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利用预测模型和风险评分系统识别美国先天性梅毒高发县。

Identification of US Counties at Elevated Risk for Congenital Syphilis Using Predictive Modeling and a Risk Scoring System.

机构信息

From the Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, GA.

Oak Ridge Institute for Science and Education, Oak Ridge, TN.

出版信息

Sex Transm Dis. 2020 May;47(5):290-295. doi: 10.1097/OLQ.0000000000001142.

DOI:10.1097/OLQ.0000000000001142
PMID:32044864
Abstract

BACKGROUND

Although preventable through timely screening and treatment, congenital syphilis (CS) rates are increasing in the United States, occurring in 5% of counties in 2015. Although individual-level factors are important predictors of CS, given the geographic focus of CS, it is also imperative to understand what county-level factors are associated with CS.

METHODS

This is a secondary analysis of reported county CS cases to the National Notifiable Diseases Surveillance System during the periods 2014-2015 and 2016-2017. We developed a predictive model to identify county-level factors associated with CS and use these to predict counties at elevated risk for future CS.

RESULTS

Our final model identified 973 (31.0% of all US counties) counties at elevated risk for CS (sensitivity, 88.1%; specificity, 74.0%). County factors that were predictive of CS included metropolitan area, income inequality, primary and secondary syphilis rates among women and men who have sex with men, and population proportions of those who are non-Hispanic black, Hispanic, living in urban areas, and uninsured. The predictive model using 2014-2015 CS outcome data was predictive of 2016-2017 CS cases (area under the curve value, 89.2%) CONCLUSIONS: Given the dire consequences of CS, increasing prevention efforts remains important. The ability to predict counties at most elevated risk for CS based on county factors may help target CS resources where they are needed most.

摘要

背景

尽管通过及时的筛查和治疗可以预防先天性梅毒(CS),但在美国,CS 的发病率仍在上升,2015 年有 5%的县发生了 CS。尽管个体因素是 CS 的重要预测因素,但鉴于 CS 的地理重点,了解与 CS 相关的县一级因素也至关重要。

方法

这是对全国传染病监测系统报告的 2014-2015 年和 2016-2017 年期间县 CS 病例的二次分析。我们开发了一个预测模型,以确定与 CS 相关的县一级因素,并利用这些因素预测未来 CS 风险较高的县。

结果

我们最终的模型确定了 973 个(所有美国县的 31.0%)CS 风险较高的县(敏感性为 88.1%;特异性为 74.0%)。预测 CS 的县一级因素包括都市区、收入不平等、女性和男男性行为者的原发性和继发性梅毒率,以及非西班牙裔黑人、西班牙裔、居住在城市地区和没有保险的人口比例。使用 2014-2015 年 CS 结果数据的预测模型可以预测 2016-2017 年 CS 病例(曲线下面积值为 89.2%)。

结论

鉴于 CS 的严重后果,增加预防工作仍然很重要。根据县一级的因素预测 CS 风险最高的县的能力,可能有助于将 CS 资源集中在最需要的地方。

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