Hotspot detection and socio-ecological factor analysis of asthma hospitalization rate in Guangxi, China

Asthma is a major public health concern throughout the world. Numerous researches have shown that the spatial-temporal patterns of asthma are inconsistent, leading to the suggestion that these patterns are determined by multiple factors. This study aims to detect spatial-temporal clusters of asthma...

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Veröffentlicht in:Environmental research 2020-04, Vol.183, p.109201-109201, Article 109201
Hauptverfasser: Ma, Rui, Liang, Lizhong, Kong, Yunfeng, Zhai, Shiyan, Gu, Jiangyan, Zhang, Guangli, Wang, Tuanhui
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container_title Environmental research
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creator Ma, Rui
Liang, Lizhong
Kong, Yunfeng
Zhai, Shiyan
Gu, Jiangyan
Zhang, Guangli
Wang, Tuanhui
description Asthma is a major public health concern throughout the world. Numerous researches have shown that the spatial-temporal patterns of asthma are inconsistent, leading to the suggestion that these patterns are determined by multiple factors. This study aims to detect spatial-temporal clusters of asthma and analyze socio-ecological factors associated with the asthma hospitalization rate in Guangxi, China. Asthma hospitalization and socio-ecological data for 88 counties/municipal districts in Guangxi, China in 2015 was collected. Space-time scan statistics were applied to identify the high-risk periods and areas of asthma hospital admissions. We further used GeoDetector and Spearman correlation coefficient to investigate the socio-ecological factors associated with the asthma hospitalization rates. There were a total of 7804 asthma admissions in 2015. The high-risk period was from April to June. The age groups of 0–4 and ≥65 years were both at the highest risk, with hospital admission rates of 45.0/105 and 46.5/105, respectively. High-risk areas were found in central and western Guangxi with relative risk (RR) values of asthma hospitalizations greater than 2.0. GDP per capita and altitude were positively associated with asthma hospitalizations, while air pressure and wind speed had a negative association. The explanatory powers of these factors (i.e., GDP per capita, altitude, air pressure, wind speed) were 22%, 20%, 14% and 10%, respectively. The GDP per capita appears to have the strongest correlation with asthma hospitalization rates. High-risk areas were identified in central and western Guangxi characterized by high GDP per capita. These findings may be helpful for authorities developing targeted asthma prevention policies for high-risk areas and vulnerable populations, especially during high-risk periods. •In Guangxi, the high-risk period for asthma was from April to June.•Age groups of 0–4 and ≥65 years were at the highest risk.•Hotspots were mainly found in central and western Guangxi where the GDP per capita was high.•GDP per capita played an important role in asthma, which was positively correlated with asthma hospitalization rate.•GeoDetector is suitable for detecting risk factors of disease.
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Numerous researches have shown that the spatial-temporal patterns of asthma are inconsistent, leading to the suggestion that these patterns are determined by multiple factors. This study aims to detect spatial-temporal clusters of asthma and analyze socio-ecological factors associated with the asthma hospitalization rate in Guangxi, China. Asthma hospitalization and socio-ecological data for 88 counties/municipal districts in Guangxi, China in 2015 was collected. Space-time scan statistics were applied to identify the high-risk periods and areas of asthma hospital admissions. We further used GeoDetector and Spearman correlation coefficient to investigate the socio-ecological factors associated with the asthma hospitalization rates. There were a total of 7804 asthma admissions in 2015. The high-risk period was from April to June. The age groups of 0–4 and ≥65 years were both at the highest risk, with hospital admission rates of 45.0/105 and 46.5/105, respectively. High-risk areas were found in central and western Guangxi with relative risk (RR) values of asthma hospitalizations greater than 2.0. GDP per capita and altitude were positively associated with asthma hospitalizations, while air pressure and wind speed had a negative association. The explanatory powers of these factors (i.e., GDP per capita, altitude, air pressure, wind speed) were 22%, 20%, 14% and 10%, respectively. The GDP per capita appears to have the strongest correlation with asthma hospitalization rates. High-risk areas were identified in central and western Guangxi characterized by high GDP per capita. 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High-risk areas were found in central and western Guangxi with relative risk (RR) values of asthma hospitalizations greater than 2.0. GDP per capita and altitude were positively associated with asthma hospitalizations, while air pressure and wind speed had a negative association. The explanatory powers of these factors (i.e., GDP per capita, altitude, air pressure, wind speed) were 22%, 20%, 14% and 10%, respectively. The GDP per capita appears to have the strongest correlation with asthma hospitalization rates. High-risk areas were identified in central and western Guangxi characterized by high GDP per capita. 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Numerous researches have shown that the spatial-temporal patterns of asthma are inconsistent, leading to the suggestion that these patterns are determined by multiple factors. This study aims to detect spatial-temporal clusters of asthma and analyze socio-ecological factors associated with the asthma hospitalization rate in Guangxi, China. Asthma hospitalization and socio-ecological data for 88 counties/municipal districts in Guangxi, China in 2015 was collected. Space-time scan statistics were applied to identify the high-risk periods and areas of asthma hospital admissions. We further used GeoDetector and Spearman correlation coefficient to investigate the socio-ecological factors associated with the asthma hospitalization rates. There were a total of 7804 asthma admissions in 2015. The high-risk period was from April to June. The age groups of 0–4 and ≥65 years were both at the highest risk, with hospital admission rates of 45.0/105 and 46.5/105, respectively. High-risk areas were found in central and western Guangxi with relative risk (RR) values of asthma hospitalizations greater than 2.0. GDP per capita and altitude were positively associated with asthma hospitalizations, while air pressure and wind speed had a negative association. The explanatory powers of these factors (i.e., GDP per capita, altitude, air pressure, wind speed) were 22%, 20%, 14% and 10%, respectively. The GDP per capita appears to have the strongest correlation with asthma hospitalization rates. High-risk areas were identified in central and western Guangxi characterized by high GDP per capita. These findings may be helpful for authorities developing targeted asthma prevention policies for high-risk areas and vulnerable populations, especially during high-risk periods. •In Guangxi, the high-risk period for asthma was from April to June.•Age groups of 0–4 and ≥65 years were at the highest risk.•Hotspots were mainly found in central and western Guangxi where the GDP per capita was high.•GDP per capita played an important role in asthma, which was positively correlated with asthma hospitalization rate.•GeoDetector is suitable for detecting risk factors of disease.</abstract><cop>Netherlands</cop><pub>Elsevier Inc</pub><pmid>32050128</pmid><doi>10.1016/j.envres.2020.109201</doi><tpages>1</tpages></addata></record>
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subjects Asthma hospitalization
GeoDetector
Socio-ecological factors
Spatial-temporal clusters
title Hotspot detection and socio-ecological factor analysis of asthma hospitalization rate in Guangxi, China
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