Driving Factors in Pediatric Emergency Department Use: an Ecological Retrospective Study
Bibliographic record
Abstract
Abstract Background Pediatric emergency departments (PEDs) often face high volumes of low-acuity visits, reflecting gaps in primary care access and socio-economic disparities. We investigated how neighborhood socio-economic vulnerability, pediatrician availability, and proximity to the PED jointly influence PED utilization in Geneva, Switzerland. Methods In this retrospective ecological study (Jan 2023-Dec 2024), we aggregated all PED visits for children aged 0-16 years by neighborhood and Canadian Triage Acuity Scale (CTAS) level. Neighborhood visit incidence (unique patients per child population) was modeled using mixed-effects regression against a composite socio-economic vulnerability index (NSVI), pediatrician density within a 2 km radius, and distance to the PED, incorporating an exponential decay function for distance and postal code as a random intercept. Results There were 68,482 PED visits by 35,994 children (35.1% of Geneva under-16 population). Low-acuity visits (CTAS 4-5) comprised ∼50% of encounters. Both distance and socio-economic vulnerability showed clear dose-response relationships, with stronger effects observed for lower-acuity visits, and no interaction effect between them. Overall, proximity accounted for up to 20.8% of non-urgent PED use, while neighborhood socio-economic vulnerability explained up to 19.7% of low acuity visits across Geneva. Pediatrician density showed a modest inverse association for low-acuity visits only. Conclusions Both proximity and socio-economic vulnerability are independent determinants of non-urgent PED use. Policies focusing only on primary care access risk missing key drivers of PED use, highlighting the need for locally tailored strategies such as community outreach near hospitals or programs to strengthen health literacy among families. Summary Proximity and socio-economic vulnerability each explain up to 20% of non-urgent pediatric emergency visits, highlighting major contextual drivers of avoidable care use. What’s Known on This Subject Pediatric emergency department crowding, often driven by low-acuity visits, has been linked to socio-economic disadvantage, poor primary care access, and proximity. Yet, few studies have jointly examined these factors or compared their impact within a universal healthcare setting. What This Study Adds This study disentangles the effects of distance, socio-economic vulnerability, and pediatrician density on PED use. Proximity and disadvantage each explain ∼20% of low-acuity visits, while pediatrician density has limited impact, highlighting the need for targeted, locally adapted 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".