Impact of socioeconomic- and lifestyle-related risk factors on poor mental health conditions: A nationwide longitudinal 5-wave panel study in Japan

Abstract

The association of socioeconomic status and lifestyle behaviours on mental health appears well-established in the literature, as several studies report that better socioeconomic status such as higher levels of disposable income and employment as well as practising healthy lifestyles can enhance mental well-being. However, the reliance on cross-sectional correlations and lack of adequate statistical controls are possible limitations. This study aims to add the evidence of longitudinal association to the literature by using Japanese representative longitudinal household panel data. We employed panel data analytical techniques such as the random-effects conditional logistic regression (RE-CLR) and the fixed-effects conditional logistic regression (FE-CLR) models with possible time variant confounders being controlled. Our sample was comprised of 14,717 observations of 3,501 individuals aged 22-59 years for five waves of the Japanese Household Panel Survey. We confirmed many of the factors associated with mental health reported in existing studies by analysing crosssectional data. These significant associations are also longitudinal (within) associations estimated by the FE-CLR models. Such factors include unemployment, low household income, short nightly sleeping duration, and lack of exercise. However, we also found that several factors such as disposable income, living alone, and drinking habits are not significantly associated with mental health in the FE-CRL models. The results imply the reverse causality that poor mental health conditions cause lower disposal income, possibly due to the inability to exhibit higher productivity, but an increase in disposal income would not necessarily improve mental health conditions. In this case, aggressive policy interventions to increase the disposal income of people of lower socioeconomic backgrounds would not necessarily be effective to minimize health inequalities.

Socio-economic Status as Correlate of Health Status of Undergraduates in Public Universities, Rivers State, Nigeria

“…The finding of this study gives credence to Oyeboade [16] whose study on socio-economic status and use of social media by undergraduate students in university of Ibadan, Ibadan, Oyo state, Nigeria showed that, socio-economic status has significant relationship with use of social media (Χ 2 = 9.797, p < 0.05) which influences health status of the university students. This result supports that of Nagasu and Yamamoto [17] whose study on the impact of socioeconomicand lifestyle-related risk factors on poor mental health conditions in Japan revealed that there was a statistically significant relationship between low household income and mental health condition. The similarity between the previous study and the present one might be due to the homogeneity of the study population.…”

“…The finding of the study showed that there was a significant positive relationship between parent employment status and mental health status (r = 0.94, p<0.05) and parent employment status contributed 88.3% of the variance in the mental health of undergraduate students (R 2 = 0.883). This result supports that of Nagasu and Yamamoto [17] whose study on the impact of socioeconomic-and lifestyle-related risk factors on poor mental health conditions in Japan revealed that there was a statistically significant relationship between unemployment, and mental health condition. The finding of this study corroborates that of Navarro-Carrillonm et al [14] whose study showed significant relationship between socioeconomic status such as occupation and psychological well-being.…”

Association between the COVID-19 Vaccine and Preventive Behaviors: Panel Data Analysis from Japan

“…The estimation result using an FE model is equivalent to the results of a linear regression model with dummies of individuals who are frequently included in each period [29][30][31]. The FE model can be applied to linear regression and also to other types of regression, such as conditional logistic regression [32]. In the case of this study, 4358 dummies are included to control for individuals' characteristics that do not change during the period, such as gender, educational background, childhood experience, and so on.…”

Intervention and information effects at the individual level during the COVID-19 pandemic in Japan

“…Respondents were recruited in January and were informed about the anonymity and confidentiality of their answers. The participants received the questionnaires in their home (in February) after agreeing to join the survey [5]. Informed consent was not obtained, given that the data were analyzed anonymously.…”