Use of mobile data collection systems within large-scale epidemiological field trials: findings and lessons-learned from a vector control trial in Iquitos, Peru

• Published in: BMC public health Vol. 22; no. 1; pp. 1924 - 13
• Main Authors: Elson, William H., Kawiecki, Anna B., Donnelly, Marisa A. P., Noriega, Arnold O., Simpson, Jody K., Syafruddin, Din, Rozi, Ismail Ekoprayitno, Lobo, Neil F., Barker, Christopher M., Scott, Thomas W., Achee, Nicole L., Morrison, Amy C.
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 15.10.2022; BioMed Central; BMC
• Subjects: Aedes; Animals; Clinical trial; Clinical trials; CommCare; Communicable diseases; Data Collection; Data management; Data monitoring; Data quality; Dengue - epidemiology; Dengue - prevention & control; Distribution; Epidemiology; Evaluation; Geographic information systems; Houses; Humans; Infectious diseases; Intervention; Mobile data collection; Mobile devices; Mosquito Control; Mosquito Vectors; Open source software; Peru - epidemiology; Public health; Quality assurance; Research Design; Research in Practice; Teams; Tropical environments; Urban areas; Urban environments; Vector control; Vector-borne diseases; Vectors (Biology); Peru; Mobile data collection; Spatial repellent; Data monitoring; Vector control; Dengue; Clinical trial; Aedes aegypti; Data quality; CommCare
• ISSN: 1471-2458; 1471-2458
• DOI: 10.1186/s12889-022-14301-7

Vector-borne diseases are among the most burdensome infectious diseases worldwide with high burden to health systems in developing regions in the tropics. For many of these diseases, vector control to reduce human biting rates or arthropod populations remains the primary strategy for prevention. New vector control interventions intended to be marketed through public health channels must be assessed by the World Health Organization for public health value using data generated from large-scale trials integrating epidemiological endpoints of human health impact. Such phase III trials typically follow large numbers of study subjects to meet necessary power requirements for detecting significant differences between treatment arms, thereby generating substantive and complex datasets. Data is often gathered directly in the field, in resource-poor settings, leading to challenges in efficient data reporting and/or quality assurance. With advancing technology, mobile data collection (MDC) systems have been implemented in many studies to overcome these challenges. Here we describe the development and implementation of a MDC system during a randomized-cluster, placebo-controlled clinical trial evaluating the protective efficacy of a spatial repellent intervention in reducing human infection with Aedes -borne viruses (ABV) in the urban setting of Iquitos, Peru, as well as the data management system that supported it. We discuss the benefits, remaining capacity gaps and the key lessons learned from using a MDC system in this context in detail.