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

Guinea-Bissau causal discovery child mortality inductive-deductive machine learning targeted preventive and risk-mitigating interventions

Journal

JMIR public health and surveillance
ISSN: 2369-2960
Titre abrégé: JMIR Public Health Surveill
Pays: Canada
ID NLM: 101669345

Informations de publication

Date de publication:
09 Apr 2024
Historique:
received: 18 04 2023
accepted: 23 01 2024
revised: 22 12 2023
medline: 9 4 2024
pubmed: 9 4 2024
entrez: 9 4 2024
Statut: epublish

Résumé

The 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. This 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. We 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. We 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. The 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.

Sections du résumé

BACKGROUND BACKGROUND
The 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.
OBJECTIVE OBJECTIVE
This 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.
METHODS METHODS
We 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.
RESULTS RESULTS
We 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.
CONCLUSIONS CONCLUSIONS
The 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.

Identifiants

pubmed: 38592761
pii: v10i1e48060
doi: 10.2196/48060
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e48060

Informations de copyright

©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, Claus Thorn Ekstrøm, Christine Stabell Benn, Peter Aaby, Ane Bærent Fisker. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 09.04.2024.

Auteurs

Andreas Rieckmann (A)

Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.

Sebastian Nielsen (S)

Bandim Health Project, INDEPTH Network, Bissau, Guinea-Bissau.
Bandim Health Project, Research unit Odense Patient Data Explorative Network (OPEN), Department of Clinical Research, Odense University Hospital and University of Southern Denmark, Odense, Denmark.

Piotr Dworzynski (P)

Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.

Heresh Amini (H)

Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Institute for Climate Change, Environmental Health, and Exposomics, Icahn School of Medicine at Mount Sinai, New York, NY, United States.

Søren Wengel Mogensen (SW)

Department of Automatic Control, Lund University, Lund, Sweden.

Isaquel Bartolomeu Silva (IB)

Bandim Health Project, INDEPTH Network, Bissau, Guinea-Bissau.
Bandim Health Project, Research unit Odense Patient Data Explorative Network (OPEN), Department of Clinical Research, Odense University Hospital and University of Southern Denmark, Odense, Denmark.

Angela Y Chang (AY)

Danish Institute for Advanced Study, University of Southern Denmark, Odense, Denmark.
The Interdisciplinary Centre on Population Dynamics, University of Southern Denmark, Odense, Denmark.

Onyebuchi A Arah (OA)

Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States.
Department of Statistics and Data Science, College of Letters and Science, University of California, Los Angeles, Los Angeles, CA, United States.
Research Unit for Epidemiology, Department of Public Health, University of Aarhus, Aarhus, Denmark.

Wojciech Samek (W)

Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany.
Department of Electrical Engineering and Computer Science, Technical University of Berlin, Berlin, Germany.
Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.

Naja Hulvej Rod (NH)

Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.

Claus Thorn Ekstrøm (CT)

Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.

Christine Stabell Benn (CS)

Bandim Health Project, INDEPTH Network, Bissau, Guinea-Bissau.
Bandim Health Project, Research unit Odense Patient Data Explorative Network (OPEN), Department of Clinical Research, Odense University Hospital and University of Southern Denmark, Odense, Denmark.
Danish Institute for Advanced Study, University of Southern Denmark, Odense, Denmark.

Peter Aaby (P)

Bandim Health Project, INDEPTH Network, Bissau, Guinea-Bissau.
Bandim Health Project, Research unit Odense Patient Data Explorative Network (OPEN), Department of Clinical Research, Odense University Hospital and University of Southern Denmark, Odense, Denmark.

Ane Bærent Fisker (AB)

Bandim Health Project, INDEPTH Network, Bissau, Guinea-Bissau.
Bandim Health Project, Research unit Odense Patient Data Explorative Network (OPEN), Department of Clinical Research, Odense University Hospital and University of Southern Denmark, Odense, Denmark.

Classifications MeSH