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Record W2799578117 · doi:10.1002/hpja.165

How much does Australia spend on prevention and how would we know whether it is enough?

2018· article· en· W2799578117 on OpenAlexaboutno aff
Alan Shiell, Hannah Jackson

Bibliographic record

VenueHealth Promotion Journal of Australia · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilFoundation for Alcohol Research and Education
KeywordsGross domestic productPer capitaHealth economicsGovernment (linguistics)Health careWelfareGovernment spendingPopulation healthDemographic economicsEconomic growthMedicineDevelopment economicsBusinessEconomicsEnvironmental healthPopulation

Abstract

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Chronic disease is responsible for 83% of all premature deaths in Australia and 85% of the burden of disease. Conditions such as cardiovascular disease, chronic kidney disease and type 2 diabetes impose significant costs on the healthcare system and yet are also largely preventable. This raises questions about whether Australia is doing enough to prevent disease and in particular, whether governments should be spending more. Here, we summarise what is known about how much Australian governments spend on prevention, and we compare this with spending in other OECD countries. We then consider arguments about whether we spend enough.1 According to the Australian Institute of Health and Welfare, Australia spent a little more than $2 billion on prevention in 2013-2014 or about $89 per person.2 This represented 1.34% of all health spending and 0.13% of gross domestic product (GDP). Total spending has increased in real terms since 2000, but has remained fairly constant as a share of GDP (with the exception of 2007-2008 when the federal government invested heavily to support the introduction of vaccination against HPV). The share of total health expenditure going to prevention has fallen since 2000 from 1.74% to its current level of 1.34%. Internationally, Australia's spending on prevention is distinctly “mid-table”. Of the 31 OECD countries reporting spending on prevention in 2013, Australia ranked 16th in terms of per capita spending, 19th in terms of share of GDP allocated to prevention and 20th in terms of share of current spending on health.3 Australia reportedly spends less than one half of the amount spent on prevention in the USA, the United Kingdom, Canada and New Zealand.4 Such comparisons should be made carefully, however, as despite efforts to standardise the way jurisdictions report their health expenditures, differences still exist, both within Australia and internationally, in how prevention spending is coded. The Australian accounts, for example, do not report spending on prevention by agencies other than health departments, nor do they include all that health agencies spend on preventive measures under the “public health” tab. The cost of cholesterol-lowering drugs, for example, is reported alongside all other pharmaceuticals, and measures taken by general practitioners are all accounted for under primary care. By one estimate, spending on prevention in Australia could be up to 12 times greater than that which is reported in the national accounts.5 More formal efforts to quantify the shortfall in recording prevention activity in national accounts elsewhere suggest that spending could be between three and five times as much as appears in the accounts.6, 7 However, this cannot explain Australia's position relative to other OECD countries as the same sorts of accounting issues apply elsewhere. Accounting methods therefore explain some but not all of the differences between Australia and other countries in the amount that is spent on prevention. And against the backdrop of the increasing burden of disease, the fact that Australia appears to spend considerably less on preventing disease than the USA, the United Kingdom, Canada and New Zealand is seen by some public health advocates as reason enough to increase spending here.8, 9 Unfortunately, this argument is quite easy to undermine. With the exception of Aboriginal and Torres Strait Islander people, the health of Australians is as good if not better than the countries with which we are compared. If they are spending more on disease prevention, then they are not reaping any obvious benefit. Thus, we should resist the temptation to infer that Australia should spend more on prevention simply because it appears to spend less than our neighbours. Instead, the key to determining how much we should spend involves assessing both the costs and benefits of changes in resources allocated to prevention.10 Step 1 involves looking for opportunities to reallocate resources away from relatively cost-ineffective options to policies or programs that are more cost-effective. Step 2 is to compare the added value of an increase in spending to the opportunity cost of that increase. That is, we could compare the benefits of increasing prevention spending annually by $100 million, for example, with the benefits lost because that $100 million can no longer be spent on something else, such as reducing hospital waiting lists, or improving the quality of early child development programs. If the value of the benefits derived from spending more on prevention exceeds the value of the opportunity cost, then there is a case for increasing spending. We should also look at what prevention activities might be curtailed if spending were to be reduced by $100 million and compare the impact of this with the benefits that would be gained by allocating that $100 million to something else. This process is what economists refer to as marginal analysis.11 There is clear evidence that many preventive health interventions are cost-effective. The 2010 Assessing Cost-Effectiveness (ACE) in Prevention study12 evaluated more than 120 such interventions in the Australian context. Several of these were found to be “cost-saving”: the cost of the intervention offset by savings resulting from a reduced need to treat disease. These typically involved policy actions to reduce consumption of hazardous goods such as alcohol through changes in tax rates. Other interventions improved health at a cost that would be deemed reasonable in comparison with what we currently spend to treat disease. These results have been confirmed in other evaluations of actions to promote health and prevent disease.13-16 Apart from the policy interventions, there is often no pattern to what is and is not likely to be cost-effective. For example, in preventing HIV/AIDS, distribution of condoms can be highly cost-effective or highly cost-ineffective depending on the specific characteristics of the intervention.11 Furthermore, the ACE study only considered cost-effectiveness. An intervention will also have value if it reduces inequalities in health, and while equity is not easily incorporated into cost-effectiveness calculations, the marginal analysis does allow such considerations to be factored into the decision-making process.10 A strong case can be made for increasing spending on preventive health in Australia, but the argument does not rely on comparing current spending in Australia with that in selected OECD countries. Instead, it comes from studies that have examined the cost-effectiveness of preventive health interventions. These confirm that the health of Australians would benefit both by reorganising the current suite of preventive health activities (reallocating resources within the current prevention spend) and by increasing spending in those activities assessed as most cost-effective. Funding for this work was provided by Prevention 1st, a collaboration between the Foundation for Alcohol Research and Education, the Public Health Association of Australia, Alzheimer's Australia and the Consumers Health Forum, with contributions also from the Heart Foundation, Kidney Australia and the Australian Health Promotion Association.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.122
Threshold uncertainty score0.243

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0050.006
Open science0.0010.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0090.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.201
GPT teacher head0.419
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations18
Published2018
Admission routes1
Has abstractyes

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