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Use of remotely sensed data to evaluate the relationship between living environment and blood pressure
Maurice G Estes1, Mohammad Z Al-Hamdan, William Crosson
1Universities Space Research Association, NASA-Marshall Space Flight Center, Huntsville, Alabama 35805, USA. maury.g.estes@nasa.gov
Background:
Urbanization has been correlated with hypertension (HTN) in developing countries undergoing rapid economic and environmental transitions.
Objectives:
We examined the relationships among living environment (urban, suburban, and rural), day/night land surface temperatures (LST), and blood pressure in selected regions from the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. Also, the linking of data on blood pressure from REGARDS with National Aeronautics and Space Administration (NASA) science data is relevant to NASA's strategic goals and missions, particularly as a primary focus of the agency's Applied Sciences Program.
Methods:
REGARDS is a national cohort of 30,228 people from the 48 contiguous United States with self-reported and measured blood pressure levels. Four metropolitan regions (Philadelphia, PA; Atlanta, GA; Minneapolis, MN; and Chicago, IL) with varying geographic and health characteristics were selected for study. Satellite remotely sensed data were used to characterize the LST and land cover/land use (LCLU) environment for each area. We developed a method for characterizing participants as living in urban, suburban, or rural living environments, using the LCLU data. These data were compiled on a 1-km grid for each region and linked with the REGARDS data via an algorithm using geocoding information.
Results:
REGARDS participants in urban areas have higher systolic and diastolic blood pressure than do those in suburban or rural areas, and also a higher incidence of HTN. In univariate models, living environment is associated with HTN, but after adjustment for known HTN risk factors, the relationship was no longer present.
Conclusion:
Further study regarding the relationship between HTN and living environment should focus on additional environmental characteristics, such as air pollution. The living environment classification method using remotely sensed data has the potential to facilitate additional research linking environmental variables to public health concerns.
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