Use of Payer as a Proxy for Health Insurance Status on Admission Results in Misclassification of Insurance Status among Pediatric Trauma Patients

2016 ◽  
Vol 82 (2) ◽  
pp. 146-151 ◽  
Author(s):  
Elizabeth A. Carter ◽  
Lauren J. Waterhouse ◽  
Roy Xiao ◽  
Randall S. Burd

The purpose of this study was to quantify health insurance misclassification among children treated at a pediatric trauma center and to determine factors associated with misclassification. Demographic, medical, and financial information were collected for patients at our institution between 2008 and 2010. Two health insurance variables were created: true (insurance on hospital admission) and payer (source of payment). Multivariable logistic regression was used to determine which factors were independently associated with health insurance misclassification. The two values of health insurance status were abstracted from the hospital financial database, the trauma registry, and the patient medical record. Among 3630 patients, 123 (3.4%) had incorrect health insurance designation. Misclassification was highest in patients who died: 13.9 per cent among all deaths and 30.8 per cent among emergency department deaths. The adjusted odds of misclassification were 6.7 (95% confidence interval: 1.7, 26.6) among patients who died and 16.1 (95% confidence interval: 3.2, 80.77) among patients who died in the emergency department. Using payer as a proxy for health insurance results in misclassification. Approaches are needed to accurately ascertain true health insurance status when studying the impact of insurance on treatment outcomes.

2018 ◽  
Vol 150 (1) ◽  
pp. 67-72 ◽  
Author(s):  
Michelle Davis ◽  
Kyle Strickland ◽  
Sarah Rae Easter ◽  
Michael Worley ◽  
Colleen Feltmate ◽  
...  

Cancer ◽  
2010 ◽  
Vol 116 (2) ◽  
pp. 476-485 ◽  
Author(s):  
Joseph Kwok ◽  
Scott M. Langevin ◽  
Athanassios Argiris ◽  
Jennifer R. Grandis ◽  
William E. Gooding ◽  
...  

2021 ◽  
Author(s):  
Ibrahim Gwarzo ◽  
Maria Perez-Patron ◽  
Xiaohui Xu ◽  
Tiffany Radcliff ◽  
Jennifer Horney

Abstract Background: The population health implications of the growing burden of trauma-related mortality may be influenced by access to health insurance coverage, and demographic characteristics such as race and ethnicity. We investigated the effects of health insurance status and race/ethnicity on the risk of mortality among trauma victims in Texas.Methods: Using Texas trauma registry data from 2014 - 2016, we categorized health insurance coverage into private, public, and uninsured, and categorized patients with serious injuries into Non-Hispanic Whites, Non-Hispanic Blacks, Hispanics Any-Race, and Others. Multivariate logistic regression was used to estimate the effects of health insurance status and race/ethnicity on mortality, controlling for age, gender, severity of the trauma, cause of trauma, presence of comorbid conditions, trauma center designation, presence of a traumatic brain injury (TBI), and severity of a TBI. Results: From January 1, 2014, to December 31, 2016, there were 415,159 trauma cases in Texas; 8,827 (2.1%) were fatal. Among patients with at least a moderate injury, 24, 606 (17.4%) were uninsured, and 98, 237 (69.4%) identified as Non-Hispanic White. In the multivariate analysis, Hispanics of any race and Non-Hispanic Blacks had higher adjusted odds of trauma mortality compared to Non-Hispanic Whites [ORHispanics= 1.25: 95% CI (1.16 – 1.36)] [ORBlacks= 2.11: 95% CI (1.87 – 2.37)]. Similarly, compared to privately insured, uninsured patients had 86% higher odds of trauma-related death [OR= 1.86: 95% CI (1.66 – 2.05)]. The effects of lack of health insurance on trauma mortality varied across race/ethnicity of the victims; uninsured Non-Hispanic Blacks had disproportionately higher adjusted odds of trauma mortality than uninsured Whites. Conclusion: Using Texas trauma registry data, we found significant disparities in trauma-related mortality risk based on race/ethnicity and health insurance coverage. The identification of trauma mortality inequalities could inform the design and implementation of future public health interventions.


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