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      Associations between Source-Specific Particulate Matter and Respiratory Infections in New York State Adults

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          Abstract

          The response of respiratory infections to source-specific particulate matter (PM) is an area of active research. Using source-specific PM 2.5 concentrations at six urban sites in New York State, a case-crossover design, and conditional logistic regression, we examined the association between source-specific PM and the rate of hospitalizations and emergency department (ED) visits for influenza or culture-negative pneumonia from 2005 to 2016. There were at most N = 14 764 influenza hospitalizations, N = 57 522 influenza ED visits, N = 274 226 culture-negative pneumonia hospitalizations, and N = 113 997 culture-negative pneumonia ED visits included in our analyses. We separately estimated the rate of respiratory infection associated with increased concentrations of source-specific PM 2.5, including secondary sulfate (SS), secondary nitrate (SN), biomass burning (BB), pyrolyzed organic carbon (OP), road dust (RD), residual oil (RO), diesel (DIE), and spark ignition vehicle emissions (GAS). Increased rates of ED visits for influenza were associated with interquartile range increases in concentrations of GAS (excess rate [ER] = 9.2%; 95% CI: 4.3%, 14.3%) and DIE (ER = 3.9%; 95% CI: 1.1%, 6.8%) for lag days 0–3. There were similar associations between BB, SS, OP, and RO, and ED visits or hospitalizations for influenza, but not culture-negative pneumonia hospitalizations or ED visits. Short-term increases in PM 2.5 from traffic and other combustion sources appear to be a potential risk factor for increased rates of influenza hospitalizations and ED visits.

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          The ASA's Statement onp-Values: Context, Process, and Purpose

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            Association of fine particulate matter from different sources with daily mortality in six U.S. cities.

            Previously we reported that fine particle mass (particulate matter [less than and equal to] 2.5 microm; PM(2.5)), which is primarily from combustion sources, but not coarse particle mass, which is primarily from crustal sources, was associated with daily mortality in six eastern U.S. cities (1). In this study, we used the elemental composition of size-fractionated particles to identify several distinct source-related fractions of fine particles and examined the association of these fractions with daily mortality in each of the six cities. Using specific rotation factor analysis for each city, we identified a silicon factor classified as soil and crustal material, a lead factor classified as motor vehicle exhaust, a selenium factor representing coal combustion, and up to two additional factors. We extracted daily counts of deaths from National Center for Health Statistics records and estimated city-specific associations of mortality with each source factor by Poisson regression, adjusting for time trends, weather, and the other source factors. Combined effect estimates were calculated as the inverse variance weighted mean of the city-specific estimates. In the combined analysis, a 10 microg/m(3) increase in PM(2.5) from mobile sources accounted for a 3.4% increase in daily mortality [95% confidence interval (CI), 1.7-5.2%], and the equivalent increase in fine particles from coal combustion sources accounted for a 1.1% increase [CI, 0.3-2.0%). PM(2.5) crustal particles were not associated with daily mortality. These results indicate that combustion particles in the fine fraction from mobile and coal combustion sources, but not fine crustal particles, are associated with increased mortality.
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              Referent selection in case-crossover analyses of acute health effects of air pollution.

              The case-crossover design was proposed for the study of a transient effect of an intermittent exposure on the subsequent occurrence of a rare acute-onset disease. This design can be an alternative to Poisson time series regression for studying the health effects of fine particulate matter air pollution. Characteristics of time-series of particulate matter, including long-term time trends, seasonal trends, and short-term autocorrelations, require that referent selection in the case-crossover design be considered carefully and adapted to minimize bias. We performed simulations to evaluate the bias associated with various referent selection strategies for a proposed case-crossover study of associations between particulate matter and primary cardiac arrest. Some a priori reasonable strategies were associated with a relative bias as large as 10%, but for most strategies the relative bias was less than 2% with confidence interval coverage within 1% of the nominal level. We show that referent selection for case-crossover designs raises the same issues as selection of smoothing method for time series analyses. In addition, conditional logistic regression analysis is not strictly valid for some case-crossover designs, introducing further bias.
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                Author and article information

                Journal
                Environ Sci Technol
                Environ. Sci. Technol
                es
                esthag
                Environmental Science & Technology
                American Chemical Society
                0013-936X
                1520-5851
                22 November 2019
                21 January 2020
                : 54
                : 2
                : 975-984
                Affiliations
                [1] Department of Medicine, Department of Biostatistics and Computational Biology, §Department of Public Health Sciences, and Department of Environmental Medicine, University of Rochester Medical Center , Rochester, New York 14642, United States
                []Department of Environmental Health Sciences, University at Albany, The State University of New York , Rensselaer, New York 12203, United States
                [# ]Center for Air Resources Engineering and Science, Clarkson University , Potsdam, New York 13699, United States
                Author notes
                [* ]E-mail: daniel_croft@ 123456urmc.rochester.edu . Phone: 585 275 4161. Fax: 585 271 1171.
                Article
                10.1021/acs.est.9b04295
                6978840
                31755707
                15ff8a8a-ac2e-4be2-8aa1-aeabacc62994
                Copyright © 2019 American Chemical Society

                This is an open access article published under a Creative Commons Non-Commercial No Derivative Works (CC-BY-NC-ND) Attribution License, which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes.

                History
                : 18 July 2019
                : 22 November 2019
                : 18 November 2019
                Categories
                Article
                Custom metadata
                es9b04295
                es9b04295

                General environmental science
                General environmental science

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