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

Publication Type
Journal Article
Year of Publication
2017
Authors
Watson, Nora L; Prosperi, Christine; Driscoll, Amanda J; Higdon, Melissa M; Park, Daniel E; Sanza, Megan; DeLuca, Andrea N; Awori, Juliet O; Goswami, Doli; Hammond, Emily; Hossain, Lokman; Johnson, Catherine; Kamau, Alice; Kuwanda, Locadiah; Moore, David P; Neyzari, Omid; Onwuchekwa, Uma; Parker, David; Sapchookul, Patranuch; Seidenberg, Phil; Shamsul, Arifin; Siazeele, Kazungu; Srisaengchai, Prasong; Sylla, Mamadou; Levine, Orin S; Murdoch, David R; O'Brien, Katherine L; Wolff, Mark; Deloria Knoll, Maria
Secondary
Clin Infect Dis
Volume
64
Start Page
S238
Pagination
S238-S244
Date Published
06/2017
Keywords
data management; data quality; PERCH.; electronic data capture
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.