HealthGIS '12最新文献

{"title":"Studying neonatal TSH distribution by using GIS","authors":"G. Tradigo, P. Veltri, O. Marasco, Giovanna Scozzafava, G. Parlato, S. Greco","doi":"10.1145/2452516.2452523","DOIUrl":"https://doi.org/10.1145/2452516.2452523","url":null,"abstract":"Geographical Information Systems, i.e. GIS, are used to help communities in managing data related to their geographical location. Associating textual data with spatial extension and time can be crucial to understand and improve human health. Exploiting available data and extracting new knowledge can lead to disease distribution and migration models (e.g., epidemiology).\u0000 In this paper we report the experience of using GIS technologies to analyze clinical data containing TSH values about newborn in a spatially delimited region. TSH neonatal screening has been performed on blood of newborn with the aim to discover diseases at an early stage and to study the detect any possible arise of hypothyroidism. We present a flexible geographical system called Geomedica which is being used to analyze such data according to a two steps approach: (i) study of the last 10 years of data distribution in an Italian region with over 18 thousands newborn per year and (ii) identify possible clusters by querying and projecting results geometry on a thematic map.\u0000 Queries performed on the available dataset were able to correctly correlate health data about patients with geographical features (e.g. points of interest, boundaries, coastline vectors) and to visualize diseases distributions on a geographical map. However, the proposed queries may be considered as an important starting point for similar environment dependent pathologies.","PeriodicalId":168309,"journal":{"name":"HealthGIS '12","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128772043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated epidemiologic simulation for person to person contagion through urban mobility within GIS","authors":"Hedi Haddad, B. Moulin, M. Thériault, Daniel Navarro-Velazquez","doi":"10.1145/2452516.2452529","DOIUrl":"https://doi.org/10.1145/2452516.2452529","url":null,"abstract":"In recent years, advances in Health Geography, Geographical Epidemiology and Public Health Informatics have led to an extensive use of Geographic Information Systems (GIS) to study a variety of public health issues. Considering infectious disease outbreaks, time becomes a critical factor and Public Health officers require tools to support rapid decision making. In this context, GIS technology presents some limits. Mainly, the study of communicable diseases requires the development of complicated spatial-temporal models which is often time and effort consuming. In addition, this type of dynamic analysis is hard to realize by means of the GIS functionalities commonly available. Addressing such limits, we present in this paper a new GIS-based spatial-temporal simulation approach and software to support public health decision making in the context of communicable diseases. Our approach stands out by the integrative perspective and the explicit spatial aspect that it offers. On the one hand, it fully integrates epidemiological, mobility and GIS-data models at an aggregate population level in order to support public health decision making. This is made possible because our approach is built on data automatically processed from transportation surveys that are widely available, at least in North America and Europe. Our approach is thus simple and can be promptly put into use. On the other hand, our approach particularly aims at supporting decision makers with respect to \"spatialized\" intervention policies. Mainly, it allows for the assessment of different public intervention actions in different spatial locations of the studied area and the evaluation of their effects on the disease spatial evolution and distribution.","PeriodicalId":168309,"journal":{"name":"HealthGIS '12","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121904100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using routine geo-coded data to identify geographical heterogeneity to reduce disparities: case studies in UK","authors":"A. Poots, S. Green, R. Barnes, D. Bell","doi":"10.1145/2452516.2452524","DOIUrl":"https://doi.org/10.1145/2452516.2452524","url":null,"abstract":"This paper outlines a structured argument for the use of routine health and demographic data to support the delivery of equitable services that are better aligned to the needs of the populations they serve. The paper describes case studies from a nationally funded research and quality improvement programme in London, UK as examples of targeting existing services, without top-down reconfiguration, using quality improvement methodology.\u0000 Three case studies are presented each demonstrating a differing use of geocoded routine data. The first demonstrates the use of a novel composite metric for the prospective targeting of service improvement; the second shows how routine geo-coded health data can be used to support the geographical location of services; the third demonstrates how routine data can be used to evaluate the impact of improvement initiatives on disparities in healthcare. All methods provide a novel way of analyzing current service provision to ensure targeting of services where needed and contributing to the quality and cost challenges faced by healthcare providers and commissioners.","PeriodicalId":168309,"journal":{"name":"HealthGIS '12","volume":"45 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130754794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerating investigation of food-borne disease outbreaks using pro-active geospatial modeling of food supply chains","authors":"Daniel Doerr, K. Hu, Sondra R. Renly, S. Edlund, Matthew A. Davis, J. Kaufman, J. Lessler, M. Filter, A. Käsbohrer, B. Appel","doi":"10.1145/2452516.2452525","DOIUrl":"https://doi.org/10.1145/2452516.2452525","url":null,"abstract":"Over the last decades the globalization of trade has significantly altered the topology of food supply chains. Even though food-borne illness has been consistently on the decline, the hazardous impact of contamination events is larger [1-3]. Possible contaminants include pathogenic bacteria, viruses, parasites, toxins or chemicals. Contamination can occur accidentally, e.g. due to improper handling, preparation, or storage, or intentionally as the melamine milk crisis proved. To identify the source of a food-borne disease it is often necessary to reconstruct the food distribution networks spanning different distribution channels or product groups. The time needed to trace back the contamination source ranges from days to weeks and significantly influences the economic and public health impact of a disease outbreak. In this paper we describe a model-based approach designed to speed up the identification of a food-borne disease outbreak source. Further, we exploit the geospatial information of wholesaler-retailer food distribution networks limited to a given food type and apply a gravity model for food distribution from retailer to consumer. We present a likelihood framework that allows determining the likelihood of wholesale source(s) distributing contaminated food based on geo-coded case reports. The developed method is independent of the underlying food distribution kernel and thus particularly applicable to empirical distributions of food acquisition.","PeriodicalId":168309,"journal":{"name":"HealthGIS '12","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115923424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health risk assessment of zoonotic infections agents through plant products in areas with high livestock pressure","authors":"G. Tradigo, M. Cannataro, P. Guzzi, F. Casalinuovo, P. Veltri, C. Graziani","doi":"10.1145/2452516.2452530","DOIUrl":"https://doi.org/10.1145/2452516.2452530","url":null,"abstract":"In areas where there is a high livestock pressure, small variabilities in the infection agents distributions can lead to disastrous consequences on the territory for both animals and humans. Agriculture and animal health parameters are strictly correlated factors and as such they should be considered when analysing pasture behaviours for a certain area with respect to animal diseases. A Geographical Information System (GIS) is a solid tool for the veterinary to manage data and find correlations with its location and spatial extension over time. GIS can lead to novel animal disease distribution models, thus helping in the interpretation of, e.g., an epidemic episode or a groundwater contamination. Contaminated or polluted areas have bad effects on herds insisting on that area and, as a side effect, on products and food connected with both animals and plants on the same area. Moreover, many misbehaviours in human-managed farms higher risks for human health. In mixed farms (i.e. where both animals and vegetable crops are managed) in fact, animal droppings are often used to manure vegetables, thus creating a potential risk for the diffusion of pathogenic micro-organisms polluting deep and superficial groundwater or producing even more serious problems.\u0000 In this paper we report about the project of using a GIS technology-based tool to monitor the land use of a large agricultural area, where high quality land products and animal based foods (such as milk production or cheese or meat); the idea is to verify whereas high concentration of animals related production, are land related with possible bacteria or problems in agricultural productions. The idea is to use GIS as potential prevention models for studying (i) farms as potential accumulators of pathogens, (ii) the environment (i.e. ground and water) as a collector of pathogens coming from herbs or farms and (iii) contaminated vegetable and crop production as a vehicle of infection for humans. We plan to use a tool implemented as a general purpose geographical information system with querying and spatial data management capabilities. Such a tool, called Geomedica, can be used easily and efficiently to design and implement ad hoc queries. A first set of queries performed over the preliminary dataset were able to validate our models and verify the correctness of our assumptions and to visualize results on a geographical map.","PeriodicalId":168309,"journal":{"name":"HealthGIS '12","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132735679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing colorectal cancer disparities: the identification of geographic targets for screening interventions in Miami-Dade County, Florida","authors":"Recinda Sherman, Kevin A. Henry, David J Lee","doi":"10.1145/2452516.2452520","DOIUrl":"https://doi.org/10.1145/2452516.2452520","url":null,"abstract":"This paper describes an analysis of spatial clustering of colorectal cancer (CRC) in Miami-Dade County, Florida. The objective was to identify geographically based targets for colorectal cancer screening interventions for Blacks and Hispanic Whites, two groups with demonstrated disparities in stage at diagnosis and mortality for CRC. The initial cluster detection analysis identified areas with high risk of late stage CRC, however, none of the results were statistically significant.\u0000 The analysis was not based on an academic research question, but instead was an application intended to guide appropriate and targeted strategies for high risk populations. Only about 50% of the general population receives CRC screening, so, while all groups would benefit from increased CRC screening, high risk communities may potentially benefit the most. Because public health resources are limited, geographically targeting high risk populations for enhanced screening efforts is pragmatic public health policy.\u0000 Despite the lack of statically significant results, we still needed to develop a helpful answer to the question, where should we market a screening intervention?\u0000 The selected geographic areas must have real potential for attenuating excess CRC burden through increased screening efforts. Through evaluating a combination of clusters of late stage and overall CRC risk (two separate models of cluster detection), probable communities with low CRC screening uptake were identified. Although they did not meet statistical significance, they were determined to have public health importance.","PeriodicalId":168309,"journal":{"name":"HealthGIS '12","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129083539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolution of respiratory syncytial virus (RSV) over space and time in rural Filipino children","authors":"E. Root, James T Gaensbauer, H. Nohynek, M. Lucero, A. Tanskanen, Leilani Nillos, V. Tallo, E. Simões","doi":"10.1145/2452516.2452528","DOIUrl":"https://doi.org/10.1145/2452516.2452528","url":null,"abstract":"Very little is known about how spatial distance influences viral evolution or what local spatio-temporal patterns of evolution look like. In this study, we use data from a randomized controlled efficacy trial of an 11-valent pneumococcal vaccine (PCV) undertaken in the Bohol province of the Philippines from July 2000 to December 2004. Viral culture and multiplex PCR were done on nasal wash specimens, collected from a sample of infants visiting the regional hospital or outpatient clinics during the vaccine trial. We performed a nested phylogeographic analysis of respiratory syncytial virus (RSV) positive samples and classified virus samples into distinct subgroups. The geographic coordinates of household of residence were obtained for study participants using GPS and used to link phylogenetic results to the geographic location of each patient. We then performed a retrospective space-time scan statistic to identify the spatial location and temporal extent of clusters of each subgroup and visualized geographic patterns using GIS. The spatio-temporal scan statistic identified several unique space-time clusters of RSV-A and RSV-B subgroups. The results show that RSV subgroups evolve in distinct localized areas at different points in time, suggesting that spatial distance a population factors play an important role in viral evolution. Spatial analysis and geovisualization is the first step in exploring the effects of distance on viral evolution and potential ecological pressures that contribute to evolutionary pressures.","PeriodicalId":168309,"journal":{"name":"HealthGIS '12","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124345611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health-optimal routing in pedestrian navigation services","authors":"Monir H. Sharker, H. Karimi, J. Zgibor","doi":"10.1145/2452516.2452518","DOIUrl":"https://doi.org/10.1145/2452516.2452518","url":null,"abstract":"People use various criteria for choosing routes, which may vary depending on location and time, purpose of trip, and personal preferences. Common routing criteria supported by current navigation services include shortest, fastest, least traffic, and least expensive (e.g., less fuel cost, toll free). While each optimal route is computed by using one of these criteria, there is currently no criterion that can be used to compute routes that are health-optimal. In this paper, we focus on a new routing criterion to compute health-optimal routes with the main objective of increasing physical activity. Those who are physically capable and motivated to walk can adapt a lifestyle that includes walking as a means to mitigate or prevent obesity. To that end, a routing criterion for computing health-optimal routes suitable for those who are concerned with obesity must take into account both environmental and individual factors. Computing optimal routes requires that each road segment of a road network be assigned a weight; like, distance for shortest routes and travel time for fastest routes. In this paper, we present and discuss a new weight for segments of pedestrian paths used in pedestrian navigation services to compute health-optimal routes. While health-optimal routes may address various health conditions, the objective of this work is to provide options for walking routes to increase regular physical activity as one means to help mitigate or prevent obesity. Weights are calculated by considering both environmental and individual parameters. The optimal-health weight is simulated using various scenarios. The results of the simulations show that the computed weights can be used to find health-optimal routes that are meaningful and consistent with walkability and obesity standards.","PeriodicalId":168309,"journal":{"name":"HealthGIS '12","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127195532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk analysis based on spatio-temporal characterization: a case study of disease risk mapping","authors":"Vipul Raheja, K. Rajan","doi":"10.1145/2452516.2452527","DOIUrl":"https://doi.org/10.1145/2452516.2452527","url":null,"abstract":"One of the challenges in risk analysis has been that the determinants which are identified are based on a causality-driven approach drawn largely from the correlation studies of underlying factors. These approaches not only require numerous thematic information layers - spatial and non-spatial, that may potentially represent the factors of interest, but also tend to ignore the spatial and temporal variability of the outcome itself (say, disease incidence). On the other hand, owing to the advances in surveillance and monitoring systems resulting in enhanced availability of spatially explicit data over the last 25 years, there is a need to use these effectively at understanding or explaining the phenomenon itself. In this paper, we propose a method to leverage the observed event data - both spatial and temporal characterizations of disease occurrences, to generate a risk map that will provide valuable insights into its geographical spread and to help quantify the spatial risk factor associated with it. It is evident that such a methodology will help prioritize decision-making process for better risk assessment and management including disease outbreak. Illustrative case studies of Salmonellosis disease in two states of USA are presented to demonstrate the utility of the method. It is observed that this method, per se, can be applied to other domains that exhibit similar spatio-temporal dynamic behavior.","PeriodicalId":168309,"journal":{"name":"HealthGIS '12","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116679391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does location matter: effects of distance & practice size on consumer preferences for seeking primary healthcare","authors":"Lindsay Aspen, T. Shah, K. Wilson, S. Bell","doi":"10.1145/2452516.2452532","DOIUrl":"https://doi.org/10.1145/2452516.2452532","url":null,"abstract":"This article examines distance to healthcare services and physician practice size as factors influencing consumer preference and choice when seeking primary healthcare (PHC) in an urban setting. Data from a multipurpose telephone survey for the Canadian city of Saskatoon, Saskatchewan was analyzed. Using network analyst in ArcGIS and information drawn from this survey, distances to respondents' regular family physicians were compared against distances to the location where healthcare was alternatively received. Statistical analysis demonstrated preferences for larger, more local practices at the expense of continuity of care. These findings suggest erratic utilization of healthcare services that could lead to further healthcare access issues. This paper contributes to a growing body of work that recognizes the complexity of access to healthcare; most importantly it suggests that lower neighbourhood level access can result in health care decisions that might reduce continuity of care.","PeriodicalId":168309,"journal":{"name":"HealthGIS '12","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127642029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}