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      Estimation of Short-Term Effects of Air Pollution on Stroke Hospital Admissions in Wuhan, China

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          Abstract

          Background and Objective

          High concentrations of air pollutants have been linked to increased incidence of stroke in North America and Europe but not yet assessed in mainland China. The aim of this study is to evaluate the association between stroke hospitalization and short-term elevation of air pollutants in Wuhan, China.

          Methods

          Daily mean NO 2, SO 2 and PM 10 levels, temperature and humidity were obtained from 2006 through 2008. Data on stroke hospitalizations (ICD 10: I60–I69) at four hospitals in Wuhan were obtained for the same period. A time-stratified case-crossover design was performed by season (April-September and October-March) to assess effects of pollutants on stroke hospital admissions.

          Results

          Pollution levels were higher in October-March with averages of 136.1 µg/m 3 for PM 10, 63.6 µg/m 3 for NO 2 and 71.0 µg/m 3 for SO 2 than in April-September when averages were 102.0 µg/m 3, 41.7 µg/m 3 and 41.7 µg/m 3, respectively ( p<.001). During the cold season, every 10 µg/m 3 increase in NO 2 was associated with a 2.9% (95%C.I. 1.2%–4.6%) increase in stroke admissions on the same day. Every 10 ug/m 3 increase in PM 10 daily concentration was significantly associated with an approximate 1% (95% C.I. 0.1%–1.4%) increase in stroke hospitalization. A two-pollutant model indicated that NO 2 was associated with stroke admissions when controlling for PM 10. During the warm season, no significant associations were noted for any of the pollutants.

          Conclusions

          Exposure to NO 2 is significantly associated with stroke hospitalizations during the cold season in Wuhan, China when pollution levels are 50% greater than in the warm season. Larger and multi-center studies in Chinese cities are warranted to validate our findings.

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          Most cited references36

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          Exposure measurement error in time-series studies of air pollution: concepts and consequences.

          S Zeger, Owen D Thomas, F Dominici … (2000)
          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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            Increased particulate air pollution and the triggering of myocardial infarction.

            Annette Peters, Douglas Dockery, James Muller … (2001)
            Elevated concentrations of ambient particulate air pollution have been associated with increased hospital admissions for cardiovascular disease. Whether high concentrations of ambient particles can trigger the onset of acute myocardial infarction (MI), however, remains unknown. We interviewed 772 patients with MI in the greater Boston area between January 1995 and May 1996 as part of the Determinants of Myocardial Infarction Onset Study. Hourly concentrations of particle mass <2.5 microm (PM(2.5)), carbon black, and gaseous air pollutants were measured. A case-crossover approach was used to analyze the data for evidence of triggering. The risk of MI onset increased in association with elevated concentrations of fine particles in the previous 2-hour period. In addition, a delayed response associated with 24-hour average exposure 1 day before the onset of symptoms was observed. Multivariate analyses considering both time windows jointly revealed an estimated odds ratio of 1.48 associated with an increase of 25 microg/m(3) PM(2.5) during a 2-hour period before the onset and an odds ratio of 1.69 for an increase of 20 microg/m(3) PM(2.5) in the 24-hour period 1 day before the onset (95% CIs 1.09, 2.02 and 1.13, 2.34, respectively). The present study suggests that elevated concentrations of fine particles in the air may transiently elevate the risk of MIs within a few hours and 1 day after exposure. Further studies in other locations are needed to clarify the importance of this potentially preventable trigger of MI.
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              Case-crossover analyses of air pollution exposure data: referent selection strategies and their implications for bias.

              Holly Janes, Lianne Sheppard, Thomas Lumley (2005)
              The case-crossover design has been widely used to study the association between short-term air pollution exposure and the risk of an acute adverse health event. The design uses cases only; for each individual case, exposure just before the event is compared with exposure at other control (or "referent") times. Time-invariant confounders are controlled by making within-subject comparisons. Even more important in the air pollution setting is that time-varying confounders can also be controlled by design by matching referents to the index time. The referent selection strategy is important for reasons in addition to control of confounding. The case-crossover design makes the implicit assumption that there is no trend in exposure across the referent times. In addition, the statistical method that is used-conditional logistic regression-is unbiased only with certain referent strategies. We review here the case-crossover literature in the air pollution context, focusing on key issues regarding referent selection. We conclude with a set of recommendations for choosing a referent strategy with air pollution exposure data. Specifically, we advocate the time-stratified approach to referent selection because it ensures unbiased conditional logistic regression estimates, avoids bias resulting from time trend in the exposure series, and can be tailored to match on specific time-varying confounders.
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                Author and article information

                Contributors
                Michelle L. Block: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2013
                Publication date (Electronic): 12 April 2013
                Volume: 8
                Issue: 4
                Electronic Location Identifier: e61168
                Affiliations
                [1 ]Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, Hubei, China
                [2 ]Wuhan University Global Health Institute, Wuhan University, Wuhan, Hubei, China
                [3 ]Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
                [4 ]Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
                [5 ]Department of Epidemiology, College of Public Health and Health Professions, College of Medicine, University of Florida, Gainesville, Florida, United States of America
                [6 ]Department of Labor and Environmental Hygiene, School of Public Health, Wuhan University, Wuhan, Hubei, China
                Virginia Commonwealth University, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: EOT YB. Performed the experiments: HX YB KJM. Analyzed the data: HX KJM LLB VCA XX. Contributed reagents/materials/analysis tools: VCA XX LLB KJM EOT. Wrote the paper: HX KJM VCA XX EOT LLB.

                Article
                Publisher ID: PONE-D-12-23786
                DOI: 10.1371/journal.pone.0061168
                PMC ID: 3625157
                PubMed ID: 23593421
                SO-VID: 0e6b0122-f38a-418f-9892-cf0f49d17f58
                Copyright statement: Copyright @ 2013
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 24 July 2012
                Date accepted : 7 March 2013
                Page count
                Pages: 5
                Funding
                These authors have no support or funding to report.
                Categories
                Subject: Research Article
                Subject: Earth Sciences
                Subject: Environmental Sciences
                Subject: Environmental Engineering
                Subject: Pollution
                Subject: Medicine
                Subject: Cardiovascular
                Subject: Stroke
                Subject: Epidemiology
                Subject: Cardiovascular Disease Epidemiology
                Subject: Environmental Epidemiology
                Subject: Epidemiological Methods
                Subject: Neurology
                Subject: Cerebrovascular Diseases
                Subject: Ischemic Stroke
                Subject: Hemorrhagic Stroke
                Subject: Public Health
                Subject: Environmental Health
                Subject: Preventive Medicine

                ScienceOpen disciplines: Uncategorized
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                ScienceOpen disciplines: Uncategorized

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