Population Research and Policy Review

, Volume 37, Issue 1, pp 117–135 | Cite as

Estimating Educational Differences in Low-Risk Cesarean Section Delivery: A Multilevel Modeling Approach

  • Andrea M. TilstraEmail author


U.S. rates of cesarean section, and in particular, low-risk cesarean section (LRC) births rose dramatically across the late 1990s and early 2000s, and have since remained high. Although previous research explores how trends in LRC vary between states and across maternal characteristics, within-state heterogeneity has not yet been accounted for, nor has the extent to which maternal and county characteristics might interact to shape the likelihood of a LRC birth. Using U.S. county-level birth data for years 2008–2010 from the restricted National Vital Statistics Systems Cohort Linked Birth-Infant Death Files and the Area Health Resource Files, I conduct race-stratified multilevel analyses to explore the association between the mother’s education, the income of the county in which she gives birth, and the odds of LRC delivery. I find that regardless of race/ethnicity, less education at the individual level and lower income at the county level are associated with higher odds of LRC delivery. There are also persistent racial disparities in these relationships. Non-Hispanic black mothers have the highest overall odds of LRC delivery, yet the effect of both education and county income is greatest for non-Hispanic white mothers. The results highlight the importance of analyzing both individual resources and contextual effects of the county when assessing birthing processes, as both contribute to a mother’s access to and knowledge of natal care.


United States C-sections Fertility Education Contextual effects BID files AHRF 



I thank the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)-funded University of Colorado Population Center (Award Number P2C HD066613) for development, administrative, and computing support; the National Association for Public Health Statistics and Information Systems and the National Vital Statistics Systems for providing data access; and Ryan K. Masters and Richard G. Rogers for guidance on analyses and manuscript preparation. This manuscript also benefitted from comments and discussion from the Population Health Working group at the University of Colorado Boulder. The content is solely the responsibility of the author and does not necessarily represent the official views of the NICHD, the National Institutes, NAPHSIS, or the NVSS. A previous version of this manuscript was presented at the Population Association of America annual meeting in Chicago, IL, April 27–29, 2017.

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© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of SociologyUniversity of ColoradoBoulderUSA
  2. 2.Population Program, Institute of Behavioral ScienceUniversity of ColoradoBoulderUSA

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