A
Health workers' knowledge, perceptions, and practices relating to antimicrobial use and resistance in Cross River State, NigeriaAkaninyene Asuquo Otu1,2,3*, Obiageli Chiezey Onwusaka3,4*, Ubong Aniefiok Udoh5, Ugbe Maurice-Joel Ugbe3,4, Emmanuel Edet Effa3,6, Joel Inyang3,4, Bassey Ebenso1, Fatumo Abdi2, Chikezie Okwesileze7, Bassey Ewa Ekeng5, Ita Okokon Ita5, John Walley1
1.
Nuffield Centre for International Health and Development, University of Leeds, Leeds, United Kingdom
2.
Faculty of Public Health, 4 St. Andrews Place, London, NW1 4LB, United Kingdom
3.
Foundation for Healthcare Innovation and Development (FHIND), Calabar, Cross River State, Nigeria.
4.
Department of Public Health, Faculty of Allied Medical Sciences, College of Medical Sciences, University of Calabar, Calabar, Cross River State, Nigeria.
5.
Department of Medical Microbiology and Parasitology, Faculty of Medicine, College of Medical Sciences, University of Calabar, Calabar, Cross River State, Nigeria.
6.
Department of Internal Medicine, University of Calabar, Calabar, Cross River State, Nigeria.
7.
University of Calabar Teaching Hospital, Calabar, Cross River State, Nigeria.
* These authors are equal contributors to this work and designated as co-first authors.
*Corresponding author:
Akaninyene Asuquo Otu
Nuffield Centre for International Health and Development
University of Leeds, Leeds
United Kingdom
E-mail: akanotu@yahoo.com
ORCID: 0000-0002-6009-2707
Running title
Antimicrobial use and resistance among health workers in Cross River State, Nigeria
Key words:
Health workers
Antimicrobial use
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Antimicrobial resistanceAntimicrobial stewardship
Cross River State
Abstract
Introduction:
Antimicrobial resistance (AMR) is a serious global health issue with health workers (HWs) being central to addressing this issue. This study aimed to assess the knowledge, perceptions, and practices of HWs regarding antimicrobial use/AMR in Cross River State, Nigeria.
Methodology
: This cross-sectional study involved 431 HWs in randomly selected healthcare facilities (13 primary and 23 secondary facilities) in Cross River State. Data were collected using a structured questionnaire. Descriptive and inferential analysis were performed using SPSS version 23.
Results
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Of 431 HWs approached, 427 (99.1%) responded. Nurses comprised the largest group (27%), followed by medical doctors (18%). Overall, 60% (M = 18.2, SD 3.4) demonstrated good knowledge (defined as ≥ 80% or ≥ 19/24 points), 30% (M = 14.5, SD 2.1) moderate (50–79% or 12–18 points), and 10% (M = 9.8, SD 1.7) poor knowledge (< 50% or < 12 points) of AMR and antimicrobial use. Perceptions were positive, with 81% (M = 128.6, SD 15.2) categorized as good (defined as > 105/175 points). Practices were good in 65% (M = 25.3, SD 4.1) of respondents (defined as ≥ 70% or ≥ 23/33 points), though gaps included prescribing due to patient pressure (47%) and stock availability (66%). Significant associations were found between professional role and years of experience with knowledge (p = 0.003 and p = 0.013) and practices (p = 0.008 and p = 0.012), but not perceptions.Conclusion
A
HWs in Cross River exhibited predominantly good knowledge and perceptions of AMR, but practices, though good, revealed areas for improvement, particularly in guideline adherence and resistance to external pressures.A
A
Introduction
Antimicrobial resistance (AMR) represents one of the most pressing threats to global health security, rendering once-effective treatments ineffective against common infections and potentially reversing decades of medical progress [1, 2]. The World Health Organization (WHO) has recognized AMR as a global public health concern, as it makes infectious diseases harder to treat and results in increased mortality rates [3]. Recent estimates have revealed that bacterial AMR directly caused 1.27 million deaths in 2019, and indirectly caused 4.95 million deaths, with projections estimating 1.9 million attributable and 8.22 million associated deaths annually by the year 2050 unless addressed [3]. This crisis is exacerbated in low- and middle-income countries (LMICs), due to low infection prevention and control, over-the-counter availability of antibiotics, low diagnostic capacity driving the emergence of resistance and poor national surveillance systems coverage in African countries [4, 5]. Apart from health impacts, AMR is predicted to exacerbate economic costs, with the potential of driving millions of individuals into poverty due to prolonged illness and higher healthcare costs [6, 7].
A
In Nigeria, AMR is worse due to widespread antimicrobial misuse in animals, agriculture and humans, with sub-optimal antimicrobial prescription in healthcare facilities and low community awareness [8–10]. Although national surveillance data remain limited, available reports indicate high resistance among priority pathogens e.g., MRSA and ESBL-producing Enterobacteriaceae in Nigeria. In 2019, Nigeria had an estimated 263,000 deaths associated with bacterial AMR. [8]. The drivers of AMR are improper dosing regimens, self-treatment, and poor sanitation, which encourage the spread of resistance [8]. Nigeria's National Action Plan for AMR (2024–2028) calls for multisectoral interventions, but gaps in implementation such as a lack of proper training for health workers continue to exist [11].Health workers (HWs), such as doctors, nurses, pharmacists, and community health extension workers (CHEWs), play crucial roles in preventing AMR through appropriate prescribing, patient counseling, and compliance with antimicrobial stewardship (AMS) principles [12]. In LMICs like Nigeria, where most antibiotic prescriptions are made in primary care units and HWs' knowledge, attitudes, and practices drive resistance patterns [13]. Levels of awareness in Nigeria are low due to resource and educational deficits. A national survey showed that half (56%) of the 482 respondents were aware of the term AMR, but only 8.3% of them had good knowledge of AMR [14]. While a study in Delta State of Nigeria reported that 58% of 420 adult respondents had poor awareness of AMR [15].
This study evaluates these domains among HWs in Cross River State (CRS) to identify gaps and suggest evidence-based strategies for enhancing AMS in resource-limited situations.
Methodology
Study setting
CRS has a population of 3,866,300 people who are served by 1,028 publicly owned health facilities made up of 3 tertiary level facilities, 16 secondary level facilities (general hospitals) and 909 primary level facilities comprising 722 health posts, 269 Primary Health Centres (PHCs) and 18 comprehensive health centres (CHCs). Approximately 70% of clients in CRS access care from public facilities where antibiotics are prescribed by doctors, nurses and community health extension workers (CHEWs) [16]. Cross River State is divided into North, South and Central zones, each with six local government areas. These LGAs are further divided into political wards and then communities.
Study design
A cross-sectional survey was conducted from November to December 2023 in Cross River State, Nigeria. HWs from primary, secondary and tertiary healthcare facilities were selected using stratified random sampling. A structured questionnaire, validated through a pilot study, was used for data collection. The questionnaire comprised sections on demographics, knowledge of AMR, perceptions about its impact, and practices related to antimicrobial use.
Sampling Procedure
In the North and South senatorial zones, two out of six Local Government Areas (LGAs) were randomly selected, while in the more populous Central zone three out of six LGAs were selected. ‘The three senatorial zones in the study area were used, with stratified sampling techniques categorizing LGAs into different strata and a simple random sampling technique selecting LGAs from each stratum. Healthcare facilities were stratified into primary, secondary, and tertiary levels. A simple random sampling technique was used to select four Primary Health Centers (PHCs) from the Northern and Southern senatorial districts, and five PHCs from the Central senatorial district, making a total of thirteen PHCs. Seventeen secondary-level facilities were randomly picked from the forty-four facilities in the selected LGAs. One of the three tertiary healthcare facilities was purposively selected, as it is the largest and most representative of tertiary care in the state, ensuring inclusion of high-level HWs. Figure 1 below provides a diagrammatic view of the procedure.
Fig. 1
Diagrammatic presentation of the sampling procedure
Survey administration
In each healthcare facility, the respondents were approached and informed about the study objectives. We minimised social desirability bias by training independent data collectors to use a neutral, standardised script and non-leading probes. Interviews were conducted in a private area without supervisors present. Participants were assured of anonymity and told clearly that responses would not affect employment or supervision. No names or personal identifiers were collected questionnaires carried only study identification numbers. Sensitive items were read verbatim with neutral wording and respondents could point to response options on a card.
Structured questionnaire was interviewer-administered to each consenting healthcare practitioner, and entered directly into open data kit collect (ODK). In cases where respondents experienced challenges with internet access, the survey was administered to them using a paper-based questionnaire, which was later entered into the electronic app. Study participation was explained to the respondents to be anonymous, confidential and voluntary, and the respondents could withdraw at any time. The data collection process was completed within a three-week period.
Method of Data Analysis
Data were entered into Microsoft Excel 2016 and checked for completeness. Data were then transferred to Statistical Products and Service Solutions (SPSS) Version 23 for analysis. Descriptive statistics (means, standard deviations, and frequency tables) and inferential statistics were used. Twenty-four knowledge questions were assessed with ‘yes’ or ‘no’ responses, where ‘yes’ indicated the correct answer and ‘no’ an incorrect answer. Each correct response scored 1, and incorrect responses scored 0, with a minimum score of 0 and a maximum of 24. Using a modified Bloom’s cut-off [17], knowledge was categorized as follows: ≥80% (≥ 19/24 points) as “good knowledge,” 50–79% (12–18 points) as “moderate knowledge,” and < 50% (< 12 points) as “poor knowledge.” The 80% cut-off assumes HWs have access to AMR training and information sources, reflecting an expectation of high proficiency in this critical area for healthcare professionals.
Perceptions were assessed using a 35-item questionnaire with a 5-point Likert scale: Strongly disagree (1), Disagree (2), Neutral (3), Agree (4), and Strongly agree (5). The minimum and maximum perception scores were 35 and 175, respectively. Items 14, 23, 25, 26, 33, and 35 were reverse-coded for consistent directionality, with higher scores indicating positive perceptions toward antimicrobial stewardship. A midpoint score of 105 was used to categorize perceptions as “good” (> 105) or “poor” (≤ 105).
Practices were evaluated using a 33-item questionnaire. Appropriate practices scored 1, while inappropriate or non-practiced actions scored 0. A Bloom’s cut-off of ≥ 70% (≥ 23/33 points) denoted “good practices,” and < 70% (< 23 points) denoted “poor practices.” The lower threshold accounts for systemic barriers like resource constraints and patient pressure. Chi-square tests of independence were used to assess associations between categorical variables, with Fisher’s exact test applied when expected cell counts were violated. The significance threshold was set at α = 0.05.
Results
Demographic Characteristics of Respondents
Of 431 HWs approached, 427 responded, thus yielding a 99% response rate. Table 1 shows Nurses as the largest group (27%), followed by medical doctors (18%). Those with 5–9 years of experience of working in as HWs were the majority in the study (28%). Respondents with greater than 20 years of experience were the least represented in the study.
Variables | Frequency (n = 427) | Percent % |
|---|---|---|
Role | ||
Pharmacist | 28 | 6.5 |
Nurse | 116 | 27.2 |
Medical Microbiologist | 8 | 1.9 |
Pharmacy Technician | 9 | 2.1 |
Medical Doctor | 75 | 17.6 |
Health Assistant | 5 | 1.2 |
CHEW | 28 | 6.5 |
Lab Scientist | 20 | 4.7 |
Junior Community Health Extension Worker | 6 | 1.4 |
Health Supervisor | 2 | 0.5 |
Years of experience | ||
Less than 1 year | 30 | 7.0 |
1–4 years | 100 | 23.4 |
5–9 years | 120 | 28.1 |
10–14 years | 90 | 21.1 |
15–20 years | 60 | 14.1 |
Greater than 20 years | 27 | 6.3 |
HWs’ Knowledge of Antimicrobial Use and AMR
Table 2 shows knowledge question responses. A significant majority (95.0%, n = 406) correctly identified that antibiotics are effective against bacteria, and 92.0% (n = 393) recognized that antibiotics are not effective against viruses. Additionally, 89.9% (n = 384) accurately defined antimicrobial resistance, and 92.0% (n = 393) acknowledged that bacteria can develop resistance to antimicrobials. However, knowledge gaps were evident in certain areas, with 20.8% (n = 89) thinking parasites can develop resistance to antibiotics. Regarding AMR causes, 88.1% (n = 376) identified overuse of antibiotics, and 86.0% (n = 367) recognized poor infection prevention and control as contributors.
A
Strategies to tackle AMR, such as developing institutional guidelines (95.0%, n = 406) and implementing infection prevention and control measures (88.1%, n = 376), were widely acknowledged. However, only 65.1% (n = 278) recognized the importance of investing in new medicines. Figure 2 shows that 60.0% had good knowledge, 30.0% moderate, and 10.0% poor knowledge.Item | Yes N (%) | No N (%) |
|---|---|---|
1. Antibiotics are effective against bacteria | 406 (95.0) | 21 (5.0) |
2. Antibiotics are effective against viruses | 34 (8.0) | 393 (92.0) |
3. Antibiotics are effective against fungi | 43 (10.1) | 384 (89.9) |
4. Antibiotics are effective against parasites | 51 (11.9) | 376 (88.1) |
5. Definition of antimicrobial resistance correctly answered | 384 (89.9) | 43 (10.1) |
6. Bacteria can become resistant to antimicrobials | 393 (92.0) | 34 (8.0) |
7. Viruses can become resistant to antimicrobials | 85 (19.9) | 342 (80.1) |
8. Fungi can become resistant to antimicrobials | 350 (82.0) | 77 (18.0) |
9. Parasites can become resistant to antimicrobials | 338 (79.2) | 89 (20.8) |
10. Humans can become resistant to antimicrobials | 34 (8.0) | 393 (92.0) |
11. Animals can become resistant to antimicrobials | 384 (89.9) | 43 (10.1) |
12. Causes of AMR: Poor infection prevention and control | 367 (86.0) | 60 (14.0) |
13. Causes of AMR: Inadequate hand hygiene | 363 (85.0) | 64 (15.0) |
14. Causes of AMR: Use of antibiotics | 351 (82.2) | 76 (17.8) |
15. Causes of AMR: Overuse of antibiotics | 376 (88.1) | 51 (11.9) |
16. Tackling AMR: Surveillance | 342 (80.1) | 85 (19.9) |
17. Tackling AMR: Public awareness | 346 (81.0) | 81 (19.0) |
18. Tackling AMR: Healthcare professional training | 350 (82.0) | 77 (18.0) |
19. Tackling AMR: Infection Prevention and Control | 376 (88.1) | 51 (11.9) |
20. Tackling AMR: Antimicrobial Stewardship | 363 (85.0) | 64 (15.0) |
21. Tackling AMR: Investment in new medicines | 278 (65.1) | 149 (34.9) |
22. Tackling AMR: Development of institutional guidelines | 406 (95.0) | 21 (5.0) |
23. Tackling AMR: Audit and feedback | 307 (71.9) | 120 (28.1) |
24. Correctly ordered the WHO antibiotic categories (Access, Reserve, Watch) order of use | 342 (80.1) | 85 (19.9) |
Fig. 2
Knowledge level of HWs on antimicrobial use and AMR in Cross River State
HWs’ Perceptions on Antimicrobial Use and AMR
Table 3 shows perceptions of the respondents on AMR and antimicrobial use. A notable 64.4% (n = 275) either agreed (47.3%, n = 202) or strongly agreed (17.1%, n = 73) that they are worried about AMR, and 75.4% (n = 322) agreed or strongly agreed that AMR is an important issue in their daily practice. Additionally, 54.1% (n = 231) believed their actions could protect antimicrobial effectiveness, and 79.2% (n = 338) felt that everyone can promote AMR awareness.
A
Compliance with prescribing guidelines was seen as effective in preventing AMR by 63.2% (n = 270), though only 36.5% (n = 156) strongly agreed that guideline compliance prevents AMR. A
Hand hygiene was strongly endorsed as a preventive measure, with 55.5% (n = 237) agreeing or strongly agreeing. However, confidence in challenging inappropriate prescribing was low, with 65.1% (n = 278) strongly disagreeing that they felt confident doing so. Furthermore, 60.2% (n = 257) strongly agreed that easy access to antibiotics without a prescription contributes to AMR, and 89.0% (n = 380) agreed or strongly agreed that AMR is a significant problem in their hospital, underscoring localized concerns about AMR prevalence and management. A total of 347 (81.4%) had good perceptions while 80 (18.6%) had perceptions on antimicrobial use and AMR among HWs.Items | SD n (%) | D n (%) | N n (%) | A n (%) | SA n (%) |
|---|---|---|---|---|---|
1. I am worried about antimicrobial resistance | 16 (3.7) | 118 (27.6) | 18 (4.2) | 202 (47.3) | 73 (17.1) |
2. AMR is an important issue in my daily practice | 39 (9.1) | 1 (0.2) | 65 (15.2) | 264 (61.8) | 58 (13.6) |
3. My actions can protect antimicrobial effectiveness | 141 (33.0) | 18 (4.2) | 37 (8.7) | 134 (31.4) | 97 (22.7) |
4. Everyone can promote AMR awareness | 99 (23.2) | 14 (3.3) | 86 (20.1) | 201 (47.1) | 27 (6.3) |
5. Prescribing guidelines support preventing AMR | 66 (15.5) | 57 (13.3) | 1 (0.2) | 168 (39.3) | 135 (31.6) |
6. Prescribing guidelines are easy to implement | 148 (34.7) | 26 (6.1) | 97 (22.7) | 135 (31.6) | 21 (4.9) |
7. Guideline compliance prevents AMR | 6 (1.4) | 14 (3.3) | 137 (32.1) | 114 (26.7) | 156 (36.5) |
8. Hand hygiene prevents infection and AMR | 84 (19.7) | 34 (8.0) | 72 (16.9) | 43 (10.1) | 194 (45.4) |
9. I am motivated to advocate against AMR | 9 (2.1) | 59 (13.8) | 13 (3.0) | 314 (73.5) | 32 (7.5) |
10. Sharing AMR knowledge improves practice | 25 (5.9) | 18 (4.2) | 58 (13.6) | 130 (30.4) | 196 (45.9) |
11. Challenging inappropriate antimicrobial use is important | 4 (0.9) | 117 (27.4) | 29 (6.8) | 186 (43.6) | 91 (21.3) |
12. I am confident in challenging inappropriate prescribing | 278 (65.1) | 1 (0.2) | 28 (6.6) | 45 (10.5) | 75 (17.6) |
13. Advocating for infection prevention is key to AMS | 52 (12.2) | 114 (26.7) | 120 (28.1) | 93 (21.8) | 48 (11.2) |
14. Patients take antibiotics inappropriately regardless of my actions | 110 (25.8) | 15 (3.5) | 30 (7.0) | 189 (44.3) | 83 (19.4) |
15. Advising patients/public about AMR is important | 30 (7.0) | 112 (26.2) | 101 (23.7) | 111 (26.0) | 73 (17.1) |
16. Quantifying antimicrobial use identifies AMS gaps | 25 (5.9) | 58 (13.6) | 80 (18.7) | 123 (28.8) | 141 (33.0) |
17. I consider AMR when treating a patient | 93 (21.8) | 53 (12.4) | 11 (2.6) | 61 (14.3) | 209 (48.9) |
18. AMR is a significant problem worldwide | 0 (0.0) | 28 (6.6) | 242 (56.7) | 109 (25.5) | 48 (11.2) |
19. AMR is a significant problem in my country | 82 (19.2) | 149 (34.9) | 1 (0.2) | 148 (34.7) | 47 (11.0) |
20. AMR is a significant problem in my hospital | 92 (21.5) | 50 (11.7) | 26 (6.1) | 258 (60.4) | 1 (0.2) |
21. Easy access to antibiotics without a prescription contributes to AMR | 6 (1.4) | 52 (12.2) | 30 (7.0) | 82 (19.2) | 257 (60.2) |
22. My institution performs adequate surveillance for resistant organisms | 0 (0.0) | 177 (41.5) | 77 (18.0) | 136 (31.9) | 37 (8.7) |
23. Lack of diagnostic tests leads to antimicrobial overuse | 9 (2.1) | 22 (5.2) | 127 (29.7) | 118 (27.6) | 151 (35.4) |
24. My institution provides adequate AMR education | 105 (24.6) | 189 (44.3) | 22 (5.2) | 79 (18.5) | 32 (7.5) |
25. I suspect antimicrobials in my institution are of poor quality | 77 (18.0) | 61 (14.3) | 69 (16.2) | 160 (37.5) | 60 (14.1) |
26. Sporadic antimicrobial supply leads to therapy interruptions | 17 (4.0) | 110 (25.8) | 30 (7.0) | 217 (50.8) | 53 (12.4) |
27. Cost considerations affect my antimicrobial choice | 11 (2.6) | 108 (25.3) | 25 (5.9) | 222 (52.0) | 61 (14.3) |
28. AMS improves the quality of patient care | 104 (24.4) | 57 (13.3) | 43 (10.1) | 51 (11.9) | 172 (40.3) |
29. AMS reduces antibiotic use overall | 100 (23.4) | 41 (9.6) | 212 (49.6) | 45 (10.5) | 29 (6.8) |
30. AMS reduces hospital stay and costs | 35 (8.2) | 40 (9.4) | 58 (13.6) | 174 (40.7) | 120 (28.1) |
31. My institution can implement an effective AMS program | 53 (12.4) | 43 (10.1) | 58 (13.6) | 98 (22.9) | 175 (41.0) |
32. My institution has the capacity for effective AMS | 38 (8.9) | 40 (9.4) | 40 (9.4) | 124 (29.0) | 185 (43.3) |
33. AMS can be an obstacle to good patient care | 58 (13.6) | 15 (3.5) | 97 (22.7) | 139 (32.6) | 118 (27.6) |
34. Infectious disease experts are available for guidance | 22 (5.2) | 3 (0.7) | 157 (36.8) | 124 (29.0) | 121 (28.3) |
35. Only prescribing physicians need to understand AMS | 109 (25.5) | 51 (11.9) | 47 (11.0) | 181 (42.4) | 39 (9.1) |
| SD = Strongly Disagree; D = Disagree; N = Neutral; A = Agree; SA = Strongly agree | |||||
HWs’ Practices relating to Antimicrobial Use and AMR
Table 4 presents the practices of HWs related to AMR and antimicrobial use. A substantial proportion of respondents 87.1% (n = 372) reported advising colleagues on the appropriate use of antimicrobials, and 82.0% (n = 350) felt confident advising patients on antibiotic use.
A
Additionally, 90.9% (n = 388) were aware of safe antimicrobial disposal protocols, and 79.9% (n = 341) said they referred to standard treatment guidelines (STG) before prescribing. A
However, concerning practices were also prevalent, with 54.3% (n = 232) admitting to supplying antibiotics not in accordance with guidelines, and 65.8% (n = 281) determining formulation and dose based on stock availability rather than guidelines. Furthermore, 46.8% (n = 200) prescribed antibiotics due to patient pressure, and 46.4% (n = 198) provided broader-spectrum antibiotics due to doubts about suitability. On a positive note, 72.8% (n = 311) of those queried prescriptions lacking evidence of infection, and 76.6% (n = 327) were certain of protocols to follow when concerned. A total of 277 (65%) of HWs had good practices relating to antimicrobial use and AMR while 150 (35%) did not.A
Practices | Frequency (n) | % of respondents |
|---|---|---|
1. Supplied an antibiotic not first recommended due to limited stock | 145 | 34.0 |
2. Provided less than the recommended dose of an antibiotic | 145 | 34.0 |
3. Queried a prescription due to insufficient evidence of infection | 311 | 72.8 |
4. Had doubts on the efficacy of the antimicrobial batch | 215 | 50.4 |
5. Reported/sent for testing an antimicrobial batch I doubted | 304 | 71.2 |
6. Supplied an antibiotic not in line with guidelines | 232 | 54.3 |
7. Advised a colleague on the most appropriate antimicrobial | 372 | 87.1 |
8. Safely disposed of antimicrobials at work | 248 | 58.1 |
9. Contributed to AMS strategies at my workplace | 296 | 69.3 |
10. Involved in collecting data on AMR | 372 | 87.1 |
11. Followed up with a patient supplied with an antimicrobial | 294 | 68.9 |
12. Referred to the Standard Treatment Guideline (STG) for a prescribed antimicrobial | 260 | 60.9 |
13. Formulation and dose provided were determined by stock rather than guidelines | 281 | 65.8 |
14. Worried about the quality of antibiotic formulations and their impact on care | 380 | 89.0 |
15. Sure of protocol to check with prescriber when concerned about a prescription | 327 | 76.6 |
16. Prescriber expects me to query concerning prescriptions | 246 | 57.6 |
17. Check antibiotic choice with a peer or superior when uncertain | 317 | 74.2 |
18. Felt pressure to supply antibiotics when not clinically required | 161 | 37.7 |
19. Confident in independently supplying for infection treatment without a prescription | 171 | 40.0 |
20. Confident in advising patients on how to use/take antibiotics | 350 | 82.0 |
21. Aware of how antimicrobials can be safely disposed of at work | 388 | 90.9 |
22. My role includes contributing to the hospital’s AMR goals | 347 | 81.3 |
23. My role includes collecting data to support tackling AMR | 321 | 75.2 |
24. My role includes giving feedback to colleagues about antimicrobial use | 333 | 78.0 |
25. Provided an antibiotic due to fear of patient deterioration | 160 | 37.5 |
26. Provided multiple antimicrobials to the same patient | 259 | 60.7 |
27. Stopped an antibiotic supply earlier than prescribed | 137 | 32.1 |
28. Checked my choice with a senior colleague | 330 | 77.3 |
29. Prescribed an antibiotic due to patient pressure to maintain the relationship | 200 | 46.8 |
30. Prescribed an antibiotic due to uncertainty about the infection diagnosis | 144 | 33.7 |
31. Provided a broad/wider spectrum antibiotic due to doubts about suitability | 198 | 46.4 |
32. Received feedback on my antimicrobial prescribing | 246 | 57.6 |
33. Referred to the Standard Treatment Guideline before prescribing an antimicrobial | 341 | 79.9 |
Test of Hypotheses
There is no statistically significant association between HWs’ socio-demographic characteristics and their knowledge of antimicrobial use and AMR.
Table 5 shows a significant association between role and knowledge level (χ²=38.45, df = 18, p = 0.003). Nurses (65.5%) and Medical Doctors (64.0%) had higher proportions of good knowledge. Cramer’s V (0.212, p = 0.003) indicates a small effect of respondents’ roles on their knowledge of AMR and antimicrobial use. Years of experience were significant (χ²=26.78, df = 10, p = 0.013), with > 20 years showing higher knowledge. Cramer’s V (0.177, p = 0.013) suggests a small but statistically significant effect of years of experience on knowledge of AMR and antimicrobial use.
Knowledge level | |||||
|---|---|---|---|---|---|
Poor n (%) | Moderate n (%) | Good n (%) | χ² | p-Value | |
Role | |||||
CHEW | 6 (21.4) | 12 (42.9) | 10 (35.7) | ||
Health Assistant | 1 (20.0) | 2 (40.0) | 2 (40.0) | ||
Health Supervisor | 0 (0.0) | 1 (50.0) | 1 (50.0) | ||
JCHEW | 2 (33.3) | 2 (33.3) | 2 (33.3) | ||
Lab Scientist | 2 (10.0) | 6 (30.0) | 12 (60.0) | ||
Medical Microbiologist | 1 (12.5) | 2 (25.0) | 5 (62.5) | ||
Medical Doctor | 6 (8.0) | 21 (28.0) | 48 (64.0) | ||
Nurse | 8 (6.9) | 32 (27.6) | 76 (65.5) | 38.45 | 0.003* |
Pharmacist | 3 (10.7) | 9 (32.1) | 16 (57.1) | ||
Pharmacy Technician | 1 (11.1) | 3 (33.3) | 5 (55.6) | ||
Years of experience | |||||
Less than 1 year | 5 (16.7) | 12 (40.0) | 13 (43.3) | ||
1–4 years | 13 (13.0) | 34 (34.0) | 53 (53.0) | ||
5–9 years | 12 (10.0) | 36 (30.0) | 72 (60.0) | ||
10–14 years | 7 (7.8) | 27 (30.0) | 56 (62.2) | ||
15–20 years | 4 (6.7) | 17 (28.3) | 39 (65.0) | ||
Greater than 20 years | 2 (7.4) | 7 (25.9) | 18 (66.7) | 26.78 | 0.013* |
| χ²= Chi-square statistic; P-value = Probability value; *=Statistical significance (P < 0.05) | |||||
There was no statistically significant association between HW by cadre and their years of experience and their perceptions on antimicrobial use and AMR.
Table 6 shows no significant associations between role (χ²=5.12, df = 9, p = 0.118) or years of experience (χ²= 4.98, df = 5, p = 0.418) and perceptions. The null hypothesis is not rejected.
Perceptions | ||||
|---|---|---|---|---|
Variables | Poor n (%) | Good n (%) | χ² | p-Value |
Role | 5.12 | 0.118 | ||
CHEW | 3 (10.7) | 25 (89.3) | ||
Health Assistant | 1 (20.0) | 4 (80.0) | ||
Health Supervisor | 1 (50.0) | 1 (50.0) | ||
JCHEW | 2 (33.3) | 4 (66.7) | ||
Lab Scientist | 3 (15.0) | 17 (85.0) | ||
Medical Microbiologist | 1 (12.5) | 7 (87.5) | ||
Medical Doctor | 13 (17.3) | 62 (82.7) | ||
Nurse | 14 (12.1) | 102 (87.9) | ||
Pharmacist | 5 (17.9) | 23 (82.1) | ||
Pharmacy Technician | 2 (22.2) | 7 (77.8) | ||
Years of experience | 4.98 | 0.418 | ||
Less than 1 year | 3 (10.0) | 27 (90.0) | ||
1–4 years | 21 (21.0) | 79 (79.0) | ||
5–9 years | 28 (23.3) | 92 (76.7) | ||
10–14 years | 20 (22.2) | 70 (77.8) | ||
15–20 years | 9 (15.0) | 51 (85.0) | ||
Greater than 20 years | 4 (14.8) | 23 (85.2) | ||
| χ²= Chi-square statistic; P-value = Probability value | ||||
There was no statistically significant association between HWs’ cadre and demographic characteristics and their practices towards antimicrobial use and AMR.
Table 7 shows a significant association between role and practices (χ²=22.34, df = 9, p = 0.008), with nurses showing good practices. Cramer’s V (0.229, p = 0.008) indicates a small but statistically significant association of roles (cadres e.g. nurse) on antimicrobial stewardship. Years of experience were statistically significant (χ²=14.67, df = 5, p = 0.012), with HWs with greater than 20 years’ experience showing better practices. Cramer’s V (0.185, p = 0.012) suggests a small but statistically significant effect of years of experience on antimicrobial stewardship.
Practices | ||||
|---|---|---|---|---|
Variables | Poor n (%) | Good n (%) | χ² | p-Value |
Role | ||||
CHEW | 17 (60.7) | 11 (39.3) | ||
Health Assistant | 3 (60.0) | 2 (40.0) | ||
Health Supervisor | 1 (50.0) | 1 (50.0) | ||
JCHEW | 3 (50.0) | 3 (50.0) | ||
Lab Scientist | 8 (40.0) | 12 (60.0) | ||
Medical Microbiologist | 3 (37.5) | 5 (62.5) | ||
Medical Doctor | 21 (28.0) | 54 (72.0) | ||
Nurse | 30 (25.9) | 86 (74.1) | 22.34 | 0.008* |
Pharmacist | 10 (35.7) | 18 (64.3) | ||
Pharmacy Technician | 4 (44.4) | 5 (55.6) | ||
Years of experience | ||||
Less than 1 year | 18 (60.0) | 12 (40.0) | ||
1–4 years | 46 (46.0) | 54 (54.0) | ||
5–9 years | 47 (39.2) | 73 (60.8) | ||
10–14 years | 28 (31.1) | 62 (68.9) | ||
15–20 years | 18 (30.0) | 42 (70.0) | ||
Greater than 20 years | 7 (25.9) | 20 (74.1) | 14.67 | 0.012* |
| χ²= Chi-square statistic; P-value = Probability value; *=Statistical significance (P < 0.05) | ||||
Discussion
This study showed that knowledge and perceptions among this cohort of HWs in Cross River State were generally good as most respondents scored well on knowledge (60% “good”) and held positive perceptions (81.4% “good”), but practice was constrained by stock availability and patient pressure (between 47–66%). It highlights opportunities for HWs to mitigate AMR in an LMIC setting.
Knowledge of Antimicrobial Use and AMR
Most of the HWs had good knowledge of AMR and antimicrobial use (60% and above). This level is different from the 70% found among healthcare professionals in Zambia [18]. Similarly, AMR knowledge was found to be significantly high (66.1% − 88%) among HWs in the Benin Republic [19]. A study in Niger State of Nigeria [20] had similar the findings of this present study, with 62.3% of healthcare professionals were found to have good knowledge of AMR and antimicrobial use. In neighboring Akwa Ibom State, however, there was a notable deficit in knowledge of AMR and WHO Access, Watch, Reserve (AwaRe) classification among healthcare providers, reflecting regional variation guided by access to training [21]. However, common misconceptions, as evident in current observed data, such as the belief that viral or parasitic infections do not respond to antibiotics, are in line with broader global (e.g., European) trends [22]. This is typically driven by inadequate education on microbial dynamics [20, 21].
A
We found that 54.3% of the prescribers supplied antibiotics not in line with guidelines which suggests that practice lags behind what people know and believe. Good knowledge of AMR among HWs is a result of increased awareness campaigns, targeted educational interventions, National Action Plans, the widely adopted One Health Approach, and international interventions [23]. The knowledge gaps reported in this study and others [21, 22, 24] highlight the need for educational interventions to increase knowledge and attitudes. After discussing the survey data by professional cadre, potential options include in-venue or online training, educational outreach, opinion leaders, peer groups, and prescription review (clinical audit) among health care professionals, with targeted outreaches to different cadres [25].The robust correlation between professional status (e.g., physicians and nurses having higher knowledge) and years of experience (> 20 years translating to better scores) shows that specialist training and experiential learning enhance understanding. This is consistent with findings from studies in which healthcare professionals’ knowledge was poor but improved with increasing practice years, and with emphasis being placed on professional continuous development [26, 27]. In turn, CHEWs can lag due to limited formal education, a not unusual scenario in primary care settings in Africa. The robust correlation between professional cadre (e.g., physicians and nurses having higher knowledge) and years of experience (> 20 years translating to better scores) shows that specialist training and experiential learning enhance understanding of AMR concepts [28].
Perceptions on Antimicrobial Use and AMR
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Perceptions of HWs on AMR and antimicrobial use were significantly positive, with strong agreement over the efficacy of preventive measures such as hand hygiene and guideline adherence. Such positivity is corroborated by studies [23, 29, 30] and is evidence of growing awareness, perhaps spurred locally by national efforts such as under Nigeria's AMR Action Plan. Perceptions in the African setting tend to follow with institutional support; a regional review [31] revealed gaps in KAP due to system-level reasons like an absence of resources, which could explain the lack of demographic correlations in the current study.In contrast to practices and knowledge, perceptions were not significantly associated with experience or role, indicating that they may be more affected by the outside world, such as media and policy exposure rather than profession.
Practices on Antimicrobial Use and AMR
While 65.0% of the reported practices were good, system vulnerabilities are expressed via alarming practices like unethical prescribing driven by external non-clinical factors, such as financial incentives, and patient pressure. These behaviours perpetuate AMR as exemplified by the prevalence of high broad-spectrum antibiotic prescribing due to diagnostic challenges [31–33], an issue prevalent in Nigerian health centers with limited training (e.g. CHEWs) and limited laboratories. Comparative studies in Nigeria and across Africa demonstrate similar problems, with only 45–51% having good prescribing practices, due often to health financing reasons and patient pressures [34]. Correlation with experience and role (nurses showing enhanced practice) indicates the significance of seniority in coping with these pressures. These practices-knowledge/perceptions differences highlight the "know-do" gap [35, 36], whereby positive perceptions are not being converted into meaningful practices due to impediments like workload and stockouts. Stock-based prescribing such as giving a broad-spectrum antibiotic when a narrow-spectrum or required antibiotic is out of stock-further exacerbates the issue.
Implications for policy and practice in Cross River State and beyond
In general, the study suggests that multi-dimensional interventions are required to reconcile practice with knowledge and perceptions. Implications are for integrating AMR modules within pre-service training curriculum, with enhanced supply chains, and for interprofessional team working to counteract external pressures. Specifically, both pre-service and in-service training modules are required, including realistic case study exercises for skills development. Face-to-face training is likely preferable for in-service, but online training offers greater reach and scale-up at lower cost compared to venue-based sessions. Other options include peer groups facilitated by senior respected HWs/supervisors, which can incorporate prescription review (also called clinical audit). More targeted training needs to be channeled to junior-level staff, such as CHEWs and Junior CHEWs, to improve their knowledge and corresponding practices regarding AMR.
Limitations
This study has some limitations. Self-reported bias may overestimate positive responses, and a cross-sectional design limits causality. The focus on public facilities may limit the generalizability of findings to the private sector. Subsequent studies need to employ mixed methods approaches, such as prescribing audits and longitudinal surveys, to track AMS impacts. Multisite research in Nigeria would inform national scaling while exploring determinants of behaviour. Interventions should be pilot-tested e.g. with pre/post training tests, and refined, prior to scale-up. Supervisors need to be involved in trainings, and follow-up visits to guide/ support change in practice.
Conclusion
Our study underscores the potential role of HWs in combating AMR in Cross River State, Nigeria, and highlights the intricate interplay of strengths and weaknesses in their knowledge, perceptions, and practices. The generally good knowledge and positive perceptions among HWs indicate a good platform for AMS, fueled by awareness of AMR's global and local consequences. However, the gap in practices highlights systemic barriers undermining effective AMS. The results align with overall trends in LMICs, where resource and time constraints, and patient expectation pressures often undermine the translation of knowledge into practice.
The significant correlations between professional cadres (doctors and nurses) and years of experience, along with better knowledge and practices, suggest that targeted interventions can utilize experienced HWs to mentor others in lower cadres, thereby fostering a culture of evidence-based prescribing. With HW capacity bolstered and structural challenges addressed, Cross River State can serve as a model for effective AMR control, supporting the global effort to maintain antimicrobial efficacy for generations to come.
A
Acknowledgement
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We thank the Ministry of Health, Cross River State, and all participating health facilities and health workers for their cooperation and support in this study.Declarations
Ethical approval
A
Ethical approval was obtained from the Health Research Ethics Committee of the Ministry of Health Cross River State to conduct this survey. The approval certificate identifier: CRS/MH/HREC/023/Vol.V1/255. This study was conducted in compliance with the Declaration of Helsinki. A
All participants were adults and written informed consent was obtained from them prior to commencing the survey.Consent to Participate
A
Written and verbal informed consents were obtained from parents/guardians to authorize their adolescents' participation in the study. Additionally, consent was obtained from adolescents.Consent for publication
Not applicable
A
Funding
Commonwealth Partnerships for Antimicrobial Stewardship (CwPAMS) is managed by the Tropical Health and Education Trust (THET) and the Commonwealth Pharmacists Association (CPA).
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This project is funded by the UK Department of Health and Social Cares Fleming Fund using UK aid.
Conflicts of interest/Competing interests
The authors declare no conflicts of interest.
A
Data Availability
The datasets used and/or analysed during the current study will be available from the corresponding author on reasonable request.
A
Author Contribution
AAO, OCO, UAU conceived the study. Material preparation, data collection and analysis were performed by OCO, UAU, JI, AAO, UMU, FA, EEE. The first draft of the manuscript was written by AAO, OCO, UMU, EE, UAU, BE, FA and JW. The manuscript was edited by AAO, UMU, CO, BEE, BE, IOI, E, JI, CE, JW. All authors discussed the results and contributed substantially to the final manuscript.
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