A doubly robust estimation framework to quantify potential bias in linked crash-EMS-trauma data with multi-cohort...

Reliable estimation of injury severity is essential for informing trauma care, evaluating crash interventions, and guiding EMS resource allocation; however, analyses based on linked administrative datasets are often compromised by incomplete linkage and selection bias. This study employs a doubly robust estimation framework to address potential bias in injury severity estimation when integrating multiple datasets. Using Augmented Inverse Probability Weighting (AIPW), we adjust for selection bias introduced by incomplete linkage while improving robustness to misspecification in either the selection or outcome model. Using data from a multi-source linkage of crash, EMS, and trauma records, we estimate the Injury Severity Score (ISS) under three approaches: naïve complete-case analysis, inverse probability weighting (IPW), and AIPW. The naïve approach yielded a mean ISS of 13.52, while both IPW (10.86) and AIPW (10.93) provided adjusted estimates accounting for selection. Subgroup analyses revealed substantial differences in effect size and direction between models. For instance, the impact of male gender on ISS was estimated at 3.98 in AIPW versus 2.22 in naïve analysis. Similarly, secondary collisions and frontage-road crashes showed ISS increases exceeding 10 points under AIPW, compared to considerably lower naïve estimates. Several protective factors, including airbag deployment and crash setting, also demonstrated stronger effects when adjusted for bias. Our results demonstrate that traditional analyses of linked data may underestimate or misstate key risk and protective associations. The proposed AIPW framework offers a practical, statistically rigorous solution for producing population-level inferences in injury severity research using linked administrative data.