Contextual and Individual Determinants of Mental Health: A Cross-sectional Multilevel Study in Tehran, Iran

This is the place: a multi-level analysis of neighbourhood correlates of adolescent wellbeing

OBJECTIVE

Adolescent wellbeing is a key research and policy priority, but little is known about neighbourhood-level influences. This study examined the extent to which adolescents' life satisfaction and internalising symptoms vary between neighbourhoods, and which neighbourhood characteristics are associated with individual outcomes.

METHOD

Baseline data from the #BeeWell cohort study in Greater Manchester (England) including 35,902 adolescents (aged 12-15) across 243 neighbourhoods were linked to neighbourhood characteristics (e.g. access to education and health services, leisure facilities) from the Co-op's Community Wellbeing Index and analysed using multi-level regression.

RESULTS

Neighbourhoods explained 0.61% and 1.17% of the variation in life satisfaction and internalising symptoms, respectively. Socio-demographic inequalities in these outcomes varied across neighbourhoods. Several neighbourhood characteristics were associated with wellbeing, but differences across model specifications were observed (e.g. adjusted vs unadjusted; unique associations vs grouped domains). However, higher levels of perceived wellbeing support from local people were associated with lower internalising symptoms in all models. Other characteristics associated with better wellbeing outcomes in various models included lower GP antidepressant prescription rates, and better access to health services, areas for leisure, and good places to spend free time.

CONCLUSION

Neighbourhoods account for a small but significant proportion of the variance in adolescent life satisfaction and internalising symptoms. Some neighbourhood characteristics (notably neighbourhood social capital) are associated with these outcomes at the individual level, and disparities in these outcomes for some groups vary across neighbourhoods. Our findings speak to the role of place as a determinant of adolescent wellbeing, with consequent implications for intervention.

Measuring and exploring mental health determinants: a closer look at co-residents' effect using a multilevel structural equations model

OBJECTIVE

Previous research has demonstrated that individual risk of mental illness is associated with individual, co-resident, and household risk factors. However, modelling the overall effect of these risk factors presents several methodological challenges. In this study we apply a multilevel structural equation model (MSEM) to address some of these challenges and the impact of the different determinants when measuring mental health risk.

STUDY DESIGN AND SETTING

Two thousand, one hundred forty-three individuals aged 16 and over from 888 households were analysed based on the Household Survey for England-2014 dataset. We applied MSEM to simultaneously measure and identify psychiatric morbidity determinants while accounting for the dependency among individuals within the same household and the measurement errors.

RESULTS

Younger age, female gender, non-working status, headship of the household, having no close relationship with other people, having history of mental illness and obesity were all significant (p < 0.01) individual risk factors for psychiatric morbidity. A previous history of mental illness in the co-residents, living in a deprived household, and a lack of closeness in relationships among residents were also significant predictors. Model fit indices showed a very good model specification (CFI = 0.987, TLI = 0.980, RMSEA = 0.023, GFI = 0.992).

CONCLUSION

Measuring and addressing mental health determinants should consider not only an individual's characteristics but also the co-residents and the households in which they live.

Multidimensional poverty index: a multilevel analysis of deprivation among Iranian older adults

Although the older adult population faces a higher risk of poverty compared to others, there is no clear picture of their poverty in Iran. The aim of this study was to measure multidimensional poverty and its related factors among Iranian older adults. This cross-sectional study was conducted from July to November 2019 and collected data by interviewing 1,280 participants in Tehran, Iran. To compute multidimensional poverty, four dimensions were used: health (disabilities), education, housing and standard of living. Single and multidimensional poverty and the joint distribution of deprivation were calculated. Multilevel logistic regression models were used to determine the relationship between predictor variables and outcome (multidimensional poverty). Multidimensional poverty among Tehran's older people was 59.0 per cent. The prevalence of health, housing, education and standard of living deprivations were 15.4, 25.3, 29.5 and 29.9 per cent, respectively. Furthermore, multivariate analysis shows that living with a spouse, being employed, and having health and social insurance coverage were protective factors, while being female was a risk factor for multidimensional poverty. Approximately 21 per cent of multidimensional poverty variance was attributed to the district level and the remaining was assigned to individual-level factors. This study showed that the older adults living in different areas of Tehran experience different aspects of poverty. So paying attention to the dimensions of multidimensional poverty can play an important role in customising the policies of each district. Also, the findings of this study on risk and protective factors of multidimensional poverty can be effective in designing and implementing interventions to mitigate poverty among the older adults.