Background: In India, the prevalence of depression among older adults dealing with multiple health conditions varies between rural and urban areas due to disparities in healthcare access and cultural factors. The distinct patterns observed underscore the necessity for tailored research and interventions to address mental health inequalities among multimorbid older patients in diverse geographic contexts. Methods: This study used data from the Longitudinal Ageing Study in India (LASI) wave 1 (2017–18). A total of 7,608 adults aged ≥ 60 years who were diagnosed with two or more chronic conditions (such as hypertension, diabetes, cancer, chronic lung disease, chronic heart diseases, stroke, bone/joint disease, any neurological or psychiatric diseases, and high cholesterol) were included in this study. Descriptive statistics, bivariate analysis, logistic regression estimates, and Fairlie decomposition method were used to accomplish the study’s objectives. Results: The prevalence of depression among older adults with multimorbidity was 9.48% higher in rural areas (38.33%) than in urban areas (28.85%).. Older adults with multimorbidity belonging to the scheduled caste group were 40% more likely to experience depression. Moreover, those with multimorbidity and any form of disability in activities of daily living (ADL) were 93% more likely to experience depression than those without disability, whereas those with multimorbidity and perceived good general health were 65% less likely to suffer from depression than those with poor self-perceived health. Additionally, decomposition analysis revealed that education (35.99%), caste status (10.30%), IADL disability (19.30%), and perceived discrimination (24.25%) were the primary factors contributing to the differences in depression prevalence among older adults with multimorbidity between rural and urban areas. Conclusions: We found significant rural–urban differences in depression among older Indians with multimorbidity. The findings underscore the need for targeted interventions that address the unique challenges faced by older patients in rural areas, including lack of social capital, discrimination, and limited resources that enable access to healthcare services. Policymakers and healthcare professionals must collaboratively design and implement effective strategies to improve the mental health and overall well-being of rural older adults, particularly those with multiple comorbidities.

Sewer Treatment Plants (STPs) are essential pieces of infrastructure given the growing scarcity of water sources due to the challenges of urbanization. The positioning of STPs is a complex multidimensional process that involves integrative decision-making approaches that consider multiple sustainability criteria to ensure their optimal placement. The Multi-Criteria Decision Method (MCDM) is a suite of approaches available to decision-makers when making systematic and scientifically informed decisions on siting wastewater treatment plants. Although MCDM methods have manifold applications in different geographic contexts, there is a paucity of studies employing MCDM models for the siting of STPs within the context of Oman. In this study, we assessed the locations of existing STPs and identified suitable locations for future STPs within the Muscat Governorate of Oman using a Multi-Criteria Decision-Making Analytic Hierarchy Process (MCDM-AHP) model in a Geographic Information System (GIS) environment. Eight factors were considered in the MCDM-AHP model: slope, elevation, proximity to built-up areas, airports, valleys, road networks, the sea, parks, and golf courses. Each factor was assigned priority weights based on its importance using the AHP method. Thematic maps were generated to categorize the potential sites into different suitability levels. The results showed that the coastal areas of A’Seeb and Bowsher were the most suitable locations for STPs, representing only 1.19% of the total study area. The novelty of this study stems from the perspective of an original application within the context of Oman, which has generated novel results and interpretations. This has significant implications for urban policy and planning with respect to better informing decision-makers with a systematic framework for efficient wastewater treatment.

We explore the spatial patterning of precarious forms of employment (PFE) by immigration status using Statistics Canada's 2011‐2016 Labor Force Surveys. We provide new evidence showing that different PFEs exhibited distinct spatial patterns, with the census metropolitan areas showing the greatest spatial variation for immigrants. More so, immigrants were represented in high shares in PFEs in geographies where they tend to concentrate. Our findings raise a series of important questions on the efficacy of the regionally differentiated entitlement to Employment Insurance (EI) especially as immigrants are represented in high PFE shares in regions where EI coverage rates are low.

With a rapidly aging population, physical frailty has become a significant public health concern globally. While the association between frailty, depression, and cognitive decline has been widely studied in developed countries, there is limited evidence from low- and middle-income countries, including India. Additionally, little is known about the sex-specific associations between frailty, and mental and cognitive health outcomes. We examined the associations of physical frailty with depressive symptoms and cognitive functioning among older Indian men and women, while also exploring how the frailty-cognition link differs between those with and without depression. We used data from the Longitudinal Aging Study in India, conducted between 2017 and 2019. The analytic sample consisted of 14,652 males and 15,899 females aged ≥ 60 years. Frailty was assessed using a modified version of Fried’s frailty phenotype, depressive symptoms were evaluated using the Composite International Diagnostic Interview- Short Form, and cognitive ability was measured through memory, orientation, arithmetic, executive function, and object naming tasks. Univariate and multivariable linear regression models were used to examine the associations between frailty, depressive symptoms, and cognitive ability in older men and women, as well as frailty-cognition association by depressive status. The prevalence of frailty was higher in older women than that in older men (32.2% vs. 27.4%). The mean depressive symptom score was higher (0.8 vs 1.0), and the mean cognitive score was lower (26.4 vs 22.1) among older women than men. Physical frailty was associated with higher levels of depressive symptoms (β = 0.51; 95% CI: 0.39, 0.64), and poor cognitive performance (β = -1.06; 95% CI: -1.37, -0.75). Non-frail older men had lower depressive symptoms than frail older men (β = -0.48; 95% CI: -0.66, -0.31), however, the association was not significant among older women. Furthermore, frail older women performed worse on cognitive tests than did frail older men (β = -2.14; 95% CI: -2.40, -1.87). In addition, non-frail older women had poorer cognitive performance than frail older men (β = -0.77; 95% CI: -1.22, -0.32). Conversely, stratification by depressive status showed that frailty was associated with worse cognitive ability, with no difference between individuals with and without depression. We found that frail older individuals, particularly women, have significant mental and cognitive deficits compared with their non-frail counterparts. Our findings have major implications in both community and clinical settings. Appropriate policies and programs should be implemented to reinforce the strength of pre-frail and frail older adults and maintain improved mental health and cognition in older adults.

Using Statistics Canada’s 2011-2016 Labor Force Surveys, this paper examines the spatial dimensions of precarious forms of employment (PFE) in Canada. We first compare different PFEs across a range of geographies including national, provincial, census metropolitan areas and urban/rural areas. The results show that different PFEs exhibited distinct spatial patterns across space and scale. Second, using logistic regression models, results show that patterns in PFEs were reinforced by factors such as immigration status, gender, age, education, and income. These models further confirm that spatial variations in PFEs were robust even when controlling for socio-demographic and socio-economic effects. Taken together, these marked spatial patterns advances our understanding of the spatial divisions of precariousness in Canada.