Epidemiology plays a crucial role in improving the health of populations, and this study offers a systematic approach to the basic methods and principles of epidemiology. The aim of this study is to:

1. Interpret the principles of disease occurrence with stress on modifiable environmental factors, incorporating environmentally-determined behaviors;

2. Promote the application of epidemiology in preventing of disease and promoting health;

3. Develop health-related professionals for improving the health services, and to ensure that resources of health have the maximum possible effect, and;

4. Promote healthy clinical practice by implementing the conceptualization of clinical epidemiology.

Epidemiology is also significant in explaining the health status of groups. Understanding of the disease in populations is necessary for health caring authorities, who seek to implement limited resources to achieve the optimum effect by prioritizing health measures for prevention and care. In some specialized fields, such as occupational and environmental epidemiology, the study emphasizes on populations with peculiar types of exposure to the environment. Implementing epidemiological methods and principles to problems faced, while practicing medicine, these studies have led to the expansion of clinical epidemiology.  

This paper highlights the purpose of this study and determines the scope with examples and applications of epidemiological studies. Further, it also signifies the measurement of disease and exposure, identifying potential threats to the validity of epidemiological studies and also summarizes identified five threats to the validity of studies.

Definition of Epidemiology: Last defines the Epidemiology as the discipline of the statistical distribution and factors of health-concerning events in defined populations and the implementation of this branch for controlling and preventing health related problems (last, 2001).

1. Identified 5 threats to the validity of studies

Potential threats and errors in epidemiological studies

The aim of epidemiological investigations is to provide precise measures of occurrence of disease. However, there exist many possibilities of threats and errors in assessment. Epidemiologists pay much attention to reducing threats and evaluating the impact of threats that cannot be eradicated. Sources of threat and error can be systematic or random.

1. Random Error

Random error occurs when a value of the sample assessment diverges due to probability alone from the true population value. Random error leads to inaccurate measures and, commonly, there are three key sources of random error:

1.  Individual biological variation;

2.  Sampling error;

3. Measurement error.

Random error cannot be fully eliminated as it only studies a sample of the population. Sampling error commonly results due to the truth that a small sampling does not represent all population variables. The best method to minimize sampling error is by increasing the size of the research. Individual variation will likely to occur, and no measurement is perfectly precise. Measurement error can be minimized by stringent protocols and by conducting individual measurements as accurate as possible. Researchers must understand the measurement methods carried in the study, and the errors that these procedures can cause. Preferably, laboratories must be able to derive the precision and accuracy of their measurements by strict, systematic quality control methods (Bernstein, 2006).

2. Sample size

The size of the sample must be fairly large for studying so that there is enough statistical power to find the differences, and possible to do sample size calculations with standard formulae. The following information is necessary before carrying the calculation:

1. Necessary level of statistical importance of the ability to find a difference:

2. Quantum of disease in the sample population;

3. Error acceptable, or chance of lacking a real effect;

4. Corresponding sizes of the groups to be compared;

5. Magnitude of the impact under investigation.

Usually, sample size determination is through financial and logistic consideration, and adjustment has always to be done between costs and sample size.  The accuracy of a study can be enhanced by confirming that the groups consist of appropriate relative size. Occasionally, this is an issue concerning in case-control researches when needs a decision, while choosing the number of controls for each case. It is not feasible to be decisive about the operating ratio of controls to cases, as this relies on the comparative costs of controls and accumulating cases. If the controls are plentiful and cases are scarce, it is proper to enhance the ratio of controls to cases. Therefore, it is always essential to be assured that there is enough similarity between controls and cases when the collected data has to be examined.

3. Systematic error

In epidemiology, systematic error or bias happens when the results are differing in a methodical manner from the true values. A research or study having a small systematic error has a high accuracy, and the sample size does not influence the accuracy. The possible sources of bias or systematic error are at large, and fluctuate; more than 32 types of systematic error exist in epidemiology studies. The principal biases or systematic error are selection bias and measurement (or classification) bias.

4. Selection bias

Selection bias takes place when occurs a systematic difference between the characteristics of the population chosen for study and the characteristics of those that are not. An apparent source of selection bias takes place when participants select themselves for a study. Besides these participants also worry about an exposure, or they are unwell. For example, people who consent to participate in a study on the smoking effects differ in their smoking habits from non repliers; usually the latter are heavy smokers.

In a study of children’s health, where parents cooperation is necessary, selection bias is also possible. In a group study of newly born, successful 12 month follow up differed according to income of the parents. If individuals remaining in study own different characteristics from those who are not participating, the outcome is a biased estimate of the relationship between outcome and exposure (Grimes, 2002).

5. Measurement bias

Measurement bias happens when the individual classifications or measurements of exposure or disease are inaccurate – they do not measure accurately what is essential to measure. There exist numerous sources of measurement bias and their impacts are varying. For example, physiological and biochemical measurements are never fully accurate, and all laboratories produce different outcomes on the same specimen. If different laboratories carry the analysis of the specimens from the control and exposed groups randomly, then there can be fewer chances for systematic measurement bias, than in the condition, where a single laboratory performs analysis all specimens from the exposed group, and analysis of those from the control group in another (Grimes, 2002).

2.  Identify potential threats to the validity of epidemiological studies


Confounding is another prominent obstacle and threat in epidemiological studies. While studying the association between the occurrence of disease and exposure to a cause, confounding can take place when another exposure prevails in the population, which is under study and associate with both the exposure and disease.

A problem occurs if this alien factor itself is a decisive factor or risk factor to the health and unevenly distributed among the exposure groups. Confounding can occur when the separation of outcomes of two exposures is not proper, and the examination determines that the effect is because of one variable rather than the other. Two conditions must be present for a confounding factor.

Confounding occurs because the nonrandom distribution of risk causes in the source population thus offering misleading outcomes of effect. In this perception, it may seem to be a bias, but usually it does not come out from systematic error in research studies. Social class and age are usually confounders in epidemiological researches.

A relationship between coronary heart disease and high blood pressure in the true sense may represent subsequent changes in the two variables, which take place with increasing age. The potential of confounding impact of age should be taken into consideration, and once this is complete, it will show that high blood pressure certainly is a cause of coronary heart disease (Greenberg, 2001).

In the above, illustrated Figure 1, confounding can be the explanation for the relationship between risk of coronary heart disease and coffee drinking; since everybody knows that coffee consumption associate with smoking. Coffee drinkers are most likely smokers than people who do not consume coffee. It is a common observation that cigarette smoking causes coronary heart disease. Thus, it may be possible that the connection between coronary heart disease and coffee drinking merely depicts the known casual relationship between coronary heart disease and coffee drinking. In such situation, cigarette smoking confounds the apparent association between coronary heart disease and coffee drinking; smoking always correlates with coffee consumption and also a risk factor for those people who do no consume coffee (Rothman, 1998).

3. Integrated threats to the validity with the study examples

Validity is a reflection of the degree up to which a test has a capability of measuring what it intends to measure. A study will be valid if its outcomes coincide with the truth; there must no systematic error and the random error must be minimal as possible.

Internal Validity

Internal validity is the degree, which measures correctness of results for the group of people under study. For example, while measuring of blood hemoglobin it should be accurately distinguished among participants suffering from anemia. Analysis of the blood sample in different laboratory can produce varying results due to systematic error, but the rating of relationships with anemia done by one laboratory will be still internally valid.

External Validity

External validity is the degree to which the outcomes of a research apply to people who are not participants. External validity needs external quality control of judgments and measurements up to the extent to which the results of a research can be extrapolated. It is not necessary that the study sample should represent as a reference. For example, evidence of the low blood cholesterol in males is also relevant to females requires a determination about the external validity of studies in males. Usually, study designs support external validity that analyze clear hypothesis in well defined groups (Coggan, 1997).


The main epidemiological study methods are the case-control study, cross-sectional survey, the randomized controlled trial, and the cohort study. This study shows that measurement bias can be a problem, since it may not be possible to remember past disease accurately, and the growth of the disease may influence recall. Further, due to chance alone, random error is the deviation of a monitored value from the true population value. This can be minimized by increasing the size of the sample under study and enhancing the trustworthiness of the measurement method.

This paper observes that systematic error can also happen when there is a disposition to produce results that usually differ systematically from the true values. The prime sources of systematic error are measurement bias and selection bias.

The above errors depict that there are many biased factors to the interpretation of epidemiology; however, minimum set standards, while conducting a study, ensure that any decision made should be appropriate. The investigator must ensure that assessing epidemiological studies not only connote knowing how to analyze information but also is correct. Bias, chance, and confounding can always threat the validity of studies at all stages. Thus, the methodology should be well thought, and it must reflect in the study paper. Investigators need to show that they plan to reduce bias and account for confounding while explaining statistical methods and also report any potential effect of limitations on the results found.

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