Pediatric Emergency Medicine Fellow Dayton Children's Hospital Dayton, Ohio, United States
Background: Bacterial meningitis (BM), a severe infectious disease characterized by inflammation of the membranes surrounding the brain and spinal cord, remains a feared entity in pediatric medicine. Despite advancements in medical care, BM confers significant risk for permanent neurologic morbidity and mortality, with treatment commonly requiring admission to intensive care. With the continuous rollout of vaccines against common BM pathogens over the last fifty years, the epidemiologic landscape of BM has shifted markedly, with surveillance data indicating decreasing incidence and shifting prevalence of the causative bacteria. However, despite national surveillance programs, there is a lack of data detailing the relative burden of illness and case fatality rate of BM among different patient populations, namely racial/ethnic groups, socioeconomic groups, and those of different geographic regions. With the advent of large, shared pediatric healthcare databases, we propose that potential disparities across these groups, as well as broad questions about the burden of BM on the healthcare system, can be studied. Objective: The objective of this study is to analyze epidemiologic and demographic features of emergency room visits and hospital admissions for pediatric bacterial meningitis from 2016 through 2023, with particular attention paid to the intersection of race/ethnicity, socioeconomic factors, and geographic regions on case fatality, intensive-care-unit admission, and healthcare utilization. Design/Methods: Using the Pediatric Hospital Information System (PHIS), a large, multicenter database, the cohort of patients with a diagnosis code for BM discharged between January 1, 2016 and December 31, 2023 will be identified. Demographic factors including age, race, ethnicity, and region in the United States will be assessed and associations between these factors and case fatality rates will be assessed utilizing chi-square tests and multiple logistic regression.
