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.

Concurrence of multiple risk factors Low-and middle-income countries Under-5 mortality

Journal

EClinicalMedicine
ISSN: 2589-5370
Titre abrégé: EClinicalMedicine
Pays: England
ID NLM: 101733727

Informations de publication

Date de publication:
May 2024
Historique:
received: 01 11 2023
revised: 13 03 2024
accepted: 19 03 2024
medline: 15 4 2024
pubmed: 15 4 2024
entrez: 15 4 2024
Statut: epublish

Résumé

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. 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. 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 ( 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. This research is funded by Research Fund, Vanke School of Public Health, Tsinghua University.

Sections du résumé

Background UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
This research is funded by Research Fund, Vanke School of Public Health, Tsinghua University.

Identifiants

DOI: 10.1016/j.eclinm.2024.102583 PMID: 38618201 PMC: PMC11015335
pubmed: 38618201
doi: 10.1016/j.eclinm.2024.102583
pii: S2589-5370(24)00162-7
pmc: PMC11015335
doi:

Types de publication

Journal Article

Langues

eng

Pagination

102583

Informations de copyright

© 2024 The Author(s).

Déclaration de conflit d'intérêts

The authors declare no competing interests.

Auteurs

Yuhao Kong (Y)

Vanke School of Public Health, Tsinghua University, Beijing, China.

Shaoru Chen (S)

Vanke School of Public Health, Tsinghua University, Beijing, China.

Ning Ma (N)

Vanke School of Public Health, Tsinghua University, Beijing, China.

Zekun Chen (Z)

Vanke School of Public Health, Tsinghua University, Beijing, China.

Peter Karoli (P)

National Institute for Medical Research, Dar es salaam, Tanzania.

John Lapah Niyi (JL)

Ghana Health Service, Gushegu Municipal Health Directorate, Gushegu, Ghana.

Pengyang Fan (P)

Vanke School of Public Health, Tsinghua University, Beijing, China.

Günther Fink (G)

Swiss TPH and University of Basel, Basel, Switzerland.

Xiaoxiao Jiang Kwete (XJ)

Global Health Research and Consulting, Yaozhi, Yangzhou, China.

Fernando C Wehrmeister (FC)

Department of Epidemiology, Universidade Federal de Pelotas, Pelotas, Brazil.
Institute for Global Public Health, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada.

Feng Cheng (F)

Vanke School of Public Health, Tsinghua University, Beijing, China.
Institute for Healthy China, Tsinghua University, 100084, Beijing, China.

Dongqing Wang (D)

Department of Global and Community Health, College of Public Health, George Mason University, Fairfax, VA, USA, 22030.

Melkamu Aderajew Zemene (MA)

Department of Public Health, College of Health sciences, Debre Tabor University, Debre Tabor, Ethiopia.

Samwel Maina Gatimu (SM)

Diabetes Foot Foundation of Kenya, Kenya.

Nuruzzaman Khan (N)

Centre for Women's Health Research, Faculty of Health and Medicine, University of Newcastle, Australia.

Ashfikur Rahman (A)

Development Studies Discipline, Social Science School, Khulna University, Bangladesh.

Lelisa Fekadu (L)

Health Economist, Health Economics and Financing Program, Africa CDC, Addis Ababa, Ethiopia.

Gebretsadik Shibre (G)

Department of Reproductive, Family, and Population Health, School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia.

Lhuri Dwianti Rahmartani (LD)

Faculty of Public Health, Department of Epidemiology, Universitas Indonesia, Depok, Jawa Barat, Indonesia.

Justice Moses K Aheto (JMK)

Department of Biostatistics, School of Public Health, University of Ghana, Ghana.
WorldPop, School of Geography and Environmental Science, University of Southampton, United Kingdom.

Pascal Geldsetzer (P)

Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Chan Zuckerberg Biohub - San Francisco, CA, USA.

Zhihui Li (Z)

Vanke School of Public Health, Tsinghua University, Beijing, China.
Institute for Healthy China, Tsinghua University, 100084, Beijing, China.

Classifications MeSH