Evidence map›Paper›PMID 31086756›Full record

ArticleCurrent epidemiology reports2018

The curse of the perinatal epidemiologist: inferring causation amidst selection.

Jonathan M Snowden, Marit L Bovbjerg, Mekhala Dissanayake, Olga Basso

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Current epidemiology reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed.

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  14. Epidemiology of elective induction of labour: a timeless exposure.International journal of epidemiology · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jonathan M SnowdenSchool of Public Health, Oregon Health and Science University-Portland State University, 3181 SW Sam Jackson Park Rd, Mail Code: CB-669, Portland, OR 97239-3098, USA.
Marit L BovbjergCollege of Public Health and Human Sciences, Oregon State University, Milam Hall 103, Corvallis, OR 97331, USA.
Mekhala DissanayakeDepartment of Obstetrics and Gynecology, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Mail Code: L-466, Portland, OR 97239-3098, USA.
Olga BassoDepartment of Obstetrics & Gynecology; Research Institute of the McGill University Health Centre.

Funding

Cesarean delivery in overweight and obese women: patient and systems factorsR00HD079658 · NICHD · OREGON HEALTH & SCIENCE UNIVERSITY · PI SNOWDEN, JONATHAN M · 2016 to 2018
$725k
NICHD NIH HHS R00 HD079658
6 · The paper itself

Abstract

purpose of reviewHuman reproduction is a common process and one that unfolds over a relatively short time, but pregnancy and birth processes are challenging to study. Selection occurs at every step of this process (e.g., infertility, early pregnancy loss, and stillbirth), adding substantial bias to estimated exposure-outcome associations. Here we focus on selection in perinatal epidemiology, specifically, how it affects research question formulation, feasible study designs, and interpretation of results. RECENT

findingsApproaches have recently been proposed to address selection issues in perinatal epidemiology. One such approach is the ongoing pregnancies denominator for gestation-stratified analyses of infant outcomes. Similarly, bias resulting from left truncation has recently been termed "live birth bias," and a proposed solution is to control for common causes of selection variables (e.g., fecundity, fetal loss) and birth outcomes. However, these approaches have theoretical shortcomings, conflicting with the foundational epidemiologic concept of populations at risk for a given outcome. SUMMARY: We engage with epidemiologic theory and employ thought experiments to demonstrate the problems of using denominators that include units not "at risk" of the outcome. Fundamental (and commonsense) concerns of outcome definition and analysis (e.g., ensuring that all study participants are at risk for the outcome) should take precedence in formulating questions and analysis approach, as should choosing questions that stakeholders care about. Selection and resulting biases in human reproductive processes complicate estimation of unbiased exposure- outcome associations, but we should not focus solely (or even mostly) on minimizing such biases.

Indexed as

birth outcomescausationearly pregnancy lossinfertilitypopulation at riskSelection bias

Identifiers

PMID31086756
PMCPMC6510491

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.