A spatial-temporal approach to surveillance of prostate cancer disparities in population subgroups

BACKGROUND: Prostate cancer mortality disparities exist among racial/ethnic groups in the United States, yet few studies have explored the spatiotemporal trend of the disease burden. To better understand mortality disparities by geographic regions over time, the present study analyzed the geographic variations of prostate cancer mortality by three Texas racial/ethnic groups over a 22-year period. METHODS: The Spatial Scan Statistic developed by Kulldorff et al was used. Excess mortality was detected using scan windows of 50% and 90% of the study period and a spatial cluster size of 50% of the population at risk. Time trend was analyzed to examine the potential temporal effects of clustering. Spatial queries were used to identify regions with multiple racial/ethnic groups having excess mortality. RESULTS: The most likely area of excess mortality for blacks occurred in Dallas-Metroplex and upper east Texas areas between 1990 and 1999; for Hispanics, in central Texas between 1992 and 1996: and for non-Hispanic whites, in the upper south and west to central Texas areas between 1990 and 1996. Excess mortality persisted among all racial/ethnic groups in the identified counties. The second scan revealed that three counties in west Texas presented an excess mortality for Hispanics from 1980-2001. Many counties bore an excess mortality burden for multiple groups. There is no time trend decline in prostate cancer mortality for blacks and non-Hispanic whites in Texas. CONCLUSION: Disparities in prostate cancer mortality among racial/ethnic groups existed in Texas. Central Texas counties with excess mortality in multiple subgroups warrant further investigation.

New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^

Kawasaki syndrome: Cluster analysis of incident cases presenting to a pediatric tertiary care hospital in Texas from January 1, 2005 to December 31, 2009

Background: Despite almost 40 years of research into the etiology of Kawasaki Syndrome (KS), there is little research published on spatial and temporal clustering of KS cases. Previous analysis has found significant spatial and temporal clustering of cases, therefore cluster analyses were performed to substantiate these findings and provide insight into incident KS cases discharged from a pediatric tertiary care hospital. Identifying clusters from a single institution would allow for prospective analysis of risk factors and potential exposures for further insight into KS etiology. ^ Methods: A retrospective study was carried out to examine the epidemiology and distribution of patients presenting to Texas Children’s Hospital in Houston, Texas, with a diagnosis of Acute Febrile Mucocutaneous Lymph Node Syndrome (MCLS) upon discharge from January 1, 2005 to December 31, 2009. Spatial, temporal, and space-time cluster analyses were performed using the Bernoulli model with case and control event data. ^ Results: 397 of 102,761 total patients admitted to Texas Children’s Hospital had a principal or secondary diagnosis of Acute Febrile MCLS upon over the 5 year period. Demographic data for KS cases remained consistent with known disease epidemiology. Spatial, temporal, and space-time analyses of clustering using the Bernoulli model demonstrated no statistically significant clusters. ^ Discussion: Despite previous findings of spatial-temporal clustering of KS cases, there were no significant clusters of KS cases discharged from a single institution. This implicates the need for an expanded approach to conducting spatial-temporal cluster analysis and KS surveillance given the limitations of evaluating data from a single institution.^