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PUBH2001 Tutorial 2 Guide
Q1. The word epidemiology comes from the Greek words epi , meaning on or upon, demos, meaning
people, and logos, mea ...
PUBH2001 Tutorial 2 Guide
Q1. The word epidemiology comes from the Greek words epi , meaning on or upon, demos, meaning
people, and logos, meaning the study of. Many definitions have been proposed, but the following
captures the underlying principles and public health spirit of epidemiology:
Epidemiology is the study of the distribution and determinants of health -related states or
events in specified populations , and the application of this study to the control of health
problems. Epidemiology is the basic s cience of disease prevention and plays major roles
in the development and evaluation of public policy. It is now used together with lab
research to identify environmental and genetic risk factors for diseases and to shed light
on the mechanism s involved in pathogenesis. Keeping this in mind c omplete the
following statements by choosing from the given words :
(l) Investigating epidemics and new diseases;
(2) Studying the biologic spectrum of disease;
(3) Instituting surveillance of community health interv entions;
(4) Setting disease control priorities;
(5) Helpful in the diagnosis, treatment, and prognosis of clinical disease;
(6) Improving health services research;
(7) Providing expert testimony in courts of law.
Note: M ore than one word can be suit able for some spots.
Q2. Descriptive Epidemiology is concerned with describing the general characteristics of the
distribution of disease particularly in relation to time, place and person. What are the 4 W words that
tell us what descriptive epidemiology is about & what is the ‘W’ word for analytic epidemiology ?
What, who, where and when ( Why for analytic epidemiology)
Q3. Write two research questions that can be answered using descriptive approach and two examples
of research questions that can be answered using analytic approach.
What was the p revalence of type 2 diabetes among Australian adults in 2017?
What is the incidence of colon cancer among Australian females 60 years and older.
Is there an association bet ween obesity and the risk of uterine cancer?
Does breastfeeding reduce the risk of breast cancer?
Analytic Epidemiology is the process of testing a specific hypothesis mainly about association
between potential exposures (or determinant s) and outcome of interest. A study is performed to
examine the relationship between the exposure to a risk factor and the outcome, typically a disease
but can be disability, injury or any health related state.
Q4. Table 1.1 Water sources and cholera mortality in London, 9 th July to 26 th August
Source of Water Total no of households No of cholera deaths Deaths per 10,000 house hol
S & V Company 40, 046 1263 315
Lambeth Company 26, 107 98 38
Rest of London 256, 423 1422 55
During 19 th century two private companies that obtained water directly from river Thames supplied water in
London. Southwark & Vauxhall (S&V) and Lambeth as listed in the table. Between 1849 and 1853, when
London was free of cholera, Lambeth Company moved its water source to upstream, an area outside London
and less polluted, while the S&V Company continued to draw water from a downstream source in London.
Snow collected data on the number of houses supplied by each company and when cholera epidemic recurred
in 1954 he collected data on sources of water for households of those who had died of cholera. The above
table shows number of cholera deaths per 10,000 households, stratified by water source, during the first 7
weeks of the epidemic.
Note: In case you are wondering how t hey got the numbers in final column labelled as ‘Deaths per
10,000 households’ which are Cholera deaths per 10,000 households the formula they used was:
No of deaths due to Cholera x 10,000
Number of households supplied
Deaths due to cholera in househol ds supplied by S&V Company = (1263/40,046) x 10,000 = 315
Deaths due to cholera in households supplied by Lambeth Company = (98/26,107) x 10,000 = 38
Deaths due to cholera in households for rest of London = (1422/256,423) x 10,000 = 55
a. How many death s happened in households supplied by S&V, Lambeth and rest of London
(calculate separately for each of the three and express as percentage with 2 decimals)
Deaths due to cholera in households supplied by S&V Company = (1263/40,046) x 100 = 3.15%
Deaths due to cholera in households supplied by Lambeth Company = (98/26,107) x 100 = 0.38
Deaths due to cholera in households for rest of London = (1422/256,423) x 100 = 0.55
Please note there are more than one ways of doing this (as you can see below) but I sugge st going
with the original data (information given as number of Cholera deaths and number of households
supplied), rather than using the final column, number of deaths per 10,000 households because some
rounding up or down have been done in this column. Using a calculated answer from a previous
step may sometimes give you slightly different final answer (not in this case though). I still prefer if
you go with the method shown above rather than the one below)
Deaths due to cholera in houses supplied by S&V Company = (315/10,000) x 100 = 3.15%
Deaths due to cholera in houses supplied by Lambeth Company = (38/10,000) x 100 = 0.38%
Deaths due to cholera in houses supplied by S&V Company = (59/10,000) x 100 = 0.55%
b. Do the answers in ‘ part a’ support Snow’s hypothesis that cholera is transmitted through
water? Give reasons for your answers.
Yes the above data supports Snow’s hypothesis that cholera is transmitted through water but they do
not prove it. For example no information on other possible modes of transmission is included.
c. Are these data adequate to conclude that cholera mortality is higher in houses supplied by
the S&V Company than in houses supplied by the Lambeth Company? Give reasons for your
No. You can see that the number of deaths were 8.29 times more in households supplied by S&V
Company in comparison to Lambeth Company (3.15/0.38 = 8.29) and 5.73 times compared to the
rest of London (3.15/0.55 = 5.73 ). Although the data suggest that cholera deaths were higher in
households supplied by S&V Company but they do not prove the causality and are not adequate to
make firm conclusions that cholera mortality is higher in households supplied by S&V Company.
d. What further questions you might ask before reaching any conclusions based on these data?
Before reaching any conclusions we should consider whether the number of people per household,
their socio-economic status, and other potential factors associated with the risk of cholera are
comparable (similar) between the populations we are comparing. For example, the S&V Company
might have supplied water to multiple occupancy buildings while Lambeth supplied individual
family houses. If this were the case, then the risk of cholera death per house between these
populations will not be comparable since the average number of people per house would differ
between them. Also since S&V Company was drawing water from downstream, it is possible that
the households supplied by the company would have been in downstream areas and might be poorer
than households upstream. Therefore, although the data appear to support Snow’s hypothesis, more
information is needed to be convincing.
Q5. What is the name of the following graph? Write a short interpretation for the
following graph (what information is being conveyed) . What do the ‘error bars’ represent
and what is their importance ?
Figure1: Prevalence of self -reported diabetes by age group and gender (dark bands males
& light for females)
It is a ‘Bar chart’ which is suitable for displaying categorical data (number of people with
diabetes per 100 in the population within each gender and age group) . Males have higher
prevalence of self -reported diabetes than females in all age groups and the prevalence of
diabetes increases with age.
Error bars represent the 95% confidence intervals (a confidence interval reflects the
uncertainty of the population estimate) and the height of each bar shows the accuracy of
prevalence estimates. If 95% CIs for any two groups overlap it means there is no real (or
no statistically signi ficant difference) in prevalence between the two groups. In the above
graph 95%CIs for males and females overlap for all the age groups therefore we can say
that though prevalence of self -reported diabetes is slightly higher among males but this
difference is due to some random chance and there is no statistically significant
difference in prevalence among males and females.
Q 6a. Fill in the blanks: The two main types of data are Categorical (i.e. Nominal &
Ordinal) and Continuous (Interval and Ratio).
Q 6b. Label the following variables (or data) with correct measurement scale ( Nominal
(including Binary/dichotomous, having two values or categories only), Ordinal , Interval
For those who need a refresher p lease read on: A measurement scale refers to how data or
a variable of interest has been measured ( Why you need to know on what scale a variable
has been measured ? Because it determines what sorts of analysis or statistical tests we
can do with them)
Nominal scale: Categories or groups with names only, no information regarding size or
magnitude or relationship between categories e.g. name of the units you are studying this
semester . It does not tell us much other than how many are in each category (percentage
or proportion) .
Ordinal scale: categories with some form of ‘order’ or ‘ranks’ in terms of size or
magnitude (we can say one is bigger than the other) but the interval or difference
between one category and the next is not the same e.g. units you are studying as to
‘dislike, like a little, like a lot’ we can make some sense here but we can’t really say how
much exactly you like or dislike and how different one category is from another in terms
of size or magnitude. A nother example may be ‘ disease severity’ measured as (mild,
moderate and severe) we know t he disease is getting worse from mild to severe but the
difference between mild and moderate, and between moderate to severe is not known or
Interval scale: Magnitude or size can be measured precisely and ‘intervals’ are equal and
can be identified e.g. we can say A - B = B - C, if A, B, and C are IQ scores and A = 150,
B=100 and C=50 then it will be true that A - B = B – C however, we cannot say that
A=3C or A is three times more intelligent than C . Interval scale does not have meaning ful
zero ( an IQ of zero does not mean complete absence of intelligence rather it simply
means that a person has severe intellectual deficit and cannot cope with the procedure or
the tests for measuring the IQ ). Another example to clarify equal intervals is e.g. if
Claudia, Sam and Hamish weigh 60kg, 66kg and 72kg respectively 6kg difference is
‘same’ whether it is lighter or heavier i.e. difference in terms of body weight is same
between Claudia and Sam as well as between Sam and Hamish. Remember in Interva l
scale ‘zero’ does not mean ‘complete absence of that characteristic’ e.g. 0 degree
Centigrade does not mean complete absence of heat, as temperature can go down below
zero such as - 5 or -40 etc . (therefore in statistics lingo we say ‘zero is not absolute ’)
Ratio scale: It has ALL the properties Interval scale along with a meaningful or ‘absolute
zero’, so a value of zero on ratio scale actually means ‘complete absence of that
characteristic’ e .g. zero number of heart beats per minute would actually mean ‘no heart
beat’ therefore it is a variable with ‘Ratio scale’ so is ‘Money in your wallet’ now if
‘money in your valet = 0 it will actually mean there is complete absence of money (yes no
Following table shows all four scales and their characteristics/properties.
Scale/Level of Measurement
Characteristic Nominal Ordinal Interval Ratio
Distinctiveness x x x x
Ordering in magnitude x x x
Equal Intervals x x
Absolute zero x
Distinctiveness = Different numbers (labels) assigned to different properties
Ordering in magnitude = Larger values represent more of the property
Equal intervals = same distance between points on a scale.
Absolute zero = I think you already know this now.
Let’s see if the following answers make perfect se nse:
a. The number of heart beats per minute: Ratio.
b. Platform numbers at a railway station: Nominal.
c. Finishing order in a horse race: O rdinal.
d. Self -rated anxiety levels on a five- point scale: Ordinal.
e. Assignment 1 marks out of 20: Ratio.
f. Subjects’ height in centimeters: Interval.
g. Body weight measured in kilograms: Interval
h. Percentage change in serum cholesterol levels: Ratio (as zero means ‘no change’).
i. Patient identification number: Nominal .
j. Weight measured as percentage overweight in relation to healthy weight: Ratio
k. Subject status after 2 years of follo w up measured as dead or alive:
( Binary/dichotomous: a nominal variable with 2 categories only)
Please note: I f ‘k’ was a multiple choice question, between the two choices ‘Nominal’
and ‘Binary/dichotomous’ the correct answer will be ‘Binary /dichotomous’ since it is
more precise/detailed than ‘Nominal’.
Q 7. Describe the 3 levels of prevention with an example each.
1) Primary prevention: denotes an action taken to prevent the development of a
disease in a healthy person not having the disease in question. Or, if a disease is
environmentally induced, we can prevent a person’s exposure to such environment
to prevent the development of the condition. For example, we can immunize a
person against certain diseases . By getting people to stop smoking we can prevent
70- 80% of lung cancer in human beings. Primary prevention is our ultimate goal.
2) Secondary prevention: denotes the identification of people with disease at an early
stage in the disease’s natural history, through screening and early intervention. For
example, breast cancer screening , colon cancer screening (occult blood test). The
rationale is that if disease can be identified earlier, the intervention measures would
be more effective, less costly and the m ortality or complications could be
3) Tertiary prevention: Reduction of disability and promotion of rehabilitation e.g.
surgical intervention to prevent a complication (amputation of lower limb in case of
gangrene in a diabetic patient to prevent septicemia and death).
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