Factors associated with future hospitalization among children with asthma: a systematic review
Bibliographic record
Abstract
Asthma is a leading cause of emergency department (ED) visits and hospitalizations in children, though many could be prevented. Our study objective was to identify factors from the published literature that are associated with future hospitalization for asthma beyond 30 days following an initial asthma ED visit. We searched CINAHL, CENTRAL, MEDLINE, and Embase for all studies examining factors associated with asthma-related hospitalization in children from January 1, 1992 to February 7, 2022.Selecting Studies: All citations were reviewed independently by two reviewers and studies meeting inclusion criteria were assessed for risk of bias. Data on all reported variables were extracted from full text and categorized according to identified themes. Where possible, data were pooled for meta-analysis using random effects models. Of 2262 studies, 68 met inclusion criteria. We identified 28 risk factors and categorized these into six themes. Factors independently associated with future hospitalization in meta-analysis include: exposure to environmental tobacco smoke (OR = 1.94 95%CI 0.67–5.61), pets exposure (OR = 1.67 95%CI 1.17–2.37), and previous asthma hospitalizations (OR = 3.47 95% CI 2.95–4.07). Additional related factors included previous acute care visits, comorbid health conditions (including atopy), allergen exposure, severe-persistent asthma phenotype, inhaled steroid use prior to ED visit, poor asthma control, higher severity symptoms at ED presentation, warmer season at admission, longer length of stay or ICU admission, and African-American race/ethnicity. We identified multiple factors that are consistently associated with future asthma hospitalization in children and could be used to identify those who would benefit from targeted preventative interventions.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".