Epidemiology: Principles, Applications, and Measurement Scales

Posted on May 16, 2024 in Psychology and Sociology

Areas Where Epidemiology is Applied

Health-Disease-Environment

Quantification of Epidemiological Phenomena

Using Natural Experiments in Causative Research

Research Method of John Snow

He did it as follows:

  1. Diagnosis of epidemic.
  2. Chronological distribution of cases.
  3. Spatial distribution.
  4. Survey and analysis of other attributes.
  5. Set cause.
  6. Recommendations.
  7. Present a report with the conclusions reached.

Experimental Studies

The epidemiological study is based on counting and observing. Experience would be a very appropriate addition, but this may not always be done within the legal framework of ethics as the basis of epidemiology is still the observation of phenomena and counting of cases.

Uses and Purposes of Epidemiology

  • Diagnosis of Population Health

    To reach it we need to develop relevant health indicators. Before, only morbidity and mortality were considered, now more aspects are discussed: demographic structure of the population, defining environmental, biological, and social variables (characteristics of the environment in which people live, race), indicators of morbidity and mortality, and rate of global health.

  • Evaluation of Diagnostic Methods and Treatment

    We must take into account: patient characteristics, characteristics of the disease, characteristics of therapy used. * Rating: Efficacy, Cost, Administration, acceptance and control, absence of side effects.

  • Establish Probability and Risk

    We will be able to predict the probability and the risk that the person can suffer from a certain disease. This is going to be achieved with the analysis of the collective experience of a large number of representative individuals who possess the characteristics considered.

  • Better Understanding of Biological Phenomena

    We will be able to better understand the etiology of an event and see how it increases or decreases, therefore we will serve the biological event description and study of their distribution according to different variables of person, place, and time. We can find different risk factors that cause a biological event.

  • Research of Causes

    Is going to apply for health and causes of disease in order to understand these events and to contribute to better health. Phenomena that are observed: The natural conditions, possible interactions, context of all social and economic factors.

Variables in Epidemiology

a) Risk Factor (most important)

Features of a Risk Factor:

  • Endogenous or exogenous.
  • Can be controlled.
  • Precedes the beginning of the illness.
  • Responsibility in the production of the disease.

b) Risk Markers

Is reserved for:

  • Variable = endogenous.
  • Not controllable.
  • Defines vulnerable individuals.
  • Draws an increased risk of developing the disease, but will have no direct influence on production.

c) Indicators of Risk

Harbinger of the disease:

  • It demonstrates the early presence of the disease.
  • Is a significant feature attached to the disease in a preclinical state.
  • Not involved in the production of the disease.

Relationship Between Variables

Association

The relationship between two variables significantly higher or lower than chance would explain on the basis of the frequency of each of them separately.

  • Positive: when the probability of occurrence of the variable increases with the presence of another.
  • Negative: when the probability of occurrence of the variable decreases with the fact that the next one happens.

Types of Association:

  1. Association is not Causation: in turn can be:
    • Artificial: will occur when the presence of a variable associated with a factor and the disease can establish the association between the two, being the illogical association as a causal factor against scientific understanding.
    • Spurious or Error: is the error that the sample selection or failure to perform work leads to a causal association being false, i.e., we arrive at false conclusions. Causal association appears to be false: no causal association.
  2. Causal Association: is one that meets the criteria of causality. Will be:
    • Direct Causal Association: is that which immediately precedes the effect it produces.
    • Indirect Causal Association: is one that has other more immediate causes intermediate between it and the production of the effect.

Independence

Is going to occur when the increase or decrease of the variable does not follow a similar effect in the other.

Causality Criteria

Internal Validity

  • a) Strength of Association: the relationship between the frequency of occurrence of the disease in individuals exposed to a risk factor analyzed with respect to the same disease in unexposed to this factor so that, as this ratio is higher, there is more possibility of causal association.
  • b) Time Frame: a causal association requires that the risk factor precedes the onset of the effect it causes. That is, to establish a causal relationship, first must be the risk factor and then the disease.
  • c) Dose-Response Effect: the frequency of occurrence of the disease increases with dose, time, and level of exposure. A higher dose and higher level as long as more frequent illness.

Scientific Consistency

  • a) Consistency: which assesses the consistency and reproducibility of the association that the study indicates, that is, they can be compared. If there have been several studies on the same subject at different times or by different methods that produced the same results, we can talk more about a causal link between the risk factor and disease.
  • b) Consistency with Current Scientific Knowledge: that is, if the hypothetical causal relationship makes sense in the context of current scientific knowledge, it will be enhanced to accept the arguments that there is a causal interpretation.
  • c) Specificity of Association: that is, if the only factor studied is associated with the disease so that if I introduce the factor, there was the disease and if I remove the factor, the disease disappears or if the disease is associated with only one factor, the causal interpretation is easier.
  • d) Experimental Evidence: that is, causal evidence is pro-experience. If I can experience because I can see if there is, but we cannot experience.

Scale of Measurement of Variables

  1. Nominal Scale:

    Set groups according to the presence or absence of an attribute or characteristic.
    • Dichotomous Nominal Scale: 2 possibilities dead or alive.
    • Polychotomous Nominal Scale: more than 2 possibilities blood groups: A, B, AB and O.

    Features of Nominal Scale:

    • Objective.
    • Common use in medicine.

  2. Ordinal Scale

    we can establish a logical order sequence. Permits establishing that measures the intensity of the attribute.

    Features of Ordinal Scale:

    • It is common in medicine.
    • It is essential that we establish the objective criteria.

  3. Numerical Scale:

    the more developed of the measurement scales, not only will establish an order but we will be able to know the distance or degree that separates them. It is the most comprehensive level. * The more you use are: Weight (gr), Size (cm), Temperature (°C)

    Types of Numerical Scales:

    • Discrete: when the attribute is presented in an integrated form from one stage to the next but they may not exist intermediate degrees (number of teeth, number of deliveries…).
    • Continuous: measured as whole numbers or degrees (height in cm, weight in kg…).