| Title |
The diffusion of generics after patent expiry in Germany
|
|---|---|
| Published in |
HEPAC Health Economics in Prevention and Care, November 2015
|
| DOI | 10.1007/s10198-015-0744-3 |
| Pubmed ID | |
| Authors |
Katharina Elisabeth Fischer, Tom Stargardt |
| Abstract |
To identify the influences on the diffusion of generics after patent expiry, we analyzed 65 generic entries using prescription data of a large German sickness fund between 2007 and 2012 in a sales model. According to theory, several elements are responsible for technology diffusion: (1) time reflecting the rate of adaption within the social system, (2) communication channels, and (3) the degree of incremental innovation, e.g., the modifications of existing active ingredient's strength. We investigated diffusion in two ways: (1) generic market share (percentage of generic prescriptions of all prescriptions of a substance) and, (2) generic sales quantity (number of units sold) over time. We specified mixed regression models. Generic diffusion takes considerable time. An average generic market share of about 75 % was achieved not until 48 months. There was a positive effect of time since generic entry on generic market share (p < 0.001) and sales (p < 0.001). Variables describing the communication channels and the degree of innovation influenced generic market share (mostly p < 0.001), but not generic sales quantity. Market structure, e.g., the number of generic manufacturers (p < 0.001) and prices influenced both generic market share and sales. Imperfections in generic uptake through informational cascades seem to be largely present. Third-party payers could enhance means to promote generic diffusion to amplify savings through generic entry. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 51 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 11 | 22% |
| Researcher | 8 | 16% |
| Student > Ph. D. Student | 5 | 10% |
| Other | 3 | 6% |
| Unspecified | 3 | 6% |
| Other | 7 | 14% |
| Unknown | 14 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Economics, Econometrics and Finance | 7 | 14% |
| Pharmacology, Toxicology and Pharmaceutical Science | 6 | 12% |
| Social Sciences | 6 | 12% |
| Business, Management and Accounting | 5 | 10% |
| Unspecified | 3 | 6% |
| Other | 8 | 16% |
| Unknown | 16 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 28 November 2015.
All research outputs
#20,656,161
of 25,374,647 outputs
Outputs from HEPAC Health Economics in Prevention and Care
#1,039
of 1,303 outputs
Outputs of similar age
#200,512
of 274,640 outputs
Outputs of similar age from HEPAC Health Economics in Prevention and Care
#17
of 22 outputs
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