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Developing a reference standard for pertussis by applying a stratified sampling strategy to electronic medical record

Shilo H McBurney1, Jeffrey C Kwong2, Kevin A Brown3

  • 1Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.

Annals of Epidemiology
|November 13, 2022
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Developing an accurate pertussis reference standard is crucial for validation studies. This study presents a stratified sampling method to minimize bias and improve precision in pertussis surveillance data.

Keywords:
Diagnostic accuracyLow prevalencePertussisReference standardSampling strategyValidation

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Area of Science:

  • Public Health
  • Epidemiology
  • Infectious Disease Surveillance

Background:

  • Pertussis surveillance in Canada is essential but limited by ascertainment bias.
  • Improving detection through additional data sources necessitates robust validation methods.
  • Low disease prevalence and oversampling suspected cases introduce verification bias in pertussis studies.

Purpose of the Study:

  • To develop a reference standard for pertussis validation studies.
  • To achieve adequate analytic precision and minimize bias in pertussis data.
  • To create a reliable standard for evaluating surveillance data accuracy.

Main Methods:

  • A stratified sampling strategy was employed to create the reference standard.
  • Data were sampled from a primary care electronic medical record cohort.
  • Abstractor notes were used to classify pertussis cases (definite, possible, ruled-out, no mention) based on surveillance definitions.

Main Results:

  • Eight hundred records were abstracted from a cohort of 404,922.
  • A significant proportion of cases were classified as definite (26%) or possible (32.6%) pertussis.
  • Abstraction reliability showed moderate to substantial agreement.

Conclusions:

  • The stratified sampling strategy is effective for developing reference standards with limited resources for low-prevalence diseases like pertussis.
  • This approach mitigates verification and spectrum bias.
  • The method provides sufficient precision and includes a range of case severities for robust validation.