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Spatial distribution of neighborhood-level housing prices and its association with all-cause mortality in Seoul, Korea (2013–2018): A spatial panel data analysis

ABSTRACT: Housing prices are known to be a relevant indicator of the socioeconomic position of the neighborhood. In a society where the market system mainly drives housing prices, residents' spatial patterning is formulated according to their socioeconomic position. Dividing the 2013–2018 entire study period into three periods, we explored the spatial distribution of housing prices and all-cause mortality and their association in Seoul, the country's capital city. The government authorities' data and 2015 census data were used for the study. We mapped the spatial distribution of housing prices and all-cause mortality and investigated the changes in distribution. We conducted a pooled ordinary least square (OLS) and spatial panel regression analysis to estimate housing prices elasticity of all-cause mortality. We also explored the possible mediating role of housing prices on the educational composition's effect on all-cause mortality. We found the common trends of increasing spatial patterning of housing prices and all-cause mortality. The magnitude of spatial patterning was far greater in housing prices than all-cause mortality. A pooled OLS regression analysis found that a 1% increase in housing price was associated with a 0.11% reduction in all-cause mortality after controlling the explanatory variables. Attenuation in the regression coefficient's magnitude was found after adding the neighborhood's educational composition to the model. As a result of spatial panel analysis, we found a direction and scale similar to the housing price elasticity of all-cause mortality in the final pooled OLS model. The results suggested that spatial health inequality in Korea's urban space mainly stems from socioeconomic inequality. Highlights • Korea's housing system is highly market-driven, and the distribution of housing prices is spatially patterned.• Doubling the neighborhood-level price of housing reduced all-cause mortality by 5% after adjusting neighborhood-level factors.• Housing prices and educational composition were related and explained part of the variance of all-cause mortality.

SUBMITTER: Kim I

PROVIDER: S-EPMC8599165 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundWireless emergency alerts (WEAs), which deliver disaster information directly to individuals' mobile phones, have been widely used to provide information related to COVID-19 and to encourage compliance with social distancing guidelines during the COVID-19 pandemic. The floating population refers to the number of people temporarily staying in a specific area, and this demographic data can be a useful indicator to understand the level of social distancing people are complying with during the COVID-19 pandemic.ObjectiveThis study aimed to empirically analyze the impact of WEAs on the floating population where WEAs were transmitted in the early stages of the COVID-19 pandemic. As most WEA messages focus on compliance with the government's social distancing guidelines, one of the goals of transmitting WEAs during the COVID-19 pandemic is to control the floating population at an appropriate level.MethodsWe investigated the empirical impact of WEAs on the floating population across 25 districts in Seoul by estimating a panel regression model at the district-hour level with a series of fixed effects. The main independent variables were the number of instant WEAs, the daily cumulative number of WEAs, the total cumulative number of WEAs, and information extracted from WEAs by natural language processing at the district-hour level. The data set provided a highly informative empirical setting as WEAs were sent by different local governments with various identifiable district-hour-level data.ResultsThe estimates of the impact of WEAs on the floating population were significantly negative (-0.013, P=.02 to -0.014, P=.01) across all specifications, implying that an additional WEA issuance reduced the floating population by 1.3% (=100(1-e-0.013)) to 1.4% (=100(1-e-0.014)). Although the coefficients of DCN (the daily cumulative number of WEAs) were also negative (-0.0034, P=.34 to -0.0052, P=.15) across all models, they were not significant. The impact of WEAs on the floating population doubled (-0.025, P=.02 to -0.033, P=.005) when the first 82 days of observations were used as subsamples to reduce the possibility of people blocking WEAs.ConclusionsOur results suggest that issuing WEAs and distributing information related to COVID-19 to a specific district was associated with a decrease in the floating population of that district. Furthermore, among the various types of information in the WEAs, location information was the only significant type of information that was related to a decrease in the floating population. This study makes important contributions. First, this study measured the impact of WEAs in a highly informative empirical setting. Second, this study adds to the existing literature on the mechanisms by which WEAs can affect public response. Lastly, this study has important implications for making optimal WEAs and suggests that location information should be included.

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Spatial distribution of neighborhood-level housing prices and its association with all-cause mortality in Seoul, Korea (2013–2018): A spatial panel data analysis

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