Fellow Cincinnati Children's Hospital Medical Center cincinnati, Ohio, United States
Background: Latino youth (LY) are at risk of poor health outcomes shaped by social determinants of health. As a result, LY experience worse outcomes for common conditions like type 1 diabetes (T1D), asthma, and mental health disorders (MHD) than their non-Latino peers. Inequities among LY may be of greater magnitude in nontraditional destination areas like Cincinnati with smaller immigrant communities and often lack needed supporting infrastructure. Objective: To quantitatively explore utilization patterns of LY in Cincinnati, in all cause hospitalizations and in those hospitalized for specific chronic conditions: asthma, T1D, and MHD. Design/Methods: We will perform a single-center retrospective cohort study at Cincinnati Children’s, a quaternary care children’s hospital, which serves as the sole pediatric inpatient care center in our region. The study protocol is IRB-approved. We will include patients < 18 years with asthma, T1D, and/or MHD from our 8-county primary service area who were admitted between June 2015 and May 2022. Our primary outcome of interest will examine the following: 1. Hospitalizations for all causes, and for the 3 specified conditions; 2. Intensive care unit-level care during hospitalization; 3. Severity of presentation, including: need for continuous albuterol (Asthma); need for continuous insulin infusion (T1D); for MHD, patients who require medical monitoring or telemetry during their stay and/or requiring an admission to an inpatient psychiatric facility. Ethnicity (primary exposure) will be classified as Latino or non-Latino. We will classify race – a social construct – as White, Black, or Other. We will measure other sociodemographic and clinical characteristics as covariates. As LY comprise ~6% of the population, we anticipate this group will make up ~6% of hospitalizations (and severe presentations). We will build multivariable logistic regression models to examine independent associations between outcomes and exposure variables, adjusting for pertinent covariates. We anticipate completing data extraction, quality review, and analysis by April 2024.
