Bayesian generalized linear mixed modeling of breast cancer data in Nigeria.

Doctor of Philosophy in Statistics, University of KwaZulu-Natal, Westville, 2017.Breast cancer is the world’s most prevalent type of cancer among women. Statistics indicate that breast cancer alone accounted for 37% out of all the cases of cancer diagnosed in Nigeria in 2012. Data used in this study are extracted from patient records, commonly called hospital-based records, and identified key socio-demographic and biological risk factors of breast cancer. Researchers sometimes ignore the hierarchical structure of the data and the disease when analyzing data. Doing so may lead to biased parameter estimates and larger standard error. That is why the analyses undertaken in this study included the multilevel structure of cancer diagnosis, types, and medication through a Generalized Linear Mixed Model (GLMM) which consider both fixed and random effects (level 1 and 2). In addition to the classical statistics approach, this study incorporates the Bayesian GLMM approach as well as some bootstrapping techniques. All the analyses are done using R or SAS for the classical statistics approaches, and WinBUGS for the Bayesian approach. The Bayesian analyses were strengthened by advanced analyses of convergence and autocorrelation checks, and other Markov Chain assumptions using the CODA and BOA packages. The findings reveal that Bayesian techniques provide more comprehensive results, given that Bayesian analysis is a more statistically strong technique. The Bayesian methods appeared more robust than the classical and bootstrapping techniques in analyzing breast cancer data in Western Nigeria. The results identified age at diagnosis, educational status, grade tumor, and breast cancer type as prognostic factors of breast cancer

Socio-economic status as predictors of malaria transmission in KwaZulu-Natal, South Africa. A retrospective study

Background: Understanding the socioeconomic status that influences malaria transmission in KwaZulu-Natal, South Africa is vital in creating policies and strategies to combat malaria transmission, improve socioeconomic conditions and strengthen the malaria elimination campaign. Objectives: To determine the relationship between socioeconomic status and malaria incidence in KwaZulu-Natal, South Africa. Methods: Socioeconomic information (gender, age, no formal education, no electricity, no toilet facilities, unemployment) and malaria data for 2011 were obtained from Statistics South Africa and the malaria control program of KwaZulu-Natal, South Africa respectively. The analysis was conducted employing the Bayesian multiple regression model. Results: The obtained posterior samples show that all the variables employed in this study were significant and positive predictors of malaria disease at 95% credible interval. The low socioeconomic status that exhibited the strongest association with malaria risk was lack of toilet facilities (odd ratio =12.39; 95% credible interval = 0.61, 24.36). This was followed by no formal education (odd ratio =11.11; 95% credible interval = 0.51, 24.10) and lack of electricity supply (odd ratio =8.94; 95% credible interval = 0.31, 23.21) respectively. Conclusions: Low socioeconomic status potentially sustains malaria transmission and burden. As an implication, poverty alleviation and malaria intervention resources should be incorporated side by side into the socioeconomic framework to attain zero malaria transmission. Keywords: Malaria; socialeconomic status; Bayesian modelling; KwaZulu-Natal; South Africa

Meta-Analysis of Factors Influencing Student Acceptance of Massive Open Online Courses for Open Distance Learning

This study aimed to apply the meta-analysis methodology to systematically synthesize results of primary studies to discover the main significant factors influencing student acceptance of massive open online courses (MOOCs) for open distance learning (ODL). An abundance of studies on MOOCs exists, but there is a lack of meta-analysis research on student acceptance of MOOCs, which is a novel contribution of the current study. The meta-analysis methodology was applied to investigate effect sizes, statistical heterogeneity, and publication bias across 36 primary studies involving 14233 participating students. The study findings show satisfaction to be the main significant factor influencing student acceptance of MOOCs. The findings can enlighten stakeholders in the decision-making process of implementing MOOCs for ODL and advance technology acceptance models. Moreover, this study has the potential to theoretically contribute to technology acceptance research by situating the widely known technology acceptance models in the context of education

The effect of a mobile-learning curriculum on improving compliance to quality management guidelines for HIV rapid testing services in rural primary healthcare clinics, KwaZulu-Natal, South Africa : a quasi-experimental study

BACKGROUND: Despite significant achievements made towards HIV testing, linkage to antiretroviral therapy treatment and viral load suppression, the Sub-Saharan region of Africa continues to be reported to have the highest prevalence of HIV/AIDS, with over 26 million people living with the disease. In light of the added burden on already overwhelmed health systems due to the Covid-19 pandemic, maintaining the reliability and accuracy of point-of-care diagnostics (POC) results is crucial to ensure the sustainability of quality service delivery. The integration of technology-based interventions into nurse education curricula is growing, to help prepare students for the current practice environment which requires access to large amounts of information. The aim of this study was to determine the effect of a Mobile Learning (mLearning) Curriculum on improving the quality of HIV rapid testing services in rural clinics of KwaZulu-Natal (KZN), South Africa. METHODS: To achieve the aim of this study, pre-test and post-test audits were conducted in a quasi-experimental design. Eleven clinics of KZN, with the highest availability and usage of POC diagnostics were selected from a cross-sectional study survey to constitute the sample of this study. The World Health Organization On-site Monitoring Checklist-Assessment of Quality System was adapted and used as an audit tool to evaluate four key quality components. The effect of the mLearning curriculum on HIV testing quality improvement was determined through statistically comparing pre-audit and post-audit results. The independent samples t-test and the Levene's test were employed to evaluate the equality of measured variables for the two groups. The relationships between variables were estimated using the Pearson pair wise correlation coefficient (p) and correlations were reported as significant at p < 0.05. RESULTS: A total of 11 clinics was audited at the pretest and 7 clinics were audited post-piloting of the mLearning curriculum. The estimated level of compliance of the participating clinics to quality HIV rapid testing guidelines ranged between poor and moderate quality. The mLearning curriculum was shown to have no statistically significant effect on the quality of POC diagnostic services provided in rural clinics of KZN. CONCLUSION: The mLearning curriculum was shown to have no statistically significant effect on the quality of HIV rapid testing services provided in participating clinics; however, multiple barriers to the full adoption of the piloted curriculum were identified. The provision of reliable technology devices and improved internet connection were recommended to enhance the adoption of technology-based interventions necessary to improve access to relevant learning material and updated information.http://www.biomedcentral.com/bmchealthservresNursing Scienc

Medicinal plants with anti-SARS-CoV activity repurposing for treatment of COVID-19 infection: A systematic review and meta-analysis

The novel SARS-CoV-2 (severe acute respiratory syndrome coronavirus) has emerged as a significant threat to public health with startling drawbacks in all sectors globally. This study investigates the practicality of some medicinal plants for SARS-CoV-2 therapy using a systematic review and meta-analysis of their reported SARS-CoV-1 inhibitory potencies. Relevant data were systematically gathered from three databases, viz., Web of Science, PubMed and Scopus. The information obtained included botanical information, extraction method and extracts concentrations, as well as the proposed mechanisms. Fourteen articles describing 30 different plants met our eligibility criteria. Random effects model and subgroup analysis were applied to investigate heterogeneity. According to subgroup analysis, the substantial heterogeneity of the estimated mean based on the IC50 values reporting the most potent anti-SARS-CoV 3C-like protease (3CLpro) inhibitors (10.07 %, p < 0.0001), was significantly higher compared to the most active anti-SARS-CoV papain-like protease (PLpro) inhibitors (6.12 %, p < 0.0001). More importantly, the literature analysis revealed that fruit extracts of Rheum palmatum Linn. and the compound cryptotanshinone isolated from the root of Salvia miltiorrhiza (IC50 = of 0.8 ± 0.2 µmol L–1) were excellent candidates for anti-SARS-CoV targeting PLpro. Meanwhile, iguesterin (IC50 = 2.6 ± 0.6 µmol L–1) isolated from the bark of Tripterygium regelii emerged as the most excellent candidate for anti-SARS-CoV targeting 3CLpro. The present systematic review and meta-analysis provide valuable and comprehensive information about potential medicinal plants for SARS-CoV-2 inhibition. The chemotypes identified herein can be adopted as a starting point for developing new drugs to contain the novel virus

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Statistical Modeling of Determinants of Anemia Prevalence among Children Aged 6–59 Months in Nigeria: A Cross-Sectional Study

Objective. Childhood anemia remains a significant public health challenge in developing countries, and it has negative consequences on the growth of the children. Therefore, it is essential to identify the determinants of childhood anemia, as these will help in formulating appropriate health policies in order to meet the United Nations MDG goal. This study aims to assess and model the determinants of the prevalence of anemia among children aged 6–59 months in Nigeria. To accomplish the aims of the study, the authors applied single-level and multilevel binary logistic regression models. Methods. To measure the relative impact of individual and household-level factors for childhood anemia among children aged 6–59 months, this study undertakes data from Nigeria Demographic and Health Surveys with both binary logistic and multilevel logistic regression models. The fit of the model was assessed by Hosmer–Lemeshow goodness-of-fit, variance inflation factor, and likelihood ratio tests. Results. The study established that about 67.01% of the children were anemic and identified sex of children, mother’s education, religion, household wealth status, total children ever born, age of children, place of residence, and region to have a statistical significant effect on the prevalence of anemia. The adjusted odds ratio (aOR) for anemia was 0.56 (95% CI = 0.50, 0.63) in children aged from 24 to 42 months and 0.40 (95% CI = 0.36, 0.45) in children aged from 43 to 59 months. Also, children who reside in certain geographical-political zones of Nigeria are associated with increased childhood anemia. Conclusion. This study has highlighted the high prevalence of childhood anemia in Nigeria and indicated the need to improve mothers’ education and regional variations. Findings from this study can help policymakers and public health institutions to map out programs targeting these regions as a measure of tackling the prevalence of anemia among the Nigerian populace

A Bibliometric Analysis of the Literature on Norovirus Disease from 1991&ndash;2021

Norovirus (NoV) is one of the oldest recognized diseases and the leading causal pathogen for acute gastroenteritis (AGE) worldwide. Though numerous studies have been reported on NoV disease, limited research has explored the publication trends in this area. As a result, the objective of this work was to fill the void by conducting a bibliometric study in publication trends on NoV studies as well as discovering the hotspots. The Web of Science central assemblage database was hunted for publications from 1991 to 2021 with &ldquo;norovirus&rdquo; in the heading. Microsoft Excel 2016, VOSviewer, R Bibliometrix, and Biblioshiny packages were deployed for the statistical analysis of published research articles. A total of 6021 published documents were identified in the Web of Science database for this thirty-year study period (1991&ndash;2021). The analyses disclosed that the Journal of Medical Virology was the leading journal in publications on norovirus studies with a total of 215 published articles, the Journal of Virology was the most cited document with 11,185 total citations. The United States of America (USA) has the most significant productivity in norovirus publications and is the leading country with the highest international collaboration. Analysis of top germane authors discovered that X. Jiang (135) and J. Vinje (119) were the two top relevant authors of norovirus publications. The commonly recognized funders were US and EU-based, with the US emerging as a top funder. This study reveals trends in scientific findings and academic collaborations and serves as a leading-edge model to reveal trends in global research in the field of norovirus research. This study points out the progress status and trends on NoV research. It can help researchers in the medical profession obtain a comprehensive understanding of the state of the art of NoV. It also has reference values for the research and application of the NoV visualization methods. Further, the research map on AGE obtained by our analysis is expected to help researchers efficiently and effectively explore the NoV field

Accuracy of Machine Learning Classification Models for the Prediction of Type 2 Diabetes Mellitus: A Systematic Survey and Meta-Analysis Approach

Soft-computing and statistical learning models have gained substantial momentum in predicting type 2 diabetes mellitus (T2DM) disease. This paper reviews recent soft-computing and statistical learning models in T2DM using a meta-analysis approach. We searched for papers using soft-computing and statistical learning models focused on T2DM published between 2010 and 2021 on three different search engines. Of 1215 studies identified, 34 with 136952 patients met our inclusion criteria. The pooled algorithm&rsquo;s performance was able to predict T2DM with an overall accuracy of 0.86 (95% confidence interval [CI] of [0.82, 0.89]). The classification of diabetes prediction was significantly greater in models with a screening and diagnosis (pooled proportion [95% CI] = 0.91 [0.74, 0.97]) when compared to models with nephropathy (pooled proportion = 0.48 [0.76, 0.89] to 0.88 [0.83, 0.91]). For the prediction of T2DM, the decision trees (DT) models had a pooled accuracy of 0.88 [95% CI: 0.82, 0.92], and the neural network (NN) models had a pooled accuracy of 0.85 [95% CI: 0.79, 0.89]. Meta-regression did not provide any statistically significant findings for the heterogeneous accuracy in studies with different diabetes predictions, sample sizes, and impact factors. Additionally, ML models showed high accuracy for the prediction of T2DM. The predictive accuracy of ML algorithms in T2DM is promising, mainly through DT and NN models. However, there is heterogeneity among ML models. We compared the results and models and concluded that this evidence might help clinicians interpret data and implement optimum models for their dataset for T2DM prediction