Socioeconomic inequality in clusters of health-related behaviours in Europe: latent class analysis of a cross-sectional European survey

Abstract

BackgroundModifiable health-related behaviours tend to cluster among most vulnerable sectors of the population, particularly those at the bottom of the social hierarchy. This study aimed to identify the clusters of health-related behaviours in 27 European countries and to examine the socioeconomic inequalities in these clusters.MethodsData were from Eurobarometer 72.3–2009, a cross-sectional survey of 27 European countries. The analyses were conducted in 2016. The main sections of the survey included questions pertaining to sociodemographic factors, health-related behaviours, and use of services. In this study, those aged 18 years and older were included. We selected five health-related behaviours, namely smoking, excessive alcohol consumption, frequent fresh fruit consumption, physical activity and dental check-ups. Socioeconomic position was indicated by education, subjective social status and difficulty in paying bills. Latent class analysis was conducted to explore the clusters of these five behaviours. Multinomial logistic regression model was used to examine the relationships between the clusters and socioeconomic positions adjusting for age, gender, marital status and urbanisation.ResultsThe eligible total population was 23,842. Latent class analysis identified three clusters; healthy, moderate and risky clusters in this European population. Individuals with the lowest socioeconomic position were more likely to have risky and moderate clusters than healthy cluster compared to those with the highest socioeconomic position.ConclusionsThere were clear socioeconomic gradients in clusters of health-related behaviours. The findings highlight the importance of adopting interventions that address multiple health risk behaviours and policies that tackle the social determinants of health-related behaviours.

Associations of health-behavior patterns, mental health and self-rated health

“…A better understanding of how sitting and sleep co-occur with diet, physical activity, alcohol consumption and smoking may assist in identifying populations who are at higher risk of poor health outcomes. Further, exploring how identified behavioral patterns vary by sociodemographic and psychological factors may add valuable context for the development and targeting of more efficacious health interventions and public health messaging 23,[37][38][39][40] . The primary aim of this study was to explore how diet quality, alcohol consumption, smoking status, physical activity, sitting time, and sleep quality, duration, and latency co-occur, and investigate the relationships between the identified behavioral patterns and mental and selfrated health.…”

Socioeconomic inequalities in dental behaviors among Korean adults

“…Several studies have demonstrated an inverse relationship between unfavorable health-related behaviors and oral health in the context of socioeconomic disparities, 11,[14][15][16][17][18][19][20][21][22][23][24] which used various indicators for socioeconomic status such as the Index of Relative Socioeconomic Disadvantage (IRSD), 14 the poverty-income ratio, 15 and the Socioeconomic Disadvantage Index (SDI). 16 However, other studies have utilized household income 18 and/or head of household's social class.…”

“…20 Various attempts to clarify the association between socioeconomic inequality and oral health related behaviors have also been made by clustering oral health behaviors. 23,24 To identify the socioeconomic disparities in dental behaviors that play an important role on oral health status, the authors of this study selected income and education factors that represent different dimensions of "causes of the causes" leading to oral health outcomes based on Andersen's behavioral models. 11,12 Each of the two variables as well as the combination of those components were utilized as independent variables.…”

Regionální diferenciace (geo)demografických ukazatelů nemocnosti na příkladu Jihomoravského kraje

“…), longitudinálních studií (např. Kino et al, 2017) a dotazníkových šetření (Žmuk, 2015;Minicuci et al, 2016 aj.). Pro hodnocení zdraví, nemocnosti a úmrtnosti se používají jednak sekundární data národních a regionálních statistik, a dále se vychází z výběrových šetření, která se vztahují zejména k oblasti determinantů zdraví.…”