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Matching criteria in case-control studies in the field of antimicrobial resistance

Matthew E Falagas1, Eleni G Mourtzoukou, Konstantina P Giannopoulou

  • 1Alfa Institute of Biomedical Sciences, Athens, Greece. m.falagas@aibs.gr

Microbial Drug Resistance (Larchmont, N.Y.)
|May 21, 2008
PubMed
Abstract

Insights

Individual matching in antimicrobial resistance case-control studies is infrequent and limited to a few characteristics like age. Furthermore, analysis of matched pairs was often omitted, impacting study rigor.

Area of Science:

  • Microbiology
  • Epidemiology
  • Biostatistics

Background:

  • Antimicrobial resistance (AMR) poses a significant global health threat.
  • Case-control studies are crucial for investigating factors contributing to AMR.
  • Matching is a technique used in case-control studies to reduce confounding.

Purpose of the Study:

  • To assess the extent of matching in case-control studies on antimicrobial resistance.
  • To identify the specific criteria employed for matching in these studies.
  • To evaluate the frequency of matched-pair data analysis.

Main Methods:

  • Systematic literature search of the PubMed database for case-control studies on AMR (archived by August 2006).
  • Inclusion and detailed analysis of studies utilizing individual matching techniques.
  • Categorization of studies based on the bacteria investigated (Acinetobacter baumannii, Pseudomonas aeruginosa, other bacteria).

Main Results:

  • 115 case-control studies on AMR were identified.
  • Individual matching was performed in 27.8% (32/115) of the studies.
  • Age (69%), sex (34%), and underlying illness (25%) were the most common matching criteria. Matched-pair analysis was conducted in 56.3% (18/32) of matched studies.

Conclusions:

  • Individual matching is underutilized in AMR case-control studies and applied to limited variables.
  • A significant proportion of studies employing matching did not analyze matched pairs, potentially affecting results.
  • Standardization of matching and analysis methods is needed to enhance the quality of AMR research.

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