A spatiotemporal exposure model to estimate population-level environmental circadian misalignment (ECM) light exposure
From Fred Hutch Cancer Center
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- Description: We developed an environmental circadian misalignment (ECM) light exposure model that can estimate the relative amount of light exposure (from the sun) impacting a person's circadian phase based on geographic location.
- Model inputs: The model incorporates information on geographic location within a time zone, elevation, sunrise time, and sunset time.
- Modeling methodology: We apply geospatial science technologies, including geographic information systems (GIS), to utilize spatial datasets during model development.
- Data linkage for epidemiologic studies: This exposure model can be linked with any study population using geographic variables (e.g., geocoded residential addresses) to conduct population-based research.
- Geographic extent: This nationwide exposure model is available for all 50 states in the US, as well as Washington, D.C. and Puerto Rico. This model can be extended internationally given available data.
- Spatial resolution and temporal resolution: The flexible modeling methodology enables scalability across space and time. This exposure model is able to estimate ECM light exposure for any geographic variable (e.g., census tracts, ZIP Codes) and for any time period (e.g., daily, weekly, monthly, yearly).
- Light exposure(from the sun) helps the body align internal circadian rhythms with our environment.
- Circadian misalignment occurs when internal circadian rhythms and environmental rhythms are out of phase with one another.
- One source of circadian misalignment is shift work , which has been associated with increased risk for cancer.
- Another potential source is where a person is located within a time zone, which we have termed as environmental circadian misalignment (ECM).
- As the sun rises in the east and sets in the west, geographic location within a time zone is associated with differences in elevation, sunrise time, and sunset time, each of which has implications for the amount of ambient light exposure to which we are exposed.
How the exposure model works
1. Determine the geographic variable of interest, which defines the spatial resolution of the exposure model.
Examples of geographic variables include:
- Geocoded residential addresses (latitude and longitude coordinates)
- Census tracts
- ZIP Codes
2. Determine the temporal resolution.
- Identify a time period of interest (e.g., 2000 - 2010).
- Identify if daily, weekly, monthly, and/or yearly averages are relevant to examine.
3. For a given spatial resolution and temporal resolution, the model provides an estimate of ECM light exposure incorporating spatial data on:
- Geographic location within a time zone
- Sunrise time
- Sunset time
Specifically, the model uses geographic information systems (GIS) to incorporate geographic location within a time zone –
as well as elevation raster spatial data.
The model further incorporates sunrise time and sunset time. There is substantial geographic variability in sunrise time and sunset time, and thus light exposure, depending on where you are located.
Use the vertical line to drag between the two maps below to visualize geographic differences across the US in sunrise time (green map) and sunset time (orange map).
Average sunrise and sunset times in 2010 (in local time) were calculated using US Census Bureau census tracts and the NOAA Solar Calculator. Puerto Rico is not pictured.
Research & publications
- Currently in progress:
- ECM light exposure model methods paper (Ton M et al. in preparation)
- Validation study using LYS Button wearable light sensors (pilot study funded by Fred Hutchinson Cancer Center)
- Citations for preliminary research using the ECM light exposure model:
- Ton M, Weaver MD, VoPham T. Spatiotemporal exposure modeling of environmental circadian misalignment. Abstract presented at International Society for Environmental Epidemiology (ISEE) Conference. Athens, Greece. Sep 2022.
- VoPham T and Weaver MD. Environmental circadian misalignment and liver disease in a US nationwide administrative dataset. Abstract presented at Geospatial Health Research Symposium, American Association of Geographers (AAG) Conference. New York, NY. Feb 2022.
- Publication on results from an epidemiologic study examining residential location within a time zone and liver cancer risk:
- VoPham T, Weaver MD, Vetter C, Hart JE, Tamimi RM, Laden F, Bertrand KA. Circadian misalignment and hepatocellular carcinoma incidence in the United States. Cancer Epidemiology, Biomarkers and Prevention. 2018 Jul;27(7):719-727.
- This US nationwide spatiotemporal ECM light exposure model can be used for exposure assessment in epidemiologic studies to enable comprehensive characterization of geographic variation in light exposure potentially impacting circadian phase.
- The model is scalable in its applications, enabling the estimation of ECM light exposure for any geographic variable (across the US) and for any time period – accommodating the analysis of any epidemiologic research question of interest.
- This model can be extended internationally given available data.
Funding & acknowledgments
This research was supported by the University of Washington Exposures, Diseases, Genomics and Environment (EDGE) Center (NIH/NIEHS P30 ES007033), NIH/NIDDK K01 DK12561, and Fred Hutchinson Cancer Center.
NIH: National Institutes of Health; NIEHS: National Institute of Environmental Health Sciences; NIDDK: National Institute of Diabetes and Digestive and Kidney Diseases
The ECM light exposure model was developed by:
- Mimi Ton, MPH, PhD Candidate (Department of Epidemiology, University of Washington): email@example.com
- Matthew D. Weaver, PhD (Division of Sleep Medicine, Harvard Medical School): firstname.lastname@example.org
- Trang VoPham, PhD, MS (Epidemiology Program, Public Health Sciences Division, Fred Hutchinson Cancer Center): email@example.com