Evidence map›Paper›PMID 36353264›Full record

ArticleEClinicalMedicine2022

Independent and cumulative effects of risk factors associated with stillbirths in 50 low- and middle-income countries: A multi-country cross-sectional study.

Zhihui Li, Yuhao Kong, Shaoru Chen, Maya Subramanian, Chunling Lu, Rockli Kim, Fernando C Wehrmeister, Yi Song, S V Subramanian

Open access · goldAbstract read
In one paragraph

Article in EClinicalMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
8.4field-weighted citation impact, top 2% 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, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.

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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

9 authors at 7 institutions in 5 countries.

Zhihui LiVanke School of Public Health, Tsinghua University, 100084, Beijing, China.
Yuhao KongVanke School of Public Health, Tsinghua University, 100084, Beijing, China.
Shaoru ChenVanke School of Public Health, Tsinghua University, 100084, Beijing, China.
Maya SubramanianBrandeis University, Waltham, MA, 02453, USA.
Chunling LuDepartment of Global Health and Social Medicine, Harvard Medical School, Boston, MA, 02115, USA.
Rockli KimDivision of Health Policy and Management, College of Health Science, Korea University, Seoul, South Korea.
Fernando C WehrmeisterDepartment of Epidemiology, Universidade Federal de Pelotas, Pelotas, Brazil.
Yi SongInstitute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China.
S V SubramanianHarvard Center for Population & Development Studies, Cambridge, MA, 02115, USA.
Harvard University · USTsinghua University · CNBrandeis University · USKorea University · KRNational Health and Family Planning Commission · CNUniversity of Manitoba · CAVanke (China) · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Early identification of high-risk pregnancies could reduce stillbirths, yet remains a challenge in low- and middle-income countries (LMICs). This study aims to estimate the associations between easily observable risk factors and stillbirths, and construct a risk score which could be adopted in LMICs to identify pregnancies with high risk of stillbirths. Methods: Using the most recent Demographic and Health Surveys from 50 low- and middle-income countries (LMICs) with available data between January 1, 2010 and December 31, 2021, we analysed a total of 22 factors associated with stillbirths in a series of single-adjusted and mutually adjusted logistic regression models. Upon identification of the risk factors with the strongest associations, we constructed a risk score on the basis of the magnitude of the β coefficient to examine the cumulative effects of risk factors on stillbirths. To assess whether the associations between risk scores and stillbirths were moderated by protective factors, we added an interaction term between the identified protective factor and risk scores to the regression model. We also conducted two sets of subgroup analyses for previous history of pregnancy and maternal age at pregnancy and four sets of supplementary analyses to test the robustness of the results. Findings: Among the 795,642 women identified for analysis with at least one pregnancy within the five years before the survey, the most recent pregnancy of 8968 (1.13%) ended as stillbirths. Using a mutually adjusted regression model, we found that the top factors showing the strongest associations with stillbirths were short maternal height (odds ratio [OR]: 1.99, 95% confidence interval [CI]: 1.48-2.67, Interpretation: Our study suggested that short maternal height, low socioeconomic status, previous stillbirth history, low maternal education, and very short interpregnancy interval had the strongest associations with stillbirths. The construction of risk scores using easily observable risk factors could be an effective way to identify high-risk pregnancies in resource-poor settings. Funding: This research was supported by Sanming Project of Medicine in Shenzhen (NO. SZSM202111001) and China National Natural Science Foundation (NO. 72203119).

Indexed as

Low- and middle-income countriesRisk factorsRisk scoresStillbirth

Identifiers

PMID36353264
PMCPMC9637680
OpenAlexW4307750376

What OpenQuestion holds

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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.