Clinical epidemiology thyroid disease and test results
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Clinical Epidemiology: Thyroid disease and test results. Wiley D. Jenkins, PhD, MPH Research Assistant Professor Southern Illinois University School of Medicine Department of Family and Community Medicine. Who I am.

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Clinical epidemiology thyroid disease and test results l.jpg

Clinical Epidemiology:Thyroid disease and test results

Wiley D. Jenkins, PhD, MPH

Research Assistant Professor

Southern Illinois University School of Medicine

Department of Family and Community Medicine


Who i am l.jpg
Who I am

  • My name is Wiley D. Jenkins and I am currently Research Assistant Professor at the SIU-SOM Department of Family and Community Medicine. Prior to this I spent 13 years in the state health department laboratory.

  • I received my MPH-Epidemiology from Tulane University in 2002. This was followed by my PhD in Health policy from the University of Illinois at Chicago in 2007.

  • Much of my research and work experience has concerned laboratory testing, STDs and the quality of laboratory data.


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Learning objectives

  • To understand the concepts of test sensitivity, specificity, positive predictive value and negative predictive value.

  • To understand how these factors effect the utility of individual tests when diagnosing a condition.

  • To understand how these factors are manipulated by targeting screening tests to specific populations.


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Performance objectives

  • To be able to calculate the sensitivity, specificity, positive predictive value and negative predictive value for a given test.

  • To be able to determine if a test’s result is useful given its calculated values.

  • To be able to show how screening guidelines should be adjusted to increase positive and negative predictive values to maximize result usefulness.


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There is always uncertainty

  • Our common language incorporates uncertainty.

    • “Usually” implies error bars

  • Physics tells us that in an infinite universe, anything is possible. Some things are just more or less likely.

  • Heisenberg uncertainty principle:

    • statement that locating a particle in a small region of space makes the momentum of the particle uncertain; and conversely, that measuring the momentum of a particle precisely makes the position uncertain

  • As a matter of practicality, some things are essentially “100%” or “always” something. HOWEVER, its important to know when this is not the case, and that is not always obvious.


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Quick review of terms

  • Sensitivity – the ability of a test to correctly identify those who have a condition

  • Specificity – the ability of a test to correctly identify those who do not have a condition

  • Positive predictive value – the number of individuals who have a condition from all those who test positive

  • Negative predictive value - the number of individuals who do not have a condition from all those who test negative


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The 2 x 2 table

  • You’ll use this a lot later in life…


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Sensitivity

  • 90% sensitivity implies that of all those who have the disease, 10% will not be identified by the test. If prevalence is 20% of the population…


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Specificity

  • 75% specificity implies that of all those who do not have the disease, 25% will not be identified by the test. If prevalence is 20% of the population…


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Positive/negative predictive value

  • We complete the remaining marginals and find:

    • PPV for our example test is 180/380 = 47%

    • NPV is 600/620 = 97%.

    • What do we draw from this about the usefulness of the test?


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Time for a clinical example

  • 27-year-old woman

  • 10 lb weight loss in past two months, not trying

  • Some difficulty sleeping

  • Never had anything like this before

  • No signs/symptoms of depression

  • Meds: Oral contraceptive pills

  • 1-cm, firm, smooth nodule in right lobe of thyroid

  • BMI = 20

  • Skin slightly dry

  • Remainder of physical examination normal

  • What do you think?

  • What should we do?



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What next?

  • Order more tests?

  • Schedule for surgery?

  • Prescribe medication, therapy, hamburgers…?

  • 1st, let’s see what the tests are really telling us.


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Thyroid stimulating hormone

  • Our patient has a (low) normal TSH

    • Sensitivity = 92%

    • Specificity = 94%

    • Are these good values?

  • Assume prevalence for thyroid disease of 4% in large populations

  • Calculate PPV and NPV for TSH

  • Do we care more about the PPV or NPV for this scenario?


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TSH 2 x 2 table

  • Complete the table and calculate the PPV and NPV assuming: sens = 92%, spec = 94% and prevalence = 4%


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TSH 2 x 2 table - completed

  • We find:

    • PPV = 37/95 = 31%

    • NPV = 902/905 = 100%

    • Which do we care about and what conclusions do we draw?


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Free T4

  • Our patient has an elevated Free T4

  • Sensitivity = 82%

  • Specificity = 94%

  • Assume prevalence for thyroid disease of 4% in large populations

  • Calculate PPV and NPV for Free T4

  • Do we care more about the PPV or NPV for this scenario?


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Free T4 table

  • Complete the table and calculate the PPV and NPV assuming: sens = 82%, spec = 94% and prevalence = 4%


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Free T4 table - completed

  • We find:

    • PPV = 33/91 = 36%

    • NPV = 902/909 = 99%

    • Which do we care about and what conclusions do we draw?


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So…

  • We have:

    • A symptomatic woman on OCPs with a thyroid nodule

    • A normal TSH

    • An elevated Total T4

    • An elevated Free T4

  • What next?

    • Scintigraphy?

    • Fine Needle Aspiration Biopsy?

    • Excisional Biopsy?


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Fine needle aspiration biopsy

  • Indeterminate result

  • 15-20% false positive rate (assume 20% for calculations to follow)

  • 3% false negative rate

  • If we assume a 4% prevalence of thyroid cancer, calculate the sensitivity and specificity of the biopsy.

  • Calculate the positive and negative predictive value.


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The FNAB 2 x 2 table

  • What do we know?

    • Prevalence – 4%

    • False positive rate – 20%

    • False negative rate – 3%


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The FNAB 2 x 2 table

  • False positives = FP rate x all negatives = 0.20 x 960 = 192

  • False negatives = FN rate x all positives = .03 x 40 = 1


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The FNAB 2 x 2 table - completed

  • We find:

    • PPV = 39/231 = 17%

    • NPV = 768/769 = 100%

    • Which do we care about and what conclusions do we draw?


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Clinical course

  • The patient was referred to a surgeon for excisional biopsy.

  • Nodule was removed, was a benign colloid goiter, no malignancy and no evidence of Hashimoto’s or other disease.



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How do laboratory tests contribute to medical errors?

  • Are not always right

  • May result in unnecessary further testing

  • May result in unnecessary surgery

    • With attendant complications

  • If we assume that tests are correct 95% of the time, what is the likelihood that, in a battery of 20 tests, one will be a false result?

  • So, for every Chem 20 you order (or other battery of 20 tests), 1 will be either a FALSE POSITIVE or a FALSE NEGATIVE.

  • Need to know how to work with sensitivity and specificity in order to know what to believe.


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Time for a population example

  • Why, because we like you! (M – I – C…)

  • Seriously though, population-level studies are translated into clinical guidelines.

  • In 2006, the number of reported cases of Chlamydia trachomatis (Ct) in the US exceeded 1,000,000 for the 1st time.

  • The great majority of cases (~70% in women) are entirely asymptomatic.

  • Upwards of 40% of untreated Ct progress to PID; followed by chronic pelvic pain, ectopic pregnancy and infertility.

  • How do we address this?


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Chlamydia trachomatis screening

  • Diagnostic companies have spent considerable money developing rapid and accurate tests for the detection of Ct.

  • Current tests offer

    • ~95% sensitivity

    • ~98% specificity

  • So, do we just test everyone……? Lets’ see. (~150,000,000 women) x (~$10/test) = need for other alternative.

  • Who has Ct?

    • 0.35% all Americans

      • 0.52% women

      • 0.17% men

    • 1.76% Black women

    • 0.24% White women

    • 2.9% women aged 15-19

    • 2.8% women aged 20-24


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The Ct 2 x 2 table - completed

  • For the general population (0.35%) we find:

    • PPV = 33/233 = 14%

    • NPV = 9765/9767 = 100%


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The Ct 2 x 2 table - completed

  • For all women (0.52%) we find:

    • PPV = 49/248 = 20%

    • NPV = 9749/9752 = 100%


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The Ct 2 x 2 table - completed

  • For all women aged 16-24 (2.9%) we find :

    • PPV = 276/470 = 59%

    • NPV = 9516/9530 = 100%


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Utility of targeted testing

  • By purposefully targeting our testing to at-risk populations, we increase the PPV of the test and better allocate resources.

    • General population

      • Prevalence = 0.35% PPV = 14%

    • All women

      • Prevalence = 0.52% PPV = 20%

    • Women aged 16-24

      • Prevalence = 2.9% PPV = 59%

    • Females admitted into juvenile detention centers??

      • Prevalence = 12-20% PPV = >90%!

    • Other risk factors important.

  • This works for clinical guidelines for screening, such as mammography, prostate exams, cholesterol…


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Take away items

  • Not a good practice to order tests “just because we can” or for “fishing expeditions.”

  • Costs can quickly become quite significant (e.g. compare HC expenditure for US versus other industrialized countries and resultant health outcomes).

  • Utility of the results is directly impacted by the population/person to which they are given.

  • Multiple tests increase the likelihood of a correct diagnosis.

    • E.g. Ct in 16-24, PPV = 59%

    • Additional test on just these positives (e.g. 59% prevalence) with same sens/spec results in PPV of 99%!

  • In the absence (always) of the “ultimate test”, use multiple results to arrive at the best conclusion.


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Questions or comments??

Contact info:

Wiley D. Jenkins, PhD, MPH

[email protected]

217-545-8717


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