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

BMI COVID-19 Epidemic response plan Obesity Pandemic preparedness Population health

Journal

Lancet regional health. Americas
ISSN: 2667-193X
Titre abrégé: Lancet Reg Health Am
Pays: England
ID NLM: 9918232503006676

Informations de publication

Date de publication:
Feb 2024
Historique:
received: 30 07 2023
revised: 09 01 2024
accepted: 15 01 2024
medline: 9 2 2024
pubmed: 9 2 2024
entrez: 9 2 2024
Statut: epublish

Résumé

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. 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. 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. 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. 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.

Sections du résumé

Background UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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.

Identifiants

pubmed: 38332937
doi: 10.1016/j.lana.2024.100682
pii: S2667-193X(24)00009-7
pmc: PMC10850772
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100682

Informations de copyright

© 2024 Published by Elsevier Ltd.

Déclaration de conflit d'intérêts

KDH declares receipt of personal fees from WHO, Pfizer and GSK for work unrelated to this study, Payments for expert testimony by Infected Blood Inquiry (UK) and stocks from Astra Zeneca. GF declares receipt of personal fees from WHO and Imperial College London for consultancies. RJ and PD declare receipt of personal fees from WHO for consultancies on pandemic vulnerabilities and integrated epidemiological—economic modelling, respectively.

Auteurs

Rob Johnson (R)

MRC 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 Carnalla (M)

Centro de Salud en Investigación Poblacional, Instituto Nacional de Salud Pública, Cuernavaca, México.

Ana Basto-Abreu (A)

Centro de Salud en Investigación Poblacional, Instituto Nacional de Salud Pública, Cuernavaca, México.

David Haw (D)

Department of Mathematical Sciences and Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, UK.

Christian Morgenstern (C)

MRC 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 Doohan (P)

MRC 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 Forchini (G)

MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute for Disease and Emergency Analytics, Imperial College London, UK.
USBE, Umeå Universitet, Umeå, Sweden.

Katharina D Hauck (KD)

MRC 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-Gutiérrez (T)

Centro de Salud en Investigación Poblacional, Instituto Nacional de Salud Pública, Cuernavaca, México.

Classifications MeSH