Data Management and Data Quality in PERCH, a Large International Case-Control Study of Severe Childhood Pneumonia

Abstract

The Pneumonia Etiology Research for Child Health (PERCH) study is the largest multicountry etiology study of pediatric pneumonia undertaken in the past 3 decades. The study enrolled 4232 hospitalized cases and 5325 controls over 2 years across 9 research sites in 7 countries in Africa and Asia. The volume and complexity of data collection in PERCH presented considerable logistical and technical challenges. The project chose an internet-based data entry system to allow real-time access to the data, enabling the project to monitor and clean incoming data and perform preliminary analyses throughout the study. To ensure high-quality data, the project developed comprehensive quality indicator, data query, and monitoring reports. Among the approximately 9000 cases and controls, analyzable laboratory results were available for ≥96% of core specimens collected. Selected approaches to data management in PERCH may be extended to the planning and organization of international studies of similar scope and complexity.

Data Quality Methods and Applications in Health Care System: A Systematic Literature Review

“…The case treated by Watson et al (2017) was born within a health program aimed at children suffering from pneumonia. The first data quality action was the creation of a centralized database that eliminates the logistics problem of transporting information between different workplaces.…”

“…Both the aforementioned cases also respond to the principle of internal coherence, in the case of Mitsunaga et al (2015) this refers to the consistency between the information in the registers and the interviews made by the medical staff, finally, for Watson et al (2017) represents the percentage of data present and corrected in the 9000 clinical cases analyzed.…”

“…To this end the results belonging to the collections of Taylor & Francis (Bai et al, 2018), BioMed Central (Ledikwe et al, 2014), Springer (Mitsunaga et al, 2015), Journal of Health Management & Informatics (Firouraghi et al, 2018), BioMed Central (Skyttberg et al, 2016), Elsevier (Verma, 2014), Oxford University Press (Watson et al, 2017), American College of Healthcare Executives (Wills, 2014) and National Institutes of Health (NIH) Health Care Systems Research Collaboratory (Zozus M. N., et al, 2014).…”

“…42 2. monitoring of clinical trial operations in real time (e.g., through sample volume and time from sample collection to laboratory reception); 3. the insertion of data at several collection points in each site (e.g., Laboratory, clinic, first aid); 4. rapid implementation of any changes to the information system required by the clinical staff.The validation of the system has provided for a training activity aimed at homogenizing data insertion in the database and monthly monitoring systems on the lack of data.The quantitative cases also examined partially meet the criteria outlined by the World Health Organization. Completeness and timeliness is analysed by Mitsunaga et al (2015) as an essential element during the home visits of health personnel and the compilation of digital folders, in the case of Watson et al (2017) refers exclusively to the completeness referred to demographic data, clinical vaccinations, environmental elements, and risk factors.…”

“…The quantitative cases also examined partially meet the criteria outlined by the World Health Organization. Completeness and timeliness is analysed by Mitsunaga et al (2015) as an essential element during the home visits of health personnel and the compilation of digital folders, in the case of Watson et al (2017) refers exclusively to the completeness referred to demographic data, clinical vaccinations, environmental elements, and risk factors.…”