Measuring Quality of Data Collection Process to Ensure Data Quality for Public Health Information Systems

Background: High quality data and effective data quality assessment are vital for accurate detection and diagnosis of public health risks, for the design, implementation, and evaluation of public health intervention impact and public health outcome measurement. Effective data quality assessment not only reports the status of data quality but also determines the causes of data quality problems. To date, there is scarce research on the quality of the data collection process for public health information systems (PHISs), in which data quality problems frequently occur. Aims: This PhD project aims to develop a framework to evaluate the quality of the PHIS data collection process. The aim is achieved through realizing three research objectives: (1) review and synthesize the existing PHIS data quality assessment methods; (2) conceptualize and validate a framework to measure the quality of the PHIS data collection process; (3) use the developed framework to evaluate the data collection process for a country-level PHIS. Methods: The project systematically reviews PHIS data quality assessment methods and the essential components of the quality of the PHIS data collection process. An expert elicitation research approach is used to qualitatively validate a 4D (data collection management, data collection personnel, data collection environment, data collection system) component framework to evaluate the PHIS data collection process in the context of Chinese National HIV/AIDS Information Management Systems (CRIMS). Evaluation of the quality of the CRIMS data collection process is completed using the validated 4D Framework.