HIV transmission dynamics and population-wide drug resistance in rural South Africa.

Despite the scale-up of antiretroviral therapy (ART) in South Africa, HIV-1 incidence remains high. The anticipated use of potent integrase strand transfer inhibitors and long-acting injectables aims to enhance viral suppression at the population level and diminish transmission. Nevertheless, pre-existing drug resistance could impede the efficacy of long-acting injectable ART combinations, such as rilpivirine (an NNRTI) and cabotegravir (an INSTI). Consequently, a thorough understanding of transmission networks and geospatial distributions is vital for tailored interventions, including pre-exposure prophylaxis with long-acting injectables. However, empirical data on background resistance and transmission networks remain limited. In a community-based study in rural KwaZulu-Natal (2018-2019), prior to the widespread use of integrase inhibitor-based first-line ART, we performed HIV testing with reflex HIV-1 RNA viral load quantification on 18,025 participants. From this cohort, 6,096 (33.9%) tested positive for HIV via ELISA, with 1,323 (21.7%) exhibiting detectable viral loads (> 40 copies/mL). Of those with detectable viral loads, 62.1% were ART-naïve, and the majority of the treated were on an efavirenz + cytosine analogue + tenofovir regimen. Deep sequencing analysis, with a variant abundance threshold of 20%, revealed NRTI resistance mutations such as M184V in 2% of ART-naïve and 32% of treated individuals. Tenofovir resistance mutations K65R and K70E were found in 12% and 5% of ART-experienced individuals, respectively, and in less than 1% of ART-naïve individuals. Integrase inhibitor resistance mutations were notably infrequent (< 1%). Prevalence of pre-treatment drug resistance to NNRTIs was 10%, predominantly consisting of the K103N mutation. Among those with viraemic ART, NNRTI resistance was 50%, with rilpivirine-associated mutations observed in 9% of treated and 6% of untreated individuals. Cluster analysis revealed that 20% (205/1,050) of those sequenced were part of a cluster. We identified 171 groups with at least two linked participants; three quarters of clusters had only two individuals, and a quarter had 3-6 individuals. Integrating phylogenetic with geospatial analyses, we revealed a complex transmission network with significant clustering in specific regions, notably peripheral and rural areas. These findings derived from population scale genomic analyses are encouraging in terms of the limited resistance to DTG, but indicate that transitioning to long-acting cabotegravir + rilpivirine for transmission reduction should be accompanied by prior screening for rilpivirine resistance. Whole HIV-1 genome sequencing allowed identification of significant proportions of clusters with multiple individuals, and geospatial analyses suggesting decentralised networks can inform targeting public health interventions to effectively curb HIV-1 transmission.

Mapping regional variability of exclusive breastfeeding and its determinants at different infant's age in Tanzania.

INTRODUCTION: Despite its numerous benefits, exclusive breastfeeding (EBF) remains an underutilized practice. Enhancing EBF uptake necessitates a focused approach targeting regions where its adoption is suboptimal. This study aimed to investigate regional disparities in EBF practices and identify determinants of EBF among infants aged 0-1, 2-3, and 4-5 months in Tanzania. METHODS: This cross-sectional study utilized data from the 2015/16 Tanzania Demographic and Health Survey. A total of 1,015 infants aged 0-5 met the inclusion criteria, comprising 378 aged 0-1 month, 334 at 2-3 months, and 303 at 4-5 months. EBF practices were assessed using a 24-hour recall method. A generalized linear mixed model, with fixed covariates encompassing infant and maternal attributes and clusters for enumeration areas (EAs) and regions, was employed to estimate EBF proportions. RESULTS: Regional disparities in EBF were evident among infants aged 0-1, 2-3, and 4-5 months, with decline in EBF proportions as an infant's age increases. This pattern was observed nationwide. Regional and EA factors influenced the EBF practices at 0-1 and 2-3 months, accounting for 17-40% of the variability at the regional level and 40-63% at the EA level. Literacy level among mothers had a significant impact on EBF practices at 2-3 months (e.g., women who could read whole sentences; AOR = 3.2, 95% CI 1.1,8.8). CONCLUSION: Regional disparities in EBF proportions exist in Tanzania, and further studies are needed to understand their underlying causes. Targeted interventions should prioritize regions with lower EBF proportions. This study highlights the clustering of EBF practices at 0-1 and 2-3 months on both regional and EA levels. Conducting studies in smaller geographical areas may enhance our understanding of the enablers and barriers to EBF and guide interventions to promote recommended EBF practices.

Machine learning algorithms for predicting determinants of COVID-19 mortality in South Africa.

Background: COVID-19 has strained healthcare resources, necessitating efficient prognostication to triage patients effectively. This study quantified COVID-19 risk factors and predicted COVID-19 intensive care unit (ICU) mortality in South Africa based on machine learning algorithms. Methods: Data for this study were obtained from 392 COVID-19 ICU patients enrolled between 26 March 2020 and 10 February 2021. We used an artificial neural network (ANN) and random forest (RF) to predict mortality among ICU patients and a semi-parametric logistic regression with nine covariates, including a grouping variable based on K-means clustering. Further evaluation of the algorithms was performed using sensitivity, accuracy, specificity, and Cohen's K statistics. Results: From the semi-parametric logistic regression and ANN variable importance, age, gender, cluster, presence of severe symptoms, being on the ventilator, and comorbidities of asthma significantly contributed to ICU death. In particular, the odds of mortality were six times higher among asthmatic patients than non-asthmatic patients. In univariable and multivariate regression, advanced age, PF1 and 2, FiO2, severe symptoms, asthma, oxygen saturation, and cluster 4 were strongly predictive of mortality. The RF model revealed that intubation status, age, cluster, diabetes, and hypertension were the top five significant predictors of mortality. The ANN performed well with an accuracy of 71%, a precision of 83%, an F1 score of 100%, Matthew's correlation coefficient (MCC) score of 100%, and a recall of 88%. In addition, Cohen's k-value of 0.75 verified the most extreme discriminative power of the ANN. In comparison, the RF model provided a 76% recall, an 87% precision, and a 65% MCC. Conclusion: Based on the findings, we can conclude that both ANN and RF can predict COVID-19 mortality in the ICU with accuracy. The proposed models accurately predict the prognosis of COVID-19 patients after diagnosis. The models can be used to prioritize COVID-19 patients with a high mortality risk in resource-constrained ICUs.

Trends of Exclusive Breastfeeding Practices and Its Determinants in Tanzania from 1999 to 2016.

Introduction: The benefits of exclusive breastfeeding (EBF) are widely reported. However, it is crucial to examine potential disparities in EBF practices across different regions of a country. Our study uses Tanzania demographic and health survey data to report on the trends of EBF across regions from 1999 to 2016, the patterns of the practice based on geographical location and socioeconomic status, and explores its determinants across the years. Methods: Descriptive statistics were used to establish the trends of EBF by geographical location and wealth quintile. A generalized linear mixed model was developed to incorporate both infant and maternal attributes as fixed covariates while considering enumeration areas and regions as clusters. The fitted model facilitated the estimation of EBF proportions at a regional level and identified key determinants influencing EBF practices across the survey periods. Moreover, we designed breastfeeding maps, visually depicting the performance of different regions throughout the surveys. Results: Across the various survey rounds, a notable regional variation in EBF practices was observed, with coastal regions generally exhibiting lower adherence to the practice. There was a linear trend between EBF and geographical residence (p < 0.05) and socioeconomic standing (p < 0.05) across the survey periods. Rural-dwelling women and those from the least affluent backgrounds consistently showcased a higher proportion of EBF. The prevalence of EBF declined as infants aged (p < 0.001), a trend consistent across all survey waves. The associations between maternal attributes and EBF practices displayed temporal variations. Furthermore, a correlation between exclusive breastfeeding and attributes linked to both regional disparities and enumeration areas was observed. The intra-cluster correlation ranged from 18% to 41.5% at the regional level and from 40% to 58.5% at the enumeration area level. Conclusions: While Tanzania's progress in EBF practices is laudable, regional disparities persist, demanding targeted interventions. Sustaining achievements while addressing wealth-based disparities and the decline in EBF with infant age is vital. The study highlights the need for broad national strategies and localized investigations to understand and enhance EBF practices across different regions and socioeconomic contexts.

Role of clusters in exclusive breastfeeding practices in Tanzania: A secondary analysis study using demographic and health survey data (2015/2016).

Background: While the benefits of exclusive breastfeeding are widely acknowledged, it continues to be a rare practice. Determinants of exclusive breastfeeding in Tanzania have been studied; however, the existence and contribution of regional variability to the practice have not been explored. Methods: Tanzania demographic and health survey data for 2015/2016 were used. Information on infants aged up to 6 months was abstracted. Exclusive breastfeeding was defined using a recall of feeding practices in the past 24 h. Enumeration areas and regions were treated as random effects. Models without random effects were compared with those that incorporated random effects using the Akaike information criterion. The determinants of exclusive breastfeeding were estimated using the generalized linear mixed model with enumeration areas nested within the region. Results: The generalized linear mixed model with an enumeration area nested within a region performed better than other models. The intra-cluster variability at region and enumeration area levels was 3.7 and 24.5%, respectively. The odds of practicing exclusive breastfeeding were lower for older and male infants, for mothers younger than 18, among mothers residing in urban areas, among those who were employed by a family member or someone else, those not assisted by a nurse/midwife, and those who were not counseled on exclusive breastfeeding within 2 days post-delivery. There was no statistical evidence of an association between exclusive breastfeeding practices and the frequency of listening to the radio and watching television. When mapping the proportion of exclusive breastfeeding, a variability of the practice is seen across regions. Conclusion: There is room to improve the proportion of those who practice exclusive breastfeeding in Tanzania. Beyond individual and setting factors, this analysis shows that a quarter of the variability in exclusive breastfeeding practices is at the community level. Further studies may explore the causes of variabilities in regional and enumeration area and how it operates. Interventions to protect, promote, and support exclusive breastfeeding in Tanzania may target the environment that shapes the attitude toward exclusive breastfeeding in smaller geographical areas.

Dictionary Based Global Twitter Sentiment Analysis of Coronavirus (COVID-19) Effects and Response.

In December 2019, a new pandemic called the coronavirus began ravaging the world. By May 2020, the pandemic had caused great loss of lives and disrupted the way of lives in more ways than one. The nature of the disease saw several strategies to curb its spread rolled out. These strategies included closing of businesses and borders, restriction of movements and working from home, mask mandate among others. With these measures and the effects, many individuals have taken to the social media to express their frustrations, opinions and how the pandemic is affecting them. This study employs dictionary based method for sentiment polarization from tweets related to coronavirus posted on Twitter. We also examine the co-occurrence of words to gain insights on the aspects affecting the masses. The results showed that mental health issues, lack of supplies were some of the direct effects of the pandemic. It was also clear that the COVID-19 prevention guidelines were well understood by those who tweeted. The results from this study may help governments combat the consequences of COVID-19 like mental health issues, lack of supplies e.g. food and also gauge the effectiveness or the reach of their guidelines.

Spatial modeling of HIV and HSV-2 among women in Kenya with spatially varying coefficients.

BACKGROUND: Disease mapping has become popular in the field of statistics as a method to explain the spatial distribution of disease outcomes and as a tool to help design targeted intervention strategies. Most of these models however have been implemented with assumptions that may be limiting or altogether lead to less meaningful results and hence interpretations. Some of these assumptions include the linearity, stationarity and normality assumptions. Studies have shown that the linearity assumption is not necessarily true for all covariates. Age for example has been found to have a non-linear relationship with HIV and HSV-2 prevalence. Other studies have made stationarity assumption in that one stimulus e.g. education, provokes the same response in all the regions under study and this is also quite restrictive. Responses to stimuli may vary from region to region due to aspects like culture, preferences and attitudes. METHODS: We perform a spatial modeling of HIV and HSV-2 among women in Kenya, while relaxing these assumptions i.e. the linearity assumption by allowing the covariate age to have a non-linear effect on HIV and HSV-2 prevalence using the random walk model of order 2 and the stationarity assumption by allowing the rest of the covariates to vary spatially using the conditional autoregressive model. The women data used in this study were derived from the 2007 Kenya AIDS indicator survey where women aged 15-49 years were surveyed. A full Bayesian approach was used and the models were implemented in R-INLA software. RESULTS: Age was found to have a non-linear relationship with both HIV and HSV-2 prevalence, and the spatially varying coefficient model provided a significantly better fit for HSV-2. Age-at first sex also had a greater effect on HSV-2 prevalence in the Coastal and some parts of North Eastern regions suggesting either early marriages or child prostitution. The effect of education on HIV prevalence among women was more in the North Eastern, Coastal, Southern and parts of Central region. CONCLUSIONS: The models introduced in this study enable relaxation of two limiting assumptions in disease mapping. The effects of the covariates on HIV and HSV-2 were found to vary spatially. The effect of education on HSV-2 status for example was lower in North Eastern and parts of the Rift region than most of the other parts of the country. Age was found to have a non-linear effect on HIV and HSV-2 prevalence, a linearity assumption would have led to wrong results and hence interpretations. The findings are relevant in that they can be used in informing tailor made strategies for tackling HIV and HSV-2 in different counties. The methodology used here may also be replicated in other studies with similar data.

Semi-parametric spatial joint modeling of HIV and HSV-2 among women in Kenya.

Several diseases have common risk factors. The joint modeling of disease outcomes within a spatial statistical context may provide more insight on the interaction of diseases both at individual and at regional level. Spatial joint modeling allows for studying of the relationship between diseases and also between regions under study. One major approach for joint spatial modeling is the multivariate conditional autoregressive approach. In this approach, it is assumed that all the covariates in the study have linear effects on the multiple response variables. In this study, we relax this linearity assumption and allow some covariates to have nonlinear effects using the penalized regression splines. This model was used to jointly model the spatial variation of human immunodeficiency virus (HIV) and herpes simplex virus-type 2 (HSV-2) among women in Kenya. The model was applied to HIV and HSV-2 prevalence data among women aged 15-49 years in Kenya, derived from the 2007 Kenya AIDS indicator survey. A full Bayesian approach was used and the models were implemented in WinBUGS software. Both diseases showed significant spatial variation with highest disease burdens occurring around the Lake Victoria region. There was a nonlinear association between age of an individual and HIV and HSV-2 infection. The peak age for HIV was around 30 years while that of HSV-2 was about 40 years. A positive significant spatial correlation between HIV and HSV-2 was observed with a correlation of 0.6831(95% CI: 0.3859, 0.871).