Exploitations Familiales de Production Agricole des Cercles de Niono et de Banamba (Mali): Caractérisation et Stratégies de Prise en Charge des Besoins en Rapport Avec L’intervention Pour le Développement Agricole et Rural

The development of family farming is an important strategic option to support the food needs of the rural and urban populations. This is the reason it has been the subject of several studies which is based on characterization and typology to better orientate agricultural and rural development interventions. This study was conducted to characterize family farm in Mali and to determine the strategies to satisfy the country’s needs in rapport with the implementation of rural development. In doing this, field data were first collected in the family farms at Niono County (Niger River valley) and the Banamba County. After data collection, it was then analyzed in a systemic way. This analysis helps to characterize the family farm of Mali as a “Traditional peasant farm”. In Niono County, ‘peasant family farm’ which ensures its food self-sufficiency is centered on the production unit with an option of diversification. In Banamba County, “peasant family farm’ centered on unity of consumption develops food security strategy through pluri-activity. Thus, despite the intensity of government intervention, particularly in the area of Niger River Valley, malian “peasant family farmg” did not turn into “Modern family farm business”. In doing this, intervention for agricultural and rural development should move based on its strategies and practices from a sectorial approach to a systemic approach. This passage from the deterministic and holistic paradigm to the systemic and interactionist paradigm, should better redefine agricultural education and agricultural subsidy in relation with the structure of production

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Prevalence of Group B Streptococcus among Pregnant Women in Bobo-Dioulasso (Burkina Faso)

info:eu-repo/semantics/publishe

Differences in the Cardiovascular Risk Assessment in Cardiology Outpatients in Mali: Comparison between Framingham Body Mass Index-Based Tool and Low-Information World Health Organization Chart

Objective. This study aimed to compare 2 laborless tools, namely, the body mass index-based Framingham (bmi-Frm) and low-information WHO- (li-WHO-) based risk scores, and assess their agreement in outpatients in a cardiology department. Methodology. Data stem from a cross-sectional previous study performed from May to September 2016 in the Cardiology Department of University Hospital Gabriel Touré (UH-GT) in Bamako. All patients aged 40 and more were included in the study allowing the assessment of bmi-Frm and li-WHO prediction charts. The cardiovascular risk (CVR) was evaluated using a calculator prepared by D‘Agostino et al. for the bmi-Frm and the li-WHO chart for the Afro-D region of the WHO. The risk score for both ranged from <10 to ≥40. The data were entered in an ACCESS 2010 database, then processed by MS Excel 2010, and finally analysed using IBM SPSS Statistics 20. Continuous variables were presented as means and standard deviations, and categorical variables were presented as frequencies with percentages. P<0.05 was considered the statistical significance level. After sample description, the risk score was assessed using bmi-Frm and li-WHO prediction tools. Finally, a kappa test was performed to check for the interreliability of both methods. For weighted kappa, coefficients were given all five classes of risk groups in 0, 25 steps from 1 for total concordance to 0 for total discordance. Results. This study involved 793 outpatients, 63.7% being female, 35.1% of them younger than 50 years, 57.9% with no formal education, and 67.7% with no medical insurance. Means for age, body mass index (BMI), and systolic blood pressure (SBP) were, respectively, 53.81 ± 16.729 years, 25.29 ± 06.151 kg/m2, and 139.49 ± 27.110 mm Hg. Using the li-WHO prediction chart gives a much higher proportion of low-risk patients compared to bmi-Frm (83.6 vs. 37.7). Sociodemographic characteristics such as education or income level were not different in risk score neither for the bmi-Frm nor for the li-WHO risk score. The percentage of agreement between both tools was 40.4%, and agreement (kappa of 0.1 and weighted kappa of 0.2) was found to be slight. Conclusion. Using the bmi-Frm and li-WHO tool gives a similar risk estimation in younger female patients. Older patients must be evaluated using high-information tools with cholesterol, e.g., versions of the Framingham risk equation or WHO using cholesterol. These must be confirmed in further studies and compared to data from prospective studie