Natalia Soldevila-Domenech1,2,3, Carlos G Forero4, Itxaso Alayo1,5, Jordina Capella6,7, Joan Colom6, Davide Malmusi5,8, Anna Mompart9, Philippe Mortier1,5, Beatriz Puértolas1,5, Néstor Sánchez4, Anna Schiaffino9,10, Gemma Vilagut11,12, Jordi Alonso13,14,15. 1. Health Services Research Group, Epidemiology and Public Health Program, Hospital del Mar Medical Research Institute (IMIM), Carrer del Doctor Aiguader, 88, PRBB Building, 08003, Barcelona, Spain. 2. Integrative Pharmacology and Systems Neurosciences Research Group, Neurosciences Research Program, Hospital del Mar Medical Research Institute (IMIM), Carrer del Doctor Aiguader, 88, PRBB Building, 08003, Barcelona, Spain. 3. Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), Carrer del Doctor Aiguader, 88, PRBB Building, 08003, Barcelona, Spain. 4. School of Medicine, Universitat Internacional de Catalunya (UIC), Barcelona, Spain. 5. CIBER en Epidemiología y Salud Pública (CIBERESP), Av. Monforte de Lemos, 3-5, 28029, Madrid, Spain. 6. Programme on substance Abuse, Agency of Public Health of Catalonia, Carrer Roc Boronat, 81-95, 08005, Barcelona, Spain. 7. Direcció General d'Ordenació i Regulació Sanitàries, Departament de Salut, Generalitat de Catalunya, Travessera de les Corts, 131-159, 08028, Barcelona, Spain. 8. Ajuntament de Barcelona, Barcelona, Spain. 9. Direcció General de Planificació en Salut, Departament de Salut, Generalitat de Catalunya, Travessera de les Corts, 131-159, 08028, Barcelona, Spain. 10. Institut Català D'Oncologia, Gran Via de l'Hospitalet 199-203, 08908, l'Hospitalet de Llobregat, Spain. 11. Health Services Research Group, Epidemiology and Public Health Program, Hospital del Mar Medical Research Institute (IMIM), Carrer del Doctor Aiguader, 88, PRBB Building, 08003, Barcelona, Spain. gvilagut@imim.es. 12. CIBER en Epidemiología y Salud Pública (CIBERESP), Av. Monforte de Lemos, 3-5, 28029, Madrid, Spain. gvilagut@imim.es. 13. Health Services Research Group, Epidemiology and Public Health Program, Hospital del Mar Medical Research Institute (IMIM), Carrer del Doctor Aiguader, 88, PRBB Building, 08003, Barcelona, Spain. jalonso@imim.es. 14. Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), Carrer del Doctor Aiguader, 88, PRBB Building, 08003, Barcelona, Spain. jalonso@imim.es. 15. CIBER en Epidemiología y Salud Pública (CIBERESP), Av. Monforte de Lemos, 3-5, 28029, Madrid, Spain. jalonso@imim.es.
Abstract
PURPOSE: The aim of this study was to analyse the association between individual mental well-being and social, economic, lifestyle and health factors. METHODS: Cross-sectional study on a representative sample of 13,632 participants (> 15y/o) from the Catalan Health Interview Survey 2013-2016 editions. Mental well-being was assessed with the Warwick-Edinburg Mental Well-being Scale (WEMWBS). Linear regressions were fitted to associate well-being and sociodemographic, relational, lifestyle and health variables according to minimally sufficient adjustment sets identified using directed acyclic graphs. Predictors entered the model in blocks of variable types and analysed individually. Direct and total effects were estimated. RESULTS: Health factors significantly contributed to mental well-being variance. Presence of a mental disorder and self-reported health had the largest effect size (eta2 = 13.4% and 16.3%). The higher individual impact from a variable came from social support (β = - 12.8, SE = 0.48, eta2 = 6.3%). A noticeable effect gradient (eta2 = 4.2%) from low to high mental well-being emerged according to economic difficulties (from β = 1.59, SE = 0.33 for moderate difficulties to β = 6.02 SE = 0.55 for no difficulties). Younger age (β = 5.21, SE = 0.26, eta2 = 3.4%) and being men (β = 1.32, SE = 0.15, eta2 = 0.6%) were associated with better mental well-being. Direct gender effects were negligible. CONCLUSIONS: This study highlights health and social support as the most associated factors with individual mental well-being over socioeconomic factors. Interventions and policies aimed to these factors for health promotion would improve population mental well-being.
PURPOSE: The aim of this study was to analyse the association between individual mental well-being and social, economic, lifestyle and health factors. METHODS: Cross-sectional study on a representative sample of 13,632 participants (> 15y/o) from the Catalan Health Interview Survey 2013-2016 editions. Mental well-being was assessed with the Warwick-Edinburg Mental Well-being Scale (WEMWBS). Linear regressions were fitted to associate well-being and sociodemographic, relational, lifestyle and health variables according to minimally sufficient adjustment sets identified using directed acyclic graphs. Predictors entered the model in blocks of variable types and analysed individually. Direct and total effects were estimated. RESULTS: Health factors significantly contributed to mental well-being variance. Presence of a mental disorder and self-reported health had the largest effect size (eta2 = 13.4% and 16.3%). The higher individual impact from a variable came from social support (β = - 12.8, SE = 0.48, eta2 = 6.3%). A noticeable effect gradient (eta2 = 4.2%) from low to high mental well-being emerged according to economic difficulties (from β = 1.59, SE = 0.33 for moderate difficulties to β = 6.02 SE = 0.55 for no difficulties). Younger age (β = 5.21, SE = 0.26, eta2 = 3.4%) and being men (β = 1.32, SE = 0.15, eta2 = 0.6%) were associated with better mental well-being. Direct gender effects were negligible. CONCLUSIONS: This study highlights health and social support as the most associated factors with individual mental well-being over socioeconomic factors. Interventions and policies aimed to these factors for health promotion would improve population mental well-being.
Entities:
Keywords:
Directed acyclic graphs; Health determinants; Health survey; Mental health; WEMWBS; Well-being