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[Quality assurance of data collection and data processing in epidemiologic study data].

M Greiner1, M P Baumann, K H Zessin

  • 1Fachrichtung Internationale Tiergesundheit, Institut für Parasitologie und Internationale Tiergesundheit, Freie Universität Berlin. mgreiner@vetmed.fu-berlin.de

DTW. Deutsche Tierarztliche Wochenschrift
|January 5, 2002
PubMed
Summary

Implementing a robust quality assurance system is crucial for ensuring the internal validity of epidemiologic study results. This system covers planning, data gathering, entry, and processing, using tools like Microsoft ACCESS for data integrity.

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Area of Science:

  • Epidemiology
  • Data Management

Background:

  • Internal validity of epidemiologic studies relies on rigorous quality assurance of data generation processes.
  • Existing quality assurance systems often focus on continuous data streams, not project-specific data.

Purpose of the Study:

  • To present practical aspects of a quality assurance system for project-based epidemiologic studies.
  • To detail quality assurance measures across study planning, data gathering, entry, and processing phases.

Main Methods:

  • Development of standard operating protocols for observation, coding, and data entry.
  • Predefined and documented database structure with validation rules and queries.
  • Implementation of a data safety concept for integrity, physical safety, and availability.
  • Utilizing relational database facilities (Microsoft ACCESS) for data processing, including re-coding.

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Main Results:

  • Standard operating protocols ensure reliable performance in data handling.
  • Well-defined database structures and validation rules enhance data accuracy.
  • Data safety concepts and relational database tools improve data integrity and accessibility.

Conclusions:

  • A comprehensive quality assurance system is essential for valid epidemiologic study outcomes.
  • Systematic implementation of protocols and database management ensures data quality.
  • Relational database technologies offer effective solutions for data processing challenges in epidemiologic research.