Modeling the positive testing rate of COVID-19 in South Africa using a semi-parametric smoother for binomial data.

Identification and isolation of COVID-19 infected persons plays a significant role in the control of COVID-19 pandemic. A country's COVID-19 positive testing rate is useful in understanding and monitoring the disease transmission and spread for the planning of intervention policy. Using publicly available data collected between March 5th, 2020 and May 31st, 2021, we proposed to estimate both the positive testing rate and its daily rate of change in South Africa with a flexible semi-parametric smoothing model for discrete data. There was a gradual increase in the positive testing rate up to a first peak rate in July, 2020, then a decrease before another peak around mid-December 2020 to mid-January 2021. The proposed semi-parametric smoothing model provides a data driven estimates for both the positive testing rate and its change. We provide an online R dashboard that can be used to estimate the positive rate in any country of interest based on publicly available data. We believe this is a useful tool for both researchers and policymakers for planning intervention and understanding the COVID-19 spread.

Development of Microbiome Biomarkers in Intervention Studies.

AIMS: There has been an increased interest in studying the association between microbial communities and different diseases and in discovering microbiome biomarkers. This association is pivotal to discover such biomarkers. In this paper, we present a unified modelling approach that can be used to detect and develop microbiome biomarkers for different clinical responses of interest at different levels of the microbiome ecosystem. METHODS AND RESULTS: We extended the methodology rooted in the information theory and joint modelling approaches for the evaluation of surrogate endpoints in randomized clinical trials to the high-dimensional microbiome setting. The unified modelling approach introduced in this paper allows for detecting biomarkers associated with a clinical response of interest, adjusting for the intervention applied to the subjects. For some microbiome features, the association is driven by the treatment, while for others, the association reflects the correlation between the microbiome biomarker and the clinical response of interest. CONCLUSIONS: The results have demonstrated that biomarkers can be identified at different levels of the microbiome phylogenetic tree using various measures as biomarkers.

Modelling trends of CD4 counts for patients on antiretroviral therapy (ART): a comprehensive health care clinic in Nairobi, Kenya.

BACKGROUND: In resource-limited settings, changes in CD4 counts constitute an important component in patient monitoring and evaluation of treatment response as these patients do not have access to routine viral load testing. In this study, we quantified trends on CD4 counts in patients on highly active antiretroviral therapy (HAART) in a comprehensive health care clinic in Kenya between 2011 and 2017. We evaluated the rate of change in CD4 cell count in response to antiretroviral treatment. We further assessed factors that influenced time to treatment change focusing on baseline characteristics of the patients and different initial drug regimens used. This was a retrospective study involving 432 naïve HIV patients that had at least two CD4 count measurements for the period. The relationship between CD4 cell count and time was modeled using a semi parametric mixed effects model while the Cox proportional hazards model was used to assess factors associated with the first regimen change. RESULTS: Majority of the patients were females and the average CD4 count at start of treatment was 362.1 [Formula: see text]. The CD4 count measurements increased nonlinearly over time and these trends were similar regardless of the treatment regimen administered to the patients. The change of logarithm CD4 cell count rises fast for in the first 450 days of antiretroviral initiation. The average time to first regimen change was 2142 days. Tenoforvir (TDF) based regimens had a lower drug substitution(aHR 0.2682, 95% CI:0.08263- 0.8706) compared to Zidovudine(AZT). CONCLUSION: The backbone used was found to be associated with regimen changes among the patients with fewer switches being observed, with the use of TDF when compared to AZT. There was however no significant difference between TDF and AZT in terms of the rate of change in logarithm CD4 count over time.

A Roadmap for Building Data Science Capacity for Health Discovery and Innovation in Africa.

Technological advances now make it possible to generate diverse, complex and varying sizes of data in a wide range of applications from business to engineering to medicine. In the health sciences, in particular, data are being produced at an unprecedented rate across the full spectrum of scientific inquiry spanning basic biology, clinical medicine, public health and health care systems. Leveraging these data can accelerate scientific advances, health discovery and innovations. However, data are just the raw material required to generate new knowledge, not knowledge on its own, as a pile of bricks would not be mistaken for a building. In order to solve complex scientific problems, appropriate methods, tools and technologies must be integrated with domain knowledge expertise to generate and analyze big data. This integrated interdisciplinary approach is what has become to be widely known as data science. Although the discipline of data science has been rapidly evolving over the past couple of decades in resource-rich countries, the situation is bleak in resource-limited settings such as most countries in Africa primarily due to lack of well-trained data scientists. In this paper, we highlight a roadmap for building capacity in health data science in Africa to help spur health discovery and innovation, and propose a sustainable potential solution consisting of three key activities: a graduate-level training, faculty development, and stakeholder engagement. We also outline potential challenges and mitigating strategies.

Epidemiology of Mycobacterium tuberculosis lineages and strain clustering within urban and peri-urban settings in Ethiopia.

BACKGROUND: Previous work has shown differential predominance of certain Mycobacterium tuberculosis (M. tb) lineages and sub-lineages among different human populations in diverse geographic regions of Ethiopia. Nevertheless, how strain diversity is evolving under the ongoing rapid socio-economic and environmental changes is poorly understood. The present study investigated factors associated with M. tb lineage predominance and rate of strain clustering within urban and peri-urban settings in Ethiopia. METHODS: Pulmonary Tuberculosis (PTB) and Cervical tuberculous lymphadenitis (TBLN) patients who visited selected health facilities were recruited in the years of 2016 and 2017. A total of 258 M. tb isolates identified from 163 sputa and 95 fine-needle aspirates (FNA) were characterized by spoligotyping and compared with international M.tb spoligotyping patterns registered at the SITVIT2 databases. The molecular data were linked with clinical and demographic data of the patients for further statistical analysis. RESULTS: From a total of 258 M. tb isolates, 84 distinct spoligotype patterns that included 58 known Shared International Type (SIT) patterns and 26 new or orphan patterns were identified. The majority of strains belonged to two major M. tb lineages, L3 (35.7%) and L4 (61.6%). The observed high percentage of isolates with shared patterns (n = 200/258) suggested a substantial rate of overall clustering (77.5%). After adjusting for the effect of geographical variations, clustering rate was significantly lower among individuals co-infected with HIV and other concomitant chronic disease. Compared to L4, the adjusted odds ratio and 95% confidence interval (AOR; 95% CI) indicated that infections with L3 M. tb strains were more likely to be associated with TBLN [3.47 (1.45, 8.29)] and TB-HIV co-infection [2.84 (1.61, 5.55)]. CONCLUSION: Despite the observed difference in strain diversity and geographical distribution of M. tb lineages, compared to earlier studies in Ethiopia, the overall rate of strain clustering suggests higher transmission and warrant more detailed investigations into the molecular epidemiology of TB and related factors.

Global prevalence of Mycobacterium bovis infections among human tuberculosis cases: Systematic review and meta-analysis.

Tuberculosis (TB) is a chronic communicable bacterial disease caused by Mycobacterium tuberculosis complex (MTBC) species. M. tuberculosis is the main causative agent of human TB, and cattle are the primary host of Mycobacterium bovis; due to close interaction between cattle and humans, M. bovis poses a zoonotic risk. This review summarizes and estimates the prevalence of M. bovis infection among human cases. Studies reporting TB prevalence data that were published in English during 10 years from 20 April 2009 to 17 April 2019 were identified through search of PubMed and other sources. Quality of studies and risk of bias were assessed using standard tools for prevalence study reports. Characteristics of included studies and their main findings were summarized in tables and discussed with narrative syntheses. Meta-analysis was performed on 19 included studies, with a total of 7,185 MTBC isolates identified; 702 (9.7%) of them were characterized as of subspecies M. bovis, but there was a large prevalence difference between the studies, ranging from 0.4% to 76.7%. The genotyping-based studies reported significantly lower prevalence of zoonotic TB than did the studies based on older techniques. The overall pooled prevalence of M. bovis aggregated from all included studies was 12.1% of the total MTBC isolates, while the corresponding pooled figure from the 14 genotyping-based studies was only 1.4%. Generally, human M. bovis cases reported from different countries of the world suggest that the impact of zoonotic TB is still important in all regions. However, it was difficult to understand the true picture of the disease prevalence because of methodological differences. Future investigations on zoonotic TB should carefully consider these differences when evaluating prevalence results.

Factors associated with localization of tuberculosis disease among patients in a high burden country: A health facility-based comparative study in Ethiopia.

INTRODUCTION: In contrast to most tuberculosis (TB) high burden countries, Ethiopia has for a long time reported a very high percentage of extra pulmonary TB (EPTB), which is also reflected in population based estimations reported by the World Health Organization (WHO). Particularly a steadily higher proportion of cervical tuberculous lymphadenitis (TBLN) has been described. Here we identify clinical and demographic factors associated with anatomic site of the TB disease. METHOD: A health facility based comparative study was conducted among TBLN and PTB patients who visited selected health facilities in Ethiopia during 2016 and 2017. Associated risk factors were identified through a multivariate logistic regression model using R-studio. RESULT: A total of 1,890 study participants, 427 TBLN and 1,463 PTB patients, were included. The mean age of TBLN patients (29 years ± 14.4 SD) was lower than that of PTB cases (36 years ± 15.0 SD). There were slightly more women diagnosed with TBLN (51.1%) while nearly 6 out of 10 male patients were diagnosed with PTB (58.9%). Most significantly, younger age groups (<15 Years) were more likely to develop cervical TBLN than older people (>56 years), with an AOR of 9.76 (95% CI: 4.87, 19.56). The odds of cervical TBLN among women [1.69 (1.30, 2.20)] was higher than that for men. In addition, adjusted estimates suggested that, compared with PTB, renal diseases [3.41 (1.29, 9.02)] and the presence of other concomitant chronic illness [1.61 (1.23, 2.09)] had a significant association with TBLN. CONCLUSION: Generally, the risk of developing a particular form of TB disease is usually associated with demographic and medical history of an infected individual. Hence, the current symptom based screening, which primarily rely on chronic cough in many countries, may lead to missing significant portions of TBLN cases.

Informal mhealth at scale in Africa: Opportunities and challenges.

The extraordinary global growth of digital connectivity has generated optimism that mobile technologies can help overcome infrastructural barriers to development, with 'mobile health' (mhealth) being a key component of this. However, while 'formal' (top-down) mhealth programmes continue to face challenges of scalability and sustainability, we know relatively little about how health-workers are using their own mobile phones informally in their work. Using data from Ghana, Ethiopia and Malawi, we document the reach, nature and perceived impacts of community health-workers' (CHWs') 'informal mhealth' practices, and ask how equitably these are distributed. We implemented a mixed-methods study, combining surveys of CHWs across the three countries, using multi-stage proportional-to-size sampling (N = 2197 total), with qualitative research (interviews and focus groups with CHWs, clients and higher-level stake-holders). Survey data were weighted to produce nationally- or regionally-representative samples for multivariate analysis; comparative thematic analysis was used for qualitative data. Our findings confirm the limited reach of 'formal' compared with 'informal' mhealth: while only 15% of CHWs surveyed were using formal mhealth applications, over 97% reported regularly using a personal mobile phone for work-related purposes in a range of innovative ways. CHWs and clients expressed unequivocally enthusiastic views about the perceived impacts of this 'informal health' usage. However, they also identified very real practical challenges, financial burdens and other threats to personal wellbeing; these appear to be borne disproportionately by the lowest-paid cadre of health-workers, especially those serving rural areas. Unlike previous small-scale, qualitative studies, our work has shown that informal mhealth is already happening at scale, far outstripping its formal equivalent. Policy-makers need to engage seriously with this emergent health system, and to work closely with those on the ground to address sources of inequity, without undermining existing good practice.

Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach.

BACKGROUND: The rising burden of the ongoing COVID-19 epidemic in South Africa has motivated the application of modeling strategies to predict the COVID-19 cases and deaths. Reliable and accurate short and long-term forecasts of COVID-19 cases and deaths, both at the national and provincial level, are a key aspect of the strategy to handle the COVID-19 epidemic in the country. METHODS: In this paper we apply the previously validated approach of phenomenological models, fitting several non-linear growth curves (Richards, 3 and 4 parameter logistic, Weibull and Gompertz), to produce short term forecasts of COVID-19 cases and deaths at the national level as well as the provincial level. Using publicly available daily reported cumulative case and death data up until 22 June 2020, we report 5, 10, 15, 20, 25 and 30-day ahead forecasts of cumulative cases and deaths. All predictions are compared to the actual observed values in the forecasting period. RESULTS: We observed that all models for cases provided accurate and similar short-term forecasts for a period of 5 days ahead at the national level, and that the three and four parameter logistic growth models provided more accurate forecasts than that obtained from the Richards model 10 days ahead. However, beyond 10 days all models underestimated the cumulative cases. Our forecasts across the models predict an additional 23,551-26,702 cases in 5 days and an additional 47,449-57,358 cases in 10 days. While the three parameter logistic growth model provided the most accurate forecasts of cumulative deaths within the 10 day period, the Gompertz model was able to better capture the changes in cumulative deaths beyond this period. Our forecasts across the models predict an additional 145-437 COVID-19 deaths in 5 days and an additional 243-947 deaths in 10 days. CONCLUSIONS: By comparing both the predictions of deaths and cases to the observed data in the forecasting period, we found that this modeling approach provides reliable and accurate forecasts for a maximum period of 10 days ahead.

Exploring the Microbiome Analysis and Visualization Landscape.

Research on the microbiome has boomed recently, which resulted in a wide range of tools, packages, and algorithms to analyze microbiome data. Here we investigate and map currently existing tools that can be used to perform visual analysis on the microbiome, and associate the including methods, visual representations and data features to the research objectives currently of interest in microbiome research. The analysis is based on a combination of a literature review and workshops including a group of domain experts. Both the reviewing process and workshops are based on domain characterization methods to facilitate communication and collaboration between researchers from different disciplines. We identify several research questions related to microbiomes, and describe how different analysis methods and visualizations help in tackling them.

High dimensional surrogacy: computational aspects of an upscaled analysis.

Identification of genomic biomarkers is an important area of research in the context of drug discovery experiments. These experiments typically consist of several high dimensional datasets that contain information about a set of drugs (compounds) under development. This type of data structure introduces the challenge of multi-source data integration. High-Performance Computing (HPC) has become an important tool for everyday research tasks. In the context of drug discovery, high dimensional multi-source data needs to be analyzed to identify the biological pathways related to the new set of drugs under development. In order to process all information contained in the datasets, HPC techniques are required. Even though R packages for parallel computing are available, they are not optimized for a specific setting and data structure. In this article, we propose a new framework, for data analysis, to use R in a computer cluster. The proposed data analysis workflow is applied to a multi-source high dimensional drug discovery dataset and compared with a few existing R packages for parallel computing.

Correlations between the metabolic profile and 18F-FDG-Positron Emission Tomography-Computed Tomography parameters reveal the complexity of the metabolic reprogramming within lung cancer patients.

Several studies have demonstrated that the metabolite composition of plasma may indicate the presence of lung cancer. The metabolism of cancer is characterized by an enhanced glucose uptake and glycolysis which is exploited by 18F-FDG positron emission tomography (PET) in the work-up and management of cancer. This study aims to explore relationships between 1H-NMR spectroscopy derived plasma metabolite concentrations and the uptake of labeled glucose (18F-FDG) in lung cancer tissue. PET parameters of interest are standard maximal uptake values (SUVmax), total body metabolic active tumor volumes (MATVWTB) and total body total lesion glycolysis (TLGWTB) values. Patients with high values of these parameters have higher plasma concentrations of N-acetylated glycoproteins which suggest an upregulation of the hexosamines biosynthesis. High MATVWTB and TLGWTB values are associated with higher concentrations of glucose, glycerol, N-acetylated glycoproteins, threonine, aspartate and valine and lower levels of sphingomyelins and phosphatidylcholines appearing at the surface of lipoproteins. These higher concentrations of glucose and non-carbohydrate glucose precursors such as amino acids and glycerol suggests involvement of the gluconeogenesis pathway. The lower plasma concentration of those phospholipids points to a higher need for membrane synthesis. Our results indicate that the metabolic reprogramming in cancer is more complex than the initially described Warburg effect.

Model-based prediction of CD4 cells counts in HIV-infected adults on antiretroviral therapy in Northwest Ethiopia: A flexible mixed effects approach.

BACKGROUND: CD4 cell counts is widely used as a biomarker for treatment progression when studying the efficacy of drugs to treat HIV-infected patients. In the past, it had been also used in determining eligibility to initiate antiretroviral therapy. The main aim of this was to model the evolution of CD4 counts over time and use this model for an early prediction of subject-specific time to cross a pre-specified CD4 threshold. METHODS: Hospital based retrospective cohort study of HIV-infected patients was conducted from January 2009 to December 2014 at University of Gondar hospital, Northwest Ethiopia. Fractional polynomial random effect model is used to model the evolution of CD4 counts over time in response to treatment and to estimate the individual probability to be above a pre-selected CD4 threshold. Human subject research approval for this study was received from University of Gondar Research Ethics Committee and the medical director of the hospital. RESULTS: A total of 1347 patients were included in the analysis presented in this paper. The cohort contributed a total of 236.58 per 100 person-years of follow-up. Later the data were divided into two periods: the first is the estimation period in which the parameters of the model are estimated and the second is the prediction period. Based on the parameters from the estimation period, model based prediction for the time to cross a threshold was estimated. The correlations between observed and predicted values of CD4 levels in the estimation period were 0.977 and 0.982 for Neverapine and Efavirenz containing regimens, respectively; while the correlation between the observed and predicted CD4 counts in the prediction period are 0.742 and 0.805 for NVP and EFV, respectively. CONCLUSIONS: The model enables us to estimate a subject-specific expected time to cross a CD4 threshold and to estimate a subject-specific probability to have CD4 count above a pre-specified threshold at each time point. By predicting long-term outcomes of CD4 count of the patients one can advise patient about the potential ART benefits that accrue in the long-term.

A multivariate linear model for investigating the association between gene-module co-expression and a continuous covariate.

A way to enhance our understanding of the development and progression of complex diseases is to investigate the influence of cellular environments on gene co-expression (i.e. gene-pair correlations). Often, changes in gene co-expression are investigated across two or more biological conditions defined by categorizing a continuous covariate. However, the selection of arbitrary cut-off points may have an influence on the results of an analysis. To address this issue, we use a general linear model (GLM) for correlated data to study the relationship between gene-module co-expression and a covariate like metabolite concentration. The GLM specifies the gene-pair correlations as a function of the continuous covariate. The use of the GLM allows for investigating different (linear and non-linear) patterns of co-expression. Furthermore, the modeling approach offers a formal framework for testing hypotheses about possible patterns of co-expression. In our paper, a simulation study is used to assess the performance of the GLM. The performance is compared with that of a previously proposed GLM that utilizes categorized covariates. The versatility of the model is illustrated by using a real-life example. We discuss the theoretical issues related to the construction of the test statistics and the computational challenges related to fitting of the proposed model.

Differential effects of two therapeutic cancer vaccines on short- and long-term survival populations among patients with advanced lung cancer.

BACKGROUND: Progress in immunotherapy has revolutionized the treatment landscape for advanced lung cancer, with emerging evidence of patients experiencing long-term survivals. The goal of this study was to explore the existence of short- and long-term survival populations and to assess the effect of immunotherapy on them. METHODS: Data from two randomized, multicenter, controlled clinical trials was used to evaluate the effect of two therapeutic vaccines (anti-idiotypic vaccine VAXIRA and anti-EGF vaccine CIMAVAX) on survival curves in advanced non-small cell lung cancer patients. Data were fitted to Kaplan-Meier, standard Weibull survival, and two-component Weibull mixture models. Bayesian Information Criterion was used for model selection. RESULTS: VAXIRA did not modify, neither the fraction of patients with long-term survivals (0.18 in the control group v 0.19 with VAXIRA, P = .88), nor the median overall survival of the patients in the short-term survival subpopulation (6.8 v 7.8 months, P = .24). However, this vaccine showed great benefit for the patients belonging to the subpopulation of patients with long-term survival (33.8 v 76.6 months, P <.0001). CIMAVAX showed impact in the overall survival of both short- and long-term populations (6.8 v 8.8 months, P = .005 and 33.8 v 61.8 months, P = .007). It also increased the proportion of patients with long-term survival (from 0.18 to 0.28, P = .02). CONCLUSIONS: This study shows that therapeutic vaccines produce differential effects on short- and long-term survival populations and illustrates the application of advanced statistical methods to deal with the long-term evolution of patients with advanced lung cancer in the era of immunotherapy.

The Usage of Exon-Exon Splice Junctions for the Detection of Alternative Splicing using the REIDS model.

Alternative gene splicing is a common phenomenon in which a single gene gives rise to multiple transcript isoforms. The process is strictly guided and involves a multitude of proteins and regulatory complexes. Unfortunately, aberrant splicing events have been linked to genetic disorders. Therefore, understanding mechanisms of alternative splicing regulation and differences in splicing events between diseased and healthy tissues is crucial in advancing personalized medicine and drug developments. We propose a linear mixed model, Random Effects for the Identification of Differential Splicing (REIDS), for the identification of alternative splicing events using Human Transcriptome Arrays (HTA). For each exon, a splicing score is calculated based on two scores, an exon score and an array score. The junction information is used to rank the identified exons from strongly confident to less confident candidates for alternative splicing. The design of junctions was also discussed to highlight the complexity of exon-exon and exon-junction interactions. Based on a list of Rt-PCR validated probe sets, REIDS outperforms AltAnalyze and iGems in the % recall rate.

Diffusion kurtosis imaging allows the early detection and longitudinal follow-up of amyloid-ß-induced pathology.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the elderly population. In this study, we used the APP/PS1 transgenic mouse model to explore the feasibility of using diffusion kurtosis imaging (DKI) as a tool for the early detection of microstructural changes in the brain due to amyloid-ß (Aß) plaque deposition. METHODS: We longitudinally acquired DKI data of wild-type (WT) and APP/PS1 mice at 2, 4, 6 and 8 months of age, after which these mice were sacrificed for histological examination. Three additional cohorts of mice were also included at 2, 4 and 6 months of age to allow voxel-based co-registration between diffusion tensor and diffusion kurtosis  metrics and immunohistochemistry. RESULTS: Changes were observed in diffusion tensor (DT) and diffusion kurtosis (DK) metrics in many of the 23 regions of interest that were analysed. Mean and axial kurtosis were greatly increased owing to Aß-induced pathological changes in the motor cortex of APP/PS1 mice at 4, 6 and 8 months of age. Additionally, fractional anisotropy (FA) was decreased in APP/PS1 mice at these respective ages. Linear discriminant analysis of the motor cortex data indicated that combining diffusion tensor and diffusion kurtosis metrics permits improved separation of WT from APP/PS1 mice compared with either diffusion tensor or diffusion kurtosis metrics alone. We observed that mean kurtosis and FA are the critical metrics for a correct genotype classification. Furthermore, using a newly developed platform to co-register the in vivo diffusion-weighted magnetic resonance imaging with multiple 3D histological stacks, we found high correlations between DK metrics and anti-Aß (clone 4G8) antibody, glial fibrillary acidic protein, ionised calcium-binding adapter molecule 1 and myelin basic protein immunohistochemistry. Finally, we observed reduced FA in the septal nuclei of APP/PS1 mice at all ages investigated. The latter was at least partially also observed by voxel-based statistical parametric mapping, which showed significantly reduced FA in the septal nuclei, as well as in the corpus callosum, of 8-month-old APP/PS1 mice compared with WT mice. CONCLUSIONS: Our results indicate that DKI metrics hold tremendous potential for the early detection and longitudinal follow-up of Aß-induced pathology.

Choice of initial antiretroviral drugs and treatment outcomes among HIV-infected patients in sub-Saharan Africa: systematic review and meta-analysis of observational studies.

BACKGROUND: The effectiveness of antiretroviral therapy (ART) depends on the choice of regimens during initiation. Most evidences from developed countries indicated that there is difference between efavirenz (EFV) and nevirapine (NVP). However, the evidences are limited in resource poor countries particularly in Africa. Thus, this systematic review and meta-analysis was carried out to summarize reported long-term treatment outcomes among people on first line therapy in sub-Saharan Africa. METHODS: Observational studies that reported odds ratio, relative risk, hazard ratio, or standardized incidence ratio to compare risk of treatment failure among HIV/AIDS patients who initiated ART with EFV versus NVP were systematically searched. Searches were conducted using the MEDLINE database within PubMed, Google Scholar, HINARI, and Research Gates between 2007 and 2016. Information was extracted using standardized form. Pooled risk ratios (RR) and 95% confidence intervals (CI) were calculated using random-effect, generic inverse variance method. RESULT: A total of 6394 articles were identified, of which, 29 were eligible for review and abstraction in sub-Saharan Africa. Seventeen articles were used for the meta-analysis. Of a total of 121,092 independent study participants, 76,719 (63.36%) were females. Of these, 40,480 (33.43%) initiated with NVP containing regimen. Two studies did not report the median CD4 cell counts at initiation. Patients who have low CD4 cell counts initiated with EFV containing regimen. The pooled effect size indicated that treatment failure was reduced by 15%, 0.85 (95%CI: 0.75-0.98), and non-nucleoside reverse transcriptase inhibitor (NNRTI) switch was reduced by 43%, 0.57 (95%CI: 0.37-0.89). CONCLUSION: The risk of treatment failure and NNRTI switch were lower in patients who initiated with EFV than NVP-containing regimen. The review suggests that initiation of patients with EFV-containing regimen will reduce treatment failure and NNRTI switch.

Disease severity in patients with visceral leishmaniasis is not altered by co-infection with intestinal parasites.

Visceral leishmaniasis (VL) is a neglected tropical disease that affects the poorest communities and can cause substantial morbidity and mortality. Visceral leishmaniasis is characterized by the presence of Leishmania parasites in the spleen, liver and bone marrow, hepatosplenomegaly, pancytopenia, prolonged fever, systemic inflammation and low body mass index (BMI). The factors impacting on the severity of VL are poorly characterized. Here we performed a cross-sectional study to assess whether co-infection of VL patients with intestinal parasites influences disease severity, assessed with clinical and haematological data, inflammation, cytokine profiles and BMI. Data from VL patients was similar to VL patients co-infected with intestinal parasites, suggesting that co-infection of VL patients with intestinal parasites does not alter disease severity.

Multi-state models for the analysis of time-to-treatment modification among HIV patients under highly active antiretroviral therapy in Southwest Ethiopia.

BACKGROUND: Highly active antiretroviral therapy (HAART) has shown a dramatic change in controlling the burden of HIV/AIDS. However, the new challenge of HAART is to allow long-term sustainability. Toxicities, comorbidity, pregnancy, and treatment failure, among others, would result in frequent initial HAART regimen change. The aim of this study was to evaluate the durability of first line antiretroviral therapy and to assess the causes of initial highly active antiretroviral therapeutic regimen changes among patients on HAART. METHODS: A Hospital based retrospective study was conducted from January 2007 to August 2013 at Jimma University Hospital, Southwest Ethiopia. Data on the prescribed ARV along with start date, switching date, and reason for change was collected. The primary outcome was defined as the time-to-treatment change. We adopted a multi-state survival modeling approach assuming each treatment regimen as state. We estimate the transition probability of patients to move from one regimen to another. RESULT: A total of 1284 ART naive patients were included in the study. Almost half of the patients (41.2%) changed their treatment during follow up for various reasons; 442 (34.4%) changed once and 86 (6.69%) changed more than once. Toxicity was the most common reason for treatment changes accounting for 48.94% of the changes, followed by comorbidity (New TB) 14.31%. The HAART combinations that were robust to treatment changes were tenofovir (TDF) + lamivudine (3TC)+ efavirenz (EFV), tenofovir + lamivudine (3TC) + nevirapine (NVP) and zidovudine (AZT) + lamivudine (3TC) + nevirapine (NVP) with 3.6%, 4.5% and 11% treatment changes, respectively. CONCLUSION: Moving away from drugs with poor safety profiles, such as stavudine(d4T), could reduce modification rates and this would improve regimen tolerability, while preserving future treatment options.