| Literature DB >> 30442145 |
Philippe J Domeyer1, Vasiliki Katsari1, Pavlos Sarafis2, Vassilis Aletras3, Dimitris Niakas1,4.
Abstract
BACKGROUND: The penetration of generic medicines in the pharmaceutical market is influenced, among others, by the consumer's attitude upon them. The attitude of students in health management and recent alumni is particularly important, as they constitute tomorrow's policymakers. The aim of our study was to assess their attitude, perception and knowledge towards generic medicines.Entities:
Keywords: Attitude; Drug substitution; Fiscal impact; Generics; Greece; Knowledge; Quality of generics; State audit; Students; Trust in generics
Mesh:
Substances:
Year: 2018 PMID: 30442145 PMCID: PMC6238271 DOI: 10.1186/s12909-018-1379-8
Source DB: PubMed Journal: BMC Med Educ ISSN: 1472-6920 Impact factor: 2.463
Descriptive statistics of items included in the original version of the ATTOGEN questionnaire only
| Item | Item description (ordinal variables) | Mean (SD) score | Median score (IQR)a |
| I | “I am satisfied with the information I have regarding generic medicines” | 2.781 (1.369) | 3 (2) |
| II | “I have noted substantial differences between brand-name and generic medicines” | 3.164 (1.107) | 3 (1) |
| III | “I am skeptical about generic medicines because of their lower price” | 3.647 (1.074) | 4 (1) |
| IV | “I believe that generics were invented and promoted in order to resolve the financial crisis of social security institutions at the expense of citizens” | 3.267 (1.251) | 3 (2) |
| V | “Among two generic medicines, I would trust more the one that is manufactured in Greece” | 2.216 (0.967) | 2 (2) |
| Item description (categorical variables) | N (%) | ||
| VI | “I know if my current medications include generic medicines” | ||
| | 375 (38.03%) | ||
| | 24 (2.43%) | ||
| | 587 (59.53%) | ||
aIQR Interquartile range
Inter-scale correlationsa
| Scale | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| 1 (Trust) | – | |||||
| 2 (State audit) | −0.276§ | – | ||||
| 3 (Knowledge) | −0.262§ | 0.032 | – | |||
| 4 (D quality) | −0.620§ | 0.361§ | 0.352§ | – | ||
| 5 (Drug substitution) | −0.232§ | 0.034§ | 0.172 | 0.208§ | – | |
| 6 (Fiscal impact) | −0.376§ | 0.075§ | 0.371¶ | 0.433§ | 0.235§ | – |
aCalculated with Spearman’s correlation coefficient
§ p < 0.001, ¶ p = 0.018
Inter-item correlations for items I-V*
| Items | I | II | III | IV | V |
|---|---|---|---|---|---|
| I | – | ||||
| II | 0.059 | – | |||
| III | −0.227§ | 0.149§ | – | ||
| IV | −0.265§ | 0.202§ | 0.474§ | – | |
| V | 0.186§ | 0.016 | −0.056 | −0.029 | – |
* Calculated with Spearman’s correlation coefficient, § p < 0.001
Item-scale correlations for items I-V and scales 1–6*
| Items/scales | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| I | −0.193§ | 0.132§ | 0.441§ | 0.317§ | 0.028 | 0.084¶ |
| II | 0.434§ | −0.110§ | 0.040 | −0.310§ | − 0.148§ | −0.241§ |
| III | 0.512§ | −0.085¶ | −0.334§ | − 0.323§ | −0.102¶ | − 0.115§ |
| IV | 0.525§ | −0.093 | −0.320§ | − 0.367§ | −0.113§ | − 0.121§ |
| V | −0.011 | 0.041 | 0.198§ | 0.017 | −0.060 | −0.035 |
* Calculated with Spearman’s correlation coefficient, § p < 0.001, ¶ p < 0.01
Statistically significant associations between sociodemographic factors and the questionnaire’s scales
| Scales | Associated factors | Mean (SD)* | Median (IQR) ** | Spearman’s rho† |
|
|---|---|---|---|---|---|
| State audit | Sex | --- | < 0.001¶ | ||
| Male | 3.397 (0.977) | 3.333 (1.333) | |||
| Female | 3.164 | 3.000 | |||
| Profession | (0.952) | (1.667) | --- | < 0.001§ | |
| Doctor | 3.455 (1.045) | 3.667 (1.667) | |||
| Dentist | 3.406 (1.029) | 3.667 (1.333) | |||
| Pharmacist | 3.380 (1.008) | 3.500 (1.333) | |||
| Nurse | 2.991 (0.923) | 3.000 (1.333) | |||
| Other health professional | 3.214 (0.931) | 3.333 (1.333) | |||
| Other profession | 3.225 (0.864) | 3.333 (1.333) | |||
| Units completed | --- | --- | 0.076 | (0.018) ‡ | |
| Knowledge | Sex | --- | < 0.001¶ | ||
| Male | 1.348 (0.581) | 1.000 (0.667) | |||
| Female | 1.648 (0.722) | 1.333 (1.000) | |||
| Profession | --- | < 0.001§ | |||
| Doctor | 1.231 (0.441) | 1.000 (0.333) | |||
| Dentist | 1.188 (0.359) | 1.000 (0.167) | |||
| Pharmacist | 1.019 (0.077) | 1.000 (0.000) | |||
| Nurse | 1.524 (0.579) | 1.333 (1.000) | |||
| Other health professional | 1.700 (0.734) | 1.667 (1.000) | |||
| Other profession | 1.918 (0.831) | 2.000 (0.667) | |||
| Units completed | --- | --- | −0.140 | < 0.001 | |
| Drug quality | Age | --- | --- | −0.064 | (0.046) |
| Professional status | --- | (0.039)¶ | |||
| Employed | 2.693 (0.965) | 2.667 (1.333) | |||
| Unemployed | 2.986 (1.047) | 3.167 (1.667) | |||
| Drug substitution | Age | --- | --- | 0.099 | 0.002 |
| Sex | --- | (0.048)¶ | |||
| Male | 3.872 (1.209) | 4.000 (2.000) | |||
| Female | 3.802 (1.076) | 4.000 (2.000) | |||
| Marrital status | --- | (0.048)§ | |||
| Single | 3.685 (1.185) | 4.000 (2.000) | |||
| Married | 3.889 (1.096) | 4.000 (2.000) | |||
| Divorced | 4.012 (1.056) | 4.500 (1.500) | |||
| Widowed | 3.333 (1.155) | 4.000 (2.000) | |||
| Profession | --- | < 0.001§ | |||
| Doctor | 4.343 (0.913) | 5.000 (1.000) | |||
| Dentist | 3.969 (1.177) | 4.250 (1.500) | |||
| Pharmacist | 2.139 (1.382) | 1.500 (2.000) | |||
| Nurse | 3.644 (1.101) | 4.000 (1.500) | |||
| Other health professional | 3.682 (1.117) | 4.000 (1.500) | |||
| Other profession | 3.782 (0.940) | 4.000 (1.500) | |||
| Fiscal impact | Profession | --- | < 0.001§ | ||
| Doctor | 2.622 (0.873) | 2.667 (1.333) | |||
| Dentist | 2.427 (0.963) | 2.000 (1.000) | |||
| Pharmacist | 2.611 (1.006) | 2.667 (1.500) | |||
| Nurse | 2.103 (0.798) | 2.000 (1.000) | |||
| Other health professional | 2.159 (0.812) | 2.000 (1.000) | |||
| Other profession | 2.151 (0.773) | 2.000 (1.000) | |||
* for categorical variables, ** IQR Interquartile rage, †for ordinal or continuous variables, ¶ Wilcoxon rank-sum test, § Kruskal-Wallis rank test, ‡p-values in parentheses denote p-values< 0.05 who become statistically insignificant after application of the Bonferonni correction (corrected p-value = 0.007)