Wildlife policy, the food system and One Health: a complex systems analysis of unintended consequences for the prevention of emerging zoonoses in China, the Democratic Republic of the Congo and the Philippines

INTRODUCTION

Evolving human-wildlife interactions have contributed to emerging zoonoses outbreaks, and pandemic prevention policy for wildlife management and conservation requires enhanced consideration from this perspective. However, the risk of unintended consequences is high. In this study, we aimed to assess how unrecognised complexity and system adaptation can lead to policy failure, and how these dynamics may impact zoonotic spillover risk and food system outcomes.

METHODOLOGY

This study focused on three countries: China, the Democratic Republic of the Congo (DRC) and the Philippines. We combined evidence from a rapid literature review with key informant interviews to develop causal loop diagrams (CLDs), a form of systems map representing causal theory about system factors and interconnections. We analysed these CLDs using the 'fixes that fail' (FTF) systems archetype, a conceptual tool used to understand and communicate how system adaptation can lead to policy failure. In each country, we situated the FTF in the wider system of disease ecology and food system factors to highlight how zoonotic risk and food system outcomes may be impacted.

RESULTS

We interviewed 104 participants and reviewed 303 documents. In each country, we identified a case of a policy with the potential to become an FTF: wildlife farming in China, the establishment of a new national park in the DRC, and international conservation agenda-setting in the Philippines. In each country, we highlighted context-specific impacts of the FTF on zoonotic spillover risk and key food system outcomes.

CONCLUSION

Our use of systems thinking highlights how system adaptation may undermine prevention policy aims, with a range of unintended consequences for food systems and human, animal and environmental health. A broader application of systems-informed policy design and evaluation could help identify instruments approporiate for the disruption of system traps and improve policy success. A One Health approach may also increase success by supporting collaboration, communication and trust among actors to imporove collective policy action.

Role of Antioxidant Therapy in the Treatment and Prognosis of COVID-19: A Systematic Review and Meta-analysis of Randomized Controlled Trials

Background

A significant aspect of the SARS-CoV-2 pathology involves oxidative stress, characterized by an imbalance between the production of harmful free radicals and the body's antioxidant defenses. With the ongoing evolution of SARS-CoV-2, the investigation into non-virus-specific therapeutic options, such as antioxidant therapy, has gained importance.

Objectives

This systematic review and meta-analysis aimed to summarize data from randomized control trials (RCTs) to evaluate the effectiveness and safety of antioxidant therapy in patients with SARS-CoV-2 infection.

Methods

We searched the peer-reviewed indexed literature on MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), CINAHL, EMBASE, International Pharmaceutical Abstracts, and Scopus, from inception to July 2023.

Results

The search identified 3306 articles from which 25 were included for quantitative synthesis, with 5 studies eligible for meta-analysis. Antioxidant therapies included zinc, vitamin A, vitamin C, and combination treatments. Zinc interventions showed mixed results regarding intensive care unit admissions and hospital stays. Vitamin A studies indicated improvements in inflammatory markers. Vitamin C studies displayed inconsistent effects on clinical improvement and hospitalization. Combination treatments suggested benefits in symptom clearance and cytokine storm reduction. Meta-analysis of vitamin C studies found no significant difference in C-reactive protein concentrations (-0.50; 95% CI: -3.63, 2.63; I2 = 0%), intensive care unit stay duration (pooled mean difference: 1.44; 95% CI: 0.07, 2.81; I2 = 0%), or mortality (pooled odds ratio: 0.55; 95% CI: 0.28, 1.09; I2 = 0%), with a slight trend favoring reduced hospitalization duration (pooled mean difference: -2.37; 95% CI: -2.99, -1.76; I2 = 49%). Of the 25 studies, 8 were high quality with low bias, 6 had some concerns, and 11 were low quality with high bias.

Conclusions

The review presents mixed efficacy of antioxidant therapies for SARS-CoV-2, with some studies indicating potential benefits. Further well-designed large-scale RCTs are warranted to determine the definitive role of antioxidants in SARS-CoV-2 treatment.This systematic review was registered at PROSPERO as CRD42023430805.

Exploring Machine Learning Applications in Pediatric Asthma Management: Scoping Review

BACKGROUND

The integration of machine learning (ML) in predicting asthma-related outcomes in children presents a novel approach in pediatric health care.

OBJECTIVE

This scoping review aims to analyze studies published since 2019, focusing on ML algorithms, their applications, and predictive performances.

METHODS

We searched Ovid MEDLINE ALL and Embase on Ovid, the Cochrane Library (Wiley), CINAHL (EBSCO), and Web of Science (core collection). The search covered the period from January 1, 2019, to July 18, 2023. Studies applying ML models in predicting asthma-related outcomes in children aged <18 years were included. Covidence was used for citation management, and the risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool.

RESULTS

From 1231 initial articles, 15 met our inclusion criteria. The sample size ranged from 74 to 87,413 patients. Most studies used multiple ML techniques, with logistic regression (n=7, 47%) and random forests (n=6, 40%) being the most common. Key outcomes included predicting asthma exacerbations, classifying asthma phenotypes, predicting asthma diagnoses, and identifying potential risk factors. For predicting exacerbations, recurrent neural networks and XGBoost showed high performance, with XGBoost achieving an area under the receiver operating characteristic curve (AUROC) of 0.76. In classifying asthma phenotypes, support vector machines were highly effective, achieving an AUROC of 0.79. For diagnosis prediction, artificial neural networks outperformed logistic regression, with an AUROC of 0.63. To identify risk factors focused on symptom severity and lung function, random forests achieved an AUROC of 0.88. Sound-based studies distinguished wheezing from nonwheezing and asthmatic from normal coughs. The risk of bias assessment revealed that most studies (n=8, 53%) exhibited low to moderate risk, ensuring a reasonable level of confidence in the findings. Common limitations across studies included data quality issues, sample size constraints, and interpretability concerns.

CONCLUSIONS

This review highlights the diverse application of ML in predicting pediatric asthma outcomes, with each model offering unique strengths and challenges. Future research should address data quality, increase sample sizes, and enhance model interpretability to optimize ML utility in clinical settings for pediatric asthma management.