Disparities in Misclassification of Race and Ethnicity in Electronic Medical Records Among Patients with Traumatic Injury

Racial disparities persist in mortality after firearm assault injuries: A national analysis of the Trauma Quality Improvement Program database

OBJECTIVES

It is unclear whether improvements in the delivery of trauma care over the last decade have diminished racial disparities in mortality after firearm injuries.

METHODS

The American College of Surgeons Trauma Quality Improvement Program was queried for patients (≥18 years old) who experienced penetrating firearm assault injuries between 2008 and 2018. Multivariable logistic regression was used to assess the association between mortality and race, controlling for demographics, comorbidities, shock index, injury severity score injury location, and hospital characteristics.

RESULTS

This retrospective cohort study included 261,116 patients who experienced firearm injuries secondary to assault between 2008 and 2018. Patients most frequently identified as Black (64.1%, n = 167,494), followed by White (16.3%, n = 42,649), Hispanic/Latino (15.7%, n = 41,044), and other racial groups (3.8%, n = 9,929). On multivariable logistic regression, Black patients (odds ratio, 1.53; 95% confidence interval, 1.45-1.62), and Hispanic/Latino patients (odds ratio, 1.11; 95% confidence interval, 1.03-1.19) were more likely to die after a firearm assault injury than White patients. Black patients were more likely to die in the emergency department (62.2%, n = 13,438) compared with White patients (52.7%, n = 2,838), Hispanic/Latino patients (53.0%, n = 2,635), and patients of other races (54.8%, n = 681). Across all years of the study period, Black patients had a greater risk-adjusted mortality rate than White patients and Hispanic/Latino patients.

CONCLUSION

Significant racial disparities in mortality after firearm assault continue to persist. Early death within the emergency department appears to be a significant driver of these persistent disparities.

Unwinding of Continuous Medicaid Coverage Among Pediatric Community Health Center Patients

Importance

During the COVID-19 pandemic public health emergency, states provided continuous Medicaid coverage to enrollees. In April 2023, states began to unwind this continuous coverage, prompting concern about the impact of this on pediatric patients.

Objective

To examine loss of coverage during the unwinding of continuous Medicaid coverage among pediatric patients seen at community-based health care organizations.

Design, Setting, and Participants

This cohort study with statistical analysis in November 2024 included pediatric patients from a multistate network of community-based health care organizations. Participants were aged up to 17 years at both their last Medicaid-insured visit during the continuous coverage period and at their first visit during the unwinding period (April 1, 2023, to March 31, 2024).

Exposures

Age, sex, race and ethnicity, language, and medical complexity.

Main Outcome and Measures

The main outcome was Medicaid disenrollment to uninsured status during the unwinding period. Logistic regression was used to estimate the odds of ever being uninsured during unwinding, and Cox proportional hazards regression models were used to examine the time to uninsured status from the start of unwinding in each patient's state of residence through the end of March 2024. Associations between age, sex, race and ethnicity, language, and medical complexity and the outcome were assessed.

Results

Among 450 146 pediatric patients, mean (SD) patient age was 8.11 (5.07) years and 50.1% were male. Overall, 8.7% were disenrolled from Medicaid to uninsured status. Patients aged 12 to 17 years had the highest estimated disenrollment among age groups (10.5%), but after adjustment, all other age groups had lower odds of disenrollment compared with those younger than 1 year. Females had higher odds of disenrollment (adjusted odds ratio [AOR], 1.15 [95% CI, 1.13-1.18]; adjusted hazard ratio [AHR], 1.14 [95% CI, 1.12-1.17]) than males. American Indian or Alaska Native patients had higher odds of disenrollment (AOR, 1.95 [95% CI, 1.81-2.09]; AHR, 1.81 [95% CI, 1.05-3.13]) than White patients, with estimated disenrollment of 17.1% vs 9.4%. Compared with patients with low medical complexity, those with either chronic but noncomplex (AOR, 1.83 [95% CI, 1.79-1.88]; AHR, 1.80 [95% CI, 1.44-2.27]) or complex chronic (AOR, 1.95 95% CI, 1.89-2.00]; AHR, 1.92 [95% CI, 1.67-2.21]) medical complexity had higher odds of disenrollment.

Conclusions and Relevance

In this cohort study of previously Medicaid-insured pediatric patients seen at community-based health care organizations, a meaningful proportion of patients were disenrolled to uninsured status during the unwinding of continuous Medicaid coverage, with associated differences by demographic and clinical characteristics. This could impact access to care and health outcomes for the patients.

Comparing Self-Reported and Aggregated Racial Classification for American Indian/Alaska Native Youths in YRBSS: 2021

Objectives. To identify how race and ethnicity were reclassified with survey variables for children self-reporting as American Indian/Alaska Native (AI/AN) using the 2021 Youth Risk Behavior Surveillance System (YRBSS). Methods. We conducted a cross-sectional analysis of the US Centers for Disease Control and Prevention's 2021 YRBSS. YRBSS collects behaviors and demographics of students in grades 9 through 12, including race and ethnicity via self-report, and then reclassifies data into a "raceeth" variable. To examine the classification of AI/AN in YRBSS, we compared AI/AN composition between self-report and raceeth variables. Results. A total of 816 adolescents self-reported as AI/AN alone (145; 17.70%), AI/AN alone with Hispanic/Latino background (246; 30.15%), or AI/AN in combination with 1 or more race (425; 52.08%). Of those, only 145 were classified as being AI/AN in the calculated raceeth variable. With YRBSS survey weighting, the percentage of AI/AN in the raceeth variable was 13.4%. Conclusions. Misclassification, noncollection, or the use of categories such as "other" and "multirace" without allowing disaggregation can misrepresent disease burden, morbidity, and mortality. Consequently, it is critical to disaggregate data to adequately capture race/ethnicity in self-report surveys and data sources. (Am J Public Health. 2024;114(4):403-406. https://doi.org/10.2105/AJPH.2023.307561).