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Genetic testing is becoming more prevalent in detecting risk and guiding cancer treatment in our increasingly personalized medicine model. However, few studies have examined underserved populations' perceptions of genetic testing, especially those of rural dwelling populations. We asked residents of three rural communities to complete a self-administered survey gauging their knowledge, attitudes, and perceived barriers for genetic testing. 64.8% of participants of the overall study completed the survey. Most participants were aware of genetic testing for cancer screening (69.0%) and would likely share results with their family (88.5% if it indicated low risk, 85.9% for high risk). Some barriers were noted, including genetic testing not offered in a clinic nearby (46.9%), insurance company knowing the results (54.0%), cost (49.1%), and no accessible genetic counselors with whom to discuss results (45.6%). Our rural participants were generally knowledgeable about genetic testing, but this may not be reflective of all rural populations. Opportunities exist to mitigate use barriers, expand the utilization of telehealth services and regulatory agency-approved assays, and increase knowledge regarding privacy and protections offered by statute, such as the Genetic Information Nondiscrimination Act (US) and General Data Protection Regulation (Europe).

We have previously hypothesized that relatively small and isolated rural communities may experience founder effects, defined as the genetic ramifications of small population sizes at the time of a community's establishment. To explore this, we used an Illumina Infinium Omni2.5Exome-8 chip to collect data from 157 individuals from four Illinois communities, three rural and one urban. Genetic diversity estimates of 999,259 autosomal markers suggested that the reduction in heterozygosity due to shared ancestry was approximately 0, indicating a randomly mating population. An eigenanalysis, which is similar to a principal component analysis but run on a genetic coancestry matrix, conducted in the SNPRelate R package revealed that most of these individuals formed one cluster, with a few putative outliers obscuring population variation. An additional eigenanalysis on the same markers in a combined data set including the 2,504 individuals in the 1000 Genomes database found that most of the 157 Illinois individuals clustered into one group in close proximity to individuals of European descent. A final eigenanalysis of the Illinois individuals with the 503 individuals of European descent (within the 1000 Genomes Project) revealed two clusters of individuals and likely two source populations; one British and one consisting of multiple European subpopulations. We therefore demonstrate the feasibility of examining genetic relatedness across Illinois populations and assessing the number of source populations using publicly available databases. When assessed, population structure information can contribute to the understanding of genetic history in rural populations.

Rural status in the United States can be objectively measured using multiple designations within different geographic extents, often considering both population density and proximity to urban areas. However, these measurements are often incomplete for assessing the relationship between rural status and health and are often inadequately considered in analysis. To address these limitations, we posit four recommendations: two recommendations to improve current measures by including additional factors and making measures continuous and two recommendations to improve regression analysis by considering rural status as a contextual factor in multilevel modeling and as a variable in conjunction with socioeconomic factors derived through principal component analysis.

More than 46 million Americans live in rural areas, but rural populations remain relatively understudied in cancer disparities research. However, several analyses of multistate cancer registry data that describe the rural cancer incidence burden have been recently published. In light of this, our article aims to characterize the utility and generalizability of multistate, population-based cancer registry datasets for rural cancer surveillance research. First, we describe the accessibility, geographic coverage, available variables, and strengths and weaknesses of five data sources. Second, we evaluate two of these data sources-the North American Association of Central Cancer Registries (NAACCR) public use dataset (93% population coverage) and the Surveillance Epidemiology and End Results (SEER) 18 dataset (28% population coverage)-on their characterization of rural-urban cancer incidence rates and sociodemographic representation. The five data sources varied in geographic coverage and extent of available variables. SEER 18's cancer rates sociodemographic representation differed from the more geographically representative NAACCR data. We suggest that SEER increase its geographic coverage to improve their generalizability and to take advantage of their utility to assess disparities along the cancer control continuum. We also suggest that non-SEER data sources be utilized more frequently to capitalize on their extensive geographic coverage. Cancer Epidemiol Biomarkers Prev; 27(11); 1252-60. ©2018 AACR., (©2018 American Association for Cancer Research.)

Background: Cancer incidence and mortality rates in the United States are declining, but this decrease may not be observed in rural areas where residents are more likely to live in poverty, smoke, and forego cancer screening. However, there is limited research exploring national rural-urban differences in cancer incidence and trends. Methods: We analyzed data from the North American Association of Central Cancer Registries' public use dataset, which includes population-based cancer incidence data from 46 states. We calculated age-adjusted incidence rates, rate ratios, and annual percentage change (APC) for: all cancers combined, selected individual cancers, and cancers associated with tobacco use and human papillomavirus (HPV). Rural-urban comparisons were made by demographic, geographic, and socioeconomic characteristics for 2009 to 2013. Trends were analyzed for 1995 to 2013. Results: Combined cancers incidence rates were generally higher in urban populations, except for the South, although the urban decline in incidence rate was greater than in rural populations (10.2% vs. 4.8%, respectively). Rural cancer disparities included higher rates of tobacco-associated, HPV-associated, lung and bronchus, cervical, and colorectal cancers across most population groups. Furthermore, HPV-associated cancer incidence rates increased in rural areas (APC = 0.724, P < 0.05), while temporal trends remained stable in urban areas. Conclusions: Cancer rates associated with modifiable risks-tobacco, HPV, and some preventive screening modalities (e.g., colorectal and cervical cancers)-were higher in rural compared with urban populations. Impact: Population-based, clinical, and/or policy strategies and interventions that address these modifiable risk factors could help reduce cancer disparities experienced in rural populations. Cancer Epidemiol Biomarkers Prev; 27(11); 1265-74. ©2017 AACR., (©2017 American Association for Cancer Research.)

Introduction: Despite having lower overall incidence rates, rural populations tend to have higher cancer mortality rates. Rural populations often have higher rates of cancers with primary and secondary prevention modalities. However, there is limited research on rural-urban differences in incidence by stage. Therefore, the objective was to assess rural-urban differences in cancer rates by stage., Methods: The North American Association of Central Cancer Registries public use data set (2009-2013) was used to calculate age-adjusted incidence rates and rate ratios (rural versus urban) for all stageable cancers, tobacco-associated cancers, human papillomavirus-associated cancers, and individual cancers with screening modalities. Analyses were performed in summer 2017 for all populations and stratified by race/ethnicity and region for localized and distant stages., Results: For all cancers, rural populations had lower rates of localized stage cancers (rate ratio=0.95, 95% CI=0.95, 0.95) and higher rates of distant stage cancer (rate ratio=1.05, 95% CI=1.05, 1.06). Higher rates of distant stage human papillomavirus-associated, tobacco-associated, colorectal, oropharyngeal, lung, cervical cancers, and melanoma were identified in rural populations. Racial/ethnic stratifications identified higher rates of distant stage cancers in rural non-Hispanic whites, but not non-Hispanic blacks and Hispanics. Distant stage lung cancer rates were higher in all rural groups, whereas rural whites had higher distant rates of tobacco-associated, colorectal, and cervical cancers, and rural blacks had higher distant rates for human papillomavirus-associated and oral cancers. Regional stratifications showed the greatest disparity in stage at diagnosis in the South., Conclusions: These findings might help explain the higher rural cancer mortality rates and provide additional evidence to support targeted interventions., (Copyright © 2018 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

Statistical analyses of health and disease in rural communities is frequently limited by low sample counts. Still, some studies indicate increased risk for some diseases even after adjustment for known risk factors. It has been hypothesized that the context of community formation in rural areas facilitates the propagation of genetic founder effects-potentially impacting disease susceptibility. However, outright examination of genetic diversity in such communities has not been performed. Our objective was to engage otherwise research-inexperienced rural communities of largely European descent in genomic research in the context of cancer susceptibility. From September 2015 to February 2016, we implemented a systematic process of progressive community engagement. This iterative method sought project buy-in from first the town mayor, then village council. If approved by both, a focus group of community members examined how residents might view the research, informed consent and specimen collection, and issues of privacy. We were successful in engaging three of the four communities approached for the research project. There was universal enthusiasm for the project by all mayors and village councils. The focus groups' main point of discussion involved wording in the informed consent, with little concern regarding the research question or privacy. Perhaps contrary to popular thought, we found each community we approached to be both welcoming and enthusiastic about collaborating in research on genomic diversity. The systematic method of engagement did much to preserve community respect and autonomy and facilitated buy-in.

Context: Although rural-urban cancer disparities have been explored with some depth, disparities within seemingly homogeneous rural areas have received limited attention. However, exploration of intrarural cancer incidence may have important public health implications for risk assessment, cancer control, and resource allocation., Objective: The objective of this study was to explore intrastate rural cancer risk and incidence differences within Illinois., Design: Illinois's 83 rural counties were categorized into northern, central, and southern regions (IL-N, IL-C, and IL-S, respectively). Chi-square test for independence and analysis of variance calculations were performed to assess regional differences in demographic characteristics, socioeconomic deprivation, smoking history, obesity, cancer-screening adherence, and density of general practitioners. Age-adjusted incidence rates were calculated for 5 cancer categories: all cancers combined, lung, colorectal, breast (female), and prostate cancers. Unadjusted and adjusted incidence rate ratios (IRRs) were calculated to evaluate regional differences in rates for each cancer category., Results: Socioeconomic deprivation varied by region: 4.5%, 6.9%, and 40.6% of IL-N, IL-C, and IL-S counties, respectively (P < .001). Smoking history also significantly differed by region. Mean former/current smoking prevalence in IL-N, IL-C, and IL-S counties was 46.4%, 48.2%, and 51.4%, respectively (P = .006). In unadjusted analysis, IL-C (IRR = 1.12; 95% confidence interval [CI], 1.02-1.23) and IL-S (IRR = 1.24; 95% CI, 1.13-1.35) had increased lung cancer incidence compared with IL-N. Elevated risk remained in IL-S after adjusting for relevant factors such as smoking and socioeconomic deprivation (IRR = 1.14; 95% CI, 1.04-1.26)., Conclusions: Socioeconomic deprivation, health behaviors, and lung cancer incidence varied across rural regions. Our findings underscore the importance of identifying cancer risk heterogeneity, even within a state, to effectively target risk factor reduction and cancer control interventions.

Over 19% of the US population resides in rural areas, where studies of disease risk and disease outcomes are difficult to assess due to smaller populations and lower incidence. While some studies suggest rural disparities for different chronic diseases, the data are inconsistent across geography and definitions of rurality. We reviewed the literature to examine if local variations in population genomic diversity may plausibly explain inconsistencies in estimating disease risk. Many rural communities were founded over 150 years ago by small groups of ethnically and ancestrally similar families. These have since endured relative geographical isolation, similar to groups in other industrialized nations, perhaps resulting in founder effects impacting local disease susceptibility. Studies in Europe and Asia have found that observably different phenotypes may appear in isolated communities within 100 years, and that genomic variation can significantly vary over small geographical scales. Epidemiological studies utilizing common "rural" definitions may miss significant disease differences due to assumptions of risk homogeneity and misinterpretation of administrative definitions of rurality. Local genomic heterogeneity should be an important aspect of chronic disease epidemiology in rural areas, and it is important to consider for designing studies and interpreting results, enabling a better understanding of the heritable components of complex diseases.

Some studies have shown disproportionate cancer incidence burden in rural areas which may be attributable partly due to the use of 'rural' as a generic term implying homogeneity of risk/protective factors across wide geographic spans. Counties in SEER 18 registries (years 2001-2011) were classified by their Rural-Urban Continuum Code (RUCC) and aggregated into urban, adjacent rural, and non-adjacent rural and were also aggregated into 3 regions: North, South, and West. Two-way ANCOVA was performed with region and RUCC as factors with adjustment for rates of common risk factors obtained from the County Health Rankings (2013). RUCC has a significant effect on incidence rate in urban areas on breast (P =0.001) and prostate (P =0.009). Colorectal significantly varies by region (P<0.0001), and the effect of rurality significantly varies across regions with North highest (P=0.0005). Lung rates significantly vary across both region and RUCC (P<0.0001 and P=0.0001, respectively). The analysis shows that risk-adjusted cancer incidence varies significantly across regions. However, we also found that rural cancer incidence significantly varied across otherwise-similar rural areas implying that 'rural' is not a homogeneous classification.