INTRODUCTION Chronic kidney disease has reached epidemic levels in several Central American countries since the early years of this century. In El Salvador, it is the second cause of death in men, the fi fth in persons over 18 years old and the third cause of hospital deaths in the adult population. Its features, especially those of a subtype unassociated with traditional risk factors such as diabetes and high blood pressure, are only partially understood.OBJECTIVE Estimate the magnitude of chronic kidney disease in the adult population of El Salvador, considering both prevalence of the disease in its diverse forms as well as presence of potential risk factors nationally and in major subpopulations.METHODS A descriptive, cross-sectional analysis was conducted on data obtained from the Survey of Chronic Non-communicable Diseases in Adults in El Salvador, completed in 2015. The original data (interviews and measurements) were collected between October 2014 and March 2015 from 4817 adults employing a two-stage probabilistic cluster sample, with stratifi cation of primary sampling units. Our analysis, using 20 of the 118 primary variables included in the original survey, focused on point estimation of prevalence rates and means, related to both traditional biological risk factors and nontraditional ones, such as insuffi cient hydration, strenuous working conditions and exposure to toxic agents. A separate analysis was performed to estimate prevalence of chronic kidney disease from nontraditional causes. Corresponding confi dence intervals were calculated with proper weighting. RESULTSThe general prevalence of chronic kidney disease in El Salvador was 12.8% (men 18.0%; women 8.7%). Of the chronically ill kidney patients, 13.1% were between 20 and 40 years of age. Among biological risk factors, the most frequent was high blood pressure (37.0%). Among nontraditional risk factors, high levels of sugary drink consumption (81.0%), insuffi cient hydration (65.9%) and high levels of exposure to agrochemicals in the work environment (12.6%) were also observed. Prevalence of chronic kidney disease from nontraditional causes was 3.9% (men 6.1%; women 2.2%).CONCLUSIONS Chronic kidney disease has reached epidemic proportions in El Salvador. The data confi rm a health tragedy that, although especially striking older men, also takes a severe toll on young men and women. The results confi rm fi ndings of previous research in several Salvadoran agricultural communities. The relatively high level of population exposure to agrochemicals is important and alarming, especially in rural areas, meriting health-impact studies that include and go beyond possible impact on chronic kidney disease.
Introduction: technological developments in artificial intelligence and health are necessary for Latin American health systems. Objective: to describe the trends in scientific production on artificial intelligence and health in Latin America in Scopus. Method: This is a retrospective bibliometric study of Latin American authors' scientific production on artificial intelligence and health in Scopus between 2012 and 2021. Production, visibility and impact indicators were used. VOSviewer and SciVal were used for data analysis. Results: 2871 articles were published, with a variation between 2012 and 2021 of 94.98%. 2,397 articles were original, and 2,741 were written in English. 58.3% were published in first-quartile journals, the most productive being Sensors (Ndoc=79) and Plos One (Ndoc=66). 64,128 citations were received (mean of 22.3 citations per article). Brazil was the most productive country (Ndoc=1420), and the institution was the University of São Paulo (Ndoc=288). 498 thematic groups were identified, and 1376 themes. 54% of the articles had international collaboration and 3.3% with academic-corporation collaboration. Conclusions: there is a growing scientific production on artificial intelligence and health in Latin America, written mainly in English, medical, engineering and computer science research areas, disseminated in specialized magazines in the first quartiles. Brazil and its institutions were the top producers. The main topics were predictive models and the application of artificial intelligence for classifying, diagnosing and treating diseases.
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