6.1 Epidemiology: the foundation of public health

6.1 Epidemiology: the foundation of public health
Oxford Textbook of Public Health

Epidemiology: the foundation of public health

Roger Detels

What is epidemiology?
Uses of epidemiology in support of public health

Describing the spectrum of the disease

Describing the natural history of disease

Identifying factors that increase or decrease the risk of acquiring disease

Predicting disease trends

Elucidating mechanisms of disease transmission

Testing the efficacy of intervention strategies

Evaluating intervention programmes

Identifying the health needs of a community

Evaluating public health programmes
Applications of epidemiology

Ecological studies

Cross-sectional/prevalence surveys

Case–control studies

Cohort studies

Experimental studies


Methodological issues
Chapter References

The subsequent chapters in this section present detailed discussions of the principles and methods of epidemiology. This introductory chapter will attempt to define epidemiology, present ways in which epidemiology is used in the advancement of public health, and discuss the range of applications of epidemiological methodologies.
What is epidemiology?
There are probably as many definitions of epidemiology as there are epidemiologists, although every epidemiologist will know exactly what it is that he or she does! Defining epidemiology is difficult primarily because it does not represent a body of knowledge, as does, for example, anatomy, nor does it target a specific organ system, as does cardiology. Epidemiology represents a philosophical method of studying a health problem and can be applied to a wide range of problems, from transmission of an infectious disease agent to the design of a new strategy of health-care delivery. Furthermore, that methodology is continually changing as it is adapted to a greater range of health problems and more techniques are borrowed and adapted from other disciplines such as mathematics and statistics.
Maxcy, one of the pioneer epidemiologists of the twentieth century, offered the following definition: ‘Epidemiology is that field of medical science which is concerned with the relationship of various factors and conditions which determine the frequencies and distributions of an infectious process, a disease, or a physiologic state in a human community’ (Lilienfeld 1978). The word itself comes from the Greek epi, demos, and logos; literally translated it means the study (logos) of what is among (epi) the people (demos). Last, in the Dictionary of Epidemiology, has defined epidemiology as ‘The study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the control of health problems’. Last’s definition emphasizes that epidemiologists are not only concerned with disease but also with ‘health-related events’, and that ultimately epidemiology is committed to the control of disease. All epidemiologists, however, will agree that epidemiology concerns itself with populations rather than individuals, thereby separating itself from the rest of medicine and constituting the basic science of public health. Following from this, therefore, is the need to describe health and disease in terms of frequencies and distributions in the population. The epidemiologist relates these frequencies and distributions of specific health parameters to the frequencies of other factors to which populations are exposed in order to identify those that may be causes of a disease or promoters of good health. Inherent in the philosophy of epidemiology is the idea that ill health is not randomly distributed in populations, and that elucidating the reasons for this non-random distribution will provide clues regarding the risk factors for disease and the biological mechanisms that result in loss of health. Because epidemiology usually focuses on health in specifically human populations it is rarely able to provide experimental proof in the sense of Koch’s postulates, as can often be done in the laboratory sciences. Epidemiology more often provides an accumulation of increasingly convincing indirect evidence of a relationship between health or disease and other factors. This process, referred to as causal inference (Chapter 6.11), includes considering an observed relationship in term of its strength, consistency, specificity, temporality, biological gradient, plausibility, coherence, and experimental evidence (Hill 1965).
Although they will differ on the exact definitions of epidemiology, most epidemiologists will agree that they try to characterize the relationship between the agent, the environment, and the host (usually human). The epidemiologist considers health to represent a balance among these three forces, as shown in Fig. 1.

Fig. 1 The relationship between agent, host, and environment in epidemiology.

Changes in any one of these three factors may result in loss of health. For example, the host may be compromised as a result of treatment with steroids, making him or her more susceptible to agents that do not ordinarily cause disease. Conversely, a breakdown in the water supply system may result in an increased exposure of people to hepatitis B, as happened some years ago when the main water supply of New Delhi, the River Jumna, was drastically reduced by drought. Finally, some agents may become more or less virulent over time—often because of the promiscuous use of antibiotics—thereby disturbing the dynamic balance among agent, host, and environment. Two examples are the cases of acute necrotizing fasciitis caused by Streptococcus A (Communicable Disease Surveillance Centre 1994) and the development of multidrug-resistant tuberculosis (Chapman and Henderson 1994).
The epidemiologist uses another triad to study the relationship of agent, host, and environment: time–place–person. Using various epidemiological techniques described in subsequent chapters, the epidemiologist describes disease or disease factors occurring in the population in terms of characteristics of time (for example, trends, outbreaks, and so on), place (the geographic area in which the disease is occurring), and person (the characteristics of the affected individuals, for example age, gender, and so on) to elucidate the causative agent, the natural history of the disease, and the environmental factors that increase the likelihood of the host acquiring the disease. With this information the epidemiologist is able to suggest ways to intervene in the disease process to prevent disease or death.
Epidemiology has been described as the ‘art of the possible’. Because epidemiologists work with human populations, they are rarely able to manipulate events or the environment as can the laboratory scientist. They must, therefore, exploit situations as they exist naturally to advance knowledge. They must be both pragmatic and realistic. They must realize both what is possible and what are the limitations of the discipline. Morris has said that the ‘epidemiologic method is the only way to ask some questions…, one way of asking others and no way at all to ask many’ (Morris 1975). The art of epidemiology is to know both when epidemiology is the method of choice and how to use it to answer the question.
Applying the epidemiological method to resolve a health question successfully can be compared to constructing a memorable Chinese banquet. It is not enough to have the best ingredients and to know the various Chinese cooking methods. The truly great Chinese chefs must be able to select the appropriate ingredients and cooking methods for each individual dish and, furthermore, must know how to construct the correct sequence of dishes to excite the palate without overwhelming it. These chefs create a memorable banquet by adding their creative genius to the raw ingredients and the established cooking methods. Similarly, it is not enough for the epidemiologist to know the various strategies and methods of epidemiology; she or he must be able to apply them creatively to obtain the information needed to understand the natural history of the disease. It is not enough to know what a cohort study is; the epidemiologist must know when the cohort design is the appropriate design for the question at hand, and then must apply that design appropriately and creatively. These skills make epidemiology more than a methodology. It is this opportunity for creativity and innovation that provides excitement for the practitioner and makes the successful practice of epidemiology an art.
For example, Imagawa et al. (1989) identified probable transient HIV-1 infection in men, implying clearance of the virus by the immune system of the men by focusing their viral isolation studies on relatively few HIV-1 antibody-negative homosexual men who had many different sexual partners. A simple cohort study of antibody-negative individuals would have required a cohort of thousands of men rather than the 133 studied. Similarly, the effects of passive smoking were demonstrated by cohort studies of non-smoking family members of smokers. The effects of passive smoking/second-hand smoking were demonstrated in nursing students by comparing the reported symptoms in room-mates of smokers and non-smokers who kept diaries of their symptoms. The room-mates of the smokers had a 1.8 greater risk of episodes of phlegm than room-mates of non-smokers (Schwartz and Zeger 1990). Colley et al. (1974), Tager et al. (1979), and Tashkin et al. (1984) demonstrated that children of smokers had lower levels of lung function than children of non-smokers. All of these investigators used traditional study designs, but demonstrated their creativity by applying that design to specific populations which were most likely to reveal a relationship if it existed.
Epidemiological studies rarely provide ‘proof’ of a causal relationship. Thus, there is continuing debate among epidemiologists about what constitutes adequate criteria for inferring a causal relationship from epidemiological studies (Rothman 1988). Hill (1965) suggested the following criteria for establishing a causal relationship: strength of association (statistical probability and risk ratio), consistency of finding across multiple studies, specificity of the relationship, temporality (outcome follows causation), biological gradient (a dose–response relationship), plausibility, coherence (consistency with prior knowledge), experimental evidence, and analogy (relationship hypothesized similar to that in known relationships). Susser has added to these criteria the ability of the observed relationship to predict other relationships correctly (Rothman 1988). The debate goes on, but the principle is the same: epidemiologic studies seldom provide ‘proof’ of a causal relationship in the sense of Koch’s postulates, but may be used to reveal a possible relationship and build a convincing case that this relationship is causal.
Uses of epidemiology in support of public health
Epidemiology is the basic science of public health because it is the health science that describes health and disease in populations rather than in individuals, information essential for the formulation of effective public health initiatives to prevent disease and promote health in the community. Epidemiology can (a) describe the spectrum of the disease, (b) describe the natural history of the disease, (c) identify factors that increase or decrease the risk of acquiring disease, (d) predict disease trends, (e) elucidate mechanisms of disease transmission, (f) test the efficacy of intervention strategies, (g) evaluate intervention programmes, (h) identify the health needs of a community, and (i) evaluate public health programmes.
Describing the spectrum of the disease
Disease represents the endpoint of a process of alteration of the host’s biological systems. Although many disease agents are limited in the range of alterations they can initiate, others, such as measles, can cause a variety of disease endpoints. For example, the majority of infections with measles virus result in the classical febrile disease with a blotchy rash, but the measles virus can also cause generalized haemorrhagic rash and acute encephalitis. Years after initial infection, measles can also cause subacute sclerosing panencephalitis, a fatal disease of the central nervous system.
Various types of epidemiological studies have been used to elucidate the spectrum of disease resulting from many agents and conditions. Cohort studies have been used to document the role of high blood pressure as a major cause of stroke, myocardial infarction, and chronic kidney disease. For rare diseases such as subacute sclerosing panencephalitis and multiple sclerosis, case–control studies have been useful to identify the role of the measles virus (Detels et al. 1973; Sullivan et al. 1984). Knowing the spectrum of disease that can result from specific infections and conditions allows the public health professional to design more effective intervention strategies; for example, education, screening, and treatment programmes to reduce the prevalence of high blood pressure will also reduce the incidence of myocardial infarction, stroke, and chronic kidney disease (Hypertension Detection and Follow-up Program Cooperative Group 1979).
Describing the natural history of disease
Epidemiological studies can be used to describe the natural history of disease, to elucidate the specific alterations in the biological system in the host, and to improve diagnostic accuracy. For example, cohort studies of individuals who were infected with HIV revealed that a drop in the level of T lymphocytes having the CD4 marker was associated with being infected with HIV, and that a further decline in CD4 cells was associated with developing clinical symptoms and AIDS (Detels et al. 1987; Polk et al. 1987). This observation stimulated immunologists to focus their research on the interaction of the immune system and HIV. From a clinical perspective, clinicians can target HIV-positive individuals who have declining CD4 cells for prophylactic treatment when it is most likely to be effective. Epidemiology can also be used to describe the impact of treatment on the natural history of disease. For example, a cohort study design was used to demonstrate the public health efficacy of combined highly active retroviral therapy on reducing both the incidence of AIDS and extending survival of those who already had the disease (Detels et al. 1998). Thus, describing the natural history of AIDS has assisted researchers to focus their studies and clinicians to use the limited treatment modalities available more effectively (Phair et al. 1992). The field of ‘clinical epidemiology’ applies research on the natural history of disease to improving the diagnostic accuracy of doctors in their clinical practice (Sackett et al. 1991).
Identifying factors that increase or decrease the risk of acquiring disease
Having specific characteristics increases the probability that individuals will develop disease. These ‘risk factors’ may be social (smoking, drinking), genetic (ethnicity), dietary (saturated fats, vitamin deficiencies), and so on. Knowing these risk factors can often provide public health professionals with the necessary tools to design effective programmes to intervene before disease occurs. For example, descriptive, cross-sectional, case–control, cohort, and intervention studies have all shown that smoking is the biggest single risk factor for ill health, because it is a major risk factor for cardiovascular disease, chronic respiratory disease, and many cancers (for example, of the lung, nasopharynx, and bladder). Thus, smoking is the leading cause of disability and death in developed countries, if not the world. Health education campaigns and other strategies to stop or reduce smoking, based on these epidemiological studies, are now a major public health activity in most countries.
Predicting disease trends
The ability to predict future epidemics provides the public health professional with the opportunity to muster the most effective forces to combat the disease. Descriptive studies of many infectious diseases, such as measles, polio, and influenza, have revealed a periodicity of pandemics and epidemics caused by them. Knowledge of these disease patterns has been useful to public health officials to prevent these epidemics.
Studies of the trends of HIV infection in those engaging in high-risk activities, and of changing frequency of high-risk activities, have permitted epidemiologists and statisticians to develop models that predict the number of cases of AIDS likely to occur in 5 to 10 years (Brookmeyer and Damiano 1989; Taylor 1989; Chin et al. 1990). This information is particularly useful for public health professionals who must anticipate future health-care needs.
Elucidating mechanisms of disease transmission
Understanding the mechanisms of disease transmission can suggest ways in which public health professionals can protect the public by stopping transmission of the disease agent. Epidemiological studies of the various arboviral encephalitides have incriminated certain species of mosquitoes as the vectors of disease and specific animals as the reservoirs for the viruses. For example, public health efforts in California to prevent western equine encephalitis have concentrated on control of the mosquito vector and vaccination of horses, which are a reservoir of the virus. Although an effective vaccine for smallpox had been available for almost two hundred years, eradication of the disease was not achieved until the recognition that the low infectivity of varicella virus and the relatively long incubation for development of smallpox could be used to develop a strategy of surveillance for cases, with identification and immediate vaccination of all susceptible contacts (containment). Using this containment strategy based on epidemiological principles, smallpox was eradicated through a worldwide effort in less than 10 years (Fenner et al. 1988).
Testing the efficacy of intervention strategies
A primary objective of public health is to prevent disease through intervention in the disease process. But a vaccine or other intervention programme must be proven to be effective before it is used in the community. Epidemiological studies (double-blind placebo-controlled trials) are a necessary step in developing an intervention programme, whether that programme is administration of a new vaccine, a behavioural intervention strategy to stop smoking, or a community intervention study to lower heart disease. Although it may be argued that injection of a saline placebo is no longer considered ethical, a proven vaccine, such as polio, can often be used as a placebo for a trial of a new vaccine for a different disease, as was used for trials of rubella vaccines in Taiwan (Detels et al. 1969). Widespread use of an intervention not subjected to epidemiological studies of efficacy may result in implementation of an ineffective intervention programme at great public expense and may actually result in greater morbidity and mortality because of an increased reliance on the favoured but unproved intervention and a reduced use of other strategies which are thought to be less effective but which are actually more effective.
Evaluating intervention programmes
Although an intervention such as a vaccine may have been demonstrated to have efficacy in double-blind trials, it may fail to provide protection when used in the community. Double-blind trials may demonstrate the ‘biological efficacy’ of the vaccine; but if the vaccine is not acceptable to the majority of the public, they will refuse to be vaccinated, and the ‘public health efficacy’ of the vaccine will be very low. For example, the typhoid vaccine provided some protection against small infecting inocula, but the frequency of unpleasant side-effects with the whole-cell vaccines and the need for multiple injections in the past influenced many people against being vaccinated (Chin 2000).
Another problem of inferring public health efficacy from small vaccine trials is that volunteers for vaccine trials may not be representative of the general public which needs to be protected against a specific disease. Thus broad-based intervention trials also need to be carried out, to demonstrate the acceptability and public health efficacy of a vaccine or other intervention to the population in need of protection.
Since there are adverse side-effects associated with any vaccine, ongoing evaluations of the cost–benefit relationship of specific vaccines are important. By comparing the incidence of smallpox with the incidence of adverse side-effects from the smallpox vaccine, Lane et al. (1969) demonstrated that more disease resulted from routine use of the vaccine in the United States than by transmission from imported cases.
There are several epidemiological strategies that can be used for ongoing evaluation of intervention programmes. Serial cross-sectional studies can be used to determine if there has been a change in the prevalence of disease or of indicators of health status over time. The cohort design can be used to compare incidence of disease in comparable populations receiving and not receiving the prevention programme. The case–control design can be used to determine if there are differences in the proportion of cases and non-cases who had the intervention programme.
Identifying the health needs of a community
To be effective in promoting the health of a community or country, health agencies must know what the major health problems of that community are and which subgroups in it are most affected. Cross-sectional studies will reveal the prevalence of disease in the community as well as in specific subgroups of the population, while surveillance programmes can identify trends in disease, infection, and/or health status over time. For example, the prevalence of HIV/AIDS is low in most developed countries, including the United States, but is disproportionately high in some subgroups such as African Americans and poorer communities. With this information, many health departments are now focusing their education, testing, and support programmes for HIV/AIDS on African-Americans and poorer communities.
Evaluating public health programmes
Departments of health are engaged in a variety of activities to promote the health of the community, ranging from vaccination programmes to clinics for the treatment of specific diseases in the poor. Ongoing evaluation of such programmes is necessary to assure that they continue to be cost-effective. Periodic review of routinely collected health statistics can provide information about the effectiveness of many programmes. For those programmes for which relevant statistics are not routinely available, cohort studies and serial cross-sectional studies of the incidence and changing prevalence of the targeted disease in the populations which are the intended recipients of these programmes can measure whether the programmes have had an impact and are cost-effective.
Applications of epidemiology
Specific epidemiological study designs are used to achieve specific public health goals. These goals range from identifying a suspected exposure–disease relationship to establishing that relationship, to designing an intervention to prevent it, and, finally, to assessing the effectiveness of that intervention. The usual sequence of study designs in the identification and resolution of a disease problem are:

ecological studies

cross-sectional (prevalence) surveys

case–control studies

cohort studies

experimental studies.
There are, however, many exceptions to the application of this sequence of study designs, depending on such things as the prevalence and virulence of the agent and the nature of the human response to the agent.
The earliest suspicion that a relationship exists between a disease and a possible causative factor is frequently obtained from observing correlations between exposure and disease from existing data such as mortality statistics and surveys of personal or national characteristics. These can be correlations observed across geographical areas (ecological studies) or over time, or a combination of both. Many of the initial epidemiological investigations into chronic bronchitis used vital statistics data, particularly data on mortality. Case–control studies identified smoking as a possible causal factor for chronic bronchitis. Subsequent prevalence studies confirmed the relationship, as have cohort studies. Finally, a decline in respiratory symptoms of chronic bronchitis and a concurrent, but slower decline in lung function, has been observed in individuals who cease smoking (Colley 1991).
Although this is the usual sequence in which the various epidemiological study designs are applied, there are exceptions to this sequence. Furthermore, all study designs are not appropriate to answer all health questions. The usual applications of each of the different epidemiological study designs and the limitations of each are therefore presented briefly below and in greater depth in subsequent chapters.
Ecological studies (Chapter 6.2)
The use of existing statistics to correlate the prevalence or incidence of disease in groups or populations to the frequency or trends over time of suspected causal factors in specific localities has often provided the first clues that a particular factor may cause a specific disease. These epidemiological strategies, however, document only the co-occurrence of disease and other factors in a population; the risk factors and the disease may not be occurring in the same people within the population. These types of descriptive studies are inexpensive and relatively easy to do, but the co-occurrence observed may be due merely to chance. For example, the incidence of both heart disease and lung cancer has increased concurrently with the prevalence of automatic dishwashers in the United States. Few people, however, would attribute the increase in these two diseases to the use of automatic dishwashers. Thus, ecological studies must be interpreted with caution. Nonetheless, they often reveal important relationships and provide a strong rationale for undertaking more expensive analytical studies.
Cross-sectional/prevalence surveys (Chapter 6.3, Chapter 6.4, and Chapter 6.16)
Cross-sectional/prevalence surveys establish the frequency of disease and other factors in a community. Since they require the collection of data, however, they can be expensive. They are useful to estimate the number of people in a population who have disease and can also identify the difference in frequency of disease in different subpopulations. This descriptive information is particularly useful to health administrators who are responsible for developing appropriate and effective public health programmes. Cross-sectional studies can also be used to document the co-occurrence of disease and suspected risk factors not only in the population but also in specific individuals within the population. The cross-sectional study design is useful to study chronic diseases such as multiple sclerosis and chronic bronchitis, which have a high prevalence, but an incidence that is too low to make a cohort study feasible (Detels et al. 1978). Conversely, they are not useful for studying diseases that have a very low prevalence, such as subacute sclerosing panencephalitis. Cross-sectional studies are subject to problems of respondent bias, recall bias, and undocumented confounders. Also, unless historical information is obtained from all the individuals surveyed, the time relationship between the factor and the disease is not known. Furthermore, prevalence surveys identify people who have survived to that time point with disease and, thus, under-represent people with a short course of disease.
The cross-sectional study design is used in two special types of studies: field studies and surveillance. Field studies are usually investigations of acute outbreaks which require immediate identification of the causative factors if effective public health interventions are to be implemented in a timely fashion. Surveillance is the monitoring of disease- or health-related factors over time and uses serial cross-sectional surveys to observe trends. Surveillance is important to identify diseases that are becoming an increasing public health problem, to assure that diseases already brought under control remain under control, and to evaluate the impact of public health intervention strategies.
Case–control studies (Chapter 6.5)
The case–control study compares the prevalence of suspected causal factors between cases and controls. If the prevalence of the factor is significantly different in cases than it is in controls, it suggests that this factor is associated with the disease. Although case–control studies can identify associations, they do not measure risk. An estimate of relative risk, however, can be derived by calculating the odds ratio. Case–control studies are often the analytical study design used initially to investigate a suspected association. Compared to cohort and experimental studies, they are usually relatively cheap and easy to do. Cases can often be selected from hospital patients and controls either from hospital-admitted patients with other diseases or by using algorithms or formulas for selecting community (neighbourhood) or other types of controls, although selection bias is often a problem, especially when using either hospital-admitted cases or controls. The participants are seen only once, and no follow-up is necessary. Although time sequences can often be established for factors elicited by interview, they usually cannot be for laboratory test results. Thus, an elevation in factor ‘B’ may either be causally related, or it may be a result of the disease process and not a cause. Furthermore, factors elicited from interview are subject to recall bias; for example, patients are often better motivated for recall of events than controls because they are concerned about their disease. The case–control study is particularly useful for exploring relationships noted in observational studies. A hypothesis, however, is necessary for case–control studies. Relationships will be observed only for those factors studied. Case–control studies are not useful for determining the spectrum of health outcomes resulting from specific exposures, since a definition of a case is required in order to do a case–control study. Conversely, case–control studies are the method of choice for studying rare diseases. Case–control studies are often indicated when a specific health question needs to be answered quickly.
Cohort studies (Chapter 6.6)
Cohort studies follow defined groups of people without disease to identify risk factors associated with disease occurrence. Cohort studies have the advantage of establishing the temporal relationship between an exposure and a health outcome, and, thus, they measure risk directly. Because the population studied is defined on the basis of its exposure to the suspected factor, cohort studies are particularly suitable for investigating health hazards associated with environmental or occupational exposures. Furthermore, cohort studies will measure more than one outcome of a given exposure and, therefore, are useful for defining the spectrum of disease resulting from exposure to a given factor. Occasionally a cohort study is done to elucidate the natural history of a disease when a group can be identified that has a high incidence of disease but in which specific risk factors are not known. Although this cohort is not defined on the basis of a known exposure, questions are asked and biological specimens are collected from which exposure variables can be identified concurrently or in the future. Unfortunately, cohort studies are both expensive and time-consuming. Unless the investigator can define a cohort in which risk factors were measured at some time in the past and has assurance that the cohort has been completely followed up for disease outcome in the interim, the cohort design can take years to decades to yield information about the risks of disease resulting from exposure to specific factors. Assuring that participants remain in a cohort study for such long periods of time is both difficult and expensive. Furthermore, the impact of those who drop out of follow-up must be taken into account in the analysis and interpretation of cohort studies. Finally, exposures may vary over time, complicating the analysis of their impact. Because of the cost and complexity of cohort studies, they are usually done only after descriptive, cross-sectional, and/or case–control studies have suggested a causal relationship. The size of the cohort to be studied is partly dependent on the anticipated incidence of the disease resulting from the exposure. For diseases with a very low incidence, cohort studies usually are not feasible, either in terms of the logistics or of the expense of following very large numbers of people, or both. Cohort studies establish the risk of disease associated with exposure to a factor, but do not ‘prove’ that the factor is causal. The observed factor merely may be very closely correlated with the real causative factor or may even be related to the participants’ choice to be exposed.
A variant of the cohort study which has become popular is the ‘nested case–control’ study (Gange et al. 1997). Cases which arise from a cohort study are compared to individuals followed in the cohort who have not developed disease using the usual case–control analytical strategies. The advantage of this type of study is that the exposure variables are collected before knowledge of the outcome and therefore are unlikely to be tainted by recall bias.
Experimental studies (Chapter 6.7, Chapter 6.8 and Chapter 6.9)
Experimental studies differ from cohort studies because it is the investigator who makes the decision about who will be exposed to the factor based on the specific design factors to be employed (for example, randomization, matching, and so on). Therefore, confounding factors that may have led to the subjects being exposed in the cohort studies are not a problem in experimental studies. Because epidemiologists usually study human populations, there are few opportunities for an investigator to deliberately expose participants to a suspected factor. Conversely, intervention studies of individuals randomly assigned to receive or not receive an intervention programme that demonstrates a subsequent reduction in a specific health outcome in the intervention group do provide strong evidence, if not proof, of a causal relationship. Because of the serious implications of applying an intervention that may alter the biological status of an individual, intervention studies are not undertaken until the probability of a causal relationship has been well established using the other types of study designs.
Meta-analysis (Chapter 6.12)
A recent trend has been to combine similar studies to increase the power of the analysis because individual epidemiological studies rarely provide proof of causation and results of different studies can vary for a number of reasons, including small sample size. This strategy for data synthesis is known as ‘meta-analysis’. It has been especially helpful in studying diseases with a low incidence or where different studies have given conflicting results.
Methodological issues (Chapter 6.10 and Chapter 6.11)
Epidemiological studies, because they deal with humans, are subject to problems such as bias (deviation of results from truth) due to the strategies of recruiting participants, or to differential recall among persons with and without disease and confounding due to factors which are associated with both the exposure variables and outcome variable under study. In the last several decades many new techniques have been developed to reduce the effect of these factors which can influence the outcome of a study and, in some instances, can cause apparent relationships to be observed which are false.
Epidemiology is the basic science of public health because it is the science that describes the relationship of health or disease with other factors in human populations. Furthermore, epidemiology is used to generate much of the information required by public health professionals to develop, implement, and evaluate effective intervention programmes for the prevention of disease and the promotion of health, such as the eradication of smallpox, the anticipated eradication of polio and guinea worm disease, and the recent decline in heart disease in most of the developed countries. Finally, it is the best strategy to evaluate the effectiveness of public health programmes.
Unlike pathology, which constitutes a basic area of knowledge, and cardiology, which is the study of a specific organ, epidemiology is a medical philosophy or methodology that can be applied to learning about and resolving a very broad range of health problems. The art of epidemiology is knowing when and how to apply epidemiological methods creatively to answer specific health questions; it is not enough to know what the various study designs and statistical methodologies are. The uses and limitations of the various epidemiological study designs have been presented to illustrate and underscore the fact that the successful application of epidemiology requires more than a knowledge of study designs and epidemiological methods. These designs and methods must be applied both appropriately and innovatively if they are to yield the desired information. The field of epidemiology has been expanding dramatically over the last two decades, as more epidemiologists have demonstrated new uses and variations of traditional study designs and methods. The uses of epidemiology will expand even more in the future as increasing numbers of creative epidemiologists develop new strategies and techniques of epidemiology.
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6 comments on “6.1 Epidemiology: the foundation of public health

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      Dynamic variations in the peripheral blood lymphocyte subgroups of patients with 2009 pandemic H1N1 swine-origin influenza A virus infection Novel Influenza A (H1N1) is a……

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