The document provides an overview of a training workshop on epidemiological analysis. It discusses key concepts in epidemiology including study design, data collection, and analytical approaches. It emphasizes the importance of designing an analysis plan from the beginning to determine the appropriate statistical techniques for answering research questions based on the study objectives, design, and variables. The document uses an example of a cross-sectional study of schistosomiasis in Cameroon to demonstrate how to develop an analysis plan, including exploring variables, examining relationships, and considering regression modeling to adjust for confounding factors.

1. Training workshop: Epidemiological analysis
Dr Suzy Campbell
Postdoctoral Research Associate
LSTM, Liverpool
Suzanne.Campbell@lstmed.ac.uk

2. Overview of Workshop
Welcome, introduction and overview
Brief introduction to epidemiological data
Key measures encountered in helminthology
Epidemiological data collection
Designing an analysis plan for your work
Useful sources of information – where to go next?
Final thoughts, questions and close

3. Epidemiology
The ‘who’, ‘what’, ‘where’, ‘when’, ‘why’, and ‘how’ of disease
Field epidemiology – study design, data collection
Analytical epidemiology – analysing the results of the data to meet the stated research
objectives (quantitative and qualitative epidemiology, biostatistics)
- Determine disease distributions in populations
- Determine associations between outcome (eg disease status) and explanatory
variables (factors of interest/’determinants’ of disease)
 Extremely useful (often, essential) for targeting control strategies appropriately

4. Introduction to epidemiological data
Baseline surveying useful:
- To find out what disease/s present in which areas
- To define where interventions are needed (may not be all areas: can rank order of
priority)
- To estimate intervention (drug) requirements
- To determine the frequency of interventions
Follow-up surveying useful:
- To assess changes in diseases (and intervention requirements) over time
- To assess whether the intervention is working (reductions in morbidity, increases in
coverage)
- To know when you can stop (theoretically)

5. Epidemiological measures encountered in helminthology
Prevalence of schistosomiasis and STH by area (and infection intensity): stool and urine
examination
Also essential to collect information on other risk factors (eg demography, WASH,
environmental variables etc)
Treatment coverage data (usually school-based) AND age and sex
School-based data: also interested in
• Size of school-aged population
• Number of schools (and teachers) by district/province)
• School enrolment rate

6. The ‘evidence pyramid’ of study designs
Image credit: http://libraryguides.unh.edu/health-
literacy/pyramid
Most evidence tends to be observational  you
CANNOT attribute causality to observational data
Few experimental studies undertaken  you CAN
attribute causality (if well-designed and analysed)
Few systematic reviews/meta-analyses
(observational evidence)

7. Epidemiological data collection
1. Know your research question/s at the BEGINNING!
1. That way you are collecting the right data
2. What do you need, what don’t you need, who is the population (inclusion/exclusion)
3. Where could the mistakes be made
4. Quality quality quality!
2. Design data collection forms to collect this information
1. Use sources of information eg literature review, expert advice
2. Test them! Pre-testing, pilot testing before you commence
3. Be open to adaptation (but STRONG note of caution here!)

8. Some examples of data collection forms for helminth surveys

9. Laboratory specific

14. Electronic data capture with ODK Collect

15. Using Open Data Kit (ODK) Collect and ‘tablets’ to collect data

16. So you’ve collected the data: now what?
We need to carry out an analysis (often more than one) to answer certain questions.
But where do we start?
• What analyses should be carried out?
• What statistical techniques are appropriate to use?
• Will these techniques be sufficient to answer the research questions?
• We need to choose the correct methods of analysis for each question.
• It is POOR PRACTICE to apply statistical techniques without thinking about their
relevance to the research question and the study design.
 You need an analysis plan: this essentially involves thinking ahead, from the time of
designing the study, to determine the nature of the analysis to be conducted.

17. Designing an analysis plan for your study
1. This should be done AT THE BEGINNING
2. Know your data
1. What is your research question (null hypothesis)?
2. What are your outcome variables? Your explanatory variables?
3. Always start with investigating the variables individually – describe them
4. Then move on to consideration of two variables – is there anything happening?
5. Then move on to consideration of modelling (univariable before multivariable)
ALWAYS: start simple – explore your data, do descriptive analyses, build up to
more advanced modelling. NEVER just do statistical modelling without basic
investigations!

18. Steps to designing an analysis plan
1. List the objective
2. Describe the study design
3. Identify variables of interest
4. Define inclusion/exclusion criteria
5. Explore variable(s) needed
6. Examine relationships of interest
7. Dummy tables and figures
8. Going further: confounding and effect modification, statistical modelling

19. Example:
• A study investigating
prevalence, infection intensity
and risk factors associated
with schistosomiasis and soil-
transmitted helminths (STH),
in Barombi Kotto and Barombi
Mbo crater lakes, Cameroon
• Cross-sectional
schistosomiasis and STH study
undertaken in May-June 2016

20. Steps to designing an analysis plan
1. List the objective/s
1. Describe the prevalence and infection intensity of schistosomiasis in the populations of
Barombi Mbo and Barombi Kotto
2. Identify some of the risk factors associated with schistosomiasis (water contact behaviours)
2. Describe the study design
“The schistosomiasis and STH study is a cross-sectional study that was undertaken in
May-June 2016 in the two crater lakes of Barombi Mbo and Barombi Kotto, in South
West Region, Cameroon. It involved 338 children and adults who supplied a faecal and
urine sample at commencement, for the purpose of ascertaining prevalence and
intensity of schistosomiasis and STH. They also completed an interviewer-administered
questionnaire.”

21. Steps to designing an analysis plan
3. Identify variables of interest:
Haematobium (binary), Mansoni (binary), Eggsper10mL (quantitative continuous), age (quantitative
continuous), sex (categorical nominal), B1_Swim (categorical ordinal), B2_Wash (categorical ordinal),
B3_Fish (categorical ordinal), B4_Other (categorical ordinal)
Get to know your data – don’t be scared of it!
• What kind of variables do you have? Categorical, binary, ordinal, continuous etc
• What are your outcome variables?
• What are your explanatory variables?
Are all variables listed in a codebook for quick reference? If not, write this!

22. Variable name Code Variable name Code
P_Number Person’s unique ID number. NB: People with the same
number (but different letters) are in the same family
Guineensis 0 = No S. guineensis
1 = S. guineensis
888 = No data
Area BM = Barombi Mbo
BK = Barombi Kotto
B1_Swim Swims in lake: 1 = Never; 2 = Monthly; 3 = Weekly; 4 =
Daily; 5 = Refused; 6 = Unsure; 888 = No data
Gender F = Female
M = Male
B2_Wash Washes self in lake: 1 = Never; 2 = Monthly; 3 =
Weekly; 4 = Daily; 5 = Refused; 6 = Unsure; 888 = No
data
Age Age in years B3_Fish Fishes in lake: 1 = Never; 2 = Monthly; 3 = Weekly; 4 =
Daily; 5 = Refused; 6 = Unsure; 888 = No data
Agecohort Categorised age:
1 = Preschool-aged (1≤6 years);
2 = School-aged (>6≤18 years)
3 = Adults (>18 years)
B4_Other Enters lake for any other reason: 1 = Never; 2 =
Monthly; 3 = Weekly; 4 = Daily; 5 = Refused; 6 =
Unsure; 888 = No data
Haematobium 0 = No S. haematobium
1 = S. haematobium
888 = No data
B4Otherreason Other reason for entering the lake: As written
Eggsper10mL Number of eggs B5Home_water Source of water used at home: 1 = Lake; 2 = Bore
hole; 3 = Pool; 4 = Well; 5 = Pump; 6 = Other; 888 =
No data
Mansoni 0 = No S. mansoni
1 = S. mansoni
888 = No data

23. Steps to designing an analysis plan
4. Define inclusion/exclusion criteria
• Exclude those with missing stool or urine results for prevalence and intensity analysis
• Exclude those without questionnaire answers for risk factor analysis
• Exclude all children aged less than 1 year

24. 5.Explore the variable(s) needed
Always start with investigating the variables individually – describe them
1. Frequency, percentage, summary statistics (mean, median, standard deviation, minimum
and maximum for continuous variables)
2. How much data is missing? What will you do about it?
3. What graphs are useful?
Histograms, bar charts, box plots, scatter plots
In our example:
Descriptive statistics of Haematobium (number, percent), Eggsper10mL (Mean, median, SD),
Haematobium_IOI (number, percent)
Graphical representation of the variable, such as histograms or boxplots of Haematobium and
Eggsper10mL (to check range and distribution). If you were thinking of doing a regression—
then a scatterplot would be appropriate

25. Standard (normal) distribution
VERY important for the outcome variable: it
helps you determine the statistical techniques
to use later on:
- Parametric
- Non-parametric

26. 5.Explore the variable(s) needed (cont’d)
Does a variable need to be categorised?
Eggsper10mL  classes of infection intensity:

27. Calculation of categorised infection intensity:
New variable Haematobium_IOI:
if Eggsper10mL = 0 then Haematobium_IOI = 0 "No infection"
if Eggsper10mL > 1 and <= 50 then Haematobium_IOI = 1 "Light"
if Eggsper10mL > 50 then Haematobium_IOI = 2 "Heavy"
if Eggsper10mL missing then Haematobium_IOI = NA
 Descriptive statistics for Haematobium_IOI: Table showing Haematobium_IOI
(%, N)

28. 6. Examine relationships of interest:
One of our objectives is about relationships between Haematobium_IOI (likely,
heavy infection intensity) and other variables.
Treat each of the selected 'other variables' separately, and come up with a plan
to analyse the relationship. Are the relationships significant?
(i) Ask questions about the types of variables, and design issues, and the nature of the
question itself
(ii) Choose a statistical method
(iii) Consider whether additional outputs are needed or useful: standard errors, confidence
intervals, effect estimates and so on. Will graphs help?

29. 6. Examine relationships of interest:
Investigate the relationship between two variables of interest: Outcome variable
and a risk factor: how much does one influence the other?
Eg: prevalence of schistosomiasis and age
The way in which these relationships are analysed depends on the types of
variable combinations involved
y = mx + b

30. Basic statistical techniques and when to use them

31. In our example:
Variation of heavy infection intensity with sex:
• Contingency (2x2) table showing sex (rows) by Haematobium_IOI and row %s
• Chi-squared test for prevalence of Haematobium Males vs Females (assessing
the role of ‘chance’) – a test of statistical significance
• E.g. there is a statistically significant difference between males and females in
prevalence of Schistosoma haematobium
• Odds ratio of prevalence of Haematobium in Males:Females and 95%CI
Variation of heavy infection intensity with age:
• Use AgeCohort (or construct new variable for age groups if this is not
provided)

32. 6. Examine relationships of interest:
Remember:
The statistical program is quite capable of calculating something which is
mathematically correct, but statistically invalid, because, for example,
assumptions are not met. Therefore, for each statistical test, it should be
indicated in the analysis plan what the assumptions are for each test and how
they will be tested.

33. 7. Consider using ‘dummy’ tables
By this point, you will have quite a lot of output.
What is most useful?
How will you present it?

34. In our example:
Table X Barombi Mbo Schistosomiasis and Soil-transmitted helminths Study: demographic determinants
of heavy infection intensity
Variable/values N Intensity of infection 95% CI for
Heavy %
P-value from chi-
squared
No
infection
Light Heavy
Sex Females X % % % %, % Chi-squared = X,
P = X, 1 dfMales X % % % %, %
Age 1<6 yrs X % % % %, % Chi-squared = X,
P = X, 5 df6<18 yrs X % % % %, %
18 yrs or
older
X % % %
%, %

35. 8. Going further: Confounding & effect modification,
statistical modelling
This is where you consider
• univariable and
• multivariable
regression analysis (statistical modelling)
This is where:
• we get the most important results: how much does one factor influence risk of
getting schistosmiasis, when ADJUSTED for other factors?
• we get the most detail to guide decision-making: where to target resources
This is where you CONTACT an epidemiologist!

36. Regression made simple!
You are simply exploring the relationship between one (or more) explanatory
variable (X) on an outcome variable (Y):
e.g. for a linear relationship, it is Y = a + b*X (a is the intercept, b is the slope of
the line – ie the amount in which Y changes for each unit of X)

37. Multiple regression:
Having established that an outcome variable and an explanatory variable are
associated, and estimated the regression coefficient, we might be interested in
further exploring the other influences on the outcome variable. We can set up a
multivariate (or multivariable) linear model such as:
Y = a + b X + c X + dX +......
Where a, b, c,... are regression coefficients indicating the dependence of Y upon
variables X1, X2, X3.
The underlying mathematics is a bit more complex, and there are some key
assumptions, but ESSENTIALLY NO NEW PRINCIPLES EMERGE.

38. Multiple regression
When a multiple regression model is fitted, we obtain estimates of the separate
or adjusted relationships between the dependent variable and the independent
(or explanatory) variables. Thus, the association between the outcome variable
and one explanatory variable is adjusted for confounding with the other
explanatory variables. This is the method for dealing with confounding and
multiple exposures.
A confounding variable is one that is associated with both the dependent variable and an
independent variable. If the confounder is left out of the analysis then a direct association will
be found between the dependent and independent variable that only exists because of the
indirect association due to the confounding variable.

39. Summary
Statistical analyses should not proceed in an ad hoc fashion
Good research requires construction of an analysis plan, which is driven by
research objectives, and takes account of study design, including sampling and
measurement issues, and variable types.

40. Any questions?

41. Thank you!