Transparency, traceability and deforestation in the Ivorian cocoa supply chain

Cocoa production has been identified as a major global driver of deforestation, but its precise contribution to deforestation dynamics in West Africa remains unclear. It is also unknown to what degree companies and international markets are able to trace their cocoa imports, and satisfy their sustainable sourcing commitments. Here, we use publicly-available remote-sensing and supply chain data for Côte d’Ivoire, the world’s largest cocoa producer, to quantify cocoa-driven deforestation and trace 2019 cocoa exports and the associated deforestation from their department of origin, via trading companies, to international markets. We find 2.4 Mha of cocoa deforestation and degradation over 2000–2019, i.e. 125 000 ha y ^−1 , representing 45% of the total deforestation and forest degradation over that period. Only 43.6% (95% CI: 42.6%–44.7%) of exports can be traced back to a specific cooperative and department. The majority of cocoa (over 55%) thus remains untraced, either indirectly sourced from local intermediaries by major traders (23.9%, 95% CI: 22.9%–24.9%), or exported by untransparent traders—who disclose no information about their suppliers (32.4%). Traceability to farm lags further behind, and is insufficient to meet the EU due-diligence legislation’s proposed requirement for geolocation of product origins. We estimate that trading companies in the Cocoa and Forests Initiative have mapped 40% of the total farms supplying them, representing only 22% of all Ivorian cocoa exports in 2019. We identify 838 000 hectares of deforestation over 2000–2015 associated with 2019 EU imports, 56% of this arising through untraced sourcing. We discuss issues of company- and state-led traceability systems, often presented as solutions to deforestation, and stress the need for transparency and for the sector to work beyond individual supply chains, at landscape-level, calling for collaboration, stronger regulatory policies, and investments to preserve the remaining stretches of forests in West Africa

Investigating COVID-19 Vaccine Impact on the Risk of Hospitalisation through the Analysis of National Surveillance Data Collected in Belgium

The national vaccination campaign against SARS-CoV-2 started in January 2021 in Belgium. In the present study, we aimed to use national hospitalisation surveillance data to investigate the recent evolution of vaccine impact on the risk of COVID-19 hospitalisation. We analysed aggregated data from 27,608 COVID-19 patients hospitalised between October 2021 and February 2022, stratified by age category and vaccination status. For each period, vaccination status, and age group, we estimated risk ratios (RR) corresponding to the ratio between the probability of being hospitalised following SARS-CoV-2 infection if belonging to the vaccinated population and the same probability if belonging to the unvaccinated population. In October 2021, a relatively high RR was estimated for vaccinated people > 75 years old, possibly reflecting waning immunity within this group, which was vaccinated early in 2021 and invited to receive the booster vaccination at that time. In January 2022, a RR increase was observed in all age categories coinciding with the dominance of the Omicron variant. Despite the absence of control for factors like comorbidities, previous infections, or time since the last administered vaccine, we showed that such real-time aggregated data make it possible to approximate trends in vaccine impact over time

Conceptual causal framework to assess the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients.

BACKGROUND: SARS-CoV-2 strains evolve continuously and accumulate mutations in their genomes over the course of the pandemic. The severity of a SARS-CoV-2 infection could partly depend on these viral genetic characteristics. Here, we present a general conceptual framework that allows to study the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients. METHODS: A causal model is defined and visualized using a Directed Acyclic Graph (DAG), in which assumptions on the relationship between (confounding) variables are made explicit. Various DAGs are presented to explore specific study design options and the risk for selection bias. Next, the data infrastructure specific to the COVID-19 surveillance in Belgium is described, along with its strengths and weaknesses for the study of clinical impact of variants. DISCUSSION: A well-established framework that provides a complete view on COVID-19 disease severity among hospitalized patients by combining information from different sources on host factors, viral factors, and healthcare-related factors, will enable to assess the clinical impact of emerging SARS-CoV-2 variants and answer questions that will be raised in the future. The framework shows the complexity related to causal research, the corresponding data requirements, and it underlines important limitations, such as unmeasured confounders or selection bias, inherent to repurposing existing routine COVID-19 data registries. TRIAL REGISTRATION: Each individual research project within the current conceptual framework will be prospectively registered in Open Science Framework (OSF identifier: https://doi.org/10.17605/OSF.IO/UEF29 ). OSF project created on 18 May 2021

Association between COVID-19 Primary Vaccination and Severe Disease Caused by SARS-CoV-2 Delta Variant among Hospitalized Patients: A Belgian Retrospective Cohort Study

We aimed to investigate vaccine effectiveness against progression to severe COVID-19 (acute respiratory distress syndrome (ARDS), intensive care unit (ICU) admission and/or death) and in-hospital death in a cohort of hospitalized COVID-19 patients. Mixed effects logistic regression analyses were performed to estimate the association between receiving a primary COVID-19 vaccination schedule and severe outcomes after adjusting for patient, hospital, and vaccination characteristics. Additionally, the effects of the vaccine brands including mRNA vaccines mRNA-1273 and BNT162b2, and adenovirus-vector vaccines ChAdOx1 (AZ) and Ad26.COV2.S (J&J) were compared to each other. This retrospective, multicenter cohort study included 2493 COVID-19 patients hospitalized across 73 acute care hospitals in Belgium during the time period 15 August 2021–14 November 2021 when the Delta variant (B1.617.2) was predominant. Hospitalized COVID-19 patients that received a primary vaccination schedule had lower odds of progressing to severe disease (OR (95% CI); 0.48 (0.38; 0.60)) and in-hospital death (OR (95% CI); 0.49 (0.36; 0.65)) than unvaccinated patients. Among the vaccinated patients older than 75 years, mRNA vaccines and AZ seemed to confer similar protection, while one dose of J&J showed lower protection in this age category. In conclusion, a primary vaccination schedule protects against worsening of COVID-19 to severe outcomes among hospitalized patients