Sociodemographic data
Of 1778 questionnaires, 1324 (74% response rate) were completed and returned from 11 states and 12 hospitals across all geopolitical zones of Nigeria (Figure 1). Of the 1324 respondents, 889 (67.3%) were males, 1003 (75.6%) were between age 25 and 39 years and 834 (63%) were of the residency professional cadre. The south-south (26.9%) and the north-west (25.8%) regions had the highest proportions of respondents (Table 1).
Table 1: Demographic Characteristics and Professional features of Respondents
Characteristics
|
Frequency (N = 1324)
|
Percent (%)
|
Sex
|
|
|
Male
|
891
|
67.3
|
Female
|
433
|
32.7
|
Age group
|
|
|
15-24yrs
|
39
|
2.9
|
25-39yrs
|
1003
|
75.8
|
40-59yrs
|
264
|
19.9
|
Over 59yrs
|
18
|
1.4
|
Department
|
|
|
Emergency unit
|
35
|
2.7
|
Family medicine
|
81
|
6.1
|
Internal medicine
|
259
|
19.6
|
Obstetrics & Gynaecology
|
514
|
38.8
|
Paediatrics
|
132
|
10.0
|
Public health
|
37
|
2.8
|
Surgery
|
163
|
12.3
|
Others
|
103
|
7.8
|
Professional Cadre
|
|
|
House officer
|
268
|
20.2
|
Medical officer
|
45
|
3.4
|
Resident
|
834
|
63.0
|
Consultant
|
177
|
13.4
|
Prior Training on AMR
|
|
|
Yes
|
417
|
31.5
|
No
|
907
|
68.5
|
Region and Health Facility
|
|
|
North-central
|
|
|
UATH
|
98
|
7.4
|
North-east
|
|
|
FMCN
|
38
|
2.9
|
North-west
|
|
|
ABUTH
|
235
|
17.7
|
AKTH
|
107
|
8.1
|
FMCBK
|
43
|
3.2
|
South-east
|
|
|
UNTH
|
88
|
6.6
|
FMCO
|
136
|
10.3
|
South-south
|
|
|
NDUTH
|
109
|
8.2
|
UCTH
|
164
|
12.4
|
FMCY
|
83
|
6.3
|
South-west
|
|
|
UCH
|
100
|
7.6
|
LUTH
|
123
|
9.3
|
Abbreviation: UNTH-University of Nigeria Teaching Hospital, Enugu; AKTH- Aminu Kano University Teaching Hospital, Kano; NDUTH-Niger Delta University Teaching Hospital, Bayelsa; UCH-University College Hospital, Oyo State; LUTH-Lagos University Teaching Hospital, Lagos; UCTH-University of Calabar Teaching Hospital; UATH-University of Abuja Teaching Hospital
Implementation of Antimicrobial stewardship among hospitals and AMR training
Out of a possible total score of 12, the ASP scores of hospitals surveyed ranged from 0 to 7, with median (and interquartile range-IQR) of 2 (0, 2). None of the hospitals surveyed had a formal ASP or a policy on antibiotic restrictions (Figure 2). Three (25%) hospitals routinely monitored antibiotic consumption and had an antibiotic use policy, while four (33.3%) had a protocol for management of IDs in various departments. Although, 6 (50%) hospitals reported routine monitoring of antibiotic resistance patterns, only one (8.3%) routinely monitored specific AMRO such as ESBL, MRSA and CRE (Figure 2). Only one hospital (UNTH) had above average ASP score (58.3%). All other hospitals had poor ASP score (between 0 to 33.3%).
Of the 1324 participants, 417 (31.5%) reported participating in a prior training on antibiotic use and resistance.
Figure 2: Implementation of antimicrobial stewardship programs and strategies by tertiary hospitals in Nigeria.
Figure legend
None of the hospitals had a formal ASP or policy on antibiotic restriction. Half (50%) of the hospitals routinely monitored local AMR patterns. Abbreviation: UNTH-University of Nigeria Teaching Hospital, Enugu; AKTH- Aminu Kano University Teaching Hospital, Kano; NDUTH-Niger Delta University Teaching Hospital, Bayelsa; UCH-University College Hospital, Oyo State; LUTH-Lagos University Teaching Hospital, Lagos; UCTH-University of Calabar Teaching Hospital; UATH-University of Abuja Teaching Hospital
Knowledge, attitude and practice of antibiotic use and resistance
The majority (>80%) of study participants had heard of rational antibiotic prescription and AMR, but only 40.6% had heard of ASP (Table 2). Awareness of antibiotic resistant organisms ranged from 49% to 83%. Regarding knowledge of rational antibiotic use, 85.4% knew that antibiotics should only be stopped after completion of recommended doses, but 51% did not know that parenteral antibiotics are not necessarily more effective than oral antibiotics. Although 90% of participants knew that widespread use of antibiotics could promote emergence of AMR, most participants did not know that AMR could arise from use of antibiotics in farming and animal husbandry and from poor practice of hand hygiene in hospitals. The participants had lower knowledge scores for rational use of antibiotics compared to awareness about antibiotic use and resistance, and knowledge of causes of AMR (Table 2).
Table 2: Knowledge of antibiotic prescriptions and antimicrobial resistance among physicians in Nigeria
|
Knowledge questions
|
% Correct
|
% Wrong
|
A
|
Awareness of antibiotic use and resistance
|
|
|
|
Have you previously heard about any of the following?
|
|
|
1
|
Rational antibiotic use
|
89.6
|
10.4
|
2
|
Antibiotic resistance
|
97.7
|
2.3
|
3
|
Antibiotic stewardship program
|
40.6
|
59.4
|
4
|
Antibiogram
|
45.3
|
54.7
|
5
|
Methicillin-resistant Staphylococcus aureus (MRSA)
|
83.2
|
16.8
|
6
|
Vancomycin-resistant Staphylococcus aureus (VRSA)
|
74.2
|
25.8
|
7
|
Extended spectrum beta-lactamases (ESBL)
|
65.3
|
34.7
|
8
|
Carbapenem-resistant Enterobacteriaceae (CRE)
|
48.9
|
51.1
|
|
Overall % Correct- Median (IQR)=75 (62.5, 87.5)
|
|
|
B
|
Knowledge of rational antibiotic use
|
|
|
|
Concerning antibiotic use which of the following statements are correct?
|
|
|
9
|
Antibiotics should not be used to treat non-bacterial infections
|
76.6
|
23.4
|
10
|
Antibiotics may be used to treat common cold
|
71.5
|
28.5
|
11
|
Antibiotics should be avoided in cases of acute diarrhoea
|
51.5
|
48.5
|
12
|
Antibiotics should be stopped as soon as patient symptoms resolve
|
84.1
|
15.9
|
13
|
Antibiotics should only be stopped after completing recommended doses
|
85.4
|
14.6
|
14
|
Parenteral antibiotics are more effective than oral antibiotics
|
49
|
51
|
15
|
Prophylactic surgical antibiotic should be discontinued after 24hours
|
39.4
|
60.6
|
16
|
Prophylactic surgical antibiotic should not be given for less than 3days
|
47.1
|
52.9
|
|
Overall % Correct- Median (IQR)=62.5 (50, 75)
|
|
|
C
|
Knowledge of definition of AMR
|
|
|
17
|
Antibiotic Resistance means the micro-organism is resistant to the antibiotic
|
69.3
|
30.7
|
D
|
Knowledge of causes of AMR
|
|
|
|
The following are known to promote emergence of antibiotic resistance?
|
|
|
18
|
Widespread use of antibiotics
|
90
|
10
|
19
|
Use of broad-spectrum antibiotics
|
56.6
|
43.4
|
20
|
Antibiotic use in animal husbandry
|
41.5
|
58.5
|
21
|
Antibiotic use in farming
|
39.7
|
60.3
|
22
|
Poor practice of hand hygiene in hospitals
|
47.7
|
52.3
|
23
|
Prescribing parenteral antibiotics
|
62.6
|
37.4
|
24
|
Lack of antibiotics prescribing guidelines
|
91.8
|
8.2
|
25
|
Microbial mutations
|
94.3
|
5.7
|
26
|
Premature interruption of antibiotics
|
92.7
|
7.3
|
27
|
Use of antibiotics to treat common cold
|
74.5
|
25.5
|
28
|
Vaccination
|
74.5
|
25.5
|
|
Overall % Correct- Median (IQR)=72.7 (63.6, 81.8)
|
|
|
Concerning attitude toward antibiotic use and resistance, majority of participants agreed that AMR is a serious public health issue in Nigeria and worldwide and could be a problem for their hospital (Table 3). However, about 79% disagreed that routine hand washing could prevent AMR, 50% agreed that pharmaceutical companies could influence their choice of antibiotic prescriptions, and 27.9% wrongly believed that antibiotic could be used to prevent bacterial infection in patients with upper respiratory tract infection (URTI) due to viruses.
Table 3 shows that about 62% of participants sometimes prescribe antibiotics because they did not trust the available laboratory results, 43% have ever prescribed antibiotics for malaria and 28% sometimes prescribe antibiotics for common cold. About 78% of participants stated that they had rarely or never stopped antibiotics immediately patient symptoms resolve, and 29.5% had sometimes prescribe antibiotics based on recommendation of pharmaceutical companies.
Table 3: Attitude and Practice of antibiotic prescriptions and antimicrobial resistance among physicians in Nigeria
Sn
|
Attitude
|
Strongly agree
|
Agree
|
Neutral
|
Disagree
|
Strongly disagree
|
|
|
%
|
%
|
%
|
%
|
%
|
1
|
Antibiotic resistance is a serious public health issue worldwide
|
79.3
|
18.5
|
1.8
|
0.3
|
0.2
|
2
|
Antibiotic resistance is a serious public health issue in Nigeria
|
82.0
|
16.3
|
1.1
|
0.2
|
0.4
|
3
|
Antibiotic resistance is a problem in other hospitals but not in our hospital
|
1.3
|
2.2
|
6.4
|
46.8
|
43.3
|
4
|
Antibiotic could be used to prevent bacterial infection in patients with viral URTI
|
6.6
|
20.4
|
12.3
|
29.6
|
31.2
|
5
|
Any patient with fever would benefit from antibiotic therapy
|
1.8
|
6.3
|
9.1
|
43.8
|
38.9
|
6
|
Prolonged use of broad-spectrum antibiotics is a risk factor for antibiotic resistance
|
42.9
|
38.9
|
7.0
|
7.4
|
3.8
|
7
|
Patients may feel better if you prescribe antibiotics to satisfy their demands and expectations
|
4.3
|
22.3
|
17.9
|
29.8
|
25.7
|
8
|
There is nothing I can do as a person to lower the risk of antibiotic resistance in our hospital
|
2.2
|
1.9
|
6.1
|
38.6
|
51.2
|
9
|
There is no risk of resistance if antibiotics are taken as prescribed
|
21.1
|
32.4
|
20.4
|
17.6
|
8.5
|
10
|
Persons who have never taken antibiotics have no risk of resistance
|
8.0
|
19.1
|
10.1
|
46.1
|
16.8
|
11
|
It is better to stop antibiotics as soon as a patient feels better
|
3.5
|
11.3
|
11.5
|
49.7
|
24.0
|
12
|
Regular hand washing can prevent antibiotics resistance
|
4.6
|
7.9
|
8.3
|
42.6
|
36.6
|
13
|
A longer course of antibiotic is less likely to cause resistance than a short course
|
7.8
|
19.2
|
18.6
|
37.7
|
16.7
|
14
|
Pharmaceutical companies sometimes influence my choice of antibiotics
|
9.1
|
40.5
|
15.2
|
24.0
|
11.2
|
15
|
In hospital setting, antibiotic resistance could be transmitted from healthcare worker to patients
|
26.7
|
37.2
|
12.0
|
15.2
|
8.9
|
|
Practice
|
Always
|
Most of the time
|
Sometimes
|
Rarely
|
Never
|
|
|
%
|
%
|
%
|
%
|
%
|
1
|
Prescribe antibiotics for common cold
|
1.4
|
3.3
|
28.0
|
44.1
|
23.2
|
2
|
Prescribe antibiotics for pneumonia
|
45.4
|
45.1
|
6.3
|
1.5
|
1.7
|
3
|
Prescribe antibiotics for malaria
|
2.0
|
1.5
|
8.1
|
21.7
|
66.7
|
4
|
Stop antibiotics immediately patient symptoms resolve
|
1.4
|
5.0
|
15.8
|
26.5
|
51.4
|
5
|
Prescribe antibiotics because patient insists on it
|
0.2
|
0.8
|
11.2
|
27.8
|
60.0
|
6
|
Prescribe antibiotics because you do not trust the available laboratory results
|
1.1
|
8.6
|
61.9
|
17.4
|
10.9
|
7
|
Wait for culture result before prescribing antibiotics
|
0.8
|
10.6
|
49.2
|
30.5
|
8.9
|
8
|
Prescribe antibiotics based on culture results
|
17.9
|
48.0
|
28.7
|
3.8
|
1.6
|
9
|
Prescribe antibiotic based on recommendation of pharmaceutical companies
|
1.4
|
4.8
|
29.5
|
37.7
|
26.6
|
10
|
Prescribe antibiotics inappropriately because patient cannot afford the appropriate antibiotic
|
0.4
|
3.4
|
34.0
|
29.0
|
33.2
|
11
|
Prescribe antibiotics inappropriately because the appropriate antibiotic is not available
|
0.6
|
3.4
|
38.1
|
30.2
|
27.8
|
12
|
De-escalate from broad spectrum to narrow spectrum antibiotics as soon as culture results are available
|
24.0
|
37.1
|
22.3
|
9.9
|
6.7
|
13
|
Prescribe prophylaxis antibiotics for more than 24hours
|
6.4
|
19.2
|
41.9
|
23.3
|
9.1
|
Antibiotic prescriptions
All study participants (100%) had prescribed one or more antibiotics in the previous 6 months. A median prescription of 15 antibiotics (IQR-13, 17) were prescribed per study participant in the previous 6 months. The prescription scores ranged from 20 to 90 with median (IQR) of 48 (43, 53). Amoxicillin-clavulanate (98%), ciprofloxacin/ofloxacin (97%), ceftriaxone (96.8%) and metronidazole (96.5%) were the most frequently prescribed antibiotics (Figure 3). According to AWaRe categories, 100%, 99.3%, 67.8% of respondents had prescribed antibiotics from the Access, Watch and Reserve group of antibiotics, respectively.
Respondents practicing in FMC Nguru, Yobe State and AKTH, Kano and those working in Paediatrics and Internal Medicine departments reported higher rates of prescriptions of Cefepime than respondents from other hospitals and departments (Figure 4)
Figure 3: Frequency of prescription of various antibiotics by physicians in Nigeria in the prior six months.
Figure legend
Penicillins with β-lactamase inhibitors, fluoroquinolones and third generation cephalosporins were the most frequently classes of antibiotics prescribed. 68.6% had prescribed the Reserved antibiotic Cefepime.
Figure 4. Frequency of prescription of reserve antibiotic (Cefepime) among physicians in Nigeria
Figure legend
There was significant difference in prescription of Cefepime in relation to hospital of practice* and department of practice†. Physicians practicing in AKTH (92.5%), FMCN (85.9%) and UATH (85.7%), all located in northern Nigeria, had the highest rates prescriptions of Cefepime. The highest rates of prescription of Cefepime were also observed among physicians practicing in and Internal Medicine (77.6%) and Paediatrics (72.8%) departments.
Abbreviations: UNTH-University of Nigeria Teaching Hospital, Enugu; AKTH- Aminu Kano University Teaching Hospital, Kano; NDUTH-Niger Delta University Teaching Hospital, Bayelsa; UCH-University College Hospital, Oyo State; LUTH-Lagos University Teaching Hospital, Lagos; UCTH-University of Calabar Teaching Hospital; UATH-University of Abuja Teaching Hospital; FMCN-Federal Medical Centre, Nguru, Yobe; FMCY-Federal Medical Centre, Yenagoa, Bayelsa; FMCO- Federal Medical Centre, Owerri, Imo; FMCBK- Federal Medical Centre, Birnin Kudu, Jigawa; ABUTH-Ahmadu Bello University Teaching Hospital, Kaduna;
Correlations between knowledge, attitude, practice, and prescription (KAPPr) scores
The correlations between KAPPr scores are shown in table 4. There were weak positive correlations between knowledge, attitude, and practice scores, and weak negative correlations when prescriptions scores are compared with knowledge, attitude, and practice scores.
Table 4. Spearman rho’s correlations between knowledge, attitude, practice, and prescription scores
Scores
|
Knowledge score –
r (p value)
|
Attitude score-
r (p value)
|
Practice score-
r (p value)
|
Prescription score-
r (p value)
|
Knowledge score
|
1
|
0.360 (<0.0001)
|
0.191 (<0.0001)
|
-0.074
(0.008)
|
Attitude score
|
0.360
(<0.0001)
|
1
|
0.296 (<0.0001)
|
-0.217 (<0.0001)
|
Practice score
|
0.191
(>0.0001)
|
0.296 (<0.0001)
|
1
|
-0.192 (<0.0001)
|
Prescription score
|
-0.074
(0.008)
|
-0.217 (<0.0001)
|
-0.192 (<0.0001)
|
1
|
NB: there were weak positive correlations between knowledge, attitude and practice scores and weak negative correlations between when prescription scores where compared with knowledge, attitude, and practice scores
Prevalence and factors associated with good knowledge, attitude, practice, and prescription.
The descriptive statistics of KAPPr scores are summarized in Table 5. Of the 1324 study participants, 295 (22.3%), 534 (40.3%), 418 (31.6%) and 420 (31.7%) had good knowledge, attitude, practice, and prescription, respectively. The factors associated with good KAPPr on univariate and multivariate analysis are shown in Table 6 and 7, respectively. Professional rank was independently associated with good knowledge, attitude, and prescription but not with good practice. Senior physicians (consultants and resident doctors) were between 1.6 to 3.3 times more likely to have good KAPPr than house officers. Those who previously participated in any training on antibiotic use and resistance were 2.6 times more likely to have good knowledge than those without any prior training.
Department of practice was independently associated with good prescription, but not associated with good knowledge, attitude, and practice. Respondents practicing in Paediatrics departments were significantly less likely to have good prescription compared to respondents from other departments. Hospital of practice was independently associated with good KAPPr. Overall, respondents practicing in South West Hospitals (UCH and LUTH) were more likely to have good KAPPr than those respondents from other hospitals (Figure 5). Respondents practicing in FMCN and FMCBK, both located in northern Nigeria, generally had lowest rates of good KAPPr.
Table 5. Descriptive statistics of knowledge, attitude, practice, and prescription scores of physicians in Nigeria
Variable
|
Score
|
Score (n)
|
Score (%)
|
Good Score
|
Average Score
|
Poor Score
|
|
Range
|
Median (IQR)
|
Median (IQR)
|
n (%)
|
n (%)
|
n (%)
|
Knowledge
|
0-28
|
19 (16, 21)
|
71.1(62, 79)
|
293 (22.3)
|
963 (73.4)
|
56 (4.3)
|
Attitude
|
15-75
|
57 (52, 62)
|
77 (72, 83)
|
531 (40.3)
|
786 (59.6)
|
1 (0.1)
|
Practice
|
13-65
|
49 (46, 52)
|
75 (72, 80)
|
416 (31.6)
|
899 (68.4)
|
0 (0)
|
Prescription
|
18-90
|
48 (43, 53)
|
53.3 (47.8, 58.9)
|
420 (31.7)
|
896 (67.7)
|
8 (0.6)
|
Key: n-number of participants; IQR-interquartile range
Figure 5. Differences in prevalence of good knowledge, attitude, practice, and prescription in relation to hospital of practice of physicians in Nigeria.
Figure legend
LUTH and UCH both located in South-West Nigeria generally had higher prevalence of good KAPPr than other hospitals. Abbreviations: UNTH-University of Nigeria Teaching Hospital, Enugu; AKTH- Aminu Kano University Teaching Hospital, Kano; NDUTH-Niger Delta University Teaching Hospital, Bayelsa; UCH-University College Hospital, Oyo State; LUTH-Lagos University Teaching Hospital, Lagos; UCTH-University of Calabar Teaching Hospital; UATH-University of Abuja Teaching Hospital; FMCN-Federal Medical Centre, Nguru, Yobe; FMCY-Federal Medical Centre, Yenagoa, Bayelsa; FMCO- Federal Medical Centre, Owerri, Imo; FMCBK- Federal Medical Centre, Birnin Kudu, Jigawa; ABUTH-Ahmadu Bello University Teaching Hospital, Kaduna;
Table 6. Univariate analysis of factors associated with good knowledge, attitude, practice, and prescription among physicians in Nigeria
Variable
|
N
|
Good knowledge
|
Good attitude
|
Good practice
|
Good prescription
|
|
|
n (%)
|
n (%)
|
n (%)
|
n (%)
|
Gender
|
|
|
|
|
|
Female
|
430
|
84 (19.8)
|
177 (41.5)
|
138 (32.5)
|
149 (34.7)
|
Male
|
889
|
208 (23.6)
|
353 (39.8)
|
277 (31.3)
|
268 (30.1)
|
Age group
|
|
|
|
|
|
<36yrs
|
817
|
146 (18.1) †
|
298 (36.7) †
|
238 (29.4) †
|
244 (29.9)
|
36-49yrs
|
456
|
128 (28.1)
|
206 (45.3)
|
151 (33.3)
|
160 (35.1)
|
>49yrs
|
51
|
19 (37.3)
|
27 (52.9)
|
27 (52.9)
|
16 (31.4)
|
Professional rank
|
|
|
|
|
|
House officers
|
268
|
33 (12.5) *
|
71 (26.9) *
|
69 (26.2) *
|
61 (22.8)
|
Medical officers/Residents
|
879
|
197 (22.6)
|
362 (41.2)
|
290 (33.1)
|
280 (31.9)
|
Consultants
|
177
|
63 (35.6)
|
98 (55.7)
|
57 (32.4)
|
79 (44.6)
|
Prior AMR training
|
|
|
|
|
|
No
|
870
|
142 (16.5) *
|
323 (37.3) †
|
257 (29.8) ¶
|
279 (32.1)
|
Yes
|
417
|
143 (34.5)
|
190 (45.7)
|
147 (35.3)
|
132 (31.7)
|
Department
|
|
|
|
|
|
Paediatrics
|
132
|
30 (22.9) †
|
54 (41.2)
|
38 (29) *
|
23 (17.4) ¶
|
Internal Medicine
|
259
|
75 (29)
|
121 (46.9)
|
81 (31.5)
|
77 (29.7)
|
O&G
|
515
|
105 (20.5)
|
197 (38.5)
|
150 (29.5)
|
161 (31.3)
|
Surgery
|
164
|
42 (25.8)
|
67 (41.1)
|
58 (35.4)
|
60 (36.6)
|
Family Medicine
|
80
|
14 (18.2)
|
25 (31.2)
|
39 (48.8)
|
22 (27.5)
|
Others
|
174
|
27 (15.9)
|
67 (38.5)
|
50 (28.7)
|
77 (44.3)
|
Hospital
|
|
|
|
|
|
LUTH
|
123
|
50 (40.7) *
|
86 (69.9) *
|
52 (42.3) *
|
45 (36.6) †
|
UCH
|
100
|
37 (37.4)
|
51 (52)
|
40 (40.8)
|
47 (47)
|
UATH
|
98
|
25 (25.5)
|
41 (42.3)
|
37 (37.8)
|
24 (24.5)
|
ABUTH
|
235
|
48 (20.4)
|
103 (43.8)
|
63 (26.9)
|
80 (34)
|
FMCY
|
83
|
13 (16.2)
|
18 (21.7)
|
33 (39.8)
|
24 (28.9)
|
NDUTH
|
109
|
21 (19.4)
|
32 (29.6)
|
39 (36.1)
|
34 (31.2)
|
UNTH
|
88
|
12 (13.6)
|
23 (26.1)
|
13 (15.1)
|
25 (28.4)
|
AKTH
|
107
|
29 (28.2)
|
45 (42.1)
|
19 (17.8)
|
30 (28)
|
UCTH
|
164
|
27 (16.5)
|
52 (31.7)
|
61 (37.2)
|
57 (34.8)
|
FMCO
|
136
|
27 (20.3)
|
62 (46.3)
|
42 (31.6)
|
39 (28.7)
|
FMCN
|
38
|
2 (5.3)
|
7 (18.4)
|
9 (23.7)
|
5 (13.2)
|
FMCBK
|
43
|
2 (4.7)
|
11 (25.6)
|
8 (18.6)
|
10 (23.3)
|
All participants
|
1324
|
293 (22.3)
|
531 (40.3)
|
416 (31.6)
|
420 (31.7)
|
Key: p<0.0001*, p<0.001†, p<0.05¶; N-number of participants; AMR-antimicrobial resistance
UNTH-University of Nigeria Teaching Hospital, Enugu; AKTH- Aminu Kano University Teaching Hospital, Kano; NDUTH-Niger Delta University Teaching Hospital, Bayelsa; UCH-University College Hospital, Oyo State; LUTH-Lagos University Teaching Hospital, Lagos; UCTH-University of Calabar Teaching Hospital; UATH-University of Abuja Teaching Hospital; FMCN-Federal Medical Centre, Nguru, Yobe; FMCY-Federal Medical Centre, Yenagoa, Bayelsa; FMCO- Federal Medical Centre, Owerri, Imo; FMCBK- Federal Medical Centre, Birnin Kudu, Jigawa; ABUTH-Ahmadu Bello University Teaching Hospital, Kaduna.
Table 7. Multivariate analysis of the predictors of good knowledge, attitude, practice, and prescription among physicians in Nigeria
Variable
|
Good Knowledge
|
Good Attitude
|
Good Practice
|
Good Prescription
|
|
AOR (95% CI)
|
AOR (95% CI)
|
AOR (95% CI)
|
AOR (95% CI)
|
Gender
|
|
|
|
|
Female (Ref)
|
1
|
1
|
1
|
1
|
Male
|
1.5 (1, 2.1)
|
1.1 (0.9, 1.5)
|
1 (0.8, 1.4)
|
0.8 (0.6, 1)
|
Age group
|
|
|
|
|
<36years (Ref)
|
1
|
1
|
1
|
1
|
36-49years
|
1.2 (0.9, 1.7)
|
1 (0.7, 1.3)
|
1.1 (0.8, 1.5)
|
1 (0.8, 1.3)
|
>49years
|
1.7 (0.8, 3.5)
|
1.2 (0.6, 2.4)
|
2.7b (1.4, 5.4)
|
0.6 (0.3, 1.1)
|
Professional Category
|
|
|
|
|
House officers
|
1¶
|
1*
|
1
|
1*
|
Medical officers/Residents
|
1.6¶ (1, 2.5)
|
1.8b (1.2, 2.5)
|
1.4 (1, 2)
|
1.6¶ (1.1, 2.3)
|
Consultants
|
2.4¶ (1.3, 4.5)
|
3c (1.8, 5)
|
1 (0.6, 1.7)
|
3.3c (2, 5.7)
|
Prior AMR training
|
|
|
|
|
No (Ref)
|
1*
|
1
|
1
|
1
|
Yes
|
2.6 (1.9, 3.4)
|
1.3 (1, 1.7)
|
1.2 (0.9, 1.5)
|
0.9 (0.7, 1.2)
|
Department
|
|
|
|
|
Paediatrics (Ref)
|
1
|
1
|
1
|
1*
|
Internal Medicine
|
1.3 (0.7, 2.2)
|
1.2 (0.7, 2)
|
1.3 (0.8, 2.1)
|
2.2b (1.3, 3.9)
|
O&G
|
1.1 (0.6, 1.9)
|
0.8 (0.5, 1.3)
|
1.2 (0.7, 1.9)
|
2.7c (1.6, 4.6)
|
Surgery
|
0.9 (0.5, 1.6)
|
0.8 (0.5, 1.4)
|
1.3 (0.8, 2.2)
|
2.9c (1.6, 5.2)
|
Family Medicine
|
0.7 (0.3, 1.5)
|
0.6 (0.3, 1.1)
|
2.3 (1.2, 4.2)
|
1.9 (0.9, 3.8)
|
Others
|
0.6 (0.3, 1.2)
|
0.8 (0.5, 1.3)
|
1.1 (0.7, 1.9)
|
4.5c (2.5, 8)
|
Hospital
|
|
|
|
|
LUTH (Ref)
|
1*
|
1*
|
1*
|
1†
|
UCH
|
0.9 (0.5, 1.6)
|
0.5† (0.3, 0.9)
|
0.8 (0.5, 1.4)
|
1.8¶ (1, 3.2)
|
UATH
|
0.5 (0.3, 1)
|
0.4† (0.2, 0.7)
|
0.8 (0.4, 1.4)
|
0.7 (0.4, 1.4)
|
ABUTH
|
0.4† (0.2, 0.7)
|
0.3* (0.2, 0.6)
|
0.5† (0.3, 0.8)
|
0.8 (0.5, 1.3)
|
FMCY
|
0.3† (0.1, 0.6)
|
0.1*(0.1, 0.2)
|
0.7 (0.4, 1.4)
|
0.9 (0.5, 1.8)
|
NDUTH
|
0.3† (0.2, 0.6)
|
0.2* (0.1, 0.4)
|
0.7 (0.4, 1.3)
|
1 (0.6, 1.9)
|
UNTH
|
0.2† (0.1, 0.5)
|
0.2* (0.1, 0.4)
|
0.3† (0.1, 0.6)
|
0.7 (0.4, 1.4)
|
AKTH
|
0.6 (0.3, 1.1)
|
0.3* (0.2, 0.5)
|
0.3† (0.2, 0.6)
|
0.6 (0.3, 1.1)
|
UCTH
|
0.3* (0.2, 0.6)
|
0.2* (0.1, 0.4)
|
0.8 (0.5, 1.4)
|
1.2 (0.7, 2)
|
FMCO
|
0.3† (0.2, 0.7)
|
0.5† (0.3, 0.9)
|
0.7 (0.4, 1.2)
|
0.8 (0.4, 1.4)
|
FMCN
|
0.1† (0, 0.4)
|
0.1* (0, 0.3)
|
0.4 (0.2, 1)
|
0.3 (0.1, 1)
|
FMCBK
|
0.1* (0, 0.3)
|
0.1* (0.1, 0.3)
|
0.3† (0.1, 0.7)
|
0.5 (0.2, 1.2)
|
Key: p<0.0001*, p<0.001†, p<0.05¶; Ref-reference group; AOR-adjusted odds ratio; CI-Confidence Interval; AMR-antimicrobial resistance
UNTH-University of Nigeria Teaching Hospital, Enugu; AKTH- Aminu Kano University Teaching Hospital, Kano; NDUTH-Niger Delta University Teaching Hospital, Bayelsa; UCH-University College Hospital, Oyo State; LUTH-Lagos University Teaching Hospital, Lagos; UCTH-University of Calabar Teaching Hospital; UATH-University of Abuja Teaching Hospital; FMCN-Federal Medical Centre, Nguru, Yobe; FMCY-Federal Medical Centre, Yenagoa, Bayelsa; FMCO- Federal Medical Centre, Owerri, Imo; FMCBK- Federal Medical Centre, Birnin Kudu, Jigawa; ABUTH-Ahmadu Bello University Teaching Hospital, Kaduna;