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      Air Pollution and Hospitalization for Headache in Chile

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          Abstract

          The authors performed a time-series analysis to test the association between air pollution and daily numbers of hospitalizations for headache in 7 Chilean urban centers during the period 2001–2005. Results were adjusted for day of the week and humidex. Three categories of headache—migraine, headache with cause specified, and headache not otherwise specified—were all associated with air pollution. Relative risks for migraine associated with interquartile-range increases in specific air pollutants were as follows: 1.11 (95% confidence interval (CI): 1.06, 1.17) for a 1.15-ppm increase in carbon monoxide; 1.11 (95% CI: 1.06, 1.17) for a 28.97-μg/m 3 increase in nitrogen dioxide; 1.10 (95% CI: 1.04, 1.17) for a 6.20-ppb increase in sulfur dioxide; 1.17 (95% CI: 1.08, 1.26) for a 69.51-ppb increase in ozone; 1.11 (95% CI: 1.00, 1.19) for a 21.51-μg/m 3 increase in particulate matter less than 2.5 μm in aerodynamic diameter (PM 2.5); and 1.10 (95% CI: 1.04, 1.15) for a 37.79-μg/m 3 increase in particulate matter less than 10 μm in aerodynamic diameter (PM 10). There was no significant effect modification by age, sex, or season. The authors conclude that air pollution appears to increase the risk of headache in Santiago Province. If the relation is causal, the morbidity associated with headache should be considered when estimating the burden of illness and costs associated with poor air quality.

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          Fine particulate air pollution and mortality in 20 U.S. cities, 1987-1994.

          Air pollution in cities has been linked to increased rates of mortality and morbidity in developed and developing countries. Although these findings have helped lead to a tightening of air-quality standards, their validity with respect to public health has been questioned. We assessed the effects of five major outdoor-air pollutants on daily mortality rates in 20 of the largest cities and metropolitan areas in the United States from 1987 to 1994. The pollutants were particulate matter that is less than 10 microm in aerodynamic diameter (PM10), ozone, carbon monoxide, sulfur dioxide, and nitrogen dioxide. We used a two-stage analytic approach that pooled data from multiple locations. After taking into account potential confounding by other pollutants, we found consistent evidence that the level of PM10 is associated with the rate of death from all causes and from cardiovascular and respiratory illnesses. The estimated increase in the relative rate of death from all causes was 0.51 percent (95 percent posterior interval, 0.07 to 0.93 percent) for each increase in the PM10 level of 10 microg per cubic meter. The estimated increase in the relative rate of death from cardiovascular and respiratory causes was 0.68 percent (95 percent posterior interval, 0.20 to 1.16 percent) for each increase in the PM10 level of 10 microg per cubic meter. There was weaker evidence that increases in ozone levels increased the relative rates of death during the summer, when ozone levels are highest, but not during the winter. Levels of the other pollutants were not significantly related to the mortality rate. There is consistent evidence that the levels of fine particulate matter in the air are associated with the risk of death from all causes and from cardiovascular and respiratory illnesses. These findings strengthen the rationale for controlling the levels of respirable particles in outdoor air.
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            Exposure measurement error in time-series studies of air pollution: concepts and consequences.

            Misclassification of exposure is a well-recognized inherent limitation of epidemiologic studies of disease and the environment. For many agents of interest, exposures take place over time and in multiple locations; accurately estimating the relevant exposures for an individual participant in epidemiologic studies is often daunting, particularly within the limits set by feasibility, participant burden, and cost. Researchers have taken steps to deal with the consequences of measurement error by limiting the degree of error through a study's design, estimating the degree of error using a nested validation study, and by adjusting for measurement error in statistical analyses. In this paper, we address measurement error in observational studies of air pollution and health. Because measurement error may have substantial implications for interpreting epidemiologic studies on air pollution, particularly the time-series analyses, we developed a systematic conceptual formulation of the problem of measurement error in epidemiologic studies of air pollution and then considered the consequences within this formulation. When possible, we used available relevant data to make simple estimates of measurement error effects. This paper provides an overview of measurement errors in linear regression, distinguishing two extremes of a continuum-Berkson from classical type errors, and the univariate from the multivariate predictor case. We then propose one conceptual framework for the evaluation of measurement errors in the log-linear regression used for time-series studies of particulate air pollution and mortality and identify three main components of error. We present new simple analyses of data on exposures of particulate matter < 10 microm in aerodynamic diameter from the Particle Total Exposure Assessment Methodology Study. Finally, we summarize open questions regarding measurement error and suggest the kind of additional data necessary to address them. Images Figure 1 Figure 2 Figure 3
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              Nonlinear mixed effects models for repeated measures data.

              We propose a general, nonlinear mixed effects model for repeated measures data and define estimators for its parameters. The proposed estimators are a natural combination of least squares estimators for nonlinear fixed effects models and maximum likelihood (or restricted maximum likelihood) estimators for linear mixed effects models. We implement Newton-Raphson estimation using previously developed computational methods for nonlinear fixed effects models and for linear mixed effects models. Two examples are presented and the connections between this work and recent work on generalized linear mixed effects models are discussed.
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                Author and article information

                Journal
                Am J Epidemiol
                amjepid
                aje
                American Journal of Epidemiology
                Oxford University Press
                0002-9262
                1476-6256
                15 October 2009
                9 September 2009
                9 September 2009
                : 170
                : 8
                : 1057-1066
                Author notes
                Correspondence to Dr. Sabit Cakmak, Division of Statistics, Health Canada, 50 Columbine Driveway, Ottawa, Ontario, Canada K1A 0K9 (e-mail: sabit_cakmak@ 123456hc-sc.gc.ca ).
                Article
                10.1093/aje/kwp217
                2755178
                19741041
                1c2f7402-f4ab-4dd0-b746-ca452f90701c
                American Journal of Epidemiology © 2009 The Authors

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 February 2009
                : 25 June 2009
                Categories
                Original Contributions

                Public health
                headache,air pollution,environment
                Public health
                headache, air pollution, environment

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