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      Acute effects of air pollution on mortality: A 17-year analysis in Kuwait

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          Abstract

          Background:

          The health burden from exposure to air pollution has been studied in many parts of the world. However, there is limited research on the health effects of air quality in arid areas where sand dust is the primary particulate pollution source.

          Objective:

          Study the risk of mortality from exposure to poor air quality days in Kuwait.

          Methods:

          We conducted a time-series analysis using daily visibility as a measure of particulate pollution and non-accidental total mortality from January 2000 through December 2016. A generalized additive Poisson model was used adjusting for time trends, day of week, and temperature. Low visibility (yes/no), defined as visibility lower than the 25th percentile, was used as an indicator of poor air quality days. Dust storm events were also examined. Finally, we examined these associations after stratifying by gender, age group, and nationality (Kuwaitis/non-Kuwaitis).

          Results:

          There were 73,748 deaths from natural causes in Kuwait during the study period. The rate ratio comparing the mortality rate on low visibility days to high visibility days was 1.01 (95% CI: 0.99–1.03). Similar estimates were observed for dust storms (1.02, 95% CI: 1.00–1.04). Higher and statistically significant estimates were observed among non-Kuwaiti men and non-Kuwaiti adolescents and adults.

          Conclusion:

          We observed a higher risk of mortality during days with poor air quality in Kuwait from 2000 through 2016.

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          Most cited references28

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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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            The distributed lag between air pollution and daily deaths.

            Many studies have reported associations between air pollution and daily deaths. Those studies have not consistently specified the lag between exposure and response, although most have found associations that persisted for more than 1 day. A systematic approach to specifying the lag association would allow better comparison across sites and give insight into the nature of the relation. To examine this question, I fit unconstrained and constrained distributed lag relations to the association between daily deaths of persons 65 years of age and older with PM10 in 10 U.S. cities (New Haven, Birmingham, Pittsburgh, Canton, Detroit, Chicago, Minneapolis, Colorado Springs, Spokane, and Seattle) that had daily monitoring for PM10. After control for temperature, humidity, barometric pressure, day of the week, and seasonal patterns, I found evidence in each city that the effect of a single day's exposure to PM10 was manifested across several days. Averaging over the 10 cities, the overall effect of an increase in exposure of 10 microg/m3 on a single day was a 1.4% increase in deaths (95% confidence intervals (CI) = 1.15-1.68) using a quadratic distributed lag model, and a 1.3% increase (95% CI = 1.04-1.56) using an unconstrained distributed lag model. In contrast, constraining the model to assume the effect all occurs in one day resulted in an estimate of only 0.65% (95% CI = 0.49-0.81), indicating that this constraint leads to a substantial underestimate of effect. Combining the estimated effect at each day's lag across the 10 cities showed that the effect was spread over several days and did not reach zero until 5 days after the exposure. Given the distribution of sensitivities likely in the general population, this result is biologically plausible. I also found a protective effect of barometric pressure in all 10 locations.
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              Visibility: Science and Regulation

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                Author and article information

                Journal
                7807270
                22115
                Environ Int
                Environ Int
                Environment international
                0160-4120
                1873-6750
                28 April 2019
                04 March 2019
                May 2019
                13 May 2019
                : 126
                : 476-483
                Affiliations
                [a ]Cyprus International Institute for Environmental and Public Health, Cyprus University of Technology, Limassol, Cyprus
                [b ]Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
                [c ]Department of Medicine, Faculty of Medicine, Kuwait University, Kuwait City, Kuwait
                [d ]Dasman Diabetes Institute, Kuwait City, Kuwait
                [e ]Pulmonary, Allergy, Sleep, and Critical Care Medicine Section, VA Boston Healthcare System, Boston, MA, USA
                [f ]Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
                [g ]Harvard Medical School, Boston, MA, USA
                [h ]Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, USA
                [i ]Chemical Engineering Department, Kuwait University, Kuwait City, Kuwait
                [j ]Environment and Life Sciences Center, Kuwait Institute for Scientific Research (KISR), Kuwait City, Kuwait
                Author notes
                [* ]Corresponding author at: Cyprus International Institute for Environmental and Public Health, Cyprus University of Technology, Limassol, Cyprus., souzana.achilleos@ 123456cut.ac.cy (S. Achilleos).
                Article
                VAPA1025041
                10.1016/j.envint.2019.01.072
                6511973
                30844583
                85aa8976-1fa3-47e1-9bd5-48791219acfb

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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                Categories
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                desert dust,dust storm,kuwait,mortality,time series
                desert dust, dust storm, kuwait, mortality, time series

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