Objectives: We hypothesized that the proportion of Black individuals in a county would be associated with higher rates of coronavirus disease 2019 (COVID-19) cases and deaths, even after accounting for other high-risk socioecologic factors such as poverty, population density, and household crowding, and uninsured rates. We also expected that counties designated as primary care health professional...

Racial and ethnic minorities in the US have been disproportionately affected by the COVID-19 pandemic and are at risk for disparities in COVID-19 vaccinations. The H1N1 flu vaccine experience provides lessons learned to address and prevent racial and ethnic disparities in COVID-19 vaccinations. We aim to identify racial/ethnic and geographic disparities in H1N1 vaccinations among Medicaid enrollees to inform equitable COVID-19 vaccination policies and strategies. The study population included people under 65 who were continuously enrolled in Medicaid in 2009 and 2010 from 28 states and the District of Columbia. H1N1 vaccinations were identified from Medicaid outpatient claims. Vaccination rates were calculated for the overall sample and subpopulations by race/ethnicity and state. 3,708,894 (12.3%) Medicaid enrollees in the sample were vaccinated for H1N1 in 2009–2010. Race-specific vaccination rates ranged from 8.1% in American Indian/Alaska Native (AI/AN) to 19.8% in Asian/Pacific Islander Medicaid enrollees. NHB enrollees had lower vaccination rates than non-Hispanic White (NHW) enrollees in all states, with the exceptions of Maryland, Missouri, Ohio, and Washington. The largest disparity between NHB and NHW was in Pennsylvania (1.0% vs. 7.0%), while the largest absolute difference between NHB and NHW enrollees was in Georgia (17.4% vs. 30.7%). Our study found huge variation in H1N1 vaccinations across states and racial/ethnic disparities in H1N1 vaccinations within states. In most states, NHB and AI/AN Medicaid enrollees had lower vaccination rates than Whites. Hispanic and Asian/Pacific Islander Medicaid enrollees in most states had higher vaccination rates than Whites.

Race and ethnicity are frequently missing in electronic health records (EHRs), where excluding these records can bias pediatric research and disparity estimates. Imputing missing values may reduce this bias but can perform unevenly across methods and subgroups, especially for smaller or heterogeneous categories. We compared four approaches—logistic regression, random forest, k-nearest neighbors (KNN), and multiple imputation by chained equations (MICE)—to impute missing race (Black/White/Other) and ethnicity (Hispanic/Non-Hispanic) using individual- and census-tract-level sociodemographic measures. We analyzed 5309 children (<19 years) treated for Staphylococcus aureus infections at two pediatric hospitals in metropolitan Atlanta (2002–2015). The performance was evaluated on a held-out test set (n = 554) using accuracy and weighted F1. For race, logistic regression and KNN performed best (accuracy/weighted F1: 0.838/0.822 and 0.839/0.823), followed by random forest (0.798/0.787), with MICE being the lowest (0.736/0.743). For ethnicity, KNN achieved the highest accuracy (0.912) and random forest the highest weighted F1 (0.895) (logistic regression 0.901/0.876; random forest 0.904/0.895; KNN 0.912/0.887; MICE 0.866/0.864). Performance was the lowest for Hispanic ethnicity and the “Other” race category, consistent with the class imbalance. Imputation performance depends on the demographic attribute and modeling approach; subgroup-specific evaluation is essential when imputing race and ethnicity in pediatric EHR research.

(1) Background: With increasing international travel and mass population displacement due to war, famine, climate change, and immigration, pathogens, such as Staphylococcus aureus (S. aureus), can also spread across borders. Methicillin-resistant S. aureus (MRSA) most commonly causes skin and soft tissue infections (SSTIs), as well as more invasive infections. One clonal strain, S. aureus USA300, originating in the United States, has spread worldwide. We hypothesized that S. aureus USA300 would still be the leading clonal strain among US-born compared to non-US-born residents, even though risk factors for SSTIs may be similar in these two populations (2) Methods: In this study, 421 participants presenting with SSTIs were enrolled from six community health centers (CHCs) in New York City. The prevalence, risk factors, and molecular characteristics for MRSA and specifically clonal strain USA300 were examined in relation to the patients’ self-identified country of birth. (3) Results: Patients born in the US were more likely to have S. aureus SSTIs identified as MRSA USA300. While being male and sharing hygiene products with others were also significant risks for MRSA SSTI, we found exposure to animals, such as owning a pet or working at an animal facility, was specifically associated with risk for SSTIs caused by MRSA USA300. Latin American USA300 variant (LV USA300) was most common in participants born in Latin America. Spatial analysis showed that MRSA USA300 SSTI cases were more clustered together compared to other clonal types either from MRSA or methicillin-sensitive S. aureus (MSSA) SSTI cases. (4) Conclusions: Immigrants with S. aureus infections have unique risk factors and S. aureus molecular characteristics that may differ from US-born patients. Hence, it is important to identify birthplace in MRSA surveillance and monitoring. Spatial analysis may also capture additional information for surveillance that other methods do not.