Observational studies offer a non-experimental and minimally disruptive approach for generating real-world evidence, making them particularly valuable for informing clinical practice, research and health system strengthening – especially in primary care. This article, part of the African Journal of Primary Health Care & Family Medicine (PHCFM) methods series, introduces key observational study designs including cross-sectional, cohort and (nested) case–control studies and discusses their application in doctoral-level research. Drawing on historical and contemporary examples, we examine methodological considerations, ethical issues and modern analytical strategies essential for the careful planning and execution of observational research. By integrating conceptual frameworks and causal inference methods, this primer aims to equip researchers at different career stages with a foundational understanding of how to choose and implement observational designs that are both methodologically robust and relevant to primary care contexts.

Artificial intelligence (AI) is being broadly integrated into processes to manage and analyze large amounts of data accurately and efficiently. In this work, we explored how AI methods, in particular machine learning (ML), are being implemented in human biomonitoring using a mixed methodology approach that consisted of: (1) a scoping literature review and (2) an international scan of biomonitoring programs to contextualize current practices and perceptions from researchers in the field. We synthesized findings from the review according to the year of publication, biomonitoring study and location, study outcomes, and the most frequent ML methods. We additionally categorized all published ML methods from the review according to three dimensions (paradigm, type of task, and model structure), mapped studies to biomonitoring themes and other applications, and provided details for the more commonly applied ML methods. The international scan was administered through a 30-question online survey and gathered information on current uses, perspectives, and barriers related to AI. Scoping review: We found 286 studies that applied a ML method to human biomonitoring data. Eighty-two ML methods were identified, with the most common being supervised approaches. ML was predominantly applied to predict health-related outcomes based on chemical exposure. International scan: The survey yielded 30 responses from programs across 15 countries. Approximately 27% of respondents reported implementing AI-related methods in the collection and analysis of biomonitoring data, and the primary barrier to adopting these methods was a lack of technical expertise (80%). This exploratory work provides an integrated understanding of ML applications in the human biomonitoring field. ML clearly holds promise for furthering our understanding of chemical exposure in people and will likely undergo continued growth in applications.