BACKGROUND: Older adults in Japan have the highest drowning mortality rate globally due to frequent bathing practices. Low outdoor temperatures have been linked to bath-related deaths; however, previous studies employed limited statistical models and focused on a single prefecture. Given Japan's aging population, preventing bath-related deaths is a public health priority. This study aimed to analyze the association between outdoor temperature and bath-related drowning deaths across Japan from 1995 to 2020 (n = 110,938), examining regional variations and identifying contributing prefectural characteristics. METHODS: Daily counts of bath-related drowning deaths per prefecture were matched with daily mean temperature data from the Japan Meteorological Agency. Prefecture-level demographic and environmental data were obtained from Japan's Official Statistics. We applied a generalized additive mixed model to examine the association between daily mean temperature and bath-related drowning death risk. Meta-regression was used to identify prefecture-level modifiers. RESULTS: Bath-related drowning death risk peaked at a daily mean temperature of 1.8 °C (relative risk [RR] 9.7, 95% confidence interval [CI]: 9.5-9.9), compared with the lowest risk at 30.3 °C. The association was stronger at mid-range temperatures, particularly among males and individuals aged ≥65 years. Among prefectures, Kagoshima-the southernmost prefecture on Japan's main islands-had the highest maximum RR at 19.6 (95% CI: 16.2-23.6), while Hokkaido-the northernmost prefecture-had the lowest at 3.8 (95% CI: 3.4-4.3). Prefecture-level factors that strengthened this relationship included a lower prevalence of double-pane windows as a proxy of housing insulation and higher annual mean temperatures with ratio of RR change per one standard deviation increase of 0.76 (95% CI: 0.69-0.83) and 1.27 (95% CI: 1.18-1.37), respectively. CONCLUSIONS Warmer prefectures in southern regions exhibited greater maximum-to-minimum risk ratios compared to cooler northern prefectures. This paradoxical finding underscores the importance of region-specific interventions to reduce bath-related deaths.
Japan/epidemiology* ; Humans ; Drowning/epidemiology* ; Male ; Aged ; Female ; Baths/statistics & numerical data* ; Middle Aged ; Temperature ; Aged, 80 and over ; Adult ; Young Adult ; Risk Factors ; Adolescent

BACKGROUND: Globally, Japan has the highest drowning mortality among older adults, largely because of bathing customs. Although this mortality rate peaks in winter, the nationwide impact of outdoor temperature has not been quantified, and whether specific days carry greater risks for bathtub drowning deaths remains unclear. Therefore, we aimed to address these gaps using nationwide data from Japan. METHODS: We collected daily data on outdoor temperatures and bathtub drowning deaths (from death certificates), along with population data, across 47 prefectures from 1995-2020. A time series regression model incorporating a cyclic spline for day-of-year and a cross-basis function for outdoor temperature was used to estimate seasonality and temperature attributable fractions (AFs). Prefecture-specific estimates were pooled using meta-analysis. National holidays were defined by the Act on National Holidays. RESULTS: During the study period, 99,930 home bathtub drowning deaths were recorded. The AF for seasonality modelled with a cyclic spline for day-of-year was 77.8% (empirical confidence interval [eCI]: 76.7-78.8%), which decreased to 15.3% (eCI: 13.1-18.0%) after adjusting for outdoor temperature, indicating that outdoor temperature accounted for 80.3% of the seasonal effect. Elevated risks were observed on Sundays (relative risk = 1.16, 95% CI: 1.12-1.20), holidays (1.12, 95% CI: 1.08-1.16), New Year's Day (1.72, 95% CI: 1.61-1.84), and New Year's Eve (1.63, 95% CI: 1.52-1.74) in the adjusted model, which included a cyclic spline for day-of-year and a cross-basis function for outdoor temperature. CONCLUSION Our findings highlight the importance of mitigating the impact of outdoor temperature on bath-related death risk. Identifying high-risk days can be used to help develop targeted preventive strategies.
Seasons ; Humans ; Drowning/epidemiology* ; Japan/epidemiology* ; Temperature ; Aged ; Male ; Adult ; Female ; Middle Aged ; Child ; Young Adult ; Adolescent ; Aged, 80 and over ; Child, Preschool

OBJECTIVES: To study the clinical and microbiological characteristics of children with drowning-associated aspiration pneumonia, so as to provide a reference for empirical selection of antibacterial agents. METHODS: A retrospective analysis was performed on the medical data of 185 children with drowning-associated aspiration pneumonia who were admitted to Children's Hospital of Chongqing Medical University from January 2010 to October 2020. According to the drowning environment, these children were divided into four groups: fecal group (n=44), freshwater group (n=69), swimming pool group (n=41), and contaminant water group (n=31). The clinical characteristics and pathogen detection results were reviewed and compared among the four groups. RESULTS: The 185 children had an age of 4 months to 17 years (median 34 months). Sputum cultures were performed on 157 children, and 103 were tested positive (65.6%), with 87 strains of Gram-negative bacteria (68.5%), 37 strains of Gram-positive bacteria (29.1%), and 3 strains of fungi (2.4%). Gram-negative bacteria were the main pathogen in the fecal group and the contaminant water group, accounting for 88.2% (30/34) and 78.3% (18/23), respectively. The freshwater group had a significantly higher detection rate of Gram-positive bacteria than the fecal group (P<0.008), and the swimming pool group had an equal detection rate of Gram-negative bacteria and Gram-positive bacteria. CONCLUSIONS For pulmonary bacterial infection in children with drowning in feces or contaminant water, antibiotics against Gram-negative bacteria may be applied empirically, while for children with drowning in a swimming pool or freshwater, broad-spectrum antibiotics may be used as initial treatment, and subsequently the application of antibiotics may be adjusted according to the results of the drug sensitivity test.
Anti-Bacterial Agents/therapeutic use* ; Child ; Drowning ; Gram-Negative Bacteria ; Humans ; Pneumonia, Aspiration/drug therapy* ; Retrospective Studies ; Water

OBJECTIVE: To analyze the mortality of injuries among children and adolescents aged 5 to 24 in China from 1990 to 2019, and to provide the theoretical basis for the formulation of policies related to injury prevention. METHODS: The mortality data of children and adolescents aged 5 to 24 years in China between 1990 and 2019 were obtained from Global Burden of Disease (GBD) 2019, and the change in mortality between 1990 and 2019 was described. Age-period-cohort analysis was utilized to determine the age effect, period effect and cohort effect for road injuries, drowning and self-harm. RESULTS: Injury mortality of Chinese children and adolescents aged 5 to 24 years decreased from 46.22 [95% uncertainty interval (UI): 40.88-52.12] per 100 000 to 20.36 (95%UI: 17.58-23.38) per 100 000 between 1990 and 2019. Sub-group analysis revealed a pattern that was basically consistent with the overall trend. From 1990 to 2019, drowning declined from the first leading cause of injury death among children and adolescents aged 5 to 24 years in China to the second while road injuries became the one which caused the most death among them, and self-harm was the third leading cause of injury death. The top three causes of injury death in each subgroup were basically the same as the overall, but the order was different in each subgroup. Age-period-cohort analysis showed that the death risk of road injuries, drowning, and self-harm all decreased with period and cohort. Aside from that, the death risk of road injuries showed a U-shape trend, which decreased at first but increased soon afterwards, with the increase of age, while the death risk of drowning decreased with age and the death risk of self-harm increased with age. CONCLUSION In China, the injuries mortality among children and adolescents aged 5 to 24 years has decreased over the last three decades. However, specific cause-related injury deaths, manifested differently in different sub-groups. Targeted policies and intervention should be proposed to reduce the mortality of children and adolescents in accordance with the characteristics of injuries death in different genders and age groups.
Adolescent ; Asians ; Cause of Death ; Child ; China/epidemiology* ; Drowning ; Female ; Global Burden of Disease ; Humans ; Infant ; Male ; Wounds and Injuries

Autopsy ; Drowning/diagnosis* ; Forensic Pathology ; Humans ; Lung ; Postmortem Changes ; Water

Drowning is the death caused by asphyxiation due to fluid blocking the airway. In the practice of forensic medicine, it is the key to determine whether the corpse was drowned or entered the water after death. At the same time, the drowning site inference and postmortem submersion interval (PMSI) play an important role in the investigating the identity of the deceased, narrowing the investigation scope, and solving the case. Based on diatoms testing, molecular biology, imaging and artificial intelligence and other technologies, domestic and foreign forensic scientists have done relative research in the identification of the cause of death, drowning site inference and PMSI, and achieved certain results in forensic medicine application. In order to provide a reference for future study of bodies in the water, this paper summarizes the above research contents.
Artificial Intelligence ; Diatoms ; Drowning/diagnosis* ; Forensic Pathology ; Humans ; Lung ; Water

Diatom test is the main laboratory test method in the diagnosis of drowning in forensic medicine. It plays an important role in differentiating the antemortem drowning from the postmortem drowning and inferring drowning site. Artificial intelligence (AI) automatic diatom test is a technological innovation in forensic drowning diagnosis which is based on morphological characteristics of diatom, the application of AI algorithm to automatic identification and classification of diatom in tissues and organs. This paper discusses the morphological diatom test methods and reviews the research progress of automatic diatom recognition and classification involving AI algorithms. AI deep learning algorithm can assist diatom testing to obtain objective, accurate, and efficient qualitative and quantitative analysis results, which is expected to become a new direction of diatom testing research in the drowning of forensic medicine in the future.
Artificial Intelligence ; Autopsy ; Diatoms ; Drowning/diagnosis* ; Humans ; Lung

Diatom detection is an important method for identifying drowning and throwing corpses after death and inferring the drowning sites in forensic examination of corpses in water. In recent years,high-throughput sequencing technology has achieved rapid development and has been widely used in research related to diatom taxonomic investigations. This paper reviews the research status and prospects of high-throughput sequencing technology and its application in forensic diatom detection.
Cadaver ; Diatoms/genetics* ; Drowning/diagnosis* ; Forensic Pathology/methods* ; High-Throughput Nucleotide Sequencing ; Humans ; Lung ; Technology

OBJECTIVES: To explore the application values of diatom artificial intelligence (AI) search system in the diagnosis of drowning. METHODS: The liver and kidney tissues of 12 drowned corpses were taken and were performed with the diatom test, the view images were obtained by scanning electron microscopy (SEM). Diatom detection and forensic expert manual identification were carried out under the thresholds of 0.5, 0.7 and 0.9 of the diatom AI search system, respectively. Diatom recall rate, precision rate and image exclusion rate were used to detect and compare the efficiency of diatom AI search system. RESULTS: There was no statistical difference between the number of diatoms detected in the target marked by the diatom AI search system and the number of diatoms identified manually (P>0.05); the recall rates of the diatom AI search system were statistically different under different thresholds (P<0.05); the precision rates of the diatom AI system were statistically different under different thresholds(P<0.05), and the highest precision rate was 53.15%; the image exclusion rates of the diatom AI search system were statistically different under different thresholds (P<0.05), and the highest image exclusion rate was 99.72%. For the same sample, the time taken by the diatom AI search system to identify diatoms was only 1/7 of that of manual identification. CONCLUSIONS Diatom AI search system has a good application prospect in drowning cases. Its automatic diatom search ability is equal to that of experienced forensic experts, and it can greatly reduce the workload of manual observation of images.
Artificial Intelligence ; Diatoms ; Drowning/diagnosis* ; Humans ; Liver ; Lung ; Microscopy, Electron, Scanning

OBJECTIVES: To construct a YOLOv3-based model for diatom identification in scanning electron microscope images, explore the application performance in practical cases and discuss the advantages of this model. METHODS: A total of 25 000 scanning electron microscopy images were collected at 1 500× as an initial image set, and input into the YOLOv3 network to train the identification model after experts' annotation and image processing. Diatom scanning electron microscopy images of lung, liver and kidney tissues taken from 8 drowning cases were identified by this model under the threshold of 0.4, 0.6 and 0.8 respectively, and were also identified by experts manually. The application performance of this model was evaluated through the recognition speed, recall rate and precision rate. RESULTS: The mean average precision of the model in the validation set and test set was 94.8% and 94.3%, respectively, and the average recall rate was 81.2% and 81.5%, respectively. The recognition speed of the model is more than 9 times faster than that of manual recognition. Under the threshold of 0.4, the mean recall rate and precision rate of diatoms in lung tissues were 89.6% and 87.8%, respectively. The overall recall rate in liver and kidney tissues was 100% and the precision rate was less than 5%. As the threshold increased, the recall rate in all tissues decreased and the precision rate increased. The F1 score of the model in lung tissues decreased with the increase of threshold, while the F1 score in liver and kidney tissues with the increase of threshold. CONCLUSIONS The YOLOv3-based diatom electron microscope images automatic identification model works at a rapid speed and shows high recall rates in all tissues and high precision rates in lung tissues under an appropriate threshold. The identification model greatly reduces the workload of manual recognition, and has a good application prospect.
Diatoms ; Drowning/diagnosis* ; Humans ; Liver/diagnostic imaging* ; Lung/diagnostic imaging* ; Microscopy, Electron, Scanning