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Record W2147283553 · doi:10.1093/eurpub/ckn035

Promoting economic value in public health

2008· editorial· en· W2147283553 on OpenAlexaboutno aff
Reiner Leidl

Bibliographic record

VenueEuropean Journal of Public Health · 2008
Typeeditorial
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsReimbursementEuropean unionHealth carePublic healthGermanBusinessMedicinePublic economicsPolitical scienceEconomic growthInternational tradeEconomicsGeographyNursing

Abstract

fetched live from OpenAlex

In the pharmaceutical field, the term ‘fourth hurdle’ has become common currency in many health systems. It means that only products that can show their cost-effectiveness will gain unrestricted access to patients. The preceding hurdles of safety, efficacy and quality have existed for several decades and have been harmonized within the European Union years ago. In the early 1990s, Australia and Canada pioneered the integration of cost-effectiveness into the regulation of drug reimbursement; by 2005, a survey identified 10 more European countries to consider this aspect in drug regulation.1 Adding France and Germany leads to at least a dozen European countries going this way today. The emphasis of the ‘fourth hurdle’ is on individual clinical care. ‘Concentrating on drugs and clinical procedures does not really have a great influence on population health’2 Holland criticized after having reviewed the first experience of health technology assessment in Europe. Banta and de Wit3 in a recent review also emphasize that economic evidence plays a minor role outside pharmaceutical care. To improve decision making, they suggest that researchers should economically study portfolios of curative and preventive interventions. Holland2 had argued that public-health interventions often are too complex to be assessed adequately. Indeed the evaluation of public-health intervention may be more difficult than that of drugs as experimental designs may not be possible, and as long-term time frames and reference to heterogeneous target populations may require more comprehensive evaluation approaches. The population investigated may furthermore feature medical and economic characteristics that are both relevant and specific to the decision context at stake. In consequence, assessments have to be tailor-made, and good transferability of economic results from other study contexts is less likely than it is for clinical studies. On an international scale, more efforts and resources may thus be needed to show the cost-effectiveness of public-health intervention as compared with clinical care strategies. This does certainly not mean that basing decisions on effectiveness and cost-effectiveness should be restricted to individual clinical care. The benefits of systematically integrating the economic aspects into evidence-based decision making apply to public-health interventions as well: more cost-effectiveness will produce more health with a given budget, or given health benefits may be reached by less-resource input. Acknowledging this, UK health policy has extended the responsibility of the much respected National Institute of Health and Clinical Excellence (NICE) to also develop cost-effective guidance for public health.4 Such guidance might move the benefits of public-health intervention more into the focus of the public. However, the adequate use of such economic evidence also poses significant challenges. The responsibility for public-health action is typically attributed to a number of institutions at the national, regional and local level. In order to increase efficiency in public health intervention, more decision makers have to be provided with advanced knowledge of how to best make use of the economic evidence. In practice, the role and use of economics in public-health decision making may significantly differ by sector, as Grosse et al.5 found in their review on the situation in the United States. They had included such diverse areas as environmental regulation, injury prevention and screening issues from newborns up to cancer. Behavioural prevention is a key topic for public-health research. Life style may substantially influence the large-scale health problems such as smoking, obesity, heart disease and diabetes. The knowledge on genetic risks is quickly expanding. Humphries et al.6 suggest that the combination of genetics with conventional risk factors, including life style, may improve the understanding of disease. If future screening approaches shall include early recognition of individual disease risk, public health and clinical medicine will move closer together. Intervention strategies may then integrate individual and population aspects in disease management—provided they are effective and cost-effective. Promoting the economic value of public-health intervention makes up a Sisyphus task, but it also provides a measure to further improve population health. Today, decision making based on economic assessments primarily focuses on individual clinical care. Only adding the population perspective and public-health intervention will provide the full health benefit that evidence-based decision making is able to achieve.

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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)0.021
metaresearch head score (Gemma)0.081
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.032
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.081
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0040.003
Science and technology studies0.0030.012
Scholarly communication0.0130.013
Open science0.0050.004
Research integrity0.0320.044
Insufficient payload (model declined to judge)0.0060.002

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 head0.455
GPT teacher head0.424
Teacher spread0.032 · 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
GenreEditorial

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".

Quick stats

Citations1
Published2008
Admission routes1
Has abstractyes

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