WIP 72 - Health Outcomes of Very Low Birth Weight Infants based on Maternal Language Status

Poster Number: WIP 72
Publication Number: WIP 72.974

Background: While racial disparities in preterm birth are well-known, the extent that language disparities may also impact preterm birth has not been well-studied. A key component of high-quality care in the neonatal intensive care unit (NICU) is communication between providers and families. The extent that language discordance between staff and families due to parental non-English language status may impact care quality and neonatal morbidities is unclear.

Objective: We aim to examine the extent that maternal primary language is associated with (1) preterm birth ( < 37 weeks vs. >= 37 weeks); and among very low birth weight (VLBW; <=1,500 grams) infants (2) known VLBW infant morbidities (necrotizing enterocolitis, chronic lung disease and late onset sepsis), and (3) quality of NICU care including length of stay, number of days after admission of the first social work consultation and lactation consultation, any readmission within 6 months of NICU discharge, and outpatient developmental follow-up.

Design/Methods: This is a single-center retrospective cohort study of infants admitted to a level 3 NICU in Worcester, Massachusetts from 10/2017 to 04/2023. The study was reviewed and approved by the UMass Chan Medical School IRB.All data were abstracted from the medical record. Maternal primary language was defined as English and non-English speaking. We used definitions of the Vermont Oxford Network to define VLBW infant morbidities. Length of stay and days after admission until the first social work and lactation consultation was measured continuously in days. Any readmission in the first 6 months to our hospital and any follow-up to our high risk developmental clinic within the first year after discharge was categorized as yes or no. We will examine differences in language status among our outcomes of interest using t-tests for continuous variables and chi-square tests for categorical variables. We will use multivariate regression analyses to adjust for covariates. We plan to complete data collection by December 31, 2024 and complete data analysis by March 1, 2024.