Annual and Lifetime Prevalence
General medical and psychological concerns are reported in terms of annual or lifetime prevalence. Whereas a season of participation may define the time frame for athletes, the medical literature will use a longer period for reporting. Annual prevalence refers to an onset in the previous 12 months, and lifetime prevalence means any history of that illness or disease. Because lifetime prevalence covers a longer time period, it is significantly lower than annual prevalence data. For example, the lifetime prevalence of an eating disorder in adults in the United States is 4.7%, but the annual prevalence of eating disorders among the most at-risk group, teenagers, is 2.7%.
Validity of Clinical Tests
Clinical tests are an integral part of the injury evaluation. After a review of the patient’s records, thorough history, and performance measures, the clinician will administer clinical diagnostic tests that may include the use of medical instruments or technology. Often, special tests and diagnostic tests have statistical information available to assist the clinician to properly implement and interpret the test. Through statistical analysis, one may discover that a test is not indicated or is best paired with others. A test’s sensitivity and specificity must be taken into account when selecting a test and interpreting results. High sensitivity means the test correctly identifies those with the condition. High specificity means that the test rules out those without the condition. Both are important for an accurate diagnosis. Being an evidence-based clinician involves considering the available data about a clinical test and integrating it appropriately into the clinical examination. To be efficient, accurate, and effective, athletic trainers should research and use epidemiological evidence in their evaluations.
A 2×2 table is a useful tool to visualize clinical test results (Figure 3-14). Each box represents
2 pieces of information. First, it represents the test result being positive or negative. Second, it represents whether the patient has the condition. Therefore, box “a” represents a true positive, meaning the patient tested positive and truly has the disorder. Box “b” represents a false positive, meaning the patient tested positive when they did not have the condition. Box “c” represents a false negative, meaning the test result was negative, but the patient does have the condition. Therefore, the condition is undetected. Finally, box “d” represents a true negative, meaning the patient tested negative and does not have the condition. The information from the 2×2 table is used to calculate sensitivity and specificity. This statistical information is used to assist clinicians in knowing the strengths and weaknesses of diagnostic tests. Sensitivity and specificity are inversely proportional, meaning that if one goes up, the other goes down.4 One cannot have a clinical test that has high specificity and sensitivity. Therefore, a combination of tests is often recommended.
Sensitivity: A test with high sensitivity is effective as correctly classifying the patient with the condition. Therefore, if a condition is suspected, and a special test with a high sensitivity is administered and yields a positive result, the clinician can feel confident that the patient has that condition. Sensitivity is expressed as a percentile and can be calculated using the following formula:
When a clinician obtains a negative result using a highly sensitive test, the clinician can have a high level of confidence that the condition can be ruled out. This concept is termed SnNOut. SnNOut is an acronym for sensitive (Sn), negative (N), condition ruled out (Out). Highly sensitive tests are good at identifying people with the condition.
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An example of this from daily life could be drawn from going through security at the airport. Say there have been several threats of attack, and security is heightened. The metal detectors are turned on high and will beep, even with the smallest infraction. So, whereas a belt buckle may not have set it off in the past, even a small earring back will now trigger the alarm on the detector. Therefore, if someone passes through the detector and it does not beep (a negative test), one can be sure that they are not carrying a metal weapon. Because the detector has high sensitivity and there was a negative result, one can feel confident that the individual is not carrying a weapon. However, many people may receive a false positive, meaning that they receive a beep when they are not carrying a weapon.
Specificity: A test with high specificity is effective at correctly categorizing patients without the condition. Therefore, if a condition is suspected and a special test with a high specificity is administered and yields a positive result, the clinician can feel confident that the patient has that condition. Specificity is expressed as a percentile and can be calculated using the following formula:
When a clinician obtains a positive result using a highly specific test, the clinician can have a high level of confidence that the condition can be ruled in. This concept is termed SpPin (Specific, Positive, In). Highly specific tests are good at correctly classifying people without the condition.
Here is an example using the Ottawa Ankle Rules. In 2013, Clifton et al5 proposed a “Clinical Bottom Line” for the utilization of the Ottawa Ankle Rules. A meta-analysis revealed that the Ottawa Ankle Rules have a high sensitivity of 98.5%. Specificity ranges from the meta-analysis ranged from 26% to 50%. That is, if the clinician obtains a negative result using the Ottawa Ankle Rules, he or she can feel confident in ruling out an ankle fracture. The high sensitivity levels of this clinical prediction rule make it valuable in determining when to avoid referral. The authors noted that the poor specificity indicates that positive findings may not indicate a fracture and should not inform the clinician’s decision-making process. This is a good display of the inverse relationship of sensitivity and specificity, as well as the proper and improper usage of a clinical test in decision making. Figure 3-15 provides the qualities and uses for sensitivity and specificity.
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