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.

Socioeconomic status and prevalence of obesity and diabetes in a Mexican American community, Cameron County, Texas, 2004-2007.

INTRODUCTION: Mexican Americans are at increased risk for obesity and diabetes. We established a cohort on the United States-Mexico border to determine the prevalence of obesity and diabetes in this Mexican American population and to see whether minor economic advantages had any effect on health. METHODS: We randomly selected and extensively documented 810 people aged 35 to 64 years. Weighted data were analyzed to establish prevalence of obesity and diabetes and other markers of poor health such as elevated glycated hemoglobin levels. RESULTS: Rates of obesity (body mass index > or = 30 kg/m(2)) were 57% in the first (lower) of 4 socioeconomic strata by income and were 55.5% in the third (higher). People in the higher socioeconomic stratum were significantly less likely to have undiagnosed diabetes (2% vs 9%). Among people aged 55 to 64 years, rates of diabetes were significantly higher among those in the lower socioeconomic stratum than among those in the higher stratum. Rates of undiagnosed diabetes had similar differences. Approximately three-fourths of the respondents reported having no health insurance, and we found no difference between people in different socioeconomic strata. CONCLUSION: Rates of obesity and diabetes in this border community are among the highest in the United States. Belonging to the lower socioeconomic stratum significantly increased the likelihood of having undiagnosed diabetes and, in patients too young to be eligible for Medicare, the overall risk of developing diabetes. Modest improvement in income has a beneficial effect on health in this racial/ethnic minority community.

An emission-weighted proximity model for air pollution exposure assessment.

BACKGROUND: Among the most common spatial models for estimating personal exposure are Traditional Proximity Models (TPMs). Though TPMs are straightforward to configure and interpret, they are prone to extensive errors in exposure estimates and do not provide prospective estimates. METHOD: To resolve these inherent problems with TPMs, we introduce here a novel Emission Weighted Proximity Model (EWPM) to improve the TPM, which takes into consideration the emissions from all sources potentially influencing the receptors. EWPM performance was evaluated by comparing the normalized exposure risk values of sulfur dioxide (SO(2)) calculated by EWPM with those calculated by TPM and monitored observations over a one-year period in two large Texas counties. In order to investigate whether the limitations of TPM in potential exposure risk prediction without recorded incidence can be overcome, we also introduce a hybrid framework, a 'Geo-statistical EWPM'. Geo-statistical EWPM is a synthesis of Ordinary Kriging Geo-statistical interpolation and EWPM. The prediction results are presented as two potential exposure risk prediction maps. The performance of these two exposure maps in predicting individual SO(2) exposure risk was validated with 10 virtual cases in prospective exposure scenarios. RESULTS: Risk values for EWPM were clearly more agreeable with the observed concentrations than those from TPM. Over the entire study area, the mean SO(2) exposure risk from EWPM was higher relative to TPM (1.00 vs. 0.91). The mean bias of the exposure risk values of 10 virtual cases between EWPM and 'Geo-statistical EWPM' are much smaller than those between TPM and 'Geo-statistical TPM' (5.12 vs. 24.63). CONCLUSION: EWPM appears to more accurately portray individual exposure relative to TPM. The 'Geo-statistical EWPM' effectively augments the role of the standard proximity model and makes it possible to predict individual risk in future exposure scenarios resulting in adverse health effects from environmental pollution.

Air pollution exposure assessment methods utilized in epidemiological studies.

The assessment of personal exposure to air pollution is a critical component of epidemiological studies associating air pollution and health effects. This paper critically reviewed 157 studies over 29 years that utilized one of five categories of exposure methods (proximity, air dispersion, hybrid, human inhalation, and biomarkers). Proximity models were found to be a questionable technique as they assume that closer proximity equates to greater exposure. Inhalation models and biomarker estimates were the most effective in assessing personal exposure, but are often cost prohibitive for large study populations. This review suggests that: (i) factors such as uncertainty, validity, data availability, and transferability related to exposure assessment methods should be considered when selecting a model; and (ii) although an entirely discreet new class of approach is not necessary, significant progress could be made through the development of a 'hybrid' model utilizing the strengths of several existing methods. Future work should systematically evaluate the performance of hybrid models compared to other individual exposure assessment methods utilizing geospatial information technologies (e.g. geographic information systems (GIS) and remote sensing (RS)) to more robustly refine estimates of ambient exposure and quantify the linkages and differences between outdoor, indoor and personal exposure estimates.