Evidence map›Paper›PMID 38516346›Full record

ArticleJAMIA open2024

Diving into CDC pregnancy data in the United States: longitudinal study and interactive application.

Andrea O Clark-Sevilla, Yun C Lin, Arnav Saxena, Qi Yan, Ronald Wapner, Anita Raja, Itsik Pe'er, Ansaf Salleb-Aouissi

Open access · goldAbstract read
In one paragraph

Article in JAMIA open, 2024. 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
2.3field-weighted citation impact, top 13% of its field
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, 4 citations in OpenAlex.

  1. Derivation and validation of an algorithm for maternal-child linkage in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  2. Article
  3. Article
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

8 authors at 2 institutions in 1 country.

Andrea O Clark-SevillaDepartment of Computer Science, Columbia University, New York, NY 10027, United States.
Yun C LinDepartment of Computer Science, Columbia University, New York, NY 10027, United States.
Arnav SaxenaDepartment of Data Science, Columbia University, New York, NY 10027, United States.
Qi YanDepartment of Obstetrics and Gynecology, Columbia University, New York, NY 10027, United States.ORCID https://orcid.org/0000-0002-5236-9673
Ronald WapnerDepartment of Obstetrics and Gynecology, Columbia University, New York, NY 10027, United States.
Anita RajaDepartment of Computer Science, CUNY Hunter College, New York, NY 10065, United States.
Itsik Pe'erDepartment of Computer Science, Columbia University, New York, NY 10027, United States.
Ansaf Salleb-AouissiDepartment of Computer Science, Columbia University, New York, NY 10027, United States.
Columbia University · USHunter College · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Preterm birth (PTB) is a major determinant of neonatal mortality, morbidity, and childhood disability. In this article, we present a longitudinal analysis of the risk factors associated with PTB and how they have varied over the years: starting from 1968 when the CDC first started, reporting the natality data, up until 2021. Along with this article, we are also releasing an RShiny web application that will allow for easy consumption of this voluminous dataset by the research community. Further, we hope this tool can aid clinicians in the understanding and prevention of PTB. Materials and Methods: This study used the CDC Natality data from 1968 to 2021 to analyze trends in PTB outcomes across the lens of various features, including race, maternal age, education, and interval length between pregnancies. Our interactive RShiny web application, CDC NatView, allows users to explore interactions between maternal risk factors and maternal morbidity conditions and the aforementioned features. Results: Our study demonstrates how CDC data can be leveraged to conduct a longitudinal analysis of natality trends in the United States. Our key findings reveal an upward trend in late PTBs, which is concerning. Moreover, a significant disparity exists between African American and White populations in terms of PTB. These disparities persist in other areas, such as education, body-mass index, and access to prenatal care later in pregnancy. Discussion: Another notable finding is the increase in maternal age over time. Additionally, we confirm that short interpregnancy intervals (IPIs) are a risk factor for PTBs. To facilitate the exploration of pregnancy risk factors, infections, and maternal morbidity, we developed an open-source RShiny tool called CDC NatView. This software offers a user-friendly interface to interact with and visualize the CDC natality data, which constitutes an invaluable resource. Conclusion: In conclusion, our study has shed light on the rise of late PTBs and the persistent disparities in PTB rates between African American and White populations in the US. The increase in maternal age and the confirmation of a short IPI as a risk factor for PTB are noteworthy findings. Our open-source tool, CDC NatView, can be a valuable resource for further exploration of the CDC natality data to enhance our understanding of pregnancy risk factors and the interaction of PTB outcomes and maternal morbidities.

Indexed as

CDCdata analysislongitudinal studymaternal morbiditypreterm birthR Shiny

Identifiers

PMID38516346
PMCPMC10955523
OpenAlexW4394739205

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.