BackgroundHypertension is a worldwide health issue that primarily affects the elderly in our society. However, in comparison to the developed world, the prevalence of hypertension is higher in Sub-Saharan Africa.ObjectiveThis paper examines the prevalence of hypertension and its associated risk factors among older adults in Ghana.MethodsUsing the World Health Organization’s study on global AGEing and adult health (WHO SAGE) Wave 1 cross-sectional data collected via in-person structured interviews; paper and pencil interviews (PAPI) from ten administrative regions of Ghana using stratified multistage cluster design from respondents aged 50+ grouped by decade, this study analyzed a nationally representative sub-sample of 3,997 respondents employing binary logistic regression. Odds ratios (OR) and 95% confidence intervals (95% CI) were used to estimate risk factors associated with hypertension (blood pressure ≥ 130/80 mm/Hg).ResultsThere was a 53.72% hypertension prevalence rate among older adults. Hypertension prevalence tends to increase with increasing age. The prevalence of hypertension was associated with residency (B = −0.18, OR = 0.84, p < 0.017), with urban residents being more at risk of hypertension than rural residents. The prevalence of hypertension increased with overweight (B = 0.66, OR = 1.94, p < 0.001) and obesity (B = 0.82, OR = 2.28, p < 0.001). The amount of fruit and vegetable intake was insignificant but had an inverse relationship with hypertension prevalence.ConclusionThis study has shown that demographic and lifestyle factors significantly affect and explain the hypertension risk among older adults. Medical factors, such as chronic diseases, were largely insignificant and accounted for less hypertension prevalence. Therefore, when interpreting test findings in clinical practice, such as hypertension, it is essential to consider demographic and lifestyle factors. In addition, health policies and primary interventions that seek to improve the standard of living, lifestyle, and wellbeing of older adults need to be critically considered moving forward to lower hypertension prevalence among older adults in Ghana.

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Recommender systems (RS) have been widely deployed in many real-world applications, but usually suffer from the long-standing user/item cold-start problem. As a promising approach, cross-domain recommendation (CDR), which has attracted a surge of interest, aims to transfer the user preferences observed in the source domain to make recommendations in the target domain. Traditional machine learning and deep learning methods are not designed to learn from complex data representations such as graphs, manifolds and 3D objects. However, current trends in data generation include these complex data representations. In addition, existing research works do not consider the complex dimensions and the locality structure of items, which however, contain more discriminative information essential for improving the performance accuracy of the recommender system. Furthermore, similar outcomes between test samples and their neighboring training data restrained in the kernel space are not fully realized from the recommended objects belonging to the same object category to capture the embedded discriminative information effectively. These challenges leave the problem of sparsity and the cold-start of items/users unsolved and hence impede the performance of the cross-domain recommender system, causing it to suggest less relevant and undistinguished items to the user. To handle these challenges, we propose a novel deep learning (DL) method, Discriminative Geometric Deep Learning (D-GDL) for cross-domain recommender systems. In the proposed D-GDL, a discriminative function based on sparse local sensitivity is introduced into the structure of the DL network. In the D-GDL, a local representation learning (i.e., a local sensitivity-based deep convolutional belief network) is introduced into the structure of the DL network to effectively capture the local geometric and visual information from the structure of the recommended 3D objects. A kernel-based method (i.e., a local sensitivity deep belief network) is also incorporated into the structure of the DL framework to map the complex structure of recommended objects into high dimensional feature space and achieve an effective recognition result. An improved kernel density estimator is created to serve as a weighing function in building a high dimensional feature space, which makes it more resistant to geometric noise and computation performance. The experiment results show that the proposed D-GDL significantly outperforms the state-of-the-art methods in both sparse and dense settings for cross-domain recommendation tasks.

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The purpose of this study was to conduct a meta-analysis of studies that have examined the prevalence of falls among older adults living in Africa. Three investigators independently searched the databases of PubMed, EMBASE, Google Scholar, and Web of Science from their inception date until September 2019. Participants were 5,815 older adults aged 60 years and above. The prevalence of falls was determined using the random effects meta-analysis, whereas meta-regression was conducted to investigate the moderating factors. Eleven of the 921 potentially relevant studies met the inclusion criteria and were included in the meta-analysis. The meta-analysis revealed a pooled prevalence of fall rate of 24.2% (95% CI: 23.1%-25.3%, I2 = 95.2%). Multivariate meta-regression analysis found no moderating effects of study sub-region, study year, and sample size on fall prevalence (p values > 0.05). Falls among older adults living in Africa are common and therefore need continuous research to examine the possible risk factors associated with falls among older adults and to establish effective policies and prevention approaches to reduce risk.