Evidence map›Paper›PMID 38332937›Full record

ArticleLancet regional health. Americas2024

Promoting healthy populations as a pandemic preparedness strategy: a simulation study from Mexico.

Rob Johnson, Martha Carnalla, Ana Basto-Abreu, David Haw, Christian Morgenstern, Patrick Doohan, Giovanni Forchini, Katharina D Hauck, Tonatiuh Barrientos-Gutiérrez

Open access · goldAbstract read
In one paragraph

Article in Lancet regional health. Americas, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.7field-weighted citation impact, top 28% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
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 at 3 institutions in 3 countries.

Rob JohnsonMRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute for Disease and Emergency Analytics, Imperial College London, UK.
Martha CarnallaCentro de Salud en Investigación Poblacional, Instituto Nacional de Salud Pública, Cuernavaca, México.
Ana Basto-AbreuCentro de Salud en Investigación Poblacional, Instituto Nacional de Salud Pública, Cuernavaca, México.
David HawDepartment of Mathematical Sciences and Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, UK.
Christian MorgensternMRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute for Disease and Emergency Analytics, Imperial College London, UK.
Patrick DoohanMRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute for Disease and Emergency Analytics, Imperial College London, UK.
Giovanni ForchiniMRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute for Disease and Emergency Analytics, Imperial College London, UK.
Katharina D HauckMRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute for Disease and Emergency Analytics, Imperial College London, UK.
Tonatiuh Barrientos-GutiérrezCentro de Salud en Investigación Poblacional, Instituto Nacional de Salud Pública, Cuernavaca, México.
Imperial College London · GBInstituto Nacional de Salud Pública · MXUniversity of Liverpool · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The underlying health status of populations was a major determinant of the impact of the COVID-19 pandemic, particularly obesity prevalence. Mexico was one of the most severely affected countries during the COVID-19 pandemic and its obesity prevalence is among the highest in the world. It is unknown by how much the COVID-19 burden could have been reduced if systemic actions had been implemented to reduce excess weight in Mexico before the onset of the pandemic. Methods: Using a dynamic epidemic model based on nationwide data, we compare actual deaths with those under hypothetical scenarios assuming a lower body mass index in the Mexican population, as observed historically. We also model the number of deaths that would have been averted due to earlier implementation of front-of-pack warning labels or due to increases in taxes on sugar-sweetened beverages and non-essential high-energy foods in Mexico. Findings: We estimate that 52.5% (95% prediction interval (PI) 43.2, 61.6%) of COVID-19 deaths were attributable to obesity for adults aged 20-64 and 23.8% (95% PI 18.7, 29.1%) for those aged 65 and over. Had the population BMI distribution remained as it was in 2000, 2006, or 2012, COVID-19 deaths would have been reduced by an expected 20.6% (95% PI 16.9, 24.6%), 9.9% (95% PI 7.3, 12.9%), or 6.9% (95% PI 4.5, 9.5%), respectively. If the food-labelling intervention introduced in 2020 had been introduced in 2018, an expected 6.2% (95% PI 5.2, 7.3%) of COVID-19 deaths would have been averted. If taxes on sugar-sweetened beverages and high-energy foods had been doubled, trebled, or quadrupled in 2018, COVID-19 deaths would have been reduced by an expected 4.1% (95% PI 2.5, 5.7%), 7.9% (95% PI 4.9, 11.0%), or 11.6% (95% PI 7.3, 15.8%), respectively. Interpretation: Public health interventions targeting underlying population health, including non-communicable chronic diseases, is a promising line of action for pandemic preparedness that should be included in all pandemic plans. Funding: This study received funding from Bloomberg Philanthropies, awarded to Juan A. Rivera from the National Institute of Public Health; Community Jameel, the UK Medical Research Council (MRC), Kenneth C Griffin, and the World Health Organization.

Indexed as

BMICOVID-19Epidemic response planObesityPandemic preparednessPopulation health

Identifiers

PMID38332937
PMCPMC10850772
OpenAlexW4391482401

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.