• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>BMC Public Health >High quality standards for a large-scale prospective population-based observational cohort: Constances
【24h】

High quality standards for a large-scale prospective population-based observational cohort: Constances

机译：大规模的基于人群的预期前瞻性观察队列的高质量标准：康斯坦斯

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Background Long-term multicentre studies are subject to numerous factors that may affect the integrity of their conclusions. Quality control and standardization of data collection are crucial to minimise the biases induced by these factors. Nevertheless, tools implemented to manage biases are rarely described in publications about population-based cohorts. This report aims to describe the processes implemented to control biases in the Constances cohort taking lung function results as an example. Methods Constances is a general-purpose population-based cohort of 200,000 participants. Volunteers attend physical examinations at baseline and then every 5?years at selected study sites. Medical device specifications and measurement methods have to comply with Standard Operating Procedures developed by experts. Protocol deviations are assessed by on-site inspections and database controls. In February 2016, more than 94,000 participants yielding around 30 million readings from physical exams, had been covered by our quality program. Results Participating centres accepted to revise their practices in accordance with the study research specifications. Distributors of medical devices were asked to comply with international guidelines and Constances requirements. Close monitoring enhanced the quality of measurements and recordings of the physical exams. Regarding lung function testing, spirometry acceptability rates per operator doubled in some sites within a few months and global repeatability reached 96.7?% for 29,772 acceptable maneuvers. Conclusions Despite Constances volunteers being followed in multiple sites with heterogeneous materials, the investment of significant resources to set up and maintain a continuous quality management process has proved effective in preventing drifts and improving accuracy of collected data.
机译：背景技术长期的多中心研究受到多种因素的影响，这些因素可能会影响其结论的完整性。质量控制和数据收集的标准化对于最小化由这些因素引起的偏差至关重要。然而，有关基于人群的队列研究很少描述用于管理偏见的工具。本报告旨在以肺功能结果为例，描述为控制Constances队列中的偏见而实施的过程。方法Constances是一个基于通用人群的队列，共有200,000名参与者。志愿者在基线时参加体格检查，然后每5年在选定的研究地点参加体格检查。医疗设备的规格和测量方法必须符合专家制定的标准操作程序。协议偏差通过现场检查和数据库控制进行评估。 2016年2月，我们的质量计划涵盖了94,000多名参与者，从身体检查中获得了大约3,000万份读数。结果参与中心接受了根据研究研究规范对其实践进行修订。要求医疗器械的分销商遵守国际准则和Constances要求。密切监视提高了身体检查的测量和记录的质量。关于肺功能测试，在几个月内，某些地方的每个操作人员的肺活量测定可接受率翻了一番，并且在29,772次可接受的操作中，总体重复性达到96.7％。结论尽管Constances志愿者在多个地点使用异构材料进行跟踪，但是事实证明，投入大量资源来建立和维护连续的质量管理过程可有效防止漂移和提高收集数据的准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号