Advertisement

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

  • Patricia Finnerty
  • Lloyd Provost
  • Emily O’Donnell
  • Sabrina Selk
  • Kaerin Stephens
  • Jamie Kim
  • Scott Berns
Methodological Notes

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.

Keywords

Infant mortality Statistical process control (SPC) Rare events Quality improvement 

Notes

Acknowledgements

This project is supported by the Health Resources and Services Administration (HRSA) of the U.S. Department of Health and Human Services (HHS) under Grant No. UF3MC26524, Providing Support for the Collaborative Improvement and Innovation Network (CoIIN) to Reduce Infant Mortality, from 9/30/2013 through 9/29/2017 for $11,910,957 (no NGO sources). This information or content and conclusions are those of the author and should not be construed as the official position or policy of, nor should any endorsements be inferred by HRSA, HHS or the U.S. Government.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Benneyan, J. C., Lloyd, R. C., & Plsek, P. E. (2003). Statistical process control as a tool for research and healthcare improvement. Quality and Safety in Health Care, 12(6), 458–464.CrossRefGoogle Scholar
  2. Brown Speights, J. S., Goldfarb, S. S., Wells, B. A., Beitsch, L., Levine, R. S., & Rust, G. (2017). State-level progress in reducing the black–white infant mortality gap, United States, 1999–2013. American Journal of Public Health, 107(5), 775–782.  https://doi.org/10.2105/AJPH.2017.303689.CrossRefGoogle Scholar
  3. Kochanek, K. D., Xu, J., Murphy, S. L., Minino, A. M., & Kung, H.-C. (2016). National vital statistics reports deaths: Final data for 2014. National Center for Health Statistics, 65(4), 1–117.Google Scholar
  4. Lau, C., Ambalavanan, N., Chakraborty, H., Wingate, M. S., & Carlo, W. A. (2013). Extremely low birth weight and infant mortality rates in the United States. Pediatrics, 131(5), 855–860. Retrieved from http://pediatrics.aappublications.org/content/131/5/855.
  5. Macdorman, M. F., Hoyert, D. L., & Mathews, T. J. (2013). Recent declines in infant mortality in the United States, 2005–2011. NCHS Data Brief, 120. Retrieved from https://www.cdc.gov/nchs/data/databriefs/db120.pdf.
  6. Macdorman, M. F., & Mathews, T. J. (2014). International comparisons of infant mortality and related factors: United States and Europe, 2010. National Vital Statistics Report, 63(5), 1–7. Retrieved from https://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_05.pdf.
  7. Mathews, T. J., & Driscoll, A. K. (2017). Trends in infant mortality in the United States, 2005–2014. NCHS Data Brief, 279. Retrieved from https://www.cdc.gov/nchs/data/databriefs/db279.pdf.
  8. Mohammed, M. (2004). Using statistical process control to improve the quality of health care. Quality & Safety in Health Care, 13(4), 242–243.  https://doi.org/10.1136/qshc.2004.010454.CrossRefGoogle Scholar
  9. Organisation for Economic Co-operation and Development. (2014). OECD stat extracts: Health status. Retrieved August 30, 2017, from http://stats.oecd.org/Index.aspx?DatasetCode=HEALTH_STAT.
  10. Process Improvement Products. (2017). QI Charts. Retrieved October 23, 2017, from http://www.pipproducts.com.
  11. Provost, L. P., & Murray, S. (2011). The health care data guide: Learning from data for improvement. San Francisco: Wiley.Google Scholar
  12. Shewhart, W. A. (1980). Economic control of quality of manufactured product. American Society for Quality. Retrieved from https://asq.org/quality-press/display-item?item=H0509.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Education Development CenterWalthamUSA
  2. 2.Associates in Process ImprovementWimberleyUSA
  3. 3.National Institute for Children’s Health QualityBostonUSA
  4. 4.State of Alaska, Department of Health and Social Services, Department of Public Health, Section of Women’s, Children’s & Family HealthAnchorageUSA
  5. 5.Kansas Department of Health and Environment, Maternal and Child Health EpidemiologyBureau of Family HealthTopekaUSA

Personalised recommendations