|Alternative Title||Study on Spatial-temporal Characteristics and Prediction for Tuberculosis Trends in China during 2004-2015|
|Place of Conferral||兰州|
|Keyword||肺结核 流行 时空特征 空间自相关分析 空间扫描统计 预测|
Objectives: The aims of this study were to explore the epidemic situations and the general epidemiological characteristics of tuberculosis in China, and to analyze the temporal-spatial characteristics of it's distribution, and to detect the aggregation area, scope and time. Then, a prediction model for forecasting the trends of tuberculosis incidence was established. Results from the analysis were visualized. This research can be developed for tuberculosis epidemic monitoring, prevention and controlling, so as to provide references for making plans. At the same time, the study could provide analytical methods and references for the infectious disease data which has space attributes and seasonal characteristics.
Methods: In this research, tuberculosis data were collected from the Public Health Scientific data platform of the National Centre for Disease Control and Prevention. The population data were obtained from the Statistical Yearbook of the National Bureau of Statistics. The provincial administrative electronic maps were collected from the National Geomatics Center of China (NGCC).
The research methods are as follows:(1)General descriptive statistics were used to analyze the epidemic situation and characteristics of tuberculosis. H-P filtering method and seasonal index were applied to analyze the periodicity and seasonal characteristics of tuberculosis, and Cochran-Armitage trend test method was used to analyze the incidence trend.(2)The global auto-correlation and local disease distribution pattern were analyzed by the spatial 3D trend surface, distribution direction (standard deviation ellipse), Maoran`s I and LISA value.(3)Using the retrospective discrete Poisson spatio-temporal scanning statistics to detect the aggregation of tuberculosis in space-time. Then, quantitatively evaluate the risk of the aggregation area. (4) A single SARIMA model was used to fit the monthly incidence, and a combined model of the SARIMA-GRNN was constructed to predict the incidence trend of tuberculosis. Finally, the best model was produced by evaluating the applicability and effects of the prediction model.
Results: (1) From 2004 to 2015, a total of 12,321,559 tuberculosis cases were reported in 31 provinces (municipalities and autonomous regions), with an annual reported incidence of 77.31/100,000. The annual decreasing rate of tuberculosis incidence was 1.35%, showing a declining trend (Z=-231.67, P < 0.001). There were obvious seasonal and periodic characteristic in monthly incidence with a double peak distribution. The highest registration rate appeared in January, followed by March, the lowest appeared in December. Peak season of morbidity was January to July, the off-season of morbidity arised in winter. Obviously, males account for 69.50%. The number of middle and old people were the highest, followed by the 20-24 age groups (10.21%). The numbers of males in all age groups were higher than females. The most patients were farmers (61.84%). Farmers, factory workers, students, housekeeping workers and unemployed people were higher risk in all kinds of occupations, and the reported case of these occupations increased by years. The number of laboratory confirmed tuberculosis cases (55.41%) were higher than clinical diagnosis. The reported incidence of +TB and unsputum cases showed a downward trend. The areas with relatively low annual incidence were mainly located along the Bohai Sea and Huanghai (municipalities directly under the Central Government), Ningxia hui autonomous region and Yunnan province.(2) The incidence rate of tuberculosis in China was non-random distribution in space (the overall Moran`I were 0.2614~0.607, P < 0.05). The distribution of tuberculosis in the east and west direction was linear, and the incidence of tuberculosis in the east is obviously lower than in the west. The distribution of tuberculosis in the north and south shown shaped nearly like the letter "U", with the incidence of the central region was higher than the north and the south. In 2015, the center of tuberculosis incidence moved towards the southwest direction 157.19 km compared with 2004. The incidence of tuberculosis spread from southeast to northwest and the direction of incidence showed a weaker trend. The provinces of High-High distribution pattern had progressively increased, mainly distributed in the western regions (Xinjiang Uygur autonomous region, Tibet autonomous region and Qinghai province), Hunan province and Guangxi zhuang autonomous region. The Low-Low distribution patterns concentrated in Jingjin-hebei district (Beijing city, Tianjing city and Heibei province), Jiangsu province and Zhejiang province. (3) Five spatial-temporal clustering areas were detected by space-time scanning. The first concentration area was located in Lhasa city with a radius of 1601.35 km (LLR, RR were 121196.13 and 1.56, respectively). The aggregation range included the Tibet autonomous region, Xinjiang Uygur autonomous region, Guangxi zhuang autonomous region, Qinghai province, Gansu province, Sichuan province, Chongqing city and Yunnan province. Some areas properly coincided with hot spots in local spatial auto-correlation analysis.(4) The MSE, RMSE, MAE, MAPE, R2 of the SARIMA-GRNN model were 0.1069, 0.3269, 0.2032, 0.0313 and 0.9495, respectively. The results of indicates that the prediction accuracy of the combined model were better than the SARIMA model.
Conclusions:(1)The annual incidence rate showed a downward trend, indicating that the effectiveness of prevention and control of tuberculosis in China was significant. The monthly incidence had an evidently seasonal and cyclical with a bimodal distribution, Simultaneously the onset season from January to July. Male, middle aged people, farmers, workers, domestic and unemployed workers and students were the main targets of prevention and control of tuberculosis. The quality of diagnosis and registration of tuberculosis had gradually improved. Some provinces (autonomous regions) in the western region and the southern region were key prevention and control regions for tuberculosis epidemics.(2)The incidence was non random distribution in space, and there was an aggregation region. The center of the gravity moves to south-west. Incidence diffused in the direction of southeast to northwest and the direction trend was weakens. The “hot spot” epidemic regions were mainly distributed in the western regions (Xinjiang Uygur autonomous region, Tibet autonomous region, Qinghai province) as well as Hunan province and Guangxi zhuang autonomous region. The “cold spots” epidemic area were located in Beijing, Tianjin, Hebei, and Zhejiang provinces. The statistical results of spatio-temporal scans were consistent with the spatial auto-correlation analysis and seasonal analysis. The first-order spatio-temporal cluster area was located in the high-incidence area in the west, and the aggregation time was the peak season of onset.(3)The SARIMA-GRNN model fitted the linear and non-linear characteristics of tuberculosis time series data and possessed a higher prediction accuracy, which could be used for the early warning of the epidemic situation.(4)The conclusions of this study are notable by the combination of seasonal characteristics analysis, geographic information system spatial analysis, and space-time scan statistics analysis to detect the seasonal variation, spatial-temporal characteristics distribution, space time evolution and spatiotemporal aggregation.
|毛强. 2004-2015年全国肺结核流行趋势时空特征及预测研究[D]. 兰州. 兰州大学,2018.|
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