Neonatal-Perinatal Medicine Fellow Children's Hospital of the University of Illinois Chicago, Illinois, United States
Background: Sudden Infant Death Syndrome (SIDS) is among the leading causes of overall infant mortality and post neonatal mortality in the United States. Most SIDS cases involve unhealthy infant sleep practices such as bed-sharing and using unsafe items in infants’ sleeping areas. Despite interventions to improve safe sleep for infants, the rates of SIDS are one of the highest among African Americans, reflecting racial and cultural disparities in safe sleep practices.
Addressing the challenges in reducing SIDS among African American infants is important in improving overall SIDS mortality rates. Despite receiving education prior to discharge, most do not follow through with implementation. Reasons include being easier to bed-share, inability to fight culture/extended family in implementing safe sleep, and lack of effective teaching of safe sleep to mothers that address their beliefs and perceptions on what is best for their infant. Objective: We will conduct a Quality Improvement (QI) project, in conjunction with the Illinois Perinatal Quality Collaborative’s (ILPQC) Equity for Safe Sleep in Infants (ESSI) initiative, to improve adherence of safe sleep among African American mothers. We hypothesize that we will increase the understanding of and adherence to safe sleep practices among African American mothers of infants prior to their discharge from the UIC NICU by 5% from baseline by March 2024. Design/Methods: We will apply for IRB approval. The study will be conducted from September 2023 to March 2024. We will adapt and utilize a survey for pre/post- education on safe sleep evaluating: self-efficacy, knowledge of and attitudes towards safe sleep recommendations, and intent with implementation. We will then provide culturally sensitive information on safe sleep to African American mothers in person, utilize print resources with enhanced messages, and use visual aids to quiz mothers. We will Identify and use relevant methods of data analysis including, but not limited to: descriptive statistics, process control charts, and chi-square/t-test using STATA software.