Data management and data quality in PERCH, a large international case-control study of severe childhood pneumonia

Nora L. Watson, Christine Prosperi, Amanda J. Driscoll, Melissa M. Higdon, Daniel E. Park, Megan Sanza, Andrea N. DeLuca, Juliet O. Awori, Doli Goswami, Emily Hammond, Lokman Hossain, Catherine Johnson, Alice Kamau, Locadiah Kuwanda, David P. Moore, Omid Neyzari, Uma Onwuchekwa, David Parker, Patranuch Sapchookul, Phil SeidenbergArifin Shamsul, Kazungu Siazeele, Prasong Srisaengchai, Mamadou Sylla, Orin S. Levine, David R. Murdoch, Katherine L. O'Brien, Mark Wolff, Maria Deloria Knoll

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


The Pneumonia Etiology Research for Child Health (PERCH) study is the largest multicountry etiology study of pediatric pneumonia undertaken in the past decades. The study enrolled 423hospitalized cases and 532controls over years across research sites in 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 900cases 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.

Original languageEnglish (US)
Pages (from-to)S238-S244
JournalClinical Infectious Diseases
StatePublished - 2017


  • Data management
  • Data quality
  • Electronic data capture

ASJC Scopus subject areas

  • Microbiology (medical)
  • Infectious Diseases


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