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

被引:0
|
作者
Rieckmann, Andreas [1 ]
Nielsen, Sebastian [2 ,3 ,4 ]
Dworzynski, Piotr [5 ]
Amini, Heresh [6 ,7 ]
Mogensen, Soren Wengel [6 ]
Silva, Isaquel Bartolomeu [2 ,3 ,4 ]
Chang, Angela Y. [8 ,9 ,10 ]
Arah, Onyebuchi A. [11 ,12 ,13 ]
Samek, Wojciech [14 ,15 ,16 ]
Rod, Naja Hulvej [1 ]
Ekstrom, Claus Thorn [17 ]
Benn, Christine Stabell [3 ,4 ]
Aaby, Peter [2 ,3 ,4 ]
Fisker, Ane Baerent [2 ,3 ,4 ]
机构
[1] Univ Copenhagen, Dept Publ Hlth, Sect Epidemiol, Oster Farimagsgade 5, CH-1353 Copenhagen, Denmark
[2] INDEPTH Network, Bandim Hlth Project, Bissau, Guinea Bissau
[3] Odense Univ Hosp, Dept Clin Res, Bandim Hlth Project, Res unit Odense Patient Data Explorat Network OPEN, Odense, Denmark
[4] Univ Southern Denmark, Odense, Denmark
[5] Univ Copenhagen, Novo Nord Fdn Ctr Basic Metab Res, Copenhagen, Denmark
[6] Icahn Sch Med Mt Sinai, Dept Environm Med & Climate Sci, New York, NY USA
[7] Icahn Sch Med Mt Sinai, Inst Climate Change Environm Hlth & Expos, New York, NY 10029 USA
[8] Lund Univ, Dept Automat Control, Lund, Sweden
[9] Univ Southern Denmark, Danish Inst Adv Study, Odense, Denmark
[10] Univ Southern Denmark, Interdisciplinary Ctr Populat Dynam, Odense, Denmark
[11] Univ Calif Los Angeles, Fielding Sch Publ Hlth, Dept Epidemiol, Los Angeles, CA USA
[12] Univ Calif Los Angeles, Coll Letters & Sci, Dept Stat & Data Sci, Los Angeles, CA USA
[13] Univ Aarhus, Dept Publ Hlth, Res Unit Epidemiol, Aarhus, Denmark
[14] Fraunhofer Heinrich Hertz Inst, Dept Artificial Intelligence, Berlin, Germany
[15] Tech Univ Berlin, Dept Elect Engn & Comp Sci, Berlin, Germany
[16] Berlin Inst Fdn Learning & Data, Berlin, Germany
[17] Univ Copenhagen, Dept Publ Hlth, Sect Biostat, Copenhagen, Denmark
来源
基金
美国国家卫生研究院;
关键词
child mortality; causal discovery; Guinea-Bissau; inductive-deductive; machine learning; targeted preventive and risk-mitigating; AFRICA; HEALTH; AGE;
D O I
10.2196/48060
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Background: The decline in global child mortality is an important public health achievement, yet child mortality remainsd is proportionally 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: 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: 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: 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: The study's results underscore the need for targeted interventions to address the specific risks faced by theseidentified 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 tooptimize health outcomes for all children in high-mortality settings, leveraging a strategic mix of targeted and general healthinterventions to address the varied needs of different child subgroups.
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页数:16
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