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. 2012 Feb 15;8(1):77-86.
doi: 10.5664/jcsm.1668.

State and regional prevalence of sleep disturbance and daytime fatigue

Michael A Grandner  1 , Nicholas J Jackson, Wilfred R Pigeon, Nalaka S Gooneratne, Nirav P Patel
Affiliations

State and regional prevalence of sleep disturbance and daytime fatigue

Michael A Grandner et al. J Clin Sleep Med. .

Abstract

Study objectives: Social and demographic influences are important for sleep attainment. Geographic location has not been previously explored.

Methods: Data from the 2006 Behavioral Risk Factor Surveillance System (BRFSS) were used (N = 157,319). Participants answered a question on Sleep Disturbance and Daytime Fatigue. Thirty-six states/regions provided data on these items. Prevalence estimates were adjusted for age, sex, ethnoracial group, education, income, employment, general health, healthcare access, and depression. Chi-squared tests were conducted across states and census regions, and pseudo-R(2) values were computed for the effect of state, relative to other predictors. To evaluate potential mediators of census region differences, an analysis of p value change associated with specific covariates and covariate groups was undertaken.

Results: Adjusted prevalence rates of Sleep Disturbance differed across states/regions overall (χ(2) = 412.3, p < 0.0001), as well as separately for men (χ(2) = 139.5, p < 0.0001) and women (χ(2) = 350.0, p < 0.0001), as did rates of Daytime Fatigue overall (χ(2) = 245.7, p < 0.0001), and separately for men (χ(2) = 117.5, p < 0.0001) and women (χ(2) = 181.2, p < 0.0001). Analysis of pseudo-R(2) values revealed that despite these significant findings, state differences were an overall weak predictor, representing 1.30% to 1.73% of the magnitude of the effect of the best predictor (mental health). When Census regions were compared, significant differences were found for Sleep Disturbance (p = 0.002), but after adjustment for covariates, these were no longer significant. Differences existed for Daytime Fatigue in adjusted analyses overall (p < 0.0001), with the West reporting the fewest complaints and the South reporting the most.

Conclusions: These results demonstrate that reports of sleep related complaints vary across states, independent (at least partially) of factors that influence circadian rhythms (e.g., latitude).

Keywords: Sleep; epidemiology; fatigue; geographic factors; health services.

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Figures

Figure 1
Figure 1
Graphical representation of prevalence rates for Sleep Disturbance by state
Figure 2
Figure 2
Graphical representation of prevalence rates for Daytime Fatigue by state
See this image and copyright information in PMC

References

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