There is longstanding evidence that immunoglobulin G (IgG) has a role in protection against clinical malaria, and human antibodies of the cytophilic subclasses are thought to be particularly critical in this respect. In this cohort study, 286 Burkinabè children 6 months to 15 years old were kept under malaria surveillance in order to assess the protective role of antibody responses against four antigens which are currently being evaluated as vaccine candidates: apical membrane antigen 1 (AMA1), merozoite surface protein 1-19 (MSP1-19), MSP3, and glutamate-rich protein (GLURP). Total IgG, IgM, and IgG subclass responses were measured just before the malaria transmission season. The incidence of malaria was 2.4 episodes per child year of risk. After adjusting for the confounding effects of age, the level of total IgG to GLURP was strongly associated with reduced malaria incidence (incidence rate ratio associated with a doubling of total IgG, 0.79; 95% confidence interval, 0.66 to 0.94;P= 0.009.); there was a borderline statistically significant association between the level of total IgG to MSP3 and malaria incidence and no evidence of an association for total IgG to AMA1 and to MSP1-19. Of the IgG subclass responses studied, only IgG3 and IgG4 against GLURP and IgG1 against AMA1 were associated with reduced risk of clinical malaria. There was no evidence of an interaction between responses to AMA1 and baseline parasitemia in their effects on malaria incidence. Currently included in malaria vaccine formulations for clinical trials in humans, these blood-stage antigens, AMA1 and GLURP, offer good prospects for malaria vaccine development.

Antigen-specific antibody-mediated immune responses play an important role in natural protection against clinical malaria, but conflicting estimates of this association have emerged from immuno-epidemiological studies in different geographical settings. This study was aimed at assessing in a standardized manner the relationship between the antibody responses to four malaria vaccine candidate antigens and protection from clinical malaria, in a cohort of Ghanaian children.

Sub-Saharan Africa is experiencing an epidemiological transition as the burden of NCDs overtake communicable diseases. However, it is unknown what capacity and gaps exist at primary care level to address the growing burden of NCDs. This study aimed to assess the Zambian health system’s capacity to address in NCDs, using an adapted WHO Essential Non Communicable Disease Interventions (WHO PEN) tool. This was a cross-sectional facility survey in the three districts conducted from September 2017 to October 2017. We defined facility readiness along five domains: basic equipment, essential services, diagnostic capacity, counseling services, and essential medicines. For each domain, we calculated an index as the mean score of items expressed as percentage. These indices were compared to an agreed cutoff at 70%, meaning that a facility index or district index below 70% off was considered as ‘not ready’ to manage NCDs at that level. All analysis were performed using Stata 15 MP. There appeared to be wide heterogeneity between facilities in respect of readiness to manage NCDs. Only 6 (including the three 1^st level hospitals) out of the 46 facilities were deemed ready to manage NCDs. Only the first level hospitals scored a mean index higher than the 70% cut off; With regard to medications needed to manage NCDs, urban and rural health facilities were comparably equipped. However, there was evidence that calcium channel blockers (p = 0.013) and insulin (p = 0.022) were more likely to be available in urban and semi-urban health facilities compared to rural facilities. Our study revealed gaps in primary health care capacity to manage NCDs in Zambia, with almost all health facilities failing to reach the minimum threshold. These results could be generalized to other similar districts in Zambia and the sub-region, where health systems remain focused on infectious rather than non-communicable Disease. These results should attract policy attention and potentially form the basis to review current approach to NCD care at the primary care level in Zambia and Sub-Saharan Africa.

Stunting is a global public health issue. We sought to train and evaluate machine learning (ML) classification algorithms on the Zambia Demographic Health Survey (ZDHS) dataset to predict stunting among children under the age of five in Zambia. We applied Logistic regression (LR), Random Forest (RF), SV classification (SVC), XG Boost (XgB) and Naïve Bayes (NB) algorithms to predict the probability of stunting among children under five years of age, on the 2018 ZDHS dataset. We calibrated predicted probabilities and plotted the calibration curves to compare model performance. We computed accuracy, recall, precision and F1 for each machine learning algorithm. About 2327 (34.2%) children were stunted. Thirteen of fifty-eight features were selected for inclusion in the model using random forest. Calibrating the predicted probabilities improved the performance of machine learning algorithms when evaluated using calibration curves. RF was the most accurate algorithm, with an accuracy score of 79% in the testing and 61.6% in the training data while Naïve Bayesian was the worst performing algorithm for predicting stunting among children under five in Zambia using the 2018 ZDHS dataset. ML models aids quick diagnosis of stunting and the timely development of interventions aimed at preventing stunting.

Live attenuated oral vaccines against rotavirus (RV) have been shown to be less efficacious in children from developing countries. Reasons for this disparity are not fully understood. We assessed the role of maternal factors including breast milk RV-specific IgA, transplacentally acquired infant serum RV-specific IgG and maternal HIV status in seroconversion among Zambian infants routinely immunized with Rotarix™ (RV1). 420 mother-child pairs were recruited at infant age 6–12 weeks in Lusaka. Clinical information and samples were collected at baseline and at one month following the second dose of RV1. Determination of breast milk RV-specific IgA and serum RV-specific IgA and IgG was done using standardized ELISA. Seroconversion was defined as a ≥ 4 fold rise in serum IgA titre from baseline to one-month post RV1 dose 2, while seropositivity of IgA was defined as serum titre ≥ 40 and antibody variables were modelled on log-base 2. Logistic regression was used to identify predictors of the odds of seroconversion. Baseline infant seropositivity was 25.5% (91/357). The seroconversion frequency was 60.2% (130/216). Infants who were IgA seropositive at baseline were less likely to seroconvert compared to their seronegative counterparts (P = 0.04). There was no evidence of an association between maternal HIV status and seroconversion (P = 0.25). Higher titres of breast milk rotavirus-specific IgA were associated with a lower frequency of seroconverson (Nonparametric test for trend Z = -2.84; P<0.01): a two-fold increase in breast milk RV-specific IgA titres was associated with a 22% lower odds of seroconversion (OR = 0.80; 95% CI = 0.68–0.94; P = 0.01). There was seasonal variation in baseline breast milk rotavirus-specific IgA titres, with significantly higher GMTs during the cold dry months (P = 0.01). Low immunogenicity of RV1 vaccine could be explained in part by exposure to high antibody titres in breast milk and early exposure to wild-type rotavirus infections. Potential interference of anti-RV specific IgA in breast milk and pre-vaccination serum RV specific-IgA and IgG titres with RV1 seroconversion and effectiveness requires further research.

Background: Inappropriate antimicrobial usage is a key driver of antimicrobial resistance (AMR). Low- and middle-income countries (LMICs) are disproportionately burdened by AMR and young children are especially vulnerable to infections with AMR-bearing pathogens. The impact of antibiotics on the microbiome, selection, persistence, and horizontal spread of AMR genes is insufficiently characterized and understood in children in LMICs. This systematic review aims to collate and evaluate the available literature describing the impact of antibiotics on the infant gut microbiome and resistome in LMICs. Methods and findings: In this systematic review, we searched the online databases MEDLINE (1946 to 28 January 2023), EMBASE (1947 to 28 January 2023), SCOPUS (1945 to 29 January 2023), WHO Global Index Medicus (searched up to 29 January 2023), and SciELO (searched up to 29 January 2023). A total of 4,369 articles were retrieved across the databases. Duplicates were removed resulting in 2,748 unique articles. Screening by title and abstract excluded 2,666 articles, 92 articles were assessed based on the full text, and 10 studies met the eligibility criteria that included human studies conducted in LMICs among children below the age of 2 that reported gut microbiome composition and/or resistome composition (AMR genes) following antibiotic usage. The included studies were all randomized control trials (RCTs) and were assessed for risk of bias using the Cochrane risk-of-bias for randomized studies tool. Overall, antibiotics reduced gut microbiome diversity and increased antibiotic-specific resistance gene abundance in antibiotic treatment groups as compared to the placebo. The most widely tested antibiotic was azithromycin that decreased the diversity of the gut microbiome and significantly increased macrolide resistance as early as 5 days posttreatment. A major limitation of this study was paucity of available studies that cover this subject area. Specifically, the range of antibiotics assessed did not include the most commonly used antibiotics in LMIC populations. Conclusion: In this study, we observed that antibiotics significantly reduce the diversity and alter the composition of the infant gut microbiome in LMICs, while concomitantly selecting for resistance genes whose persistence can last for months following treatment. Considerable heterogeneity in study methodology, timing and duration of sampling, and sequencing methodology in currently available research limit insights into antibiotic impacts on the microbiome and resistome in children in LMICs. More research is urgently needed to fill this gap in order to better understand whether antibiotic-driven reductions in microbiome diversity and selection of AMR genes place LMIC children at risk for adverse health outcomes, including infections with AMR-bearing pathogens.