Causal inference based on a restricted version of the potential outcomes approach reasoning is assuming an increasingly prominent place in the teaching and practice of epidemiology. The proposed concepts and methods are useful for particular problems, but it would be of concern if the theory and practice of the complete field of epidemiology were to become restricted to this single approach to causal inference. Our concerns are that this theory restricts the questions that epidemiologists may ask and the study designs that they may consider. It also restricts the evidence that may be considered acceptable to assess causality, and thereby the evidence that may be considered acceptable for scientific and public health decision making. These restrictions are based on a particular conceptual framework for thinking about causality. In Section 1, we describe the characteristics of the restricted potential outcomes approach (RPOA) and show that there is a methodological movement which advocates these principles, not just for solving particular problems, but as ideals for which epidemiology as a whole should strive. In Section 2, we seek to show that the limitation of epidemiology to one particular view of the nature of causality is problematic. In Section 3, we argue that the RPOA is also problematic with regard to the assessment of causality. We argue that it threatens to restrict study design choice, to wrongly discredit the results of types of observational studies that have been very useful in the past and to damage the teaching of epidemiological reasoning. Finally, in Section 4 we set out what we regard as a more reasonable ‘working hypothesis’ as to the nature of causality and its assessment: pragmatic pluralism.

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This chapter explores the idea that causal inference is warranted if and only if the mechanism underlying the inferred causal association is identified. This mechanistic stance is discernible in the epidemiological literature, and in the strategies adopted by epidemiologists seeking to establish causal hypotheses. But the exact opposite methodology is also discernible, the black box stance, which asserts that epidemiologists can and should make causal inferences on the basis of their evidence, without worrying about the mechanisms that might underlie their hypotheses. I argue that the mechanistic stance is indeed a bad methodology for causal inference. However, I detach and defend a mechanistic interpretation of causal generalisations in epidemiology as existence claims about underlying mechanisms. Causal hypotheses in epidemiologyWhat does it take to establish a causal hypothesis in epidemiology? What standards need to be met? Or, if establishment comes in degrees, degrees of what?The most obvious aspect of this problem concerns inferring causation in a particular study. A study reveals a statistical association between smoking and lung cancer, or a certain gene and obesity. Statistical analysis reveals a low p-value -a measure of the chance that the association is due to chance. Study design controls for confounding variables (what philosophers would call common causes of the putative cause and effect).Can it be inferred that, for this group, a causal relationship exists between smoking and lung cancer, or having that gene and obesity?Oddly enough, this is not a question that epidemiologists like to answer. A single study would not normally be considered a sufficient basis for a causal inference. Replication is a guiding epidemiological principle. From a methodological point of view this is extremely interesting. Epidemiologists' credence in a causal hypothesis about Study Group A increases when the effect is replicated in Study Group B. Explaining (or, I suppose, refuting) this attitude is a central task for any methodological analysis. 2A second difficulty concerns the inference from a study, or a collection of studies, to a wider population. Epidemiologists are centrally concerned with extrapolating from the people they study to people they have not studied. Replication is important here too, because one way to argue that differences between the population studied and the target population are causally irrelevant is to replicate the study among people who are drawn from the target population. However, replication cannot solve the problem of generalisation. Often the study group will already be drawn from the target population:for example, when generalising from the Whitehall studies to the population of Britain. 1 Differences between those studied and those not studied will always remain; the difficulty is working out when these differences make a difference. On other occasions, studies on a subset of the target population may be impractical: for an obvious example, consider future populations. Q...

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In the literature on health, naturalism and normativism are typically characterized as espousing and rejecting, respectively, the view that health is objective and value-free. This article points out that there are two distinct dimensions of disagreement, regarding objectivity and value-ladenness, and thus arranges naturalism and normativism as diagonal opposites on a two-by-two matrix of possible positions. One of the remaining quadrants is occupied by value-dependent realism, holding that health facts are value-laden and objective. The remaining quadrant, which holds that they are non-objective but value-free, is unexplored. The article endorses a view in the latter quadrant, namely, the view that health is a secondary property. The article argues that a secondary property framework provides the resources to respond to the deepest objections to a broadly Boorsean account of natural function, and so preserves the spirit, though not the letter, of that account. Treating health as a secondary property permits a naturalistic explanation—specifically, an evolutionary explanation—of the health concept, in terms of the assistance such a concept might have provided to the survival and reproduction of those organisms that had it. (This approach is completely distinct from evolutionary and aetiological accounts of natural functions.) This provides the explanation, missing from Boorse's account, for the fact that function is determined with reference to the contribution to the goals of survival and reproduction, relative to the age of the sex of the species, rather than some other equally natural goals or reference classes. Introduction Two Ways to Disagree about Health Secondary Properties Health as a Secondary Property Conclusion

Diabetes has been shown to be a risk factor for corona virus disease-2019 (COVID-19) infection. The characteristics of patients with diabetes vulnerable to this infection are less specified. We aim to present the characteristics of patients with diabetes admitted to hospital with COVID-19. Design: A retrospective case series. Setting: A single clinical centre in the UK. Methods: We have retrospectively collected the demographics, medical characteristics and outcome of all patients with diabetes admitted to hospital over two-week period with COVID-19 infection. All cases were diagnosed by a reverse transcription polymerase chain reaction (RT-PCR) of pharyngeal and nasal swabs. Results: A total of 71 COVID-19 patients were admitted during the study period of whom 16 (22.5%) patients had diabetes and were included in this case series. There was no significant difference between patients with compared to those without diabetes regarding age, gender or clinical presentation. However, comorbidities were more common in patients with diabetes specially hypertension {75% v 36.4%, a difference of 38.6%, 95% confidence interval (CI) 6.5-58.3} and chronic kidney disease (37.5 v 5.5, a difference of 32% (1.6-51.6). Patients with diabetes were significantly more obese than those without diabetes (56.2% v 21.8% a difference of 34.4%, 95% CI 7.7-61.1). About one third (31.3%) of patients with diabetes were frail. Mean {standard deviation (SD)} duration of diabetes was 10 (2.8) years and mean (SD) HbA1c was 60.3 (15.6) mmol/mol. The use of angiotensin converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARBs) and non-steroidal

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