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Introduction: Population 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. Methods: The 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. Results: The 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. Conclusion: The 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. [ABSTRACT FROM AUTHOR]

Background: Schistosomiasis affects 218 million people worldwide, with most infections in Africa. Prevalence studies suggest that people with chronic schistosomiasis may have higher risk of HIV-1 acquisition and impaired ability to control HIV-1 replication once infected. We hypothesized that: (1) pre-existing schistosome infection may increase the odds of HIV-1 acquisition and that the effects may differ between men and women, and (2) individuals with active schistosome infection at the time of HIV-1 acquisition may have impaired immune control of HIV-1, resulting in higher HIV-1 viral loads at HIV-1 seroconversion. Methology/Principal findings: We conducted a nested case-control study within a large population-based survey of HIV-1 transmission in Tanzania. A population of adults from seven villages was tested for HIV in 2007, 2010, and 2013 and dried blood spots were archived for future studies with participants’ consent. Approximately 40% of this population has Schistosoma mansoni infection, and 2% has S. haematobium. We tested for schistosome antigens in the pre- and post-HIV-1-seroconversion blood spots of people who acquired HIV-1. We also tested blood spots of matched controls who did not acquire HIV-1 and calculated the odds that a person with schistosomiasis would become HIV-1-infected compared to these matched controls. Analysis was stratified by gender. We compared 73 HIV-1 seroconverters with 265 controls. Women with schistosome infections had a higher odds of HIV-1 acquisition than those without (adjusted OR = 2.8 [1.2–6.6], p = 0.019). Schistosome-infected men did not have an increased odds of HIV-1 acquisition (adjusted OR = 0.7 [0.3–1.8], p = 0.42). We additionally compared HIV-1 RNA levels in the post-seroconversion blood spots in HIV-1 seroconverters with schistosomiasis versus those without who became HIV-infected in 2010, before antiretroviral therapy was widely available in the region. The median whole blood HIV-1 RNA level in the 15 HIV-1 seroconverters with schistosome infection was significantly higher than in the 22 without schistosomiasis: 4.4 [3.9–4.6] log10 copies/mL versus 3.7 [3.2–4.3], p = 0.017. Conclusions/Significance: We confirm, in an area with endemic S. mansoni, that pre-existing schistosome infection increases odds of HIV-1 acquisition in women and raises HIV-1 viral load at the time of HIV-1 seroconversion. This is the first study to demonstrate the effect of schistosome infection on HIV-1 susceptibility and viral control, and to differentiate effects by gender. Validation studies will be needed at additional sites. [ABSTRACT FROM AUTHOR]

The Data Documentation Initiative (DDI) specification has gone through significant development in recent years. Most Health and Demographic Surveillance System (HDSS) researchers in Low and Middle Income Countries (LMIC) are, however, unclear on how to apply it to their work. This paper sets out considerations that LMIC HDSS researchers need to make regarding DDI use. We use the Kisesa HDSS in Mwanza Tanzania as a prototype. First, we mapped the Kisesa HDSS data production process to the Generic Longitudinal Business Process Model (GLBPM). Next, we used existing GLBPM to DDI mapping to guide us on the DDI elements to use. We then explored implementation of DDI using the tools Nesstar Publisher for the DDI Codebook version and Colectica Designer for the DDI Lifecycle version. We found the amounts of metadata entry comparable between Nesstar Publisher and Colectica Designer when documenting a study from scratch. The majority of metadata had to be entered manually. Automatically extracted metadata amounted to at most 48% in Nesstar Publisher and 33% in Colectica Designer. We found Colectica Designer to have stiffer staff training needs and software costs than Nesstar Publisher. Our study shows that, at least for HDSS in LMIC, it is unlikely to be the amount of metadata entry that determines the choice between DDI Codebook and DDI Lifecycle but rather staff training needs and software costs. LMIC HDSS studies would need to invest in extensive staff training to directly start with DDI Lifecycle or they could start with DDI Codebook and move to DDI Lifecycle later. [ABSTRACT FROM AUTHOR]

Background: The Tanzanian national HIV care and treatment programme has provided free antiretroviral therapy (ART) to HIV-positive persons since 2004. ART has been available to participants of the Kisesa open cohort study since 2005, but data to 2007 showed a slow uptake of ART and a modest impact on mortality. Additional data from the 2010 HIV serological survey provide an opportunity to update the estimated impact of ART in this setting. Methods: The Kisesa Health and Demographic Surveillance Site (HDSS) has collected HIV serological data and demographic data, including verbal autopsy (VA) interviews since 1994. Serological data to the end of 2010 were used to make two estimates of HIV-attributable mortality, the first among HIV positives using the difference in mortality between HIV positives and HIV negatives, and the second in the population using the difference between the observed mortality rate in the whole population and the mortality rate among the HIV negatives. Four time periods (1994-1999, 2000-2004, 2005-2007, and 2008-2010) were used and HIV-attributable mortality estimates were analysed in detail for trends over time. A computer algorithm, InterVA-4, was applied to VA data to estimate the HIV-attributable mortality for the population, and this was compared to the estimates from the serological survey data. Results: Among HIV-positive adults aged 45-59 years, high mortality rates were observed across all time periods in both males and females. In HIV-positive men, the HIV-attributable mortality was 91.6% (95% confidence interval (CI): 84.6%-95.3%) in 2000-2004 and 86.3% (95% CI: 71.1%-93.3%) in 2008-2010, while among women, the HIV-attributable mortality was 87.8% (95% CI: 71.1%-94.3%) in 2000-2004 and 85.8% (95% CI: 59.6%-94.4%) in 2008-2010. In the whole population, using the serological data, the HIV-attributable mortality among men aged 30-44 years decreased from 57.2% (95% CI: 46.9%-65.3%) in 2000-2004 to 36.5% (95% CI: 18.8%-50.1%) in 2008-2010, while among women the corresponding decrease was from 57.3% (95% CI: 49.7%-63.6%) to 38.7% (95% CI: 27.4%-48.2%). The HIV-attributable mortality in the population using estimates from the InterVA model was lower than that from HIV sero-status data in the period prior to ART, but slightly higher once ART became available. Discussion: In the Kisesa HDSS, ART availability corresponds with a decline in adult overall mortality, although not as large as expected. Using InterVA to estimate HIV-attributable mortality showed smaller changes in HIV-related mortality following ART availability than the serological results. [ABSTRACT FROM AUTHOR]

Background: Metadata 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., Objective: To 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., Methods: The 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., Results: The 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., Conclusions: The 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., (©David Amadi, Sylvia Kiwuwa-Muyingo, Tathagata Bhattacharjee, Amelia Taylor, Agnes Kiragga, Michael Ochola, Chifundo Kanjala, Arofan Gregory, Keith Tomlin, Jim Todd, Jay Greenfield. Originally published in the Online Journal of Public Health Informatics (https://ojphi.jmir.org/), 01.08.2024.)