Evidence map›Paper›PMID 31259002›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019

Detecting Patterns of Prescription Drug Use During Pregnancy and Lactation with Visualization Techniques.

Ou Stella Liang, Jeanne S Sheffield, Casey Overby Taylor

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

3 authors.

Ou Stella LiangDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Jeanne S SheffieldDepartment of Gynecology and Obstetrics.
Casey Overby TaylorDivision of General Internal Medicine.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPharmacovigilance studies for the pregnancy population are an important tool to address the teratogenic and maternal effects of prescription drugs.

objectivesWe sought to leverage existing data to provide empirical evidence that reflects current drug utilization by pregnant/lactating women for pharmacovigilance and effective study designs.

methodsTemporal patterns of prescription drug use during pregnancy and lactation for four clinical conditions were visualized using a R package and an administrative claims dataset representing nationwide privately-insured women.

resultsDuring the years 2010 to 2014, there was a significant increase (p < 0.001) in overall use of opioid, antidepressant, antihypertensive, and antidiabetic prescriptions. Hydrocodone, sertraline, labetalol, and metformin were the most common prescriptions for perinatal use in its respective category. Trimester-specific prescription patterns were reported.

conclusionsVisualization of administrative claims data can quickly detect temporal patterns of prescription drug use during pregnancy and lactation.

Identifiers

PMID31259002
PMCPMC6568088

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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.