Epidemeology

Epidemiology Module
Exercises 2015
Module Exercise 2: Public health (large area) epidemiology

The exercise:
The Australian Government Department of Health (Federal) produces reports each year which of great use to those wishing to identify changes in the distribution of communicable diseases with space and time, in order to plan their control on a national basis. Epidemiologists working for the National Notifiable Diseases Surveillance System (NNDSS) produce tables showing this complex and extensive data in a user-friendly format.

Part 1: Access a table from a Department of Health report which shows notification rates for the years 2005 to 2010 for pertussis and chlamydial infection for all of Australia, and produce a labelled, computer generated time-trend graph (or graphs) for these two diseases.
Part 2: Briefly discussthreepossible reasons why the rates for each of these graphs are changing with time.

Aims of Exercise:
i. To acquire skills in extraction, analysis and presentation of quantitative information from an epidemiological report, and the interpretation of that information.
ii. To develop sound perspectives on risk factors for specific disease examples, and as to how and why these might change change with time.

Hints:
i. Trawl through the question and identify the key words or phrases, such as “notifiable”, “communicable”, “time-trend graph”, “rate” and “surveillance”. Research these before starting the answer to improve your understanding of the question, and to lock yourself into the correct “mind set” to answer it.
ii. Make sure the table or table section you select to plot your graphs shows the correct data.
iii. Search for information in later study modules which discusses conventions (formalities or rules) for producing epidemiological graphs.
iv. Read up on the two diseases, and identify risk factors for each, then base your discussion in part 2 on the reasons why risk may have changed with time, using references to support your case.
v. Retain this exercise and consolidate it with those for three other module exercises for submission as four consolidated module exercises by the date indicated in the Learning Guide.

Module Exercise 3: Bivariate linear regression analysis (correlation)
Background to the exercise:
Asa preliminary step in a large-scale study of asthma in Armidale, New South Wales, you are asked to carry out a study to identify the impact of ambient atmospheric particulate pollution (PM10) on the incidenceof asthmatic wheeze in primary school children.

To ensure an accurate medical diagnosis you select all primary school children attending a day clinic over a 30-day period in April. In this month, other “confounding” risk factors are thought to be atrelatively low levels, and therefore to some level controlled. Thermal inversions, however, can occur in the basin setting, trapping pollutants in the lower atmosphere and causing entrapment of air pollutants from point and diffuse sources.

From trained clinical staff you obtain a daily record of asthmatic wheeze incidence in children presenting for all medical conditionsat the clinicduring the study period. Thedaily air quality record is obtained from the Department of the Environment and a short latency period (minutes to hours) between exposure to ambient air particulates and production of symptoms is assumed. You produce the tabulated data shown on the next page.

The exercise:
Part 1:Plot a graph of the relationship between asthma wheeze and ambient atmospheric particulate matter (PM10)using a recognised computer application (eg: Excel®). Add a computer-generated line of best fit, assuming a linear relationship. Present the graph for assessment with a comment on the type of correlation (direct or inverse), its computed strength in terms of Pearson’s Product Moment Correlation Coefficient, r, and qualitative interpretation of this result.

Part 2: Using the formula given in the module notes, hand-calculate Pearson’s Product Moment Correlation Coefficient, r. Submit the tabulation used to generate values for the algebraic formula. Comment on the possible reason for any differences noted between the result obtained in parts 1 and 2.

Aims of the Exercise:
i. To gain an understanding of the use of bivariate linear regression analysis as a fundamental epidemiological analytical tool.
ii. To gain a conceptual idea of risk factors and management of an important health condition
Day Total number of children with asthmatic wheeze Total number of children attending the clinic that day Ambient atmospheric particulates(PM10 in µg/m3)
1 11 420 40
2 8 230 45
3 11 190 90
4 24 550 60
5 31 643 50
6 39 710 60
7 39 560 360
8 26 302 320
9 19 200 110
10 31 587 70
11 22 589 80
12 21 632 64
13 14 585 50
14 27 602 50
15 22 320 130
16 16 245 220
17 24 558 100
18 26 570 60
19 29 603 40
20 28 555 40
21 31 599 100
22 17 197 160
23 16 197 190
24 26 520 80
25 22 476 50
26 19 600 40
27 14 557 30
28 17 481 40
29 10 225 50
30 10 190 40

Hints:
i. If the question looks foreign and perplexing you probably need to go back to the module notes where the approach is clearly explained, and work through an example.
ii. When calculating, it is essential to check your calculations thoroughly. While marks are given for method, marks are also awarded for the correct answer so it is relatively easy to obtain 100% with careful checking of your calculation.
iii. The first step when working with raw data is to classify (construct a table). When in doubt, tabulate, when numerical problems will become clearer.
iv. Use one more decimal place in your calculations than you want to give in your answer.
v. Use the formula in the module notes rather than the one given in text books, which is primarily for statisticians.
vi. When studying health states (diseases and fitness) always calculate and use rates, and not absolute numbers.
vi. Excel® does not do as much as SPSS and Minitab, but is probably the most user-friendly program to use, and links well with Word®. Adding the line of best fit in Excel® involves highlighting the graph first by clicking on it, when the menu tab for this function will appear.
Module Exercise 4: Association analysis

Background to the exercise:
Given rising concerns about obesity in the children of Western Sydney, You are asked to carry out a nutrition and health study of 474 children between the ages of 5 and 12 at a primary school. Age-adjusted BMIs reveal that 87 are overweight. This figure includes a number of potentially obese children. Of these overweight children, 43 eat lunch in the school canteen which is run by a private contractor. 130 of the children who also eat lunch in the canteen, however, are not overweight. Children who do not eat in the canteen mainly bring in lunch from home (prepared by parents or themselves), miss lunch, and a few have prepared-lunch or fast food brought the gate at lunchtime by their parents.

The exercise:

Part 1: Carry out an assessment to see if eating the canteen meals is associated with being overweight, in comparison to the other, informal lunchtime arrangements described. Show all tabulations, calculations and comparative processes required to reach this answer.
Part 2: Based on the information revealed in this study, present a motivated discussion on the school’s options for securing improved eating arrangements which will ensure a healthier lifestyle for its entire student body. Do not simply give a general list of health dietary practices to complete this answer.

Aims of Exercise:
i. To gain an understanding of single stage, association analysis as a fundamental analytical epidemiological tool.
ii. To gain a conceptual idea of risk factors and their management for an important chronic health condition.

Hints:
iWhen calculating, it is essential to check your calculations thoroughly. While marks are given for method, marks are also awarded for the correct answer so it is relatively easy to obtain 100% with careful checking of your calculation.
ii. The first step when working with raw data is to classify (construct a table). When in doubt, tabulate, when numerical problems inevitably become clearer.
iii. Use one more decimal place in your calculations than you want to give in your answer.
iii. In part 2 it is important to remember that the epidemiological method requires deductive reasoning to be carried out based on the results of analysis (remember how John Snow thought his way around the problem based on the numerical facts he had collected ). Simply giving general dietary information is of little relevance when you have been called in to diagnose and solve a specific problem at that school.
Module Exercise 5: Relative risk analysis

Background to the exercise:
As a food hygienist you are telephoned at homeon Saturday evening by your departmental director because 84 of the 146 chemists who attended a banquetin a convention centre a few hours before have become violently ill with nausea and repeated vomiting, many vomiting in the restaurant itself. They were not together at this gathering prior to this meeting. A few of the older guests have collapsed from dehydration and were rushed to hospital, although no deaths have occurred.

With a team of trained interviewers you visit all of the guests and collect the following information regarding each item of food eaten:

Food item Guests ill after
consuming the item Guests not ill after consuming the item
Paté foie gras in aspic 25 26
Tomato and basil soup 18 2
Surf and turf 11 42
Vegetarian mushroom lasagne 13 60
Pavlova 30 0
Coffee 38 32

The exercise:
Part 1: Carry out relative risk analysis on the data provided and rank your results. Show all tabulations and calculations.
Part 2:From the types of food most effected, the incubation period of the illness, and the symptoms, deduce and name the food poisoning type/s most likely to have been involved. Explain the process of deduction used.

Aims of the exercise:
i. To gain an understanding of single stage, association analysis as a fundamental analytical epidemiological tool.
ii. To gain a conceptual idea of risk factors and how they can be integrated with deductive reasoning in the management of an important acute health condition.

Hints:
i. Do not resort to guesswork based on common knowledge (often incorrect) to make your deductions. Observe the facts and develop hypotheses based on these observations and information in the scientific literature.
ii. When calculating, it is essential to check your calculations thoroughly. While marks are given for method, marks are also awarded for the correct answer so it is relatively easy to obtain 100% with careful checking of your calculation.