1
|
Abstract
Similar to other countries, the Japanese government quickly undertook preventative measures against increasing suicides during the pandemic, but could not suppress the increase. Suicide mortality among both sexes under 20 and females aged 20-39 significantly increased during the pandemic, but unexpectedly had already slowed decreasing trends before the pandemic onset. Furthermore, before the pandemic, a higher complete unemployment rate contributed to increasing suicide mortality of both sexes, whereas during the pandemic, the positive relationship between females suicide mortalities and complete unemployment rates was not observed.
Collapse
Affiliation(s)
- Motohiro Okada
- Department of Neuropsychiatry, Division of Neuroscience, Graduate School of Medicine, Mie University, Tsu 514-8507, Japan.
| |
Collapse
|
2
|
Okada M, Matsumoto R, Motomura E, Shiroyama T, Murata M. Exploring characteristics of increased suicide during the COVID-19 pandemic in Japan using provisional governmental data. Lancet Reg Health West Pac 2022; 24:100481. [PMID: 35664440 PMCID: PMC9160839 DOI: 10.1016/j.lanwpc.2022.100481] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Background The Japanese age-standardised death rate of suicide (SDR) had decreased during 2009-2019, but increased in 2020-2021, during the COVID-19 pandemic. Methods This study aimed to explain the trend change in the SDR during the pandemic, disaggregated by prefecture, gender, suicide method and household, as compared to predicted SDR derived from pre-pandemic data, using linear mixed-effect and hierarchical linear regression models with robust standard error analyses. Findings The SDR was lower during March-June 2020 (during the first wave of the pandemic), but higher during July-December 2020 than the predicted SDR. In 2021, males' SDR was nearly equal to the predicted SDR, whereas females' SDR in the metropolitan-region (17.5%: 95% confidence interval: 13.9-21.2%) and non-metropolitan-region (24.7%: 95% confidence interval: 22.8-26.7%) continued to be higher than the predicted SDR. These gender- and region-dependent temporal fluctuations of SDR were synchronised with those of SDRs caused by hanging, at home and single-person-households. Additionally, the rising number of infected patients with the SARS-CoV-2 and polymerase chain reaction (PCR) diagnostic examinations were positively (β = 0.024) and negatively (β =-0.002) related to the SDR during the pandemic, respectively. Interpretation Japanese suicide statistics have previously established that the predominant method and place of suicide were by hanging and at the individual's home, respectively. The present findings suggest that transformed lifestyles during the pandemic, increasing time spent at home, enhanced the suicide risk of Japanese people by hanging and at home. Funding Regional Suicide Countermeasures Emergency Enhancement Fund of Mie Prefecture (2021-40).
Collapse
Affiliation(s)
- Motohiro Okada
- Department of Neuropsychiatry, Division of Neuroscience, Graduate School of Medicine, Mie University, Tsu 514-8507, Japan
| | - Ryusuke Matsumoto
- Department of Neuropsychiatry, Division of Neuroscience, Graduate School of Medicine, Mie University, Tsu 514-8507, Japan
| | - Eishi Motomura
- Department of Neuropsychiatry, Division of Neuroscience, Graduate School of Medicine, Mie University, Tsu 514-8507, Japan
| | - Takashi Shiroyama
- Department of Neuropsychiatry, Division of Neuroscience, Graduate School of Medicine, Mie University, Tsu 514-8507, Japan
| | - Masahiko Murata
- Department of Psychiatry, National Hospital Organization Sakakibara Hospital, 777 Sakakibara, Tsu, Mie 514-1292, Japan
| |
Collapse
|
3
|
Iwasaki-Motegi R, Yoshioka-Maeda K, Honda C, Yamamoto-Mitani N. Prefectural public health nurses' support in human resource development of municipal public health nurses in Japan. Nihon Koshu Eisei Zasshi 2022; 69:417-423. [PMID: 35400724 DOI: 10.11236/jph.21-078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective This study aimed to explore the support extended by prefectural public health nurses (PHNs) toward the human resource development (HRD) of municipal PHNs in Japan.Methods We performed a qualitative descriptive study involving nine prefectural PHNs from April 2019 to May 2020. The data were collected through semi-structured interviews using an interview guide, described qualitatively, coded, and then categorized.Results Five categories were extracted. "Clarifying the needs and problems related to HRD and daily PHN activities in the municipalities," "Creating an environment where all municipal PHNs have equal opportunities for off-the-job training," and "Helping municipal PHNs recognize the meaning of practice and develop an evaluation perspective" were extracted from the prefectural government PHNs and prefectural health center (HC) PHNs. "Clarifying problems and future prospects to encourage the growth of PHNs" and "Creating an environment where the significance and value of the activities of PHNs are recognized within the organization and HRD can easily take place" were extracted from the HC PHNs.Conclusion Much of the HRD support provided by the prefectural PHNs to the municipal PHNs was analogous to the PHN activities provided to the community and residents. To promote HRD effectively, prefectural PHNs should apply their individual care skills to the HRD of municipal PHNs.
Collapse
Affiliation(s)
- Riho Iwasaki-Motegi
- Department of Community Health Nursing, Division of Health Sciences and Nursing, Graduate School of Medicine & Global Nursing Research Center, The University of Tokyo
| | | | - Chikako Honda
- Department of Community Health Nursing, Division of Health Sciences and Nursing, Graduate School of Medicine & Global Nursing Research Center, The University of Tokyo
| | - Noriko Yamamoto-Mitani
- Department of Community Health Nursing, Division of Health Sciences and Nursing, Graduate School of Medicine & Global Nursing Research Center, The University of Tokyo
| |
Collapse
|
4
|
Liu MY, Li QH, Zhang YJ, Ma Y, Liu Y, Feng W, Hou CB, Amsalu E, Li X, Wang W, Li WM, Guo XH. Spatial and temporal clustering analysis of tuberculosis in the mainland of China at the prefecture level, 2005-2015. Infect Dis Poverty 2018; 7:106. [PMID: 30340513 PMCID: PMC6195697 DOI: 10.1186/s40249-018-0490-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Accepted: 10/04/2018] [Indexed: 12/25/2022] Open
Abstract
Background Tuberculosis (TB) is still one of the most serious infectious diseases in the mainland of China. So it was urgent for the formulation of more effective measures to prevent and control it. Methods The data of reported TB cases in 340 prefectures from the mainland of China were extracted from the China Information System for Disease Control and Prevention (CISDCP) during January 2005 to December 2015. The Kulldorff’s retrospective space-time scan statistics was used to identify the temporal, spatial and spatio-temporal clusters of reported TB in the mainland of China by using the discrete Poisson probability model. Spatio-temporal clusters of sputum smear-positive (SS+) reported TB and sputum smear-negative (SS-) reported TB were also detected at the prefecture level. Results A total of 10 200 528 reported TB cases were collected from 2005 to 2015 in 340 prefectures, including 5 283 983 SS- TB cases and 4 631 734 SS + TB cases with specific sputum smear results, 284 811 cases without sputum smear test. Significantly TB clustering patterns in spatial, temporal and spatio-temporal were observed in this research. Results of the Kulldorff’s scan found twelve significant space-time clusters of reported TB. The most likely spatio-temporal cluster (RR = 3.27, P < 0.001) was mainly located in Xinjiang Uygur Autonomous Region of western China, covering five prefectures and clustering in the time frame from September 2012 to November 2015. The spatio-temporal clustering results of SS+ TB and SS- TB also showed the most likely clusters distributed in the western China. However, the clustering time of SS+ TB was concentrated before 2010 while SS- TB was mainly concentrated after 2010. Conclusions This study identified the time and region of TB, SS+ TB and SS- TB clustered easily in 340 prefectures in the mainland of China, which is helpful in prioritizing resource assignment in high-risk periods and high-risk areas, and to formulate powerful strategy to prevention and control TB. Electronic supplementary material The online version of this article (10.1186/s40249-018-0490-8) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Meng-Yang Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China
| | - Qi-Huan Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China
| | - Ying-Jie Zhang
- Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Yuan Ma
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China
| | - Yue Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China
| | - Wei Feng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China
| | - Cheng-Bei Hou
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China
| | - Endawoke Amsalu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China
| | - Xia Li
- Department of Mathematics and Statistics, La Trobe University, Melbourne, 3086, Australia
| | - Wei Wang
- School of Medical Sciences and Health, Edith Cowan University, WA6027, Perth, Australia
| | - Wei-Min Li
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China. .,National Tuberculosis Clinical Laboratory of China, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China. .,Beijing Tuberculosis and Thoracic Tumour Research Institute, Beijing, 101149, China.
| | - Xiu-Hua Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China. .,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China.
| |
Collapse
|