卫生研究中的社会经济地位:一个尺寸不适合所有人
Paula A Braveman1, Catherine Cubbin, Susan Egerter
1Center on Social Disparities in Health and Department of Family and Community Medicine, University of California, San Francisco 94143-0900, USA. braveman@fcm.ucsf.edu
JAMA
|December 15, 2005
概括
在研究中衡量社会经济地位 (SES) 是复杂的. 不同的SES措施可能会导致不同的结论,特别是在健康差异方面,需要采取更细微的方法.
科学领域:
- 公共卫生 公共卫生
- 社会学 社会学 社会学
- 健康差距 研究 研究 研究 研究
背景情况:
- 社会经济地位 (SES) 在健康研究中至关重要,经常被用作控制变量.
- 目前的SES测量方法带来了重大挑战,可能会影响研究结果并影响政策.
- 在收入和财富方面存在种族/民族差异,质疑基于单一SES指标的个人可比性.
研究的目的:
- 在健康研究中批判性地评估标准社会经济地位 (SES) 测量技术.
- 突出SES测量问题对研究结论,实践和政策的影响.
- 建议改进的策略来测量在不同的人群中SES.
主要方法:
- 对SES测量现有文献的分析.
- 检查新的数据,说明跨SES指标 (收入,教育,财富) 的种族/民族差异.
- 审查不同SES措施如何影响有关健康差异的结论.
主要成果:
- 标准的SES测量可能无法准确地比较不同社会经济层面的个人,特别是种族/族群之间的个人.
- 选择用于控制社会经济因素的SES测量方法可以改变关于健康差异原因的结论.
- 在研究中有限的SES测量可能导致不准确的发现,需要重新评估.
结论:
- 对SES测量的"一刀切"方法是不够的.
- 建议对SES测量采取结果和特定社会群体的方法.
- 强调测量多个相关的SES因素,并承认更强大的研究的局限性.
更多相关视频
相关概念视频
01:50Cross-Sectional Research
In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
01:52Regression Toward the Mean
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
01:20Study 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.
01:27Confounding 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...
01:25Strategies 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 can be addressed at both the design phase of a study and through analytical methods after data...
01:29Bias 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: