相关实验视频

Updated: Jan 7, 2026

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

2.9K

欧洲581个地区的可避免死亡率的空间差异,2002-2019年

Sophie Stroisch1,2, Michael Mühlichen3, Pavel Grigoriev3

  • 1Population Research Centre, Faculty of Spatial Sciences, University of Groningen, Groningen, The Netherlands. s.stroisch@rug.nl.

European journal of population = Revue europeenne de demographie
|December 9, 2025
PubMed
概括

欧洲的健康差异仍然存在,可避免的死亡率 (通过医疗保健或公共卫生预防的死亡) 根据地区有很大差异. 持续存在的热点和冷点凸显了需要基于地方的跨国战略来减少这些不平等.

关键词:
可以避免的死亡率.跨境活动 跨境活动欧洲联盟 欧洲联盟热点分析的分析.地区不平等 地区不平等

更多相关视频

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K
Determining Gender-Based Differences in Retinal and Choroidal Thickness in Underweight Individuals via Swept-Source Optical Coherence Tomography03:35

Determining Gender-Based Differences in Retinal and Choroidal Thickness in Underweight Individuals via Swept-Source Optical Coherence Tomography

Published on: December 1, 2023

577

相关概念视频

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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:  
1.2K
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
274
Causality in Epidemiology01:21

Causality in Epidemiology

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...
1.4K
Relative Risk01:12

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
1.7K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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:
858
Confounding in Epidemiological Studies01:27

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...
550