Olaf Dammann

The philosophy of epidemiology is a recent addition to the philosophy of science. Its focus is on metaphysical and epistemic problems concerning the design and analysis of population studies performed to generate novel knowledge for medicine and public health. The chapter offers a brief introduction to the scope and goals of epidemiology and to a specific discourse about the relevance of Karl Popper’s philosophy for epidemiologists. The chapter also provides three examples of current work in the field: causal inference (with a focus on the work of Alex Broadbent), Olaf Dammann’s work on explanation in public health, and Jonathan Fuller’s work on one central aspect of the “ecological fallacy” in epidemiology, i.e., the question of how to interpret probabilities as individual risk.

The consequences of experiences and exposures suffered by those living in poverty can last a lifetime and can even be passed on to the next generation. The challenges associated with poverty have been labeled the "social determinants of health" (SDoH), but this is something of a misnomer. A more appropriate label would be the "social determinants of disease." This essay is a broad overview of the processes, including allostatic load and epigenetic aging, that might contribute to prolonging the adverse effects of the social determinants of disease.

In the US, there has historically been strong public opposition to health-care reform involving "socialized medicine." This resistance, at least in part, is influenced by a deeply entrenched individualistic ethos. It is becoming increasingly clear, however, that the current US health-care system is broken, and that existing systems around the world achieve better outcomes while costing less. This article argues that learning from these systems should be possible. The authors describe roadblocks to health-care reform in the US, including social fragmentation, resistance to global models, and the prioritization of profit over well-being; argue that a value system that prioritizes the individual over the collective precludes successful health-care reform; highlight shortcomings of the US model; and propose that reimagining health care through a socially oriented lens, emphasizing collective and reciprocal moral obligations, may offer a path forward.

In this chapter, I bring together the concepts of etiological explanation and explanatory-predictive coherence by offering a proposed outline for a multi-evidence map, in which evidence of association, biology, confirmation, and difference can be documented with the goal to reduce interventional uncertainty. Moreover, I also proposed that high quality evidence that is mutually supportive and non-contradictory might be a good starting point for judging the degree of explanatory-predictive coherence. More work is needed to outline formal ways to extract evidence from the literature and organize it using evidence mapping to curb interventional uncertainty and justify interventions in medicine.

In the previous chapters we have focused on metaphysical and epistemological concepts of causation, in medicine and population health. In this chapter, we discuss risk estimation, the focus of public health informatics methods. First, we introduce the concepts of risk and prediction. We contrast individual and population risk and discuss why using quantitative risk estimates in individuals is problematic. We describe methods for risk estimation in population health science and conclude with the proposal that risk estimation is giving a causal explanation in population health science.

In this concluding chapter we describe our view how different kinds of information are integrated in order to arrive at causal explanation in population health science. In particular, such information comes from individuals and populations (target), from epidemiology and the bench sciences (method), and from observation and experiment (manipulation). We discuss recent “systems” approaches in biology, medicine, and epidemiology in the section on “method” and the question what it is about “manipulation” that convinces many of its capability to support causal inference. In the final section, we look at complexity versus simplicity and suggest that the simplification inherent in epidemiological evidence generation may be an asset, not a liability.

Agent-based models (ABMs) are one type of simulation model used in the context of the COVID-19 pandemic. In contrast to equation-based models, ABMs are algorithms that use individual agents and attribute changing characteristics to each one, multiple times during multiple iterations over time. Based on my discussion in this chapter, I conclude that ABMs can explain causal mechanisms but cannot provide emergence explanations, because they cannot provide information about exactly why low-level phenomena give rise to those emergent phenomena. This is also one reason why I believe that ABMs cannot help with causal inference. Another reason is that ABMs do not reflect real-world processes but the causal-mechanical intuitions of the modeler. However, ABMs can integrate “impossible” multi-scale interactions between initiators, mediators, moderators, and conditions, and may be useful as comprehensive etio-prognostic explanations of illness occurrence and outcome, which in turn can go a long way reducing the uncertainty in medicine about disease etiology and prognosis.

This book offers a comprehensive account of how uncertainty is tackled in medicine and the health sciences. Olaf Dammann explores recent accounts of medicine as ineffective and suggests that the impression that medicine does not achieve its goal is, at least in part, due to the aleatoric (natural) uncertainty of biomedical processes and the subsequent epistemic (cognitive) uncertainty of those who desire solid information about such processes. Dammann shows how concepts like inference, explanation, and causometry help mitigate this disconnect. He points toward the possibility that some of the statistically rigid and formalized approaches (such as the randomized controlled trial as the gold standard for the justification of medical interventions) might better be replaced by approaches that emphasize the coherence of evidence and the people's needs for helpful health interventions (auxiliarianism). Olaf Dammann is professor of Public Health and Community Medicine at Tufts University School of Medicine, USA. His main fields of research are philosophy of health science and perinatal epidemiology.

In this chapter, I argue that medicine is not science. Medicine is care and not research, and medical science is research, not care. Medicine is done by doctors, nurses, and so on; medical science is done by pharmacologists, microbiologists, epidemiologists, etc. Medicine and science are completely separate activities; they do not overlap. While some doctors are also scientists, their work in the lab and in the clinic does not overlap. I suggest keeping medicine and science separate until we have more and better arguments in support of the notion that doing medicine and doing science are the same.

When Martha Montello took over as the Editor of Perspectives in 2014, she noted that the Journal had “flourished for 58 years because of its unique vision and mission and the ability of its remarkable editors to stay focused on that mission,” referring to the work of her predecessors, D. J. Ingle, Richard Landau, and Robert Perlman. Now we can add 10 more years to this fantastic track record of editorial excellence and count Martha herself among those to be credited for this past success.Between 2014 and 2024, Martha Montello and our Managing Editor Solveig Robinson have improved the Journal appreciably by steadying the publication schedule, securing a high quality of publications, and recruiting outstanding guest... Read More.

In this chapter we provide a philosophical discussion of the nature of causation, as applied to the investigation of disease etiology and preventive and curative interventions. This chapter is primarily an exercise in metaphysics and conceptual analysis, in which we analyze existing concepts of causation dating back to David Hume’s eighteenth century empiricism, right up to the public health-specific analysis provided by Kenneth Rothman, and the more recent dispositionalist ontologies of Stephen Mumford and Rani Lill Anjum. We argue that different metaphysical concepts of causation are implicit in different research methodologies in the medical sciences. We relate J. L. Mackie’s “Inus condition” metaphysic of causation to Rothman’s epidemiologic account of disease etiology, and go on to discuss “The counterfactual account of causation”. The latter is often used by epidemiologists as an alternative nomenclature of the “potential outcomes approach” to causal inference. We will show why this is the case, and identify differences between this strategy for causal inference in public health and the popular philosophical view. We conclude that the concepts of causation employed in the health sciences are integral to their practice, whether used consciously or unconsciously, and that a clear insight into the nature of the metaphysical and conceptual schemes underlying the etiological process would help all those working in the medicine and public health.

This chapter revolves around the idea that knowledge is generated from data. We briefly describe Ackoff’s hierarchy, which starts with data and proceeds via information to knowledge, understanding and wisdom. In contrast, we propose to de-emphasize understanding and wisdom, and to insert evidence between information and knowledge. We outline a framework that takes data as raw symbols, which morph into information when contextualized. Information becomes evidence when compared to relevant standards. Evidence is used to test hypotheses and is transformed into knowledge by consensus. As quality checkpoints for the transition between levels we offer relevance, robustness, repeatability, and reproducibility.

In this chapter, we introduce the concept of Health Data Science and define its three domains: technology, analytics, and conceptual. In the technology domain, we drill down from computer science via health informatics to public health informatics. The analytics domain includes biostatistics, bioinformatics, epidemiology, and simulation. In the conceptual domain, we introduce the philosophy of information, and of health and causation. Taken together, these domains provide the theoretical backdrop for the remainder of the book.

This book covers the overlap between informatics, computer science, philosophy of causation, and causal inference in epidemiology and population health research. Key concepts covered include how data are generated and interpreted, and how and why concepts in health informatics and the philosophy of science should be integrated in a systems-thinking approach. Furthermore, a formal epistemology for the health sciences and public health is suggested. Causation in Population Health Informatics and Data Science provides a detailed guide of the latest thinking on causal inference in population health informatics. It is therefore a critical resource for all informaticians and epidemiologists interested in the potential benefits of utilising a systems-based approach to causal inference in health informatics.

The goal of this book is to take a first step towards a framework for causal explanation in public/population health informatics and analytics. We first provide an introduction to the concepts of public health informatics (PHI) and population health informatics (PopHI). Next, we introduce the general approach we take – the etiological stance – and the idea that risk and causation are two ways of looking at etiology, the process of illness occurrence. We offer a brief description of how the discussion of causation and causal inference in epidemiology relates to concepts in philosophy of science and contrast deterministic folk psychology of causation with a pragmatic perspective built on probabilistic concepts of causation. Finally, we clarify the agenda of this book with a focus on what it is not about and give a roadmap of the remaining chapters.

In philosophy of science, discussions of explanation are often restricted to causal or causal-mechanical explanations. Some have called these explanations “answers to why-questions”. In this chapter, I suggest going beyond explanations thus defined. I propose a workflow-based framework for explanation and how it is used in medicine and public health. This proposal covers epistemic, justificatory, methodological, and anticipatory explanations, each of which appears twice (resulting in eight explanatory functions) in a cycle that starts with problem definition and proceeds to intervention, evaluation, and assessment.

My goal in this paper is twofold. First, I want to analyze two early texts by Vilém Flusser in order to explore what may have been his conceptualization of the relationship between science and philosophy. My analysis suggests that Flusser thought of both as tools to analyze reality by analyzing language. While he saw science as a (sometimes too vigorous) force forward, he viewed philosophy as what can prevent some of the negative consequences of such progress. In direct comparison, Flusser thought of science as a discourse with the purpose to provide novel information and of philosophy as what can keep objective science in check by moving the discourse into the realm of the subjective. It remains to be explored whether these results also apply to Flusser’s later writings. My second goal is to show the relationship between three aspects of modern science (crisis, contrast, and trying out) and what I see as Flusser’s early (mid 1960s) view of science in relation to philosophy and poetry.

In recent philosophy of medicine, skeptics have raised the concern that medicine is less effective than is commonly assumed. Broadbent suggests that the goal of medicine is cure and cure is not achieved in most cases. Stegenga says that many medical interventions are barely effective at all and blames faulty or even fraudulent research. My position is that the goal of medicine is not cure, but help. From this auxiliary perspective, medicine seems less ineffective than Broadbent and Stegenga want us to believe. Moreover, part of medical ineffectiveness should not be attributed to medicine or medical science but to the inherent uncertainty of biological processes and, thus, medical interventions.

In this essay, I argue that Ted Poston's theory of explanatory coherentism is well-suited as a tool for causal explanation in the health sciences, particularly in epidemiology. Coherence has not only played a role in epidemiology for more than half a century as one of Hill's viewpoints, it can also provide background theory for the development of explanatory systems by integrating epidemiologic evidence with a diversity of other error-independent data. I propose that computational formalization of Hill's viewpoints in an explanatory coherentist framework would provide an excellent starting point for a formal epistemological (knowledge-theoretical) project designed to improve causal explanation in the health sciences. As an example, I briefly introduce Paul Thagard's ECHO system and offer my responses to possible objections to my proposal.

This chapter provides an example of how uncertainty about the origin of a neonatal disorder, retinopathy of prematurity (ROP), can be reduced. Retinopathy of prematurity is a complex neonatal disorder with multiple contributing factors. In this chapter I use the model of etiological explanation and combined contribution to mount the evidence in support of the proposal that neonatal sepsis meets all requirements for being a cause of ROP (not a condition, mechanism, or even innocent bystander) by means of initiating the early stages of the patho-mechanism of ROP occurrence, systemic inflammation. It can be shown that sepsis can initiate the early stages of the patho-mechanism via systemic inflammation (causation process) and that systemic inflammation can contribute to growth factor aberrations and the retinal characteristics of ROP (disease process). The combined combination of these factors with immaturity at birth (as intrinsic risk modifier) and prenatal inflammation (as extrinsic modifier) seems to provide a cogent functional framework of ROP occurrence. Finally, I apply the Bradford Hill’s heuristics to the available evidence as a quasi-causometer. Taken together, the evidence suggests that neonatal sepsis is a causal initiator of ROP.

I introduce some epidemiologic principles and context and describe how epidemiologists employ different kinds of inference to move from a study sample as a reflection of what is going on in the underlying population to intervention. In a study sample they collect data, create estimates, use these as evidence, to generate causal knowledge, which helps justify interventions (transitional inference). Related kinds of inference include the inference from association to causation, from animal experiment to human application, from population to individual, and from research setting to real world (translational inference).

Causation is of major importance in medicine and the health sciences. To establish causation in a watertight fashion, I hold, one would need to have a way to measure token causation, i.e., to do “causometry”. I summarize what can be called causal principles in causal data science, i.e., data observation, quasi-determinism, statistical independence, Bayesian interpretation of probability, manipulation, and randomization based on counterfactualism. I review three causal methods, including causal inference based on randomization, graph-based causal discovery, and coherentist heuristics. The chapter ends with the suggestion that a token “causometer” cannot exist but that it is possible to establish the presence of type causation and to quantify the causal vigor of the cause using epidemiological measures.

Having discussed the metaphysics of disease etiology in Chap. 3, in this chapter we discuss a number of important epistemological problems concerning causal inference in medicine and population health informatics. With origins tracing back to at least the eighteenth century, the problem of induction (that is, the problem of justifying inferences from observed data to likely future or unobserved outcomes) is one of the most discussed issues in the philosophical literature. Given that causal inference plays such a large role in the health sciences and that all causal inferences are inductive in kind, we take a detailed look at how those working in the health sciences go about identifying, and justifying their causal claims. With this in mind, we examine Austin Bradford Hill’s heuristics for causal inference in epidemiology, and argue that the postulation of criteria such as Hill’s is nothing but a replacement of one dilemma with another: why should it be easier to identify the truth condition of the heuristics, than identifying the truth condition of the relationship between exposure and effect if they were hard-and-fast criteria? We conclude by asking whether genuine medical knowledge is possible. We outline a number of historically popular responses to the ultimate question in epistemology: “what is knowledge”, and demonstrate the profound effect one’s answer to that question has on the ability to claim medical knowledge of any form.

Agent-based models (ABMs) are one type of simulation model used in the context of the COVID-19 pandemic. In contrast to equation-based models, ABMs are algorithms that use individual agents and attribute changing characteristics to each one, multiple times during multiple iterations over time. This paper focuses on three philosophical aspects of ABMs as models of causal mechanisms, as generators of emergent phenomena, and as providers of explanation. Based on my discussion, I conclude that while ABMs cannot help much with causal inference, they can be viewed as etio-prognostic explanations of illness occurrence and outcome.