University of Zambia

About: University of Zambia is a education organization based out in Lusaka, Lusaka, Zambia. It is known for research contribution in the topics: Population & Health care. The organization has 2593 authors who have published 4402 publications receiving 122411 citations. The organization is also known as: UNZA.

Benchmarking health system performance across districts in Zambia: a systematic analysis of levels and trends in key maternal and child health interventions from 1990 to 2010.

Abstract: Achieving universal health coverage and reducing health inequalities are primary goals for an increasing number of health systems worldwide. Timely and accurate measurements of levels and trends in key health indicators at local levels are crucial to assess progress and identify drivers of success and areas that may be lagging behind. We generated estimates of 17 key maternal and child health indicators for Zambia’s 72 districts from 1990 to 2010 using surveys, censuses, and administrative data. We used a three-step statistical model involving spatial-temporal smoothing and Gaussian process regression. We generated estimates at the national level for each indicator by calculating the population-weighted mean of the district values and calculated composite coverage as the average of 10 priority interventions. National estimates masked substantial variation across districts in the levels and trends of all indicators. Overall, composite coverage increased from 46% in 1990 to 73% in 2010, and most of this gain was attributable to the scale-up of malaria control interventions, pentavalent immunization, and exclusive breastfeeding. The scale-up of these interventions was relatively equitable across districts. In contrast, progress in routine services, including polio immunization, antenatal care, and skilled birth attendance, stagnated or declined and exhibited large disparities across districts. The absolute difference in composite coverage between the highest-performing and lowest-performing districts declined from 37 to 26 percentage points between 1990 and 2010, although considerable variation in composite coverage across districts persisted. Zambia has made marked progress in delivering maternal and child health interventions between 1990 and 2010; nevertheless, substantial variations across districts and interventions remained. Subnational benchmarking is important to identify these disparities, allowing policymakers to prioritize areas of greatest need. Analyses such as this one should be conducted regularly and feed directly into policy decisions in order to increase accountability at the local, regional, and national levels.

Antibiograms of faecal Escherichia coli and Enterococci species isolated from pastoralist cattle in the interface areas of the Kafue basin in Zambia - short communication

Abstract: Antimicrobial resistance to a panel of ten agents was determined by the disc diffusion technique for 83 escherichia coli isolates, 29 enterococcus faecium isolates and 62 enterococcus faecalis isolates from faecal samples of apparently healthy pastoral cattle in the wildlife/livestock interface areas. Of all the e. coli isolates, 8% were diarrhoeagenic e. coli strains, 6% were enteropathogenic e. coli strains and 2% were enterotoxigenic e. coli strains. A high frequency of e. coli resistance to penicillin, erythromycin, cotrimoxazole and nitrofurantoin was observed. enterococci showed the highest percentage of resistance to gentamycin, amoxycillin, ampicillin and tetracycline. None of the e. coli strains and enterococci strains was resistant to tetracycline and vancomycin respectively. The results of this study underscore the presence of an animal reservoir of antibiotic resistant microorganisms that have the potential to enter the food chain.

Uptake of intermittent preventive treatment for malaria during pregnancy with Sulphadoxine-Pyrimethamine (IPTp-SP) among postpartum women in Zomba District, Malawi: a cross-sectional study

Abstract: Malaria in pregnancy causes adverse birth outcomes. Intermittent preventive treatment of malaria during pregnancy with sulphadoxine-pyrimethamine (IPTp-SP) is recommended as a chemoprevention therapy. Zomba district IPTp uptake falls far below the national average. The study was conducted to assess determinants of IPTp-SP uptake during pregnancy among postpartum women in Zomba district after adoption of new IPTp-SP policy in 2014. This was a cross-sectional survey. Two public health facilities (HFs) were randomly selected from urban and rural areas in Zomba district. Study participants were postpartum women selected by using exit poll method from HFs. A total of 463 postpartum women were interviewed using structured questionnaire. Bivariate and multiple logistic regression was used in data analysis. Out of all the enrolled participants (n = 463), 92% women had complete information for analysis. Of these, (n = 426) women, 127 (29.8%, 95% CI: 25.6%–34.3%) received three or more doses of SP, 299 (70.2%, 95% CI: 65.7%–74.4%) received two or less doses. Women receiving SP from rural HF were less likely to get at least three doses of SP than urban women, (AOR = 0.31, 95% CI 0.13–0.70); Others less likely were those with three or few antenatal care (ANC) visits versus four or more visits (AOR = 0.29, 95% CI 0.18–0.48); not taking SP under direct observation therapy (DOT) (AOR = 0.18, 95% CI (0.05–0.63). There is low utilisation of at least three doses of SP in this population and this seems to be associated with the number of ANC visits and use of DOTs. These determinants may therefore be important in shaping interventions aimed at increasing the uptake of IPTp in this district. In addition, the rural urban differential suggests the need for further research to understand the barriers and enablers of uptake in each context in order to improve the health of the community.

HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach

Abstract: Image classification is central to the big data revolution in medicine. Improved information processing methods for diagnosis and classification of digital medical images have shown to be successful via deep learning approaches. As this field is explored, there are limitations to the performance of traditional supervised classifiers. This paper outlines an approach that is different from the current medical image classification tasks that view the issue as multi-class classification. We performed a hierarchical classification using our Hierarchical Medical Image classification (HMIC) approach. HMIC uses stacks of deep learning models to give particular comprehension at each level of the clinical picture hierarchy. For testing our performance, we use biopsy of the small bowel images that contain three categories in the parent level (Celiac Disease, Environmental Enteropathy, and histologically normal controls). For the child level, Celiac Disease Severity is classified into 4 classes (I, IIIa, IIIb, and IIIC).