Air pollution-related health impacts from domestic waste burning and associated interventions: the merits of a traditional versus machine learning scoping review methodology

Clinical Snapshot

Key Findings

• Effect Size: Not quantified - qualitative comparison

• Primary Outcome: Machine learning approach showed tendency towards false positives but could conduct initial searches effectively

• Nnt Or Sensitivity: Machine learning model requires manual screening for final acceptance

• Confidence Interval: Not reported

Clinical Application

Hybrid methodology approach feasible for future systematic reviews in environmental health Relevant for Indigenous communities and rural areas where waste burning occurs; aligns with NHMRC environmental health priorities and state EPA regulations Communities exposed to domestic waste burning, particularly rural and disadvantaged populations

Abstract

Regularly updating repositories of air pollution impacts on health is essential for evidence-based policies, interventions, and progress monitoring. However, this is often time-consuming and labour-intensive. Automation can ensure up-to-date evidence; reduce retrieval, screening time, and costs; and make information accessible to non-academic stakeholders. This study investigated whether a machine learning approach can perform any stages of a traditional scoping review on the health impacts, policies, and interventions related to air pollution due to domestic waste burning. A traditional approach was conducted in parallel with machine learning methods enabling a comparison of the efficiency and quality of the partially automated approach against the manual review. Findings from the two approaches were compared and the final set of included articles were considered for (1) reported impacts of waste burning on health outcomes and (2) recommendations on solutions and interventions to prevent/reduce adverse effects on health from waste burning. We found a range of health impacts associated with waste burning, including low birth weight, hypertensive disorders of pregnancy, adverse respiratory outcomes like asthma and wheeze, cancer risk, and mortality. Few studies proposed solutions or evaluated the effectiveness of interventions. The ML approach showed a tendency towards false positives, which are preferable to false negatives (where relevant papers were excluded). Results showed that the model can conduct initial searches and decisions for the review. However, the articles included in the model should be screened manually for final acceptance. Therefore, we propose a hybrid approach be used until the automated model can be further refined.

References

1. 1.Kapwata, T., Webster, C., Chen, W. G. G., Suder, M. I., Jarvis, D., Vogt, A., Mahlangeni, N., Laban, T., Naidoo, N., Mudu, P., Shairsingh, K., & Wright, C. Y. (2026). Air pollution-related health impacts from domestic waste burning and associated interventions: the merits of a traditional versus machine learning scoping review methodology. Environmental Monitoring and Assessment. https://doi.org/10.1016/S2542-5196(21)00350-8