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. 2013 Aug 30;12:39.
doi: 10.1186/1476-072X-12-39.

A Cross-Sectional Analysis of Light at Night, Neighborhood Sociodemographics and Urinary 6-sulfatoxymelatonin Concentrations: Implications for the Conduct of Health Studies

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A Cross-Sectional Analysis of Light at Night, Neighborhood Sociodemographics and Urinary 6-sulfatoxymelatonin Concentrations: Implications for the Conduct of Health Studies

Susan Hurley et al. Int J Health Geogr. .
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Abstract

Background: There is accumulating evidence that circadian disruption, mediated by alterations in melatonin levels, may play an etiologic role in a wide variety of diseases. The degree to which light-at-night (LAN) and other factors can alter melatonin levels is not well-documented. Our primary objective was to evaluate the degree to which estimates of outdoor environmental LAN predict 6-sulftoxymelatonin (aMT6s), the primary urinary metabolite of melatonin. We also evaluated other potential behavioral, sociodemographic, and anthropomorphic predictors of aMT6s.

Methods: Study participants consisted of 303 members of the California Teachers Study who provided a 24-hour urine specimen and completed a self-administered questionnaire in 2000. Urinary aMT6s was measured using the Bühlmann ELISA. Outdoor LAN levels were estimated from satellite imagery data obtained from the U.S. Defense Meteorological Satellite Program's (DMSP) Operational Linescan System and assigned to study participants' geocoded residential address. Information on other potential predictors of aMT6s was derived from self-administered surveys. Neighborhood socioeconomic status (SES) was based on U.S. Census block group data.

Results: Lower aMT6s levels were significantly associated with older age, shorter nights, and residential locations in lower SES neighborhoods. Outdoor sources of LAN estimated using low-dynamic range DMSP data had insufficient variability across urban neighborhoods to evaluate. While high-dynamic range DMSP offered much better variability, it was not significantly associated with urinary aMT6s.

Conclusions: Future health studies should utilize the high-dynamic range DMSP data and should consider other potential sources of circadian disruption associated with living in lower SES neighborhoods.

Figures

Figure 1
Figure 1
Marginal distribution of Outdoor LAN estimates and distribution by urban classification for the low-dynamic range (year 2000) and the high-dynamic range (year 2006) data. The units in the “low dynamic range” data are “DN” units, while the units in the “high dynamic range data” are scaled radiance units (see Methods).
Figure 2
Figure 2
Estimated relationship between neighborhood SES and aMT6s, along with the pointwise 95% confidence band (shaded in gray). The relationship increases until SES equals zero, at which point it flattens out and no statistically significant slope remains (the dashed vertical line indicated where the SES PCA component is equal to zero). The lines across the bottom indicate the SES values for the sample. Note that the spline only shows deviation of aMT6s relative to the average aMT6s value across the sample.

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