Prevention Campaigns for Non-infectious Disease

Q1, Classifying prevention levels

Bowel screening test: secondary prevention [1]. The people invited feel well, but the aim is to detect disease that has already begun, such as polyps or early cancer, before symptoms appear so it can be treated cheaply and successfully [1].

Foot and retinal checks in diabetes: tertiary prevention [1]. The disease is already established, and the aim is to limit further damage by catching complications early rather than to prevent the diabetes itself [1].

Tobacco excise: primary prevention [1]. It reduces exposure to the risk factor across the whole population before anybody is ill, by making the product less affordable [1].

Folic acid fortification: primary prevention [1]. It removes the deficiency that raises neural tube defect risk before any pregnancy is affected, acting on the exposure rather than detecting or managing disease [1].

Q2, Tobacco control graph

2.1 Adult daily smoking prevalence falls steadily throughout, from about 34% in 1980 to about 11% in 2020 [1]. Male lung-cancer incidence continues to rise until about 1990, reaching roughly 60 per 100,000, then declines gradually to about 42 per 100,000 by 2020 [1]. The smoking curve therefore begins falling roughly a decade before the lung-cancer curve turns, and the two move in the same direction only after that [1]. Accept values read reasonably from the graph.

2.2 Lung cancer is caused by accumulated exposure to carcinogens in tobacco smoke, which produce mutations over many years before a tumour becomes clinically detectable [1]. When smoking prevalence begins to fall, the people already developing disease carry decades of prior exposure that cannot be undone, so incidence continues to reflect historical rather than current smoking [1]. The disease curve therefore lags the exposure curve by roughly two to three decades, and a fall in incidence appears only once cohorts with lower lifetime exposure reach the ages at which lung cancer occurs [1].

2.3 The flaw is judging a prevention measure on an outcome measure before the expected lag has passed; three years cannot show a change in a disease that takes decades to develop [1]. Concluding "it did nothing" mistakes absence of outcome data for absence of effect [1]. The correct evidence over that interval is an impact measure such as smoking prevalence, cigarette consumption, quit-attempt rates or uptake among young people, reported against a baseline or comparison group [1].

Q3, Campaign design

Award 1 mark per row correctly and specifically completed. A sample strategy:

• Disease and risk factor. Melanoma; harmful ultraviolet exposure, particularly intermittent intense exposure and sunburn.
• Audience and barrier. Secondary students during lunchtime and outdoor sport; the barrier is lack of shade plus the cost and inconvenience of carrying sunscreen and a hat.
• Education action. Teach when UV protection is needed using the UV index rather than temperature, since UV is high on cool, cloudy days.
• Upstream action. Install shade over the courts and place free sunscreen dispensers at the gate and the sports shed, with a hat rule applying to everyone.
• Impact measure. Observed proportion of students wearing hats and using sunscreen at lunch, plus reported sunburn episodes over the term, compared with a pre-campaign baseline.
• Outcome measure. Melanoma incidence, named as the long-term goal and explicitly not measurable within the school year.
• Equity choice. Supply sunscreen and hats free rather than recommending families buy them, so protection does not depend on household income; report uptake separately for the highest-exposure group.
• Secondary source. Cancer Council Australia, the Australian Institute of Health and Welfare, the Australian Bureau of Statistics or a peer-reviewed journal article.

Q4, Source evaluation

4.1 Who produced it, and do they benefit from the conclusion? The site belongs to a sunscreen manufacturer, which has a direct commercial interest in reporting rising sunscreen use, so there is a clear conflict of interest [1]. When was it published? No date is given, and disease rates, campaigns and behaviour change over time, so the figure cannot be placed in context [1]. Does it report real data? It reports a claim, not data: no study is named, no sample size is given and there is no comparison group or baseline, so the 30 percent has nothing to be 30 percent of [1]. Does it agree with independent sources? No corroboration is offered, and the figure cannot be traced back to a study to check whether the behaviour was observed or self-reported, which matters because self-reported behaviour is routinely overstated [1].

4.2 Any two of the Australian Institute of Health and Welfare, the Australian Bureau of Statistics, Cancer Council Australia, or a peer-reviewed journal [1 mark for two named]. They are stronger because they publish the underlying data with sample sizes and methods rather than only a claim [1], and because they have no commercial interest in the conclusion, so their reporting can be cross-checked against other independent sources [1].

Q5, Comparison table

What it changes. Education-only: knowledge and attitudes, by supplying information [1]. Layered: knowledge plus the physical and economic conditions in which the decision is made, for example by adding shade, free sunscreen and a universal hat rule [1].

Why behaviour may or may not follow. Education-only: behaviour is shaped by cost, convenience, habit, social norms and environment far more than by information, so a student can know UV causes melanoma and still have nowhere shaded to sit [1]. Layered: the upstream actions remove effort and cost from the protective choice, making the safer option the easy one, so the knowledge can actually be acted on [1].

Strongest realistic evidence after one term. Education-only: usually only a knowledge change, which is a process result and the weakest evidence of effectiveness available [1]. Layered: an impact measure, such as observed hat wearing, sunscreen use or reported sunburn against baseline, because the behaviour itself is the mechanism through which risk falls [1].

Effect on equity. An education-only campaign works best for students who can already afford sunscreen and hats, so it can widen the gap it was meant to close. A layered strategy that removes cost and access barriers and is co-designed with the community allows the highest-risk group to act, which is why equity must be designed in and reported separately rather than averaged away [2].