Application of advanced data collection and quality assurance methods in open prospective study – a case study of PONS project
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Institute of Electronics Systems, Warsaw University of Technology, Warsaw, Poland
Faculty of Health Sciences, Medical University, Warsaw, Poland
Department of Cancer Epidemiology and Prevention, Maria Skłodowska-Curie Cancer Centre and Institute of Oncology, Warsaw, Poland
European Health Inequalities Observatory, Institute of Rural Health, Lublin, Poland
Ann Agric Environ Med. 2011;18(2):207–214
Large-scale epidemiologic studies can assess health indicators differentiating social groups and important health outcomes of the incidence and mortality of cancer, cardiovascular disease, and others, to establish a solid knowledge base for the prevention management of premature morbidity and mortality causes. This study presents new advanced methods of data collection and data management systems with current data quality control and security to ensure high quality data assessment of health indicators in the large epidemiologic PONS study (The Polish-Norwegian Study).

Material and Methods:
The material for experiment is the data management design of the large-scale population study in Poland (PONS) and the managed processes are applied into establishing a high quality and solid knowledge.

The functional requirements of the PONS study data collection, supported by the advanced IT web-based methods, resulted in medical data of a high quality, data security, with quality data assessment, control process and evolution monitoring are fulfilled and shared by the IT system. Data from disparate and deployed sources of information are integrated into databases via software interfaces, and archived by a multitask secure server.

The practical and implemented solution of modern advanced database technologies and remote software/hardware structure successfully supports the research of the big PONS study project. Development and implementation of follow-up control of the consistency and quality of data analysis and the processes of the PONS sub-databases have excellent measurement properties of data consistency of more than 99%. The project itself, by tailored hardware/software application, shows the positive impact of Quality Assurance (QA) on the quality of outcomes analysis results, effective data management within a shorter time. This efficiency ensures the quality of the epidemiological data and indicators of health by the elimination of common errors of research questionnaires and medical measurements.

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