how do we delay disease progress once it has started? epidemiology matters: a new introduction to...

How do we delay disease progress once it has started?

Epidemiology matters: a new introduction to methodological foundations

Chapter 13

2Epidemiology Matters – Chapter 13

1. What is screening?

2. When should we screen?

3. How do we evaluate a screening test?

4. How do we decide on a cut-off?

5. PPV, NPV, and prevalence

6. From screening test to screening program

7. Summary








7. Summary


Screening

Screening is the process in which we use a test to determine whether an individual likely has a particular health indicator or not or is likely to develop a particular health indicator or notScreening is not the same as diagnosis; screening tests give us information about whether the disease is likely to be presentA screening test assesses the presence of an underlying marker that is associated with outcome of interest


Screening, examples

Women receive regular screening tests beginning in young adulthood for cervical cancer (Pap smear)

Physicians assess blood pressure and cholesterol as screening tools for the development of cardiovascular disease

Women use home pregnancy tests to screen for presence of an embryo or fetus








7. Summary


When to screen

We screen for disease when we have the opportunity to reduce costs and risk

associated with diagnoses on large proportions of at-risk individuals

1. We screen for health indicators that affect population health principally, not for

rare diseases (although there are exceptions for rare diseases screen in utero)

2. There should be sufficient time between biological onset of disease and

appearance of signs and symptoms of the disease exist so that screening could

detect the presence of the disease earlier than it would come to clinical

attention

3. There should be available treatment for the disease so that early detection

improves the lives of affected

4. Screening tests should be cheaper and less invasive than best available

diagnostic tool








7. Summary


Screening test evaluation

1. Sensitivity2. Specificity3. Positive predictive value4. Negative predictive value


Screening test evaluation, example

Questions: Should all men be tested for prostate cancer with the new screening tool? High rates of prostate cancer among Farrlandian men New test characterizes level of antigen in blood

demonstrated to be associated with prostate cancer Test is inexpensive and requires only a blood specimen


Screening test evaluation, example

240 men with confirmed incident diagnoses of prostate cancer

2,500 men confirmed free of prostate cancer Measure antigen level for all men


Screening test evaluationantigen among sample

Antigen

D- D+

Mean level of antigen is higher among men with prostate cancer

Distributions of antigen overlap

Between 37 and 56 ng/mL - both diseased and non-diseased men

A cut-off if typically declared within this uncertain range

Individuals above the cut-off are screening positive or below, screen negative

Cut-off is often largely arbitrary and informs performance of screening test


Screening test evaluationsensitivity and specificity

To assess the validity of a screening tool in establishing the presence of disease we compare with a gold standard

Sensitivity: Whether individuals with disease are correctly identified by the screening test as having the disease

Specificity: Whether individuals without the disease are correctly identified by the screening test as not having the disease


Screening test evaluationcut-off

False Positives

ScreensNegative

Screens Positive

D+

Screening Cut-Off

D-

Antigen

Lowest value on the antigen distribution of men with prostate cancer = 38 ng/mL.

Cut-off score = 38 ng/mL


Screening test evaluationsensitivity

Among those with disease, what proportion does the screening test detect?

Interpretation: With a cut-off of 38 ng/mL, we have a test with 100% sensitivity.

Among those with prostate cancer, the test captures all cases. There are no false

negatives.


Screening test evaluationspecificity

Among those without disease, what proportion does the screening test correctly

identify as disease free?

Interpretation: With a cut-off of 38 ng/mL, screening test classifies 61.2% of men

without prostate cancer as not having prostate cancer, remaining 38.8% are false

positive cases.


Screening test evaluationsensitivity and specificity tradeoffs

Test cut-off is very sensitive: All those who have the disease will be captured by the testTest cut-off is very specific: Individuals who do not have the disease are will not screen positiveHigh sensitivity/low specificity tests are common in practice; i.e., we will not miss many individuals with disease but we also will screen positive those who do not have disease


Screening test evaluationsensitivity and specificity, example

Primary care physicians query patients about whether they

engaged in at least one episode of heavy drinking in the past year

as a screening tool for identifying individuals with an alcohol

disorder

Almost all individuals with an alcohol disorder will have engaged in

at least one episode of heavy drinking in the past year, but many

individuals without an alcohol disorder may have engaged in one

or more heavy drinking episodes as well.

The test is sensitive, but not specific


Screening test evaluationPPV, NPV, and prevalence

How well does our screening test predict who is diseased and who is not?


Screening test evaluationpositive predictive value

Among those who screen positive, what proportion actually has the disease?

Interpretation: With a cut-off of 38 ng/mL, one fifth of the men who screen positive

on test have prostate cancer, leaving about 80% of men screening positive falsely.


Screening test evaluationnegative predictive value

Among those who screen negative, what proportion actually do not have the

disease?

Interpretation: With a cut-off of 38 ng/mL, the test has perfect negative predictive

value. Among those who are negative on the screening test, we can be perfectly

confident that none of those individuals actually have the disease.



Assess sensitivity and specificity to understand the proportion

of diseased and non-diseased individuals correctly categorized as

diseased and non-diseased

Assess PPV and NPV to understand the proportion of positively

screened and negatively screened individuals that have disease

or are disease-free


Screening test evaluationcut-offs and tradeoffs, example

Hypothetical screening test for prostate cancer set a cut-off of 38 ng/mL Perfect sensitivity and negative predictive value Low specificity and positive predictive value

Change cut-off for positivity to 45 ng/mL Individuals are positive on screening test if level of

antigen is 45 ng/mL or above


Screening test evaluationcut-offs and tradeoffs, example

Compared with original cut-off of ≥ 38 ng/mL

Sensitivity decreased and specificity increased

Positive predictive value increased


Screening test evaluationcut-offs and tradeoffs

Original cut-off

Why did sensitivity decrease?



Original cut-off

Why did sensitivity decrease?

Some individuals who have prostate cancer will screen negative now - false negatives

As increase cut-off for positivity on a screening test, number of individuals with the disease who screen negative will increase - leading to lower sensitivity

New cut-off








7. Summary


Ramifications of false positives vs. false negatives

Low rate of false negatives preferred Infectious diseases critical to maintain low rate of false

negative When disease can be readily remediated if caught early but

devastating if notLow rate of false positivity preferred When subsequent diagnostic test is invasive and expensive

procedures Screening is done routinely on low burden diseases


A note

Empirical guidelines for screening can be created to optimize screening cut-points; these are usually derived from receiver operating characteristic (ROC) curves


Multiple-stage screening

Two-stage screening test1. Screen individuals using test with high sensitivity2. Follow-up with test with high specificity








7. Summary


Prevalence of screening health indicator, example

Antigen screening test Cut-off of 45 ng/mL Screen two samples for prostate cancerA. 1,500 men with prostate cancer family history; > 60

years oldB. 1,500 men with no prostate cancer family history; 40-60

years old


Prostate cancer screening, sample A

Men with prostate cancer family history and > 60


Prostate cancer screening, sample B

Men with no prostate cancer family history and 40 - 60


Prostate cancer screening example

Sensitivity and specificity

Sample A and Sample B sensitivity = 90%

Sample A and Sample B specificity = 83%

PPV, positive predictive value

Sample A (family history, >60) PPV = 72.7%

Sample B (no family history, 40-60) PPV = 3.4%

NPV, negative predictive value

Sample A (family history, >60) NPV = 94.5%

Sample B (no family history, 40-60) NPV = 99.9%

PPV and NPV differ across samples. Why?


Why does PPV change?

PPV is dependent on the prevalence of a health indicator in screened population

As prevalence probability that individual who screens positive will be true case

As prevalence probability of being true negative case

Sensitivity and specificity are not directly influenced by prevalence

Sensitivity and specificity look among those who have the health indicator versus those who do

not

PPV and NPV are dependent on prevalence, and PPV increases as the prevalence of disease

increases.








7. Summary


Screening test to screening program

Example 1: Detection based on screening compared with detection based on symptoms Example 2: Detection based on screening compared with effect of screening on mortality


Example 1: Cancer duration in Farrlandia


Example 1: Cancer duration in Farrlandia, screening at 10 years


Example 1: Cancer duration in Farrlandia, screening at 10 years

Screening at single time point is estimating the prevalence of cancer at time pointCancer cases that are more likely to be detected have long duration and perhaps slow-growing tumorsProblems with this1. Slow-growing tumors may not eventually cause death or disability - difficult to

predict which tumors will become symptomatic and which will not2. Compare cancer-related mortality among screened sample to non-screened

sample Screened group may have fewer cancer-related deaths because may have

better cancer-related outcomes Because tumors that are detected more likely to be slow growing tumors

that did not influence morbidity and mortalityLength-time bias: Inability to estimate the causal impact of screening test on morbidity and mortality


Example 2: Disease detection by screening vs. symptoms


Example 2: Screening vs. symptom diagnosis

Person 1’s cancer detected through screening

Person 2’s cancer detected due to symptoms

Person 1 and person 2 have equal time from cancer onset to death

Time from cancer detection to death is longer in Person 1

Lead time: The time from detection from screening and detection through

symptoms afforded by screening test. This will always show a benefit in

the screened group - even if early detection did not really lengthen life


Example 2: Disease detection by screening vs. symptoms


Example 2: observation time

When time from start of observation to death is the same in both individuals - no association between screening and length of lifeWhen the time from start of observation to death shorter for the not screened compared with screened - evidence that early detection through screening has benefit on mortality








7. Summary


Summary

Screening for health indicators is integral part of improving population

health

Screening predicts who will develop a specific disease and detects

disease among those in early stages

Screening tests need to be studied for validity (sensitivity and

specificity)

We often have a trade-off between sensitivity and specificity

Predictive value of screening test is maximized in populations with high

prevalence of health indicator of interest

Value of screening program will depend on cost-effectiveness, minimal

invasiveness, availability of effective treatment


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