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This chapter focuses on the forms of regulation that shape food habits in ways that we are often unaware of. Invisible rules comprise the forms of regulation that influence food production and consumption at levels that we may be unable to control, such as globalising economic processes, labour processes, dynamics of supply and demand, advertising, and marketing. However, they also comprise the ‘cultural’ rules that – often in implicit ways – dictate who can use which spaces, how it is appropriate to shop or cook, and who may associate with whom. The invisibility of both sets of rules within urban spaces lead many people to answer, in the first instance, that they are the ones who get to decide what is in their fridge. However, through collective reflection, we unearthed the ways in which: labelling misleads us; work and life rhythms combine with street design to influence where we shop; and advertising affects what our children want us to buy. Consequently, the agenda to transform the invisible rules that shape urban food spaces is strongly grounded in the need to render these rules more visible as part of a ‘re-regulation’ (as opposed to a deregulation) of the forces that overdetermine and enclose our food habits, driven by the experience of people whose concerns have been marginalised.
Here, we present some of the results of our co-produced research project that explored how people experience the regulation of food habits in their communities. We draw on multiple sources produced through the project (audio recordings, visual mapping, photography, workshop notes, focus groups and interviews with participants) to explore three key ideas. We begin by exploring the notion of food justice, which seeks to embed discussion of food regulation in attention to the spatial dimensions of food access. Here, we point to the ways in which the project sought to make visible invisible rules and to develop processes of ‘commoning’ in order to address the spatial inequalities of urban food spaces. In the second section, we challenge notions of ‘cheapness’ and instead present ideas of food affordability, following Michael Carolan's (2013) argument that cheap food is part of the problem of unequal food access.
The introduction explores the challenges that periodicity poses to literary history. We argue that a self-conscious awareness of how periods are inevitably implicated in expanded networks of temporality and geography nevertheless allows us to explore how particular moments of literary history (in this case the 1880s) might exhibit specific and characteristic formal, thematic or cultural forms. The 1880s is a decade that has been too readily overlooked in the rush to embrace end-of-century decadence and aestheticism. The contributors to this book explore the case for the 1880s as both a discrete point of literary production, with its own pressures and provocations, and as part of a series of broader networks of affilation and contestation. The essays address a wide variety of authors, topics and genres, offering incisive readings of the diverse forces at work in the shaping of the literary 1880s.
What does it mean to focus on the decade as a unit of literary history? Emerging from the shadows of iconic Victorian authors such as Eliot and Tennyson, the 1880s is a decade that has been too readily overlooked in the rush to embrace end-of-century decadence and aestheticism. The 1880s witnessed new developments in transatlantic networks, experiments in lyric poetry, the decline of the three-volume novel, and the revaluation of authors, journalists and the reading public. The contributors to this collection explore the case for the 1880s as both a discrete point of literary production, with its own pressures and provocations, and as part of literature's sense of its expanded temporal and geographical reach. The essays address a wide variety of authors, topics and genres, offering incisive readings of the diverse forces at work in the shaping of the literary 1880s.
In the run-up to the 1936 presidential election in America, the Literary Digest conducted a poll of more than two million voters and confidently predicted that the Republican candidate, Alf Landon, would win. On the day it was the Democrat candidate, Franklin D. Roosevelt, who won a landslide victory. The Digest had correctly predicted the winner of the previous five elections, so what went wrong in 1936?
The Digest sent polling papers to households listed in telephone directories and car registration records. In 1936, however, telephone and car ownership were more common among more affluent households and these were the people who were also more likely to vote Republican. The generally less-affluent Democrat voters were thus under-represented in the sample of voters polled. In contrast, a young George Gallup conducted a much smaller poll of a few thousand representative voters and correctly predicted the Roosevelt win. As a result of this fiasco the Digest folded but Gallup polls are still conducted today.
We saw in Chapter 6 that larger studies are less likely to get the wrong results due to chance (or random sampling error) than smaller studies; however, the example in Box 7.1 shows that a large sample size is not sufficient to ensure we get the right results. The enormous presidential poll conducted by the Literary Digest didn't get the right answer because it included the ‘wrong’ people, i.e. they were not representative of everybody in the voting population. Furthermore, in epidemiology we frequently rely on records that have been collected for some other purpose, and we have already discussed some of the problems inherent in this in Chapter 3. Even when the data we use have been collected specifically for our research they are unlikely to be completely free of error. We often have to rely on people's memories, but how accurate are they? And biological measurements such as blood pressure and weight are often subject to natural variation as well as being affected by the performance of the measurement system that we use.
The rates and measures that we explored in Chapter 2 provide a variety of ways to describe the health of a population and thus also enable us to compare patterns of health and disease between populations and over time. This allows us to answer the core questions relating to disease burden that are the essential first step in setting health planning and service priorities. As we discussed in Chapter 1, this descriptive epidemiology, concerned as it is with ‘person, place and time’, attempts to answer the questions ‘Who?’, ‘What?’, ‘Where?’ and ‘When?’. This can include anything from a description of disease in a single person (a case report) or a special survey conducted to measure the prevalence of a particular health issue in a specific population, to reports from national surveys and data collection systems showing how rates of disease or other health-related factors vary in different geographical areas or over time (time trends).
Although descriptive data may be collected specifically to answer a defined question, they often come from governments, health care providers and statistical agencies that routinely collect vast amounts of information. Summary data – often the various forms of rate which you met in Chapter 2 – can be accessed from published reports and, increasingly, from online databanks. In some cases it is also possible to obtain information from which the rates are calculated at the individual level. These descriptive data are essential to identify health problems and for health planning and, although they cannot usually answer the question ‘Why?’, they may provide the first ideas about causality and thus generate hypotheses that can then be tested in more formal ‘analytic’ studies that we will discuss in Chapter 4. As you will come to see in later chapters, descriptive studies also play a critical and often under-appreciated role in monitoring the effects of large-scale interventions.
In this chapter we will look in more detail at some of the most common types of descriptive data and where they come from. However, before embarking on a data hunt, we first need to decide exactly what it is we want to know, and this can pose a challenge; to make good use of the most relevant descriptive data, it is critical to formulate our question as precisely as possible.
Taking a practical approach and supported by global examples from all areas of health, the new edition of this popular and highly commended textbook has been updated to reflect current epidemiological thinking and teaching. Based on feedback from teachers and students, material has been reordered to better suit courses and reflect the underlying logic and purpose of epidemiology.Provides students with a rounded picture of the field by emphasizing the commonalities across different areas of epidemiology, including clinical epidemiology, and highlighting the key role of epidemiology in public healthAvoids complex mathematics by restricting this to optional material, thereby keeping the book accessible to students from non-quantitative backgrounds Integrated and supplementary questions help students to reinforce conceptsA wealth of online material is available at www.cambridge.org/essential_epidemiology, including additional questions, advanced material for key concepts, recommendations for further reading, links to useful websites and slides for teaching, supporting both students and teachers.
Box 16.1 The role of epidemiology in translational research
Translational research can be divided into five separate phases (T0–T4) and epidemiology has a key role to play in all of these (from Khoury et al., 2010):
T0:Description and discovery: describing patterns of health and disease by person, place and time; observational studies to identify potential ‘causes’ of health outcomes.
T1:From discovery to application (e.g. tests, interventions): clinical and population studies to further characterise discoveries from T0 and identify potential interventions to improve health.
T2:From application to evidence-based guidelines: observational and experimental studies to assess the efficacy of an intervention to inform guidelines and recommendations.
T3:From guidelines to practice: studies to assess the implementation and uptake of guidelines (e.g. identifying barriers to uptake).
T4:From practice to health outcomes: evaluation studies to assess the effectiveness of interventions (e.g. a screening programme) in practice.
In the preceding chapters we have covered the core principles and methods of epidemiology and have shown you some of the main areas where epidemiological evidence is crucial for policy and planning. You will also have gained a sense of the breadth and depth of the subject from the examples throughout the book. To finish off we will take a broader look at the role of epidemiological practice and logic in improving health. This process where research evidence is used to change practice or policy is known as translation (see Box 16.1).
Translating epidemiological research into practice
When epidemiological evidence is both sufficient and sound it has the potential for direct translation into public health (and clinical) practice and policy. In addition to providing primary research evidence to identify and test potential interventions to improve health, you saw in Chapter 11 the fundamental role of epidemiology in knowledge synthesis through systematic reviews and meta-analyses to better inform all stages of the research and translation continuum. You have also encountered many examples of the application of simple descriptive tools to evaluate disease control programmes once they have been implemented. However, policy makers must have confidence in the quality of our data and the soundness and impartiality of our interpretations of those data. As you have seen, the evidence will often not include any data from experiments. How then can we give assurances as to the soundness of our data and their meaning?
While it is important to be able to read and interpret individual papers, as we have noted previously the results of a single study are never going to provide the complete answer to a question. To move towards this we need to review the literature more widely. There can be a number of reasons for doing this, some of which require a more comprehensive approach than others. If the aim is simply to increase our personal understanding of a new area then a few papers might provide adequate background material. Traditional narrative reviews, which give less emphasis to complete coverage of the literature and tend to be more qualitative, have value for exploring areas of uncertainty or novelty, but it is harder to scrutinise them for flaws. In contrast, a major decision regarding policy or practice should be based on a systematic review and perhaps a meta-analysis of all the relevant literature and it is this systematic approach that we will focus on here.
What is a systematic review?
A systematic review should be a helpful synthesis of all of the relevant data – highlighting patterns but not hiding differences. Although its primary data units are whole studies rather than individuals, it should still have a clearly formulated research question and be conducted with the same rigour as its component studies. So how should we go about conducting a systematic review? This is a major undertaking and excellent guidelines are widely available for would-be reviewers (see e.g. the Cochrane Collaboration website www.cochrane.org) so we will not attempt to cover all of the issues here. But, in brief, it involves:
• identifying all potentially relevant primary research studies that address the question of interest and including or excluding them according to predetermined criteria;
• abstracting the data in a standard format and critically appraising the included studies;
• summarising the findings of the studies, this might include a formal meta-analysis to combine the results of all of the studies into a single summary estimate; and
• an overall evaluation of the evidence with appropriate conclusions.
If the results of a study reveal an interesting association between some exposure and a health outcome, there is a natural tendency to assume that it is real. (Note that we are considering whether two things are associated. This does not necessarily imply that one causes the other to occur. We will discuss approaches to determining causality further in Chapter 10.) However, before we can even contemplate this possibility we have to try to rule out other possible explanations for the results. There are three main ‘alternative explanations’ that we have to consider whenever we analyse epidemiological data or read the reports of others, no matter what the study design: namely, could the results be due to
• bias or error, or
We will discuss the first of these, chance, in this chapter and will cover bias and confounding in Chapters 7 and 8, respectively.
Random sampling error
When we conduct a study or survey it is rarely possible to include the whole of a population so we usually have to rely on a sample of that population and trust that this sample will give us an answer that holds true for the general population. If we select the sample of people wisely so they are truly representative of the target population (the population that we want to study) and, importantly, if most of those selected agree to participate, then we will not introduce any selection bias into the study (we touched on the issue of selection bias in Box 3.2, and will discuss it further in Chapter 7). However, even in the absence of any selection bias, if we were to study several different samples of people from the same population it is unlikely that we would find exactly the same answer each time, and unlikely that any of the answers would be exactly the same as the true population value. This is because each sample we take will include slightly different people and their characteristics will tend to vary from those in other samples – just by chance. This is known as random sampling error.
According to the Brewers Association of Japan, the Chinese drink the most beer in the world (44,201 million litres in 2012, up from 28,640 in 2004) followed by the Americans (24,186 million litres). In contrast, the Czech Republic ranked a lowly 21st in terms of total consumption (1905 million litres) and Ireland didn't even make the top 25. This information may be useful for planning production, but do the Chinese and Americans really drink more beer than the rest of us? An alternative and possibly more informative way to look at these data is in terms of consumption per capita. When we do this, the USA falls to 14th position in the ‘beer drinking league table’ (77 litres per capita) and China falls way off the screen (a mere 33 litres per capita). The Czechs are now the champions (149 litres per capita), followed by Austria (108 litres) and Germany (106 litres) in 2nd and 3rd place and Ireland comes in 6th place (98 litres per capita). While Australia held the 4th spot in 2004 with an average of 110 litres per capita, by 2012 they had fallen to 11th on the table (83 litres).
(Source: www.kirinholdings.co.jp/english/news/2014/0108_01.html, accessed 2 May 2015.)
The goal of public health is to improve the overall health of a population by reducing the burden of disease and premature death. To do this we need to be able to quantify the levels of ill-health or disease in a population in order to monitor our progress towards eliminating existing problems and to identify the emergence of new problems. Many different measures are used by researchers and policy makers to describe the health of populations. You have already met some of these for example the attack rate, which was used to investigate the source of the food poisoning outbreak in the previous chapter. In this chapter we will introduce some more of the most commonly used measures so that you can use and interpret them correctly.