This article evaluates the accuracy of effect-size estimates for some estimation procedures in meta-analysis. The dilemma of which effect-size estimate is suitable is still a problem in meta-analysis. Monte Carlo simulations were used to generate random variables from a normal distribution or contaminated normal distribution for primary studies. The primary studies were hypothesised to have equal variance under different population effect sizes. The primary studies were also hypothesised to have unequal variance. Meta-analysis was done on the simulated hypothesized-primary-studies. The effect sizes for the simulated design of the primary studies were estimated using Cohen's d , Hedges' g , Glass' △, Cliff's delta d and the Probability of Superiority. Their corresponding standard error and confidence interval were computed and a comparison of an efficient estimator was done using statistical bias, percentage error and confidence interval width. The statistical bias, percentage error and confidence interval width pointed to Probability of Superiority as an accurate effect size estimate under contaminated normal distribution, and Hedges' g as the most accurate effect size estimates compared to Cohen's d and Glass' △ when equal variance assumptions are violated. This study suggests that the accuracy of effect size estimates depends on the details of the primary studies included in the meta-analysis.

In this paper, a deterministic mathematical model to investigate the transmission dynamics of malaria in Ghana is formulated taking into account human and mosquito populations. The model consists of seven non-linear differential equations which describe the dynamics of malaria with 4 variables for humans and 3 variables for mosquitoes. The state vector for the model is $(S_h, E_h, I_h, R, S_m, E_m, I_m,)$ where $S_h$, $E_h$, $I_h$, $R$, $S_m$, $E_m$ and $I_m$ respectively represent populations of susceptible humans, exposed humans, infectious humans, recovered humans, susceptible mosquitoes, exposed mosquitoes and infectious mosquitoes. Stability analysis of the model is performed and we make use of the next generation method to derive the basic reproduction number $R_0$. A mathematical analysis of the dynamic behaviour indicates that the estimated model has a unique endemic equilibrium point and malaria will persist in Ghana. The basic reproduction number for Ghana is found to be $R_0=0.8939$. Further, both the disease-free and endemic equilibria are locally asymptotically stable. Numerical simulations indicate that reducing current biting rate of female Anopheles mosquitoes by 1/16 could assist Ghana to achieve malaria free status by the year 2037. If, in addition, the number of days it takes to recover from malaria infection were reduced to three 3 days malaria free status could be achieved by the year 2029

We describe the rate of death and identify the determinants of survival in a cohort of adults starting antiretroviral therapy (ART) in 2 hospitals in Upper West Region, Ghana. Kaplan-Meier model was used to estimate the survival probability after ART initiation and Cox proportional hazard model used to assess the relationship between baseline variables and mortality. A total of 91 clients who were initiated on ART in both hospitals participated in the study. Clients staged in the World Health Organization (WHO) clinical stage III/IV had a higher risk of mortality than those staging I/II (hazard ratio [HR] of 3.93). Hemoglobin value at baseline with a cutoff ≥12 g/dL for women (and ≥13 for men) was strongly associated with mortality in participants with an HR of 3.87 (95% confidence interval [CI]: 0.71-21.19) for severe anemia, 2.11 (95% CI: 0.45-9.93) for moderate anemia, and 0.88 (95% CI: 0.16-4.82) for mild anemia. Anemia and WHO staging were independent predictors of mortality.

In this paper, we investigate the linkage between FDI and economic growth using macro econometric model in the Ghanaian context. Structural shocks in an SVAR model were used to identify the contemporaneous and short run relationships effects of these variables. The AB model restriction approach was used for the Identification and was compared to the Cholesky decomposition. We showed that, there exit a contemporaneous short run positive effects of FDI inflows on GDP growth but as the time horizon expands these effects tend to converge to the equilibrium, however FDI's deteriorate domestic investment.

Background: Low birth weight refers to new borns weighting less than 2.5 kg at birth. In November 2017, the WHO reported a global prevalence of 15.5% with 96.5% of these cases happening in developing countries. Whilst this is a global canker, the risk factors differ from locality to locality.  This study aims at determining which maternal factors explains low birth weight baby delivery in the Lower Manya Krobo Municipality.Methods: The chi-square test for independence was used to test for independence. The binary logistic model is fitted for the associated factors. The receiver operating characteristic (ROC) is used to classify unbiased estimators.Results: ANC (yes β= -2.769 sig.=0.000); Alcohol (none β=-1.479 sig.=0.000, occasionally β= −2.043 sig.=0.000); Age (<20years β=0.178 sig. =0.676, 20 to 25years β= -1.487 sig.=0.000, 26 to 30 β= -0.941 sig.=0.086); Education level (None β=2.778 sig. =0.000, primary β=3.090 sig.=0.000, JHS β=1.913 sig.=0.002, SHS/Secondary β=1.951 sig.=0.000); Exposure to Heat (Yes β=4.507 sig.=0.000). AUC education=0.67, 95% CI=0.6,0.7 and AUC Exposure to heat=0.73, 95% CI=0.68,0.77 of low birth weight.Conclusions: Social status was not significant factor. Mothers exposed to heat had the highest risk (odds=90 times). Adolescent mothers stand high risk with odds 1.195. Mothers who attended antenatal clinics were at 94% less likelihood. Mild drinkers had lesser risk compared to no and heavy drinkers. Mothers with primary education (odds=21 times) were the riskiest compared to mothers with tertiary education. This differs from researches where no education mothers were riskiest. Only mother’s exposure to heat was found to be fairly good unbiased estimators.