2/1/2011natural history; population screening1 natural history of disease / population screening...

2/1/2011 Natural history; population screening 1

Natural history of disease / population screening

Principles of Epidemiology for Public Health (EPID600)

Victor J. Schoenbach, PhD home page

Department of EpidemiologyGillings School of Global Public Health

University of North Carolina at Chapel Hill

www.unc.edu/epid600/

2/3/2004 2

SHE shouldn’ts (courtesy of www.flylady.net

# 8: SHE's shouldn't let themselves get too tired – Last week I was going over some homeschooling with my 11yo DD when I realized I hadn't seen or heard my fast-crawling 13-month old DD in a while. I said, "Anyone know where the baby is?" My older daughter just looked at me and said, "Mom?" Lo and behold, I'm nursing the baby! - in Colorado

9/24/2001 3

What not to say in your job interview

“Herb Greenberg, a leading authority on work-related personality testing, keeps a list of the dumbest things people have told his corporate clients during recent job interviews.” (Cheryl Hall, Knight Ridder, Herald-Sun, 1/26/2003: F2)(Greenberg is the 73-year-old chief executive officer of Caliper, in Princeton NJ)

9/24/2001 4

Have you ever thought of saying …

• “I will definitely work harder for you than I did for my last employer.”

• “I don’t think I’m capable of doing this job, but I sure would like the money.”

• “Do you know of any companies where I could get a job I would like better than this one?”

9/24/2001 5


• “I’m quitting my present job because I hate to work hard.”

• An apology for yawning “I usually sleep until my soap operas are on.”


• Knowledge of the natural history of disease is fundamental for effective prevention• Levels of prevention:

- Primary – prevent the disease [Primordial – prevent the risk factors]- Secondary – early detection and Rx- Tertiary – treat and minimize disability

Disease natural history and prevention


•Phenomenon of disease- What is disease?- Natural history of disease

•Requirements for screening programs• Detection of disease

- Sensitivity- Specificity

• Interpreting diagnostic & screening tests- Predictive value

Disease natural history & population screening


World Health Organization:

“a state of complete physical, mental, [and] social well-being and not merely the absence of disease or infirmity”

Phenomenon of health: what is health?


Difficult to define, e.g.:“a type of internal state which is either an impairment of normal functional ability–that is, a reduction of one or more functional abilities below typical efficiency–or a limitation on functional ability caused by environmental agents” (C. Boorse, What is disease? In: Humber M, Almeder RF, eds. Biomedical ethics reviews. Humana Press, Totowa NJ, 1997, 7-8 (quoted in Temple et al., 2001)

Phenomenon of disease: what is disease?


Difficult to define, e.g.:

“a state that places individuals at increased risk of adverse consequences”

(Temple LKF et al., Defining disease in the genomics era. Science 3 Aug 2001;293:807-808)

Phenomenon of disease: what is disease?


• Disease is a process that unfolds over time

• Natural history – sequence of developments from earliest pathological change to resolution of disease or death

Phenomenon of disease: natural history


• Induction – time to disease initiation

• Incubation – time to symptoms (infectious disease)

• Latency – time to detection (for non-infectious disease) or to infectiousness



Natural history of coronary heart disease

“Spontaneous atherosclerosis”

“Lipid lesion”Fibrointimal

lesionPlaque growth,

occlusion

Chronic minimal injury (blood flow, CHL, smoking, infection?) (youth?)

Accumulation of lipids and monocytes, toxic products, platelet adhesion(adolescence)

Migration & proliferation of smooth muscle cells

(adulthood)

Disruption

thrombi

(adulthood)






occlusion




(adulthood)

Disruption

thrombi

(adulthood)


Natural history is central to screening

Pre-detectableDetectable, preclinical

ClinicalDisability or death

Possible detection via screening

Clinical detection

Age: 35 45 55 65 75


“application of a test to asymptomatic people to detect occult disease or a precursor state” (Alan Morrison, Screening in Chronic Disease, 1985)

Population screening


Immediate objective of a screening test – to classify people as being likely or unlikely of having the disease

• Ultimate objective: to reduce mortality and morbidity

Population screening


Test that can help save your life


1. Suitable disease

2. Suitable test

3. Suitable program

4. Good use of resources

Requirements for a screening program


• Serious consequences if untreated

• Detectable before symptoms appear

• Better outcomes if treatment begins before clinical diagnosis

1. Suitable disease


• Detect during pre-symptomatic phase

• Safe

• Accurate

• Acceptable, cost-effective

2. Suitable test


• Reaches appropriate target population

• Quality control of testing

• Good follow-up of positives

• Efficient

3. Suitable program


• Cost of screening tests

• Cost of follow-up diagnostic tests

• Cost of treatment

• Benefits versus alternatives

4. Good use of resources


Summary of Recommendations•The USPSTF recommends biennial screening mammography for women aged 50 to 74 years. Grade: B recommendation.•The decision to start regular, biennial screening mammography before the age of 50 years should be an individual one and take patient context into account, including the patient's values regarding specific benefits and harms. Grade: C recommendation. •The USPSTF recommends against teaching breast self-examination (BSE).Grade: D recommendation.. . .

Screening for Breast CancerU.S. Preventive Services Task ForceDecember 4, 2009


David ShabtaiFaculty Peer Reviewed In a bold move, the U.S. Preventive Services Task Force recently changed their breast cancer screening guidelines – recommending beginning screening at age 50 and even then only every other year until age 75. Bold, because the Task Force members are certainly aware of the media circus that ensued when in 1997, an NIH group issued similar guidelines, prompting comparisons to Alice in Wonderland.

Revisiting the USPSTF Breast Cancer Screening Guidelines: Ethics, and Patient Responsibilities


September 10, 2010

Recommended Weekend ReadingBy NATASHA SINGER

“Can we trust doctors’ recommendations on cancer screening, given that the medical profession has a vested financial interest in treating patients? That is one of the questions posed in a provocative article this week in The New England Journal of Medicine that looks at the fallout last year after a government panel recommended that women start having mammograms later in life and less frequently.”

Mammography Wars


September 29, 2010

Mammogram Benefit Seen for Women in Their 40sBy GINA KOLATA

Researchers reported Wednesday that mammograms can cut the breast cancer death rate by 26 percent for women in their 40s. But their results were greeted with skepticism by some experts who say they may have overestimated the benefit.

Who should get a mammogram?


Newsweek

The Mammogram HustleThere is no evidence digital mammograms improve cancer detection in older women. But thanks to political pressure, Medicare pays 65 percent more for them.

This story was reported and written by Center for Public Integrity.

What should we pay for?


By Julie SteenhuysenCHICAGO | Wed Jan 26, 2011 12:26pm EST

(Reuters) - A new analysis of evidence used by a U.S. advisory panel to roll back breast cancer screening guidelines suggests it may have ignored evidence that more frequent mammograms save more lives, U.S. researchers said on Tuesday.

New U.S. analysis backs annual breast screening


“The U.S. Preventive Services Task Force (USPSTF) “chose to ignore the science available to them” and brought about “potential damage to women’s health” in its 2009 recommendations for more limited mammography screening, costing an estimated 6,500 deaths in women each year, a study published in the February issue of the American Journal of Roentgenology concluded.”

AJR: USPSTF mammo recommendations could cost 6,500 lives yearly


Survival time after diagnosis – lead time

Pre-detectable Detectable, preclinical Clinical Disability

or death

Possible detection via screening

Clinical detection

Age: 35 45 55 65 75

Lead time


Survival time must increase > lead time

Pre-detectable Undetected(no screening)

Clinicaldiagnosis &treatment

Disability or death

Age: 35 45 55 65 75

Pre-detectable Early detect, diagnosis, &

treatment

Monitoringfor recurrence ?

Survival time after diagnosis

Lead time


Slowly progressing diseases are easier to detect by screening

Pre-detectable

Clinical diagnosis,treatment

Disability or death

Age: 35 45 55 65 75

Pre-detectable Detectable,pre-clinical

Clinical diagnosis &

treatment

Disabilityor death




Early detection may over-diagnose

Pre-detectable Undetected(no screening)

Mild or no symptoms

Favorableoutcome

Age: 35 45 55 65 75

Pre-detectable Early detect, diagnosis, &

treatment

Monitoringfor recurrence

Favorableoutcome


Survival time after dx


Screening test

Reliable – get same result each time

Validity – get the correct result

Sensitive – correctly classify cases

Specificity – correctly classify non-cases

[screening and diagnosis are not identical]


Reliability

Repeatability – get same result• Each time• From each instrument• From each rater

If don’t know correct result, then can examine reliability only.


Reliability

• Percent agreement is inflated due to agreement by chance

• Kappa statistic considers agreement beyond that expected by chance

• Reliability does not ensure validity, but lack of reliability constrains validity


Validity: 1) Sensitivity

Probability (proportion) of correct classification of cases

Cases found / all cases


Validity: 2) Specificity

Probability (proportion) of correct classification of noncases

Noncases identified / all noncases


O

OO

OO

O

O

OO

O

O

Remember this slide? 2 cases / month

O


O

OO

OO

O

O

O

O

O

Pre-detectable preclinical clinical old

OO

OO

O


O

OO

OOO

O

O

OO

O

O

OO

O

O

O

OO

O

O

OO

OO

O

O

OO

OO

O

O

O OO

OO

Pre-detectable pre-clinical clinical old


O

OO

OOO

O

O

OO

O

O

OO

O

O

O

OO

O

O

OO

OO

O

O

OO

OO

O

O

O OO

OO

What is the prevalence of “the condition”?


Sensitivity of a screening test

Probability (proportion) of correct classification of detectable, pre-clinical cases


O

OO

OOO

O

O

OO

O

O

Pre-detectable pre-clinical clinical old (8) (10) (6) (14)

OO

O

O

O

OO

O

O

OO

OO

O

O

OO

OO

O

O

O OO

OO


O

OO

OOO

O

O

OO

O

O

Correctly classifiedSensitivity: ––––––––––––––––––––––––––– Total detectable pre-clinical (10)

OO

O

O

O

OO

O

O

OO

OO

O

O

OO

OO

O

O

O OO

OO


Specificity of a screening test

Probability (proportion) of correct classification of noncases

Noncases identified / all noncases


O

OO

OOO

O

O

OO

O

O

Pre-detectable pre-clinical clinical old (8) (10) (6) (14)

OO

O

O

O

OO

O

O

OO

OO

O

O

OO

OO

O

O

O OO

OO


O

OO

OOO

O

O

OO

O

O

Correctly classifiedSpecificity: ––––––––––––––––––––––––––––– Total non-cases (& pre-detect) (162 or 170)

OO

O

O

O

OO

O

O

OO

OO

O

O

OO

OO

O

O

O OO

OO


Truepositive

Truenegative

Falsepositive

Falsenegative

Sensitivity = True positives

All cases

a + c b + d

= a

a + c

Specificity = True negatives All non-cases

= db + d

a + b

c + d

True Disease Status

Cases Non-cases

Positive

Negative

ScreeningTest

Results

a d b

c


True Disease Status

Cases Non-cases

Positive

Negative

ScreeningTest

Results

a d

1,000 b

c60

Sensitivity = True positives

All cases

200 20,000

= 140200

Specificity = True negatives All non-cases

= 19,00020,000

1,140

19,060

140

19,000

=

= 70%

95%


Interpreting test results: predictive value

Probability (proportion) of those tested who are correctly classified

Cases identified / all positive tests

Noncases identified / all negative tests


Truepositive

Truenegative

Falsepositive

Falsenegative

PPV = True positives

All positives

a + c b + d

= a

a + b

NPV = True negatives All negatives

=d

c + d

a + b

c + d

True Disease Status

Cases Non-cases

Positive

Negative

ScreeningTest

Results

a d b

c


True Disease Status

Cases Non-cases

Positive

Negative

ScreeningTest

Results

a d

1,000 b

c60

PPV = True positives

All positives

200 20,000

= 1401,140

NPV = True negatives All negatives

= 19,00019,060

1,140

19,060

140

19,000

=

= 12.3%

99.7%


Positive predictive value, Sensitivity, specificity, and prevalence

Prevalence (%) PV+ (%) Se (%) Sp (%) 0.1 1.4 70 95

1.0 12.3 70 95

5.0 42.4 70 95

50.0 93.3 70 95


Example: Mammography screening of unselected women

Disease status

Cancer No cancer Total Positive 132 985 1,117 Negative 47 62,295 62,342

Total 179 63,280 63,459

Prevalence = 0.3% (179 / 63,459)

Se = 73.7% Sp = 98.4% PV+ = 11.8% PV– = 99.9%

Source: Shapiro S et al., Periodic Screening for Breast Cancer


Effect of Prevalence on Positive Predictive Value

PV+ = 64%

PV+ = 88%

Sensitivity = 93%, Specificity = 92%

Surgical biopsy (“gold standard”) Cancer No cancer Prev.

Without palpable mass in breast

Fine needle Positive 14 8 13%aspiration Negative 1 91

With palpable mass in breast

Fine needle Positive 113 15 38%aspiration Negative 8 181

See http://www.meddean.luc.edu/lumen/MedEd/ipm/IPM1/Biostats/diagnostic_test_example1_Solutions1011.pdf


What is used as a “gold standard”

1. Most definitive diagnostic procedure e.g. microscopic examination of a tissue specimen

2. Best available laboratory teste.g. polymerase chain reaction (PCR)

for HIV virus

3. Comprehensive clinical evaluatione.g. clinical assessment of arthritis


Main concepts1. Requirements for a screening program2. Concept of natural history – possible biases include lead time, “length”, over-diagnosis3. Reliability (repeatable) – can occur by chance4. Validity (correct) – sensitivity, specificity5. Sensitivity and specificity relate to the detectable pre-clinical stage of the disease6. Predictive value – the population perspective on disease detection

9/24/2001 63


• “My resume might make it look like I’m a job hopper. But I want you to know that I never left any of those jobs voluntarily.”

• “What job am I applying for anyway?”

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