县级社会脆弱性与美国疾病控制和预防中心资助的艾滋病毒检测计划结果之间的关联,2020-2022年
Wei Song1, Mesfin S Mulatu, Nicole Crepaz
1Division of HIV Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA.
Journal of acquired immune deficiency syndromes (1999)
|January 9, 2025
概括
社区层面的社会脆弱性会影响艾滋病毒检测结果. 虽然CDC资助的项目到达脆弱地区,但艾滋病毒阳性,护理联系和预防转诊的差异仍然存在,需要有针对性的资源分配.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 健康的社会决定因素
背景情况:
- 社区层面的社会脆弱性对艾滋病毒的结果有很大影响.
- 这项研究检查了县级社会脆弱性与由疾病控制和预防中心 (CDC) 资助的艾滋病毒检测计划结果之间的联系.
研究的目的:
- 评估县级社会脆弱性与CDC资助的艾滋病毒检测计划的关键绩效指标之间的关系.
- 根据社会脆弱性指标,识别艾滋病毒检测结果的差异.
主要方法:
- 利用来自60个州/地方卫生部门和119个社区组织 (2020-2022) 的艾滋病毒检测数据.
- 综合艾滋病毒检测数据与县级的少数民族健康社会脆弱性指数 (SVI).
- 高和低SVI县之间的HIV阳性,与护理联系,伴侣服务和暴露前预防 (PrEP) 转诊的计算差异措施.
主要成果:
- 在490万例艾滋病毒检测中,超过85%发生在高度社会脆弱的地区.
- 在高SVI县观察到更高的HIV阳性 (1.1%) 和与护理的联系 (77.5%).
- 较低的合作伙伴服务率 (72.1%) 和PrEP推率 (48.1%) 在高SVI县发现,因人口统计和地点类型而有差异.
结论:
- 美国疾病预防控制中心 (CDC) 资助的艾滋病毒检测计划有效地接触到弱势群体.
- 艾滋病毒检测计划的结果,包括链接和预防转诊,表明受社会脆弱性,人口统计和测试地点影响的变化.
- 持续监测社会脆弱性对艾滋病毒检测的影响对于资源分配和结束艾滋病毒流行病至关重要.
相关概念视频
Statistical Methods for Analyzing Epidemiological Data
299
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:
299
Bias in Epidemiological Studies
151
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:
151
Prevalence and Incidence
342
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...
342
Causality in Epidemiology
289
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...
289
Hazard Ratio
85
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
For example, in a clinical trial...
85
Longitudinal Research
11.8K
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...
11.8K


