The trend of mortality rates following hospitals downgrading and closures due to outbreak of COVID‐19 in Fars province: A comparative cohort study
Bibliographic record
Abstract
Background and Aims: Hospitals are one of the most important healthcare centers for providing the patients with different medical needs. Several different factors might cause hospitals to downgrade their services or departments or close down overall. One of the most multifaceted reasons for hospital downgrading or closure is infectious disease outbreaks. In this regard, we aimed to evaluate the effects of hospital closure and downgrading due to the COVID-19 pandemic on the mortality rate of the people residing in Fars province, Iran. Methods: We gathered mortality information, including the cause of death, age, sex, place, and time of death of all deceased cases occurring during a period of 3 years, from February 20, 2018 to March 2021 from the forensic medicine and also the Department of Biostatistics in Shiraz University of Medical Sciences. Results: A total of 71,331 deaths have been reported since 2018 through the first quarter of 2021, with 57.9% of total mortality cases attributed to male gender. The total mortality counts ranged from 4229 to 9809 deaths per quarter, from which the minimum rate was reported in the first quarter of 2018 and the maximum in the fourth quarter of 2020. Based on the causes of death, diseases of the circulatory system were shown to be the all-time most frequent cause of death, accounting for a total of 42.8% of recorded deaths, followed by neoplasms (9.77%) and diseases of the respiratory system (9.45%). Conclusion: Although the large number of deaths at the time of the pandemic are immediately due to COVID-19 infection, deaths due to a notable number of other causes have had a significant increase which, along with the specific trend of place and causes of death, shows that the downgrading and closure of hospitals have had a significant impact on overall population mortality.
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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 | 0.009 | 0.001 |
| 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".