https://geospatialhealth.net/index.php/gh/issue/feed Geospatial Health 2019-11-16T21:16:52+01:00 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/828 The future is now: New United Nations’ Sustainable Development Goals report provides a perspective on vector-borne diseases 2019-11-16T21:16:52+01:00 Anna-Sofie Stensgaard asstensgaard@bio.ku.dk Laura Rinaldi lrinaldi@unina.it Robert Bergquist editor@geospatialhealth.net <p>Not available.</p> 2019-11-06T09:54:57+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/771 Spatiotemporal dengue fever hotspots associated with climatic factors in Taiwan including outbreak predictions based on machine-learning 2019-11-16T21:16:51+01:00 Sumiko Anno sumiko_anno@sophia.ac.jp Takeshi Hara hara@info.gifu-u.ac.jp Hiroki Kai kai@restec.or.jp Ming-An Lee malee@ntou.edu.tw Yi Chang yichang@mail.ncku.edu.tw Kei Oyoshi ohyoshi.kei@jaxa.jp Yousei Mizukami mizukami.yousei@jaxa.jp Takeo Tadono tadono.takeo@jaxa.jp <p>Early warning systems (EWS) have been proposed as a measure for controlling and preventing dengue fever outbreaks in countries where this infection is endemic. A vaccine is not available and has yet to reach the market due to the economic burden of development, introduction and safety concerns. Understanding how dengue spreads and identifying the risk factors will facilitate the development of a dengue EWS, for which a climate-based model is still needed. An analysis was conducted to examine emerging spatiotemporal hotspots of dengue fever at the township level in Taiwan, associated with climatic factors obtained from remotely sensed data in order to identify the risk factors. Machinelearning was applied to support the search for factors with a spatiotemporal correlation with dengue fever outbreaks. Three dengue fever hotspot categories were found in southwest Taiwan and shown to be spatiotemporally associated with five kinds of sea surface temperatures. Machine-learning, based on the deep AlexNet model trained by transfer learning, yielded an accuracy of 100% on an 8-fold cross-validation test dataset of longitudetime sea surface temperature images.</p> 2019-11-06T10:03:09+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/770 Addressing operational challenges of combatting malaria in a remote forest area of Vietnam using spatial decision support system approaches 2019-11-16T21:16:50+01:00 Thang Duc Ngo thangnimpevn@yahoo.com Sara E. Canavati saracanavati@yahoo.com Ha Son Dinh nimpe.vn@gmail.com Thinh Duc Ngo Ngoducthinh68@gmail.com Duong T. Tran tranthanhduong@hotmail.com Nicholas J. Martin Martin.Nicholas.mil@afrims.org Gerard C. Kelly gerardckelly@gmail.com <p>This study examines the development of a spatial decision support system (SDSS) to address operational challenges for combatting malaria in a priority remote forest area of Vietnam including locating active malaria transmission, guiding targeted response, and identifying mobile and high-risk populations. A customized SDSS was developed for three communes in Phu Yen Province, Vietnam. Geographical reconnaissance (GR) was conducted to map and enumerate all households in the study site. During 2015 and 2016, detected malaria cases were reported to the SDSS and georeferenced to household residence. Individual case data were analysed in the SDSS to guide targeted response. Case investigation data, including suspected forest and remote area transmission locations, were also integrated into the SDSS. Surveys and in-depth interviews were conducted to assess utility and user acceptability. In 2015 and 2016, 4,667 households and a population of 17,563 were captured during GR. During the study period, 128 malaria cases were reported and automatically mapped in the SDSS. Targeted response interventions were conducted in 12 villages, testing 1,872 individuals. Intervention and remote-area sleeping site data were mapped and analysed using the SDSS. During follow-up interviews in 2017 the SDSS was found to be highly acceptable to malaria surveillance personnel. Results suggest that an SDSS can provide an effective tool in Vietnam to support the implementation of specialized surveillance, and calls for further research, application and roll-out in the Greater Mekong Subregion.</p> 2019-11-06T10:14:06+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/773 A spatial analysis of referrals to a primary mental health programme in Western Sydney from 2012 to 2015 2019-11-16T21:16:50+01:00 Cailin Maas cailin.maas@sydney.edu.au Jose A. Salinas-Perez jsalinas@uloyola.es Nasser Bagheri nasser.bagheri@anu.edu.au Sebastian Rosenberg sebastian.rosenberg@anu.edu.au William Campos Bill.campos@wentwest.com.au James A. Gillespie james.gillespie@sydney.edu.au Luis Salvador-Carulla luis.salvador-carulla@anu.edu.au <p>Access to Allied Psychological Services is a primary mental health programme targeting hard-to-reach populations throughout Australia. This research aims to identify patterns of referrals to the programme in the Western Sydney Primary Health Network region from 2012 to 2015. The referral rates were analysed by using spatial autocorrelation indexes and spatial regression. The study area was described through the identification of the most disadvantaged areas and through consideration of three socio-economic indicators: percentage of Aboriginal and Torres Strait Islander Australians, low educational attainment and low weekly incomes. A large hot spot (identifying high referral rates) was located across the duration of the study in the south-western urban area that partially covered a disadvantaged area. The main cold spot (identifying low referral rates) was located in the south-eastern urban area, covering another disadvantaged area, however critically this association disappeared over time. Our modelling showed that the referral rates had a direct association with the percentage of Aboriginal and Torres Strait Islander peoples with low incomes, and an indirect association with low educational attainment. The results and technique are useful in monitoring and addressing inequality in health planning and policy.</p> 2019-11-06T10:14:57+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/783 Adjusted, non-Euclidean cluster detection of Vibrio parahaemolyticus in the Chesapeake Bay, USA 2019-11-16T21:16:49+01:00 Anton Kvit antonkvit@gmail.com Benjamin Davis bdavis64@jhmi.edu John Jacobs john.jacobs@noaa.gov Frank C. Curriero fcurriero@jhu.edu <p><em>Vibrio parahaemolyticus</em> (<em>V. parahaemolyticus</em>) is a naturallyoccurring bacterium found in estuaries, such as the Chesapeake Bay (USA), that can cause vibriosis, a food - and waterborne illness, in humans. Tracking the spatial and temporal distribution of <em>V. parahaemolyticus</em> in the Chesapeake Bay, which varies in part due to water temperature, salinity, and other environmental variables, can help identify areas and time periods of high risk. These observations can support interventions used to reduce the burden of vibriosis. Spatial and spatiotemporal clusters of high <em>V. parahaemolyticus</em> abundance were identified among surface water samples in the Chesapeake Bay between 2007 and 2010. While Euclidean distances between geographic points in spatial analyses are often used for cluster detection, non-Euclidean distances should be considered for cluster detection due to the complex nature of the Chesapeake Bay shoreline. Comparison of both methods consistently showed the non-Euclidean cluster detection providing unique and more reasonable clusters than the Euclidean approach. Residuals from univariate and multivariate models were used to identify how clusters changed after controlling for environmental variables. Most clusters tended to decrease in space, time, or significance after adjustment, suggesting these covariates contributed to the original formation of the clusters and as such are useful observation tools for vibriosis risk managers. Clusters that remained after adjustment suggest areas for further study and intervention. These findings reinforce the importance of using non-Euclidean distances when tracking the spatiotemporal variation of <em>V. parahaemolyticus</em> as well as the benefits of cluster detection methods for <em>V. parahaemolyticus</em> risk management in estuaries.</p> 2019-11-06T10:15:45+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/793 Colorectal cancer risk factors in north-eastern Iran: A retrospective cross-sectional study based on geographical information systems, spatial autocorrelation and regression analysis 2019-11-16T21:16:48+01:00 Ladan Goshayeshi GoshayeshiL@mums.ac.ir Ali Pourahmadi Ali.pourahmadi@mums.ac.ir Majid Ghayour-Mobarhan GhayourM@mums.ac.ir Soheil Hashtarkhani hashtarkhanis951@mums.ac.ir Sajad Karimian Sajjad.Karimian94@gmail.com Reza Shahhosein Dastjerdi Dastjerdi09@gmail.com Babak Eghbali Kianib@mums.ac.ir Efat Seyfi Kianib@mums.ac.ir Behzad Kiani kiani.behzad@gmail.com <p>Colorectal cancer (CRC) is the second most common cancer among females and the third most common malignancy in males in the world. No single risk factor has been identified, but there are many interrelated factors that together cause the disease. This retrospective, cross-sectional study aimed to identify potential spatial factors contributing to its geographical distribution. Data concerning 1,089 individuals with CRC from the Khorasan-Razavi Province in Iran, located in the North-East of the country, were obtained from the national CRC registry. Local Moran’s <em>I</em> statistic was performed to identify clustered areas of CRC occurrence and ordinary least squared regression was calculated to predict frequency of the disease based on a set of variables, such as diet, body mass index (BMI) and the proportion of the population ≥50 years of age. Some dissimilarities related to the geography in the province and also some neighbourhood-related similarities and dissimilarities of CRC occurrence in the city of Mashhad were found. A significant regression equation was found (F (4,137)=38.304, P&lt;.000) with an adjusted R<sup>2</sup> of 0.6141. The predicted CRC frequency was -58.3581 with the coefficients for average BMI=+1.594878; fibre intake=-0.610335; consumption of red meat +0.078970; and ≥50-year age group =+0.000744. All associations were statistically significant, except the <em>consumption of red meat</em> one. The study results illuminate the potential underlying risk factors in areas where the CRC risk is comparatively high and how the CRC risk factors may play a role in CRC geographic disparity. Further research is required to explain the patterns observed. We conclude that people should include more fibre in their daily diet and decline their BMI to decrease risk of CRC.</p> 2019-11-06T10:16:52+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/786 Evaluation and planning of Chagas control activities using geospatial tools 2019-11-16T21:16:48+01:00 Diego Weinberg dweinberg@mundosano.org Mario Lanfri lamfri@conae.gov.ar Carlos M. Scavuzzo scavuzzo@conae.gov.ar Marcelo Abril mabril@mundosano.org Sofía Lanfri sofia.lanfri.vgb@gmail.com <p>Chagas continues to be a relevant public health problem in Latin America. In this work, we present a spatiotemporal analysis applied for the evaluation and planning of Chagas vector control strategies. We analysed the spatial distribution of the vector <em>Triatoma infestans</em> infestation related to ongoing control interventions cycles in rural communities near Añatuya, Santiago del Estero, Argentina. A geographical information system was developed for the spatial analysis obtaining, for each house, variables that describe the history of spraying and infestation at each time of interventions. Bi-dimensional histograms were used to describe the spatiotemporal pattern of these activities and peri-domestic infestation at the last intervention was modelled by a neural network model. We qualitatively evaluate control programmes considering the history of infestation and spraying from a spatiotemporal point of view, incorporating new ways of visualising this information. Predictions are based on novel, non-linear models and spatiotemporal indices, which should be useful for strategically allocating Chagas control resources in the future and thus help to better plan spraying strategies.</p> 2019-11-06T10:18:16+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/769 Exploring the geographical distribution of cryptosporidiosis in the cattle population of Southern Ontario, Canada, 2011-2014 2019-11-16T21:16:47+01:00 Andrea Nwosu anwosu@uoguelph.ca Olaf Berke anwosu@uoguelph.ca David L. Pearl anwosu@uoguelph.ca Lise A. Trotz-Williams anwosu@uoguelph.ca <p>Cryptosporidiosis is an infectious disease of relevance to the cattle industry. The southern region of the Canadian province of Ontario is characterised by widespread cattle farming that is a key contributor to the Canadian dairy industry. Given Ontario’s key role in the Canadian dairy industry and the potential impact that cryptosporidiosis can have on cattle operations, identifying areas of increased risk for bovine cryptosporidiosis is important. The primary goal of this study was to explore the distribution of bovine cryptosporidiosis, across the geographical areas served by the 29 Public Health Units (PHUs) of Southern Ontario, in the period 2011-2014. Laboratory data on bovine cryptosporidiosis were collected from the Animal Health Laboratory at the University of Guelph, Canada. Using veterinary clinic locations as a proxy for farm location, choropleth and isopleth maps were produced. Highrisk clusters of bovine cryptosporidiosis were identified using the flexible spatial scan test. Assessment of the potential for spatial misclassification bias resulting from a proxy location variable was conducted. The overall raw farm-level prevalence of bovine cryptosporidiosis was 45% [95% confidence interval, CI: 42%-48%]. A cluster was identified in the central-west region of Southern Ontario (relative risk 1.30 [95% CI: 1.07-1.54, P=0.026]) meaning that cattle in the areas served by the Bruce-Grey-Owen Sound, Huron, Wellington-Dufferin Guelph and Waterloo PHUs were at a higher risk for infection. Given that this area is known for having a high-density of dairy cattle, it should be considered as a target for further surveillance.</p> 2019-11-06T11:36:53+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/805 Spatiotemporal analysis of rabies in cattle in central Mexico 2019-11-16T21:16:46+01:00 Isabel Bárcenas-Reyes ibr.mvz@hotmail.com Diana Paulina Nieves-Martínez paum352@gmail.com José Quintín Cuador-Gil jcuador@gmail.com Elizabeth Loza-Rubio eli_rubio33@hotmail.com Sara González-Ruíz sagoru2410@hotmail.com Germinal Jorge Cantó-Alarcón gcanto07@uaq.mx Feliciano Milian-Suazo feliciano.milian@uaq.mx <p>Spatial epidemiology of bat-transmitted rabies in cattle has been limited to spatial distribution of cases, an approach that does not identify hidden patterns and the spread resulting in outbreaks in endemic and susceptible areas. Therefore, the purpose of this study was to determine the relationship between the three variables average annual maximum, annual minimum temperature and precipitation in the region on the one hand, and the spatial distribution of cases on the other, using geographic information systems and co-Kriging considering that these environmental variables condition the existence of the rabies vector <em>Desmodus rotundus</em>. A stationary behaviour between the primary and the secondary variables was verified by basic statistics and moving window statistics. The directions of greater and lesser spatial continuity were determined by experimental cross-semivariograms. It was found that the highest risk for bovine paralytic rabies occurs in areas known as <em>La Huasteca Potosina</em> and <em>La Sierra Gorda</em> that are characterized by a maximum temperature of 29.5 °C, a minimum temperature of 16.5 °C and precipitation of 1200 mm. A risk estimation map was obtained for the presence of rabies with a determination coefficient greater than 95%, and a correlation coefficient greater than 0.95. Our conclusion is that ordinary co- Kriging provides a better estimation of risk and spatial distribution of rabies than simple Kriging, making this the method recommended for risk estimation and regional distribution of rabies.</p> 2019-11-06T11:37:22+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/812 Residential mobility impacts relative risk estimates of space-time clusters of chlamydia in Kalamazoo County, Michigan 2019-11-16T21:16:46+01:00 Claudio Owusu cowusu@uncc.edu Michael R. Desjardins mdesjar2@uncc.edu Kathleen M. Baker kathleen.baker@wmich.edu Eric Delmelle Eric.Delmelle@uncc.edu <p>We determine the impact of residential mobility in the prevalence and transmission dynamics of sexually transmitted infections. We illustrate our approach on reported chlamydia infections obtained from the Michigan Disease Surveillance System for Kalamazoo County, USA, from 2006 to 2014. We develop two scenarios, one with fixed residential addresses and one considering residential mobility. We then compare the resulting space-time clusters and relative risk (RR) of infection. The space-time scan statistics showed increased RR in an area with previously low risk of sexually transmitted infections. In addition, even though the spatial extent of the three clusters identified did not change significantly at the scale we conducted our analysis at, the temporal extent (duration) did exhibit significant changes and could be considered for unique interventions. The results indicate that residential mobility has some dependency on the prevalence and transmission dynamics of sexually transmitted infections to new areas. We suggest that strategies adopted to reduce the burden of sexually transmitted infections take into consideration the relatively high residential mobility of at-risk populations to reduce spreading the infections to new areas.</p> 2019-11-06T11:39:13+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/809 A geospatial model to determine the spatial cost-efficiency of anticoagulation drug therapy: Patients’ perspective 2019-11-16T21:16:45+01:00 Mikko Pyykönen mikko.pyykonen@uef.fi Aapeli Leminen mikko.pyykonen@uef.fi Juho Tynkkynen mikko.pyykonen@uef.fi Markku Tykkyläinen mikko.pyykonen@uef.fi Tiina Laatikainen mikko.pyykonen@uef.fi <p>Most atrial fibrillation (AF) patients need anticoagulation management to reduce the risk of thromboembolic events and stroke. Currently, two major drug therapies are available: warfarin and direct oral anticoagulant (DOAC). This study examined the spatial costs of these therapies and derived the least-cost market areas for both therapies in the study area. The concepts of spatial costs and the principles of forming market areas were used as theoretical starting points, and the patients’ travel, time-loss, and medication cost parameters combined with geographical information systems methods were incorporated into the geospatial model. Results showed that for AF patients who live near the international normalized ratio (INR) monitoring sample collection point and have less than 15 annual INR monitoring visits, warfarin therapy resulted in the lowest cost regardless of patient’s travel mode and their assumed working or retirement status. If the AF patient needs more frequent INR monitoring visits or lives farther from the nearest sample collection point, DOAC would be the least costly option. The modelled results reveal the variety and importance of patients’ cost of time loss and travel costs when a physician selects the appropriate anticoagulation therapy.</p> 2019-11-06T11:39:46+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/789 Spatial clustering of people with memories and responses six years after an earthquake in Cauquenes, Chile 2019-11-16T21:16:45+01:00 Marcelo Leiva-Bianchi marcleiva@utalca.cl Carlos Mena cmena@utalca.cl Yony Ormazábal yormazabal@utalca.cl Carlos Serrano cserrano@utalca.cl <p>The occurrence of earthquakes can cause psychiatric problems expressed as unpleasant and uncontrollable memories of the event termed post-traumatic stress disorder (PTSD). Mapping the location of people and identifying their exposure and reactions to an earthquake can be extremely valuable from a public, mental health point of view. The main objective of this study was to examine people with respect to PTSD and healthy post-traumatic growth (PTG) after an earthquake searching for expression of geographic clustering that could be useful for a better understanding of mental health conditions. Geographic information systems analyses were performed to detect global and local geographic clustering. Investigating 171 randomly selected adults from Cauquenes, Chile, we demonstrated spatially clustered variables related to PTSD and PTG in Cauquenes six years after an earthquake. Urban and peri-urban areas had clear differences (hotspots/coldspots). The spatial identifications found should facilitate exploring the impact of mental health programmes in communities exposed to disasters like earthquakes, thereby improving their quality of life as well as reducing overall costs.</p> 2019-11-06T11:40:20+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/767 Malaria risk map for India based on climate, ecology and geographical modelling 2019-11-16T21:16:44+01:00 Soma Sarkar ssarkar.delhi@gmail.com Poonam Singh punamsingh10@gmail.com Mercy Aparna L. Lingala mercy_aparna@yahoo.co.in Preeti Verma verma.preeti07@gmail.com Ramesh C. Dhiman r.c.dhiman@gmail.com <p>Mapping the malaria risk at various geographical levels is often undertaken considering climate suitability, infection rate and/or malaria vector distribution, while the ecological factors related to topography and vegetation cover are generally neglected. The present study abides a holistic approach to risk mapping by including topographic, climatic and vegetation components into the framework of malaria risk modelling. This work attempts to delineate the areas of <em>Plasmodium falciparum</em> and <em>Plasmodium vivax</em> malaria transmission risk in India using seven geo-ecological indicators: temperature, relative humidity, rainfall, forest cover, soil, slope, altitude and the normalized difference vegetation index using multi-criteria decision analysis based on geographical information system (GIS). The weight of the risk indicators was assigned by an analytical hierarchical process with the climate suitability (temperature and humidity) data generated using fuzzy logic. Model validation was done through both primary and secondary datasets. The spatio-ecological model was based on GIS to classify the country into five zones characterized by various levels of malaria transmission risk (very high; high; moderate; low; and very low. The study found that about 13% of the country is under very high malaria risk, which includes the malaria- endemic districts of the states of Chhattisgarh, Odisha, Jharkhand, Tripura, Assam, Meghalaya and Manipur. The study also showed that the transmission risk suitability for <em>P. vivax</em> is higher than that for <em>P. falciparum</em> in the Himalayan region. The field study corroborates the identified malaria risk zones and highlights that the low to moderate risk zones are outbreak-prone. It is expected that this information will help the National Vector Borne Disease Control Programme in India to undertake improved surveillance and conduct target based interventions.</p> 2019-11-06T11:40:51+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/784 Assessing joint spatial autocorrelations between mortality rates due to cardiovascular conditions in South Africa 2019-11-16T21:16:43+01:00 Timotheus B. Darikwa timotheus.darikwa@ul.ac.za Samuel Manda Samuel.Manda@mrc.ac.za ‘Maseka Lesaoana Lesaoana.Maseka@ul.ac.za <p>South Africa is experiencing an increasing burden of noncommunicable diseases (NCDs). There is evidence of co-morbidity of several NCDs at small geographical areas in the country. However, the extent to which this applies to joint spatial autocorrections of NCDs is not known. The objective of this study was to derive and quantify multivariate spatial autocorrections for NCDrelated mortality in South Africa. The study used mortality attributable to cerebrovascular, ischaemic heart failure and hypertension captured by the country’s Department of Home Affairs for the years 2001, 2007 and 2011. Both univariate and pairwise spatial clustering measures were derived using observed, empirical Bayes smoothed and age-adjusted standardised mortality rates. Cerebrovascular and ischaemic heart co-clustering was significant for the years 2001 and 2011. Cerebrovascular and hypertension co-clustering was significant for the years 2007 and 2011, while hypertension and ischaemic heart co-clustering was significant for the year 2011. Co-clusters of cerebrovascular-ischaemic heart disease are the most profound and located in the south-western part of the country. It was successfully demonstrated that bivariate spatial autocorrelations can be derived for spatially dependent mortality rates as exemplified by mortality rates attributed to three cardiovascular conditions. The identified co-clusters of spatially dependent health outcomes may be targeted for an integrated intervention and monitoring programme.</p> 2019-11-06T11:41:20+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/796 Space and time predictions of schistosomiasis snail host population dynamics across hydrologic regimes in Burkina Faso 2019-11-16T21:16:43+01:00 Javier Perez-Saez javier.perezsaez@gmail.com Theophile Mande mandetheophile@gmail.com Andrea Rinaldo andrea.rinaldo@epfl.ch <p>The ecology of the aquatic snails that serve as obligatory intermediate hosts of human schistosomiasis is driven by climatic and hydrological factors which result in specific spatial patterns of occurrence and abundance. These patterns in turn affect, jointly with other determinants, the geography of the disease and the timing of transmission windows, with direct implications for the success of control and elimination programmes in the endemic countries. We address the spatial distribution of the intermediate hosts and their seasonal population dynamics within a predictive ecohydrological framework developed at the national scale for Burkina Faso, West Africa. The approach blends river network-wide information on hydrological ephemerality which conditions snail habitat suitability together with ensembles of discrete time ecological models forced by remotely sensed estimates of temperature and precipitation. The models were validated against up to four years of monthly snail abundance data. Simulations of model ensembles accounting for the uncertainty in remotely sensed products adequately reproduce observed snail demographic fluctuations observed in the field across habitat types, and produce national scale predictions by accounting for spatial patterns of hydrological conditions in the country. Geospatial estimates of seasonal snail abundance underpin large-scale, spatially explicit predictions of schistosomiasis incidence. This work can therefore contribute to the development of disease control and elimination programmes.</p> 2019-11-06T11:44:49+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/791 Searching for space-time clusters: The CutL method compared to Kulldorff’s scan statistic 2019-11-16T21:16:42+01:00 Barbara Więckowska basia@ump.edu.pl Ilona Górna igorna@ump.edu.pl Maciej Trojanowski maciej.trojanowski@wco.pl Agata Pruciak agata@ump.edu.pl Barbara Stawińska-Witoszyńska bwitoszynska@ump.edu.pl <p>Both epidemiology and health care planning require analytical tools, especially for cluster detection in cases with unusually high rates of disease incidence. The aim of this work was to extend the application of the CutL method, which is used for detecting spatial clusters of any shape, to detecting space-time clusters, and to show how the method works compared to Kulldorff’s scan statistic. In the CutL method, clusters with disease incidence rates higher than the one entered by the researcher are searched for. The way in which the space-time version of that method works is illustrated with the example of data simulating the distribution of people affected by health problems in Polish counties in the period 2013- 2017. With respect to detection of irregularly shaped space-time clusters, the CutL method turned out to be more effective than Kulldorff’s scan statistic; for cylinder-shaped space-time clusters, the two methods produced similar results. The CutL method has also the important advantage of being widely accessible through the PQScut and PQStat programmes (PQStat Software Company, Poznan, Poland).</p> 2019-11-06T11:45:45+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/823 Bayesian conditional autoregressive models to assess spatial patterns of diarrhoea risk among children under the age of 5 years in Mbour, Senegal 2019-11-16T21:16:42+01:00 Sokhna Thiam sokhna.thiam@swisstph.ch Guéladio Cissé gueladio.cisse@swisstph.ch Anna-Sofie Stensgaard asstensgaard@bio.ku.dk Aminata Niang-Diène aminata.niang@ucad.edu.sn Jürg Utzinger juerg.utzing@swisstph.ch Penelope Vounatsou penelope.vounatsou@swisstph.ch <p>Diarrhoeal diseases remain a major public health problem, causing more than half a million child deaths every year, particularly in low- and middle-income countries (LMICs). Despite existing knowledge on the aetiologies and causes of diarrhoeal diseases, relatively little is known about its spatial patterns in LMICs, including Senegal. In the present study, data from a cross-sectional survey carried out in 2016 were analysed to describe the spatial pattern of diarrhoeal prevalence in children under the age of 5 years in the secondary city of Mbour in the south-western part of Senegal. Bayesian conditional autoregressive (CAR) models with spatially varying coefficients were employed to determine the effect of sociodemographic, economic and climate parameters on diarrhoeal prevalence. We observed substantial spatial heterogeneities in diarrhoea prevalence. Risk maps, stratified by age group, showed that diarrhoeal prevalence was higher in children aged 25-59 months compared to their younger counterparts with the highest risk observed in the north and south peripheral neighbourhoods, especially in Grand Mbour, Médine, Liberté and Zone Sonatel. The posterior relative risk estimate obtained from the Bayesian CAR model indicated that a unit increase in the proportion of people with untreated stored drinking water was associated with a 29% higher risk of diarrhoea. A unit increase in rainfall was also associated with an increase in diarrhoea risk. Our findings suggest that public health officials should integrate disease mapping and cluster analyses and consider the varying effects of sociodemographic factors in developing and implementing areaspecific interventions for reducing diarrhoea.</p> 2019-11-06T11:55:03+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/820 Stunting spatial pattern in Rwanda: An examination of the demographic, socio-economic and environmental determinants 2019-11-16T21:16:41+01:00 Vestine Uwiringiyimana v.uwiringiyimana@utwente.nl Antonie Veldkamp a.veldkamp@utwente.nl Sherif Amer s.amer@utwente.nl <p>Stunting is recognised as a major public health problem in Rwanda. We therefore aimed to study the demographic, socio-economic and environmental factors determining the spatial pattern of stunting. A cross-sectional study using the data from the 2014- 2015 Rwanda Demographic and Health Survey and environmental data from external geospatial datasets were conducted. The study population was children less than two years old with their mothers. A multivariate linear regression model was used to estimate the effects of demographic, socio-economic and biophysical factors and a proxy measure of aflatoxins exposure on height-for-age. Also, a spatial prediction map of height-for-age to examine the stunting pattern was produced. It was found that age of child, height of mother, secondary education and higher, a child being male and birth weight were associated with height-for-age. After adjusting for demographic and socioeconomic factors, elevation and being served by a rural market were also significantly associated with low height-for-age in children. The spatial prediction map revealed the variability of height-for-age at the cluster-level that was lost when the levels are aggregated at the district level. No associations with height-for-age were found for exclusive breastfeeding, use of deworming tablets, improved water source and improved sanitation in the study population. In addition to the child and mother factors known to determine height-for-age, our study confirms the influence of environmental factors in determining the height-of-age of children in Rwanda. A consideration of the environmental drivers of anthropometric status is crucial to have a holistic approach to reduce stunting.</p> 2019-11-06T11:55:59+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/727 Assessment of the health impact of paper mulberry (Broussonetia papyrifera L.), an invasive plant species in Islamabad, Pakistan 2019-11-16T21:16:40+01:00 Sana Qazi junaid@igis.nust.edu.pk Javed Iqbal junaid@igis.nust.edu.pk Junaid Aziz Khan junaid@igis.nust.edu.pk <p>This study focuses on the risk of pollen allergy due to paper mulberry (<em>Broussonetia papyrifera L</em>.), an Asian invasive plant species now common in large parts of the world. Pollen plays a key role in the pathogenesis of respiratory allergic diseases, particularly rhinitis and asthma, and Islamabad, a major metropolitan city, is severely affected by allergy owing to <em>B. papyrifera</em> pollen. Due to its seasonality and other relationships with climatic variables, we used remote sensing to monitor the trend of pollen count. We also mapped the localisation of patients affected by pollen allergy using geographic information systems. The maximum likelihood algorithm was applied to SPOT-5 satellite imagery for land use/land cover classification. Temporal analysis of remotely sensed data revealed an increasing trend of paper mulberry density towards the southern and south-western part of Islamabad. Although not evident during rainfall, a clear positive correlation was found between patient count and pollen count. Field survey data and hotspot spatial analysis of allergy patients revealed that residents of Shakerperiyan and Lok Virsa areas (Sectors H-8, I-8, I-9, G-8, G-7 and G-6 in Islamabad) had more pronounced symptoms compared to residents of other sectors. The methodology adopted used in this study can be used to map the distribution of similar invasive species in other parts of the country.</p> 2019-11-12T10:52:00+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/752 Using global positioning system methods to explore mobility patterns and exposure to high HIV prevalence neighbourhoods among transgender women in New York 2019-11-16T21:16:39+01:00 William C. Goedel william_goedel@brown.edu Seann D. Regan seann.regan@gmail.com Basile Chaix basile.chaix@iplesp.upmc.fr Asa Radix ARadix@callen-lorde.org Sari L. Reisner Sari.Reisner@childrens.harvard.edu Aron C. Janssen Aron.Janssen@nyumc.org Dustin T. Duncan dd3018@columbia.edu <p>The aim of this study was to assess mobility patterns among a sample of transgender women (n=14) in New York City via survey and Global Positioning System (GPS) monitoring. We found varying levels of concordance between the residential neighbourhood and each of the non-residential contexts: 64.3% considered the neighbourhood that they socialised in most often to be different from their residential neighbourhood. While participants’ residences represented 10 zone improvement plan code tabulation areas (ZCTAs), GPS data were recorded in 124 of 263 ZCTAs (47.1%). Overall, 58.2% (n=373,262) were recorded in ZCTAs in the highest quartile of human immunodeficiency virus (HIV) prevalence. The association between place, community HIV prevalence, mobility, and factors that increase the vulnerability of transgender women to HIV infection are worthy of future investigation in reducing the burden of the HIV epidemic in these communities.</p> 2019-11-12T10:57:10+01:00 ##submission.copyrightStatement## https://geospatialhealth.net/index.php/gh/article/view/798 Mapping routine malaria incidence at village level for targeted control in Papua New Guinea 2019-11-16T21:16:40+01:00 Daniela Rodríguez-Rodríguez daniela.rodriguez@swisstph.ch Seri Maraga seri.maraga@pngimr.org.pg Sharon Jamea-Maiasa sharon.jamea@pngimr.org.pg Anthony Tandrapah ttandrapah@gmail.com Leo Makita leo.makita@gmail.com Peter M. Siba pmaxsiba@gmail.com Ivo Mueller ivo.mueller@pasteur.fr Justin Pulford Justin.Pulford@lstmed.ac.uk Manuel Hetzel manuel.hetzel@swisstph.ch <p>Malaria surveillance and response-systems are essential for identifying the areas most affected by malaria and for targeting interventions and optimising resources. This study aimed to assess whether the visualisation of routinely collected health facility data linked to village of residence provides evidence for targeting control interventions in four sentinel health facilities in Papua New Guinea. A video format was used to visualise the dynamics in case incidence over time and space alongside photographs illustrating the context of the data collection in the study sites. Incidence changes overtime were illustrated in animated maps. Despite limitations, this approach appeared useful in sites with very few remaining cases or with increasingly marked heterogeneity. Villages that could benefit from targeted interventions or investigations were identified.</p> 2019-11-07T16:47:16+01:00 ##submission.copyrightStatement##