Evaluating the Knowledge, Practice, and Regulatory Situation of Veterinary Experts Regarding Counterfeit Veterinary Medications in the Selected Districts of Central Gondar Zone, Ethiopia

Figures & data

Table 1 The Socio-Demographic Study Participants Profile in the Study Area

Study Variables		Frequency (%)
Age	15–25 years	3(7.3%)
	26–36 years	25(61.0%)
	37–47 years	8(19.5%)
	Above 48 years	5(12.2%)
Sex	Male	33(80.5%)
	Female	8(19.5%)
Work position	Private pharmacy	12(29.3%)
	Government clinics	26(63.4%)
	Government pharmacy	3(7.3%)
Educational level	Level- 4	15(36.6%)
	Bachelor degree of veterinary pharmacy	4(9.8%)
	DVM	14(34.1%)
	MSc	6(14.6%)
	Others	2(4.9%)
Work experience	Less than 1years	1(2.4%)
	1–4 years	7(17.1%)
	5–9 years	23(56.1%)
	Above 10 years	10(24.4%)
Training	Yes	16(39%)
	No	25(61.0%)

Table 2 Knowledge of Study Participants Towards Veterinary Drug Counterfeits

Parameters		Frequency (%)
Have you any information on counterfeit vet drugs?	Yes	34(82.9%)
	No	7(12.2%)
Source of information on counterfeit Drugs?	Education	23(56.1%)
	Media	4(9.8%)
	Friends	8(19.5%)
	Others	6(14.6%)
Is there method for detecting counterfeit drugs?	Yes	29(70.7%)
	No	12(29.3%)
Is there Penalty for supplying products without manufacture or entrance license?	Yes	23(56.1%)
	No	18(43.9%)
Counterfeit drugs affect the quality of the drugs	Yes	34(82.9%)
	No	7(17.1%)
Are there factors escalating the distribution of the counterfeit drugs	Yes	29(70.7)
	No	12(29.3%)
What are counterfeit drugs?	Correctly answered	24(58.5%)
	Incorrectly answered	17(41.5%)
Hazards associated with counterfeit drug	Treatment failure	2(4.9%)
	More side effects	1(2.5%)
	Treatment failure, more side effects, safety, and efficacy problems	3(7.3%)
	Treatment failure, increase in cost of treatment, and economic problem	1(2.4%)
	No hazard	1(2.4%)
	All	33(80.5%)

Figure 1 Knowledge distribution level of respondents to ward counterfeit medicine.

Table 3 Practice of Study Participants Towards Veterinary Drug Counterfeits

Statement of Study Variables		Frequency (%)
Do you think you are able to distinguish a counterfeit drug from the genuine medicine?	Yes	15(36.6%)
	No	26(63.4%)
Do you accept to distribute a drug if it is come into the country illegally	Yes	13(31.7%)
	No	28(68.3%)
Ways of identifying counterfeit medicines?	By physical observation	1(2.4%)
	By its unexpected side effects	8(19.5%)
	By lack of effectiveness	5(12.2%)
	By physical observation, By its unexpected side effects, and by lack of effectiveness	11(26.8%)
	By its unexpected side effect, and by lack of effectiveness	5(12.2%)
	By physical observation, By its unexpected side effects	2(4.9%)
Have you taken any training course about identifying counterfeit drugs and dealing with this problem	Yes	13(31.7%)
	No	28(68.3%)
Do you report if you observe counterfeited drugs in your drug outlets to drug regulatory body?	Yes	21(51.2%)
	No	20(48.8%)
Do you have economic exchange with whole sellers and suppliers of drugs without a manufacture license or distribution permit from the health ministry	Yes	13(31.7%)
	No	28(68.3%)
Do your counterpart pharmacists inform the authorities, if the suppliers of counterfeit drugs referred to them	Yes	21(51.2%)
	No	20(48.8%)
Do you regularly check the batch number of received medicines against that in purchasing invoices	Yes	26(63.4%)
	No	15(36.6%)

Figure 2 Practice distribution level of respondents to ward counterfeit medicine.

Table 4 Regulatory Activities Towards Veterinary Drug Counterfeits

Statement of the Study Variables	Measuring Tools	Freq (%)
Have you taken training from regulatory bodies regarding counterfeit drugs?	Yes	7(17.1%)
	No	34(82.9%)
Is there any special regulation in drugs by regulatory body?	Yes	16(39%)
	No	25(61%)
Do you know authorized or licensed pharmaceutical importers and whole sellers?	Yes	15(36.6%)
	No	26(63.4%)
Do you know regulatory body that authorized or licensed veterinary drug professionals?	Yes	14(34.1%)
	No	27(65.9%)
What do you think the main contributing factor for the presence of illegal drugs sources?	Lack of informal market control	1(2.4%)
	Poor control at entry port	1(2.4%)
	Extra profit from illegal products	2(4.9%)
	All are possible contributing factors	37(90.2%)
Do you have reporting practice about counterfeited drugs to regulatory body?	Yes	12(29.3%)
	No	29(70.7%)

Figure 3 Regulatory distribution level of respondents to ward counterfeit medicine.

Table 5 Association of Knowledge with Socio Demographic Characteristics

Variables		Knowledge			χ2	Sig.
		High (%)	Moderate (%)	Low (%)
Age	15–25 years	3(100)	0(0.0)	0(0.0)	3.398	0.757
	26–36 years	16(64)	8(32)	1(4)
	37–47 years	4(50)	3(37.5)	1(12.5)
	Above 48 years	3(60)	2(40)	0(0.0)
Sex	Male	21(63.6)	10(30.3)	2(6.1)	0.591	0.744
	Female	5(62.5)	3(37.5)	0(0.0)
Work position	Private pharmacy	6(50.0)	5(41.7)	1(8.3)	2.8203	0.588
	Government clinic	17(65.4)	8(30.8)	1(3.8)
	Government pharmacy	3(100)	0(0.0)	0(0.0)
Education level	Level-4	7(46.7)	8(53.3)	0(0.0)	9.4090	0.309
	MSc	4(66.6)	1(16.7)	1(16.7)
	BSc of veterinary pharmacy	4(100)	0(0.0)	0(0.0)
	DVM	9(64.3)	4(28.6)	1(7.1)
	Others	2(100)	(0.0)	(0.0)
Work experience	Less than 1 year	1(100)	0(0.0)	0(0.0)	3.7885	0.705
	1–4 years	6(85.7)	1(14.3)	0(0.0)
	5–9 years	13(56.5)	8(34.8)	2(8.7)
	Above 10 years	6(60.0)	4(40.0)	0(0.0)
Training	Yes	11(68.7)	4(25.0)	1(6.3)	0.5913	0.744
	No	15(60.0)	9(36.0)	1(4.0)

Table 6 Association of Practice with Socio Demographic Characteristics

Variables		Practice			χ2	P-value
		Good (%)	Fair (%)	Poor (%)
Age	15–25 years	2(66.7)	0(0.0)	1(33.3)	11.521	0.074
	26–36 years	7(28.0)	15(60.0)	3(12.0)
	37–47 years	2(25.0)	3(37.5)	3(37.5)
	Above 48 years	4(80.0)	0(0.0)	1(20.0)
Sex	Male	13(39.4)	16(48.5)	4(12.1)	5.9084	0.052
	Female	2(25.0)	2(25.0)	4(50.0)
Work position	Private pharmacy	6(50.0)	4(33.3)	2(16.7)	3.4021	0.493
	Government clinic	7(26.9)	13(50.0)	6(23.1)
	Government pharmacy	2(66.7)	1(33.3)	0(0.0)
Education level	Level-4	8(53.3)	3(20.0)	4(26.7)	13.717	0.089
	MSc	1(16.7)	5(83.3)	0(0.0)
	BSc of veterinary pharmacy	3(75.0)	1(25.0)	0(0.0)
	DVM	2(14.3)	8(57.1)	4(28.6)
	Others	1(50.0)	1(50.0)	0(0.0)
Work experience	Less than 1 year	1(100)	0(0.0)	0(0.0)	11.177	0.083
	1–4 years	3(42.8)	2(28.6)	2(28.6)
	5–9 years	4(17.4)	14(60.9)	5(21.7)
	Above 10 years	7(70.0)	2(20.0)	1(10.0)
Training	Yes	10(62.5)	4(25.0)	2(12.5)	7.6165	0.022*
	No	5(20.0)	14(56.0)	6(24.0)

Table 7 Association of Regulatory with Socio Demographic Characteristics

Variable		Regulatory			χ2	P-value
		High (%)	Moderate (%)	Low (%)
Age	15–25 years	2(66.7)	1(33.3)	0(0.0)	4.154	0.656
	26–36 years	22(88.0)	2(8.0)	1(4.0)
	37–47 years	6(75.0)	2(25.0)	0(0.0)
	Above 48 years	5(100)	0(0.0)	0(0.0)
Sex	Male	31(93.9)	1(3.0)	1(3.0)	13.34	0.001*
	Female	4(50.0)	4(50.0)	0(0.0)
Work position	Private pharmacy	10(83.3)	1(3.3)	1(3.3)	3.829	0.430
	Government clinic	23(88.5)	3(11.5)	0(0.0)
	Government pharmacy	2(66.7)	1(33.3)	0(0.0)
Education level	Level-4	13(86.7)	2(13.3)	0(0.0)	13.37	0.100
	MSc	6(100)	0(0.0)	0(0.0)
	Bachelor degree of veterinary pharmacy	3(75.0)	0(0.0)	1(25.0)
	DVM	12(85.7)	2(14.3)	0(0.0)
	Others	1(50.0)	1(50.0)	0(0.0)
Work experience	Less than 1 year	1(100)	0(0.0)	0(0.0)	13.67	0.033*
	1–4 years	3(42.9)	3(42.9)	1(14.2)
	5–9 years	21(91.3)	2(8.7)	0(0.0)
	Above 10 years	10(100)	0(0.0)	0(0.0)
Training	Yes	14(87.5)	2(12.5)	0(0.0)	0.656	0.720
	No	21(84.0)	3(12.0)	1(4.0)

Table 8 Association Between Knowledge and Practice Level

Knowledge	Level	Practice				χ2	p-value
		Good	Fair	Poor	Total
		N (%)	N (%)	N (%)	N (%)
	High	12	11	3	26	4.7220	0.317
	Moderate	3	6	4	13
	Low	0	1	1	2
	Total	15	18	8	41

Table 9 Association Between Knowledge and Regulatory Level

Knowledge	Level	Regulatory				χ2	p-value
		High	Moderate	Low	Total
		N (%)	N (%)	N (%)	N (%)
	High	22	3	1	26	0.9912	0.911
	Moderate	11	2	0	13
	Low	2	0	0	2
	Total	35	5	1	41

Table 10 Association Between Practice and Regulatory Level

Practice	Level	Regulatory				χ2	p-value
		High	Moderate	Low	Total
		N (%)	N (%)	N (%)	N (%)
	Good	14	0	1	15	15.0463	0.005*
	Fair	17	1	0	18
	Poor	4	4	0	8
	Total	35	5	1	41

Note: NB: *Significant difference.