Back

Prevalence of chronic kidney disease in the general population in Latin America and the Caribbean: Protocol for a systematic review and meta-analysis

Villa Ato, C. S.; Weiss, M.; Sequeiros, D. A.; Carrillo-Larco, R. M.

2021-01-28 epidemiology
10.1101/2021.01.26.21250540 medRxiv
Show abstract

BackgroundChronic kidney disease (CKD) is a global health issue with a general prevalence of 9%. Although the most affected populations are in low- and middle-income countries, the epidemiology of CKD in these countries remains poorly understood and prevalence estimates come from global efforts informed by data from high-income countries; these prevalence estimates need to be compared -and if needed updated-with local estimates. ObjectiveTo estimate the prevalence of CKD in adults in Latin America and the Caribbean (LAC). MethodsSystematic review and meta-analysis. We will search Embase, Medline, Global Health (these three through Ovid), Scopus and LILACS. No date or language restrictions will be set. We seek observational studies with a random sample of the general population. We will screen titles and abstracts, we will then study the selected reports. Both phases will be done by two reviewers independently. Data extraction will be performed by two researchers independently using a pre-specified Excel form. We will evaluate the risk of bias with the scale proposed by Hoy et al. for prevalence studies. We will conduct a meta-analysis of prevalence estimates, if there are at least three reports homogeneous enough to be pooled; we will use a random-effects model. ConclusionsThis systematic review and meta-analysis will provide the prevalence of CKD in adults in countries of LAC. Currently, information regarding CKD in the region is limited. This work will provide evidence to elucidate the magnitude of CKD prevalence in LAC. In so doing, we will provide evidence to inform the scientific community about the burden of CKD in LAC so that research, policies and health interventions can be planned accordingly.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.