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Multiwave validation sampling for error-prone electronic health records.

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Electronic health record (EHR) data quality is crucial for research. This study introduces a novel multiwave validation sampling method to accurately assess links between maternal weight gain and childhood obesity or asthma risks.

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Area of Science:

  • Biomedical Informatics
  • Epidemiology
  • Biostatistics

Background:

  • Electronic health records (EHR) are vital for research but present data quality challenges.
  • Complete validation of EHR data is often infeasible due to resource limitations.
  • Accurate analysis requires robust methods to address data quality issues in EHR.

Purpose of the Study:

  • To develop and illustrate a prospective, multiwave, two-phase validation sampling strategy for EHR data.
  • To estimate the association between maternal weight gain and risks of childhood obesity or asthma.
  • To compare estimates derived from validated versus unvalidated EHR data.

Main Methods:

  • Employed a multiwave, two-phase validation sampling design using EHR data.
  • Estimated influence functions iteratively using unvalidated and validated data to refine sampling.
  • Combined sampling frames for childhood obesity and asthma, using generalized raking for weight calibration.
  • Validated 996 mother-child dyads out of 10,335 in six sampling waves.

Main Results:

  • Demonstrated that validation sampling significantly impacts estimates of associations.
  • Observed marked differences between estimates using validated versus unvalidated EHR data.
  • Highlighted the importance of efficient validation for accurate biomedical research findings.

Conclusions:

  • Prospective, multiwave validation sampling is essential for reliable EHR data analysis.
  • Efficient sampling strategies are critical for overcoming data quality limitations in EHR research.
  • Accurate estimation of health risks, like childhood obesity and asthma, relies on validated EHR data.