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The current humanitarian crises in Ukraine and Gaza, along with the chronic crises, and the climate-related disasters, have exposed the limitations of the humanitarian system. Within these contexts, humanitarian organisations frequently struggle with collecting, analysing, interpreting, and utilising health data, due to the challenging environments in which they operate and funding constraints. It is precisely in these contexts that field epidemiology plays a crucial, but often overlooked role.Field epidemiologists face unique challenges, including rapidly changing conditions, poor-quality data, and biases. Despite these difficulties, accurate epidemiological data are essential for needs assessment, guidance on interventions, and advocacy. Conventional methods often need adaptation for crisis settings, and there are still gaps in measurement.This article discusses the role of epidemiology in such contexts, noting a shortage of trained \'humanitarian epidemiologists\' and specialised training as major issues.To address these needs, the Italian Association of Epidemiology organised a course in early 2024 to enhance the epidemiological skills of staff working in humanitarian crises and introduce traditional epidemiologists to crisis-specific challenges. The course covered key concepts and methods of field epidemiology, emphasising the use of secondary health data. Its positive reception underscored the demand for such specialised training.Improving public health information collection and use in humanitarian crises is an ethical and practical necessity. Indeed, investing in field epidemiology and recognising its importance can enhance humanitarian interventions and better serve vulnerable populations.

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M-estimation is a statistical procedure that is particularly advantageous for some comon epidemiological analyses, including approaches to estimate an adjusted marginal risk contrast (i.e. inverse probability weighting and g-computation) and data fusion. In such settings, maximum likelihood variance estimates are not consistent. Thus, epidemiologists often resort to bootstrap to estimate the variance. In contrast, M-estimation allows for consistent variance estimates in these settings without requiring the computational complexity of the bootstrap. In this paper, we introduce M-estimation and provide four illustrative examples of implementation along with software code in multiple languages. M-estimation is a flexible and computationally efficient estimation procedure that is a powerful addition to the epidemiologist\'s toolbox.

In Massachusetts (MA), partner notification is routinely offered for new HIV and infectious syphilis cases, but there are no formal partner notification services for gonorrhea and chlamydia. Electronic partner notification (ePN), which allows patients to anonymously notify their partners of sexually transmitted infection exposure, could fill this gap. We evaluated the acceptability of and ideal characteristics for a statewide ePN service in MA. We performed semistructured interviews with patients at a Boston area sexual health clinic and conducted focus groups with clinicians and Massachusetts Department of Public Health Field Epidemiologists (FEs). We developed a codebook and thematically analyzed interview and focus group data; 25% of interviews were double coded. We identified six main themes from our data: (1) partner notification is a relational process and (2) partner notification is situation dependent. There are three pairs of challenges and core values for an effective ePN system: (3) stigmatization versus inclusivity, (4) trust versus mistrust, and (5) privacy versus helpful information sharing. Therefore, (6) a statewide ePN platform must be customizable at each possible step. Although ePN was acceptable across all three groups, the likelihood of individual use was grounded in a patient\'s sociocultural context, interpersonal relationships, trust in the platform and health authorities, desire to avoid stigmatization, and privacy needs. These factors are best accommodated by a platform that adapts to users\' preferences and needs. ePN presents an opportunity to link partners at risk for gonorrhea or chlamydia to clinical care that is complementary to the more labor-intensive FE role.

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COVID-19 underscored the importance of field epidemiology training programmes (FETPs) as countries struggled with overwhelming demands. Experts are calling for more field epidemiologists with better training. Since 1951, FETPs have been building public health capacities across the globe, yet explorations of learning in these programmes are lacking. This qualitative study will (1) describe approaches to training field epidemiologists in FETP; (2) describe strategies for learning field epidemiology among FETP trainees and (3) explain the principles and practices aligning training approaches with learning strategies in FETP.
The research design, implementation and interpretation are collaborative efforts with FETP trainers. Data collection will include interviews with FETP trainers and trainees and participant observations of FETP training and learning events in four FETP in the Western Pacific Region. Data analysis will occur in three phases: (1) we will use the constant comparison method of Charmaz\'s grounded theory during open coding to identify and prioritise categories and properties in the data; (2) during focused coding, we will use constant comparison and Polkinghorne\'s analysis of narratives, comparing stories of prioritised categories, to fill out properties of those categories and (3) we will use Polkinghorne\'s narrative analysis to construct narratives that reflect domains of interest, identifying correspondence among Carr and Kemmis\'s practices, understandings and situations to explain principles and processes of learning in FETP.
We have obtained the required ethics approvals to conduct this research at The Australian National University (2021/771) and Taiwan\'s Ministry of Health and Welfare (112206). Data will not be available publicly, but anonymised findings will be shared with FETP for collaborative interpretation. Ultimately, findings and interpretations will appear in peer-reviewed journals and conferences.

During the early 1980s both cancer biology and epidemiological methods were being transformed. In 1984 the leading cancer epidemiologist Richard Peto - who, in 1981, had published the landmark Causes of Cancer with Richard Doll - wrote a short chapter on \"The need for ignorance in cancer research\", in which the worlds of epidemiology and speculative Darwinian biology met. His reflections on how evolutionary theory related to cancer have become known as \"Peto\'s paradox\", whilst his articulation of \"black box epidemiology\" provided the logic of subsequent practice in the field. We reprint this sparkling and prescient example of biologically-informed epidemiological theorising at its best in this issue of the European Journal of Epidemiology, together with four commentaries that focus on different aspects of its rich content. Here were provide some contextual background to the 1984 chapter, and our own speculations regarding various paradoxes in cancer epidemiology. We suggest that one reason for the relative lack of progress in indentifying novel modifiable causes of cancer over the last 40 years may reflect such exposures being ubiquitous within environments, and discuss the lessons for epidemiology that would follow from this.

The Covid-19 pandemic has laid bare a tension around scientific expertise that has major implications for the effectiveness of health systems. Critical engagement with this tension, however, is largely missing from the lessons and programs consolidating in the wake of the emergency. Lacking good frameworks for discussing the tension, the vague term \"public trust\" has proliferated into a buzzword that stands in for more articulate discussion. The tension between experts and the public is not new, however. It is useful to look back to the 1930s, when health experts identifying as \"new epidemiologists\" imagined a new modern science of epidemiology that, some believed, would resolve evident failures in public cooperation. Historical analysis of different approaches to the production and use of epidemiological knowledge in these years reveals a debate about power at the heart of epidemiology, and a critical framework for discussing the tension around epidemiological expertise in public health.