
==== Front
medRxiv
MEDRXIV
medRxiv
Cold Spring Harbor Laboratory

10.1101/2024.08.19.24312249
preprint
1
Article
Benchmarking commercial healthcare claims data
Dahlen Alex
Deng Yaowei
Charu Vivek http://orcid.org/0000-0002-7147-9026

20 8 2024
2024.08.19.24312249https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
http://medrxiv.org/lookup/doi/10.1101/2024.08.19.24312249
nihpp-2024.08.19.24312249.pdf
Abstract

Importance

Commercial healthcare claims datasets represent a sample of the US population that is biased along socioeconomic/demographic lines; depending on the target population of interest, results derived from these datasets may not generalize. Rigorous comparisons of claims-derived results to ground-truth data that quantify this bias are lacking.

Objectives

(1) To quantify the extent and variation of the bias associated with commercial healthcare claims data with respect to different target populations; (2) To evaluate how socioeconomic/demographic factors may explain the magnitude of the bias.

Design

This is a retrospective observational study. Healthcare claims data come from the Merative™ MarketScan® Commercial Database; reference data for comparison come from the State Inpatient Databases (SID) and the US Census. We considered three target populations, aged 18-64 years: (1) all Americans; (2) Americans with health insurance; (3) Americans with commercial health insurance.

Participants

We analyzed inpatient discharge records of patients aged 18-64 years, occurring between 01/01/2019 to 12/31/2019 in five states: California, Iowa, Maryland, Massachusetts, and New Jersey.

Outcomes

We estimated rates of the 250 most common inpatient procedures, using claims data and using reference data for each target population, and we compared the two estimates.

Results

The average rate of inpatient discharges per 100 person-years was 5.39 in the claims data (95% CI: [5.37, 5.40]) and 7.003 (95% CI: [7.002, 7.004]) in the reference data for all Americans, corresponding to a 23.1% underestimate from claims. We found large variation in the extent of relative bias across inpatient procedures, including 22.8% of procedures that were underestimated by more than a factor of 2. There was a significant relationship between socioeconomic/demographic factors and the magnitude of bias: procedures that disproportionately occur in disadvantaged neighborhoods were more underestimated in claims data ( R 2 = 51.6%, p < 0.001). When the target population was restricted to commercially insured Americans, the bias decreased substantially (3.2% of procedures were biased by more than factor of 2), but some variation across procedures remained.

Conclusions and relevance

Naïve use of healthcare claims data to derive estimates for the underlying US population can be severely biased. The extent of bias is at least partially explained by neighborhood-level socioeconomic factors.
==== Body
pmc
