geospatial health data

BioMeanings Update January 2015

1.12.15

Two proposal tips

We write a fair number of research proposals, with most of them going to the National Institutes of Health, and the Centers for Disease Control and surveillance. We also submit proposals to the National Science Foundation and NASA. For those researchers who are new to a proposal writing role here are a couple tips.

One of the first things to do after identifying the next “Big Idea” is to assess its potential viability and impact. Two items top the list. The first is the priority in terms of disease targeted; it is much easier to obtain funding for a disease with a large human and economic impact. One resource that may be of use is the global burden of disease, and of years of life lost (YYL). This article, published recently in The Lancet provides estimates from around the globe for both chronic and infectious disease burdens.

Second, if you are looking for funding in the U.S. it is important to consider what the funding organization’s budget and plan are for the near term. Remember the reviewers may not even be aware of the funding organizations research priorities. For NIH, the reviewers are external, and it is good proposal practice to remind them of the relevant NIH research priorities. A good portion of BioMedware’s research portfolio has been funded by the National Cancer Institute. Our 2015 proposals likely will incorporate information from NCI’s 2016 budget proposal and plan, which may be found here.

In summary, the message is to (1) seek to address important diseases, where importance is defined by human and economic costs; and (2) go after those proposal ideas that are highly relevant to the funding organization’s mission and budget plan.

Save the Date: April 13-14, 2015

GIS for Community Impact: From Technology to Translation

Organized by The Cancer Prevention Institute of California and Zero Breast Cancer
Location: The California Endowment, 1111 Broadway, Oakland, CA
Geoffrey Jacquez, PhD, President of BioMedware, and Keynote speaker is conducting the preconference workshop on April 13th, titled “Space Time Analysis Using SpaceStat”.

In the news: SpaceStat for spatial analysis

Recently published articles demonstrating how SpaceStat’s rigorous, statistical analysis methods are being utilized by researchers.

Geographical Disparities of Lung Cancer Mortality Centered on Central Appalachia

Timothy S. Hare, Chad Wells, Nicole Johnson; Morehead State University, Morehead, KY, USA
International Journal of Applied Geospatial Research, 5(4), 35-53, October-December 2014 35

“…We use ESRI’s ArcGIS for data processing and visualization and BioMedware’s SpaceStat for EDA, ESDA, OLS regression, spatial regression, and GWR. SpaceStat is a set of software tools for conducting a variety of spatial statisti¬cal analysis techniques (BioMedware, 2012). It supports dynamic and interactive analysis of linked tables, charts, and maps. SpaceStat calculates probability values (p-values) for observed test statistics by comparing them to their null distributions, which estimates the likelihood of the observed values in comparison with the null distribution. The spatial weights matrix used is queen’s case contiguity.”

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The RUSLE erosion index as a proxy indicator for debris flow susceptibility

Alessandro Zini, Sergio Grauso, Vladimiro Verrubbi, Luca Falconi, Gabriele Leoni, Claudio Puglisi
Landslides, DOI 10.1007/s10346-014-0515-8
Received: 25 September 2013; Accepted: 14 August 2014

Background. Debris flows represent dangerous occurrences in many parts of the world. Several disasters are documented due to this type of fast-moving landslides, therefore, natural-hazard assessment of debris flows is crucial for safety of life and property. In this paper we investigated the effectiveness of the soil erosion index as debris flows susceptibility indicator. The relation between the erosion index, which was assessed by means of the Revised Universal Soil Loss Equation (RUSLE) model, and the inventory of debris flows that have occurred in an area in Sicily was investigated.

Method. To this aim, a geographically weighted logistic regression analysis (GWR) was carried out making use of SpaceStat. While traditional logistic regression aims to obtain an average parameter estimate over the whole territory under observation, the GWR attempts to gain point-to-point estimates. Moreover, the GWR technique may provide a better fit with experimental data.

Results. The results of the statistical analysis prove the existence of a significant connection between the observed debris flows and the RUSLE erosion index. Secondly, the complexity of the GWR model, based on non-fixed regression coefficients, points out that the same value of RUSLE index can have different effects in different locations. This result highlights the behavior of complex land systems in the real world, where different forces are acting (rain, running waters, internal water pressures, internal strains, gravity, etc.) and determine different point-to-point degrees of susceptibility to debris flows. On the basis of GWR parameters a scenario analysis regarding the area was conducted.

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Follow Geoffrey Jacquez, PhD, on Twitter @GeoffJacquez.

Best wishes for your research efforts in 2015 and thank you for your interest in BioMedware.

BioMedware is pleased to announce its membership in the 43pl Consortium

9.21.14

The 43pl consortium operates a unit trust which facilitates participation in the CRCSI by a large number of small to medium sized enterprises. Founded in 2003, the CRC for Spatial Information (CRCSI) is a research organisation funded by Australia’s Cooperative Research Centre Program and by participant contributions.

Announcing the Release of SpaceStat 4: software for the visualization, analysis, modeling and interactive exploration of spatiotemporal data

5.3.14

Download a Free 14-day evaluation of SpaceStat

SpaceStat 4.0 represents a major reworking of the underlying architecture of the application. Multithreading has been introduced improving the performance of many methods. A LePace-Sage estimator for spatial-error and spatial-lag analyses has been added to the spatial regression method.

Based on customer feedback, we have designed feature enhancements in SpaceStat 4.0 that improve the appearance, functionality and performance of maps and graphs. You will also find that the extensive help documentation has been updated, revised and expanded.

Additionally, we’ve responded to your requests for a SpaceStat virtual class by adding a series of tutorials to our website. Each tutorial comes with a SpaceStat project designed to get you started working with a specific concept, and provides a landing page with a description, time estimate and associated project links.

New LeSage-Pace Estimator

A LeSage-Pace estimator for spatial-error and spatial-lag analyses has been added to the spatial regression method.

“I am involved in developing and applying multiple regression models for the mass valuation of residential real estate properties. Modelers such as me are always seeking to find improved model accuracy. The spatial regression models in SpaceStat are of particular interest. The addition of the LeSage-Pace output makes it easier to compare to other methods. Incidentally the Spatial Error Model has been the best performer among all models I have tested lately. It is featured in a book I have written on spatio-temporal methods in mass appraisal to be published June 2014. Also thanks to BioMedware for making this change to the product.”

Richard A. Borst, PhD
Tyler Technologies, Inc.

Recently Published Research using SpaceStat…

Spatial Relationship Quantification between Environmental, Socioeconomic and Health Data at Different Geographic Levels

Int. J. Environ. Res. Public Health 2014, 11(4), 3765-3786; doi:10.3390/ijerph110403765
Authors: Mahdi-Salim Saib, Julien Caudeville, Florence Carre, Olivier Ganry, Alain Trugeon and Andre Cicolella

“We used Spacestat to evaluate relationships between spatial data collected at different geographic scales. Spacestat is easy-to-use and provides powerful tools that make possible spatial data processing, exploratory analysis, and the quantification of spatial relationships in environmental health research. Spacestat is extraordinarily useful for stakeholders seeking to prioritize prevention actions in the context of environmental inequalities reduction.”

Julien Caudeville
French National Institute for Industrial Environment and Risks (INERIS)
Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France

Space-time clusters of breast cancer using residential histories: A Danish case control study

BMC Cancer.2014, 14:255. DOI: 10.1186/1471-2407-14-255
Authors: Nordsborg Baastrup Rikke, Meliker R Jaymie, Ersbøll Kjær Annette, Jacquez M Geoffrey, Poulsen Harbo Aslak, Raaschou-Nielsen Ole

Background

A large proportion of breast cancer cases are thought related to environmental factors. Identification of specific geographical areas with high risk (clusters) may give clues to potential environmental risk factors. The aim of this study was to investigate whether clusters of breast cancer existed in space and time in Denmark, using 33 years of residential histories.

Methods

We conducted a population-based case–control study of 3138 female cases from the Danish Cancer Registry, diagnosed with breast cancer in 2003 and two independent control groups of 3138 women each, randomly selected from the Civil Registration System. Residential addresses of cases and controls from 1971 to 2003 were collected from the Civil Registration System and geo-coded. Q-statistics were used to identify space-time clusters of breast cancer. All analyses were carried out with both control groups, and for 66% of the study population we also conducted analyses adjusted for individual reproductive factors and area-level socioeconomic indicators.

Results

In the crude analyses a cluster in the northern suburbs of Copenhagen was consistently found throughout the study period (1971–2003) with both control groups. When analyses were adjusted for individual reproductive factors and area-level socioeconomic indicators, the cluster area became smaller and less evident.

Conclusions

The breast cancer cluster area that persisted after adjustment might be explained by factors that were not accounted for such as alcohol consumption and use of hormone replacement therapy. However, we cannot exclude environmental pollutants as a contributing cause, but no pollutants specific to this area seem obvious.

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Thoughts from Austin: NAACCR Annual Meeting

6.12.13

This week I am attending the meetings of the North American Association of Central Cancer Registries that is being held in Austin, Texas. The topic of this year’s conference is “Thinking big, the future of cancer surveillance”, and I’m involved in two activities. The first was a series of workshops that occurred on Saturday and Sunday titled “Evaluation of Homomorphic Cryptography for Geospatial Studies with Human Subjects”. This workshop was convened as part of a grant funded by National Library of Medicine that is evaluating the feasibility of using homomorphic cryptography to accelerate the pace of research and discovery for studies that use human subjects data. “Homomorphic” means mathematical operations can be conducted on encrypted data (e.g. in the encrypted space), greatly reducing the risk to privacy of confidential data.

My co-organizer, Dr. Khaled El Emam of Privacy Analytics and the University of Ottawa e-health laboratory were very happy with the recommendations that came out of the working group. These are being written up as a BioMedware report to the National Library of Medicine, and will be available in our Publications when they are ready. But here is a preview of some of the “low-hanging fruit” that homomorphic cryptography may make possible.

First, increased data security greatly enhances data sharing, and hence participation in all manner of activities where data sharing plays an important role. It turns out a key bugaboo in the processing of disease registry data is deduplication; the removal of duplicate data records that may appear in several data bases. This arises, for example, when snowbirds flit between Michigan and Florida, yet have records of cancer tumor treatment in both States. The data providers must be very satisfied that the potential for unintentional release of their highly confidential patient records is absolutely minimal, meaning, in practice, that two data providers may be reluctant to share data to search for record duplicates. Homomorphic encryption solves this by having deduplication take place in the encrypted space – hence even if the data security is breached the records appear as complete gibberish.

Second, increased data sharing means data aggregation across data providers becomes far less of a concern. Hence activities that involve pooling data, such as determining the number of cases anticipated in projected enrollment reports for NIH grant applications, suddenly becomes very easy.
Other opportunities were identified – keep checking back for our release of the workshop report!

Advancing the use of GIS and spacial analysis to improve public health: First Annual Western New York GIS Day

11.15.12

The first annual Western New York GIS Day was held August 15, 2012, at the Roswell Park Cancer Institute in Buffalo, New York. The meeting’s objective was to advance the use of GIS and spatial analysis to improve public health, health services, health planning and health research, and was the product of a collaboration between the State University of New York at Buffalo (UB) and Buffalo State University.

Speakers included Gale R. Burstein, MD, and Congresswoman Kathy Hochul, with keynote addresses by William F. Wieczorek, PhD, Director and Professor, Center for Health and Social Research, Buffalo State College, and Geoffrey M. Jacquez, PhD, Professor, Department of Geography, University at Buffalo and President, BioMedware.

Announcing Our Membership in Esri’s Business Partner Network and the Release of SpaceStat 3 with Geodatabase File Format Integration

9.5.11

I am very excited by our release of SpaceStat 3, which links BioMedware to Esri technology using the geodatabase file format and is an important step towards addressing unmet needs in data access and geohealth analysis techniques. This is very timely, as BioMedware recently joined Esri’s Business Partner Network.

With this latest release, SpaceStat readily imports the wealth of geospatial health data provided by Esri and Esri’s business partners. SpaceStat’s advanced visualization, space-time analysis, and modeling techniques are easily integrated into workflows that use Esri technologies. For example, you can use ArcGIS to acquire, edit, and manipulate your data, and then use SpaceStat to analyze time-dynamic data; to target health interventions, to assess health disparities, and to undertake predictive modeling. (See my last blog for a list of 20 health analysis activities commonly accomplished in SpaceStat.)

Importing geodatabase (gdb) files is straightforward. After starting SpaceStat, select “File->Import->Esri geodatabase” and you will be taken to the import dialog. In the shown example we are importing CentralBusinessDistrect.gdb, using the CBDOutline as the feature class.

A new geography will be created in SpaceStat, called CentralBusinessDistrict (CBDOutline). The data may be time-stamped, which is a useful mechanism for representing time-dynamic geographies, such as business districts, where buildings are demolished, new ones built, and streets are rerouted.

Here we are looking at the Pittsburgh business district, and have displayed the different neighborhoods that comprise it. The circular feature is Mellon Arena, home of the NHL’s Pittsburgh Penguins. The Mellon Arena features the largest retractable, stainless steel dome roof in the world–170,000 total square feet and 2,950 tons of Pittsburgh steel. It certainly stands out on the map!

Interested in giving SpaceStat 3 a spin? Go here for a free 14-day evaluation copy.

Esri, and esri.com are trademarks, registered trademarks, or service marks of Esri in the United States, the European Community, or certain other jurisdictions.

BioMedware Joins Esri Partner Network

9.1.11

Partnership Provides an Easy Way for Esri Users to Add SpaceStat’s Advanced Space-Time Analysis Into Their Workflows

Ann Arbor, MI – Sept. 6, 2011 – BioMedware, a leader in geohealth software development and research solutions, today announced its membership in the Esri Partner Network.

BioMedware’s President, Dr. Geoffrey Jacquez, also revealed that BioMedware’s flagship product, SpaceStat (formerly known as STIS), now links to Esri technology using the geodatabase file format. “Formalizing our partnership with Esri is an important step towards addressing unmet needs in data access and geohealth analysis techniques.” said Jacquez. “By including geodatabase support in SpaceStat v3.0, Esri customers will easily be able to integrate SpaceStat’s advanced space-time analysis methods and visualization techniques into their workflows.” Jacquez added, “We’re looking forward to growing this new relationship and leveraging our Esri partner status in ways that will benefit both our customers and Esri’s clients, and improve data analysis.”

Dr. Jacquez, an expert in geohealth research and spatial-time analysis software development, is presenting a talk tomorrow entitled “Does geocoding positional error matter in health GIS studies?” at the Esri Health GIS Conference being held in Washington, D.C.

BioMedware counts over 600 academic and government organizations among their global customer base.

Esri customers can sign up for a free 14-day evaluation release of SpaceStat v3.0, with geodatabase file format support.

About Esri
Since 1969, Esri has been giving customers around the world the power to think and plan geographically. The market leader in geographic information system (GIS) technology, Esri software is used in more than 300,000 organizations worldwide including each of the 200 largest cities in the United States, most national governments, more than two-thirds of Fortune 500 companies, and more than 7,000 colleges and universities. Esri applications, running on more than one million desktops and thousands of Web and enterprise servers, provide the backbone for the world’s mapping and spatial analysis. Esri is the only vendor that provides complete technical solutions for desktop, mobile, server, and Internet platforms. Visit us at esri.com/news.

Esri, the Esri globe logo, GIS by Esri, ArcLogistics, esri.com, and @esri.com are trademarks, registered trademarks, or service marks of Esri in the United States, the European Community, or certain other jurisdictions. Other companies and products mentioned herein may be trademarks or registered trademarks of their respective trademark owners.

About BioMedware
BioMedware was founded in 1995 to develop statistical analysis methods and to make the methods more available to non-statisticians in user-friendly software. To date, BioMedware has received nearly ten million dollars in grants to develop new methods and software for the spatial analysis and modeling of disease. This research has resulted in three commercial software products: SpaceStat, ClusterSeer and BoundarySeer. For more information, visit www.biomedware.com or email [email protected]

SpaceStat, ClusterSeer and BoundarySeer are trademarks of BioMedware, Inc. All other trademarks are the property of their respective holders.

For media inquiries or product information contact Susan Hinton
+1 734-913-1098 ext 201
[email protected]