Adherence to evidence-based implementation of antimicrobial treatment guidelines among prescribers in sub-Saharan Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Adherence to evidence-based standard treatment guidelines (STGs) enable healthcare providers to deliver consistently appropriate diagnosis and treatment. Irrational use of antimicrobials significantly contributes to antimicrobial resistance in sub-Saharan Africa (SSA). The best available evidence is needed to guide healthcare providers on adherence to evidence-based implementation of STGs. This systematic review and meta-analysis aimed to determine the pooled prevalence of adherence to evidence-based implementation of antimicrobial treatment guidelines among prescribers in SSA. METHODS: statistics. Heterogeneity and publication bias were validated using Duval and Tweedie's nonparametric trim and fill analysis using the random-effect analysis. The summary prevalence and the corresponding 95% confidence interval (CI) of healthcare professionals' compliance with evidence-based implementation of STG were estimated using random effect model. The review protocol has been registered with PROSPERO code CRD42023389011. The PRISMA flow diagram and checklist were used to report studies included, excluded and their corresponding section in the manuscript. RESULTS: Twenty-two studies with a total of 17,017 study participants from 14 countries in sub-Saharan Africa were included. The pooled prevalence of adherence to evidence-based implementation of antimicrobial treatment guidelines in SSA were 45%. The pooled prevalence of the most common clinical indications were respiratory tract (35%) and gastrointestinal infections (18%). Overall prescriptions per wards were inpatients (14,413) and outpatients (12,845). Only 391 prescribers accessed standard treatment guidelines during prescription of antimicrobials. CONCLUSIONS: Healthcare professionals' adherence to evidence-based implementation of STG for antimicrobial treatment were low in SSA. Healthcare systems in SSA must make concerted efforts to enhance prescribers access to STGs through optimization of mobile clinical decision support applications. Innovative, informative, and interactive strategies must be in place by the healthcare systems in SSA to empower healthcare providers to make evidence-based clinical decisions informed by the best available evidence and patient preferences, to ultimately improving patient outcomes and promoting appropriate antimicrobial use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.073 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.047 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".