Lack of insurance is associated with lower probability of diagnostic imaging use among US trauma patients: An instrumental variable analysis and simulation

Abstract

ABSTRACT:Background: Uninsured trauma patients have higher mortality than their insured counterparts. One possible reason is disparities in utilization of appropriate diagnostic imaging, including computed tomography (CT), X-ray, ultrasound (US), and magnetic resonance imaging (MRI). We examined the association between lack of insurance and use of diagnostic imaging. Methods: Data come from the National Trauma Databank 2010-2015. Patients were determined uninsured if payment mode was self-pay or missing. The primary outcome was any diagnostic imaging procedure, and secondary outcomes included CT, X-ray, US, or MRI. Risk ratios (RRs) were adjusted for demographics, comorbidities, injury characteristics, facility characteristics. We also used the 2010 Patient Protection and Affordable Care Act as an instrumental variable (IV), with linear terms for year to account for annual trends in imaging use. Monte carlo simulations to test effect of hypothetical violations to IV assumptions of relevance, no direct effect, and no confounding. Results: Of 4,373,554 patients, 953,281 (21.8%) were uninsured. After adjusting, uninsured patients had lower chance of any imaging (RR 0.98, 95% CI 0.98 to 0.98), x-ray (RR 0.99, 95% CI 0.99 to 1.00), and MRI (RR 0.82, 95% CI 0.81 to 0.83), and higher chance of ultrasound (RR 1.01, 95% CI 1.01 to 1.02). In IV analysis, uninsured status was associated with reduction in any imaging (RR 0.60, 95% CI 0.52 to 0.70), tomography (RR 0.52, 95% CI 0.44 to 0.62) ultrasound (RR 0.46, 95% CI 0.32 to 0.65), and MRI (RR 0.19, 95% CI 0.10 to 0.37) and increased likelihood of x-ray use (RR 1.74, 95% CI 1.31 to 2.32). Simulations indicated that a direct effect RD of -0.02 would be necessary to produce observed results under the null hypothesis. Discussion: Our study suggests an association between insurance status and use of imaging that is unlikely to be driven by confounding or violations of IV assumptions. Mechanisms for this remain unclear, but could include unconscious provider bias or institutional financial constraints. Further research is warranted to elucidate mechanisms and assess whether differences in diagnostic imaging use mediate the association between insurance and mortality.

simstudy: Illuminating research methods through data generation

“…The SimDesign Package (Chalmers & Adkins, 2020) and the related MonteCarlo Package (Leschinski, 2019) follow a similar line of thought but focus on easy replication of the analyses and providing summaries of simulated data. simstudy has been used in a variety of fields for theoretical exploration of research methodology (Anderson, Wennberg, & McMullen, 2019;El Alili et al, 2020;Kirasich, Smith, & Sadler, 2018;Krzykalla, Benner, & Kopp-Schneider, 2020;Liu, Chrysanthopoulou, Chang, Hunnicutt, & Lapane, 2019;Nickodem, 2020;Thoya, Waititu, Magheto, & Ngunyi, 2018;Wang & Ma, 2020), power calculation for trials (Wei et al, 2019) and other simulation tasks supporting researchers (Chukwu, 2019;Forthun et al, 2020;Horry, Fitzgerald, & Mansour, 2020;Renson, Schubert, & Bjurlin, 2017).…”