摘要
目的基于调查摸底数据,分析甘肃省大骨节病的空间分异规律及主要环境风险因子,为大骨节病的预防、干预及消除提供科学支持。方法主要采用了数理统计的分析方法,采用R语言对结果进行分析和绘图,采用全局莫兰指数(Moran’s I)和局部莫兰指数(Local Moran’s I)分析大骨节病的发病空间聚集或随机分散特征,利用地理探测器来探讨主要环境风险因子及其影响机制。结果①甘肃省的大骨节病历史病区有7个市(州)、37个县(区)、381个乡镇,主要集中在陇东、陇南地区;②地理环境风险因子对大骨节病的解释率较高,分别解释了52.9%(P<0.05)、41.9%(P<0.05)、38.1%(P<0.05)、36.3%(P<0.01)和36.3%(P<0.1)的空间贡献率,具有统计学意义;③除了地理环境风险,社会经济风险方面,人均可支配收入、人均财政预算支出、老年人口比例等指标的贡献率最高,解释率分别为44.3%(P<0.05)、28.8%(P=0.153)和24.7%(P=0.187),说明个人经济状况能很大程度上影响大骨节病的发病率,较好的经济状况可以及早地改善生活条件和质量,对大骨节病的预防提供了一定条件。结论今后应围绕大骨节病的集中集聚区开展预防、干预和治疗工作。
Objective Based on investigated data,we analyzed the spatial differentiation patterns and main environmental risk factors of Kashin-Beck disease(KBD)in Gansu Province,so as to provide scientific support for the prevention,intervention and elimination of KBD.Methods The main analysis methods adopted were mathematical statistics,and the R language was used to analyze and plot the results.The global Moran's I and local Moran's I were used to analyze the spatial aggregation or random dispersion characteristics of KBD,and geographic detectors were used to explore the main environmental risk factors and their influencing mechanisms.Results①The historical KBD concentrated areas in Gansu Province cover 7 cities(prefectures),37 counties(districts)and 381 towns.They are mainly concentrated in the east and south of Gansu province.②Geographical environmental risk factors have a high explanation rate for KBD,explaining 52.9%(P<0.05),41.9%(P<0.05),38.1%(P<0.05),36.3%(P<0.05)and 36.3%(P<0.1)of the spatial contribution rate,respectively,which was statistically significant.For socioeconomic risks,per capita disposable income,per capita fiscal budget,and the proportion of the elderly had the highest contribution rates,with explanation rates of 44.3%(P<0.05),28.8%(P=0.153)and 24.7%(P=0.187),respectively.The result indicated that personal economic conditions could greatly affect the incidence of KBD.Better economic conditions could improve living conditions and quality early,and provide certain conditions for the prevention of KBD.Conclusion In the future,
作者
罗泽刚
柳亚亚
韩志坚
LUO Zegang;LIU Yaya;HAN Zhijian(People's Hospital of Zhuanglang County,Pingliang,Gansu744600,China;不详)
出处
《中国地方病防治》
CAS
2024年第5期361-364,377,共5页
Chinese Journal of Control of Endemic Diseases
基金
甘肃省卫生健康行业2022年度科研管理项目(GSWSKY2022-84)。
关键词
大骨节病
甘肃省
空间聚类分析
地理探测器
风险因子分析
莫兰指数
Kaschin-Beck disease
Gansu Province
Spatial cluster analysis
Geographic detector
Risk factor analysis
Moran's I