Background: Hypertension has become an important public health concern in the developing world owing to rising prevalence and its adverse impact on ailing health systems. Despite being a modifiable risk factor for cardiovascular disease, hypertension has not received the needed attention in Ghana as a result of various competing interests for scarce health resources. This systematic review and meta-analysis provides a comprehensive and updated summary of the literature on the prevalence of hypertension in Ghana. Methods: Major databases such as MEDLINE, EMBASE, and Google Scholar and local thesis repositories were accessed to identify population-based studies on hypertension among Ghanaians. Data extracted from retrieved reports were screened independently by two reviewers. The quality of eligible studies was evaluated and reported. A reliable pooled estimate of hypertension prevalence was calculated utilizing a random-effects model and reported according to the GRADE framework. Additionally, a meta-regression analysis was performed to analyze the contribution of study-level variables to variance in hypertension prevalence. Results: In general, a total of 45,470 subjects (n = 22,866 males and 22,604 females) were enrolled from urban (n = 12), rural (n = 8), and mixed populations (n = 7). Blood pressure (BP) was measured across studies according to a validated and clinically approved protocol by trained field workers or healthcare workers including nurses and physicians. A combined total of 30,033 participants across twenty studies reporting on the population prevalence of hypertension were pooled with 10,625 (35.4%) identified to satisfy study criteria for elevated BP. The pooled prevalence across 24 studies was 30.3% (95% CI 26.1–34.8%) after fitting a random effects model. Prevalence of hypertension was 30.1% (95% CI 25.6–36.0%) among females and 34.0% (95% CI 28.5–40.0%) among males. Significant differences in pooled estimates across regions emerged from subgroup comparisons of regional estimates with an increasing trend in the north-to-south direction and with increasing age. Compared to rural settings, the burden of hypertension in urban populations was significantly higher. Age structure and population type accounted for 65.0% of the observed heterogeneity in hypertension estimates. Conclusions: The prevalence of hypertension in Ghana is still high. The gap in hypertension prevalence between rural and urban populations is closing especially in elderly populations. These findings must claim the attention of public health authorities in Ghana to explore opportunities to reduce rural hypertension. Systematic review registration: The protocol for this review has been published previously with PROSPERO (CRD42020215829).

Students pick up the perception that mathematics is abstract and therefore, the learning of mathematics would yield to them no benefit. With their attitude towards mathematics modelled and their interest for mathematics impacted by this automatic generated perception, they may never again appreciate the beauty of mathematics. In this paper, the researchers used structural equation modeling (SEM), to investigate the variables that affect students’ interest, among the variables, students’ confidence and motivation. The foregoing variables were conceptualized to have a direct effect on students’ interest in mathematics, whilst mathematics anxiety and students’ knowledge of the usefulness of mathematics were conceptualized to have indirect effects on their interest in mathematics moderated by students’ confidence and motivation. The result showed that significantly students’ confidence directly affects students’ interest in the learning of mathematics and there is a direct relationship between confidence and motivation. A student’s knowledge about the usefulness of mathematics indirectly increases the student’s interest in mathematics.

Objective In developing countries, malnutrition is a noteworthy concern related to the well-being of people, and this study aimed to determine the factors that affect malnutrition among children below 5 years in Ghana. Design The study used a secondary data source, specifically the Ghanaian Multiple Indicator Cluster Survey Six (MICS 6), which was conducted by the Ghana Statistical Service in 2017–2018. The MICS data are hierarchical, as children are categorised within households, and households are further grouped within a higher cluster, violating the independence assumption that must be addressed in the analyses. This study used a Bayesian multilevel ordinal logistic regression to model, identify and analyse the factors linked to child malnutrition in Ghana. Setting The setting of the study was the household level across the previous 10 administrative regions in Ghana. Participants Data for 8875 children under 5 years were used for the study. The data were gathered from households in all 10 administrative regions of Ghana using a sampling procedure consisting of stratification and random selection to ensure national representation. Results The results showed that the Northern Region of Ghana had the highest occurrence rate of severe and moderate malnutrition, and factors such as the count of children’s books or picture books, whether the child experienced fever in the last 2 weeks, age and sex of the child, and the child’s household wealth index quintile were strongly linked to malnutrition among Ghanaian children. Conclusion These findings underscore the intricate interplay of factors contributing to child nutrition in Ghana and suggest that addressing malnutrition necessitates a comprehensive approach that considers factors such as access to healthcare and reading materials, household wealth, and other social and environmental factors.

Malaria is considered endemic in over hundred countries across the globe. Many cases of malaria and deaths due to malaria occur in Sub-Saharan Africa. The disease is of great public health concern since it affects people of all age groups more especially pregnant women and children because of their vulnerability. This study sought to use vector autoregression (VAR) models to model the impact of climatic variability on malaria. Monthly climatic data (rainfall, maximum temperature, and relative humidity) from 2010 to 2015 were obtained from the Ghana Meteorological Agency while data on malaria for the same period were obtained from the Ghana Health Service. Results of the Granger and instantaneous causality tests led to a conclusion that malaria is influenced by all three climatic variables. The impulse response analyses indicated that the highest positive effect of maximum temperature, relative humidity, and rainfall on malaria is observed in the months of September, March, and October, respectively. The decomposition of forecast variance indicates varying degree of malaria dependence on the climatic variables, with as high as 12.65% of the variability in the trend of malaria which has been explained by past innovations in maximum temperature alone. This is quite significant and therefore, policy-makers should not ignore temperature when formulating policies to address malaria.

Students’ satisfaction in the university environment is essential to both the student (customer) and management of the university. Satisfied students are determined to succeed in their academics, and this sustains their loyalty and trust, which results in an improved image and esteem of the university. This study examined the level of students’ satisfaction with campus facilities and infrastructure, campus social life, student support services, and the quality of academics in the University of Energy and Natural Resources (UENR) in Ghana and further investigated how students’ satisfaction with the above four areas of the university environment affect each other. A questionnaire was administered to continuous students in UENR, and the collected data were analysed using structural equation modelling within the maximum likelihood and Bayesian frameworks whose results and performance were compared. Results showed that students’ satisfaction levels with available campus facilities, campus social life, and student support services were low but were fairly satisfied with the quality of academics. Both maximum likelihood and Bayesian techniques showed positive significant effects of students’ satisfaction with campus facilities and infrastructure on satisfaction with campus social life, students’ support services, and academics. Moreover, students’ satisfaction with social life was positively associated with their satisfaction with academics and student support services. Although both estimation methods obtained similar estimates and inferences, the Bayesian SEM outperformed the ML-SEM based on the recommended fit indices. Findings of the study highlight the significant effects of satisfaction with campus facilities and student support services on students’ satisfaction with academics and the university environment at large. The study further underpins the important role of the availability of adequate facilities and quality students’ services in improving and sustaining satisfaction.

In this study, we extend Codeço’s classical SI-B epidemic and endemic model from a deterministic framework into a stochastic framework. Then, we formulated it as a stochastic differential equation for the number of infectious individuals under the role of the aquatic environment. We also proved that this stochastic differential equation (SDE) exists and is unique. The reproduction number, , was derived for the deterministic model, and qualitative features such as the positivity and invariant region of the solution, the two equilibrium points (disease-free and endemic equilibrium), and stabilities were studied to ensure the biological meaningfulness of the model. Numerical simulations were also carried out for the stochastic differential equation (SDE) model by utilizing the Euler-Maruyama numerical method. The method was used to simulate the sample path of the SI-B stochastic differential equation for the number of infectious individuals , and the findings showed that the sample path or trajectory of the stochastic differential equation for the number of infectious individuals is continuous but not differentiable and that the SI-B stochastic differential equation model for the number of infectious individuals fluctuates inside the solution of the SI-B ordinary differential equation model. Another significant feature of our proposed SDE model is its simplicity.