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Wu Y, Grant S, Chen W, Szarka A. Refining acute human exposure assessment to pesticides in surface water: An integrated data-driven modeling approach. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 865:161190. [PMID: 36581287 DOI: 10.1016/j.scitotenv.2022.161190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 12/03/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
The substantial spatial and temporal variability of pesticides has led to large uncertainties when determining their peak aqueous concentrations. There is however a lack of large-scale studies dealing with accurate determination of annual maximum daily concentration (AMDC) across the landscape and over time based on the publicly available monitoring data. We developed a novel data-driven approach that firstly used time series modeling to generate AMDCs for qualified water monitoring sites in the conterminous U.S. With feature variables such as pesticide use and land cover compiled into the dataset, machine learning models using eXtreme Gradient Boosting (XGBoost) and Random Forest Regressor (RF) were then developed to estimate AMDCs in surface waters across the U.S. Both models exhibited significant predictability, while a hybrid model consisting of the average predictions by XGBoost and RF model had the highest prediction accuracy (mean absolute error (MAE): 1.23; R2: 0.61). The analysis of permutation variable importance indicated that pesticide use and drainage area were the two most important drivers. Partial dependence analysis revealed that pesticide use, precipitation, cultivated crop land cover and solubility exhibited concentration-promoting effects, whereas drainage area and molecular weight had concentration-demoting effects. Soil adsorption coefficient (Koc) showed nonmonotonic effects. The hybrid model was used to predict and map AMDCs of four example pesticides, including 2,4-dichlorophenoxyacetic acid (2,4-D), atrazine, glyphosate and imidacloprid during 2016-2019 at national scale. The predictive capability was validated using independent monitoring datasets. The fully evaluated approach significantly reduced the uncertainties in modeling annual peak concentrations and served as a valuable solution for conducting geographically oriented, highly refined exposure assessments for pesticides.
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Affiliation(s)
- Yaoxing Wu
- Product Safety, Syngenta Crop Protection LLC, Greensboro, NC 27409, USA.
| | - Shanique Grant
- Product Safety, Syngenta Crop Protection LLC, Greensboro, NC 27409, USA
| | - Wenlin Chen
- Product Safety, Syngenta Crop Protection LLC, Greensboro, NC 27409, USA
| | - Arpad Szarka
- Product Safety, Syngenta Crop Protection LLC, Greensboro, NC 27409, USA
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2
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Li C, Chen K, Yang K, Li J, Zhong Y, Yu H, Yang Y, Yang X, Liu L. Progress on application of spatial epidemiology in ophthalmology. Front Public Health 2022; 10:936715. [PMID: 36033806 PMCID: PMC9399620 DOI: 10.3389/fpubh.2022.936715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/19/2022] [Indexed: 01/25/2023] Open
Abstract
Most ocular diseases observed with cataract, chlamydia trachomatis, diabetic retinopathy, and uveitis, have their associations with environmental exposures, lifestyle, and habits, making their distribution has certain temporal and spatial features based essentially on epidemiology. Spatial epidemiology focuses on the use of geographic information systems (GIS), global navigation satellite systems (GNSS), and spatial analysis to map spatial distribution as well as change the tendency of diseases and investigate the health services status of populations. Recently, the spatial epidemic approach has been applied in the field of ophthalmology, which provides many valuable key messages on ocular disease prevention and control. This work briefly reviewed the context of spatial epidemiology and summarized its progress in the analysis of spatiotemporal distribution, non-monitoring area data estimation, influencing factors of ocular diseases, and allocation and utilization of eye health resources, to provide references for its application in the prevention and control of ocular diseases in the future.
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Affiliation(s)
- Cong Li
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Kang Chen
- Department of Ophthalmology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Kaibo Yang
- Department of Ophthalmology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jiaxin Li
- Department of Graduate, China Medical University, Shenyang, China
| | - Yifan Zhong
- Department of Ophthalmology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yajun Yang
- Department of Cataract, Baotou Chaoju Eye Hospital, Baotou, China,*Correspondence: Yajun Yang
| | - Xiaohong Yang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China,Xiaohong Yang
| | - Lei Liu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China,Department of Ophthalmology, Jincheng People's Hospital, Jincheng, China,Lei Liu
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Ramìrez-Aldana R, Gomez-Verjan JC, Bello-Chavolla OY, Naranjo L. A spatio-temporal study of state-wide case-fatality risks during the first wave of the COVID-19 pandemic in Mexico. GEOSPATIAL HEALTH 2022; 17. [PMID: 35352540 DOI: 10.4081/gh.2022.1054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 03/10/2022] [Indexed: 06/14/2023]
Abstract
spatio-temporal analysis of the first wave of the coronavirus (COVID-19) pandemic in Mexico (April to September 2020) was performed by state. Descriptive analyses through diagrams, mapping, animations and time series representations were carried out. Greater risks were observed at certain times in specific regions. Various trends and clusters were observed and analysed by fitting linear mixed models and time series clustering. The association of co-morbidities and other variables were studied by fitting a spatial panel data linear model (SPLM). On average, the greatest risks were observed in Baja California Norte, Chiapas and Sonora, while some other densely populated states, e.g., Mexico City, had lower values. The trends varied by state and a four-order polynomial, including fixed and random effects, was necessary to model them. The most common risk development was observed in states belonging to two clusters and consisted of an initial increase followed by a decrease. Some states presented cluster configurations with a retarded risk increase before the decrease, while the risk increased throughout the time of study in others. A cyclic behaviour with a second increasing trend was also observed in some states. The SPLM approach revealed a positive significant association with respect to case fatality risk between certain groups, such as males and individuals aged 50 years and more, and the prevalence of chronic kidney disease, cardiovascular disease, asthma and hypertension. The analysis may provide valuable insight into COVID-19 dynamics applicable in future outbreaks, as well as identify determinants signifying certain trends at the state level. The combination of spatial and temporal information may provide a better understanding of the fatalities due to COVID-19.
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Affiliation(s)
| | | | | | - Lizbeth Naranjo
- 2Department of Mathematics, Faculty of Sciences, National Autonomous University of Mexico, Mexico City.
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Gourishankar A. Geospatial analysis of salmonellosis and its association with socioeconomic status in Texas. Fam Med Community Health 2021; 9:fmch-2021-001214. [PMID: 34625486 PMCID: PMC8504352 DOI: 10.1136/fmch-2021-001214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Objective The study’s objective was to find the association between salmonellosis and socioeconomic status (SES) in hot spot areas and statewide counties. Design A retrospective cohort study. Setting The data were recorded regarding salmonellosis in 2017 from the Texas surveillance database. It included assessment of hot spot analysis and SES association with salmonellosis at the county level. Participants Patients with salmonellosis of all age groups in Texas. Results There were a total of 5113 salmonellosis from 254 counties with an unadjusted crude rate of 18 per 100 000 person-years. Seven SES risk factors in the hot spot counties were as follows: low values of the severe housing problem, unemployment, African American and high values of social association rate, fast food/full-service restaurant use, Hispanic and Hispanic senior low access-to-store (p<0.05). A 12% difference existed between local health departments in hot (25%) and cold spot (37%) counties (χ2 (1, n=108)=0.5, p=0.81). Statewide independent risk factors were severe housing problem (incidence rate ratio (IRR)=1.1; 95% CI: 1.05 to 1.14), social association rate (IRR=0.89; 95% CI: 0.87 to 0.92), college education (IRR=1.05; 95% CI: 1.04 to 1.07) and non-Hispanic senior local access-to-store (IRR=1.98; 95% CI: 1.26 to 3.11). The severe housing problem predicted zero occurrences of infection in a county (OR=0.51; 95% CI: 0.28 to 0.95). Conclusions Disparity exists in salmonellosis and SES. Attention to unmet needs will decrease salmonellosis. Severe housing problem is a notable risk.
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Affiliation(s)
- Anand Gourishankar
- Division of Pediatric Hospital Medicine, Children's National Hospital, Washington, District of Columbia, USA
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Nieves JJ, Bondarenko M, Kerr D, Ves N, Yetman G, Sinha P, Clarke DJ, Sorichetta A, Stevens FR, Gaughan AE, Tatem AJ. Measuring the contribution of built-settlement data to global population mapping. SOCIAL SCIENCES & HUMANITIES OPEN 2021; 3:100102. [PMID: 33889839 PMCID: PMC8041065 DOI: 10.1016/j.ssaho.2020.100102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 12/11/2020] [Accepted: 12/20/2020] [Indexed: 11/24/2022]
Abstract
Top-down population modelling has gained applied prominence in public health, planning, and sustainability applications at the global scale. These top-down population modelling methods often rely on remote-sensing (RS) derived representation of the built-environment and settlements as key predictive covariates. While these RS-derived data, which are global in extent, have become more advanced and more available, gaps in spatial and temporal coverage remain. These gaps have prompted the interpolation of the built-environment and settlements, but the utility of such interpolated data in further population modelling applications has garnered little research. Thus, our objective was to determine the utility of modelled built-settlement extents in a top-down population modelling application. Here we take modelled global built-settlement extents between 2000 and 2012, created using a spatio-temporal disaggregation of observed settlement growth. We then demonstrate the applied utility of such annually modelled settlement data within the application of annually modelling population, using random forest informed dasymetric disaggregations, across 172 countries and a 13-year period. We demonstrate that the modelled built-settlement data are consistently the 2nd most important covariate in predicting population density, behind annual lights at night, across the globe and across the study period. Further, we demonstrate that this modelled built-settlement data often provides more information than current annually available RS-derived data and last observed built-settlement extents.
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Affiliation(s)
- Jeremiah J. Nieves
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
| | - Maksym Bondarenko
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
| | - David Kerr
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
| | - Nikolas Ves
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
| | - Greg Yetman
- Center for International Earth Science Information Network (CIESIN), Columbia University, Palisades, NY, USA
| | - Parmanand Sinha
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
- Department of Geography and Geosciences, University of Louisville, Kentucky, USA
| | - Donna J. Clarke
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
| | - Alessandro Sorichetta
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
| | - Forrest R. Stevens
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
- Department of Geography and Geosciences, University of Louisville, Kentucky, USA
| | - Andrea E. Gaughan
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
- Department of Geography and Geosciences, University of Louisville, Kentucky, USA
| | - Andrew J. Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
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Kigozi SP, Kigozi RN, Sebuguzi CM, Cano J, Rutazaana D, Opigo J, Bousema T, Yeka A, Gasasira A, Sartorius B, Pullan RL. Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019. BMC Public Health 2020; 20:1913. [PMID: 33317487 PMCID: PMC7737387 DOI: 10.1186/s12889-020-10007-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 12/04/2020] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND As global progress to reduce malaria transmission continues, it is increasingly important to track changes in malaria incidence rather than prevalence. Risk estimates for Africa have largely underutilized available health management information systems (HMIS) data to monitor trends. This study uses national HMIS data, together with environmental and geographical data, to assess spatial-temporal patterns of malaria incidence at facility catchment level in Uganda, over a recent 5-year period. METHODS Data reported by 3446 health facilities in Uganda, between July 2015 and September 2019, was analysed. To assess the geographic accessibility of the health facilities network, AccessMod was employed to determine a three-hour cost-distance catchment around each facility. Using confirmed malaria cases and total catchment population by facility, an ecological Bayesian conditional autoregressive spatial-temporal Poisson model was fitted to generate monthly posterior incidence rate estimates, adjusted for caregiver education, rainfall, land surface temperature, night-time light (an indicator of urbanicity), and vegetation index. RESULTS An estimated 38.8 million (95% Credible Interval [CI]: 37.9-40.9) confirmed cases of malaria occurred over the period, with a national mean monthly incidence rate of 20.4 (95% CI: 19.9-21.5) cases per 1000, ranging from 8.9 (95% CI: 8.7-9.4) to 36.6 (95% CI: 35.7-38.5) across the study period. Strong seasonality was observed, with June-July experiencing highest peaks and February-March the lowest peaks. There was also considerable geographic heterogeneity in incidence, with health facility catchment relative risk during peak transmission months ranging from 0 to 50.5 (95% CI: 49.0-50.8) times higher than national average. Both districts and health facility catchments showed significant positive spatial autocorrelation; health facility catchments had global Moran's I = 0.3 (p < 0.001) and districts Moran's I = 0.4 (p < 0.001). Notably, significant clusters of high-risk health facility catchments were concentrated in Acholi, West Nile, Karamoja, and East Central - Busoga regions. CONCLUSION Findings showed clear countrywide spatial-temporal patterns with clustering of malaria risk across districts and health facility catchments within high risk regions, which can facilitate targeting of interventions to those areas at highest risk. Moreover, despite high and perennial transmission, seasonality for malaria incidence highlights the potential for optimal and timely implementation of targeted interventions.
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Affiliation(s)
- Simon P Kigozi
- Department of Disease Control, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK. .,Infectious Diseases Research Collaboration, PO Box 7475, Kampala, Uganda.
| | - Ruth N Kigozi
- USAID's Malaria Action Program for Districts, PO Box 8045, Kampala, Uganda
| | - Catherine M Sebuguzi
- Infectious Diseases Research Collaboration, PO Box 7475, Kampala, Uganda.,National Malaria Control Division, Uganda Ministry of Health, Kampala, Uganda
| | - Jorge Cano
- Department of Disease Control, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Damian Rutazaana
- National Malaria Control Division, Uganda Ministry of Health, Kampala, Uganda
| | - Jimmy Opigo
- National Malaria Control Division, Uganda Ministry of Health, Kampala, Uganda
| | - Teun Bousema
- Department of Medical Microbiology, Radboud University, Nijmegen, Netherlands
| | - Adoke Yeka
- Department of Disease Control and Environmental Health, College of Health Sciences, School of Public Health, Makerere University, PO Box 7072, Kampala, Uganda
| | - Anne Gasasira
- African Leaders Malaria Alliance (ALMA), Kampala, Uganda
| | - Benn Sartorius
- Department of Disease Control, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Rachel L Pullan
- Department of Disease Control, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
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Fine-scale spatial clustering of measles nonvaccination that increases outbreak potential is obscured by aggregated reporting data. Proc Natl Acad Sci U S A 2020; 117:28506-28514. [PMID: 33106403 DOI: 10.1073/pnas.2011529117] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
The United States experienced historically high numbers of measles cases in 2019, despite achieving national measles vaccination rates above the World Health Organization recommendation of 95% coverage with two doses. Since the COVID-19 pandemic began, resulting in suspension of many clinical preventive services, pediatric vaccination rates in the United States have fallen precipitously, dramatically increasing risk of measles resurgence. Previous research has shown that measles outbreaks in high-coverage contexts are driven by spatial clustering of nonvaccination, which decreases local immunity below the herd immunity threshold. However, little is known about how to best conduct surveillance and target interventions to detect and address these high-risk areas, and most vaccination data are reported at the state-level-a resolution too coarse to detect community-level clustering of nonvaccination characteristic of recent outbreaks. In this paper, we perform a series of computational experiments to assess the impact of clustered nonvaccination on outbreak potential and magnitude of bias in predicting disease risk posed by measuring vaccination rates at coarse spatial scales. We find that, when nonvaccination is locally clustered, reporting aggregate data at the state- or county-level can result in substantial underestimates of outbreak risk. The COVID-19 pandemic has shone a bright light on the weaknesses in US infectious disease surveillance and a broader gap in our understanding of how to best use detailed spatial data to interrupt and control infectious disease transmission. Our research clearly outlines that finer-scale vaccination data should be collected to prevent a return to endemic measles transmission in the United States.
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Carrasco-Escobar G, Manrique E, Tello-Lizarraga K, Miranda JJ. Travel Time to Health Facilities as a Marker of Geographical Accessibility Across Heterogeneous Land Coverage in Peru. Front Public Health 2020; 8:498. [PMID: 33042942 PMCID: PMC7524891 DOI: 10.3389/fpubh.2020.00498] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 07/31/2020] [Indexed: 12/19/2022] Open
Abstract
To better estimate the travel time to the most proximate health care facility (HCF) and determine differences across heterogeneous land coverage types, this study explored the use of a novel cloud-based geospatial modeling approach. Geospatial data of 145,134 cities and villages and 8,067 HCF were gathered with land coverage types, roads and river networks, and digital elevation data to produce high-resolution (30 m) estimates of travel time to HCFs across Peru. This study estimated important variations in travel time to HCFs between urban and rural settings and major land coverage types in Peru. The median travel time to primary, secondary, and tertiary HCFs was 1.9-, 2.3-, and 2.2-fold higher in rural than urban settings, respectively. This study provides a new methodology to estimate the travel time to HCFs as a tool to enhance the understanding and characterization of the profiles of accessibility to HCFs in low- and middle-income countries.
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Affiliation(s)
- Gabriel Carrasco-Escobar
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt, " Universidad Peruana Cayetano Heredia, Lima, Peru.,Division of Infectious Diseases, Department of Medicine, University of California, San Diego, La Jolla, CA, United States
| | - Edgar Manrique
- Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt, " Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Kelly Tello-Lizarraga
- Facultad de Salud Publica y Administración, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - J Jaime Miranda
- CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru.,School of Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru
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Yamaoka K, Suzuki M, Inoue M, Ishikawa H, Tango T. Spatial clustering of suicide mortality and associated community characteristics in Kanagawa prefecture, Japan, 2011-2017. BMC Psychiatry 2020; 20:74. [PMID: 32070316 PMCID: PMC7029524 DOI: 10.1186/s12888-020-2479-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 01/31/2020] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Suicide mortality is high in Japan and early interventional strategies to solve that problem are needed. An accurate evaluation of the regional status of current suicide mortality would be useful for community interventions. A few studies in Kanagawa prefecture, located next to Tokyo and with the second largest population in Japan, have identified spatial clusters of suicide mortality at regional levels. This study examined spatial clustering and clustering over time of such events using spatial data from regional statistics on suicide deaths. METHODS Data were obtained from regional statistics (58 regions in Kanagawa prefecture) of the National Vital Statistics of Japan from 2011 to 2017. The standardized mortality ratio (SMR) and Empirical Bayes estimator for the SMR (EBSMR) were used as measures. Spatial clusters were examined by Kulldorff's circular spatial scan statistic, Tango-Takahashi's flexible spatial scan statistic and Tango's test. Linear regression and conditional autoregressive (CAR) models were used not only to adjust for covariates but also to estimate regional effects. The analyses were conducted for each year, inclusive. RESULTS Among male suicide deaths, being unemployed (50%) was most frequently related to suicide while among female health problem (50%) were frequent. Spatial clusters with significance detected by FlexScan, SatScan and Tango's test were few and varied somewhat according to the method used. Spatial clusters were detected in some regions including Kawasaki ward after adjustment by covariates. By the linear regression models, selected variables with significance were different between the sexes. For males, unemployment, family size, and proportion of higher education were detected for several of the years studied while for females, family size and divorce rate were detected over this period. These variables were also observed by the CAR model with 5 covariates. Regional effects were much clearer by considering the spatial parameter for both males and females and especially, Kawasaki ward was detected as a high risk region in many years. CONCLUSION The present results detected some spatial clustering of suicide deaths within certain regions. Factors related to suicide deaths were also indicated. These results would provide important information in policy making for suicide prevention.
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Affiliation(s)
- Kazue Yamaoka
- Teikyo University Graduate School of Public Health, 2-11-1, Kaga, Itabashi-ku, Tokyo, 173-8605, Japan.
| | - Masako Suzuki
- grid.264706.10000 0000 9239 9995Teikyo University Graduate School of Public Health, 2-11-1, Kaga, Itabashi-ku, Tokyo, 173-8605 Japan
| | - Mariko Inoue
- grid.264706.10000 0000 9239 9995Teikyo University Graduate School of Public Health, 2-11-1, Kaga, Itabashi-ku, Tokyo, 173-8605 Japan
| | - Hirono Ishikawa
- grid.264706.10000 0000 9239 9995Teikyo University Graduate School of Public Health, 2-11-1, Kaga, Itabashi-ku, Tokyo, 173-8605 Japan
| | - Toshiro Tango
- grid.264706.10000 0000 9239 9995Teikyo University Graduate School of Public Health, 2-11-1, Kaga, Itabashi-ku, Tokyo, 173-8605 Japan ,Center for Medical Statistics, Tokyo, Japan
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