Predictors of persistent moderate and severe food insecurity in a longitudinal survey in Mexico during the Covid-19 pandemic

Bibliographic Data

ID22067243
AuthorsPablo Gaitán-Rossi (0000-0002-7617-6366, Ibero American University), Alan Martín Hernández Solano (0000-0002-1112-2885, Ibero American University, corresponding author), Alan Hernández-Solano, Vitervo López-Caballero (Tecnológico Nacional de México), René Zurita-Corro (Ibero American University), Ximena García-Ruiz (Ibero American University), Víctor Hugo Pérez Hernández (0000-0003-0960-7821, Ibero American University), Víctor Pérez-Hernández, Mireya Vilar-Compte (0000-0001-9047-1102, Montclair State University)
Year2024
Volume12
Pages1374815-1374815
Publication date2024-06-26
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2024.1374815
PMID38989123
OpenAlexW4400059469
LanguageEN
References cited32

Background Household food insecurity (HFI) increased in Latin America by 9% between 2019 and 2020. Scant evidence shows who was unable to recover from the COVID-19 pandemic. Our aim was to use a Machine Learning (ML) approach to identify consistent and influential predictors of persistent moderate or severe HFI over 2 years. Methods We use a three-wave longitudinal telephone survey with a probabilistic sample representative of the Mexican population. With a response rate of 51.3 and 60.8% for the second and third waves, the final sample size consisted of 1,074 individuals. The primary outcome was persistent HFI, i.e., respondents who reported moderate or severe HFI in 2021 and 2022. Twelve income-related predictors were measured in 2020, including baseline HFI. We employed 6 supervised ML algorithms to cross-validate findings in models, examined its precision with 4 standard performance indicators to assess precision, and used SHAP values (Shapley Additive exPlanations) to identify influential predictors in each model. Results Prevalence of persistent moderate/severe HFI in 2021 and 2022 was 8.8%. Models with only a HFI 2020 baseline measure were used as a reference for comparisons; they had an accuracy of 0.79, a Cohen’s Kappa of 0.57, a sensitivity of 0.68, and a specificity of 0.88. When HFI was substituted by the suite of socioeconomic indicators, accuracy ranged from 0.70 to 0.84, Cohen’s Kappa from 0.40 to 0.67, sensitivity from 0.86 to 0.90, and specificity from 0.75 to 0.82. The best performing models included baseline HFI and socioeconomic indicators; they had an accuracy between 0.81 and 0.92, a Cohen’s Kappa between 0.61 and 0.85, a sensitivity from 0.74 to 0.95, and a specificity from 0.85 to 0.92. Influential and consistent predictors across the algorithms were baseline HFI, socioeconomic status (SES), adoption of financial coping strategies, and receiving government support. Discussion Persistent HFI can be a relevant indicator to identify households that are less responsive to food security policies. These households should be prioritized for innovative government support and monitored to assess changes. Forecasting systems of HFI can be improved with longitudinal designs including baseline measures of HFI and socioeconomic predictors

Environmental health · Kappa · Pandemic · Population · Socioeconomic status · Agricultural risk and resilience · COVID-19 Pandemic Impacts · Demography · Food Security and Health in Diverse Populations · Mathematics · Medicine · Internal Medicine

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