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GIS and Evidence-Based Policy Making - Chapter 8

The need to identify patterns of illness and disease is not uncommonin public health, for example the identification of disease clusters and tendencies toward clustering, such as outbreaks of communicable disease (e.g., tuberculosis), and higher than expected prevalence=incidence of diseases (e.g., childhood leukemia). The basic building blocks or units for such patterns may be individuals or geographical units, but the key factor is the association between units in terms of time, space, or other complex links. However, searching for patterns of disease using geographical-based data can help not only to identify disease clusters in a geographical area but also can be helpful in seeking to identify potential causes of. | 8__ Pattern Identification ỉn Public Health Data Sets The Potential Offered by Graph Theory Peter A. Bath Cheryl Craigs Ravi Maheswaran John Raymond and Peter Willett CONTENTS 8.1 Introduction.159 8.1.1 Background.160 8.1.2 Computational Chemistry and Graph Theory.161 8.2 Methods.162 8.2.1 Program . 162 8.2.2 Data . 162 8.2.2.1 Geographical Area . 162 8.2.2.2 Deprivation.163 8.2.2.3 Standardized Long-Term Limiting Illness for People Aged Less Than 75.164 8.2.2.4 Adjacency Information.165 8.2.3 Storage of Information.165 8.2.4 Queries.166 8.2.4.1 Query Patterns.166 8.2.4.2 Query Data File.167 8.3 Results. 169 8.4 Discussion.172 Acknowledgments.175 References . 175 8.1 Introduction Pattern identification is an important issue in public health and current methods are not designed to deal with identifying complex geographical patterns of illness and disease. Graph theory has been used successfully within the field of chemoinformatics to identify complex user-defined patterns 2007 by Taylor Francis Group LLC. or substructures within molecules in databases of two-dimensional 2D and three-dimensional 3D chemical structures. In this paper we describe a study in which one graph theoretical method the maximum common substructure MCS algorithm which has been successful in identifying such patterns has been adapted for use in identifying geographical patterns in public health data. We describe how the RASCAL RApid Similarity CALculator program Raymond and Willett 2002 Raymond et al. 2002a b which uses the MCS method was utilized for identifying user-specified geographical patterns of socioeconomic deprivation and long-term limiting illness. The paper illustrates the use of this method presents the results from searches in a large database of public health data and then discusses the potential of graph theory for use in searching for geographical-based information. 8.1.1 Background The need to identify patterns of illness and disease is