泰国5岁以下死亡率的空间关联和建模,2020年
Suparerk Suerungruang1, Kittipong Sornlorm2, Wongsa Laohasiriwong3
1Faculty of Public Health, Khon Kaen University, Khon Kaen. iampokpak@gmail.com.
Geospatial health
|September 5, 2023
概括
泰国5岁以下死亡率 (U5MR) 的差异与社会经济,卫生服务和环境因素有关. 解决这些问题需要有针对性的多部门战略,以改善儿童健康结果.
科学领域:
- 公共卫生 公共卫生
- 空间流行病学 空间流行病学
- 健康的社会经济决定因素
背景情况:
- 5岁以下的死亡率 (U5MR) 是儿童健康和国家发展的关键指标.
- 尽管取得了进展,但泰国在各省的U5MR仍然存在差异.
- 了解U5MR的空间分布及其决定因素对于有效的干预至关重要.
研究的目的:
- 检查影响泰国U5MR的社会经济,卫生服务和环境因素.
- 用空间分析建模这些因素与U5MR之间的空间关系.
- 确定U5MR的关键驱动因素和保护因素,以制定有针对性的政策.
主要方法:
- 利用来自泰国国家来源的二次数据.
- 采用全球和本地莫兰的I统计数据来评估空间自相关性.
- 应用空间回归模型,包括普通最小平方 (OLS),空间滞后模型 (SLM) 和空间错误模型 (SEM).
主要成果:
- 在泰国各地观察到U5MR的显著空间差异.
- 低出生体重和失业率等因素与U5MR正相关.
- 高等教育,社区组织,获得医疗保健 (人均床位) 和母乳养都显示出保护作用 (SEM R2 = 0.588).
结论:
- 综合性,多部门战略对于减少泰国的U5MR差异至关重要.
- 政策干预应侧重于改善社会经济条件,医疗保健质量和高死亡率地区的可访问性.
- 为了有效的儿童健康干预,需要根据空间分析制定量身定制的,本地化的卫生政策.
相关概念视频
Applications of Life Tables
91
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
91
Life Tables
128
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
128
Statistical Methods for Analyzing Epidemiological Data
408
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
408
Principles of Disease Surveillance
122
Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
122
Causality in Epidemiology
463
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...
463
Assumptions of Survival Analysis
153
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.
153


