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一种用于优先安排卫生部门进行调查的梅毒血清学记录自动搜索和审查算法。

An Automated Syphilis Serology Record Search and Review Algorithm to Prioritize Investigations by Health Departments.

机构信息

From the Division of Sexually Transmitted Diseases Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA.

New York City Department of Health and Mental Hygiene, New York City, NY.

出版信息

Sex Transm Dis. 2021 Dec 1;48(12):909-914. doi: 10.1097/OLQ.0000000000001489.

Abstract

BACKGROUND

Reactive syphilis serologies are investigated by health departments to determine if they represent new infection, reinfection, or treatment failure. Serologies prioritized for investigation based on nontreponemal test titer and age (using a "reactor grid") undergo manual record search and review. We developed a computerized algorithm that automates the record search and review.

METHODS

We developed and tested the algorithm using a Florida Department of Health data set containing serologies reported January 2016 to December 2018 and previous records linked to each individual. The algorithm was based on the syphilis case definition, which requires (except primary cases with signs and symptoms) (1) a positive treponemal test result and a newly positive nontreponemal test result or (2) a 4-fold increase in nontreponemal test titer. Two additional steps were added to avoid missing cases. New York City Department of Health and Mental Hygiene validated this algorithm.

RESULTS

The algorithm closed more investigations (49.9%) than the reactor grid (27.0%). The algorithm opened 99.4% of the individuals investigated and labeled as cases by the health department; it missed 75 cases. Many investigations opened by the algorithm were closed by the reactor grid; we could not assess how many would have been cases. In New York City, the algorithm closed 70.9% of investigations, likely because more individuals had previous test in the database (88.2%) compared with Florida (56.5%).

CONCLUSIONS

The automated algorithm successfully searched and reviewed records to help identify cases of syphilis. We estimate the algorithm would have saved Florida 590 workdays for 3 years.

摘要

背景

为确定是否代表新感染、再感染或治疗失败,卫生部门会调查反应性梅毒血清学。根据非梅毒螺旋体试验滴度和年龄(使用“反应网格”)对血清学进行优先调查,并进行手动记录搜索和审查。我们开发了一种自动记录搜索和审查的计算机算法。

方法

我们使用包含 2016 年 1 月至 2018 年 12 月报告的血清学和与每个人相关的以前记录的佛罗里达州卫生部数据集开发和测试了该算法。该算法基于梅毒病例定义,该定义要求(除有症状和体征的原发性病例外)(1)阳性梅毒螺旋体检测结果和新的非梅毒螺旋体检测阳性结果,或(2)非梅毒螺旋体试验滴度增加 4 倍。为避免漏诊病例,增加了两个额外步骤。纽约市卫生局验证了该算法。

结果

该算法比反应网格(27.0%)关闭了更多的调查(49.9%)。该算法开启了健康部门调查并标记为病例的 99.4%的个体;它错过了 75 个病例。算法开启的许多调查被反应网格关闭;我们无法评估有多少会成为病例。在纽约市,该算法关闭了 70.9%的调查,可能是因为数据库中以前的测试(88.2%)比佛罗里达州(56.5%)更多。

结论

自动算法成功地搜索和审查了记录,以帮助识别梅毒病例。我们估计该算法将为佛罗里达州节省 3 年 590 个工作日的工作量。

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