Longitudinal and time series analyses are needed to characterize the associations between hydrometeorological parameters and health outcomes. Earth Observation (EO) climate data products derived from satellites and global model-based reanalysis have the potential to be used as surrogates in situations and locations where weather-station based observations are inadequate or incomplete. However, these products often lack direct evaluation at specific sites of epidemiological interest. Standard evaluation metrics of correlation, agreement, bias and error were applied to a set of ten hydrometeorological variables extracted from two quasi-global, commonly used climate data products - the Global Land Data Assimilation System (GLDAS) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) - to evaluate their performance relative to weather-station derived estimates at the specific geographic locations of the eight sites in a multi-site cohort study. These metrics were calculated for both daily estimates and 7-day averages and for a rotavirus-peak-season subset. Then the variables from the two sources were each used as predictors in longitudinal regression models to test their association with rotavirus infection in the cohort after adjusting for covariates. The availability and completeness of station-based validation data varied depending on the variable and study site. The performance of the two gridded climate models varied considerably within the same location and for the same variable across locations, according to different evaluation criteria and for the peak-season compared to the full dataset in ways that showed no obvious pattern. They also differed in the statistical significance of their association with the rotavirus outcome. For some variables, the station-based records showed a strong association while the EO-derived estimates showed none, while for others, the opposite was true. Researchers wishing to utilize publicly available climate data - whether EO-derived or station based - are advised to recognize their specific limitations both in the analysis and the interpretation of the results. Epidemiologists engaged in prospective research into environmentally driven diseases should install their own weather monitoring stations at their study sites whenever possible, in order to circumvent the constraints of choosing between distant or incomplete station data or unverified EO estimates.

Objective: To characterize the operations of the street food enterprise in the Vhembe district, focusing on business profile, sold foods, inputs, pricing, record-keeping practices and total running cost.Methods: A descriptive cross-sectional face-to-face study of 511 vendors was done using a structured researcher-administered questionnaire comprising demographic and operational characteristics. Convenience sampling was used to choose the vending sites. Chi-square tests were conducted between four categorical variables (gender, age, marital status and citizenship) and operational characteristics. P-values were considered significant at p < 0.05. However, a Bonferroni adjustment decreased the significant value to p < 0.013.Results: The findings highlight the dominance of single middle-aged (35–44) women (63.1%) with some high school education. About 14% migrated from Zimbabwe. Most vendors were owners (86.1%), with 70.0% in business for at least 1–10 years. Food sold included mielie pap (stiff porridge) served with beef or chicken, sometimes with vegetables. Plate prices were R40.00 (2.6 USD) for a full plate and R30.00 (2 USD) for half a plate. The typical street food consumers were government officials, middlemen, and schoolchildren. Social media such as Whatsapp were used to communicate between the street food vendors and customers. The results highlight poor managerial skills as only 15.5% kept records, most of which were sales records (59.5%). On average, street vendors made a monthly profit of R3200.00 (213 USD) while spending R1800.00 (120 USD) on daily running expenses. There were statistically significant variations in some operational characteristics of vendor variables and gender, age, marital status, and citizenship.Conclusions: There is a need for capital and management for small businesses and food training for rural street food vendors. Therefore, government officials, policymakers, and NGOs could target street vendors to offer training and microfinance to improve their business skills while promoting food safety and consumption of nutritious foods.

Background: There is strong evidence that exclusive breastfeeding (EBF) in the first 6 months of life reduces the risk of diseases in infancy and in later life. Objective: To understand the maternal reasoning that influences optimum infant feeding practices of caregivers in semirural communities of Limpopo province. Methods: Nested qualitative study among mothers in an ongoing birth cohort study was conducted; structured and semi-structured interviews were used to collect data. Data from 234 infants after 6 months of follow-up was included for quantitative analysis. Four focus discussion groups comprising 7 to 10 caregivers were used to obtain perception of mothers on breastfeeding. A semi-structured interview guide was used to stimulate discussions. Thematic content analyses were conducted to identify the main themes that influence breastfeeding practices of caregivers. Results: Over 90% of the caregivers initiated breastfeeding after delivery. However, less than 1% of mothers practiced EBF by 3 months, and none of the children were exclusively breastfed for up to 6 months. All caregivers introduced non–breast milk liquids and solids by the second month of child’s life. Common reasons for introducing non–breast milk foods included insufficiency of breast milk production, going back to work or school, and influence by elderly women (mothers/mothers-in-law) and church members. Conclusion: Exclusive breastfeeding was not practiced in this community due to cultural and religious beliefs and misinformation. The involvement of elderly women and church members in infant feeding education and promotion programs and the dissemination of breastfeeding information through mobile phones to younger mothers are recommended.

The WHO recommends exclusive breastfeeding for the first 6 months of life. However, the transition of the infants' diet to partial breastfeeding with the addition of animal milks and/or solids typically occurs earlier than this. Here, we explored factors associated with the timing of an early transition to partial breastfeeding across seven sites of a birth cohort study in which twice weekly information on infant feeding practices was collected. Infant (size, sex, illness and temperament), maternal (age, education, parity and depressive symptoms), breastfeeding initiation practices (time of initiation, colostrum and pre‐lacteal feeding) and household factors (food security, crowding, assets, income and resources) were considered. Three consecutive caregiver reports of feeding animal milks and/or solids (over a 10‐day period) were characterized as a transition to partial breastfeeding, and Cox proportional hazard models with time (in days) to partial breastfeeding were used to evaluate associations with both fixed and time‐varying characteristics. Overall, 1470 infants were included in this analysis. Median age of transition to partial breastfeeding ranged from 59 days (South Africa and Tanzania) to 178 days (Bangladesh). Overall, higher weight‐for‐lengthz‐scores were associated with later transitions to partial breastfeeding, as were food insecurity, and infant cough in the past 30 days. Maternal depressive symptoms (evaluated amongst 1227 infants from six sites) were associated with an earlier transition to partial breastfeeding. Relative thinness or heaviness within each site was related to breastfeeding transitions, as opposed to absolutez‐scores. Further research is needed to understand relationships between local perceptions of infant body size and decisions about breastfeeding.

Background: Antimicrobial resistance (AMR) is a global health threat that increases the burden of infectious diseases and disproportionately affects communities of low socioeconomic status. Despite the call for community-level AMR data, prospective studies from rural sub-Saharan African communities to inform appropriate targeted interventions remain scarce. Given the role of enteric bacteria in AMR transmission dynamics, there is a need to understand the timing, risk factors, and ecological drivers of gut resistome acquisition and development during infancy. Objective: This study aimed to characterize the temporal dynamics of enteric bacterial resistomes during the first 2 years of life and to identify drivers of AMR acquisition and development in a community-based, prospective, observational birth cohort study in a rural South African community. Methods: The study aims to enroll 200 newborns and their mothers within 17 days post partum. Data on key exposures and variables include sociodemographics; perinatal and anthropometrics; feeding practices and dietary exposures; illness, medication, and vaccination history; breast milk metabolomic profiles; household socioeconomic status; maternal psychosocial and behavioral factors; hygiene and sanitation practices; and environmental exposures including hydro-meteorological variables, in-house livestock and pets, and drinking water quality. Biological samples include stools from monthly collections and diarrhea episodes for metagenomic analysis and breast milk for metabolomics. Planned analyses include assessing the infant microbiome and resistome structure (diversity, abundance, and composition) across time points and modeling associations between risk factors and AMR outcomes. Additionally, a cross-sectional community survey on knowledge, attitudes, and practices regarding antimicrobial use is conducted to inform knowledge translation through responsive dialogues, thereby developing ethnographically relevant packages for community-level AMR stewardship. Results: Participant identification and enrollment began in August 2023. By October 2025, 167 newborns had been enrolled, with 20 having completed the 24-month follow-up. The characteristics of the enrolled participants are presented in this protocol. Conclusions: This study will offer a unique opportunity to generate longitudinal resistome data from a rural sub-Saharan African setting. The study is expected to contribute knowledge on the microbiome and resistome structure dynamics and trajectories associated with key risk factors of acquisition and development. In addition, co-produced ethnographically tailored educational packages, informed by knowledge, attitudes, and practices and bacterial resistome data, will drive sustainable community-centered AMR awareness interventions.

Background: Growth trajectories are highly variable between children, making epidemiological analyses challenging both to the identification of malnutrition interventions at the population level and also risk assessment at individual level. We introduce stochastic differential equation (SDE) models into child growth research. SDEs describe flexible dynamic processes comprising: drift - gradual smooth changes – such as physiology or gut microbiome, and diffusion - sudden perturbations, such as illness or infection. Methods: We present a case study applying SDE models to child growth trajectory data from the Haydom, Tanzania and Venda, South Africa sites within the MAL-ED cohort. These data comprise n=460 children aged 0-24 months. A comparison with classical curve fitting (linear mixed models) is also presented. Results: The SDE models offered a wide range of new flexible shapes and parameterizations compared to classical additive models, with performance as good or better than standard approaches. The predictions from the SDE models suggest distinct longitudinal clusters that form distinct ‘streams’ hidden by the large between-child variability. Conclusions: Using SDE models to predict future growth trajectories revealed new insights in the observed data, where trajectories appear to cluster together in bands, which may have a future risk assessment application. SDEs offer an attractive approach for child growth modelling and potentially offer new insights.