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Introduction: An ongoing Zika virus pandemic in Latin America and the Caribbean has raised concerns that travel-related introduction of Zika virus could initiate local transmission in the United States (U.S.) by its primary vector, the mosquito Aedes aegypti., Methods: We employed meteorologically driven models for 2006-2015 to simulate the potential seasonal abundance of adult Aedes aegypti for fifty cities within or near the margins of its known U.S. range. Mosquito abundance results were analyzed alongside travel and socioeconomic factors that are proxies of viral introduction and vulnerability to human-vector contact.    , Results: Meteorological conditions are largely unsuitable for Aedes aegypti over the U.S. during winter months (December-March), except in southern Florida and south Texas where comparatively warm conditions can sustain low-to-moderate potential mosquito abundance. Meteorological conditions are suitable for Aedes aegypti across all fifty cities during peak summer months (July-September), though the mosquito has not been documented in all cities. Simulations indicate the highest mosquito abundance occurs in the Southeast and south Texas where locally acquired cases of Aedes-transmitted viruses have been reported previously. Cities in southern Florida and south Texas are at the nexus of high seasonal suitability for Aedes aegypti and strong potential for travel-related virus introduction. Higher poverty rates in cities along the U.S.-Mexico border may correlate with factors that increase human exposure to Aedes aegypti.    , Discussion: Our results can inform baseline risk for local Zika virus transmission in the U.S. and the optimal timing of vector control activities, and underscore the need for enhanced surveillance for Aedes mosquitoes and Aedes-transmitted viruses.

We describe a remote sensing and GIS-based study that has three objectives: (1) characterize fine particulate matter (PM 2.5 ), insolation and land surface temperature using NASA satellite observations, EPA ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes; and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making.

Studies of the effect of air pollution on cognitive health are often limited to populations living near cities that have air monitoring stations. Little is known about whether the estimates from such studies can be generalized to the U.S. population, or whether the relationship differs between urban and rural areas. To address these questions, we used a satellite-derived estimate of fine particulate matter (PM2.5) concentration to determine whether PM2.5 was associated with incident cognitive impairment in a geographically diverse, biracial US cohort of men and women (n = 20,150). A 1-year mean baseline PM2.5 concentration was estimated for each participant, and cognitive status at the most recent follow-up was assessed over the telephone using the Six-Item Screener (SIS) in a subsample that was cognitively intact at baseline. Logistic regression was used to determine whether PM2.5 was related to the odds of incident cognitive impairment. A 10 µg/m(3) increase in PM2.5 concentration was not reliably associated with an increased odds of incident impairment, after adjusting for temperature, season, incident stroke, and length of follow-up [OR (95% CI): 1.26 (0.97, 1.64)]. The odds ratio was attenuated towards 1 after adding demographic covariates, behavioral factors, and known comorbidities of cognitive impairment. A 10 µg/m(3) increase in PM2.5 concentration was slightly associated with incident impairment in urban areas (1.40 [1.06-1.85]), but this relationship was also attenuated after including additional covariates in the model. Evidence is lacking that the effect of PM2.5 on incident cognitive impairment is robust in a heterogeneous US cohort, even in urban areas.

Most of currently reported models for predicting PM(2.5) concentrations from satellite retrievals of aerosol optical depth are global methods without considering local variations, which might introduce significant biases into prediction results. In this paper, a geographically weighted regression model was developed to examine the relationship among PM(2.5), aerosol optical depth, meteorological parameters, and land use information. Additionally, two meteorological datasets, North American Regional Reanalysis and North American Land Data Assimilation System, were fitted into the model separately to compare their performances. The study area is centered at the Atlanta Metro area, and data were collected from various sources for the year 2003. The results showed that the mean local R(2) of the models using North American Regional Reanalysis was 0.60 and those using North American Land Data Assimilation System reached 0.61. The root mean squared prediction error showed that the prediction accuracy was 82.7% and 83.0% for North American Regional Reanalysis and North American Land Data Assimilation System in model fitting, respectively, and 69.7% and 72.1% in cross validation. The results indicated that geographically weighted regression combined with aerosol optical depth, meteorological parameters, and land use information as the predictor variables could generate a better fit and achieve high accuracy in PM(2.5) exposure estimation, and North American Land Data Assimilation System could be used as an alternative of North American Regional Reanalysis to provide some of the meteorological fields., (Copyright © 2012 Elsevier Inc. All rights reserved.)

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.

This study describes and demonstrates different techniques for surface fitting daily environmental hazards data of particulate matter with aerodynamic diameter less than or equal to 2.5 microm (PM2.5) for the purpose of integrating respiratory health and environmental data for the Centers for Disease Control and Prevention (CDC) pilot study of Health and Environment Linked for Information Exchange (HELIX)-Atlanta. It presents a methodology for estimating daily spatial surfaces of ground-level PM2.5 concentrations using the B-Spline and inverse distance weighting (IDW) surface-fitting techniques, leveraging National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectrometer (MODIS) data to complement U.S. Environmental Protection Agency (EPA) ground observation data. The study used measurements of ambient PM2.5 from the EPA database for the year 2003 as well as PM2.5 estimates derived from NASA's satellite data. Hazard data have been processed to derive the surrogate PM2.5 exposure estimates. This paper shows that merging MODIS remote sensing data with surface observations of PM,2. not only provides a more complete daily representation of PM,2. than either dataset alone would allow, but it also reduces the errors in the PM2.5-estimated surfaces. The results of this study also show that although the IDW technique can introduce some numerical artifacts that could be due to its interpolating nature, which assumes that the maxima and minima can occur only at the observation points, the daily IDW PM2.5 surfaces had smaller errors in general, with respect to observations, than those of the B-Spline surfaces. Finally, the methods discussed in this paper establish a foundation for environmental public health linkage and association studies for which determining the concentrations of an environmental hazard such as PM2.5 with high accuracy is critical.