Using Infant Mortality Data to Improve Maternal and Child Health Programs: An Application of Statistical Process Control Techniques for Rare Events

Abstract

Introduction The infant mortality rate (IMR) in the United States remains higher than most developed countries. To understand this public health issue and support state public health departments in displaying and analyzing data in ways that support learning, states participating in the Collaborative Improvement and Innovation Network to Reduce Infant Mortality (IM CoIIN) created statistical process control (SPC) charts for rare events. Methods State vital records data on live births and infant deaths was used to create U, T and G charts for Kansas and Alaska, two states participating in the IM CoIIN who sought methods to more effectively analyze IMR for subsets of their populations with infrequent number of deaths. The IMR and the number of days and number of births between infant deaths was charted for Kansas Non-Hispanic black population and six Alaska regions for the time periods 2013–2016 and 2011–2016, respectively. Established empirical patterns indicated points of special cause variation. Results The T and G charts for Kansas and G charts for Alaska depict points outside the upper control limit. These points indicate special cause variation and an increased number of days and/or births between deaths at these time periods. Discussion T and G charts offer value in examining rare events, and indicate special causes not detectable by U charts or other more traditional analytic methods. When small numbers make traditional analysis challenging, SPC has potential in the MCH field to better understand potential drivers of improvements in rare outcomes, inform decision making and take interventions to scale.

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