AMR KNUST Basic
METHODS/MATERIALS
Measures
The knowledge assessment consisted of 10 questions, categorized into two areas: knowledge about antibiotic resistance (four questions), and knowledge about antibiotic use (six questions) . Each question related to antibiotics awarded one mark if answered correctly, and zero if incorrect. The maximum possible score for the assessment was 10 and minimum was 0. The scores were later transformed into percentages. To categorise the knowledge, we used the Bloom’s cut-off point; 80–100% being good knowledge level, 60–79% being moderate knowledge level, and <60% and below for poor knowledge level (Seid and Hussen 2018).
Data Analysis
Descriptive statistics were used to summarise the study variables; categorical variables were presented in frequency and percentages, and continuous variables were expressed using the median and interquartile ranges. To evaluate the significant difference in overall knowledge levels between the pre-test and post-test, a McNemar test was applied. A simple linear regression model was also used using the difference between scores (post test minus pre test) as the dependent variable and the independent variables to estimate crude beta coefficients. From this approach, we were able to quantify the impact of each of the independent variables on observed change in knowledge. After adjusting for confounding, variables with a p-value < 0.10 in the unadjusted analysis were included as covariates in a multivariate linear regression to obtain adjusted beta coefficient.
Despite the study design inherently violating the assumption of independence in linear regression due to repeated measures on the same individuals, the difference in scores was used as the dependent variable to address this issue (Allison 1990). By analyzing the difference directly, we effectively reduced the within-subject correlation, focusing on the net change attributable to the independent variables. This approach assumes that individual-specific effects are implicitly controlled since each participant serves as their own baseline, mitigating some of the dependency concerns. Significance level was set at 5%. All analyses were carried out using R Programming Language version 4.4.0(2024)
RESULTS
Sociodemographic characteristics of respondents
The median age of the respondents was 13 years, with more than one-third of the respondents in JHS 1. More than half of the respondents were females. Regarding their living situation, almost ninety-four percent of the respondents resided with their parents. The parents’ median age was 46 and 41 years for the father and mother, respectively. More than three-quarters of the respondents’ fathers had a tertiary education, and nearly two-thirds of the respondents’ mothers also obtained tertiary education as the highest educational qualification. One-quarters of respondents residing with their guardians had their guardians have tertiary education (Table 1).
| Characteristic | N = 6111 |
|---|---|
| Class | |
| JHS 1 | 228 (37.32%) |
| JHS 2 | 212 (34.70%) |
| JHS 3 | 171 (27.99%) |
| Age, years | |
| Median (Q1, Q3) | 13.00 (12.00, 13.00) |
| Gender | |
| Female | 340 (55.65%) |
| Male | 271 (44.35%) |
| Living Situation | |
| Extended Family | 31 (5.23%) |
| Immediate Family | 1 (0.17%) |
| Legal Guardian | 4 (0.67%) |
| Parents | 557 (93.93%) |
| Missing | 18 |
| Father's Age, years | |
| Median (Q1, Q3) | 46 (40, 50) |
| Missing | 9 |
| Father's Educational Level | |
| Basic | 15 (2.66%) |
| Secondary | 39 (6.91%) |
| Technical | 82 (14.54%) |
| Tertiary | 428 (75.89%) |
| Missing | 47 |
| Mother's Age | |
| Median (Q1, Q3) | 41 (35, 45) |
| Missing | 10 |
| Mother's Educational Level | |
| Basic | 34 (5.95%) |
| Secondary | 74 (12.96%) |
| Technical | 95 (16.64%) |
| Tertiary | 368 (64.45%) |
| Missing | 40 |
| Guardian Educational Level | |
| Basic | 1 (2.44%) |
| Secondary | 6 (14.63%) |
| Technical | 9 (21.95%) |
| Tertiary | 25 (60.98%) |
| Missing | 570 |
| 1 n (%) | |
Antibiotic Resistance Awareness and Source of Information
More than ninety per cent of the respondents were aware of antibiotic resistance. Health workers (61.0%) were the most cited source of information concerning antimicrobial resistance, followed by an educational campaign (30%). Ninety-three per cent of the respondents knew the definition of antibiotic resistance. Two-thirds of the respondents had first-degree family members being healthcare professionals (Table 2).
| Characteristic | N = 6111 |
|---|---|
| Information about Antibiotic Resistance | 558 (94.58%) |
| Missing | 21 |
| Source of Information (Health Professional) | 339 (60.75%) |
| Missing | 53 |
| Source of Information (Educational Campaign) | 166 (29.75%) |
| Missing | 53 |
| Source of Information (Media - TV,Radio, Social Media, etc) | 66 (11.83%) |
| Missing | 53 |
| Source of Information (Textbook - School Curriculum) | 32 (5.73%) |
| Missing | 53 |
| Source of Information (Family ) | 11 (1.97%) |
| Missing | 53 |
| Source of Information (Friend) | 13 (2.33%) |
| Missing | 53 |
| Antibiotic Resistance Explanation | 557 (92.99%) |
| Missing | 12 |
| First-degree family healthcare occupation | 381 (65.58%) |
| Missing | 30 |
| 1 n (%) | |
Perception of Respondents towards Antimicrobial Use
Concern about antibiotic resistance showed a borderline significant increase, with positive responses rising from 71.0% at the pre-test to 75.6% at the post-test (p = 0.050). Furthermore, awareness of antibiotic resistance as a global issue remained essentially unchanged, with 73.0% of participants at the pre-test and 75.5% at the post-test recognising its global nature (p = 0.3). Similarly, perceptions of the limited impact of antibiotic resistance showed no significant variation, with positive responses remaining stable at 64.2% pre-intervention and 63.3% post-intervention (p = 0.8). However, a considerable improvement was observed in participants acknowledging their responsibility in mitigating antibiotic resistance. Positive responses increased from 67.4% at the pre-test to 85.1% at the post-test (p < 0.001).
| Characteristic | Pre Test N = 6111 |
Post Test N = 6111 |
p-value2 |
|---|---|---|---|
| Stopping Antibiotics When Better | <0.001 | ||
| Negative | 223 (36.5%) | 111 (18.2%) | |
| Positive | 388 (63.5%) | 500 (81.8%) | |
| Missed Doses and Resistance | 0.12 | ||
| Negative | 267 (43.7%) | 241 (39.4%) | |
| Positive | 344 (56.3%) | 370 (60.6%) | |
| Antibiotics for Viral Infections? | 0.013 | ||
| Negative | 314 (51.4%) | 269 (44.0%) | |
| Positive | 297 (48.6%) | 342 (56.0%) | |
| Unconcerned About Antibiotic Resistance | 0.050 | ||
| Negative | 177 (29.0%) | 149 (24.4%) | |
| Positive | 434 (71.0%) | 462 (75.6%) | |
| Antibiotic Resistance Beyond Ghana? | 0.3 | ||
| Negative | 165 (27.0%) | 150 (24.5%) | |
| Positive | 446 (73.0%) | 461 (75.5%) | |
| Antibiotic Resistance Limited Impact? | 0.8 | ||
| Negative | 219 (35.8%) | 224 (36.7%) | |
| Positive | 392 (64.2%) | 387 (63.3%) | |
| Personal Role in Antibiotic Resistance | <0.001 | ||
| Negative | 199 (32.6%) | 91 (14.9%) | |
| Positive | 412 (67.4%) | 520 (85.1%) | |
| 1 n (%) | |||
| 2 McNemar’s Chi-squared test with continuity correction | |||
Knowledge of Respondents on Antimicrobial and Antimicrobial Resistance
Knowledge of Respondents on Antimicrobial Resistance
After the educational intervention, knowledge was significantly changed across all the questions assessing antimicrobial and antimicrobial resistance. Regarding antibiotic resistance, there was a significant increase in the knowledge in that domain from 59.2% to 78.4%, p-value <0.001. Also, there was a more than a quarter increase in knowledge in understanding of the risks of self-medicating with antibiotics without consulting a health professional or purchasing them without a prescription improved markedly (57.6% to 83.6%, p-value <0.001). There was also a substantial increase in knowledge about the risks of using leftover antibiotics from previous infections, rising from 47.6% to 76.9% (p < 0.001). Finally, knowledge regarding the risk of treatment failure due to not completing antibiotics as instructed by a doctor improved by nearly 20 percentage points, from 70.9% to 90.5% (p < 0.001).
| Characteristic | Pre Test N = 6111 |
Post Test N = 6111 |
p-value2 |
|---|---|---|---|
| Antibiotic Resistance: Loss of Effectiveness | <0.001 | ||
| Correct | 362 (59.2%) | 479 (78.4%) | |
| Wrong | 249 (40.8%) | 132 (21.6%) | |
| Antibiotic Resistance Risk: Leftover Medication | <0.001 | ||
| Correct | 291 (47.6%) | 470 (76.9%) | |
| Wrong | 320 (52.4%) | 141 (23.1%) | |
| Antibiotic Treatment Failure: Incomplete Course | <0.001 | ||
| Correct | 433 (70.9%) | 553 (90.5%) | |
| Wrong | 178 (29.1%) | 58 (9.5%) | |
| Antibiotic Resistance Risk: Self-Medication | <0.001 | ||
| Correct | 352 (57.6%) | 511 (83.6%) | |
| Wrong | 259 (42.4%) | 100 (16.4%) | |
| 1 n (%) | |||
| 2 McNemar’s Chi-squared test with continuity correction | |||
Knowledge of Respondents on Antibiotic Use
The intervention led to significant improvements in respondents’ knowledge of antibiotic use. Familiarity with antibiotics showed a marked increase, with correct responses rising from 43.7% at pre-test to 94.8% at the post-test (p < 0.001). Awareness that antibiotics are ineffective against viral infections also improved significantly, with correct responses increasing from 34.4% to 56.1% (p < 0.001). Similarly, the proportion of respondents correctly identifying that antibiotics are not appropriate for treating cough and wheezing rose from 39.8% at pre-test to 59.6% at post-test (p < 0.001). Knowledge about the spread of antibiotic-resistant bacteria also demonstrated significant gains, with correct responses increasing from 46.3% to 69.4% (p < 0.001). Furthermore, the understanding that low-dose antibiotics are not beneficial improved significantly, with correct responses rising from 38.3% at pre-test to 44.8% at post-test (p = 0.007). These findings underscore substantial progress in participants’ knowledge across critical aspects of antibiotic use and resistance
| Characteristic | Pre Test N = 6111 |
Post Test N = 6111 |
p-value2 |
|---|---|---|---|
| Familiar with Antibiotics | <0.001 | ||
| Correct | 267 (43.7%) | 579 (94.8%) | |
| Wrong | 344 (56.3%) | 32 (5.2%) | |
| Antibiotics Don't Treat Viruses | <0.001 | ||
| Correct | 210 (34.4%) | 343 (56.1%) | |
| Wrong | 401 (65.6%) | 268 (43.9%) | |
| Early Antibiotics Prevent Infection? | 0.037 | ||
| Correct | 171 (28.0%) | 201 (32.9%) | |
| Wrong | 440 (72.0%) | 410 (67.1%) | |
| Antibiotics for Cough and Wheezing? | <0.001 | ||
| Correct | 243 (39.8%) | 364 (59.6%) | |
| Wrong | 368 (60.2%) | 247 (40.4%) | |
| Low Dose Antibiotics Beneficial? | 0.007 | ||
| Correct | 234 (38.3%) | 274 (44.8%) | |
| Wrong | 377 (61.7%) | 337 (55.2%) | |
| Antibiotic Resistant Bacteria Spread | <0.001 | ||
| Correct | 283 (46.3%) | 424 (69.4%) | |
| Wrong | 328 (53.7%) | 187 (30.6%) | |
| 1 n (%) | |||
| 2 McNemar’s Chi-squared test with continuity correction | |||
Overall Knowledge of Participants After Intervention
The educational intervention led to significant improvements in knowledge across the knowledge levels. A McNemar test demonstrated a significant overall change (( ^2(3) = 445.08 ), ( p = p = <0.001), with a moderate effect size} = 0.38 ), CI({95%}) [0.35, 0.40]).
Among participants initially categorized as Poor, 41% significantly improved to Moderate, and 24% significantly improved to Good ( p = <0.001). For those in the Moderate category, 38% significantly improved to Good ( p = <0.001). Among participants in the Good category, the majority (62%) retained their Good scores, with a small but significant decline of 34% to Moderate (p = <0.001 ).
Determinants of Knowledge on on Antimicrobial and Antimicrobial Resistance
In the simple linear regression model, being in JHS 2 was associated with a significant increase in knowledge relative to being in JHS 1 (β = 0.66, 95% CI: 0.24, 1.1, p = 0.002). Male respondents had significantly lower knowledge scores than females (β = -0.67, 95% CI: -1.0, -0.32, p < 0.001). Media sources such as TV, radio, and social media were associated with lower knowledge scores compared to those who did not report these sources (β = -0.57, 95% CI: -1.1, -0.05, p = 0.033).
In the multivariate linear regression model, gender and information sources remained significant. Male respondents continued to demonstrate lower knowledge scores than females (β = -0.51, 95% CI: -1.0, -0.01, p = 0.045). Media as a source of information was still associated with lower knowledge scores (β = -0.56, 95% CI: -1.1, -0.04, p = 0.035). Educational campaigns were also found to negatively influence knowledge scores (β = -1.4, 95% CI: -2.6, -0.17, p = 0.025).
| Characteristic | N | Beta | 95% CI1 | p-value |
|---|---|---|---|---|
| Class | 611 | |||
| JHS 1 | Reference | Reference | ||
| JHS 2 | 0.66 | 0.24, 1.1 | 0.002 | |
| JHS 3 | 0.01 | -0.42, 0.45 | 0.948 | |
| Age, years | 611 | -0.04 | -0.20, 0.13 | 0.663 |
| Gender | 611 | |||
| Female | Reference | Reference | ||
| Male | -0.67 | -1.0, -0.32 | <0.001 | |
| Father's Age, years | 591 | -0.01 | -0.02, 0.00 | 0.108 |
| Father's Educational Level | 551 | |||
| Basic | Reference | Reference | ||
| Secondary | 0.68 | -0.56, 1.9 | 0.283 | |
| Technical | 0.34 | -0.77, 1.5 | 0.545 | |
| Tertiary | 0.67 | -0.37, 1.7 | 0.208 | |
| Mother's Age | 596 | -0.01 | -0.02, 0.00 | 0.166 |
| Mother's Educational Level | 548 | |||
| Basic | Reference | Reference | ||
| Secondary | -0.10 | -0.98, 0.78 | 0.830 | |
| Technical | -0.13 | -1.0, 0.73 | 0.764 | |
| Tertiary | 0.03 | -0.75, 0.81 | 0.939 | |
| Staying with biological parents | 611 | |||
| Staying with biological parents | Reference | Reference | ||
| No | 0.60 | -0.79, 2.0 | 0.395 | |
| Yes | 0.37 | -0.82, 1.6 | 0.542 | |
| Living Situation | 597 | |||
| Extended Family | Reference | Reference | ||
| Immediate Family | 0.07 | -2.6, 2.7 | 0.960 | |
| Legal Guardian | 1.1 | -1.6, 3.7 | 0.432 | |
| Parents | -0.13 | -0.97, 0.70 | 0.754 | |
| Source of Information (Health Professional) | 244 | |||
| No | Reference | Reference | ||
| Yes | -0.01 | -0.54, 0.51 | 0.958 | |
| Source of Information (Educational Campaign) | 244 | |||
| No | Reference | Reference | ||
| Yes | -1.2 | -2.4, 0.03 | 0.056 | |
| Source of Information (Media - TV,Radio, Social Media, etc) | 244 | |||
| No | Reference | Reference | ||
| Yes | -0.57 | -1.1, -0.05 | 0.033 | |
| Source of Information (Textbook - School Curriculum) | 244 | |||
| No | Reference | Reference | ||
| Yes | 0.03 | -0.65, 0.71 | 0.931 | |
| Source of Information (Family ) | 244 | |||
| No | Reference | Reference | ||
| Yes | 0.02 | -0.96, 1.0 | 0.971 | |
| Source of Information (Friend) | 244 | |||
| No | Reference | Reference | ||
| Yes | 0.90 | -0.13, 1.9 | 0.086 | |
| First-degree family healthcare occupation | 579 | |||
| No | Reference | Reference | ||
| Yes | 0.00 | -0.37, 0.36 | 0.994 | |
| 1 CI = Confidence Interval | ||||
| Characteristic | Beta | 95% CI1 | p-value |
|---|---|---|---|
| Class | |||
| JHS 1 | Reference | Reference | |
| JHS 2 | 0.57 | -0.04, 1.2 | 0.065 |
| JHS 3 | 0.25 | -0.38, 0.87 | 0.437 |
| Gender | |||
| Female | Reference | Reference | |
| Male | -0.51 | -1.0, -0.01 | 0.045 |
| Source of Information (Media - TV,Radio, Social Media, etc) | |||
| No | Reference | Reference | |
| Yes | -0.56 | -1.1, -0.04 | 0.035 |
| Source of Information (Educational Campaign) | |||
| No | Reference | Reference | |
| Yes | -1.4 | -2.6, -0.17 | 0.025 |
| Source of Information (Friend) | |||
| No | Reference | Reference | |
| Yes | 0.85 | -0.18, 1.9 | 0.107 |
| 1 CI = Confidence Interval | |||