Spatial Analysis Of Main Risk Factors Of Chronic Diseases A Mong Residents In XX District Of Kunming

Objective:This thesis explores the current status of chronic diseases in residents of a district in Kunming,studies the geographical distribution of local chronic diseases and deeply analyzes the risk factors affecting the prevalence rate of chronic diseases in the jurisdiction by using geographic information systems and spatial analysis techniques.It provides theoretical basis and data support for the prevention and management of chronic diseases in the area and meanwhile,provides references for related types of research.Methods:The study selects a certain district in Kunming as the survey site On-site questionnaire surveys and physical examinations were conducted on the streets and towns in the district to collect the data required for the research.Use Epidata software to enter data,and then connect chronic disease data with administrative vector maps of jurisdictions made by ArcGIS to establish a geographic information system database of chronic disease in the district.Descriptive analysis of the prevalence rate of chronic diseases is conducted by the use of SPSS.Then,use Geoda software to perform global and local spatial autocorrelation analysis and make related thematic maps.The risk factors related to the prevalence rate of chronic diseases are selected to perform ordinary least squares regression(OLS),and the screened variables are used to construct a geographic weighted regression model(GWR).The selection of risk factors for chronic disease is mainly carried out from the aspects of population,economy,society and behavior.The establishment of spatial regression model is completed by GWR4 software.Results:1.The prevalence rate of chronic diseases among residents aged 18 years and over in the jurisdiction is 38.3%and the prevalence rate increases with age(Χ2=404.46,p<0.01),among which,the prevalence rate is the highest among those over 60 years old,up to 64.9%.There was statistical significant difference in prevalence rate between men and women(x2=9.680,p<0.01).The prevalence of male(40.7%)was higher than that of female(36.2%).The chronic diseases in the area are mainly dyslipidemia(34.5%),hypertension(19.5%)and diabetes(8.6%).2.The global spatial autocorrelation Moran’s I index of chronic disease prevalence rate is 0.310,Z value being 2.113 and P value being 0.033(p<0.05).There are three statistically significant local autocorrelation Moran’s I values,of which the coefficients are all positive.These areas include:A,C,and H.Regions A and C are high-high clustering,while region H is low-low clustering.3.OLS regression analysis of risk factors shows that the proportion of elderly population,overweight rate and awareness rate are statistically significant.The F statistic of OLS regression model was 28.413 with P<0.05,the model-corrected R2 being 0.859,the geographic weighted regression model(GWR)corrected R2 to 0.896 and AIC to 63.26.The effect of the proportion of the elderly population on the prevalence rate of chronic diseases is a positive correlation.The regression coefficient is the largest in the I area,and the smallest in G and F areas.The effect of overweight rate on the chronic disease prevalence is a positive relationship.The regression coefficient is the largest in I and J areas and the the smallest in E area.The effect of the awareness rate on the prevalence of chronic diseases is a negative correlation.The regression coefficient is the largest in the J area and the smallest in the I area.Conclusion:1.The district has a high prevalence rate of chronic diseases,which are mainly dyslipidemia,hypertension and diabetes.The prevalence rate increases with the increase of age,especially among the elderly over 60 years old,whose chronic diseases are more obvious,so chronic disease management and monitoring of big chronic diseases should be strengthened.2.The global spatial autocorrelation shows that there is a positive spatial correlation in the prevalence rate of chronic diseases,and the prevalence rate is high in south and low in north.The local spatial autocorrelation indicates that the prevalence rate of chronic diseases in the jurisdiction has phenomenon of spatial clustering,and regions A and C are high-high clustering,while region H is low-low clustering.The corresponding measures are carried out aiming at the above disease characteristics,mainly preventing and controlling the disease in the south region and adjacent defending in the concentration areas.3.Taking the prevalence rate of chronic diseases as the dependent variable and the influencing factors as the independent variables,the analysis of OLS model shows that the population proportion of the elderly,the overweight rate and the awareness rate are the three independent variables with statistical significance,which are the main influencing factors of the local chronic diseases.However,the adjusted R2 of GWR regression model is higher than OLS,with a high degree of fitting and strong explanatory power.In terms of data with spatial attributes,spatial analysis can be demonstrated by intuitive legends,and spatial regression model is more regionally targeted in the exploration of influencing factors.4.Different risk factors have obvious differences in the degree of chronic diseases in diverse regions,among which,the proportion of elderly population has a positive correlation on the influence of chronic diseases with greater effect in the south and north of the jurisdiction,but lower in the central part,the influence of overweight rate on chronic diseases is positively correlated with greater effect in central and northern regions,and lower in southern regions,and the influence of awareness rate on chronic diseases is negatively correlated,higher in the south and lower in the north.Therefore,it is necessary to carry out targeted management of chronic diseases according to local actual conditions.