Abstract

Clinical trial data management tools are widely available—some free to access and others relatively expensive, particularly for low- and middle-income countries. Such tools also do not always permit adaptation for local conditions nor include options to capture environmental and meteorological data. In the context of climate change and pressing environmental health threats, more studies that aim to assess the impacts of environmental change on public health are being carried out. Here, using freely available software, we tailor-made a clinical trial data management tool that managed all aspects of an intervention-based clinical trial to assess the impact of personal solar ultraviolet radiation exposure on vaccine effectiveness. Data captured and associated procedures included patient data, scheduling, reporting, analysis and data management. Moreover, patient enrolment, recruitment, follow-up and decision-making in response to patient data were managed. Given the multidisciplinary study approach, the tool also managed all environmental and meteorological data for the rural African study site. Application of the tool ensured efficient communication between rural sites, a relatively high overall participant response rate (87%) and minimal loss to follow-up. This study suggests that it is possible to tailor-make a clinical trial data management tool for environmental and public health studies.

Highlights

  • Good Clinical Practice (GCP) [1] calls for excellent data management in research, especially when conducting clinical trials

  • As part of a larger intervention and control study that aimed to investigate whether or not providing sun protection to mothers of children prost-vaccination would elicit a more effective immune response in the child [9], we developed a clinical trial data management tool to assist in the study execution

  • 10) nwith from the Black African population group participated in the study (Figure 10) with recruitment taking place from December 2015 to March 2016

Read more

Summary

Introduction

Good Clinical Practice (GCP) [1] calls for excellent data management in research, especially when conducting clinical trials. Quality, recording, maintenance and retrieval of source data from a clinical trial. The aim of data management is to transform responses from the study participants, efficiently and without errors, into data that are accurate and accessible. It can be transformed into information that can be disseminated and understood through statistical analysis. Data management needs to be ordered using a standard operating procedure that includes checklists for assessment at each stage of a research project. Good data management ensures data integrity and accuracy [1]

Objectives
Methods
Results
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.