在跨国HIV队列协作中协调酒精使用数据和死亡率
Suzanne M Ingle1, Adam Trickey1, Anastasia Lankina2
1Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Alcohol, clinical & experimental research
|January 8, 2025
概括
在艾滋病毒感染者 (PWH) 中协调酒精使用措施揭示了与死亡率的J形关联. 不使用酒精和大量饮酒都与增加死亡风险有关,支持干预措施.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 艾滋病毒研究 艾滋病毒研究
背景情况:
- 酒精使用评估在各个研究环境中存在很大差异.
- 协调酒精措施对于多队列研究至关重要,特别是那些涉及艾滋病毒感染者 (PWH) 的研究.
研究的目的:
- 在艾滋病毒感染者 (PWH) 中协调各种酒精使用措施.
- 调查协调酒精消费 (克/天) 与PWH死亡风险之间的关联.
主要方法:
- 来自14项艾滋病毒队列研究 (欧洲和北美) 的综合数据.
- 根据国家特定的标准饮料定义,以克/天的标准化酒精使用量.
- 使用的酒精使用障碍识别测试 (AUDIT-C) 和队列特定措施.
- 采用多变量考克斯模型来评估酒精使用和死亡率的关联.
主要成果:
- 包括83,424个PWH; 27%有AUDIT-C措施.
- 观察到日常饮酒量 (克/天) 和死亡率之间存在J形关系.
- 与轻度饮酒 (0.1-5.5g/day) 相比,不饮酒 (aHR 1.46) 和重度饮酒 (>61.0g/day,aHR 1.92) 的死亡率更高.
- 对于非AUDIT-C措施,也发现了类似的关联.
结论:
- 克/天是协调各种酒精使用数据的有效指标.
- 在PWH中,酒精使用与死亡率之间的关系可能因研究设置和测量方法而异.
- 随着饮酒量增加而增加的死亡率,强调了需要针对性干预措施来减少PWH饮酒的需要.
相关概念视频
Hypothesis Test for Test of Independence
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
H0: The two variables (factors)...
Bioavailability Study Design: Healthy Subjects Versus Patients
Bioavailability studies are essential for evaluating a drug's therapeutic efficacy and understanding its absorption patterns under various physiological conditions. Conducting such studies on target patient populations provides more relevant data by simulating real-world disease states. However, practical challenges often necessitate the use of young, healthy adult volunteers as study subjects.Patients may exhibit altered drug absorption patterns due to the effects of the disease itself,...
Study Designs in Epidemiology
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control 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 phenomenon...
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:
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:


