The increasing burden of asthma acute care in Singapore: an update on 15-year population-level evidence
Bibliographic record
Abstract
BACKGROUND: In Singapore, there is currently scarce population-based research informing the recent trends of asthma-related healthcare burdens. In this study, we investigated the past 25-year trends of asthma-related hospitalisations, emergency department (ED) visits and deaths in Singapore and projected the future burdens from 2023 to 2040. METHODS: We acquired annually-measured data from the Singapore Ministry of Health Clinical and National Disease Registry, containing 25-year asthma-related hospitalisation and death rates as well as 15-year ED visit rates. We conducted change-point analysis and generalised linear modelling to identify time intervals with stable trends and estimate asthma-related healthcare utilisation and mortality rates. To project future asthma-related burdens, we developed a probabilistic model which combined projections of future population size with the estimated rate outcomes from the last stable period. RESULTS: Our results show that the asthma hospitalisation rate in Singapore had remained at approximately 80 episodes per 100,000 from 2003 to 2019 and are likely to grow by 1.7% each year (95% CI: 0.7, 5.0%), leading to a total of 163,633 episodes from 2023 to 2040 which corresponds to an estimated $103,075,820 based on 2022 USD. Besides, Singapore's asthma-related ED visit rate was 390 per 100,000 in 2019 and is expected to decline by 3.4% each year (95% CI: - 5.8, 0.0%), leading to a total of 208,145 episodes from 2023 to 2040 which corresponds to USD$15,053,795. In contrast, the 2019 asthma-related mortality rate in Singapore was approximately 0.57 per 100,000 and is likely to stay stably low (change per year: -1.3, 95% CI: - 11.0, 4.3%). Between 2023 and 2040, Singapore's estimated total number of asthma-related deaths is 638 episodes. CONCLUSIONS: Currently, the burden of asthma acute care in Singapore is high; Singapore's asthma-related hospitalisation and ED visit rates are relatively higher than those of other developed economies, and its asthma admission rate is expected to increase significantly over time, possibly indicating excess resource use for asthma. The established national asthma programme in Singapore, together with recent efforts in reinforcing primary care at the national level, provides opportunities to reduce avoidable asthma admissions.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".