Forecasting daily bathtub-drowning mortality in Japan

Background: Japan reports the highest global mortality rate from drowning among older adults, predominantly owing to bathtub-related incidents. Despite sustained public health interventions, this mortality has increased over several decades. Timely warnings advising older adults to avoid unsupervised bathing during high-risk conditions may mitigate this issue; however, no nationwide forecasting model currently exists. Methods: We integrated death certificate records from 1995-2020 (99,930 bathtub-drowning deaths) with meteorological, temporal, and demographic data across all 47 prefectures (446,359 prefecture-days). Daily mortality counts were modeled using a distributed-lag non-linear model (DLNM), extreme gradient boosting (XGBoost), and long short-term memory network (LSTM). Data were partitioned chronologically into training (1995-2015), validation (2016-2018), and test (2019-2020) sets. Predictive accuracy was evaluated using root mean square error (RMSE) and mean absolute error (MAE), whereas feature importance was quantified via Shapley additive explanations. Results: During the test period, DLNM, XGBoost, and LSTM exhibited comparable predictive performance (RMSE = 0.577, 0.574, 0.575; MAE = 0.345, 0.333, 0.347, respectively). The most important features across all models were daily mean temperature, prefectural population, and binary prefecture indicators. Restricting DLNM meteorological inputs to routinely forecasted variables-daily maximum and minimum temperatures-did not reduce predictive accuracy [RMSE = 0.577 (95% confidence interval, 0.566-0.590); MAE = 0.344 (95% confidence interval, 0.340-0.349)]. Conclusions: The DLNM-based framework provides a practical means of forecasting the daily bathtub-drowning deaths. Integration into routine meteorological broadcasts and mobile platforms may facilitate timely warnings, prompting older adults to avoid unsupervised bathing on high-risk days, thereby reducing Japan's ongoing preventable bath-related mortality

Artificial neural network · Bathing · Deep learning · Deep neural networks · Unsupervised learning · Flood Risk Assessment and Management · Injury Epidemiology and Prevention · Traffic and Road Safety

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