Barriers and determinants of asthma control in children and adolescents in Africa: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To identify reasons for poor asthma control in African children and adolescents. DESIGN: Systematic review DATA SOURCES: PubMed, Scopus, CINHAL, PsycINFO, MEDLINE and Web of Science databases were systematically searched up to 31 May 2020. Hand searching was done on Sabinet, African Journal online and Google Scholar. ELIGIBILITY CRITERIA: Studies identifying barriers to asthma control, where asthma control was assessed by the validated Asthma Control Test/Child Asthma Control Test and/or Asthma Control Questionnaire were included. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently selected studies for inclusion with disagreements resolved by a research team discussion, including a third reviewer. Data were extracted using the Cochrane Effective Practice and Organization of Care data collection form. The quality of the included studies was assessed using the modified Newcastle-Ottawa quality assessment scale. Identified barriers were reported in a thematic narrative synthesis. PRIMARY OUTCOMES: Poorly controlled asthma and associated factors. RESULTS: From 914 records, three studies conducted between 2014 and 2019 in Nigeria, Uganda and South Africa met the inclusion criteria. A total of 883 children aged 4-19 years were analysed. Older age, concurrent allergy and city-dwelling significantly impacted asthma control. Few children with asthma symptoms in the community had ever used inhaled corticosteroids (6.7%) and identified reasons included lack of asthma diagnosis (38.8%) and no prescribed treatment (47.6%). CONCLUSION: Asthma control in African children is impacted by age, allergy, urbanisation and lack of access to asthma diagnosis and treatment. More studies focusing on identifying barriers to asthma control in Africa are needed. PROSPERO: Registration no: CRD42020196755).
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".