Antibiotic prescription sources and use among under-5 children with fever/cough in sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: Childhood febrile illness is among the leading causes of hospital admission for children <5 y of age in sub-Saharan Africa (SSA). Antibiotics have played a pivotal role in enhancing health outcomes, especially for children <5 y of age. Antibiotics prescription pattern evidence exists for SSA, however, prescription sources (either from qualified or unqualified sources) and use among children with fever or cough have not been explored. Thus the present study assessed antibiotic prescription sources and use among children <5 y of age with fever and cough in SSA. METHODS: We used Demographic and Health Survey data from 37 countries with a total of 18 866 children <5 y of age who had fever/cough. The surveys span from 2006 to 2021. The dependent variable was antibiotics taken for fever/cough based on prescriptions from qualified sources. The data were weighted using sampling weight, primary sampling unit and strata. A mixed-effects logistic regression model (both fixed and random effects) was fitted since the outcome variable was binary. Model comparison was made based on deviance (-2 log likelihood) and likelihood ratio tests were used for model comparison. Variables with p≤0.2 in the bivariable analysis were considered for the multivariable mixed-effects binary logistic regression model. In the final model, the adjusted odds ratio (AOR) with a 95% confidence interval (CI) and p<0.05 in the multivariable model were used to declare a significant association with taking antibiotics for fever/cough prescribed from qualified sources. RESULTS: The percentage of unqualified antibiotic prescriptions among children <5 y of age who had a fever/cough and took antibiotics was 67.19% (95% CI 66.51 to 67.85), ranging from 40.34% in Chad to 92.67% in Sao Tome. The odds of taking antibiotics prescribed from unqualified sources for fever/cough among children <5 y of age living in rural areas were 1.23 times higher (AOR 1.23 [95% CI 1.13 to 1.33]) compared with urban children. The odds of taking antibiotics prescribed from qualified sources for fever/cough among children <5 y of age whose mothers had primary, secondary and higher education decreased by 14% (AOR=0.86 [95% CI 0.79 to 0.93]), 21% (AOR 0.79 [95% CI 0.72 to 0.86]) and 21% (AOR 0.79 [95% CI 0.65 to 0.95]) compared with those whose mother had no formal education, respectively. CONCLUSIONS: The study showed that the majority of the children who received antibiotics obtained them from unqualified sources in the 37 SSA countries. Our findings underscore the significance of addressing healthcare disparities, improving access to qualified healthcare providers, promoting maternal education and empowering mothers in healthcare decision-making to ensure appropriate antibiotic use in this vulnerable population. Further research and interventions targeted at these factors are warranted to optimize antibiotic prescribing practices and promote responsible antibiotic use in the management of fever and cough in children <5 y of age.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".