Spatial-Temporal Risk Assessment And Prediction Of Gout Disease Admission In Hefei,Anhui Province Based On GIS

Objective:With the continuous development of epidemiological research,only exploring the relationship between diseases and risk factors in space or time dimension has gradually failed to meet the needs of research.This study combines Geographical Information System（GIS）with Distributed Lag Nonlinear Model（DLNM）,To explore the comprehensive relationship between gout hospital admission risk and air pollutants in Hefei city in both spatial and temporal dimensions,in order to discover the possible hidden links,and to provide certain direction and reference for the modeling and accurate prediction of gout diseases.Methods:In this study,a total of 2893 patients admitted from seven Grade A hospitals in Hefei during 2015-2018 were screened out.These hospitals are the main choice of gout patients in Hefei.The hospital included the First Affiliated Hospital of Anhui Medical University,the First People’s Hospital of Hefei City,Anhui Provincial Hospital,the Second People’s Hospital of Hefei City,the Second Affiliated Hospital of Anhui Medical University,the Third People’s Hospital of Hefei City and the 901Hospital of the People’s Liberation Army.The collected admission data were stratified by region,sex and age.In addition,pollutant and meteorological data came from Hefei Environmental Monitoring Center and Hefei Meteorological Bureau,respectively.Pollutant data include daily inhalable particulate matter(PM₁₀,μg/m³),carbon monoxide（CO,μg/m³）,fine particulate matter(PM_2.5,μg/m³),carbon dioxide（NO₂,μg/m³）,8-hour maximum ozone concentration（O₃,μg/m³）and sulfur dioxide（SO₂,μg/m³）in the air.The meteorological data included the 24-hour 24-hour temperature and relative humidity（RH,%）in Hefei from 2015 to 2018.The daily temperature mean（Tmean,℃）was calculated using the single-hour temperature value.The daily diurnal temperature range（DTR,℃）is calculated from the one-hour difference between the maximum temperature and the minimum temperature within24 hours.The spatial analysis models used in this study include Natural break classification（NBC）and Logical Option Method（LOM）,Moran’s index（Moran’sⅠ）,Getis Gi statistics and kriging interpolation prediction method（kriging）.Structural Equation Modeling（SEM）was used before the time series analysis.In order to determine the exposure factors for subsequent temporal analysis,DLNM was used in the temporal analysis model.By using the collected data,we adjusted the parameters of each model,divided the admission risk areas of gout disease in Hefei into high,medium and low three categories,and found the differences in the exposure of pollutants in different areas among people of different genders and ages.The test level was set as two-sided test（p<0.05）.Results:The average annual admission rate of gout diseases in each district and county of Hefei from 2015 to 2018 was:Luyang District 246/（1 million）,Shushan District 127/（1 million）,Changfeng County 103/（1 million）,Yaohai District 98/（1million）,Baohe District 93/（1 million）,Feidong County 83/（1 million）,Feixi County74/（1 million）,Lujiang County 9/（1 million）,Chaohu City 9/（1 million）.The results of natural discontinuous grading showed that Luyang District and Shushan District were the high-risk areas for gout disease admission,Changfeng County,Yaohai District and Baohe District were the medium-risk areas,Feidong County,Feixi County,Lujiang County and Chaohu City were the low-risk areas.In general,the risk of gout disease admission in Hefei gradually decreased from north to south,and presented a middle high in the east-west direction.The trend of lower east and west.The results of spatial autocorrelation analysis showed that the index value of Moran’sⅠwas 0.384 and the Z score was 3.196,indicating that there was spatial clustering in the admission of gout patients in Hefei,and the index value of Moran’sⅠwas greater than 0,indicating that the clustering was positive spatial autocorrelation.The results of the analysis of high-low aggregation and cold-hot aggregation showed that the"high-high"aggregation phenomenon existed in Luyang District,Shushan District,Yaohai District and Changfeng County of Hefei,and Luyang District,Shushan District and Changfeng County were the hot spots for gout disease admission（p<0.05）,the result value of Getis Gi was 0.135,and the Z score was 2.777.Kriging interpolation prediction results showed that in 2015,the admission of gout disease in Hefei gradually spread from the northwest area to the southeast area.In 2018,compared with 2016 and 2017,the admission risk not only increased to the northeast and southwest areas,but also gradually increased to the central area of Hefei.The standard deviation root-mean-square results of the predicted results of Kriging interpolation from 2015 to 2018 were 0.9418,0.9852,0.9982 and 0.9737,respectively,indicating that the predicted results were stable.Before the time series analysis,we conducted SEM analysis on the data,and the results showed that O₃,Tmean,NO₂and PM_2.5had a positive impact on the admission risk of gout,while DTR,RH,PM₁₀,CO and SO₂had a negative impact on the admission risk of gout.The linear correlation coefficient between NO₂and gout admission was 0.29.Overall,the linear correlation between pollutants and gout admissions was higher than that of meteorological factors.The overall results of time series analysis show that the hospital admission risk of the general population in high-risk areas decreases under low PM_2.5and high O₃exposure,the hospital admission risk of the population in medium-risk areas decreases under both low and high SO₂exposure,and the hospital admission risk of the population in low-risk areas increases under high PM_2.5exposure.NO₂is reduced at lower levels of exposure.Stratified analysis showed that exposure to ultra-low levels of PM_2.5reduced the risk of gout hospitalization in men and young adults in high-risk areas,as well as in women in moderate-risk areas.Low levels of PM_2.5exposure reduce the risk of hospitalization in moderately at-risk women;Exposure to ultra-high levels of PM_2.5increases the risk of admission to hospital in young people in high-risk areas,as well as in men and the elderly in low-risk areas.Exposure to ultra-low levels of NO₂decreased the risk of admission to hospital for young people in medium-risk areas and increased the risk of admission to hospital for gout in elderly people in low-risk areas.High levels of NO₂exposure were associated with an increased risk of hospital admission in the elderly population in medium-risk areas and a decreased risk of hospital admission for gout disease in women in low-risk areas.Exposure to ultra-low levels of SO₂reduced the risk of admission for men in medium-risk areas and increased the risk of admission for women in low-risk areas.High levels of SO₂exposure increase the risk of admission to hospital for gout in women and older adults in medium-risk areas.Ultra-low O₃exposure increases the risk of hospitalization for gout in the elderly in low-risk areas.Low O₃exposure reduces the risk of hospital admission for women in low-risk areas.High O₃exposure reduces the risk of gout hospital admission in young people from high-risk areas and increases the risk of gout hospital admission in women from low-risk areas.Ultra-high O₃exposure reduces the risk of gout admission in men,women,young people,the elderly and men in medium-risk areas,and increases the risk of gout admission in women in low-risk areas.Conclusion:The overall level of SO₂in the air of Hefei is low.People living in cities need to improve their living habits and diet;more trees need to be planted in rural areas to prevent the impact of high PM_2.5;men living in rural areas need to pay attention to the impact of high PM_2.5;rural women need to pay attention to the impact of low SO₂and high O₃;young people in cities need to be aware of the effects of high PM_2.5on gout;elderly people need to pay special attention to the impact of gout hospitalization caused by exposure to extreme pollutants.In addition,this study only discussed the impact of air pollutant exposure on hospital admission for gout diseases in Hefei City,while other regions and risk factors have not been included in the study scope.Therefore,in the follow-up study,we will expand the study area,include more risk factors for analysis,and add institutional studies to further support the study results.In order to investigate the risk factors of admission to hospital for gout disease more completely,and to evaluate and predict them accurately and quickly.