Abstract
Abstract
Study Objectives:
To date, conflicting observations have been made regarding ethnic differences in sleep patterns. Plausibly, differing sampling strategies and disparity in the cohorts investigated might help explain discrepant findings. To our knowledge population-based studies investigating ethnic differences in sleep complaints have not addressed within-group ethnic heterogeneity, although within-group health disparities have been documented.
Design
Volunteers (n =1118) in this study were community-residing older European Americans and African Americans residing in Brooklyn, New York, which were recruited by a stratified, cluster sampling technique. Trained interviewers of the same race as the respondents gathered data during face-to-face interviews conducted either in the respondent's home or another location of their choice. Data included demographic and health risk factors, physical health, social support, and emotional experience. Relationships of demographic and health risk factors to sleep disturbances were examined in multiple linear regression analyses. Withingroup differences in sleep complaints were also explored.
Setting
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Participants
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Interventions
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Measurements and Results
Of the factors showing significant associations with sleep disturbance, European American ethnicity was the most significant predictor (r2 = 0.20). Worse sleep and greater reliance on sleep medicine were observed among European Americans. Caribbean Americans reported less sleep complaints than did U.S.-born African Americans, and immigrant European Americans reported greater complaints than did US-born European Americans.
Conclusions
As expected several health risk factors were predictive of sleep disturbance among urban community-dwelling older adults, but ethnicity was the most significant predictor. The present data suggest both between-group and within-group ethnic differences in sleep complaints. Understanding of demographic and cultural differences between African Americans and European Americans may be critical in interpreting subjective health-related data.
