| Title |
Decision analysis, economic evaluation, and newborn screening: challenges and opportunities
|
|---|---|
| Published in |
Genetics in Medicine, April 2012
|
| DOI | 10.1038/gim.2012.24 |
| Pubmed ID | |
| Authors |
Lisa A. Prosser, Scott D. Grosse, Alex R. Kemper, Beth A. Tarini, James M. Perrin |
| Abstract |
The number of conditions included in newborn screening panels has increased rapidly in the United States during the past decade, and many more conditions are under consideration for addition to state panels. The rare nature of candidate conditions for newborn screening makes their evaluation challenging. The scarcity of data on the costs of screening, follow-up, treatment, and long-term disability must be addressed to improve the evaluation process for nominated conditions. Decision analyses and economic evaluations can help inform policy decisions for newborn screening programs by providing a systematic approach to synthesizing available evidence and providing projected estimates of long-term clinical and economic outcomes when long-term data are not available. In this review, we outline the types of data required for the development of decision analysis and cost-effectiveness models for newborn screening programs and discuss the challenges faced when applying these methods in the arena of newborn screening to help inform policy decisions.Genet Med advance online publication 5 April 2012. |
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 % |
|---|---|---|
| India | 1 | 50% |
| Brazil | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 2 | 3% |
| Poland | 1 | 1% |
| Spain | 1 | 1% |
| Unknown | 66 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 21% |
| Student > Ph. D. Student | 12 | 17% |
| Student > Master | 8 | 11% |
| Other | 6 | 9% |
| Professor > Associate Professor | 6 | 9% |
| Other | 16 | 23% |
| Unknown | 7 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 28 | 40% |
| Social Sciences | 6 | 9% |
| Economics, Econometrics and Finance | 5 | 7% |
| Agricultural and Biological Sciences | 4 | 6% |
| Biochemistry, Genetics and Molecular Biology | 2 | 3% |
| Other | 10 | 14% |
| Unknown | 15 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 21 August 2012.
All research outputs
#15,092,197
of 25,374,917 outputs
Outputs from Genetics in Medicine
#2,410
of 2,943 outputs
Outputs of similar age
#99,442
of 173,560 outputs
Outputs of similar age from Genetics in Medicine
#19
of 34 outputs
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We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.