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

Jennifer A. Hutcheon, Susan Moskosky, Cande V. Ananth, Olga Basso, Peter A. Briss, Cynthia D. Ferré, Brittni N. Frederiksen, Sam Harper, Sonia Hernández-Díaz, Ashley H. Hirai, Russell S. Kirby, Mark A. Klebanoff, Laura Lindberg, Sunni L. Mumford, Heidi Nelson, Robert W. Platt, Lauren M. Rossen, Alison M. Stuebe, Marie E. Thoma, Catherine J. Vladutiu & 1 others Katherine A. Ahrens

Research output: Contribution to journalArticle

6 Citations (Scopus)

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.

Original languageEnglish (US)
JournalPaediatric and Perinatal Epidemiology
DOIs
StateAccepted/In press - Jan 1 2018

Fingerprint

Birth Intervals
Observational Studies
Pregnancy
Health
Parturition
Maternal Age
Spontaneous Abortion
Pregnancy Outcome
Research
Gestational Age
Meta-Analysis
Economics
Mothers

Keywords

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

ASJC Scopus subject areas

  • Epidemiology
  • Pediatrics, Perinatology, and Child Health

Cite this

Good practices for the design, analysis, and interpretation of observational studies on birth spacing and perinatal health outcomes. / 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; Platt, Robert W.; Rossen, Lauren M.; Stuebe, Alison M.; Thoma, Marie E.; Vladutiu, Catherine J.; Ahrens, Katherine A.

In: Paediatric and Perinatal Epidemiology, 01.01.2018.

Research output: Contribution to journalArticle

Hutcheon, JA, Moskosky, S, Ananth, CV, Basso, O, Briss, PA, Ferré, CD, Frederiksen, BN, Harper, S, Hernández-Díaz, S, Hirai, AH, Kirby, RS, Klebanoff, MA, Lindberg, L, Mumford, SL, Nelson, H, Platt, RW, Rossen, LM, Stuebe, AM, Thoma, ME, Vladutiu, CJ & Ahrens, KA 2018, 'Good practices for the design, analysis, and interpretation of observational studies on birth spacing and perinatal health outcomes', Paediatric and Perinatal Epidemiology. https://doi.org/10.1111/ppe.12512
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 ; Platt, Robert W. ; Rossen, Lauren M. ; Stuebe, Alison M. ; Thoma, Marie E. ; Vladutiu, Catherine J. ; Ahrens, Katherine A. / Good practices for the design, analysis, and interpretation of observational studies on birth spacing and perinatal health outcomes. In: Paediatric and Perinatal Epidemiology. 2018.
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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.",
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T1 - Good practices for the design, analysis, and interpretation of observational studies on birth spacing and perinatal health outcomes

AU - Hutcheon, Jennifer A.

AU - Moskosky, Susan

AU - Ananth, Cande V.

AU - Basso, Olga

AU - Briss, Peter A.

AU - Ferré, Cynthia D.

AU - Frederiksen, Brittni N.

AU - Harper, Sam

AU - Hernández-Díaz, Sonia

AU - Hirai, Ashley H.

AU - Kirby, Russell S.

AU - Klebanoff, Mark A.

AU - Lindberg, Laura

AU - Mumford, Sunni L.

AU - Nelson, Heidi

AU - Platt, Robert W.

AU - Rossen, Lauren M.

AU - Stuebe, Alison M.

AU - Thoma, Marie E.

AU - Vladutiu, Catherine J.

AU - Ahrens, Katherine A.

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N2 - 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.

AB - 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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