1. Epidemiology and Global Health
  2. Medicine
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Development, validation and application of a machine learning model to estimate salt consumption in 54 countries

  1. Wilmer Cristobal Guzman-Vilca
  2. Manuel Castillo-Cara
  3. Rodrigo M Carrillo-Larco  Is a corresponding author
  1. Universidad Peruana Cayetano Heredia, Peru
  2. Universidad de Lima, Peru
  3. Imperial College London, United Kingdom
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Cite this article as: eLife 2022;11:e72930 doi: 10.7554/eLife.72930

Abstract

Global targets to reduce salt intake have been proposed but their monitoring is challenged by the lack of population-based data on salt consumption. We developed a machine learning (ML) model to predict salt consumption at the population level based on simple predictors and applied this model to national surveys in 54 countries. We used 21 surveys with spot urine samples for the ML model derivation and validation; we developed a supervised ML regression model based on: sex, age, weight, height, systolic and diastolic blood pressure. We applied the ML model to 54 new surveys to quantify the mean salt consumption in the population. The pooled dataset in which we developed the ML model included 49,776 people. Overall, there were no substantial differences between the observed and ML-predicted mean salt intake (p<0.001). The pooled dataset where we applied the ML model included 166,677 people; the predicted mean salt consumption ranged from 6.8 g/day (95% CI: 6.8-6.8 g/day) in Eritrea to 10.0 g/day (95% CI: 9.9-10.0 g/day) in American Samoa. The countries with the highest predicted mean salt intake were in Western Pacific. The lowest predicted intake was found in Africa. The country-specific predicted mean salt intake was within reasonable difference from the best available evidence. A ML model based on readily available predictors estimated daily salt consumption with good accuracy. This model could be used to predict mean salt consumption in the general population where urine samples are not available.

Data availability

This study used nationally-representative survey data that are in the public domain, which was requested through the online repository (https://extranet.who.int/ncdsmicrodata/index.php/home). We provide the analysis code of data preparation and data analysis as supplementary materials to this paper (Source Code File - "Analysis Code | Python and R")

The following previously published data sets were used

Article and author information

Author details

  1. Wilmer Cristobal Guzman-Vilca

    Universidad Peruana Cayetano Heredia, Lima, Peru
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2194-8496
  2. Manuel Castillo-Cara

    Universidad de Lima, Lima, Peru
    Competing interests
    The authors declare that no competing interests exist.
  3. Rodrigo M Carrillo-Larco

    Imperial College London, London, United Kingdom
    For correspondence
    rcarrill@ic.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2090-1856

Funding

Wellcome Trust (214185/Z/18/Z)

  • Rodrigo M Carrillo-Larco

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Gian Paolo Rossi, University of Padua, Italy

Publication history

  1. Received: August 9, 2021
  2. Preprint posted: September 2, 2021 (view preprint)
  3. Accepted: December 15, 2021
  4. Accepted Manuscript published: January 5, 2022 (version 1)

Copyright

© 2022, Guzman-Vilca et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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