Assignment # 1
e-Module 1 – Basics of Birth defect Epidemiology
Deadline: 29 April 2013 (Monday)
Questions & Answers
Q1. The new Maternal and Child Health officer in Loa, Dr Jorge, has made the following observations
relative to 2012: please summarize these data in a table and compute the total prevalence of neural
tube defects.
 Number of neural tube defects observed = 23
o Among these:
 Spontaneous abortion = 2
 Elective termination of pregnancy for fetal anomaly = 6
 Stillbirths = 4
 Live births = 11
 Total reported spontaneous abortions = 28
 Total reported elective termination of pregnancy for fetal anomaly = 66
 Total Stillbirths = 54
 Total Live births = 23,402
A1. 21/23,456 or 8.95 per 10,000 births (alternatively, 8.97 per 10,000 live births)
Comments:
a) Let’s compute the numerator: in this case 11+4+6 = 21
NOTE: spontaneous abortions are not included (can you imagine why?)
b) Now the denominator: 23,402 (livebirth) + 54 (stillbirths) = 23,456
NOTE: if the system does not report stillbirths, then use only livebirths (and be clear about the
denominator when reporting prevalence, i.e., per 10,000 live births). You will have noted that stillbirths
are typically in the order of 1 for every 100 live births (two orders of magnitude fewer) so excluding
stillbirths from the denominator does not materially change the estimated prevalence.
c) 21/23,456 = 8.952 per 10,000; it’s a good habit in these situations to use at least one decimal (=9.0) ,
and there is nothing wrong with two (=8.95)
Q2. What is the opposite of a risk factor? Suggest a term (and explain why you think it is a good
term) that might be used to indicate factors that reduce the probability of occurrence of a disease
when compared to individuals in whom the factor is not present
A2. Protective
This is not a trick question! The term is commonly used with a positive meaning: whereas a risk factor
increases the likelihood of disease, a protective factor reduces it. Note that the term protective factor is
not commonly used in classic epidemiology (in which the emphasis is on risk factors), and when used, it
is typically in relation to behaviors (e.g.: http://www.publicsafety.gc.ca/prg/cp/risk-factors-eng.aspx ,
http://www.cdc.gov/healthyyouth/adolescenthealth/protective.htm). One reason for this question is to
emphasize that identifying and understanding protective factors (e.g., using a folic acid supplement) is at
least as important as researching risk factors.
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Q3. Referring to Segment 1.2 slide 21, a study is mentioned in which “the exposure (e.g.: medication
use during first trimester of pregnancy) [was] ascertained by available clinical records or interview”.
Do you think that there is a difference on the validity of the ascertainment of exposure between
mother’s interview and data extraction from clinical records? Why?
A3. The main point here is the concern that ascertaining the exposure after the outcome is known (by
parents or investigators) may bias recall or reporting and consequently the risk estimates. For example,
mothers of babies with birth defects may recall or report differently past exposures in their effort to find
an explanation for what has occurred. The extent to which this really happens in birth defect studies is
unclear most of the time, but remains a real concern that needs to be addressed, most effectively during
study design and study implementation, and, to the extent possible, also in the analysis. Note also that
bias can occur in more complicated ways as well (e.g., it may vary by type and severity of birth defect, by
educational level, and so on).
Extracting data from clinical records that document events and exposures (including prescriptions)
before the discovery of the birth defects would seem to take care of that problem. This is certainly true
to some extent, but (internal) validity may still be an issue: for example, reporting (by the subject or
health care provider) can still be partial, selective, or imprecise, leading to differential or non-differential
misclassification of the exposure, both of which can impact validity.
Q4. Interventions aimed at eliminating risk factors in a population (segment 1.3) are typically
effective up to a point. Can you give an example of one or more situations (preferably real) in which a
risk factor can or has been completely eliminated? What were the crucial characteristics (of the risk
factor, population, or intervention) that made such an intervention successful?
A4. The question focuses on “risk factors” that contribute to causing a “non-communicable disease”
such as a birth defect. One classic example is smoking, a risk factor associated with many adverse health
outcomes. (Immunizations are a classic example in the world of communicable diseases—and to some
extent, with congenital rubella also in the context of birth defects—but here we would like to focus on
other exposures).
There are several examples of real situations in which complete or near complete elimination of a risk
factor can in theory be achieved. However, few of these have been successful. Examples of successful
interventions may include the following:
(1) Public Health Agencies implement a mandatory action:
a. restricting the use or withdrawing from the market a teratogenic drug: thalidomide is
the best known example;
b. fortification of staple foods with folic acid.
(2) Public Health Agencies together with professional groups implement an awareness campaign:,
e.g.: the ‘Back to Sleep’ campaign to encourage parents to place the infants to sleep on the
back and not on the belly, to prevent sudden infant death (SIDS)
Determinants of success in these examples include the following (you can think of more):
(a) Strong evidence of protective effect of the intervention before the implementation decision.
(b) Strong and sustained role of Public Health Agencies in the battle against the risk factor.
(c) Collaboration of multiple organizations in echoing the positive messages.
(d) The intervention was simple, inexpensive (or cost effective), and acceptable by the population.
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Q5. Examine the following table of observations in this population of 100,000:
With birth
defects
Without birth
defects
Total
Exposed
1,000
19,000
20,000
Unexposed
1,600
78,400
80,000
Total
2,600
97,400
100,000
Compute the following measures of risk and impact:
A5:
Note 1: the formulas are in the slides (not repeated here).
Note 2: Let’s use a multiplicator “per 1,000” to denote risk, to avoid confusion with percent (%) used
typically for fractions. Let’s also always use at least a decimal.
Q5.1 Risk of birth defects in the population:
A5.1 2,600/100,000 = 0.026 = 26.0 per 1,000
Q5.2 Risk of birth defects among exposed:
A5.2 1,000/20,000 = 0.05 = 50.0 per 1,000
Q5.3 Risk of birth defects among unexposed:
A5.3 1,600/80,000 = 0.02 = 20.0 per 1,000
Q5.4 Risk difference (attrib. risk in exposed):
A5.4 1,000/20,000 – 1,600/80,000 = 0.03 = 30.0 per 1,000
Q5.5 Relative risk (RR):
A5.5 (1,000/20,000)/(1,600/80,000) = 2.5
Note: 2.5 is an absolute number, not a percent .i.e. 2.5% is incorrect).
Q5.6 Frequency of exposure in the population (%):
A5.6 20,000/100,000=0.2 = 20%
Q5.7 Attributable fraction in exposed
A5.7 We already know how much higher is the risk among the exposed population compared to the
unexposed. Now we want to express this information as a proportion: the risk attributable to the
exposure as a fraction of the overall risk observed among exposed (which includes the risk associated
with other factors and is identical to the unexposed population). This is simple: a division suffices—
divide what you have computed in question 5.4 = 0.03 (or 30.0 per 1,000) by what you computed in
question 5.2 = 0.05. The result is 0.6 = 60%
What does this mean? In terms of cases, this is interpreted as saying that in 60% of exposed cases, the
birth defect is attributable to the exposure. Three comments, obvious but worth mentioning:
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- for most weak to moderate risk factors (e.g., 1 < RR <3) you cannot attribute the birth defect to the
exposure
- this is a group attribute: on an individual exposed case, one cannot say whether or not the exposure
caused the birth defect (these two points are relevant in individual counseling)
- the concept of attributable fraction is different from that of risk: ‘attributable’ assumes causality, and
causality is based on a evidence that is well beyond these simple mathematical constructs. When
reporting attributable fraction, it is helpful to preface with a statement such as ‘ Assuming causality….’,
(not only for the reader, but also a good reminder for oneself!).
Q5.8 Population risk difference (population attributable risk) :
A5.8 We are now interested attributable risk in the total population, exposed and unexposed: what is
the risk increase in the total population attributable to the fact that in this population there are some
exposed person? Obviously this depends not only on how ‘strong’ the risk factor is, per se, but also on
how many exposed people there are (how frequent is the exposure).
In this example 20,000 out of 100,000 people are exposed (20%).
The answer is easy, right? The attributable risk in this population is precisely the difference between the
risk in THIS population and the risk in a ‘ideal’ (counterfactual) population in which no one is exposed to
the risk factor. What is the risk in this ideal population? It is simply the risk among the unexposed
(which we computed in question 5.3 = 20.0 per 1,000). So, the population attributable risk is 26.0 per
1,000 minus 20,0 per 1,000 = 6.0 per 1,000.
What does this mean? 6.0 per 1,000 cases of birth defects in this population (with its specific rate of
exposure to the risk factor and other characteristics) are attributable to the exposure. One can express
this as absolute number of birth defects = 600 cases, 6.0 per 1,000 or 600 per 100,000 (the total
population we have). Of course, the same caveats about causality (discussed in answer 5.7 above) apply
here as well.
Reflection: What do you think will happen if the risk (e.g., relative risk) is higher, or lower, or if the
frequency of exposure is higher, or lower? Do you think the attributable risk increases linearly or in
some other fashion?
Q5.9 Population attributable fraction:
A5.9 This question is very similar to the previous one (question 5-8). The point here is that we want to
know the fraction or proportion of risk, and not the absolute value. The answer is similar to the answer
5.7 and is simple: divide the population risk difference (see question 5.8 above = 6.0 per 1,000) by the
population risk (= 26.0 per 1,000), and voila’ = the result is 23.1%
Note: this means that 23.1% of the risk is attributable to the risk factor in this particular situation: 20%
of population exposed, with the stated relative risk associated to the exposure.
In term of absolute number of birth defects this means 600 cases, 23.1% of the 2,600 total birth defects
we have in this example.
Bonus question: if an intervention decreases by half the frequency of exposure in this population, what
measures of risk and impact will change? Which will remain the same?
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If the frequency of exposure in the population changes from 20,000/100,000 = 20.0% to 10,000/100,000
= 10.0%
These changes will occur:
Risk of birth defects in the population: from 26.0 per 1,000 to 23.0 per 1,000
Population risk difference (population attributable risk) from = 26.0 per 1,000 ‐ 20.0 per 1,000 = 6.0 per
1,000 to 23.0 per 1,000 – 20.0 per 1,00 = 3.0 per 1,000
Population attributable fraction = from 6.0 per 1,000 / 26.0 per 1,000 = 23.1% to 3.0 per 1,000 / 23.0
per 1,000 = 13.0%
Same:
Risk of birth defects among exposed: = 50.0 per 1,000
Risk of birth defects among unexposed: = 20.0 per 1,000
Relative risk (RR): = 2.5
Risk difference (attributable risk in exposed): = 30.0 per 1,000
Attributable fraction in exposed = 60.0%
See also this table:
Prevalence of
exposure in
Population
20%
Exposed
Unexposed
with birth
defects
1,000
1,600
2,600
Risk of birth defects
Risk among exposed
Risk among unexposed
Relative Risk
Risk difference (AR exposed)
Attrib. Fraction in exposed
Population Risk Difference (pop. Attr. Risk)
Attrib. Fraction in population
without
birth
defects
19,000
78,400
97,400
Total
20,000
80,000
100,000
26.00 per 1,000
50.00 per 1,000
20.00 per 1,000
2.5
30.00 per 1,000
60%
6.00 per 1,000
23.1%
Prevalence of
exposure in
Population
10%
Risk
50.00
20.00
26.00
with birth
defects
Exposed
500
Unexposed
1,800
2,300
without
birth
defects
9,500
88,200
97,700
Total
10,000
90,000
100,000
23.00 per 1,000
50.00 per 1,000
20.00 per 1,000
2.5
30.00 per 1,000
60%
3.00 per 1,000
13.0%
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Risk
50.00
20.00
23.00
Q6. (Segment 1.4). Compute relative risk and odds ratio from the data in segment 1.3, slides 7, 14 and
15. Are the results similar or very different? Why?
A6. Here is the table:
with
without
birth
birth
defects defects
Exposed
513
5,650
Unexposed 17,546 285,265
Total
6,163
302,811
Risk
8.32
5.79
Relative Risk
Odds ratio
1.44 8.32/5.79
1.48 (513/17546)/(5650/285265)
1.48 (513*285265)/(5650*17546)
Note: Odds and risk (probability), as well as their corresponding metrics OR and RR, have similar values
when the event frequency relative to the comparator is low. In other words, the numerator is small
relative to the denominator. (For example, numerators are 10 to 20 times smaller than denominators)
Q7. If odds ratio and relative risk point to and try to measure the same concept, why do they not
provide identical results?
A7. In the typical case-control study of birth defects* the odds ratio tends to approximate by excess (it is
always a bit higher) the relative risk, and the approximation is better for rare disease.
Note: consider however that the odds ratio remains a valid measure of association regardless of the
diseases being common or rare – the rare disease caveat relates only to the ability of the odds ratio to
estimate the relative risk of disease as computed (or imagined) in a cohort study, that is, the ratio
between disease rate in the exposed over disease rate in the unexposed.
*small point: in a typical study, cases and controls are selected at the end of a theoretical follow up
period (e.g., at birth, after pregnancy). In a density-based case-control study or a case-cohort study the
odds ratio does not require the rare disease assumption in order to estimate the relative risk accurately.
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