IDEAS home Printed from https://ideas.repec.org/a/plo/pgph00/0002304.html
   My bibliography  Save this article

Pediatric chronic kidney disease mortality in Brazil—A time trend analysis

Author

Listed:
  • Arnauld Kaufman
  • André L Barreira
  • Marcelo G P Land

Abstract

Chronic kidney disease (CKD) is defined based on structural or functional abnormalities of the kidneys, or a glomerular filtration rate (GFR) below the threshold of 60 ml/min per 1.73 m2 for more than 3 months. It is an important noncommunicable disease with a rising worldwide, becoming a global public health problem. There are few studies about this problem, especially in low- and middle-income countries (LMIC), including Brazil, an upper-middle-income country. The objective of the study was to determine the cause-specific mortality rates for pediatric CKD patients (CKDMR) from 0 to 19 years old, based on the 10th revision of the International Classification of Diseases (ICD-10) and the Global Burden of Diseases Injuries and Risk Factors Study’s (GBD) list. We calculated the impact of the annual human development indexes (HDI) in CKDMR in Brazil and its regions at two different times and compared it with the literature results. We obtained data from the Department of Informatics of the Brazilian Unified Health System (DATASUS) from 1996 to 2017. The Joinpoint regression analyses estimated the average annual percentage changes (AAPCs). The correlation between the HDI values and the number of deaths from each age group in Brazil and its different regions were assessed using the time series autoregressive integrated moving average (ARIMA) models. There were 8838 deaths in a pediatric and adolescent population of about 1.485 x 109 person-years observed in Brazil from 1996 to 2017. Our results demonstrated a significant increase in the AAPC in Brazil’s less than 1-year-old age group and a decrease in children from 5 to 19 years old. We observed a positive correlation between CKDMR and HDI among children under 1 year of age. Conversely, there is a negative association in the age groups ranging from 5 to 19 years, indicating an inverse relationship between CKDMR and HDI.

Suggested Citation

  • Arnauld Kaufman & André L Barreira & Marcelo G P Land, 2024. "Pediatric chronic kidney disease mortality in Brazil—A time trend analysis," PLOS Global Public Health, Public Library of Science, vol. 4(1), pages 1-14, January.
  • Handle: RePEc:plo:pgph00:0002304
    DOI: 10.1371/journal.pgph.0002304
    as

    Download full text from publisher

    File URL: https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0002304
    Download Restriction: no

    File URL: https://journals.plos.org/globalpublichealth/article/file?id=10.1371/journal.pgph.0002304&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pgph.0002304?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Nancy R. Zhang & David O. Siegmund, 2007. "A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data," Biometrics, The International Biometric Society, vol. 63(1), pages 22-32, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yana Melnykov & Marcus Perry, 2024. "On Robust Change Point Detection and Estimation in Multisubject Studies," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(2), pages 827-879, August.
    2. Yann Guédon, 2013. "Exploring the latent segmentation space for the assessment of multiple change-point models," Computational Statistics, Springer, vol. 28(6), pages 2641-2678, December.
    3. Debi P Bal & Badri N Rath, 2019. "Nonlinear causality between crude oil price and exchange rate: A comparative study of China and India - A Reassessment," Economics Bulletin, AccessEcon, vol. 39(1), pages 592-604.
    4. Davis, Richard A. & Hancock, Stacey A. & Yao, Yi-Ching, 2016. "On consistency of minimum description length model selection for piecewise autoregressions," Journal of Econometrics, Elsevier, vol. 194(2), pages 360-368.
    5. Kurozumi, Eiji & Tuvaandorj, Purevdorj, 2011. "Model selection criteria in multivariate models with multiple structural changes," Journal of Econometrics, Elsevier, vol. 164(2), pages 218-238, October.
    6. Fryzlewicz, Piotr, 2020. "Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection," LSE Research Online Documents on Economics 103430, London School of Economics and Political Science, LSE Library.
    7. Lee Jaeeun & Chen Jie, 2019. "A penalized regression approach for DNA copy number study using the sequencing data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(4), pages 1-14, August.
    8. Neil Kellard & Denise Osborn & Jerry Coakley & Alastair R. Hall & Denise R. Osborn & Nikolaos Sakkas, 2015. "Structural Break Inference Using Information Criteria in Models Estimated by Two-Stage Least Squares," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 741-762, September.
    9. Arnaud Dufays & Elysee Aristide Houndetoungan & Alain Coën, 2022. "Selective Linear Segmentation for Detecting Relevant Parameter Changes [Risks and Portfolio Decisions Involving Hedge Funds]," Journal of Financial Econometrics, Oxford University Press, vol. 20(4), pages 762-805.
    10. Chulwoo Han & Abderrahim Taamouti, 2017. "Partial Structural Break Identification," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(2), pages 145-164, April.
    11. Lu Shaochuan, 2020. "Bayesian multiple changepoints detection for Markov jump processes," Computational Statistics, Springer, vol. 35(3), pages 1501-1523, September.
    12. Lu Shaochuan, 2023. "Scalable Bayesian Multiple Changepoint Detection via Auxiliary Uniformisation," International Statistical Review, International Statistical Institute, vol. 91(1), pages 88-113, April.
    13. Harris, David & Kew, Hsein & Taylor, A.M. Robert, 2020. "Level shift estimation in the presence of non-stationary volatility with an application to the unit root testing problem," Journal of Econometrics, Elsevier, vol. 219(2), pages 354-388.
    14. Brennen T. Fagan & Marina I. Knight & Niall J. MacKay & A. Jamie Wood, 2020. "Change point analysis of historical battle deaths," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 909-933, June.
    15. Guédon, Yann & Legave, Jean Michel, 2008. "Analyzing the time-course variation of apple and pear tree dates of flowering stages in the global warming context," Ecological Modelling, Elsevier, vol. 219(1), pages 189-199.
    16. Picard, F. & Lebarbier, E. & Budinskà, E. & Robin, S., 2011. "Joint segmentation of multivariate Gaussian processes using mixed linear models," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1160-1170, February.
    17. Brigida, Matt & Pratt, William R., 2017. "Fake news," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 564-573.
    18. Jaromír Antoch & Daniela Jarušková, 2013. "Testing for multiple change points," Computational Statistics, Springer, vol. 28(5), pages 2161-2183, October.
    19. Sean Jewell & Paul Fearnhead & Daniela Witten, 2022. "Testing for a change in mean after changepoint detection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1082-1104, September.
    20. Cho, Haeran & Kirch, Claudia, 2024. "Data segmentation algorithms: Univariate mean change and beyond," Econometrics and Statistics, Elsevier, vol. 30(C), pages 76-95.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pgph00:0002304. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: globalpubhealth (email available below). General contact details of provider: https://journals.plos.org/globalpublichealth .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.