Severe Maternal Morbidity: A Comparison of Definitions and Data Sources. Issue 9 (23rd March 2021)

• Record Type: Journal Article
• Authors: Snowden, Jonathan M; Lyndon, Audrey; Kan, Peiyi; El Ayadi, Alison; Main, Elliott; Carmichael, Suzan L
• Is Part Of: American journal of epidemiology. Volume 190:Issue 9(2021)
• Page Start: 1890
• Page End: 1897
• Publication Date: 2021-03-23
• Subjects: data quality -- maternal health -- perinatal outcomes -- quality improvement -- reliability -- severe maternal morbidity; Epidemiology -- Periodicals; Public health -- Periodicals; 614.4
• DOI: 10.1093/aje/kwab077
• Languages: English
• ISSNs: 0002-9262

Abstract

Abstract: Severe maternal morbidity (SMM) is a composite outcome measure that indicates serious, potentially life-threatening maternal health problems. There is great interest in defining SMM using administrative data for surveillance and research. In the United States, one common way of defining SMM at the population level is an index developed by the Centers for Disease Control and Prevention. Modifications to this index (e.g., exclusion of maternal blood transfusion) have been proposed; some research defines SMM using an index introduced by Bateman et al. ( Obstet Gynecol . 2013;122(5):957–965). Birth certificate data are also increasingly being used to define SMM. We compared commonly used US definitions of SMM among all California births (2007–2012) using the kappa (κ) statistic and other measures. We also evaluated agreement between maternal morbidity fields on the birth certificate as compared with health insurance claims data. Concordance between the 7 definitions of SMM analyzed was generally low (i.e., κ < 0.41 for 13 of 21 two-way comparisons). Low concordance was particularly driven by the presence/absence of transfusion and claims data versus birth certificate definitions. Low agreement between administrative data–based definitions of SMM highlights that results can be expected to differ between them. Further research on validity of SMM definitions, using more fine-grained data sources, is needed.