The Association of Daily Diabetes Mortality and Outdoor Air Pollution in Shanghai, China

By Kan, Haidong; Jia, Jian et al. | Journal of Environmental Health, October 2004 | Go to article overview
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The Association of Daily Diabetes Mortality and Outdoor Air Pollution in Shanghai, China


Kan, Haidong, Jia, Jian, Chen, Bingheng, Journal of Environmental Health


Introduction

During the past decade, numerous epidemiological studies have confirmed that ambient air pollution is associated with increases in daily mortality, especially mortality due to cardiovascular and respiratory diseases (Committee of the Environmental and Occupational Health Assembly of the American Thoracic Society, 1996). Subpopulations especially susceptible to ambient air pollution still need to be identified, however, and this issue has been regarded as a key research need (National Research Council, 1998). Also, the issue is obviously of great importance in the exploration of potential biological mechanisms of air pollutants and in setting relevant public health policies.

Recently, elevated exposure to air pollution has been associated with triggering of myocardial infarction (Peters, Dockery, Muller, & Mittleman, 2001), initiation of life-threatening arrhythmias (Peters et al., 2000), changes in cardiac rhythm and autonomic function (Pope et al., 1999), endothelial dysfunction (Brook, Brook, Urch, Vincent, Rajagopalan, & Silverman, 2002), increased plasma viscosity (Peters, Doring, Wichmann, & Koenig, 1997), and increased C-reactive protein (Peters et al., 2001). These findings suggest possible pathways by which air pollutants, especially particulate matter, affect the incidence and death rate of cardiovascular diseases.

Diabetes is known to be a chronic disease characterized by disturbance in the cardiovascular system (Stec et al., 2000). Therefore, diabetics have been suspected to be at higher risk of air pollution-related health events. Recently, the relationship has been investigated and confirmed positive in Canada (Goldberg et al., 2001) and the United States (Zanobetti & Schwartz, 2001, 2002). In Shanghai, the largest city of China, diabetes has become one of the leading causes of death, and the mortality from diabetes has increased sharply from 0.52 per 100,000 in 1966 to 16.95 per 100,000 in 1998 (Ling, Song, & Zhou, 2001). Therefore, it is worthwhile to explore the relationship between air pollution and diabetes mortality; air pollution is a potentially preventable risk factor that does not rely on behavioral changes and genetic characteristics. In the study reported here, the authors used a time-series approach to assess the effects of air pollution on daily diabetes mortality, and also explored the exposure-response patterns for major air pollutants with respect to diabetes mortality in Shanghai.

Methods

Data

The authors obtained daily diabetes mortality data (International Classification of Diseases, 9th revision [ICD-9]. Code 250) for the Zhabei District of Shanghai between January 1, 2001, and December 31, 2002. The Shanghai death certificate data should be considered reliable because all data were reported by physicians, not by relatives of the deceased. All mortality data and their accuracy were rechecked by the staffs of the local Center of Disease Control before being entered into the database. Meteorological data (daily average temperature, relative humidity, and dew point), which were measured by one station in central Shanghai, were provided by the Shanghai Meteorological Bureau. Air pollution data (daily average P[M.sub.10], S[O.sub.2], and N[O.sub.2] concentrations) were retrieved from the database of the Shanghai Environmental Monitoring Center. A total of six fixed-site monitoring stations are scattered among the urban districts of Shanghai. To match mortality and air pollution data in this research, the authors selected the results monitored by the station located in the Zhabei District. No data were found missing for the variables described above.

Statistical Analysis

The analysis followed a time-series procedure. The authors used log-linear models to estimate air pollution/diabetes mortality relative risks (RRs), while controlling for longer-term trends, seasonality, weather, and day of the week.

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