Effects of 2018 Japan floods on healthcare costs and service utilization in Japan: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Floods and torrential rains are natural disasters caused by climate change. Unfortunately, such events are more frequent and are increasingly severe in recent times. The 2018 Japan Floods in western Japan were one of the largest such disasters. This study aimed to evaluate the effect of the 2018 Japan Floods on healthcare costs and service utilization. METHODS: This retrospective cohort study included all patients whose receipts accrued between July 2017 and June 2019 in Hiroshima, Okayama, and Ehime prefectures using the National Database of Health Insurance Claims. We used Generalized Estimating Equations (GEEs) to investigate yearly healthcare costs during the pre-and post-disaster periods, quarterly high-cost patients (top 10%), and service utilization (outpatient care, inpatient care, and dispensing pharmacy) during the post-disaster period. After the GEEs, we estimated the average marginal effects as the attributable disaster effect. RESULTS: The total number of participants was 5,534,276. Victims accounted for 0.65% of the total number of participants (n = 36,032). Although there was no significant difference in pre-disaster healthcare costs (p = 0.63), post-disaster costs were $3,382 (95% CI: 3,254-3,510) for victims and $3,027 (95% CI: 3,015-3,038) for non-victims (p < 0.001). The highest risk difference among high-cost patients was 0.8% (95% CI: 0.6-1.1) in the fourth quarter. In contrast, the highest risk difference of service utilization was in the first quarter (outpatient care: 7.0% (95% CI: 6.7-7.4), inpatient care: 1.3% (95% CI: 1.1-1.5), and dispensing pharmacy: 5.9% (95% CI: 5.5-6.4)). CONCLUSION: Victims of the 2018 Japan Floods had higher medical costs and used more healthcare services than non-victims. In addition, the risk of higher medical costs was highest at the end of the observation period. It is necessary to estimate the increase in healthcare costs according to the disaster scale and plan for appropriate post-disaster healthcare service delivery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".