Power outage impacts on adult hospital occupancy and summary of health indicators in Puerto Rico
Bibliographic record
Abstract
Puerto Rico’s electric grid has experienced mounting grid reliability challenges since Hurricane Maria devastated its infrastructure in 2017. This study examines trends in intermittent power outages in 2022 and their association with adult hospital occupancy across selected municipalities (Arecibo, Bayamón, Caguas, Carolina, Mayagüez, Ponce, and San Juan). A comprehensive literature search and analysis of health data revealed negative trends in public health – specifically all-cause mortality and hypertension on the island. Outage data from the U.S. Department of Energy’s EAGLE-I system were integrated with publicly available hospital occupancy data from the Department of Health in Puerto Rico. Mortality and hypertension data from the World Health Organization were reviewed with a time-series analysis. Results indicate that frequent outages coincided with some increases in adult hospital occupancy during certain months (January, May, July, August, September, October, November, December), though the association was relatively weak when aggregated over the entire year (adjusted R2 = 0.2458). Outage events clustered in less populated and more remote western municipalities such as Mayagüez, Arecibo, and Ponce, where restoration was slower and outage durations longer. Both mortality and hypertension rates in Puerto Rico have trended upward since 2015, with cardiovascular disease consistently noted as a leading cause of death. These trends suggest that stress from grid instability and barriers to healthcare access may compound preexisting vulnerabilities, including a large population dependent on electrically powered medical devices. Findings underscore a need for more reliable electricity infrastructure and targeted community-level mitigation strategies to address public health risks. Further research incorporating granular hospital and emergency department data is warranted to identify causal pathways linking power interruptions to declining health outcomes in Puerto Rico.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".