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Meng YY, Yu Y, Al-Hamdan MZ, Marlier ME, Wilkins JL, Garcia-Gonzales D, Chen X, Jerrett M. Short-Term total and wildfire fine particulate matter exposure and work loss in California. Environ Int 2023; 178:108045. [PMID: 37352581 DOI: 10.1016/j.envint.2023.108045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 05/25/2023] [Accepted: 06/14/2023] [Indexed: 06/25/2023]
Abstract
BACKGROUND Few studies investigated the impact of particulate matter (PM2.5) on some symptom exacerbations that are not perceived as severe enough to search for medical assistance. We aimed to study the association of short-term daily total PM2.5 exposure with work loss due to sickness among adults living in California. METHODS We included 44,544 adult respondents in the workforce from 2015 to 2018 California Health Interview Survey data. Daily total PM2.5 concentrations were linked to respondents' home addresses from continuous spatial surfaces of PM2.5 generated by a geostatistical surfacing algorithm. We estimated the effect of a 2-week average of daily total PM2.5 exposure on work loss using logistic regression models. RESULTS About 1.69% (weighted percentage) of adult respondents reported work loss in the week before the survey interview. The odds ratio of work loss was 1.45 (odds ratio [OR] = 1.45, 95% confidence interval [CI]: 1.03, 2.03) when a 2-week average of daily total PM2.5 exposure was higher than 12 µg/m3. The OR for work loss was 1.05 (95% CI: 0.98, 1.13) for each 2.56ug/m3 increase in the 2-week average of daily total PM2.5 exposure, and became stronger among those who were highly exposed to wildfire smoke (OR = 1.06, 95% CI: 1.00, 1.13), compared to those with lower wildfire smoke exposure (OR = 1.04, 95% CI: 0.79, 1.39). CONCLUSIONS Our findings suggest that short-term ambient PM2.5 exposure is positively associated with work loss due to sickness and the association was stronger among those with higher wildfire smoke exposure. It also indicated that the current federal and state PM2.5 standards (annual average of 12 µg/m3) could be further strengthened to protect the health of the citizens of California.
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Affiliation(s)
- Ying-Ying Meng
- UCLA Center for Health Policy Research, University of California at Los Angeles, CA, USA.
| | - Yu Yu
- UCLA Center for Health Policy Research, University of California at Los Angeles, CA, USA; Department of Environmental Health Sciences, Fielding School of Public Health, University of California at Los Angeles, CA, USA
| | - Mohammad Z Al-Hamdan
- National Center for Computational Hydroscience and Engineering, School of Engineering, University of Mississippi, Oxford, MS, USA; Department of Civil Engineering, School of Engineering, University of Mississippi, Oxford, MS, USA
| | - Miriam E Marlier
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California at Los Angeles, CA, USA
| | - Joseph L Wilkins
- School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA; Interdisciplinary Studies Department, Howard University, Washington, D.C, USA
| | - Diane Garcia-Gonzales
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California at Los Angeles, CA, USA
| | - Xiao Chen
- UCLA Center for Health Policy Research, University of California at Los Angeles, CA, USA
| | - Michael Jerrett
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California at Los Angeles, CA, USA
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Juarez PD, Tabatabai M, Burciaga Valdez R, Hood DB, Im W, Mouton C, Colen C, Al-Hamdan MZ, Matthews-Juarez P, Lichtveld MY, Sarpong D, Ramesh A, Langston MA, Rogers GL, Phillips CA, Reichard JF, Donneyong MM, Blot W. The Effects of Social, Personal, and Behavioral Risk Factors and PM 2.5 on Cardio-Metabolic Disparities in a Cohort of Community Health Center Patients. Int J Environ Res Public Health 2020; 17:E3561. [PMID: 32438697 PMCID: PMC7277630 DOI: 10.3390/ijerph17103561] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 03/30/2020] [Accepted: 05/15/2020] [Indexed: 12/26/2022]
Abstract
(1) Background: Cardio-metabolic diseases (CMD), including cardiovascular disease, stroke, and diabetes, have numerous common individual and environmental risk factors. Yet, few studies to date have considered how these multiple risk factors together affect CMD disparities between Blacks and Whites. (2) Methods: We linked daily fine particulate matter (PM2.5) measures with survey responses of participants in the Southern Community Cohort Study (SCCS). Generalized linear mixed modeling (GLMM) was used to estimate the relationship between CMD risk and social-demographic characteristics, behavioral and personal risk factors, and exposure levels of PM2.5. (3) Results: The study resulted in four key findings: (1) PM2.5 concentration level was significantly associated with reported CMD, with risk rising by 2.6% for each µg/m3 increase in PM2.5; (2) race did not predict CMD risk when clinical, lifestyle, and environmental risk factors were accounted for; (3) a significant variation of CMD risk was found among participants across states; and (4) multiple personal, clinical, and social-demographic and environmental risk factors played a role in predicting CMD occurrence. (4) Conclusions: Disparities in CMD risk among low social status populations reflect the complex interactions of exposures and cumulative risks for CMD contributed by different personal and environmental factors from natural, built, and social environments.
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Affiliation(s)
- Paul D. Juarez
- Department of Family and Community Medicine, Meharry Medical College, Nashville, TN 37208, USA; (W.I.); (P.M.-J.)
| | - Mohammad Tabatabai
- School of Graduate Studies and Research, Meharry Medical College, Nashville, TN 37208, USA;
| | - Robert Burciaga Valdez
- RWJF Professor, Department of Family & Community Medicine AND Economics, University of New Mexico, Albuquerque, NM 87131, USA;
| | - Darryl B. Hood
- Department of Environmental Health Sciences, College of Public Health, Ohio State University, Columbus, OH 43210, USA;
| | - Wansoo Im
- Department of Family and Community Medicine, Meharry Medical College, Nashville, TN 37208, USA; (W.I.); (P.M.-J.)
| | - Charles Mouton
- Department of Family Medicine, University of Texas Medical Branch, Galveston, TX 77555, USA;
| | - Cynthia Colen
- Department of Sociology, Ohio State University, Columbus, OH 43210, USA;
| | - Mohammad Z. Al-Hamdan
- Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL 35805, USA;
| | - Patricia Matthews-Juarez
- Department of Family and Community Medicine, Meharry Medical College, Nashville, TN 37208, USA; (W.I.); (P.M.-J.)
| | - Maureen Y. Lichtveld
- Department of Environmental Health Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA;
| | - Daniel Sarpong
- Department of Biostatistics, Xavier University, Cincinnati, OH 45207, USA;
| | - Aramandla Ramesh
- Department of Biochemistry, Cancer Biology, Neuroscience & Pharmacology, Meharry Medical College, Nashville, TN 37208, USA;
| | - Michael A. Langston
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA; (M.A.L.); (C.A.P.)
| | - Gary L. Rogers
- National Institute for Computational Sciences, University of Tennessee, Knoxville, TN 37996, USA;
| | - Charles A. Phillips
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA; (M.A.L.); (C.A.P.)
| | - John F. Reichard
- Department of Environmental Health, Risk Science Center, University of Cincinnati, Cincinnati, OH 45221, USA;
| | - Macarius M. Donneyong
- Division of Outcomes and Translational Sciences, College of Pharmacy, Ohio State University, Columbus, OH 43210, USA;
| | - William Blot
- Center for Population-based Research, Vanderbilt University, Nashville, TN 37235, USA;
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Crosson WL, Al-Hamdan MZ, Insaf TZ. Downscaling NLDAS-2 daily maximum air temperatures using MODIS land surface temperatures. PLoS One 2020; 15:e0227480. [PMID: 31945081 PMCID: PMC6964900 DOI: 10.1371/journal.pone.0227480] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 12/19/2019] [Indexed: 11/18/2022] Open
Abstract
We have developed and applied a relatively simple disaggregation scheme that uses spatial patterns of Land Surface Temperature (LST) from MODIS warm-season composites to improve the spatial characterization of daily maximum and minimum air temperatures. This down-scaling model produces qualitatively reasonable 1 km daily maximum and minimum air temperature estimates that reflect urban and coastal features. In a 5-city validation, the model was shown to provide improved daily maximum air temperature estimates in the three coastal cities, compared to 12 km NLDAS-2 (North American Land Data Assimilation System). Down-scaled maximum temperature estimates for the other two (non-coastal) cities were marginally worse than the original NLDAS-2 temperatures. For daily minimum temperatures, the scheme produces spatial fields that qualitatively capture geographic features, but quantitative validation shows the down-scaling model performance to be very similar to the original NLDAS-2 minimum temperatures. Thus, we limit the discussion in this paper to daily maximum temperatures. Overall, errors in the down-scaled maximum air temperatures are comparable to errors in down-scaled LST obtained in previous studies. The advantage of this approach is that it produces estimates of daily maximum air temperatures, which is more relevant than LST in applications such as public health. The resulting 1 km daily maximum air temperatures have great potential utility for applications such as public health, energy demand, and surface energy balance analyses. The method may not perform as well in conditions of strong temperature advection. Application of the model also may be problematic in areas having extreme changes in elevation.
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Affiliation(s)
- William L. Crosson
- Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL, United States of America
| | - Mohammad Z. Al-Hamdan
- Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL, United States of America
| | - Tabassum Z. Insaf
- New York State Department of Health & University at Albany- State University of New York, Albany, NY, United States of America
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4
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Bi J, Stowell J, Seto EYW, English PB, Al-Hamdan MZ, Kinney PL, Freedman FR, Liu Y. Contribution of low-cost sensor measurements to the prediction of PM 2.5 levels: A case study in Imperial County, California, USA. Environ Res 2020; 180:108810. [PMID: 31630004 PMCID: PMC6899193 DOI: 10.1016/j.envres.2019.108810] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2019] [Revised: 08/13/2019] [Accepted: 10/07/2019] [Indexed: 05/22/2023]
Abstract
Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment while emerging low-cost sensors have the potential to fill in the gaps. To date, limited studies, if any, have been conducted to utilize low-cost sensor measurements to improve PM2.5 prediction with high spatiotemporal resolutions based on statistical models. Imperial County in California is an exemplary region with sparse Air Quality System (AQS) monitors and a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study aims to evaluate the contribution of IVAN measurements to the quality of PM2.5 prediction. We adopted the Random Forest algorithm to estimate daily PM2.5 concentrations at a 1-km spatial resolution using three different PM2.5 datasets (AQS-only, IVAN-only, and AQS/IVAN combined). The results show that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 prediction with an increase of cross-validation (CV) R2 by ~0.2. The IVAN measurements also contributed to the increased importance of emission source-related covariates and more reasonable spatial patterns of PM2.5. The remaining uncertainty in the calibrated IVAN measurements could still cause apparent outliers in the prediction model, highlighting the need for more effective calibration or integration methods to relieve its negative impact.
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Affiliation(s)
- Jianzhao Bi
- Department of Environmental Health, Emory University, Rollins School of Public Health, Atlanta, GA, 30322, United States
| | - Jennifer Stowell
- Department of Environmental Health, Emory University, Rollins School of Public Health, Atlanta, GA, 30322, United States
| | - Edmund Y W Seto
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA, 98195, United States
| | - Paul B English
- California Department of Public Health, Richmond, CA, 94804, United States
| | - Mohammad Z Al-Hamdan
- Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL, 35808, United States
| | - Patrick L Kinney
- Department of Environmental Health, Boston University, School of Public Health, Boston, MA, 02118, United States
| | - Frank R Freedman
- Department of Meteorology and Climate Science, San Jose State University, San Jose, CA, 95192, United States.
| | - Yang Liu
- Department of Environmental Health, Emory University, Rollins School of Public Health, Atlanta, GA, 30322, United States.
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5
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Diao M, Holloway T, Choi S, O’Neill SM, Al-Hamdan MZ, van Donkelaar A, Martin RV, Jin X, Fiore AM, Henze DK, Lacey F, Kinney PL, Freedman F, Larkin NK, Zou Y, Kelly JT, Vaidyanathan A. Methods, availability, and applications of PM 2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models. J Air Waste Manag Assoc 2019; 69:1391-1414. [PMID: 31526242 PMCID: PMC7072999 DOI: 10.1080/10962247.2019.1668498] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 08/01/2019] [Accepted: 08/22/2019] [Indexed: 05/20/2023]
Abstract
Fine particulate matter (PM2.5) is a well-established risk factor for public health. To support both health risk assessment and epidemiological studies, data are needed on spatial and temporal patterns of PM2.5 exposures. This review article surveys publicly available exposure datasets for surface PM2.5 mass concentrations over the contiguous U.S., summarizes their applications and limitations, and provides suggestions on future research needs. The complex landscape of satellite instruments, model capabilities, monitor networks, and data synthesis methods offers opportunities for research development, but would benefit from guidance for new users. Guidance is provided to access publicly available PM2.5 datasets, to explain and compare different approaches for dataset generation, and to identify sources of uncertainties associated with various types of datasets. Three main sources used to create PM2.5 exposure data are ground-based measurements (especially regulatory monitoring), satellite retrievals (especially aerosol optical depth, AOD), and atmospheric chemistry models. We find inconsistencies among several publicly available PM2.5 estimates, highlighting uncertainties in the exposure datasets that are often overlooked in health effects analyses. Major differences among PM2.5 estimates emerge from the choice of data (ground-based, satellite, and/or model), the spatiotemporal resolutions, and the algorithms used to fuse data sources.Implications: Fine particulate matter (PM2.5) has large impacts on human morbidity and mortality. Even though the methods for generating the PM2.5 exposure estimates have been significantly improved in recent years, there is a lack of review articles that document PM2.5 exposure datasets that are publicly available and easily accessible by the health and air quality communities. In this article, we discuss the main methods that generate PM2.5 data, compare several publicly available datasets, and show the applications of various data fusion approaches. Guidance to access and critique these datasets are provided for stakeholders in public health sectors.
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Affiliation(s)
- Minghui Diao
- San Jose State University, Department of Meteorology and Climate Science, One Washington Square, San Jose, California, USA, 95192-0104
| | - Tracey Holloway
- University of Wisconsin-Madison, Nelson Institute Center for Sustainability and the Global Environment (SAGE) and Department of Atmospheric and Oceanic Sciences, 201A Enzyme Institute, 1710 University Ave., Madison, Wisconsin, USA, 53726
| | - Seohyun Choi
- University of Wisconsin-Madison, Nelson Institute Center for Sustainability and the Global Environment (SAGE) and Department of Atmospheric and Oceanic Sciences, 201A Enzyme Institute, 1710 University Ave., Madison, Wisconsin, USA, 53726
| | - Susan M. O’Neill
- United States Department of Agriculture Forest Service, Pacific Northwest Research Station, Seattle, WA, USA, 98103-8600
| | - Mohammad Z. Al-Hamdan
- Universities Space Research Association, NASA Marshall Space Flight Center, National Space Science and Technology Center, 320 Sparkman Dr., Huntsville, Alabama, USA, 35805
| | - Aaron van Donkelaar
- Dalhousie University, Department of Physics and Atmospheric Science, 6299 South St, Halifax, Nova Scotia, Canada, B3H 4R2
| | - Randall V. Martin
- Dalhousie University, Department of Physics and Atmospheric Science, 6299 South St, Halifax, Nova Scotia, Canada, B3H 4R2
- Smithsonian Astrophysical Observatory, Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA, 02138
- Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA, 63130
| | - Xiaomeng Jin
- Columbia University, Department of Earth and Environmental Sciences and Lamont-Doherty Earth Observatory, 61 Route 9W, Palisades, New York, USA, 10964
| | - Arlene M. Fiore
- Columbia University, Department of Earth and Environmental Sciences and Lamont-Doherty Earth Observatory, 61 Route 9W, Palisades, New York, USA, 10964
| | - Daven K. Henze
- University of Colorado, Mechanical Engineering Department, 1111 Engineering Drive UCB 427, Boulder, CO, USA, 80309
| | - Forrest Lacey
- University of Colorado, Mechanical Engineering Department, 1111 Engineering Drive UCB 427, Boulder, CO, USA, 80309
- National Center for Atmospheric Research, Atmospheric Chemistry Observations and Modeling, 3450 Mitchell Ln, Boulder, CO, USA, 80301
| | - Patrick L. Kinney
- Boston University School of Public Health, Department of Environmental Health, 715 Albany Street, Talbot 4W, Boston, Massachusetts, USA, 02118
| | - Frank Freedman
- San Jose State University, Department of Meteorology and Climate Science, One Washington Square, San Jose, California, USA, 95192-0104
| | - Narasimhan K. Larkin
- United States Department of Agriculture Forest Service, Pacific Northwest Research Station, Seattle, WA, USA, 98103-8600
| | - Yufei Zou
- University of Washington, School of Environmental and Forest Sciences, Anderson Hall, Seattle, WA, USA, 98195
| | - James T. Kelly
- Office of Air Quality Planning & Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA 27711
| | - Ambarish Vaidyanathan
- Asthma and Community Health Branch, Centers for Disease Control and Prevention, 1600 Clifton Road, Mail Stop E-19, Atlanta, Georgia, USA, 30333
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Zhou Y, Meng X, Belle JH, Zhang H, Kennedy C, Al-Hamdan MZ, Wang J, Liu Y. Compilation and spatio-temporal analysis of publicly available total solar and UV irradiance data in the contiguous United States. Environ Pollut 2019; 253:130-140. [PMID: 31306820 DOI: 10.1016/j.envpol.2019.06.074] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 06/17/2019] [Accepted: 06/18/2019] [Indexed: 06/10/2023]
Abstract
Skin cancer is the most common type of cancer in the United States, the majority of which is caused by overexposure to ultraviolet (UV) irradiance, which is one component of sunlight. National Environmental Public Health Tracking Program at CDC has collaborated with partners to develop and disseminate county-level daily UV irradiance (2005-2015) and total solar irradiance (1991-2012) data for the contiguous United States. UV irradiance dataset was derived from the Ozone Monitoring Instrument (OMI), and solar irradiance was extracted from National Solar Radiation Data Base (NSRDB) and SolarAnywhere data. Firstly, we produced daily population-weighted UV and solar irradiance datasets at the county level. Then the spatial distributions and long-term trends of UV irradiance, solar irradiance and the ratio of UV irradiance to solar irradiance were analyzed. The national average values across all years are 4300 Wh/m2, 2700 J/m2 and 130 mW/m2 for global horizontal irradiance (GHI), erythemally weighted daily dose of UV irradiance (EDD) and erythemally weighted UV irradiance at local solar noon time (EDR), respectively. Solar, UV irradiances and the ratio of UV to solar irradiance all increased toward the South and in some areas with high altitude, suggesting that using solar irradiance as indicator of UV irradiance in studies covering large geographic regions may bias the true pattern of UV exposure. National annual average daily solar and UV irradiances increased significantly over the years by about 0.3% and 0.5% per year, respectively. Both datasets are available to the public through CDC's Tracking network. The UV irradiance dataset is currently the only publicly-available, spatially-resolved, and long-term UV irradiance dataset covering the contiguous United States. These datasets help us understand the spatial distributions and temporal trends of solar and UV irradiances, and allow for improved characterization of UV and sunlight exposure in future studies.
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Affiliation(s)
- Ying Zhou
- Environmental Health Tracking Section, Division of Environmental Health Practice and Science, National Center for Environmental Health (NCEH), Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA.
| | - Xia Meng
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Jessica Hartmann Belle
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Huanxin Zhang
- Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA
| | - Caitlin Kennedy
- Environmental Health Tracking Section, Division of Environmental Health Practice and Science, National Center for Environmental Health (NCEH), Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Mohammad Z Al-Hamdan
- Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL 35805G, USA
| | - Jun Wang
- Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA
| | - Yang Liu
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
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Adeyeye TE, Insaf TZ, Al-Hamdan MZ, Nayak SG, Stuart N, DiRienzo S, Crosson WL. Estimating policy-relevant health effects of ambient heat exposures using spatially contiguous reanalysis data. Environ Health 2019; 18:35. [PMID: 30999920 PMCID: PMC6471902 DOI: 10.1186/s12940-019-0467-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 03/19/2019] [Indexed: 05/28/2023]
Abstract
BACKGROUND Regional National Weather Service (NWS) heat advisory criteria in New York State (NYS) were based on frequency of heat events estimated by sparse monitoring data. These may not accurately reflect temperatures at which specific health risks occur in large geographic regions. The objectives of the study were to use spatially resolved temperature data to characterize health risks related to summertime heat exposure and estimate the temperatures at which excessive risk of heat-related adverse health occurs in NYS. We also evaluated the need to adjust current heat advisory threshold and messaging based on threshold temperatures of multiple health outcomes. METHODS We assessed the effect of multi-day lag exposure for maximum near-surface air temperature (Tmax) and maximum Heat Index derived from the gridded National Land Data Assimilation System (NLDAS) reanalysis dataset on emergency department (ED) visits/ hospitalizations for heat stress, dehydration, acute kidney failure (AKF) and cardiovascular diseases (CVD) using a case-crossover analysis during summers of 2008-2012. We assessed effect modification using interaction terms and stratified analysis. Thresholds were estimated using piecewise spline regression. RESULTS We observed an increased risk of heat stress (Risk ratio (RR) = 1.366, 95% confidence interval (CI): 1.347, 1.386) and dehydration (RR = 1.024, 95% CI: 1.021, 1.028) for every 1 °C increase in Tmax on the day of exposure. The highest risk for AKF (RR = 1.017, 95% CI: 1.014, 1.021) and CVD (RR = 1.001, 95% CI: 1.000, 1.002) were at lag 1 and 4 respectively. The increased risk of heat-health effects persists up to 6 days. Rural areas of NYS are at as high a risk of heat-health effects as urban areas. Heat-health risks start increasing at temperatures much lower than the current NWS criteria. CONCLUSION Reanalysis data provide refined exposure-response functions for health research, in areas with sparse monitor observations. Based on this research, rural areas in NYS had similar risk for health effects of heat. Heat advisories in New York City (NYC) had been reviewed and lowered previously. As such, the current NWS heat advisory threshold was lowered for the upstate region of New York and surrounding areas. Enhanced outreach materials were also developed and disseminated to local health departments and the public.
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Affiliation(s)
- Temilayo E. Adeyeye
- Bureau of Environmental and Occupational Epidemiology, New York State Department of Health, Albany, NY USA
| | - Tabassum Z. Insaf
- Bureau of Environmental and Occupational Epidemiology, New York State Department of Health, Albany, NY USA
- Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, State University of New York, Rensselaer, NY USA
| | - Mohammad Z. Al-Hamdan
- Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL USA
| | - Seema G. Nayak
- Bureau of Environmental and Occupational Epidemiology, New York State Department of Health, Albany, NY USA
| | - Neil Stuart
- National Oceanic and Atmospheric Administration/ National Weather Service, Albany, NY USA
| | - Stephen DiRienzo
- National Oceanic and Atmospheric Administration/ National Weather Service, Albany, NY USA
| | - William L. Crosson
- Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL USA
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Wortzel JR, Norden JG, Turner BE, Haynor DR, Kent ST, Al-Hamdan MZ, Avery DH, Norden MJ. Ambient temperature and solar insolation are associated with decreased prevalence of SSRI-treated psychiatric disorders. J Psychiatr Res 2019; 110:57-63. [PMID: 30594025 DOI: 10.1016/j.jpsychires.2018.12.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 11/02/2018] [Accepted: 12/12/2018] [Indexed: 12/31/2022]
Abstract
Serotonergic function is known to fluctuate in association with light and temperature. Serotonin-related behaviors and disorders similarly vary with climatic exposure, but the associations are complex. This complexity may reflect the importance of dose and timing of exposure, as well as acclimation. This cross-sectional study tests how average climate exposures (ambient temperature and solar insolation) vary with the prevalence of a group of SSRI-treated disorders. For comparison, we similarly studied a group of disorders not treated by SSRIs (i.e substance use disorders). Psychiatric prevalence data were obtained from the Collaborative Psychiatric Epidemiology Surveys (CPES). Average yearly solar insolation was obtained from NASA's NLDAS-2 Forcing Dataset Information. Average yearly temperature was obtained from NOAA's US Climate Normals. Logistic regression models were generated to assess the relationship between these two climatic factors and the prevalence of SSRI-treated and substance use disorders. Age, gender, race, income, and education were included in the models to control for possible confounding. Temperature and insolation were significantly associated with the SSRI-responsive group. For an average 1 GJ/m2/year increase, OR was 0.90 (95% CI 0.85-0.96, p = 0.001), and for an average 10 °F increase, OR was 0.93 (95% CI 0.88-0.97, p = 0.001). This relationship was not seen with substance use disorders (insolation OR: 0.97, p = 0.682; temperature OR: 0.96, p = 0.481). These results warrant further investigation, but they support the hypothesis that chronic exposure to increased temperature and light positively impact serotonin function, and are associated with reduced prevalence of some psychiatric disorders. They also support further investigation of light and hyperthermia treatments.
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Affiliation(s)
- J R Wortzel
- School of Medicine, Stanford University, Stanford, CA, 94305, USA.
| | - J G Norden
- School of Medicine, Stanford University, Stanford, CA, 94305, USA
| | - B E Turner
- School of Medicine, Stanford University, Stanford, CA, 94305, USA
| | - D R Haynor
- University of Washington, Seattle, WA, 98195, USA
| | - S T Kent
- School of Public Health, University of Alabama at Birmingham Universities, AL, 35294, USA
| | - M Z Al-Hamdan
- Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL, 35812, USA
| | - D H Avery
- University of Washington, Seattle, WA, 98195, USA
| | - M J Norden
- University of Washington, Retired Associate Professor on the Axillary Faculty, USA
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Sarmiento EJ, Moore JX, McClure LA, Griffin R, Al-Hamdan MZ, Wang HE. Fine Particulate Matter Pollution and Risk of Community-Acquired Sepsis. Int J Environ Res Public Health 2018; 15:ijerph15040818. [PMID: 29690517 PMCID: PMC5923860 DOI: 10.3390/ijerph15040818] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 04/17/2018] [Accepted: 04/19/2018] [Indexed: 12/28/2022]
Abstract
While air pollution has been associated with health complications, its effect on sepsis risk is unknown. We examined the association between fine particulate matter (PM2.5) air pollution and risk of sepsis hospitalization. We analyzed data from the 30,239 community-dwelling adults in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort linked with satellite-derived measures of PM2.5 data. We defined sepsis as a hospital admission for a serious infection with ≥2 systemic inflammatory response (SIRS) criteria. We performed incidence density sampling to match sepsis cases with 4 controls by age (±5 years), sex, and race. For each matched group we calculated mean daily PM2.5 exposures for short-term (30-day) and long-term (one-year) periods preceding the sepsis event. We used conditional logistic regression to evaluate the association between PM2.5 exposure and sepsis, adjusting for education, income, region, temperature, urbanicity, tobacco and alcohol use, and medical conditions. We matched 1386 sepsis cases with 5544 non-sepsis controls. Mean 30-day PM2.5 exposure levels (Cases 12.44 vs. Controls 12.34 µg/m3; p = 0.28) and mean one-year PM2.5 exposure levels (Cases 12.53 vs. Controls 12.50 µg/m3; p = 0.66) were similar between cases and controls. In adjusted models, there were no associations between 30-day PM2.5 exposure levels and sepsis (4th vs. 1st quartiles OR: 1.06, 95% CI: 0.85–1.32). Similarly, there were no associations between one-year PM2.5 exposure levels and sepsis risk (4th vs. 1st quartiles OR: 0.96, 95% CI: 0.78–1.18). In the REGARDS cohort, PM2.5 air pollution exposure was not associated with risk of sepsis.
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Affiliation(s)
- Elisa J Sarmiento
- Department of Emergency Medicine, University of Alabama at Birmingham, Birmingham, AL 35233, USA.
- Department of Epidemiology, University of Alabama at Birmingham, 1665 University Boulevard, RPHB, Birmingham, AL 35233, USA.
| | - Justin Xavier Moore
- Department of Emergency Medicine, University of Alabama at Birmingham, Birmingham, AL 35233, USA.
- Department of Epidemiology, University of Alabama at Birmingham, 1665 University Boulevard, RPHB, Birmingham, AL 35233, USA.
- Division of Public Health Sciences, Department of Surgery, Washington University in Saint Louis School of Medicine, St. Louis, MO 63110, USA.
| | - Leslie A McClure
- Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, PA 19104, USA.
| | - Russell Griffin
- Department of Epidemiology, University of Alabama at Birmingham, 1665 University Boulevard, RPHB, Birmingham, AL 35233, USA.
| | - Mohammad Z Al-Hamdan
- Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL 35805, USA.
| | - Henry E Wang
- Department of Emergency Medicine, University of Alabama at Birmingham, Birmingham, AL 35233, USA.
- Department of Emergency Medicine, University of Texas Health Science Center at Houston, 6431 Fannin St., JJL 434, Houston, TX 77030, USA.
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Loop MS, McClure LA, Levitan EB, Al-Hamdan MZ, Crosson WL, Safford MM. Fine particulate matter and incident coronary heart disease in the REGARDS cohort. Am Heart J 2018; 197:94-102. [PMID: 29447790 DOI: 10.1016/j.ahj.2017.11.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 11/16/2017] [Indexed: 11/16/2022]
Abstract
Chronic exposure to fine particulate matter (PM2.5) is accepted as a causal risk factor for coronary heart disease (CHD). However, most of the evidence for this hypothesis is based upon cohort studies in whites, comprised of either only males or females who live in urban areas. It is possible that many estimates of the effect of chronic exposure to PM2.5 on risk for CHD do not generalize to more diverse samples. METHODS Therefore, we estimated the relationship between chronic exposure to PM2.5 and risk for CHD in among participants in the REasons for Geographic And Racial Differences in Stroke (REGARDS) cohort who were free from CHD at baseline (n=17,126). REGARDS is a sample of whites and blacks of both genders living across the continental United States. We fit Cox proportional hazards models for time to CHD to estimate the hazard ratio for baseline 1-year mean PM2.5 exposure, adjusting for environmental variables, demographics, and other risk factors for CHD including the Framingham Risk Score. RESULTS The hazard ratio (95% CI) for a 2.7-μg/m3 increase (interquartile range) 1-year mean concentration of PM2.5 was 0.94 (0.83-1.06) for combined CHD death and nonfatal MI, 1.13 (0.92-1.40) for CHD death, and 0.85 (0.73-0.99) for nonfatal MI. We also did not find evidence that these associations depended upon overall CHD risk factor burden. CONCLUSIONS Our results do not provide strong evidence for an association between PM2.5 and incident CHD in a heterogeneous cohort, and we conclude that the effects of chronic exposure to fine particulate matter on CHD require further evaluation.
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Affiliation(s)
- Matthew Shane Loop
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Leslie A McClure
- Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, PA, USA
| | - Emily B Levitan
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Mohammad Z Al-Hamdan
- Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL, USA
| | - William L Crosson
- Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL, USA
| | - Monika M Safford
- Division of General Internal Medicine, Weill Cornell Medical College, New York City, NY, USA
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Al-Hamdan AZ, Preetha PP, Albashaireh RN, Al-Hamdan MZ, Crosson WL. Investigating the effects of environmental factors on autism spectrum disorder in the USA using remotely sensed data. Environ Sci Pollut Res Int 2018; 25:7924-7936. [PMID: 29299867 DOI: 10.1007/s11356-017-1114-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Accepted: 12/20/2017] [Indexed: 06/07/2023]
Abstract
This study aimed to assess the association between exposures to outdoor environmental factors and autism spectrum disorder (ASD) prevalence in a diverse and spatially distributed population of 8-year-old children from the USA (n = 2,097,188) using the air quality index (AQI) of the US Environmental Protection Agency as well as satellite-derived data of PM2.5 concentrations, sunlight, and maximum heat index. Multivariable logistic regression analyses were performed to determine whether the unhealthy AQI, PM2.5, sunlight, and maximum heat index were related to the odds of ASD prevalence based on gender and race and taking into consideration the confounding factors of smoking and socioeconomic status. The logistic regression odds ratios for ASD per 10% increase in the unhealthy AQI were greater than 1 for all categories, indicating that unhealthy AQI is related to the odds of ASD prevalence. The odds ratio of ASD due to the exposure to the unhealthy AQI was higher for Asians (OR = 2.96, 95% CI = 1.11-7.88) than that for Hispanics (OR = 1.308, 95% CI = 0.607-2.820), and it was higher for Blacks (OR = 1.398, 95% CI = 0.827-2.364) than that for Whites (OR = 1.219, 95% CI = 0.760-1.954). The odds ratio of ASD due to the unhealthy AQI was slightly higher for males (OR = 1.123, 95% CI = 0.771-1.635) than that for females (OR = 1.117, 95% CI = 0.789-1.581). The effects of the unhealthy environmental exposures on the odds ratios of ASD of this study were inconclusive (i.e., statically insignificant; p value > 0.05) for all categories except for Asians. The odds ratios of ASD for Asians were increased by 5, 12, and 14% with increased levels of the environmental exposures of 10 μg/m3 of PM2.5, 1000 kJ/m2 of sunlight, and 1 °F of maximum heat index, respectively. The odds ratios of ASD prevalence for all categories, except for Asians, were increased with the inclusion of the smoking covariate, reflecting the effect of smoking on ASD prevalence besides the unhealthy environmental factors.
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Affiliation(s)
- Ashraf Z Al-Hamdan
- Department of Civil and Environmental Engineering, University of Alabama in Huntsville, 301 Sparkman Drive, Huntsville, AL, 35899, USA.
| | - Pooja P Preetha
- Department of Civil and Environmental Engineering, University of Alabama in Huntsville, 301 Sparkman Drive, Huntsville, AL, 35899, USA
| | - Reem N Albashaireh
- Department of Mathematics, Alabama Agricultural and Mechanical University, Normal, AL, 35762, USA
| | - Mohammad Z Al-Hamdan
- Universities Space Research Association, NASA Marshall Space Flight Center, National Space Science and Technology Center, 320 Sparkman Drive, Huntsville, AL, 35805, USA
| | - William L Crosson
- Universities Space Research Association, NASA Marshall Space Flight Center, National Space Science and Technology Center, 320 Sparkman Drive, Huntsville, AL, 35805, USA
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Al-Hamdan AZ, Albashaireh RN, Al-Hamdan MZ, Crosson WL. The association of remotely sensed outdoor fine particulate matter with cancer incidence of respiratory system in the USA. J Environ Sci Health A Tox Hazard Subst Environ Eng 2017; 52:547-554. [PMID: 28276881 DOI: 10.1080/10934529.2017.1284432] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This study aimed to assess the association between exposure to fine particulate matter (PM2.5) and respiratory system cancer incidence in the US population (n = 295,404,580) using a satellite-derived estimate of PM2.5 concentrations. Linear and logistic regression analyses were performed to determine whether PM2.5 was related to the odds of respiratory system cancer (RSC) incidence based on gender and race. Positive linear regressions were found between PM2.5 concentrations and the age-adjusted RSC incidence rates for all groups (Males, Females, Whites, and Blacks) except for Asians and American Indians. The linear relationships between PM2.5 and RSC incidence rate per 1 μg/m3 PM2.5 increase for Males, Females, Whites, Blacks, and all categories combined had slopes of, respectively, 7.02 (R2 = 0.36), 2.14 (R2 = 0.14), 3.92 (R2 = 0.23), 5.02 (R2 = 0.21), and 4.15 (R2 = 0.28). Similarly, the logistic regression odds ratios per 10 μg/m3 increase of PM2.5 were greater than one for all categories except for Asians and American Indians, indicating that PM2.5 is related to the odds of RSC incidence. The age-adjusted odds ratio for males (OR = 2.16, 95% CI = 1.56-3.01) was higher than that for females (OR = 1.50, 95% CI = 1.09-2.06), and it was higher for Blacks (OR = 2.12, 95% CI = 1.43-3.14) than for Whites (OR = 1.72, 95% CI = 1.23-2.42). The odds ratios for all categories were attenuated with the inclusion of the smoking covariate, reflecting the effect of smoking on RSC incidence besides PM2.5.
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Affiliation(s)
- Ashraf Z Al-Hamdan
- a Department of Civil and Environmental Engineering , University of Alabama in Huntsville , Huntsville , Alabama , USA
| | - Reem N Albashaireh
- b Department of Mathematics , Alabama Agricultural and Mechanical University , Normal , Alabama , USA
| | - Mohammad Z Al-Hamdan
- c Universities Space Research Association , NASA Marshall Space Flight Center, National Space Science and Technology Center , Huntsville , Alabama , USA
| | - William L Crosson
- c Universities Space Research Association , NASA Marshall Space Flight Center, National Space Science and Technology Center , Huntsville , Alabama , USA
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Loop MS, Howard G, de Los Campos G, Al-Hamdan MZ, Safford MM, Levitan EB, McClure LA. Heat Maps of Hypertension, Diabetes Mellitus, and Smoking in the Continental United States. Circ Cardiovasc Qual Outcomes 2017; 10:e003350. [PMID: 28073852 PMCID: PMC5234692 DOI: 10.1161/circoutcomes.116.003350] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Accepted: 11/11/2016] [Indexed: 11/16/2022]
Abstract
BACKGROUND Geographic variations in cardiovascular mortality are substantial, but descriptions of geographic variations in major cardiovascular risk factors have relied on data aggregated to counties. Herein, we provide the first description of geographic variation in the prevalence of hypertension, diabetes mellitus, and smoking within and across US counties. METHODS AND RESULTS We conducted a cross-sectional analysis of baseline risk factor measurements and latitude/longitude of participant residence collected from 2003 to 2007 in the REGARDS study (Reasons for Geographic and Racial Differences in Stroke). Of the 30 239 participants, all risk factor measurements and location data were available for 28 887 (96%). The mean (±SD) age of these participants was 64.8(±9.4) years; 41% were black; 55% were female; 59% were hypertensive; 22% were diabetic; and 15% were current smokers. In logistic regression models stratified by race, the median(range) predicted prevalence of the risk factors were as follows: for hypertension, 49% (45%-58%) among whites and 72% (68%-78%) among blacks; for diabetes mellitus, 14% (10%-20%) among whites and 31% (28%-41%) among blacks; and for current smoking, 12% (7%-16%) among whites and 18% (11%-22%) among blacks. Hypertension was most prevalent in the central Southeast among whites, but in the west Southeast among blacks. Diabetes mellitus was most prevalent in the west and central Southeast among whites but in south Florida among blacks. Current smoking was most prevalent in the west Southeast and Midwest among whites and in the north among blacks. CONCLUSIONS Geographic disparities in prevalent hypertension, diabetes mellitus, and smoking exist within states and within counties in the continental United States, and the patterns differ by race.
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Affiliation(s)
- Matthew Shane Loop
- From the Department of Epidemiology (M.S.L., E.B.L.) and Department of Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Epidemiology & Biostatistics and Department of Statistics & Probability, Michigan State University, East Lansing (G.d.l.C.); Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL (M.Z.A.-H.); Division of General Internal Medicine, Weill Department of Medicine, Weill Cornell Medical College, New York, NY (M.M.S.); and Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, PA (L.A.M.).
| | - George Howard
- From the Department of Epidemiology (M.S.L., E.B.L.) and Department of Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Epidemiology & Biostatistics and Department of Statistics & Probability, Michigan State University, East Lansing (G.d.l.C.); Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL (M.Z.A.-H.); Division of General Internal Medicine, Weill Department of Medicine, Weill Cornell Medical College, New York, NY (M.M.S.); and Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, PA (L.A.M.)
| | - Gustavo de Los Campos
- From the Department of Epidemiology (M.S.L., E.B.L.) and Department of Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Epidemiology & Biostatistics and Department of Statistics & Probability, Michigan State University, East Lansing (G.d.l.C.); Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL (M.Z.A.-H.); Division of General Internal Medicine, Weill Department of Medicine, Weill Cornell Medical College, New York, NY (M.M.S.); and Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, PA (L.A.M.)
| | - Mohammad Z Al-Hamdan
- From the Department of Epidemiology (M.S.L., E.B.L.) and Department of Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Epidemiology & Biostatistics and Department of Statistics & Probability, Michigan State University, East Lansing (G.d.l.C.); Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL (M.Z.A.-H.); Division of General Internal Medicine, Weill Department of Medicine, Weill Cornell Medical College, New York, NY (M.M.S.); and Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, PA (L.A.M.)
| | - Monika M Safford
- From the Department of Epidemiology (M.S.L., E.B.L.) and Department of Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Epidemiology & Biostatistics and Department of Statistics & Probability, Michigan State University, East Lansing (G.d.l.C.); Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL (M.Z.A.-H.); Division of General Internal Medicine, Weill Department of Medicine, Weill Cornell Medical College, New York, NY (M.M.S.); and Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, PA (L.A.M.)
| | - Emily B Levitan
- From the Department of Epidemiology (M.S.L., E.B.L.) and Department of Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Epidemiology & Biostatistics and Department of Statistics & Probability, Michigan State University, East Lansing (G.d.l.C.); Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL (M.Z.A.-H.); Division of General Internal Medicine, Weill Department of Medicine, Weill Cornell Medical College, New York, NY (M.M.S.); and Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, PA (L.A.M.)
| | - Leslie A McClure
- From the Department of Epidemiology (M.S.L., E.B.L.) and Department of Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Epidemiology & Biostatistics and Department of Statistics & Probability, Michigan State University, East Lansing (G.d.l.C.); Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL (M.Z.A.-H.); Division of General Internal Medicine, Weill Department of Medicine, Weill Cornell Medical College, New York, NY (M.M.S.); and Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, PA (L.A.M.)
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Jiao Y, Bower JK, Im W, Basta N, Obrycki J, Al-Hamdan MZ, Wilder A, Bollinger CE, Zhang T, Hatten L, Hatten J, Hood DB. Application of Citizen Science Risk Communication Tools in a Vulnerable Urban Community. Int J Environ Res Public Health 2015; 13:ijerph13010011. [PMID: 26703664 PMCID: PMC4730402 DOI: 10.3390/ijerph13010011] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Revised: 10/02/2015] [Accepted: 10/09/2015] [Indexed: 01/08/2023]
Abstract
A public participatory geographical information systems (PPGIS) demographic, environmental, socioeconomic, health status portal was developed for the Stambaugh-Elwood (SE) community in Columbus, OH. We hypothesized that soil at SE residences would have metal concentrations above natural background levels. Three aims were developed that allowed testing of this hypothesis. Aim 1 focused on establishing partnerships between academia, state agencies and communities to assist in the development of a community voice. Aim 2 was to design and conduct soil sampling for residents of the SE community. Aim 3 was to utilize our interactive, customized portal as a risk communication tool by allowing residents to educate themselves as to the potential risks from industrial sources in close proximity to their community. Multiple comparisons of means were used to determine differences in soil element concentration by sampling location at p < 0.05. The results demonstrated that eight metals (As, Cd, Cu, Pb, Mo, Se, Tl, Zn) occurred at statistically-significantly greater levels than natural background levels, but most were below risk-based residential soil screening levels. Results were conveyed to residents via an educational, risk-communication informational card. This study demonstrates that community-led coalitions in collaboration with academic teams and state agencies can effectively address environmental concerns.
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Affiliation(s)
- Yuqin Jiao
- Division of Environmental Health Sciences, College of Public Health, The Ohio State University, Columbus, OH 43210, USA.
| | - Julie K Bower
- Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH 43210, USA.
| | - Wansoo Im
- VERTICES, LLC 303 George Street Suite 406, New Brunswick, NJ 08901, USA.
| | - Nicholas Basta
- Environmental Science Graduate Program, School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA.
| | - John Obrycki
- Environmental Science Graduate Program, School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA.
| | - Mohammad Z Al-Hamdan
- Universities Space Research Association at NASA Marshall Space Flight Center, Huntsville, AL 35805, USA.
| | - Allison Wilder
- Division of Environmental Health Sciences, College of Public Health, The Ohio State University, Columbus, OH 43210, USA.
| | - Claire E Bollinger
- Division of Environmental Health Sciences, College of Public Health, The Ohio State University, Columbus, OH 43210, USA.
| | - Tongwen Zhang
- Division of Environmental Health Sciences, College of Public Health, The Ohio State University, Columbus, OH 43210, USA.
| | - Luddie Hatten
- Stambaugh-Elwood Citizens for the Environment, LLC Columbus, OH 43207, USA.
| | - Jerrie Hatten
- Stambaugh-Elwood Citizens for the Environment, LLC Columbus, OH 43207, USA.
| | - Darryl B Hood
- Division of Environmental Health Sciences, College of Public Health, The Ohio State University, Columbus, OH 43210, USA.
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Loop MS, Kent ST, Al-Hamdan MZ, Crosson WL, Estes SM, Estes MG, Quattrochi DA, Hemmings SN, Wadley VG, McClure LA. Correction: Fine particulate matter and incident cognitive impairment in the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. PLoS One 2015; 10:e0125137. [PMID: 25886257 PMCID: PMC4401742 DOI: 10.1371/journal.pone.0125137] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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Juarez PD, Matthews-Juarez P, Hood DB, Im W, Levine RS, Kilbourne BJ, Langston MA, Al-Hamdan MZ, Crosson WL, Estes MG, Estes SM, Agboto VK, Robinson P, Wilson S, Lichtveld MY. The public health exposome: a population-based, exposure science approach to health disparities research. Int J Environ Res Public Health 2014; 11:12866-95. [PMID: 25514145 PMCID: PMC4276651 DOI: 10.3390/ijerph111212866] [Citation(s) in RCA: 103] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2014] [Revised: 11/12/2014] [Accepted: 11/27/2014] [Indexed: 11/16/2022]
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 has led 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.
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Affiliation(s)
- Paul D Juarez
- Research Center on Health Disparities, Equity, and the Exposome, University of Tennessee Health Science Center, 66 N. Pauline, Memphis, TN 38105, USA.
| | - Patricia Matthews-Juarez
- Research Center on Health Disparities, Equity, and the Exposome, University of Tennessee Health Science Center, 66 N. Pauline, Memphis, TN 38105, USA.
| | - Darryl B Hood
- Department of Environmental Health Sciences, College of Public Health, Ohio State University, Columbus, OH 43210, USA.
| | - Wansoo Im
- Vertices, Inc., 317 George Street 411, New Brunswick, NJ 08901, USA.
| | - Robert S Levine
- Department of Family & Community Medicine, Meharry Medical College, Nashville, TN 37208, USA.
| | - Barbara J Kilbourne
- Department of Sociology, Tennessee State University, Nashville, TN 37209, USA.
| | - Michael A Langston
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA.
| | - Mohammad Z Al-Hamdan
- National Space Science and Technology Center, Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL 35805, USA.
| | - William L Crosson
- National Space Science and Technology Center, Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL 35805, USA.
| | - Maurice G Estes
- National Space Science and Technology Center, University of Alabama, Huntsville, AL 35805, USA.
| | - Sue M Estes
- National Space Science and Technology Center, Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL 35805, USA.
| | - Vincent K Agboto
- Department of Family & Community Medicine, Meharry Medical College, Nashville, TN 37208, USA.
| | - Paul Robinson
- Department of Ophthalmology, Charles R. Drew University of Medicine and Science, Los Angeles, CA 90059, USA.
| | - Sacoby Wilson
- Research Center on Health Disparities, Equity, and the Exposome, University of Tennessee Health Science Center, 66 N. Pauline, Memphis, TN 38105, USA.
| | - Maureen Y Lichtveld
- Research Center on Health Disparities, Equity, and the Exposome, University of Tennessee Health Science Center, 66 N. Pauline, Memphis, TN 38105, USA.
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Kent ST, Cushman M, Howard G, Judd SE, Crosson WL, Al-Hamdan MZ, McClure LA. Sunlight exposure and cardiovascular risk factors in the REGARDS study: a cross-sectional split-sample analysis. BMC Neurol 2014; 14:133. [PMID: 24946776 PMCID: PMC4075775 DOI: 10.1186/1471-2377-14-133] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2013] [Accepted: 06/13/2014] [Indexed: 12/31/2022] Open
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. Methods 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. Results 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]). Conclusions 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.
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Affiliation(s)
| | | | | | | | | | | | - Leslie A McClure
- Department of Biostatistics, 1665 University Blvd, University of Alabama at Birmingham, Birmingham 35294, Alabama.
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Kent ST, Kabagambe EK, Wadley VG, Howard VJ, Crosson WL, Al-Hamdan MZ, Judd SE, Peace F, McClure LA. The relationship between long-term sunlight radiation and cognitive decline in the REGARDS cohort study. Int J Biometeorol 2014; 58:361-370. [PMID: 23340910 PMCID: PMC3665728 DOI: 10.1007/s00484-013-0631-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2012] [Revised: 08/01/2012] [Accepted: 01/02/2013] [Indexed: 06/01/2023]
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.
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Affiliation(s)
- Shia T Kent
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, 1665 University Blvd, Birmingham, AL, USA,
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Al-Hamdan MZ, Crosson WL, Economou SA, Estes MG, Estes SM, Hemmings SN, Kent ST, Puckett M, Quattrochi DA, Rickman DL, Wade GM, McClure LA. Environmental Public Health Applications Using Remotely Sensed Data. Geocarto Int 2014; 29:85-98. [PMID: 24910505 PMCID: PMC4044865 DOI: 10.1080/10106049.2012.715209] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
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.
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Affiliation(s)
- Mohammad Z Al-Hamdan
- Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL, USA
| | - William L Crosson
- Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL, USA
| | - Sigrid A Economou
- Office of Surveillance, Epidemiology and Laboratory Services, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Maurice G Estes
- Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL, USA
| | - Sue M Estes
- Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL, USA
| | - Sarah N Hemmings
- Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL, USA
| | - Shia T Kent
- School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Mark Puckett
- Office of Surveillance, Epidemiology and Laboratory Services, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Dale A Quattrochi
- Earth Science Office, NASA Marshall Space Flight Center, Huntsville, AL, USA
| | - Douglas L Rickman
- Earth Science Office, NASA Marshall Space Flight Center, Huntsville, AL, USA
| | - Gina M Wade
- Von Braun Center for Science and Innovation, National Space Science and Technology Center (Previously with USRA at NASA/MSFC), Huntsville, AL, USA
| | - Leslie A McClure
- School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
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McClure LA, Loop MS, Crosson WL, Kleindorfer DO, Kissela BM, Al-Hamdan MZ. Abstract TP211: Fine Particulate Matter (PM2.5) and the Risk of Stroke in the REGARDS Cohort. Stroke 2013. [DOI: 10.1161/str.44.suppl_1.atp211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Introduction:
Recent work has suggested that there is some association between acute exposures to fine particulate matter with aerodynamic diameter less than or equal to 2.5 micrometers (PM2.5) and ischemic stroke; however, the evidence is conflicting. Thus, we assessed whether PM2.5 was associated with ischemic stroke in participants in the Reasons for Geographic And Racial Differences in Stroke (REGARDS) cohort.
Methods:
We used a time-stratified case-crossover design to determine if exposure to PM2.5 was associated with an increased risk of ischemic stroke. We fit conditional logistic regression models to determine the odds ratio of ischemic stroke for those exposed to moderate (PM2.5 15-40 μg/m3) relative to good (PM2.5 ≤ 15 μg/m3) levels of PM2.5. We adjusted for temperature at the time of exposure, and assessed whether the association differed by region of residence (stroke belt vs. non-belt regions).
Results:
Among 442 participants who experienced an incident ischemic stroke in REGARDS, we found that there was no association with PM2.5 exposure (OR: 0.89, 95% CI: 0.69-1.15), and that there was no impact of region of residence on these results (p for interaction=0.14).
Conclusions:
We did not confirm earlier research indicating that there is an acute association between PM2.5 and ischemic stroke. More research is needed to understand these conflicting results, and to assess the impact of longer term exposures of PM2.5 on stroke incidence.
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Affiliation(s)
| | | | - William L Crosson
- Science and Technology Institute/Universities Space Rsch Association, Huntsville, AL
| | | | | | - Mohammad Z Al-Hamdan
- Universities Space Rsch Association at NASA Marshall Space Flight Cente, Huntsville, AL
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Hu X, Waller LA, Al-Hamdan MZ, Crosson WL, Estes MG, Estes SM, Quattrochi DA, Sarnat JA, Liu Y. Estimating ground-level PM(2.5) concentrations in the southeastern U.S. using geographically weighted regression. Environ Res 2013; 121:1-10. [PMID: 23219612 DOI: 10.1016/j.envres.2012.11.003] [Citation(s) in RCA: 130] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2011] [Revised: 06/21/2012] [Accepted: 07/13/2012] [Indexed: 04/14/2023]
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.
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Affiliation(s)
- Xuefei Hu
- Department of Environmental Health, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA
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Kent ST, McClure LA, Judd SE, Howard VJ, Crosson WL, Al-Hamdan MZ, Wadley VG, Peace F, Kabagambe EK. Short- and long-term sunlight radiation and stroke incidence. Ann Neurol 2012; 73:32-7. [PMID: 23225379 DOI: 10.1002/ana.23737] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2012] [Revised: 07/17/2012] [Accepted: 08/10/2012] [Indexed: 01/15/2023]
Abstract
OBJECTIVE Examine whether long- and short-term sunlight radiation is related to stroke incidence. METHODS 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. RESULTS 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). INTERPRETATION 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.
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Affiliation(s)
- Shia T Kent
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, USA.
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Kent ST, McClure LA, Howard VJ, Crosson WL, Al-Hamdan MZ, Wadley VG, Judd SE, Peace F, Kabagambe EK. Abstract 2591: Relationship Between Sunlight and Temperature Exposure to Stroke Incidence in the Reasons for Geographic and Racial Differences in Stroke (regards) Study. Stroke 2012. [DOI: 10.1161/str.43.suppl_1.a2591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
INTRODUCTION:
Stroke incidence is higher during the winter, indicating that long and short-term sunlight exposure may contribute to stroke incidence.
HYPOTHESIS:
Reduced sunlight exposure and extreme temperatures are associated with increased stroke incidence.
METHODS:
Data were obtained from the REGARDS study, a national population-based cohort of 30,239 enrolled from 2003-2007 consisting of black and white participants, aged 45+. A centralized phone interview was used for medical history, in-home evaluation for physical measures, and a self-administered questionnaire for complete residential histories (city/state) from birth. The risk of stroke and sunlight exposure was studied in the 16,529 participants that were free of stroke and coronary artery disease at baseline and had lifetime residential histories available. Fifteen-year residential history merged with satellite and ground monitor data were used to determine sunlight and temperature exposure. Since long-term sunlight and temperature measures have not been extensively used in health studies, we performed exploratory analyses to determine which measures carried the strongest relationships with stroke. Fifteen, ten, five, two and one-year exposures were used to predict stroke incidence using Cox proportional hazard models. Potential demographic, behavioral, and medical confounders and mediators were included during model-building.
RESULTS:
Over an average follow-up period of 5.0 years, 351 had an incident stroke. Monthly average sunlight and maximum temperature exposures at residence exhibited the strongest relationships with stroke. It was determined that the shortest period of sunlight exposure, one year, exhibited the strongest relationship. After adjustment for other covariates, the previous year’s monthly average sunlight exposure below the median predicted increased risk of stroke (HR=1.61 (95% CI: 1.15, 2.26)). Temperature exhibited a J-shaped relationship with stroke incidence.
CONCLUSION:
This is the first report showing a relationship between sunlight and stroke. In addition, we confirmed earlier studies that both hot and cold temperatures are related to increased 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 if it is due to short or long-term exposures.
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Affiliation(s)
- Shia T Kent
- Univ of Alabama at Birmingham, Birmingham, AL
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Estes MG, Al-Hamdan MZ, Crosson W, Estes SM, Quattrochi D, Kent S, McClure LA. Use of remotely sensed data to evaluate the relationship between living environment and blood pressure. Environ Health Perspect 2009; 117:1832-8. [PMID: 20049200 PMCID: PMC2799455 DOI: 10.1289/ehp.0900871] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2009] [Accepted: 08/04/2009] [Indexed: 05/07/2023]
Abstract
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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Affiliation(s)
- Maurice G Estes
- Universities Space Research Association, NASA-Marshall Space Flight Center, Huntsville, Alabama 35805, USA.
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Al-Hamdan MZ, Crosson WL, Limaye AS, Rickman DL, Quattrochi DA, Estes MG, Qualters JR, Sinclair AH, Tolsma DD, Adeniyi KA, Niskar AS. Methods for characterizing fine particulate matter using ground observations and remotely sensed data: potential use for environmental public health surveillance. J Air Waste Manag Assoc 2009; 59:865-881. [PMID: 19645271 DOI: 10.3155/1047-3289.59.7.865] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
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.
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Affiliation(s)
- Mohammad Z Al-Hamdan
- Universities Space Research Association, National Aeronautics and Space Administration, Marshall Space Flight Center, National Space Science and Technology Center, Huntsville, AL 35805 , USA.
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