Cohort Profile: The Cohorts Consortium of Latin America and the Caribbean (CC-LAC)
Abstract
Latin America and the Caribbean (LAC) are characterized by much diversity in terms of socio-economic status, ecology, environment, access to health care,1,2 as well as the frequency of risk factors for and prevalence or incidence of non-communicable diseases;3–7 importantly, these differences are observed both between and within countries in LAC.8,9 LAC countries share a large burden of non-communicable (e.g. diabetes and hypertension) and cardiovascular (e.g. ischaemic heart disease) diseases, with these conditions standing as the leading causes of morbidity, disability and mortality in most of LAC.10–12 These epidemiological estimates—e.g. morbidity—cannot inform about risk factors or risk prediction, which are relevant to identify prevention avenues. Cohort studies, on the other hand, could provide this evidence. Pooled analysis, using data from multiple cohort studies, have additional strengths such as increased statistical power and decreased statistical uncertainty.13 LAC cohort studies have been under-represented,14 or not included at all,15–17 in international efforts aimed at pooling data from multiple cohort studies. We therefore set out to pool data from LAC cohorts to address research questions that individual cohort studies would not be able to answer. Drawing from previous successful regional enterprises (e.g. Asia Pacific Cohort Studies Collaboration),18,19 we established the Cohorts Consortium of Latin America and the Caribbean (CC-LAC). The main aim of the CC-LAC is to start a collaborative cohort data pooling in LAC to examine the association between cardio-metabolic risk factors (e.g. blood pressure, glucose and lipids) and non-fatal and fatal cardiovascular outcomes (e.g. stroke or myocardial infarction). In so doing, we aim to provide regional risk estimates to inform disease burden metrics, as well as other ambitious projects including a cardiovascular risk score to strengthen cardiovascular prevention in LAC. Initial funding has been provided by a fellowship from the Wellcome Trust Centre for Global Health Research at Imperial College London (Strategic Award, Wellcome Trust–Imperial College Centre for Global Health Research, 100693/Z/12/Z). Additional funding is being provided by an International Training Fellowship from the Wellcome Trust (214185/Z/18/Z). At the time of writing, the daily operations and pooled database are hosted at Imperial College London, though a mid-term goal is to transfer this expertise and operations to LAC. The collaboration relies fundamentally on a strong regional network of health researchers and practitioners. We have harmonized and pooled approximately population-based cohort data on cardio-metabolic risk factors and outcomes, i.e. participants were not recruited based on disease (e.g. cohort of stroke survivors) or risk factor (e.g. cohort of smokers) history. Data have been collated by the CC-LAC, a LAC network of health researchers and practitioners. The database was collated using multiple data identification sources. First, we accessed publicly available cohort data through each study’s website or data repository. Second, we conducted a systematic search in Medline, Embase and SciELO, a LAC-based search engine to identify cohort studies with peer-reviewed publications in regional journals. The search query included country terms (countries in LAC), cohort studies (e.g. cohort stud*), and cardiovascular outcomes (e.g. stroke); the search query is available in Supplementary data p. 02, available as Supplementary data at IJE online. We invited all eligible cohorts to join the CC-LAC requesting access to anonymized individual-level data. Third, enquiries among LAC researchers also helped identify or approach principal investigators of the identified cohorts. LAC members of the Non-Communicable Disease Risk Factor Collaboration (NCD-RisC)20 also helped identify additional data sources. Sources that led to the identification of the collaborating cohort studies are shown in Supplementary data pp. 03–06, available as Supplementary data at IJE online. The project procedures were approved by the Institutional Review Board at Universidad Peruana Cayetano Heredia (UPCH), Lima, Peru. Data sources were included in the CC-LAC database if they had at least a baseline and one follow-up round, included LAC populations living in LAC, and participants were not recruited exclusively based on the presence of a risk factor or disease. No restrictions were set regarding a minimum sample size at baseline or follow-up duration, sex or age profiles. Anonymized individual record data were received, harmonized and pooled by the CC-LAC. During the harmonization process, we excluded participants who by the time of the baseline assessment had had a cardiovascular event (e.g. stroke or myocardial infarction). Implausible values in selected risk factors were excluded as follows:5–7,21 body mass index (BMI <10 kg/m2 or >80 kg/m2), systolic blood pressure (<70 mmHg or >270 mmHg), diastolic blood pressure (<30 mmHg or >150 mmHg), total cholesterol (<1.75 mmol/L or >20.00 mmol/L), high-density lipoprotein (HDL)-cholesterol (<0.40 mmol/L or >5.00 mmol/L), and fasting glucose (<2.50 mmol/L or >30.00 mmol/L) (Supplementary data are available at IJE online p. 07). Notably, BMI was computed based on measured weight and height in all but one Mexican cohort,22 yet the self-reported weight and height closely correlated with measured estimates.23 At the time of writing, i.e. first datalock on 29 March 2020, 32 cohorts with data on cardiovascular outcomes have been pooled. Seven additional cohorts have joined the CC-LAC but are currently working to provide cardiovascular outcomes, thus these 7 cohorts have not been pooled yet. To date, the CC-LAC is therefore a consortium of 39 cohorts in 13 LAC countries (Figure 1). Number of pooled cohorts and participants by country in Latin America and the Caribbean. Based on the 32 pooled cohort units (total sample size = 174 180). A…
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BibTeX
@article{carrillolarco2026cohort,
title = {{Cohort Profile: The Cohorts Consortium of Latin America and the Caribbean (CC-LAC)}},
author = {Rodrigo M And Carrillo-Larco and Mariachiara Di Cesare and Ian R Hambleton and Anselm Hennis and Vilma Irazola and Dalia Stern and Catterina Ferreccio and Paulo Lotufo and Pablo Perel and Edward W Gregg and Majid Ezzati and Goodarz Danaei and J Jaime Miranda and Carlos A Aguilar-Salinas and Ramón Alvarez-Váz and Marselle B Amadio and Cecilia Baccino and Claudia Bambs and João Luiz Bastos and Gloria Beckles and Antonio Bernabe-Ortiz and Carla D O Bernardo and Katia V Bloch and Juan E Blümel and Jose G Boggia and Pollyanna K Borges and Miguel Bravo and Gilbert Brenes-Camacho and Horacio A Carbajal and Maria S Castillo Rascon and Blanca H Ceballos and Veronica Colpani and Susana C Confortin and Jackie A Cooper and Adrian Cortés-Valencia and Sandra Cortes and Roberto S Cunha and Eleonora D’orsi and William H Dow and Walter G Espeche and Flavio D Fuchs and Sandra C Fuchs and Suely G A Gimeno and Donaji Gomez-Velasco and Clicerio Gonzalez-Villalpando and María-Elena Gonzalez-Villalpando and David A Gonzalez-Chica and Gonzalo Grazioli and Ricardo O Guerra and Laura Gutierrez and Fernando L Herkenhoff and Andrea R V R Horimoto and Andrea Huidobro and Elard Koch and Martin Lajous and Maria Fernanda Lima-Costa and Ruy Lopez-Ridaura and Alvaro C C Maciel and Betty S Manrique-Espinoza and Larissa P Marques},
journal = {International Journal of Epidemiology},
year = {2026},
month = oct,
doi = {10.1093/ije/dyaa073},
url = {https://hal.science/hal-05791964v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Cohort Profile: The Cohorts Consortium of Latin America and the Caribbean (CC-LAC)
AU - Rodrigo M And Carrillo-Larco
AU - Mariachiara Di Cesare
AU - Ian R Hambleton
AU - Anselm Hennis
AU - Vilma Irazola
AU - Dalia Stern
AU - Catterina Ferreccio
AU - Paulo Lotufo
AU - Pablo Perel
AU - Edward W Gregg
AU - Majid Ezzati
AU - Goodarz Danaei
AU - J Jaime Miranda
AU - Carlos A Aguilar-Salinas
AU - Ramón Alvarez-Váz
AU - Marselle B Amadio
AU - Cecilia Baccino
AU - Claudia Bambs
AU - João Luiz Bastos
AU - Gloria Beckles
AU - Antonio Bernabe-Ortiz
AU - Carla D O Bernardo
AU - Katia V Bloch
AU - Juan E Blümel
AU - Jose G Boggia
AU - Pollyanna K Borges
AU - Miguel Bravo
AU - Gilbert Brenes-Camacho
AU - Horacio A Carbajal
AU - Maria S Castillo Rascon
AU - Blanca H Ceballos
AU - Veronica Colpani
AU - Susana C Confortin
AU - Jackie A Cooper
AU - Adrian Cortés-Valencia
AU - Sandra Cortes
AU - Roberto S Cunha
AU - Eleonora D’orsi
AU - William H Dow
AU - Walter G Espeche
AU - Flavio D Fuchs
AU - Sandra C Fuchs
AU - Suely G A Gimeno
AU - Donaji Gomez-Velasco
AU - Clicerio Gonzalez-Villalpando
AU - María-Elena Gonzalez-Villalpando
AU - David A Gonzalez-Chica
AU - Gonzalo Grazioli
AU - Ricardo O Guerra
AU - Laura Gutierrez
AU - Fernando L Herkenhoff
AU - Andrea R V R Horimoto
AU - Andrea Huidobro
AU - Elard Koch
AU - Martin Lajous
AU - Maria Fernanda Lima-Costa
AU - Ruy Lopez-Ridaura
AU - Alvaro C C Maciel
AU - Betty S Manrique-Espinoza
AU - Larissa P Marques
PY - 2026
DA - 2026/10/11
JO - International Journal of Epidemiology
DO - 10.1093/ije/dyaa073
UR - https://hal.science/hal-05791964v1
AB - Latin America and the Caribbean (LAC) are characterized by much diversity in terms of socio-economic status, ecology, environment, access to health care,1,2 as well as the frequency of risk factors for and prevalence or incidence of non-communicable diseases;3–7 importantly, these differences are observed both between and within countries in LAC.8,9 LAC countries share a large burden of non-communicable (e.g. diabetes and hypertension) and cardiovascular (e.g. ischaemic heart disease) diseases, with these conditions standing as the leading causes of morbidity, disability and mortality in most of LAC.10–12 These epidemiological estimates—e.g. morbidity—cannot inform about risk factors or risk prediction, which are relevant to identify prevention avenues. Cohort studies, on the other hand, could provide this evidence. Pooled analysis, using data from multiple cohort studies, have additional strengths such as increased statistical power and decreased statistical uncertainty.13 LAC cohort studies have been under-represented,14 or not included at all,15–17 in international efforts aimed at pooling data from multiple cohort studies. We therefore set out to pool data from LAC cohorts to address research questions that individual cohort studies would not be able to answer. Drawing from previous successful regional enterprises (e.g. Asia Pacific Cohort Studies Collaboration),18,19 we established the Cohorts Consortium of Latin America and the Caribbean (CC-LAC). The main aim of the CC-LAC is to start a collaborative cohort data pooling in LAC to examine the association between cardio-metabolic risk factors (e.g. blood pressure, glucose and lipids) and non-fatal and fatal cardiovascular outcomes (e.g. stroke or myocardial infarction). In so doing, we aim to provide regional risk estimates to inform disease burden metrics, as well as other ambitious projects including a cardiovascular risk score to strengthen cardiovascular prevention in LAC. Initial funding has been provided by a fellowship from the Wellcome Trust Centre for Global Health Research at Imperial College London (Strategic Award, Wellcome Trust–Imperial College Centre for Global Health Research, 100693/Z/12/Z). Additional funding is being provided by an International Training Fellowship from the Wellcome Trust (214185/Z/18/Z). At the time of writing, the daily operations and pooled database are hosted at Imperial College London, though a mid-term goal is to transfer this expertise and operations to LAC. The collaboration relies fundamentally on a strong regional network of health researchers and practitioners. We have harmonized and pooled approximately population-based cohort data on cardio-metabolic risk factors and outcomes, i.e. participants were not recruited based on disease (e.g. cohort of stroke survivors) or risk factor (e.g. cohort of smokers) history. Data have been collated by the CC-LAC, a LAC network of health researchers and practitioners. The database was collated using multiple data identification sources. First, we accessed publicly available cohort data through each study’s website or data repository. Second, we conducted a systematic search in Medline, Embase and SciELO, a LAC-based search engine to identify cohort studies with peer-reviewed publications in regional journals. The search query included country terms (countries in LAC), cohort studies (e.g. cohort stud*), and cardiovascular outcomes (e.g. stroke); the search query is available in Supplementary data p. 02, available as Supplementary data at IJE online. We invited all eligible cohorts to join the CC-LAC requesting access to anonymized individual-level data. Third, enquiries among LAC researchers also helped identify or approach principal investigators of the identified cohorts. LAC members of the Non-Communicable Disease Risk Factor Collaboration (NCD-RisC)20 also helped identify additional data sources. Sources that led to the identification of the collaborating cohort studies are shown in Supplementary data pp. 03–06, available as Supplementary data at IJE online. The project procedures were approved by the Institutional Review Board at Universidad Peruana Cayetano Heredia (UPCH), Lima, Peru. Data sources were included in the CC-LAC database if they had at least a baseline and one follow-up round, included LAC populations living in LAC, and participants were not recruited exclusively based on the presence of a risk factor or disease. No restrictions were set regarding a minimum sample size at baseline or follow-up duration, sex or age profiles. Anonymized individual record data were received, harmonized and pooled by the CC-LAC. During the harmonization process, we excluded participants who by the time of the baseline assessment had had a cardiovascular event (e.g. stroke or myocardial infarction). Implausible values in selected risk factors were excluded as follows:5–7,21 body mass index (BMI <10 kg/m2 or >80 kg/m2), systolic blood pressure (<70 mmHg or >270 mmHg), diastolic blood pressure (<30 mmHg or >150 mmHg), total cholesterol (<1.75 mmol/L or >20.00 mmol/L), high-density lipoprotein (HDL)-cholesterol (<0.40 mmol/L or >5.00 mmol/L), and fasting glucose (<2.50 mmol/L or >30.00 mmol/L) (Supplementary data are available at IJE online p. 07). Notably, BMI was computed based on measured weight and height in all but one Mexican cohort,22 yet the self-reported weight and height closely correlated with measured estimates.23 At the time of writing, i.e. first datalock on 29 March 2020, 32 cohorts with data on cardiovascular outcomes have been pooled. Seven additional cohorts have joined the CC-LAC but are currently working to provide cardiovascular outcomes, thus these 7 cohorts have not been pooled yet. To date, the CC-LAC is therefore a consortium of 39 cohorts in 13 LAC countries (Figure 1). Number of pooled cohorts and participants by country in Latin America and the Caribbean. Based on the 32 pooled cohort units (total sample size = 174 180). A…
SN - 0300-5771
SN - 1464-3685
ER -