Urban–Rural Differences in Older Adult Depression: A Systematic Review and Meta-analysis of Comparative Studies

Context: Depression among older adults (aged 60 years or older) is a problem that could be exacerbated by global trends in urbanization and population aging. The study purpose was to assess whether urban, relative to rural, residence is associated with depression among older adults and whether associations differ in countries with developed versus developing economies. Evidence acquisition: In 2017, the authors identified and extracted information from comparative studies of urban–rural depression prevalence among older adults. Studies were identified in PubMed, PsychINFO, and Web of Science and limited to English language articles published after 1985. Eighteen studies met inclusion criteria. Random effects meta-analysis was conducted to produce weighted pooled ORs estimating the association between urban–rural residence and depression for all study participants (N=31,598) and sub-analyses were conducted for developed (n=12,728) and developing (n=18,870) countries. Evidence synthesis: Depression prevalence was significantly higher among urban residents in ten studies and significantly higher among rural residents in three studies (all three conducted in China). Associations between urban–rural residence and depression generally remained significant after adjusting for covariates. In developed countries, the odds of depression were significantly higher among urban than rural residents (pooled OR=1.44, 95% CI=1.10, 1.88). However, in developing countries, this association was not observed (pooled OR=0.91, 95% CI=0.46, 1.77). Conclusions: Converging trends of urbanization and population aging could increase the global burden of depression among older adults. The pathways through which urban–rural residence influences depression risk among older adults might differ by country context. Future research should focus on measuring variation in these contexts

Depression and alcohol misuse among older adults: exploring mechanisms and policy impacts using agent-based modelling

Purpose: To: (1) explore how multi-level factors impact the longitudinal prevalence of depression and alcohol misuse among urban older adults (≥ 65 years), and (2) simulate the impact of alcohol taxation policies and targeted interventions that increase social connectedness among excessive drinkers, socially isolated and depressed older adults; both alone and in combination. Methods: An agent-based model was developed to explore the temporal co-evolution of depression and alcohol misuse prevalence among older adults nested in a spatial network. The model was based on Los Angeles and calibrated longitudinally using data from the Multi-Ethnic Study of Atherosclerosis. Results: Interventions with a social component targeting depressed and socially isolated older adults appeared more effective in curbing depression prevalence than those focused on excessive drinkers. Targeting had similar impacts on alcohol misuse, though the effects were marginal compared to those on depression. Alcohol taxation alone had little impact on either depression or alcohol misuse trajectories. Conclusions: Interventions that improve social connectedness may reduce the prevalence of depression among older adults. Targeting considerations could play an important role in determining the success of such efforts

Examining the possible impact of daily transport on depression among older adults using an agent-based model

_Objectives:_ Daily transport may impact depression risk among older adults through several pathways including facilitating the ability to meet basic needs, enabling and promoting contact with other people and nature, and promoting physical activity (e.g. through active transportation such as walking or walking to public transit). Both daily transport and depression are influenced by the neighborhood environment. To provide insights into how transport interventions may affect depression in older adults, we developed a pilot agent-based model to explore the contribution of daily transport and neighborhood environment to older adults’ depression in urban areas. _Method:_ The model includes about 18,500 older adults (i.e. agents) between the ages of 65 and 85 years old, living in a hypothetical city. The city has a grid space with a number of neighborhoods and locations. Key dynamic processes in the model include aging, daily transport use and feedbacks, and the development of depression. Key parameters were derived from US data sources. The model was validated using empirical studies. _Results:_ An intervention that combines a decrease in bus fares, shorter bus waiting times, and more bus lines and stations is most effective at reducing depression. Lower income groups are likely to be more sensitive to the public transit-oriented intervention. _Conclusion:_ Preliminary results suggest that promoting public transit use may be a promising strategy to increase daily transport and decrease depression. Our results may have implications for transportation policies and interventions to prevent depression in older adults

Complex Systems Approaches to Understand Drivers of Mental Health and Inform Mental Health Policy: A Systematic Review

We conducted a systematic review of studies employing complex systems approaches (i.e., agent based and system dynamics models) to understand drivers of mental health and inform mental health policy. We extracted key data (e.g., purpose, design, data) for each study and provide a narrative synthesis of insights generated across studies. The studies investigated drivers and policy intervention strategies across a diversity of mental health outcomes. Based on these studies and the extant literature, we propose a typology of mental health research and policy areas that may benefit from complex systems approaches

A genome-wide association study of depressive symptoms

Background: Depression is a heritable trait that exists on a continuum of varying severity and duration. Yet, the search for genetic variants associated with depression has had few successes. We exploit the entire continuum of depression to find common variants for depressive symptoms. Methods: In this genome-wide association study, we combined the results of 17 population-based studies assessing depressive symptoms with the Center for Epidemiological Studies Depression Scale. Replication of the independent top hits (p<1×10-5) was performed in five studies assessing depressive symptoms with other instruments. In addition, we performed a combined meta-analysis of all 22 discovery and replication studies. Results: The discovery sample comprised 34,549 individuals (mean age of 66.5) and no loci reached genome-wide significance (lowest p = 1.05×10-7). Seven independent single nucleotide polymorphisms were considered for replication. In the replication set (n = 16,709), we found suggestive association of one single nucleotide polymorphism with depressive symptoms (rs161645, 5q21, p = 9.19×10-3). This 5q21 region reached genome-wide significance (p = 4.78×10-8) in the overall meta-analysis combining discovery and replication studies (n = 51,258). Conclusions: The results suggest that only a large sample comprising more than 50,000 subjects may be sufficiently powered to detect genes for depressive symptoms