Evidence map›Paper›PMID 32213523›Full record

ArticleBMJ open2020

Overview of the burden of chronic kidney disease in Mexico: secondary data analysis based on the Global Burden of Disease Study 2017.

Marcela Agudelo-Botero, Rafael Valdez-Ortiz, Liliana Giraldo-Rodríguez, María Cecilia González-Robledo, Dolores Mino-León, María Fernanda Rosales-Herrera, Lucero Cahuana-Hurtado, Mario Enrique Rojas-Russell, Claudio Alberto Dávila-Cervantes

Abstract read
In one paragraph

Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed.

  1. Article
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  7. [Association of serum vitamin D and acute renal graft dysfunction].Revista medica del Instituto Mexicano del Seguro Social · 2025
    Observational
  8. Article
  9. [Elevated neutrophil-lymphocyte ratio and delayed graft function].Revista medica del Instituto Mexicano del Seguro Social · 2025
    Observational
  10. Inflammation in glomerular diseases.Frontiers in immunology · 2025
    Review
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Observational
  17. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Marcela Agudelo-BoteroPolitics, Population and Health Research Center, School of Medicine, National Autonomous University of Mexico, Mexico City, Mexico.ORCID 0000-0002-4489-4224
Rafael Valdez-OrtizService of Nephrology, General Hospital of Mexico, Dr. Eduardo Liceaga, Mexico City, Mexico.ORCID 0000-0003-3504-5869
Liliana Giraldo-RodríguezDemographic Epidemiology and Social Determinants Department, Subdirection of Research on Geriatrics, National Institute of Geriatrics, Mexico City, Mexico.ORCID 0000-0001-6541-865X
María Cecilia González-RobledoCenter for Research in Health Systems, National Institute of Public Health, Cuernavaca, Morelos, Mexico.ORCID 0000-0002-4987-3684
Dolores Mino-LeónResearch Unit in Clinic Epidemiology, Specialties Hospital of the National Medical Centre SXXI, Mexican Institute of Social Security, Mexico City, Mexico.ORCID 0000-0002-5144-2728
María Fernanda Rosales-HerreraPolitics, Population and Health Research Center, School of Medicine, National Autonomous University of Mexico, Mexico City, Mexico.
Lucero Cahuana-HurtadoSchool of Public Health and Administration, Cayetano Heredia University, Lima, Peru.ORCID 0000-0002-9438-1443
Mario Enrique Rojas-RussellFaculty of Higher Studies Zaragoza, National Autonomous University of Mexico, Mexico City, Mexico.ORCID 0000-0001-6644-1384
Claudio Alberto Dávila-CervantesLatin American Faculty of Social Sciences, Mexico City, Mexico claudio.davila@flacso.edu.mx.ORCID 0000-0002-7656-3606

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo describe the evolution of the burden of chronic kidney disease (CKD) in Mexico by states, sex and subtypes from 1990 to 2017.

designSecondary data analysis based on the Global Burden of Disease Study (GBD) 2017.

participantsMexico and its 32 states. Data were publicly available and de-identified and individuals were not involved.

methodsWe analysed age-standardised mortality rates, years of life lost (YLL) due to premature death, years lived with disability (YLD) and disability-adjusted life years (DALY), as well as the percentage of change of these indicators between 1990 and 2017.

resultsFrom 1990 to 2017, the number of deaths, YLL, YLD and DALY due to CKD increased from 12 395 to 65 033, from 330 717 to 1 544 212, from 86 416 to 210 924 and from 417 133 to 1 755 136, respectively. Age-standardised rates went from 28.7 to 58.1 for deaths (% of change 102.3), from 601.2 to 1296.7 for YLL (% of change 115.7), from 158.3 to 175.4 for YLD (% of change 10.9) and from 759.4 to 1472.2 for DALY (% of change 93.8). The highest burden of CKD was for Puebla and the lowest for Sinaloa. It was also greater for men than women. By subtypes of CKD, diabetes and hypertension were the causes that contributed most to the loss of years of healthy life in the Mexican population.

conclusionsMexico has experienced exponential and unprecedented growth in the burden of CKD with significant differences by states, sex and subtypes. Data from the GBD are key inputs to guide decision-making and focus efforts towards the reduction of inequities in CKD. These results should be considered a valuable resource that can help guide the epidemiological monitoring of this disease and prioritise the most appropriate health interventions.

Indexed as

FemaleGlobal Burden of DiseaseHealth StatusHumansMaleMexicoRenal Insufficiency, ChronicResidence CharacteristicsSex FactorsSocioeconomic Factorsepidemiologyhealth policynephrologypublic health

Identifiers

PMID32213523
PMCPMC7170614

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.