From data to decisions: understanding information flows within regulatory water quality monitoring programs
From data to decisions: understanding information flows within regulatory water quality monitoring programs
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DOI：
10.1038/s41545-020-00084-0
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发表时间：
2020-08-24
期刊：
影响因子：
11.4
通讯作者：
Peletz, Rachel
中科院分区：
文献类型：
--
作者：
Kumpel, Emily;MacLeod, Clara;Peletz, Rachel
大多数国家对饮用水供应的测试都有监管要求，以指导处理程序，确保向消费者提供安全的水。然而，目前尚不清楚水质数据是否始终得到有效利用，特别是在资源匮乏的环境中。全面了解现有的信息管理和共享系统将有助于改进水质数据的使用。本研究评估了撒哈拉以南非洲6个国家的26个供水商或监测机构和两个监管机构用于组织，分析和传输饮用水质量数据的方法。在广泛的定性和定量数据收集之后，我们开发了数据流程图来绘制正式和非正式的水质网络。我们发现，在不同监管结构下运作的不同机构建立的信息系统之间存在高度相似性。我们确定信息流动的主要障碍是数据汇总和分析有限以及数据共享要求执行不力。我们的研究结果表明，广泛的改革是必要的，以改善这些水质数据的使用，以管理水安全。这些措施可包括加强检测和报告的执行，建设工作人员管理和使用数据的能力，以及将水质数据的收集与其他信息系统相结合。
Most countries maintain regulatory requirements for testing of drinking water supplies to guide treatment procedures and ensure safe water delivery to consumers. It is unclear, however, if water quality data are always used effectively, particularly in low-resource settings. Efforts to improve the use of water quality data will benefit from a comprehensive understanding of existing systems for managing and sharing information. This study evaluates the methods used to organize, analyze, and transmit drinking water quality data among 26 water supplier or surveillance institutions and two regulatory agencies in six countries of sub-Saharan Africa. Following extensive qualitative and quantitative data collection, we developed data flow diagrams to map formal and informal water quality networks. We found high levels of similarities between the information systems established by different institutions operating under different regulatory structures. We determined that the key barriers to information flows were the limited aggregation and analysis of data and the poor enforcement of data sharing requirements. Our results suggest that broad reforms are necessary to improve the use of these water quality data to manage water safety. These measures could include strengthening enforcement of testing and reporting, building staff capacity for managing and using data, and integrating collection of water quality data with other information systems.