Publications by authors named "Lance A Waller"

117 Publications

A Bayesian Downscaler Model to Estimate Daily PM Levels in the Conterminous US.

Int J Environ Res Public Health 2018 09 13;15(9). Epub 2018 Sep 13.

Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.

There has been growing interest in extending the coverage of ground particulate matter with aerodynamic diameter ≤ 2.5 μm (PM) monitoring networks based on satellite remote sensing data. With broad spatial and temporal coverage, a satellite-based monitoring network has a strong potential to complement the ground monitor system in terms of the spatiotemporal availability of the air quality data. However, most existing calibration models focus on a relatively small spatial domain and cannot be generalized to a national study. In this paper, we proposed a statistically reliable and interpretable national modeling framework based on Bayesian downscaling methods to be applied to the calibration of the daily ground PM concentrations across the conterminous United States using satellite-retrieved aerosol optical depth (AOD) and other ancillary predictors in 2011. Our approach flexibly models the PM versus AOD and the potential related geographical factors varying across the climate regions and yields spatial- and temporal-specific parameters to enhance model interpretability. Moreover, our model accurately predicted the national PM with an ² at 70% and generated reliable annual and seasonal PM concentration maps with its SD. Overall, this modeling framework can be applied to national-scale PM exposure assessments and can also quantify the prediction errors.
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http://dx.doi.org/10.3390/ijerph15091999DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6164266PMC
September 2018

A multicity study of air pollution and cardiorespiratory emergency department visits: Comparing approaches for combining estimates across cities.

Environ Int 2018 11 11;120:312-320. Epub 2018 Aug 11.

Department of Environmental Health, Emory University, 1518 Clifton Rd. NE, Atlanta, GA 30322, United States. Electronic address:

Determining how associations between ambient air pollution and health vary by specific outcome is important for developing public health interventions. We estimated associations between twelve ambient air pollutants of both primary (e.g. nitrogen oxides) and secondary (e.g. ozone and sulfate) origin and cardiorespiratory emergency department (ED) visits for 8 specific outcomes in five U.S. cities including Atlanta, GA; Birmingham, AL; Dallas, TX; Pittsburgh, PA; St. Louis, MO. For each city, we fitted overdispersed Poisson time-series models to estimate associations between each pollutant and specific outcome. To estimate multicity and posterior city-specific associations, we developed a Bayesian multicity multi-outcome (MCM) model that pools information across cities using data from all specific outcomes. We fitted single pollutant models as well as models with multipollutant components using a two-stage chemical mixtures approach. Posterior city-specific associations from the MCM models were somewhat attenuated, with smaller standard errors, compared to associations from time-series regression models. We found positive associations of both primary and secondary pollutants with respiratory disease ED visits. There was some indication that primary pollutants, particularly nitrogen oxides, were also associated with cardiovascular disease ED visits. Bayesian models can help to synthesize findings across multiple outcomes and cities by providing posterior city-specific associations building on variation and similarities across the multiple sources of available information.
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http://dx.doi.org/10.1016/j.envint.2018.07.033DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6218942PMC
November 2018

Contributions from the silent majority dominate dengue virus transmission.

PLoS Pathog 2018 05 3;14(5):e1006965. Epub 2018 May 3.

Department of Biological Sciences, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, United States.

Despite estimates that, each year, as many as 300 million dengue virus (DENV) infections result in either no perceptible symptoms (asymptomatic) or symptoms that are sufficiently mild to go undetected by surveillance systems (inapparent), it has been assumed that these infections contribute little to onward transmission. However, recent blood-feeding experiments with Aedes aegypti mosquitoes showed that people with asymptomatic and pre-symptomatic DENV infections are capable of infecting mosquitoes. To place those findings into context, we used models of within-host viral dynamics and human demographic projections to (1) quantify the net infectiousness of individuals across the spectrum of DENV infection severity and (2) estimate the fraction of transmission attributable to people with different severities of disease. Our results indicate that net infectiousness of people with asymptomatic infections is 80% (median) that of people with apparent or inapparent symptomatic infections (95% credible interval (CI): 0-146%). Due to their numerical prominence in the infectious reservoir, clinically inapparent infections in total could account for 84% (CI: 82-86%) of DENV transmission. Of infections that ultimately result in any level of symptoms, we estimate that 24% (95% CI: 0-79%) of onward transmission results from mosquitoes biting individuals during the pre-symptomatic phase of their infection. Only 1% (95% CI: 0.8-1.1%) of DENV transmission is attributable to people with clinically detected infections after they have developed symptoms. These findings emphasize the need to (1) reorient current practices for outbreak response to adoption of pre-emptive strategies that account for contributions of undetected infections and (2) apply methodologies that account for undetected infections in surveillance programs, when assessing intervention impact, and when modeling mosquito-borne virus transmission.
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http://dx.doi.org/10.1371/journal.ppat.1006965DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5933708PMC
May 2018

Source-specific pollution exposure and associations with pulmonary response in the Atlanta Commuters Exposure Studies.

J Expo Sci Environ Epidemiol 2018 06 3;28(4):337-347. Epub 2018 Jan 3.

Department of Environmental Health, Emory University, Atlanta, USA.

Concentrations of traffic-related air pollutants are frequently higher within commuting vehicles than in ambient air. Pollutants found within vehicles may include those generated by tailpipe exhaust, brake wear, and road dust sources, as well as pollutants from in-cabin sources. Source-specific pollution, compared to total pollution, may represent regulation targets that can better protect human health. We estimated source-specific pollution exposures and corresponding pulmonary response in a panel study of commuters. We used constrained positive matrix factorization to estimate source-specific pollution factors and, subsequently, mixed effects models to estimate associations between source-specific pollution and pulmonary response. We identified four pollution factors that we named: crustal, primary tailpipe traffic, non-tailpipe traffic, and secondary. Among asthmatic subjects (N = 48), interquartile range increases in crustal and secondary pollution were associated with changes in lung function of -1.33% (95% confidence interval (CI): -2.45, -0.22) and -2.19% (95% CI: -3.46, -0.92) relative to baseline, respectively. Among non-asthmatic subjects (N = 51), non-tailpipe pollution was associated with pulmonary response only at 2.5 h post-commute. We found no significant associations between pulmonary response and primary tailpipe pollution. Health effects associated with traffic-related pollution may vary by source, and therefore some traffic pollution sources may require targeted interventions to protect health.
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http://dx.doi.org/10.1038/s41370-017-0016-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6013329PMC
June 2018

Exploring regional variability in utilization of antireflux surgery in children.

J Surg Res 2017 06 8;214:49-56. Epub 2017 Mar 8.

Division of Pediatric Surgery, Department of Surgery, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, Georgia. Electronic address:

Background: There is significant variation surrounding the indications, surgical approaches, and outcomes for children undergoing antireflux procedures (ARPs) resulting in geographic variation of care. Our purpose was to quantify this geographic variation in the utilization of ARPs in children.

Methods: A cross-sectional analysis of the 2009 Kid's Inpatient Database was performed to identify patients with gastroesophageal reflux disease or associated diagnoses. Regional surgical utilization rates were determined, and a mixed effects model was used to identify factors associated with the use of ARPs.

Results: Of the 148,959 patients with a diagnosis of interest, 4848 (3.3%) underwent an ARP with 2376 (49%) undergoing a laparoscopic procedure. The Northeast (2.0%) and Midwest (2.2%) had the lowest overall utilization of surgery, compared with the South (3.3%) and West (3.4%). After adjustment for age, case-mix, and surgical approach, variation persisted with the West and the South demonstrating almost two times the odds of undergoing an ARP compared with the Northeast. Surgical utilization rates are independent of state-level volume with some of the highest case volume states having surgical utilization rates below the national rate. In the West, the use of laparoscopy correlated with overall utilization of surgery, whereas surgical approach was not correlated with ARP use in the South.

Conclusions: Significant regional variation in ARP utilization exists that cannot be explained entirely by differences in patient age, race/ethnicity, case-mix, and surgical approach. In order to decrease variation in care, further research is warranted to establish consensus guidelines regarding indications for the use ARPs for children.
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http://dx.doi.org/10.1016/j.jss.2017.02.075DOI Listing
June 2017

Estimating PM Concentrations in the Conterminous United States Using the Random Forest Approach.

Environ Sci Technol 2017 Jun 1;51(12):6936-6944. Epub 2017 Jun 1.

School of Community Health Sciences, University of Nevada Reno , Reno, Nevada 89557, United States.

To estimate PM concentrations, many parametric regression models have been developed, while nonparametric machine learning algorithms are used less often and national-scale models are rare. In this paper, we develop a random forest model incorporating aerosol optical depth (AOD) data, meteorological fields, and land use variables to estimate daily 24 h averaged ground-level PM concentrations over the conterminous United States in 2011. Random forests are an ensemble learning method that provides predictions with high accuracy and interpretability. Our results achieve an overall cross-validation (CV) R value of 0.80. Mean prediction error (MPE) and root mean squared prediction error (RMSPE) for daily predictions are 1.78 and 2.83 μg/m, respectively, indicating a good agreement between CV predictions and observations. The prediction accuracy of our model is similar to those reported in previous studies using neural networks or regression models on both national and regional scales. In addition, the incorporation of convolutional layers for land use terms and nearby PM measurements increase CV R by ∼0.02 and ∼0.06, respectively, indicating their significant contributions to prediction accuracy. A pair of different variable importance measures both indicate that the convolutional layer for nearby PM measurements and AOD values are among the most-important predictor variables for the training process.
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http://dx.doi.org/10.1021/acs.est.7b01210DOI Listing
June 2017

Landscape and environmental influences on Mycobacterium ulcerans distribution among aquatic sites in Ghana.

PLoS One 2017 24;12(4):e0176375. Epub 2017 Apr 24.

Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America.

Buruli ulcer, caused by Mycobacterium ulcerans, is highly endemic in West Africa. While the mode of transmission is unknown, many studies associate Buruli ulcer with different types of water exposure. We present results from the largest study to date to test for M. ulcerans in aquatic sites and identify environmental attributes associated with its presence. Environmental samples from 98 aquatic sites in the Greater Accra, Ashanti, and Volta regions of Ghana were tested for the presence of M. ulcerans DNA by polymerase chain reaction. The proportion of aquatic sites positive for M. ulcerans varied by region: Ashanti 66% (N = 39), Greater Accra 34% (N = 29), and Volta 0% (N = 30). We explored the spatial distribution of M. ulcerans positive and negative water bodies and found no significant clusters. We also determined both highly localized water attributes and broad scale remotely sensed land cover and terrain environmental characteristics associated with M. ulcerans presence through logistic regression. Our results concur with published results regarding conditions suitable for M. ulcerans growth and associations with Buruli ulcer disease burden with regards to water characteristics and disturbed environments, but differ from others with regards to spatial associations and topographic effects such as elevation and wetness. While our results suggest M. ulcerans is an environmental organism existing in a specific ecological niche, they also reveal variation in the elements defining this niche across the sites considered. In addition, despite the causal association between Buruli ulcer and M. ulcerans, we observed no significant statistical association between case reports of Buruli ulcer and presence of M. ulcerans in nearby waterbodies.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176375PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5402941PMC
September 2017

Spatial Prediction of Coxiella burnetii Outbreak Exposure via Notified Case Counts in a Dose-Response Model.

Epidemiology 2017 01;28(1):127-135

From the aJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands; bCentre for Infectious, Disease Control, RIVM, Bilthoven, The Netherlands; cDepartment of Sexual Health, Infectious Diseases, and Environmental Health, South Limburg Public Health Service, Heerlen, The Netherlands; dDepartment of Medical Microbiology, School of Public Health and Primary Care, Maastricht University Medical Center, Maastricht, The Netherlands; eHubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA; and fDepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA.

We develop a novel approach to study an outbreak of Q fever in 2009 in the Netherlands by combining a human dose-response model with geostatistics prediction to relate probability of infection and associated probability of illness to an effective dose of Coxiella burnetii. The spatial distribution of the 220 notified cases in the at-risk population are translated into a smooth spatial field of dose. Based on these symptomatic cases, the dose-response model predicts a median of 611 asymptomatic infections (95% range: 410, 1,084) for the 220 reported symptomatic cases in the at-risk population; 2.78 (95% range: 1.86, 4.93) asymptomatic infections for each reported case. The low attack rates observed during the outbreak range from (Equation is included in full-text article.)to (Equation is included in full-text article.). The estimated peak levels of exposure extend to the north-east from the point source with an increasing proportion of asymptomatic infections further from the source. Our work combines established methodology from model-based geostatistics and dose-response modeling allowing for a novel approach to study outbreaks. Unobserved infections and the spatially varying effective dose can be predicted using the flexible framework without assuming any underlying spatial structure of the outbreak process. Such predictions are important for targeting interventions during an outbreak, estimating future disease burden, and determining acceptable risk levels.
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http://dx.doi.org/10.1097/EDE.0000000000000574DOI Listing
January 2017

Relations Between Residential Proximity to EPA-Designated Toxic Release Sites and Diffuse Large B-Cell Lymphoma Incidence.

South Med J 2016 10;109(10):606-614

From the Division of Epidemiology and Biostatistics, University of Illinois at Chicago School of Public Health, Chicago, the University of Texas MD Anderson Cancer Center, Houston, the Departments of Hematology and Oncology and Biostatistics and Bioinformatics Shared Resource, Winship Cancer Institute, and the Departments of Epidemiology and Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, and the Georgia Department of Public Health, Atlanta.

Objectives: Examining the spatial patterns of diffuse large B-cell lymphoma (DLBCL) incidence and residential proximity to toxic release locations may provide insight regarding environmental and sociodemographic risk factors.

Methods: We linked and geocoded cancer incidence data for the period 1999-2008 from the Georgia Comprehensive Cancer Registry with population data from the US Census and the Environmental Protection Agency's Toxics Release Inventory. We conducted cluster analyses and constructed Poisson regression models to assess DLBCL incidence as a function of mean distance to the toxic release sites.

Results: In total, 3851 incident DLBCL cases occurred among adults residing in Georgia between 1999 and 2008. Significant focal clustering was observed around 57% of ethylene oxide sites, 5% of benzene sites, 9% of tetrachloroethylene sites, 7% of styrene sites, 10% of formaldehyde sites, 5% of trichloroethylene sites, and 10% of all release sites. Mean distance to sites was significantly associated with DLBCL risk for all chemicals.

Conclusions: Proximity to Toxics Release Inventory sites can be linked to increased DLBCL risk as assessed through focal clustering and Poisson regression, and confirmatory studies using geospatial mapping can aid in further specifying risk factors for DLBCL.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5076563PMC
http://dx.doi.org/10.14423/SMJ.0000000000000545DOI Listing
October 2016

Characterizing the spatial distribution of multiple pollutants and populations at risk in Atlanta, Georgia.

Spat Spatiotemporal Epidemiol 2016 08 24;18:13-23. Epub 2016 Mar 24.

Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States.

Background: Exposure metrics that identify spatial contrasts in multipollutant air quality are needed to better understand multipollutant geographies and health effects from air pollution. Our aim is to improve understanding of: (1) long-term spatial distributions of multiple pollutants; and (2) demographic characteristics of populations residing within areas of differing air quality.

Methods: We obtained average concentrations for ten air pollutants (p=10) across a 12 km grid (n=253) covering Atlanta, Georgia for 2002-2008. We apply a self-organizing map (SOM) to our data to derive multipollutant patterns observed across our grid and classify locations under their most similar pattern (i.e, multipollutant spatial type (MST)). Finally, we geographically map classifications to delineate regions of similar multipollutant characteristics and characterize associated demographics.

Results: We found six MSTs well describe our data, with profiles highlighting a range of combinations, from locations experiencing generally clean air to locations experiencing conditions that were relatively dirty. Mapping MSTs highlighted that downtown areas were dominated by primary pollution and that suburban areas experienced relatively higher levels of secondary pollution. Demographics show the largest proportion of the overall population resided in downtown locations experiencing higher levels of primary pollution. Moreover, higher proportions of nonwhites and children in poverty reside in these areas when compared to suburban populations that resided in areas exhibiting relatively lower pollution.

Conclusion: Our approach reveals the nature and spatial distribution of differential pollutant combinations across urban environments and provides helpful insights for identifying spatial exposure and demographic contrasts for future health studies.
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http://dx.doi.org/10.1016/j.sste.2016.02.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4977533PMC
August 2016

Associations between Source-Specific Fine Particulate Matter and Emergency Department Visits for Respiratory Disease in Four U.S. Cities.

Environ Health Perspect 2017 01 17;125(1):97-103. Epub 2016 Jun 17.

Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, USA.

Background: Short-term exposure to ambient fine particulate matter (PM2.5) concentrations has been associated with increased mortality and morbidity. Determining which sources of PM2.5 are most toxic can help guide targeted reduction of PM2.5. However, conducting multicity epidemiologic studies of sources is difficult because source-specific PM2.5 is not directly measured, and source chemical compositions can vary between cities.

Objectives: We determined how the chemical composition of primary ambient PM2.5 sources varies across cities. We estimated associations between source-specific PM2.5 and respiratory disease emergency department (ED) visits and examined between-city heterogeneity in estimated associations.

Methods: We used source apportionment to estimate daily concentrations of primary source-specific PM2.5 for four U.S. cities. For sources with similar chemical compositions between cities, we applied Poisson time-series regression models to estimate associations between source-specific PM2.5 and respiratory disease ED visits.

Results: We found that PM2.5 from biomass burning, diesel vehicle, gasoline vehicle, and dust sources was similar in chemical composition between cities, but PM2.5 from coal combustion and metal sources varied across cities. We found some evidence of positive associations of respiratory disease ED visits with biomass burning PM2.5; associations with diesel and gasoline PM2.5 were frequently imprecise or consistent with the null. We found little evidence of associations with dust PM2.5.

Conclusions: We introduced an approach for comparing the chemical compositions of PM2.5 sources across cities and conducted one of the first multicity studies of source-specific PM2.5 and ED visits. Across four U.S. cities, among the primary PM2.5 sources assessed, biomass burning PM2.5 was most strongly associated with respiratory health. Citation: Krall JR, Mulholland JA, Russell AG, Balachandran S, Winquist A, Tolbert PE, Waller LA, Sarnat SE. 2017. Associations between source-specific fine particulate matter and emergency department visits for respiratory disease in four U.S. cities. Environ Health Perspect 125:97-103; http://dx.doi.org/10.1289/EHP271.
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http://dx.doi.org/10.1289/EHP271DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5226704PMC
January 2017

Exploring spatial patterns in the associations between local AIDS incidence and socioeconomic and demographic variables in the state of Rio de Janeiro, Brazil.

Spat Spatiotemporal Epidemiol 2016 05 4;17:85-93. Epub 2016 May 4.

Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA. Electronic address:

Access to antiretroviral therapy (ART), universally provided in Brazil since 1996, resulted in a reduction in overall morbidity and mortality due to AIDS or AIDS-related complications, but in some municipalities of Rio de Janeiro, AIDS incidence remains high. Public health surveillance remains an invaluable tool for understanding current AIDS epidemiologic patterns and local socioeconomic and demographic factors associated with increased incidence. Geographically Weighted Poisson Regression (GWPR) explores spatial varying impacts of these factors across the study area focusing attention on local variations in ecological associations. The set of sociodemographic variables under consideration revealed significant associations with local AIDS incidence and these associations varied geographically across the study area. We find the effects of predictors on AIDS incidence are not constant across the state, contrary to assumptions in the global models. We observe and quantify different local factors driving AIDS incidence in different parts of the state.
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http://dx.doi.org/10.1016/j.sste.2016.04.008DOI Listing
May 2016

Time Lags between Exanthematous Illness Attributed to Zika Virus, Guillain-Barré Syndrome, and Microcephaly, Salvador, Brazil.

Emerg Infect Dis 2016 08 15;22(8):1438-44. Epub 2016 Aug 15.

Zika virus infection emerged as a public health emergency after increasing evidence for its association with neurologic disorders and congenital malformations. In Salvador, Brazil, outbreaks of acute exanthematous illness (AEI) attributed to Zika virus, Guillain-Barré syndrome (GBS), and microcephaly occurred in 2015. We investigated temporal correlations and time lags between these outbreaks to identify a common link between them by using epidemic curves and time series cross-correlations. Number of GBS cases peaked after a lag of 5-9 weeks from the AEI peak. Number of suspected cases of microcephaly peaked after a lag of 30-33 weeks from the AEI peak, which corresponded to time of potential infections of pregnant mothers during the first trimester. These findings support the association of GBS and microcephaly with Zika virus infection and provide evidence for a temporal relationship between timing of arboviral infection of pregnant women during the first trimester and birth outcome.
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http://dx.doi.org/10.3201/eid2208.160496DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4982160PMC
August 2016

Resolving uncertainty in the spatial relationships between passive benzene exposure and risk of non-Hodgkin lymphoma.

Cancer Epidemiol 2016 Apr 2;41:139-51. Epub 2016 Mar 2.

Department of Hematology and Oncology, Winship Cancer Institute, Emory University, Atlanta, GA, USA.

Background: Benzene is a known occupational carcinogen associated with increased risk of hematologic cancers, but the relationships between quantity of passive benzene exposure through residential proximity to toxic release sites, duration of exposure, lag time from exposure to cancer development, and lymphoma risk remain unclear.

Methods: We collected release data through the Environmental Protection Agency's Toxics Release Inventory (TRI) from 1989 to 2003, which included location of benzene release sites, years when release occurred, and amount of release. We also collected data on incident cases of non-Hodgkin lymphoma (NHL) from the Georgia Comprehensive Cancer Registry (GCCR) for the years 1999-2008. We constructed distance-decay surrogate exposure metrics and Poisson and negative binomial regression models of NHL incidence to quantify associations between passive exposure to benzene and NHL risk and examined the impact of amount, duration of exposure, and lag time on cancer development. Akaike's information criteria (AIC) were used to determine the scaling factors for benzene dispersion and exposure periods that best predicted NHL risk.

Results: Using a range of scaling factors and exposure periods, we found that increased levels of passive benzene exposure were associated with higher risk of NHL. The best fitting model, with a scaling factor of 4 kilometers (km) and exposure period of 1989-1993, showed that higher exposure levels were associated with increased NHL risk (Level 4 (1.1-160kilograms (kg)) vs. Level 1: risk ratio 1.56 [1.44-1.68], Level 5 (>160kg) vs. Level 1: 1.60 [1.48-1.74]).

Conclusions: Higher levels of passive benzene exposure are associated with increased NHL risk across various lag periods. Additional epidemiological studies are needed to refine these models and better quantify the expected total passive benzene exposure in areas surrounding release sites.
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http://dx.doi.org/10.1016/j.canep.2016.01.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4946246PMC
April 2016

Coupled Heterogeneities and Their Impact on Parasite Transmission and Control.

Trends Parasitol 2016 05 2;32(5):356-367. Epub 2016 Feb 2.

Department of Environmental Sciences, Emory University, Atlanta, GA, USA; Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.

Most host-parasite systems exhibit remarkable heterogeneity in the contribution to transmission of certain individuals, locations, host infectious states, or parasite strains. While significant advancements have been made in the understanding of the impact of transmission heterogeneity in epidemic dynamics and parasite persistence and evolution, the knowledge base of the factors contributing to transmission heterogeneity is limited. We argue that research efforts should move beyond considering the impact of single sources of heterogeneity and account for complex couplings between conditions with potential synergistic impacts on parasite transmission. Using theoretical approaches and empirical evidence from various host-parasite systems, we investigate the ecological and epidemiological significance of couplings between heterogeneities and discuss their potential role in transmission dynamics and the impact of control.
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http://dx.doi.org/10.1016/j.pt.2016.01.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4851872PMC
May 2016

More than Manuscripts: Reproducibility, Rigor, and Research Productivity in the Big Data Era.

Toxicol Sci 2016 Feb;149(2):275-6

Editor-in-Chief, Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322

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http://dx.doi.org/10.1093/toxsci/kfv330DOI Listing
February 2016

Understanding Local Spatial Variation Along the Care Continuum: The Potential Impact of Transportation Vulnerability on HIV Linkage to Care and Viral Suppression in High-Poverty Areas, Atlanta, Georgia.

J Acquir Immune Defic Syndr 2016 May;72(1):65-72

*Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA; †Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, GA; ‡Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA; §Department of Behavioral Sciences and Health Education, Rollins School of Public Health, Emory University, Atlanta, GA; ‖Georgia Department of Public Health, Atlanta, GA; and ¶Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA.

Background: Engagement in care is central to reducing mortality for HIV-infected persons and achieving the White House National AIDS Strategy of 80% viral suppression in the US by 2020. Where an HIV-infected person lives impacts his or her ability to achieve viral suppression. Reliable transportation access for healthcare may be a key determinant of this place-suppression relationship.

Methods: ZIP code tabulation areas (ZCTAs) were the units of analysis. We used geospatial and ecologic analyses to examine spatial distributions of neighborhood-level variables (eg, transportation accessibility) and associations with: (1) community linkage to care, and (2) community viral suppression. Among Atlanta ZCTAs with data for newly diagnosed HIV cases (2006-2010), we used Moran I to evaluate spatial clustering and linear regression models to evaluate associations between neighborhood variables and outcomes.

Results: In 100 ZCTAs with 8413 newly diagnosed HIV-positive residents, a median of 60 HIV cases were diagnosed per ZCTA during the 5-year period. We found significant clustering of ZCTAs with low linkage to care and viral suppression (Moran I = 0.218, P < 0.05). In high-poverty ZCTAs, a 10% point increase in ZCTA-level household vehicle ownership was associated with a 4% point increase in linkage to care (P = 0.02, R = 0.16). In low-poverty ZCTAs, a 10% point increase in ZCTA-level household vehicle ownership was associated with a 30% point increase in ZCTA-level viral suppression (P = 0.01, R = 0.08).

Conclusions: Correlations between transportation variables and community-level care linkage and viral suppression vary by area poverty level and provide opportunities for interventions beyond individual-level factors.
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http://dx.doi.org/10.1097/QAI.0000000000000914DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4837075PMC
May 2016

Optimizing Human Immunodeficiency Virus Testing Interventions for Men Who Have Sex With Men in the United States: A Modeling Study.

Open Forum Infect Dis 2015 Dec 20;2(4):ofv153. Epub 2015 Oct 20.

Department of Epidemiology , Laney Graduate School.

Background.  In the United States, public health recommendations for men who have sex with men (MSM) include testing for human immunodeficiency virus (HIV) at least annually. We model the impact of different possible HIV testing policies on HIV incidence in a simulated population parameterized to represent US MSM. Methods.  We used exponential random graph models to explore, among MSM, the short-term impact on baseline (under current HIV testing practices and care linkage) HIV incidence of the following: (1) increasing frequency of testing; (2) increasing the proportion who ever test; (3) increasing test sensitivity; (4) increasing the proportion of the diagnosed population achieving viral suppression; and combinations of 1-4. We simulated each scenario 20 times and calculated the median and interquartile range of 3-year cumulative incidence of HIV infection. Results.  The only intervention that reduced HIV incidence on its own was increasing the proportion of the diagnosed population achieving viral suppression; increasing frequency of testing, the proportion that ever test or test sensitivity did not appreciably reduce estimated incidence. However, in an optimal scenario in which viral suppression improved to 100%, HIV incidence could be reduced by an additional 17% compared with baseline by increasing testing frequency to every 90 days and test sensitivity to 22 days postinfection. Conclusions.  Increased frequency, coverage, or sensitivity of HIV testing among MSM is unlikely to result in reduced HIV incidence unless men diagnosed through enhanced testing programs are also engaged in effective HIV care resulting in viral suppression at higher rates than currently observed.
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http://dx.doi.org/10.1093/ofid/ofv153DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653969PMC
December 2015

Current Methods and Challenges for Epidemiological Studies of the Associations Between Chemical Constituents of Particulate Matter and Health.

Curr Environ Health Rep 2015 Dec;2(4):388-98

Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta, GA, 30322, USA.

Epidemiological studies have been critical for estimating associations between exposure to ambient particulate matter (PM) air pollution and adverse health outcomes. Because total PM mass is a temporally and spatially varying mixture of constituents with different physical and chemical properties, recent epidemiological studies have focused on PM constituents. Most studies have estimated associations between PM constituents and health using the same statistical methods as in studies of PM mass. However, these approaches may not be sufficient to address challenges specific to studies of PM constituents, namely assigning exposure, disentangling health effects, and handling measurement error. We reviewed large, population-based epidemiological studies of PM constituents and health and describe the statistical methods typically applied to address these challenges. Development of statistical methods that simultaneously address multiple challenges, for example, both disentangling health effects and handling measurement error, could improve estimation of associations between PM constituents and adverse health outcomes.
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http://dx.doi.org/10.1007/s40572-015-0071-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4626265PMC
December 2015

Exploring associations between multipollutant day types and asthma morbidity: epidemiologic applications of self-organizing map ambient air quality classifications.

Environ Health 2015 Jun 23;14:55. Epub 2015 Jun 23.

Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States.

Background: Recent interest in the health effects of air pollution focuses on identifying combinations of multiple pollutants that may be associated with adverse health risks.

Objective: Present a methodology allowing health investigators to explore associations between categories of ambient air quality days (i.e., multipollutant day types) and adverse health.

Methods: First, we applied a self-organizing map (SOM) to daily air quality data for 10 pollutants collected between January 1999 and December 2008 at a central monitoring location in Atlanta, Georgia to define a collection of multipollutant day types. Next, we conducted an epidemiologic analysis using our categories as a multipollutant metric of ambient air quality and daily counts of emergency department (ED) visits for asthma or wheeze among children aged 5 to 17 as the health endpoint. We estimated rate ratios (RR) for the association of multipollutant day types and pediatric asthma ED visits using a Poisson generalized linear model controlling for long-term, seasonal, and weekday trends and weather.

Results: Using a low pollution day type as the reference level, we found significant associations of increased asthma morbidity in three of nine categories suggesting adverse effects when combinations of primary (CO, NO2, NOX, EC, and OC) and/or secondary (O3, NH4, SO4) pollutants exhibited elevated concentrations (typically, occurring on dry days with low wind speed). On days with only NO3 elevated (which tended to be relatively cool) and on days when only SO2 was elevated (which likely reflected plume touchdowns from coal combustion point sources), estimated associations were modestly positive but confidence intervals included the null.

Conclusions: We found that ED visits for pediatric asthma in Atlanta were more strongly associated with certain day types defined by multipollutant characteristics than days with low pollution levels; however, findings did not suggest that any specific combinations were more harmful than others. Relative to other health endpoints, asthma exacerbation may be driven more by total ambient pollutant exposure than by composition.
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http://dx.doi.org/10.1186/s12940-015-0041-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4477305PMC
June 2015

Comparing methods of measuring geographic patterns in temporal trends: an application to county-level heart disease mortality in the United States, 1973 to 2010.

Ann Epidemiol 2015 May 19;25(5):329-335.e3. Epub 2015 Feb 19.

Division for Heart Disease and Stroke Prevention, Centers for Disease Control and Prevention, Atlanta, GA.

Purpose: To demonstrate the implications of choosing analytical methods for quantifying spatiotemporal trends, we compare the assumptions, implementation, and outcomes of popular methods using county-level heart disease mortality in the United States between 1973 and 2010.

Methods: We applied four regression-based approaches (joinpoint regression, both aspatial and spatial generalized linear mixed models, and Bayesian space-time model) and compared resulting inferences for geographic patterns of local estimates of annual percent change and associated uncertainty.

Results: The average local percent change in heart disease mortality from each method was -4.5%, with the Bayesian model having the smallest range of values. The associated uncertainty in percent change differed markedly across the methods, with the Bayesian space-time model producing the narrowest range of variance (0.0-0.8). The geographic pattern of percent change was consistent across methods with smaller declines in the South Central United States and larger declines in the Northeast and Midwest. However, the geographic patterns of uncertainty differed markedly between methods.

Conclusions: The similarity of results, including geographic patterns, for magnitude of percent change across these methods validates the underlying spatial pattern of declines in heart disease mortality. However, marked differences in degree of uncertainty indicate that Bayesian modeling offers substantially more precise estimates.
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http://dx.doi.org/10.1016/j.annepidem.2015.02.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4397179PMC
May 2015

GeoMedStat: an integrated spatial surveillance system to track air pollution and associated healthcare events.

Geospat Health 2014 Dec 1;8(3):S631-46. Epub 2014 Dec 1.

GIS and Remote Sensing, University of Mississippi Medical Center, Jackson, Mississippi.

Air pollutants, such as particulate matter with a diameter ≤2.5 microns (PM2.5) and ozone (O3), are known to exacerbate asthma and other respiratory diseases. An integrated surveillance system that tracks such air pollutants and associated disease incidence can assist in risk assessment, healthcare preparedness and public awareness. However, the implementation of such an integrated environmental health surveillance system is a challenge due to the disparate sources of many types of data and the implementation becomes even more complicated for a spatial and real-time system due to lack of standardised technological components and data incompatibility. In addition, accessing and utilising health data that are considered as Protected Health Information (PHI) require maintaining stringent protocols, which have to be supported by the system. This paper aims to illustrate the development of a spatial surveillance system (GeoMedStat) that is capable of tracking daily environmental pollutants along with both daily and historical patient encounter data. It utilises satellite data and the groundmonitor data from the US National Aeronautics and Space Administration (NASA) and the US Environemental Protection Agenecy (EPA), rspectively as inputs estimating air pollutants and is linked to hospital information systems for accessing chief complaints and disease classification codes. The components, developmental methods, functionality of GeoMedStat and its use as a real-time environmental health surveillance system for asthma and other respiratory syndromes in connection with with PM2.5 and ozone are described. It is expected that the framework presented will serve as an example to others developing real-time spatial surveillance systems for pollutants and hospital visits.
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http://dx.doi.org/10.4081/gh.2014.293DOI Listing
December 2014

Assessment of critical exposure and outcome windows in time-to-event analysis with application to air pollution and preterm birth study.

Biostatistics 2015 Jul 7;16(3):509-21. Epub 2015 Jan 7.

Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.

In reproductive epidemiology, there is a growing interest to examine associations between air pollution exposure during pregnancy and the risk of preterm birth (PTB). One important research objective is to identify critical periods of exposure and estimate the associated effects at different stages of pregnancy. However, population studies have reported inconsistent findings. This may be due to limitations from the standard analytic approach of treating PTB as a binary outcome without considering time-varying exposures together over the course of pregnancy. To address this research gap, we present a Bayesian hierarchical model for conducting a comprehensive examination of gestational air pollution exposure by estimating the joint effects of weekly exposures during different vulnerable periods. Our model also treats PTB as a time-to-event outcome to address the challenge of different exposure lengths among ongoing pregnancies. The proposed model is applied to a dataset of geocoded birth records in the Atlanta metropolitan area between 1999-2005 to examine the risk of PTB associated with gestational exposure to ambient fine particulate matter [Formula: see text]m in aerodynamic diameter (PM[Formula: see text]). We find positive associations between PM[Formula: see text] exposure during early and mid-pregnancy, and evidence that associations are stronger for PTBs occurring around week 30.
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http://dx.doi.org/10.1093/biostatistics/kxu060DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5963471PMC
July 2015

Using a geolocation social networking application to calculate the population density of sex-seeking gay men for research and prevention services.

J Med Internet Res 2014 Nov 18;16(11):e249. Epub 2014 Nov 18.

Department of Epidemiology, Laney Graduate School, Emory University, Atlanta, GA, United States.

Background: In the United States, human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) continues to have a heavy impact on men who have sex with men (MSM). Among MSM, black men under the age of 30 are at the most risk for being diagnosed with HIV. The US National HIV/AIDS strategy recommends intensifying efforts in communities that are most heavily impacted; to do so requires new methods for identifying and targeting prevention resources to young MSM, especially young MSM of color.

Objective: We piloted a methodology for using the geolocation features of social and sexual networking applications as a novel approach to calculating the local population density of sex-seeking MSM and to use self-reported age and race from profile postings to highlight areas with a high density of minority and young minority MSM in Atlanta, Georgia.

Methods: We collected data from a geographically systematic sample of points in Atlanta. We used a sexual network mobile phone app and collected application profile data, including age, race, and distance from each point, for either the 50 closest users or for all users within a 2-mile radius of sampled points. From these data, we developed estimates of the spatial density of application users in the entire city, stratified by race. We then compared the ratios and differences between the spatial densities of black and white users and developed an indicator of areas with the highest density of users of each race.

Results: We collected data from 2666 profiles at 79 sampled points covering 883 square miles; overlapping circles of data included the entire 132.4 square miles in Atlanta. Of the 2666 men whose profiles were observed, 1563 (58.63%) were white, 810 (30.38%) were black, 146 (5.48%) were another race, and 147 (5.51%) did not report a race in their profile. The mean age was 31.5 years, with 591 (22.17%) between the ages of 18-25, and 496 (18.60%) between the ages of 26-30. The mean spatial density of observed profiles was 33 per square mile, but the distribution of profiles observed across the 79 sampled points was highly skewed (median 17, range 1-208). Ratio, difference, and distribution outlier measures all provided similar information, highlighting areas with higher densities of minority and young minority MSM.

Conclusions: Using a limited number of sampled points, we developed a geospatial density map of MSM using a social-networking sex-seeking app. This approach provides a simple method to describe the density of specific MSM subpopulations (users of a particular app) for future HIV behavioral surveillance and allow targeting of prevention resources such as HIV testing to populations and areas of highest need.
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http://dx.doi.org/10.2196/jmir.3523DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4260063PMC
November 2014

Improving satellite-driven PM models with Moderate Resolution Imaging Spectroradiometer fire counts in the southeastern U.S.

J Geophys Res Atmos 2014 Oct 8;119(19):11375-11386. Epub 2014 Oct 8.

Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.

Multiple studies have developed surface PM (particle size less than 2.5 µm in aerodynamic diameter) prediction models using satellite-derived aerosol optical depth as the primary predictor and meteorological and land use variables as secondary variables. To our knowledge, satellite-retrieved fire information has not been used for PM concentration prediction in statistical models. Fire data could be a useful predictor since fires are significant contributors of PM. In this paper, we examined whether remotely sensed fire count data could improve PM prediction accuracy in the southeastern U.S. in a spatial statistical model setting. A sensitivity analysis showed that when the radius of the buffer zone centered at each PM monitoring site reached 75 km, fire count data generally have the greatest predictive power of PM across the models considered. Cross validation (CV) generated an of 0.69, a mean prediction error of 2.75 µg/m, and root-mean-square prediction errors (RMSPEs) of 4.29 µg/m, indicating a good fit between the dependent and predictor variables. A comparison showed that the prediction accuracy was improved more substantially from the nonfire model to the fire model at sites with higher fire counts. With increasing fire counts, CV RMSPE decreased by values up to 1.5 µg/m, exhibiting a maximum improvement of 13.4% in prediction accuracy. Fire count data were shown to have better performance in southern Georgia and in the spring season due to higher fire occurrence. Our findings indicate that fire count data provide a measurable improvement in PM concentration estimation, especially in areas and seasons prone to fire events.
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http://dx.doi.org/10.1002/2014JD021920DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5619254PMC
October 2014

Contact investigation of melioidosis cases reveals regional endemicity in Puerto Rico.

Clin Infect Dis 2015 Jan 30;60(2):243-50. Epub 2014 Sep 30.

Bacterial Special Pathogens Branch, Centers for Disease Control and Prevention, Atlanta, Georgia.

Background: Melioidosis results from infection with Burkholderia pseudomallei and is associated with case-fatality rates up to 40%. Early diagnosis and treatment with appropriate antimicrobials can improve survival rates. Fatal and nonfatal melioidosis cases were identified in Puerto Rico in 2010 and 2012, respectively, which prompted contact investigations to identify risk factors for infection and evaluate endemicity.

Methods: Questionnaires were administered and serum specimens were collected from coworkers, neighborhood contacts within 250 m of both patients' residences, and injection drug user (IDU) contacts of the 2012 patient. Serum specimens were tested for evidence of prior exposure to B. pseudomallei by indirect hemagglutination assay. Neighborhood seropositivity results guided soil sampling to isolate B. pseudomallei.

Results: Serum specimens were collected from contacts of the 2010 (n = 51) and 2012 (n = 60) patients, respectively. No coworkers had detectable anti-B. pseudomallei antibody, whereas seropositive results among neighborhood contacts was 5% (n = 2) for the 2010 patient and 23% (n = 12) for the 2012 patient, as well as 2 of 3 IDU contacts for the 2012 case. Factors significantly associated with seropositivity were having skin wounds, sores, or ulcers (odds ratio [OR], 4.6; 95% confidence interval [CI], 1.2-17.8) and IDU (OR, 18.0; 95% CI, 1.6-194.0). Burkholderia pseudomallei was isolated from soil collected in the neighborhood of the 2012 patient.

Conclusions: Taken together, isolation of B. pseudomallei from a soil sample and high seropositivity among patient contacts suggest at least regional endemicity of melioidosis in Puerto Rico. Increased awareness of melioidosis is needed to enable early case identification and early initiation of appropriate antimicrobial therapy.
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http://dx.doi.org/10.1093/cid/ciu764DOI Listing
January 2015

A study of adverse birth outcomes and agricultural land use practices in Missouri.

Environ Res 2014 Oct 27;134:420-6. Epub 2014 Sep 27.

University of Illinois at Chicago, School of Public Health, Epidemiology and Biostatistics Division, 1603W. Taylor Street, Chicago, IL 60607, United States of America.

Background: Missouri is an agriculturally intensive state, primarily growing corn and soybeans with additional rice and cotton farming in some southeastern counties. Communities located in close proximity to pesticide-treated fields are known to have increased exposure to pesticides and may be at increased risk of adverse birth outcomes. The study aims were to assess the relationship between county-level measures of crop-specific agricultural production and adverse birth outcomes in Missouri and to evaluate the most appropriate statistical methodologies for doing so.

Methods: Potential associations between county level data on the densities of particular crops and low birth weight and preterm births were examined in Missouri between 2004-2006. Covariates considered as potential confounders and effect modifiers included gender, maternal race/ethnicity, maternal age at delivery, maternal smoking, access to prenatal care, quarter of birth, county median household income, and population density. These data were analyzed using both standard Poisson regression models as well as models allowing for temporal and spatial correlation of the data.

Results: There was no evidence of an association between corn, soybean, or wheat densities with low birth weight or preterm births. Significant positive associations between both rice and cotton density were observed with both low birth weight and preterm births. Model results were consistent using Poisson and alternative models accounting for spatial and temporal variability.

Conclusions: The associations of rice and cotton with low birth weight and preterm births warrant further investigation. Study limitations include the ecological study design and limited available covariate information.
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http://dx.doi.org/10.1016/j.envres.2014.06.016DOI Listing
October 2014

Using self-organizing maps to develop ambient air quality classifications: a time series example.

Environ Health 2014 Jul 3;13:56. Epub 2014 Jul 3.

Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.

Background: Development of exposure metrics that capture features of the multipollutant environment are needed to investigate health effects of pollutant mixtures. This is a complex problem that requires development of new methodologies.

Objective: Present a self-organizing map (SOM) framework for creating ambient air quality classifications that group days with similar multipollutant profiles.

Methods: Eight years of day-level data from Atlanta, GA, for ten ambient air pollutants collected at a central monitor location were classified using SOM into a set of day types based on their day-level multipollutant profiles. We present strategies for using SOM to develop a multipollutant metric of air quality and compare results with more traditional techniques.

Results: Our analysis found that 16 types of days reasonably describe the day-level multipollutant combinations that appear most frequently in our data. Multipollutant day types ranged from conditions when all pollutants measured low to days exhibiting relatively high concentrations for either primary or secondary pollutants or both. The temporal nature of class assignments indicated substantial heterogeneity in day type frequency distributions (~1%-14%), relatively short-term durations (<2 day persistence), and long-term and seasonal trends. Meteorological summaries revealed strong day type weather dependencies and pollutant concentration summaries provided interesting scenarios for further investigation. Comparison with traditional methods found SOM produced similar classifications with added insight regarding between-class relationships.

Conclusion: We find SOM to be an attractive framework for developing ambient air quality classification because the approach eases interpretation of results by allowing users to visualize classifications on an organized map. The presented approach provides an appealing tool for developing multipollutant metrics of air quality that can be used to support multipollutant health studies.
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http://dx.doi.org/10.1186/1476-069X-13-56DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4098670PMC
July 2014

Spatial-temporal disease mapping of illicit drug abuse or dependence in the presence of misaligned ZIP codes.

GeoJournal 2013 Jun;78(3):463-474

Surveillance Research Program, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892.

Geo-referenced data often are collected in small, administrative units such as census enumeration districts or postal code areas. Such areas vary in geographic area and population size and may change over time. In research into drug-related health issues within the United States, U.S. Postal Service ZIP codes represent a commonly used unit for data collection, storage, and spatial analysis because of their widespread availability in health databases through patient contact and billing information. However, the ZIP code was developed for the specific purpose of delivering mail and may be changed at any time, and its design and development does not take into consideration problems that may arise in data collection, analysis, and presentation in health studies. In this paper, we propose a spatial hierarchical modeling approach to quantify trends within ZIP-code based counts when some fraction of ZIP codes change over the study period, that is, when the data are spatially misaligned across time. We propose a data vector approach and adjust the spatial auto-correlation structure within our Bayesian hierarchical model to provide inference for our misaligned data. We motivate and illustrate our approach to explore spatio-temporal patterns of amphetamine abuse and/or dependence in Tracy, California over the years 1995-2005. Uncertainty associated with misaligned data is modeled, quantified, and visualized. The approach offers a framework for further investigation into other risk factors in order to more fully understand the dynamics of illicit drug abuse or dependence across time and space in imperfectly measured data.
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http://dx.doi.org/10.1007/s10708-011-9429-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3724536PMC
June 2013