Environmental Quality and Invasive Breast Cancer

Geospatial Modeling Methods in Epidemiological Kidney Research: An Overview and Practical Example

Geospatial modeling methods in population-level kidney research have not been used to full potential because few studies have completed associative spatial analyses between risk factors and exposures and kidney conditions and outcomes. Spatial modeling has several advantages over traditional modeling, including improved estimation of statistical variation and more accurate and unbiased estimation of coefficient effect direction or magnitudes by accounting for spatial data structure. Because most population-level kidney research data are geographically referenced, there is a need for better understanding of geospatial modeling for evaluating associations of individual geolocation with processes of care and clinical outcomes. In this review, we describe common spatial models, provide details to execute these analyses, and perform a case-study to display how results differ when integrating geographic structure. In our case-study, we used U.S. nationwide 2019 chronic kidney disease (CKD) data from Centers for Disease Control and Prevention's Kidney Disease Surveillance System and 2006 to 2010 U.S. Environmental Protection Agency environmental quality index (EQI) data and fit a nonspatial count model along with global spatial models (spatially lagged model [SLM]/pseudo-spatial error model [PSEM]) and a local spatial model (geographically weighted quasi-Poisson regression [GWQPR]). We found the SLM, PSEM, and GWQPR improved model fit in comparison to the nonspatial regression, and the PSEM model decreased the positive relationship between EQI and CKD prevalence. The GWQPR also revealed spatial heterogeneity in the EQI-CKD relationship. To summarize, spatial modeling has promise as a clinical and public health translational tool, and our case-study example is an exhibition of how these analyses may be performed to improve the accuracy and utility of findings.

Ambient air pollutants and breast cancer stage in Tehran, Iran

This study aimed to examine the impacts of single and multiple air pollutants (AP) on the severity of breast cancer (BC). Data of 1148 diagnosed BC cases (2008-2016) were obtained from the Cancer Research Center and private oncologist offices in Tehran, Iran. Ambient PM10, SO2, NO, NO2, NOX, benzene, toluene, ethylbenzene, m-xylene, p-xylene, o-xylene, and BTEX data were obtained from previously developed land use regression models. Associations between pollutants and stage of BC were assessed by multinomial logistic regression models. An increase of 10 μg/m3 in ethylbenzene, o-xylene, m-xylene, and 10 ppb of NO corresponded to 10.41 (95% CI 1.32-82.41), 4.07 (1.46-11.33), 2.89 (1.08-7.73) and 1.08 (1.00-1.15) increase in the odds of stage I versus non-invasive BC, respectively. Benzene (OR, odds ratio = 1.16, 95% CI 1.01-1.33) and o-xylene (OR = 1.18, 1.02-1.38) were associated with increased odds of incidence of BC stages III & IV versus non-invasive stages. BC stage I and stage III&IV in women living in low SES areas was associated with significantly higher levels of benzene, ethylbenzene, o-xylene, and m-xylene. The highest multiple-air-pollutants quartile was associated with a higher odds of stage I BC (OR = 3.16) in patients under 50 years old. This study provides evidence that exposure to AP is associated with increased BC stage at diagnosis, especially under premenopause age.

Cumulative environmental quality is associated with breast cancer incidence differentially by summary stage and urbanicity

Individual environmental contaminants have been associated with breast cancer; however, evaluations of multiple exposures simultaneously are limited. Herein, we evaluated associations between breast cancer summary stages and the Environmental Quality Index (EQI), which includes a range of environmental factors across five domains. The EQI (2000-2005) was linked to county-level age-standardized incidence rates (SIRs) obtained from the North Carolina Central Cancer Registry (2010-2014). Incidence rates and SIRs of total, in situ, localized, regional, and distant breast cancers were evaluated stratified by rural-urban status. In counties with poor environmental quality compared to those with good environmental quality, total breast cancer incidence was higher by 10.82 cases per 100,000 persons (95% CI 2.04, 19.60, p = 0.02). This association was most pronounced for localized breast cancer (β = 5.59, 95% CI 0.59, 10.58, p = 0.03). Higher incidence of early-stage disease (carcinoma in situ β = 5.25, 95% CI 2.34, 8.16, p = 0.00 and localized breast cancer β = 6.98, 95% CI 2.24, 11.73, p = 0.00) and total breast cancer (β = 11.44, 95% CI 3.01, 19.87, p = 0.01) occurred in counties with poor land quality, especially urban counties. Our analyses indicate significant associations between environmental quality and breast cancer incidence, which differ by breast cancer stage and urbanicity, identifying a critical need to assess cumulative environmental exposures in the context of cancer stage.

An overview of real-world data sources for oncology and considerations for research

Generating evidence on the use, effectiveness, and safety of new cancer therapies is a priority for researchers, health care providers, payers, and regulators given the rapid pace of change in cancer diagnosis and treatments. The use of real-world data (RWD) is integral to understanding the utilization patterns and outcomes of these new treatments among patients with cancer who are treated in clinical practice and community settings. An initial step in the use of RWD is careful study design to assess the suitability of an RWD source. This pivotal process can be guided by using a conceptual model that encourages predesign conceptualization. The primary types of RWD included are electronic health records, administrative claims data, cancer registries, and specialty data providers and networks. Careful consideration of each data type is necessary because they are collected for a specific purpose, capturing a set of data elements within a certain population for that purpose, and they vary by population coverage and longitudinality. In this review, the authors provide a high-level assessment of the strengths and limitations of each data category to inform data source selection appropriate to the study question. Overall, the development and accessibility of RWD sources for cancer research are rapidly increasing, and the use of these data requires careful consideration of composition and utility to assess important questions in understanding the use and effectiveness of new therapies.

The Intersection of Rural Residence and Minority Race/Ethnicity in Cancer Disparities in the United States

One in every twenty-five persons in America is a racial/ethnic minority who lives in a rural area. Our objective was to summarize how racism and, subsequently, the social determinants of health disproportionately affect rural racial/ethnic minority populations, provide a review of the cancer disparities experienced by rural racial/ethnic minority groups, and recommend policy, research, and intervention approaches to reduce these disparities. We found that rural Black and American Indian/Alaska Native populations experience greater poverty and lack of access to care, which expose them to greater risk of developing cancer and experiencing poorer cancer outcomes in treatment and ultimately survival. There is a critical need for additional research to understand the disparities experienced by all rural racial/ethnic minority populations. We propose that policies aim to increase access to care and healthcare resources for these communities. Further, that observational and interventional research should more effectively address the intersections of rurality and race/ethnicity through reduced structural and interpersonal biases in cancer care, increased data access, more research on newer cancer screening and treatment modalities, and continued intervention and implementation research to understand how evidence-based practices can most effectively reduce disparities among these populations.

Toxicoproteomics Disclose Pesticides as Downregulators of TNF-α, IL-1β and Estrogen Receptor Pathways in Breast Cancer Women Chronically Exposed

Deleterious effects have been widely associated with chronic pesticide exposure, including cancer development. In spite of several known consequences that pesticides can trigger in the human body, few is known regarding its impact on breast cancer women that are chronically exposed to such substances during agricultural work lifelong. In this context, the present study performed a high-throughput toxicoproteomic study in association with a bioinformatics-based design to explore new putative processes and pathways deregulated by chronic pesticide exposure in breast cancer patients. To reach this goal, we analyzed comparatively non-depleted plasma samples from exposed (n = 130) and non-occupationally exposed (n = 112) women diagnosed with breast cancer by using a label-free proteomic tool. The list of proteins differentially expressed was explored by bioinformatics and the main pathways and processes further investigated. The toxicoproteomic study revealed that women exposed to pesticides exhibited mainly downregulated events, linked to immune response, coagulation and estrogen-mediated events in relation to the unexposed ones. Further investigation shown that the identified deregulated processes and pathways correlated with significant distinct levels tumor necrosis factor alpha and interleukin 1 beta in the blood, and specific clinicopathological characteristics pointed out by bioinformatics analysis as adipose-trophic levels, menopause and intratumoral clots formation. Altogether, these findings reinforce pesticides as downregulators of several biological process and highlight that these compounds can be linked to poor prognosis in breast cancer.