Exercise 1: Public health (large area) epidemiology

The exercise: The Australian government Department of Health (federal) produces reports each year containing data on notifiable diseases which are of great use to those studying changes in disease distributions with space or time with the aim of planning country-wide control initiatives. To facilitate similar regional operations, states and territories produce annual Public Health Bulletins, zooming-in on the data at a higher level of resolution.

Part 1: Access a table for NSW showing disease incidence for the years 2003 to 2012, and produce labelled, computer-generated time trend graphs for giardiasis and HIV infections using an application such as Excel®.

Part 2: Briefly discuss two possible reasons why each of these diseases might have increased or decreased over this period. Reference this discussion.

Aims of the exercise:

i. To acquire skills in the extraction, presentation, analysis and use of quantitative information from a large-area epidemiological report.

ii. To develop early perspectives on risk factors for specific diseases, and insight as to how and why these might change with time.

Exercise 2: Bivariate linear regression analysis (correlation)

Background to the exercise: As a 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 general particulate pollution (PM10) on the incidence of asthmatic wheeze in primary school children. Thermal inversions can occur periodically in the Armidale basin, trapping pollutants from point and diffuse sources in the lower atmosphere.

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 (such as rainfall) are at relatively low levels, and therefore to some extent controlled.

From trained clinical staff you obtain a daily record of asthmatic wheeze incidence in children presenting for all medical conditions at the clinic during the study period. The daily 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 showing the relationship between asthma wheeze and ambient atmospheric particulate matter (PM10) using a recognised computer application such as 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 electronically-computed strength in terms of Pearson's Product Moment Correlation Coefficient r (some versions of the graph on Excel also give this), and a qualitative interpretation of this result (eg: "low correlation", "moderate correlation", etc.)

Part 2: Using the formula and table 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, along with your calculated value for r. Comment on the possible reason for any differences noted between the result obtained in parts 1 and 2.

Aim of the Exercise:

i. To gain an understanding of the use of bivariate linear regression analysis as a fundamental but powerful epidemiological analytical tool.

ii. To gain a conceptual idea of an industrially generated, environmental risk factor for an important health condition.