ArticleFrontiers in public health2023
Enabling data sharing and utilization for African population health data using OHDSI tools with an OMOP-common data model.
Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Opportunities and challenges of artificial intelligence in public health: a systematic review on technological efficacy, ethical dilemmas, and governance pathways.Frontiers in public health · 2025Pooled it
- Unleashing potential: assessing Africa's readiness for the data science revolution to impact health.Nature communications · 2026Review
- Evaluating the impact of OMOP-CDM on data quality insight generation in respiratory disease management.Frontiers in big data · 2026Article
- Harmonizing patient health data into the OMOP common data model: a case study using tuberculosis data from Douala General Hospital, Cameroon.Frontiers in digital health · 2026Article
- From fragmentation to federation: multi-partner OMOP implementation in Uganda enabling global real-world evidence generation.Frontiers in digital health · 2026Article
- Data science and artificial intelligence for maternal, newborn and child health: scoping review and thematic analysis.BMC public health · 2025Article
- Article
- Harmonizing population health data into OMOP common data model: a demonstration using COVID-19 sero-surveillance data from Nairobi Urban Health and Demographic Surveillance System.Frontiers in digital health · 2025Article
- Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable Population Health Data: Framework Development and Implementation Study.Online journal of public health informatics · 2024Article
- A call-to-action: integrate a learning health system framework into longitudinal population studies to improve health response in Africa.Health affairs scholar · 2024Article
- Integrating longitudinal mental health data into a staging database: harnessing DDI-lifecycle and OMOP vocabularies within the INSPIRE Network Datahub.Frontiers in big data · 2024Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
Abstract
The COVID-19 pandemic has spurred the use of AI and DS innovations in data collection and aggregation. Extensive data on many aspects of the COVID-19 has been collected and used to optimize public health response to the pandemic and to manage the recovery of patients in Sub-Saharan Africa. However, there is no standard mechanism for collecting, documenting and disseminating COVID-19 related data or metadata, which makes the use and reuse a challenge. INSPIRE utilizes the Observational Medical Outcomes Partnership (OMOP) as the Common Data Model (CDM) implemented in the cloud as a Platform as a Service (PaaS) for COVID-19 data. The INSPIRE PaaS for COVID-19 data leverages the cloud gateway for both individual research organizations and for data networks. Individual research institutions may choose to use the PaaS to access the FAIR data management, data analysis and data sharing capabilities which come with the OMOP CDM. Network data hubs may be interested in harmonizing data across localities using the CDM conditioned by the data ownership and data sharing agreements available under OMOP's federated model. The INSPIRE platform for evaluation of COVID-19 Harmonized data (PEACH) harmonizes data from Kenya and Malawi. Data sharing platforms must remain trusted digital spaces that protect human rights and foster citizens' participation is vital in an era where information overload from the internet exists. The channel for sharing data between localities is included in the PaaS and is based on data sharing agreements provided by the data producer. This allows the data producers to retain control over how their data are used, which can be further protected through the use of the federated CDM. Federated regional OMOP-CDM are based on the PaaS instances and analysis workbenches in INSPIRE-PEACH with harmonized analysis powered by the AI technologies in OMOP. These AI technologies can be used to discover and evaluate pathways that COVID-19 cohorts take through public health interventions and treatments. By using both the data mapping and terminology mapping, we construct ETLs that populate the data and/or metadata elements of the CDM, making the hub both a central model and a distributed model.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.