Good practices for the design, analysis, and interpretation of observational studies on birth spacing and perinatal health outcomes. Issue 1 (12th October 2018)

• Record Type: Journal Article
• Authors: Hutcheon, Jennifer A.; Moskosky, Susan; Ananth, Cande V.; Basso, Olga; Briss, Peter A.; Ferré, Cynthia D.; Frederiksen, Brittni N.; Harper, Sam; Hernández‐Díaz, Sonia; Hirai, Ashley H.; Kirby, Russell S.; Klebanoff, Mark A.; Lindberg, Laura; Mumford, Sunni L.; Nelson, Heidi D.; Platt, Robert W.; Rossen, Lauren M.; Stuebe, Alison M.; Thoma, Marie E.; Vladutiu, Catherine J.; Ahrens, Katherine A.
• Is Part Of: Paediatric and perinatal epidemiology. Volume 33:Issue 1(2019:Jan.)
• Page Start: O15
• Page End: O24
• Publication Date: 2018-10-12
• Subjects: adverse perinatal outcomes -- birth spacing -- causal inference -- epidemiologic bias -- interpregnancy interval -- preterm birth; Pediatrics -- Periodicals; Perinatology -- Periodicals; Pediatric epidemiology -- Periodicals; Infants (Newborn) -- Diseases -- Periodicals; 618.92
• DOI: 10.1111/ppe.12512
• Languages: English
• ISSNs: 0269-5022

Abstract

Abstract: Background: 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. Methods: 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. Results: 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. Conclusion: 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.