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10.5B: Analytical Epidemiology

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Epidemiology draws statistical inferences, mostly about causes of disease in populations based on available samples of it.

Learning Objectives

  • Describe the role of an analytical epidemiologist
  • Epidemiologists employ a range of study designs from the observational to experimental and they are generally categorized as descriptive, analytic, and experimental.
  • Analytic epidemiology aims to further examine known associations or hypothesized relationships.
  • Analytical observations deal more with the ‘how’ of a health -related event.
  • analytical : pertaining to or emanating from analysis.
  • epidemiology : Epidemiology is the study (or the science of the study) of the patterns, causes, and effects of health and disease conditions in defined populations.

Epidemiology is the study (or the science of the study) of the patterns, causes, and effects of health and disease conditions in defined populations. It is the cornerstone of public health, and informs policy decisions and evidence-based medicine by identifying risk factors for disease and targets for preventive medicine. Epidemiologists help with study design, collection and statistical analysis of data, and interpretation and dissemination of results (including peer review and occasional systematic review). Epidemiology has helped develop methodology used in clinical research, public health studies and, to some extent, basic research in the biological sciences.

Epidemiologists employ a range of study designs from observational to experimental and generally categorized as descriptive, analytic (aiming to further examine known associations or hypothesized relationships), and experimental (a term often equated with clinical or community trials of treatments and other interventions).

Where descriptive epidemiology describes occurrence of disease (or of its determinants) within a population, the analytical epidemiology aims to gain knowledge on the quality and the amount of influence that determinants have on the occurrence of disease. The usual way to gain this knowledge is by group comparisons. Such a comparison starts from one or more hypotheses about how the determinant may influence occurrence of disease. Analytical epidemiology attempts to determine the cause of an outbreak. Using the case control method, the epidemiologist can look for factors that might have preceded the disease. Often, this entails comparing a group of people who have the disease with a group that is similar in age, sex, socioeconomic status, and other variables, but does not have the disease. In this way, other possible factors, e.g., genetic or environmental, might be identified as factors related to the outbreak.

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Statistical Approaches for Epidemiology pp 1–18 Cite as

Descriptive and Analytical Epidemiology

  • Kiran Sapkota 2  
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Both descriptive and analytical epidemiology are important for advancing clinical medicine and public health. Descriptive epidemiology assesses the burden and magnitude of health problems in a population, whereas analytical epidemiology identifies the causes and risk factors of health problems. This chapter provides the scopes, designs, data analytics approaches, ethical issues, and examples of various epidemiological studies. Descriptive epidemiological studies include: (1) case reports, (2) case series, (3) descriptive cross-sectional (prevalence) studies, and (4) descriptive cohort (incidence) studies. Analytical epidemiological studies include: (a) observational studies, such as (1) ecological studies (correlational studies), (2) analytical cross-sectional studies, (3) analytical cohort studies (prospective and retrospective), and (4) case–control studies, and (b) experimental studies, such as (1) community-based interventions and (2) clinical trials.

  • Descriptive studies
  • Analytical studies
  • Hierarchy of studies
  • Person–place–time model
  • Cross-sectional studies
  • Ecological studies
  • Case–control studies
  • cohort studies
  • clinical trials

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Sapkota, K. (2024). Descriptive and Analytical Epidemiology. In: Mitra, A.K. (eds) Statistical Approaches for Epidemiology. Springer, Cham. https://doi.org/10.1007/978-3-031-41784-9_1

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examples of analytical studies in epidemiology

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Part 2 - Measures of Association Between Exposure and Disease

When searching for the determinants of a health outcome, one generally relies first on descriptive epidemiology to generate hypotheses about associations between health-related exposures and outcomes. Once hypotheses are generated, analytical epidemiology is employed to  test hypotheses by drawing samples of people and comparing groups to determine whether health outcomes differ based on exposure status. If individuals with a given exposure are found to have a greater probability of developing a particular outcome, it suggests an association, and, conversely, if the groups have the same probability of developing the outcome regardless of their exposure status, it suggests that particular exposure is not associated with a greater risk of disease. In either event one must then consider whether the findings were misleading because of sampling error, bias, or confounding (the issue of validity is one that we will address later), In other words, we must consider alternative explanations that might invalidate our conclusions. The remainder of this module we will focus on methods for comparing groups and computing an estimate of the magnitude of association between an exposure and a health outcome and how to interpret the findings.

Basic Strategies for Analytical Epidemiology Studies

Study designs will be discussed more completely in a later module, but several basic design strategies are introduced here in order facilitate an understanding of how one measures the magnitude of an association.

Cross-sectional surveys were discussed in module 1B on descriptive studies. These studies assess exposure status and health outcome status at a single point in time, providing prevalence measures.

Cohort studies enroll a sample of subjects who do not yet have the health outcomes of interest and assess their exposure status, e.g., whether they smoke or not, and then follow the subjects forward in time and record whether they develop a particular health outcome, such as coronary heart disease. When sufficient time has passed, the subjects are grouped according to their exposure status, and the incidence of disease is compared among two or more levels of exposure. For example, a greater incidence of heart disease in smokers than in non-smokers suggests that smoking is associated with a greater risk of coronary heart disease. If the cohort study is conducted in a way that provides individual follow-up, it is possible to record when outcomes occur and if and when subjects become lost to follow-up. This then provides the option of computing either cumulative incidence or incidence rates. The basic design of a cohort study is illustrated in the figure below.

Intervention studies (randomized clinical trials) are similar in design to cohort studies in that they enroll subjects who do not yet have the outcome of interest, and subjects are followed over time in order to compare incidence of the outcome among two or more exposure groups. However, in contrast to cohort studies, exposures are allocated to subjects by the investigators, usually in a randomized fashion. For example, if investigators wanted to test the efficacy of low-dose aspirin (test agent A) in preventing heart attacks compared to an inactive placebo (test agent B), eligible subjects would be randomly allocated to take either agent A or agent B, as illustrated in the image below.

examples of analytical studies in epidemiology

Case-control studies use a different analytic strategy. They enroll a group of "cases" who already have the outcome of interest, and then they enroll a comparison group of "controls" who do not. Investigators then assess past exposures in the two groups and compare the odds of past exposure. Greater odds of exposure in the cases suggests an association. Note that in the illustration below the odds of exposure (indicated by the letter "E") are greater among the case subjects than in the non-diseased control subjects.

examples of analytical studies in epidemiology

We will address these design strategies in greater detail in subsequent modules, but for now we will focus on formulating hypotheses and then testing hypotheses by computing measures of association in cross-sectional surveys, cohort studies, and intervention studies.

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5 Study Designs Commonly used in Epidemiology

Study designs commonly used in epidemiology.

Learning Objectives

By the end of this chapter, the learner will be able to

  • Describe the most common research study designs used in epidemiology
  • Differentiate between Non-experimental Observational studies, and Experimental/Interventional epidemiological studies
  • Differentiate among individual and population based studies, and also between observational, descriptive and analytic studies.
  • Understand the use of randomization in experimental studies such as clinical trials, and other types of experimental field trials.

Introduction to the chapter

This chapter will present the most commonly used epidemiological study designs,listing main characteristics and then, focusing on their benefits, strengths, weaknesses, and uses in public health.

Most epidemiologists are trained to do their investigation based on a series of designs called, Study Designs. The study Designs commonly used in epidemiology are based on several premises, but a series of questions can help the investigator to decide what design best fits its needs, some of these questions are, what types of study designs are there? How and when we use specific type of study designs? Which study design is the most appropriate to use in certain investigations? The list of questions could continue but it is important to generate these questions in order to arrive to a decision of what fits better the investigators needs. Also, investigators need to be familiar with these study designs so they can use them when needed.

Main Question

What are study designs in epidemiology? Study designs refer to the different approaches mainly used to conduct research for investigative purposes. They are called, ‘designs’ because they represent a specific manner of conducting the research process, which is mainly based on the scientific method. Study designs are more of a framework to guide the researcher in the process. [1] And, although basically all research process starts with a research question, there is need to follow a process that will convert this research question into a hypothesis, and then, to a real life situation, or, scenario that need to be framed in order to arrive to valid conclusions. [2] In other words, study designs assists the researcher providing a type of road map that will help to not get lost. It is easy to get lost, especially when complex health phenomena is studied/researched, but the study design is expected to assist in providing direction. [3] In sum, study designs are road maps, or, frameworks that assist in the research/investigative process.

It is also probably useful to mention that some of the study designs mentioned here, are not only used in the sciences of epidemiology, they are used by other areas of study, especially those areas that belong to the social sciences, including public health, but also mathematics, statistics, and of course, medical sciences. So, this study designs are not unique to the field of epidemiology, but they are highly used in public health, and medical research.

Descriptive versus Analytic In a broadly manner, epidemiologic study designs can be divided into two broad categories: 1) Descriptive and 2) Analytic.

Descriptive studies as the word implies, ‘describe’ situations, problems, and other health phenomena (diseases, disorders, health behavior, healthy lifestyles, etc.). These type of study designs are mainly used to generate hypotheses, especially in those cases in which the health issue in study is unknown, or, there is not much information on it, or, simply, because the topic is a ‘new’ problem found in sciences, in this case, social sciences, including epidemiology. On the other hand, Analytic studies, ‘analyze’ the health phenomena, situation, or, problem.

More elements used to distinguish between descriptive and analytic research studies Since epidemiology is by nature quantitative, the division between descriptive and analytic studies can be also clearly recognized by the type of quantitative methods, in this case, the methodology, data collection, and statistical analyses that are used in the research process. For example, in descriptive study designs, the most common data analysis is the use of ‘descriptive statistics’ such as numbers, percentages, sums (total number of cases), mean, mode, standard deviation, etc. Since, the descriptive study is looking mainly for a ‘description’ of the problem, the use of descriptive analyses suffice for the type of research that mainly intents to generate hypotheses, or, to add more information for future studies.

Overview of Study Designs – the following is a list of study designs:

Descriptive Studies In this category, the following are the most commonly used/listed: a) Case Study or, Reports b) Case Series c) Cross-sectional studies d) Ecologic studies

Analytics Studies , since they are more complex, they are subdivided into two additional categories: Observational and Experimental.

Observational studies are mainly represented by the following: a) Cross-sectional studies – as you probably had noted, this category is shared also with the ‘descriptive’ category, which means that a cross-sectional study can be descriptive, observational, or, analytic. b) Case- control studies c) Cohort studies

Experimental studies In this category, it is customary to include the following: a) Clinical Trials b) Community Trials c) Other forms of experimental research.

examples of analytical studies in epidemiology

Now, the question is, how do we know what study design to use? As it is seen (listed) above, there is a repertoire of study designs available to the investigator. The use of any of these designs depends on the mainly purpose/reason for the study, and also, the resources, which are mainly financial, and expertise of the researcher team.

How do we choose certain study design from the list of options? Many times, the answer to this question depends on the purposes/reasons for the research, or, investigation of a certain health phenomena. For example, if we want to conduct a study about a topic/area that is unknown, or, not well-known, and the research question is still a work in process, or, it is clear, but there is need to elaborate a hypothesis/or, hypotheses; then, the most appropriate study design in this case is the descriptive, why? It is because descriptive studies are commonly used for hypotheses generating. Does it mean that there is no other way to conduct the study? Not necessarily, because they may be another way to do the same, but over and over, especially in social sciences, public health, and specifically in epidemiology, descriptive study designs have been used for those purposes, in this case, hypotheses generating.

Another example that refers to the purposes/reasons for the research but that also refers to the financial aspects of medical, and public health research is the cohort study. In this case, if the main reason/purpose of the study is to find a causal relationship, the cohort study is the answer. This type of study design will help overtime to elucidate the associated risk factors, and social  health determinants that are related to the health problem that is researched. What is the only reason that stop the use of the cohort study? The main obstacle is financial, cohort studies can be highly costly, since, study subjects are basically follow over a long period of time until the health outcome is developed, as it is the case of for example the cardiovascular disease cohort studies conducted in the United States. More information about cohort studies will be provided/expanded later in the context of this chapter, and the overall book content itself. [5]

Descriptive Study Designs

The Case Studies/Case Series This study design is commonly used when there is no much information about the case, as it is the example of a recently reported disease, or, a disease that is very rare, so, the investigator wants to share the information with the scientific community, but since there is only one, or, three cases, it is much better to choose this study design, which sometimes become a case series, if a continuation of cases are reported in a limited fashion. The main limitation is that the cases are not necessarily representative of the general population, but the benefits are that the reported case brings an opportunity for future studies on the subject. [6] , [7]

In most epidemiological textbooks, the case studies are commonly used by the medical community to report a case, or, a series of cases that usually represent patients suffering from certain diseases. The medical model usually reports cases that are diseased. But the model hardly applied to other health phenomena, and since it is usually oriented to report cases in a limited fashion, there is small use of this design in epidemiology, which focuses on populations at large.

Ecologic Studies

For this study design, the unit of analysis is the group, not the individual. In this case, correlations are obtained between exposures rates and disease rates among different groups, or, populations. Because of the word, ‘ecologic’, ecological studies tend to be confused with ‘environmental’ studies, and this is possible especially if an environmental issue is studied using this design, but in general, ecologic studies refer to the group(s) investigated.

examples of analytical studies in epidemiology

The ecological study provides a setting in which observations made at the group level may not represent the exposure-disease relationship at the individual level, this is called, the ecologic fallacy , which occurs when incorrect inferences about the individual are made from the group level data. [8] .

Essentially, this means that inferences from ‘the results of ecological studies can only be applied to the group but not to the individual’. You may said, why? And the answer is because the intention of the ecologic studies is to capture how the health events are affecting the group, the community and not the individual. An example of this, is this study done in alcohol consumption and coronary heart disease (CHD), see the image below that presents the respective information:

In the study of the image presented above, the data across countries (the ‘population’ or, ‘group’ mentioned in the definition of ecologic studies) showed that moderate use of alcohol was not beneficial to the heart, on the contrary, it increases the risk for CHD, why? It is the typical case in which what is true at the group or, population level, it is not true to the individual level. Studies done on individuals or, specific groups had shown that moderate alcohol consumption is beneficial to prevent CHD. [9]

As it has been presented in the example above, the ecologic fallacy which is part of the nature of an ecologic study, it is also considered a disadvantage of this type of study design. Another disadvantage is that ecologic studies could make imprecise measurement of exposure and disease. [10] .

On the other hand, the following are advantages of ecologic studies, they are quick, simple and less costly than other studies, and their completion is faster compared to other designs used in epidemiology and related sciences. One more advantage is that they can be used (and very useful in this sense) for generating hypotheses, especially when a disease is of unknown etiology.

Common uses of ecologic studies Specific applications of the ecologic study design has been classified by some authors as the following: geographical comparisons, time trends, migrants, occupation and social class. More details are included below:

Geographical comparisons, which for example can be used to find prevalence of risk factors by comparing incidence or, mortality in two, or, more geographic areas. Time trends, which essentially means to study the fluctuations on the incidence of chronic diseases which tend to change over time. Migrants , the study of migrant groups can be used to identify those factors that are predominantly genetic from those who are environmental, in this case, first or, second generation of an ethnic group maybe affected differently depending on their degree acculturation . Occupation and social class, in this case, it refers on how some morbidity and mortality area associated with certain occupations, and also with the socioeconomic status of the groups working on those type of jobs. [11]

Cross-Sectional Studies

This study is a commonly used design. As a way to understand the most basic principle of a cross sectional study, is to think on the total study population as a pie, in which each percentage represents a section of the pie, then, for study purposes only a piece (or, section) of the pie is investigated. Since in the majority of cases, the characteristics of a population are very similar, choosing to study one portion (section) of the pie (the population) will be representative of the total population. Assuming that we are talking about a study population, not the general population. This analogy takes us to the term, cross (cutting) section (a piece of the pie) study.

examples of analytical studies in epidemiology

Since cross-sectional studies collect data at a point in time, they are commonly used to calculate prevalence for public health reports, and also for the designing and location of health services in a community. This study design is used frequently, especially when there is not much money to afford another type of study. So, cross sectional studies are very popular not only because they are less costly, but also they are fast to complete. Another great advantage of cross sectional studies is that they are used to generate hypotheses, or, specific research questions about exposure and disease, however, this type of study does not address the issue of temporality , due to being one shot only (one point in time), it does not provide information to know what was first between cause and effect. [12]

Example An example of this type of study design is, an Australian cross-sectional study on the effects of screen and non-screen sedentary time in adolescents, and how these types of behaviors affect their weight and overall well-being. The study found that although screen sedentary time (SST) is a contributing factor in the amount of fatness observed in school-age children and adolescents, there are two other factors that need to be studied if a significant change is expected to be observed, and these are active lesson breaks in the classroom, or active transport to school. These two last factors are part of what the study called, the NSST or, Non-screen sedentary time. [13] As part of the study design (cross-sectional), some of the study variables are presented in the figure below (taken directly from the published article), see below:

Finally, one of the major benefits of cross sectional studies is they are quick (compared to other type of designs such as the cohort study) to complete, making them very efficient in terms of time and cost.

examples of analytical studies in epidemiology

An example of a case control study is the work on a group of investigators who compared the Impact of windows and daylight exposure on overall health and sleep quality of office workers. The results showed that workers in windowless environments tend to experience limitations in their role in terms of physical problems and vitality and some sleep disturbances. When the two groups were compared, workers with windows had more light exposure, more physical activity, and longer sleep duration. [15] A selected image included in the article shows graphically how two (more variables were studied) of the characteristics between both groups show clearly the difference that makes to have windows or, not in the workplace.

Common uses of the case-control study design Because of all of the mentioned characteristics, the case control-study design is used to find the prevalence in the community of certain diseases, and from their results, a subsequent study is designed. Also, the case-control study design has been used especially for the study of rare diseases.

The selection of the cases and the controls in the case-control study design This is an important step in the use of the case-control study design. The cases need to be selected based on a set of criteria, which defines the characteristics and manifestation of the disease, including laboratory and other medical tests such as imaging, including x-rays. Then, these criteria is used to identified the cases. And, what about the controls, how are those individuals found? The controls  can be for example patients from the same clinic/hospitals as the cases, or, the same population, for example, college campus, or, factory workers. The major characteristic of the controls is the similarity in terms of the selection criteria used for the cases, so, a comparison can be established. When there is time to assess the risk, both groups cases and controls are included in the statistical calculations based on the exposed, and not exposed criteria and the development of the disease under study. It is important to note that the word, ‘exposed’ here is a matter of semantics, because the exposed do not necessarily existed, they are cases (they have the disease already). [16]

Cohort Study This type of study design is considered the prototype (the model) of an almost ‘perfect’ design to investigate causality. In the cohort study, the main measure of disease frequency is, incidence. The following diagram presents how the study population is selected, and the major steps in the implementation of this type of study design:

examples of analytical studies in epidemiology

In the field of epidemiology is also accepted that cohort study address the issues of temporality (most studies do not) making possible to avoid logical errors. Cohort studies are usually conducted for longer periods of time compared to other designs; and the reason for it is that cohorts start with individuals who are free of the disease under study, and are followed up over the years to observe the development of this disease (or, group of diseases such as cardiovascular diseases). Due to the fact that most of the time, the data is collected in the future (from one specific point in time – the now, and the upcoming time of observation), the word retrospective is commonly used to reflect this concept, which makes most cohorts, examples of perspective studies. [17]

In some cases, a cohort study can be designed by using data that has already been collected (which resembles the case-control design), and since this is data collected in the past, then, the cohort is called, a retrospective cohort. And, when, retrospective data, present time data, and prospective data collection (which is essentially the classic cohort study) are included, the name of the cohort is, ambispective . But in reality most people when they hear the word, cohort, they are referring to prospective data collection studies (or, prospective cohorts). [18]

Cohort studies can be used to study more than one disease, or, multiple exposures, so, investigators can take some data (already collected in the cohort), and design a case-control study known as the ‘nested case-control,’ it is nested because it comes from inside the cohort. [19] , [20]

Because of all of the mentioned benefits and advantages of cohorts, especially the calculation of incidence about a disease, makes cohorts an ideal study design, but at the same time it limitation is that cohorts are highly expensive, and that is mainly due to the fact that they last for a long time, especially for those diseases that take a long time to develop, so, the investigators have to wait for a while before they see the first generation of cases. [21]

Examples of Cohort Studies To provide a mental picture of the cohort study design, I am including here, what I called, ‘Famous Cohorts in the United States.’ Famous because they reflect the reality of the country in terms of race segregation, and other socio-demographic factors that have shaped the country. The Framingham Heart Study

The Framingham Heart Study takes its name from the town in which the study was conducted, Framingham. Framingham is located in Massachusetts, United States, and it within Middlesex County and the MetroWest subregion of the Greater Boston metropolitan area. [22] A picture of this city is shown below:

The Framingham Heart Study is a classic example of a cohort study that assessed multiple exposures and multiple outcomes. This study, a collaboration between the US National Heart, Lung, and Blood Institute (a division of the National Institutes of Health) and Boston University, began in 1948 by enrolling just over 5,000 adults living in Framingham, Massachusetts. Investigators measured numerous exposures and outcomes, then repeated the measurements every few years. As the cohort aged, their spouses, children, children’s spouses, and grandchildren have been enrolled. [23]

The Framingham study is responsible for much of our knowledge about heart disease, stroke, and related disorders, as well as of the intergenerational effects of some lifestyle habits. [24] More information and a list of additional publications (more than 3,500 studies have been published using Framingham data) can be found extensively and especially in the project’s website.  [25]

The Bogalusa Heart Study Although, the Framingham Heart Study is a model cohort that influenced the work in public health and medicine. There was one flaw with the study, the participants were all white or, Caucasian; which from the beginning introduced a confounding factor that is race, which is a health determinant that is also linked to income, socio-economic status, social class, and among others, access to health services. So, to study cardiovascular disease beyond the white population generated the need to conduct a study on another major ethnic population group in the U.S. population, which is the black or African American community. Although not many studies on black, the Bogalusa Heart Study in Louisiana was born in 1972. Bogalusa is a small town in Louisiana (almost in the limit with Mississippi), it is mainly a biracial (black/white) rural community in which entire families have lived there for generations. The population in Bogalusa is mostly constant with few, or, no migration is ideal study population brought the attention of a famous Tulane University School of Public Health in New Orleans, Dr. Berenson, who lived through the entire duration of the study. [26] , [27] . See a picture of  the town, and also of Dr. Berenson:

He survived the study which was taken down after hurricane Katrina due to major damage by the lack of electricity in New Orleans during Katrina in which many of the study samples that were stored were damaged, and also, the lack of funding after the impacts of Hurricane Katrina devastation in New Orleans in 2005.

The Bogalusa Heart Study started as an epidemiological study of cardiovascular risk factors in children and adolescents; it eventually evolved into observations of young adults. This study main milestones confirmed the findings of the Framingham Heart Study, but also superseed in terms of adding new variables to the study of cardiovascular disease, especially with the findings of the presence of cardiovascular disease in children, which had not been studied before. The study reported an African American child who died of cardiovascular disease and had atherosclerotic deposits in his arteries at the age of eight years old. This finding moved the American Heart Association to recommend that children stop been fed with whole cow’s milk after the child is 1; recommending 2% cow’s milk for children over 1 years old in the U.S. population. Another major milestone of the Bogalusa heart study is that identified several risk factors such as obesity, essential hypertension linked to kidney disease, and also how early onset of diabetes can also increase highly the development of cardiovascular disease at earlier ages, a finding that was also new to the medical and public health community. [28] [29]

The San Antonio Heart Study

The San Antonio Heart Study conducted in Texas takes its name from this city. San Antonio, Texas, is a city of the Southern United States, and it is considered one of the seven most populous city in the U.S. [30]

The San Antonio Heart Study (SAHS), is another study that is no commonly mentioned in most epidemiology textbooks, and that brings another important perspective in the study of cardiovascular disease in the U.S. is the San Antonio Heart Study, which focused its efforts in identifying cardiovascular risk factors in the Latino population in the U.S. Again, as in the case of the Bogalusa Heart Study; the need to study in detail what happened to another major ethnic group in the U.S. was critical, what was found among Caucasians or, Whites in the U.S. cannot necessarily be applied (extrapolated) to the African American community, nor, to the Latino community, so, the study was justified. And its findings among others discovered that the Latino heart is hard to die. Among all of the major ethnic groups in the U.S., Latinos have the lowest rates of heart attacks after accounting for several confounding factors. [31] , [32] , [33]

Note: the above content about cohorts had presented the problem of heart disease among the white, black and Latino population in the U.S. but there is literature available about other ethnic groups in the U.S. such as Native Americans, Asians, and Pacific Islanders, however, those studies are not cohort studies, only reports about the health status of these mentioned groups, for example, there is one report about Native Americans in the U.S. [34]

Cohort Studies major disadvantages and historical ‘mistakes’ Besides the disadvantage already mentioned before in the content, that cohorts are very expensive (they usually costs millions over the years of duration), it is not possible to have a cohort for every disease that exist, or, that is highly prevalence in the population. There is another limitation of cohorts, they cannot be used for the study of rare diseases, because one of the characteristics of a rare disease is that it is not suffered by a great number of people in the population, making the sample usually small, or, it may take long times to get enough data that can be used meaningfully in the practice.

Another major disadvantage of cohorts, which is the reason I included the examples of the ‘famous cohorts in the U.S.,’ is that in this country, cohorts were linked to the problem of racial preference, the first ethnic group represented in a popular cohort such as the Framingham heart study is the white population, which excluded the other ethnic groups in the U.S. who also are affected by heart disease. This fact is an example of the historical exclusion of people of color and indigenous populations in the U.S., so, the history of major cohorts in the U.S. is also reflecting the need to study those oppressed, and ignored throughout history. Another observation in this context is, that for example, the Bogalusa Heart Study, and the San Antonio Heart Study are not well known in the scientific community, teaching medical schools, and other similar educational institutions do not know – or, pretend to not knowing about the existing of these studies, which provides extremely important data for the prevention of coronary heart disease in the nation.

Clinical Trials

Clinical Trials They are considered the highest level of the research designs discussed above in this chapter, and they are very much the standard design used especially for pharmaceutical companies to assess the effectiveness and safety of drugs, certain medical procedures, sophisticated medical equipment, etc. There at least two types of clinical trials as it was mentioned in the introduction of this chapter in the summary of types of study design; 1) Preventive or, Community Trials, and 2) Therapeutic clinical trials . The discussion in this chapter will be mainly focused in the second group or, therapeutic clinical trials, also, just called, clinical trials.

Community (or, preventive) trials These type of studies are used to determine the potential benefit of new policies and programs. They are called, community because it refers to the population, or, specific groups in the population. In general, the community trials will evaluate the impact of specific interventions that intent to produce changes in a target population. For example, the knowledge, attitudes and practices related to the Medicare program; or, the use (by the target population) of health care services to prevent and treat heart disease, etc.

The first step in the process of a community trial is to determine eligible communities, or, groups, and their willingness to participate. Then, baseline data is collected, this type of information can be for example, target population demographics, cultural traits, data from the national census, disease rates, etc., of the problem to be addressed in the intervention, and the collected information is also used for the control communities. In addition, the trial participants are selected by randomization (which is described in more details later in this section) and the selected individuals (or, groups) are followed over time. Data is entered, analyzed, and reports generated. Finally, the outcomes of interest are measured and used to assess the effectiveness, or, to identified weak points in the program intervention, and how to improve the quality of the programs and services offered to the target population, or, group. An example of a community trial and its protocol is summarized in the image below:

Advantages and disadvantages of community trials The major advantages of community trials is that are unique in providing information that be can used to estimate the impact of change in the behavior or modifiable exposure of the incidence of disease in a community or, group; and also, the effectiveness of services and programs offered to the target group. As any other study design, the community trials have some also some disadvantages, for example, in general they are considered inferior to clinical (therapeutic) trials – discussed in detail in the rest of this chapter); and that is because selection of participants into the study, delivery of the intervention, and monitoring of the study outcomes are not as strict (or, rigorous) as it is in a clinical trial. Other disadvantages is that the study results are affected by some population dynamics, especially secular trends because of the mobility, or, changes in the target population. Also, it is hard to avoid the influence of non intervention forces surrounding the study population or, group.

Clinical trials Since the content above has been mostly about the clinical community (or, preventive) trials. The information that follows will focus mainly in the conduction of therapeutic clinical trials, commonly called just, ‘clinical trials.’ What are clinical trials ? A common definition is that, clinical trials are planned experiments that assesses the efficacy of a treatment (or, medical procedure) in people. It is medical research involving people. In a clinical trial, the study outcomes in a treated group are compared with outcomes in an equivalent control group. Participants in both groups are enrolled, treated, and followed over the same time period. [35]

Methods commonly used in clinical trials There is a series of methods or, strategies used for clinical trials, and these are at the same time considered the major strengths of clinical trials. The most important are discussed in the following paragraphs. Study Protocol The clinical trials protocol is usually an extensive and detailed manual that outline the major steps of the study, especially outlining what could happen in certain situations during the completion of the study. For example, what to do is the investigators deviate from the originally planned study assignments of the participants? How many deviations in the protocol would  be allowed? It is customary that for example, no  more than three deviations would be allowed during the duration of the trial. Also, the protocol include the data collection instruments, data input procedures, and analyses once the data is collected. The presence of a protocol is a valuable tool to assure that the clinical trial is conducted under the required academic and scientific rigor.

One of the major elements of the mentioned document is to plan ahead for any deviation of the study protocol. To prevent for this to happen, planned crossovers are part of the protocol. In this case, the study participant may server as his/her own control. And, when unplanned situations occurs for example a change of treatment is requested by a study participant; this change is called, an unplanned crossover , which could exist  in for example situations in which the study participant request a change of treatment. It is recommended that no many unplanned crossovers occur during the duration of the trial, because it can compromise the study results, and the quality of the study in general.

Selection of the study participants One of the methods (and strengths) of clinical trials is the careful selection of study participants. For this purpose, research in clinical trials used what is known as randomization , , a statistical method to sort the possible study participants before they can participate in the study. Essentially, the randomization procedure allows to use random selection twice, for example, a person is assigned a number that will be randomly picked for the study participation, and then, if selected, the person (now, a study participant) will be randomly assigned to the drug, procedure, placebo or, no treatment branch of the trial.

Randomization is the preferred method for assigning subjects to the treatment or control conditions of a clinical trial. If not random assignment is used then, mixing of effects of the intervention can occur, which at the same time, create differences among the study participants in the trial.

Blinding An additional method or strategy commonly used in clinical trials is also one  of the major strengths of clinical trials is that they control bias, especially selection bias. And, to control for this, the procedure known as blinding is used.  At least three types of blinding are known, 1) single blinding, it is when the study participants (clients, patients) don’t know about the type of drug, or, medical procedure they have been assigned, or, they don’t know if the treatment they are receiving is placebo. 2) double blinding, it is when the client/patient doesn’t know what type of treatment or, placebo they are receiving (the clients/patient), or, the health care providers (doctors, nurses, technicians, etc.) are administering. 3) triple blind, the client/patient, the health care providers, nor the data collection and analysis have knowledge of the treatment given to the study participants. Additional blinding strategies can be used, but the mentioned here are the most commonly used. Phases of clinical trials

Strengths and limitations of clinical trials The major strengths of clinical trials is for the investigator to have the greatest control over the amount of exposure, the timing and the frequency of that exposure, and the observation period. Also, the use of randomization greatly reduces the likelihood that groups will differ significantly, which is of enormous benefit to the enhancement of the results. The limitations of clinical trials include ethical dilemmas such as how the benefits would outweigh the risk, how to protect the interests of the study participants and not only the investigators, when to stop a trial if a major adverse health outcome occurs during the completion of the study, and overall, how much of the information in the trial is shared with the study participant in the informed consent form.

Summary This chapter has covered the most common epidemiology study designs and its uses. These designs include: the case study/case reports, ecologic study, cross-sectional, case controls, cohort studies, and clinical trials. When they are classified by type of study, they can be descriptive or, analytic studies. The most common descriptive studies are, the case report/case series, ecologic and cross sectional studies. Analytic studies, they can be classified into intervention and observational.  Examples of analytic ‘intervention’ studies are, community (preventive) trials and clinical (therapeutic) trials. And, observational studies, examples, the case control, and the cohort studies. The use of these mentioned study designs depending of the research questions, the purposes of the study, and the availability of resources to conduct them. Investigators always look for those study designs that are relatively quick, less expensive, and efficient.

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observations made at the group level may not represent the exposure-disease relationship at the individual level

in epidemiology this term is used to refer to 'time' for example, the changes in disease prevalence over time.

the term refers to a defined unit, for example, a county, state, or, school district, etc.

Any program or other planned effort designed to produce changes in a target population. For example, health care use, etc.

essentially randomly selecting a study participant twice.

clinical trials are planned experiments that assesses the efficacy of a treatment (or, medical procedure) in people. It is medical research involving people.

Any planned, or, unplanned deviation of the study protocol.

To 'blind' the study participants, or, the providers or, both by not knowing to what clinical intervention of drug they are assigned or, participating, and it is commonly used in clinical trials.

Principles of Epidemiology Copyright © by H. Giovanni Antunez is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Epidemiological Studies: A Practical Guide (3 edn)

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Epidemiological Studies: A Practical Guide (3 edn)

17 Introductory data analysis: Analytical epidemiology

  • Published: October 2018
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This chapter builds on the previous one on the analysis of descriptive epidemiological studies and illustrates statistical methods appropriate for analysis of analytical epidemiological studies. It mainly focuses on data obtained from case–control and cohort studies, but also considers other study designs presented in Chapter 6. There are also several practical examples to help with the analysis and interpretation of the results of analytical epidemiological studies. In practice, relatively little mathematical calculation is done without computers. In this chapter, however, formulae are presented for the main measures of effect together with worked examples. Indeed, when data are available in tabulated form, as opposed to raw data files, it is frequently an easy task to calculate the important measures ‘by hand’. The formulae presented will permit the reader, for example, to check or further explore data published by others.

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  • Chapter 9. Experimental studies

Randomised controlled trials

Crossover studies, experimental study of populations.

  • Chapter 1. What is epidemiology?
  • Chapter 2. Quantifying disease in populations
  • Chapter 3. Comparing disease rates
  • Chapter 4. Measurement error and bias
  • Chapter 5. Planning and conducting a survey
  • Chapter 6. Ecological studies
  • Chapter 7. Longitudinal studies
  • Chapter 8. Case-control and cross sectional studies
  • Chapter 10. Screening
  • Chapter 11. Outbreaks of disease
  • Chapter 12. Reading epidemiological reports
  • Chapter 13. Further reading

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Home » Health » What is the Difference Between Descriptive and Analytic Epidemiology

What is the Difference Between Descriptive and Analytic Epidemiology

The main difference  between descriptive and analytical epidemiology is that  descriptive epidemiology generates hypotheses on risk factors and causes of disease, whereas analytical epidemiology tests hypotheses by assessing the determinants of diseases, focusing on risk factors and causes as well as, analyzing  the distribution of exposures and diseases.  Furthermore, descriptive epidemiology is comparatively a small and less complex study area, while analytical epidemiology is a larger and more complex study area.  

Descriptive and analytical epidemiology are two main areas of epidemiology that studies the distribution, patterns, and determinants of health and diseases in defined populations .  

Key Areas Covered  

1. What is Descriptive Epidemiology      – Definition, Features, Importance 2. What is Analytical Epidemiology      – Definition, Features, Importance 3. What are the Similarities Between Descriptive and Analytical Epidemiology      – Outline of Common Features 4. What is the Difference Between Descriptive and Analytical Epidemiology      – Comparison of Key Differences

Key Terms  

Analytical Epidemiology, Descriptive Epidemiology, Making Hypotheses, Occurrence of Diseases, Testing Hypotheses

Difference Between Descriptive and Analytic Epidemiology - Comparison Summary

What is Descriptive Epidemiology  

Descriptive epidemiology is one of the two main areas of epidemiological studies. It is responsible for the determination of the patterns of disease occurrence, focusing on clinical information, person, place, and time. Here, the clinical information includes the signs and symptoms of the disease, laboratory results, data on hospitalization, and live or dead numbers. Besides, it uses demographic information, including age, sex, material status, personal habits, etc. Also , it studies socioeconomic information such as education, occupation, income, residence, place of work, etc. Furthermore, cultural information, including ethnicity, dietary habits, and religious preferences, also have an effect on causing diseases. 

Difference Between Descriptive and Analytic Epidemiology

Figure 1: Bar Graph of the Incidence of Mild Traumatic Brain Injury by Age Range

Generally, descriptive epidemiologists collect relatively accessible data used for program planning , generating hypotheses, and suggesting ideas for further studies. Moreover, the hypotheses produced by descriptive epidemiological studies are confirmed by the analytical epidemiology. Furthermore, the three main types of descriptive epidemiology are the case report, case studies, and incidence. Case reports describe the person, place, and time of a specific case while case series describes the person, place, and time of a group of cases. Incidence studies, on the other hand, describe the number of new cases during a specific time.  

What is Analytical Epidemiology  

Analytical epidemiology is the second area of epidemiology, and it is a more complex and broader area than descriptive epidemiology. It is responsible for testing the hypotheses built in descriptive epidemiology. Therefore, the main objective of analytical epidemiology is to assess the determinants of diseases, risk factors and causes, as well as, to analyze the distribution of diseases and their exposures. Additionally, the key feature of analytical epidemiology is that it uses comparison groups. 

Difference Between Descriptive and Analytic Epidemiology

Figure 2: Table of Comparison of Prostate Screening Results Globally

Moreover, the two main types of analytical epidemiology are the experimental epidemiology and observational epidemiology. In experimental epidemiology , a randomized selection process based on chance is used to study different study groups. Sometimes, it can be clinical procedures, which study new drugs to prevent a particular disease in a community. In contrast, observational epidemiology is based on non-randomized studies. Moreover, they mainly study patterns of exposure. Furthermore, the four types of analytical epidemiology studies are cohort, case-control, cross-sectional, and ecologic.  

Similarities Between Descriptive and Analytical Epidemiology  

  • Descriptive and analytical epidemiology are two main study areas of epidemiology.  
  • Moreover, both study the distribution, patterns, and determinants of health and diseases in defined populations.  
  • Also, their main goals are to identify who is at risk and to provide  clues to the cause of diseases.  
  • Therefore, they are a type of important activities in public health authorities.  

Difference Between Descriptive and Analytical Epidemiology  

Definition  .

Descriptive epidemiology refers to the area of epidemiology that focuses on describing disease distribution by characteristics relating to time, place, and people, while analytical epidemiology refers to the area of epidemiology, which measures the association between a particular exposure and a disease, using information collected from individuals, rather than from the aggregate population.

Importance  

While descriptive epidemiology generates hypotheses on risk factors and causes of disease, analytical epidemiology tests hypotheses by assessing the determinants of diseases focusing on risk factors and causes as well as, analyzing  the distribution of exposures and diseases. Thus, this is the main difference between descriptive and analytical epidemiology.

Focuses on  

Another difference between descriptive and analytical epidemiology is that descriptive epidemiology focuses on what, who, when, and where disease can occur, while analytical epidemiology focuses on why and how disease occurs.  

Significance  

Furthermore, descriptive epidemiology is comparatively a small and less complex study area, while analytical epidemiology is a larger and more complex study area.  

Broadness  

Descriptive epidemiology uses individuals or a group of individuals to make hypotheses, while analytical epidemiology uses comparison groups to test hypotheses. Hence, this is also a difference between descriptive and analytical epidemiology.

Moreover, descriptive epidemiology includes case reports, case series, and incidence, while analytical epidemiology includes observational studies and experimental studies.  

As an example, descriptive epidemiology examines case series using person, place, and time of first 100 patients with SARS, while analytical epidemiology measures risk factors for  SARS such as contact with animals and infected people.  

Conclusion  

Descriptive epidemiology is one of the two main areas of epidemiology that produces hypotheses about the risk factors and causes of diseases. Analytical epidemiology, on the other hand, is the area of epidemiology which tests the above hypotheses. Moreover, it assesses the risk factors and analyzes  the distribution of diseases. Therefore, the main difference between descriptive and analytical epidemiology is the type of study.  

References:

1. Kobayashi, John. “Study Types in Epidemiology.”  Nwcphp.org , Northwest Center for Public Health Practice. Available Here .

Image Courtesy:

1. “MTBI incidince bar graph” By self – Own work ( CC BY-SA 3.0 ) via Commons Wikimedia     2. “ Prostate cancer global epidemiology ” By US govt (Public Domain) via Commons Wikimedia   

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About the Author: Lakna

Lakna, a graduate in Molecular Biology and Biochemistry, is a Molecular Biologist and has a broad and keen interest in the discovery of nature related things. She has a keen interest in writing articles regarding science.

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THE CDC FIELD EPIDEMIOLOGY MANUAL

Describing Epidemiologic Data

Robert E. Fontaine

  • Organizing Epidemiologic Data
  • Characterizing The Cases (What?)
  • Counts and Rates (How Much?)
  • Time (When?)
  • Person (Among Whom?)
  • Best Practices

As a field epidemiologist, you will collect and assess data from field investigations, surveillance systems, vital statistics, or other sources. This task, called descriptive epidemiology, answers the following questions about disease, injury, or environmental hazard occurrence:

  • Among whom?

The first question is answered with a description of the disease or health condition. “How much?” is expressed as counts or rates. The last three questions are assessed as patterns of these data in terms of time, place, and person. After the data are organized and displayed, descriptive epidemiology then involves interpreting these patterns, often through comparison with expected (e.g., historical counts, increased surveillance, or output from prevention and control programs) patterns or norms. Through this process of organization, inspection, and interpretation of data, descriptive epidemiology serves multiple purposes ( Box 6.1 ).

Descriptive epidemiology

  • Provides a systematic approach for dissecting a health problem into its component parts.
  • Ensures that you are fully versed in the basic dimensions of a health problem.
  • Identifies populations at increased risk for the health problem under investigation.
  • Provides timely information for decision-makers, the media, the public, and others about ongoing investigations.
  • Supports decisions for initiating or modifying control and prevention measures.
  • Measures the progress of control and prevention programs.
  • Enables generation of testable hypotheses regarding the etiology, exposure mode, control measure effectiveness, and other aspects of the health problem.
  • Helps validate the eventual incrimination of causes or risk factors.

Your analytic findings must explain the observed patterns by time, place, and person.

Organizing descriptive data into tables, graphs, diagrams, maps, or charts provides a rapid, objective, and coherent grasp of the data. Whether the tables or graphs help the investigator understand the data or explain the data in a report or to an audience, their organization should quickly reveal the principal patterns and the exceptions to those patterns. Tables, graphs, maps, and charts all have four elements in common: a title, data, footnotes, and text ( Box 6.2 ). In this chapter, additional guidelines for preparing these data displays will appear where the specific data display type is first applied.

Tables are commonly used for characterizing disease cases or other health events and are ideal for displaying numeric values. In addition to the previously mentioned elements in common to all data displays ( Box 6.2 ), tables have column and row headings that identify the data type and any units of measurement that apply to all data in that column or row. A well-structured analytical table that is organized to focus on comparisons will help you understand the data and explain the data to others. In arranging analytical tables, you should begin with the arrangement of the data space by following a simple set of guidelines ( Box 6.3 ) ( 1 ).

Cases are customarily organized in a table called a line-listing ( Table 6.1 ) ( 2 ). This arrangement facilitates sorting to reorganize cases by relevant characteristics. The line-listing in Table 6.1 has been sorted by days between vaccination and onset to reveal the pattern of this important time–event association. Commonly in descriptive epidemiology, you organize cases by frequency of clinical findings ( Table 6.2 ) (3). If the disease cause is unknown, this arrangement can assist the epidemiologist in developing hypotheses regarding possible exposures. For example, initial respiratory symptoms might indicate exposure through the upper airways, as in Table 6.2 .

A statistical data display should include, at a minimum,

  • A title that includes the what, where, and when that identifies the data it introduces.
  • A data space where the data are organized and displayed to indicate patterns.
  • Footnotes that explain any abbreviations used, the data sources, units of measurement, and other necessary details or data.
  • Text that highlights the main patterns of the data (this text might appear within the table or graphic or in the body of the report).
  • Round data to two statistically significant or effective numbers.
  • Using three or more significant figures interferes with comparison and comprehension.
  • More precision is usually not needed for epidemiologic purposes.
  • Effective figures refers to numbers that contain additional, leading non-zero digits that do not vary (e.g., 123, 145, 168, or 177) or vary slightly (see BMI columns in Table 6.3 ) within a column or row.
  • Provide marginal averages, rates, totals, or other summary statistics for rows and columns whenever possible.
  • Use columns for most crucial data comparisons.
  • Numbers are more easily compared down a column than across a row.
  • Organize data by magnitude (sort) across rows and down columns.
  • Use the most important epidemiologic features on which to sort the data.
  • Organizing data columns and rows by the magnitude of the marginal summary statistics is often helpful.
  • When the row or column headings are numeric (e.g., age groups), they should govern the order of the data.
  • Use the table layout to guide the eye. For example,
  • Align columns of numbers on the decimal point (or ones column).
  • Place numbers close together, which might require using abbreviations in column headings.
  • Avoid using dividing lines, grids, and other embellishments within the data space.
  • Use alternating light shading of rows to assist readers in following data across a table.

Source: Adapted from Reference 1 .

F, female; M, male. a RotaShield®, Wyeth-Lederle, Collegeville, Pennsylvania b Days from vaccine dose to illness onset Source: Adapted from Reference 2

a Defined as a symptom that improved while away from the facility, either on days off or on vacation. b During the previous 12 months. c Defined as current use of asthma medicine or one or more of the following symptoms during the previous 12 months: wheezing or whistling in the chest, awakening with a feeling of chest tightness, or attack of asthma.

Source: Adapted from Reference 3

A first and simple step in determining how much is to count the cases in the population of interest. Always check whether data sources are providing incident (new events among the population) or prevalent (an existing event at a specific point in time) cases. For incident cases, specify the period during which the cases occurred. This count of incident cases over time in a population is called incidence . Never mix incident with prevalent cases in epidemiologic analyses.

The counts of incident or prevalent cases can be compared with their historical norm or another expected or target value. These case counts are valid for epidemiologic comparisons only when they come from a population of the same or approximately the same size.

Rates, Ratios, and Alternative Denominators

Rates correct counts for differences among population sizes or study periods. Thus, incidence divided by an appropriate estimation of the population yields several versions of incidence rates. Similarly, prevalent case counts divided by the population from which they arose produce a proportion (termed prevalence ). Strictly speaking, in computing rates, the disease or health event you have counted should have been derived from the specific population used as the denominator. However, sometimes the population is unknown, costly to determine, or even inappropriate. For example, a maternal mortality ratio and infant mortality rate use births in a calendar year as a denominator for deaths in the same calendar year, yet the deaths might be related to births in the previous calendar year. To assess adverse effects from a vaccine or pharmaceutical, consider using total doses distributed as the denominator. Another example is injuries from snowmobile use, which have been calculated both as ratios per registered vehicle and as per crash incident ( 4 ). Returning now to counts, you can calculate expected case counts for a population by multiplying an expected (e.g., historical counts, increased surveillance, or output from prevention and control programs) or a target rate by the population total. This expected or target case count is now corrected for the population and can be compared with the actual observed case counts.

Mean, median, range, and interquartile range of body mass index measurements of 1,800 residents, by education level: Ajloun and Jerash Governorates, Jordan, 2012.

examples of analytical studies in epidemiology

Source: Adapted from: Ajloun Non-Communicable Disease Project, Jordan, unpublished data, 2017.

Measurements on a Continuous Scale

Disease or unhealthy conditions also can be measured on a continuous scale rather than counted directly (e.g., body mass index [BMI], blood lead level, blood hemoglobin, blood sugar, or blood pressure). You can use empirical cutoff points (e.g., BMI ≥26 for overweight). These can then be counted and the rates calculated. However, a person’s measurements can fluctuate above or below these cutoff values. To calculate incidence, special care therefore is needed to avoid counting the same person every time a fluctuation occurs above or below the cutoff point. For prevalence, this fluctuation amplifies the statistical error. A more precise approach involves computing the average and dispersion of the individual measurements. These can then be compared among groups, against expected values, or against target values. The averages and dispersions can be displayed in a table or visualized in a box-and-whisker plot that indicates the median, mean, interquartile range, and outliers ( Figure 6.1 ) ( 5 ).

Time has special importance in interpreting epidemiologic data in that the initial exposure to a causative agent must precede disease. Often, this will follow a biologically determined interval. The disease or health condition onset time is the preferred statistic for studying time patterns. Onset might not always be available. In surveillance systems, you might have only the report date or another onset surrogate. Moreover, with slowly developing health conditions, a discernable onset might not exist. On the opposite end of the scale, injuries and acute poisonings have instantaneous and obvious onsets.

Similarly, times of suspected exposures vary in their precision. With acute infections, poisonings, and injuries, you will often have precise exposure times to different suspected agents. Contrast this with chronic diseases that can have exposures lasting for decades before development of overt disease. Other relevant events supplementing a chronologic framework of a health problem include underlying environmental conditions, changes in health policy, and application of control and prevention measures.

Relating disease with these events in time can support calculation of key characteristics of the disease or health event. If you know both time of onset and time of the presumed exposure, you can estimate the incubation or latency period. When the agent is unknown, the time interval between presumed exposures and onset of symptoms helps in hypothesizing the etiology. For example, the consistent time interval between rotavirus vaccination and onset of intussusception ( Table 6.1 ) helped build the hypothesis that the vaccine precipitated the disease ( 1 ). Similarly, when the incubation period is known, you can estimate a time window of exposure and identify exposures to potential causative agents during that window.

Depicting Data by Time: Graphs

Graphs are most frequently used for displaying time associations and patterns in epidemiologic data. These graphs can include line graphs, histograms (epidemic curves), and scatter diagrams (see Box 6.4 for general guidelines in construction of epidemiologic graphs).

  • Take care in selecting a graph type in computer graphics programs. In Microsoft Excel (Microsoft Corporation, Redmond, WA), for example, you should use “scatter,” not “line” to produce numerically scaled line graphs.
  • Adhere to mathematical principles in plotting data and scaling axes.
  • On an arithmetic scale, represent equal numerical units with equal distances on an axis.
  • When using transformed data (e.g., logarithmic, normalized, or ranked), represent equal units of the transformed data with equal distances on the axis.
  • Represent dependent variables on the vertical scale and independent variables on the horizontal scale.
  • No line at all (use data markers only).
  • A trend line of best fit underlying the data markers.
  • A moving average line underlying the data markers.
  • Aspect ratios (data space width to height) of approximately 2:1 work well. Extreme aspect ratios distort data.
  • Scale the graph to fill the data space and to improve resolution. If this means that you must exclude the zero level, exclude it, but note for the reader that this has been done.
  • Do not insist on a zero level unless it is an integral feature of the data (e.g., an endpoint).
  • Use graphic designs that reveal the data from the broad overview to the fine detail.
  • To compare two lines, plot their difference directly.
  • Use visually prominent symbols to plot and emphasize the data.
  • Make sure overlapping plotting symbols are distinguishable.
  • Design point markers and lines for visual discrimination; and
  • Differentiate them with labels, legends, or keys.
  • To avoid clutter and maintain undistorted comparisons, consider using two or more separate panels for different strata on the same graph.
  • When comparing two graphs of the same dependent variable, use scaling that improves comparison and resolution.
  • Clearly indicate scale divisions and scaling units.
  • Minimize frames, gridlines, and tick marks (6–10/axis is sufficient) to avoid interference with the data.
  • Use six or fewer tick mark labels on the axes. More than that becomes confusing clutter.
  • Keep keys, legends, markers, and other annotations out of the data space. Instead, put them just outside the data region.
  • Proofread your graphs.

Contact Diagrams

Contact diagrams are versatile tools for revealing relationships between individual cases in time. In contact diagrams ( Figure 6.2 , panel A) ( 5 ), which are commonly used for visualizing person-to-person transmission, different markers are used to indicate the different groups exposed or at risk. Epidemic Curves

Epidemic curves ( Box 6.5 ) are histograms of frequency distributions of incident cases of disease or other health events displayed by time intervals. Epidemic curves often have patterns that reveal likely transmission modes. The following sections describe certain kinds of epidemic situations that can be diagnosed by plotting cases on epidemic curves.

  • Time intervals are indicated on the x -axis and case counts on the y -axis.
  • Upright bars in each interval represent the case counts during that interval.
  • No gaps should exist between the bars.
  • Use time intervals of half an incubation or latency period or less.
  • Decrease the time interval size as case numbers increase.
  • Indicate an interval of 1–2 incubation periods before the outbreak increases from the background and after it returns to background levels.
  • Use separate, equally scaled epidemic curves to indicate different groups.
  • Do not stack columns for different groups atop one another in the same graph.
  • Use an overlaid line graph, labels, markers, and reference lines to indicate suspected exposures, interventions, special cases, or other key features.
  • Compare the association of cases during these pre-and post-epidemic periods with the main outbreak.

Point Source

An epidemic curve with a tight clustering of cases in time (≤1.5 times the range of the incubation period, if the agent is known) and with a sharp upslope and a trailing downslope is consistent with a point source ( Figure 6.3 ) ( 6 ). Variations in slopes (e.g., bimodal or a broader than expected peak) might indicate different ideas about the appearance, persistence, and disappearance of exposure to the source. Of note, administration of antimicrobials, immunoglobulins, antitoxins, or other quickly acting drugs can lead to a shorter than expected outbreak with a curtailed downslope.

To approximate the time of exposure, count backward to the average incubation period before the peak, the minimum incubation period from the initial cases, and the maximum incubation period from the last cases. These three points should bracket the exposure period. If a rapidly acting intervention was taken early enough to prevent cases, discount the contribution of the last cases to this estimation.

Point source outbreaks result in infected persons who might have transmitted the agent directly or through a vehicle to others. These secondary cases might appear as a prominent wave after a point source by one incubation period, as observed after a point source hepatitis E outbreak that resulted from repairs on a broken water main ( Figure 6.4 ) ( 7 ). With diseases of shorter incubation and lower rates of secondary spread, the secondary wave might appear only as a more prolonged downslope.

Continuing Common Source

Outbreaks can arise from common sources that continue over time. The continuing common source epidemic curve will increase sharply, similar to a point source. Rather than increase to a peak, however, this type of epidemic curve has a plateau. The downslope can be precipitous if the common source is removed or gradual if it exhausts itself. The rapid increase, plateau, and precipitous downslope all appeared with a salmonellosis outbreak from cheese distributed to multiple restaurants and then recalled ( Figure 6.5 ).

Contact between severe acute respiratory syndrome (SARS) cases among a group of relatives and health care workers: Beijing, China, 2003.

Contact between severe acute respiratory syndrome (SARS) cases among a group of relatives and health care workers: Beijing, China, 2003.

Source: Adapted from Reference 5 .

Cases of salmonellosis among passengers on a flight from London to the United States, by time of onset, March 13– 14, 1984.

Cases of salmonellosis among passengers on a flight from London to the United States, by time of onset, March 13–14, 1984.

Source: Adapted from Reference 6 .

Cases of jaundice, by week of onset: Jafr, Ma’an Governorate, Jordan,  June– October 1999.

Cases of jaundice, by week of onset: Jafr, Ma’an Governorate, Jordan, June–October 1999.

Source : Adapted from Reference 7 .

Cases of Salmonella enterica serovar Heidelberg infection, by illness onset date: Colorado, July 10– August 17, 1976.

Cases of Salmonella enterica serovar Heidelberg infection, by illness onset date: Colorado, July 10–August 17, 1976.

A propagated pattern arises with agents that are communicable between persons, usually directly but sometimes through an intermediate vehicle. This propagated pattern has four principal characteristics ( Box 6.6 ).

The epidemic curve accompanying the severe acute respiratory syndrome (SARS) contact diagram ( Figure 6.2 , panel B) illustrates these features, including waves with an approximate 1-week periodicity. Certain behaviors (e.g., drug addiction or mass sociogenic illness) might propagate from person to person, but the epidemic curve will not necessarily reflect generation times. Epidemic curves for large geographic areas might not reveal the early periodicity or the characteristic increase and decrease of a propagated outbreak. For these larger areas, stratifying the epidemic curves by smaller subunits can reveal the underlying periodicity.

  • They encompass multiple generation periods for the agent.
  • They begin with a single or limited number of cases and increase with a gradually increasing upslope.
  • Often, a periodicity equivalent to the generation period for the agent might be obvious during the initial stages of the outbreak.
  • After the outbreak peaks, the exhaustion of susceptible hosts usually results in a rapid downslope.

Human–Vector–Human

Vectorborne diseases propagate between an arthropod vector and a vertebrate host. Six biologic differences in human–vector–human propagation affect the size and the shape of the epidemic curve ( Box 6.7 ). The last two factors listed in the box will lead to irregular peaks during the progression of the outbreak and precipitous decreases.

  • Arthropod vectors feed indiscriminately. Contrast this with human social interactions that govern person-to-person transmission. Sequential waves of human–vector–human transmission tend to be larger than person-to-person transmission.
  • Generation periods between waves of an outbreak are usually longer than with simple person-to-person transmission because two sequential incubation periods, extrinsic in the vector and intrinsic in the human, are involved.
  • Arthropod vectors, after becoming infected, remain so until they perish. This tends to prolong waves of vectorborne outbreaks.
  • Increasing environmental temperatures accelerate the multiplication of infectious agents in an arthropod. Consequently, they also accelerate and amplify epidemic development.
  • Mean daily temperatures of less than 68ºF (<20ºC) typically arrest multiplication of infectious agents in the arthropod.
  • Arthropod populations can grow explosively and can decline even more rapidly. This will be reflected by an instability of the epidemic curve.

An outbreak of dengue arising from a single imported case in a South China town reveals several of these features ( Figure 6.6 ) ( 8 ). After the initial case, 15 days elapsed until the peak of the first generation of new cases. Control measures targeting the larva and adults of the mosquito vectors Aedes aegypti and A. albopictus began late in the first generation. The line indicates the rapid decrease in Aedes -infested houses (house index). A rapid decrease in dengue cases follows this decrease in vector density.

The epidemic curve for a zoonotic disease among humans typically mirrors the variations in prevalence among the reservoir animal population. This will be modified by the variability of contact between humans and the reservoir animal and, for vectorborne zoonoses, contact with the arthropod vector.

Environmental

Epidemic curves from environmentally spread diseases reflect complex interactions between the agent and the environment and the factors that lead to exposure of humans to the environmental source. Outbreaks that arise from environmental sources usually encompass multiple generations or incubation periods for the agent. You should include on the epidemic curve a representation of the suspected environmental factor (e.g., rainfall connected with leptospirosis in Figure 6.7 [ 9 ]). In this example, nearly every peak of rainfall precedes a peak in leptospirosis, supporting the hypothesis regarding the importance of water and mud in transmission.

Relative Time

As an alternative to plotting onset by calendar time, plotting the time between suspected exposures and onset can help you understand the epidemiologic situation. For example, a plot of the days between contact with a SARS patient and onset of SARS in the person having contact indicates an approximation of the incubation period ( Figure 6.8 ) ( 5 ).

Multiple Strata Display

To reveal distinctive internal patterns (e.g., by exposure, method of case detection, place, or personal characteristics) in time distributions, epidemic curves should be stratified ( Figure 6.9 ). This puts each stratum on a flat baseline, enabling undistorted comparisons. Stacking different strata atop one another (as in Figure 6.7 , which is not recommended) defeats attempts to compare the time patterns by group.

Date of onset of 185 cases of dengue in a fishing port: Guangdong Province, China, 2007.

Date of onset of 185 cases of dengue in a fishing port: Guangdong Province, China, 2007.

Source : Adapted from Reference 8 .

Cases of leptospirosis by week of hospitalization and rainfall in Salvador, Brazil, March 10–November 2, 1996.

Cases of leptospirosis by week of hospitalization and rainfall in Salvador, Brazil, March 10–November 2, 1996.

Source : Adapted from Reference 9 .

Days (2-day intervals) between onset of a case of severe acute respiratory syndrome and onset of the corresponding source case: Beijing, China, March–April 2003.

Days (2-day intervals) between onset of a case of severe acute respiratory syndrome and onset of the corresponding source case: Beijing, China, March–April 2003.

Source : Adapted from Reference 5 .

Cases of typhoid fever by date of onset: Tabuk, Saudi Arabia, April–June 1992.

Cases of typhoid fever by date of onset: Tabuk, Saudi Arabia, April–June 1992.

Examining Rates by Time

Temporal disease rates are usually illustrated by using a line graph ( Box 6.4 ). The x -axis represents a period of interest. The y -axis represents the rate of the health event. For most conditions, when the rates vary over one or two orders of magnitude, an arithmetic scale is recommended. For rates that vary more widely, a logarithmic scale for the y -axis is recommended for epidemiologic purposes ( Figure 6.10 ) ( 10 ). You should also use a logarithmic scale for comparing two or more population groups. Equal rates of change in time (e.g., a 10% decrease/year) will yield misleading, divergent lines on an arithmetic plot; a logarithmic scale will yield parallel lines.

Secular Trend

For most conditions, a time characteristic of interest is the secular trend—the rate of disease over multiple years or decades. Secular trends of invasive cervical cancer ( Figure 6.11 ) reveal steady decreases over 37 years ( 11 ). New health policies in 1970 and 1995 that broadened coverage of Papanicolaou smear screenings for women were initially followed by steeper decreases and subsequent leveling off of the downward trend. This demonstrates how review of secular trends can bring attention to key events, improvements in control, changes in policy, sociologic phenomena, or other factors that have modified the epidemiology of a disease.

Seasonal and Cyclical Patterns

For certain conditions, a description by season, month, day of the week, or even time of day can be revealing. Seasonal patterns might be summarized in a seasonal curve ( Box 6.8 ). Stratifying seasonal curves can further expose key differences by place, person, or other features ( Figure 6.12 ) ( 12 ).

  • Use multiple years (≥5) of data.
  • Summarize with average rates, average counts, or totals for all the Januarys, Februarys, and so on for each of the 12 months.
  • Use other intervals (e.g., weeks or days) accordingly.
  • Plot the rate, average, or total for each interval on a histogram or line graph.
  • Plot the percentage of the total for the year represented by each interval; however, take care when interpreting the total percentage.
  • Use redundant beginning and end points (see Figures 6.9 through 6.14) to visualize the trend between the last and first months of the cycle.
  • This type of curve can be made for any time cycle (e.g., time of day, day of week, or week of influenza season).

Age-specific mortality rates per 100,000 population/year: United States, 1910, 1950, and 1998.

Age-specific mortality rates per 100,000 population/year: United States, 1910, 1950, and 1998.

Source : Adapted from Reference 10 .

Cervical cancer (invasive) Surveillance Epidemiology, and End Results Program incidence and death rate: United States, 1999–2013.

Cervical cancer (invasive) Surveillance Epidemiology, and End Results Program incidence and death rate: United States, 1999–2013.

Source : Adapted from Reference 11 .

Seasonal distribution of malaria cases, by month of detection by voluntary collaborators in four villages: El Salvador, 1970–1977.

Seasonal distribution of malaria cases, by month of detection by voluntary collaborators in four villages: El Salvador, 1970–1977.

Source : Adapted from Reference 12 .

When creating graphics and interpreting distributions of disease by place, keep in mind Waldo Tobler’s first law of geography: “Everything is related to everything else, but near things are more related than distant things” ( 13 ). These distance associations of cases or rates are best understood on maps. In addition, maps display a wealth of underlying detail to compare against disease distributions. In creating epidemiologic maps, you should follow certain basic guidelines ( Box 6.9 ).

  • Indicate scaling as a ratio (e.g., 1:100,000), a scale bar (e.g., a 1-cm bar = 50 m), or tick marks on the x -and y -axes (indicating linear distance or longitude and latitude).
  • On maps representing land areas, indicate longitude and latitude and orientation (i.e., by using a northward-pointing arrow).
  • Ensure that scaling applies accurately to all features in the map area, especially indicators of location of disease and potential exposures.
  • Reduce embellishments that obstruct a clear vision of disease and potential exposures. These might include detailed administrative boundaries or a longitude-latitude grid.

Information about place of affected persons might include residence, workplace, school, recreation site, other relevant locales, or movement between fixed geographic points. Distinguish between place of onset, place of known or suspected exposure, and place of case identification. They are often different and have distinct epidemiologic implications. Information about place can range in precision from the geographic coordinates of a residence or bed in a hospital to simply the state of residence. Because population estimates or censuses follow standard geographic areas (e.g., city, census tract, county, state, or country), determination of rates is also restricted to these same areas.

Use spot maps to reveal spatial associations between cases and between cases and geographic features. Cases can be plotted on a base map ( Figure 6.13 [ 14 ]), a satellite view of the area, a floor plan, or other accurately scaled diagram to create a spot map. Dots, onset times, case identification numbers for indexing with a line listing, or other symbols might represent disease cases ( Box 6.10 ). The example spot map of a dengue outbreak uses larger dots to represent cases clustered in time and space and numbers these clusters to reference to a table (not shown). It reveals the location of the first case in the business district and the large initial cluster surrounding it ( Figure 6.13 ) ( 14 ). Cases not included in clusters are marked with smaller dots. These are widely dispersed, indicating that they did not acquire their infection from their local environs.

You might also use spot maps to represent affected villages, towns, or other smaller population units. If the denominator of the population unit is known, spots of different size or shading ( Box 6.10 ) can represent rates or ratios.

Spot maps that plot cases have a general weakness. The observed pattern might represent variability in the distribution of the underlying population. When interpreting spot maps, keep in mind the population distribution with particular attention to unpopulated (e.g., parks, vacant lots, or abandoned warehouses) or densely populated areas.

Area Maps and Rates

Rates are normally displayed on area maps (e.g., patch or choropleth). The map is divided into population enumeration areas for which rates or ratios can be computed. The areas are then ranked into strata by the rates, and the strata are shaded ( Box 6.10 ) according to the magnitude of the rate.

Compute and plot rates for the smallest area possible. For example, the map of spotted fever rickettsioses in the United States effectively displays multiple levels of risk for human infection ( Figure 6.14 ) ( 15 ). Avoid using area maps to display case counts. Plotting only numerators loses the advantage of both the spot map (indicating exact location and detailed background features) and the area map (indicating rates).

  • Place all spots accurately.
  • Ensure that overlapping spots are distinguishable.
  • Ensure that potential exposures are easily discerned and labeled.
  • Indicate underpopulated or depopulated areas.
  • Highlight high-interest cases.

AREA (PATCH OR CHOROPLETH) MAPS

  • To indicate numeric intensity, use increasing intensity of gray from white to black. If using color, use increasing intensities of the same hue.
  • To indicate divergence from an average range, use white for the center range and deepening intensities of two different hues for divergent strata on opposite extremes.
  • To indicate nominative (non-numeric) qualities, use different hues or fill patterns.
  • To indicate no data, use a different hue or fill pattern.
  • Let the difference in shading of map areas define and replace detailed internal boundary lines.
  • Include a legend or key to clarify map features (e.g., disease cases, rates, and exposures).
  • Consider indicating the zero-level separately.
  • Indicate the data range in the legend; do not leave it open-ended.
  • Create multiple maps to indicate associations of cases to different background features to fully communicate the geographic association between disease and exposure.
  • Use the smallest possible administrative area that the numerator and denominator will allow.

Scatter Plots

Scatter plots are versatile instruments for exploring and communicating data. They indicate the association between two numerically scaled variables ( Figure 6.15 ) ( 16 ). Each spot in the plotting area represents the joint magnitude of the two variables. As a convention in plotting epidemiologic or geographic association, the explanatory variable (exposure, environmental, or geographic) is plotted on the x -axis, and the outcome (rate or individual health measurement [e.g., BMI]) is plotted on the y -axis.

When the pattern of the spots forms a compact, linear pattern, suspect a strong association between the two variables. In Figure 6.15 , a distinctive pattern of rapidly increasing cholera death rates is apparent as the altitude approaches the level of the River Thames. This reveals that factor and that an environmental exposure also related to low altitude (e.g., poor drainage of sewage) might have contributed to cholera incidence.

Significant space–time clustering (assessed by the Knox test) of dengue cases in the city of Cairns, Australia, during January–August 2003.

Significant space–time clustering (assessed by the Knox test) of dengue cases in the city of Cairns, Australia, during January–August 2003.

Source : Adapted from Reference 14 .

Reported incidence rate of spotted fever rickettsiosis† by county: United States, 2000–2013.

Reported incidence rate of spotted fever rickettsiosis† by county: United States, 2000–2013.

Cholera deaths per 10,000 inhabitants and altitude above the average high-tide level, by district in London, England, 1849.

Cholera deaths per 10,000 inhabitants and altitude above the average high-tide level, by district in London, England, 1849.

Source : Adapted from Reference 15 .

Recognizing disease patterns by personal attributes (e.g., age, sex, education, income, or immunization status) constitutes the fifth element in descriptive epidemiology. Two important qualifications apply to person data assessments. First, determining rates is more often necessary than for time and place. Second, age is a strong independent determinant for many causes of morbidity and mortality.

Social Groupings and Personal Contact

Social groupings might be as compact as a household or as diffuse as a social network linked by a common interest. The underlying epidemiologic process might produce disease distributions within and among social groupings that range from strong aggregation to randomness or uniformity. Clustered distributions might result from common exposures of group members, an agent that is transmissible through personal contact, an environmental exposure in the living or meeting areas, or localization of houses near or within an environmental area of high risk. Random or uniform distributions indicate that the exposure lies outside the group.

For diseases or behaviors spread through personal contact or association, contact diagrams reveal the pattern of spread plus such key details as index cases and outliers. In the example diagram, closeness and quality of relationships, timing between onsets, and places of contact are all displayed through different symbols and shading ( Figure 6.2 ) ( 5 ). To support person-to-person transmission, you should also see that the timing between onsets of cases approximates the known incubation periods for the disease ( Figure 6.8 ) ( 5 ).

In most descriptive analyses, the epidemiologist will determine disease rates by age. This can be as simple as finding that a health event is affecting only a limited age group or as complicated as comparing age-specific rates among multiple groups. Age represents three different categories of determinants of disease risk ( Box 6.11 ). Because age is a pervasive determinant of disease and because population groups often differ in their age structures, age adjustment (standardization) is a useful tool for comparing rates between population groups ( 17 ). Age-adjusted rates can be used for comparing populations from different areas, from the same area at different times, and among other characteristics (e.g., ethnicity or socioeconomic status).

  • The condition of the host and its susceptibility to disease. Persons of different ages often differ in susceptibility or predisposition to disease. Age is one of the most important determinants of chronic diseases, many infectious diseases, and mortality.
  • Differing intensities of exposure to causative agents .
  • The passage of time. Older persons have had greater overall time of exposure or might have been exposed at different periods when background exposures to certain agents were greater. A disease with a long latency period (e.g., tuberculosis) might reflect exposures decades in the past.

An analysis of BMI by age from Ajloun and Jerash Governorates, Jordan, draws attention to increasing BMI and accumulating overweight prevalence for persons aged 18–75 years ( Table 6.3 ) (Ajloun Non-Communicable Disease Project, Jordan, unpublished data, 2017). As an alternative to using tables, charts ( Box 6.12 ) (e.g., dot charts) ( Figure 6.16 , panel A) or horizontal cluster bar charts ( Figure 6.16 , panel B) improve perception of the patterns in the data, compared with a table. Cluster bar charts with more than two bars per cluster (e.g., Figure 6.16 , panel B) are not recommended.

  • Charts present statistical information comparing numeric values for sets of multiple nominative characteristics or grouped numeric characteristics.
  • Data presentation is interchangeable with tables. The choice between tables and charts depends on the purpose, the audience, and the complexity of the data.
  • The best charts for quick and accurate understanding are dot plots, box-and-whisker plots, and simple bar charts.
  • Avoid pie charts, cluster bar charts, stacked bar charts, and other types not presented in this chapter.
  • Dot plots, box plots, and bar charts are easier to understand and read if aligned horizontally (with the numeric axis horizontal).
  • Sorting nominative categories by the magnitude of the numeric value helps the reader’s understanding. If the classification variable is numeric (e.g., age group), sort by the numeric category.
  • The dot chart is the most versatile and the easier to understand, particularly as categories increase in number.
  • Dot and box-and-whisker charts are plotted against a numeric scale and thus do not need a zero level.
  • Bar charts usually need a zero level because viewers judge magnitude by the length of the bar.

Other Personal Attributes

Analysis by other personal attributes in descriptive epidemiology involves comparing rates or other numeric data by different classes of the attribute. For example, overweight prevalence in the Ajloun data can be compared by using different education levels. A more precise approach to estimating how much for measurements on a continuous scale, discussed earlier in this chapter, might be to compute the average and dispersion of the individual BMI measurements, as shown on a box-and-whisker plot ( Figure 6.1 ).

Dot chart (A) and bar chart (B) comparison of mean body mass index among adults, by age group and sex: Ajloun and Jerash Governorates, Jordan, 2012.

Dot chart (A) and bar chart (B) comparison of mean body mass index among adults, by age group and sex: Ajloun and Jerash Governorates, Jordan, 2012.

Source : Adapted from Ajloun Non-Communicable Disease Project, Jordan, unpublished data, 2017.

BMI, Body mass index; F, female; M, male; SD, standard deviation.

Source: Ajloun Non-Communicable Disease Project, Jordan, Unpublished data, 2017.

The tables, graphs, and charts presented in this chapter have been determined experimentally to perform best in conveying information and data patterns to you and others. Accordingly, less efficient and inaccurate displays, although common, were avoided or noted as not recommended.

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  • Registrar-General. Report on the mortality of cholera in England 1848–49 . London, UK: Her Majesty’s Stationery Office; 1852.
  • Fleiss JC. Statistical methods for rates and proportions . New York: John Wiley & Sons; 1981.

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  • 1. 1 Presented by: reMAN dhaKAL CODSH-NMC FIRST BATCH
  • 2.  Introduction  Types of epidemiology  Types of analytical epidemiology  Case control study  Cohort study  Comparison between case control and cohort study 2
  • 3.  John M. last: "the study of the distribution and determinants of health-related states in specified populations, and the application of this study to control health problems. 3
  • 4. 4 EPIDEMIOLOGY
  • 5. 5 Analytical epidemiology  Second major type of epidemiology.  Focus on individual within population unlike descriptive epidemiology..  Objective not to formulate hypothesis but to test hypothesis. TYPES A. CASE CONTROL STUDY B. COHORT STUDY
  • 6.  Retrospective or trohoc study  Distinct features: 1. Both exposure and outcome have occurred before the start of disease 2. Study proceed backward from effect to cause 3. Uses a control or comparison group to support or refute an inference. 6
  • 7. 7 The basic study design Cases (those with condition) eg: cases with oral cancer characteristic or risk factor) Control Unexposed (without Eg. Non chewers Exposed (with characteristic (those without condition) eg: those free of oral cancer or risk factor) Eg. tobacoo chewers
  • 8. 1. Selection of cases and controls 2. Matching 3. Measurement of exposure , and 4. Analysis and interpretation. 8
  • 9. 9 A. Selection of case Definition of a case: I). diagnostic criteria: ii). Eligibility criteria Sources of cases: i). Hospital ii). General population B. Selection of control Sources of controls: i). Hospital controls(common source of selection bias) ii). Relatives iii). General population Number of controls/control groups
  • 10.  Define as:”process by which we select controls in such a way that they are similar to cases with regard to certain pertinent selected variables(eg. Age) which are known to influence the outcome of disease and which, if not adequately matched for comparability, could distort or confounded the result”.  CONFOUNDING FACTOR 10 EXPOSURE (eg. Consumption of alcohol) DISEASE (eg. Oesophageal cancer) CONFOUNDING FACTOR (eg. smoking, age)
  • 11.  Definition and criteria about exposure are just as important as those used to define cases and controls. This may be obtained by :  Interviews  Questionnaries  Studying past record of cases such as hospital records, employment records etc.  Clinical or laboratory examination. Investigator should not know whether a subject is in case or control group. 11
  • 12. The final step is analysis, to find out: a) Exposure rates among cases and controls to suspected factors b) Estimation of disease risk associated with exposure (ODD RATIO) 12
  • 13. Example of case control study of tobacco chewing and oral cancer Tobacco chewers Non-chewers Cases (with oral cancer) 33 (a) 2 (c) Controls (without oral cancer) 55(b) 27(d) 13 Total 35 (a +c) 82 (b +d) EXPOSURE RATES a. Cases =a/(a+c) = 33/35 = 94.2% b. Controls =b/(b+d) = 55/82 = 67.0%
  • 14. 1. Relative risk = Incidence among exposed incidence among non exposed = a c (a+b) (c+d) 2. Odds ratio (OR) = (a/b) (c/d)  It measure strength of the association between risk factor and outcome. ,
  • 15. 1. Selection bias : special types: a) Prevalence incidence (selective survival) b) Admission rate ( Berkson/Berkesonian) 2. Information bias: a) Memory or recall bias b) Telescopic bias c) Interviewer’s bias 3. Bias due to confounding 15
  • 16. 16 ADVANTAGES: CASE CONTROL STUDY 1. Relatively easy to carry out. 2. Rapid and inexpensive 3. Require fewer subjects. 4. Suitable for investigation of rare diseases. 5. No risk of subject. 6. Allows the study of several different etiological factors. 7. Risk factor can be identify 8. No attrition problem because do not require follow up. 9. Minimal ethical problem. DISADVANTAGES: 1. Problem of bias since it relies on past memory or past records. 2.Difficulty in selection of appropriate control group. 3. Can not measure incidence only RR. 4. Doesn’t distinguish between cause and associated factors. 5.Not suited for the evaluation of therapy or prophylaxis of disease.
  • 17.  Prospective ,longitudinal, incidence and forward-looking study  Distinguishing features: a) The cohorts are identified prior to the appearance of the disease b) The study groups, so defined, are observed over a period of time to determine the frequency of disease among them c) Study proceeds from cause to effect. 17
  • 18. time Study begins here Study population free of disease Factor present Factor absent disease no disease disease no disease present future
  • 19. When there is good association between exposure and disease.  When exposure is rare, but the incidence of disease is high among exposed. When attrition of study population can be minimized. 19
  • 20. 20 1. PROSPECTIVE COHORT STUDY 1. RETROSPECTIVE COHORT STUDY 2. A COMBINATION OF PROSPECTIVE AND RETROSPECTIVE COHORT STUDY.
  • 21.  - Outcome has not yet occurred the time of investigation begins. Measure exposure and confounder variables Exposed Non-exposed Outcome Outcome Baseline time Study begins here
  • 22.  Outcomes have all occurred before the start of the investigation. 22 Measure exposure and confounder variables Exposed Non-exposed Outcome Outcome Baseline time Study begins here
  • 23. 1. Selecting of study subject 2. Obtaining data on exposure 3. Selection of comparison group 4. Follow up 5. analysis 23
  • 24. 24 1. Selecting of study subject When exposure or cause of death is fairly frequent in the population i. Select group – Professional group ( doctors,nurses ) ii. Exposure group- High risk situation (eg.radiologist exposed to x-ray) Obtaining data on exposure Information about exposure may be obtained directly from:-
  • 25. 25 Selection of comparison group a. Internal comparisons:-  Comparison groups are in built (eg. Smoking, bp etc.) within same cohort group. b. External comparisons:-  Eg. Smoker and non smoker, radiologists with opthalmologists. c. With General population:-  If none is available, mortality of exposed group with general population Follow up  Main problem  Procedures to obtain data for assessing the outcome are: a. Periodic medical checkup b. Reviewing hospital records c. Routine surveillance of death records d. Mailed questionnaries, telephone calls, periodic home visits.
  • 26. Data are analysed in term of: a. Incidence rates of outcome among exposed and non-exposed: 26
  • 27. 27 Data are analysed in term of: a. Incidence rates of outcome among exposed and non-exposed RISK FACTOR (TOBACCO) 5. ANALYSIS DEVELOPED ORAL CANCER DID NOT DEVELOP TOTAL PRESENT (CHEWERS) 45 (a) 9955 (c) 10000 (a + c) ABSENT (NON CHEWERS) 5 (b) 9995 (d) 10000 (b + d) Incidence rates: 1. Among tobacco chewers: = 45/10000 =4.5 per 1000 2.Among non chewers = 5/10000 = 0.5 per 1000
  • 28. b. Estimation of risk A. Relative risk (RR): = incidence of disease among exposed incidence of disease among non-exposed = 4.5/0.5 = 9  It implies 9 times higher risk of development of oral carcinoma in tobacco chewers compared to non chewers. 28
  • 29. B. Attributable risk (AR) Or “risk difference”: Incidence of disease rate among exposed- incidence among non exposed Incidence rate among exposed = 4.5 – 0.5 4.5 = 88.9%  Indicates to what extent the disease under study can be attributed to the exposure. 29
  • 30. 1. Selection bias:  Non consent bias  Missing data bias 2. information bias:  Error in classification of individual  Diagnostic bias 3. Confounding bias :  Due to confounding factors 4. Post hoc bias: 30
  • 31. 1. Incidence can be calculated 2. Several possible outcomes related to exposure can be studied simultaneously. 3. Provide a direct estimate of RR. 4. Dose response ratios can be calculated. 5. Since comparison groups are formed before disease develops, certain forms of bias can be minimized like misclassification of individual. 31
  • 32. 1. Unsuitable for investigating uncommon disease. 2. Long time to complete study and obtain results. 3. Administrative problem –  Extensive record keeping 4.Expensive 5. Alter people behavior  Stop or decrease smoking  Loss of interest  migration 5. Ethical problem of varying important 32
  • 33.  Start disease :effect cause  First approach to test hypothesis.  Involve fewer subject.  Yield result quickly.  Suitable for studying rare disease.  Gives RR only.  Start people: cause effect.  Reserved for testing precisely formulated hypothesis.  Involve larger number of subjects.  Results are delayed due to long follow up.  Unsuitable for studying for rare diseases.  Yield RR and AR.  Relatively inexpensive.  Expensive  Does not give information  Can give information more about diseases other than that than one disease. selected for the disease. 33 1 2 3 4 5 6 7 8

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Medical Microbiology. 4th edition.

Chapter 9 epidemiology.

Philip S. Brachman .

  • General Concepts

Definitions

Epidemiology is the study of the determinants, occurrence, and distribution of health and disease in a defined population. Infection is the replication of organisms in host tissue, which may cause disease. A carrier is an individual with no overt disease who harbors infectious organisms. Dissemination is the spread of the organism in the environment.

  • Chain of Infection

There are three major links in disease occurrence: the etiologic agent, the method of transmission (by contact, by a common vehicle, or via air or a vector), and the host.

  • Epidemiologic Methods

Epidemiologic studies may be (1) descriptive, organizing data by time, place, and person; (2) analytic, incorporating a case-control or cohort study; or (3) experimental. Epidemiology utilizes an organized approach to problem solving by: (1) confirming the existence of an epidemic and verifying the diagnosis; (2) developing a case definition and collating data on cases; (3) analyzing data by time, place, and person; (4) developing a hypothesis; (5) conducting further studies if necessary; (6) developing and implementing control and prevention measures; (7) preparing and distributing a public report; and (8) evaluating control and preventive measures.

  • Introduction

This chapter reviews the general concepts of epidemiology, which is the study of the determinants, occurrence, distribution, and control of health and disease in a defined population. Epidemiology is a descriptive science and includes the determination of rates, that is, the quantification of disease occurrence within a specific population. The most commonly studied rate is the attack rate: the number of cases of the disease divided by the population among whom the cases have occurred. Epidemiology can accurately describe a disease and many factors concerning its occurrence before its cause is identified. For example, Snow described many aspects of the epidemiology of cholera in the late 1840s, fully 30 years before Koch described the bacillus and Semmelweis described puerperal fever in detail in 1861 and recommended appropriate control and prevention measures a number of years before the streptococcal agent was fully described. One goal of epidemiologic studies is to define the parameters of a disease, including risk factors, in order to develop the most effective measures for control. This chapter includes a discussion of the chain of infection, the three main epidemiologic methods, and how to investigate an epidemic ( Table 9-1 ).

Table 9-1. Epidemiologic Methods and Investigation.

Epidemiologic Methods and Investigation.

Proper interpretation of disease-specific epidemiologic data requires information concerning past as well as present occurrence of the disease. An increase in the number of reported cases of a disease that is normal and expected, representing a seasonal pattern of change in host susceptibility, does not constitute an epidemic. Therefore, the regular collection, collation, analysis, and reporting of data concerning the occurrence of a disease is important to properly interpret short-term changes in occurrence.

A sensitive and specific surveillance program is important for the proper interpretation of disease occurrence data. Almost every country has a national disease surveillance program that regularly collects data on selected diseases. The quality of these programs varies, but, generally, useful data are collected that are important in developing control and prevention measures. There is an international agreement that the occurrence of three diseases—cholera, plague, and yellow fever—will be reported to the World Health Organization in Geneva, Switzerland. In the United States, the Centers for Disease Control and Prevention (CDC), U.S. Public Health Service, and the state health officers of all 50 states have agreed to report the occurrence of 51 diseases weekly and of another 10 diseases annually from the states to the CDC. Many states have regulations or laws that mandate reporting of these diseases and often of other diseases of specific interest to the state health department.

The methods of case reporting vary within each state. Passive reporting is one of the main methods. In such a case, physicians or personnel in clinics or hospitals report occurrences of relevant diseases by telephone, postcard, or a reporting form, usually at weekly intervals. In some instances, the report may be initiated by the public health or clinical laboratory where the etiologic agent is identified. Some diseases, such as human rabies, must be reported by telephone as soon as diagnosed. In an active surveillance program, the health authority regularly initiates the request for reporting. The local health department may call all or some health care providers at regular intervals to inquire about the occurrence of a disease or diseases. The active system may be used during an epidemic or if accurate data concerning all cases of a disease are desired.

The health care provider usually makes the initial passive report to a local authority, such as a city or county health department. This unit collates its data and sends a report to the next highest health department level, usually the state health department.

The number of cases of each reportable disease are presented weekly, via computer linkage, by the state health department to the CDC. Data are analyzed at each level to develop needed information to assist public health authorities in disease control and prevention. For some diseases, such as hepatitis, the CDC requests preparation of a separate case reporting form containing more specific details.

In addition, the CDC prepares and distributes routine reports summarizing and interpreting the analyses and providing information on epidemics and other appropriate public health matters. Most states and some county health departments also prepare and distribute their own surveillance reports. The CDC publishes Morbidity and Mortality Weekly Report, which is available for a small fee from the Massachusetts Medical Society. The CDC also prepares more detailed surveillance reports for specific diseases, as well as an annual summary report, all of which can also be obtained through the Massachusetts Medical Society.

Infection is the replication of organisms in the tissue of a host; when defined in terms of infection, disease is overt clinical manifestation. In an inapparent (subclinical) infection, an immune response can occur without overt clinical disease. A carrier (colonized individual) is a person in whom organisms are present and may be multiplying, but who shows no clinical response to their presence. The carrier state may be permanent, with the organism always present; intermittent, with the organism present for various periods; or temporary, with carriage for only a brief period. Dissemination is the movement of an infectious agent from a source directly into the environment; when infection results from dissemination, the source, if an individual, is referred to as a dangerous disseminator.

Infectiousness is the transmission of organisms from a source, or reservoir (see below), to a susceptible individual. A human may be infective during the preclinical, clinical, postclinical, or recovery phase of an illness. The incubation period is the interval in the preclinical period between the time at which the causative agent first infects the host and the onset of clinical symptoms; during this time the agent is replicating. Transmission is most likely during the incubation period for some diseases such as measles; in other diseases such as shigellosis, transmission occurs during the clinical period. The individual may be infective during the convalescent phase, as in diphtheria, or may become an asymptomatic carrier and remain infective for a prolonged period, as do approximately 5% of persons with typhoid fever.

The spectrum of occurrence of disease in a defined population includes sporadic (occasional occurrence); endemic (regular, continuing occurrence); epidemic (significantly increased occurrence); and pandemic (epidemic occurrence in multiple countries).

The chain of infection includes the three factors that lead to infection: the etiologic agent, the method of transmission, and the host ( Fig. 9-1 ). These links should be characterized before control and prevention measures are proposed. Environmental factors that may influence disease occurrence must be evaluated.

Summary of important aspects involved in the chain of any infection.

Etiologic Agent

The etiologic agent may be any microorganism that can cause infection. The pathogenicity of an agent is its ability to cause disease; pathogenicity is further characterized by describing the organism's virulence and invasiveness. Virulence refers to the severity of infection, which can be expressed by describing the morbidity (incidence of disease) and mortality (death rate) of the infection. An example of a highly virulent organism is Yersinia pestis, the agent of plague, which almost always causes severe disease in the susceptible host.

The invasiveness of an organism refers to its ability to invade tissue. Vibrio cholerae organisms are noninvasive, causing symptoms by releasing into the intestinal canal an exotoxin that acts on the tissues. In contrast, Shigella organisms in the intestinal canal are invasive and migrate into the tissue.

No microorganism is assuredly avirulent. An organism may have very low virulence, but if the host is highly susceptible, as when therapeutically immunosuppressed, infection with that organism may cause disease. For example, the poliomyelitis virus used in oral polio vaccine is highly attenuated and thus has low virulence, but in some highly susceptible individuals it may cause paralytic disease.

Other factors should be considered in describing the agent. The infecting dose (the number of organisms necessary to cause disease) varies according to the organism, method of transmission, site of entrance of the organism into the host, host defenses, and host species. Another agent factor is specificity; some agents (for example, Salmonella typhimurium) can infect a broad range of hosts; others have a narrow range of hosts. Styphi, for example, infects only humans. Other agent factors include antigenic composition, which can vary within a species (as in influenza virus or Streptococcus species); antibiotic sensitivity; resistance transfer plasmids (see Ch. 5 ); and enzyme production.

The reservoir of an organism is the site where it resides, metabolizes, and multiplies. The source of the organism is the site from which it is transmitted to a susceptible host, either directly or indirectly through an intermediary object. The reservoir and source can be different; for example, the reservoir for S typhi could be the gallbladder of an infected individual, but the source for transmission might be food contaminated by the carrier. The reservoir and source can also be the same, as in an individual who is a permanent nasal carrier of S aureus and who disseminates organisms from this site. The distinction can be important when considering where to apply control measures.

Method of Transmission

The method of transmission is the means by which the agent goes from the source to the host. The four major methods of transmission are by contact, by common vehicle, by air or via a vector.

In contact transmission the agent is spread directly, indirectly, or by airborne droplets. Direct contact transmission takes place when organisms are transmitted directly from the source to the susceptible host without involving an intermediate object; this is also referred to as person-to-person transmission. An example is the transmission of hepatitis A virus from one individual to another by hand contact. Indirect transmission occurs when the organisms are transmitted from a source, either animate or inanimate, to a host by means of an inanimate object. An example is transmission of Pseudomonas organisms from one individual to another by means of a shaving brush. Droplet spread refers to organisms that travel through the air very short distances, that is, less than 3 feet from a source to a host. Therefore, the organisms are not airborne in the true sense. An example of a disease that may be spread by droplets is measles.

Common-vehicle transmission refers to agents transmitted by a common inanimate vehicle, with multiple cases resulting from such exposure. This category includes diseases in which food or water as well as drugs and parenteral fluids are the vehicles of infection. Examples include food-borne salmonellosis, waterborne shigellosis, and bacteremia resulting from use of intravenous fluids contaminated with a gram-negative organism.

The third method of transmission, airborne transmission, refers to infection spread by droplet nuclei or dust. To be truly airborne, the particles should travel more than 3 feet through the air from the source to the host. Droplet nuclei are the residue from the evaporation of fluid from droplets, are light enough to be transmitted more than 3 feet from the source, and may remain airborne for prolonged periods. Tuberculosis is primarily an airborne disease; the source may be a coughing patient who creates aerosols of droplet nuclei that contain tubercle bacilli. Infectious agents may be contained in dust particles, which may become resuspended and transmitted to hosts. An example occurred in an outbreak of salmonellosis in a newborn nursery in which Salmonella -contaminated dust in a vacuum cleaner bag was resuspended when the equipment was used repeatedly, resulting in infections among the newborns.

The fourth method of transmission is vector borne transmission, in which arthropods are the vectors. Vector transmission may be external or internal. External, or mechanical, transmission occurs when organisms are carried mechanically on the vector (for example, Salmonella organisms that contaminate the legs of flies). Internal transmission occurs when the organisms are carried within the vector. If the pathogen is not changed by its carriage within the vector, the carriage is called harborage (as when a flea ingests plague bacilli from an infected individual or animal and contaminates a susceptible host when it feeds again; the organism is not changed while in the flea). The other form of internal transmission is called biologic. In this form, the organism is changed biologically during its passage through the vector (for example, malaria parasites in the mosquito vector).

An infectious agent may be transmitted by more than one route. For example, Salmonella may be transmitted by a common vehicle (food) or by contact spread (human carrier). Francisella tularensis may be transmitted by any of the four routes.

The third link in the chain of infection is the host. The organism may enter the host through the skin, mucous membranes, lungs, gastrointestinal tract, or genitourinary tract, and it may enter fetuses through the placenta. The resulting disease often reflects the point of entrance, but not always: meningococci that enter the host through the mucous membranes may nonetheless cause meningitis. Development of disease in a host reflects agent characteristics (see above) and is influenced by host defense mechanisms, which may be nonspecific or specific.

Nonspecific defense mechanisms include the skin, mucous membranes, secretions, excretions, enzymes, the inflammatory response, genetic factors, hormones, nutrition, behavioral patterns, and the presence of other diseases. Specific defense mechanisms or immunity may be natural, resulting from exposure to the infectious agent, or artificial, resulting from active or passive immunization (see Ch. 8 ).

The environment can affect any link in the chain of infection. Temperature can assist or inhibit multiplication of organisms at their reservoir; air velocity can assist the airborne movement of droplet nuclei; low humidity can damage mucous membranes; and ultraviolet radiation can kill the microorganisms. In any investigation of disease, it is important to evaluate the effect of environmental factors. At times, environmental control measures are instituted more on emotional grounds than on the basis of epidemiologic fact. It should be apparent that the occurrence of disease results from the interaction of many factors ( Table 9-2 ). Some of these factors are outlined here.

Table 9-2. General Factors That Influence the Occurence of Infectious Disease.

General Factors That Influence the Occurence of Infectious Disease.

The three major epidemiologic techniques are descriptive, analytic, and experimental. Although all three can be used in investigating the occurrence of disease, the method used most is descriptive epidemiology. Once the basic epidemiology of a disease has been described, specific analytic methods can be used to study the disease further, and a specific experimental approach can be developed to test a hypothesis.

Descriptive Epidemiology

In descriptive epidemiology, data that describe the occurrence of the disease are collected by various methods from all relevant sources. The data are then collated by time, place, and person. Four time trends are considered in describing the epidemiologic data. The secular trend describes the occurrence of disease over a prolonged period, usually years; it is influenced by the degree of immunity in the population and possibly nonspecific measures such as improved socioeconomic and nutritional levels among the population. For example, the secular trend of tetanus in the United States since 1920 shows a gradual and steady decline.

The second time trend is the periodic trend. A temporary modification in the overall secular trend, the periodic trend may indicate a change in the antigenic characteristics of the disease agent. For example, the change in antigenic structure of the prevalent influenza A virus every 2 to 3 years results in periodic increases in the occurrence of clinical influenza caused by lack of natural immunity among the population. Additionally, a lowering of the overall immunity of a population or a segment thereof (known as herd immunity) can result in an increase in the occurrence of the disease. This can be seen with some immunizable diseases when periodic decreases occur in the level of immunization in a defined population. This may then result in an increase in the number of cases, with a subsequent rise in the overall level of herd immunity. The number of new cases then decreases until the herd's immunity is low enough to allow transmission to occur again and new cases then appear.

The third time trend is the seasonal trend. This trend reflects seasonal changes in disease occurrence following changes in environmental conditions that enhance the ability of the agent to replicate or be transmitted. For example, food-borne disease outbreaks occur more frequently in the summer, when temperatures favor multiplication of bacteria. This trend becomes evident when the occurrence of salmonellosis is examined on a monthly basis ( Fig. 9-2 ).

An example of a disease showing a seasonal trend. Reported human Salmonella isolations, by 4-week average, in the United States from 1968 to 1980.

The fourth time trend is the epidemic occurrence of disease. An epidemic is a sudden increase in occurrence due to prevalent factors that support transmission.

A description of epidemiologic data by place must consider three different sites: where the individual was when disease occurred; where the individual was when he or she became infected from the source; and where the source became infected with the etiologic agent. Therefore, in an outbreak of food poisoning, the host may become clinically ill at home from food eaten in a restaurant. The vehicle may have been undercooked chicken, which became infected on a poultry farm. These differences are important to consider in attempting to prevent additional cases.

The third focus of descriptive epidemiology is the infected person. All pertinent characteristics should be noted: age, sex, occupation, personal habits, socioeconomic status, immunization history, presence of underlying disease, and other data.

Once the descriptive epidemiologic data have been analyzed, the features of the epidemic should be clear enough that additional areas for investigation are apparent.

Analytic Epidemiology

The second epidemiologic method is analytic epidemiology, which analyzes disease determinants for possible causal relations. The two main analytic methods are the case-control (or case-comparison) method and the cohort method. The case-control method starts with the effect (disease) and retrospectively investigates the cause that led to the effect. The case group consists of individuals with the disease; a comparison group has members similar to those of the case group except for absence of the disease. These two groups are then compared to determine differences that would explain the occurrence of the disease. An example of a case-control study is selecting individuals with meningococcal meningitis and a comparison group matched for age, sex, socioeconomic status, and residence, but without the disease, to see what factors may have influenced the occurrence in the group that developed disease.

The second analytic approach is the cohort method, which prospectively studies two populations: one that has had contact with the suspected causal factor under study and a similar group that has had no contact with the factor. When both groups are observed, the effect of the factor should become apparent. An example of a cohort approach is to observe two similar groups of people, one composed of individuals who received blood transfusions and the other of persons who did not. The occurrence of hepatitis prospectively in both groups permits one to make an association between blood transfusions and hepatitis; that is, if the transfused blood was contaminated with hepatitis B virus, the recipient cohort should have a higher incidence of hepatitis than the nontransfused cohort.

The case-control approach is relatively easy to conduct, can be completed in a shorter period than the cohort approach, and is inexpensive and reproducible; however, bias may be introduced in selecting the two groups, it may be difficult to exclude subclinical cases from the comparison group, and a patient's recall of past events may be faulty. The advantages of a cohort study are the accuracy of collected data and the ability to make a direct estimate of the disease risk resulting from factor contact; however, cohort studies take longer and are more expensive to conduct.

Another analytic method is the cross-sectional study, in which a population is surveyed over a limited period to determine the relationship between a disease and variables present at the same time that may influence its occurrence.

Experimental Epidemiology

The third epidemiologic method is the experimental approach. A hypothesis is developed and an experimental model is constructed in which one or more selected factors are manipulated. The effect of the manipulation will either confirm or disprove the hypothesis. An example is the evaluation of the effect of a new drug on a disease. A group of people with the disease is identified, and some members are randomly selected to receive the drug. If the only difference between the two is use of the drug, the clinical differences between the groups should reflect the effectiveness of the drug.

  • Epidemic Investigation

An epidemic investigation describes the factors relevant to an outbreak of disease; once the circumstances related to the occurrence of disease are defined, appropriate control and prevention measures can be identified. In an epidemic investigation, data are collected, collated according to time, place, and person, and analyzed and inferences are drawn.

In the investigation, the first action should be to confirm the existence of the epidemic by noting from past surveillance data the number of cases suspected and comparing this with the number of cases initially reported. Additionally, the investigator should discuss the occurrence of the disease with physicians or others who have seen or reported cases after examining patients and reviewing laboratory and hospital records. These diagnoses should then be verified. A case definition should be developed to differentiate patients who represent actual cases, those who represent suspected or presumptive cases, and those who should be omitted from further study. Additional cases may be sought or additional patient data obtained, and a rough case count made.

This initial phase consists basically of collecting data, which then must be organized according to time, place, and person. The population at risk should be identified and a hypothesis developed concerning the occurrence of the disease. If appropriate, specimens should be collected and transported to the laboratory. More specific studies may be indicated. Additional data from these studies should be analyzed and the hypothesis confirmed or altered. After analysis, control and prevention measures should be developed and, as far as possible, implemented. A report containing this information should be prepared and distributed to those involved in investigating the outbreak and in implementing control and/or prevention measures. Continued surveillance activities may be appropriate to evaluate the effectiveness of the control and prevention measures.

In the United States, the CDC assists state health departments by providing epidemiologic and laboratory support services on request. Its assistance supports disease investigations and diagnostic laboratory activities and includes various training programs conducted in the states and at the CDC. A close working relationship exists between the CDC and state health departments. Additionally, physicians frequently consult with CDC personnel on a variety of health-related problems and attend public health training programs.

The use of epidemiology to characterize a disease before its etiology has been identified is exemplified by the initial studies of acquired immune deficiency syndrome (AIDS). The first cases came to the attention of the CDC late in 1981 when an increase was observed in requests for pentamidine for treatment of Pneumocystis carinii pneumonia. This initiated specific surveillance activities and epidemiologic studies that provided important information about this newly diagnosed disease.

Initial symptoms include fever, loss of appetite, weight loss, extreme fatigue, and enlargement of lymph nodes. A severe immune deficiency then develops, which appears to be associated with opportunistic infections. These infections include P carinii pneumonia, diagnosed in 52 percent of cases; Kaposi sarcoma in 26 percent of cases; and both P carinii pneumonia and Kaposi sarcoma in 7 percent of cases. The remaining 15 percent of AIDS patients have other parasitic, fungal, bacterial, or viral infections associated with immunodeficiencies. Among the first 2,640 cases reported to the CDC, there were 1,092 deaths, a case-fatality rate of 41 percent. Approximately 95 percent of the cases were male; 70 percent were 20 to 49 years of age at the time of diagnosis. Approximately 40 percent of the cases were reported from New York City, 12 percent from San Francisco, 8 percent from Los Angeles, and the remainder from 32 other states. Cases were reported from at least 16 other countries. Among the 90 percent of patients who were categorized according to possible risk factors, those at highest risk were homosexuals or bisexuals (70 percent), intravenous drug abusers ( 17 percent), Haitian entrants into the United States (9.5 percent), and persons with hemophilia (1 percent).

Analysis of these initial data, collected before the etiologic agent of AIDS was identified, supported the hypothesis that transmission occurred primarily by sexual contact, receipt of contaminated blood or blood products, or contact with contaminated intravenous needles. Spread through casual contact did not seem likely. The epidemiologic data indicated that AIDS was an infectious disease. It has now been determined that AIDS results from infection with a retrovirus of the human T cell leukemia/lymphoma virus family, which has been designated human immunodeficiency virus type I (HIV-l). The initial hypotheses have been proven as shown by analysis of data subsequently collected.

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  • Cite this Page Brachman PS. Epidemiology. In: Baron S, editor. Medical Microbiology. 4th edition. Galveston (TX): University of Texas Medical Branch at Galveston; 1996. Chapter 9.

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  1. PDF Descriptive and Analytic Studies

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