PDF(Pubmed)
Abstract：
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: 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 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.
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 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.
摘要：
背景：全球儿童死亡率的下降是一项重要的公共卫生成就，然而，在几内亚比绍等许多低收入国家，儿童死亡率仍然高得不成比例。持续的高死亡率需要进行有针对性的研究，以确定脆弱的儿童亚群并制定有效的干预措施。
目的：本研究的目的是发现在几内亚比绍城市环境中死亡风险较高的儿童亚组，几内亚比绍，西非。通过识别这些群体，我们打算为制定有针对性的卫生干预措施提供基础，并为公共卫生政策提供信息。
方法：我们使用了来自健康和人口监测点的数据，班迪姆健康项目，涵盖2003年至2019年。我们确定了儿童达到6周龄之前记录的基线变量。重点是确定与3岁以下死亡率增加相关的因素。我们的多方面方法论方法结合了空间分析，用于可视化死亡风险的地理变化，因果调整回归分析，找出特定的危险因素，和用于识别多因素风险因素集群的机器学习技术。为了确保健壮性和有效性，我们暂时划分了数据集，评估不同时期已识别亚组的持久性。死亡风险的重新评估使用有针对性的最大似然估计（TMLE）方法来实现更可靠的因果模型。
结果：我们分析了21,005名儿童的数据。2003年至2011年出生的儿童的死亡风险（6周至3岁）为5.2％（95％CI4.8％-5.6％），2012年至2016年出生的儿童为2.9％（95％CI2.5％-3.3％）。我们的发现揭示了3个不同的高风险亚组，死亡率明显较高，居住在特定城市地区的儿童(调整后死亡率风险差异为3.4%，95%CI0.3%-6.5%），没有产前咨询的母亲所生的孩子(调整后的死亡率风险差异为5.8%，95%CI2.6%-8.9%），和在旱季出生的一夫多妻制家庭的儿童(调整后的死亡率风险差异为1.7%，95%CI0.4%-2.9%）。这些子组，虽然小,随着时间的推移，显示出更高的死亡风险的一致模式。共同的社会和经济因素与儿童死亡总数的更大比例有关。
结论：研究结果强调需要有针对性的干预措施，以解决这些已确定的高风险亚组所面临的特定风险。这些干预措施应旨在补充更广泛的公共卫生战略，制定全面的方法来降低儿童死亡率。我们建议未来的研究侧重于发展，测试,并比较有针对性的干预策略，揭示本研究中提出的假设。最终目标是为高死亡率环境中的所有儿童优化健康结果，利用有针对性和一般健康干预措施的战略组合，以满足不同儿童亚组的不同需求。
目的：本研究的目的是发现在几内亚比绍城市环境中死亡风险较高的儿童亚组，几内亚比绍，西非。通过识别这些群体，我们打算为制定有针对性的卫生干预措施提供基础，并为公共卫生政策提供信息。
方法：我们使用了来自健康和人口监测点的数据，班迪姆健康项目，涵盖2003年至2019年。我们确定了儿童达到6周龄之前记录的基线变量。重点是确定与3岁以下死亡率增加相关的因素。我们的多方面方法论方法结合了空间分析，用于可视化死亡风险的地理变化，因果调整回归分析，找出特定的危险因素，和用于识别多因素风险因素集群的机器学习技术。为了确保健壮性和有效性，我们暂时划分了数据集，评估不同时期已识别亚组的持久性。死亡风险的重新评估使用有针对性的最大似然估计（TMLE）方法来实现更可靠的因果模型。
结果：我们分析了21,005名儿童的数据。2003年至2011年出生的儿童的死亡风险（6周至3岁）为5.2％（95％CI4.8％-5.6％），2012年至2016年出生的儿童为2.9％（95％CI2.5％-3.3％）。我们的发现揭示了3个不同的高风险亚组，死亡率明显较高，居住在特定城市地区的儿童(调整后死亡率风险差异为3.4%，95%CI0.3%-6.5%），没有产前咨询的母亲所生的孩子(调整后的死亡率风险差异为5.8%，95%CI2.6%-8.9%），和在旱季出生的一夫多妻制家庭的儿童(调整后的死亡率风险差异为1.7%，95%CI0.4%-2.9%）。这些子组，虽然小,随着时间的推移，显示出更高的死亡风险的一致模式。共同的社会和经济因素与儿童死亡总数的更大比例有关。
结论：研究结果强调需要有针对性的干预措施，以解决这些已确定的高风险亚组所面临的特定风险。这些干预措施应旨在补充更广泛的公共卫生战略，制定全面的方法来降低儿童死亡率。我们建议未来的研究侧重于发展，测试,并比较有针对性的干预策略，揭示本研究中提出的假设。最终目标是为高死亡率环境中的所有儿童优化健康结果，利用有针对性和一般健康干预措施的战略组合，以满足不同儿童亚组的不同需求。
