Pattern of OPD utilisation during the COVID-19 pandemic under the Universal Coverage Scheme in Thailand: what can 850 million records tell us?
Bibliographic record
Abstract
Abstract Background Out-patient department (OPD) is a crucial component of the healthcare systems in low- and middle-income countries including Thailand. A considerable impact of coronavirus disease 2019 (COVID-19) pandemic and its control measures, especially the lockdown, on utilisation of OPD services was expected. This study thus aims to estimate the pattern of OPD utilisation during the COVID-19 pandemic in Thailand including overall utilisation and within each sub-groups including diagnostic group, age group, and health region. Methods This study was a secondary data analysis of aggregated outpatient data from patients covered under the Universal Coverage Scheme (UCS) in Thailand over a 4-year period (2017–2020). Interrupted time series analyses and segmented Quasi-Poisson regression were used to examine the impact of COVID-19 on the overall OPD utilisation including the impact on each diagnostic group, age groups, health regions, and provinces. Results Analysis of 845,344,946 OPD visits in this study showed a seasonal pattern and increasing trend in monthly OPD visits before the COVID-19 pandemic. A 28% (rate ratio (RR) 0.718, 95% confidence interval (CI): 0.631–0.819) and 11% (RR 0.890, 95% CI: 0.811–0.977) reduction in OPD visits was observed during the lockdown and post-lockdown periods, respectively, when compared to the pre-lockdown period. Diseases of respiratory system were most affected with a RR of 0.411 (95% CI: 0.320–0.527), while the number of visits for non-communicable diseases (ICD-10: E00–E90, I00–I99) and elderly (> 60 years) dropped slightly. The post-lockdown trend in monthly OPD visits gradually increased to the pre-pandemic levels in most groups. Conclusions Thailand's OPD utilisation rate during the COVID-19 lockdown decreased in some diseases, but the service for certain group of patients appeared to remain available. After the COVID-19 lockdown, the rate returned to the pre-pandemic level in a timely manner. Equipped with a knowledge of OPD utilisation pattern during COVID-19 based on a national real-world database could aid with a better preparation of healthcare system for future pandemics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".