Assessment of Hypertension-Induced Deaths in Ghana: A Nation-Wide Study from 2012 to 2016

Globally, hypertension is one of the leading causes of death. It can potentially lead to heart disease and stroke, among others, that could result to premature death. In Ghana, hypertension is considered as a disease that contributes to an increase in outpatients’ attendance. To assess the trend differentials of hypertension-induced deaths in Ghana, Chi-square test for equal proportions and Marascuilo procedure for pairwise comparison were performed using surveillance data on reported number of deaths from 2012 to 2016 across the then ten regions. The results show that incidence of hypertension-induced mortality was significantly different for almost all the regions and over the years. The incidence of hypertension-induced mortality has significantly reduced from 2012 to 2016. However, Volta Region recorded the highest incidence of mortality cases (p-value less of 0.0001) than the other regions during the period under review, while the Upper East Region recorded continuous increase in incidence of mortality cases with the highest in 2016. The Eastern Region, Central Region, and Greater Accra Region recorded significantly (p-value less of 0.0001) higher incidence of hypertension-induced mortality than the Ashanti Region, Brong Ahafo Region, Northern Region, Western Region and Upper West Region. The Upper West Region and Western Region had the lowest incidence of mortality. The decline in trend of hypertension-induced mortality could be attributed to some healthcare interventions put in place during the period. One of these interventions was the introduction of health insurance in 2003, a development which has been shown to affect the health seeking behaviors of the people. It is, therefore, important to investigate factors affecting these spatial and temporal dynamics in order to determine appropriate ways to actively control the hypertension-induced deaths in the country. Public education on health should be intensified so as to totally curb hypertension and its attendant risks.

Review of Dimension Reduction Methods

Purpose: This study sought to review the characteristics, strengths, weaknesses variants, applications areas and data types applied on the various Dimension Reduction techniques. Methodology: The most commonly used databases employed to search for the papers were ScienceDirect, Scopus, Google Scholar, IEEE Xplore and Mendeley. An integrative review was used for the study where 341 papers were reviewed. Results: The linear techniques considered were Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), Latent Semantic Analysis (LSA), Locality Preserving Projections (LPP), Independent Component Analysis (ICA) and Project Pursuit (PP). The non-linear techniques which were developed to work with applications that have complex non-linear structures considered were Kernel Principal Component Analysis (KPCA), Multi-dimensional Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Self-Organizing Map (SOM), Latent Vector Quantization (LVQ), t-Stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). DR techniques can further be categorized into supervised, unsupervised and more recently semi-supervised learning methods. The supervised versions are the LDA and LVQ. All the other techniques are unsupervised. Supervised variants of PCA, LPP, KPCA and MDS have been developed. Supervised and semi-supervised variants of PP and t-SNE have also been developed and a semi supervised version of the LDA has been developed. Conclusion: The various application areas, strengths, weaknesses and variants of the DR techniques were explored. The different data types that have been applied on the various DR techniques were also explored.

Box-Jenkins’ Methodology in Predicting Maternal Mortality Records from a Public Health Facility in Ghana

The Millennium Development Goal (MDG) 5 advocated the reduction of maternal mortality rates significantly by 2015, however, maternal mortality rates continue to rise. Here, we modelled maternal mortality data for the years 2000 to 2013 obtained from a public hospital in Kumasi, Ghana. We applied the Box-Jenkins approach of univariate form of time series autoregressive integrated moving average (ARIMA). The output revealed that the ARIMA (1, 1, 1) model was most appropriate to model and predict monthly maternal cases with Akaike information criterion (AIC) value of 117.02 and Bayesian information criterion (BIC) value of 125.91. The Shapiro-Wilk normality test confirmed normality of the residuals. The Ljung-Box test on the residuals showed no serial correlation. The model was then validated based on the measures of accuracy. The results showed that the maternal mortality cases for the years 2000 to 2011 are high: minimum 3, median 11, mean 12 and maximum cases of 26 per month. The predicted mortality cases were 10 to 11 monthly for years 2012 to 2013, indicating that the target of MDG 5 could not be achieved by 2015. Fresh and perceptive strategies are urgently needed to arrest the unacceptably high death rates.