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3 "Spatial analysis"
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Original Articles
Spatio-temporal Distribution of Suicide Risk in Iran: A Bayesian Hierarchical Analysis of Repeated Cross-sectional Data
Seyed Saeed Hashemi Nazari, Kamyar Mansori, Hajar Nazari Kangavari, Ahmad Shojaei, Shahram Arsang-Jang
J Prev Med Public Health. 2022;55(2):164-172.   Published online February 10, 2022
DOI: https://doi.org/10.3961/jpmph.21.385
  • 2,548 View
  • 120 Download
  • 2 Web of Science
  • 1 Crossref
AbstractAbstract PDFSupplementary Material
Objectives
We aimed to estimate the space-time distribution of the risk of suicide mortality in Iran from 2006 to 2016.
Methods
In this repeated cross-sectional study, the age-standardized risk of suicide mortality from 2006 to 2016 was determined. To estimate the cumulative and temporal risk, the Besag, York, and Mollié and Bernardinelli models were used.
Results
The relative risk of suicide mortality was greater than 1 in 43.0% of Iran’s provinces (posterior probability >0.8; range, 0.46 to 3.93). The spatio-temporal model indicated a high risk of suicide in 36.7% of Iran’s provinces. In addition, significant upward temporal trends in suicide risk were observed in the provinces of Tehran, Fars, Kermanshah, and Gilan. A significantly decreasing pattern of risk was observed for men (β, -0.013; 95% credible interval [CrI], -0.010 to -0.007), and a stable pattern of risk was observed for women (β, -0.001; 95% CrI, -0.010 to 0.007). A decreasing pattern of suicide risk was observed for those aged 15-29 years (β, -0.006; 95% CrI, -0.010 to -0.0001) and 30-49 years (β, -0.001; 95% CrI, -0.018 to -0.002). The risk was stable for those aged >50 years.
Conclusions
The highest risk of suicide mortality was observed in Iran’s northwestern provinces and among Kurdish women. Although a low risk of suicide mortality was observed in the provinces of Tehran, Fars, and Gilan, the risk in these provinces is increasing rapidly compared to other regions.
Summary

Citations

Citations to this article as recorded by  
  • Spatial, geographic, and demographic factors associated with adolescent and youth suicide: a systematic review study
    Masoud Ghadipasha, Ramin Talaie, Zohreh Mahmoodi, Salah Eddin Karimi, Mehdi Forouzesh, Masoud Morsalpour, Seyed Amirhosein Mahdavi, Seyed Shahram Mousavi, Shayesteh Ashrafiesfahani, Roya Kordrostami, Nahid Dadashzadehasl
    Frontiers in Psychiatry.2024;[Epub]     CrossRef
Trends and Spatial Pattern Analysis of Dengue Cases in Northeast Malaysia
Afiqah Syamimi Masrani, Nik Rosmawati Nik Husain, Kamarul Imran Musa, Ahmad Syaarani Yasin
J Prev Med Public Health. 2022;55(1):80-87.   Published online January 6, 2022
DOI: https://doi.org/10.3961/jpmph.21.461
  • 3,685 View
  • 192 Download
  • 2 Web of Science
  • 3 Crossref
AbstractAbstract PDF
Objectives
Dengue remains hyperendemic in Malaysia despite extensive vector control activities. With dynamic changes in land use, urbanisation and population movement, periodic updates on dengue transmission patterns are crucial to ensure the implementation of effective control strategies. We sought to assess shifts in the trends and spatial patterns of dengue in Kelantan, a north-eastern state of Malaysia (5°15’N 102°0’E).
Methods
This study incorporated data from the national dengue monitoring system (eDengue system). Confirmed dengue cases registered in Kelantan with disease onset between January 1, 2016 and December 31, 2018 were included in the study. Yearly changes in dengue incidence were mapped by using ArcGIS. Hotspot analysis was performed using Getis-Ord Gi to track changes in the trends of dengue spatial clustering.
Results
A total of 10 645 dengue cases were recorded in Kelantan between 2016 and 2018, with an average of 10 dengue cases reported daily (standard deviation, 11.02). Areas with persistently high dengue incidence were seen mainly in the coastal region for the 3-year period. However, the hotspots shifted over time with a gradual dispersion of hotspots to their adjacent districts.
Conclusions
A notable shift in the spatial patterns of dengue was observed. We were able to glimpse the shift of dengue from an urban to peri-urban disease with the possible effect of a state-wide population movement that affects dengue transmission.
Summary

Citations

Citations to this article as recorded by  
  • Digital Health Interventions in Dengue Surveillance to Detect and Predict Outbreak: A Scoping Review
    Marko Ferdian Salim, Tri Baskoro Tunggul Satoto, Danardono Danardono, D. Daniel
    The Open Public Health Journal.2024;[Epub]     CrossRef
  • Solid waste management and Aedes aegypti infestation interconnections: A regression tree application
    Fernanda Klafke, Virgínia Grace Barros, Elisa Henning
    Waste Management & Research: The Journal for a Sustainable Circular Economy.2023; 41(11): 1684.     CrossRef
  • Entomo-Virological Aedes aegypti Surveillance Applied for Prediction of Dengue Transmission: A Spatio-Temporal Modeling Study
    André de Souza Leandro, Mario J. C. Ayala, Renata Defante Lopes, Caroline Amaral Martins, Rafael Maciel-de-Freitas, Daniel A. M. Villela
    Pathogens.2022; 12(1): 4.     CrossRef
Systematic Review
A Systematic Review of Spatial and Spatio-temporal Analyses in Public Health Research in Korea
Han Geul Byun, Naae Lee, Seung-sik Hwang
J Prev Med Public Health. 2021;54(5):301-308.   Published online August 26, 2021
DOI: https://doi.org/10.3961/jpmph.21.160
  • 5,219 View
  • 210 Download
  • 11 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Objectives
Despite its advantages, it is not yet common practice in Korea for researchers to investigate disease associations using spatio-temporal analyses. In this study, we aimed to review health-related epidemiological research using spatio-temporal analyses and to observe methodological trends.
Methods
Health-related studies that applied spatial or spatio-temporal methods were identified using 2 international databases (PubMed and Embase) and 4 Korean academic databases (KoreaMed, NDSL, DBpia, and RISS). Two reviewers extracted data to review the included studies. A search for relevant keywords yielded 5919 studies.
Results
Of the studies that were initially found, 150 were ultimately included based on the eligibility criteria. In terms of the research topic, 5 categories with 11 subcategories were identified: chronic diseases (n=31, 20.7%), infectious diseases (n=27, 18.0%), health-related topics (including service utilization, equity, and behavior) (n=47, 31.3%), mental health (n=15, 10.0%), and cancer (n=7, 4.7%). Compared to the period between 2000 and 2010, more studies published between 2011 and 2020 were found to use 2 or more spatial analysis techniques (35.6% of included studies), and the number of studies on mapping increased 6-fold.
Conclusions
Further spatio-temporal analysis-related studies with point data are needed to provide insights and evidence to support policy decision-making for the prevention and control of infectious and chronic diseases using advances in spatial techniques.
Summary
Korean summary
본 연구는 국내 시공간 분석을 활용한 역학연구를 체계적 문헌고찰을 통해 검토하였다. 의료이용, 형평성, 건강행동 관련 주제가 가장 많았고, 두 가지 이상의 공간분석 기법을 적용한 사례가 늘었으며, 단순 지도화를 적용한 연구가 가장 많았다. 향후 시공간 분석 결과를 이용해 질병 예방과 관리 정책에 적극적으로 활용할 필요가 있다.

Citations

Citations to this article as recorded by  
  • Group I pharmaceuticals of IARC and associated cancer risks: systematic review and meta-analysis
    Woojin Lim, Sungji Moon, Na Rae Lee, Ho Gyun Shin, Su-Yeon Yu, Jung Eun Lee, Inah Kim, Kwang-Pil Ko, Sue K. Park
    Scientific Reports.2024;[Epub]     CrossRef
  • Spatiotemporal models of dengue epidemiology in the Philippines: Integrating remote sensing and interpretable machine learning
    Don Enrico Buebos-Esteve, Nikki Heherson A. Dagamac
    Acta Tropica.2024; 255: 107225.     CrossRef
  • Spatio-Temporal Analysis of Leptospirosis Hotspot Areas and Its Association With Hydroclimatic Factors in Selangor, Malaysia: Protocol for an Ecological Cross-sectional Study
    Muhammad Akram Ab Kadir, Rosliza Abdul Manaf, Siti Aisah Mokhtar, Luthffi Idzhar Ismail
    JMIR Research Protocols.2023; 12: e43712.     CrossRef
  • Epidemiological characteristics and spatiotemporal analysis of mumps at township level in Wuhan, China, 2005–2019
    Ying Peng, Peng Wang, De-guang Kong, Wen-zhen Li, Dong-ming Wang, Li Cai, Sha Lu, Bin Yu, Bang-hua Chen, Pu-Lin Liu
    Epidemiology and Infection.2023;[Epub]     CrossRef
  • A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research
    Zemenu Tadesse Tessema, Getayeneh Antehunegn Tesema, Susannah Ahern, Arul Earnest
    International Journal of Environmental Research and Public Health.2023; 20(13): 6277.     CrossRef
  • Use of geographically weighted regression models to inform retail endgame strategies in South Korea: application to cigarette and ENDS prevalence
    Heewon Kang, Eunsil Cheon, Jaeyoung Ha, Sung-il Cho
    Tobacco Control.2023; : tc-2023-058117.     CrossRef
  • EpiVECS: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization
    Lee Mason, Blànaid Hicks, Jonas S. Almeida
    Scientific Reports.2023;[Epub]     CrossRef
  • Spatiotemporal Trends and Distributions of Malaria Incidence in the Northwest Ethiopia
    Teshager Zerihun Nigussie, Temesgen T. Zewotir, Essey Kebede Muluneh, Wei Wang
    Journal of Tropical Medicine.2022; 2022: 1.     CrossRef
  • Small-scale spatiotemporal epidemiology of notifiable infectious diseases in China: a systematic review
    Junyao Zheng, Guoquan Shen, Siqi Hu, Xinxin Han, Siyu Zhu, Jinlin Liu, Rongxin He, Ning Zhang, Chih-Wei Hsieh, Hao Xue, Bo Zhang, Yue Shen, Ying Mao, Bin Zhu
    BMC Infectious Diseases.2022;[Epub]     CrossRef
  • Characteristics of Atmospheric Compounds based on Regional Multicorrelation Analysis in Honam Area
    Sung-Hyun Oh, Sea-Ho Oh, Min-Suk Bae
    Journal of Environmental Analysis, Health and Toxicology.2022; 25(3): 85.     CrossRef
  • Spatiotemporal analyses of the epidemiological characteristics of diabetes mellitus
    Sang Youl Rhee
    Epidemiology and Health.2021; 43: e2021102.     CrossRef

JPMPH : Journal of Preventive Medicine and Public Health