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Sensitivity and Specificity

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Sensitivity and Specificity

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    1. Sensitivity and Specificity Bioterrorism Epidemiology Module 10 Missouri Department of Health And Senior Services This module will discuss precision, sensitivity, specificity, and positive and negative predictive values.This module will discuss precision, sensitivity, specificity, and positive and negative predictive values.

    2. Precision (Reliability) Measure of a random variation around a specific value Variation in method Variation in observer Intra- and inter observer Epidemiology assumes that background fluctuations in disease rates are random Increasing study size can usually increase precision Every time you obtain data such as a laboratory value or an answer on a survey, that measurement is subject to random variation. For example, if you measure the number of anthrax spores in a nasal sample more than one time, those values will differ slightly. Thus, if the first time you measure the number of spores, the value is 1010, the second time it might be 890, and the third time 1160 spores. These differences might be due to variation in the method or in the observer. The variation can increase further if more than one observer interprets the measurements. If an investigator measures the rate of respiratory disease on three successive days, the measurements will vary. If, however, there are no factors that would cause a change in those rates, the fluctuations are random. Since epidemiologists assume that the background fluctuations in disease rates are random, they need to measure these rates as precisely as possible in order to determine when rates exceed background levels. The more you can decrease this random variability, the more precise your measurement will be. Usually increased sample size will increase precision.Every time you obtain data such as a laboratory value or an answer on a survey, that measurement is subject to random variation. For example, if you measure the number of anthrax spores in a nasal sample more than one time, those values will differ slightly. Thus, if the first time you measure the number of spores, the value is 1010, the second time it might be 890, and the third time 1160 spores. These differences might be due to variation in the method or in the observer. The variation can increase further if more than one observer interprets the measurements. If an investigator measures the rate of respiratory disease on three successive days, the measurements will vary. If, however, there are no factors that would cause a change in those rates, the fluctuations are random. Since epidemiologists assume that the background fluctuations in disease rates are random, they need to measure these rates as precisely as possible in order to determine when rates exceed background levels. The more you can decrease this random variability, the more precise your measurement will be. Usually increased sample size will increase precision.

    3. Sensitivity and Specificity Sensitivity and specificity are important for a surveillance system. Sensitivity is particularly important for a system that is designed to detect an outbreak of disease, however if specificity is low, then the surveillance system might indicate an outbreak when one has not occurred.Sensitivity and specificity are important for a surveillance system. Sensitivity is particularly important for a system that is designed to detect an outbreak of disease, however if specificity is low, then the surveillance system might indicate an outbreak when one has not occurred.

    4. Sensitivity Calculate Sensitivity for this data. Calculate Sensitivity for this data.

    5. Sensitivity This calculation indicates that the surveillance systems were able to detect 70% of the people with respiratory disease.This calculation indicates that the surveillance systems were able to detect 70% of the people with respiratory disease.

    6. Specificity Calculate specificity for this dataCalculate specificity for this data

    7. Specificity Only 20% of the people without the disease were labeled by the system as diseased.Only 20% of the people without the disease were labeled by the system as diseased.

    8. Positive and Negative Predictive Values Positive predictive value is the proportion of people the surveillance system indicates as having the disease who actually have it. Negative predictive value is the proportion of people the surveillance system indicates as not having the disease who actually do not have it. Let us look at a couple of examples to illustrate these terms.Positive predictive value is the proportion of people the surveillance system indicates as having the disease who actually have it. Negative predictive value is the proportion of people the surveillance system indicates as not having the disease who actually do not have it. Let us look at a couple of examples to illustrate these terms.

    9. Positive Predictive Value Calculate the positive predictive value for this surveillance data.Calculate the positive predictive value for this surveillance data.

    10. Positive Predictive Value The calculation indicates that of the 2250 people the surveillance system identified as having the disease only 16% actually have the disease. A surveillance system with such a low positive predictive value is of little use because epidemiologists would be following up on false indications of an outbreak of respiratory disease.The calculation indicates that of the 2250 people the surveillance system identified as having the disease only 16% actually have the disease. A surveillance system with such a low positive predictive value is of little use because epidemiologists would be following up on false indications of an outbreak of respiratory disease.

    11. Negative Predictive Value Calculate the negative predictive value for this data.Calculate the negative predictive value for this data.

    12. Negative Predictive Value 98% of all the people indicated by the surveillance system as not having respiratory disease actually do not have the disease. This slide concludes this module.98% of all the people indicated by the surveillance system as not having respiratory disease actually do not have the disease. This slide concludes this module.

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