Open access Original research Knowledge, attitudes and practices regarding antimicrobial use and resistance among healthcare seekers in two tertiary hospitals in Ghana: a quasi- experimental study Evans Otieku ,1,2 Ama Pokuaa Fenny ,1 Appiah- Koran Labi,3 Alex Kwame Owusu- Ofori ,4,5 Jørgen Kurtzhals ,6,7 Ulrika Enemark2 To cite: Otieku E, Fenny AP, ABSTRACT Labi A-K , et al. Knowledge, Objective To evaluate knowledge of antimicrobial STRENGTHS AND LIMITATIONS OF THIS STUDY attitudes and practices resistance (AMR), to study how the judgement of health ⇒ This study included an intervention to evaluate how regarding antimicrobial use and value (HVJ) and economic value (EVJ) affects antibiotic access to antimicrobial resistance (AMR) informa- resistance among healthcare use, and to understand if access to information on AMR tion may influence AMR mitigation strategies in seekers in two tertiary hospitals in Ghana: a quasi- implications may influence perceived AMR mitigation Ghana. experimental study. BMJ Open strategies. ⇒ The study involved 800 participants, and the data 2023;13:e065233. doi:10.1136/ Design A quasi- experimental study with interviews were collected using a validated instrument. bmjopen-2022-065233 performed before and after an intervention where ⇒ Reporting and methodological quality followed hospital staff collected data and provided one group the Strengthening the Reporting of Observational ► Prepublication history and Studies in Epidemiology checklist. additional supplemental material of participants with information about the health and for this paper are available economic implications of antibiotic use and resistance ⇒ One limitation was that the intervention was admin- online. To view these files, compared with a control group not receiving the istered to participants by the same person who per- please visit the journal online intervention. formed the interview leading to a power imbalance (http://dx.doi.org/10.1136/ Setting Korle-B u and Komfo Anokye Teaching Hospitals, in which participants may have felt a need to give bmjopen-2022-065233). Ghana. an expected answer rather than their own opinion. Participants Adult patients aged 18 years and older ⇒ Another limitation was that though the intervention Received 30 May 2022 seeking outpatient care. group received examples of antibiotics, we were Accepted 12 February 2023 Main outcome measures We measured three outcomes: unsure if they had a correct perception of drugs in (1) level of knowledge of the health and economic mind when assessing statements about antibiotics. implications of AMR; (2) HVJ and EVJ behaviours influencing antibiotic use and (3) differences in perceived AMR mitigation strategy between participants exposed and INTRODUCTION not exposed to the intervention. WHO has declared antimicrobial resistance Results Most participants had a general knowledge of (AMR) a top 10 public health emergency,1 the health and economic implications of antibiotic use posing an aggravated threat to fighting infec- and AMR. Nonetheless, a sizeable proportion disagreed or tious diseases.2 Moreover, AMR has adverse disagreed to some extent that AMR may lead to reduced health and economic consequences.3 4 productivity/indirect costs (71% (95% CI 66% to 76%)), Studies show that AMR is attributable to increased provider costs (87% (95% CI 84% to 91%)) and costs for carers of AMR patients/societal costs (59% non- prescribed access to antibiotics, and (95% CI 53% to 64%)). Both HVJ- driven and EVJ- driven WHO and others thus recommend restricting this access.1 5 6behaviours influenced antibiotic use, but the latter was a Consumers and providers are © Author(s) (or their better predictor (reliability coefficient >0.87). Compared responsible for the inappropriate use of anti- employer(s)) 2023. Re- use 7with the unexposed group, participants exposed to the biotics. Evidence shows an AMR knowledge permitted under CC BY- NC. No intervention were more likely to recommend restrictive gap among patients8 9commercial re-u se. See rights and there may be a and permissions. Published by access to antibiotics (p<0.01) and pay slightly more for problem for providers due to information BMJ. a health treatment strategy to reduce their risk of AMR asymmetry between patients and providers, For numbered affiliations see (p<0.01). which results in inappropriate prescription end of article. Conclusion There is a knowledge gap about antibiotic or treatment. use and the implications of AMR. Access to AMR Correspondence to We hypothesised that a person with ill information at the point of care could be a successful way Evans Otieku; health faces several decisions. Whether to mitigate the prevalence and implications of AMR. o tieku@ yahoo.c om to: (1) self- treat with leftover medicine or Otieku E, et al. BMJ Open 2023;13:e065233. doi:10.1136/bmjopen-2022-065233 1 BMJ Open: first published as 10.1136/bmjopen-2022-065233 on 22 February 2023. Downloaded from http://bmjopen.bmj.com/ on April 18, 2023 by guest. Protected by copyright. Open access over-t he- counter medicine or consult a doctor10–12; (2) about the health and economic implications of antibiotic press for a prescription for antibiotics even if a doctor use and AMR, while a control group was not given the finds it unnecessary and13 (3) comply with a prescription information. The hospital staff collecting data did not from a certified provider.14 15 provide medical care to the study participants. This study From the theories of utility and demand, we assumed followed the Strengthening the Reporting of Observa- that an ill person makes choices that maximise utility for tional Studies in Epidemiology checklist.28 a given information and preference structure, consid- ering income, price and health effects.16 17 Behavioural Setting economics principles18–20 suggest utility expectations The study settings were four outpatient departments may explain decisions on antibiotic use. Notably, among (OPDs) in each of the two participating hospitals. The other factors, the health benefits from previous use of OPDs included medical, surgical, child health and obstet- the same antibiotic may influence the non-p rescribed use rics/gynaecology. The KBTH is in the Greater Accra of antibiotics. Likewise, the prices of antibiotics and the Region, the national capital and KATH is in Kumasi, the income of the patient could be determining factors for Ashanti Regional capital of Ghana. Both facilities have antibiotic use.21–23 This explains why a patient is likely to more than six decades of rendering specialist clinical and use cheaper and more generic and accessible first- line diagnostic services with a current hospital bed capacity of antibiotics like penicillin, gentamicin, metronidazole and about 2000 and 1200, respectively, and attending to about ciprofloxacin rather than second-l ine and third-l ine anti- 1500 outpatients daily before the COVID-1 9 outbreak.29 30 biotics like meropenem and vancomycin.24 Data collection lasted 3 months from July to September Antibiotic use presents a typical public goods problem. 2021. Excessive unauthorised demand and access to antibiotics may cause a shortage for others in need.24 25 Therefore, Participants restricting antibiotic use is a preferred AMR mitigation Inclusion criteria were patients aged 18 years and older strategy. We hypothesised that if people are made aware seeking outpatient care at the four OPDs in each of the of the implications of AMR, they may decide to use anti- two hospitals during the study period. Patients were biotics only when prescribed by a certified healthcare excluded if they had a medical condition requiring urgent professional. intervention or if they declined participation. The selec- In this study, we equate health value judgement (HVJ) tion of eligible participants was based on a first-c ome- first- behaviours to situations where people consume antibi- select basis in consultation with the in- charge nurses and otics because of the health value they place on the drug. ward matrons. Ten participants from each OPD equal to Thus, the expected effectiveness of a drug is based on 80 per week were selected from both hospitals for a total peoples’ own experience or perfect/imperfect informa- of 10 weeks. If a patient among the first 10 was eligible tion sources, whereas economic value judgement (EVJ) but declined participation, the next patient was selected. may refer to financial or productivity decisions for antibi- Consequently, we selected a total of 800 participants for otic use. A perfect information source for the decision to 10 weeks spanning July to September 2021. The overall use antibiotics may be a certified provider, while imper- sample size of 800 (95% CI 786 to 814) was determined fect information sources may encompass friends, family from a 95% confidence level (z score 1.96) and 3000 OPD and other uncertified/unauthorised sources. attendances per week due to COVID- 19. To assess the The study setting is Ghana, a lower- middle-i ncome impact of the intervention on perceived AMR mitigation country (LMIC) challenged by inappropriate antibiotic strategies, participants were divided into two groups, A use and an AMR knowledge gap.26 27 A recent global and B. Group A, the control group, was enrolled in weeks study named Ghana as one of five countries where unau- 1, 3, 5, 7 and 9. Group B, the intervention group, was thorised antibiotic use may escalate disproportionately enrolled in weeks 2, 4, 6, 8 and 10. if actions are not taken to address inappropriate use.11 The aim of this study was first to ascertain the knowledge The intervention: AMR knowledge dissemination package gap of the implications of AMR; second, to evaluate the In collaboration with staff at the OPDs and AMR stew- importance of HVJ and EVJ in decisions to use antibiotics ards, we designed a simple intervention involving an and third, to understand if access to information on the AMR knowledge dissemination package. The choice of health and economic implications of AMR may signifi- intervention was discussed and accepted, agreeing that it cantly influence perceived AMR mitigation strategies. could be scaled up in LMIC settings if the intervention succeeds in changing participant attitudes about ways to mitigate the impact of AMR. METHODS The intervention was presented midway into the inter- Design view after eliciting data on participants’ knowledge and We conducted a quasi-e xperimental study where hospital awareness of the health and economic consequences of staff collected data among patients on knowledge and antibiotic use and AMR. Illustrations and examples of the attitudes regarding antibiotic use and AMR. An interven- most used antibiotics in Ghana were given as examples of tion group was provided with point-o f-c are information drugs that should be accessible as the first treatment and 2 Otieku E, et al. BMJ Open 2023;13:e065233. doi:10.1136/bmjopen-2022-065233 BMJ Open: first published as 10.1136/bmjopen-2022-065233 on 22 February 2023. Downloaded from http://bmjopen.bmj.com/ on April 18, 2023 by guest. Protected by copyright. Open access antibiotics that should be watched or reserved, including 31 May 2021. The pilot involved 80 participants, equiva- third- generation cephalosporins.31 Refer online supple- lent to 10% of the study sample. The aim was to ensure mental material 1 for a qualitative description of the that the target population understood the questions and intervention. that any difficulties translating the questions were docu- mented and resolved. The aim was also to identify a smooth Variables and measurement sequence in the arrangement of the questions. The term We analysed three primary outcomes: (1) knowledge of antimicrobial resistance and its acronym AMR were alien health and economic implications of antibiotic use and to about 73% of the participants who preferred the term resistance; (2) stated HVJ and EVJ behaviours influencing antibiotic resistance. Also, about 61% of the participants antibiotic use and (3) differences in AMR mitigation validated a rewording of the Likert scale measures. For strategies between the intervention and control groups. example, they were familiar with ‘agree to some extent’ We used numeric variables to capture responses and ‘disagree to some extent’ instead of ‘strongly agree’ to questions used to analyse objectives 1 and 2 and and ‘strongly disagree’. Similar experiences relating to measured them on a 4-p oint Likert scale, where 1=agree, translation and understanding of terminology and ques- 2=agree to some extent, 3=disagree to some extent and tions have been documented in another study.32 These 4=disagree. Knowledge was measured by five questions preferences led to changes in the Likert scale measures. on perceived economic and health implications, respec- tively. If the correct answer to a question was ‘agree’, then responses 1 or 2 on the Likert scale indicated the Data sources participant has a degree of knowledge about the health Data were collected with a structured questionnaire and and economic consequences of antibiotic use and resis- administered in person by trained hospital staff (intern tance. If the correct answer to a question was ‘disagree’, nurses) using a computer-a ssisted personal interviewing then responses 3 or 4 showed some degree of awareness. tool embedded with CS Pro V.7.6.0 software. The tool Objective 2 was measured by seven HVJ questions and five comprises a list of 28 closed and open-e nded questions EVJ questions. For objective 3, we used the simple ‘yes’ classified into 6 modules (online supplemental material or ‘no’ categorical variables to capture responses, except 2). Table 1 presents a summary of the number and cate- for the question ‘do you think a doctor’s prescription gory of questions contained in each module, the purpose for antibiotics should be more or less restrictive?’; this of the questions and the data source for the inclusion had three categorical responses, that is, more restrictive, of those questions. For instance, questions contained unchanged and less restrictive. in modules 1, 2, 3 and 5 were drawn from the WHO protocol on antibiotic resistance multicountry public Patient and public involvement awareness survey33 and supplemented with a few selected Patient and public involvement in this study were three- questions from a validated tool previously used to assess fold. First, we conducted a week-l ong pilot of the data antibiotics knowledge.8 Questions contained in modules collection tool at the study sites between 24 May 2021 and 4 and 6 were developed by the authors for objectives 2 Table 1 Summary of data used in this study and the source No of questions Data category Participants Purpose Source Module 1 11 Sociodemographic All Analyse the socio- demographic characteristics of WHO,33 plus respondents. the Authors Module 2 3* Antibiotics knowledge and All† Assess knowledge and implications of antibiotic WHO,33 implications of antibiotics use. use. Jairoun et al8 Module 3 5 Previous/current use of antibiotics All† Evaluate the use of antibiotics. WHO33 Module 4 1‡ HVJ and EVJ determinants of All† Examine if antibiotic use was influenced by either Authors antibiotics use. HVJ or EVJ or both and find out which socio- demographic variables relate to HVJ and EVJ. Module 5 5§ AMR knowledge and implication All† Assess participant’s knowledge of AMR WHO33 plus tests. Authors Module 6 4 AMR mitigation attitude tests Group A¶ and Compare AMR mitigation attitudes between the Authors Group B** intervention and control groups. *Subdivided into 13 antibiotics knowledge test questions. †Some subquestions do not apply to some respondents by design. ‡Subdivided into 12 HVJ and EVJ questions. §Has 27 subquestions. ¶Group A—participants not exposed to the intervention. **Group B—participants exposed to the intervention. AMR, antimicrobial resistance; EVJ, economic value judgement; HVJ, health value judgement. Otieku E, et al. BMJ Open 2023;13:e065233. doi:10.1136/bmjopen-2022-065233 3 BMJ Open: first published as 10.1136/bmjopen-2022-065233 on 22 February 2023. Downloaded from http://bmjopen.bmj.com/ on April 18, 2023 by guest. Protected by copyright. Open access Table 2 Participants’ sociodemographic characteristics Study sites Category Characteristics Overall N (%) KATH N (%) KBTH N (%) Alpha* Gender Female 527 (65.88) 249 (62.25) 278 (69.50) Male 273 (34.13) 151 (37.75) 122 (30.50) Total 800 (100.00) 400 (100.00) 400 (100.00) P<0.05† Age group (years) 18–19 27 (3.38) 11 (2.75) 16 (4.00) 20–29 192 (24.00) 81 (20.25) 111 (27.75) 30–39 269 (33.63) 135 (33.75) 134 (33.50) 40–49 198 (24.75) 102 (25.50) 96 (24.00) 50–59 95 (11.88) 61 (15.25) 34 (8.50) 60+ 19 (2.38) 10 (2.50) 9 (2.25) Total 800 (100.00) 400 (100.00) 400 (100.00) P<0.05† Completed level of schooling None 55 (6.88) 40 (10.00) 15 (3.75) Basic 337 (42.13) 173 (43.25) 164 (41.00) Secondary 246 (30.75) 124 (31.00) 122 (30.50) Tertiary 162 (20.25) 63 (15.75) 99 (24.75) Total 800 (100.00) 400 (100.00) 400 (100.00) P<0.001† Occupation type Formal 122 (15.25) 55 (13.75) 67 (16.75) Informal 557 (69.63) 287 (71.75) 270 (67.50) Not working 121 (15.13) 58 (14.50) 63 (15.75) P>0.05† Total 800 (100.00) 400 (100.00) 400 (100.00) Residential location Rural 333 (41.63) 245 (61.25) 88 (22.00) Urban 467 (58.38) 155 (38.75) 312 (78.00) Total 800 (100.00) 400 (100.00) 400 (100.00) P<0.001† Ever registered for health insurance Yes 573 (71.63) 260 (65.00) 313 (78.25) No 227 (28.38) 140 (35.00) 87 (21.75) Total 800 (100.00) 400 (100.00 400 (100.00) P<0.001† Currently have a valid health insurance Yes 491 (85.69 205 (78.85) 286 (91.37) No 82 (14.31) 55 (21.15) 27 (8.63) Total 573 (100.00) 260 (100.00) 313 (100.00) P<0.001† Type of valid health insurance in possession NHIS 436 (88.80) 186 (90.73) 250 (87.41) PHIS 55 (11.20) 19 (9.27) 36 (12.59) Total 491 (100.00) 205 (100.00) 286 (100.00) P>0.05† Economic status relative to others Best 20 (2.50) 11 (2.75) 9 (2.25) Better 249 (31.13) 150 (37.50) 99 (24.75) Good 195 (24.38) 119 (29.75) 76 (19.00) Worse 336 (42.00) 120 (30.00) 216 (54.00) Total 800 (100.00) 400 (100.00) 400 (100.00) P<0.001† Household size Mean (95% CI) 4.9 (4.7 to 5.0) 5.1 (4.8 to 5.4) 4.6 (4.4 to 4.8) P<0.05‡ Years of schooling Mean (95% CI) 10.8 (10.5 to 11.1) 10.2 (9.7 to 10.6) 11.4 (11.0 to 11.8) P<0.05‡ *Comparing differences in observation between study sites. †Derived from χ2 test for categorical variables. ‡Alpha derived from t-t est for count variables. KATH, Komfo Anokye Teaching Hospital; KBTH, Korle-B u Teaching Hospital; NHIS, National health insurance scheme; PHIS, Private health insurance scheme. and 3 (table 1). The questions were kept brief and admin- response bias.34 All participants gave written informed istered in a preferred language, mainly English, Akan and consent and kept copies for reference. Ga. The translation of the questionnaire into Akan or Ga language followed consensus by medical staff with the Statistical analysis relevant language competencies. The questions consid- Knowledge of the health and economic consequences ered the need to avoid technical language bias and no of antibiotic use was evaluated using the proportionate 4 Otieku E, et al. BMJ Open 2023;13:e065233. doi:10.1136/bmjopen-2022-065233 BMJ Open: first published as 10.1136/bmjopen-2022-065233 on 22 February 2023. Downloaded from http://bmjopen.bmj.com/ on April 18, 2023 by guest. Protected by copyright. Open access Figure 1 Participants’ knowledge of how antibiotics affect health. *Only 0.29% disagree to some extent and is invisible due to the approximation. ratings and their 95% CI for each category of response measure of sampling adequacy (KMO=0.85). A sampling and presented the result in a 1%–100% stacked bar. adequacy of 0.8 or more is recommended for EFA.35 36 Second, Further analysis of mean knowledge scores and 95% CI Eigenvalues generated from the EFA were assessed for their for each response are presented in the online supple- unique variance and communality. To preserve orthogonality, mental material. factor loadings from the EFA were subject to Varimax rota- For objective 2, the analysis was in phases. Phase 1 followed tion and those with factor loadings >0.4 plus a scale reliability the procedure used to analyse and report results for objective coefficient of 0.87 were extracted for inclusion in the multi- 1. Results of the mean scores and 95% CI of each of the 12 factor regression analysis (online supplemental table S1B,C). lists of HVJ and EVJ items/questions, disaggregated by study Though arbitrary rule, studies recommend that factor sites are presented in online supplemental file. In phase 2, we loadings >0.4 is statistically significant/reliable and must be refined the measure for HVJ and EVJ behaviours. We antic- retained.36 37 In phase 3, we analysed the factors affecting HVJ ipated that not all HVJ and EVJ items would influence the and EVJ behaviours through multifactor regression. We first use of antibiotics. To that end, we performed an exploratory undertook an analysis of the correlation between retained factor analysis (EFA) by first testing the assumption of collin- factor loading items from the EFA and numerically coded earity between the 12 HVJ and EVJ items/questions. Results sociodemographic variables (independent/explanatory vari- of the correlation matrix and the corresponding alpha values ables), that is, age, years of schooling, household size, gender (p<0.01 and p<0.05) are presented in online supplemental (male=1, female=2), occupation type (formal=1, informal=2, table S1A. The analysis includes Bartlett’s test for interrelat- not working=3), residential location (rural=1, urban=2), edness between items (p<0.001) and the Kaiser-M ayer- Olkin valid health insurance status (no=1, yes=2) and self-r ated Figure 2 Participants’ knowledge of the health implications of antibiotics resistance/AMR. Note: In the questionnaire, we used the synonym antibiotic resistance instead of AMR. AMR, antimicrobial resistance. Otieku E, et al. BMJ Open 2023;13:e065233. doi:10.1136/bmjopen-2022-065233 5 BMJ Open: first published as 10.1136/bmjopen-2022-065233 on 22 February 2023. Downloaded from http://bmjopen.bmj.com/ on April 18, 2023 by guest. Protected by copyright. Open access Figure 3 Participants’ knowledge of the economic implications of antibiotics resistance/AMR. *Approximately 1.0% of the participants agree to some extent; **About 6% of the participants disagree. Note: In the questionnaire, we used the synonym antibiotic resistance instead of AMR. AMR, antimicrobial resistance. economic status relative to others (best=1, better=2, good=3, All the analyses were performed with STATA analytical worse=4) (online supplemental table S2). The retained factor software V.14.0 (STATA) and Microsoft Excel to generate loading items of HVJ and EVJ constituted the dependent vari- graphs. ables for the regression analysis. To identify the best model fit with the least Akaike information and the error term, we performed a forward, backwards and bidirectional selection RESULTS of variables. For each regression model, we included a test of Descriptive heteroscedasticity-c onsistent SEs to rule out biases in residual A total of 800 adult outpatients from two hospitals (KBTH: values,38 and a check for omitted variable bias using the N=400, KATH: N=400) participated in this study. Female Ramsey test of powers of the fitted values (p>0.1). participants accounted for 65.9%. The mean years of Finally, we computed a χ2 test for non-p arametric cate- schooling, less the years spent in preschool, was 10.8 gorical variables (p<0.05) in module 6 questions for objec- years (95% CI 10.5 to 11.1) and subjects with no formal tive 3 and reported alpha values for statistical differences education accounted for 6.9%. Approximately 42% of the in reported AMR mitigation strategies between partici- participants lived in rural communities and about 89% pants with and without exposure to the intervention. had active health insurance to access healthcare when Figure 4 (Stated) influence of health value judgements on participant use of antibiotics. The response denotes ‘I use antibiotics if/when or anytime…’. *About 1% disagree, **Less than 0.5% disagree. 6 Otieku E, et al. BMJ Open 2023;13:e065233. doi:10.1136/bmjopen-2022-065233 BMJ Open: first published as 10.1136/bmjopen-2022-065233 on 22 February 2023. Downloaded from http://bmjopen.bmj.com/ on April 18, 2023 by guest. Protected by copyright. Open access Figure 5 (Stated) influence of economic value judgement (EVJ) on participant use of antibiotics The response denotes ‘I use antibiotics if/when or anytime…’. *About 1% disagree, **Less than 0.5% disagree. ill. Participants’ average household size was 4.9 people from work occasioned by AMR, (3) costs to carers of AMR (95% CI 4.7 to 5.0). Participants from Kumasi were more patients, (4) healthcare provider costs and (5) healthcare- rural with less schooling and lower insurance coverage but related costs for other patients. The result showed that considered themselves better off than others (table 2). more than half of the participants could not relate to how AMR affects provider costs (87% (95% CI 84% to Knowledge of how antibiotic use affects health 91%)), productivity loss/indirect cost (71% (95% CI 66% A total of 698 (87.3%) participants had heard/knew to 76%)), the cost to carers of AMR patients/societal cost about antibiotics and could self-i ndicate the health impli- (59% (95% CI 53% to 64%)), but knew that AMR could cations of antibiotic use. We gave them six simple- framed increase the out- of-p ocket costs of treatment (93% (95% standard statements on how antibiotics affect health and CI 90% to 96%)). asked them to indicate whether they agree or disagree. In addition, we found statistically significant differ- The result showed that participants had varying degrees ences in AMR knowledge between participants with and of knowledge on how antibiotic use affects human health. without formal education regarding how AMR affects When asked if antibiotics work against viral infections and the duration of illness and treatment (p<0.01), death whether antibiotics efficacy is better if the dosage is more (p<0.01), 3), length of hospital stays (p<0.05) and how than is prescribed, the proportion of participants who inappropriate use of antibiotics increase the risk of AMR correctly disagreed was 72% (95% CI 68% to 75%) and (p<0.05) (online supplemental table S3A). However, 47% (95% CI 44 to 51%), respectively (figure 1). there was no statistical difference in AMR knowledge Knowledge of the health and economic implications of AMR between males and females and between rural and urban Less than 40% (39.3%, n=314) of the participants said residents (online supplemental table S3B,C). Further, in they knew about antibiotics resistance or AMR, and we all categories of AMR knowledge questions, the propor- gave them 10 statements about the health and economic tion of the participants who responded wrongly was much implications of AMR and asked them to indicate whether similar across gender and residence, but slightly different they agreed or disagreed. The first five questions related between participants with and without formal education to knowledge of the health burden of AMR. We found (online supplemental table S3D). that most of the participants correctly indicated that AMR could affect the length of morbidity and treatment (64% How HVJ and EVJ influence antibiotic use (95% CI 59% to 69%)), hospital length of stay (59% (95% Figure 4 shows the list of items used to assess the influ- CI 53% to 64%)) and mortality (74% (95% CI 72% to ence of HVJ on antibiotic use. We posed seven HVJ ques- 77%)) and that inappropriate use of antibiotics increased tions to participants and found that they were more likely the risk of AMR (62% (95% CI 57% to 67%)). However, a to use antibiotics if (1) they had used the drug previously large minority—a quarter to one-t hird of respondents had to treat similar health conditions (70% (95% CI 67% to incorrect perceptions about the effect of AMR on health. 73%)); (2) the drug had been recommended by a known Finally, we found that 56% (95% CI 50% to 61%) of the person for the treatment of similar infection(s) (69% participants either disagreed or disagreed to some extent (95% CI 66% to 72%)); (3) the antibiotic was prescribed that AMR is a major health problem in Ghana (figure 2). by a health professional such as a doctor or pharmacist Regarding the economic implications of AMR (73% (95% CI 70% to 76%)); (4) the drug was advertised (figure 3), we asked the participants whether they agreed in the media for treatment of same illness (77% (95% CI that AMR increases the following: (1) out-o f- pocket cost 74% to 80%)) and (5) they have a bacterial infection such of treatment, (2) productivity loss/cost due to absence as a urinary tract infection (76% (95% CI 73% to 79%)). Otieku E, et al. BMJ Open 2023;13:e065233. doi:10.1136/bmjopen-2022-065233 7 BMJ Open: first published as 10.1136/bmjopen-2022-065233 on 22 February 2023. Downloaded from http://bmjopen.bmj.com/ on April 18, 2023 by guest. Protected by copyright. Open access Table 3 Regression results identifying sociodemographic factors influencing HVJ and EVJ items for antibiotic use Overall KATH KBTH HVJ EVJ HVJ EVJ HVJ EVJ β SE β SE β SE β SE β SE β SE Age 0.05 0.04 0.04 0.04 0.04 0.06 0.02 0.06 0.02 0.04 0.01 0.05 Female sex −0.21* 0.08 −0.16 0.08 −0.03 0.12 0.01 0.13 −0.36* 0.10 −0.30* 0.10 Years of schooling −0.05* 0.01 −0.04* 0.01 −0.06* 0.01 −0.05* 0.01 −0.04* 0.01 −0.04* 0.01 Occupation: Informal 0.10 0.08 0.08 0.11 0.08 0.15 0.04 0.17 0.13 0.10 0.07 0.15 Not working 0.06 0.12 −0.08 0.12 0.09 0.19 −0.14 0.19 0.06 0.16 −0.04 0.18 Residential location (Urban) −0.15 0.08 −0.22† 0.09 0.05 0.13 −0.06 0.14 −0.27† 0.13 −0.27† 0.13 Household size 0.02 0.01 0.01 0.02 0.01 0.02 −0.01 0.02 0.04 0.03 −0.04 0.03 Ever registered for HS (yes) – Currently have a valid HS (yes) – Type of valid HS in possession: NHIS 0.05 0.09 −0.64 0.13 0.05 0.23 0.08 0.22 0.06 0.11 −0.16 0.17 Economic status relative to others 0.09† 0.04 0.06 0.04 0.11 0.07 0.06 0.07 0.10 0.05 0.09 0.05 Model fit (%) 22.7 18.1 20.7 17.4 26.9 20.0 HVJ and EVJ are the health and economic value judgement items (dependent variables) with the best model fit in the regression analysis. For HVJ, the item used denotes whether participants agree to use antibiotics if/when they have a bacterial infection such as a urinary tract infection. The EVJ item denotes whether participants agree to use antibiotics if the drug can make him/her return to work as soon as possible. In both instances, the responses were coded as 1=agree, 2=agree to some extent, 3=disagree to some extent and 4=disagree. *P<0.01. †P<0.05. EVJ, economic value judgment; HS, health insurance; HVJ, health value judgement; KATH, Komfo Anokye Teaching Hospital; KBTH, Korle-B u Teaching Hospital; NHIS, national health insurance scheme. Interestingly, we found some AMR knowledge gaps. For use antibiotics if the drug would make them return to instance, participants would not use antibiotics anytime work as soon as possible. In the overall sample, we found they experienced symptoms like nasal congestion and the factors predicting HVJ for antibiotic use to include headache (87% (95% CI 84% to 90%)) but would use female sex (Co-e ff. −0.21; p<0.01), years of schooling (Co- them anytime if they had a sore throat, stomach ulcers eff. −0.05; p<0.01) and economic status of participants and infections like a cold and the influenza (76% (95% relative to others (Co-e ff. 0.09; p<0.05), all accounting CI 73% to 79%)), which may be inappropriate without for about 20% of the model fit. Regarding EVJ, signifi- proper diagnosis and prescription. Between study sites, cant predictors in descending order of magnitude to the trend in how HVJ and EVJ influenced the use of anti- the model fit include years of schooling (Co- eff. −0.04; biotics was similar (online supplemental table S4). p<0.01) and urban residence (Co-e ff. −0.22; p<0.05), From a scale reliability coefficient >0.87, we found two suggesting that, for example, a unit decrease in partici- out of five EVJ items may strongly influence participants pant years of schooling may cause a 4% increase in the to use antibiotics (figure 5). For example, 75% (95% CI chances that they would press to get antibiotics if they 72% to 78%) of the participants indicated they would use believed the drug would heal them quickly and make antibiotics anytime if the drug could make them recover them able to resume productive work fast. quickly and resume productive work, and if they could In the stratified model, the only predictor of HVJ and afford the prescription by a certified healthcare profes- EVJ for antibiotic use in the KATH sample was years of sional (56% (95% CI 52% to 60%)). Again, possession of schooling (Co- eff. −0.06; p<0.01), whereas female sex, health insurance was a determining factor for antibiotic years spent in school and urban residence emerged as use for less than half of the participants (49% (95% CI significant predictors of both HVJ and EVJ in the KBTH 45% to 53%)). strata (table 3). Factors predicting HVJ and EVJ for antibiotic use AMR mitigation strategy Factor loadings extracted from the EFA were regressed We posed four questions related to AMR mitigation with the demographic characteristics of participants strategy to all study participants. For the control group to find out which of the demographics influenced how (group A), less than half (42.5% (95% CI. 36.9% to HVJ and EVJ affected the use of antibiotics among our 46.6%)) indicated that antibiotic prescription should be study participants. The HVJ item with the best model fit more restrictive, and 49.5% (95% CI. 44.6% to 54.4%) was whether participants agreed to use antibiotics if they suggested they would be willing to pay slightly more for a had a bacterial infection such as a urinary tract infec- health treatment strategy that reduced their risk of AMR tion, whereas the EVJ item was whether they agreed to (table 4). Among participants in the intervention group 8 Otieku E, et al. BMJ Open 2023;13:e065233. doi:10.1136/bmjopen-2022-065233 BMJ Open: first published as 10.1136/bmjopen-2022-065233 on 22 February 2023. Downloaded from http://bmjopen.bmj.com/ on April 18, 2023 by guest. Protected by copyright. Open access Table 4 Patient attitudes towards AMR mitigation, stratified health and economic implications of antibiotic use and by level of information we asked them to indicate their preference for each. The result suggested that most participants without exposure The patient was provided with additional information on the health and economic to the intervention preferred to receive AMR information consequences of AMR (N, %) at the point of care, that is, in a clinic/hospital (73.5%) or No (group A) Yes (group B) Total Alpha* licensed pharmacy/drug stores (83.5%) or via television broadcast (91.8%). Compared with the control group, a Antibiotic prescription should be: higher proportion of the participants in the intervention Less restrictive 63 35 98 group preferred point of care access to AMR information 15.75 8.75 12.25 (93% (95% CI 90.5% to 95.5%)), while a lower propor- Unchanged 170 81 251 tion (84.3% (95% CI 82.5% to 86.1%)) preferred the 42.50 20.25 31.37 same information via television broadcast. More restrictive 167 284 451 P<0.01 Between groups, we observed a statistically significant 41.75 71.00 56.38 difference in all measurements, that is, antibiotic prescrip- Total 400 400 800 tion (p<0.01), willingness to pay slightly more for health 100.00 100.00 100.00 treatment to reduce the risk of AMR (p<0.01) (online Willingness to pay more for health treatment that reduced the supplemental tables S5,6) and preference for AMR infor- risk of AMR mation sources (p<0.01; p<0.05) (table 5). Yes 198 322 280 49.50 80.50 35.00 No 202 78 520 P<0.01 DISCUSSION 50.50 19.50 65.00 The study showed that providing participants with point- of-c are information about the health and economic impli- Total 400 400 800 cations of inappropriate antibiotic use may yield positive 100.00 100.00 100.00 attitudinal changes for better use of antibiotics and accep- Note. Group A—participants not exposed to the intervention (n=400); group tance of restrictive access to antimicrobials. Participants B—participants exposed to the intervention (n=400). *Indicate alpha for statistically significant difference between groups. in the intervention group were willing to pay slightly AMR, antimicrobial resistance. more for health treatment to avert the risk of AMR in the medium to long term than those in the control group. Thus, if patients have more information regarding their (group B), a considerably higher proportion of 71% safety and do value their health, they may be likely to (95% CI 66.6% to 75.4%) suggested more restrictive anti- protect their health, provided they have the means. biotic prescription and 80.5% (95% CI 78.5% to 82.5%) Our findings are consistent with others such as a WHO were willing to pay slightly more for a health treatment multicountry study on antibiotic resistance awareness in strategy that reduced their risk of AMR in the short term 12 countries, including Russia, South Africa, Nigeria and to medium term. Indonesia. In that study, 87% of the participants knew Eight possible sources of acquiring AMR information when to use antibiotics and 72% understood that inap- were presented to the intervention and control group to propriate antibiotic use expedites AMR in humans and keep updated with and improve their knowledge of the prolongs morbidity and treatment of AMR patients.33 A Table 5 Preferred sources of AMR information by participants with and without exposure to the intervention The patient was provided with additional information on the health and economic consequences of AMR Description Overall N (%) No (group A) Yes (group B) Alpha Point of healthcare delivery, that is, clinic and hospital (yes) 667 (83.4) 294 (73.5) 373 (93.3) P<0.01 Licensed pharmacy and drug store (yes) 706 (88.3) 334 (83.5) 372 (93.0) P<0.01 Community information system (yes) 224 (28.0) 141 (35.3) 83 (20.8) P<0.01 Print media* (yes) 128 (16.0) 52 (13.0) 76 (19.0) P<0.05 Digital/social media platforms† (yes) 426 (53.3) 230 (57.5) 196 (49.0) P<0.05 Television broadcast and information sharing (yes) 704 (88.0) 367 (91.8) 337 (84.3) P<0.01 Radio broadcast (yes) 255 (31.9) 206 (51.5) 49 (12.3) P<0.01 School textbooks (should be taught in school) (yes) 619 (77.4) 289 (72.3) 330 (82.5) P<0.01 Note: Group A—participants not exposed to the intervention (n=400); group B—participants exposed to the intervention (n=400). *Book covers, newspapers, fliers, etc. †Voice messages, short message service, Facebook, Twitter. AMR, antimicrobial resistance. Otieku E, et al. BMJ Open 2023;13:e065233. doi:10.1136/bmjopen-2022-065233 9 BMJ Open: first published as 10.1136/bmjopen-2022-065233 on 22 February 2023. Downloaded from http://bmjopen.bmj.com/ on April 18, 2023 by guest. Protected by copyright. Open access study in China found substantial evidence of misguided more information and stated their preferences regarding knowledge regarding the use of antibiotics for viral various information channels. Some of these channels infections such as colds and influenza39; these results are may be costly, but the benefit of AMR information inter- comparable to ours. It is possible these misconceptions ventions may outweigh the costs. can explain inappropriate antibiotic use in Ghana and The strength of our study is the use of a validated elsewhere.6 In addition to the similarities, we observed instrument for data gathering, a reliable data source and some differences compared with other studies.8 40 We a rigorous methodology that rules out several sources of found that the proportion of our participants with the bias and supports external validity and generalisability of misconception that antibiotics efficacy is better if the the result. Again, the study involves a simple intervention drug is new and costly was about two-t hirds and higher that yielded the expected results and can encourage the than in the UAE and Italy.8 appropriate use of antibiotics in the population. One interesting observation concerned inconsistency One limitation of this study was the possibility of bias in antibiotic use knowledge responses. Ideally, the same caused by a perceived need to please the health staff proportion of participants agreeing that antibiotics work performing both the interviews and the intervention. against bacterial infection only would also disagree that This bias would have been strong if the same person that antibiotics only work against viral infections. However, interviewed the patients was the one who treated them. A that was not the case in our study, indicating some confu- second limitation is the possibility of selection bias caused sion about when to use antibiotics appropriately. by the need to adhere to COVID- 19 safety protocol and We found commonalities in HVJ behaviours influ- ethics approval guidelines. As a result, we replaced 11 encing antibiotic use in other LMIC settings. For participants and postponed 8 interviews to minimise the example, a systematic review in LMIC showed that most risk of physical contact. When necessary, patients unsure people, including the educated, used antibiotics based on about the difference between antibiotics and other experience using the same drug41 or recommendations drugs were given examples of antibiotics as illustrations. by family/friends.12 33 42 Our data suggested that antibi- However, we cannot be completely sure that patients when otic consumption among patients in Ghana was not only assessing statements about antibiotics, actually have the influenced by HVJ but also by EVJ. The multifactor and right group of drugs in mind. Even if they have a broader stratified regression analyses showed that HVJ and EVJ group of drugs in mind, it would still be problematic if behaviours that may predict appropriate antibiotic use they thought antibiotics could cure both bacterial and were influenced predominantly by participants’ years viral infections. In any case, we have no reason to believe of schooling. Thus, the more educated they were, the that any misconceptions about antibiotics versus other easier it was for them to assimilate information regarding drugs would differ between the intervention and control antibiotic use. Our finding is congruent with a recently groups, so this is unlikely to bias the comparison between published study on the drivers of antibiotic use and the two groups. Also, we detected a few missing data for misuse in Australia, which reached a similar conclusion six participants. However, a check for omitted variable that knowledge gained through formal education has a bias using the Ramsey test of powers of the fitted values moderating effect on behaviours for antibiotic use.42 and heteroscedasticity- consistent standard errors showed To a large extent, the results of this study are in line no significant effect of missing values on study outcomes. with our hypotheses. For example, we hypothesised that demand for antibiotics was influenced by EVJ, like pricing, co- payments and the need to resume productive CONCLUSION work. Of the 11 factors extracted from the EFA, 6 were Among study participants, there was a general under- HVJ and 5 were EVJ, suggesting a wide range of health standing of when and why to use antibiotics, as well as the and economic value factors influenced antibiotic use. implications of AMR. Nonetheless, there was no attrib- However, EVJ was a better predictor for antibiotic use. utable change in attitude towards antibiotic use among We argue that although our study participants seemed study subjects. Our data showed that both HVJ and EVJ to have a general knowledge about antibiotic use and influenced antibiotic use, but the latter was a better resistance, this knowledge did not seem to matter much predictor among participants when deciding which anti- for some participants when considering the use of antibi- biotic to use. Creating public awareness of the health and otics when ill. Therefore, we agree with prior studies6 10 43 economic implications of AMR at the point of care may suggesting the need for an intervention to promote atti- lead to a behavioural change towards more appropriate tudinal changes in antibiotic use. A solution may be to antibiotic use to mitigate AMR. provide more persuasive information which in addition to the health consequences of AMR must include the Author affiliations economic implications of inappropriate antibiotic use. 1Economics Division, Institute of Statistical, Social, and Economic Research (ISSER), We demonstrated that information at the point of care has University of Ghana, Legon, Ghana2Department of Public Health, Aarhus University, Aarhus, Denmark an immediate effect on attitude, but we cannot conclude 3Department of Medical Microbiology, University of Ghana Medical School, Accra, whether this is sustained over time and to what extent it Ghana changes actual behaviour. Patients indicated they wanted 4Laboratory Services Directorate, Komfo Anokye Teaching Hospital, Kumasi, Ghana 10 Otieku E, et al. BMJ Open 2023;13:e065233. doi:10.1136/bmjopen-2022-065233 BMJ Open: first published as 10.1136/bmjopen-2022-065233 on 22 February 2023. Downloaded from http://bmjopen.bmj.com/ on April 18, 2023 by guest. Protected by copyright. Open access 5Department of Clinical Microbiology, Kwame Nkrumah University of Science and 5 Broniatowski DA, Klein EY, May L, et al. Patients’ and clinicians’ Technology, Kumasi, Ghana perceptions of antibiotic prescribing for upper respiratory infections 6ISIM, University of Copenhagen, Copenhagen, Denmark in the acute care setting. Med Decis Making 2018;38:547–61. 7 6 Afari- Asiedu S, Hulscher M, Abdulai MA, et al. Every medicine is Department of Clinical Microbiology, Rigshospitalet, Kobenhavn, Denmark medicine; exploring inappropriate antibiotic use at the community level in rural ghana. BMC Public Health 2020;20:1103. Acknowledgements The authors acknowledge the support of the team of data 7 Bekoe SO, Ahiabu M- A, Orman E, et al. Exposure of consumers collectors at the Komfo Anokye and Korle Bu Teaching Hospitals. We appreciate the to substandard antibiotics from selected authorised and support of the in- charge nurses and matrons at the various outpatient departments unauthorised medicine sales outlets in ghana. Trop Med Int Health who facilitated access to the patients during recruitment. 2020;25:962–75. 8 Jairoun A, Hassan N, Ali A, et al. Knowledge, attitude and practice of Contributors Conceptualisation: EO, APF, JK and UE; Data curation and antibiotic use among university students: a cross sectional study in investigation: EO, A- KL and AKO- O; Methodology: EO, APF, JK and UE; Formal UAE. BMC Public Health 2019;19:518. analysis and writing original draft: EO; Project Administration: EO, APF, JK and UE; 9 Effah CY, Amoah AN, Liu H, et al. A population- base survey on Supervision: APF, JK, UE; Validation: A- KL, AKO- O, APF, JK and UE; Visualisation: EO; knowledge, attitude and awareness of the general public on Writing reviews and editing: A- KL, AKO- O, APF, JK and UE; Guarantor: EO. antibiotic use and resistance. Antimicrob Resist Infect Control 2020;9:105. Funding A research training supplement was awarded to the corresponding 10 Mok CZ, Sellappans R, Ee Loo JS. The prevalence and perception author by the Graduate School of Health at Aarhus University to cover the cost of of self- medication among adults in the klang valley, malaysia. Int J subsistence as a PhD student. Grant/award number: N/A. Pharm Pract 2021;29:29–36. 11 STOECKLE JD, ZOLA IK, DAVIDSON GE. ON going to see the Competing interests None declared. doctor, the contributions of the patient to the decision to seek Patient and public involvement Patients and/or the public were involved in the medical aid. A selective review. J Chronic Dis 1963;16:975–89. 12 Ackermann Rau S, Sakarya S, Abel T. When to see a doctor for design, or conduct, or reporting, or dissemination plans of this research. Refer to common health problems: distribution patterns of functional health the Methods section for further details. literacy across migrant populations in Switzerland. Int J Public Health Patient consent for publication Consent obtained directly from patient(s). 2014;59:967–74. 13 Bosley H, Henshall C, Appleton JV, et al. A systematic review Ethics approval The study received ethics approval from the Komfo Anokye to explore influences on parental attitudes towards antibiotic and Korle Bu Teaching Hospitals with approval registration numbers KATH-I RB/ prescribing in children. J Clin Nurs 2018;27:892–905. AP/030/21 and KBTH/MD/93/21, respectively. 14 Schweim H, Ullmann M. Media influence on risk competence in self- medication and self- treatment. Ger Med Sci 2015;9:13. Provenance and peer review Not commissioned; externally peer reviewed. 15 Kim Y, Kornfield R, Shi Y, et al. Effects of televised direct- to- Data availability statement Data are available on reasonable request. The consumer advertising for varenicline on prescription dispensing in the united states, 2006-2 009. Nicotine Tob Res 2016;18:1180–7. anonymised data used for this study is available on reasonable request to the 16 Kong LS, Islahudin F, Muthupalaniappen L, et al. Knowledge and corresponding author due to ethics review guidelines. expectations on antibiotic use among older adults in malaysia: A Supplemental material This content has been supplied by the author(s). It has cross- sectional survey. Geriatrics (Basel) 2019;4:61. not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been 17 Chow SKY, Tao X, Zhu X, et al. How socioeconomic, health seeking peer- reviewed. Any opinions or recommendations discussed are solely those behaviours, and educational factors are affecting the knowledge and use of antibiotics in four different cities in Asia. Antibiotics (Basel) of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and 2021;10:1522. responsibility arising from any reliance placed on the content. Where the content 18 Abellan- Perpiñan JM, Bleichrodt H, Pinto- Prades JL. The predictive includes any translated material, BMJ does not warrant the accuracy and reliability validity of prospect theory versus expected utility in health utility of the translations (including but not limited to local regulations, clinical guidelines, measurement. J Health Econ 2009;28:1039–47. terminology, drug names and drug dosages), and is not responsible for any error 19 Bleichrodt H, Abellan- Perpiñan JM, Pinto-P rades JL, et al. and/or omissions arising from translation and adaptation or otherwise. 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