Effects of urbanization on vulnerability to heat-related mortality in urban and rural areas in South Korea: a nationwide district-level time-series study

Abstract

Background Although urbanization is often an important topic in climate change studies, the complex effect of urbanization on heat vulnerability in urban and rural areas has rarely been studied. We investigated the disparate effects of urbanization on heat vulnerability in urban and rural areas, using nationwide data. Methods We collected daily weather data for all 229 administrative districts in South Korea (2011–17). Population density was applied as an urbanization indicator. We calculated the heat-mortality risk using a distributed lag nonlinear model and analysed the relationship with population density. We also examined district characteristics that can be related to the spatial heterogeneity in heat-mortality risk. Results We found a U-shaped association between population density and heat-mortality risk, with the highest risk for rural populations; in urban areas, risk increases with increasing population density. Higher heat-mortality risk was associated with a lower number of hospital beds per person and higher percentage of people requiring recuperation. The association between hospital beds and heat-mortality risk was prominent in high-density urban areas, whereas the association between the percentage of people requiring recuperation and heat-mortality risk was pronounced in rural areas. Conclusions Our findings indicate that the association between population density and heat-mortality risk is different in urban and rural areas, and that district characteristics related to heat-mortality risk also differ by urbanicity. These results can contribute to understanding the complex role of urbanization on heat vulnerability and can provide evidence to policy makers for prioritizing resources.

Heat-related mortality in U.S. state and private prisons: A case-crossover analysis

“…When looking at effect modification by urbanization, we found the largest increases in very urban counties and rural counties with no increase in mostly urban areas. Interestingly, this “u-shape” effect has been reported elsewhere in the epidemiologic literature such that the risk of heat-related mortality is high is rural locations but also increases with population density [ 5 , 16 , 24 ]. Some of the proposed hypotheses for this relationship include hospital access, proportion of families living in poverty, and proportion of elderly residents.…”

“…Based on previous studies [ 1 , 5 , 16 , 23 , 24 ], as well as our own hypotheses, we examined several personal characteristics and prison characteristics as potential modifiers of the impact of heat on total mortality. We considered age at death (≤ 44, 45–54, 55–64, ≥ 65) due to increasing risk mortality with age.…”

Social vulnerability indicators to the Health Impacts of Climate Change: A Scoping Review

“…The spatial distribution of human populations in urban, sub-urban, and rural areas increases vulnerability to the health impacts of climate change (16,48,49,59,62,67,70,90,91,94,96,98,105,109,116,118,144,149,162,168,172,187,193,194). Urban and sub-urban areas have higher population densities (25,41,45,48,58,62,75,98,105,109,113,119,127,151,162,171,178,(196)(197)(198)(199)(200)(201) and larger population sizes (98,202,203...…”

“…Urban and sub-urban areas have higher population densities (25,41,45,48,58,62,75,98,105,109,113,119,127,151,162,171,178,(196)(197)(198)(199)(200)(201) and larger population sizes (98,202,203) that are associated with elevated exposure to extreme heat due to processes associated with urban heat island effects that put urban populations at increased risk of adverse health impacts, while inequality within urban settings can further decrease adaptive capacity (48). On the other end, rural areas have higher vulnerability at equivalent temperatures, attributable to demographic and socioeconomic factors such as older age, lower education, less air conditioner use, agricultural occupations, and less access to health care services (150,200). Though the heterogeneity in the health effects of extreme temperatures between urban and rural areas varies across settings (150).…”

“…Multiple dimensions of housing and housing affordances as a resource that is inequitably distributed in society were used as indicators of social vulnerability in the review (44 of 205 studies) (28, 35, 36, 38, 41, 42, 44, 46, 47, 58, 67, 75, 87, 89, 95, 100, 105, 112-114, 118, 119, 162, 176, 179, 182, 186, 188, 189, 195, 200, 201, 208-212). Social structures (i.e., tenure type, percentage of homeownership, and percentage of rental housing) (42,44,67,75,89,105,114,119,176,182,188,201) and housing affordances (i.e., air conditioning ownership) (41,58,87,100,112,119,179,189,195) have been more often than covered, followed by building age (38,47,89,95,113,118,162), dwelling type (28,35,36,46,118,186,200,212), dwelling size/overcrowding (22,36,42,45,75,102,123), building materials…”