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Benchmarking commercial healthcare claims data.

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Commercial healthcare claims data show significant bias, underestimating procedure rates by 23.1% for the general US population. This bias is linked to socioeconomic factors and is reduced when focusing on commercially insured individuals.

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Area of Science:

  • Health Services Research
  • Biostatistics
  • Health Economics

Background:

  • Commercial healthcare claims datasets are widely used for health services research but may not accurately represent the entire US population due to socioeconomic and demographic biases.
  • Existing research lacks rigorous comparisons to quantify the bias in claims data and understand its drivers.

Purpose of the Study:

  • To quantify the bias in commercial healthcare claims data across different target populations.
  • To evaluate the influence of socioeconomic and demographic factors on the magnitude of this bias.

Main Methods:

  • Retrospective observational study using Merative MarketScan Commercial Database and State Inpatient Databases (SID) for reference.
  • Analysis of inpatient discharge records for individuals aged 18-64 in five states from 2019.
  • Estimation and comparison of rates for the 250 most common inpatient procedures between claims data and reference data for three target populations.

Main Results:

  • Claims data underestimated inpatient discharges by 23.1% for the general US population (aged 18-64).
  • Significant variation in bias was observed across procedures, with 22.8% underestimated by over a factor of two.
  • Bias was significantly associated with neighborhood-level socioeconomic factors, particularly for procedures more common in disadvantaged areas (R²=51.6%, p<0.001).
  • Restricting the target population to commercially insured individuals substantially reduced bias (3.2% of procedures biased by >2x), though some variation persisted.

Conclusions:

  • The naive use of commercial healthcare claims data for estimating US population rates can lead to severe underestimation.
  • Neighborhood socioeconomic factors partially explain the observed bias in claims data.
  • While bias is reduced for commercially insured populations, careful consideration of data limitations is still necessary.