scholarly journals PO 8490 PROMOTING GOOD DATA MANAGEMENT PRACTICES IN CLINICAL RESEARCH IN RESOURCE-POOR SETTINGS

2019 ◽  
Vol 4 (Suppl 3) ◽  
pp. A45.1-A45
Author(s):  
Harry Van Loen ◽  
Diana Arango ◽  
Hanne Landuyt ◽  
Christophe Burm ◽  
Yven Van Herrewege
Keyword(s):  
Clinical Trials ◽  
Clinical Research ◽  
Data Management ◽  
Capacity Building ◽  
Management Practices ◽  
Theory And Practice ◽  
Sub Saharan Africa ◽  
The South ◽  
Good Data ◽  
Resource Poor

BackgroundAccurate and timely data management (DM) is of key importance in clinical research to generate high-quality and GCP-compliant data for analysis and/or sharing. Our objective is to strengthen the capacity for DM in clinical research in resource-poor settings by organising several teaching initiatives.MethodsOur teaching initiatives have a twofold approach. First, a generic and comprehensive approach with capacity building on various thematical modules. These include a research component (overviewing the research data management procedures) and a technological component (introducing databases and software). In addition, a component on legislation, guidelines and standards specific towards DM is discussed, as well as a project management component on how to organise DM efficiently and timely. Second, we apply a more focused and study-specific approach which details roles and responsibilities in data management, milestones and documentation practices. Both approaches are based upon successful implementation in EDCTP-funded clinical trials, such as the 4ABC, PREGACT and Microbicide Safety Biomarkers studies, as well as the FP7 sponsored NIDIAG project. The target audience comprises various study stakeholders such as data managers, IT administrators, clinicians, laboratory researchers and statisticians, coming from sub-Saharan Africa, South-East Asia and Latin America.ResultsA teaching model for promoting Good Data Management Practices has been developed with theory- and practice-based modules. This model is used at face-to-face workshops in remote settings and has been re-used by colleagues and implemented by other research institutions to promote further capacity building and sustainable development in the South. In addition, it has led to mutual learning and enhanced institutional and personal North-South collaborations.ConclusionThere is a clear case for promoting DM and providing guidelines for Good Data Management Practices. Our twofold approach has enabled the successful conduct of GCP compliant non-commercial clinical trials in the South.

scholarly journals VARIABILITY IN CLINICAL RESEARCH DATA MANAGEMENT PRACTICES: LESSONS FROM THE MALARIA COMMUNITY

2017 ◽  
Vol 2 (Suppl 2) ◽  
pp. A19.1-A19
Author(s):  
Amélie Julé ◽  
Hazel Ashurst ◽  
Laura Merson ◽  
Piero Olliaro ◽  
Vicki Marsh ◽  
...  
Keyword(s):  
Clinical Research ◽  
Data Management ◽  
Management Practices ◽  
Research Data ◽  
Research Data Management ◽  
Clinical Research Data

scholarly journals The conundrum of clinical trials and standard of care in sub-Saharan Africa – the research nurse perspective

2019 ◽  
Vol 24 (8) ◽  
pp. 649-660 ◽  
Author(s):  
Jane Frances Ndyetukira ◽  
Richard Kwizera ◽  
Florence Kugonza ◽  
Cynthia Ahimbisibwe ◽  
Carol Namujju ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Clinical Research ◽  
Standard Of Care ◽  
Sub Saharan Africa ◽  
Research Nurse ◽  
Loss To Follow Up ◽  
Resource Limited ◽  
Research Nurses ◽  
Sub Saharan

Background Nurses form a very important part of the health workforce in sub-Saharan Africa. Research nurses are critical to the implementation of clinical trials. The duties and responsibilities of a research nurse are complex and continue to evolve as new practices and guidelines are formulated. Aims In this paper, we have highlighted the major contributions of research nurses in HIV clinical trials in sub-Saharan Africa from the unique perspective of Ugandan nurses. Methods The requirements and challenges of two multi-site, randomised cryptococcal meningitis clinical trials in Uganda were assessed from the perspective of research nurses conducting complex research in resource-limited settings. Results Over the course of 8 years, approximately 1739 participants were screened and 934 people were enrolled into the two trials. The nurses found that patient education and engagement were among the most important predictors of success in minimising loss to follow-up. Conclusions Research nurses played a key role in communicating clinical research goals to patients, obtaining informed consent, minimising loss to follow-up, and ensuring that research practices are translated and implemented into standard of care. However, there remains a need to integrate the same level of care provided in clinical research studies to non-study patients.

scholarly journals How Clinical Research Can Contribute to Strengthening Health Systems in Low Resource Countries

2020 ◽  
Vol 5 (2) ◽  
pp. 48
Author(s):  
Florent Mbo ◽  
Wilfried Mutombo ◽  
Digas Ngolo ◽  
Patrice Kabangu ◽  
Olaf Valverde Mordt ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Clinical Research ◽  
Health System ◽  
Health Systems ◽  
Neglected Tropical Diseases ◽  
Information Provision ◽  
International Standards ◽  
Sub Saharan Africa ◽  
Health System Strengthening ◽  
Low Resource

Clinical research on neglected tropical diseases is a challenge in low-resource countries, and the contribution of clinical and operational research to health system strengthening is poorly documented. Developing new, simple, safe, and effective treatments may improve the effectiveness of health systems, and conducting research directly in health structures may have an additional impact. This study describes the process of conducting clinical trials in the Democratic Republic of Congo (DRC) in compliance with international standards, and the role of the trials in strengthening health system functions, including governance, human resources, health information, provision of care, and the equipping of health services with the necessary supplies and infrastructure. We conclude that conducting clinical trials in endemic areas has not only reinforced and supported the aim of conducting high-level clinical research in endemic countries, but has also brought lasting benefits to researchers, staff, and hospitals, as well as to broader health systems, which have positive knock-on effect on patients outside of the clinical trials and their communities. Sustainability, however, remains a challenge in an underfunded health system, especially with respect to specialized equipment. Clinical research in most of sub-Saharan Africa is highly dependent on international input and external technical support; there are areas of weaknesses in trial design and documentation, as well as in data management and analysis. Financing remains a critical issue, as African investigators have difficulties in directly accessing sources of international research funding.

Creating an Effort Tracking Tool to Improve Therapeutic Cancer Clinical Trials Workload Management and Budgeting

2011 ◽  
Vol 9 (11) ◽  
pp. 1228-1233 ◽  
Author(s):  
Pam James ◽  
Patty Bebee ◽  
Linda Beekman ◽  
David Browning ◽  
Mathew Innes ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Clinical Research ◽  
Data Management ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
University Of Michigan ◽  
Web Based ◽  
Workload Management ◽  
Effective Cost ◽  
The University

Quantifying data management and regulatory workload for clinical research is a difficult task that would benefit from a robust tool to assess and allocate effort. As in most clinical research environments, The University of Michigan Comprehensive Cancer Center (UMCCC) Clinical Trials Office (CTO) struggled to effectively allocate data management and regulatory time with frequently inaccurate estimates of how much time was required to complete the specific tasks performed by each role. In a dynamic clinical research environment in which volume and intensity of work ebbs and flows, determining requisite effort to meet study objectives was challenging. In addition, a data-driven understanding of how much staff time was required to complete a clinical trial was desired to ensure accurate trial budget development and effective cost recovery. Accordingly, the UMCCC CTO developed and implemented a Web-based effort-tracking application with the goal of determining the true costs of data management and regulatory staff effort in clinical trials. This tool was developed, implemented, and refined over a 3-year period. This article describes the process improvement and subsequent leveling of workload within data management and regulatory that enhanced the efficiency of UMCCC's clinical trials operation.

scholarly journals Clinical research data management in the United States: Where we've been and where we're going

2021 ◽  
Author(s):  
Mary Banach ◽  
Kaye H Fendt ◽  
Johann Proeve ◽  
Dale Plummer ◽  
Samina Qureshi ◽  
...  
Keyword(s):  
Clinical Research ◽  
Data Management ◽  
Clinical Data ◽  
Management Practices ◽  
The United States ◽  
Research Data ◽  
Fda Guidance ◽  
Research Data Management ◽  
Challenges And Opportunities ◽  
Clinical Research Data

With the focus of the COVID-19 pandemic, we wanted to reach all stakeholders representing communities concerned with good clinical data management practices. We wanted to represent not only data managers but bio-statisticians, clinical monitors, data scientists, informaticians, and all those who collect, organize, analyze, and report on clinical research data. In our paper we will discuss the history of clinical data management in the US and its evolution from the early days of FDA guidance. We will explore the role of biomedical research focusing on the similarities and differences in industry and academia clinical research data management and what we can learn from each other. We will talk about our goals for recruitment and training for the CDM community and what we propose for increasing the knowledge and understanding of good clinical data practice to all – particularly our front-line data collectors i.e., nurses, medical assistants, patients, other data collectors. Finally, we will explore the challenges and opportunities to see CDM as the hub for good clinical data research practices in all of our communities.We will also discuss our survey on how the COVID-19 pandemic has affected the work of CDM in clinical research.

scholarly journals PO 8471 A SURVEY OF CURRENT CLINICAL DATA MANAGEMENT PRACTICES IN SUB-SAHARAN AFRICA

2019 ◽  
Vol 4 (Suppl 3) ◽  
pp. A41.3-A42
Author(s):  
Hanne Landuyt ◽  
Harry Van Loen ◽  
Bai L Dondeh ◽  
Badou M Gaye ◽  
Yven Van Herrewege
Keyword(s):  
Data Management ◽  
Clinical Data ◽  
Online Survey ◽  
Management Practices ◽  
Descriptive Analysis ◽  
Relevant Information ◽  
Sub Saharan Africa ◽  
Management Tools ◽  
Sub Saharan ◽  
Personalised Feedback

BackgroundInformation on clinical data management (CDM) practices in clinical trials in sub-Saharan Africa is scarce. As part of ALERRT (the African coalition for Epidemic Research, Response and Training, an EDCTP-funded project) we want to gauge current CDM and ICT practices and identify possible gaps within different research institutions in sub-Saharan Africa. This information will be used to develop a scalable, GCP-compliant, robust CDM/ICT infrastructure suitable for resource-poor settings and response-ready in the event of an outbreak.MethodsAn online survey was designed to assess the experience of the participating sites with the various CDM processes, CDM documentation and facilities, the availability of dedicated staff and their experience with GCP. In addition, ICT features essential to CDM will be assessed. Lastly, information on the use of CDM software will be obtained. Respondents can request to receive personalised feedback (aimed to improve their CDM practices) based on their results. The survey, in English and French, will be sent out to 100 sites in sub-Saharan Africa. Sites with intermittent internet connections will receive an MS-Office Word-version of the survey.ResultsThe survey will be closed after a month. Personalised feedback (if requested) will be sent to the respondents. Descriptive analysis of the survey results will be done, and results will be used to design standard data management tools, tailored to the needs of research sites in sub-Saharan Africa and suitable for emergency research. Both results and tools will be disseminated to the scientific community.ConclusionThe results of this survey will provide relevant information on the current CDM and ICT practices in sub-Saharan Africa. Potential pitfalls will be identified and opportunities for improvement will be addressed. Furthermore, the survey will offer a chance to exchange ideas between African and European partners on how to implement good CDM and ICT practices.

scholarly journals PO 8538 STRENGTHENING SOCIAL STRATEGIES TO ENGAGE SUB-SAHARAN AFRICAN SOCIETIES TO VALUE CLINICAL RESEARCH: THE EXPERIENCE IN CONDUCTING CLINICAL TRIALS IN LAMBARéNé

2019 ◽  
Vol 4 (Suppl 3) ◽  
pp. A51.1-A51
Author(s):  
Pamela C Angoissa Minsoko ◽  
Selidji T Agnandji
Keyword(s):  
Clinical Trials ◽  
Clinical Research ◽  
Community Engagement ◽  
Sub Saharan Africa ◽  
Social Meaning ◽  
Willingness To Participate ◽  
The Social ◽  
Social Meanings ◽  
Sub Saharan ◽  
Social Components

BackgroundOver the last two decades, clinical research activities have increased significantly in sub-Saharan Africa but societal engagement to make research socially and economically valuable is limited. Thus, engaging communities when designing clinical research and promoting social impacts of research are becoming key objectives among stakeholders involved in clinical research in sub-Saharan Africa. However, there is a need to define concepts and indicators to assess the strength of community engagement as well as the social impacts of clinical research.Here, we hypothesised that the social meanings of willingness to participate and compliance to clinical trial procedures are relevant indicators to assess community engagement.MethodsWe conducted a retrospective, prospective case study of clinical trials conducted in CERMEL between 1995 and 2017. We performed a social meaning framework analysis of the following processes: protocol design, ethical and regulatory clearance, informed consent and medical study procedures. We identified the social meanings of each procedure according to the involvement of social components (actors, ideas, communication strategies).ResultsA total of 42 clinical trials were identified in the ClinicalTrials.gov and Pan-African Clinical Trials Registry databases and confirmed by the top management of CERMEL. Between 1995 and 2004, there was little social meaning connected to trial procedures. This period was associated with poor compliance to study procedures. Between 2005 and 2017, compliance to study procedures improved. Detailed results will be presented during the meeting.ConclusionThe rise in willingness to participate in clinical research and improved compliance with study procedures were associated with the introduction of social components to medical procedures. Both indicators may be relevant to assess the strength of community engagement.

scholarly journals Foundational Practices of Research Data Management

2020 ◽  
Vol 6 ◽  
Author(s):  
Kristin Briney ◽  
Heather Coates ◽  
Abigail Goben
Keyword(s):  
Data Analysis ◽  
Best Practices ◽  
Data Management ◽  
Management Practices ◽  
Research Data ◽  
Data Reuse ◽  
Data Handling ◽  
Good Data ◽  
Research Data Management ◽  
The Future

The importance of research data has grown as researchers across disciplines seek to ensure reproducibility, facilitate data reuse, and acknowledge data as a valuable scholarly commodity. Researchers are under increasing pressure to share their data for validation and reuse. Adopting good data management practices allows researchers to efficiently locate their data, understand it, and use it throughout all of the stages of a project and in the future. Additionally, good data management can streamline data analysis, visualization, and reporting, thus making publication less stressful and time-consuming. By implementing foundational practices of data management, researchers set themselves up for success by formalizing processes and reducing common errors in data handling, which can free up more time for research. This paper provides an introduction to best practices for managing all types of data.

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