Browsing by Author "Gyasi, R.M."
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Item Ageing Happily in Ghana: How Does Social Inclusion Contribute?(Psychogeriatrics, 2023) Gyasi, R.M.; Hajek, A.; Hervie, L.M.; et al.Background: Promoting happiness has become increasingly important in old age for a wide range of reasons. In this study, we aim to examine the association between social inclusion (SI) and happiness among older adults in Ghana and identify the mediating factors. Methods: The study included 1201 community-dwelling adults aged ≥50 (mean age = 66.4 SD 11.9 years; 63.3% female) from the 2016–2018 Ageing, Health, Psychological Well-being, and Health-seeking Behaviour Study. We assessed happiness with a self-rated and cross-culturally vali dated item on a five-point scale. SI was operationalised using the modified Berkman–Syme Social Network Index. The hypothesised associations were evaluated by hierarchical regressions and bootstrapping techniques from Hayes’ PROCESS macro programme. Results: The prevalence of happiness was 24.3% (all of the time), 43.6% (most of the time), 28.3% (little of the time), and 3.7% (none of the time). After controlling for potential confounders, higher SI was associated with increased levels of happiness (odds ratio (OR) = 1.36; 95% confidence interval (CI) = 1.16–2.51). Aside from family/friends contacts, all other SI domains positively influenced happiness (OR = 1.45–1.81). The link between SI and happiness was mediated by depressive symptoms (65.2%), generalised anxiety (30.1%), and sleep problems (9.5%). Conclusions: Our data suggest that psychological factors may largely explain the positive SI-happiness link. Efforts should target these factors to promote happiness in old age. Longitudinal analysis may confirm our findingsItem Urban sprawl and land use/land-cover transition probabilities in peri-urban Kumasi, Ghana(West African Journal of Applied Ecology, 2018-10) Abass, K.; Adanu, S.K.; Gyasi, R.M.This paper examines Land Use and Land Cover (LULC) transition probabilities and its implications for KumasiMetropolis using remote sensing image analysis technique. Methods used for the study include sub-setting ofsatellite images for the metropolis using the metropolitan shapefile boundary and classification of the images using maximum likelihood image classification algorithm. A Markov Model was applied to predict probabilities of LULC changes in 15 years (2016 - 2031). Study results show the probability of urban lands changing to agricultural land as low and so is the probability of farmland transitioning to urban land use. Vegetation however shows a high probability of change to built-up area while the likelihood of change from water to other cover types is not a possibility. The study recommends enforcement of relevant land use policies backed by vigorous public education to make sustainable urban land use in the Metropolis a reality. Also, vertical rather than horizontal construction of buildings could stem the sprawling city.