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BackgroundLongitudinal studies are essential for understanding the progression of mental health disorders over time, but combining data collected through different methods to assess conditions like depression, anxiety, and psychosis presents significant challenges. This study presents a mapping technique allowing for the conversion of diverse longitudinal data into a standardized staging database, leveraging the Data Documentation Initiative (DDI) Lifecycle and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standards to ensure consistency and compatibility across datasets.MethodsThe “INSPIRE” project integrates longitudinal data from African studies into a staging database using metadata documentation standards structured with a snowflake schema. This facilitates the development of Extraction, Transformation, and Loading (ETL) scripts for integrating data into OMOP CDM. The staging database schema is designed to capture the dynamic nature of longitudinal studies, including changes in research protocols and the use of different instruments across data collection waves.ResultsUtilizing this mapping method, we streamlined the data migration process to the staging database, enabling subsequent integration into the OMOP CDM. Adherence to metadata standards ensures data quality, promotes interoperability, and expands opportunities for data sharing in mental health research.ConclusionThe staging database serves as an innovative tool in managing longitudinal mental health data, going beyond simple data hosting to act as a comprehensive study descriptor. It provides detailed insights into each study stage and establishes a data science foundation for standardizing and integrating the data into OMOP CDM.

BackgroundMetadata describe and provide context for other data, playing a pivotal role in enabling findability, accessibility, interoperability, and reusability (FAIR) data principles. By providing comprehensive and machine-readable descriptions of digital resources, metadata empower both machines and human users to seamlessly discover, access, integrate, and reuse data or content across diverse platforms and applications. However, the limited accessibility and machine-interpretability of existing metadata for population health data hinder effective data discovery and reuse. ObjectiveTo address these challenges, we propose a comprehensive framework using standardized formats, vocabularies, and protocols to render population health data machine-readable, significantly enhancing their FAIRness and enabling seamless discovery, access, and integration across diverse platforms and research applications. MethodsThe framework implements a 3-stage approach. The first stage is Data Documentation Initiative (DDI) integration, which involves leveraging the DDI Codebook metadata and documentation of detailed information for data and associated assets, while ensuring transparency and comprehensiveness. The second stage is Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardization. In this stage, the data are harmonized and standardized into the OMOP CDM, facilitating unified analysis across heterogeneous data sets. The third stage involves the integration of Schema.org and JavaScript Object Notation for Linked Data (JSON-LD), in which machine-readable metadata are generated using Schema.org entities and embedded within the data using JSON-LD, boosting discoverability and comprehension for both machines and human users. We demonstrated the implementation of these 3 stages using the Integrated Disease Surveillance and Response (IDSR) data from Malawi and Kenya. ResultsThe implementation of our framework significantly enhanced the FAIRness of population health data, resulting in improved discoverability through seamless integration with platforms such as Google Dataset Search. The adoption of standardized formats and protocols streamlined data accessibility and integration across various research environments, fostering collaboration and knowledge sharing. Additionally, the use of machine-interpretable metadata empowered researchers to efficiently reuse data for targeted analyses and insights, thereby maximizing the overall value of population health resources. The JSON-LD codes are accessible via a GitHub repository and the HTML code integrated with JSON-LD is available on the Implementation Network for Sharing Population Information from Research Entities website. ConclusionsThe adoption of machine-readable metadata standards is essential for ensuring the FAIRness of population health data. By embracing these standards, organizations can enhance diverse resource visibility, accessibility, and utility, leading to a broader impact, particularly in low- and middle-income countries. Machine-readable metadata can accelerate research, improve health care decision-making, and ultimately promote better health outcomes for populations worldwide.

Background The completion of case-based surveillance forms was vital for case identification during COVID-19 surveillance in Malawi. Despite significant efforts, the resulting national data suffered from gaps and inconsistencies which affected its optimal usability. The objectives of this study were to investigate the processes of collecting and reporting COVID-19 data, to explore health workers’ perceptions and understanding of the collection tools and processes, and to identify factors contributing to data quality. Methods A total of 75 healthcare professionals directly involved in COVID-19 data collection from the Malawi Ministry of Health in Lilongwe and Blantyre participated in Focus Group Discussions and In-Depth Interviews. We collected participants’ views on the effectiveness of surveillance forms in collecting the intended data, as well as on the data collection processes and training needs. We used MAXQDA for thematic and document analysis. Results Form design significantly influenced data quality and, together with challenges in applying case definitions, formed 44% of all issues raised. Concerns regarding processes used in data collection and training gaps comprised 49% of all the issues raised. Language issues (2%) and privacy, ethical, and cultural considerations (4%), although mentioned less frequently, offered compelling evidence for further review. Conclusions Our study highlights the integral connection between data quality and the design and utilization of data collection forms. While the forms were deemed to contain the most relevant fields, deficiencies in format, order of fields, and the absence of an addendum with guidelines, resulted in large gaps and errors. Form design needs to be reviewed so that it appropriately fits into the overall processes and systems that capture surveillance data. This study is the first of its kind in Malawi, offering an in-depth view of the perceptions and experiences of health professionals involved in disease surveillance on the tools and processes they use.

Euphorbia lagascae Spreng is a promising emerging oilseed crop, with its seed oil accounting for approximately 50% of the seed weight. Euphorbia oil contains a significant amount of vernolic acid, comprising two-thirds of its composition, which boasts various industrial applications, including acting as a stabilizer-plasticizer and natural dye. However, this species was known to have a high degree of seed-shattering and a low germination rate, which act as two important barriers to large-scale production and exploitation. Therefore, the objective of this study is to determine the genetic control of seed germination and seed-shattering traits in order to develop a reliable pipeline that would be applicable for industries and breeders to select superior E. lagascae lines and design a robust breeding scheme in a short time at reduced labor costs. For this objective, five different wild-type genotypes of E. lagascae that demonstrated high germination potential were crossed with an ethyl methanesulfonate (EMS) mutant genotype that produces non-shattering capsules. The F2 populations from two successful crosses (A and B) were separated into three different treated groups for seed germination evaluation and to study the segregation of 200 individuals per F2 population. The three treatments were: light, gibberellic acid (GA3), and control treatment. Consequently, plants treated with approximately 250 μmol/m2/s of light showed significant improvement in germination up to 75% in cross A and 82.4 % in cross B compared with the control plants and the group treated with 0.05% GA3. According to the chi-square test results, the inheritance pattern of seed germination in response to light treatment follows a 3:1 segregation ratio between germinated and non-germinated seeds, indicating a dominant gene action in the F2 generation. The same conclusion was followed for the shattering trait in the group treated with light, which was also simply inherited as a 3:1 ratio for shattering vs. non-shattering capsules. Our results emphasize the importance and significance of light treatment in producing uniform populations through acceptable germination and shattering resistance of the mutant genotypes of E. lagascae. This is the first report of light treatment that significantly improved seed germination of E. lagascae, which may enhance efforts in the development of this new industrial crop as a feedstock for vernolic acid production.

IntroductionPopulation health data integration remains a critical challenge in low- and middle-income countries (LMIC), hindering the generation of actionable insights to inform policy and decision-making. This paper proposes a pan-African, Findable, Accessible, Interoperable, and Reusable (FAIR) research architecture and infrastructure named the INSPIRE datahub. This cloud-based Platform-as-a-Service (PaaS) and on-premises setup aims to enhance the discovery, integration, and analysis of clinical, population-based surveys, and other health data sources.MethodsThe INSPIRE datahub, part of the Implementation Network for Sharing Population Information from Research Entities (INSPIRE), employs the Observational Health Data Sciences and Informatics (OHDSI) open-source stack of tools and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to harmonise data from African longitudinal population studies. Operating on Microsoft Azure and Amazon Web Services cloud platforms, and on on-premises servers, the architecture offers adaptability and scalability for other cloud providers and technology infrastructure. The OHDSI-based tools enable a comprehensive suite of services for data pipeline development, profiling, mapping, extraction, transformation, loading, documentation, anonymization, and analysis.ResultsThe INSPIRE datahub's “On-ramp” services facilitate the integration of data and metadata from diverse sources into the OMOP CDM. The datahub supports the implementation of OMOP CDM across data producers, harmonizing source data semantically with standard vocabularies and structurally conforming to OMOP table structures. Leveraging OHDSI tools, the datahub performs quality assessment and analysis of the transformed data. It ensures FAIR data by establishing metadata flows, capturing provenance throughout the ETL processes, and providing accessible metadata for potential users. The ETL provenance is documented in a machine- and human-readable Implementation Guide (IG), enhancing transparency and usability.ConclusionThe pan-African INSPIRE datahub presents a scalable and systematic solution for integrating health data in LMICs. By adhering to FAIR principles and leveraging established standards like OMOP CDM, this architecture addresses the current gap in generating evidence to support policy and decision-making for improving the well-being of LMIC populations. The federated research network provisions allow data producers to maintain control over their data, fostering collaboration while respecting data privacy and security concerns. A use-case demonstrated the pipeline using OHDSI and other open-source tools.