GIS-based spatiotemporal mapping of malaria prevalence and exploration of environmental inequalities.

Malaria poses a significant threat to global health, with particular severity in Nigeria. Understanding key factors influencing health outcomes is crucial for addressing health disparities. Disease mapping plays a vital role in assessing the geographical distribution of diseases and has been instrumental in epidemiological research. By delving into the spatiotemporal dynamics of malaria trends, valuable insights can be gained into population dynamics, leading to more informed spatial management decisions. This study focused on examining the evolution of malaria in Nigeria over twenty years (2000-2020) and exploring the impact of environmental factors on this variation. A 5-year-period raster map was developed using malaria indicator survey data for Nigeria's six geopolitical zones. Various spatial analysis techniques, such as point density, spatial autocorrelation, and hotspot analysis, were employed to analyze spatial patterns. Additionally, statistical methods, including Principal Component Analysis, Spearman correlation, and Ordinary Least Squares (OLS) regression, were used to investigate relationships between indicators and develop a predictive model. The study revealed regional variations in malaria prevalence over time, with the highest number of cases concentrated in northern Nigeria. The raster map illustrated a shift in the distribution of malaria cases over the five years. Environmental factors such as the Enhanced Vegetation Index, annual land surface temperature, and precipitation exhibited a strong positive association with malaria cases in the OLS model. Conversely, insecticide-treated bed net coverage and mean temperature negatively correlated with malaria cases in the same model. The findings from this research provide valuable insights into the spatiotemporal patterns of malaria in Nigeria and highlight the significant role of environmental drivers in influencing disease transmission. This scientific knowledge can inform policymakers and aid in developing targeted interventions to combat malaria effectively.

Identifying Potential Factors Associated with High HIV viral load in KwaZulu-Natal, South Africa using Multiple Correspondence Analysis and Random Forest Analysis.

BACKGROUND: Sustainable Human Immunodeficiency Virus (HIV) virological suppression is crucial to achieving the Joint United Nations Programme of HIV/AIDS (UNAIDS) 95-95-95 treatment targets to reduce the risk of onward HIV transmission. Exploratory data analysis is an integral part of statistical analysis which aids variable selection from complex survey data for further confirmatory analysis. METHODS: In this study, we divulge participants' epidemiological and biological factors with high HIV RNA viral load (HHVL) from an HIV Incidence Provincial Surveillance System (HIPSS) sequential cross-sectional survey between 2014 and 2015 KwaZulu-Natal, South Africa. Using multiple correspondence analysis (MCA) and random forest analysis (RFA), we analyzed the linkage between socio-demographic, behavioral, psycho-social, and biological factors associated with HHVL, defined as ≥400 copies per m/L. RESULTS: Out of 3956 in 2014 and 3868 in 2015, 50.1% and 41% of participants, respectively, had HHVL. MCA and RFA revealed that knowledge of HIV status, ART use, ARV dosage, current CD4 cell count, perceived risk of contracting HIV, number of lifetime HIV tests, number of lifetime sex partners, and ever diagnosed with TB were consistent potential factors identified to be associated with high HIV viral load in the 2014 and 2015 surveys. Based on MCA findings, diverse categories of variables identified with HHVL were, did not know HIV status, not on ART, on multiple dosages of ARV, with less likely perceived risk of contracting HIV and having two or more lifetime sexual partners. CONCLUSION: The high proportion of individuals with HHVL suggests that the UNAIDS 95-95-95 goal of HIV viral suppression is less likely to be achieved. Based on performance and visualization evaluation, MCA was selected as the best and essential exploration tool for identifying and understanding categorical variables' significant associations and interactions to enhance individual epidemiological understanding of high HIV viral load. When faced with complex survey data and challenges of variables selection in research, exploratory data analysis with robust graphical visualization and reliability that can reveal divers' structures should be considered.

Enteropathogenic viruses associated with acute gastroenteritis among African children under 5 years of age: A systematic review and meta-analysis.

Gastroenteritis viruses are the leading etiologic agents of diarrhea in children worldwide. We present data from thirty-three (33) eligible studies published between 2003 and 2023 from African countries bearing the brunt of the virus-associated diarrheal mortality. Random effects meta-analysis with proportion, subgroups, and meta-regression analyses were employed. Overall, rotavirus with estimated pooled prevalence of 31.0 % (95 % CI 24.0-39.0) predominated in all primary care visits and hospitalizations, followed by norovirus, adenovirus, sapovirus, astrovirus, and aichivirus with pooled prevalence estimated at 15.0 % (95 % CI 12.0-20.0), 10 % (95 % CI 6-15), 4.0 % (95 % CI 2.0-6.0), 4 % (95 % CI 3-6), and 2.3 % (95 % CI 1-3), respectively. Predominant rotavirus genotype was G1P[8] (39 %), followed by G3P[8] (11.7 %), G9P[8] (8.7 %), and G2P[4] (7.1 %); although, unusual genotypes were also observed, including G3P[6] (2.7 %), G8P[6] (1.7 %), G1P[6] (1.5 %), G10P[8] (0.9 %), G8P[4] (0.5 %), and G4P[8] (0.4 %). The genogroup II norovirus predominated over the genogroup I-associated infections (84.6 %, 613/725 vs 14.9 %, 108/725), with the GII.4 (79.3 %) being the most prevalent circulating genotype. In conclusion, this review showed that rotavirus remains the leading driver of viral diarrhea requiring health care visits and hospitalization among under-five years children in Africa. Thus, improved rotavirus vaccination in the region and surveillance to determine the residual burden of rotavirus and the evolving trend of other enteric viruses are needed for effective control and management of cases.

A Forty-Year Analysis of the Literature on Babesia Infection (1982-2022): A Systematic Bibliometric Approach.

Babesia infection is a tick-borne protozoan disease associated with significant veterinary, economic, and medical importance. This infection affects many hosts, ranging from wild to domestic animals and including man. All vertebrates serve as potential carriers due to the huge diversity of the species. Babesiosis has been associated with severe economic loss in livestock production, especially in cattle farming, and is also a major public health concern in man, which could be fatal. The infection is usually opportunistic, ranging from asymptomatic to symptomatic, usually in immunocompromised subjects or under conditions of stressful management. This study was designed to uncover trends in relation to publication growth and further explore research output regarding babesiosis from data indexed in the WoS. The WoS is the only platform used to map publications on Babesia infection. The search term "babesiosis" or "Babesia infection" was used to extract articles published across the study period from 1982 to 2022. The inclusion criteria were restricted to only articles for the analysis. The results from the search query showed that a total of 3763 articles were published during the study period with an average of 91.70 ± 43.87 articles annually and an average total citation (n = 1874.8). An annual growth rate of 2.5% was recorded during the study period. The year 2021 had the highest number of published articles (n = 193, 5.1%) and citations (n = 7039). The analysis of the most relevant keywords and titles showed that infection (n = 606, 16.1%), babesiosis (n = 444, 11.7%), and Babesia (n = 1302, 16%) were the most relevant keyword plus (ID), author keyword (DE), and title, respectively. The common conceptual framework analysis through K-means clustering showed two clusters comprising 4 and 41 elements, respectively. The United States of America is the top-performing country in terms of article production (n = 707, 20.8%) and the leading funder for babesiosis research, with two of its agencies ranked at the top. These are the Department of Health and Human Services (n = 254, 6.7%) and the National Institute of Health (n= 238,6.3%). Igarashi I. is the top-performing author (n = 231, 6.1%), while Veterinary Parasitology is ranked the top journal (n = 393, 10.4%) in terms of babesiosis publications. Overall, an increase in publications was observed in the study period, with significant output from developed nations.

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.

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'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.

A Multilevel Analysis of the Associated and Determining Factors of TB among Adults in South Africa: Results from National Income Dynamics Surveys 2008 to 2017.

TB is preventable and treatable but remains the leading cause of death in South Africa. The deaths due to TB have declined, but in 2017, around 322,000 new cases were reported in the country. The need to eradicate the disease through research is increasing. This study used population-based National Income Dynamics Survey data (Wave 1 to Wave 5) from 2008 to 2017. By determining the simultaneous multilevel and individual-level predictors of TB, this research examined the factors associated with TB-diagnosed individuals and to what extent the factors vary across such individuals belonging to the same province in South Africa for the five waves. Multilevel logistic regression models were fitted using frequentist and Bayesian techniques, and the results were presented as odds ratios with statistical significance set at p < 0.05. The results obtained from the two approaches were compared and discussed. Findings reveal that the TB factors that prevailed consistently from wave 1 to wave 5 were marital status, age, gender, education, smoking, suffering from other diseases, and consultation with a health practitioner. Also, over the years, the single males aged 30-44 years suffering from other diseases with no education were highly associated with TB between 2008 and 2017. The methodological findings were that the frequentist and Bayesian models resulted in the same TB factors. Both models showed that some form of variation in TB infections is due to the different provinces these individuals belonged. Variation in TB patients within the same province over the waves was minimal. We conclude that demographic and behavioural factors also drive TB infections in South Africa. This research supports the existing findings that controlling the social determinants of health will help eradicate TB.

Global Research Trends on Monkeypox Virus: A Bibliometric and Visualized Study.

Monkeypox is a zoonotic viral disease that has recently emerged as another global infection disease. A double-stranded enveloped deoxyribonucleic acid virus the cause of this disease. Since monkeypox is an evolving field of study with a growing interest in public health, it is crucial to study the scientific trend and research activities. This study provides an essential insight into the research response to scientific trends of monkeypox using the bibliometric analysis technique. A literature search for published articles on LSD from 2001 to 2021 was conducted in Scopus on 24 July 2022. Visualization analysis was performed using R statistical software. The growth and trend of documents, country-level distribution of publications and collaborations, and the relationship between authors and co-authors were analyzed. Findings revealed a significant increase in the research conducted, mainly from the United States (US). The top 12 institutions published papers on the monkeypox virus, accounting for 33.09 percent of the articles. The US was the most productive nation, producing 275 documents (54.34%), or one-third of all publications in this sector worldwide. Centers for Disease Control and Prevention in Georgia in the United States were the organization that produced the most (365 publications). The Journal of Virology garnered the most citations, with an h-index of 18. In the last year, there has been an increase in the publication of monkeypox virus-related studies. The importance of the monkeypox virus highlights the necessity for continued research to help international health organizations identify areas that require prompt action to implement suitable solutions. This study also provides scaling-up analysis, evidence dissemination on the monkeypox virus, emerging hotspots, and perceptive remarks on the technological advances in this field.

Spatial pattern and determinants of anemia among women of childbearing age in Nigeria.

BACKGROUND: The risk of anemia in Nigeria is of public health importance, with an increasing number of women of reproductive age being anemic. This study sought to identify the spatial distribution and examine the geographical variation of anemia risk at a regional level while accounting for risk factors associated with anemia among women of childbearing age in Nigeria. The significant interest in spatial statistics lies in identifying associated risk factors that enhance the risk of infection. However, most studies make no or limited use of the data's spatial structure and possible non-linear effects of the risk factors. METHODS: The data used in this study were extracted from the 2015 Nigeria Demographic and Health Survey (NDHS). A full Bayesian semi-parametric regression model was fitted to data to accomplish the aims of the study. Model estimation and the inference was fully Bayesian approach via integrated nested Laplace approximations (INLA). The fixed effects were modeled parametrically; non-linear effects were modeled non-parametrically using second-order random walk priors. RESULTS: Wealth index, level of education, type of residence, and unprotected drinking water source were found to be the risk factors associated with anemia. The risk of anemia was found to vary across different regions, with North Central, North East, and North West regions having the highest number of cases and South East with the least number of cases. The spatial analysis result indicated that statistically high hot-spots of anemia were observed in all the northern parts of the country. CONCLUSION: The study revealed associations between anemia risk and women residing in rural settlements, wealth index, women with no formal education, and unprotected drinking water sources. Community and household-related change interventions should, therefore, be pertinent to the prevention of anemia. The spatial analysis further revealed a significant anemia risk towards the Northern areas of Nigeria. We propose that interventions targeting women of reproductive age should initially focus on these regions and subsequently spread across Nigeria.

Meta-analysis of studies on depression prevalence among diabetes mellitus patients in Africa.

Diabetes mellitus is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion or insulin action. It can be caused by the consumption of carbohydrate meals or medication side effects. Depression as a comorbid condition in an individual with diabetes is accountable for increased disability, mortality, and significant health problem in patients. As a continent, Africa does not have an overall estimation of depression prevalence among diabetes mellitus patients at a regional level. Consequently, this study's purpose was to use the meta-analysis method to summarize estimates of extant studies that have reported depression prevalence among patients with diabetes mellitus in Africa. The literature search method was executed to classify studies with reported depression prevalence with evidently designed inclusion and exclusion criteria. In total, 20 studies from sundry screened articles were appropriate for ultimate inclusion in the meta-analysis. Since substantial heterogeneity was expected, a random-effects meta-analysis was carried out using the number of cases with a total sample size to estimate the prevalence of diabetes mellitus at a regional level. The residual amount of heterogeneity was found to be high according to the statistics of τ2 = 0.06; I2 = 99.10%, chi-square = 2184.85, degree of freedom = 19 and P =< 0.001. The pooled depression prevalence was 40% within a 95% confidence interval of 29%-51%. The meta-regression analysis result showed that none of the included moderators contributed to the heterogeneity of studies. The result of effect size estimates against its standard error showed publication bias with a P-value of 0.001. The meta-analysis findings of this study have indicated that depression prevalence in Africa is still high. Reporting on numerous risk factors like socio-demographic characteristics were not possible in this study because of a lack of completeness in the included articles. Consequently, screening diabetes patients for comorbid depression with its associated risk factors is highly recommended.

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.

Bayesian Inference on Malignant Breast Cancer in Nigeria: A Diagnosis of MCMC Convergence

Background: There has been no previous study to classify malignant breast tumor in details based on Markov Chain Monte Carlo (MCMC) convergence in Western, Nigeria. This study therefore aims to profile patients living with benign and malignant breast tumor in two different hospitals among women of Western Nigeria, with a focus on prognostic factors and MCMC convergence. Materials and Methods: A hospital-based record was used to identify prognostic factors for malignant breast cancer among women of Western Nigeria. This paper describes Bayesian inference and demonstrates its usage to estimation of parameters of the logistic regression via Markov Chain Monte Carlo (MCMC) algorithm. The result of the Bayesian approach is compared with the classical statistics. Results: The mean age of the respondents was 42.2 ±16.6 years with 52% of the women aged between 35-49 years. The results of both techniques suggest that age and women with at least high school education have a significantly higher risk of being diagnosed with malignant breast tumors than benign breast tumors. The results also indicate a reduction of standard errors is associated with the coefficients obtained from the Bayesian approach. In addition, simulation result reveal that women with at least high school are 1.3 times more at risk of having malignant breast lesion in western Nigeria compared to benign breast lesion. Conclusion: We concluded that more efforts are required towards creating awareness and advocacy campaigns on how the prevalence of malignant breast lesions can be reduced, especially among women. The application of Bayesian produces precise estimates for modeling malignant breast cancer.