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Epidemiological Evidence Definition Essay

 

PEOPLE'S EPIDEMIOLOGY LIBRARY

PEL ESSAYS ON EPIDEMIOLOGY

S.D. Walter, McMaster University, Hamilton, Canada

Updated: 120103

Essay # 1:

Introduction to Epidemiology


This essay provides a brief summary of what epidemiology is, what it is used for and the kind of people and skills required to do it.  We will touch on the history of the subject, and identify some examples of important findings from epidemiology studies.

Definition and uses of epidemiology

Epidemiology is the study of health and disease in populations. [Porta, 2008]  Epidemiology studies are used to identify which people might be more or less likely to get certain diseases, whether the rates of disease are changing over time, and whether some geographic areas of a community might be at higher risk of disease than other areas. 

In the next essays, it will become clear how epidemiology uses comparisons between groups of people, and thereby thinks in terms of populations, to arrive at its conclusions. The results of epidemiologic studies are very important for the administration of health care systems.  Public health authorities frequently use this information to monitor the state of health of a population.  They may also base public health programs for the early detection or prevention of disease on epidemiology findings.

The epidemiologic approach is equally important for clinical medicine.  Then, the populations studied are patients, their diagnosis, prognosis and therapy. It provides much of the input needed in evidence-based medicine.[Sackett et al., 1996]  An evidence-based framework is often used to develop reliable guidelines and provides advice to doctors about which are likely to be the best diagnostic tests and treatments for their patients. 

Epidemiologists are usually involved in the design of research studies, in data collection for those studies, and in the analysis and interpretation of the results.  They are also increasingly involved in knowledge transfer activities, which are communicating the results of their studies to assist health care policy makers, clinicians, and the general public. 

Epidemiologic evidence is fundamental in the development of recommendations to the community about healthy lifestyles, and there can also be commercial implications, such as in developing the health messages and labels that may be attached to foods and other consumer products such as tobacco. Likewise, clinical epidemiologic evidence is the basis of developing guidelines for clinical practice.

Skills required to do epidemiology

The discipline of epidemiology involves many different aspects.  Typically, epidemiologic studies require the expertise of doctors, biologists, biochemists or other scientists to inform the biological aspects of the disease or clinical question being studied. 

Many epidemiologic studies are highly quantitative, and they draw on the skills of biostatisticians and information technologists to design appropriate ways to capture and store the data that is needed, and to carry out statistical analysis of the results.  Additionally, qualitative approaches are often used in epidemiology, for instance, involving social scientists who determine important themes in the perception of a particular disease concern, and how the community might respond. 

Because many scientific and other disciplines are required in epidemiology, the background and training of epidemiologists varies considerably.  Some epidemiologists begin their careers in a laboratory science (for instance, chemistry or microbiology), but later move into doing epidemiologic studies in the general community. 

Similarly, many doctors, nurses and other health professionals begin working in clinical practice, but then go on to work in epidemiology and public health, treating the whole population as their patient, and focussing on patient populations. Still others, such as biostatisticians, may begin their training in mathematics and statistics, and become involved in epidemiology through the development of appropriate study designs and methods of data analysis for epidemiologic data.

Epidemiology in history


Epidemiology has both a long and a short history.  It might be argued that Hippocrates (a variant of this famous oath is taken by new doctors almost all over the world) was an epidemiologist, because he had a hypothesis that some diseases were caused by environmental factors that act upon human populations.

On the other hand, epidemiology as we know it today has a short history since there were no real attempts at comparisons between groups to consider illnesses in populations before the 17th century. In the 17th century John Graunt carried out a series of analyses on the newly implemented “Bills of Mortality” in London, which for the first time systematically determined which were the leading causes of death.[Graunt, 1662]  Lists of numbers of deaths from various causes, as they were understood in those times, including examples such as “Frighted”, “Griping in the Guts” and “Worms”, were published every week as a kind of early warning signal for the onset of plague. More importantly, from our present-day view, he showed that overall mortality evolved smoothly from year to year while mortality from plague demonstrated  peaks, suggesting that it had an environmental cause that came and went.

Another well known pioneer or epidemiologic thinking was James Lind who compared six pairs of sick seamen with different treatments in each member of the pair and observed that oranges and lemons were effective treatment for scurvy.[Lind, 1853]

In London, some 200 years after Graunt, the epidemiologist John Snow’s studies compared the mortality from cholera between the clients of two companies providing water in London.[Snow, 1855] The study confirmed his hypothesis that getting cholera was somehow linked to contaminated drinking water. The specific inquiry of a cluster of cases which occurred in the vicinity of Broad Street famously led to the removal of the handle of the Broad Street water pump.  This was a controversial move at the time, because several other theories about the cause of cholera existed, including the notion that living at a low altitude would increase the population’s exposure to dangerous vapours.  We know nowadays that Snow was right, and the provision of a clean and secure drinking water supply was one of the main elements of a major movement towards improving public health during the 19th century and subsequently.[Vandenbroucke et al, 1991]

Other important examples of 19th century epidemiology include the study of Dr Pierre Louis on the efficacy of bloodletting in the treatment of pneumonia [Louis 1835] and the intervention of Ignaz Semmelweis demonstrating the effectiveness of hand washing in the prevention of puerperal fever [Carter, 2005][Semmelweis, 1861]

More recently, in the mid-20th century, a number of epidemiologic studies were central in the discovery that smoking could increase the risk of lung cancer. Some compared the frequency of smoking in cases of lung cancer and carefully chosen controls [Wynder and Graham, 1950][Doll and Hill, 1950][Levin et al., 1950]  Other compared the occurrence of lung cancer in smokers and non smokers.[Link to [Doll and Hill, 1954, 1964] [Hammond & Horn, 1958]

Although this idea is now well accepted, at the time it too was controversial and much contested by scientists [Fisher, 1959;Stolley, 1991] and to Berkson [Berkson, 1946] and the tobacco industry.[US surgeon general report, 1964]  Epidemiology studies over the years since then have confirmed the existence of numerous adverse health effects of smoking, including, heart disease and several other types of cancer. [Pettiti, 2001]

Epidemiology in the community


Use of the epidemiologic method is an important practical way to study health and disease, because epidemiologists work in the general community.[Holland et al., 2007]  Epidemiologists can be found working in the general population, based in hospitals or local health units, or working for government or commercial enterprises such as the pharmaceutical industry.  They all have in common that they are working on studying health events in various groups of people, making comparisons over time and between various places.  Laboratory scientists and research doctors often use experimental designs for their studies, focusing on one particular risk-factor and one outcome, in such a way that all other risk-factors are suitably controlled, often by the use of randomisation: random numbers, i.e., pure chance determines who will receive what treatment. 

In contrast, epidemiologists may be engaged in studying several diseases and several risk factors for those diseases simultaneously, and sometimes refined study designs are required to do so.  Epidemiology is characterized by attempts to deal with the numerous determinants of disease in the community; epidemiologists are often concerned with trying to isolate the association between one risk factor and one particular health outcome while taking into account other important associations that exist in people’s daily lives. Epidemiologists do not limit themselves to factors that are measured at the individual level. They also measure factors at the community level, and integrate both type of information in their analysis.[Susser, 1998, McMichael, 1999 Diex-Roux,1998] They also take into account the fact that some factors may have different effects at different stages of life, and that the effects of exposures at a younger age may only become manifest at an older age.[Kuh & Shlomo, 2004]

While important and useful information can emerge from laboratory studies and randomised clinical trials of treatment, the findings from those studies may not be generalisable to the population at large, and here epidemiology has an advantage by directly studying what is actually occurring in the general population.  Furthermore, many health-related questions of interest, for example the health effects of smoking, cannot be studied in humans using experimental randomisation, and we are therefore obliged to consider the problem by  comparing groups of people, using the study designs that are explained in essay.

In some special circumstances, epidemiologists can use randomisation in the community.  For instance, in order to evaluate the potential benefits and harms of screening for the early detection of cancer, they might randomise portions of the population or particular individuals to be offered screening or not, and then observe their subsequent experience of cancer detection and prognosis.[Shapiro et al., 1988]  However, this type of study is unusual because of the large sample sizes and extended follow-up periods that are required to do them. 

Instead, epidemiologists are more usually concerned with studying health effects in people as they live their daily lives but without the ability to randomise people to categories of risk exposure.  Studying health effects in people who go about their usual daily lives is one major advantage over other scientific methods, but it is also one that presents epidemiology with some of its greatest challenges.  In the community, epidemiology is instrumental in detecting and tracking epidemics of infectious diseases, like flu, HIV, measles of outbreaks like food poisoning.


Epidemiology in the clinic


One of the greater triumphs of epidemiology was the acceptance of its instruments, i.e., its study designs, as ’arbiters’ in clinical medicine: a great impact was made when the randomized controlled trial became accepted as the gold standard for deciding which therapy works best.[Streptomycin Trial, 1948] Similarly, adverse effects of treatments are nowadays commonly researched using epidemiologic studies, i.e., the same study designs as were used to assess how much smoking causes lung cancer.[James Lind Library]

Abstract

Interpreting observational epidemiological evidence can involve both the quantitative method of meta-analysis and the qualitative criteria-based method of causal inference. The relationships between these two methods are examined in terms of the capacity of meta-analysis to contribute to causal claims, with special emphasis on the most commonly used causal criteria: consistency, strength of association, dose-response, and plausibility. Although meta-analysis alone is not sufficient for making causal claims, it can provide a reproducible weighted average of the estimate of effect that seems better than the rules-of-thumb (e.g. majority rules and all-or-none) often used to assess consistency. A finding of statistical heterogeneity, however, need not preclude a conclusion of consistency (e.g. consistently greater than 1.0). For the criteria of strength of association and dose-response, meta-analysis provides more precise estimates, but the causal relevance of these estimates remains a matter of judgement. Finally, meta-analysis may be used to summarize evidence from biological, clinical, and social levels of knowledge, but combining evidence across levels is beyond its current capacity. Meta-analysis has a real but limited role in causal inference, adding to an understanding of some causal criteria. Meta-analysis may also point to sources of confounding or bias in its assessment of heterogeneity.

Causation, epidemiology, inference, meta-analysis, systematic reviews

The interpretation of observational epidemiological studies offers both promise and peril.1,2 Aetiological hypotheses with public health implications may gain support from these studies which in turn are subject to many alternative interpretations, especially bias and confounding. Traditionally, epidemiologists have used a mostly qualitative narrative review technique that includes causal criteria—strength, consistency, plausibility, dose-response and others—as well as considerations such as bias, confounding, and study designs.3–9

How meta-analysis can help solve this difficult interpretative problem is not immediately obvious. In a recent paper in this Journal, for example, traditional narrative reviews and meta-analyses are portrayed as methodological alternatives, each with strengths and weaknesses.10 Others argue that a systematic review may include a meta-analysis but that it should not be a prominent component of that review.11

A key unexamined concern is the relationship between meta-analysis and criteria-based methods of causal inference. This paper describes how these two methods—one more quantitative and the other more qualitative—can be used together in causal assessments.

Background: Practical and Theoretical Accounts

The relationship between meta-analysis and causal inference has both practical and theoretical dimensions. In practice, causal claims are sometimes made in a single review in which both methods are applied to the evidence. A recent and controversial example involved induced abortion and breast cancer;12 the authors claimed that several thousand breast cancer deaths each year can be attributed to induced abortion. Another review of the same evidence—sans meta-analysis and published almost simultaneously—concluded that there was no association between induced abortion and breast cancer much less a causal one.13

Indeed, causal claims may appear in published narrative reviews in which meta-analysis is not performed; these claims arise from the use of methods with the traditional causal criteria at their core.14 Finally, some causal claims appear to arise from meta-analysis alone with minimal reference to causal inference methods and in the absence of a systematic narrative review.15

Such a wide range of practices provides no firm basis upon which the relationship between meta-analytical and causal inference methods can be teased out. Turning to theoretical accounts, however, is only marginally helpful. Many commentators have cautioned that meta-analysis may not yet be ready for widespread use, especially for observational studies.16–21 Beyond that, the main concern of methodologists is less about the role of meta-analysis in assessing causation than it is about the synthetic approach to meta-analysis that emphasizes summarization of evidence over the search for heterogeneity.22–27 On the topic of causation, for example, a recent paper notes only that statistical methods alone cannot provide a causal explanation of associations.23 A recent text argues that meta-analysis of observational data does not provide good evidence of causation because competing hypotheses (e.g. bias and confounding) cannot be ruled out.28

From these accounts, it is reasonable to conclude only that meta-analysis may not, by itself, provide enough information to warrant a causal claim. Although this conclusion calls into question one practice described earlier, it does not provide much help in determining the extent to which meta-analysis makes causal claims easier or more difficult. A look at the finer structure of causal inference methods, the individual causal criteria, provides clues.

Meta-analysis and Criteria of Causation

Consistency

One of the most frequently used criteria in the practice of causal inference14 is consistency, the extent to which the association is observed in different circumstances, by different investigators, using different study designs, and in different locations.6 Consistency is the epidemiologist's expanded equivalent of the experimentalist's search for replicability,29 although for observational studies, replication of findings in the face of different, rather than similar, circumstances is more highly valued.8,30,31

Without meta-analysis, reviewers often assess consistency with simple quantitative summarizing techniques, tallying up the per cent of studies that are positive and then utilizing a rule-of-thumb for declaring them consistent: a simple majority in some cases, a higher threshold in others.32 Indeed, it has been shown that in some controversial, high-profile situations, different rules-of-thumb for consistency (coupled with its relatively high prioritization among causal considerations) have contributed to vastly different, even exactly opposite, causal judgements.33 To examine whether meta-analysis can provide a less subjective assessment of consistency requires a look at how summarization of effect estimates across studies occurs; assessing heterogeneity is a key consideration.

Heterogeneity tests assess the extent to which the studies are similar enough to warrant summarization. When a lack of statistical heterogeneity across all studies occurs, a weighted average of the estimated effect size (under one or another statistical model) seems a defensible and reproducible way to approach the assessment of consistency and therefore likely better than the rules-of-thumb used in the practice of causal inference.

When heterogeneity exists, however, then exploring its sources11 will not only guard against the concerns that meta-analysis too often buries important inconsistencies in statistical aggregations19 but will also provide relevant information for the practice of causal inference. For example, summarization of results within heterogeneous groups provides estimates of the overall effect by measurement technique, study design, population studied, or other sources of the observed heterogeneity, although the extent to which each heterogeneous group estimate represents bias (or not) may be a matter, like so much else in causal inference, of judgement.

Heterogeneity, however, does not necessarily preclude a conclusion of consistency in causal inference. Consider a hypothetical situation in which meta-analysis reveals heterogeneous groups of summarizable studies each of which (i.e. each group) has a statistically significant estimate in the same direction although of different magnitudes. A more extreme example can be imagined in which no known covariates are associated with variation in the study results, yet the results of all individual studies show strong effect sizes (and are heterogeneous). In both instances, it is too simplistic a rule to declare that evidence of heterogeneity disallows a causal judgement due to a lack of consistency.23 Indeed, in these instances, it may be reasonable to argue that the results are both consistent (i.e. consistently greater than 1.0) and heterogeneous.

Biological plausibility

The extent to which an observed association in epidemiological studies is supported or not by what is known about the mechanism of action and the underlying disease process is commonly referred to as biological plausibility or sometimes coherence.34 In metaphorical terms, assessing this causal criterion is like opening a window into the many-tiered structure of biological knowledge pertinent to the aetiology of disease: cellular and subcellular systems, experimental animal models, physiological parameters and genetic or other biomarkers.35 Inasmuch as several similar studies may examine the same biological parameters, meta-analysis could assist in summarizing information and in identifying heterogeneity analogous to the way in which it is used for randomized clinical trials and for epidemiological studies.36 A recent example involved the role of MDR1/gp170 expression in breast cancer tumour samples wherein substantial heterogeneity was linked to differences in assay techniques.37 This uncommon application of meta-analysis is a step towards a more systematic approach to the assessment of biological evidence, although it seems unlikely that meta-analysis in its current quantitative form will be useful for summarizing different kinds of studies from different levels of biological knowledge.

Strength of association and dose-response

There is an important distinction to be made between improving the precision of the relative risk estimate—which meta-analysis offers—and interpreting the causal relevance of the absolute value of the summarized estimate—which it does not.11,18,20,38 Simply put, the practitioner of causal inference, having completed a meta-analysis, remains faced with the problem of judging whether the summarized estimate—small (i.e. weak) or large (i.e. strong)—can be explained by confounding or bias. Thus it is reasonable to accept a relative risk estimate of 2.0 emerging from a meta-analysis as a better estimate than that which we may have opined through a judicious application of our ‘judgement’, somehow estimating a summary average value from a long list of single study estimates. Nevertheless, that same conclusion, emerging as it does from a meta-analysis, provides no additional warrant for a causal claim. Put another way, a summary estimate of 2.0—whether it emerges from a meta-analysis or not—remains on the borderline of what is typically called a ‘weak’ association. The same argument applies to dose-response curves,39 which can also be made more precise by judicious application of meta-analysis. Nevertheless, whatever the form of those curves—increasing, decreasing, ‘S’ or ‘J’, shaped—the practitioner of causal inference is left with the task of explaining them in terms of the biological sense of the revealed pattern, potential biases, and other concerns.

Other criteria

Specificity, temporality, and analogy are less frequently employed criteria, but are nevertheless a part of the rich historical tradition of causal inference methods.32 Meta-analysis has indirect connections to the consideration of specificity; it is possible that one group of studies will have a summarizable effect estimate that differs from another because the exposure (or disease) was measured more narrowly. Meta-analysis may also provide a way to summarize evidence that examines the relationships of time-related variables to outcomes, but it is not clear that meta-analysis has important implications for the classic consideration of the temporal order of cause and effect nor for the criterion of analogy.

Meta-analysis, Confounding, and Bias

Causal criteria may be central to causal assessments of epidemiological evidence, but other concerns, especially confounding and bias, are also important. The extent to which meta-analysis can assist in the assessment of confounding and bias is closely related to the capacity for meta-analysis to reveal heterogeneity among studies. Potential sources of heterogeneity include study design differences, exposure assessment differences, confounders (known and unknown), and bias.11,24–26 Publication bias is another concern in the practice of meta-analysis that could potentially affect interpretation of results.24

Conclusion

Both causal inference methods and meta-analysis can appear within the context of a systematic review, the latter characterized by a clearly stated purpose, careful literature searches, explicit inclusion and exclusion criteria, assessments of study validity and thus bias, and well-articulated definitions and rules of inference for selected causal criteria.4,5 Several important causal criteria may be determined or made more precise through quantitative assessments of heterogeneity and summarization of effects, in particular, using meta-analyses of groups of studies from observational or biological levels of knowledge.

Thus meta-analysis has an important role to play in causal assessments, although meta-analysis alone is not sufficient for making causal claims. Meta-analysis provides a more formal statistical approach to the criterion of consistency as well as a way to identify heterogeneous groups of studies, whether epidemiological or biological in origin. It also can provide more precise estimates of the magnitude of the effect estimate and of dose-response relationships. These are the clearest and perhaps most important links between the quantitative method of meta-analysis and the more qualitative method of causal inference.

Discussion

These conclusions imply that practitioners of meta-analysis should refrain from making causal claims without first considering the issues brought out by applying the largely qualitative causal inference methods. Practitioners of causal inference, on the other hand, should recognise the strengths and limitations of applying meta-analysis to epidemiological evidence and understand that melding the quantitative and qualitative components of these methods within the context of a systematic review is helpful and yet also only a rough solution to a longstanding methodological problem.9

Indeed, the scope of the problem of making causal claims from scientific evidence is widening. Recently, some have argued that an understanding of the determinants and distribution of disease will be improved by examining not only epidemiological and biological evidence but also social evidence for causation.40–42 Causal inference, in this expanded context, will require both an ability to combine studies within the social arena, but perhaps as importantly, to assess evidence across many levels of scientific knowledge from the molecular to the social, with classic epidemiology near the middle. Following the arguments above, meta-analysis in its current form cannot be a solution because it requires that studies be reasonably similar in design and in measurements of effect, two characteristics not shared by studies of, say, DNA repair mechanisms in tumour cell lines, tissue biomarker levels in mice exposed to an environmental insult, case-control studies of cancer in workers exposed to potential carcinogens, and analyses of the impact of income or class differences on selection processes into and out of jobs in hazardous workplaces. Indeed, making inferences across many levels of scientific knowledge may require a systems-based approach43,44 not captured by the methods discussed here.

Helpful comments and suggestions were provided by Drs Graham Colditz, Matthew Longnecker, Karin Michels, and Beverly Rockhill.

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© International Epidemiological Association 2000

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