Analysis of rubella epidemic characteristics in Nanning from 2005 to 2022
Objective To analyze the epidemic characteristics of rubella in Nanning from 2005 to 2022,providing a reference for rubella prevention and control.Methods Reported rubella cases in Nanning from 2005 to 2022 were collected,and the Join Point regression model was utilized to analyze the trend of incidence.The spatial autocorrelation analysis and spatiotem-poral scanning were employed to assess the clustering of rubella cases.Results A total of 4 001 rubella cases were reported in Nanning from 2005 to 2022,with 12 outbreaks and no fatalities.The average annual incidence rate was 3.17 per 100 000,displaying an initial increase followed by a decrease,then another increase and decrease,with significant peaks in 2011 and 2018.Cases were concentrated between March and June,with the incidence rate in urban areas(3.70 per 100 000)being higher than that in rural counties(2.48 per 100 000).Males(3.58 per 100 000)had a higher incidence than females(2.72 per 100 000),and the highest average annual incidence rate was observed in the age group of 10 to 19 years,predominantly among students(73.53％).The proportion of students decreased,while the proportions of farmers,commercial service workers,homemakers,the unemployed,and other professions increased.Spatial autocorrelation analysis indicated significant cluster-ing,with a trend of"high-high"clusters shifting from surrounding counties to the urban center,ultimately shrinking and dis-appearing,while"low-low"clusters exhibited a merging trend.Spatiotemporal scanning identified two clusters covering seven counties and districts.Conclusion Significant progress has been made in rubella prevention and control in Nanning,with the incidence still primarily affecting the 10 to 19 age group and a noted shift in the age of onset.It is recommended to continue strengthening rubella vaccination for age-appropriate children,while also targeting vaccination efforts towards key populations such as middle school students and women of childbearing age.
RubellaIncidence rateJoin point regression modelClustering
NETL
NSTL
万方数据
