Journal
PAEDIATRIC AND PERINATAL EPIDEMIOLOGY
Volume 33, Issue 1, Pages O15-O24Publisher
WILEY
DOI: 10.1111/ppe.12512
Keywords
adverse perinatal outcomes; birth spacing; causal inference; epidemiologic bias; interpregnancy interval; preterm birth
Funding
- Canadian Institutes of Health Research
- Michael Smith Foundation for Health Research
- Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health
- Office of Population Affairs [HHSP233201450040A]
- Atlas Research, LLC [HHSP233201450040A]
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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.
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