Open AccessEstimating the population impact of hypothetical breastfeeding interventions in a low-income population in Los Angeles County: An agent-based model
Author(s) -
Linghui Jiang,
Xiaoyan Li,
May D. Wang,
Nathaniel D. Osgood,
Shan E. Whaley,
Catherine M. Crespi
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0231134
Subject(s) - breastfeeding , psychological intervention , population , medicine , theory of planned behavior , behavior change , context (archaeology) , intervention (counseling) , stakeholder , psychology , nursing , environmental health , social psychology , pediatrics , computer science , public relations , geography , control (management) , archaeology , artificial intelligence , political science
Background Breastfeeding has clear benefits. Yet, breastfeeding practices fall short of recommendations in low-income populations including participants of the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). To promote breastfeeding, it is important to understand breastfeeding-related behaviors such as initiation and maintenance within the context of a complex societal system. For individual women, making choices about infant feeding (whether to breastfeed or formula-feed a newborn, or when to stop breastfeeding) is a dynamic process involving interactions with health professionals, family, peers and workplaces. Integrating behavioral change theories with systems science tools such as agent-based modeling can help illuminate patterns of breastfeeding behaviors, identify key factors affecting breastfeeding behaviors within this complex dynamic system, and estimate the population impact of hypothetical interventions. Methods An agent-based model (ABM) was developed to investigate the influences of multiple levels of factors affecting breastfeeding behaviors among WIC participants. Health behavioral change theories were applied and stakeholder input obtained to improve the model, particularly during the conceptual design and model specification steps. The model was then used to identify critical points for intervention and assess the effects of five common interventions (improving knowledge through education, implementing Baby-Friendly Hospital Initiative practices, providing postpartum breastfeeding counselling, strengthening partner support, and fostering supportive workplace environments.) Results The ABM developed in this study produced outcomes (i.e., breastfeeding rates) that were concordant with empirical data. Increasing the coverage of the five selected interventions produced various levels of improvement in breastfeeding practices in the target population. Specifically, improving breastfeeding knowledge had a positive impact on women’s intent to breastfeed, while increasing the availability of the Baby-Friendly Hospital Initiative improved breastfeeding initiation rates. However, neither of these two interventions showed a significant impact on breastfeeding maintenance , which was supported by postpartum breastfeeding counseling, partner support and a supportive workplace environment. These three intervention strategies each improved breastfeeding rates at 6 months from 55.6% to 57.1%, 59.5% and 59.3%, respectively. Increasing the coverage of multiple interventions simultaneously had a synergistic effect on breastfeeding maintenance with their effects being greater than the cumulative effects of increasing the coverage of these interventions individually. Conclusion The ABM we developed was helpful for understanding the dynamic process of decision-making regarding infant feeding modalities in a low-income population, and for evaluating the aggregated population-level impact of breastfeeding promotion interventions.