Webrod Mufwambi1,2,3, Julia Stingl4, Collen Masimirembwa2, Justen Manasa2,3, Charles Nhachi3, Nadina Stadler5, Chiluba Mwila1, Aubrey Chichonyi Kalungia1, Moses Mukosha1, Chenai S Mutiti2,3, Alfred Kamoto2,3, Patrick Kaonga6,7, Brian Godman8,9,10, Derick Munkombwe1. 1. Department of Pharmacy, School of Health Sciences, University of Zambia, Lusaka, Zambia. 2. African Institute of Biomedical Science and Technology, Harare, Zimbabwe. 3. University of Zimbabwe, College of Health Sciences, Harare, Zimbabwe. 4. RWTH University Hospital Aachen, Aachen, Germany. 5. Research Division, Federal Institute for Drugs and Medical Devices, Bonn, Germany. 6. Department of Epidemiology and Biostatistics, School of Public Health, University of Zambia, Lusaka, Zambia. 7. Tropical Gastroenterology and Nutrition Group, School of Medicine, University of Zambia, Lusaka, Zambia. 8. Division of Public Health Pharmacy and Management, School of Pharmacy, Sefako Makgatho Health Sciences University, Rankuwa, South Africa. 9. Division of Clinical Pharmacology, Karolinska Institute, Stockholm, Sweden. 10. Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, United Kingdom.
Abstract
Introduction: Sub-Saharan Africa and other low- and middle-income countries (LMICs) have the highest rates of antimicrobial resistance (AMR) driven by high rates of antimicrobial utilization. This is a concern as AMR appreciably increases morbidity, mortality and costs. Pharmacogenetics (PGx) and precision medicine are emerging approaches to combat AMR. Consequently, as a first step there is a need to assess AMR knowledge and attitudes, and knowledge of PGx, among healthcare professionals and use the findings to guide future interventions. Methodology: We conducted a cross-sectional study involving 304 healthcare professionals at tertiary hospitals in Lusaka, Zambia. Structural Equation Modeling (SEM) was used to analyze relationships among latent variables. Results: Overall correctness of answers concerning AMR among healthcare professionals was 60.4% (7/11). Knowledge of pharmacogenetics was low (38%). SEM showed that high AMR knowledge score correlated with a positive attitude toward combating AMR (p < 0.001). Pharmacists had relatively higher AMR knowledge scores (mean = 7.67, SD = 1.1), whereas nurses had lower scores (mean = 5.57, SD = 1.9). A minority of respondents [31.5% (n = 95)] indicated that poor access to local antibiogram data promoted AMR, with the majority [56.5% (n = 190)] responding that poor adherence to prescribed antimicrobials can lead to AMR. Pharmacists had the highest scores for attitude (mean = 5.60, SD = 1.6) whereas nurses had the lowest scores (mean = 4.02, SD = 1.4). Conclusion: AMR knowledge and attitudes, as well as knowledge on PGx among healthcare professionals in Zambia, is sub-optimal and has the potential to affect the uptake of precision medicine approaches to reduce AMR rates. Educational and positive behavioral change interventions are required to address this and in future, we will be seeking to introduce these to improve the use of antimicrobials.
Introduction: Sub-Saharan Africa and other low- and middle-income countries (LMICs) have the highest rates of antimicrobial resistance (AMR) driven by high rates of antimicrobial utilization. This is a concern as AMR appreciably increases morbidity, mortality and costs. Pharmacogenetics (PGx) and precision medicine are emerging approaches to combat AMR. Consequently, as a first step there is a need to assess AMR knowledge and attitudes, and knowledge of PGx, among healthcare professionals and use the findings to guide future interventions. Methodology: We conducted a cross-sectional study involving 304 healthcare professionals at tertiary hospitals in Lusaka, Zambia. Structural Equation Modeling (SEM) was used to analyze relationships among latent variables. Results: Overall correctness of answers concerning AMR among healthcare professionals was 60.4% (7/11). Knowledge of pharmacogenetics was low (38%). SEM showed that high AMR knowledge score correlated with a positive attitude toward combating AMR (p < 0.001). Pharmacists had relatively higher AMR knowledge scores (mean = 7.67, SD = 1.1), whereas nurses had lower scores (mean = 5.57, SD = 1.9). A minority of respondents [31.5% (n = 95)] indicated that poor access to local antibiogram data promoted AMR, with the majority [56.5% (n = 190)] responding that poor adherence to prescribed antimicrobials can lead to AMR. Pharmacists had the highest scores for attitude (mean = 5.60, SD = 1.6) whereas nurses had the lowest scores (mean = 4.02, SD = 1.4). Conclusion: AMR knowledge and attitudes, as well as knowledge on PGx among healthcare professionals in Zambia, is sub-optimal and has the potential to affect the uptake of precision medicine approaches to reduce AMR rates. Educational and positive behavioral change interventions are required to address this and in future, we will be seeking to introduce these to improve the use of antimicrobials.
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