J. Gaines Wilson

Performance of AERMOD at different time scales

As high-density monitoring networks observing pollutant concentrations are costly to establish and maintain, researchers often employ various models to estimate concentrations of air pollutants. The AMS/EPA Regulatory Model (AERMOD) is a fairly recent and promising model for estimating concentrations of air pollutants, but the effectiveness of this model at different time scales remains to be verified. This paper evaluates the performance of AERMOD in estimating sulfur dioxide (SO2) concentrations in Dallas and Ellis counties in Texas. Results suggest that SO2 concentrations simulated by AERMOD at the 8 h, daily, monthly, and annual intervals match their respective observed concentrations much better compared with the simulated 1 and 3 h SO2 concentrations. In addition, AERMOD performs better in simulating SO2 concentrations when combined point and mobile emission sources are used as model inputs rather than using point or mobile emission sources alone. Results also suggest that, at the monthly scale, AERMOD performs much better in simulating the high end of the spectrum of SO2 concentrations in the study area compared to results at the 1, 3, 8 h, and daily scales. These results not only help us better understand the performance of AERMOD but also provide useful information to researchers who are interested in applying AERMOD in various applications, such as the utilization of AERMOD in chronic exposure assessment in epidemiological studies where long-term (i.e., monthly and/or annual) air pollution concentration estimations are often used.

Identifying Risk Factors for Disparities in Breast Cancer Mortality among African-American and Hispanic Women

BACKGROUND This study evaluated the risk factors associated with racial disparities in female breast cancer mortality for African-American and Hispanic women at the census tract level in Texas from 1995 to 2005. METHODS Data on female breast cancer cases were obtained from the Texas Cancer Registry. Socioeconomic and demographic data were collected from Census 2000. Network distance and driving times to mammography facilities were estimated using Geographic Information System techniques. Demographic, poverty and spatial accessibility factors were constructed using principal component analysis. Logistic regression models were developed to predict the census tracts with significant racial disparities in breast cancer mortality based on racial disparities in late-stage diagnosis and structured factors from the principal component analysis. RESULTS Late-stage diagnosis, poverty factors, and demographic factors were found to be significant predictors of a census tract showing significant racial disparities in breast cancer mortality. Census tracts with higher poverty status were more likely to display significant racial disparities in breast cancer mortality for both African Americans (odds ratio [OR], 2.43; 95% confidence interval [CI], 1.95-3.04) and Hispanics (OR, 5.30; 95% CI, 4.26-6.59). Spatial accessibility was not a consistent predictor of racial disparities in breast cancer mortality for African-American and Hispanic women. CONCLUSION Physical access to mammography facilities does not necessarily reflect a greater utilization of mammogram screening, possibly owing to financial constraints. Therefore, a metric measuring access to health care facilities is needed to capture all aspects of access to preventive care. Despite easier physical access to mammography facilities in metropolitan areas, great resources and efforts should also be devoted to these areas where racial disparities in breast cancer mortality are often found.

Spatial association of racial/ethnic disparities between late-stage diagnosis and mortality for female breast cancer: where to intervene?

BackgroundOver the past twenty years, racial/ethnic disparities between late-stage diagnoses and mortality outcomes have widened due to disproportionate medical benefits that different racial/ethnic groups have received. Few studies to date have examined the spatial relationships of racial/ethnic disparities between breast cancer late-stage diagnosis and mortality as well as the impact of socioeconomic status (SES) on these two disparities at finer geographic scales.MethodsThree methods were implemented to assess the spatial relationship between racial/ethnic disparities of breast cancer late-stage diagnosis and morality. First, this study used rate difference measure to test for racial/ethnic disparities in both late-stage diagnosis and mortality of female breast cancer in Texas during 1995-2005. Second, we used linear and logistic regression models to determine if there was a correlation between these two racial/ethnic disparities at the census tract level. Third, a geographically-weighted regression analysis was performed to evaluate if this correlation occurred after weighting for local neighbors.ResultsThe spatial association of racial disparities was found to be significant between late-stage diagnosis and breast cancer mortality with odds ratios of 33.76 (CI: 23.96-47.57) for African Americans and 30.39 (CI: 22.09-41.82) for Hispanics. After adjusting for a SES cofounder, logistic regression models revealed a reduced, although still highly significant, odds ratio of 18.39 (CI: 12.79-26.44) for African-American women and 11.64 (CI: 8.29-16.34) for Hispanic women. Results of the logistic regression analysis indicated that census tracts with low and middle SES were more likely to show significant racial disparities of breast cancer late-stage diagnosis and mortality rates. However, values of local correlation coefficients suggested that the association of these two types of racial/ethnic disparities varied across geographic regions.ConclusionsThis study may have health-policy implications that can help early detection of breast cancer among disadvantaged minority groups through implementing effective intervention programs in targeted regions.

Female breast cancer mortality clusters within racial groups in the United States.

Although breast cancer is the second leading cause of cancer deaths among women in the Unites States, to date there have been no nationwide studies systematically analyzing geographic variation and clustering. An assessment of spatial-temporal clusters of cancer mortality by age and race at the county level in the lower 48 United States indicated a primary cluster in the Northeast US for both younger (RR = 1.349; all RR are p < or = 0.001) and older (RR = 1.283) women in the all-race category. Similar cluster patterns in the North were detected for younger (RR = 1.390) and older (RR = 1.292) white women. The cluster for both younger (RR = 1.337) and older (RR = 1.251) black women was found in the Midwest. The clusters for all other racial groups combined were in the West for both younger (RR = 1.682) and older (RR = 1.542) groups. Regression model results suggest that lower socioeconomic status (SES) was more protective than higher status at every quartile step (Medium-high SES, OR = 0.374; Medium-low, OR = 0.137; Low, OR = 0.061). This study may provide insight to aid in identifying geographic areas and subpopulations at increased risk for breast cancer.

Spatial Access to Health Care Services and Disparities in Colorectal Cancer Stage at Diagnosis in Texas

Early diagnosis of colorectal cancer (CRC) is important for improving the survival rate of the disease. Disparities in CRC diagnosis among different population groups have persisted in the United States. This study examines whether spatial access to medical services contributes to disparities of late-stage diagnosis of CRC in Texas. Analysis results suggest that there are significant disparities in late-stage CRC diagnosis by race/ethnicity, socioeconomic status, and geographic location in Texas. It is concluded that spatial access to primary care is associated with CRC stage at diagnosis, but not with racial/ethnic and socioeconomic disparities.

Utilizing spatiotemporal analysis of influenza-like illness and rapid tests to focus swine-origin influenza virus intervention

In the spring of 2009, a novel strain of H1N1 swine-origin influenza A virus (S-OIV) emerged in Mexico and the United States, and soon after was declared a pandemic by the World Health Organization. This work examined the ability of real-time reports of influenza-like illness (ILI) symptoms and rapid influenza diagnostic tests (RIDTs) to approximate the spatiotemporal distribution of PCR-confirmed S-OIV cases for the purposes of focusing local intervention efforts. Cluster and age adjusted relative risk patterns of ILI, RIDT, and S-OIV were assessed at a fine spatial scale at different time and space extents within Cameron County, Texas on the US-Mexico border. Space-time patterns of ILI and RIDT were found to effectively characterize the areas with highest geographical risk of S-OIV within the first two weeks of the outbreak. Based on these results, ILI and/or RIDT may prove to be acceptable indicators of the location of S-OIV hotspots. Given that S-OIV data is often difficult to obtain real-time during an outbreak; these findings may be of use to public health officials targeting prevention and response efforts during future flu outbreaks.

Response to H1N1 in a U.S.-Mexico Border Community

Public health experts from a county health department and a school of public health collaborated to establish a simple, functional surveillance system to monitor swine-origin influenza virus as it crossed from Mexico into a Texas border community during the 2009 pandemic. The draft national and state preparedness plans were found to be cumbersome at the local level, so a simple, more practical real-time surveillance and response system was developed, in part by modifying these documents, and immediately implemented. Daily data analyses, including geographical information system mapping of cases and reports of school and daycare absences, were used for outbreak management. Aggregate reports of influenzalike illness and primary school absences were accurate in predicting influenza activity and were practical for use in local tracking, making decisions, and targeting interventions. These simple methods should be considered for local implementation and for integration into national recommendations for epidemic preparedness and response.