相关实验视频

Updated: Jun 30, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

评估用于绘制历史红线图的方法,以对人口普查区域进行卫生公平研究

Hannah De Los Santos¹, Carla P Bezold², Karen M Jiang²

¹The MITRE Corporation, McLean, VA, USA. hdelossantos@mitre.org.

Journal of urban health : bulletin of the New York Academy of Medicine

|March 23, 2024

概括

历史上的红线图对当今的健康状况产生了重大影响. 考虑当前人口的连续绘图方法为红线和健康研究提供了更好的见解.

科学领域:

环境健康环境健康
城市规划城市规划
公共卫生公共卫生

背景情况:

邻里地区的特点,包括住房状况,对健康结果有着深刻的影响.
历史性的红线划分,一个导致投资减少的区域分类实践,越来越多地被认为是它对当今健康的影响.
关于在当代健康研究中测量历史红线的最佳方法的指导是有限的.

研究的目的:

评估不同的红线图对齐方法如何影响历史红线和当今健康结果之间的关联.
为了比较各种红线测量方法的统计学意义和解释能力 (R2值).
为健康研究中最佳的红线测量提供建议.

主要方法:

确定了11种现有的红线地图对齐方法和37种逻辑扩展,共48种方法.
将这些方法与202个城市的人口普查区预期寿命数据合并,构建了9696个线性模型.
评估模型使用统计学意义,R2值和根平均平方误差 (RMSE) 来评估地理和人口变化.

主要成果:

根平均平方误差 (RMSE) 达到0.175,表明历史和当代地理和人口之间的持续差异.
使用低值的连续方法显示了更高的社区覆盖率.
权重方法显示了更显著的关联,而高值方法则产生了更高的R2值.

结论:

建议在健康研究中采用包含当代人口分布和映射重叠的连续红线测量方法.
选择红线测量方法显著影响观察到的与健康结果的关联.
一个R应用程序{holcmapr}被开发,以促进方法的比较和选择,用于红线和健康研究.

关键词:

地理空间的方法.卫生公平性健康公平性人口健康人口健康红线线路是指红线路.

更多相关视频

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

相关概念视频

Strategies for Assessing and Addressing Confounding

Strategies for Assessing and Addressing Confounding

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 in Epidemiological Studies

Confounding in Epidemiological Studies

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...

Selected Data About Geographic Locations

Selected Data About Geographic Locations

Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...

Longitudinal Research

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...

Thematic Layering in GIS

Thematic Layering in GIS

In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...

Introduction to Epidemiology

Introduction to Epidemiology

Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...