通过前性队列研究和基于人口的对照来估计心房的成本影响
Helena Aebersold1, Fabienne Foster-Witassek2, Miquel Serra-Burriel2
1Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland helena.aebersold@uzh.ch.
BMJ open
|September 14, 2023
概括
与普通人群相比,心房动 (AF) 显著增加了50%的直接医疗保健成本. 了解这种经济负担对于有效的医疗保健规划和资源分配至关重要.
科学领域:
- 卫生经济学 卫生经济学
- 心脏病学 心脏病学
- 公共卫生 公共卫生
背景情况:
- 心房动 (AF) 是一种普遍的疾病,预计会带来大量的医疗费用.
- 在AF患者和普通人群之间的直接成本比较是有限的.
- 估计AF的经济影响对于医疗保健政策和规划至关重要.
研究的目的:
- 在瑞士估计与心房 (AF) 相关的直接医疗保健成本.
- 为了比较AF患者的医疗保健支出与基于人口的对照.
- 为了建模瑞士AF的全国疾病成本.
主要方法:
- 利用了来自前性瑞士-AF队列研究的数据,并匹配了基于人口的对照.
- 分析了5年的随访数据,包括临床信息和医疗保险索赔.
- 采用五种不同的统计方法来估算成本,包括回归模型和倾向得分匹配.
- 使用GBD流行率,瑞士人口数据和衍生成本估计来建模疾病的国家成本.
主要成果:
- 每年,AF患者在AF相关的直接医疗费用中额外支付5600瑞士法郎 (5091欧元).
- 总体而言,AF患者的直接医疗费用比对照患者高出50%.
- 据估计,与AF相关的费用占瑞士医疗保健总支出的1%.
结论:
- 心房动会带来相当大的直接医疗费用负担.
- 结果提供了强有力的证据,支持AF患者更高的医疗保健利用率.
- 这些数据可以为医疗保健能力规划和AF管理的资源配置提供信息.
相关概念视频
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
147
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
147
01:26Coronary Artery Disease IV: Preventive Measures
23
Effective preventive measures for coronary artery disease (CAD) focus on controlling modifiable risk factors, including cholesterol abnormalities and lifestyle changes.Cholesterol ManagementFirst, the Mediterranean diet and the American Heart Association advocate for maintaining low-density lipoprotein (LDL) cholesterol levels below 100 mg/dL, with a more stringent recommendation of below 70 mg/dL for individuals at high risk. LDL cholesterol, often termed "bad cholesterol," can lead to the...
23
01:29Bias in Epidemiological Studies
343
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:
343
01:08Prevalence and Incidence
621
In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
621
01:22Rheumatic Heart Disease II: Clinical Manifestations and Diagnostic Studies
38
The key clinical manifestations of Rheumatic heart disease (RHD) include several distinct cardiac symptoms.Carditis, a hallmark of acute rheumatic fever, involves inflammation of the heart's endocardium, myocardium, and pericardium. Chronic RHD often results from recurrent episodes of carditis. Its symptoms include the following:Murmurs are caused by valvular damage, especially to the mitral and aortic valves. Mitral stenosis or regurgitation is common, with characteristic heart murmurs...
38
01:27Confounding in Epidemiological Studies
188
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...
188