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Zhang Y, Chang HH, Warren JL, Ebelt ST. A scalar-on-quantile-function approach for estimating short-term health effects of environmental exposures. Biometrics 2024; 80:ujae008. [PMID: 38477485 PMCID: PMC10934338 DOI: 10.1093/biomtc/ujae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 12/27/2023] [Accepted: 01/17/2024] [Indexed: 03/14/2024]
Abstract
Environmental epidemiologic studies routinely utilize aggregate health outcomes to estimate effects of short-term (eg, daily) exposures that are available at increasingly fine spatial resolutions. However, areal averages are typically used to derive population-level exposure, which cannot capture the spatial variation and individual heterogeneity in exposures that may occur within the spatial and temporal unit of interest (eg, within a day or ZIP code). We propose a general modeling approach to incorporate within-unit exposure heterogeneity in health analyses via exposure quantile functions. Furthermore, by viewing the exposure quantile function as a functional covariate, our approach provides additional flexibility in characterizing associations at different quantile levels. We apply the proposed approach to an analysis of air pollution and emergency department (ED) visits in Atlanta over 4 years. The analysis utilizes daily ZIP code-level distributions of personal exposures to 4 traffic-related ambient air pollutants simulated from the Stochastic Human Exposure and Dose Simulator. Our analyses find that effects of carbon monoxide on respiratory and cardiovascular disease ED visits are more pronounced with changes in lower quantiles of the population's exposure. Software for implement is provided in the R package nbRegQF.
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Affiliation(s)
- Yuzi Zhang
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, United States
| | - Howard H Chang
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, United States
| | - Joshua L Warren
- Department of Biostatistics, Yale University, New Haven, CT 06511, United States
| | - Stefanie T Ebelt
- Gangarosa Department of Environmental Health, Emory University, Atlanta, GA 30322, United States
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2
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Baker E, Barbillon P, Fadikar A, Gramacy RB, Herbei R, Higdon D, Huang J, Johnson LR, Ma P, Mondal A, Pires B, Sacks J, Sokolov V. Analyzing Stochastic Computer Models: A Review with Opportunities. Stat Sci 2022. [DOI: 10.1214/21-sts822] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Evan Baker
- Evan Baker is Postdoctoral Research Fellow, Living Systems Institute, University of Exeter, Stocker Road, Exeter, EX4 4QD, UK
| | - Pierre Barbillon
- Pierre Barbillon is Associate Professor, Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA-Paris, 16 rue Claude Bernard, 75231 Paris Cedex 05, France
| | - Arindam Fadikar
- Arindam Fadikar is Postdoctoral Appointee, Mathematics and Computer Science Division, Argonne National Laboratory, 9700 South Cass Ave., Lemont, Illinois 60439, USA
| | - Robert B. Gramacy
- Robert B. Gramacy is Professor, Department of Statistics, Virginia Tech, 250 Drillfield Drive Blacksburg, Virginia 24061, USA
| | - Radu Herbei
- Radu Herbei is Professor of Statistics, Department of Statistics, College of Arts and Sciences, The Ohio State University, 1958 Neil Ave., Columbus, Ohio 43210, USA
| | - David Higdon
- David Higdon is Professor, Department of Statistics, Virginia Tech, MC0439, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - Jiangeng Huang
- Jiangeng Huang is Senior Statistical Scientist, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, USA
| | - Leah R. Johnson
- Leah R. Johnson is Associate Professor, Department of Statistics, Computational Modeling and Data Analytics (CMDA), Virginia Tech, Hutcheson Hall, RM 409-B, 250 Drillfield Drive, Blacksburg, Virginia 24061, USA
| | - Pulong Ma
- Pulong Ma is Postdoctoral Fellow, Duke University and Statistical and Applied Mathematical Sciences Institute, 19 T.W. Alexander Drive, P.O. Box 110207, Durham, North Carolina 27709, USA
| | - Anirban Mondal
- Anirban Mondal is Assistant Professor, Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, 10900 Euclid Avenue, Yost Hall Room 337, Cleveland, Ohio 44106-7058, USA
| | - Bianica Pires
- Bianica Pires is Lead Modeling & Simulation Engineer, The MITRE Corporation, 7515 Colshire Dr, McLean, Virginia 22102, USA
| | - Jerome Sacks
- Jerome Sacks is Ph.D., NISS, 1460 N. Sandburg Ter, Apt 2902, Chicago, Illinois 60610, USA
| | - Vadim Sokolov
- Vadim Sokolov is Assistant Professor, Systems Engineering and Operations Research, George Mason University, Nguyen Engineering Building MS 4A6, Fairfax, Virginia 22302, USA
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Qian W, Chang HH. Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18041992. [PMID: 33670819 PMCID: PMC7922393 DOI: 10.3390/ijerph18041992] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 02/12/2021] [Accepted: 02/15/2021] [Indexed: 01/21/2023]
Abstract
Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50-70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.
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Statistical downscaling with spatial misalignment: Application to wildland fire PM 2.5 concentration forecasting. JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2020; 26:23-44. [PMID: 33867783 DOI: 10.1007/s13253-020-00420-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Fine particulate matter, PM2.5, has been documented to have adverse health effects and wildland fires are a major contributor to PM2.5 air pollution in the US. Forecasters use numerical models to predict PM2.5 concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce bias and quantify uncertainty. Typical model calibration techniques do not allow for errors due to misalignment of geographic locations. We propose a spatiotemporal downscaling methodology that uses image registration techniques to identify the spatial misalignment and accounts for and corrects the bias produced by such warping. Our model is fitted in a Bayesian framework to provide uncertainty quantification of the misalignment and other sources of error. We apply this method to different simulated data sets and show enhanced performance of the method in presence of spatial misalignment. Finally, we apply the method to a large fire in Washington state and show that the proposed method provides more realistic uncertainty quantification than standard methods.
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Yang CC, Chen YH, Chang HY. Composite marginal quantile regression analysis for longitudinal adolescent body mass index data. Stat Med 2017; 36:3380-3397. [PMID: 28574584 DOI: 10.1002/sim.7355] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Revised: 04/24/2017] [Accepted: 05/10/2017] [Indexed: 11/11/2022]
Abstract
Childhood and adolescenthood overweight or obesity, which may be quantified through the body mass index (BMI), is strongly associated with adult obesity and other health problems. Motivated by the child and adolescent behaviors in long-term evolution (CABLE) study, we are interested in individual, family, and school factors associated with marginal quantiles of longitudinal adolescent BMI values. We propose a new method for composite marginal quantile regression analysis for longitudinal outcome data, which performs marginal quantile regressions at multiple quantile levels simultaneously. The proposed method extends the quantile regression coefficient modeling method introduced by Frumento and Bottai (Biometrics 2016; 72:74-84) to longitudinal data accounting suitably for the correlation structure in longitudinal observations. A goodness-of-fit test for the proposed modeling is also developed. Simulation results show that the proposed method can be much more efficient than the analysis without taking correlation into account and the analysis performing separate quantile regressions at different quantile levels. The application to the longitudinal adolescent BMI data from the CABLE study demonstrates the practical utility of our proposal. Copyright © 2017 John Wiley & Sons, Ltd.
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Affiliation(s)
- Chi-Chuan Yang
- Institute of Statistical Science, Academia Sinica, Taipei, 11529, Taiwan
| | - Yi-Hau Chen
- Institute of Statistical Science, Academia Sinica, Taipei, 11529, Taiwan
| | - Hsing-Yi Chang
- Center for Health Policy Research and Development, National Health Research Institutes, Miaoli County, 35053, Taiwan
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Sanderson M, Arbuthnott K, Kovats S, Hajat S, Falloon P. The use of climate information to estimate future mortality from high ambient temperature: A systematic literature review. PLoS One 2017; 12:e0180369. [PMID: 28686743 PMCID: PMC5501532 DOI: 10.1371/journal.pone.0180369] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 06/14/2017] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Heat related mortality is of great concern for public health, and estimates of future mortality under a warming climate are important for planning of resources and possible adaptation measures. Papers providing projections of future heat-related mortality were critically reviewed with a focus on the use of climate model data. Some best practice guidelines are proposed for future research. METHODS The electronic databases Web of Science and PubMed/Medline were searched for papers containing a quantitative estimate of future heat-related mortality. The search was limited to papers published in English in peer-reviewed journals up to the end of March 2017. Reference lists of relevant papers and the citing literature were also examined. The wide range of locations studied and climate data used prevented a meta-analysis. RESULTS A total of 608 articles were identified after removal of duplicate entries, of which 63 were found to contain a quantitative estimate of future mortality from hot days or heat waves. A wide range of mortality models and climate model data have been used to estimate future mortality. Temperatures in the climate simulations used in these studies were projected to increase. Consequently, all the papers indicated that mortality from high temperatures would increase under a warming climate. The spread in projections of future climate by models adds substantial uncertainty to estimates of future heat-related mortality. However, many studies either did not consider this source of uncertainty, or only used results from a small number of climate models. Other studies showed that uncertainty from changes in populations and demographics, and the methods for adaptation to warmer temperatures were at least as important as climate model uncertainty. Some inconsistencies in the use of climate data (for example, using global mean temperature changes instead of changes for specific locations) and interpretation of the effects on mortality were apparent. Some factors which have not been considered when estimating future mortality are summarised. CONCLUSIONS Most studies have used climate data generated using scenarios with medium and high emissions of greenhouse gases. More estimates of future mortality using climate information from the mitigation scenario RCP2.6 are needed, as this scenario is the only one under which the Paris Agreement to limit global warming to 2°C or less could be realised. Many of the methods used to combine modelled data with local climate observations are simplistic. Quantile-based methods might offer an improved approach, especially for temperatures at the ends of the distributions. The modelling of adaptation to warmer temperatures in mortality models is generally arbitrary and simplistic, and more research is needed to better quantify adaptation. Only a small number of studies included possible changes in population and demographics in their estimates of future mortality, meaning many estimates of mortality could be biased low. Uncertainty originating from establishing a mortality baseline, climate projections, adaptation and population changes is important and should be considered when estimating future mortality.
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Affiliation(s)
| | - Katherine Arbuthnott
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Centre for Radiation, Chemical and Environmental Hazards, Public Health England, Didcot, United Kingdom
| | - Sari Kovats
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Shakoor Hajat
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
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Smith LB, Reich BJ, Herring AH, Langlois PH, Fuentes M. Multilevel quantile function modeling with application to birth outcomes. Biometrics 2015; 71:508-19. [PMID: 25761678 PMCID: PMC6601633 DOI: 10.1111/biom.12294] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2013] [Revised: 12/01/2014] [Accepted: 01/01/2015] [Indexed: 11/29/2022]
Abstract
Infants born preterm or small for gestational age have elevated rates of morbidity and mortality. Using birth certificate records in Texas from 2002 to 2004 and Environmental Protection Agency air pollution estimates, we relate the quantile functions of birth weight and gestational age to ozone exposure and multiple predictors, including parental age, race, and education level. We introduce a semi-parametric Bayesian quantile approach that models the full quantile function rather than just a few quantile levels. Our multilevel quantile function model establishes relationships between birth weight and the predictors separately for each week of gestational age and between gestational age and the predictors separately across Texas Public Health Regions. We permit these relationships to vary nonlinearly across gestational age, spatial domain and quantile level and we unite them in a hierarchical model via a basis expansion on the regression coefficients that preserves interpretability. Very low birth weight is a primary concern, so we leverage extreme value theory to supplement our model in the tail of the distribution. Gestational ages are recorded in completed weeks of gestation (integer-valued), so we present methodology for modeling quantile functions of discrete response data. In a simulation study we show that pooling information across gestational age and quantile level substantially reduces MSE of predictor effects. We find that ozone is negatively associated with the lower tail of gestational age in south Texas and across the distribution of birth weight for high gestational ages. Our methods are available in the R package BSquare.
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Affiliation(s)
- Luke B. Smith
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, 27695-8203, U.S.A
| | - Brian J. Reich
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, 27695-8203, U.S.A
| | - Amy H. Herring
- Department of Biostatistics and Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599-7420, U.S.A
| | - Peter H. Langlois
- Texas Department of State Health Services, Austin, Texas 78714-9347, U.S.A
| | - Montserrat Fuentes
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, 27695-8203, U.S.A
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Reich BJ, Chang HH, Foley KM. A spectral method for spatial downscaling. Biometrics 2014; 70:932-42. [PMID: 24965037 DOI: 10.1111/biom.12196] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2013] [Revised: 03/01/2014] [Accepted: 05/01/2014] [Indexed: 11/30/2022]
Abstract
Complex computer models play a crucial role in air quality research. These models are used to evaluate potential regulatory impacts of emission control strategies and to estimate air quality in areas without monitoring data. For both of these purposes, it is important to calibrate model output with monitoring data to adjust for model biases and improve spatial prediction. In this article, we propose a new spectral method to study and exploit complex relationships between model output and monitoring data. Spectral methods allow us to estimate the relationship between model output and monitoring data separately at different spatial scales, and to use model output for prediction only at the appropriate scales. The proposed method is computationally efficient and can be implemented using standard software. We apply the method to compare Community Multiscale Air Quality (CMAQ) model output with ozone measurements in the United States in July 2005. We find that CMAQ captures large-scale spatial trends, but has low correlation with the monitoring data at small spatial scales.
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Affiliation(s)
- Brian J Reich
- North Carolina State University, Raleigh, North Carolina, U.S.A
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Chang HH, Hao H, Sarnat SE. A Statistical Modeling Framework for Projecting Future Ambient Ozone and its Health Impact due to Climate Change. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2014; 89:290-297. [PMID: 24764746 PMCID: PMC3994127 DOI: 10.1016/j.atmosenv.2014.02.037] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
The adverse health effects of ambient ozone are well established. Given the high sensitivity of ambient ozone concentrations to meteorological conditions, the impacts of future climate change on ozone concentrations and its associated health effects are of concern. We describe a statistical modeling framework for projecting future ozone levels and its health impacts under a changing climate. This is motivated by the continual effort to evaluate projection uncertainties to inform public health risk assessment. The proposed approach was applied to the 20-county Atlanta metropolitan area using regional climate model (RCM) simulations from the North American Regional Climate Change Assessment Program. Future ozone levels and ozone-related excesses in asthma emergency department (ED) visits were examined for the period 2041-2070. The computationally efficient approach allowed us to consider 8 sets of climate model outputs based on different combinations of 4 RCMs and 4 general circulation models. Compared to the historical period of 1999-2004, we found consistent projections across climate models of an average 11.5% higher ozone levels (range: 4.8%, 16.2%), and an average 8.3% (range: -7% to 24%) higher number of ozone exceedance days. Assuming no change in the at-risk population, this corresponds to excess ozone-related ED visits ranging from 267 to 466 visits per year. Health impact projection uncertainty was driven predominantly by uncertainty in the health effect association and climate model variability. Calibrating climate simulations with historical observations reduced differences in projections across climate models.
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Affiliation(s)
- Howard H. Chang
- Department of Biostatistics and Bioinformatics, Emory University
| | - Hua Hao
- Department of Environmental Health, Emory University
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