Tonny J. Oyana

Exploration of preterm birth rates using the public health exposome database and computational analysis methods.

Recent advances in informatics technology has made it possible to integrate, manipulate, and analyze variables from a wide range of scientific disciplines allowing for the examination of complex social problems such as health disparities. This study used 589 county-level variables to identify and compare geographical variation of high and low preterm birth rates. Data were collected from a number of publically available sources, bringing together natality outcomes with attributes of the natural, built, social, and policy environments. Singleton early premature county birth rate, in counties with population size over 100,000 persons provided the dependent variable. Graph theoretical techniques were used to identify a wide range of predictor variables from various domains, including black proportion, obesity and diabetes, sexually transmitted infection rates, mother’s age, income, marriage rates, pollution and temperature among others. Dense subgraphs (paracliques) representing groups of highly correlated variables were resolved into latent factors, which were then used to build a regression model explaining prematurity (R-squared = 76.7%). Two lists of counties with large positive and large negative residuals, indicating unusual prematurity rates given their circumstances, may serve as a starting point for ways to intervene and reduce health disparities for preterm births.

Scalable combinatorial tools for health disparities research

Despite staggering investments made in unraveling the human genome, current estimates suggest that as much as 90% of the variance in cancer and chronic diseases can be attributed to factors outside an individual’s genetic endowment, particularly to environmental exposures experienced across his or her life course. New analytical approaches are clearly required as investigators turn to complicated systems theory and ecological, place-based and life-history perspectives in order to understand more clearly the relationships between social determinants, environmental exposures and health disparities. While traditional data analysis techniques remain foundational to health disparities research, they are easily overwhelmed by the ever-increasing size and heterogeneity of available data needed to illuminate latent gene x environment interactions. This has prompted the adaptation and application of scalable combinatorial methods, many from genome science research, to the study of population health. Most of these powerful tools are algorithmically sophisticated, highly automated and mathematically abstract. Their utility motivates the main theme of this paper, which is to describe real applications of innovative transdisciplinary models and analyses in an effort to help move the research community closer toward identifying the causal mechanisms and associated environmental contexts underlying health disparities. The public health exposome is used as a contemporary focus for addressing the complex nature of this subject.

Using an External Exposome Framework to Examine Pregnancy-Related Morbidities and Mortalities: Implications for Health Disparities Research.

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.

A field assessment of land use systems and soil properties at varied landscape positions in a fragile ecosystem of Mount Elgon, Uganda

The purpose of this field study was to identify and characterize land use systems at varied spatial hierarchies, and to differentiate soil properties along varied landscape positions. Key data about existing land use systems and soil properties were systematically collected in 12 sites. Topographic profiles across catchment and geomorphic units were identified to show the relation typical of a mountain ecosystem. We found statistically significant differences (p < .05) in soil properties along altitudinal gradients in 52% of the samples and also in land use and land cover distribution characteristics along the slope gradients. The most dominant soils were sandy loam on the hillslopes and clay loams at the footslopes; and most of the cultivated fields were located on slopes ranging from 0 to 17%. The livestock production system was based on a small constructed unit with a capacity to enable feed resources. Soil conservation practices that were evident among the study sites included the use of mulching, hedgerows, intercropping, fallowing, and minimum tillage. Deep empirical knowledge of this system will further advance our understanding on how a fragile ecosystem responds to both natural and human-caused change, and its associated impact on the surrounding environment.

Geographic Variation in Mortality Among Children and Adolescents Diagnosed With Cancer in Tennessee. Does Race Matter

Cancer is one of the leading causes of death among children in the United States. Previous research has examined geographic variation in cancer incidence and survival, but the geographic variation in mortality among children and adolescents is not as well understood. The purpose of this study was to investigate geographic variation by race in mortality among children and adolescents diagnosed with cancer in Tennessee. Using an innovative combination of spatial and nonspatial analysis techniques with data from the 2004-2011 Tennessee Cancer Registry, pediatric deaths were mapped and the effect of race on the proximity to rural areas and clusters of mortality were explored with multivariate regressions. The findings revealed that African American children and adolescents in Tennessee were more likely than their counterparts of other races to reside in rural areas with close proximity to mortality clusters of children and adolescents with a cancer. Findings have clinical implications for pediatric oncology nurses regarding the delivery of supportive care at end of life for rural African American children and adolescents.

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

ABSTRACT 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 < 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.

Assessing Performance of Leaf Area Index in a Monitored Mountain Ecosystem on Mount Elgon-Uganda

This study investigated the performance of leaf area index (LAI) and photosynthetically active radiation (PAR) in a mountain ecosystem. The authors hypothesized that significant spatial and temporal differences exist in LAI and PAR values in the Manafwa catchment on Mt. Elgon. This was accomplished through field measurements of actual LAI and PAR values of diverse vegetation types along a ~900m altitudinal gradient (1141–2029 masl) in the catchment. In-situ measurements were obtained from 841 micro-scale study plots in 28 sampling plots using high resolution LAI sensors. The findings showed a significant positive relationship exists between elevation and observed LAI (r = 0.45, p = 0.01). A regression model further shows that elevation and curvature of the landscape slope were highly significant (p

An examination of historical and future land use changes in Uganda using change detection methods and agent-based modelling

We analyse historical land use/land cover changes beginning from 1996 to 2013 and apply an agent-based model to simulate potential agricultural land use change scenarios in Uganda. The model incorporates farmers’ decision-making processes into biophysical and socioeconomic factors and uses these to analyse the effect of farmers’ decisions on agricultural land use changes. Geographic information system tools are employed to build spatial relations between farmers and land cover systems. Satellite images are used to represent the initial land cover conditions and serve as observed land cover datasets to calibrate the simulated results. Significant agricultural and grassland cover and urban land uses are experienced in 72 and 36% of the regions, respectively, while wetland land uses increased significantly in 82% of the regions. On the other hand, 91% of the regions experience reductions in forest cover except for Teso region which reports gains of 62% in forest cover. Acholi is the only region (extreme outlier) that reports dramatic gains in wetlands of over 880%. The results of the simulation model are promising, and the model was successful at representing historical and future scenarios of agricultural land use patterns at a national level.

Assessing adaptability and response of vegetation to glacier recession in the afro-alpine moorland terrestrial ecosystem of Rwenzori Mountains

The objective of this study was to explore vegetation adaptability in a changing afro-alpine moorland terrestrial ecosystem on Mt. Rwenzori and to determine whether there were any links with response of vegetation to glacier recession. We analyzed the composition and distribution of plant species in relation to soils, geomorphic processes, and landscape positions in the Alpine zone. To accomplish this objective, archival data sources and published reports for this ecosystem were reviewed. A field trip was conducted in 2010 to study in detail seven vegetation sampling plots that were systematically selected using GIS maps and a nested-quadrat sampling design framework along an altitudinal gradient in the lower and upper alpine zones. Using these sampling plots, 105 vegetation and 13 soil samples were assessed in the alpine zone. Soil samples were taken for laboratory testing and analysis. The results show statistically significant differences in pH, OM, N, P, Ca, Mg, and K pools between soils samples drawn from the lower and upper alpine sites (p < 0.0033). Furthermore, we observed a significant vegetation formation with numerous structural forms, but there was a limited diversity of species. The most significant forms included Alchemilla carpets, Bogs, Dendrosenecio woodland, and Scree slopes. The lower alpine area (3500–3900 masl) had a more diverse plant species than other areas, especially Alchemilla argyrophylla and Dendrosenecio adnivalis species that were evident due to well-drained deeper soils. The Alchemilla subnivalis were evident at a higher altitude of above 4000 masl. Shifts in the Astareceae (e.g. Senecio species) were particularly prominent even on recently deglaciated areas. The spatial variations of species distribution, structure, and composition suggest there are serious implications in terms of ecosystem adaptability, resilience, and stability that require further evaluation.

642: THE EFFECTS OF AIR POLLUTION ON SEVERITY OF PNEUMONIA IN A METROPOLITAN AREA

Crit Care Med 2016 • Volume 44 • Number 12 (Suppl.) season, we rapidly collected demographics, hospital course, ICU resources, infectious diagnoses, and outcome for a diverse patient cohort. Scalability of the same infrastructure and processes is testable and could be used to improve our understanding of SARI and foster research emergency responses by exercising capabilities annually worldwide.