社会人口特征和COVID-19测试率:时空模式和瑞典测试可访问性的影响
Beatrice Kennedy1, Georgios Varotsis1, Ulf Hammar1
1Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
European journal of public health
|November 27, 2023
概括
社会人口因素和测试可访问性影响COVID-19测试率. 改善对检测的准入可以减少健康不平等.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 诊断测试对于疾病监测和控制工作至关重要.
- 住宅区的社会人口统计特征和测试可访问性可能会影响2019年新冠肺炎 (COVID-19) 测试率.
研究的目的:
- 调查住宅区社会人口因素与COVID-19测试率之间的关联.
- 评估测试可访问性的影响,包括测试站的距离,对COVID-19测试的普及.
主要方法:
- 分析了瑞典乌普萨拉县 (2020年6月 - 2022年2月) 426,224名患者发起的COVID-19 PCR测试.
- 普森回归用于检查护理需求指数 (CNI) 与每日检测率之间的关联,调整为社区传播.
- 差异差异分析评估了一个处于劣势地区的新测试站的影响.
主要成果:
- 护理需求指数 (CNI) 显示,在5-69岁的人群中,特别是年轻人群中,与COVID-19测试率的负相关性.
- 测试站距离的增加与较低的测试率相关,每10公里与15-29岁人群测试减少10-18%有关.
- 一个处于劣势地区的新开设的检测站导致了检测率的增加,特别是老年人 (70-105岁) 的检测率翻了一番.
结论:
- 住宅区社会人口统计因素与COVID-19测试率的差异有关.
- 测试可访问性,包括接近测试设施,显著影响了测试的普及.
- 有针对性的干预措施,例如在服务不足的地区建立可访问的测试站,可以有效地减少测试不平等.
相关概念视频
01:18Steps in Outbreak Investigation
133
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
133
01:11Residuals and Least-Squares Property
7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
01:25Statistical Methods for Analyzing Epidemiological Data
372
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:
372