Effects of 2019’s social protests on emergency health services utilization and case severity in Santiago, Chile: a time-series analysis
Bibliographic record
Abstract
Background: On October 18th, 2019, protestors gathered across Chile to call for social equity, resulting in widespread civil unrest and violent confrontation with the police. In this study, we quantify the effects of the 2019 Chilean protests on emergency health services utilization and inpatient admission in Santiago. Methods: We used weekly emergency department (ED) admissions (2015-2019) from three large public hospitals near the focal point of protests in Santiago. The exposure period was from October 18th to December 31st, 2019. The outcomes were the number of weekly consultations and hospitalizations by trauma and respiratory causes and the proportion of hospitalizations among consultants per 1,000. We implemented Bayesian structural time series models to calculate the absolute and relative effects and 95% credible intervals (CrI). Findings: During the first ten weeks of protests ED consultations declined on average by 14% for trauma (95%CrI: -40·2%, 11·5%) and 30% for respiratory causes (95%CrI: -89·4%, 30·2%), 7% for respiratory hospitalizations (95%CrI: -43·6%, 30·8%); however, none of these three results were statistically distinguishable from the null. Trauma hospitalizations, on the other hand, increased by 15% (95%CrI: 4·0%, 26·4%), and the proportion of hospitalizations per consultations increased by 40% for trauma (95%CrI: 13·1%, 68·0%) and 59% for respiratory causes (95%CrI: 29·4%, 87·9%). Interpretation: The 2019 Chilean protests affected the use of emergency health services by increasing the trauma hospitalizations and the case hospitalization ratio per 1,000 consultations for trauma and respiratory causes. Crowd-control protocols must be reviewed to prevent the negative effects of civil unrest.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".