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Bovine mastitis remains a significant challenge to dairy health management worldwide, with substantial economic and public health implications. In Rwanda, where traditional dairy farming is crucial for household livelihoods and the national economy, mastitis reduces milk yield and increases the risk for bacterial contamination, posing serious food safety concerns. This study, conducted in Rwanda’s North-Western region from January 2024 to April 2024, aimed to identify key mastitis risk factors, evaluate existing control gaps, and propose evidence-based interventions. This cross-sectional study collected data from 411 smallholder dairy farms in Rwanda, assessing one lactating cow per farm through clinical examination, California Mastitis Test, and structured farmer questionnaires on management and hygiene practices. Logistic regression analysis in R identified significant cow-related and farm-level risk factors, providing a basis for targeted mastitis control and biosecurity recommendations. The overall mastitis prevalence was 60.06% (95% CI: 53.57–66.55), with subclinical cases alone accounting for 56.9%. Poor cow hygiene emerged as the strongest predictor (OR = 85.47, 95% CI: 27.18–268.74; p < 0.001). Other associated factors included exotic pure breeds, late lactation stages, and multiparity. External contributors included inadequate milking practices and limited veterinary access. In zero-grazing systems, poor housing drainage (OR = 109, 95% CI: 26.46–507.18; p < 0.001) and infrequent bedding changes (OR = 6.36, 95% CI: 3.38–12.78; p = 0.014) significantly increased mastitis risk. Identified gaps included lack of farmer knowledge, insufficient access to affordable mastitis control supplies (e.g., disinfectants such as iodine or chlorine dioxide), inappropriate mastitis treatments, poor farm biosecurity, and inefficient quality control in the milk marketing chain. Strengthening farm biosecurity, implementing a national mastitis control program, and enhancing veterinary extension are essential to reduce mastitis and improve milk safety. Coordinated stakeholder action is vital for sustainable dairy development and public health.

Despite national progress, stunting remains prevalent in specific regions of Rwanda, highlighting the limitations of coarse-resolution data for effective mapping and intervention planning. This study explored optimal spatial resolution and analytical approach to capture localised dynamics and the multifactorial nature of stunting. A cross-sectional, population-based study was conducted in the Northern Province of Rwanda, focusing on children aged 1–36 months. Data were collected using structured questionnaires covering socio-demographic, economic, health, childcare, livestock factors and anthropometric measurements. Environmental characteristics were obtained from national datasets, while household geographic coordinates were captured using a customized mobile geodata platform ( emGeo ). After data cleaning, predictors were analysed using univariable and multivariable logistic regression as well as geographically weighted logistic regression (GWLR) to account for spatial heterogeneity. Among 601 children, stunting prevalence was 27% (boys 33.8%; girls 20.9%). GWLR improved model fit, increasing adjusted deviance explained from 34% to 39%. Significant predictors included child age (adjusted OR = 2.46; 95% CI: 1.78–3.39), male sex (OR = 2.83; 95% CI: 1.65–4.86), birthweight (OR = 0.71; 95% CI: 0.54–0.94), maternal autonomy (ability to refuse sexual intercourse; OR = 0.48; 95% CI: 0.27–0.86), inconsistent maternal social support (OR = 2.30; 95% CI: 1.20–4.42), household electricity access (OR = 0.48; 95% CI: 0.27–0.84) and handwashing facilities (OR = 0.21; 95% CI: 0.07–0.67). GWLR revealed substantial spatial heterogeneity in these factors, delineating areas where each factor matters most. This household-level, spatially explicit analysis reveals localised risk patterns often masked by aggregated national data. Prioritising context-specific interventions (such as electrification, hygiene promotion, and enhanced maternal social support), can enhance effectiveness. The proposed analytical workflow provides a model for addressing persistent stunting in other resource-limited settings.

Background Wildebeest associated malignant catarrhal fever (WA-MCF) is a fatal disease of cattle. Outbreaks are seasonal and associated with close interaction between cattle and calving wildebeest. In Kenya, WA-MCF has a dramatic effect on cattle-keepers who lose up to 10% of their cattle herds per year. The objective of this study was to report the impact of WA-MCF on a commercial ranch and assess the performance of clinical diagnosis compared to laboratory diagnosis as a disease management tool. A retrospective study of WA-MCF in cattle was conducted from 2014 to 2016 at Kapiti Plains Ranch Ltd., Kenya. During this period, 325 animals showed clinical signs of WA-MCF and of these, 123 were opportunistically sampled. In addition, 51 clinically healthy animals were sampled. Nested polymerase chain reaction (PCR) and indirect enzyme linked immunosorbent assay (ELISA) were used to confirm clinically diagnosed cases of WA-MCF. A latent class model (LCM) was used to evaluate the diagnostic parameters of clinical diagnosis and the tests in the absence of a gold standard. Results By PCR, 94% (95% C.I. 89–97%) of clinically affected animals were positive to WA-MCF while 63% (95% C.I. 54–71%) were positive by indirect ELISA. The LCM demonstrated the indirect ELISA had poor sensitivity 63.3% (95% PCI 54.4–71.7%) and specificity 62.6% (95% PCI 39.2–84.9%) while the nested PCR performed better with sensitivity 96.1% (95% PCI 90.7–99.7%) and specificity 92.9% (95% PCI 76.1–99.8%). The sensitivity and specificity of clinical diagnosis were 99.1% (95% PCI 96.8–100.0%) and 71.5% (95% PCI 48.0–97.2%) respectively. Conclusions Clinical diagnosis was demonstrated to be an effective method to identify affected animals although animals may be incorrectly classified resulting in financial loss. The study revealed indirect ELISA as a poor test and nested PCR to be a more appropriate confirmatory test for diagnosing acute WA-MCF. However, the logistics of PCR make it unsuitable for field diagnosis of WA-MCF. The future of WA-MCF diagnosis should be aimed at development of penside techniques, which will allow for fast detection in the field.

A cross-sectional study on 156 smallholder dairy farms in Rwanda was carried out to assess the association between farm management practices and milk yield and quality. A pre-tested questionnaire was used to collect data on cow characteristics and farm management practices. Milk yield was recorded at household level, milk composition was monitored using a Lactoscan device (Milk Analyzer). Somatic cell count (SCC) was determined using a DeLaval cell counter (DCC). A Delvotest SP-NT kit was used to determine antibiotic residues in raw milk. Most dairy cows were kept in zero-grazing system (84.6%) and most farmers had less experience of dairy production (78.2%). Mean daily milk yield was 3.9 L/cow and was associated with type of breed, milking frequency, stage of lactation, and parity. Mean milk content of protein, fat, lactose and solid non-fat, and density were normal and showed no association with different management practices. Based on SCC analyses, 65.8% of the milk samples with less than 300,000 cells/mL were graded as acceptable for delivery to a milk collection centre (MCC) and 12.9% of the samples tested positive for antibiotic residues. These findings suggest low milk yields on smallholder farms in Rwanda that are attributable to type of breed and prevalent high level mastitis, among other factors. The results also indicate possible non-compliance with withdrawal periods, resulting in antibiotic residues in milk, which has public health implications for consumers. Routine testing at MCC for both SCC and antibiotic residues is important for quality control.

Despite national progress, stunting remains prevalent in specific regions of Rwanda, highlighting the limitations of coarse-resolution data for effective mapping and intervention planning. This study explored optimal spatial resolution and analytical approach to capture localised dynamics and the multifactorial nature of stunting. A cross-sectional, population-based study was conducted in the Northern Province of Rwanda, focusing on children aged 1-36 months. Data were collected using structured questionnaires covering socio-demographic, economic, health, childcare, livestock factors and anthropometric measurements. Environmental characteristics were obtained from national datasets, while household geographic coordinates were captured using a customized mobile geodata platform (emGeo). After data cleaning, predictors were analysed using univariable and multivariable logistic regression as well as geographically weighted logistic regression (GWLR) to account for spatial heterogeneity. Among 601 children, stunting prevalence was 27% (boys 33.8%; girls 20.9%). GWLR improved model fit, increasing adjusted deviance explained from 34% to 39%. Significant predictors included child age (adjusted OR = 2.46; 95% CI: 1.78-3.39), male sex (OR = 2.83; 95% CI: 1.65-4.86), birthweight (OR = 0.71; 95% CI: 0.54-0.94), maternal autonomy (ability to refuse sexual intercourse; OR = 0.48; 95% CI: 0.27-0.86), inconsistent maternal social support (OR = 2.30; 95% CI: 1.20-4.42), household electricity access (OR = 0.48; 95% CI: 0.27-0.84) and handwashing facilities (OR = 0.21; 95% CI: 0.07-0.67). GWLR revealed substantial spatial heterogeneity in these factors, delineating areas where each factor matters most. This household-level, spatially explicit analysis reveals localised risk patterns often masked by aggregated national data. Prioritising context-specific interventions (such as electrification, hygiene promotion, and enhanced maternal social support), can enhance effectiveness. The proposed analytical workflow provides a model for addressing persistent stunting in other resource-limited settings.