Data for population-based health analytics: the Cohorts Consortium of Latin America and the Caribbean

Carrillo-Larco et al.

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 individuallevel 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.

Article's language
English
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