| Literature DB >> 27033564 |
Aggeliki V Tsaprantzi1, Petros Kostagiolas2, Charalampos Platis3, Vassilios P Aggelidis1, Dimitris Niakas3.
Abstract
The objective of this study is to assess the impact of information on doctors' attitudes and perceptions toward generics. A cross-sectional survey based on a specially designed 21-item questionnaire was conducted. The survey involved doctors of different specialties working in a public hospital in Greece. The analysis includes descriptive and inferential statistics, reliability and validity tests, as well as structural equation modeling to evaluate the causal model. Statistical analysis was accomplished by using SPSS 20 and Amos 20. A total of 134 questionnaires out of 162 were received, providing a response rate of 82.71%. A number of significant associations were found between information and perceptions about generic medicines with demographic characteristics. It seems that the provision of quality information on generic drugs influences doctors' attitudes and prescription practices toward generic drugs. This is not a static process but a rather dynamic issue involving information provision policies for strengthening the proper doctors' attitudes toward generic drugs.Entities:
Keywords: Greece; drugs; generics; information; medical doctors; survey
Mesh:
Substances:
Year: 2016 PMID: 27033564 PMCID: PMC5798745 DOI: 10.1177/0046958016637791
Source DB: PubMed Journal: Inquiry ISSN: 0046-9580 Impact factor: 1.730
Figure 1.Research model.
Respondents’ Demographic Characteristics.
| Frequency (persons) | Frequency (%) | Cumulative % | |
|---|---|---|---|
| Gender | |||
| Male | 66 | 49.3 | 49.3 |
| Female | 68 | 50.7 | 100 |
| Age | |||
| 24-30 | 31 | 23.1 | 23.1 |
| 31-40 | 48 | 35.8 | 59 |
| 41-50 | 29 | 21.6 | 80.6 |
| 51-64 | 26 | 19.4 | 100.0 |
| Years in practice | |||
| 1-5 | 63 | 47.0 | 47.0 |
| 6-10 | 23 | 17.2 | 64.2 |
| >10 | 48 | 35.8 | 100 |
| Position | |||
| Specialist | 65 | 48.5 | 48.5 |
| Non-specialist | 69 | 51.5 | 100 |
| Responsibility position | |||
| Senior | 26 | 20.1 | 20.1 |
| Non-senior | 117 | 79.9 | 100 |
| Place of work | |||
| Public hospital | 123 | 91.8 | 91.8 |
| Health care centers | 11 | 8.2 | 100 |
Survey Results.
| Items | Strongly agree | Agree | Neutral | Disagree | Strongly disagree | Gender[ | Age[ | Years of practice[ | Position[ | Responsibility[ | Place of work[ |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Doctors’ information on generic medicines | |||||||||||
| A generic medicine is bioequivalent to brand name medicines | 16 | 58 | 51 | 9 | 0 | .109 | .989 | .819 | .732 | .207 | .223 |
| A generic medicine must be in the same dosage form (eg, tablet, capsule) as the brand name medicine | 26 | 44 | 53 | 7 | 4 | .201 | .637 | .250 | .095 | .235 | .774 |
| A generic medicine must contain the same dose as the brand name medicines | 34 | 24 | 73 | 1 | 2 | .779 | .894 | .657 | .877 | .225 |
|
| Generic medicines are less effective compared with brand name medicines (r) | 1 | 12 | 29 | 77 | 15 |
| .437 |
|
| .073 | .924 |
| Generic medicines produce more side effects compared with brand name medicines (r) | 0 | 15 | 36 | 68 | 15 |
| .789 | .440 | .606 | .308 | .857 |
| Brand name medicines are required to meet higher safety standards than generic medicines (r) | 1 | 15 | 35 | 70 | 13 |
| .921 | .601 | .759 | .899 | .891 |
| Doctors’ attitudes and perceptions toward generics | |||||||||||
| I believe we need a standard guideline for both Doctors and pharmacists on the brand substitution process | 34 | 69 | 20 | 10 | 1 | .954 | .102 | .348 | .640 | .345 | .064 |
| I think the patient should be given enough information about generic medicines to make sure they really understand about the medicines they take | 34 | 78 | 4 | 17 | 1 | .699 |
| .622 | .994 | .374 | .960 |
| I believe advertisement by the drug companies will influence my future prescribing pattern | 13 | 66 | 44 | 11 | 0 | .604 | .458 | .384 | .623 | .373 | .839 |
| I need more information on the issues pertaining to the safety and efficacy of generic medicines | 36 | 73 | 19 | 6 | 0 | .817 | .608 | .394 | .650 | .966 | .379 |
| Patient’s socio-economic factor will affect my choice of medicines | 32 | 77 | 15 | 9 | 1 | .832 |
|
| .089 | .461 | .824 |
| Credibility of the manufactures/suppliers is my concern when prescribing medicines | 31 | 82 | 12 | 8 | 1 | .900 | .661 | .755 | .949 | .733 | .672 |
| Pharmaceutical companies’ product bonuses will influence my choice of medicines | 17 | 39 | 40 | 36 | 2 | .481 | .993 | .377 | .826 | .477 | .896 |
Note. P < .05 is considered significant. Bold values indicate statistical significance. r = reversed item.
Mann-Whitney test was used.
Kruskal-Wallis test was used.
Explanatory Factor Analysis Results and Confirmatory Factor Analysis Results.
| Factor | Items | Explanatory factor analysis | Confirmatory factor analysis | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Comp. matrix | Mean | SD | KMO | Bartlett test | Total variance explained | Loadings | Squared factor loadings | Cronbach’s α | Composite reliability | Variance extracted | ||
| Doctors’ information of generic medicines | a1 | 0.779 | 3.60 | 0.785 | 0.876 | 457.14 | 66.288% | 0.73 | 0.54 | 0.898 | 0.90 | 0.60 |
| a2 | 0.782 | 3.60 | 0.958 | 0.74 | 0.55 | |||||||
| a3 | 0.877 | 3.65 | 0.920 | 0.86 | 0.74 | |||||||
| a4 | 0.841 | 3.69 | 0.816 | 0.80 | 0.63 | |||||||
| a5 | 0.826 | 3.62 | 0.830 | 0.78 | 0.61 | |||||||
| a6 | 0.775 | 3.59 | 0.843 | 0.71 | 0.51 | |||||||
| Doctors’ attitude toward generic medicines | b1 | 0.876 | 3.93 | 0.877 | 0.917 | 596.20 | 73.75% | 0.85 | 0.73 | 0.928 | 0.91 | 0.63 |
| b2 | 0.924 | 3.95 | 0.928 | 0.92 | 0.85 | |||||||
| b3 | 0.775 | 3.60 | 0.776 | 0.73 | 0.53 | |||||||
| b4 | 0.850 | 4.04 | 0.770 | 0.81 | 0.65 | |||||||
| b5 | 0.854 | 3.97 | 0.831 | 0.82 | 0.67 | |||||||
| b6 | 0.867 | 4.00 | 0.795 | 0.84 | 0.70 | |||||||
Note. KMO = Kaiser-Meyer-Olkin.
Figure 2.Confirmatory factor analysis of the model.
Note. df = degrees of freedom; RMR = root mean square residual; RMSEA = root mean square error of approximation; GFI = goodness-of-fit index; CFI = comparative fit index; NFI = normalized fit index; TLI = Tucker-Lewis index.