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

Abstract

Background: Temperature-related mortality risks have mostly been studied in urban areas, with limited evidence for urban-rural differences in the temperature impacts on health outcomes.

Objectives: We investigated whether temperature-mortality relationships vary between urban and rural counties in China.

Methods: We collected daily data on 1 km gridded temperature and mortality in 89 counties of Zhejiang Province, China, for 2009 and 2015. We first performed a two-stage analysis to estimate the temperature effects on mortality in urban and rural counties. Second, we performed meta-regression to investigate the modifying effect of the urbanization level. Stratified analyses were performed by all-cause, nonaccidental (stratified by age and sex), cardiopulmonary, cardiovascular, and respiratory mortality. We also calculated the fraction of mortality and number of deaths attributable to nonoptimum temperatures associated with both cold and heat components. The potential sources of the urban-rural differences were explored using meta-regression with county-level characteristics.

Results: Increased mortality risks were associated with low and high temperatures in both rural and urban areas, but rural counties had higher relative risks (RRs), attributable fractions of mortality, and attributable death counts than urban counties. The urban-rural disparity was apparent for cold (first percentile relative to minimum mortality temperature), with an RR of 1.47 [95% confidence interval (CI): 1.32, 1.62] associated with all-cause mortality for urban counties, and 1.98 (95% CI: 1.87, 2.10) for rural counties. Among the potential sources of the urban-rural disparity are age structure, education, GDP, health care services, air conditioners, and occupation types.

Conclusions: Rural residents are more sensitive to both cold and hot temperatures than urban residents in Zhejiang Province, China, particularly the elderly. The findings suggest past studies using exposure-response functions derived from urban areas may underestimate the mortality burden for the population as a whole. The public health agencies aimed at controlling temperature-related mortality should develop area-specific strategies, such as to reduce the urban-rural gaps in access to health care and awareness of risk prevention. Future projections on climate health impacts should consider the urban-rural disparity in mortality risks. https://doi.org/10.1289/EHP3556.

Figure 1.

(A) Locations of study area and automatic weather stations (some outside the province not shown), and (B) average of daily mean temperature across Zhejiang Province, 2009–2015.

Figure 2.

Pooled temperature–mortality associations along lag 0–21 d for cause-specific mortality for urban and rural counties in Zhejiang Province, 2009–2015, with 95% confidence intervals (CIs). Note: The vertical lines represent the minimum mortality temperature (MMT, solid) and the 1st and 99th percentiles of the temperature distribution (dashed) for 29 urban counties and 60 rural counties in Zhejiang Province, 2009–2015. The histograms represent the distributions of the daily averages of mean temperatures of urban and rural counties in Zhejiang Province, 2009–2015. The shading lines represent the 95% CI areas for risk estimates. Distributed lag nonlinear models (DLNMs) were used to model the exposure–lag–response associations between temperature and mortality. A cross-basis function was defined using a quadratic B-spline with two internal knots of temperature and a natural cubic spline for the space of 21 lag days with 4 degrees of freedom. RR, relative risk.

Figure 3.

Pooled temperature–mortality associations along lag 0–21 d for nonaccidental mortality stratified by age and sex for urban and rural counties in Zhejiang Province, 2009–2015, with 95% confidence intervals (CIs). Note: The vertical lines represent the minimum mortality temperature (MMT, solid) and the 1st and 99th percentiles of the temperature distribution (dashed) for 29 urban counties and 60 rural counties in Zhejiang Province, 2009–2015. The shading lines represent the 95% CI areas for risk estimates. Distributed lag nonlinear models (DLNMs) were used to model the exposure–lag–response associations between temperature and mortality. A cross-basis function was defined using a quadratic B-spline with two internal knots of temperature and a natural cubic spline for the space of 21 lag days with 4 degrees of freedom. RR, relative risk.