A rapid assessment of the impact of COVID-19 on the utilization of hospital services in Ondo State of South Western Nigeria
Bibliographic record
Abstract
Abstract Background: There is no doubt that COVID_19 has impacted on all facets of human activities everywhere, including ONDO state, Nigeria. However, empirical evidence of the extent of this impact in ONDO is lacking. The objective of this study was to evaluate the impact of COVID_19 epidemic on the utilization of hospital services & determine the existence of community transmission of COVID_19 in ONDO state using a proxy “Respiratory Tract Infections, RTIs”.Methods: The study used resident clinicians to conduct peer review of hospital utilization records. The review was retrospectively for the months of January to May for four consecutive years (2018 to 2021). The team reviewed outpatient records from 11 health facilities before the advent of COVID_19 (2018) to date (2021). Thousands of such records were scrutinized to reveal six parameters: number of antenatal visits; number of live births and total number of infants served BCG; number RDTs conducted for Malaria, number of RTIs seen and total number of patients for all other illnesses. Results: Results showed a decline in all parameters investigated except RTIs. Indeed, the proportion of RTIs - related hospital visits increased by about 22.8%.Discussion: The decline in the utilization of hospital services is expected given the lock down measures implemented to halt transmission of COVID_19 that makes it difficult to access health facilities. The rise in the incidence of RTIs may probably be that, they were COVID cases that were undetected due to limited COVID_19 testing capacity.Conclusion: The study concludes that COVID_19 epidemic impacted negatively on all aspect of hospital services except RTIs. The increment is statistically significant. As the most prominent of all symptoms of COVID_19 presents as respiratory tract disorder, a continued rise in RTIs despite declining rates of confirmed COVID19 cases in the state at the moment suggests that, there’s an ongoing community transmission.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".