including the gold standard Polysomnography (PSG) in sleep study field as well as several alternative techniques such as single-channel ECG, pulse oximeter and so on. However, many shortcomings still limit their generalization in home environment. In this study, we aim to propose an efficient approach to automatically assess the severity of sleep apnea syndrome based on the ballistocardiogram (BCG) signal, which is non-intrusive and suitable for in home environment.MethodsWe develop an unobtrusive sleep monitoring system to capture the BCG signals, based on which we put forward a three-stage sleep apnea syndrome severity assessment framework, i.e., data preprocessing, sleep-related breathing events (SBEs) detection, and sleep apnea syndrome severity evaluation. First, in the data preprocessing stage, to overcome the limits of BCG signals (e.g., low precision and reliability), we utilize wavelet decomposition to obtain the outline information of heartbeats, and apply a RR correction algorithm to handle missing or spurious RR intervals. Afterwards, in the event detection stage, we propose an automatic sleep-related breathing event detection algorithm named Physio_ICSS based on the iterative cumulative sums of squares (i.e., the ICSS algorithm), which is originally used to detect structural breakpoints in a time series. In particular, to efficiently detect sleep-related breathing events in the obtained time series of RR intervals, the proposed algorithm not only explores the practical factors of sleep-related breathing events (e.g., the limit of lasting duration and possible occurrence sleep stages) but also overcomes the event segmentation issue (e.g., equal-length segmentation method might divide one sleep-related breathing event into different fragments and lead to incorrect results) of existing approaches. Finally, by fusing features extracted from multiple domains, we can identify sleep-related breathing events and assess the severity level of sleep apnea syndrome effectively.ConclusionsExperimental results on 136 individuals of different sleep apnea syndrome severities validate the effectiveness of the proposed framework, with the accuracy of 94.12% (128/136).

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Associations among Neighborhood, Race, and Sleep Apnea Severity in Children. A Six-City Analysis.

Publications

Associations among Neighborhood, Race, and Sleep Apnea Severity in Children. A Six-City Analysis.

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