Academic journal article Environmental Health Perspectives

Evidence for Urban-Rural Disparity in Temperature-Mortality Relationships in Zhejiang Province, China

Academic journal article Environmental Health Perspectives

Evidence for Urban-Rural Disparity in Temperature-Mortality Relationships in Zhejiang Province, China

Article excerpt

Introduction

Nonoptimum temperatures (either heat or cold) have been widely documented to be associated with increased risks of cause-specific mortality, such as cardiovascular and respiratory mortality, mostly in developed countries (Anderson and Bell 2009; Gasparrini et al. 2015; Guo et al. 2014). Most of these studies on temperature-mortality relationships have been conducted in urban areas (Analitis et al. 2008; Basu 2009; Madrigano et al. 2015b; Medina-Ramon and Schwartz 2007). In contrast, few studies have been performed in rural areas (Hashizume et al. 2009; Todd and Valleron 2015) because of the lack of sufficient meteorological and health data. In addition, most global, national, and regional temperature-related mortality projections usually use the same exposure-response associations for the whole population (Ballester et al. 2011; Huang et al. 2011; Takahashi et al. 2007), which ignores possible urban-rural differences and may result in an incorrect estimation of the temperature-related health burden.

Due to the urban heat island (UHI) effect, it is usually assumed that urban residents are at a higher risk of extreme heat than rural dwellers (Heaviside et al. 2017; Tan et al. 2010; Tomlinson et al. 2011). However, rural residents are also sensitive to nonoptimum temperatures and exhibit different patterns of vulnerability from those of urban populations (Kovach et al. 2015; Sheridan and Dolney 2003). For example, their living conditions and their outdoor occupations mean that they are more frequently exposed to extreme temperatures. Moreover, they might have limited risk awareness and limited access to health services, particularly in developing countries (Bai et al. 2016; Li et al. 2017; Williams et al. 2013).

To date, very few studies have compared temperature-related mortality risks between urban and rural areas (Henderson et al. 2013; Li et al. 2016; Urban et al. 2014), and the results are mixed. For example, Gabriel and colleagues reported a greater increase in mortality during heat waves in the city of Berlin, Germany, compared with neighboring nonurban areas (Gabriel and Endlicher 2011). In contrast, in Jiangsu and Hubei provinces in China, findings indicated a higher mortality increase associated with high temperatures in less urbanized counties compared with urban counties (Chen et al. 2016b; Zhang et al. 2017b). Of particular note is that most of these studies focused exclusively on heat-related mortality without assessment of health risks associated with cold temperatures. However, cold effects were reported to account for most of the temperature-related attributable mortality burden in a multicountry study (Gasparrini et al. 2015). Therefore, the urban-rural disparity in mortality risks associated with both cold and heat deserves to be further investigated with equal attention. A better understanding of these risks is important for effective decision support to design spatially targeted interventions and mitigation policies. This is especially relevant for developing countries, which are more sensitive to extreme temperature events and often lag behind developed countries in health risk management capacities (Laboy-Nieves et al. 2010; Mendelsohn et al. 2006; Tol et al. 2004).

Epidemiological studies have predominantly examined temperature-mortality associations in a city using temperatures from one site or the average from a network of sites (Guo et al. 2013), which induces exposure measurement error and biases the estimates. Satellite-measured land surface temperature (LST) has been used to identify the temperature variations at a high spatial resolution (Madrigano et al. 2015a; Wan 2008). However, LST cannot serve as a proper proxy for the daily mean temperature because only two images are generally available within a day, and cloud-contaminated values often lead to incomplete data (Wan 2008). Moreover, despite sometimes high correlations, LST cannot be used as a direct substitute for ambient temperature due to the complex relationship between them (Vancutsem et al. …

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