School of Computing and Information Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, Australia.
Jiangxi Provincial Centre for Disease Control and Prevention, Nanchang, China.
J Med Internet Res. 2021 May 10;23(5):e17240. doi: 10.2196/17240.
Identification of the essential components of the quality of the data collection process is the starting point for designing effective data quality management strategies for public health information systems. An inductive analysis of the global literature on the quality of the public health data collection process has led to the formation of a preliminary 4D component framework, that is, data collection management, data collection personnel, data collection system, and data collection environment. It is necessary to empirically validate the framework for its use in future research and practice.
This study aims to obtain empirical evidence to confirm the components of the framework and, if needed, to further develop this framework.
Expert elicitation was used to evaluate the preliminary framework in the context of the Chinese National HIV/AIDS Comprehensive Response Information Management System. The research processes included the development of an interview guide and data collection form, data collection, and analysis. A total of 3 public health administrators, 15 public health workers, and 10 health care practitioners participated in the elicitation session. A framework qualitative data analysis approach and a quantitative comparative analysis were followed to elicit themes from the interview transcripts and to map them to the elements of the preliminary 4D framework.
A total of 302 codes were extracted from interview transcripts. After iterative and recursive comparison, classification, and mapping, 46 new indicators emerged; 24.8% (37/149) of the original indicators were deleted because of a lack of evidence support and another 28.2% (42/149) were merged. The validated 4D component framework consists of 116 indicators (82 facilitators and 34 barriers). The first component, data collection management, includes data collection protocols and quality assurance. It was measured by 41 indicators, decreased from the original 49% (73/149) to 35.3% (41/116). The second component, data collection environment, was measured by 37 indicators, increased from the original 13.4% (20/149) to 31.9% (37/116). It comprised leadership, training, funding, organizational policy, high-level management support, and collaboration among parallel organizations. The third component, data collection personnel, includes the perception of data collection, skills and competence, communication, and staffing patterns. There was no change in the proportion for data collection personnel (19.5% vs 19.0%), although the number of its indicators was reduced from 29 to 22. The fourth component, the data collection system, was measured using 16 indicators, with a slight decrease in percentage points from 18.1% (27/149) to 13.8% (16/116). It comprised functions, system integration, technical support, and data collection devices.
This expert elicitation study validated and improved the 4D framework. The framework can be useful in developing a questionnaire survey instrument for measuring the quality of the public health data collection process after validation of psychometric properties and item reduction.
识别数据收集过程质量的基本组成部分是为公共卫生信息系统设计有效数据质量管理策略的起点。对全球公共卫生数据收集过程质量的文献进行归纳分析，形成了初步的 4D 成分框架，即数据收集管理、数据收集人员、数据收集系统和数据收集环境。有必要对该框架进行实证验证，以便在未来的研究和实践中使用。
本研究旨在获得实证证据，以确认框架的组成部分，并在需要时进一步发展该框架。
专家征询用于评估初步框架在中国国家艾滋病综合应对信息管理系统背景下的适用性。研究过程包括制定访谈指南和数据收集表、数据收集和分析。共有 3 名公共卫生管理员、15 名公共卫生工作者和 10 名医疗保健从业者参加了征求意见会议。采用框架定性数据分析方法和定量比较分析方法，从访谈记录中提取主题，并将其映射到初步 4D 框架的要素上。
从访谈记录中提取了 302 个代码。经过迭代和递归比较、分类和映射，出现了 46 个新指标；由于缺乏证据支持，原始指标中有 24.8%（37/149）被删除，另有 28.2%（42/149）被合并。经过验证的 4D 成分框架由 116 个指标（82 个促进因素和 34 个障碍）组成。第一个组成部分是数据收集管理，包括数据收集协议和质量保证。它由 41 个指标衡量，从原始的 49%（73/149）下降到 35.3%（41/116）。第二个组成部分是数据收集环境，由 37 个指标衡量，从原始的 13.4%（20/149）增加到 31.9%（37/116）。它包括领导力、培训、资金、组织政策、高层管理支持以及平行组织之间的协作。第三个组成部分是数据收集人员，包括对数据收集的看法、技能和能力、沟通和人员配备模式。尽管其指标数量从 29 个减少到 22 个，但数据收集人员的比例（19.5%比 19.0%）没有变化。第四个组成部分是数据收集系统，使用 16 个指标衡量，百分比略有下降，从 18.1%（27/149）降至 13.8%（16/116）。它包括功能、系统集成、技术支持和数据收集设备。
本专家征询研究验证并改进了 4D 框架。该框架在验证心理测量特性和项目减少后，可以用于开发衡量公共卫生数据收集过程质量的问卷调查工具。
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