Urbanization, the shift of a growing population into urban areas, is shaping global development across infrastructure, health, and sustainability. Although it brings economic growth, innovation, and improved access to services, it may also impact mental health.

The present article was prepared on behalf of the European Psychiatric Association and explores the complexity of associations between urbanization and mental health, highlighting both potential risks and opportunities for improvement.

Urban growth often leads to increased population density, social fragmentation, and environmental stressors, including noise, pollution, and reduced green spaces, all of which might account for worsening mental health. Urban residents might be at risk of various mental disorders due to these stressors, accompanied by the risk of social disconnection. Moreover, socioeconomic disparities in urban settings can lead to unequal healthcare access, further contributing to these challenges. However, urbanization also offers unique opportunities to improve mental health through better resource allocation, innovative healthcare solutions, and community-building initiatives. Indeed, cities might serve as areas for mental health promotion by integrating mental health services into primary care, utilizing digital health technologies, and fostering environments that promote social interactions and well-being. Urban planning that prioritizes green spaces, safe housing, and accessible public transportation holds the potential to mitigate some risks related to urban living.

While urbanization presents significant challenges to mental health, it also provides grounds for transformative interventions. Addressing the mental health needs of urban populations requires a multifaceted approach that includes policy reform, community engagement, and sustainable urban planning.

Multimorbidity is increasingly common among older adults in Sub-Saharan Africa (SSA), yet the role of social determinants in shaping its prevalence and outcomes remains underexplored.

This review aimed to (a) identify the prevalence, types, and patterns of multimorbidity among older adults in SSA; (b) examine the influence of social determinants such as income, education, healthcare access, and geographic location; (c) evaluate current approaches for prevention and management; and (d) propose directions for future research.

A systematic search of six databases (PubMed, EMBASE, PsycINFO, Google Scholar, CINAHL, and Global Index Medicus) was conducted to identify quantitative studies published between 2000 and 2024 on adults aged 50 and above. Of 841 records screened, 16 studies met inclusion criteria and passed quality appraisal. The review protocol was registered in PROSPERO (CRD42024607875).

Multimorbidity ranged from 5.4% in Botswana to 71% in Nigeria. Cardiometabolic conditions often co-occurred with infectious and mental disorders. Poverty and low education significantly increased risk (OR: 1.44–7.44). Rural residents faced limited healthcare access, while urban dwellers had higher risks from lifestyle factors. Obesity and food insecurity further heightened vulnerability, especially among women and older adults.

Findings indicate that social determinants critically shape multimorbidity risk and outcomes in SSA. Integrated care models, targeted interventions, and policies addressing structural inequalities are urgently needed. Future research should apply longitudinal and qualitative approaches to clarify causal pathways and inform context-sensitive strategies.

Existing panel studies on the relationships between cognition and depressive symptoms did not systematically separate between- and within-person components, with measurement time lags that are too long for precise assessment of dynamic within-person relationships.

To investigate the bidirectional relationships between cognition and depressive symptoms and examine the effects of sociodemographic characteristics and lifestyle factors via random-intercept, cross-lagged panel modelling (RI-CLPM) in middle-aged and older adults.

The sample comprised 24 425 community-based residents aged 45 years or above, recruited via five waves of the China Health and Retirement Longitudinal Study (2011–2020). Cognition was evaluated using the Telephone Interview of Cognition Status, and depressive symptoms were assessed by the ten-item Center for Epidemiologic Studies Depression Scale. RI-CLPM included sociodemographic and lifestyle factors as time-invariant and -varying covariates. Subgroup analysis was conducted across gender, age groups and urban/rural regions.

RI-CLPM showed a superior fit to cross-lagged panel models. Male, higher education, married, urban region, non-smoking, currently working and participation in social activities were linked with better cognition and fewer depressive symptoms. Overall, cognition and depressive symptoms showed significant and negative bidirectional cross-lagged effects over time. Despite similar cross-lagged effects across gender, subgroup analysis across urbanicity found that cross-lagged effects were not significant in urban regions.

The present study provided nuanced results on negative bidirectional relationships between cognition and depressive symptoms in Chinese middle-aged and older adults. Our results highlight the health disparities in cognitive and emotional health across urbanicity and age groups.

Dissecting the exposome linked to mental health outcomes can help identify potentially modifiable targets to improve mental well-being. However, the multiplicity of exposures and the complexity of mental health phenotypes pose a challenge that requires data-driven approaches.

Guided by our previous systematic approach, we conducted hypothesis-free exposome-wide analyses to identify factors associated with 7 psychiatric diagnostic domains and 19 symptom dimensions in 157,298 participants from the UK Biobank Mental Health Survey. After quality control, 294 environmental, lifestyle, behavioral, and economic variables were included. An Exposome-Wide Association Study was conducted per outcome in two equally split datasets. Variables associated with each outcome were then tested in a multivariable model.

Across all diagnostic domains and symptom dimensions, the top three exposures were childhood adversities and traumatic events. Cannabis use was associated with common psychiatric disorders (depressive, anxiety, psychotic, and bipolar manic disorders), with ORs ranging from 1.10 to 1.79 in the multivariable models. Additionally, differential associations were identified between specific outcomes—such as neurodevelopmental disorders, eating disorders, and self-harm behaviors—and exposures, including early life experiences (being adopted), lifestyle (time spent using computers), and dietary habits (vegetarian diet).

This comprehensive mapping of the exposome revealed that several factors, particularly in the domains of those previously well-studied were shared across mental health phenotypes, providing further support for transdiagnostic pathoetiology. Our findings also showed that distinct relations might exist. Continued exposome research through multimodal mechanistic studies guided by the transdiagnostic mental health framework is required to better inform public health policies.

Adolescence is a critical developmental phase during which young people are vulnerable to the experiences of mental ill-health and social exclusion (consisting of various domains including education and employment, housing, finances and social supports and relationships). The aims of this study were to (i) obtain an understanding of the relationships between social exclusion, mental health and wellbeing of young people; and (ii) identify potentially modifiable targets, or population groups that require greater or targeted supports.

Data were obtained from the Mission Australia 2022 Youth Survey, Australia’s largest annual population-wide survey of young people aged 15–19 years (n = 18,800). Participants’ experiences of social exclusion in different domains were explored (e.g., prevalence, co-occurrence and controlling for differences in demographic characteristics). Multivariable linear regression models were used to map the relationships between social exclusion domains and mental health and wellbeing, controlling for confounding factors where necessary.

Sixty per cent of all young people experienced social exclusion in at least one domain, 25% in multiple. Young people who identified as gender diverse, Indigenous, living in a remote/rural or socio-economically disadvantaged area and with a culturally diverse background were more likely to report social exclusion. A strong association was seen between all domains of social exclusion and poor mental health (e.g., higher psychological distress and loneliness, reduced personal wellbeing, reduced sense of control over their life and a more negative outlook on the future). Notably, difficulties in socialising and obtaining social support were critical factors linked to increased psychological distress and reduced wellbeing.

Findings underscore the need to address multiple domains of social exclusion concurrently, and in collaboration with youth mental healthcare. Prevention efforts aimed at early identification and intervention should be prioritised to support young people vulnerable to social exclusion. Screening approaches are needed to identify individuals and groups of young people in need of support, and to facilitate care coordination across multiple providers.

Social prescribing is growing rapidly globally as a way to tackle social determinants of health. However, whom it is reaching and how effectively it is being implemented remains unclear.

To gain a comprehensive picture of social prescribing in the UK, from referral routes, reasons, to contacts with link workers and prescribed interventions.

This study undertook the first analyses of a large database of administrative data from over 160 000 individuals referred to social prescribing across the UK. Data were analysed using descriptive analyses and regression modelling, including logistic regression for binary outcomes and negative binomial regression for count variables.

Mental health was the most common referral reason and mental health interventions were the most common interventions prescribed. Between 72% and 85% of social prescribing referrals were from medical routes (primary or secondary healthcare). Although these referrals demonstrated equality in reaching across sociodemographic groups, individuals from more deprived areas, younger adults, men, and ethnic minority groups were reached more equitably via non-medical routes (e.g. self-referral, school, charity). Despite 90% of referrals leading to contact with a link worker, only 38% resulted in any intervention being received. A shortage of provision of community activities – especially ones relevant to mental health, practical support and social relationships – was evident. There was also substantial heterogeneity in how social prescribing is implemented across UK nations.

Mental health is the leading reason for social prescribing referrals, demonstrating its relevance to psychiatrists. But there are inequalities in referrals. Non-medical referral routes could play an important role in addressing inequality in accessing social prescribing and therefore should be prioritised. Additionally, more financial and infrastructural resource and strategic planning are needed to address low intervention rates. Further investment into large-scale data platforms and staff training are needed to continue monitoring the development and distribution of social prescribing.

Adolescent girls are at risk of anaemia due to increased nutrient demands because of growth, menstrual blood loss and possible pregnancies. Sociocultural and household conditions influence their anaemia risk. We aimed to identify the sociocultural and economic factors associated with anaemia among adolescent girls in Nepal.

The Nepal Demographic and Health Surveys (NDHS) conducted in 2006, 2011 and 2016 were pooled for secondary analysis. We used data on haemoglobin measurements for anaemia and conducted bivariate and multivariable logistic regression analyses to identify factors associated with anaemia.

Nationally representative NDHS households with adolescent girls 15–19 years of age.

Non-pregnant adolescent girls 15–19 years, with a haemoglobin measurement (n = 3731).

The overall prevalence of anaemia among adolescent girls was 39·6 %. Adolescents from socially disadvantaged caste/ethnicity groups were 1·42 times (95 % CI: 1·13, 1·78) more likely to have anaemia compared with those from Brahmin/Chhetri households. We found a counter-intuitive association between socio-economic status and anaemia where adolescents from the middle (adjusted OR (aOR) 1·37, 95 % CI: 1·01, 1·85) and highest (aOR 1·74, 95 % CI: 1·18, 2·56) quintiles were at increased odds of anaemia. Relative geographical inequality was observed where adolescents from the Terai region had 3·5 times (95 % CI: 2·32, 5·33) higher odds of anaemia.

The disparities in the distribution of anaemia among adolescents by caste/ethnicity groups, wealth quintiles and geographical regions are evident. Reducing the anaemia burden will require addressing the social determinants of anaemia by allocating resources and expanding anaemia prevention programmes to target adolescents at higher risk.

This study aims to determine health-related quality of life (QoL) and the related factors from the perspective of social determinants of health among children.

Childhood is the most intense period of life, and environmental factors surrounding children, as well as individual lifestyle factors, are related to the child’s physical and mental well-being. To our knowledge, there is a lack of studies evaluating the relationship between determinants of health and the QoL of healthy children in general.

This cross-sectional study was executed in the Bayrakli district of Izmir city. Stratified clustered sampling was used including 24 schools and 3367 7th-grade children, and 1284 students were targeted (50% prevalence, 95% CI, %5 margins of error, 2.25 design effect, and 20% replacement). The response rate was 84.9% (n = 1090). The Turkish KID-KINDL Health-Related Quality of Life Questionnaire for Children was used to assess QoL. Independent variables were examined in four layers using Dahlgren’s Determinants of Health Model: basic characteristics, lifestyle factors, family characteristics, and life conditions.

The mean QoL score was 71.3 ± 12.6. Our study explained 31.7% of the variance in QoL. Higher QoL scores were associated with better health status, perceived academic achievement, normal/thin body perception, physical activity (PA), and adequate sleep duration. Living with both parents and having fewer siblings positively influenced QoL. Moreover, the presence of structural problems in the household and poorer health perceptions were associated with lower QoL scores (P < 0.05) This study highlighted the multifaceted nature of QoL in Turkish children, revealing the importance of various determinants of health. The results show that in order to improve the general well-being of this population, interventions and policies are required that concentrate on elements including health status, academic accomplishment, body perception, physical activity, family structure, and living situations.

The COVID-19 pandemic dramatically altered social determinants of health including work, education, social connections, movement, and perceived control; and loneliness was commonly experienced. This longitudinal study examined how social determinants at the personal (micro), community (meso), and societal (macro) levels predicted loneliness during the pandemic.

Participants were 2056 Australian adults surveyed up to three times over 18 months in 2020 and 2021. Multi-level mixed-effect regressions were conducted predicting loneliness from social determinants at baseline and two follow-ups.

Loneliness was associated with numerous micro determinants: male gender, lifetime diagnosis of a mental health disorder, experience of recent stressful event(s), low income, living alone or couples with children, living in housing with low natural light, noise, and major building defects. Lower resilience and perceived control over health and life were also associated with greater loneliness. At the meso level, reduced engagement with social groups, living in inner regional areas, and living in neighbourhoods with low levels of belongingness and collective resilience was associated with increased loneliness. At the macro level, increased loneliness was associated with State/Territory of residence.

Therapeutic initiatives must go beyond psychological intervention, and must recognise the social determinants of loneliness at the meso and macro levels.