ACMT Board Review
Course
Population Health and
Assessments
Jeffrey Brent, M.D., Ph.D.
Toxicology Associates
University of Colorado
School of Medicine
and
Colorado School of Public Health
Topics for this Lecture
1.
2.
3.
4.
5.
6.
7.
Exposure monitoring and sampling
PPE
Study designs and measures of association
Statistical concepts
Bias and confounding
The Hill “criteria”
Sensitivity, specificity, predictive values
Exposure monitoring and
sampling
Exposure monitoring
Environmental sampling:
Wipe sampling
Water sampling
Air sampling
Breathing zone measurements are best for
inhalational exposures
Biological monitoring e.g.:
Blood Pb
Urine mercury
Personal protective equipment
Respiratory
Chemically protective clothing
Respiratory protection
Classification by
size
Quarter face
Half face
Full face
Classification by
function
Air-purifying
 Uses chemical
specific cartridges
Supplied air
SCBA
Protection Factor
The factor by which exposure is
reduced by use of a respirator
Ambient/protection factor = exposure
For example:
Ambient of 100 PPM
Protection factor of 10
Exposure = 100/10 = 10 PPM
Protection factors range form 5 –
10,000
The goal is to get exposure to below
safe limits
Chemically protective clothing
Simple protection
Ex. Aprons, boots, gloves
Nonencpasulating suits
1 or 2 pieces, for example:
1 piece hooded coveralls
Hooded jacket + chem protective pants
Encapsulating suit
Highest level of protection
Chemically protective clothing is usually
designated by the EPA rating system
 Level A = Max protection
 Encapsulating suit
 SCBA
 Level B
 Supplied air respirator (or SCBA)
 Non-encapsulating garment
 Level C
 Air purifying respirator
 Non-encapsulating garment
 Level D = standard work clothes
NEXT
STUDY DESIGN AND
MEASURES OF ASSOCIATION
STATISTICAL CONCEPTS
Types of human data
Anecdotal
Case-reports and series
Controlled observational
Controlled epidemiological studies
Controlled interventional
Trials
Controlled observational studies
 Cohort
 Cross-sectional
 Mortality
 Case-control
 Ecologic
For all epi studies:
 Groups should be matched for relevant variables
(e.g.)
Age
Sex
Anything else that can affect results
Cohort studies
Compares exposed group to an unexposed
group
Can be retrospective or prospective
Can assess incidence rates
Incidence = Rate of new cases
(e.g. Cases/100,00/yr)
Prevalence = Number of cases in the
population
(e.g. cases/100,000)
Results expressed as Relative Risk (aka risk
ratio or rate ratio)
Cross-sectional studies
Compares exposed group to an unexposed
group at one snapshot in time
Provides prevalence data
Example: Prevalence of drug abuse in
medial toxicologists taking the board exam
v those that are not
Results expressed as Relative Risk (aka risk
ratio or rate ratio)
Mortality Studies
Typically a variation of a cohort study
Assesses diagnoses at time of death
Results expressed as mortality rates
corrected for relevant factors
(“Standardized mortality rates”)
Usually expressed as a percentage
(Mortality rate of exposed/rate in unexposed)
X 100 = SMR)
Thus an SMR of 100 = no difference btw
exposed and unexposed
Case-control studies
 Compares individuals with a specific condition with
individuals that do not have that condition and
compares exposures (or other risk factors)
 For example: Comparing medical toxicologists with
alcohol abuse (the cases) with those w/o this dx
(controls) to see if there is a higher likelihood of
alcoholic abuse if preparing to take the boards.
 Thus assesses risk factors (e.g. exposures) related
to specific conditions
 Recall bias major problem
 Results expressed as Odds Ratios
Ecologic Studies
Assesses population numbers, not
individuals
Example: Rate of admission for asthma
exacerbations in a city with high airborne
PM10 compared to a city with low PM10
Results expressed as Relative Risk (aka risk
ratio or rate ratio)
Ex: Snow’s study of cholera rates in London
districts
Assessment of results of epi
studies
By convention a result is statistically
significant if the likelihood that it is
chance result is < 5% or approx 2SDs
from the mean
Interpretation of EPI Data
You can never assess the degree of
association based only on the magnitude of
the RR, OR or SMR
These values have an inherent uncertainty
that is determined by the nature of the
data
In modern epi this uncertainty is expressed
as Confidence Intervals
The 95% Convention
 In science the
uncertainty in a result is
expressed as that range
of data in which there is
a 95% likelihood that the
real value exists
 CIs express this range
 Ex: RR 1.6 (CI 0.7 – 2.4)
The importance of confidence
intervals
 If RR 1.6 (0.7 – 2.4)
Than there is a 95% likelihood that the real
value lies between 0.7 – 2.4
 If the real RR is:
 >1= association
 1= Non-association
 <1 = negative association (protective effect)
 The 95% rule defines “statistical significance”
 Thus, in order to be a statistically significant
result the CI must not include 1
What about “p values”?
p Values are an older way of describing
statistical significance
P < 0.05 means a result is statistically
significant
OR 1.6 (0.7 – 2.4) = OR 1.6 p > 0.05
OR 1.6 (1.1 – 2.1) = OR 1.6 p < 0.05
Now the Bad News
A statistical relationship never a
priori means a causal relationship
Does epi data show
a statistically
significant
relationship?
No
Yes
No established
association
Does the statistical
association
mean a causal
relationship?
It is not the falling of the leaves
that causes winter to come
There are many more statistical associations in
toxicology than there are causal relationships
How to get from association to
causation
Requires specific rigorous
methodology
Stems from Doll and Hills’
observation of an
association between
smoking and lung cancer
Hill’s Viewpoints
To be applied if a
statistical
association is
shown to exist
Does not account
for quality of
studies showing
such an
association
Hill’s “Viewpoints”

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






Strength of association
Consistency
Specificity
Biological gradient
Temporal precedence
Coherence
Plausibility
Experimental support
Analogy
Also must consider the quality of the
study
Bias and confounding
Bias = systematic error
Ex: You are doing a study on childhood bl Pb
concentrations and behavior. However, your
lab technique inflates blood lead values by
20% = a bias.
Confounding = uncontrolled for factor
affecting results.
Ex: You are doing a retrospective cohort study
on chronic exposures to phosgene in laboratory
workers and the incidence of lung cancer but
you do not control for smoking.
Smoking is a confounder
A LITTLE TIP -KNOW HOW
TO CALCULATE SENSITIVITY,
SPECIFICITY, AND
PREDICTIVE VALUES
Sensitivity
 The likelihood of a test being positive if the condition is
present
 Ex: Being under 16 yrs old has 100% sensitivity for the
detection of childhood Pb poisoning.
 Good for screening (few false negatives (FN))
Sensitivity = True positives (TP)/(TP + FN)
Sensitivity is often expressed as a %
In example above if screen 100 individuals and 10 had Pb
poisoning: Sens = 10/(10+0) = 10/10 = 1 (or 100%)
Another example
To determine the sensitivity of a terminal R in lead
AVR for the detecting of Na+ channel antagonist
toxicity in all OD patients.
 Screen 1,000 EKGs of OD patients, 100 had OD’d
on Na+ channel blockers and 80 had a terminal R
wave (TPs). 50 had a terminal R wave but did not
OD on these agents.
 TP = 80
 FN = 20
 Sens = TP/(TP + FN) = 80/(80 + 20)
= 80/100 = 0.8 (80%)
Specificity
The likelihood of the unaffected individuals
correctly having a negative test
Test: using criteria of being under 16 for dx
of childhood Pb poisoning.
N = 100
10 with Pb poisoning - the other 90 are false
positives (FP)
Specificity = True neg (TN)/(TN + FP)= 0/0+90 = 0
The second experiment
Screen 1,000 EKGs of OD patients, 100 had
OD’d on Na+ channel blockers and 80 had
a terminal R wave. 50 others had a
terminal R wave but did not OD on these
agents (FPs).
TN= 850
FP = 50
Sp = TN/(TN+FP) = 850/(850+50)
= 850/900 =0.94 (94%)
Comparison btw Sensitivity
and specificity
Both= True/(True + False)
Sens = TP/(TP+FN)
Specificity is the mirror image
Spec = TN/(TN+FP)
For both the “trues” in the numerator and
denominator terms are the same.
The other denominator term is the
complete opposite
Positive predicative value
PPV = likelihood that the test will correctly
Dx the condition
Test: using criteria of being under 16 for dx
of childhood Pb poisoning.
N = 100
10 with Pb poisoning (TP) - the other 90 are
false positives (FP)
PPV = TP/(TP+FP) = 10/(10 + 90) = 0.1
So 10% PPV
PPV – the second experiment
Screen 1,000 EKGs of OD patients, 100 had
OD’d on Na+ channel blockers and 80 had
a terminal R wave (TP). 50 others had a
terminal R wave but did not OD on these
agents (FPs).
TP = 80
FP = 50
PPV = TP/(TP + FP) = 80/(80+50)= 80/130
= 0.6
Negative predicative value
The likelihood that the disease is not
present if the test is negative
Test: using criteria of being under 16 for dx
of childhood Pb poisoning.
N = 100
0 are TN
0 are FN
NPV = TN/(TN+FN) = 0/(0+0) = 1 (100%)
NPV – a more rational study
Screen 1,000 EKGs of OD patients, 100 had
OD’d on Na+ channel blockers and 80 had
a terminal R wave. 50 others had a
terminal R wave but did not OD on these
agents (FPs).
NPV = TN/(TN + FN)
TN = 850
FN = 20
NPV = 850/(850+20) = 850/870 = 0.97
Predicative values - summary
PPV uses only positive terms
PPV = TP/(TP+FP)
NPV uses only negative terms and is
exactly opposite of the PPV
NPV = TN/(TN+FN)
If, when you are studying, this you have any questions call me
(24/7) @ 303-765-3800 or e-mail me at
[email protected]