Statistical data can enhance insights by establishing the relationship of events or factors to a population. However, the value and validity of these insights depends on the researchers’ skill in using data available for their research. Therefore there is a need to understand the distinctions and uses of these data to be able to develop insights regarding the population or set of information. One example understands the difference between incidence and prevalence data. By understanding characteristics unique to each, researchers will be able to use them appropriately and effectively.
Incidence refers to the probability that an individual will be fall under a constraint within a given period of time (Casella & Berger, 2001). In epidemiology, this refers to the chances that an individual will contract a specific in a particular time period. The rate of incidence is computed as a factor of the population susceptible to the constraint. Prevalence, on the other hand is defined as the probability that a constraint given a population (McClave & Sincich, 2006). Extending the definition to epidemiology, this refers to the likelihood of an individual in a population to acquire the disease. Its rate is derived from dividing the number of diagnosed cases over the total population.
The distinction between the two sets of data is based on the time and population it refers to. Incidence has a time frame or range while prevalence only considers the current data or data at a specific time. With regards to population, incidence is measured based on a constraint, usually defined because of its vulnerability to the diseases, whereas prevalence considers the whole population. If prevalence data is diminished, it means that disease is being eradicated versus in incidence where diminishing values only indicate the degree by which medical interventions are able to treat the disease (Bertoni et al, 2004). Furthermore, prevalence is the cumulative measure of incidence as of the time period which can differ significantly from incidence values (Casella & Berger, 2001).
Incidence data is often used when studying new and or short-term events. The data is independent from data gathered from other time periods, measuring only the newly diagnosed cases and does not consider populations who still have the condition or disease. As a measure of success in medical intervention, it measures the rate of success of treatments of the disease’s symptoms. They are often utilized when conditions being studied are curable or when symptoms can become absent or in studies periods of vulnerability for a disease (Bertoni et al, 2004). Thus, in the study of respiratory sensitization and allergy n due to reaction enzyme producing plant, incidence data was used because the focus of the study the significance of exposure and host factors to employee reactions (Larsen, 2007). Incidence rates will better measure this relationship since the production of the enzymes that produce the sensitivity is seasonal and the population is limited to the employees who are exposed to the plants.
Prevalence data in contrast is used more for long-term studies. As mentioned, it is a cumulative measure and therefore considers new and old diagnoses. It is often used in chronic illnesses or conditions whose symptoms will always fall under the constraints characterized for it. There is a presumption of the continuance of the disease or condition and the date is used often in conjunction with periodic measures.
Thus, in the measure of effectiveness of vaccination against Hepatitis A in north-eastern Italy, prevalence data were used because the concern was the success of the preventive measures for drug users in low prevalence areas: the measure is not among the population the cases that will be reported and the comparison of data is annually (Lugoboni et al, 2005). Prevalence studies will better develop insights to the success rate of the intervention, in this case vaccination against Hepatitis A, because vaccination implies that focus is on the non-occurrence of the condition and at the same time, the focus is not short-term.
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Casella, George and Berger, Roger L. (2001)Statistical Inference, 2nd Edition. London: Duxbury Press
Johnsen, A. I. C. R. , Frickmann, J. and Mikkelsen, S. (2007). Incidence of respiratory sensitisation and allergy to enzymes among employees in an enzyme producing plant and the relation to exposure and host factors. Occup. Environ. Med., 64: 763 – 768.
Lugoboni, Fabio, Quaglio, Gianluca, Pajusco, Benedetta, Foroni, Blengio, Maurizio Gianstefano, Talamini, Giorgio, Mezzelani, Paolo and Des Jarlais, Don C. (2005). Prevalence of hepatitis A among drug users in north-eastern Italy: Is vaccination necessary in low prevalence areas? Eur J Public Health, 15: 464 – 466.
McClave, James T. and Sincich, Terry (2006). Statistics,10th Edition. by New York: Prentice Hall