You are tasked with analyzing the negative predictive value of an experimental serum marker for ovarian cancer. You choose to enroll 2,000 patients across multiple clinical sites, including both 1,000 patients with ovarian cancer and 1,000 age-matched controls. From the disease and control subgroups, 700 and 100 are found positive for this novel serum marker, respectively. Which of the following represents the NPV for this test?
Q2
Specificity for breast examination is traditionally rather high among community practitioners. A team of new researchers sets forth a goal to increase specificity in detection of breast cancer from the previously reported national average of 74%. Based on the following results, has the team achieved its goal?
Breast cancer screening results:
Patients WITH breast cancer | Patients WITHOUT breast cancer
Test is Positive (+) 21 | 5
Test is Negative (-) 7 | 23
Q3
An inpatient psychiatrist recently had two patients who developed serious gastrointestinal infections while taking clozapine. He was concerned that his patients had developed agranulocytosis, a relatively rare but dangerous adverse event associated with clozapine. When the psychiatrist checked the absolute neutrophil count (ANC) of both patients, one was 450/mm3, while the other was 700/mm3 (N=1,500/mm3). According to the clozapine REMS (Risk Evaluation and Mitigation Strategy) program, severe neutropenia in clozapine recipients has often been defined as an absolute neutrophil count (ANC) less than 500/mm3. Changing the cutoff value to 750/mm3 would affect the test performance of ANC with regard to agranulocytosis in which of the following ways?
Q4
A home drug screening test kit is currently being developed. The cut-off level is initially set at 4 mg/uL, which is associated with a sensitivity of 92% and a specificity of 97%. How might the sensitivity and specificity of the test change if the cut-off level is changed to 2 mg/uL?
Q5
A group of investigators who are studying individuals infected with Trypanosoma cruzi is evaluating the ELISA absorbance cutoff value of serum samples for diagnosis of infection. The previous cutoff point is found to be too high, and the researchers decide to lower the threshold by 15%. Which of the following outcomes is most likely to result from this decision?
Q6
A physician at an internal medicine ward notices that several of his patients have hyponatremia without any associated symptoms. Severe hyponatremia, often defined as < 120 mEq/L, is associated with altered mental status, coma, and seizures, and warrants treatment with hypertonic saline. Because some patients are chronically hyponatremic, with serum levels < 120 mEq/L, but remain asymptomatic, the physician is considering decreasing the cutoff for severe hyponatremia to < 115 mEq/L. Changing the cutoff to < 115 mEq/L would affect the validity of serum sodium in predicting severe hyponatremia requiring hypertonic saline in which of the following ways?
Q7
A scientist in Chicago is studying a new blood test to detect Ab to EBV with increased sensitivity and specificity. So far, her best attempt at creating such an exam reached 82% sensitivity and 88% specificity. She is hoping to increase these numbers by at least 2 percent for each value. After several years of work, she believes that she has actually managed to reach a sensitivity and specificity much greater than what she had originally hoped for. She travels to China to begin testing her newest blood test. She finds 2,000 patients who are willing to participate in her study. Of the 2,000 patients, 1,200 of them are known to be infected with EBV. The scientist tests these 1,200 patients' blood and finds that only 120 of them tested negative with her new exam. Of the patients who are known to be EBV-free, only 20 of them tested positive. Given these results, which of the following correlates with the exam's specificity?
Q8
A student health coordinator plans on leading a campus-wide HIV screening program that will be free for the entire undergraduate student body. The goal is to capture as many correct HIV diagnoses as possible with the fewest false positives. The coordinator consults with the hospital to see which tests are available to use for this program. Test A has a sensitivity of 0.92 and a specificity of 0.99. Test B has a sensitivity of 0.95 and a specificity of 0.96. Test C has a sensitivity of 0.98 and a specificity of 0.93. Which of the following testing schemes should the coordinator pursue?
Q9
The World Health Organization suggests the use of a new rapid diagnostic test for the diagnosis of malaria in resource-limited settings. The new test has a sensitivity of 70% and a specificity of 90% compared to the gold standard test (blood smear). The validity of the new test is evaluated at a satellite health center by testing 200 patients with a positive blood smear and 150 patients with a negative blood smear. How many of the tested individuals are expected to have a false negative result?
Q10
A public health campaign increases vaccination rates against human papillomaviruses 16 and 18. Increased vaccination rates would have which of the following effects on the Papanicolaou test?
Sensitivity/Specificity US Medical PG Practice Questions and MCQs
Question 1: You are tasked with analyzing the negative predictive value of an experimental serum marker for ovarian cancer. You choose to enroll 2,000 patients across multiple clinical sites, including both 1,000 patients with ovarian cancer and 1,000 age-matched controls. From the disease and control subgroups, 700 and 100 are found positive for this novel serum marker, respectively. Which of the following represents the NPV for this test?
A. 700 / (700 + 300)
B. 700 / (300 + 900)
C. 700 / (700 + 100)
D. 900 / (900 + 100)
E. 900 / (900 + 300) (Correct Answer)
Explanation: ***900 / (900 + 300)***
- The **Negative Predictive Value (NPV)** is the probability that a person with a **negative test result** does not have the disease. It is calculated as **true negatives (TN)** divided by the sum of true negatives and **false negatives (FN)**, i.e., TN / (TN + FN).
- In this scenario: there are 1,000 ovarian cancer patients, and 700 tested positive, meaning **300 tested negative (false negatives)**. There are 1,000 controls, and 100 tested positive, meaning **900 tested negative (true negatives)**. Therefore, NPV = 900 / (900 + 300).
*700 / (700 + 300)*
- This calculation represents the sensitivity of the test, which is the proportion of true positives among all individuals with the disease (700 true positives / 1000 diseased individuals).
- It does not account for the true negatives or false positives, which are crucial for determining predictive values.
*700 / (300 + 900)*
- This formula mixes elements and does not correspond to a standard measure of test validity.
- The numerator (700) is the number of true positives, and the denominator incorrectly combines false negatives (300) and true negatives (900).
*700 / (700 + 100)*
- This calculation represents the **Positive Predictive Value (PPV)**, which is the probability that a person with a **positive test result** actually has the disease (700 true positives / (700 true positives + 100 false positives)).
- It does not assess the negative predictive power of the test.
*900 / (900 + 100)*
- This calculation represents the **specificity** of the test, which is the proportion of true negatives among all individuals without the disease (900 true negatives / 1000 controls).
- While this involves true negatives, it does not account for false negatives, which are essential for calculating NPV.
Question 2: Specificity for breast examination is traditionally rather high among community practitioners. A team of new researchers sets forth a goal to increase specificity in detection of breast cancer from the previously reported national average of 74%. Based on the following results, has the team achieved its goal?
Breast cancer screening results:
Patients WITH breast cancer | Patients WITHOUT breast cancer
Test is Positive (+) 21 | 5
Test is Negative (-) 7 | 23
A. No, the research team’s results lead to nearly the same specificity as the previous national average.
B. Yes, the team has achieved an increase in specificity of over 15%.
C. It can not be determined, as the prevalence of breast cancer is not listed.
D. It can not be determined, since the numbers affiliated with the first trial are unknown.
E. Yes, the team has achieved an increase in specificity of approximately 8%. (Correct Answer)
Explanation: ***Yes, the team has achieved an increase in specificity of approximately 8%.***
- Specificity is calculated as **True Negatives / (True Negatives + False Positives)**. In this case, specificity = 23 / (23 + 5) = 23 / 28 = 0.8214 or **82.14%**.
- Comparing this to the national average of 74%, the increase is 82.14% - 74% = **8.14%**.
*No, the research team’s results lead to nearly the same specificity as the previous national average.*
- The calculated specificity is **82.14%**, which is significantly higher than the 74% national average, not nearly the same.
- An **8% increase** represents a substantial improvement in the ability of the test to correctly identify individuals without the disease.
*Yes, the team has achieved an increase in specificity of over 15%.*
- The calculated increase in specificity is **8.14%**, which is less than 15%.
- This option incorrectly overestimates the magnitude of the improvement.
*It can not be determined, as the prevalence of breast cancer is not listed.*
- Prevalence is used to calculate **positive and negative predictive values**, but not sensitivity or specificity.
- Specificity can be directly calculated from the provided data on true negatives and false positives.
*It can not be determined, since the numbers affiliated with the first trial are unknown.*
- To answer the question, we only need the **original national average specificity (74%)** for comparison and the current trial's results to calculate the new specificity.
- The raw numbers from the "first trial" (national average) are not required to determine if the goal was met.
Question 3: An inpatient psychiatrist recently had two patients who developed serious gastrointestinal infections while taking clozapine. He was concerned that his patients had developed agranulocytosis, a relatively rare but dangerous adverse event associated with clozapine. When the psychiatrist checked the absolute neutrophil count (ANC) of both patients, one was 450/mm3, while the other was 700/mm3 (N=1,500/mm3). According to the clozapine REMS (Risk Evaluation and Mitigation Strategy) program, severe neutropenia in clozapine recipients has often been defined as an absolute neutrophil count (ANC) less than 500/mm3. Changing the cutoff value to 750/mm3 would affect the test performance of ANC with regard to agranulocytosis in which of the following ways?
A. Decreased true positives
B. Increased positive predictive value
C. Increased false positives (Correct Answer)
D. Unchanged specificity
E. Decreased sensitivity
Explanation: ***Increased false positives***
- Raising the **ANC cutoff** from 500/mm³ to 750/mm³ means more individuals with an ANC between 500 and 750/mm³ will now be classified as having neutropenia.
- This increases the likelihood of classifying patients without agranulocytosis as having the condition, thereby increasing **false positives**.
*Decreased true positives*
- A higher cutoff would likely lead to an **increase in true positives**, not a decrease, as more cases meeting the criteria for severe neutropenia would be identified.
- It would capture more patients who genuinely have low ANC, even if they don't develop full-blown agranulocytosis.
*Increased positive predictive value*
- An increase in **false positives** would lead to a decrease in **positive predictive value (PPV)**, as a smaller proportion of positive test results would truly represent agranulocytosis.
- PPV is the probability that a positive test result reflects the actual presence of the disease.
*Unchanged specificity*
- **Specificity** is the ability of the test to correctly identify those *without* the disease. By lowering the threshold (making it easier to test positive), specificity would decrease, not remain unchanged.
- Many healthy individuals with ANC between 500-750/mm³ would now be incorrectly classified as having severe neutropenia.
*Decreased sensitivity*
- **Sensitivity** refers to the ability of a test to correctly identify those *with* the disease. By lowering the cutoff (making it easier to test positive for neutropenia), sensitivity would increase, not decrease.
- More true cases of severe neutropenia (and potential agranulocytosis) would be detected earlier.
Question 4: A home drug screening test kit is currently being developed. The cut-off level is initially set at 4 mg/uL, which is associated with a sensitivity of 92% and a specificity of 97%. How might the sensitivity and specificity of the test change if the cut-off level is changed to 2 mg/uL?
A. Sensitivity = 92%, specificity = 97%
B. Sensitivity = 95%, specificity = 98%
C. Sensitivity = 100%, specificity = 97%
D. Sensitivity = 90%, specificity = 99%
E. Sensitivity = 97%, specificity = 96% (Correct Answer)
Explanation: ***Sensitivity = 97%, specificity = 96%***
- Lowering the cut-off from 4 mg/uL to 2 mg/uL means that more individuals will be classified as **positive** (anyone with drug levels ≥2 mg/uL instead of ≥4 mg/uL). This change will **increase the sensitivity** (capturing more true positives, fewer false negatives) but **decrease the specificity** (more false positives among those without the condition).
- Therefore, sensitivity will increase (e.g., to 97%), and specificity will decrease (e.g., to 96%), reflecting the fundamental trade-off between these metrics.
*Sensitivity = 92%, specificity = 97%*
- This option reflects the **original values** at the 4 mg/uL cut-off and does not account for the change in the threshold.
- A change in the cut-off level will inherently alter the test's performance characteristics.
*Sensitivity = 95%, specificity = 98%*
- This option suggests an increase in **both sensitivity and specificity**, which is generally not possible by simply changing the cut-off level in the same direction.
- There is typically an **inverse relationship** between sensitivity and specificity when adjusting the cut-off threshold.
*Sensitivity = 100%, specificity = 97%*
- Reaching **100% sensitivity** while maintaining a high specificity is highly unlikely with a simple cut-off adjustment.
- While sensitivity would increase with a lower cut-off, achieving perfect sensitivity is unrealistic in clinical practice.
*Sensitivity = 90%, specificity = 99%*
- This option suggests a **decrease in sensitivity** and an **increase in specificity**.
- A lower cut-off would lead to more positive results, thus increasing sensitivity and reducing specificity, which contradicts the proposed values.
Question 5: A group of investigators who are studying individuals infected with Trypanosoma cruzi is evaluating the ELISA absorbance cutoff value of serum samples for diagnosis of infection. The previous cutoff point is found to be too high, and the researchers decide to lower the threshold by 15%. Which of the following outcomes is most likely to result from this decision?
A. Increased negative predictive value (Correct Answer)
B. Unchanged true positive results
C. Decreased sensitivity
D. Increased specificity
E. Increased positive predictive value
Explanation: ***Increased negative predictive value***
- Lowering the absorbance cutoff for the ELISA test makes it **easier to test positive**, which increases **sensitivity** (more true positives are detected, fewer false negatives occur).
- **Negative predictive value (NPV)** is the probability that a person who tests negative truly does not have the disease: NPV = TN / (TN + FN).
- When the cutoff is lowered, **fewer infected individuals will be missed** (false negatives decrease). This reduction in false negatives improves the NPV because there are fewer disease-positive individuals in the "test-negative" group.
- Therefore, a negative test result becomes **more reliable at ruling out infection**, increasing the NPV.
*Unchanged true positive results*
- Lowering the cutoff means that samples with lower absorbance values (previously below threshold) from truly infected individuals will now be classified as positive.
- This directly **increases the number of true positive results**, not keeps them unchanged.
- The whole purpose of lowering the threshold is to capture more infected cases.
*Decreased sensitivity*
- **Sensitivity** = TP / (TP + FN), the ability to correctly identify those with disease.
- Lowering the cutoff **increases sensitivity** by making it easier to test positive, thereby capturing more true positives and reducing false negatives.
- A lower threshold would never decrease sensitivity—it does the opposite.
*Increased specificity*
- **Specificity** = TN / (TN + FP), the ability to correctly identify those without disease.
- Lowering the cutoff causes some uninfected individuals to now test positive (false positives increase).
- This **decreases specificity**, not increases it, as fewer true negatives remain.
*Increased positive predictive value*
- **PPV** = TP / (TP + FP), the probability that a positive test indicates true disease.
- While lowering the cutoff increases true positives, it also **increases false positives more substantially**.
- The increased false positives dilute the proportion of true positives among all positive results, thereby **decreasing the PPV**.
Question 6: A physician at an internal medicine ward notices that several of his patients have hyponatremia without any associated symptoms. Severe hyponatremia, often defined as < 120 mEq/L, is associated with altered mental status, coma, and seizures, and warrants treatment with hypertonic saline. Because some patients are chronically hyponatremic, with serum levels < 120 mEq/L, but remain asymptomatic, the physician is considering decreasing the cutoff for severe hyponatremia to < 115 mEq/L. Changing the cutoff to < 115 mEq/L would affect the validity of serum sodium in predicting severe hyponatremia requiring hypertonic saline in which of the following ways?
A. Increased sensitivity and decreased positive predictive value
B. Increased specificity and decreased positive predictive value
C. Decreased specificity and increased negative predictive value
D. Increased specificity and decreased negative predictive value (Correct Answer)
E. Decreased sensitivity and decreased positive predictive value
Explanation: ***Increased specificity and decreased negative predictive value***
- **Increasing the cutoff from <120 to <115 mEq/L makes the diagnostic criteria MORE STRINGENT** (fewer patients classified as "severe").
- **Specificity INCREASES**: With a stricter cutoff, fewer patients without true severe disease (asymptomatic chronic hyponatremia) will be falsely labeled as "severe" and unnecessarily treated with hypertonic saline. Specificity measures the ability to correctly identify patients who do NOT have the target condition (symptomatic severe hyponatremia requiring treatment).
- **Negative Predictive Value (NPV) DECREASES**: Patients with sodium levels between 115-120 mEq/L will now test "negative" for severe hyponatremia (falling above the new threshold), but some of these patients may still develop symptoms requiring treatment. Therefore, a "negative" test result (Na >115) becomes less reliable at ruling out the need for future treatment, decreasing NPV.
- **Note**: Sensitivity will DECREASE (more symptomatic patients with Na 115-120 will be missed), and PPV will INCREASE (those identified as severe are more likely to truly need treatment).
*Increased sensitivity and decreased positive predictive value*
- Moving the cutoff to a more stringent value (<115 mEq/L) would **decrease sensitivity**, not increase it, because patients with sodium 115-120 mEq/L who have symptoms would be missed.
- The positive predictive value would **increase**, not decrease, because patients classified as "severe" under the stricter criteria are more likely to truly require hypertonic saline.
*Increased specificity and decreased positive predictive value*
- **Increased specificity** is correct, as explained above.
- However, **PPV would increase**, not decrease, with a more stringent cutoff. When fewer patients are classified as "severe," those who meet the stricter criteria are more likely to truly have severe disease requiring treatment.
*Decreased specificity and increased negative predictive value*
- Specificity would **increase**, not decrease, with stricter diagnostic criteria (fewer false positives).
- NPV would **decrease**, not increase, because patients just above the new threshold (Na 115-120) who test "negative" may still require treatment.
*Decreased sensitivity and decreased positive predictive value*
- **Decreased sensitivity** is correct—the stricter cutoff will miss symptomatic patients with sodium 115-120 mEq/L.
- However, **PPV would increase**, not decrease. With stricter criteria, patients identified as "severe" are more likely to truly have severe disease requiring hypertonic saline.
Question 7: A scientist in Chicago is studying a new blood test to detect Ab to EBV with increased sensitivity and specificity. So far, her best attempt at creating such an exam reached 82% sensitivity and 88% specificity. She is hoping to increase these numbers by at least 2 percent for each value. After several years of work, she believes that she has actually managed to reach a sensitivity and specificity much greater than what she had originally hoped for. She travels to China to begin testing her newest blood test. She finds 2,000 patients who are willing to participate in her study. Of the 2,000 patients, 1,200 of them are known to be infected with EBV. The scientist tests these 1,200 patients' blood and finds that only 120 of them tested negative with her new exam. Of the patients who are known to be EBV-free, only 20 of them tested positive. Given these results, which of the following correlates with the exam's specificity?
A. 82%
B. 90%
C. 84%
D. 86%
E. 98% (Correct Answer)
Explanation: ***98%***
- **Specificity** measures the proportion of **true negatives** among all actual negatives.
- In this case, 800 patients are known to be EBV-free (actual negatives), and 20 of them tested positive (false positives). This means 800 - 20 = 780 tested negative (true negatives). Specificity = (780 / 800) * 100% = **98%**.
*82%*
- This value represents the *original sensitivity* before the scientist’s new attempts to improve the test.
- It does not reflect the *newly calculated specificity* based on the provided data.
*90%*
- This value represents the *newly calculated sensitivity* of the test, not the specificity.
- Out of 1200 EBV-infected patients, 120 tested negative (false negatives), meaning 1080 tested positive (true positives). Sensitivity = (1080 / 1200) * 100% = 90%.
*84%*
- This percentage is not directly derived from the information given for either sensitivity or specificity after the new test results.
- It does not correspond to any of the calculated values for the new test's performance.
*86%*
- This percentage is not directly derived from the information given for either sensitivity or specificity after the new test results.
- It does not correspond to any of the calculated values for the new test's performance.
Question 8: A student health coordinator plans on leading a campus-wide HIV screening program that will be free for the entire undergraduate student body. The goal is to capture as many correct HIV diagnoses as possible with the fewest false positives. The coordinator consults with the hospital to see which tests are available to use for this program. Test A has a sensitivity of 0.92 and a specificity of 0.99. Test B has a sensitivity of 0.95 and a specificity of 0.96. Test C has a sensitivity of 0.98 and a specificity of 0.93. Which of the following testing schemes should the coordinator pursue?
A. Test A on the entire student body followed by Test B on those who are positive
B. Test A on the entire student body followed by Test C on those who are positive
C. Test C on the entire student body followed by Test B on those who are positive
D. Test C on the entire student body followed by Test A on those who are positive (Correct Answer)
E. Test B on the entire student body followed by Test A on those who are positive
Explanation: ***Test C on the entire student body followed by Test A on those who are positive***
- To "capture as many correct HIV diagnoses as possible" (maximize true positives), the initial screening test should have the **highest sensitivity**. Test C has the highest sensitivity (0.98).
- To "capture as few false positives as possible" (maximize true negatives and confirm diagnoses), the confirmatory test should have the **highest specificity**. Test A has the highest specificity (0.99).
*Test A on the entire student body followed by Test B on those who are positive*
- Starting with Test A (sensitivity 0.92) would miss more true positive cases than starting with Test C (sensitivity 0.98), failing the goal of **capturing as many cases as possible**.
- Following with Test B (specificity 0.96) would result in more false positives than following with Test A (specificity 0.99).
*Test A on the entire student body followed by Test C on those who are positive*
- This scheme would miss many true positive cases initially due to Test A's lower sensitivity compared to Test C.
- Following with Test C would introduce more false positives than necessary, as it has a lower specificity (0.93) than Test A (0.99).
*Test C on the entire student body followed by Test B on those who are positive*
- While Test C is a good initial screen for its high sensitivity, following it with Test B (specificity 0.96) is less optimal than Test A (specificity 0.99) for minimizing false positives in the confirmation step.
- This combination would therefore yield more false positives in the confirmatory stage than using Test A.
*Test B on the entire student body followed by Test A on those who are positive*
- Test B has a sensitivity of 0.95, which is lower than Test C's sensitivity of 0.98, meaning it would miss more true positive cases at the initial screening stage.
- While Test A provides excellent specificity for confirmation, the initial screening step is suboptimal for the goal of capturing as many diagnoses as possible.
Question 9: The World Health Organization suggests the use of a new rapid diagnostic test for the diagnosis of malaria in resource-limited settings. The new test has a sensitivity of 70% and a specificity of 90% compared to the gold standard test (blood smear). The validity of the new test is evaluated at a satellite health center by testing 200 patients with a positive blood smear and 150 patients with a negative blood smear. How many of the tested individuals are expected to have a false negative result?
A. 60 (Correct Answer)
B. 15
C. 135
D. 155
E. 195
Explanation: ***Correct Option: 60***
- **False negatives** occur in individuals who have the disease but test negative. This is directly related to the test's **sensitivity**.
- Given a sensitivity of 70%, 30% of actual positive cases (100% - 70%) will be missed. With 200 patients having a positive blood smear (meaning they have malaria), 30% of 200 is 0.30 × 200 = **60**.
*Incorrect Option: 15*
- This number represents the expected number of **false positives** (150 patients without disease × 10% false positive rate = 15).
- However, the question asks for **false negatives**, not false positives.
*Incorrect Option: 135*
- This value represents the number of **true negatives** (150 patients without malaria × 90% specificity = 135).
- It does not represent false negative results.
*Incorrect Option: 155*
- This appears to be a distractor number that doesn't correspond to any standard diagnostic test calculation in this scenario.
- It does not represent false negatives or any meaningful combination of the given parameters.
*Incorrect Option: 195*
- This number might be derived from incorrectly applying formulas or miscalculating the relationship between sensitivity and false negatives.
- It does not represent the correct calculation for false negatives.
Question 10: A public health campaign increases vaccination rates against human papillomaviruses 16 and 18. Increased vaccination rates would have which of the following effects on the Papanicolaou test?
A. Decreased true positive rate
B. Decreased positive predictive value (Correct Answer)
C. Decreased negative predictive value
D. Increased positive likelihood ratio
E. Increased true negative rate
Explanation: ***Decreased positive predictive value***
- An increase in vaccination rates against **HPV 16 and 18** will reduce the **prevalence of cervical dysplasia and cancer** caused by these types.
- With fewer true cases in the population, a Papanicolaou (Pap) test is more likely to yield a **false positive result** when it tests positive, thus decreasing its **positive predictive value**.
- **PPV = TP/(TP+FP)** - when disease prevalence decreases, the number of true positives decreases while false positives remain relatively stable, reducing the overall PPV.
*Decreased true positive rate*
- The **true positive rate (sensitivity)** of the Pap test refers to its ability to correctly identify individuals with the disease (cervical dysplasia/cancer).
- While the overall number of true positives will decrease due to reduced disease prevalence, the inherent ability of the test to detect existing disease (i.e., its sensitivity) is **not directly affected by vaccination rates**.
- Sensitivity is an intrinsic test property: **Sensitivity = TP/(TP+FN)**.
*Decreased negative predictive value*
- The **negative predictive value** is the probability that a person with a negative test result truly does not have the disease.
- As the prevalence of the disease decreases due to vaccination, the probability of a negative test being truly negative actually **increases**, leading to an **increased negative predictive value**.
- **NPV = TN/(TN+FN)** - lower prevalence means fewer false negatives relative to true negatives.
*Increased positive likelihood ratio*
- The **positive likelihood ratio** describes how much more likely a positive test result is in someone with the disease compared to someone without the disease and is derived from sensitivity and specificity.
- **LR+ = Sensitivity/(1-Specificity)** - vaccination reduces disease prevalence but does not inherently change the **diagnostic accuracy** (sensitivity and specificity) of the Pap test, so the likelihood ratio remains unchanged.
*Increased true negative rate*
- The **true negative rate (specificity)** of the Pap test refers to its ability to correctly identify individuals who do not have the disease.
- While the overall number of true negatives will increase (because there are fewer cases to begin with), the inherent ability of the test to correctly identify healthy individuals (i.e., its specificity) is **not directly affected by the change in disease prevalence**.
- Specificity is an intrinsic test property: **Specificity = TN/(TN+FP)**.
