Evidence map›Paper›PMID 34081139›Full record

ArticleJAMA network open2021

Use of Electronic Medical Records to Estimate Changes in Pregnancy and Birth Rates During the COVID-19 Pandemic.

Molly J Stout, Cosmas J M Van De Ven, Vikas I Parekh, Jennifer L Pardo, Maxim Garifullin, Min Xu, Dee E Fenner, Roger D Smith

Open access · goldAbstract read
In one paragraph

Article in JAMA network open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.5field-weighted citation impact, top 11% 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

14 citing papers in PubMed, 28 citations in OpenAlex.

  1. Article
  2. Impact of Coronavirus Disease 2019 on Fertility: An Integrative Review.The International journal of health planning and management · 2025
    Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. 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 1 institution in 1 country.

Molly J StoutDepartment of Obstetrics and Gynecology, University of Michigan, Ann Arbor.
Cosmas J M Van De VenDepartment of Obstetrics and Gynecology, University of Michigan, Ann Arbor.
Vikas I ParekhDepartment of Internal Medicine, University of Michigan, Ann Arbor.
Jennifer L PardoOffice of Capacity Operations, University of Michigan, Ann Arbor.
Maxim GarifullinOffice of Capacity Operations, University of Michigan, Ann Arbor.
Min XuDepartment of Obstetrics and Gynecology, University of Michigan, Ann Arbor.
Dee E FennerDepartment of Obstetrics and Gynecology, University of Michigan, Ann Arbor.
Roger D SmithDepartment of Obstetrics and Gynecology, University of Michigan, Ann Arbor.
University of Michigan–Ann Arbor · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: The influence of the COVID-19 pandemic on fertility rates has been suggested in the lay press and anticipated based on documented decreases in fertility and pregnancy rates during previous major societal and economic shifts. Anticipatory planning for birth rates is important for health care systems and government agencies to accurately estimate size of economy and model working and/or aging populations. Objective: To use projection modeling based on electronic health care records in a large US university medical center to estimate changes in pregnancy and birth rates prior to and after the COVID-19 pandemic societal lockdowns. Design, Setting, and Participants: This cohort study included all pregnancy episodes within a single US academic health care system retrospectively from 2017 and modeled prospectively to 2021. Data were analyzed September 2021. Exposures: Pre- and post-COVID-19 pandemic societal shutdown measures. Main Outcomes and Measures: The primary outcome was number of new pregnancy episodes initiated within the health care system and use of those episodes to project birth volumes. Interrupted time series analysis was used to assess the degree to which COVID-19 societal changes may have factored into pregnancy episode volume. Potential reasons for the changes in volumes were compared with historical pregnancy volumes, including delays in starting prenatal care, interruptions in reproductive endocrinology and infertility services, and preterm birth rates. Results: This cohort study documented a steadily increasing number of pregnancy episodes over the study period, from 4100 pregnancies in 2017 to 4620 in 2020 (28 284 total pregnancies; median maternal [interquartile range] age, 30 [27-34] years; 18 728 [66.2%] White women, 3794 [13.4%] Black women; 2177 [7.7%] Asian women). A 14% reduction in pregnancy episode initiation was observed after the societal shutdown of the COVID-19 pandemic (risk ratio, 0.86; 95% CI, 0.79-0.92; P < .001). This decrease appeared to be due to a decrease in conceptions that followed the March 15 mandated COVID-19 pandemic societal shutdown. Prospective modeling of pregnancies currently suggests that a birth volume surge can be anticipated in summer 2021. Conclusions and Relevance: This cohort study using electronic medical record surveillance found an initial decline in births associated with the COVID-19 pandemic societal changes and an anticipated increase in birth volume. Future studies can further explore how pregnancy episode volume changes can be monitored and birth rates projected in real-time during major societal events.

Indexed as

Birth RateCOVID-19PandemicsPhysical DistancingSocial IsolationAcademic Medical CentersAdultElectronic Health RecordsFemaleFertilityForecastingHumansInterrupted Time Series AnalysisPregnancyProspective StudiesRacial Groups

Identifiers

PMID34081139
PMCPMC8176329
OpenAlexW3165737718

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.