应用指数曲线模型预测北京市一般体检人群甲状腺结节流行趋势的研究  被引量:16

Research on the application of exponential curve model to prediction of epidemic tendency of thyroid nodules in general physical examination population in Beijing

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作  者:刘博[1] 张静波[2] 韩玉梅[2] 季建英[2] 陈硕[2] 李阳桦 李爽[1] 陈艳伟[1] 杜丹[1] 王全意[1] 黎新宇[1] 

机构地区:[1]北京市疾病预防控制中心传染病与地方病控制科,北京100013 [2]北京市体检中心信息科,北京100077

出  处:《中华疾病控制杂志》2015年第2期193-195,共3页Chinese Journal of Disease Control & Prevention

摘  要:目的 建立北京市体检人群的甲状腺结节预测模型,探讨其不同年龄、性别的发展趋势,为进一步采取干预措施提供依据。方法 采用描述性研究对2010-2013年甲状腺结节患病情况进行描述;按照年龄和性别分组建立指数曲线预测模型,使用R2和U检验评价模型的应用价值。结果 甲状腺结节总患病率为36.65%,其中男性患病率为30.39%,女性患病率为43.31%,随着年龄的增加,男女患病率均呈现增加趋势;所建立的模型老年组R2最大为0.90,老年组男性R2最小为0.68;除青年组和青年女性组外,各组患病率与预测值的U检验P值均〉0.05,差异无统计学意义。结论 指数曲线模型适用干预测甲状腺结节的发病趋势,可以推算理论患病率预测甲状腺结节患病率的发展趋势。北京市近4年的甲状腺结节患病率呈上升趋势,尤其是20~45岁女性患病率上升速度最快,相关机构应尽快采取措施,降低患病率。Objective To establish prediction model of thyroid nodules among health examination population in Beijing, to explore the tendency of mobidity in ages and gender, and to provide evidence for the further intervention. Methods Descriptive study was used to describe the thyroid nodules from 2010 to 2013. Subjects were categorized accord- ing to age and sex, the index curve prediction model was established,using R2 and U to evaluate the value. Results The total prevalence of thyroid nodules was 36. 65%, the male was 30. 39% , and the female was 43.31%. With the increase of age, the prevalence showed an increasing tendency in both men and women. The highest R2 value was 0. 90 in the elder- ly group. The lowest R2 value was 0. 68 in old men group. No significant difference was noted except for youth and young female groups. Conclusions The exponential curve model may be applied to prediction of the prevalence tendency of thy- roid nodules. In Beijing, the thyroid nodules prevalence rate showed an increase tendency in the last 4 years, especially a- mong women aged from 20 to 45. Measures should be taken as soon as possible to reduce the prevalence rate.

关 键 词:甲状腺结节 人群监测 流行病学方法 

分 类 号:R581[医药卫生—内分泌] R181.2[医药卫生—内科学]

 

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