Evidence map›Paper›PMID 38592761›Full record

ArticleJMIR public health and surveillance2024

Discovering Subgroups of Children With High Mortality in Urban Guinea-Bissau: Exploratory and Validation Cohort Study.

Andreas Rieckmann, Sebastian Nielsen, Piotr Dworzynski, Heresh Amini, Søren Wengel Mogensen, Isaquel Bartolomeu Silva, Angela Y Chang, Onyebuchi A Arah, Wojciech Samek, Naja Hulvej Rod and 4 more

Open access · goldAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 13% 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, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.

  1. Pooled it
  2. Review
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

14 authors at 7 institutions in 5 countries.

Andreas RieckmannSection of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0001-8695-2376
Sebastian NielsenBandim Health Project, INDEPTH Network, Bissau, Guinea-Bissau.ORCID 0000-0003-3384-7489
Piotr DworzynskiNovo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0002-6717-8052
Heresh AminiDepartment of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, New York, NY, United States.ORCID 0000-0002-4825-1322
Søren Wengel MogensenDepartment of Automatic Control, Lund University, Lund, Sweden.ORCID 0000-0002-6652-155X
Isaquel Bartolomeu SilvaBandim Health Project, INDEPTH Network, Bissau, Guinea-Bissau.ORCID 0000-0001-9371-4253
Angela Y ChangDanish Institute for Advanced Study, University of Southern Denmark, Odense, Denmark.ORCID 0000-0003-3947-3588
Onyebuchi A ArahDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0002-9067-1697
Wojciech SamekDepartment of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany.ORCID 0000-0002-6283-3265
Naja Hulvej RodSection of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0002-6400-5105
Claus Thorn EkstrømSection of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0003-1191-373X
Christine Stabell BennBandim Health Project, INDEPTH Network, Bissau, Guinea-Bissau.ORCID 0000-0001-6102-3810
Peter AabyBandim Health Project, INDEPTH Network, Bissau, Guinea-Bissau.ORCID 0000-0001-8331-1389
Ane Bærent FiskerBandim Health Project, INDEPTH Network, Bissau, Guinea-Bissau.ORCID 0000-0002-8521-0992
Bandim Health Project · GWUniversity of Copenhagen · DKFraunhofer Institute for Telecommunications, Heinrich Hertz Institute · DEIcahn School of Medicine at Mount Sinai · USLund University · SEUniversity of California, Los Angeles · USUniversity of Southern Denmark · DK

Funding

Conduits: Mount Sinai Health System Translational Science HubUL1TR004419 · NCATS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Rosalind J Wright · 2022 to 2026
$46.4M
The Mount Sinai Transdisciplinary Center on Early Environmental ExposuresP30ES023515 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Robert O Wright · 2014 to 2026
$21.3M
Scientific and Technical CoreP2CHD041022 · NICHD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HIRAM BELTRAN-SANCHEZ · 2016 to 2026
$6.9M
NCATS NIH HHS UL1 TR004419NICHD NIH HHS P2C HD041022NIEHS NIH HHS P30 ES023515
6 · The paper itself

Abstract

backgroundThe decline in global child mortality is an important public health achievement, yet child mortality remains disproportionally high in many low-income countries like Guinea-Bissau. The persisting high mortality rates necessitate targeted research to identify vulnerable subgroups of children and formulate effective interventions.

objectiveThis study aimed to discover subgroups of children at an elevated risk of mortality in the urban setting of Bissau, Guinea-Bissau, West Africa. By identifying these groups, we intend to provide a foundation for developing targeted health interventions and inform public health policy.

methodsWe used data from the health and demographic surveillance site, Bandim Health Project, covering 2003 to 2019. We identified baseline variables recorded before children reached the age of 6 weeks. The focus was on determining factors consistently linked with increased mortality up to the age of 3 years. Our multifaceted methodological approach incorporated spatial analysis for visualizing geographical variations in mortality risk, causally adjusted regression analysis to single out specific risk factors, and machine learning techniques for identifying clusters of multifactorial risk factors. To ensure robustness and validity, we divided the data set temporally, assessing the persistence of identified subgroups over different periods. The reassessment of mortality risk used the targeted maximum likelihood estimation (TMLE) method to achieve more robust causal modeling.

resultsWe analyzed data from 21,005 children. The mortality risk (6 weeks to 3 years of age) was 5.2% (95% CI 4.8%-5.6%) for children born between 2003 and 2011, and 2.9% (95% CI 2.5%-3.3%) for children born between 2012 and 2016. Our findings revealed 3 distinct high-risk subgroups with notably higher mortality rates, children residing in a specific urban area (adjusted mortality risk difference of 3.4%, 95% CI 0.3%-6.5%), children born to mothers with no prenatal consultations (adjusted mortality risk difference of 5.8%, 95% CI 2.6%-8.9%), and children from polygamous families born during the dry season (adjusted mortality risk difference of 1.7%, 95% CI 0.4%-2.9%). These subgroups, though small, showed a consistent pattern of higher mortality risk over time. Common social and economic factors were linked to a larger share of the total child deaths.

conclusionsThe study's results underscore the need for targeted interventions to address the specific risks faced by these identified high-risk subgroups. These interventions should be designed to work to complement broader public health strategies, creating a comprehensive approach to reducing child mortality. We suggest future research that focuses on developing, testing, and comparing targeted intervention strategies unraveling the proposed hypotheses found in this study. The ultimate aim is to optimize health outcomes for all children in high-mortality settings, leveraging a strategic mix of targeted and general health interventions to address the varied needs of different child subgroups.

Indexed as

Machine LearningPublic HealthChildChild, PreschoolCohort StudiesGeographyGuinea-BissauHumansInfantcausal discoverychild mortalityGuinea-Bissauinductive-deductivemachine learningtargeted preventive and risk-mitigating interventions

Identifiers

PMID38592761
PMCPMC11040440
OpenAlexW4394603832

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.