A New-Fangled FES-k-Means Clustering Algorithm for Disease Discovery and Visual Analytics

<p/> <p>The central purpose of this study is to further evaluate the quality of the performance of a new algorithm. The study provides additional evidence on this algorithm that was designed to increase the overall efficiency of the original <it>k</it>-means clustering technique&#8212;the Fast, Efficient, and Scalable <it>k</it>-means algorithm (<it>FES-k</it>-means). The <it>FES-k</it>-means algorithm uses a hybrid approach that comprises the <it>k-d</it> tree data structure that enhances the nearest neighbor query, the original <it>k</it>-means algorithm, and an adaptation rate proposed by Mashor. This algorithm was tested using two real datasets and one synthetic dataset. It was employed twice on all three datasets: once on data trained by the innovative MIL-SOM method and then on the actual untrained data in order to evaluate its competence. This two-step approach of data training prior to clustering provides a solid foundation for knowledge discovery and data mining, otherwise unclaimed by clustering methods alone. The benefits of this method are that it produces clusters similar to the original <it>k</it>-means method at a much faster rate as shown by runtime comparison data; and it provides efficient analysis of large geospatial data with implications for disease mechanism discovery. From a disease mechanism discovery perspective, it is hypothesized that the linear-like pattern of elevated blood lead levels discovered in the city of Chicago may be spatially linked to the city's water service lines.</p

Geographic variations of childhood asthma hospitalization and outpatient visits and proximity to ambient pollution sources at a U.S.-Canada border crossing

BACKGROUND: Childhood asthma is a significant public health problem in the United States and evidence is accumulating regarding the contribution from traffic and ambient air pollution. This study is a companion piece of a related Buffalo asthma study in adults recently published in the July 2004 issue of American Journal of Public Health. This study focuses on children under 18 years of age diagnosed with asthma during a three-year period (2000–2002). In order to determine the effects of particulate air pollution on public health, we conducted an ecologic study of childhood asthma and point-source respirable particulate air pollution in patients diagnosed with asthma (n = 6,425). Patients diagnosed with gastroenteritis (n = 5,132) were used as controls. RESULTS: Although the results of this study show spatial patterns similar to the ones observed in the adult study, a multiple-comparison test shows that EPA-designated focus sites located in Buffalo's east side are statistically (p < 0.008) more linked to childhood asthma than sites located elsewhere. CONCLUSION: Findings of this study can be useful in geographic targeting and in the design of optimal and preventive measures

A Critical Assessment of Geographic Clusters of Breast and Lung Cancer Incidences among Residents Living near the Tittabawassee and Saginaw Rivers, Michigan, USA

Objectives. To assess previously determined geographic clusters of breast and lung cancer incidences among residents living near the Tittabawassee and Saginaw Rivers, Michigan, using a new set of environmental factors. Materials and Methods. Breast and lung cancer data were acquired from the Michigan Department of Community Health, along with point source pollution data from the U.S. Environmental Protection Agency. The datasets were used to determine whether there is a spatial association between disease risk and environmental contamination. GIS and spatial techniques were combined with statistical analysis to investigate local risk of breast and lung cancer. Results and Conclusion. The study suggests that neighborhoods in close proximity to the river were associated with a high risk of breast cancer, while increased risk of lung cancer was detected among neighborhoods in close proximity to point source pollution and major highways. Statistically significant (P ≤ .001) clusters of cancer incidences were observed among residents living near the rivers. These findings are useful to researchers and governmental agencies for risk assessment, regulation, and control of environmental contamination in the floodplains

A Scalable Field Study Protocol and Rationale for Passive Ambient Air Sampling: A Spatial Phytosampling for Leaf Data Collection

© 2017 American Chemical Society. Stable, bioreactive, radicals known as environmentally persistent free radicals (EPFRs) have been found to exist on the surface of airborne PM2.5. These EPFRs have been found to form during many combustion processes, are present in vehicular exhaust, and persist in the environment for weeks and biological systems for up to 12 h. To measure EPFRs in PM samples, high volume samplers are required and measurements are less representative of community exposure; therefore, we developed a novel spatial phytosampling methodology to study the spatial patterns of EPFR concentrations using plants. Leaf samples for laboratory PM analysis were collected from 188 randomly drawn sampling sites within a 500-m buffer zone of pollution sources across a sampling grid measuring 32.9 × 28.4 km in Memphis, Tennessee. PM was isolated from the intact leaves and size fractionated, and EPFRs on PM quantified by electron paramagnetic resonance spectroscopy. The radical concentration was found to positively correlate with the EPFR g-value, thus indicating cumulative content of oxygen centered radicals in PM with higher EPFR load. Our spatial phytosampling approach reveals spatial variations and potential hotspots risk due to EPFR exposure across Memphis and provides valuable insights for identifying exposure and demographic differences for health studies

Spatiotemporal patterns of childhood asthma hospitalization and utilization in Memphis Metropolitan Area from 2005 to 2015

© 2017 Taylor & Francis Group, LLC. Objective: To identify the key risk factors and explain the spatiotemporal patterns of childhood asthma in the Memphis metropolitan area (MMA) over an 11-year period (2005–2015). We hypothesize that in the MMA region this burden is more prevalent among urban children living south, downtown, and north of Memphis than in other areas. Methods: We used a large-scale longitudinal electronic health record database from an integrated healthcare system, Geographic information systems (GIS), and statistical and space-time models to study the spatiotemporal distributions of childhood asthma at census tract level. Results: We found statistically significant spatiotemporal clusters of childhood asthma in the south, west, and north of Memphis city after adjusting for key covariates. The results further show a significant increase in temporal gradient in frequency of emergency department (ED) visits and inpatient hospitalizations from 2009 to 2013, and an upward trajectory from 4 per 1,000 children in 2005 to 16 per 1,000 children in 2015. The multivariate logistic regression identified age, race, insurance, admit source, encounter type, and frequency of visits as significant risk factors for childhood asthma (p \u3c 0.05). We observed a greater asthma burden and healthcare utilization for African American (AA) patients living in a high-risk area than those living in a low-risk area in comparison to the white patients: AA vs. white [odds ratio (OR) = 3.03, 95% confidence interval (CI): 2.75–3.34]; and Hispanic vs. white (OR = 1.62, 95% CI: 1.21–2.17). Conclusions: These findings provide a strong basis for developing geographically tailored population health strategies at the neighborhood level for young children with chronic respiratory conditions

Using an external exposome framework to examine pregnancy-related morbidities and mortalities: Implications for health disparities research

© 2015 by the authors; licensee MDPI, Basel, Switzerland. Objective: We have conducted a study to assess the role of environment on the burden of maternal morbidities and mortalities among women using an external exposome approach for the purpose of developing targeted public health interventions to decrease disparities. Methods: We identified counties in the 48 contiguous USA where observed low birthweight (LBW) rates were higher than expected during a five-year study period. The identification was conducted using a retrospective space-time analysis scan for statistically significant clusters with high or low rates by a Discrete Poisson Model. Results: We observed statistically significant associations of LBW rate with a set of predictive variables. However, in one of the two spatiotemporal models we discovered LBW to be associated with five predictive variables (teen birth rate, adult obesity, uninsured adults, physically unhealthy days, and percent of adults who smoke) in two counties situated in Alabama after adjusting for location changes. Counties with higher than expected LBW rates were similarly associated with two environmental variables (ozone and fine particulate matter). Conclusions: The county-level predictive measures of LBW offer new insights into spatiotemporal patterns relative to key contributory factors. An external framework provides a promising place-based approach for identifying “hotspots” with implications for designing targeted interventions and control measures to reduce and eliminate health disparities

Spatial variations in the incidence of breast cancer and potential risks associated with soil dioxin contamination in Midland, Saginaw, and Bay Counties, Michigan, USA

<p>Abstract</p> <p>Background</p> <p>High levels of dioxins in soil and higher-than-average body burdens of dioxins in local residents have been found in the city of Midland and the Tittabawassee River floodplain in Michigan. The objective of this study is threefold: (1) to evaluate dioxin levels in soils; (2) to evaluate the spatial variations in breast cancer incidence in Midland, Saginaw, and Bay Counties in Michigan; (3) to evaluate whether breast cancer rates are spatially associated with the dioxin contamination areas.</p> <p>Methods</p> <p>We acquired 532 published soil dioxin data samples collected from 1995 to 2003 and data pertaining to female breast cancer cases (<it>n </it>= 4,604) at ZIP code level in Midland, Saginaw, and Bay Counties for years 1985 through 2002. Descriptive statistics and self-organizing map algorithm were used to evaluate dioxin levels in soils. Geographic information systems techniques, the Kulldorff's spatial and space-time scan statistics, and genetic algorithms were used to explore the variation in the incidence of breast cancer in space and space-time. Odds ratio and their corresponding 95% confidence intervals, with adjustment for age, were used to investigate a spatial association between breast cancer incidence and soil dioxin contamination.</p> <p>Results</p> <p>High levels of dioxin in soils were observed in the city of Midland and the Tittabawassee River 100-year floodplain. After adjusting for age, we observed high breast cancer incidence rates and detected the presence of spatial clusters in the city of Midland, the confluence area of the Tittabawassee, and Saginaw Rivers. After accounting for spatiotemporal variations, we observed a spatial cluster of breast cancer incidence in Midland between 1985 and 1993. The odds ratio further suggests a statistically significant (<it>α </it>= 0.05) increased breast cancer rate as women get older, and a higher disease burden in Midland and the surrounding areas in close proximity to the dioxin contaminated areas.</p> <p>Conclusion</p> <p>These findings suggest that increased breast cancer incidences are spatially associated with soil dioxin contamination. Aging is a substantial factor in the development of breast cancer. Findings can be used for heightened surveillance and education, as well as formulating new study hypotheses for further research.</p