Use of graph theory to identify patterns of deprivation and high morbidity and mortality in public health data sets.
OBJECTIVE
An important part of public health is identifying patterns of poor health and deprivation. Specific patterns of poor health may be associated with features of the geographic environment where contamination or pollution may be occurring. For example, there may be clusters of poor health surrounding nuclear power stations, whereas major roads or rivers may be associated with areas of poor health alongside the feature in chains. Current methods are limited in their capacity to search for complex patterns in geographic data sets. The objective of this study was to determine whether graph theory could be used to identify patterns of geographic areas that have high levels of deprivation, morbidity, and mortality in a public health database. The geographic areas used in the study were enumeration districts (EDs), which are the lowest level of census geography in England and Wales, representing on average 200 households in the 1991 census. More specifically, the study aimed to identify chains of EDs with high deprivation, morbidity, and mortality that might be adjacent to specific types of geographic features, i.e., rivers or major roads.


METHODS
The maximum common subgraph (MCS) algorithm was used to search for seven query patterns of deprivation and poor health within the Trent region. Query pattern 1 represented a linear chain of five EDs and query patterns 2 to 7 represented the possible clusters of the five EDs. To identify chains of EDs with high deprivation, morbidity, and mortality, the results from the query patterns 2 to 7 were used to remove patterns (option 1) and EDs (option 2) from the results of query pattern 1.


METHODS
Data on the Townsend Material Deprivation Index, standardized long-term limiting illness and standardized all-cause mortality rates were used for the 10,665 EDs within the Trent region.


RESULTS
The MCS algorithm retrieved a range of patterns and EDs from the database for the queries. Query pattern 1 identified 3,838 patterns containing a total of 195 EDs. When the patterns retrieved using query patterns 2 to 7 were removed from the 3,838 patterns using option 1, 1,704 patterns remained containing 161 EDs. When the EDs retrieved using query patterns 2 to 7 were removed from the 195 EDs identified by query pattern 1 using option 2, 12 EDs remained. The MCS algorithm was therefore able to reduce the numbers of patterns and EDs to allow manual examination for chains of EDs and for that which might be associated with them.


CONCLUSIONS
The study demonstrates the potential of the MCS algorithm for searching for specific patterns of need. This method has potential for identifying such patterns in relation to local geographic features for public health.
Bath
Peter A
PA
Centre for Health Information Management Research (CHIMR), Department of Information Studies, University of Sheffield, Western Bank, Sheffield S10 2TN UK. p.a.bath@shef.ac.uk
Craigs
Cheryl
C
Maheswaran
Ravi
R
Raymond
John
J
Willett
Peter
P
D000465:Algorithms; D016000:Cluster Analysis; D016208:Databases, Factual; D004739:England / Q000453:epidemiology; D004812:Epidemiologic Methods; D005843:Geography; D006304:Health Status; D006305:Health Status Indicators; D008962:Models, Theoretical; D009017:Morbidity; D009026:Mortality; D011203:Poverty; D011634:Public Health; D014478:Unemployment; D014852:Wales / Q000453:epidemiology