Geospatial Health
https://www.geospatialhealth.net/gh
<p><strong>Geospatial Health</strong> is the Journal of the GIS Laboratory at the Department of Veterinary Medicine and Animal Production, Regional Center for Monitoring Parasitic Infections (CREMOPAR), University of Naples Federico II (<a href="https://www.mvpa-unina.org" target="_blank" rel="noopener">https://www.mvpa-unina.org</a>).</p> <p><strong>Geospatial Health</strong> is also the official journal of the International Society of Geospatial Health (<a href="http://www.gnosisgis.org/" target="_blank" rel="noopener">www.GnosisGIS.org</a>). The journal was founded in 2006 at the University of Naples Federico II by Giuseppe Cringoli, John B. Malone, Robert Bergquist and Laura Rinaldi. The focus of the journal is on all aspects of the application of geographical information systems, remote sensing, global positioning systems, spatial statistics and other geospatial tools in human and veterinary health. The journal publishes two issues per year.</p>PAGEPress Scientific Publications, Pavia, Italyen-USGeospatial Health1827-1987<p><strong>PAGEPress</strong> has chosen to apply the <a href="http://creativecommons.org/licenses/by-nc/4.0/" target="_blank" rel="noopener"><strong>Creative Commons Attribution NonCommercial 4.0 International License</strong></a> (CC BY-NC 4.0) to all manuscripts to be published.</p>Mastering geographically weighted regression: key considerations for building a robust model
https://www.geospatialhealth.net/gh/article/view/1271
<p>Geographically weighted regression (GWR) takes a prominent role in spatial regression analysis, providing a nuanced perspective on the intricate interplay of variables within geographical landscapes (Brunsdon <em>et al.</em>, 1998). However, it is essential to have a strong rationale for employing GWR, either as an addition to, or a complementary analysis alongside, non-spatial (global) regression models (Kiani, Mamiya <em>et al.</em>, 2023). Moreover, the proper selection of bandwidth, weighting function or kernel types, and variable choices constitute the most critical configurations in GWR analysis (Wheeler, 2021). [...]</p>Behzad KianiBenn SartoriusColleen L. LauRobert Bergquist
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2024-02-292024-02-2919110.4081/gh.2024.1271Spatial and spatio-temporal clusters of lung cancer incidence by stage of disease in Michigan, United States 1985-2018
https://www.geospatialhealth.net/gh/article/view/1219
<p>Lung cancer is the most common cause of cancer-related death in Michigan. Most patients are diagnosed at advanced stages of the disease. There is a need to detect clusters of lung cancer incidence over time, to generate new hypotheses about causation and identify high-risk areas for screening and treatment. The Michigan Cancer Surveillance database of individual lung cancer cases, 1985 to 2018 was used for this study. Spatial and spatiotemporal clusters of lung cancer and level of disease (localized, regional and distant) were detected using discrete Poisson spatial scan statistics at the zip code level over the study time period. The approach detected cancer clusters in cities such as Battle Creek, Sterling Heights and St. Clair County that occurred prior to year 2000 but not afterwards. In the northern area of the lower peninsula and the upper peninsula clusters of late-stage lung cancer emerged after year 2000. In Otter Lake Township and southwest Detroit, late-stage lung cancer clusters persisted. Public and patient education about lung cancer screening programs must remain a health priority in order to optimize lung cancer surveillance. Interventions should also involve programs such as telemedicine to reduce advanced stage disease in remote areas. In cities such as Detroit, residents often live near industry that emits air pollutants. Future research should therefore, continue to focus on the geography of lung cancer to uncover place-based risks and in response, the need for screening and health care services.</p>Qiong ZhangShangrui ZhuSue C. GradyAnqi WangHollis HutchingsJessica CoxAndrew PopoffIkenna Okereke
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2024-02-142024-02-1419110.4081/gh.2024.1219Spatial associations between chronic kidney disease and socio-economic factors in Thailand
https://www.geospatialhealth.net/gh/article/view/1246
<p>Chronic kidney disease (CKD) is a persistent, progressive condition characterized by gradual decline of kidney functions leading to a range of health issues. This research used recent data from the Ministry of Public Health in Thailand and applied spatial regression and local indicators of spatial association (LISA) to examine the spatial associations with night-time light, Internet access and the local number of health personnel per population. Univariate Moran’s I scatter plot for CKD in Thailand’s provinces revealed a significant positive spatial autocorrelation with a value of 0.393. High-High (HH) CKD clusters were found to be predominantly located in the North, with Low-Low (LL) ones in the South. The LISA analysis identified one HH and one LL with regard to Internet access, 15 HH and five LL clusters related to night-time light and eight HH and five LL clusters associated with the number of health personnel in the area. Spatial regression unveiled significant and meaningful connections between various factors and CKD in Thailand. Night-time light displayed a positive association with CKD in both the spatial error model (SEM) and the spatial lag model (SLM), with coefficients of 3.356 and 2.999, respectively. Conversely, Internet access exhibited corresponding negative CKD associations with a SEM coefficient of - 0.035 and a SLM one of -0.039. Similarly, the health staff/population ratio also demonstrated negative associations with SEM and SLM, with coefficients of -0.033 and -0.068, respectively. SEM emerged as the most suitable spatial regression model with 54.8% according to R2. Also, the Akaike information criterion (AIC) test indicated a better performance for this model, resulting in 697.148 and 698.198 for SEM and SLM, respectively. These findings emphasize the complex interconnection between factors contributing to the prevalence of CKD in Thailand and suggest that socioeconomic and health service factors are significant contributing factors. Addressing this issue will necessitate concentrated efforts to enhance access to health services, especially in urban areas experiencing rapid economic growth.</p>Juree SansukKittipong Sornlorm
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2024-01-302024-01-3019110.4081/gh.2024.1246Spatial-temporal risk factors in the occurrence of rabies in Mexico
https://www.geospatialhealth.net/gh/article/view/1245
<p>Rabies is a zoonotic disease that affects livestock worldwide. The distribution of rabies is highly correlated with the distribution of the vampire bat <em>Desmodus rotundus</em>, the main vector of the disease. In this study, climatic, topographic, livestock population, vampire distribution and urban and rural zones were used to estimate the risk for presentation of cases of rabies in Mexico by co- Kriging interpolation. The highest risk for the presentation of cases is in the endemic areas of the disease, i.e. the States of Yucatán, Chiapas, Campeche, Quintana Roo, Tabasco, Veracruz, San Luis Potosí, Nayarit and Baja California Sur. A transition zone for cases was identified across northern Mexico, involving the States of Sonora, Sinaloa, Chihuahua, and Durango. The variables topography, vampire distribution, bovine population and rural zones are the most important to explain the risk of cases in livestock. This study provides robust estimates of risk and spread of rabies based on geostatistical methods. The information presented should be useful for authorities responsible of public and animal health when they plan and establish strategies preventing the spread of rabies into rabies-free regions of México.</p>Reyna Ortega-SánchezIsabel Bárcenas-ReyesJesús Luna-CozarEdith Rojas-AnayaJosé Quintín Cuador-GilGerminal Jorge Cantó-AlarcónNerina Veyna-SalazarSara González-RuizFeliciano Milián-Suazo
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2024-01-302024-01-3019110.4081/gh.2024.1245