RESUMO
Objective:To analyze the epidemic status and spatial and temporal cluster distribution of visceral leishmaniasis (VL) in Gansu Province, and to provide evidence for making precise prevention and control measures and epidemic surveillance.Methods:The data of VL cases in Gansu Province reported in the China Disease Prevention and Control Information System Infectious Disease Reporting Information Management System from 2015 to 2021 were collected, descriptive epidemiology analysis was performed (three distribution of the disease). SaTScan v9.6 and ArcMap 10.7 were used for spatio-temporal cluster analysis.Results:A total of 368 VL cases were reported from 2015 to 2021, 89.13% (328/368) were concentrated in Longnan City and Gannan Tibetan Autonomous Prefecture. The number of VL cases increased year by year from 2015 to 2017, reaching a peak in 2017 (79 cases, accounting for 21.47%), and had decreased year by year since 2018. There were cases every month throughout the year, and the peak was in April in spring (38 cases). The ratio of male to female patients was 1.3 ∶ 1.0 (208/160). Scattered children aged less than 5 years old were more common (41.30%, 152/368). The spatio-temporal cluster analysis showed that both the spatial and temporal aggregation zones of VL in Tanchang County and Chengxian County were of statistical significance ( P < 0.05). Conclusion:The incidence of VL in Gansu Province is showing a low epidemic trend, having spatio-temporal aggregation, and precise monitoring and prevention and control should be strengthened in epidemic areas with different prevalence levels.
RESUMO
Objective To investigate the factors influencing the prevalence of echinococciasis in Gansu Province.Methods With the method of stratified random sampling,all the administrative villages in pastoral areas,half pastoral areas,agriculture and urban areas in the 72 counties in Gansu Province were selected;according to the layers of the population of the county population proportion calculated,the layer investigation numbers were determined,and a total of 16 administrative villages were selected in each county.In the 16 administrative villages,more than 200 people of permanent residents were surveyed in each village (shortage was made up from a nearby village),at least 3 200 people were investigated in a county.By using the combined method of inquiry and field observation,natural factors,such as drinking water,farming and animal husbandry production data were surveyed.All data were analyzed using SPSS 19.0 statistical software.Unauy linear and multiple linear regression analysis of influencing factors and the relationship between the prevalence were analyzed.Results Unary linear regression analysis showed that longitudes,latitudes,altitudes,average annual temperature,annual rainfalls,secondary industries,animal husbandry towns,drinking water sources,the number of epidemic towns,epidemic villages,per capita net incomes in animal husbandry regions,the total number of livestock and the number of sheep were factors influencing the prevalence of echinococciasis (F =4.705,P ＜0.05).Latitude,altitude,animal husbandry towns,the number of epidemic towns,epidemic villages,per capita net incomes in animal husbandry regions,the total number of livestock and the number of sheep and the prevalence of echinococciasis was positively correlated (r =0.282,0.285,0.387,0.508,0.540,0.317,0.475,0.594,all P ＜0.05);longitude,average annual temperature,annual rainfall,secondary industries,drinking water sources and the prevalence of echinococciasis was negatively correlated (r =-0.311,-0.244,-0.244,-0.389,-0.311,all P ＜0.05).Multiple linear regression analysis showed that differences of interactions of annual rainfalls,drinking water sources,epidemic villages,per capita net incomes of herdsmen,secondary industries and the total number of livestock between groups were statistically significant (t =-1.822,-3.920,3.013,1.715,-1.609,3.264,all P ＜0.05).Conclusion The factors influencing the prevalence of echinococciasis in Gansu Province are correlated with annual rainfalls,drinking water sources,epidemic villages,per capita net incomes of herdsmen,secondary industries and the total number of livestock.