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Enregistrement 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 sur OpenAlex
Alan Shiell, Hannah Jackson

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Notice bibliographique

RevueHealth Promotion Journal of Australia · 2018
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueGlobal Public Health Policies and Epidemiology
Établissements canadiensnon disponible
Organismes subventionnairesNational Health and Medical Research CouncilFoundation for Alcohol Research and Education
Mots-clésGross domestic productPer capitaHealth economicsGovernment (linguistics)Health careWelfareGovernment spendingPopulation healthDemographic economicsEconomic growthMedicineDevelopment economicsBusinessEconomicsEnvironmental healthPopulation

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,712
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,201
Tête enseignante GPT0,419
Écart entre enseignants0,219 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle