https://geospatialhealth.net/index.php/gh/issue/feed Geospatial Health 2019-05-25T19:23:15+02:00 Francesca Baccino francesca.baccino@pagepress.org Open Journal Systems <p><strong>Geospatial Health</strong> is the official journal of the International Society of Geospatial Health (<a href="http://www.gnosisgis.org/">www.GnosisGIS.org</a>).</p> <p>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> https://geospatialhealth.net/index.php/gh/article/view/781 Good things come in small packages: New trends in acquisition of remotely-sensed data 2019-05-25T19:23:15+02:00 Robert Bergquist editor@geospatialhealth.net Sherif Amer s.amer@utwente.nl <p>Not available.</p> 2019-05-13T16:05:22+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/779 The world in your hands: GeoHealth then and now 2019-05-25T19:23:15+02:00 Robert Bergquist editor@geospatialhealth.net Samuel Manda editor@geospatialhealth.net <p>Infectious diseases transmitted by vectors/intermediate hosts constitute a major part of the economic burden related to public health in the endemic countries of the tropics, which challenges local welfare and hinders development. The World Health Organization, in partnership with pharmaceutical companies, major donors, endemic countries and non-governmental organizations, aims to eliminate the majority of these infections in the near future. To succeed, the ecological requirements and real-time distributions of the causative agents (bacteria, parasites and viruses) and their vectors must not only be known to a high degree of accuracy, but the data must also be updated more rapidly than has so far been the case. Current approaches include data collection through terrestrial capture on site and satellite-generated information. This article provides an update of currently available sources of remotely-sensed data, including specific information on satellite-borne sensors, and how such data can be handled by Geographical Information Systems (GIS). Computers, when equipped with GIS software based on common spatial denominators, can connect remotely-sensed environmental records with terrestrial-captured data and apply spatial statistics in ways uniquely suited to manage control activities in areas where vector-borne infections dominate.</p> 2019-05-13T16:09:33+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/741 Epidemiology of canine heartworm (Dirofilaria immitis) infection in domestic dogs in Ontario, Canada: Geographic distribution, risk factors and effects of climate 2019-05-25T19:23:14+02:00 Erin McGill erin.mcgill@canada.ca Olaf Berke oberke@uoguelph.ca Andrew S. Peregrine erin.mcgill@canada.ca J. Scott Weese erin.mcgill@canada.ca <p><em>Dirofilaria immitis</em> is the causal agent of heartworm, a mosquito-borne parasite that primarily infects domestic and wild canids. The infection is endemic in parts of Canada, and Ontario has been identified as the province where the majority of heartworm infections occur. Test results for blood samples submitted by veterinary clinics for the years 2007-2016 were used to conduct a spatial risk analysis of heartworm among domestic dogs in Ontario. The geographic extent of the apparent heartworm prevalence was examined through smoothed choropleth maps for all 49 census division regions. Furthermore, the regions were assessed for local clusters in apparent prevalence using the flexible spatial scan statistic. Three clusters were found and located in western, southern and eastern Ontario, respectively. A spatial Poisson regression model for heartworm prevalence among pet dog populations in southern Ontario census divisions was fit to determine the association between human population size, heartworm development units (HDUs), climate moisture index (CMI), precipitation and directions, east or north, with heartworm infection. The model identified the spatial distribution of HDUs and CMI as positively associated with heartworm infection and therefore important predictors of the infection. In contrast, human population size, increasing northern latitude and drier areas were negatively associated with heartworm infection. The east direction and precipitation were not significant.</p> 2019-05-13T16:13:33+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/736 Socioeconomic status and deaths due to unintentional injury among children: A socio-spatial analysis in Taiwan 2019-05-25T19:23:13+02:00 An-Kuo Chou dtped124@gmail.com Duan-Rung Chen duan@ntu.edu.tw <p>In Taiwan, unintentional injury is the leading cause of death among children &lt;10 years old. Low socioeconomic status is a risk factor associated with a high prevalence of injuries and our study aimed to explore the geographic distribution of mortality due to unintentional injury in this age group assessing the association between this type of injury on the one hand and socioeconomic disadvantages and family structure on the other using cluster and spatial regression analyses. Using exploratory factor analysis, we assembled nine socioeconomic variables into four composite factors including area-level poverty, family burden, family fragility and unemployment. We found significant spatial clusters of childhood deaths due to unintentional injury and identified three major causes of death involved, <em>i.e</em>. traffic accidents, drowning and suffocation. Significant associations were found between death due to unintentional injury and area-level social disadvantages including poverty, family fragility, family economic burden and unemployment, while controlling for spatial autocorrelation. Our conclusion is that socioeconomic disadvantages need to be addressed to reduce the number of deaths due to childhood unintentional injury.</p> 2019-05-13T16:22:45+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/701 A geographically weighted regression approach to investigate air pollution effect on lung cancer: A case study in Portugal 2019-05-25T19:23:12+02:00 Diogo Cardoso diogo.luzcardoso@gmail.com Marco Painho painho@novaims.unl.pt Rita Roquette rita.roquette@insa.min-saude.pt <p>The risk of developing lung cancer might to a certain extent be attributed to tobacco. Nevertheless, the role of air pollution, both form urban and industrial sources, needs to be addressed. Numerous studies have concluded that long-term exposure to air pollution is an important environmental risk factor for lung cancer mortality. Still, there are only a few studies on air pollution and lung cancer in Portugal and none addressing its spatial dimension. The goal was to determine the influence of air pollution and urbanization rate on lung cancer mortality. A geographically weighted regression (GWR) model was performed to evaluate the relation between particle matter<sub>10</sub> (PM<sub>10</sub>) emissions and lung cancer mortality relative risk (RR) for males and females in Portugal between 2007 and 2011. RR was computed with the BYM model. For a more in-depth analysis, the urbanization rate and the percentage of industrial area in each municipality were added. GWR efforts led to identifying three variables that were statistically significant in explaining lung cancer relative risk mortality, PM<sub>10</sub> emissions, urbanization rate and the percentage of industrial area with an adjusted R<sup>2</sup> of 0,63 for men and 0,59 for women. A small set of 8 municipalities with high correlation values was also identified (local R<sup>2</sup> above 0,70). Stronger relationships were found in the north-western part of mainland Portugal. The local R<sup>2</sup> tends to be higher when the emissions of PM<sub>10</sub> are joined by urbanization and industrial areas. However, when assessing the industrial areas alone, it was noted that its impact was lower overall. As one of the first communications on this subject in Portugal, we have identified municipalities where possible impacts of air pollution on lung cancer mortality RR are higher thereby highlighting the role of geography and spatial analysis in explaining the associations between a disease and its determinants.</p> 2019-05-13T16:34:28+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/745 Towards improved, cost-effective surveillance of Ixodes ricinus ticks and associated pathogens using species distribution modelling 2019-05-25T19:23:12+02:00 Manuela Signorini manusignorini@hotmail.com Anna-Sofie Stensgaard asstensgaard@snm.ku.dk Michele Drigo michele.drigo@unipd.it Giulia Simonato giulia.simonato@unipd.it Federica Marcer federica.marcer@unipd.it Fabrizio Montarsi fmontarsi@izsvenezie.it Marco Martini marco.martini@unipd.it Rudi Cassini rudi.cassini@unipd.it <p>Various ticks exist in the temperate hilly and pre-alpine areas of Northern Italy, where <em>Ixodes ricinus</em> is the more important. In this area different tick-borne pathogen monitoring projects have recently been implemented; we present here the results of a twoyear field survey of ticks and associated pathogens, conducted 2009-2010 in North-eastern Italy. The cost-effectiveness of different sampling strategies, hypothesized <em>a posteriori</em> based on two sub-sets of data, were compared and analysed. The same two subsets were also used to develop models of habitat suitability, using a maximum entropy algorithm based on remotely sensed data. Comparison of the two strategies (in terms of number of ticks collected, rates of pathogen detection and model accuracy) indicated that monitoring at many temporary sites was more cost-effective than monthly samplings at a few permanent sites. The two model predictions were similar and provided a greater understanding of ecological requirements of <em>I. ricinus</em> in the study area. Dense vegetation cover, as measured by the normalized difference vegetation index, was identified as a good predictor of tick presence, whereas high summer temperatures appeared to be a limiting factor. The study suggests that it is possible to obtain realistic results (in terms of pathogens detection and development of habitat suitability maps) with a relatively limited sampling effort and a wellplanned monitoring strategy.</p> 2019-05-13T16:45:07+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/711 Predictive risk mapping of human leptospirosis using support vector machine classification and multilayer perceptron neural network 2019-05-25T19:23:10+02:00 Mehrdad Ahangarcani mahangar@mail.kntu.ac.ir Mahdi Farnaghi mahdi.farnaghi@nateko.lu.se Mohammad Reza Shirzadi shirzadim@gmail.com Petter Pilesjö mahdi.farnaghi@nateko.lu.se Ali Mansourian ali.mansourian@nateko.lu.se <p>Leptospirosis is a zoonotic disease found wherever human is in direct or indirect contact with contaminated water and environment. Considering the increasing number of cases of this disease in the northern part of Iran, identifying areas characterized by high disease incidence risk can help policy-makers develop strategies to prevent its further spread. This study presents an approach for generating predictive risk maps of leptospirosis using spatial statistics, environmental variables and machine learning. Moran's <em>I</em> demonstrated that the distribution of leptospirosis cases in the study area in Iran was highly clustered. Pearson’s correlation analysis was conducted to examine the type and strength of relationships between climate and topographical factors and incidence of the disease. To handle the complex and nonlinear problems involved, machine learning based on the support vector machine classification algorithm and multilayer perceptron neural network was exploited to generate annual and monthly predictive risk maps of leptospirosis distribution. Performance of both models was evaluated using receiver operating characteristic curve and Kappa coefficient. The output results demonstrated that both models are adequate for the prediction of the probability of leptospirosis incidence.</p> 2019-05-14T08:40:16+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/740 A Google Earth-based database management for schistosomiasis control in Zanzibar 2019-05-25T19:23:09+02:00 Ming-Zhen He hemz1984@163.com Wei Li liwei@jipd.com Saleh Juma salehjuma2003@yahoo.com Fatma Kabole fatmaepi@yahoo.co.uk Da-Cheng Xu jtcdc_xdch@163.com Xin-Yao Wang 1248942599@qq.com Jian He hj5566@126.com Tao Jiang jiangtao_0418@126.com Robert Bergquist robert.bergquist@outlook.com Kun Yang yangkun@jipd.com <p>Schistosomiasis remains a serious health problem in Africa. Although a strong, coordinated agenda for research on this disease has been in place for the last 50 years in Zanzibar, data storage, retrieval of survey data and management remain problem areas. We investigated the use of Google Earth (GE) in conjunction with a hand-held, global positioning system as a pilot project for managing schistosomiasis control. In this way, risk areas can be surveyed and followed up by visualizing both the distribution of human infections and that of the intermediate snail host together with environmental information. A platform with three spatial databases was created: i) Distribution of infected humans; ii) Distribution of the intermediate snail host in ponds (infected and not infected specimens); iii) Distribution of the intermediate snail host in streams (infected and non-infected specimens). The GE spatial database increased the efficiency of follow-up case treatment as well as snail control and contributed also to the discovery of previously unknown areas in need of snail control. We conclude that this platform is advantageous not only by being useful for management and visualization of spatial data, but also because it is easy to operate and available free of charge.</p> 2019-05-14T08:54:10+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/738 Identification and assessment of the driving forces for the use of urban green parks and their accessibility in Colombo, Sri Lanka, through analytical hierarchical processing 2019-05-25T19:23:09+02:00 P.R.G.N. Indika Pussella indikapussella@yahoo.com Lin Li lilin@whu.edu.cn <p>Urban green parks perform a remarkable role for the physical, social and psychological wellbeing of the urban public by providing space for relaxation and recreation, directly influencing public health through mitigation of the urban heat impact, noise reduction and moderation of air and water pollution. An indicator-based approach on analytical hierarchical processing was used to identify and assess the driving forces for the utilization of urban green parks and their accessibility. Eight indicators: location, topography and geography, facility and services, safety and security, social and culture, ecology, demography, and weather and climate (further divided into 50 factors) were used in the study. Data were collected through a questionnaire survey in which 887 regular park users participated. A standardized study design was implemented to study and assess four urban green parks in the Colombo metropolitan district, Sri Lanka. The study identified park facilities and services as well as safety and security measures maintained by the park as the key factors of appeal, while location, ecology, topography and geography, including weather and climate, had a lower relative influence when selecting a park for recreation. Social, cultural and demographic factors appeared to be of the least interest. The study recommends park managers to assess their parks using this model to enhance the characteristics found to be the most important. It further suggests developing models also for other park types by considering which factors would have the highest relative influence in providing a better service for the regular park user.</p> 2019-05-14T09:04:28+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/676 Predicting malaria cases using remotely sensed environmental variables in Nkomazi, South Africa 2019-05-25T19:23:08+02:00 Abiodun Morakinyo Adeola abiodun.adeola@weathersa.co.za Joel Ondego Botai joel.botai@weathersa.co.za Jane Mukarugwiza Olwoch jane.olwoch@sasscal.org Hannes C.J. de W. Rautenbach hannes.rautenbach@weathersa.co.za Omolola Mayowa Adisa lolaadisa@yahoo.com Christiaan de Jager tiaan.dejager@up.ac.za Christina M. Botai christina.botai@weathersa.co.za Mabuza Aaron aaron.mabuza20@gmail.com <p>There has been a conspicuous increase in malaria cases since 2016/2017 over the three malaria-endemic provinces of South Africa. This increase has been linked to climatic and environmental factors. In the absence of adequate traditional environmental/climatic data covering ideal spatial and temporal extent for a reliable warning system, remotely sensed data are useful for the investigation of the relationship with, and the prediction of, malaria cases. Monthly environmental variables such as the normalised difference vegetation index (NDVI), the enhanced vegetation index (EVI), the normalised difference water index (NDWI), the land surface temperature for night (LSTN) and day (LSTD), and rainfall were derived and evaluated using seasonal autoregressive integrated moving average (SARIMA) models with different lag periods. Predictions were made for the last 56 months of the time series and were compared to the observed malaria cases from January 2013 to August 2017. All these factors were found to be statistically significant in predicting malaria transmission at a 2-months lag period except for LSTD which impact the number of malaria cases negatively. Rainfall showed the highest association at the two-month lag time (<em>r</em>=0.74; P&lt;0.001), followed by EVI (<em>r</em>=0.69; P&lt;0.001), NDVI (<em>r</em>=0.65; P&lt;0.001), NDWI (<em>r</em>=0.63; P&lt;0.001) and LSTN (<em>r</em>=0.60; P&lt;0.001). SARIMA without environmental variables had an adjusted R<sup>2</sup> of 0.41, while SARIMA with total monthly rainfall, EVI, NDVI, NDWI and LSTN were able to explain about 65% of the variation in malaria cases. The prediction indicated a general increase in malaria cases, predicting about 711 against 648 observed malaria cases. The development of a predictive early warning system is imperative for effective malaria control, prevention of outbreaks and its subsequent elimination in the region.</p> 2019-05-14T09:28:18+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/690 Understanding how distance to facility and quality of care affect maternal health service utilization in Kenya and Haiti: A comparative geographic information system study 2019-05-25T19:23:07+02:00 Xing Gao dwkelley@stthomas.edu David Wayne Kelley dwkelley@stthomas.edu <p>In 2000, the United Nations established eight Millennium Development Goals (MDG) to combat worldwide poverty, disease, and lack of primary education. Goal number five aimed to reduce the maternal mortality ratio by three quarters and provide universal access to reproductive healthcare services by 2015. While there has been some progress, MDG 5 fell far short of target goals, highlighting the necessity of further improvement in global maternal health. Using Geographic Information Systems (GIS), this study aims to understand how distance to facility and quality of care, which are components of access, affect maternal service utilization in two of the world’s poorest countries, Haiti and Kenya. Furthermore, this study examines how this relationship may change or hold between urban and rural regions. Data from the United States Agency for International Development Demographic and Health Survey and Service Provision Assessment were linked spatially in a GIS model, drawing comparisons among distance to facility, quality of care, and maternal health service utilization. Results show that in both rural and urban regions, access to maternal health service and maternal health service utilization share a similar spatial pattern. In urban regions, pockets of maternal health disparities exist despite close distance to facility and standard quality of care. In rural regions, there are areas with long distances to facilities and low quality of care, resulting in poor maternal service usage. This study highlights the usefulness of GIS as a tool to evaluate disparities in maternal healthcare provision and usage.</p> 2019-05-14T09:33:15+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/717 Japan Aerospace Exploration Agency’s public-health monitoring and analysis platform: A satellite-derived environmental information system supporting epidemiological study 2019-05-25T19:23:06+02:00 Kei Oyoshi ohyoshi.kei@jaxa.jp Yosei Mizukami ohyoshi.kei@jaxa.jp Ryosuke Kakuda ohyoshi.kei@jaxa.jp Yusuke Kobayashi ohyoshi.kei@jaxa.jp Hiroki Kai ohyoshi.kei@jaxa.jp Takeo Tadono ohyoshi.kei@jaxa.jp <p>Since the 1970s, Earth-observing satellites collect increasingly detailed environmental information on land cover, meteorological conditions, environmental variables and air pollutants. This information spans the entire globe and its acquisition plays an important role in epidemiological analysis when <em>in situ</em> data are unavailable or spatially and/or temporally sparse. In this paper, we present the development of Japan Aerospace Exploration Agency’s (JAXA) Public-health Monitoring and Analysis Platform available from JAXA, a user-friendly, web-based system providing environmental data on shortwave radiation, rainfall, soil moisture, the normalized difference vegetation index, aerosol optical thickness, land surface temperature and altitude. This system has been designed so that users should be able to download and utilize data without the need for additional data processing. The website allows interactive exchange and users can request data for a specific geographic location and time using the information gained for epidemiological analysis.</p> 2019-05-14T09:50:33+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/750 Quantifying the relationship between human Lyme disease and Borrelia burgdorferi exposure in domestic dogs 2019-05-25T19:23:11+02:00 Yan Liu yliu23@unr.edu Shila K. Nordone snordone@duke.edu Michael J. Yabsley myabsley@uga.edu Robert B. Lund lund@clemson.edu Christopher S. McMahan mcmaha2@clemson.edu Jenna R. Gettings Jenna.Gettings@uga.edu <p>Lyme disease (LD) is the most common vector-borne disease in the United States. Early confirmatory diagnosis remains a challenge, while the disease can be debilitating if left untreated. Further, the decision to test is complicated by under-reporting, low positive predictive values of testing in non-endemic areas and travel, which together exacerbate the difficulty in identification of newly endemic areas or areas of emerging concern. Spatio-temporal analyses at the national scale are critical to establishing a baseline human LD risk assessment tool that would allow for the detection of changes in these areas. A well-established surrogate for human LD incidence is canine LD seroprevalence, making it a strong candidate covariate for use in such analyses. In this paper, Bayesian statistical methods were used to fit a spatio-temporal spline regression model to estimate the relationship between human LD incidence and canine seroprevalence, treating the latter as an explanatory covariate. A strong non-linear monotonically increasing association was found. That is, this analysis suggests that mean incidence in humans increases with canine seroprevalence until the seroprevalence in dogs reaches approximately 30%. This finding reinforces the use of canines as sentinels for human LD risk, especially with respect to identifying geographic areas of concern for potential human exposure.</p> 2019-05-14T00:00:00+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/739 Spatial and statistical analysis of leptospirosis in Thailand from 2013 to 2015 2019-05-25T19:23:06+02:00 Amornrat Luenam luenam.hcu@gmail.com Nattapong Puttanapong nattapong@econ.tu.ac.th <p>This study analyzes the temporal pattern and spatial clustering of leptospirosis, a disease recognized as an emerging public health problem in Thailand. The majority of those infected are farmers and fishermen. Severe epidemics of leptospirosis in association with the rainy reason have occurred since 1996. Still, an understanding of the annual variation and spatial clustering of the disease is lacking. Data were collected from the Center of Epidemiological Information, Bureau of Epidemiology, Ministry of Public Health, covering the nationwide incidence of leptospirosis during the period 2013-2015. Clustering techniques, including local indicators of spatial association and local Getis-Ord Gi* statistic, were used for the analysis and evaluation of the annual spatial distribution of the disease. Both these statistics revealed similar results for the areas with the highest clustering patterns of leptospirosis. Specifically, there were persisting hotspots in north-eastern and southern parts of Thailand over the three years covered by the study. This outcome suggests that healthcare resources should be allocated to the areas characterized by leptospirosis clustering.</p> 2019-05-14T10:20:31+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/680 Modelling youth pregnancy in continental Portugal through geographically weighted regression 2019-05-25T19:23:05+02:00 João David jcdavid@novaims.unl.pt Pedro Cabral pcabral@novaims.unl.pt <p>Youth pregnancy, a global public health problem with important social, educational and economic impact, has mostly been studied in the least developed countries. However, this condition also affects the industrialized countries. This article presents a youth pregnancy study at the municipality level in continental Portugal based on geographically weighted regression. The results indicate that youth pregnancy rates can be explained by several variables: i) proportion (%) of social security beneficiaries; ii) number of households without amenities; iii) the rate of those prematurely leaving school; iv) the unemployment rates of youths and females, <em>ceteris paribus.</em> In addition, it was found that the beneficiaries of social security had a higher impact on youth pregnancy in the southern part of the country, and in the Centre too; that households without amenities had a higher impact along the central coast and in the South; that rates of those leaving school prematurely had a higher influence in the North and the Interior than in the rest of the country; and that youth and female unemployment rates were more widespread in the Centre, particularly along the coast. Overall, the model identified a strong association of explanatory variables with youth pregnancy rates in the country as a whole, except in the Porto metropolitan area. These findings may help health planners to define policies to mitigate this important social problem.</p> 2019-05-14T10:28:07+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/733 Spatial clusters of life expectancy and association with cardiovascular disease mortality and cancer mortality in the contiguous United States: 1980-2014 2019-05-25T19:23:04+02:00 Raid W. Amin ramin@uwf.edu Julia Steinmetz js230@students.uwf.edu <p>The average life expectancy varies greatly from county to county in USA and there are also spatial variations in the county mortality rates for cardiovascular disease (CVD) and cancer, the top two causes of death. An association between these two groups of diseases has not been identified by cluster analysis previously. The main objective in this study was to investigate and quantify the associations between mortality due to CVD, cancer mortality and life expectancy based on US county data between 1980 and 2014. Regression analysis was used to adjust life expectancy for the mortality due to CVD and that due to cancer. In addition to the spatial life expectancy trends, we also studied existing trends over time with the software JOINPOINT to see how life expectancy is influenced by changes in mortality due to CVD and cancer mortality. The study setting was the 48 contiguous US states, while participants were 3,100 counties and their populations of all ages during the period 1980-2014. The main outcomes are spatial clusters of unusually low or high levels of life expectancy in addition to identifying which county level life expectancy locations were significantly associated with mortality due to CVD and/or cancer. Life expectancy has been improving steadily from 1980 to 2014, but the rate of increase per year (indicated by variation of the trend slope) changed significantly at five joinpoints, the latest of which occurred in 2010 when the slope changed from 0.29 (1980-1982) to 0.03 (2010-2014). Our results reveal that there are significant, purely spatial clusters in some geographical areas where life expectancy rates are significantly higher (or lower) than in the rest of the contiguous US. It is also shown that there is a significant association between the life expectancy level and the corresponding CVD mortality, and there is also a significant association between life expectancy level and the corresponding overall cancer mortality. The general trends (regression slopes) over time for the USA in life expectancy mortality, CVD mortality and cancer mortality have changed significantly after 2009-2010.</p> 2019-05-14T10:31:12+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/747 Alcohol sale status and homicide victimization in Kentucky, 2005-2012: Is there a spatial association? 2019-05-25T19:23:04+02:00 Hanan Abdulghafoor Khaleel hanan_azawy2000@yahoo.com Sabrina Brown sabrina.brown@uky.edu Steven Fleming steven.fleming@uky.edu W. Jay Christian jay.christian@uky.edu <p>To date, the association between the alcohol sale status of decedents’ residence and alcohol-related homicide victimization have not been studied as far as we know. The current study aims to: i) determine whether homicide victims who were residents of <em>wet</em> counties had higher odds of testing positive for alcohol than their counterparts in <em>moist</em> or <em>dry</em> counties after adjusting for confounders; ii) determine whether homicides and alcohol-related homicides tend to cluster spatially; iii) determine whether the aforementioned associations exist only in highly-populated counties. A multilevel logistic regression analysis was used to analyze the data on homicide victims in the Kentucky Violent Death Reporting System from 2005 to 2012. Spatial statistics were used to determine the spatial autocorrelation in rates of homicides and alcohol-related homicides. Overall, 944 homicide victims were included. The male to female ratio was 3:1. About 32.8% of homicide victims tested positive for alcohol. About 33.0% of homicide decedents who were residents in <em>wet</em> counties tested positive for alcohol compared to 32.5% of their counterparts in <em>moist</em>/<em>dry</em> counties. Residence in <em>wet</em> counties was associated with a statistically insignificant increase in the unadjusted odds ratio (OR) of alcohol-related homicide victimization (OR=1.20, 95% CI=0.81-1.77) as well as the adjusted odds (aOR=1.33, 95% CI=0.83-2.12). There was no association between population size and alcohol-related homicide rate.</p> 2019-05-14T10:35:26+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/751 Future Lyme disease risk in the south-eastern United States based on projected land cover 2019-05-25T19:23:03+02:00 Logan K. Stevens LKS12@vt.edu Korine N. Kolivras korine@vt.edu Yili Hong yilihong@vt.edu Valerie A. Thomas thomasv@vt.edu James B. Campbell jayhawk@vt.edu Stephen P. Prisley LKS12@vt.edu <p>Lyme disease is the most significant vector-borne disease in the United States, and its southward advance over several decades has been quantified. Previous research has examined the potential role of climate change on the disease’s expansion, but no studies have considered the role of future land cover upon its distribution. This research examines Lyme disease risk in the south-eastern U.S. based on projected land cover developed under four Intergovernmental Panel on Climate Change Scenarios: A1B, A2, B1, and B2. Land cover types and edge indices significantly associated with Lyme disease in Virginia were incorporated into a spatial Poisson regression model to quantify potential land cover suitability for Lyme disease in the south-eastern U.S. under each scenario. Our results indicate an intensification of potential land cover suitability for Lyme disease under the A scenarios and a decrease of potential land cover suitability under the B scenarios. The decrease under the B scenarios is a critical result, indicating that Lyme disease risk can be decreased by making different land cover choices. Additionally, health officials can focus efforts in projected high incidence areas.</p> 2019-05-14T10:56:38+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/746 Population-level alcohol consumption and suicide mortality rate in South Korea: An application of multivariable spatial regression model 2019-05-25T19:23:02+02:00 Yunho Yeom yyeom@gradcenter.cuny.edu <p>This research estimates the contextual effects of populationlevel alcohol consumption on the average suicide mortality rate (SMR) in South Korea from 2013 to 2015. The effect was estimated not only in relation to the risk factors of suicide, such as divorce and being elderly, but also protective factors, such as church attendance and educational attainment. Using a multivariable spatial regression model, results show that only <em>excessive</em> population-level alcohol consumption pattern had a positive effect on SMR by increasing 0.24 standardized units in the SMR; the <em>moderate</em> pattern, however, had no significant impact. These results imply that the excessive population-level alcohol consumption pattern is a risk factor with respect to SMR. This research suggests the implementation of policies to control population- level alcohol consumption, based on a concern for public health, to reduce the suicide risk in South Korea.</p> 2019-05-14T10:59:31+02:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/673 Predicting transmission of pulmonary tuberculosis in Daerah Istimewa Yogyakarta Province, Indonesia 2019-05-25T19:23:01+02:00 Al Asyary al.asyary13@gmail.com Aries Prasetyo arewind_erika@yahoo.co.id Tris Eryando tris.eryando@gmail.com Yodi Mahendradhata yodi_mahendradhata@yahoo.co.uk <p>This study aims to explain the current dispersion of tuberculosis (TB) and provide evidence that could help predicting its future transmission in Daerah Istimewa Yogyakarta (DIY) Province, Java Island, Indonesia. One hundred thirty-two adult (&gt;14 years old) individuals, with TB diagnosed by health professionals using the Directly Observed Treatment, Short Course strategy, were identified Their residential addresses and geographical patterns of movement were investigated by global positioning systems and descriptive spatial analysis using standard deviation ellipse analysis and kernel estimation. The dispersion of TB cases was studied by ellipse regression, which showed a pattern extending in a direction oriented from north-west to south-east centred on Kasihan District, Bantul Regency, DIY Province, located near Yogyakarta City. Levels of TB risk in the study area varied from non-existent to high as calculated by kernel estimation. We conclude that suburban communities, followed by densely populated residential areas, enabled by socio-economic factors, are more likely to see increased TB transmission in the future.</p> 2019-05-14T11:07:21+02:00 ##submission.copyrightStatement##