具有混效应的时空模型:在四个撒哈拉以南非洲国家对五岁以下儿童死亡率的应用
Haile Mekonnen Fenta1,2, Ding-Geng Chen1,3, Temesgen T Zewotir4
1Department of Statistics, University of Pretoria, Pretoria, South Africa.
Frontiers in public health
|February 6, 2025
概括
撒哈拉以南非洲的五岁以下儿童死亡率 (U5M) 受空间和时间因素的影响. 改善获得水,卫生和医疗保健的机会显著降低了儿童死亡风险,特别是在尼日利亚和埃塞俄比亚.
科学领域:
- 流行病学 流行病学
- 人口统计学 人口统计学
- 地理空间健康分析
背景情况:
- 五岁以下的死亡率 (U5M) 仍然是撒哈拉以南非洲 (sSA) 的一个关键的公共卫生问题.
- 时空建模对于分析随时间推移的区域数据至关重要,但它面临着大,断开的区域的计算挑战.
- 这项研究侧重于在脱节的sSA国家中的U5M,考虑时间,空间和共变量的混效应.
研究的目的:
- 采用一个时空动态模型来分析U5M在断开连接的sSA国家.
- 调查时间,空间和固定的共变量对U5M之间的混效应.
- 强调U5M风险因素,并确定针对性干预的高风险区域.
主要方法:
- 利用了2000-2020年全国代表性的人口与健康调查 (DHS) 数据.
- 采用贝叶斯的时空层次模型与集成嵌套拉普拉斯近似 (INLA).
- 模拟U5M分布在埃塞俄比亚,尼日利亚,津巴布韦和加纳的37个地区.
主要成果:
- 分析了170,356名儿童的数据,其中15,467人患有U5M.
- 在研究期间,U5M相对风险从2.02降至0.5显著下降.
- 发现改善了获得水,卫生,清洁燃料,城市化和医疗保健设施的机会与U5M有负面联系.
结论:
- 确认了对U5M风险的强烈空间,时间和相互作用影响.
- 强调,提高妇女的识字能力,获取水,使用清洁燃料和财富指数与减少U5M相关.
- 确定尼日利亚和埃塞俄比亚的地区具有最高的U5M风险,需要专注关注.
相关概念视频
Confounding in Epidemiological Studies
132
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
132
Strategies for Assessing and Addressing Confounding
81
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
81
Causality in Epidemiology
271
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
271
Bias in Epidemiological Studies
141
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
141
Mechanistic Models: Compartment Models in Individual and Population Analysis
26
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
26
Assumptions of Survival Analysis
85
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
85


