Antimicrobial Resistance in Gram-negative bacteria from Urinary Specimens: a study of prevalence, risk factors and molecular mechanisms of resistance (ARGUS) in Zimbabwe - a study protocol.
Ioana Diana OlaruShunmay YeungRashida A FerrandRichard A StablerProsper ChonziDavid MabeyHeidi HopkinsJohn BradleyKudzai P E MasundaShungu MunyatiKatharina KranzerPublished in: Wellcome open research (2020)
Antimicrobial resistance (AMR) is compromising our ability to successfully treat infections. There are few data on gram-negative AMR prevalence in sub-Saharan Africa especially from the outpatient setting. This study aims to investigate the prevalence of and underlying molecular mechanisms for AMR in gram-negative bacilli causing urinary tract infections (UTIs) in Zimbabwe. Risk factors for AMR and how AMR impacts on clinical outcomes will also be investigated. Adults presenting with UTI symptoms at primary health clinics in Harare will be included. A questionnaire will be administered, and urine samples will be collected for culture. Participants with positive urine cultures will be followed up at 7-14 days post-enrolment. All participants will also be followed by telephone at 28 days to determine clinical outcomes. Bacterial identification and antibiotic susceptibility testing will be performed on positive cultures. The results from this study will be used to inform policy and development of treatment recommendations. Whole genome sequencing results will provide a better understanding of the prevalent resistance genes in Zimbabwe, of the spread of successful clones, and potentially will contribute to developing strategies to tackle AMR.
Keyphrases
- antimicrobial resistance
- gram negative
- risk factors
- multidrug resistant
- urinary tract infection
- public health
- healthcare
- mental health
- randomized controlled trial
- clinical trial
- gene expression
- primary care
- mass spectrometry
- health insurance
- machine learning
- dna methylation
- genome wide
- risk assessment
- case report
- physical activity
- electronic health record
- artificial intelligence
- transcription factor
- sleep quality
- data analysis
- smoking cessation