Search

BACKGROUND AND AIMS: Reduction of alcohol consumption is important for people undergoing treatment for HIV. We tested the efficacy of a brief intervention for reducing the average volume of alcohol consumed among patients on HIV antiretroviral therapy (ART). DESIGN, SETTING AND PARTICIPANTS: This study used a two-arm multi-centre randomized controlled trial with follow-up to 6 months. Recruitment occurred between May 2016 and October 2017 at six ART clinics at public hospitals in Tshwane, South Africa. Participants were people living with HIV, mean age 40.8 years [standard deviation (SD) = 9.07], 57.5% female, and on average 6.9 years (SD = 3.62) on ART. At baseline (BL), the mean number of drinks consumed over the past 30 days was 25.2 (SD = 38.3). Of 756 eligible patients, 623 were enrolled. INTERVENTION: Participants were randomly assigned to a motivational interviewing (MI)/problem-solving therapy (PST) intervention arm (four modules of MI and PST delivered over two sessions by interventionists) or a treatment as usual (TAU) comparison arm. People assessing outcomes were masked to group assignment. MEASUREMENTS: The primary outcome was the number of standard drinks (15 ml pure alcohol) consumed during the past 30 days assessed at 6-month follow-up (6MFU). FINDINGS: Of the 305 participants randomized to MI/PST, 225 (74%) completed the intervention (all modules). At 6MFU, retention was 88% for the control and 83% for the intervention arm. In support of the hypothesis, an intention-to-treat-analysis for the primary outcome at 6MFU was -0.410 (95% confidence interval = -0.670 to -0.149) units lower on log scale in the intervention group than in the control group (P = 0.002), a 34% relative reduction in the number of drinks. Sensitivity analyses were undertaken for patients who had alcohol use disorders identification test (AUDIT) scores ≥ 8 at BL (n = 299). Findings were similar to those of the whole sample. CONCLUSIONS: In South Africa, a motivational interviewing/problem-solving therapy intervention significantly reduced drinking levels in HIV-infected patients on antiretroviral therapy at 6-month follow-up.

Introduction Consumption of unhealthy foods in children contributes to high levels of childhood obesity globally. In developing countries there is paucity of empirical studies on the association. This study employed propensity-score methods to evaluate the effect of unhealthy foods on overweight among children in Malawi using observational data. Methods Data on 4625 children aged 6 to 59 months from the 2015-16 Malawi Demographic and Health Survey (MDHS) were analyzed. A multivariable logistic regression model of unhealthy foods (yes or no) on purported confounders of childhood overweight was used to obtain a child's unhealthy food propensity score. The propensity scores were then used to form matched sets of healthy and unhealthy fed children. The association between unhealthy foods and childhood overweight was assessed using the conditional logistic regression model. Results The prevalence of overweight (body mass index (BMI) z-score > 2 standard deviations) was estimated at 4.5% (3.8%, 5.3%). The proportion of children who consumed unhealthy foods was estimated at 14.6% (95% CI: 13.1%, 16.2%). Our propensity score matching achieved a balance in the distribution of the confounders between children in the healthy and unhealthy food groups. Children fed unhealthy foods were significantly more likely to be overweight than those fed healthy foods (OR = 2.5, 95% CI: (1.2, 5.2)). Conclusion The findings suggest the adverse effects of unhealthy foods on childhood overweight in Malawi. Thus, efforts to reduce unhealthy food consumption among children should be implemented and supported to address the problem of childhood overweight in Malawi and the sub-Saharan African region., Author(s): Halima S. Twabi [sup.1] , Samuel O. M Manda [sup.2] , Dylan S. Small [sup.3] Author Affiliations: (1) grid.10595.38, 0000 0001 2113 2211, Department of Mathematical Sciences, University of [...]

Background: Childhood stunting is a major indicator of child malnutrition and a focus area of Global Nutrition Targets for 2025 and Sustainable Development Goals. Risk factors for childhood stunting are well studied and well known and could be used in a risk prediction model for assessing whether a child is stunted or not. However, the selection of child stunting predictor variables is a critical step in the development and performance of any such prediction model. This paper compares the performance of child stunting diagnostic predictive models based on predictor variables selected using a set of variable selection methods. Methods: Firstly, we conducted a subjective review of the literature to identify determinants of child stunting in Sub-Saharan Africa. Secondly, a multivariate logistic regression model of child stunting was fitted using the identified predictors on stunting data among children aged 0–59 months in the Malawi Demographic Health Survey (MDHS 2015–16) data. Thirdly, several reduced multivariable logistic regression models were fitted depending on the predictor variables selected using seven variable selection algorithms, namely backward, forward, stepwise, random forest, Least Absolute Shrinkage and Selection Operator (LASSO), and judgmental. Lastly, for each reduced model, a diagnostic predictive model for the childhood stunting risk score, defined as the child propensity score based on derived coefficients, was calculated for each child. The prediction risk models were assessed using discrimination measures, including area under-receiver operator curve (AUROC), sensitivity and specificity. Results: The review identified 68 predictor variables of child stunting, of which 27 were available in the MDHS 2016–16 data. The common risk factors selected by all the variable selection models include household wealth index, age of the child, household size, type of birth (singleton/multiple births), and birth weight. The best cut-off point on the child stunting risk prediction model was 0.37 based on risk factors determined by the judgmental variable selection method. The model's accuracy was estimated with an AUROC value of 64% (95% CI: 60%-67%) in the test data. For children residing in urban areas, the corresponding AUROC was AUC = 67% (95% CI: 58–76%), as opposed to those in rural areas, AUC = 63% (95% CI: 59–67%). Conclusion: The derived child stunting diagnostic prediction model could be useful as a first screening tool to identify children more likely to be stunted. The identified children could then receive necessary nutritional interventions. [ABSTRACT FROM AUTHOR]

In sub-Saharan Africa (SSA), adolescent girls and young women (AGYW) have the highest risk of acquiring HIV. This has led to several studies aimed at identifying risk factors for HIV in AGYM. However, a combination of the purported risk variables in a multivariate risk model could be more useful in determining HIV risk in AGYW than one at a time. The purpose of this study was to develop and validate an HIV risk prediction model for AGYW. We analyzed HIV-related HERStory survey data on 4,399 AGYW from South Africa. We identified 16 purported risk variables from the data set. The HIV acquisition risk scores were computed by combining coefficients of a multivariate logistic regression model of HIV positivity. The performance of the final model at discriminating between HIV positive and HIV negative was assessed using the area under the receiver-operating characteristic curve (AUROC). The optimal cut-point of the prediction model was determined using the Youden index. We also used other measures of discriminative abilities such as predictive values, sensitivity, and specificity. The estimated HIV prevalence was 12.4% (11.7% − 14.0) %. The score of the derived risk prediction model had a mean and standard deviation of 2.36 and 0.64 respectively and ranged from 0.37 to 4.59. The prediction model's sensitivity was 16. 7% and a specificity of 98.5%. The model's positive predictive value was 68.2% and a negative predictive value of 85.8%. The prediction model's optimal cut-point was 2.43 with sensitivity of 71% and specificity of 60%. Our model performed well at predicting HIV positivity with training AUC of 0.78 and a testing AUC of 0.76. A combination of the identified risk factors provided good discrimination and calibration at predicting HIV positivity in AGYW. This model could provide a simple and low-cost strategy for screening AGYW in primary healthcare clinics and community-based settings. In this way, health service providers could easily identify and link AGYW to HIV PrEP services. [ABSTRACT FROM AUTHOR]

Although under-five mortality (U5M) rates have declined worldwide, many countries in sub-Saharan Africa still have much higher rates. Detection of subnational areas with unusually higher U5M rates could support targeted high impact child health interventions. We propose a novel group outlier detection statistic for identifying areas with extreme U5M rates under a multivariate survival data model. The performance of the proposed statistic was evaluated through a simulation study. We applied the proposed method to an analysis of child survival data in Malawi to identify sub-districts with unusually higher or lower U5M rates. The simulation study showed that the proposed outlier statistic can detect unusual high or low mortality groups with a high accuracy of at least 90%, for datasets with at least 50 clusters of size 80 or more. In the application, at most 7 U5M outlier sub-districts were identified, based on the best fitting model as measured by the Akaike information criterion (AIC). [ABSTRACT FROM AUTHOR]

Statistical techniques for detecting influential data are well developed and commonly used in linear regression, and to some extent in linear mixed-effects models. However, even though the application of multivariate survival models is widely undertaken, the development of diagnostic tools for the models has received less attention. In this article, we extend the martingale-based residuals and leverage commonly used in univariate survival regression to derive influence statistics for the multivariate survival model. The performance of the proposed statistic is evaluated by simulation studies. The statistic is illustrated with an analysis of child clustered survival data to identify influential clusters of observations and their effects on the estimate of fixed-effect coefficients. [ABSTRACT FROM AUTHOR]

The gut microbiota has been immensely studied over the past years because of its involvement in the pathogenesis of numerous diseases. However, gut microbiota data in Africa are limited. Therefore, it is crucial to have studies that reflect various populations in order to fully capture global microbial diversity. In the proposed scoping review, we will describe the gut microbiota's appearance in terms of gut microbiota markers, in both health and disease in African populations. Relevant publications will be searched for in the PubMed, Scopus, Web of Science, Academic Search Premier, Africa-Wide Information, African journals online, CINAHL, and EBSCOhost and Embase databases. We will focus on articles published between January 2005 and March 2023. We will also determine if the studies to be included in the review would provide enough data to identify quantifiable gut microbiome traits that could be used as health or disease markers, identify the types of diseases that were mostly focused on in relation to gut microbiota research in Africa, as well as to discover and analyze knowledge gaps in the gut microbiota research field in the continent. We will include studies involving African countries regardless of race, gender, age, health status, disease type, study design, or care setting. Two reviewers will conduct a literature search and screen the titles/abstracts against the eligibility criteria. The reviewers will subsequently screen full-text articles and identify studies that meet the inclusion criteria. This will be followed by charting the data using a charting tool and analysis of the evidence. The proposed scoping review will follow a qualitative approach such that a narrative summary will accompany the tabulated/graphical results which will describe how the results relate to the review objectives and questions. As a result, this review may play a significant role in the identification of microbiota-related adjunctive therapies in the African region where multiple comorbidities coexist. Scoping review registration: Open Science Framework. [ABSTRACT FROM AUTHOR]