A Framework for Global Collaborative Data Management for Malaria Research

Health Data Sciences and Cardiovascular Disease in Africa: Needs and the Way Forward

PURPOSE OF REVIEW

The rising burden of cardiovascular disease (CVD) in Africa is of great concern. Health data sciences is a rapidly developing field which has the potential to improve health outcomes, especially in low-middle income countries with burdened healthcare systems. We aim to explore the current CVD landscape in Africa, highlighting the importance of health data sciences in the region and identifying potential opportunities for application and growth by leveraging health data sciences to improve CVD outcomes.

RECENT FINDINGS

While there have been a number of initiatives aimed at developing health data sciences in Africa over the recent decades, the progress and growth are still in their early stages. Its maximum potential can be leveraged through adequate funding, advanced training programs, focused resource allocation, encouraging bidirectional international partnerships, instituting best ethical practices, and prioritizing data science health research in the region. The findings of this review explore the current landscape of CVD and highlight the potential benefits and utility of health data sciences to address CVD challenges in Africa. By understanding and overcoming the barriers associated with health data sciences training, research, and application in the region, focused initiatives can be developed to promote research and development. These efforts will allow policymakers to form informed, evidence-based frameworks for the prevention and management of CVDs, and ultimately result in improved CVD outcomes in the region.

Implementing a Redcap-based research data collection system for mental health

BACKGROUND

The implementation of new technologies in medical research, such as novel big storage systems, has recently gained importance. Electronic data capture is a perfect example as it powerfully facilitates medical research. However, its implementation in resource-limited settings, where basic clinical resources, internet access, and human resources may be reduced might be a problem.

METHODS

In this paper we described our approach for building a network architecture for data collection to achieve our objectives using a REDCap® tool in Colombia and provide guidance for data collection in similar settings.

CONCLUSIONS

REDCap is a feasible and efficient electronic data capture software to use in similar contexts to Colombia. The software facilitated the whole data management process and is a way to build research capacities in resourced-limited settings.

Expanding Research Capacity in Sub-Saharan Africa Through Informatics, Bioinformatics, and Data Science Training Programs in Mali

Bioinformatics and data science research have boundless potential across Africa due to its high levels of genetic diversity and disproportionate burden of infectious diseases, including malaria, tuberculosis, HIV and AIDS, Ebola virus disease, and Lassa fever. This work lays out an incremental approach for reaching underserved countries in bioinformatics and data science research through a progression of capacity building, training, and research efforts. Two global health informatics training programs sponsored by the Fogarty International Center (FIC) were carried out at the University of Sciences, Techniques and Technologies of Bamako, Mali (USTTB) between 1999 and 2011. Together with capacity building efforts through the West Africa International Centers of Excellence in Malaria Research (ICEMR), this progress laid the groundwork for a bioinformatics and data science training program launched at USTTB as part of the Human Heredity and Health in Africa (H3Africa) initiative. Prior to the global health informatics training, its trainees published first or second authorship and third or higher authorship manuscripts at rates of 0.40 and 0.10 per year, respectively. Following the training, these rates increased to 0.70 and 1.23 per year, respectively, which was a statistically significant increase (p < 0.001). The bioinformatics and data science training program at USTTB commenced in 2017 focusing on student, faculty, and curriculum tiers of enhancement. The program's sustainable measures included institutional support for core elements, university tuition and fees, resource sharing and coordination with local research projects and companion training programs, increased student and faculty publication rates, and increased research proposal submissions. Challenges reliance of high-speed bandwidth availability on short-term funding, lack of a discounted software portal for basic software applications, protracted application processes for United States visas, lack of industry job positions, and low publication rates in the areas of bioinformatics and data science. Long-term, incremental processes are necessary for engaging historically underserved countries in bioinformatics and data science research. The multi-tiered enhancement approach laid out here provides a platform for generating bioinformatics and data science technicians, teachers, researchers, and program managers. Increased literature on bioinformatics and data science training approaches and progress is needed to provide a framework for establishing benchmarks on the topics.

Defining and Developing a Generic Framework for Monitoring Data Quality in Clinical Research

Evidence for the need for high data quality in clinical research is well established. The rigor of clinical research conclusions rely heavily on good quality data, which relies on good documentation practices. Little attention has been given to clear guidelines and definitions to monitor data quality. To address this, a "fit-for-use" data quality monitoring framework (DQMF) for clinical research was developed based on a holistic design-oriented approach. An integrated literature review and feasibility study underpinned the framework development. Ontology of key terms, concepts, methods, and standards were recorded using a consensus approach and mind mapping technique. The DQMF is presented as a nested concentric network illustrating concept relationships and hierarchy. Face validation was conducted, and common terminology and definitions are listed. The consolidated DQMF can be adapted according to study context and data availability aiding in the development of a long-term strategy with increased efficacy for clinical data quality monitoring.

Development of a data collection and management system in West Africa: challenges and sustainability

BACKGROUND

Developing and sustaining a data collection and management system (DCMS) is difficult in malaria-endemic countries because of limitations in internet bandwidth, computer resources and numbers of trained personnel. The premise of this paper is that development of a DCMS in West Africa was a critically important outcome of the West African International Centers of Excellence for Malaria Research. The purposes of this paper are to make that information available to other investigators and to encourage the linkage of DCMSs to international research and Ministry of Health data systems and repositories.

METHODS

We designed and implemented a DCMS to link study sites in Mali, Senegal and The Gambia. This system was based on case report forms for epidemiologic, entomologic, clinical and laboratory aspects of plasmodial infection and malarial disease for a longitudinal cohort study and included on-site training for Principal Investigators and Data Managers. Based on this experience, we propose guidelines for the design and sustainability of DCMSs in environments with limited resources and personnel.

RESULTS

From 2012 to 2017, we performed biannual thick smear surveys for plasmodial infection, mosquito collections for anopheline biting rates and sporozoite rates and year-round passive case detection for malarial disease in four longitudinal cohorts with 7708 individuals and 918 households in Senegal, The Gambia and Mali. Major challenges included the development of uniform definitions and reporting, assessment of data entry error rates, unstable and limited internet access and software and technology maintenance. Strengths included entomologic collections linked to longitudinal cohort studies, on-site data centres and a cloud-based data repository.

CONCLUSIONS

At a time when research on diseases of poverty in low and middle-income countries is a global priority, the resources available to ensure accurate data collection and the electronic availability of those data remain severely limited. Based on our experience, we suggest the development of a regional DCMS. This approach is more economical than separate data centres and has the potential to improve data quality by encouraging shared case definitions, data validation strategies and analytic approaches including the molecular analysis of treatment successes and failures.

EuPathDB: the eukaryotic pathogen genomics database resource

The Eukaryotic Pathogen Genomics Database Resource (EuPathDB, http://eupathdb.org) is a collection of databases covering 170+ eukaryotic pathogens (protists & fungi), along with relevant free-living and non-pathogenic species, and select pathogen hosts. To facilitate the discovery of meaningful biological relationships, the databases couple preconfigured searches with visualization and analysis tools for comprehensive data mining via intuitive graphical interfaces and APIs. All data are analyzed with the same workflows, including creation of gene orthology profiles, so data are easily compared across data sets, data types and organisms. EuPathDB is updated with numerous new analysis tools, features, data sets and data types. New tools include GO, metabolic pathway and word enrichment analyses plus an online workspace for analysis of personal, non-public, large-scale data. Expanded data content is mostly genomic and functional genomic data while new data types include protein microarray, metabolic pathways, compounds, quantitative proteomics, copy number variation, and polysomal transcriptomics. New features include consistent categorization of searches, data sets and genome browser tracks; redesigned gene pages; effective integration of alternative transcripts; and a EuPathDB Galaxy instance for private analyses of a user's data. Forthcoming upgrades include user workspaces for private integration of data with existing EuPathDB data and improved integration and presentation of host–pathogen interactions.

REDLetr: Workflow and tools to support the migration of legacy clinical data capture systems to REDCap

OBJECTIVE

A memory clinic at an academic medical center has relied on several ad hoc data capture systems including Microsoft Access and Excel for cognitive assessments over the last several years. However these solutions are challenging to maintain and limit the potential of hypothesis-driven or longitudinal research. REDCap, a secure web application based on PHP and MySQL, is a practical solution for improving data capture and organization. Here, we present a workflow and toolset to facilitate legacy data migration and real-time clinical research data collection into REDCap as well as challenges encountered.

MATERIALS AND METHODS

Legacy data consisted of neuropsychological tests stored in over 4000 Excel workbooks. Functions for data extraction, norm scoring, converting to REDCap-compatible formats, accessing the REDCap API, and clinical report generation were developed and executed in Python.

RESULTS

Over 400 unique data points for each workbook were migrated and integrated into our REDCap database. Moving forward, our REDCap-based system replaces the Excel-based data collection method as well as eases the integration into the standard clinical research workflow and Electronic Health Record.

CONCLUSION

In the age of growing data, efficient organization and storage of clinical and research data is critical for advancing research and providing efficient patient care. We believe that the workflow and tools described in this work to promote legacy data integration as well as real time data collection into REDCap ultimately facilitate these goals.

The Ontology for Biomedical Investigations

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl.

Consistent prevalence of asymptomatic infections in malaria endemic populations in Colombia over time

Background

Malaria control programmes rely on confirmation of parasite presence in patients’ blood prior to treatment administration. Plasmodium parasites are detected mostly by microscopy or rapid diagnostic test (RDT). Although these methods contribute significantly to malaria control/elimination, they are not suitable for detecting the significant proportion of asymptomatic subjects harbouring low levels of parasitaemia, which endure untreated as potential reservoirs for transmission. Malaria prevalence was assessed in endemic regions of Colombia over a 4-year follow-up.

Methods

A series of cross-sectional surveys were conducted between 2011 and 2014 in low to moderate malaria transmission sentinel sites (SS) of Tumaco, Buenaventura and Tierralta municipalities of Colombia. A census was performed and a random sample of houses was selected from each SS prior to each survey. Inhabitants were asked to answer a questionnaire on clinical, epidemiological and demographic aspects, and to provide a blood sample for malaria diagnosis using microscopy and quantitative real time polymerase chain reaction (qPCR).

Results

A total of 3059 blood samples were obtained from all SS, 58.5 % of which were from women and displayed a malaria prevalence ranging from 4 % (95 % CI 3–5 %) to 10 % (95 % CI 8–12 %) in the 4 years’ study period. Almost all malaria cases (n = 220, 97 %) were sub-microscopic and only detectable by qPCR; 90 % of the cases were asymptomatic at the time of blood collection. While Buenaventura and Tierralta had a decreasing tendency during the follow-up, Tumaco had a rise in 2013 and then a decrease in 2014. Plasmodium vivax accounted for the majority (66–100 %) of cases in Tierralta and Buenaventura and for 25–50 % of the cases in Tumaco.

Conclusions

This study demonstrates an important prevalence of asymptomatic malaria cases not detectable by microscopy, which therefore remain untreated representing a parasite pool for malaria transmission. This demands the introduction of alternative strategies for diagnosis and treatment, especially for areas of low transmission to reduce it to appropriate levels for malaria pre-elimination efforts to start.