Antimicrobial Usage Among Acutely Ill Hospitalized Children Aged 2‒23 Months in Sub-Saharan Africa and South Asia
Bibliographic record
Abstract
Background: Understanding patterns of antimicrobial use is critical to supporting antibiotic stewardship and limiting antimicrobial resistance (AMR). We aimed to describe antimicrobial prescribing in acutely ill hospitalized children aged 2-23 months across a range of rural and urban hospital settings in Sub-Saharan Africa and South Asia. Methods: The Childhood Acute Illness & Nutrition (CHAIN) cohort collected data daily throughout hospitalization from children with acute illness aged 2-23 months admitted to 9 hospitals from November 2016 to January 2019. We determined proportions of children receiving antimicrobials, inpatient-days receiving antimicrobials, antimicrobial classes, World Health Organization (WHO) Access, Watch, and Reserve (AWaRe) classifications, and examined factors associated with Watch antimicrobial use. Results: Of 3101 admissions, 1422 (46%) received antimicrobials before hospitalization. A total of 2816 (91%) children received antimicrobials during 19 398/21 807 (93%) inpatient child-days. Two thousand four hundred seventy-seven (76%), 1092 (35%), and 12 (0.3%) children received Access, Watch, and Reserve antimicrobials, mostly within 48 hours of admission. Three hundred forty-one (11%) admissions received an antimicrobial without any indication. Prior admission, chronic illness, diagnoses of sepsis or meningitis, hypoglycemia, and duration of admission were associated with receiving Watch antimicrobials, while WHO danger signs, severe malnutrition, HIV, and receipt of prior antimicrobials were not, despite their known association with mortality and AMR. Conclusions: Antimicrobial use was similar across sites, with some overuse and notably limited escalation and de-escalation, likely due to guideline adherence. Guidelines need updating for the absence of relevant antimicrobial sensitivities, to include risk-based antimicrobial prescribing considering mortality risk and prior exposure to antimicrobials and the hospital environment. Hence, clinical trials of risk-differentiated care are needed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".