采用模糊物元法对慢性疾病监测点优化的探讨

Optimization of chronic diseases surveillance points based on the fuzzy matter-element theory

目的探讨用模糊物元法对疾病监测点布点进行优化与选择的可行性。方法采用模糊物元法对某地的慢性病监测资料进行监测点的分类和优选,并用方差分析法和系统聚类加以验证。结果某地28个监测点可以被模糊物元法分为四类,根据当年疾病监测资料所优选出的10个监测点仍可作为次年的优选监测点;多元方差分析法结果显示模糊物元法优选出的10个监测点与原28个监测点差异无统计学意义;系统聚类法与模糊物元法对监测点的分类较为一致。结论模糊物元法对疾病监测点的优化具有一定的稳定性,代表性较好,可作为疾病监测点优选时的一种推荐方法。

Objective To explore the feasibility of applying a fuzzy matter-element model to the selection and optimization of diseases surveillance points.Methods The chronic diseases surveillance data of a region were classified and optimum items picked out based on the fuzzy matter-element theory,the results of which were verified using analysis of variance and system clustering.Results The 28 surveillance points in the region could be classified into 4 groups based on the fuzzy matter-element theory,and the 10 optimum surveillance points selected according to the surveillance data of the current year could remain as the optimum ones for the next year.Multivariate analysis of variance suggested no statistical difference between the 10 surveillance points selected based on the fuzzy matter-element model and the 28 original ones.System clustering results were basically consistent with the classification derived from the fuzzy matter-element theory.Conclusion The fuzzy matter-element model,which provided stable and representative optimization results,could be a recommended method for selecting and optimizing diseases surveillance points.