Evidence map›Paper›PMID 38618201›Full record

ArticleEClinicalMedicine2024

Association between concurrence of multiple risk factors and under-5 mortality: a pooled analysis of data from Demographic and Health Survey in 61 low-and-middle-income countries.

Yuhao Kong, Shaoru Chen, Ning Ma, Zekun Chen, Peter Karoli, John Lapah Niyi, Pengyang Fan, Günther Fink, Xiaoxiao Jiang Kwete, Fernando C Wehrmeister and 12 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EClinicalMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
3.3field-weighted citation impact, top 9% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

22 authors at 14 institutions in 12 countries.

Yuhao KongVanke School of Public Health, Tsinghua University, Beijing, China.
Shaoru ChenVanke School of Public Health, Tsinghua University, Beijing, China.
Ning MaVanke School of Public Health, Tsinghua University, Beijing, China.
Zekun ChenVanke School of Public Health, Tsinghua University, Beijing, China.
Peter KaroliNational Institute for Medical Research, Dar es salaam, Tanzania.
John Lapah NiyiGhana Health Service, Gushegu Municipal Health Directorate, Gushegu, Ghana.
Pengyang FanVanke School of Public Health, Tsinghua University, Beijing, China.
Günther FinkSwiss TPH and University of Basel, Basel, Switzerland.
Xiaoxiao Jiang KweteGlobal Health Research and Consulting, Yaozhi, Yangzhou, China.
Fernando C WehrmeisterDepartment of Epidemiology, Universidade Federal de Pelotas, Pelotas, Brazil.
Feng ChengVanke School of Public Health, Tsinghua University, Beijing, China.
Dongqing WangDepartment of Global and Community Health, College of Public Health, George Mason University, Fairfax, VA, USA, 22030.
Melkamu Aderajew ZemeneDepartment of Public Health, College of Health sciences, Debre Tabor University, Debre Tabor, Ethiopia.
Samwel Maina GatimuDiabetes Foot Foundation of Kenya, Kenya.
Nuruzzaman KhanCentre for Women's Health Research, Faculty of Health and Medicine, University of Newcastle, Australia.
Ashfikur RahmanDevelopment Studies Discipline, Social Science School, Khulna University, Bangladesh.
Lelisa FekaduHealth Economist, Health Economics and Financing Program, Africa CDC, Addis Ababa, Ethiopia.
Gebretsadik ShibreDepartment of Reproductive, Family, and Population Health, School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia.
Lhuri Dwianti RahmartaniFaculty of Public Health, Department of Epidemiology, Universitas Indonesia, Depok, Jawa Barat, Indonesia.
Justice Moses K AhetoDepartment of Biostatistics, School of Public Health, University of Ghana, Ghana.
Pascal GeldsetzerDivision of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Zhihui LiVanke School of Public Health, Tsinghua University, Beijing, China.
Vanke (China) · CNAddis Ababa University · ETAfrica Centres for Disease Control and Prevention · ETChan Zuckerberg Initiative (United States) · USDebre Tabor University · ETGeorge Mason University · USGhana Health Service · GHKhulna University · BDNational Institute for Medical Research · TZSwiss Tropical and Public Health Institute · CHUniversidade Federal de Pelotas · BRUniversity of Ghana · GHUniversity of Indonesia · IDUniversity of Newcastle Australia · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Exposure to multiple risk factors is prevalent in low-and middle-income countries (LMICs), challenging one-directional strategies to address preventable under-5 mortality (U5M). This study aims to assess the associations between concurrence of multiple risk factors and U5M in LMICs. Methods: We extracted data from the Demographic and Health Surveys conducted between 2010 and 2021 across 61 LMICs. Our primary outcome was U5M, defined as deaths from birth to 59 months. Binary logistic regression model was applied to ascertain the association between U5M and a total of 20 critical risk factors. Upon identifying the risk factors demonstrating the strongest associations, we investigated the simultaneous presence of multiple risk factors in each individual and assessed their combined effects on U5M with logistic regression models. Findings: Of the 604,372 under-5 children, 18,166 (3.0%) died at the time of the survey. Unsatisfied family planning needs was the strongest risk factor for U5M (odds ratio [OR]: 2.0, 95% confidence interval [CI]: 1.9-2.1), followed by short birth interval (<18 months; OR: 2.0, 95% CI: 1.9-2.1), small birth size (OR: 2.0, 95% CI: 1.8-2.1), never breastfed or delayed breastfeeding (OR: 2.0, 95% CI: 1.9-2.0), and low maternal education (OR: 1.6, 95% CI: 1.4-1.8). 66.7% (66.6%-66.8%) of the children had 2 or more leading risk factors simultaneously. Simultaneous presence of multiple leading risk factors was significantly associated with elevated risk of U5M and children presenting with all 5 leading risk factors exhibited an exceedingly high risk of U5M (OR: 5.2, 95% CI: 4.3-6.3); a dose-response relationship between the number of risk factors and U5M was also observed-with the increment of numbers of leading risk factors, the U5M showed an increasing trend ( Interpretation: Exposure to multiple risk factors is very common in LMICs and underscores the necessity of developing multisectoral and integrated approaches to accelerate progress in reducing U5M in line with the SDG 3.2. Funding: This research is funded by Research Fund, Vanke School of Public Health, Tsinghua University.

Indexed as

Concurrence of multiple risk factorsLow-and middle-income countriesUnder-5 mortality

Identifiers

PMID38618201
PMCPMC11015335
OpenAlexW4394006598

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.