Acute symptoms related to air pollution in urban areas: a study protocol

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Abstract

Background

The harmful effects of urban air pollution on general population in terms of annoying symptoms are not adequately evaluated. This is in contrast to the hospital admissions and short term mortality. The present study protocol is designed to assess the association between the level of exposure to certain ambient air pollutants and a wide range of relevant symptoms. Awareness of the impact of pollution on the population at large will make our estimates of the pertinent covert burden imposed on the society more accurate.

Methods/design

A cross sectional study with spatial analysis for the addresses of the participants was conducted. Data were collected via telephone interviews administered to a representative sample of civilians over age four in the city. Households were selected using random digit dialling procedures and randomization within each household was also performed to select the person to be interviewed. Levels of exposure are quantified by extrapolating the addresses of the study population over the air pollution matrix of the city at the time of the interview and also for different lag times. This information system uses the data from multiple air pollution monitoring stations in conjunction with meteorological data. General linear models are applied for statistical analysis.

Discussion

The important limitations of cross-sectional studies on acute effects of air pollution are personal confounders and measurement error for exposure. A wide range of confounders in this study are controlled for in the statistical analysis. Exposure error may be minimised by employing a validated geographical information system that provides accurate estimates and getting detailed information on locations of individual participants during the day. The widespread operation of open air conditioning systems in the target urban area which brings about excellent mixing of the outdoor and indoor air increases the validity of outdoor pollutants levels that are taken as exposure levels.

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Most cited references 15

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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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Air pollution and daily mortality: a review and meta analysis.

Peter J. Schwartz (1993)

The air pollution disaster in London in 1952 established that very high levels of particulate-based smog can cause dramatic increases in daily mortality. Recently, more than a dozen studies at much lower particle concentrations have reported evidence that exposure to lower levels of airborne particles results in smaller, but nonzero increases in daily mortality. These studies were examined in a meta-analysis. A primary focus of the meta-analysis was to examine effect size estimates across large differences in both the levels of potential confounding factors and in their correlation with airborne particle concentration. In the primary meta-analysis, airborne particle concentration was a significant risk factor for elevated mortality (RR = 1.06, 95% CI = 1.05-1.07). The relative risk is for a 100 micrograms/m3 increase in TSP concentration. While mortality peaked in the cold months in all locations, in the majority of the studies airborne particle concentrations were highest in the warm months, indicating that seasonal patterns were not responsible for the observed associations. The relative risk was 1.06 (95% CI = 1.05-1.07) when the analysis was restricted to cities with summer peaking pollution. The relative risk was identical in cities with above average annual temperatures and cities with colder climates. It was also identical in drier and more humid climates, and similar across a wide range of correlations between temperature and airborne particle concentrations. These results suggest that inadequate weather control was not responsible for the association. A detailed examination of data from Philadelphia showed that control for season and weather was adequate for removing all long-term seasonal and subseasonal patterns from the mortality data, and that using a very flexible nonlinear fit to the weather factors did not disturb the association with TSP. The most reasonable interpretation of this pattern of results is that the association is causal. This is supported by other studies which have reported that particulate air pollution was associated with lung function deficits, increased symptoms, and increased hospitalization.

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Air pollution and hospital admissions for respiratory and cardiovascular diseases in Hong Kong.

T W Wong, T S Lau, T S Yu … (1999)

To investigate short term effects of concentrations of pollutants in ambient air on hospital admissions for cardiovascular and respiratory diseases in Hong Kong. Retrospective ecological study. A Poisson regression was performed of concentrations of daily air pollutant on daily counts of emergency hospital admissions in 12 major hospitals. The effects of time trend, season, and other cyclical factors, temperature, and humidity were accounted for. Autocorrelation and overdispersion were corrected. Daily concentrations of nitrogen dioxide (NO2), sulphur dioxide (SO2), ozone (O3), and particulate matter or = 65 years were at higher risk. Significant positive interactions were detected between NO2, O3, and PM10, and between O3 and winter months. Adverse health effects are evident at current ambient concentrations of air pollutants. Further reduction in air pollution is necessary to protect the health of the community, especially that of the high risk group.

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Author and article information

Journal

Journal ID (nlm-ta): BMC Public Health

Title: BMC Public Health

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2458

Publication date Collection: 2006

Publication date (Electronic): 25 August 2006

Volume: 6

Page: 218

Affiliations

[1 ]School of Public Health & Centre for Environmental Research, Tehran University of Medical Sciences; Poursina Street, Keshavarz Boulevard, P.O. Box 14155-6446, Tehran, Iran

[2 ]Medical Ethics Research Center, Tehran University of Medical Sciences, Tehran, Iran

[3 ]Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

[4 ]Department of Human Genetics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

Article

Publisher ID: 1471-2458-6-218

DOI: 10.1186/1471-2458-6-218

PMC ID: 1569380

PubMed ID: 16934138

SO-VID: d57d96eb-046d-4399-bc4a-f396a467da38

License:

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

History

Date received : 6 May 2006

Date accepted : 25 August 2006

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