Evaluating the Contribution of Nocardia spp. and Mycobacterium tuberculosis to Pulmonary Infections among HIV and Non-HIV Patients at the Komfo Anokye Teaching Hospital, Ghana.

Tuberculosis (TB) is a major cause of human mortality particularly in association with the human immunodeficiency virus (HIV). Nocardia spp. has emerged as an opportunistic infection especially in HIV patients. The high prevalence of TB and HIV coupled with the lack of a definitive laboratory diagnosis for Nocardia spp. could lead to misdiagnosed pulmonary TB. This study determined the prevalence of pulmonary infections due to Nocardia spp. and Mycobacterium tuberculosis in sputum of HIV and non-HIV patients with suspected pulmonary tuberculosis at KATH. A total of sixty sputum samples were obtained from HIV and non-HIV patients with suspected pulmonary tuberculosis. Samples were examined by fluorescence based Ziehl-Neelsen staining, culture, and PCR methods. The prevalence of Nocardia spp. and Mycobacterium tuberculosis was 18.3% and 20%, respectively, with the latter having the highest rate among patients aged 21-40 years (P=0.075). The prevalence of Nocardia spp. among HIV patients was 90.9% whilst 16.7% of the patients had HIV/Nocardia spp. coinfection. Detection of Mycobacterium tuberculosis by fluorescence-based Ziehl-Neelsen staining, culture, and PCR yielded 9 (15%), 11 (18.3%), and 12 (20%), respectively. There is a high prevalence of nocardiosis especially in HIV patients. PCR is a better diagnostic method that detects both Nocardia spp. and Mycobacterium tuberculosis and should be incorporated into routine diagnosis for pulmonary infections.

A Weather-Based Prediction Model of Malaria Prevalence in Amenfi West District, Ghana.

This study investigated the effects of climatic variables, particularly, rainfall and temperature, on malaria incidence using time series analysis. Our preliminary analysis revealed that malaria incidence in the study area decreased at about 0.35% annually. Also, the month of November recorded approximately 21% more malaria cases than the other months while September had a decreased effect of about 14%. The forecast model developed for this investigation indicated that mean minimum (P = 0.01928) and maximum (P = 0.00321) monthly temperatures lagged at three months were significant predictors of malaria incidence while rainfall was not. Diagnostic tests using Ljung-Box and ARCH-LM tests revealed that the model developed was adequate for forecasting. Forecast values for 2016 to 2020 generated by our model suggest a possible future decline in malaria incidence. This goes to suggest that intervention strategies put in place by some nongovernmental and governmental agencies to combat the disease are effective and thus should be encouraged and routinely monitored to yield more desirable outcomes.