| Title |
Decision Making and Priority Setting: The Evolving Path Towards Universal Health Coverage
|
|---|---|
| Published in |
Applied Health Economics and Health Policy, September 2017
|
| DOI | 10.1007/s40258-017-0349-3 |
| Pubmed ID | |
| Authors |
Francesco Paolucci, Ken Redekop, Ayman Fouda, Gianluca Fiorentini |
| Abstract |
Health technology assessment (HTA) is widely viewed as an essential component in good universal health coverage (UHC) decision-making in any country. Various HTA tools and metrics have been developed and refined over the years, including systematic literature reviews (Cochrane), economic modelling, and cost-effectiveness ratios and acceptability curves. However, while the cost-effectiveness ratio is faithfully reported in most full economic evaluations, it is viewed by many as an insufficient basis for reimbursement decisions. Emotional debates about the reimbursement of cancer drugs, orphan drugs, and end-of-life treatments have revealed fundamental disagreements about what should and should not be considered in reimbursement decisions. Part of this disagreement seems related to the equity-efficiency tradeoff, which reflects fundamental differences in priorities. All in all, it is clear that countries aiming to improve UHC policies will have to go beyond the capacity building needed to utilize the available HTA toolbox. Multi-criteria decision analysis (MCDA) offers a more comprehensive tool for reimbursement decisions where different weights of different factors/attributes can give policymakers important insights to consider. Sooner or later, every country will have to develop their own way to carefully combine the results of those tools with their own priorities. In the end, all policymaking is based on a mix of facts and values. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 95 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 13% |
| Student > Ph. D. Student | 11 | 12% |
| Other | 8 | 8% |
| Student > Master | 8 | 8% |
| Student > Doctoral Student | 8 | 8% |
| Other | 19 | 20% |
| Unknown | 29 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 11 | 12% |
| Nursing and Health Professions | 9 | 9% |
| Economics, Econometrics and Finance | 9 | 9% |
| Social Sciences | 8 | 8% |
| Engineering | 6 | 6% |
| Other | 19 | 20% |
| Unknown | 33 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 October 2017.
All research outputs
#3,697,381
of 23,001,641 outputs
Outputs from Applied Health Economics and Health Policy
#163
of 784 outputs
Outputs of similar age
#66,022
of 315,686 outputs
Outputs of similar age from Applied Health Economics and Health Policy
#5
of 14 outputs
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