Rural/Nonrural Differences in Colorectal Cancer Incidence in the United States, 1998--2001

BACKGROUND. Few studies of colorectal cancer incidence by rural, suburban, and metropolitan residence have been published. METHODS. The authors examined colorectal cancer incidence among men and women in U.S. counties classified as rural, suburban, and metropolitan for the period 1998–2001. They examined rural/suburban/metropolitan differences in incidence by age, race, Hispanic ethnicity, stage at diagnosis, histology, and percentage of the total county population below the poverty level, using data from the CDC’s National Program of Cancer Registries, the NCI’s Surveillance, Epidemiology, and End Results Program, and the 2000 U.S. Census. RESULTS. A total of 495,770 newly diagnosed or incident cases of colorectal cancer were included in this analysis (249,919 among men and 245,851 among women). Over the period 1998–2001, the colorectal cancer incidence rates among men tended to be lower among those who resided in rural areas, for each of the subgroups examined, with the exception of Asians and Pacific Islanders and those living in more affluent counties. Among women aged 75 years and older, the colorectal cancer incidence rates tended to be lower among rural than metropolitan or suburban residents, though the differences were slight. In multivariate analysis, the incidence of colorectal cancer was higher in metropolitan, suburban, and rural areas for blacks than that for whites (incidence rate ratios [RR] = 1.12, 1.07, and 1.06, respectively, all P \u3c 0.015). CONCLUSIONS. This study suggests that black men who reside in metropolitan areas have a higher risk of colorectal cancer than black men who reside in rural areas. This finding suggests the need for diverse approaches for reducing colorectal cancer when targeting rural compared with metropolitan areas

Use of attribute association error probability estimates to evaluate quality of medical record geocodes

BACKGROUND: The utility of patient attributes associated with the spatiotemporal analysis of medical records lies not just in their values but also the strength of association between them. Estimating the extent to which a hierarchy of conditional probability exists between patient attribute associations such as patient identifying fields, patient and date of diagnosis, and patient and address at diagnosis is fundamental to estimating the strength of association between patient and geocode, and patient and enumeration area. We propose a hierarchy for the attribute associations within medical records that enable spatiotemporal relationships. We also present a set of metrics that store attribute association error probability (AAEP), to estimate error probability for all attribute associations upon which certainty in a patient geocode depends. METHODS: A series of experiments were undertaken to understand how error estimation could be operationalized within health data and what levels of AAEP in real data reveal themselves using these methods. Specifically, the goals of this evaluation were to (1) assess if the concept of our error assessment techniques could be implemented by a population-based cancer registry; (2) apply the techniques to real data from a large health data agency and characterize the observed levels of AAEP; and (3) demonstrate how detected AAEP might impact spatiotemporal health research. RESULTS: We present an evaluation of AAEP metrics generated for cancer cases in a North Carolina county. We show examples of how we estimated AAEP for selected attribute associations and circumstances. We demonstrate the distribution of AAEP in our case sample across attribute associations, and demonstrate ways in which disease registry specific operations influence the prevalence of AAEP estimates for specific attribute associations. CONCLUSIONS: The effort to detect and store estimates of AAEP is worthwhile because of the increase in confidence fostered by the attribute association level approach to the assessment of uncertainty in patient geocodes, relative to existing geocoding related uncertainty metrics

Use of attribute association error probability estimates to evaluate quality of medical record geocodes

BACKGROUND: The utility of patient attributes associated with the spatiotemporal analysis of medical records lies not just in their values but also the strength of association between them. Estimating the extent to which a hierarchy of conditional probability exists between patient attribute associations such as patient identifying fields, patient and date of diagnosis, and patient and address at diagnosis is fundamental to estimating the strength of association between patient and geocode, and patient and enumeration area. We propose a hierarchy for the attribute associations within medical records that enable spatiotemporal relationships. We also present a set of metrics that store attribute association error probability (AAEP), to estimate error probability for all attribute associations upon which certainty in a patient geocode depends. METHODS: A series of experiments were undertaken to understand how error estimation could be operationalized within health data and what levels of AAEP in real data reveal themselves using these methods. Specifically, the goals of this evaluation were to (1) assess if the concept of our error assessment techniques could be implemented by a population-based cancer registry; (2) apply the techniques to real data from a large health data agency and characterize the observed levels of AAEP; and (3) demonstrate how detected AAEP might impact spatiotemporal health research. RESULTS: We present an evaluation of AAEP metrics generated for cancer cases in a North Carolina county. We show examples of how we estimated AAEP for selected attribute associations and circumstances. We demonstrate the distribution of AAEP in our case sample across attribute associations, and demonstrate ways in which disease registry specific operations influence the prevalence of AAEP estimates for specific attribute associations. CONCLUSIONS: The effort to detect and store estimates of AAEP is worthwhile because of the increase in confidence fostered by the attribute association level approach to the assessment of uncertainty in patient geocodes, relative to existing geocoding related uncertainty metrics

Spatial Analysis in Cancer Surveillance

The burden of cancer is a significant physical, psychological, and financial toll on individuals and populations. Cancer is more common in older populations, so advances in other areas of medicine will inevitably increase the numbers of cancer patients. The development of cancer is prolonged over years which makes it difficult to identify etiologic causes. And the treatment of the disease can also be quite protracted. In the case of advanced or aggressive disease, medical treatment is sometimes reduced to a series of clinical interventions that do little to change the timeline to mortality. Currently, our most effective tool in cancer control is prevention. Cancer risk, health behaviors, and medical care standards and access vary by geography. Epidemiologists can apply a spatial approach to epidemiology to see geographic patterns and test associations in the data based on geography in order to postulate about a community’s health, focus public health action, and choose suitable prevention interventions. The application of geographic information systems (GIS) and spatial analysis is a valuable tool for epidemiologists to address geographic discrepancies, which are often driven by race or social disparities in health. But relevant conclusions hinge on understanding the limitations of the data and the methods as well as the suitability of a spatial approach to epidemiologic research. The first part of this dissertation focuses on a specific application for cluster detection. This work indicates that clustering of disease with public health significance may be inadvertently missed if the analysis is conducted at only one scale. However, conducting spatial scan analysis at multiple scales often produces multiple, significant results that do not always overlap geographically. Until there is a practical tool to empirically assist in final result selection, manual visual assessment and local knowledge must be applied. The second part assesses the association between risk factors and the likelihood of a CRC cases being diagnosed in a cluster at high risk of late-stage at diagnosis. There was an unexpected association between area-based poverty and late-stage clustering with increasing poverty being less associated with late-stage at diagnosis cluster. This may be a “screening effect” driven by current cancer control efforts within the state which loosely correlates with state trends in CRC screening. The third part of this dissertation focuses on the data quality of cancer data and the impact on results. This work indicates that missing stage at diagnosis was spatially correlated, and that a common method of handling missing stage, removing those cases from analysis, may be the most biased method. And although misclassification of the sex code in registry data was not spatially correlated and, therefore, had limited influence on the results of spatial analysis, the impact of misclassified sex is dramatic on specific sites like male breast cancer. The theme of this dissertation is issues with application of geospatial techniques in cancer surveillance to address issues in cancer control. Relevant conclusions are that geospatial epidemiology demands a team science approach in order to adequately address software, methodological, and clinical issues that often are adapted from outside the traditional training of an epidemiologist. A geographic approach is a natural companion to population based research, but attention to data quality and relevance of results is required to make an important impact with these techniques.</p

The impact of data quality on spatial analysis of cancer registry data

Most disease surveillance systems currently geocode case data. This, coupled with advances in geographic analysis tools, has led to a rise in epidemiologic studies on distribution of disease that rely on analysis of secondary data, e.g. from cancer registries. However, while the data and tools are available for performing geospatial analyses, there are challenges with which methodologies to apply, how to interpret and translate results, and how results are impacted by data quality. The issue of data quality is the subject of this paper. Mapping cancer rates highlights spatial patterns that can help elucidate environmental, clinical, or social causality pathways that drive differences in disease burden by geographic locations. Locating areas with high rates of cancer incidence or variations by stage at diagnoses can help prioritize cancer control efforts. Once the geographic patterns of cancer are mapped, the ideal action is to follow with effective public health interventions for the high risk communities. However, before using results of spatial research to inform public health response, it is important to consider whether the results are spurious due to methodological issues, such as data quality. Missing or incorrect data can distort research conclusions and result in ineffective public health policy. Using colorectal cancer (CRC) as an example, the impact of missing stage at diagnosis on late-stage at diagnosis cluster detection is evaluated. The impact on cluster detection, area-based modeling, and distance from services analysis is described

Addressing colorectal cancer disparities the identification of geographic targets for screening interventions in Miami-Dade County, Florida

This paper describes an analysis of spatial clustering of colorectal cancer (CRC) in Miami-Dade County, Florida. The objective was to identify geographically based targets for colorectal cancer screening interventions for Blacks and Hispanic Whites, two groups with demonstrated disparities in stage at diagnosis and mortality for CRC. The initial cluster detection analysis identified areas with high risk of late stage CRC, however, none of the results were statistically significant. The analysis was not based on an academic research question, but instead was an application intended to guide appropriate and targeted strategies for high risk populations. Only about 50% of the general population receives CRC screening, so, while all groups would benefit from increased CRC screening, high risk communities may potentially benefit the most. Because public health resources are limited, geographically targeting high risk populations for enhanced screening efforts is pragmatic public health policy. Despite the lack of statically significant results, we still needed to develop a helpful answer to the question, where should we market a screening intervention? The selected geographic areas must have real potential for attenuating excess CRC burden through increased screening efforts. Through evaluating a combination of clusters of late stage and overall CRC risk (two separate models of cluster detection), probable communities with low CRC screening uptake were identified. Although they did not meet statistical significance, they were determined to have public health importance

Predictors of neighborhood risk for late-stage melanoma: addressing disparities through spatial analysis and area-based measures

Minority populations have disproportionately more advanced stage melanoma and worse survival. To clarify the impact of race and ethnicity on late-stage melanoma diagnosis, we performed spatial analysis of geocoded melanoma cases diagnosed in Florida, 1999-2008, to identify geographic clusters of higher-than-expected incidence of late-stage melanoma and developed predictive models for melanoma cases in high-risk neighborhoods accounting for area-based poverty, race/ethnicity, patient insurance status, age, and gender. In the adjusted model, Hispanic ethnicity and census tract-level poverty are the strongest predictors for clustering of late-stage melanoma. Hispanic whites were 43% more likely to live in neighborhoods with excessive late-stage melanoma (P<0.001) compared with non-Hispanic whites (NHW). For every 1% increase in population living in poverty, there is a 2% increase in late-stage melanoma clustering (P<0.001). Census tract-level poverty predicted late-stage melanoma similarly among NHW and Hispanic whites. The impact of insurance coverage varied among populations; the most consistent trend was that Medicaid coverage is associated with higher odds for late-stage melanoma. The finding that Hispanics are most likely to reside in high-risk neighborhoods, independent of poverty and insurance status, underscores the importance of addressing, and overcoming community-level barriers to melanoma care

Impact of Teaching Facility Status and High-Volume Centers on Outcomes for Lung Cancer Resection: An Examination of 13,469 Surgical Patients

Studies suggest that institutional case volume and teaching status significantly affect patient survival. We sought to compare outcomes of surgical resection for lung cancer at teaching facilities (TF) and at high-volume centers (HVC).Patients undergoing lung cancer resection with curative intent were examined using a linked dataset from 1998 to 2002 between the Florida Cancer Data System and the Florida Agency for Health Care Administration.A total of 13,469 patients were analyzed and outcomes adjusted for comorbidities. Median survival time (MST) was superior for patients treated at TF versus nonteaching facilities (NTF) (47.1 versus 40.5 months, P < 0.001). Mortality rates at NTF were higher at 30 days (2.6% versus 1.1%, P < 0.001), 90 days (6.8% versus 3.8%, P < 0.001), and at 5 years (63.9% versus 59.2%, P = 0.005). Similarly, MST was superior in the cohort treated at HVC versus low-volume center (LVC) (45.1 versus 39.8 months, P < 0.001). Mortality was observed to be higher in LVC than HVC at 30 days (2.7% versus 1.6%, P < 0.001), 90 days (7.5% versus 4.0%, P < 0.001), and at 5 years (63.5% versus 59.3%, P = 0.002). Significant preoperative, independent predictors of survival include age, sex, smoking status, and the existence of certain comorbidities. Treatment at a TF or HVC were independent predictors of better outcome. Race, use of chemotherapy or radiation did not affect outcomes.Surgical treatment for lung cancer at TF or HVC results in significantly better short- and long-term patient outcomes

Toward the identification of communities with increased tobacco-associated cancer burden: Application of spatial modeling techniques

Introduction: Smoking-attributable risks for lung, esophageal, and head and neck (H/N) cancers range from 54% to 90%. Identifying areas with higher than average cancer risk and smoking rates, then targeting those areas for intervention, is one approach to more rapidly lower the overall tobacco disease burden in a given state. Our research team used spatial modeling techniques to identify areas in Florida with higher than expected tobacco-associated cancer incidence clusters. Materials and Methods: Geocoded tobacco-associated incident cancer data from 1998 to 2002 from the Florida Cancer Data System were used. Tobacco-associated cancers included lung, esophageal, and H/N cancers. SaTScan was used to identify geographic areas that had statistically significant (P<0.10) excess age-adjusted rates of tobacco-associated cancers. The Poisson-based spatial scan statistic was used. Phi correlation coefficients were computed to examine associations among block groups with/without overlapping cancer clusters. The logistic regression was used to assess associations between county-level smoking prevalence rates and being diagnosed within versus outside a cancer cluster. Community-level smoking rates were obtained from the 2002 Florida Behavioral Risk Factor Surveillance System (BRFSS). Analyses were repeated using 2007 BRFSS to examine the consistency of associations. Results: Lung cancer clusters were geographically larger for both squamous cell and adenocarcinoma cases in Florida from 1998 to 2002, than esophageal or H/N clusters. There were very few squamous cell and adenocarcinoma esophageal cancer clusters. H/N cancer mapping showed some squamous cell and a very small amount of adenocarcinoma cancer clusters. Phi correlations were generally weak to moderate in strength. The odds of having an invasive lung cancer cluster increased by 12% per increase in the county-level smoking rate. Results were inconsistent for esophageal and H/N cancers, with some inverse associations. 2007 BRFSS data also showed a similar results pattern. Conclusions: Spatial analysis identified many nonoverlapping areas of high risk across both cancer and histological subtypes. Attempts to correlate county-level smoking rates with cancer cluster membership yielded consistent results only for lung cancer. However, spatial analyses may be most useful when examining incident clusters where several tobacco-associated cancer clusters overlap. Focusing on overlapping cancer clusters may help investigators identify priority areas for further screening, detailed assessments of tobacco use, and/or prevention and cessation interventions to decrease risk