Evaluating the generalizability of commercial healthcare claims data.

Abstract

Commercial healthcare claims datasets area non-random sample of the US population, affecting generalizability. Rigorous comparisons of claims-derived results to ground-truth data that quantify external validity bias are lacking. Our goal is to (1) quantify external validity of commercial healthcare claims data, and (2) evaluate how socioeconomic/demographic factors are related to the bias. We analyzed inpatient discharge records occurring between 01/01/2019 to 12/31/2019 in five states: California, Iowa, Maryland, Massachusetts, and New Jersey, and compared rates (per person-year) of the 250 most common inpatient procedures between claims and reference data for each target population. We used Merative™ MarketScan® Commercial Database for the claims data and State Inpatient Databases (SID) and the US Census as reference. For a target population of all Americans, commercial healthcare claims underestimate the rate of overall inpatient discharges by 23.1%. The extent of bias varied across procedures, with the rates of ~25% of procedures being underestimated by a factor of 2. Socioeconomic factors were significantly associated with the magnitude of bias (${R}^2=69.4\%,$p < 0.001). When the target population was restricted to commercially insured Americans, the bias decreased substantially (1.4% of procedures were biased by more than factor of 2), but some variation across procedures remained.