A Decision Technology System To Advance the Diagnosis and Treatment of Breast Cancer

Geographical variations in cancer rates have been observed for decades. Described spatial patterns and trends have provided clues for generating hypotheses about the etiology of cancer. For breast cancer, investigators have demonstrated that some variation can be explained by differences in the population distribution of known breast cancer risk factors such as menstrual and reproductive variables (Laden, Spiegelman, and Neas, 1997; Robbins, Bescianini, and Kelsey, 1997; Sturgeon, Schairer, and Gail, 1995). However, regional patterns also may reflect the effects of Workshop on Hormones, Hormone Metabolism, Environment, and Breast Cancer (1995): (a) environmental hazards (such as air and water pollution), (b) demographics and the lifestyle of a mobile population, (c) subgroup susceptibility, (d) changes and advances in medical practice and healthcare management, and (e) other factors. To accurately measure breast cancer risk in individuals and population groups, it is necessary to singly and jointly assess the association between such risk and the hypothesized factors. Various statistical models will be needed to determine the potential relationships between breast cancer development and estimated exposures to environmental contamination. To apply the models, data must be assembled from a variety of sources, converted into the statistical models’ parameters, and delivered effectively to researchers and policy makers. A Web-enabled decision technology system can be developed to provide the needed functionality. This chapter will present a conceptual architecture for such a decision technology system. First, there will be a brief overview of a typical geographical analysis. Next, the chapter will present the conceptual Web-based decision technology system and illustrate how the system can assist users in diagnosing and treating breast cancer. The chapter will conclude with an examination of the potential benefits from system use and the implications for breast cancer research and practice

BCAS: A Web-enabled and GIS-based Decision Support System for the Diagnosis and Treatment of Breast Cancer

For decades, geographical variations in cancer rates have been observed but the precise determinants of such geographic differences in breast cancer development are unclear. Various statistical models have been proposed. Applications of these models, however, require that the data be assembled from a variety of sources, converted into the statistical models’ parameters and delivered effectively to researchers and policy makers. A web-enabled and GIS-based system can be developed to provide the needed functionality. This article overviews the conceptual web-enabled and GIS-based system (BCAS), illustrates the system’s use in diagnosing and treating breast cancer and examines the potential benefits and implications for breast cancer research and practice

Special Libraries, February 1978

Volume 69, Issue 2https://scholarworks.sjsu.edu/sla_sl_1978/1001/thumbnail.jp

Exploring Environmental and Geographical Factors Influencing the Spread of Infectious Diseases with Interactive Maps

Publisher Copyright: © 2022 by the authors.Environmental problems due to human activities such as deforestation, urbanisation, and large scale intensive farming are some of the major factors behind the rapid spread of many infectious diseases. This in turn poses significant challenges not only in as regards providing adequate healthcare, but also in supporting healthcare workers, medical researchers, policy makers, and others involved in managing infectious diseases. These challenges include surveillance, tracking of infections, communication of public health knowledge and promotion of behavioural change. Behind these challenges lies a complex set of factors which include not only biomedical and population health determinants but also environmental, climatic, geographic, and socioeconomic variables. While there is broad agreement that these factors are best understood when considered in conjunction, aggregating and presenting diverse information sources requires effective information systems, software tools, and data visualisation. In this article, weargue that interactive maps, which couple geographical information systems and advanced information visualisation techniques, provide a suitable unifying framework for coordinating these tasks. Therefore, we examine how interactive maps can support spatial epidemiological visualisation and modelling involving distributed and dynamic data sources and incorporating temporal aspects of disease spread. Combining spatial and temporal aspects can be crucial in such applications. We discuss these issues in the context of support for disease surveillance in remote regions, utilising tools that facilitate distributed data collection and enable multidisciplinary collaboration, while also providing support for simulation and data analysis. We show that interactive maps deployed on a combination of mobile devices and large screens can provide effective means for collection, sharing, and analysis of health data.Peer reviewe

Spatiotemporal Patterns and Burden of Myocardial Infarction in Florida

Knowledge of spatiotemporal disparities in myocardial infarction (MI) risk and the determinants of those disparities is critical for guiding health planning and resource allocation. Therefore, the aims of this study were to: (i) investigate the spatial distribution and clusters of MI hospitalization (MIHosp) and MI mortality (MIMort) risks in Florida over time to identify communities with consistently high MI burdens, (ii) assess temporal trends in geographic disparities in MIHosp and MIMort risks (iii) identify predictors of MIHosp risks.Retrospective MIhosp and MImort data for Florida for 2005-2014 and 2000-2014 periods, respectively, were used. Kulldorff’s circular and Tango’s flexible spatial scan statistics were used to identify spatial clusters, and counties with persistently high or low MIHosp and MIMort risks were identified. Global and local negative binomial models were used to identify predictors of MIHosp risks.MIHosp and MIMort risks declined by 15%-20% and 48% respectively, but there were substantial disparities in space and over time. Persistent clustering of high MIHosp risks occurred in the Big Bend area, South Central and Southeast Florida. Persistent clustering of low risks occurred in southeast and southwest Florida. Clustering of high or low MIMort risks occurred in the same areas as MIHosp risks, but there was no clustering of high MIMort risks in South Central Florida. The risks declined on the overall in all clusters over the study period. However, they decreased more rapidly in high-risk clusters during the first 4-8 years of study, leading to reduced disparities in the short term. Nevertheless, MI risks for high-risk clusters lagged behind those for low-risk clusters by at least a decade. Significant predictors of MIHosp risks included race, marital status, education level, rural residence and lack of health insurance. The impacts of education level and lack of health insurance varied geographically, with strongest associations in southern Florida. In conclusion, MI interventions need to target high-risk clusters to reduce the MI burden and improve population health in Florida. Moreover, the interventions need to consider social contexts, allocating resources based on empirical evidence from global and local models to maximize their efficiency and effectivenes

Distributed usability evaluation of the Pennsylvania Cancer Atlas

<p>Abstract</p> <p>Background</p> <p>The Pennsylvania Cancer Atlas (PA-CA) is an interactive online atlas to help policy-makers, program managers, and epidemiologists with tasks related to cancer prevention and control. The PA-CA includes maps, graphs, tables, that are dynamically linked to support data exploration and decision-making with spatio-temporal cancer data. Our Atlas development process follows a user-centered design approach. To assess the usability of the initial versions of the PA-CA, we developed and applied a novel strategy for soliciting user feedback through multiple distributed focus groups and surveys. Our process of acquiring user feedback leverages an online web application (e-Delphi). In this paper we describe the PA-CA, detail how we have adapted e-Delphi web application to support usability and utility evaluation of the PA-CA, and present the results of our evaluation.</p> <p>Results</p> <p>We report results from four sets of users. Each group provided structured individual and group assessments of the PA-CA as well as input on the kinds of users and applications for which it is best suited. Overall reactions to the PA-CA are quite positive. Participants did, however, provide a range of useful suggestions. Key suggestions focused on improving interaction functions, enhancing methods of temporal analysis, addressing data issues, and providing additional data displays and help functions. These suggestions were incorporated in each design and implementation iteration for the PA-CA and used to inform a set of web-atlas design principles.</p> <p>Conclusion</p> <p>For the Atlas, we find that a design that utilizes linked map, graph, and table views is understandable to and perceived to be useful by the target audience of cancer prevention and control professionals. However, it is clear that considerable variation in experience using maps and graphics exists and for those with less experience, integrated tutorials and help features are needed. In relation to our usability assessment strategy, we find that our distributed, web-based method for soliciting user input is generally effective. Advantages include the ability to gather information from users distributed in time and space and the relative anonymity of the participants while disadvantages include less control over when and how often participants provide input and challenges for obtaining rich input.</p

Epidemics

Using Medicare claims data on prescriptions of oseltamivir dispensed to people 65 years old and older, we present a descriptive analysis of patterns of influenza activity in the United States for 579 core-based statistical areas (CBSAs) from the 2010-2011 through the 2015-2016 influenza seasons. During this time, 1,010,819 beneficiaries received a prescription of oseltamivir, ranging from 45,888 in 2011-2012 to 380,745 in 2014-2015. For each season, the peak weekly number of prescriptions correlated with the total number of prescriptions (Pearson's r\u2009 65\u20090.88). The variance in peak timing decreased with increasing severity (p\u2009<\u20090.0001). Among these 579 CBSAs, neither peak timing, nor relative timing, nor severity of influenza seasons showed evidence of spatial autocorrelation (0.02\u2009 64\u2009Moran's I\u2009 64\u20090.23). After aggregating data to the state level, agreement between the seasonal severity at the CBSA level and the state level was fair (median Cohen's weighted \u3ba\u2009=\u20090.32, interquartile range\u2009=\u20090.26-0.39). Based on seasonal severity, relative timing, and geographic place, we used hierarchical agglomerative clustering to join CBSAs into influenza zones for each season. Seasonal maps of influenza zones showed no obvious patterns that might assist in predicting influenza zones for future seasons. Because of the large number of prescriptions, these data may be especially useful for characterizing influenza activity and geographic distribution during low severity seasons, when other data sources measuring influenza activity are likely to be sparse.CC999999/Intramural CDC HHS/United States2019-05-15T00:00:00Z30249390PMC6519085626

HEALTH GeoJunction: place-time-concept browsing of health publications

<p>Abstract</p> <p>Background</p> <p>The volume of health science publications is escalating rapidly. Thus, keeping up with developments is becoming harder as is the task of finding important cross-domain connections. When geographic location is a relevant component of research reported in publications, these tasks are more difficult because standard search and indexing facilities have limited or no ability to identify geographic foci in documents. This paper introduces <it><smcaps>HEALTH</smcaps> GeoJunction</it>, a web application that supports researchers in the task of quickly finding scientific publications that are relevant geographically and temporally as well as thematically.</p> <p>Results</p> <p><it><smcaps>HEALTH</smcaps> GeoJunction </it>is a geovisual analytics-enabled web application providing: (a) web services using computational reasoning methods to extract place-time-concept information from bibliographic data for documents and (b) visually-enabled place-time-concept query, filtering, and contextualizing tools that apply to both the documents and their extracted content. This paper focuses specifically on strategies for visually-enabled, iterative, facet-like, place-time-concept filtering that allows analysts to quickly drill down to scientific findings of interest in PubMed abstracts and to explore relations among abstracts and extracted concepts in place and time. The approach enables analysts to: find publications without knowing all relevant query parameters, recognize unanticipated geographic relations within and among documents in multiple health domains, identify the thematic emphasis of research targeting particular places, notice changes in concepts over time, and notice changes in places where concepts are emphasized.</p> <p>Conclusions</p> <p>PubMed is a database of over 19 million biomedical abstracts and citations maintained by the National Center for Biotechnology Information; achieving quick filtering is an important contribution due to the database size. Including geography in filters is important due to rapidly escalating attention to geographic factors in public health. The implementation of mechanisms for iterative place-time-concept filtering makes it possible to narrow searches efficiently and quickly from thousands of documents to a small subset that meet place-time-concept constraints. Support for a <it>more-like-this </it>query creates the potential to identify unexpected connections across diverse areas of research. Multi-view visualization methods support understanding of the place, time, and concept components of document collections and enable comparison of filtered query results to the full set of publications.</p

Journal of Global Radiology, Volume 1 Issue 1 (March 2015)

Full issue of Volume 1, Issue 1 (March 2015) of the Journal of Global Radiology. Articles are available individually at http://escholarship.umassmed.edu/jgr/vol1/iss1/