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  Spatiotemporal analysis of the relationship between socioeconomic factors and stroke in the Portuguese mainland population under 65 years old

  André Oliveira, Antònio J.R. Cabral, Jorge M. Mendes, Maria R.O. Martins, Pedro Cabral

  04-11-2015

  https://doi.org/10.4081/gh.2015.365

  3229

  PDF: 1420

  HTML: 1223
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  Spatial Bayesian semi-parametric Cox-Leroux modelling of stroke patient hospitalization: aspects on survival

  Aswi Aswi, Bobby Poerwanto, Nurussyariah Hammado, Nurwan Nurwan, Oktaviana Oktaviana, Siti Djawijah, Susanna Cramb

  21-07-2025

  https://doi.org/10.4081/gh.2025.1380

  881

  PDF: 356

  HTML: 31
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  Integration of accelerometers, global positioning systems (GPS) and geographical information systems (GIS) for measuring physical activity

  Lucian Bezuidenhout, Warren Miller, Conran Joseph, David Moulaee Conradsson

  21-10-2025

  https://doi.org/10.4081/gh.2025.1382

  751

  PDF: 381

  HTML: 23

• Analysis of spatio-temporal variations in stroke incidence and case-fatality in Hong Kong

  Pui Hing Chau, Jean Woo, William B. Goggins, Moses Wong, Kam Che Chan, Suzanne C. Ho

  13-20

  01-11-2011

  https://doi.org/10.4081/gh.2011.153

  1666

  PDF: 1130
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  Spatial analysis for the epidemiological study of cardiovascular diseases: A systematic literature search

  Carlos Mena, Cesar Sepúlveda, Eduardo Fuentes, Yony Ormazábal, Iván Palomo

  07-05-2018

  https://doi.org/10.4081/gh.2018.587

  5348

  PDF: 2633

  HTML: 791
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  Diabetes prevalence in the state of Alabama: identifying the risk factors

  Kwaku Karikari Manu, Seth Appiah-Opoku

  31-03-2026

  https://doi.org/10.4081/gh.2026.1465

  114

  PDF: 74

  HTML: 0
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  Spatial analysis of the regional variation of hypertensive disease mortality and its socio-economic correlates in South Korea

  Seong-Yong Park, Jin-Mi Kwak, Eun-Won Seo, Kwang-Soo Lee

  31-05-2016

  https://doi.org/10.4081/gh.2016.420

  3054

  PDF: 1744

  HTML: 1379
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  Differences in spatial patterns of long-term care depending on severity in Hokkaido, Japan

  Kazuki Ohashi, Kensuke Fujiwara, Takumi Tanikawa, Kyohei Bando, Tomohiro Aoki, Katsuhiko Ogasawara

  16-05-2022

  https://doi.org/10.4081/gh.2022.1077

  1517

  PDF: 718

  HTML: 33
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  Comparison of GPS imputation methods in environmental health research

  Sungsoon Hwang, Kashica Webber-Ritchey, Elizabeth Moxley

  29-08-2022

  https://doi.org/10.4081/gh.2022.1081

  2294

  PDF: 1058

  HTML: 33
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  Perceived spatial stigma, body mass index and blood pressure: a global positioning system study among low-income housing residents in New York City

  Dustin T. Duncan, Ryan R. Ruff, Basile Chaix, Seann D. Regan, James H. Williams, Joseph Ravenell, Marie A. Bragg, Gbenga Ogedegbe, Brian Elbel

  31-05-2016

  https://doi.org/10.4081/gh.2016.399

  4138

  PDF: 1968

  HTML: 1502
• 

  Risk discrepancies in COVID-19-related community environments based on spatiotemporal monitoring

  Jihong Zhang, Guohua Yin, Qiuhua Zhang, Juan Fang, Duo Jiang, Chao Yang, Na Sun

  28-04-2025

  https://doi.org/10.4081/gh.2025.1286

  1417

  PDF: 398

  HTML: 84

• Geospatial epidemiology of coronary artery disease treated with percutaneous coronary intervention in Crete, Greece

  Evangelos Melidoniotis, Kleomenis Kalogeropoulos, Andreas Tsatsaris, Michail Zografakis-Sfakianakis, George Lazopoulos, Nikolaos Tzanakis, Ioannis Anastasiou, Emmanouil Skalidis

  16-05-2024

  https://doi.org/10.4081/gh.2024.1251

  2436

  PDF: 665

  HTML: 54
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  Assessing spatial inequalities in accessing community pharmacies: a mixed geographically weighted approach

  Alexander Domnich, Lucia Arata, Daniela Amicizia, Alessio Signori, Roberto Gasparini, Donatella Panatto

  16-11-2016

  https://doi.org/10.4081/gh.2016.457

  3366

  PDF: 1770

  HTML: 1324
• 

  Prevalence of hypertension in Thailand: Hotspot clustering detected by spatial analysis

  Wongsa Laohasiriwong, Nattapong Puttanapong, Atthawit Singsalasang

  07-05-2018

  https://doi.org/10.4081/gh.2018.608

  4209

  PDF: 2011

  HTML: 581
• 

  Investigating local variation in disease rates within high-rate regions identified using smoothing

  Matthew Tuson, Matthew Yap, David Whyatt

  25-05-2023

  https://doi.org/10.4081/gh.2023.1144

  654

  PDF: 412

  Supplementary Materials: 122

  HTML: 22
• 

  Nonlinear analysis of factors influencing public health in China based on machine learning

  Dehong Sun, Hui Wang

  16-03-2026

  https://doi.org/10.4081/gh.2026.1450

  114

  PDF: 77

  HTML: 4
• 

  Variability of nutrients intake, lipid profile and cardiovascular mortality among geographical areas in Spain: The DRECE study

  Agustín Gòmez de la Cámara, Eva de Andrés Esteban, Gerard Urrútia Cuchí, Enrique Calderòn Sandubete, Miguel Ángel Rubio Herrera, Miguel Menéndez Orenga, David Lora Pablos

  07-11-2017

  https://doi.org/10.4081/gh.2017.524

  2157

  PDF: 943

  HTML: 701
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  Geographic clustering of elderly people with above-norm anthropometric measurements and blood chemistry

  Carlos Mena, Eduardo Fuentes, Yony Ormazábal, Iván Palomo

  11-05-2017

  https://doi.org/10.4081/gh.2017.523

  2898

  PDF: 1272

  HTML: 886
• 

  COVID-19, air quality and space monitoring

  Yves M. Tourre, Mireille Paulin, Gilles Dhonneur, David Attias, Atul Pathak

  06-04-2022

  https://doi.org/10.4081/gh.2022.1052

  1894

  PDF: 584

  HTML: 22
• 

  Quantifying altitude of human habitation in studies of human health using geographical name server data

  Stephen Thielke, Christopher G. Slatore, William A. Banks

  21-11-2016

  https://doi.org/10.4081/gh.2016.463

  1571

  PDF: 902

  HTML: 836
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  A geospatial model to determine the spatial cost-efficiency of anticoagulation drug therapy: Patients' perspective

  Mikko Pyykönen, Aapeli Leminen, Juho Tynkkynen, Markku Tykkyläinen, Tiina Laatikainen

  06-11-2019

  https://doi.org/10.4081/gh.2019.809

  2049

  PDF: 1008

  HTML: 32

• The distribution of cardiovascular diseases in Tanzania: a spatio-temporal investigation

  Bernada E. Sianga, Maurice C. Mbago, Amina S. Msengwa

  10-09-2024

  https://doi.org/10.4081/gh.2024.1307

  2797

  PDF: 860

  HTML: 234
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  Identifying counties vulnerable to diabetes from obesity prevalence in the United States: a spatiotemporal analysis

  Xiao Li, Amanda Staudt, Lung-Chang Chien

  21-11-2016

  https://doi.org/10.4081/gh.2016.439

  2740

  PDF: 1298

  APPENDIX: 469

  HTML: 1140
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  Detection of spatial aggregation of cases of cancer from data on patients and health centres contained in the Minimum Basic Data Set

  Pablo Fernández-Navarro, Jose-Miguel Sanz-Anquela, Angel Sánchez Pinilla, Rosario Arenas Mayorga, Carmen Salido-Campos, Gonzalo Lòpez-Abente

  07-05-2018

  https://doi.org/10.4081/gh.2018.616

  1846

  PDF: 931

  SUPPLEMENTARY: 405

  HTML: 136