A graph-theory method for pattern identification in geographical epidemiology - a preliminary application to deprivation and mortality

Background: Graph theoretical methods are extensively used in the field of computational chemistry to search datasets of compounds to see if they contain particular molecular substructures or patterns. We describe a preliminary application of a graph theoretical method, developed in computational chemistry, to geographical epidemiology in relation to testing a prior hypothesis. We tested the methodology on the hypothesis that if a socioeconomically deprived neighbourhood is situated in a wider deprived area, then that neighbourhood would experience greater adverse effects on mortality compared with a similarly deprived neighbourhood which is situated in a wider area with generally less deprivation. Methods: We used the Trent Region Health Authority area for this study, which contained 10,665 census enumeration districts (CED). Graphs are mathematical representations of objects and their relationships and within the context of this study, nodes represented CEDs and edges were determined by whether or not CEDs were neighbours (shared a common boundary). The overall area in this study was represented by one large graph comprising all CEDs in the region, along with their adjacency information. We used mortality data from 1988-1998, CED level population estimates and the Townsend Material Deprivation Index as an indicator of neighbourhood level deprivation. We defined deprived CEDs as those in the top 20% most deprived in the Region. We then set out to classify these deprived CEDs into seven groups defined by increasing deprivation levels in the neighbouring CEDs. 506 (24.2%) of the deprived CEDs had five adjacent CEDs and we limited pattern development and searching to these CEDs. We developed seven query patterns and used the RASCAL (Rapid Similarity Calculator) program to carry out the search for each of the query patterns. This program used a maximum common subgraph isomorphism method which was modified to handle geographical data. Results: Of the 506 deprived CEDs, 10 were not identified as belonging to any of the seven groups because they were adjacent to a CED with a missing deprivation category quintile, and none fell within query Group 1 (a deprived CED for which all five adjacent CEDs were affluent). Only four CEDs fell within Group 2, which was defined as having four affluent adjacent CEDs and one non-affluent adjacent CED. The numbers of CEDs in Groups 3-7 were 17, 214, 95, 81 and 85 respectively. Age and sex adjusted mortality rate ratios showed a non-significant trend towards increasing mortality risk across Groups (Chi-square = 3.26, df = 1, p = 0.07). Conclusion: Graph theoretical methods developed in computational chemistry may be a useful addition to the current GIS based methods available for geographical epidemiology but further developmental work is required. An important requirement will be the development of methods for specifying multiple complex search patterns. Further work is also required to examine the utility of using distance, as opposed to adjacency, to describe edges in graphs, and to examine methods for pattern specification when the nodes have multiple attributes attached to them

The impact of the Calman–Hine report on the processes and outcomes of care for Yorkshire's colorectal cancer patients

The 1995 Calman–Hine plan outlined radical reform of the UK's cancer services with the aim of improving outcomes and reducing inequalities in NHS cancer care. Its main recommendation was to concentrate care into the hands of site-specialist, multi-disciplinary teams. This study aimed to determine if the implementation of Calman–Hine cancer teams was associated with improved processes and outcomes of care for colorectal cancer patients. The design included longitudinal survey of 13 colorectal cancer teams in Yorkshire and retrospective study of population-based data collected by the Northern and Yorkshire Cancer Registry and Information Service. The population was all colorectal cancer patients diagnosed and treated in Yorkshire between 1995 and 2000. The main outcome measures were: variations in the use of anterior resection and preoperative radiotherapy in rectal cancer, chemotherapy in Dukes stage C and D patients, and five-year survival. Using multilevel models, these outcomes were assessed in relation to measures of the extent of Calman–Hine implementation throughout the study period, namely: (i) each team's degree of adherence to the Manual of Cancer Service Standards (which outlines the specification of the ‘ideal’ colorectal cancer team) and (ii) the extent of site specialisation of each team's surgeons. Variation was observed in the extent to which the colorectal cancer teams in Yorkshire had conformed to the Calman–Hine recommendations. An increase in surgical site specialisation was associated with increased use of preoperative radiotherapy (OR=1.43, 95% CI=1.04–1.98, P<0.04) and anterior resection (OR=1.43, 95% CI=1.16–1.76, P<0.01) in rectal cancer patients. Increases in adherence to the Manual of Cancer Service Standards was associated with improved five-year survival after adjustment for the casemix factors of age, stage of disease, socioeconomic status and year of diagnosis, especially for colon cancer (HR=0.97, 95% CI=0.94–0.99 P<0.01). There was a similar trend of improved survival in relation to increased surgical site specialisation for rectal cancer, although the effect was not statistically significant (HR=0.93, 95% CI=0.84–1.03, P=0.15). In conclusion, the extent of implementation of the Calman–Hine report has been variable and its recommendations are associated with improvements in processes and outcomes of care for colorectal cancer patients

Access to palliative care for patients with advanced cancer: A longitudinal population analysis

Background The UK National Health Service is striving to improve access to palliative care for patients with advanced cancer however limited information exists on the level of palliative care support currently provided in the UK. We aimed to establish the duration and intensity of palliative care received by patients with advanced cancer and identify which cancer patients are missing out. Methods Retrospective cancer registry, primary care and secondary care data were obtained and linked for 2474 patients who died of cancer between 2010 and 2012 within a large metropolitan UK city. Associations between the type, duration, and amount of palliative care by demographic characteristics, cancer type, and therapies received were assessed using Chi-squared, Mann-Whitney or Kruskal-Wallis tests. Multinomial multivariate logistic regression was used to assess the odds of receiving community and/or hospital palliative care compared to no palliative care by demographic characteristics, cancer type, and therapies received. Results Overall 64.6% of patients received palliative care. The average palliative care input was two contacts over six weeks. Community palliative care was associated with more palliative care events (p<0.001) for a longer duration (p<0.001). Patients were less likely to receive palliative care if they were: male (p = 0.002), aged 80 years or over (p<0.05), diagnosed with lung cancer (p<0.05), had not received an opioid prescription (p<0.001), or had not received chemotherapy (p<0.001). Patients given radiotherapy were more likely to receive community only palliative care compared to no palliative care (Odds Ratio = 1.49, 95% Confidence Interval = 1.16–1.90). Conclusion Timely supportive care for cancer patients is advocated but these results suggest that older patients and those who do not receive anti-cancer treatment or opioid analgesics miss out. These patients should be targeted for assessment to identify unmet needs which could benefit from palliative care input

A systematic review of patient reported factors associated with uptake and completion of cardiovascular lifestyle behaviour change

Background: Healthy lifestyles are an important facet of cardiovascular risk management. Unfortunately many individuals fail to engage with lifestyle change programmes. There are many factors that patients report as influencing their decisions about initiating lifestyle change. This is challenging for health care professionals who may lack the skills and time to address a broad range of barriers to lifestyle behaviour. Guidance on which factors to focus on during lifestyle consultations may assist healthcare professionals to hone their skills and knowledge leading to more productive patient interactions with ultimately better uptake of lifestyle behaviour change support. The aim of our study was to clarify which influences reported by patients predict uptake and completion of formal lifestyle change programmes. Methods: A systematic narrative review of quantitative observational studies reporting factors (influences) associated with uptake and completion of lifestyle behaviour change programmes. Quantitative observational studies involving patients at high risk of cardiovascular events were identified through electronic searching and screened against pre-defined selection criteria. Factors were extracted and organised into an existing qualitative framework. Results: 374 factors were extracted from 32 studies. Factors most consistently associated with uptake of lifestyle change related to support from family and friends, transport and other costs, and beliefs about the causes of illness and lifestyle change. Depression and anxiety also appear to influence uptake as well as completion. Many factors show inconsistent patterns with respect to uptake and completion of lifestyle change programmes. Conclusion: There are a small number of factors that consistently appear to influence uptake and completion of cardiovascular lifestyle behaviour change. These factors could be considered during patient consultations to promote a tailored approach to decision making about the most suitable type and level lifestyle behaviour change support

Estimates of the absolute and relative strengths of diverse alcoholic drinks by young people

There was low but significant concordance between participants' rank-orderings of drinks by strength, and the correlation of mean ranks with correct ranks was also significant. However, their explicit estimates of the numbers of "units" in the drinks, and their % ABV values, often diverged dramatically from actual values. Participants tended to overestimate the unit contents of spirit-based drinks but underestimated the unit contents of beers and wine; women were consistently less accurate than men, typically making greater underestimates for commonly-consumed drinks. Over one-third of the sample reported that strength influenced drink choice, but its importance ranked below flavor and cost; drink strength might contribute to drink choice depending on the drinking situation. Conclusion/Importance: Young drinkers (women especially) have a poor awareness of the alcohol contents of different drinks, particularly wines and beers, but they make better judgments of relative strength

Validation of graph-theoretical methods for pattern identification in public health datasets

Pattern identification issues are commonly used in public health practice to identify disease clusters and tendencies towards clustering. 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. A range of methods has been developed for cluster detection but these methods are not designed to handle complex pattern searching. This paper describes early work in developing a novel method of tackling this problem, using graph theoretical techniques developed for computational chemistry. A modified version of the maximum common subgraph isomorphism method was used to search and retrieve enumeration districts (EDs) using 27 user-defined patterns from a set of 106 EDs. The results were then checked manually to ensure that all the appropriate and no additional patterns and EDs were retrieved. The program successfully retrieved all the relevant patterns and EDs and did not retrieve any patterns not specified by the query patterns. This study demonstrates the applicability of using graph theory for identifying and retrieving patterns in public health datasets

Use of graph theory for data mining in public health

Data mining problems are common in public health, for example for identifying disease clusters and multidimensional patterns within large databases, e.g. socioeconomic differentials in health. Although numerous data mining methods have been developed, currently available methods are not designed to handle complex pattern searching queries and no satisfactory methods are available for this purpose. The aim of the study reported here was to test graph-theoretical methods for data mining in public health databases to identify areas of high deprivation that are surrounded by affluent areas and deprived areas surrounded by deprived areas. Graph-theory (using the maximum common subgraph isomorphism (mcs) method) was used to search a database containing information on the 10920 enumeration districts (EDs) for the Trent Region of England. Each ED was allocated to a deprivation quintile based on the Townsend Deprivation Score. These mcs program was used to identify deprived EDs that are adjacent to deprived EDs and deprived EDs that are adjacent to affluent EDs. The mcs program identified 1528 deprived EDs adjacent to at least two deprived EDs, 1181 deprived EDs adjacent to at least three deprived EDs, 802 deprived EDs adjacent to at least four deprived EDs, and 505 deprived EDs adjacent to at least five deprived EDs. The program successfully identified 147 deprived EDs adjacent to at least two affluent EDs, 54 deprived EDs adjacent to at least three affluent EDs, 14 deprived EDs adjacent to at least four affluent EDs, and six deprived EDs adjacent to at least five affluent EDs. The retrieved EDs were then used for hypothesis testing using statistical methods. The study demonstrates the potential of graph theoretical techniques for data mining in public health databases

Validation of graph-theoretical methods for pattern identification in public health datasets

exterior, portico, July-Sept. 198

Use of graph theory to identify patterns of deprivation, high morbidity and mortality in public health datasets

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. Design: 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. Measurements: 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. Conclusion: 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