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Natural history of disease / population screening

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Title: Natural history of disease / population screening


1
Natural history of disease / population screening
Principles of Epidemiology for Public Health
(EPID600)
Victor J. Schoenbach, PhD home page Department of
Epidemiology Gillings School of Global Public
Health University of North Carolina at Chapel
Hill www.unc.edu/epid600/
2
SHE shouldnts (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

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)

4
Have you ever thought of saying
  • I will definitely work harder for you than I did
    for my last employer.
  • I dont think Im 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?

5
Have you ever thought of saying
  • Im quitting my present job because I hate to
    work hard.
  • An apology for yawning I usually sleep until my
    soap operas are on.

6
Disease natural history and prevention
  • 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

7
Disease natural history population screening
  • 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

8
Phenomenon of health what is health?
World Health Organization a state of complete
physical, mental, and social well-being and not
merely the absence of disease or infirmity
9
Phenomenon of disease what is disease?
Difficult to define, e.g. a type of internal
state which is either an impairment of normal
functional abilitythat is, a reduction of one or
more functional abilities below typical
efficiencyor 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)
10
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
2001293807-808)
11
Phenomenon of disease natural history
  • Disease is a process that unfolds over time
  • Natural history sequence of developments from
    earliest pathological change to resolution of
    disease or death

12
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

13
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

14
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

15
Natural history of coronary heart disease
Spontaneous atherosclerosis
Lipid lesion
Fibrointimal lesion
Plaque growth, occlusion
Accumulation of lipids and monocytes, toxic
products, platelet adhesion (adolescence)
Chronic minimal injury (blood flow, CHL, smoking,
infection?) (youth?)
Migration proliferation of smooth muscle
cells (adulthood)
Disruption thrombi (adulthood)
16
Natural history of coronary heart disease
Spontaneous atherosclerosis
Lipid lesion
Fibrointimal lesion
Plaque growth, occlusion
Accumulation of lipids and monocytes, toxic
products, platelet adhesion (adolescence)
Chronic minimal injury (blood flow, CHL, smoking,
infection?) (youth?)
Migration proliferation of smooth muscle
cells (adulthood)
Disruption thrombi (adulthood)
17
Natural history of coronary heart disease
Spontaneous atherosclerosis
Lipid lesion
Fibrointimal lesion
Plaque growth, occlusion
Accumulation of lipids and monocytes, toxic
products, platelet adhesion (adolescence)
Chronic minimal injury (blood flow, CHL, smoking,
infection?) (youth?)
Migration proliferation of smooth muscle
cells (adulthood)
Disruption thrombi (adulthood)
18
Natural history is central to screening
Pre-detectable
Detectable, preclinical
Clinical
Disability or death
Age 35 45 55
65 75
Clinical detection
Possible detection via screening
19
Population screening
application of a test to asymptomatic people to
detect occult disease or a precursor state
(Alan Morrison,
Screening in Chronic Disease, 1985)
20
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

21
Test that can help save your life
22
Requirements for a screening program
1. Suitable disease 2. Suitable test 3. Suitable
program 4. Good use of resources
23
1. Suitable disease
  • Serious consequences if untreated
  • Detectable before symptoms appear
  • Better outcomes if treatment begins before
    clinical diagnosis

24
2. Suitable test
  • Detect during pre-symptomatic phase
  • Safe
  • Accurate
  • Acceptable, cost-effective

25
3. Suitable program
  • Reaches appropriate target population
  • Quality control of testing
  • Good follow-up of positives
  • Efficient

26
4. Good use of resources
  • Cost of screening tests
  • Cost of follow-up diagnostic tests
  • Cost of treatment
  • Benefits versus alternatives

27
Screening for Breast Cancer
U.S. Preventive Services Task Force December 4,
2009
  • 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.
  • . . .

28
Revisiting the USPSTF Breast Cancer Screening
Guidelines Ethics, and Patient Responsibilities
David Shabtai Faculty 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.
29
Mammography Wars
September 10, 2010 Recommended Weekend Reading By
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.
30
Who should get a mammogram?
September 29, 2010 Mammogram Benefit Seen for
Women in Their 40s By 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.
31
What should we pay for?
Newsweek The Mammogram Hustle There 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.
32
New U.S. analysis backs annual breast screening
By Julie Steenhuysen CHICAGO Wed Jan 26, 2011
1226pm 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.
33
AJR USPSTF mammo recommendations could cost
6,500 lives yearly
The U.S. Preventive Services Task Force (USPSTF)
chose to ignore the science available to them
and brought about potential damage to womens
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.
34
Survival time after diagnosis lead time
Pre-detectable
Detectable, preclinical
Clinical
Disability or death
Age 35 45 55
65 75
Lead time
Clinical detection
Possible detection via screening
35
Survival time must increase gt lead time
Pre-detectable
Undetected (no screening)
Clinical diagnosis treatment
Disability or death
Survival time after diagnosis
Pre-detectable
Early detect, diagnosis, treatment
Monitoring for recurrence
?
Lead time
Age 35 45 55
65 75
36
Slowly progressing diseases are easier to
detect by screening
Pre- detectable
Clinical diagnosis, treatment
Disability or death
Survival time after diagnosis
Pre-detectable
Detectable, pre-clinical
Clinical diagnosis treatment
Disability or death
Survival time after diagnosis
Age 35 45 55
65 75
37
Early detection may over-diagnose
Pre-detectable
Undetected (no screening)
Mild or no symptoms
Favorable outcome
Survival time after diagnosis
Pre-detectable
Early detect, diagnosis, treatment
Monitoring for recurrence
Favorable outcome
Survival time after dx
Age 35 45 55
65 75
38
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
39
Reliability
  • Repeatability get same result
  • Each time
  • From each instrument
  • From each rater
  • If dont know correct result, then can examine
    reliability only.

40
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

41
Validity 1) Sensitivity
Probability (proportion) of correct
classification of cases Cases found / all cases
42
Validity 2) Specificity
Probability (proportion) of correct
classification of noncases Noncases identified /
all noncases
43
Remember this slide? 2 cases / month
O
O
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O
O
O
O
O
O
O
O
O
O
44
Pre-detectable preclinical clinical
old
O
O
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? ????????????????????????? ??????????????????????
??? ????????????????????????? ????????????????????
????? ????????????????????????? ??????????????????
???????
O
O
O
O
O
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O
O
O
O
45
Pre-detectable pre-clinical clinical
old
O
O
O
O
O
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? ????????????????????????? ??????????????????????
??? ????????????????????????? ????????????????????
????? ????????????????????????? ??????????????????
???????
O
O
O
O
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O
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O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
46
What is the prevalence of the condition?
O
O
O
O
O
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? ????????????????????????? ??????????????????????
??? ????????????????????????? ????????????????????
????? ????????????????????????? ??????????????????
???????
O
O
O
O
O
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O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
47
Sensitivity of a screening test
Probability (proportion) of correct
classification of detectable, pre-clinical cases
48
Pre-detectable pre-clinical clinical
old (8) (10)
(6) (14)
O
O
O
O
O
????????????????????????? ????????????????????????
? ????????????????????????? ??????????????????????
??? ????????????????????????? ????????????????????
????? ????????????????????????? ??????????????????
???????
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
49
Correctly
classified Sensitivity
Total detectable
pre-clinical (10)
O
O
O
O
O
????????????????????????? ????????????????????????
? ????????????????????????? ??????????????????????
??? ????????????????????????? ????????????????????
????? ????????????????????????? ??????????????????
???????
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
50
Specificity of a screening test
Probability (proportion) of correct
classification of noncases Noncases identified /
all noncases
51
Pre-detectable pre-clinical clinical
old (8) (10)
(6) (14)
O
O
O
O
O
????????????????????????? ????????????????????????
? ????????????????????????? ??????????????????????
??? ????????????????????????? ????????????????????
????? ????????????????????????? ??????????????????
???????
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
52
Correctly
classified Specificity
Total non-cases (
pre-detect) (162 or 170)
O
O
O
O
O
????????????????????????? ????????????????????????
? ????????????????????????? ??????????????????????
??? ????????????????????????? ????????????????????
????? ????????????????????????? ??????????????????
???????
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
53
True Disease Status
Cases
Non-cases
True positive
False positive
a b
Positive
Screening Test Results
a
b
d
c
True negative
False negative
c d
Negative
a c
b d
a
True positives

Sensitivity
a c
All cases
True negatives
d
Specificity

All non-cases
b d
54
True Disease Status
Cases
Non-cases
140
1,000
1,140
Positive
Screening Test Results
a
b
d
c
19,060
19,000
60
Negative
200
20,000
True positives
140
Sensitivity
70


All cases
200
19,000
True negatives
Specificity


95
20,000
All non-cases
55
Interpreting test results predictive value
Probability (proportion) of those tested who are
correctly classified Cases identified / all
positive tests Noncases identified / all negative
tests
56
True Disease Status
Cases
Non-cases
True positive
False positive
a b
Positive
Screening Test Results
a
b
d
c
True negative
False negative
c d
Negative
a c
b d
True positives
a
PPV

All positives
a b
True negatives
d
NPV

All negatives
c d
57
True Disease Status
Cases
Non-cases
140
1,000
1,140
Positive
Screening Test Results
a
b
d
c
19,060
19,000
60
Negative
200
20,000
True positives
140
PPV
12.3


All positives
1,140
19,000
True negatives
NPV


99.7
19,060
All negatives
58
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
59
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

60
Effect of Prevalence on Positive Predictive Value
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
PV 64
PV 88
See http//www.meddean.luc.edu/lumen/MedEd/ipm/IPM
1/Biostats/diagnostic_test_example1_Solutions1011.
pdf
61
What is used as a gold standard
1. Most definitive diagnostic procedure
e.g. microscopic examination of a tissue
specimen 2. Best available laboratory test e.g.
polymerase chain reaction (PCR) for HIV
virus 3. Comprehensive clinical evaluation e.g.
clinical assessment of arthritis
62
Main concepts
1. Requirements for a screening program 2.
Concept of natural history possible biases
include lead time, length, over-diagnosis 3.
Reliability (repeatable) can occur by chance 4.
Validity (correct) sensitivity, specificity 5.
Sensitivity and specificity relate to the
detectable pre-clinical stage of the disease 6.
Predictive value the population perspective on
disease detection
63
Have you ever thought of saying
  • My resume might make it look like Im 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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