Quality Improvement Analyses Revealed a Hidden Shift Following a Retrospective Study on Breastfeeding Rates
- PMID: 34616963
- PMCID: PMC8487780
- DOI: 10.1097/pq9.0000000000000347
Quality Improvement Analyses Revealed a Hidden Shift Following a Retrospective Study on Breastfeeding Rates
Abstract
Factors affecting exclusive breastfeeding rates are complex. Evaluations for early-onset sepsis can negatively impact breastfeeding success. We sought to determine whether implementing an algorithm utilizing the sepsis risk score (SRS) in chorioamnionitis-exposed newborns would increase exclusive breastfeeding rates. We collaborated with healthcare systems experts to analyze and understand our outcomes.
Methods: We describe a retrospective cohort study of chorioamnionitis-exposed newborns 35 weeks and older gestation in the Mother-Baby Unit at our institution following a quality improvement project that implemented an SRS algorithm. We compared exclusive breastfeeding rates over 2 time periods, 33 months before and 15 months after SRS algorithm implementation. We completed bivariate comparisons using chi-square and Mann-Whitney U tests to understand the factors contributing to exclusive breastfeeding rates. In a secondary analysis, breastfeeding rates and demographic patterns were examined using p-charts.
Results: Following algorithm implementation, exclusive breastfeeding rates increased from 49% to 58% (P = 0.10) in chorioamnionitis-exposed newborns. Factors associated with increased exclusive breastfeeding included Caucasian race, English as the primary language, private insurance, vaginal delivery, and positive group B Streptococcus status. In the secondary analysis, the proportion of non-Hispanic mothers increased from 63% to 80% during the study.
Conclusions: Despite SRS implementation, exclusive breastfeeding rates increased but not significantly, and certain sociodemographic factors remain associated with exclusive breastfeeding. Secondary analysis revealed an overall demographic shift affecting the dataset, highlighting the importance of thorough data analysis when evaluating a quality improvement project.
Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc.
Conflict of interest statement
Dr. Dukhovny is a consultant for Vermont Oxford Network. The other authors have no financial interest to declare in relation to the content of this article.
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