ArticleRevista panamericana de salud publica = Pan American journal of public health2024
Data for population-based health analytics: the Cohorts Consortium of Latin America and the Caribbean.
Article in Revista panamericana de salud publica = Pan American journal of public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: We describe the daily operations of the Cohorts Consortium of Latin America and the Caribbean (CC-LAC), detailing the resources required and offering tips to Caribbean researchers so this guide can be used to start a data pooling project. Methods: The CC-LAC began by developing a steering committee - that is, a team of regional experts who guided the project's set up and operations. The Consortium invites investigators who agree to share individual-level data about topics of interest to become members and they then have input into the project's goals and operations; they are also invited to coauthor papers. We used a systematic review methodology to identify investigators with data resources aligned with the project and developed a protocol (i.e. a manual of procedures) to document all aspects of the project's operations. Results: If a study recruited people from more than one country, then the sample from each country was counted as a separate cohort, thus in 2024 our combined data resources include >30 separate units from 13 countries, with a combined sample size of >174 000 participants. Using this unique resource, we have produced region-specific risk estimates for cardiometabolic risk factors (e.g. anthropometrics) and cardiovascular disease, and we have developed a region-specific cardiovascular risk score for use in clinical settings. Conclusions: Data pooling projects are less expensive than collecting new data, and they increase the longer-term value and impact of the data that are contributed. Data pooling efforts require systematic and transparent methodology, and expertise in data handling and analytics are prerequisites. Researchers embarking on a data pooling endeavor should understand and be able to meet the various data protection standards stipulated by national data legislation as these standards will likely vary among jurisdictions.
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