| Objective: To investigate the chronic respiratory mortality and disease burden in Qingdao from 2012 to 2020,and to analyze the time trend and geographical distribution characteristics of mortality,so as to provide a scientific basis for developing chronic respiratory disease prevention and control strategies and optimizing the allocation of health resources in Qingdao.Methods: Deaths from chronic respiratory diseases from 2012 to 2020 were collected from the cause of death monitoring system in Qingdao City.Crude mortality rate and standardized mortality rate were calculated by gender,urban and rural areas,and age groups Average annual percent change(AAPC)and annual percent change(APC)were used to calculate global time trends in standardized mortality and time trends in each time segment,respectively.Years of life lost(YLL)from premature death were used to calculate the burden of disease.Arc Map 10.7 software was used for crude mortality disease mapping,while Global Moran’s I index and high/low cluster analysis(Getis-Ord General G)for global autocorrelation analysis.Local indicators of spatial autocorrelation(LISA)clustering areas and Getis-Ord Gi* were used for local autocorrelation analysis.Unbiased optimal estimation of variables in finite regions using ordinary Kriging interpolation.Spatio-temporal scan statistics were used to identify areas with high mortality values.Results: 1.There were 23,955 deaths from chronic respiratory diseases among the registered residents in Qingdao from 2012 to 2020.The crude mortality rate was 32.46 per100 000,and the standardized rate was 29.48 per 100 000.The standard mortality rate of chronic respiratory disease was 37.028 per 100 000 for males and 2205 per 100 000 for females.The urban population was 2389/100 000,and the rural population was 4506/100000.After Chi-square test,P<0.05,urban and rural and gender differences were statistically significant.Mortality from chronic respiratory diseases increases with age,beginning to rise significantly after age 55 and peaking after age 85.2.The results of the study on disease burden show that the YLL lost by urban residents due to chronic respiratory diseases from 2012 to 2020 is 15,969 person-years,accounting for 64.39% of males and 35.61% of females.The YLL rate caused by chronic respiratory system was 0.97 per 1000 population.The YLL of rural residents who died from chronic respiratory diseases was 59,930 person-years,accounting for 59.29% of males and40.71% of females.The YLL rate caused by chronic respiratory system was 1.69/1000,1.88/1000 for males and 1.47/1000 for females.The disease burden of chronic respiratory diseases is higher in rural areas than in urban areas and for men than women.3.The results of time trend analysis showed that from 2012 to 2020,the standardized mortality rate of chronic respiratory diseases showed a downward trend in the overall time trend,and the total annual change rate was-4.1%(t=-5.1,P<0.01).The optimal fitting model found by the segmental time trend analysis is an inflection point,namely two time trends: an upward trend during 2012-2015(APC=5.4,P=0.041),and a downward trend during 2015-2016(APC=-9.4,P< 0.01).A gender breakdown found similar trends among male and female residents.4.The crude mortality map shows that in 2020,the high incidence of crude mortality of chronic respiratory diseases in Qingdao is distributed in the central area of Huangdao District,Jimo City,Laixi City and Pingdu City.The low-incidence areas are mainly distributed in the eastern part of Huangdao District,Chengyang District,Laoshan District and Licang District.The global Moran index(Moran I)was 0.292,and the P value was less than 0.01,indicating that the global spatial distribution of chronic respiratory diseases in the towns and streets of Qingdao in 2020 had a significant spatial aggregation.The Z-score of the General G test of crude mortality was positive(z=3.803),P<0.01,indicating that the global spatial distribution was a high-value cluster distribution.Local clustering and outlier cluster analysis showed that there were 41 significant clustering areas in Qingdao,including15 high-high areas,3 high-low areas,3 low-high areas and 20 low-low areas.The cold hot spot analysis showed that there were obvious cold hot spot distribution areas for chronic respiratory diseases in Qingdao.Among them,there are 21 cold spots and 18 hot spots.The spatial prediction distribution map obtained from Kriging’s analysis showed that the mortality rate of chronic respiratory diseases showed a U-shaped trend in both east-west and north-south directions.Spatiotemporal scanning analysis detected two high death aggregations in the total population,it includes Huangdao District from 2012 to 2015,and Laixi,Pingdu and Jimo from 2014 to 2017.There are three high death aggregations in males and three in females.Conclusions: 1,The mortality rate of chronic respiratory diseases in Qingdao from2012 to 2020 showed a decreasing trend,among which the mortality rate and disease burden of the elderly,rural and male populations were the highest,and these populations should be the key groups for prevention and control.2,the spatial distribution shows a positive correlation between high and low value clusters,and there are abnormal areas with lowhigh and high-low clusters.The western part of Huangdao District,Jimo City,Laixi City,most areas of Pingdu City is a possible cluster area with high mortality.It is necessary to determine key prevention and control areas for the cluster area with high mortality,further promote the early diagnosis and treatment of chronic respiratory diseases,and rationally optimize the allocation of public health resources.Reduce mortality from chronic respiratory diseases. |
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