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Paediatr Perinat Epidemiol. 2018 Oct 12. doi: 10.1111/ppe.12512. [Epub ahead of print]

Good practices for the design, analysis, and interpretation of observational studies on birth spacing and perinatal health outcomes.

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Department of Obstetrics and Gynaecology, University of British Columbia, Vancouver, British Columbia, Canada.
Office of Population Affairs, Office of the Assistant Secretary for Health, Rockville, Maryland.
Department of Obstetrics and Gynecology, Irving College of Physicians and Surgeons, Columbia University, New York, New York.
Department of Epidemiology, Joseph L. Mailman School of Public Health, Columbia University, New York, New York.
Department of Obstetrics and Gynecology, Royal Victoria Hospital, Research Institute of McGill University Health Centre, Montreal, Quebec, Canada.
Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada.
National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia.
Maternal and Infant Health Branch, Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia.
Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
US Department of Health and Human Services, Health Resources and Services Administration, Maternal and Child Health Bureau, Office of Epidemiology and Research, Rockville, Maryland.
Department of Community and Family Health, University of South Florida College of Public Health, Tampa, Florida.
Division of Epidemiology, Departments of Pediatrics and Obstetrics and Gynecology, Center for Perinatal Research, The Research Institute at Nationwide Children's Hospital, The Ohio State University, Columbus Ohio.
Guttmacher Institute, New York, New York.
Epidemiology Branch, Division of Intramural Population Health Research, National Institute of Child Health and Human Development, Bethesda, Maryland.
Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon.
Reproductive Statistics Branch, Division of Vital Statistics, Centers for Disease Control and Prevention, National Center for Health Statistics, Hyattsville, Maryland.
Department of Obstetrics and Gynecology, University of North Carolina School of Medicine, Chapel Hill, North Carolina.
Department of Maternal and Child Health, Gillings School of Global Public Health, Chapel Hill, North Carolina.
Department of Family Science, University of Maryland, College Park, Maryland.



Meta-analyses of observational studies have shown that women with a shorter interpregnancy interval (the time from delivery to start of a subsequent pregnancy) are more likely to experience adverse pregnancy outcomes, such as preterm delivery or small for gestational age birth, than women who space their births further apart. However, the studies used to inform these estimates have methodological shortcomings.


In this commentary, we summarise the discussions of an expert workgroup describing good practices for the design, analysis, and interpretation of observational studies of interpregnancy interval and adverse perinatal health outcomes.


We argue that inferences drawn from research in this field will be improved by careful attention to elements such as: (a) refining the research question to clarify whether the goal is to estimate a causal effect vs describe patterns of association; (b) using directed acyclic graphs to represent potential causal networks and guide the analytic plan of studies seeking to estimate causal effects; (c) assessing how miscarriages and pregnancy terminations may have influenced interpregnancy interval classifications; (d) specifying how key factors such as previous pregnancy loss, pregnancy intention, and maternal socio-economic position will be considered; and (e) examining if the association between interpregnancy interval and perinatal outcome differs by factors such as maternal age.


This commentary outlines the discussions of this recent expert workgroup, and describes several suggested principles for study design and analysis that could mitigate many potential sources of bias.


adverse perinatal outcomes; birth spacing; causal inference; epidemiologic bias; interpregnancy interval; preterm birth


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