[Show abstract][Hide abstract] ABSTRACT: The lack of progress in reducing health disparities suggests that new approaches are needed if we are to achieve meaningful, equitable, and lasting reductions. Current scientific paradigms do not adequately capture the complexity of the relationships between environment,personal health and population level disparities. The public health exposome is presented as a universal exposure tracking framework for integrating complex relationships between exogenous and endogenous exposures across the lifespan from conception to death. It uses a social-ecological framework that builds on the exposome paradigm for conceptualizing how exogenous exposures “get under the skin”. The public health exposome approach hasled our team to develop a taxonomy and bioinformatics infrastructure to integrate health outcomes data with thousands of sources of exogenous exposure, organized in four broad domains: natural, built, social, and policy environments. With the input of a transdisciplinary team, we have borrowed and applied the methods, tools and terms from various disciplines to measure the effects of environmental exposures on personal and population health outcomes and disparities, many of which may not manifest until many years later. As is customary with a paradigm shift, this approach has far reaching implications for research methods and design, analytics, community engagement strategies, and research training.
International journal of environmental research and public health 12/2014; 11(12):12866-95. DOI:10.3390/ijerph111212866 · 2.06 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Background: There is good evidence that both environmental air pollution and heat pose risks for heart disease mortality.
Objectives: Estimate correlations between heart disease mortality and a combined measure of environmental air pollution and heat in US counties.
Methods: Age-adjusted, race-ethnicity-gender-specific mortality from all heart disease in US Counties with reliable rates (1999-2010) was obtained for non-Hispanic white men (nhwm) and women (nhww) ages 25-64 and 65+ years and for corresponding black groups. The Public CDC WONDER web site provided county-level mortality rates and county averages for Fine Particulate Matter (FP) Air Pollution 2.5 mm3 (1/1/2003 to 12/31/2010) and
Heat Index (1/1/99 to 12/31/2010). Pearson correlations were performed for FPM and Heat Index alone and for both combined in a z-score standardized index.
Results: Correlations for heart disease mortality among persons ages 65+ years with the combined index were 0.47 (nhwm, n=3,072 counties), 0.45 (nhww, n = 3,059), 0.23 (nhbm, n = 1,282) and 0.24 (nhbw, n = 1,348). Correlations for persons ages 25-64 years were 0.47 (wnhm, n = 2,907), 0.38 nhww, n = 2,562), 0.46 (nhbm, n=1,174), and 0.40 (nhbw n=971). All p-values were <0.0001. While correlations for FP pollution alone and Heat Index alone were statistically significant, correlations with the combined index were substantially greater. Multiple regression and graph theoretical analyses to estimate the magnitude of the combined ndex association independent from socio-demographic factors are progressing.
Conclusions: The combined effects of FP air pollution and heat may constitute a preventable source of premature mortality for US adults.
142nd APHA Annual Meeting and Exposition 2014; 11/2014
[Show abstract][Hide abstract] ABSTRACT: Background
Previous research has suggested that vitamin D and sunlight are related to cardiovascular outcomes, but associations between sunlight and risk factors have not been investigated. We examined whether increased sunlight exposure was related to improved cardiovascular risk factor status.
Residential histories merged with satellite, ground monitor, and model reanalysis data were used to determine previous-year sunlight radiation exposure for 17,773 black and white participants aged 45+ from the US. Exploratory and confirmatory analyses were performed by randomly dividing the sample into halves. Logistic regression models were used to examine relationships with cardiovascular risk factors.
The lowest, compared to the highest quartile of insolation exposure was associated with lower high-density lipoprotein levels in adjusted exploratory (−2.7 mg/dL [95% confidence interval: −4.2, −1.2]) and confirmatory (−1.5 mg/dL [95% confidence interval: −3.0, −0.1]) models. The lowest, compared to the highest quartile of insolation exposure was associated with higher systolic blood pressure levels in unadjusted exploratory and confirmatory, as well as the adjusted exploratory model (2.3 mmHg [95% confidence interval: 0.8, 3.8]), but not the adjusted confirmatory model (1.6 mg/dL [95% confidence interval: −0.5, 3.7]).
The results of this study suggest that lower long-term sunlight exposure has an association with lower high-density lipoprotein levels. However, all associations were weak, thus it is not known if insolation may affect cardiovascular outcomes through these risk factors.
[Show abstract][Hide abstract] ABSTRACT: Using a geographic transect in Central Mexico, with an elevation/climate gradient, but uniformity in socio-economic conditions among study sites, this study evaluates the applicability of three widely-used remote sensing (RS) products to link weather conditions with the local abundance of the dengue virus mosquito vector, Aedes aegypti (Ae. aegypti).
Field-derived entomological measures included estimates for the percentage of premises with the presence of Ae. aegypti pupae and the abundance of Ae. aegypti pupae per premises. Data on mosquito abundance from field surveys were matched with RS data and analyzed for correlation. Daily daytime and nighttime land surface temperature (LST) values were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua cloud-free images within the four weeks preceding the field survey. Tropical Rainfall Measuring Mission (TRMM)-estimated rainfall accumulation was calculated for the four weeks preceding the field survey. Elevation was estimated through a digital elevation model (DEM). Strong correlations were found between mosquito abundance and
RS-derived night LST, elevation and rainfall along the elevation/climate gradient. These findings show that RS data can be used to predict Ae. aegypti abundance, but further studies are needed to define the climatic and socio-economic conditions under which the correlations observed herein can be assumed to apply.
[Show abstract][Hide abstract] ABSTRACT: Previous studies showed that fine particulate matter (PM2.5, particles smaller than 2.5 μm in aerodynamic diameter) is associated with various health outcomes. Ground in situ measurements of PM2.5 concentrations are considered to be the gold standard, but are time-consuming and costly. Satellite-retrieved aerosol optical depth (AOD) products have the potential to supplement the ground monitoring networks to provide spatiotemporally-resolved PM2.5 exposure estimates. However, the coarse resolutions (e.g., 10 km) of the satellite AOD products used in previous studies make it very difficult to estimate urban-scale PM2.5 characteristics that are crucial to population-based PM2.5 health effects research. In this paper, a new aerosol product with 1 km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields (e.g., wind speed) and land use parameters (e.g., forest cover, road length, elevation, and point emissions) as ancillary variables to estimate daily mean PM2.5 concentrations. The study area is the southeastern U.S., and data for 2003 were collected from various sources. A cross validation approach was implemented for model validation. We obtained R2 of 0.83, mean prediction error (MPE) of 1.89 μg/m3, and square root of the mean squared prediction errors (RMSPE) of 2.73 μg/m3 in model fitting, and R2 of 0.67, MPE of 2.54 μg/m3, and RMSPE of 3.88 μg/m3 in cross validation. Both model fitting and cross validation indicate a good fit between the dependent variable and predictor variables. The results showed that 1 km spatial resolution MAIAC AOD can be used to estimate PM2.5 concentrations.
[Show abstract][Hide abstract] ABSTRACT: The potential effects leading to geographical expansion in areas at risk of infectious diseases are among the important concerns linked to the discussion of climate change. Associations reported in the literature suggest the possibility of monitoring climate related environmental variables to estimate abundance of some vectors of disease agents. Since in situ monitoring is generally costly and time consuming, Remote Sensing technology is being increasingly used to estimate habitat suitability for a variety of vectors of disease agents. Rainfall is a weather parameter of special interest because is a well-defined indicator of vector habitats. However, satisfactory association between remotely sensed rainfall data and vector abundance is still a matter of much uncertainty. The speaker will provide a general introduction about the significance and applicability of satellite technology in this field and describe as a case the results of a cross-sectional analysis of possible association between remotely sensed rainfall data and abundance of larvae and pupae of the mosquito vector of dengue virus, Aedes aegypti, in Mexico. Remotely sensed data were derived from the NASA Tropical Rainfall Measuring Mission (TRMM). Data on abundance of larvae and pupae of Aedes aegypti were obtained through field surveys conducted in 12 communities in Mexico from ongoing NSF- and NASA-funded studies. The work presented here is aimed to contribute to the above-mentioned studies by helping identify useful indicators of a possibly ongoing climate change.
141st APHA Annual Meeting and Exposition 2013; 11/2013
[Show abstract][Hide abstract] ABSTRACT: Due to large footprints of remotely sensed microwave brightness temperatures, accuracy of microwave observations in areas of large surface heterogeneity has always been a technological challenge. Microwave observations in areas dominated by waterbodies typically exhibit observed brightness temperature several tens of kelvins lower than areas having no surface water. The non-linearity between brightness temperature and other geophysical quantities such as soil moisture makes the accuracy of microwave observations a critical element for accurate estimation of these quantities. In retrieving soil moisture estimates, an error of 1 K in remotely sensed microwave brightness temperatures results in about 0.5–1% error in volumetric soil moisture. Large uncertainties in the observed brightness temperatures make such observations unusable in areas of large brightness temperature contrast. In this article, we discuss a deconvolution method to improve accuracy using the overlap in the adjacent microwave observations. We have shown that the method results in improved accuracy of 40% in brightness temperature estimation in regions of high brightness temperature contrast.
International Journal of Remote Sensing 11/2013; 34(21):7811-7820. DOI:10.1080/01431161.2013.822595 · 1.65 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: 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.
PLoS ONE 09/2013; 8(9):e75001. DOI:10.1371/journal.pone.0075001 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Operational application of NASA satellite observations is identified as a key component of the Weather Focus Area within the NASA Earth Science Division. Thus, next generation NASA instruments, such as the Soil Moisture Active Passive (SMAP) and Global Precipitation Measurement (GPM) missions have an inherent operational objective, and the potential for the data to improve operational forecasts must be explored. This paper describes activities at the Short-term Prediction Research and Transition (SPoRT) Center to develop a suite of high-resolution, near-real-time land surface model (LSM) products integrating these two datasets that will meet the requirements of National Oceanic and Atmospheric Administration (NOAA) and National Weather Service (NWS) local operational forecasting entities. This product suite will use the current operational SPoRT-Land Information System (LIS) platform to assimilate enhanced soil moisture estimates from SMAP L-band observations and incorporate gridded precipitation analysis products from the GPM constellation of satellites to generate improved LSM results. It is anticipated that improvements to both numerical weather prediction and situational awareness for forecast challenges such as drought monitoring, excessive heat during dry-soil conditions, and convective precipitation will result from exploitation of data products from these missions.
[Show abstract][Hide abstract] ABSTRACT: Objective:
Examine whether long- and short-term sunlight radiation is related to stroke incidence.
Fifteen-year residential histories merged with satellite, ground monitor, and model reanalysis data were used to determine sunlight radiation (insolation) and temperature exposure for a cohort of 16,606 stroke and coronary artery disease-free black and white participants aged ≥45 years from the 48 contiguous United States. Fifteen-, 10-, 5-, 2-, and 1-year exposures were used to predict stroke incidence during follow-up in Cox proportional hazard models. Potential confounders and mediators were included during model building.
Shorter exposure periods exhibited similar, but slightly stronger relationships than longer exposure periods. After adjustment for other covariates, the previous year's monthly average insolation exposure below the median gave a hazard ratio (HR) of 1.61 (95% confidence interval [CI], 1.15-2.26), and the previous year's highest compared to the second highest quartile of monthly average maximum temperature exposure gave an HR of 1.92 (95%, 1.27-2.92).
These results indicate a relationship between lower levels of sunlight radiation and higher stroke incidence. The biological pathway of this relationship is not clear. Future research will show whether this finding stands, the pathway for this relationship, and whether it is due to short- or long-term exposures.
Annals of Neurology 02/2013; 73(1). DOI:10.1002/ana.23737 · 9.98 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Sunlight may be related to cognitive function through vitamin D metabolism or circadian rhythm regulation. The analysis presented here sought to test whether ground and satellite measures of solar radiation are associated with cognitive decline. The study used a 15-year residential history merged with satellite and ground monitor data to determine sunlight (solar radiation) and air temperature exposure for a cohort of 19,896 cognitively intact black and white participants aged 45+ from the 48 contiguous United States. Exposures of 15, 10, 5, 2, and 1-year were used to predict cognitive status at the most recent assessment in logistic regression models; 1-year insolation and maximum temperatures were chosen as exposure measures. Solar radiation interacted with temperature, age, and gender in its relationships with incident cognitive impairment. After adjustment for covariates, the odds ratio (OR) of cognitive decline for solar radiation exposure below the median vs above the median in the 3rd tertile of maximum temperatures was 1.88 (95 % CI: 1.24, 2.85), that in the 2nd tertile was 1.33 (95 % CI: 1.09, 1.62), and that in the 1st tertile was 1.22 (95 % CI: 0.92, 1.60). We also found that participants under 60 years old had an OR = 1.63 (95 % CI: 1.20, 2.22), those 60-80 years old had an OR = 1.18 (95 % CI: 1.02, 1.36), and those over 80 years old had an OR = 1.05 (0.80, 1.37). Lastly, we found that males had an OR = 1.43 (95 % CI: 1.22, 1.69), and females had an OR = 1.02 (0.87, 1.20). We found that lower levels of solar radiation were associated with increased odds of incident cognitive impairment.
International Journal of Biometeorology 01/2013; 58(3). DOI:10.1007/s00484-013-0631-5 · 3.25 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: 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.
Environmental Research 12/2012; 121. DOI:10.1016/j.envres.2012.11.003 · 4.37 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We describe a remote sensing and GIS-based study that has three objectives: (1) characterize fine particulate matter (PM2.5), 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.
Geocarto International 04/2012; 29(1). DOI:10.1080/10106049.2012.715209 · 1.37 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: A major shortcoming of any remotely-sensed land surface temperature (LST) dataset is the lack of observations for cloud-covered areas. A method is presented that uses the Moderate Resolution Imaging Spectroradiometer (MODIS) flying on the Terra platform to fill in spatial gaps in the Aqua MODIS LST dataset over the conterminous United States (CONUS) and limited adjacent areas. Over this domain, data are available for only about 50% of all times and pixels for each of the two MODIS sensors. Coverage is highest in summer and lowest in winter, with major regional variations.The relative close temporal proximity (~3 h) of the Aqua and Terra overpasses provides an opportunity to combine information from the two data sources, which can reduce the data loss, most of which we assume is cloud-related. We applied the approach to create a ‘merged’ data set that supplements existing Aqua and Terra daytime and nighttime LST products. We used Terra LST data to fill gaps in Aqua data, resulting in a data set tied to the ~1:30 AM/PM overpass times, so that the resulting data closely approximate daily minimum and maximum LST values. In order to use Terra LST observations to supplement Aqua data, an adjustment was applied to account for the different overpass times of the two platforms. Terra's 10:30 AM overpass usually senses a cooler surface than does Aqua with its 1:30 PM overpass. Conversely, for nighttime overpasses, Terra typically measures a warmer surface at 10:30 PM than does Aqua at 1:30 AM. Our approach was to determine, by season, mean Aqua and Terra LST values on the CONUS grid, based on data from a multi-year (2003–2008) period. Adding the mean Aqua-Terra LST differences for the respective season and time of day to a daily gridded Terra LST field removes the mean offset related to overpass time, resulting in LST values that can then be used to fill Aqua LST data gaps. Using independent offsets for each grid cell and season provides a first-order accounting for factors such as land cover, elevation, terrain slope and aspect, latitude, season and snow cover, which control the diurnal cycle of LST. For the six-year period, the merged data set increases data coverage by 24% and 30% for daytime and nighttime overpasses, respectively, relative to the Aqua LST product alone. The CONUS data set is a potentially valuable tool for weather and climate studies in which high spatial and temporal coverage are desired.
[Show abstract][Hide abstract] ABSTRACT: Evidence is mounting regarding the clinically significant effect of temperature on blood pressure.
In this cross-sectional study the authors obtained minimum and maximum temperatures and their respective previous week variances at the geographic locations of the self-reported residences of 26,018 participants from a national cohort of blacks and whites, aged 45+. Linear regression of data from 20,623 participants was used in final multivariable models to determine if these temperature measures were associated with levels of systolic or diastolic blood pressure, and whether these relations were modified by stroke-risk region, race, education, income, sex hypertensive medication status, or age.
After adjustment for confounders, same-day maximum temperatures 20 °F lower had significant associations with 1.4 mmHg (95% CI: 1.0, 1.9) higher systolic and 0.5 mmHg (95% CI: 0.3, 0.8) higher diastolic blood pressures. Same-day minimum temperatures 20 °F lower had a significant association with 0.7 mmHg (95% CI: 0.3, 1.0) higher systolic blood pressures but no significant association with diastolic blood pressure differences. Maximum and minimum previous-week temperature variabilities showed significant but weak relationships with blood pressures. Parameter estimates showed effect modification of negligible magnitude.
This study found significant associations between outdoor temperature and blood pressure levels, which remained after adjustment for various confounders including season. This relationship showed negligible effect modification.
Environmental Health 01/2011; 10(1):7. DOI:10.1186/1476-069X-10-7 · 3.37 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In the near future, data from two microwave remote sensors at L-band will enable estimation of near-surface soil moisture. The European Space Agency's Soil Moisture and Salinity Mission (SMOS) launched in November 2009, and NASA is developing a new L-band soil moisture mission named Soil Moisture Active/Passive (SMAP). Soil moisture retrieval theory is well-established, but many details of its application, including the effects of spatial scale, are still being studied. To support these two L-band missions, studies are needed to improve our understanding of the various error sources associated with retrieval of soil moisture from satellite sensors. The purpose of this study is to quantify the magnitude of the scaling error created by the existence of sub-footprint scale variability in soil and vegetation properties, which have nonlinear relationships with emitted microwave energy. The scaling error is related to different functional relationships between surface microwave emissivity and soil moisture that exist for different soils and land cover types within a satellite footprint. We address this problem using single-frequency, single-polarization passive L-band microwave simulations for an Upper Midwest agricultural region in the United States. Making several simplifying assumptions, the analysis performed here helps provide guidance and define limits for future mission requirements by indicating hydrological and landscape conditions under which large errors are expected, and other conditions that are more conducive to accurate soil moisture estimates. Errors associated with spatial aggregation of highly variable land surface characteristics within 40 km satellite ?footprints? were found to be larger than the baseline mission requirements of 0.04-0.06 Volumetric Soil Moisture (VSM) over much of the study area. Soil moisture estimation errors were especially large and positive over portions of the domain characterized by mixtures of forests, wetlands, and open wate-
r or mixtures of forest and pasture. However, by eliminating from the analysis areas with high vegetation water content or substantial surface water fractions, conditions that have well-documented adverse effects on soil moisture retrieval, we obtained errors that are in line with these mission requirements. We developed a parameterization for effective optical depth (?<sub>eff</sub>) based on the standard deviation of optical depth (?<sub>?</sub>) within a footprint in order to improve soil moisture retrieval in the presence of highly variable vegetation density. Use of the resulting parameterized optical depth in retrievals eliminated almost all of the soil moisture biases in our simulated setting. Operationally, the empirical relationship between ?<sub>eff</sub> and ?<sub>?</sub> would need to be determined a priori based on intensive measurements from ground-based instrumentation networks or via tuning of the algorithm. Due to this issue and other confounding factors, results are not expected to be as good as in the simulated cases presented here. However, the relationship found in this study is likely to be consistent across landscapes, so any correction following this functional form would very likely lead to large improvements over retrievals based simply on weighted mean properties.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 04/2010; 3(1-3):67 - 80. DOI:10.1109/JSTARS.2010.2041636 · 3.03 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Urbanization has been correlated with hypertension (HTN) in developing countries undergoing rapid economic and environmental transitions.
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.
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.
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.
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.
Environmental Health Perspectives 12/2009; 117(12):1832-8. DOI:10.1289/ehp.0900871 · 7.98 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Background: Coincident with global expansion of urban areas has been an increase in hypertension (HTN). It is unclear how much the urban environment contributes as a risk factor for blood pressure differences, and how much is due to a variety of environmental, lifestyle, and demographic correlates of urbanization.
Objectives/Purpose: The purpose of this study is to utilize high resolution satellite data to examine the relationship between living environment defined as urban, suburban, and rural and hypertension in selected regions from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort.
Methods: REGARDS is a national cohort of 30,228 participants from the 48 contiguous United States. Data from 4 metropolitan areas (Philadelphia, Atlanta, Minneapolis and Chicago) and surrounding regions were used for this study (n=3928). We used Land Cover/Land Use (LCLU) information from the Landsat-derived 30-meter National Land Cover Data (NLCD) to characterize participants into urban, suburban or rural living environments.
Results: Overall, 1996 (61%) of the participants were hypertensive. In univariate models, we found that increasing degree of urban living environment is associated with prevalence of HTN (Suburban vs. Rural: 1.3, 95% CI: 1.1-1.6; Urban vs. Rural: 1.7, 95% CI: 1.4-1.2), but that after adjustment for known HTN risk factors, the relationship was no longer present (Suburban vs. Rural: 1.1, 95% CI: 0.84-1.4; Urban vs. Rural: 1.2, 95% CI: 0.85-1.6).
Conclusions: LCLU data can be utilized to characterize the living environment, which in turn can be applied to studies of public health outcomes. Further study regarding the relationship between HTN and living environment should focus on additional characteristics of the associated environment, such as pollutants and the built environment.
137st APHA Annual Meeting and Exposition 2009; 11/2009
[Show abstract][Hide abstract] ABSTRACT: Aerosol optical depth (AOD), an indirect estimate of particulate matter using satellite observations, has shown great promise in improving estimates of PM2.5 (particulate matter with aerodynamic diameter less than or equal to 2.5 μm) surface. Currently, few studies have been conducted to explore the optimal way to apply AOD data to improve the model accuracy of PM2.5 in a real-time air quality system. We believe that two major aspects may be worthy of consideration in that area: 1) an approach that integrates satellite measurements with ground measurements in the estimates of pollutants and 2) identification of an optimal temporal scale to calculate the correlation of AOD and ground measurements. This paper is focused on the second aspect, identifying the optimal temporal scale to correlate AOD with PM2.5. Five following different temporal scales were chosen to evaluate their impact on the model performance: 1) within the last 3 days, 2) within the last 10 days, 3) within the last 30 days, 4) within the last 90 days, and 5) the time period with the highest correlation in a year. The model performance is evaluated for its accuracy, bias, and errors based on the following selected statistics: the Mean Bias, the Normalized Mean Bias, the Root Mean Square Error, Normalized Mean Error, and the Index of Agreement. This research shows that the model with the temporal scale: within the last 30 days, displays the best model performance in a southern multi-state area centered in Mississippi using 2004 and 2005 data sets.