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Food insecurity status is of added value in explaining poor health: a cross-sectional study among parents living in disadvantaged neighbourhoods in the Netherlands

OBJECTIVES: The aim of this study was to examine the added value of food insecurity in explaining poor physical and mental health beyond other socioeconomic risk factors. DESIGN, SETTING, PARTICIPANTS AND OUTCOME MEASURES: Data for this cross-sectional study were collected using questionnaires with...

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Detalles Bibliográficos
Autores principales: van der Velde, Laura A, Steyerberg, Ewout W, Numans, Mattijs E, Kiefte-de Jong, Jessica C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8830251/
https://www.ncbi.nlm.nih.gov/pubmed/35140151
http://dx.doi.org/10.1136/bmjopen-2021-052827
Descripción
Sumario:OBJECTIVES: The aim of this study was to examine the added value of food insecurity in explaining poor physical and mental health beyond other socioeconomic risk factors. DESIGN, SETTING, PARTICIPANTS AND OUTCOME MEASURES: Data for this cross-sectional study were collected using questionnaires with validated measures for food insecurity status and health status, including 199 adult participants with at least 1 child living at home, living in or near disadvantaged neighbourhoods in The Hague, the Netherlands. To assess the added value of food insecurity, optimism-corrected goodness-of-fit statistics of multivariate regression models with and without food insecurity status as a covariate were compared. RESULTS: In the multivariable models explaining poor physical health (Physical Component Summary: PCS) and mental health (Mental Component Summary: MCS), from all included socioeconomic risk factors, food insecurity score was the most important covariate. Including food insecurity score in those models led to an improvement of explained variance from 6.3% to 9.2% for PCS, and from 5.8% to 11.0% for MCS, and a slightly lower root mean square error. Further analyses showed that including food insecurity score improved the discriminative ability between those individuals most at risk of poor health, reflected by an improvement in C-statistic from 0.64 (95% CI 0.59 to 0.71) to 0.69 (95% CI 0.62 to 0.73) for PCS and from 0.65 (95% CI 0.55 to 0.68) to 0.70 (95% CI 0.61 to 0.73) for MCS. Further, explained variance in these models improved with approximately one-half for PCS and doubled for MCS. CONCLUSIONS: From these results it follows that food insecurity score is of added value in explaining poor physical and mental health beyond traditionally used socioeconomic risk factors (ie, age, educational level, income, living situation, employment status and migration background) in disadvantaged communities. Therefore, routine food insecurity screening may be important for effective risk stratification to identify populations at increased risk of poor health and provide targeted interventions.