Integrated and automated demographic surveillance data quality systems for rural areas

Abstract

The Health and Demographic Surveillance System (HDSS) is a data collection system that can track crucial events such as births, deaths, and migrations in well-defined geographic areas, particularly in low- and middle-income households. HDSS tracks the life events of approximately three million people in 18 low- and middle-income African, Asian, and Oceanian nations. Having HDSSs strategically located within a country can provide a more complete picture of health-related and other social problems affecting the public. The HDSS keeps tabs on vital demographic and health indicators as well as other metrics to help shape national policies and programmes for departments of basic education, home affairs, social development, and health. However, their establishment was plagued by several difficulties, including the difficulty of obtaining high-quality data because of the use of antiquated methods or systems. The cornerstone of a wellfunctioning HDSS is high-quality, and timely health data, which is often lacking in lowand middle-income countries. There is a paucity of high-quality, disaggregated data to monitor health inequities and promote the equitable delivery of health services. HDSSs are confronted with data quality-related problems due to how data is acquired and managed. This study addresses these problems by building a data system that integrates a novel framework known as the 3-Tier Total Data Quality Management Framework (3TTDQMF). The framework manages the quality of data from the point of collection through to the storage in the database. At the core of the framework, is an automated data quality control methodology to autonomously validate and control the quality of data. Open source technologies such as Pentaho data integration (PDI), R application programming interface (R-API), Windows task scheduler, Bash and Python programming languages were used to automate and quality control the data. The experiment was set up in Hyper-converged IT infrastructure running the Windows 2016 server operating system. The results have shown that the proposed approach greatly improved the overall efficiency of the system and the quality of data. The efficiency in dealing with data quality issues was ensured through the implementation of an automated system. The research evaluated the system’s capacity to generate high-quality data using measures such as data accuracy, completeness, consistency, timeliness, and validity. All quality metrics exhibited an increasing trend, indicating that the proposed approach led to a substantial improvement in data quality. The results further demonstrated that the use of Pareto analysis and Process control techniques in data quality management can greatly improve the quality of data by identifying and monitoring the causes of data quality issues.