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Related Concept Videos

Principles of Disease Surveillance01:26

Principles of Disease Surveillance

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Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
758

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Practical Considerations for Matching STD and HIV Surveillance Data with Data from Other Sources.

Lori M Newman1, Michael C Samuel2, Mark R Stenger3

  • 1Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, GA.

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|July 7, 2016
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Summary
This summary is machine-generated.

Linking public health data sources, known as data matching, can improve information for sexually transmitted disease (STD) and human immunodeficiency virus (HIV) programs. This method overcomes limitations of incomplete case report data for better decision-making.

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Area of Science:

  • Public Health
  • Epidemiology
  • Health Informatics

Background:

  • Public health programs require robust data for effective decision-making.
  • Routine surveillance data for notifiable conditions like STDs and HIV are often incomplete.
  • Existing supplemental data sources are frequently underutilized due to accessibility challenges.

Purpose of the Study:

  • To review practical considerations for data matching in STD and HIV surveillance.
  • To highlight the utility of linking disparate data sources for enhanced public health insights.
  • To provide examples of successful data matching applications in STD and HIV programs.

Main Methods:

  • Data matching involves linking records across multiple data sources.
  • The review focuses on practical aspects and challenges of implementing data matching.
  • Case examples illustrate the application of data matching in real-world public health surveillance.

Main Results:

  • Data matching can significantly enhance the completeness and utility of surveillance data.
  • Overcoming obstacles to data accessibility is crucial for successful data linkage.
  • Successful data matching provides richer information on disease burden, comorbidities, and risk factors.

Conclusions:

  • Data matching is a valuable strategy for improving STD and HIV surveillance.
  • Leveraging existing data resources through linkage can optimize programmatic decision-making.
  • Further exploration and implementation of data matching techniques are recommended for public health.