A Feasible Laboratory-Strengthening Intervention Yielding a Sustainable Clinical Bacteriology Sector to Support Antimicrobial Stewardship in a Large Referral Hospital in Ethiopia
Bibliographic record
Abstract
Background: Access to clinical bacteriology in low resource settings (LRS) is a key bottleneck preventing clinical management of treatable severe infections, detection of antimicrobial resistance (AMR), and implementation of effective stewardship interventions. We sought to demonstrate the feasibility of a bundle of interventions aimed at implementing sustainable clinical bacteriology services at Tikur Anbessa Specialized Hospital in Addis Ababa, Ethiopia, and report on cost and intensity of supervision. Methods: Starting in Dec 2015, an intervention based on the CLSI QMS01-A guideline was established, consisting of (i) an initial needs assessment, (ii) development of key standard operating procedures (SOP), (iii) adaptation of processes for LRS, (iv) training and supervision of laboratory staff via consultant visits and existing online resources, and (v) implementation of a practical quality systems approach. A guiding principle of the bundle was sustainability post implementation. Outcomes and challenges: An initial investment of ~US$ 26,200 for reagents, and a total of 50 visit-days annualy from three Canadian microbiologists were committed. Twelve SOPs, including antimicrobial susceptibility testing, were adapted, and an automated blood culture platform was donated (bioMerieux). In the first 18 months of implementation, the average volume of specimens analyzed in the lab went from 15/day to 75/day. The number of blood cultures tested increased from an average 2/day to over 45/day. Antimicrobial susceptibility testing was introduced and cumulative antibiograms were generated for the institution. Quality control was implemented for all procedures and quality assurance tools implemented included external quality assurance and proficiency testing of six technologists with longitudinal follow-up. The laboratory is on the path towards SLIPTA accreditation by the African Society for Laboratory Medicine. Reagent costs, staff training and retention, and engagement of clinical personnel with the lab proved to be manageable challenges. Key external challenges include in-country supply-chain management issues, lack of competition among distributors, and foreign-currency exchange distortions. Conclusions: Using a relatively low-intensity intervention based on existing training tools and accreditation schemes, we demonstrate that establishment of reasonable-quality clinical bacteriology is not only within reach but also a critical step towards assessing the burden of AMR in settings like this one and implementing effective stewardship strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".