Syphilis Record Search and Review Algorithm

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Overview

While health departments began conducting syphilis surveillance in the 1930s (consistently compiled national data are available dating back to 1941), in an effort to identify syphilis cases that might not be otherwise reported by clinicians, laboratories have been mandated to report serologies to health departments since 1962. Historical test results are routinely maintained on file to aid with interpretation of any subsequent tests. Health departments subsequently investigate reported serologies to determine if they represent a new infection, reinfection, or treatment failure. Ideally, every new reactive serology should be investigated but because of the overwhelming volume serologies reported and limited resources available at public health agencies, not all serologies are investigated. Instead, reported serologies were administratively closed using "reactor grids" which prioritize serologies for further investigation based on patient's age, sex, test type, and test titer reported on the serology.

Reactor grids, however, are widely acknowledged to be unreliable and have shown to miss syphilis cases. Recently, researchers have suggested an algorithm that, used in conjunction with electronic laboratory reporting, might be used to automatically search historical records and apply the case definition of syphilis. We developed a computerized algorithm (demonstrated in the figure below) that could be used by any public health agency to compare reported electronic laboratory reports with prior testing and intervention history in the surveillance database of the public health agency. This algorithm was developed using a surveillance dataset from Florida Department of Health and validated by New York City Department of Health and Mental Hygiene (please click the link to access the manuscript that described the algorithm). The goal of the algorithm is to accurately close those serologies that do not meet the case definition, and prioritize the rest for further investigation; and do so using evidence-based decisions and advances in technology and data management capabilities of public health agencies. The algorithm demonstrated an accuracy of 99.4% and would have saved an additional 594 workdays in the 3 years of surveillance data analyzed.

The goal of this GitHub repository is to serve as the one-stop location for public health agencies and technology vendors to test and implement this algorithm. Please contact Saugat Karki (SKarki@cdc.gov) for further discussion, or if any technical assistance is required from the Centers for Disease Control and Prevention.

This remains to be a continuous work in progress, please let us know if you find any errors or would like to discuss any changes.

Dependencies

There are 3 Python libraries necessary for this Jupyter Notebook which include the following:

• Pandas
• Numpy
• Datetime

These libraries are called in the Python Notebook. The rest of the dependencies come installed with Python 3.

Getting Started

Cloning the Repository

The repository can either be downloaded from GitHub or cloned using the following command:

$ git clone https://github.com/CDCgov/Syphilis_Record_Search_and_Review_Algorithm.git

Running the Notebook

Launch Jupyter Notebook (from the 'Notebooks' folder in the repository) in Anaconda or a Python 3 installation of your choice. Kindly follow the steps written in the Jupyter Notebook closely as there are some steps required to ensure that the data is in the correct format. See the notebook for specific details. Any initial errors would most likely result due to the formatting requirements.

Related documents

• Open Practices
• Rules of Behavior
• Thanks and Acknowledgements
• Disclaimer
• Contribution Notice
• Code of Conduct

Public Domain Standard Notice

This repository constitutes a work of the United States Government and is not subject to domestic copyright protection under 17 USC § 105. This repository is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the CC0 1.0 Universal public domain dedication. All contributions to this repository will be released under the CC0 dedication. By submitting a pull request you are agreeing to comply with this waiver of copyright interest.

License Standard Notice

The repository utilizes code licensed under the terms of the Apache Software License and therefore is licensed under ASL v2 or later.

This source code in this repository is free: you can redistribute it and/or modify it under the terms of the Apache Software License version 2, or (at your option) any later version.

This source code in this repository is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Apache Software License for more details.

You should have received a copy of the Apache Software License along with this program. If not, see http://www.apache.org/licenses/LICENSE-2.0.html

The source code forked from other open source projects will inherit its license.

Privacy Standard Notice

This repository contains only non-sensitive, publicly available data and information. All material and community participation is covered by the Disclaimer and Code of Conduct. For more information about CDC's privacy policy, please visit http://www.cdc.gov/other/privacy.html.

Contributing Standard Notice

Please contact SKarki@cdc.gov if you would like to suggest any changes.

All comments, messages, pull requests, and other submissions received through CDC including this GitHub page may be subject to applicable federal law, including but not limited to the Federal Records Act, and may be archived. Learn more at http://www.cdc.gov/other/privacy.html.

Records Management Standard Notice

This repository is not a source of government records, but is a copy to increase collaboration and collaborative potential. All government records will be published through the CDC web site.

Additional Standard Notices

Please refer to CDC's Template Repository for more information about contributing to this repository, public domain notices and disclaimers, and code of conduct.