Evidence map›Paper›PMID 30894174›Full record

ArticleReproductive health2019

Pregnancies, abortions, and pregnancy intentions: a protocol for modeling and reporting global, regional and country estimates.

Jonathan Marc Bearak, Anna Popinchalk, Gilda Sedgh, Bela Ganatra, Ann-Beth Moller, Özge Tunçalp, Leontine Alkema

Abstract read
In one paragraph

Article in Reproductive health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

7 authors.

Jonathan Marc BearakGuttmacher Institute, 125 Maiden Ln, New York, NY, 10038, USA. jbearak@guttmacher.org.ORCID http://orcid.org/0000-0002-3207-1431
Anna PopinchalkGuttmacher Institute, 125 Maiden Ln, New York, NY, 10038, USA.
Gilda SedghGuttmacher Institute, 125 Maiden Ln, New York, NY, 10038, USA.
Bela GanatraDepartment of Reproductive Health and Research, World Health Organization, Avenue Appia, 20, Geneva, Switzerland.
Ann-Beth MollerDepartment of Reproductive Health and Research, World Health Organization, Avenue Appia, 20, Geneva, Switzerland.
Özge TunçalpDepartment of Reproductive Health and Research, World Health Organization, Avenue Appia, 20, Geneva, Switzerland.
Leontine AlkemaDepartment of Biostatistics and Epidemiology, University of Massachusetts-Amherst, Amherst, MA, 01003, USA.

Funding

World Health Organization 001
6 · The paper itself

Abstract

backgroundEstimates of pregnancies, abortions and pregnancy intentions can help assess how effectively women and couples are able to fulfil their childbearing aspirations. Abortion incidence estimates are also a necessary foundation for research on the safety of abortions performed and the consequences of unsafe abortion. Furthermore, periodic estimates of these indicators are needed to help inform policy and programmes.

methodsWe will develop a Bayesian hierarchical times series model which estimates levels and trends in pregnancy rates, abortion rates, and percentages of pregnancies and births unintended for each five-year period between 1990 and 2019. The model will be informed by data on abortion incidence and the percentage of births or pregnancies that were unintended. We will develop a data classification process to be applied to all available data. Model-based estimates and associated uncertainty will take account of data sparsity and quality. Our proposed approach will advance previous work in two key ways. First, we will estimate pregnancy and abortion rates simultaneously, and model the propensity to abort an unintended pregnancy, as opposed to modeling abortion rates directly as in prior work. Secondly, we will produce estimates that are reproducible at the country level by publishing the data inputs, data classification processes and source code. DISCUSSION: This protocol will form the basis for updated global, regional and national estimates of intended and unintended pregnancy rates, abortion rates, and the percent of unintended pregnancies ending in abortion, from 1990 to 2019.

Indexed as

IntentionPregnancy RateAbortion, InducedBayes TheoremFemaleHumansIncidencePregnancyPregnancy, UnplannedAbortionBayesianEstimatesFamily planningGlobalNationalPregnancyRegionalTrendsUnintended pregnancy

Identifiers

PMID30894174
PMCPMC6425570

What OpenQuestion holds

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LicenceCC BY
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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.