Conference on Geospatial Approaches to Cancer Control and Population Sciences
September 12-14, 2016
Natcher Conference Center, NIH Campus, Bethesda MD
Cancer Epidemiology, Biomarkers & Prevention published a Focus issue in April 2017 to showcase recent, cutting-edge research in the development and application of novel geospatial approaches in cancer control and population sciences.
Growing technological capacity in mapping and spatial technology along with increasing sophistication in spatial thinking related to health has resulted in the emergence of a growing research community using geospatial approaches on diverse aspects of cancer prevention and control.
The purpose of this conference, sponsored by NCI's Division of Cancer Control and Population Sciences (DCCPS), was to bring together a community of researchers across the cancer control continuum using geospatial tools, models and approaches to address cancer prevention and control in order to 1) support and build this research community, 2) accelerate the integration of state of the art tools and theories from spatial research into cancer control and population sciences, and 3) identify future directions for data, resource, training and research funding in cancer control. This conference addressed spatial and contextual aspects of cancer across the entire cancer control continuum including etiology, prevention, detection, diagnosis, treatment and survivorship.
View agenda for Monday, September 12
|8:00 a.m. - 9:00 a.m.||Registration|
|9:00 a.m. - 9:30 a.m.||
Welcome and Introduction to the Conference
David Berrigan, Ph.D., M.P.H.
Robert T. Croyle, Ph.D.
|9:30 a.m. - 10:30 a.m.||
Opening Plenary Session
Gary L. Ellison, Ph.D., M.P.H. (Moderator)
New Directions in Cancer Control and Population Sciences
Invited Speaker: Robert Hiatt, M.D., Ph.D., University of California, San Francisco
Cancer control research has become increasingly focused on population health in the United States. The notion of 'translational research' has extended past the development of new therapeutics to impact populations… where people live, work and play. Cancer control research is now challenged with finding the best ways to make use of new types of data from electronic health records to social media, new linkages of data sets including cancer registries, administrative data and digitalized health information, and new technologies such as geospatial methods. Increasingly sophisticated molecular techniques are providing new biomarkers to assess risk, while at the other end of the research continuum, there is growing interest in implementation science. How do we best translate effective interventions to reach sectors of society where they can make a difference? There may even be a slow crumbling of the traditional walls between the medical science focus on individual patients and population health sciences with transdisciplinary approaches to complex problems including persistent inequities in cancer outcomes. The challenge will be whether we can see measureable and attributable impacts on prevention and population health, while we also pursue treating cancers more precisely in individuals.
Connecting Population, Health and Place (With Geospatial Tools and Data)
Invited Speaker: John P. Wilson, Ph.D., University of Southern California
Much has been written about the spatial turn in health research in recent years, culminating in the recent Science article authored by Richardson and colleagues, and many of the sessions and talks which follow this one will explore one or more facets of this engagement in great detail. That said, this particular talk starts with a brief synopsis of past work. From there, the value proposition for pursuing geospatial approaches to cancer control given a world that is suddenly awash with geospatial tools and data is articulated. The talk will then describe the current status of geospatial tools and data along with some of the nascent trends that will help to shape the growth and evolution of geospatial technologies moving forward. These geospatial tools and data are then connected with health through a series of enduring challenges whose resolution is likely to have a large impact on the success (or not) of geospatial approaches to cancer control. The talk will conclude with a brief discussion of the research opportunities afforded by the collaborative and connected worlds in which many of us now live our lives, the importance of activity spaces (what I like to refer as personal geographies) in clarifying what is important about specific places and spaces, and the need for cross-training to build and sustain meaningful, collaborative, and interdisciplinary research programs linking cancer control and the spatial sciences in the years ahead.
|10:30 a.m. - 10:45 a.m.||Break|
|10:45 a.m. - 12:00 p.m.||
April Oh, Ph.D., M.P.H. (Moderator)
Segregation and the People's Health: Implications for Cancer Registries, Research, and Prevention
Invited Speaker: Nancy Krieger, Ph.D., Harvard University
The counting we do for cancer registries, and for public health more generally, is for accountability, for we create knowledge critical for altering who and what drives population health and health inequities. As far as health inequities are concerned, we don't simply live in unjust societies– we live in places, and no matter how much of our lives or livelihood may roam around in cyberspace, or be engaged with non-geographically defined communities or diverse interest groups, issues of residence, economics, power, and health are tightly bound. My presentation accordingly will address three topics: (1) the context, conceptually and empirically, for current work on spatial social polarization, both globally and nationally, and its implications for population health; (2) the Index of Concentration at the Extremes, a metric for monitoring health inequities, especially in relation to racialized economic segregation, which is likely promising for our work; and (3) the ongoing embodied legacy of Jim Crow as it pertains to risk of current cancer outcomes, specifically breast cancer estrogen receptor status, and to health more generally. In closing, I will discuss why our work in cancer control and public health must be cognizant of how it is we embody history – including our societal and ecological context – and what this means for how we can and must promote health equity.
Myles Cockburn, Ph.D.
|12:00 p.m. - 1:15 p.m.||Lunch
Cafeteria on site
|1:15 p.m. - 3:00 p.m.||
Concurrent Sessions 1 - 4
Session 1: Defining Personal Environments for Risk
A Spatio-Temporal Perspective for Defining Personal and Contextual Environments for Cancer Risk Factors
Time: 1:15 - 1:45 p.m.
Individual health behaviors and outcomes are influenced by a wide range of environmental factors. Past studies mainly derived contextual variables based on static administrative areas (e.g., census tracts are used to represent people's residential neighborhoods). However, since people move around in their daily lives, their activities (and thus exposure to environmental influences) do not take place at one time point and wholly within any static, administratively bounded areal unit. The most important determinants of people's exposure to environmental influences are thus where and how much time they spend while engaged in their daily activities. In order to identify the appropriate geographic area to be used as the basis for deriving accurate measures of people's exposure to contextual factors, people's daily movement needs to be taken into account. This paper examines this need for delineating more accurate personal environments or geographic context in research on exposure to cancer risk. It first discusses the state-of-the art geospatial methods and technologies for defining and capturing neighborhood stress. It will then relate neighborhood stress to biomarkers of cancer risk and progression through the mediation of body habitus and physical activity. Implications of various environmental influences and neighborhood stress for cancer risk, cancer prevention and outcomes will be discussed.
Representing GPS-defined Walking Activity for Exposure Analysis
Time: 1:45 - 2:00 p.m.
Use of GPS in physical activity and environmental exposure research is becoming more commonplace to explore individuals' exposures to various environmental influences. However, GPS location estimates are prone to errors that can bias exposure estimates. This study evaluates the reliability of single exposure measure assessed using a variety of GPS cleaning techniques. Four established point-based GPS cleaning techniques (filtering by altitude, HDOP, and the number of satellites used and Gaussian kernel smoothing), one novel GPS-based polyline delineation method, and combinations of both cleaning and polyline methods were evaluated to remove potentially errant location estimates used to estimate exposure to fast-food restaurants while walking. Exposure to fast food restaurants was measured by determining the number of restaurants within a half-mile per minute of a single period of walking activity. Pearson's correlation coefficients served to evaluate the reliability of the exposure estimates after GPS cleaning methods were applied. Kolmogorov-Smirnov (KS) tests assessed whether the exposure distributions were significantly different from each other. Exposure estimates using point-based GPS cleaning methods were highly correlated with correlation coefficients that ranged between 0.91 and 1.00. KS tests indicated that the distribution of exposure estimates using the altitude filter was significantly different from the other methods (p < 0.001). For the polyline-based estimates simply connecting the uncleaned GPS points produced significantly higher exposure estimates when compared to the cleaned data using the KS test (p < 0.001). The altitude filter removed more points than the other methods, and should be used with care when evaluating environmental exposures. Additionally, if exposure is being measured using line-based representations of travel paths then the use of a GPS cleaning technique is necessary to avoid overestimating and individual's exposure. The results of this study indicate that GPS cleaning techniques should be applied when using polylines constructed from GPS points. Otherwise, the exposure could be overestimated.
Time: 2:00 - 2:15
The identification of environmental factors that may influence breast cancer presents a tremendous opportunity for reducing breast cancer rates. However, many exposure studies do not track exposure over time and space (dynamic exposure), which is key to assessing total exposure. Numerous studies have shown that measuring exposure solely in local neighborhoods (static exposure) underestimates effects. This study compares breast cancer survivors' exposure to walkable and recreation-promotive environments using dynamic GPS and static home measures as related to insulin levels. The Reach for Health Study enrolled 333 women; 310 completed the study protocol. Participants were postmenopausal overweight or obese breast cancer survivors diagnosed within the past 10 years, and not scheduled for or currently undergoing chemotherapy or radiotherapy. One week of hip accelerometer and GPS sensor collection, and fasting blood draw were collected at baseline. Exposure to recreation spaces and walkability was measured for each woman's home at 800m buffers (static) as well as using kernel density weight of GPS tracks (dynamic). Exposure results were used as predictor variables in linear regression models to determine if dynamic or static exposure to recreation spaces or walkability is related to breast cancer survivor's insulin levels. Dynamic exposures methods may lead to a better understanding of the link between environmental exposures to recreation/walkable spaces and insulin levels of breast cancer survivors, and may more effectively inform just-in-time interventions. This is the first study to compare dynamic and static exposure measures as related to insulin outcomes for breast cancer survivors.
Time: 2:15 - 2:30 p.m.
"Activity space" (AS) approaches can provide new insights into environmental influences on cancer risk behaviors/outcomes. This presentation addresses one major gap on AS measurement: how many days and timepoints of GPS tracking are needed to capture the area where people conduct activities and spend time. 101 adults aged 20-64 in Chicago wore GPS loggers for up to 28 consecutive days as they went about normal activities. Six-month follow-up is underway. Using a new tool, we derived counts of the number of GPS points that fell into each cell of a 240mx240m grid, and calculated cumulative measures of exposure to places across time and considering time spent in each place. 54% were women; 75% were African-American or Latino; 30% college-educated; 44% employed; and 38% owned auto. On average, individuals accumulated 48% (SD=21%) of the space where they conducted activities at day 7, 69% (SD=21%) at day 13, and 87% (SD=21%) at day 21. When considering time spent at different locations, individuals accumulated 69% (SD=17%) of the space where they spent time at day 7, 80% (SD=15%) at day 13, and 91% (SD=10%) at day 21. On average, fewer GPS days were needed for more spatially entrapped populations (African-American, unemployed, and less educated). Typical use of 7 days to measure AS is insufficient to capture where individuals typically conduct activities and spend time. Next-generation studies on environment-cancer risk/outcome relationships may more accurately measure environmental exposures through use of an AS approach with longer durations of GPS tracking.
Time: 2:30 - 2:45 p.m.
Inactivity, poor diet, and obesity are linked to cancer. "Walkable" built environments (BE) may influence health behaviors and obesity. However, most research has been fraught by methodological problems including self-selection bias, reverse causation, and structural confounding. Twin studies provide quasi-experimental data to address these issues. We use twin methods to model phenotypic and quasi-causal associations among the BE, physical activity, and BMI levels using archival survey data and geocoded home addresses among a large sample of identical and fraternal twins. We also model phenotypic and quasi-causal associations in these same exposures and outcomes using data from accelerometers, GPS devices, and mobile phones in a sample of identical twins (raised together as children, but living apart as adults), and GIS to place activity and eating behaviors within a spatial-temporal framework. Survey data from identical and fraternal twins demonstrates that the BE is causally associated with activity levels, but not with BMI. Although BE walkability decreases variance in BMI, it does so indirectly through physical activity. Using objective data, we extend these BE-related findings with evidence that age, sex, and SES also influence the BE – activity association. Twin studies support the concept that the BE is indirectly associated with obesity through its direct influence to support more activity. Longitudinal, genetically informed studies will provide further robust evidence of how the BE supports health behaviors. We propose a conceptual framework that accounts for genetic and shared environmental influences on health to provide this evidence.
Time: 2:45 - 3:00 p.m.
Cancer research studies often need data on where people have lived throughout their lifetimes to assess prior risk exposures, both socioeconomic and chemical. Recent studies have shown that commercial vendors are viable sources for information about prior residential locations. We identified three commercial vendors that could provide previous address data. To assess the accuracy of the commercially provided data, a set of self-reported residential histories was collected from volunteer participants at the National Cancer Institute and the National Institute of Environmental Health Sciences. We compared the accuracy and completeness of the residential histories derived from the vendor data with the self-reported residential histories. The commercial data start around 1980 – there is very little data available before then. Data is available for deceased individuals. Only U.S. addresses were reported. The data that commercial vendors provide consist of a set of addresses associated with each individual rather than an actual residential history for the individual. An algorithm was developed to construct residential histories from the vendor data and the derived residential histories were reasonably accurate and complete. Reasonable residential histories can be derived from vendor data and the derived histories yield significant accuracy improvements compared to assuming the person always lived at their current residence. This study demonstrates how a wide range of cancer research studies that need data on where people have lived prior to diagnosis can be conducted using existing data in cancer registries linked with commercial residential data.
Session 2: Geography of Screening and Vaccine Update
Session Chair: Jennifer Moss, Ph.D., M.P.H., National Cancer Institute
Opening Remarks from Session Chair
Variability in States' Ranks on Cancer Prevention Vaccination: Implications for Public Health Programming
Time: 1:30 - 1:45 p.m.
Performance rankings may guide public health departments in deciding where to focus cancer prevention efforts, including programming to promote uptake of cancer-preventing vaccines (human papillomavirus [HPV] and hepatitis B [HepB]). However, understanding uncertainty around ranks is important for avoiding over-interpretation. Data on uptake of HPV and HepB vaccines among 13- to 17-year-old adolescents came from 2011-2014 National Immunization Survey-Teen (n=61,207, from 50 states and Washington D.C.). Analyses included calculating means, 95% confidence intervals (CI's), and ranks for vaccination in each state, and implementing a Monte Carlo method with 100,000 simulations to generate simultaneous CI's around ranks. From 2011-2014, HPV vaccination was 55% (95% CI=54-56%; states' range: 40-76%) for girls and 21% (95% CI=20-22%; range: 13-54%) for boys. HepB vaccination was 93% (95% CI=92-93%; range: 84-97%) for all adolescents. States' ranks had substantial variability, and states in the middle had fairly wide CI's; e.g., Nevada was ranked 26th (95% CI=11-37) for girls' HPV vaccination. However, meaningful differences were apparent for states at the extremes of each outcome; e.g., Rhode Island was 1st (95% CI=1-1) and Kansas was 51st (95% CI=48-51) for girls' HPV vaccination. States' ranks of performance on uptake of cancer-preventing vaccines varied widely, but targeting programming in states with extremely poor performance may still be appropriate. Ranking states on vaccination within subpopulations (e.g., minority adolescents) will be challenged by smaller sample sizes (and larger CI's). Public health departments may over-interpret performance rankings, but ranks are still informative for identifying low-performing states.
Time: 1:45 - 2:00 p.m.
It is unknown how Medicaid expansion has affected rates of breast and cervical cancer screening in Illinois. Methods: We analyzed geocoded Medicaid enrollee claims for pap and mammography procedures. Patients who had any screening procedure (age 21-64 for pap, 40-64 for mammography) in either of two time periods (pre-expansion: 8/11-7/13, follow-up: 8/13-7/2015) were matched to census tract characteristics derived from the American Community Survey. We present change in screening rates per 1000 low-income females (household earnings 150% FPL). We compare neighborhoods based on census tract data on Chinese/Taiwanese population percentages. There was a mean of 3,636 screening mammography procedures in the pre-expansion period and 5,899 in the follow-up. There were 80 per 1000 screening mammography procedures in the pre-expansion period as compared to 120 per 1000 in the follow-up period. From the pre-expansion to the follow-up period, there was a 50% increase in patients who received screening mammography. There was a mean of 6,700 cervical screening procedures in the pre-expansion period and 5,722 in the follow-up. There were 340 per 1000 cervical screening procedures in the pre-expansion period compared to 260 per 1000 in the follow-up period, a 24% decrease in cervical cancer screenings in a period when screening recommendations changed. Neighborhood areas that were 1-20% Chinese had the highest rates of both mammography and cervical cancer screening in both periods. This study is limited by lack of data on screening procedures for the privately insured or uninsured. This study does provide a template for how Medicaid agencies can measure cancer screening utilization trends in different neighborhoods.
Time: 2:00 - 2:15 p.m.
Social determinants of health (SDH) greatly impact health outcomes. New technology allows the linkage of community-level SDH to geocoded electronic health records (EHR). We aimed to (1) demonstrate application of geospatial technology to relevant population health questions; (2) determine the association between community factors and receipt of breast cancer screening. We paired EHR data from OCHIN (a network of community health centers with a single EHR) with 'Community Vital Signs' data to determine associations between community factors (age and racial distributions, income inequality, social deprivation, and population density) and the receipt of needed breast cancer screening, 2014-2015. Patient addresses were geocoded and mapped to census tracts, zip code tabulation areas, and counties. We measured receipt of screening with a 'prevention index' (number of months appropriately screened/total months eligible) and used generalized estimating equations to model the population-averaged proportion of months covered. Our sample included 78 clinics with 32,239 patients residing in 42 states. Mean percentage of person-time up-to-date for mammography was 65% (SD=43.0). Community variables associated with higher prevention index included: social deprivation index, percent non-white Hispanic, and percent non-white (p<0.05). The ratio of older dependents to working age population was associated with a lower prevention index (p<0.05). Geocoded EHR data can be linked to community-level data to examine population health outcomes. Receipt of breast cancer screening was associated with community characteristics typically related with poverty and poor health. More research is needed to understand emerging trends in this novel application of SDH.
Time: 2:15 - 2:30 p.m.
Socioeconomic status (SES) is often considered as a risk factor for health outcomes. SES is typically measured using individual variables of educational attainment, income, housing, and employment variables or a composite or index of these variables. Approaches to building the composite variable include using arbitrary weights for each variable or estimating the weights with principal components analysis (PCA) or factor analysis. However, these methods do not consider the relationship between the health outcome and the SES variables when constructing the index. In this project, we used weighted quantile sum (WQS) regression to both estimate a neighborhood- level SES index and its effect in a model of colorectal screening adherence in Minnesota and Wisconsin. We considered several different specifications of the SES index including using different spatial scales (e.g., census block group and tract) for the SES variables. We found a significant positive association (odds ratio = 1.17, 95% CI: 1.15-1.19) between the SES index and colorectal screening adherence in the best fitting model. The model with the best goodness-of-fit included a multi-scale SES index with 10 variables at the block group-level and one at the tract-level, with home ownership, race, and income among the most important variables in the indices.
Time: 2:30 - 2:45 p.m.
Nebraska being a predominantly rural state, the people of rural Nebraska face unique challenges in accessibility of cancer screening services. The objective of this study is to identify regional (rural/ urban) variations in breast cancer screening rates, access to mammography centers and late stage at diagnosis rates in Nebraska. Area-level hot-spot analysis of breast cancer screening rates using the National Private Insurance Claims data (NPIC 2013), access to the nearest mammography center using data on screening center locations from US FDA and late-stage at diagnosis rates using the Nebraska Cancer Registry (NCR 2008-2012), were conducted using ArcGIS 10.3.1. The hot-spot analysis showed significantly higher rates of breast cancer screening in the urban areas of Eastern Nebraska, significantly lower distances to the nearest mammography center in the urban areas of Eastern Nebraska and significantly higher distances in rural Northern and Western Nebraska. It also showed significantly lower rates of late-stage diagnosis in the urban areas of Eastern Nebraska, and significantly higher rates in Western Nebraska, which is predominantly rural. There are significant regional variations in breast cancer screening rates, access to mammography centers and late stage diagnosis in Nebraska, showing rural/urban disparities both in the receipt of screening as well as in late stage at diagnosis. The lack of access to screening facilities in rural areas may be the cause for the observed regional variations in the screening rates and late stage at diagnosis of breast cancer in Nebraska. Targeted policy efforts should be developed to address the issues that rural populations in Nebraska face in access to cancer screening.
Time: 2:45 - 3:00 p.m.
Session 3: Cancer Health Disparities and the Neighborhood and Social Environments
Cancer Health Disparities: Role of Built and Ethnic Neighborhood Environments
Time: 1:15 - 1:45 p.m.
Disparities across the cancer continuum, from incidence to mortality, persist among sociodemographic population groups in the U.S. Neighborhoods are key determinants of health, such that social and built neighborhood characteristics shape opportunities for and barriers to health promotion. Evidence to date shows social and built environmental conditions facing residents affect health as much as do the individual characteristics of residents themselves. These neighborhood characteristics may further promote cancer health disparities. This presentation will highlight recent research focusing on the role of built and ethnic environment factors in cancer outcomes, across racial/ethnic groups. Methodologic considerations and future directions in this research will be discussed.
Time: 1:45 - 2:00 p.m.
Neighborhoods are important for understanding population health as structural, social and individual processes tend to coalesce at the local area level. However, there is debate among scholars over the efficacy of studying neighborhood effects on health, partly based on questions of appropriate measurement. This presentation will describe a series of recent studies in which we employed innovative approaches to measuring neighborhoods and retail environments to evaluate disparities in tobacco and substance use among young adults. The San Francisco Bay Area Young Adult Health Survey (BAYAHS) is a multimode probabilistic household sample of young adults in San Francisco and Alameda Counties, California, stratified by race/ethnicity (n=1363). The Neighborhood Determinants of Tobacco Use Disparities studies include four modes of additional data collection: 1) an observational audit of 140 neighborhoods in which BAYAHS respondents reside; 2) a survey of 300 tobacco retail outlets located within and surrounding these neighborhoods; 3) spatial video data collected in each sampled neighborhood; and 4) an ecological momentary assessment study in which 150 young adult tobacco users are followed for 30 days on their smartphones, capturing survey and geolocation data. Descriptive and geographically-weighted regression results will illustrate associations between local area characteristics and polytobacco use, marijuana use and secondhand smoke exposure. Tobacco-related disparities operate at local scales; in the Bay Area there are notable differences in availability, exposure and use of tobacco among young adults between neighborhoods that aspatial methods cannot appropriately distinguish. Tobacco policy and advocacy are often determined at local scales. Geospatial approaches to measuring tobacco-related disparities are important for identifying policy targets and populations most at risk.
Time: 2:00 - 2:15 p.m.
Geospatial factors such as neighborhood ethnic density may convey advantages through mechanisms (e.g., social support, social capital) which in turn translate into improved cancer survival for Hispanic residents. These benefits may be particularly evident for immigrant populations. However, ethnically dense neighborhoods also experience disproportionate socioeconomic deprivation, which is associated with suboptimal survival. We examine whether associations between neighborhood characteristics such as ethnic density and poverty differ depending on patient ethnicity and birthplace. Using linked Texas Cancer Registry-US Census data, we examined associations between neighborhood percent Hispanic, neighborhood poverty, patient ethnicity, and patient birthplace on all-cause and breast-cancer specific mortality among 166,254 non-Hispanic White (79.9%), Hispanic US-born (15.8%), and Hispanic foreign-born (4.2%) women with breast cancer, diagnosed 1995-2009. Shared frailty Cox proportional hazard models (patients nested within census tracts) adjusted for age, diagnosis year, stage, grade, histology, urban/rural residence, and local mammography capacity. Living in higher Hispanic density neighborhoods was associated with increased mortality for all three population groups. Associations differed by patient ethnicity, birthplace, and neighborhood poverty. The deleterious effect of Hispanic density on survival was amplified for Whites and Foreign-born Hispanics (vs. US-born Hispanics) and was somewhat attenuated in low poverty neighborhoods. Unlike prior studies, we observed no evidence that neighborhood Hispanic density confers protective effects for residents, regardless of patient ethnicity or birthplace. Future research on mechanisms underlying differences in cancer mortality by neighborhood of residence is needed to inform design of interventions for vulnerable populations who suffer disproportionate cancer burden.
Time: 2:15 - 2:30 p.m.
Neighborhood factors are critical pathways that shape and perpetuate disparities in cancer mortality. However, little is known about how neighborhood attributes work together to impact health. One approach is to use archetypes to encompass multiple neighborhood characteristics within a single classification system. Latent class analysis was applied to data on social and built environments (socioeconomic status (SES), ethnic composition, housing, population density, commute, businesses, parks, and traffic) to develop neighborhood archetypes for year 2000 and 2010 California block groups and tracts. We assessed associations with mortality among breast and prostate cancer patients using Cox proportional hazard models with geocoded cancer registry data. Goodness of fit statistics identified a 5-class and 9-class neighborhood archetype model for year 2000 tracts and a 5-class model for block groups. These archetypes showed significant associations with mortality. Compared to high SES suburbs with fewer minorities, less commuting, and more healthy food outlets and recreational facilities, all other classes in the 5-class model demonstrated higher mortality. The classes with the highest mortality were defined by lower SES, but also other characteristics, for example, rural with more older and White residents, and less commuting and less traffic, and urban with more Hispanic and Black residents, residential mobility and unhealthy food outlets. Variation by race/ethnicity and nativity was observed. The archetype approach yields insights into how neighborhood characteristics work synergistically to influence cancer mortality. This research contributes to our understanding of how place affects health and can inform multilevel interventions.
Time: 2:30 - 2:45 p.m.
In U.S., the gap in colorectal cancer (CRC) survival rates by race has persisted since the early 1980s. Although socioeconomic status (SES) and its connection to residential racial segregation are being gradually highlighted, measures of segregation are still neglected in contemporary examinations of racial disparities in health, including studies of cancer. Further, very few studies examine elements of the process by which segregation occurs and may influence health such as through housing discrimination. This study aims to determine whether observed spatial patterns of CRC survival in Southeastern Wisconsin are associated with two new measures of housing discrimination (called redlining and racial bias in mortgage lending). Invasive CRC incidence data (2002-2011) were obtained from the Wisconsin Cancer Reporting System for two MSAs in southeastern Wisconsin. Two indices of mortgage discrimination were derived from Home Mortgage Disclosure Act data for the southeastern Wisconsin. Their impacts on CRC-specific mortality and all-causes mortality among CRC survivors by race are evaluated via Cox proportional hazards regression modeling while controlling population density and/or other socioeconomic variables. Black populations experience poorer CRC survival than White populations. Model results indicate a possible relationship between institutional racism (via mortgage discrimination) and CRC survival among the Black. Racial bias in mortgage lending and residential redlining could explain CRC disparity. More research is needed to elucidate the pathways by which segregation influences caner survival disparities. Closing these gaps are necessary steps for identifying policy targets and formulating effective interventions.
Time: 2:45 - 3:00 p.m.
Breast cancer incidence is low in Chinese women, but increases upon migration to the U.S. Inflammation may be associated with risk, although the association may be less pronounced in geographic settings that offer protection against risk increase -for example those that help maintain traditional lifestyle behaviors or offer access to social networks. Thus, we examined associations of inflammatory markers with breast density, a marker of breast cancer risk, among female Chinese immigrants, and explored whether associations varied by neighborhood environment. The sample included 278 female Chinese immigrants in three geographically distinct sections of Philadelphia: (1) Chinatown, a traditional Chinese immigrant enclave; (2) South Philadelphia, an enclave including diverse Asian immigrants; and (3) Northeast Philadelphia, an emerging enclave with a smaller but rapidly growing Chinese immigrant population. Participants provided blood samples for assessment of C-reactive protein (CRP) and soluble tumor necrosis factor receptor 2 (sTNFR2) and underwent mammographic screenings to assess breast density using a computer-assisted method. In models controlling for age, body mass index (BMI), length of US residence, and acculturation, sTNFR2 was negatively associated with dense tissue area (p=0.046) and percent density (p=0.038) among women residing in Northeast Philadelphia, but not associated among women in Chinatown or South Philadelphia (p>0.16), areas representing more traditional enclaves. The test of moderation was statistically significant (p=0.040). Findings suggest that enclave residence moderates the association between inflammation and breast density, and may point to mechanisms by which local neighborhood environment can impact immigrant health trajectories.
Session 4: Identifying Priority Areas for Cancer Control Activities
Session Chair: Cynthia A. Vinson, Ph.D., M.P.A.
Opening Remarks from Session Chair
Spatial Analysis of Stomach Cancer in Central America
Time: 1:30 - 1:45 p.m.
Stomach cancer is the 3rd leading cause of global cancer mortality, and the leading infection-associated cancer. Gastric cancer demonstrates marked geographic variability, with high incidence areas in eastern Asia and mountainous Latin America ("altitude enigma"). Risk factors include host genetics and responses, H. pylori genomics, microbiota variance, diet, and environmental exposures – all of which may cluster to help explain the spatial patterns. We performed a spatial analysis of incident gastric cancer cases, as well as their subtypes and risk exposures in a high incidence region of Central America. We characterize the spatial distribution of stomach cancer in rural western Honduras, from an ongoing population-based, case-control study (n=1,139). Crude incidence rates were aggregated in 314 census units ("aldeas" = 30-50 households) and stratified by sex. Logistic regression models were constructed to examine the relationship between incidence and altitude and additional variables. GIS methodology with cluster detection tools were used to characterize stomach cancer patterns. Cluster detection results revealed unique spatial patterns identifying areas with disease burden significantly higher than expected. The incidence of stomach cancer was not associated with altitude in this circumscribed mountainous region without coastal areas. Mapping general cancer patterns, host and bacterial genomics, and environmental factors may help characterize patterns of cancer incidence and risk factors to better understand the geographic enigmas of gastric cancer. Understanding the spatial distribution of stomach cancer can help identify high burden areas, and inform geographically targeted screening and prevention program efforts in Central and Latin America.
Time: 1:45 - 2:00 p.m.
Since 1991, the Michigan Breast and Cervical Cancer Control Program (MBCCCP) has provided breast and cervical cancer screenings for uninsured/underinsured women between the ages of 40-64 as part of CDC's national program. Client outreach is an important aspect of the program. In 2002, the MBCCCP began exploring the idea of identifying sub-county areas where program eligible women might live. Maps based on Census data and client address data (internal database) were developed. After the state developed a template for online mapping applications, the MBCCCP collaborated with the pertinent state department to create a custom mapping application called "Cancer Mapper." This document covers implementation milestones and challenges for Cancer Mapper. Cancer Mapper went live in 2014. It features most aspects associated with online mapping applications, such as Google maps. It displays geocoded points of women who have participated in the MBCCCP and has custom search and report features. Custom layers show categories of race, ethnicity, poverty, cancer diagnosis information, and estimated eligible counts of women. Because Cancer Mapper shows protected health information, access is limited to internal and affiliate staff only. In its first year, local health departments used Cancer Mapper to develop targeted outreach strategies. Enhancements and updates to Cancer Mapper are in progress to keep the application relevant. Additional enhancements are being considered. Cancer Mapper may serve as a template for the GIS aspects of a large grant-funded program currently in progress.
Time: 2:00 - 2:15 p.m.
Inadequate access to screening mammography may affect its utilization, which in turn may impair the routine breast cancer screening. Health Resources and Services Administration (HRSA) develops shortage designation criteria and uses them to decide whether or not a geographic area is a Health Professional Shortage Area (HPSAs) or a Medically Underserved Area/Population (MUAs or MUPs). Such efforts, however, do not include designation of shortage areas for screening mammography. Given the importance of screening mammography in preventive care, this research considers both spatial and nonspatial factors in designating shortage area accessing screening mammography in the United States. Spatial access emphasizes the role of geographic barrier, and nonspatial factors include education and income. The location and number of all mammography machines in the US were collected from US Food and Drug Administration records of certified facilities. The census data was collected from 2010 decennial census. The spatial access was based on an adaptive two-step floating catchment area method discounted by a kernel function. Results show that counties qualified as shortage areas of screening mammography were located throughout the country, although the greatest shortage was in the south. Programs may use the findings to determine eligibility of an area and facilitate program operations.
Time: 2:15 - 2:30 p.m.
This study presents the methods by which the Project Team chose neighborhoods in which to conduct prostate cancer (PCa) educational interventions in Philadelphia. We geocoded PCa patient data (n=10750) from the Pennsylvania cancer registry from 2005-2014 by address and aggregated it by Philadelphia Census Tract (CT) to create inverse standard error-weighted standardized incidence ratios (SIRs) and mortality ratios (SMRs). For each patient, we combined PCa stage and grade into an aggressiveness variable, and aggregated by CT to create a mean aggressiveness variable. For CTs containing 300 or more men age 35+, we created a PCa composite variable by adding the SMR, SIR, and mean aggressiveness variables, each centered and scaled by their respective means and standard deviations. We mapped CTs with the highest composite scores in order to choose neighborhoods. Of the CTs with the top 13 PCa composite scores (Composite > 5.4), 11 were in one of four neighborhoods in the Lower North or West sections of Philadelphia. We chose these four neighborhoods for the PCa interventions. We selected neighborhoods by 1) ranking of CTs using a PCa composite score determined by combining SIR, SMR, and mean aggressiveness, 2) visual analysis of the geographic location of CTs within neighborhoods, and 3) local knowledge of Philadelphia by researchers and PCa survivors on the Project Team. These novel methods could be utilized by public health decision-makers when tasked to select a limited number of neighborhoods in which to intervene, due to limited resources.
Time: 2:30 - 2:45 p.m.
Cancer results from complex interactions across biologic, individual and social levels. Compared to other levels, empiric methods to assess social or neighborhood effects are limited. We propose a novel Neighborhood-Wide Association Study(NWAS), analogous to a genome-wide association study(GWAS), that utilizes high-dimensional computing approaches from biology to comprehensively and empirically assess neighborhood factors in prostate cancer.
Methods: Pennsylvania Cancer Registry data were linked to U.S. Census data. NWAS evaluated the association between neighborhood(n=14,663 census variables) and prostate cancer aggressiveness(PCA; n=6,416 aggressive Stage>3/Gleason grade>7 cases vs. n=70,670 non-aggressive Stage<3/Gleason grade>7 cases) in White men using a successively more stringent multiphase approach. Generalized estimating equations in Phase 1 and Bayesian mixed effects models in Phase 2 calculated odds ratios(OR) and credible intervals(CI). Variables meeting significance thresholds after Bonferroni adjustment were used in subsequent phases. In Phase 3, principal components analysis grouped correlated variables.
Results: We identified 17 new neighborhood variables associated with PCA. They represented income, housing, employment, immigration, access to care, social support. The top hits or most significant variables related to transportation(OR=1.05;CI=1.001-1.09) and poverty(OR=1.07;CI=1.01-1.12). Prediction models comparing NWAS findings to previously studied census variables will be reported to assess the utility of NWAS compared to current a priori approaches.
Conclusion/Impact: NWAS demonstrates how empiric, "big data" methods can be broadly applied to publically-available, social data. Findings are hypothesis-generating and suggest biologic plausibility. NWAS could potentially better inform variable selection in gene-environment studies, and could improve precision in geospatial analyses aimed at identifying areas disproportionally burdened by cancer.
Time: 2:45 - 3:00 p.m.
Including a bivariate spatial smooth of geographic location within the Cox proportional hazard models is an effective approach for spatial analyses of cancer survival. The objective of this study is to determine the impact of location and pollution burden on advanced-stage ovarian cancer survival. The Cox proportional hazard spatial methods are available in the MapGAM package implemented in R. Women diagnosed with Stage IIIC/IV epithelial ovarian cancer (1996-2006) were identified from the California Cancer Registry. The impact of pollution burden as measured by the California Office of Environmental Health Hazard Assessment CalEnvironScreen Score was assessed while adjusting for age, tumor characteristics, quality of care, race, and socioeconomic status (SES) on geographic patterns of survival. At the time of diagnosis, the median age for the 11,765 subjects was 65.0 years and 7216 patients (61.3%) had stage IIIC disease. An increase in the pollution burden from the 5th (better environment) to the 95th percentile (poor environment) was significantly associated with an increased risk of death (hazard ratio [HR], 1.17; 95% confidence interval [CI], 1.07-1.27). The pollution burden was higher for women of lower SES and racial minority, but did not differ by quality of care received. Pollution burden is highest for racial minorities and patients of low SES. The use of a bivariate spatial smoother within the survival model allows for more advanced geospatial analyses. Geographic location disproportionately affects survival among women in disadvantaged communities with high pollution burdens who are not able to travel long distances to receive quality care.
|3:00 p.m. - 3:15 p.m.||Break|
|3:15 p.m. - 4:45 p.m.||
Concurrent Sessions 5 - 8
Session 5: Accessibility to Health Services
Session Chair: Kevin A. Henry, Ph.D., Fox Chase Cancer Center and Temple University
Opening Remarks from Session Chair
Health Service Accessibility and Risk in Cervical Cancer Prevention: Comparing Rural Versus Non-Rural Residence in New Mexico
Time: 3:25 - 3:45 p.m.
Multiple intrapersonal and structural barriers, including geography, may prevent women from engaging in cervical cancer preventive care - screening, diagnostic colposcopy, and excisional pre-cancer treatment procedures. Geographic accessibility, stratified by rural and non-rural areas, to necessary services across the cervical cancer continuum of preventive care is largely unknown. Healthcare facility data for New Mexico (2010 – 2012) was provided by the New Mexico Human Papillomavirus Pap Registry, the first population-based statewide cervical cancer screening registry in the United States. Travel distance and time between the population-weighted census tract centroid to the nearest facility providing screening, diagnostic, and excisional treatment services were examined using proximity analysis by rural and non-rural census tracts. Mann-Whitney Test (P < .05) was used to determine if differences were significant and Cohen's r to measure effect. Across all cervical cancer preventive healthcare services and years, women who resided in rural areas had a significantly greater geographic accessibility burden when compared to non-rural areas (4.4 vs 2.5 km and 4.9 vs 3.0 minutes for screening; 9.9 vs 4.2 km and 10.4 and 4.9 minutes for colposcopy; and 14.8 vs 6.6 km and 14.4 and 7.4 minutes for precancer treatment services, all P < .001). Improvements in cervical cancer prevention should address the potential benefits of providing the full spectrum of screening, diagnostic and precancer treatment services within individual facilities. Accessibility assessments distinguishing rural and non-rural areas, are essential when monitoring and recommending changes to service infrastructures (e.g., mobile versus brick and mortar).
Time: 3:45 - 4:05 p.m.
Between 2003 and 2012, endometrial cancer mortality increased to a greater degree in Hispanic whites (HWs) than in non-Hispanic whites (NHWs). HWs are more likely than NHWs to be diagnosed with higher stage disease which is correlated with poorer survival. Geographic-level characteristics such as the distance traveled to surgery may influence stage at diagnosis. We identified 2538 NHW and 258 HW women in New Mexico and California who were >66 years of age diagnosed with first primary, invasive endometrial cancer using a Surveillance, Epidemiology and End Results (SEER)-Medicare linked database. Distance to surgery was determined as the shortest distance from the road nearest the centroid of the patient's census tract to the location of surgical treatment. Unconditional logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for late stage disease (vs. early stage) associated with ethnicity and distance to surgery. In adjusted models, HWs (OR = 1.3, 95%CI=1.0, 1.8) and those in the highest quartile of surgery travel distance (OR = 1.2, 95%CI=1.0, 1.5) were modestly more likely to be diagnosed with regional/distant disease than NHWs and those who traveled shorter distances, respectively. However, only HWs who drove the furthest to surgery were more likely to be diagnosed with regional/distant disease (OR = 2.5, 95%CI=1.5, 4.1), but not the NHWs who drove the furthest to surgery (OR = 1.1, 95%CI=0.9, 1.4) (interaction p-value <0.01). By elucidating and addressing the reasons behind higher stage disease in older HWs who drive the farthest distances to surgery, we can identify these cancers earlier thus improving survival.
Time: 4:05 - 4:25 p.m.
Though geographic access to healthcare may be an important determinant of prostate cancer diagnosis and treatment, differences in access to urology and radiation oncology clinics overall and by area sociodemographic characteristics have been poorly characterized. We created an inventory of urology and radiation oncology practices in Southeastern Pennsylvania. Using a 'mystery caller' approach, a research assistant posing as a medical office scheduler attempted to make a new patient appointment. Linear regression was used to determine the association between time to next available appointment and practice characteristics. Practice locations were mapped and spatial regression was used to examine the association between the number of practices within a 30-minute driving radius and census tract sociodemographic characteristics. There were 223 practices in the region. Radiation oncology practices were more likely to accept Medicaid compared to urology practices (91.3% vs 36.4%) and had shorter mean wait times for new patient appointments (9.0 vs 12.8 days). In adjusted analyses, census tracts with a higher proportion of black men had access to higher numbers of urology and radiation oncology practices within a 30-minute drive: 6% more urology practices (Risk Ratio [RR] 1.06, 95% Credible Interval [CI] 1.04, 1.08) and 6% more radiation oncology practices (RR 1.06, 95% CI 1.04, 1.08) for every 10% point increase in the proportion of black men. Conclusion and Contrary to our original hypotheses, racial differences in potential geographic access to healthcare in a large, metropolitan area were not found, suggesting a need to examine other dimensions of accessibility, beyond driving time, that may affect care delivery and racial disparities.
Time: 4:25 - 4:45 p.m.
While many cancer studies have examined how distance to the nearest facility may affect cancer treatment decisions and outcomes, little research has been undertaken on what factors affect the actual distance patients travel and what this means in terms of the types of patients different cancer facilities see. Methods: We use 2010-2014 Pennsylvania Cancer Registry data and a Stata user-provided open-source routing machine program to first calculate patients' driving time to the hospital where they receive treatment as well as the driving time to the nearest hospital. We then examine patient (age, sex, race, and insurance type, disease (primary site, stage), and facility characteristics that are associated with distance traveled to the treating hospital. For all analyses we estimate linear regression models with standard errors clustered at the county level. Results: Preliminary results suggest that younger age, being insured, VA/Tricare insurance, a higher US News hospital quality score, hospital volume, and distance to nearest hospital were significantly associated with greater travel time. In addition, we found significantly greater travel times for prostate, cervical/uterine, and ovarian cancers but significantly shorter times for colorectal, breast, and lung cancers as well as cancers at a distant stage. Conclusion: Patient, hospital, and disease characteristics are all associated with significant differences in cancer care travel times. Impact: Understanding what factors are associated with patient cancer care location choices is critical as policymakers and payers attempt to encourage patients to seek care at higher quality facilities and will be particularly critical to understanding the potential future impact of alternative care model, particularly in oncology.
Session 6: Physical Environment and Cancer Risk
Oodles of Excellent Spatially-derived Exposure Data, but Where the Heck are People Actually Geolocated?
Time: 3:15 - 3:45 p.m.
The development of efficient spatial methods and the widespread availability of spatially referenced data have changed the landscape of exposure assessment in disease etiology. Most spatial-based exposure-disease assessment approaches require knowing where study participants are located in space and time, and linking that information to spatially-referenced data that estimate potential for exposure.
Such approaches not only allow for estimation of both current and past exposures, but are efficient alternatives to traditional methods of collecting exposure data longitudinally, enhancing the utility of existing large cohorts by reverse engineering exposures when improved exposure surfaces become available.
However, while the spatial resolution of exposure surfaces has greatly improved, our ability to locate people in space (with geocoding) has not, and remains a rate- limiting factor in accurate exposure assessment. The effort engaged in improving spatially referenced exposure data is compromised without addressing the problem of misclassification of the location of people (geocoding uncertainty).
We have developed a geocoding approach that records the exact spatial extent of the final geocode that fully describes the area in which the study participant is known to be located, and a novel statistical approach to incorporate variability in exposure and covariate data based on spatial extent. These combined approaches can be extended to appropriately incorporate spatial uncertainty from geocoding misclassification into the overall exposure assessment model.
We are currently testing the inclusion of geocode uncertainty into an exposure assessment model, applying the approach in a study of the role of pesticides in childhood leukemia – an example which has a high resolution exposure surface, high variability in geocode accuracy, and is an excellent example of some of the worst case scenarios in assuming uniform spatial certainty of geocodes.
Using Geospatial Approaches to Evaluate the Association between Individual Serum PBDE Levels and Residential Proximity to Solid-Waste Facilities
Time: 3:45 - 4:00 p.m.
Polybrominated diphenyl ethers (PBDEs) are persistent environmental contaminants and have been implicated as potential carcinogens. U.S. biomonitoring data indicate widespread human exposures but the routes of exposure have not been fully explicated. Many consumer products treated with PBDEs are discarded into solid waste facilities, offering a potential reservoir for exposure. Our objective was to evaluate the association between residential proximity to solid waste facilities and serum PBDE levels among California women participating in an on-going breast cancer study. Blood samples collected from 923 breast cancer-free women were assayed for PBDEs. Information on solid waste facilities, including latitude/longitude, was downloaded from California's Solid Waste Information System (SWIS). Participants' residences at the time of blood collection were geocoded to a latitude/longitude and distances to SWIS facilities were estimated using ArcGIS. Generalized linear models were used to examine the association between serum levels of the three most common congeners (BDE-47, BDE-100, BDE-153) and distance to the nearest SWIS facility, adjusting for relevant covariates. Results: Compared to participants living >10 km from a SWIS facility, those living within 2 km had 45% higher BDE-47 and BDE-100 levels, and those living between 2-10 km had 35% higher BDE-47 and 29% higher BDE-100 levels. Living close to solid waste sites may be related to higher levels of serum BDE-47 and BDE-100, but not BDE-153. Residential proximity to solid waste sites may serve as a useful proxy for population environmental exposures to PBDEs, with associated implications for mitigating exposures through improved waste management.
Time: 4:00 - 4:15 p.m.
Animal models and epidemiologic studies suggest that exposure to light at night (LAN) may disrupt circadian patterns and decrease nocturnal secretion of melatonin, which may disturb estrogen regulation, possibly leading to increased breast cancer risk. We examined the association between residential outdoor LAN and breast cancer incidence using data from the nationwide US-based Nurses' Health Study II cohort. We followed 109,672 women from 1989-2013. Annual LAN exposure was measured using satellite data, which provides time-varying data for a composite of lights from persistent nighttime illumination at a ~1 km2 scale. Incident invasive breast cancer cases were confirmed by medical record review. We used Cox proportional hazard models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs), adjusting for anthropometric, reproductive, lifestyle and socioeconomic risk factors. Over 2,187,424 person-years, we identified 3,549 incident breast cancer cases. In multivariable models, compared to women in the lowest quintile of LAN, those in the highest quintile had a 14% increased risk of breast cancer (95% CI 1.01, 1.29). The association between LAN and breast cancer was more pronounced among premenopausal women (HR per IQR increase in LAN for premenopausal women: 1.07; 95% CI 1.01, 1.14; postmenopausal women: 1.00; 95% CI 0.91, 1.09; p for interaction: 0.08). The association between LAN and breast cancer was strongest in past and current smokers (p for interaction: 0.008) We observed a positive association between residential outdoor LAN and breast cancer risk in this cohort of adult women. Impact: Our results suggest that LAN exposure, or its correlates, may play a role in breast cancer risk.
Time: 4:15 - 4:30 p.m.
Epidemiologic studies that use residential addresses for environmental exposure assessment are subject to misclassification due to both positional error in locations and changes in residence over time. In a rural cohort, we calculated positional error in meters (m) between gold-standard rooftop coordinates at participant residences and two alternatives: E911 locations used for emergency response at the intersection of the private and public road leading to the address, and geocodes from a commercial street database. In an urban/suburban California cohort, we established participant residence histories and compared long-term exposures estimated using multiple historical addresses to exposures based only on enrollment residence. In both studies, we estimated the validity of area-based and point-sourced environmental (agricultural and air pollution) exposures proximal to residences and the expected influence on relative risk estimates in etiologic studies of cancer.
In the rural cohort, E911 locations were more accurate than address-matched geocodes (median error 39 vs. 90m). Sensitivity of exposure to common crops within 500m of the home was >95% regardless of geo-location method. However, the poor specificity of address-matched geocodes substantially attenuated odds ratios (ORs) (e.g., corn crop exposure <500m ORobserved=1.47 vs. ORtrue=2.0). We will also present our ongoing evaluation in California, including the impact of residential mobility on assessment of air pollutants with high spatial variability.
Exposure misclassification arising from location error is dependent on geocoding method and the prevalence and spatial scale of exposure. We demonstrate the influence of residence accuracy on geographic-based exposure assessment and epidemiologic inference.
Time: 4:30 - 4:45 p.m.
Studies of neighborhood context often attribute built environment measures to individuals by the type, number, or quality of selected features in proximity to static locations where participants live or work. Yet people do not spend all their time in one location. Taking inspiration from exposure science theory, we focus on the contact between a receptor (i.e. a person) and a stressor (i.e. a health-detrimental or health-supportive environment). The integration of global positioning systems (GPS) and geographic information systems (GIS) allows for spatially and temporally continuous objective measures of receptor-stressor contact. We define three types of GPS-GIS-measured contact. (1) Access is the alignment of person traits (e.g. location, demographics, attitudes) with environment traits (e.g., for food outlets, type and price of food sold, hours of operation, and the norms governing who shops there). (2) Exposure is defined by proximities that allows for a person to come into sensory contact with a stressor. (3) Use/selective mobility occurs when participants visit specific stressors. Using fast food restaurants (FFRs) in King County, WA, as an example, we introduce metrics for measuring receptor-stressor contact as well as the duration of contact. On average, people pass within 100m of 8 FFRs daily, accounting for 17 minutes per day per person. Spending 17 minutes or more was also associated with an increased odds of visiting an FFR. Additional metrics are presented and discussed. Making distinctions between types of contact and measuring duration of contact is necessary for better understanding how environmental contexts affect health and behavior.
Time: 4:45 - 5:00 p.m.
The neighborhood obesogenic environment has been associated with health behaviors. Despite limited research showing the significance of neighborhood social factors in influencing prostate cancer risk, no research has evaluated whether changes in the neighborhood environment, either by physical moves to different neighborhoods or experiencing neighborhood redevelopment or neglect, affects cancer. We examined change in the neighborhood obesogenic environment in relation to prostate cancer risk among 42,169 male participants of the Multiethnic Cohort, including 4,652 prostate cancer cases, residing in Los Angeles county between 1993 and 2010. Specifically, we evaluated four distinct neighborhood obesogenic environment factors based on Census block group level data: urban environment, unhealthy food, mixed land use, and the number of parks, as well as neighborhood socioeconomic status (nSES). Associations of change over time in the four neighborhood factors and nSES with prostate cancer risk were estimated, adjusting for age, race/ethnicity, prostate cancer risk factors, and baseline levels of neighborhood obesogenic factors. Stratified analyses were conducted by racial/ethnic group and moving status. A decline in nSES was significantly associated with lower prostate cancer risk among Latinos. An increase in nSES was linked with higher prostate cancer risk among Japanese movers. We also observed a change to a less urban neighborhood was associated with higher risk of prostate cancer. Changes over time in neighborhood obesogenic factors may influence the risk of prostate cancer with differences in effects across racial/ethnic groups and between movers and non-movers. Our results show the importance of neighborhood environment in studies of prostate cancer risk.
Session 7: Geography of Health Care Delivery
Spatial Determinants and Geographic Constructs for Assessing Cancer Care Delivery: Do We Know Where We Are Going and How We Will Get There?
Time: 3:15 - 3:45 p.m.
While evidence demonstrates independent effects of spatial factors, such as travel time, and aspatial factors, such as race/ethnicity, on cancer care delivery, our understanding of how these factors interrelate to impact geographic regions and population subgroups across the cancer control continuum is not well-developed. Examining the state-of-the-evidence for the role of geography in cancer care delivery in order to identify gaps and refine conceptual models, is a critical need for guiding meaningful advances in cancer control.
This work was a review and synthesis of published and emerging evidence that focused on measures related to geographic accessibility and geographic units for measuring variation in cancer care delivery overall, and in relation to population and clinical subgroups. Specific comparisons were made for the effects of travel time and per capita provider supply on utilization in the screening, treatment, and surveillance phases of the cancer control continuum. Variation in geographic access to specific cancer services was examined overall and in relation to utilization and outcomes. Crude preliminary analyses compared hospital service areas based on: 1) total inpatient care, 2) cardiovascular surgery, and 3) cancer-directed surgery by measuring localization index (LI), which measures border crossing of service areas for care utilization (LI=1.0 denotes no border crossing; i.e. all patients received care within their service area).
Accessibility and service area analyses have revealed differences in utilization of care and outcomes that seem to be modified by sociodemographic and clinical characteristics, with effects that may vary across the cancer control continuum. For example, the preponderance of studies examining travel time and breast cancer, reported no significant effect for screening or surveillance mammography, but greater use of mastectomy with longer travel times. No effect was seen on breast cancer stage of diagnosis. Excess travel time to more specialized services, such as breast MRI versus mammography was related to sociodemographic factors, but not risk factors. Differences in travel time between the closest facility and that actually used were small for all but rural women. When comparing the constructs of hospital service areas, those based on all discharges had a localization index of 65%, for cardiovascular surgery, 41%, and cancer surgery, 29%.
Heterogeneity of spatial effects by sociodemographic, clinical, and measurement-based factors suggests the need to develop a unifying framework to guide conceptual and methodological advances to develop targeted cancer control efforts and healthcare delivery models.
Patterns of Patient Flows Across Health Service Areas for Lung Cancer Surgery
Presenting Author: Bian Liu, Ph.D., Icahn School of Medicine at Mount Sinai
The delivery of lung cancer surgical care varies spatiotemporally. Many patient- and hospital-level factors influence the selection of surgery and the subsequent outcomes. We aimed to quantify bilateral patient flows across health service areas (HSAs) for lobectomy, the recommended surgical resection for lung cancer patients, and the impact on outcomes. The New York State Statewide Planning and Research Cooperative System inpatient database (2007-2012) was used to selected lung cancer patients who underwent lobectomy by VATS or open techniques. Logistic regressions were used to examine patient- and hospital-level factors associated with surgery occurred within or outside of patients' HSAs. There were 9,577 lobectomies, 17% of which occurred outside of the patient's HSAs; the percentage varied spatially across the eight HSAs (3.5% to 29%) but remained stable from 2007 to 2012 (16%-18%). The odds of travel-outside the HSAs vs stay-in decreased with age (ORadj: 0.99; [95% confidence interval: 0.98-0.998]), in non-whites (ORadj: 0.45 [0.39-0.53]) and with Medicaid (ORadj: 0.38 [0.27-0.53]), while it increased with hospital lung surgery volumes (ORadj: 2.03 [1.83-2.26]), urban (ORadj: 3.30 [2.40-4.53]) and teaching (ORadj: 2.23 [1.36-3.67]) hospitals. Travel-out patients were more likely to have VATS than open techniques (ORadj: 1.31 [1.17-1.47]), but their odds of having complications (ORadj: 0.89 [0.78-1.00]) or in-hospital mortality (ORadj: 1.54 [0.83-2.87]) were similar to stay-in patients. While a majority of lung cancer patients utilized their HSAs for lobectomy, patient's insurance is an independent determinant of the choice. Patients travel farther to be treated by high volume, teaching hospitals that perform VATS.
Time: 4:00 - 4:15 p.m.
Colon cancer is primarily treated by surgical resection but laparoscopic surgery (LS) varies widely, potentially leading to disparities in availability, access, and cost. We sought to identify patient, hospital and geographic characteristics associated with LS and quantify hospital and geographic variability. Claims data for 5,446 patients with non-urgent/non-emergent colon cancer resection from the 2009-2011 SEER-Medicare linked database supplemented with county demographic data were analyzed using Bayesian cross-classified multilevel logistic models to calculate adjusted odds ratios (aOR) and confidence intervals (CI) for patient/surgeon, hospital and geographic characteristics, and unexplained variability (predicted vs. observed values) using adjusted median odds ratios (aMOR) for hospitals and counties with 20+ patients. Patients from 156 counties had surgeries at 836 hospitals with 39% receiving LS. Several patient-level factors were associated with LS, including Medicare/Medicaid dual enrollment (aOR, 0.69; 95% CI, 0.57-0.83). Hospital size was positively associated with use of LS yet significant unexplained variability remained at the hospital (aMOR = 2.73, P < .001) but not county level (aMOR = 1.36, P = .07). Among units with 20+ patients, 15% of hospitals and 14% of counties were below the 95% credible interval for predicted numbers of patients receiving LS. Unexplained hospital-level variation in LS persists after adjustment for multilevel correlates. Hospitals and counties vary widely in patient receipt of LS for colon cancer. Determining sources of hospital-level variation and decreasing disparities in utilization among poor insured patients may help increase utilization to maximize health outcomes and reduce cost.
Time: 4:15 - 4:30 p.m.
Geographic and sociodemographic disparities in cancer incidence are well documented. Less is understood about where underserved populations receive cancer care as it relates to residential factors, particularly among groups newly insured through the Affordable Care Act. Using the New Jersey State Cancer Registry, we examined the relationship between residential and diagnosis/treatment locations among non-elderly (ages 21-64) invasive cervical (n=784), breast (n=13,440), and colorectal (n=4,990) cancer cases diagnosed in 2012-2014. We explored whether geospatial relationships differed by stage, insurance type, race/ethnicity, and zip code tabulation area (ZCTA)-level characteristics. Residential zip code location was available for >99% of cases. However, 28% and 14% were missing diagnosis and treatment locations respectively. Of the 10% with out-of-state treatment locations, the majority (84%) were privately insured. A significantly higher proportion of Medicaid insured cases were diagnosed (13% vs 7%) and treated (12% vs 6%) within their residential ZCTAs compared to privately insured cases. Much higher proportions of NH-blacks and residents of high-poverty areas received diagnosis/treatment within their residential ZCTA compared to other groups. Minority and low-income cancer cases are more likely to utilize care within their immediate neighborhoods compared to NH-whites and privately-insured patients. Findings indicate a need to address missing geospatial information and obtain multi-state data to accurately assess patterns of care where medical neighborhoods cross state boundaries. Understanding geographic patterns of cancer care among diverse populations will inform the development of policies and interventions aimed at improving access to care and care coordination.
Time: 4:30 - 4:45 p.m.
The Cancer Centers designated by the National Cancer Institute (NCI) form the "backbone" of the cancer care system in the United States. Awarded via a peer-review process and being re-evaluated every 3 to 5 years, an NCI Cancer Center receives substantial financial support from NCI grants. This chapter evaluates geographic disparities in spatial accessibility of the NCI Cancer Centers and examines various planning scenarios to minimize the inequalities in accessibility. Two types of optimization scenarios are considered in this exploratory research for the objective of minimizing inequality of spatial accessibility. One is to allocate additional resources to existing NCI Cancer Centers, and the other is to designate new centers from the most likely candidates (e.g., existing academic medical centers or AMCs). Quadratic programming and integer programming are used to solve different optimization problems. Several scenarios are used to illustrate the impact of optimization on reducing geographic disparities. Results from the study may inform the public policy decision making process in planning of the NCI Cancer Centers towards equal accessibility.
Session 8: Social Environment and Cancer Risk
Building a Place-based Resource for Investigations of Chronic Disease Prevention
Time: 3:15 - 3:45 p.m.
There has been an exponential increase in studies investigating the health impact of neighborhoods and other important place-based contexts over the past ten years. However, the majority of these studies are limited by crude measures of residential environments that ignore important nuances related to capturing the: 1) physical/built infrastructure, resources for health promotion, and social processes at play, and 2) dynamic nature of these features and processes over time. In this session, I will provide two examples of building comprehensive, geospatially referenced databases from a cohort of middle-aged U.S. adults and from a cohort of adults in a managed care health plan. I will then highlight specific findings in relation to chronic disease prevention and management. I will conclude with a discussion of the challenges and opportunities of using data to inform place-based strategies to improve health and reduce health disparities.
Applying the Neighborhood Environment-Wide Association Study (NE-WAS) Approach to Neighborhood Influences on Physical Activity among Older Adults
Time: 3:45 - 4:00 p.m.
Physical activity prevents cancer. Studies of neighborhood context as a correlate of physical activity typically select theoretically informed environmental characteristics, analogous, in a genetic context, to a candidate-gene study. We conducted a pilot agnostic 'Neighborhood Environment-Wide Association Study (NE-WAS)' approach to studying neighborhood influences on physical activity, analogous to a Genome Wide Association Study (GWAS) approach. NYCNAMES-II was a telephone survey of 3,497 adult residents of New York City aged 65-75. Using Geographic Information Systems and previously compiled measures of New York City's social and physical environment, we constructed 337 measures of neighborhood context for each subject. We explored survey-weighted regression models, lasso regression, and random forest approaches to select the neighborhood measures most predictive of each of 1) total physical activity, 2) gardening, 3) walking, and 4) housework. Results Proportion of residents living in extreme poverty was most strongly associated with total physical activity (estimated decrease of 0.85 Physical Activity Scale for the Elderly units (95% CI: 0.56, 1.14) per 1% increase in proportion of residents living in extreme poverty). Only neighborhood socioeconomic status and disorder measures were associated with gardening, whereas a broader range of measures was associated with walking. As expected, no neighborhood measures were associated with housework after accounting for multiple comparisons. Machine learning approaches were sensitive to tuning parameters. Conclusions A systematic approach to comparing neighborhood measures to activity measures revealed patterns in the domains of neighborhood measures associated with activity. Impact The NE-WAS approach appears promising.
Time: 4:00 - 4:15 p.m.
The size of the racial disparity in breast cancer (BC) mortality varies by state. Such a geographical variation hasn't been explained or related to disparities in other cancer sites. We examined mortality rate ratios for breast, colorectal, prostate and lung cancer across the 50 largest metropolitan statistical areas (MSAs) in U.S. to (1) determine whether mortality rate ratios were similarly elevated in MSAs for the four cancers, (2) explore potential explanatory factors. 5-year (2008-2012) cancer mortality data was obtained from the CDC. Based on the MSA level age-adjusted mortality rates, Black to White rate ratios were calculated. Correlation analyses were used to explore the associations between the size of disparities of different cancers as well as their relationships with socioeconomic predictors. The racial disparity in BC mortality was significantly correlated with the ones in colorectal/prostate cancer. The colorectal cancer disparity was significantly and positively correlated with disparities in prostate/lung cancer. Percent Black population was significantly associated with the size of the disparity for breast/colorectal/prostate cancer. Black isolation index was moderately associated with disparities in breast/prostate cancer, while the incarceration rate was significantly associated with the breast/colorectal cancer disparity. Dissimilarity index was significantly and moderately associated with the lung cancer disparity. There are relationships between the magnitude of cancer disparities across MSAs, and socioeconomic characteristics of MSAs are significantly related to these disparities. Determining causes of geographical variation could lead to new strategies to reduce gaps and facilitate knowledge translation.
Time: 4:15 - 4:30 p.m.
American Indians (AI) have one of the highest obesity rates in the US. Risk from chronic disease is high in AI groups. There is growing awareness in the general population that built and social environments play a role in the development of obesity. Environments that do not promote healthy activity and eating have been linked in multiple investigations to high levels of obesity. The environment offers promise to control obesity at the population level. Unfortunately, when faced with high obesity levels, many people attempt to lose weight through formal weight loss groups. As might be expected, these behavioral strategies are quite difficult to maintain over time and many initially successful individuals regain or overshoot their previous weight. Current intervention strategies, therefore, must consider changing environmental factors to make health easier, achievable, and maintainable, or even to serve as the primary intervention. Quantification of the built and area level environments of reservation life is a necessary first step in this line of research. Our long term goal is to implement and test a multilevel intervention to reduce obesity among Native people living on reservations. However, in order to conduct this study, we need pilot data on the exact qualities of the reservation environment that are related to obesity. Our first step is to assess the quality of the environment of Native reservations in the Northwest. We used GIS to describe the environments of Native reservations compared to nonreservation settings in the Pacific Northwest. We used the tribal headquarters location as well as the reservation boundaries to identify the important environmental areas to study. We gathered data from ReferenceUSA a commercial repository of businesses, to identify location of food, activity, and healthcare related businesses in five states in the Pacific Northwest: WA, OR, ID, WY, and MT We mapped these two sets of data using ARCGIS, and then statistically compared the existence of available businesses within the boundaries of Native lands. We found that few food related businesses existed within the boundaries of reservations in the Pacific Northwest. Similarly, there were few activity-related resources within the reservation boundaries. Even though several of the reservations were in relatively urban areas, the continued lack of resources for healthy food purchasing and activity performance could contribute to poor eating and sedentary behaviors. We believe that these food and activity poor areas are in part responsible for high levels of obesity among Native people living on reservations. If confirmed, these findings have direct implications for future opportunities to improve environmental conditions on reservations to promote health. Impact. The location of food and activity resources' for tribes has direct impact on choice of intervnetions targets for Native people.
Time: 4:30 - 4:45 p.m.
Neighborhood attributes of the socioeconomic and built environment have been linked to cancer risk, yet little is known about the underlying mechanisms. Potential biological pathways may involve systemic inflammation, adipokines, and insulin resistance. We used multi-level linear regression to examine the associations between the neighborhood obesogenic environment and fasting serum levels of C-reactive protein (CRP), leptin, adiponectin, glucose and insulin among 7,337 Multiethnic Cohort participants, predominately residing in Los Angeles County. Baseline residential addresses (1993-1996) were geocoded and linked to 1990 Census and geospatial data, capturing the obesogenic environment. Models were adjusted for demographics, health behaviors, comorbidities, and medications and stratified by sex. Overall, associations between neighborhood attributes and biomarkers were stronger among women than men and with CRP than the other biomarkers. Among women, residence in neighborhoods of lower socioeconomic status (nSES) or population density were associated with increased CRP levels (Ptrend<0.01 for both). In contrast, among men, living in neighborhoods with higher population density was associated with increased CRP levels (Ptrend<0.01). These significant associations for nSES and/or population density with CRP remained among both women and men after adjustment for body mass index. Weaker associations by sex were observed between biomarkers and neighborhood attributes capturing traffic density, restaurant and retail environments, commuting patterns, recreational facilities, and parks. Our findings provide evidence that neighborhood environments affect inflammatory processes. Systemic inflammation as measured by CRP represents a relevant pathway by which the neighborhood obesogenic environment influence cancer risk, with differential effects by sex.
Time: 4:45 - 5:00 p.m.
Prior studies have documented a link between change in neighborhood condition and weight change, but they have only examined neighborhood changes generated by residential mobility. Applying a difference-in-difference analytic framework to data from the Dallas Heart Study (DHS), a multi-ethnic, population-based cohort in Dallas County, TX, we evaluated the relationship between changes in neighborhood condition and weight change for both movers and non-movers over an approximate seven-year follow-up period. We employed a novel measure of neighborhood condition based on property appraisal data to capture temporally consistent measures of change in neighborhood condition regardless of residential mobility. We observed an inverse relationship between weight change and change in neighborhood condition that was more pronounced for non-movers (1.9 fewer kilograms gained per 1-standard deviation improvement in neighborhood condition) than for movers (1.5 fewer kilograms gained per 1-standard deviation improvement in neighborhood condition). Change in neighborhood condition, independent of the quality and condition of housing structures, is important in the causal pathway linking neighborhoods and weight gain. Our results suggest that public policy interventions which target change in neighborhood through housing policy are important to both non-mover and mover populations. Further our results illustrate the utility of extracting a measure of neighborhood condition from housing appraisal data using methods that leverage the robust economic literature related to urban housing markets.
|4:45 p.m. - 6:00 p.m.||
View agenda for Tuesday, September 13
|8:00 a.m. - 8:45 a.m.||Registration|
|8:45 a.m. - 9:00 a.m.||
Welcome Back and Overview of Conference Day 2
David Berrigan, Ph.D., M.P.H.
|9:00 a.m. – 10:00 a.m.||
Gary L. Ellison, Ph.D., M.P.H. (Moderator)
Genetic GIScience: Toward a Place-Based Synthesis of the Genome, Exposome, and Behavome
Invited Speaker: Geoffrey M. Jacquez, Ph.D., SUNY Buffalo and Biomedware Inc.
Understanding latency is critical when modeling dynamic geographic systems. This presentation describes compartmental models to estimate residence times in states defining disease progression. Two models are developed. The first models carcinogenesis based on the cascade of mutations and cellular changes that lead to metastatic cancer. The second models cancer stages defined by diagnostic criteria for cancer staging. The models are linked by mapping molecular and cellular characteristics of cancer cells to the stage at diagnosis.These models provide:
The approach may be used in disease surveillance and clustering to reveal where people lived when they were vulnerable to exposures that could have caused their disease. It also advances our understanding of disease latency, both for individuals as well as populations. Finally, it links our emerging knowledge of cancer genomics to cancer progression at the cellular level, to individuals and the stage of their cancer at diagnosis, and finally to population-level outcomes describing geographic distributions of cancer in extant populations. This provides the basis for a new synthesis in cancer control and surveillance: genetic geographic information science. An example illustrates application to PANIn pancreatic cancers in a population in southeastern Michigan. Research is needed to (1) apply the approach to different cancers, and (2) extend the models to incorporate our increasing knowledge of the cancer exposome.
Multilevel Approaches to Cancer Etiology and Control: Where Are We Now?
Invited Speaker: Timothy R. Rebbeck, Ph.D., Dana Farber Cancer Institute and Harvard University
Cancer is etiologically complex. Contextual factors including health system, neighborhood or community characteristics, have increasingly been linked to cancer incidence and mortality. In addition, social determinants and processes have been identified as cancer risk factors, including socioeconomic status or self-reported race. Environmental exposures at the level of the individual may be causally associated with CaP. Applied and fundamental investigations have identified a wide array of biologic factors mechanistically involved in carcinogenesis including those of the tumor microenvironment, metabolome, proteome, transcriptome, and genome. Hundreds of novel genetic susceptibility loci have been identified through candidate and genome-wide association studies (GWAS).
Studies of factors at a single level have provided a great deal of insight into cancer etiology. However, it is clear that genomics and risk factors reported to date do not fully explain cancer incidence or outcome. Risk factors studied in isolation and identified by standard approaches are unable to fully explain the complex, multifactorial causes of cancer. A multilevel approach may be required that simultaneously assesses the role of two or more etiological agents within hierarchical levels including the: contextual level (e.g., geocode-linked neighborhood features); individual level (e.g., behaviors, carcinogenic exposures, socioeconomic factors and psychological responses); biological level (e.g., cellular biomarkers, genomic ancestry, and inherited genetic susceptibility). For example, genomic factors may be associated with intermediate traits and contextual factors, which in turn may be associated with disease outcomes including case-control status or aggressive disease. Using this framework, it may be possible to better understand cancer etiology and outcomes, as well as identify population groups in which targeted interventions to reduce cancer incidence or poor outcomes can be focused.
|10:00 a.m. – 10:15 a.m.||Break|
|10:15 a.m. – 11:45 a.m.||
Zaria Tatalovich, Ph.D. (Moderator)
Geovisualization/Geovisual Analytics: Addressing the Cancer Burden
Invited Speaker: Alan MacEachren, Ph.D., Pennsylvania State University
This presentation will focus on advances in Geovisulization and Geovisual Analytics that have the potential to help researchers and practitioners address the cancer burden from multiple perspectives. It will begin with an overview of the related domains of Geovisualization/Geovisual Analytics research and practice, providing selected examples of their application to public health challenges (particularly related to the cancer burden), with attention to research and practice with application in epidemiology, behavioral sciences, health services, surveillance, and cancer survivorship. Then, some Geovisualization and Geovisual Analytics research challenges for the future will be outlined, with an emphasis on big data, heterogeneous information, and reasoning with uncertainty.
Kevin A. Henry, Ph.D.
|11:45 a.m. - 1:00 p.m.||Lunch
Cafeteria On Site
|1:00 p.m. – 2:30 p.m.||
Concurrent Sessions 9 - 11
Session 9: Geo-Visualization of Cancer Burden
Session Chair: Dave Stinchcomb, M.S., M.A., Westat Inc.
Opening Remarks from Session Chair
Visualizing the Diffusion of Digital Mammography in New York State, 2004-2012
Time: 1:10 - 1:25 p.m.
Innovations in cancer detection and treatment are introduced in an uneven fashion, typically originating in research and teaching hospitals and eventually diffusing to smaller community hospitals. One such major innovation was the replacement of film mammography with digital mammography in the early 2000s. We quantify the diffusion of digital mammography within the state of New York from 2004, when approximately 10% of all mammogram images were digital, to 2012, when the figure was over 90%, using Medicare claims data from a sample of over 100,000 cancer-free women for whom the zip code of residence was known. The percentage of mammograms that were digital was calculated for each zip code for each 12-month trailing period in the study. The data were smoothed using a flexible spatial filter to capture a minimum of at least 100 mammograms centered on each zip code. Viewing the resulting smoothed percentages as a series of sequential maps reveals the times, locations, and rates at which digital mammography was adopted. Early adopters tended to be in areas with younger, upscale populations and communities with teaching hospitals, but there were individual exceptions; for example, the small Binghamton market was years ahead of other upstate areas. This type of analysis can help us better understand the persistence of disparities in cancer, as differential access to technology can translate into differences in incidence and survival. The process is now being repeated with the introduction of 3-D digital tomography. Health care providers are likely interested in seeing where they fall on the innovation spectrum; maps like these could help accelerate future innovation.
Time: 1:25 - 1:40 p.m.
The epidemiology of prostate cancer (CaP) in the US is influenced by patient demographics, community, and provider characteristics which vary geographically. We explore this variation using incidence rates (IR) to identify areas of highest risk and deconstruct the relative influence of community and provider characteristics in the state of North Carolina (NC). Methods: We examine age-adjusted IR data from the NC cancer registry and county-level data (number of primary care & urology physicians per capita, race, education/insurance/employment) from the Area Resource File to describe disease risk in NC. Spatial clustering analysis methods were applied to evaluate spatial autocorrelation and identify clusters with significant high (HH) and low (LL) incidence relative to the surrounding area. Analyses were used to compare defining characteristics between clusters. Results: The geographic distribution of CaP Incidence in NC is not random (p < .001). Local Indicator of Spatial Association (LISA) analysis identified 12 HH clusters and 11 LL clusters. The HH clusters had a significantly larger proportion of black residents, fewer urologists and primary care physicians per capita, and lower SES than LL clusters. Conclusion and Impact: This study suggests that county-level socioeconomic and racial variables, as well as access to primary and specialty care, are associated with geographic disparities in prostate cancer. These observations support additional work with spatial regression to further characterize factors affecting disparities in incidence and outcomes to better identify targets for intervention.
Time: 1:40 - 1:55 p.m.
Regional disparities exist in rates of lung and bronchus cancer within the United States. Underlying reasons may include access to care, screening utilization, quality of treatment, socioeconomic, or cultural (including lifestyle) characteristics, or some combination of these. The mortality-to-incidence ratio (MIR) provides a relative measure of cancer survival and a means for identifying regional disparities that is easily derived from traditional cancer incidence and mortality data. Mortality and incidence rates for lung and bronchus cancer for 49 states (i.e., excluding Nevada) and the District of Columbia (D.C.) were obtained from the National Cancer Institute State Cancer Profiles. Rates were given as 5-year averages for the years 2008-2012. MIRs were calculated by dividing the age-adjusted mortality rate by the age-adjusted incidence rate per 100,000 for a given state. States were ranked by ascending MIR and divided into deciles. These categories were mapped using ArcGIS software in order to highlight the geographic variation in lung and bronchus cancer survival. A mean MIR of 0.75 was observed for the 49 states and D.C. MIRs ranged from 0.65 to 0.83. The five states with the lowest relative survival from lung and bronchus cancer, as measured by the highest MIRs, were all in the South: Arkansas, Oklahoma, Tennessee, Alabama, and Louisiana. The states with the lowest MIR, indicating the highest relative survival, were mostly in the Northeast: Connecticut, New York, Massachusetts, New Jersey, and Hawaii. The calculation and mapping of state level MIRs for lung and bronchus cancer revealed that many southern states have the lowest relative lung and bronchus cancer survival. Future research should focus on identifying the underlying risk factors for poor lung and bronchus cancer outcomes. This may entail exploring variation at smaller scales (e.g., counties).
Time: 1:55 - 2:10 p.m.
Session 10: Geo-Surveillance of Cancer
Time: 1:00 - 1:30 p.m.
This presentation will (1) explore the use of wearable environmental sensors to reduce cancers in vulnerable populations such as infants and children; and (2) describe a research collaboration with NAACCR that seeks to develop novel methods (geospatial cryptography) that will enhance sharing and analysis of geospatial cancer data among disease registries and researchers.
A Spatiotemporal Cluster Detection Analysis of Invasive Cervical Cancer Incidence, By County In The State Of Maryland
Time: 1:30 - 1:45 p.m.
Invasive cervical cancer (ICC) is a highly preventable cancer; yet some women in the state of Maryland, like in other parts of the United Sates, continue to develop the disease. We hypothesized that there is space-time variation in ICC incidence. Methods: Data on all 2172 ICC cases reported by the Maryland Cancer Registry between 2003 and 2012 was utilized. Our analysis involved a retrospective space-time cluster detection analysis (SaTScan), searching for clusters of both high and low rates. All analyses were conducted at the county level, using 2-year aggregates of time and adjustments were made for age distribution and race using census data as well as percentage of females per county receiving Pap test using Behavioral Risk Factor Surveillance Data. Results: Median age of cases was 50 years (IQR: 40-63.5) and at diagnosis, cases had mainly grade 2 or 3 cancers (83%) with no metastasis (88%). After adjustments, four significant clusters were identified. A cluster of higher than expected rates comprising 3 counties (Baltimore city, Anne Arundel and Howard); RR 1.52, p=0.011 was observed for the period 2011-2012. In addition three significant clusters of lower than expected rates were observed for the period 2003-2004 in Cecil county (RR 0.21, p=0.011), 2005-2008 in Somerset county (RR 0.063, p <0.001) and 2011-2012 in Fredrick county (RR 0.25, p<0.001). Conclusion and Impact: Overtime some counties have experienced significantly lower than expected rates of disease. However there is a more recent cluster of disease that is not explained by age distribution, racial composition or the percentage of females screened for cervical cancer and this cluster may require targeted efforts to decrease disease rates.
Time: 1:45 - 2:00 p.m.
Finding spatial clusters, i.e., places with unusually high disease rate, is an important task for the geo-surveillance of cancer. Many current tools (e.g., NCI GeoViewer) use spatial auto-correlation map-visualizations for manual searching of spatial clusters. Since manual searches are expensive for large datasets, other tools (e.g., SaTScan) use algorithms to automatically identify statistically significant spatial clusters with simple footprints (e.g., circles) to reduce manual labor. However, SaTScan fails to detect many interesting spatial clusters. For example, towers (or chimneys) emitting carcinogenic material may lead to a ring-shaped cluster surrounding the source. In addition, related lung-cancer clusters related to vehicular emissions may lead to linear footprints along high-traffic transportation corridors (e.g., roads, rivers, etc.). We recently proposed new algorithms for detecting (statistically significant) clusters with ring-shaped footprints. The proposed algorithms outperformed SaTScan in a case study with dataset describing the legionnaire's disease outbreak in New York in 2015, where a cooling tower that was determined to the source the outbreak by public health officials. Our algorithm identified a statistically significant ring containing the location of the source. However, the source was not contained in any cluster identified by SaTScan. We have also proposed new algorithms for detecting (statistically significant) clusters with linear footprints. In case studies with pedestrian fatality datasets, our algorithms identified several statistically significant spatial clusters with linear footprints. However, SaTScan missed all but one statistically significant cluster with linear footprints. We acknowledge support from the USDOD, the NSF and the University of Minnesota.
Time: 2:00 - 2:15 p.m.
Colorectal cancer is one of the most prevalent cancers in the United States. Cluster detection methods have been applied to colorectal cancer previously but rarely done in New York City (NYC). These analyses may yield a better understanding of how cancer incidence concentrates in certain areas and insight into key environmental determinants. The objective of this study was to detect geospatial clusters of colorectal cancer incidence in NYC. We used data on gender-specific colorectal cancer incidence of five NYC boroughs at the zip-code level from the NY State Cancer Registry in 2005-2009. A spatial scan statistic based on the unadjusted discrete Poisson model was used to identify statistically significant (p <0.05) in NYC, individuals had 19-21% greater risks compared to outside the cluster. Of two low-risk clusters (p<0.05), individuals had 20-25% lower-risks, compared to outside the cluster. For women, of three high-risk clusters, individuals had 13-92% greater risks than individuals outside the cluster. In one low-risk cluster, individuals had a 17% lower-risk, relative to outside cluster. For both gender, high risk clusters appeared in Bronx and Brooklyn, whereas low risk clusters emerged in Manhattan and a few areas in Queens. Colorectal cancer incidence spatially clustered at the zip-code level in NYC. Future research should examine the geographic stability of identified colorectal cancer clusters over time.
Time: 2:15 - 3:30 p.m.
Prostate cancer is one of the most common cancers diagnosed among males, and is an important public health issue in Pennsylvania. The incidence rate and mortality vary substantially across geographical regions (counties) and over time (years). The widely-used Cox Proportional Hazard (PH) model does not apply due to the violation of PH assumption. In this work, we propose to use Bayesian accelerated failure time (AFT) models to analyze prostate cancer survivorship by incorporating random effects with multivariate conditional autoregressive (MCAR) priors for taking spatial temporal variation into account. The models are fitted based on Monte Carlo Markov Chain (MCMC) technique under the Bayesian framework. Extensive simulations are performed to examine and compare the performances of various Bayesian AFT models with MCAR priors. The criteria for model selection via the deviance information criterion (DIC) is also evaluated in the simulation study. Finally, we implement our method to the prostate cancer data obtained from the Pennsylvania Cancer Registry which includes all reported prostate cancer diagnosed and death cases by county from years 2000-2011.
Session 11: Geo-Statistical Methods and Models for Cancer Control
Time: 1:00 - 1:30 p.m.
Cancer prevention and control is founded on inferential tools that should highlight important etiological and behavioral patterns in the variation of the disease. To this end it can be fundamentally important to consider a model-based approach to such disease analysis. A model-based approach is the most flexible tool in the inferential box. It allows the inclusion of multiple observed effects within the inference framework, something that testing-based paradigms do not provide. In addition, it can adjust for unobserved effects (confounding). Within a modeling framework, geospatial information can play a significant role in highlighting risk effects not observable in time based studies (cohort or longitudinal studies).
Bayesian disease mapping addresses these issues by focusing on hierarchical modeling of disease risk effects (e.g. clusters, hot spots, ecological relations) from a spatial view point while allowing sensible inference to be made. Confounding can easily be included within these models via predictor adjustment and random effects. These latter effects can be spatially correlated or uncorrelated and can be seen as latent effects underlying the risk. An example of the analysis of multiple cancer outcomes where between-disease shared effects are modelled and an optimal model selection strategy is pursued.
Exposure Modeling of Erythemal Ultraviolet Radiation: Spatiotemporal Downscaling Using Geostatistics
Time: 1:30 - 1:45 p.m.
Ultraviolet B (UV-B) radiation, the primary source of vitamin D for most humans, is associated with cancers. Current U.S. UV exposure models are limited in spectral, spatial, and/or temporal resolution. Area-to-point (ATP) residual kriging is a geostatistical method that can be used to create a spatiotemporal exposure model. A stratified ATP residual kriging approach was used to predict average July noon-time erythemal UV (UVEry) (mW/m2) from 1998-2012 by downscaling National Aeronautics and Space Administration (NASA) Total Ozone Mapping Spectrometer and Ozone Monitoring Instrument remote sensing images to a 1 km spatial resolution and incorporating ancillary data (aerosol optical depth [AOD], surface albedo, cloud cover, dew point, elevation, latitude, ozone, surface incoming shortwave flux, year, and/or interactions [e.g., year and AOD]). Modeling was performed separately within nine U.S. regions. Cross-validation was used to compare ATP models and NASA grids to UV-B Monitoring and Research Program (UVMRP) measurements (gold standard) by calculating mean absolute errors (MAEs) and root mean square errors (RMSEs). From 1998-2012, ATP models more accurately estimated UVEry at UVMRP stations on average compared to NASA grids in the Northeast (average MAE for ATP, NASA: 13.9, 17.4; average RMSE 16.5, 19.2), Mid-Atlantic (MAE 14.8, 14.9; RMSE 15.8, 16.4), and Southeast (MAE 25.3, 36.9; RMSE 30.6, 43.3). Preliminary results indicate ATP residual kriging can provide accurate fine-scale UVEry estimates. This spatially and temporally varying exposure model will be applied to epidemiologic studies to elucidate the role UVEry may play in cancer risk.
Time: 1:45 - 2:00 p.m.
Preliminary studies indicate that populations living near oil and gas (O&G) wells have a higher risk of childhood leukemia and other adverse health outcomes compared to populations living without O&G wells. While these studies used GIS methods to determine inverse distance weighted well counts around homes, they have not accounted for changes in intensity. Our objective is to use GIS methods to build an industrial-activity model to estimate the intensity of O&G activities surrounding homes over relevant exposure periods. We obtained dates for O&G activities such as drilling, fracturing, and production for each O&G well in Colorado. Additionally, we acquired monthly production data and other variables relevant to air emissions. This information was used to build a GIS model that estimates the wind-adjusted intensity of O&G activities for any given month. By categorizing wells into activity phases, we can produce the high degree of spatial and temporal heterogeneity in intensities expected around these sites. Our model allows us to evolve from a spatial model to a spatiotemporal one where we can assign time-varying exposure scores due to O&G development. Changes in activities, pollutants, and wind direction can have a large impact on the environmental exposure one receives from a particular source. This is particularly important when investigating associations with health outcomes such as childhood cancers where the exposure window for developing cancer may be fairly specific and relatively short. Our space/time GIS model should allow us to advance research investigating the association between decentralized sources of air toxics, such as O&G development, and childhood cancers.
Time: 2:00 - 2:15 p.m.
Age-period-cohort (APC) models are frequently used to study population trends in vital rates. However, tools for a comprehensive evaluation of geographic heterogeneity of model parameters are not well developed. We outline a general and scalable method for modeling rates in geographically-organized regions. We apply our approach to state-level variation in all-cause mortality trends for white non-Hispanic women under age 50; recent studies have noted an overall rise in mortality for this group in the United States. Thus, state-level estimates may provide new insights into this emerging public health issue. We allow region-specific parameters to be correlated spatially (e.g., among neighboring states), and to one another (e.g., allowing for correlations between state-specific mean rate and birth cohort trends (net drift)), via a random-effects formulation using a generalized multivariate conditionally auto-regressive model. This allows parameters to vary spatially, while retaining interpretability. Estimation is carried out using Bayesian methods in JAGS. Longitudinal age trend (LAT) (i.e., trend with age) exhibited significant geographic clustering with 95% posterior CI for spatial auto-correlation parameter of (0.19, 0.85). LAT varied between 5% per year in northern mountain states, and 9% per year in East North Central states. Birth cohort trend (net drift) was not significantly clustered and it varied between -0.7% per year (-0.9%, -0.5%) in New York, and 3.0% per year (2.6%, 3.5%) in West Virginia. National APC analyses that do not account for spatial heterogeneity in model parameters can over-generalize findings. Our model provides more nuanced estimates, aiding in performing reliable inference.
Time: 2:15 - 2:30 p.m.
Local health behavior and outcome data are important for program planning and evaluation, resource allocation, and policy-making. Although interest in local-level data is growing, surveys powered to make national or state-level inferences are rarely adequate to support direct estimation for small areas such as counties. Model-based small area estimation techniques have shown to be a viable solution. Using data from the 2014-2015 SC Adult Tobacco Survey, we examined several tobacco-related outcomes at the county level using a spatial multilevel, post-stratification approach. Specifically, we used a 2-level, random-intercept multilevel model and a spatially intrinsic ICAR model. Stratum-specific (race, age and sex) estimates for each county were created and then averaged based on stratum-specific population estimates obtained from the U.S. Census. The estimated prevalence of current smoking among SC adults ranged from 9-36%, and was lowest in Lee and Edgefield counties, both rural counties with a high proportion of African-Americans. The percentage of adults reporting ever trying an e-cigarette ranged from 4-29%, and was highest in Colleton County, a rural and majority-white county. Among smokers, 22-88% reported being advised to quit, and 36-92% reported a desire to quit. Data from the SC Adult Tobacco Survey was used to develop county-level estimates of >20 tobacco-related outcomes using a spatial multilevel, post-stratification approach. The results showed heterogeneity in smoking behaviors across the state along with marked spatial correlation. Our approach could be adapted to other states to produce accurate small area estimates for tobacco use and other health behaviors.
|2:30 p.m. – 2:45 p.m.||Break|
|2:45 p.m. – 4:15 p.m.||
Concurrent Sessions 12 - 14
Session 12: Spatial Analyses Using Surveillance and Health Systems Data
Time: 2:45 - 3:15 p.m.
The Environmental Health Institute at the Geisinger Health System has been engaged in epidemiologic research using electronic health records since 2007. Geisinger has > 6 hospitals and > 40 community outpatient clinics in over 40 counties in central and northeastern Pennsylvania; its EHR began in 2001; its >450,000 primary care patients represent the general population in the region; and the 40 counties have a range of communities from urban to rural with a wide range of built and social environments. Each of these features facilitates epidemiologic research. This presentation will show how EHR data can be used to identify health outcomes and give examples of how environmental and community conditions can be measured at the individual or contextual level, assigned to individuals, and evaluated in relation to health. EHR data will be contrasted with what can be learned from claims data, which are available from the Geisinger Health Plan for approximately one-third of primary care patients. Environmental and community conditions that will be discussed include unconventional natural gas development (aka "fracking"); food environments; land use environments relevant to physical activity; community socioeconomic deprivation; animal feeding operations; building radon levels; and abandoned coal mine lands. Health conditions that will be discussed include childhood obesity, methicillin-resistant Staphylococcus aureus, type 2 diabetes, and asthma exacerbations. These were selected to show the range of health conditions that can be measured with EHR data, including the use of diagnoses, laboratory tests, medications, and procedures. EHR-based epidemiologic research can be enriched with primary data collection, including to measure health-related behaviors or to obtain samples for genetic or epigenetic studies, and examples will be presented.
Space-Time Analysis of Late Stage Breast Cancer Incidence in Michigan
Time: 3:15 - 3:30 p.m.
The major difficulty in the analysis of health outcomes is that the patterns observed reflect the influence of a complex constellation of demographic, social, economic, cultural and environmental factors that likely change through time and space, and interact with the different types and scales of places where people live. A suite of techniques is introduced for the visualization and spatial analysis of time series of health data, including 3D display in a combined time and geography space, binomial kriging for noise filtering, joinpoint regression to model temporal trends, and cluster analysis to group geographical units with similar temporal trends. These techniques are used to explore spatio-temporal disparities in the incidence of breast cancer late-stage diagnosis for counties of Michigan Lower Peninsula over the period 1985-2007. Overall the proportion of late-stage diagnosis declined significantly at a rate of 3 to 5.3% per year until 1999 when it started rising again at a significant rate of 2.38% per year. Temporal trends greatly vary among counties and such discrepancies peaked in the early 1990s when the number of mammography clinics in operation started increasing steadily. The first significant decline in proportion of late-stage diagnosis started much later on the Eastern border of the State along Lake Huron, in particular in the Thumb area where late-stage diagnosis has been more prevalent over the years and both access to screening and socio-economic status are less favourable.
Time: 3:30 - 3:45 p.m.
Social observatories comprise longitudinal, linked multi-sector datasets of individuals within their local geospatial context. These datasets uniquely facilitate assessment of residential mobility and historic neighborhood exposures, thus presenting scalable, flexible platforms for longitudinal geospatial cancer prevention and control research. We link three administrative and clinical datasets tracking low-income families across the charitable food, housing, and medical sectors to evaluate potential for differential measurement of historic neighborhood exposures. Household members recorded in a food bank administrative database were matched to a safety-net healthcare system electronic medical record (EMR). Two years of food bank address histories and all associated EMR addresses were linked to housing parcel data using a hierarchical geocoding scheme. We assessed address congruence in space-time using descriptive statistics, and individual and housing correlates of congruence using logistic regression models. In all, 3,696 households and 23,192 addresses (6,289 food bank; 16,903 EMR) were included. Of 6,289 food bank addresses, 4,103 (65.2%) overlapped temporally with EMR addresses. Congruence in space-time was low (2,293 addresses; 36.5%). Congruence was more likely for older, married, Medicare/Medicaid- or un-insured and residentially-zoned households. Congruence was less likely for males and if the household moved. Multi-sector datasets measure markedly different historic neighborhood exposures. Congruence across component datasets in our social observatory is associated with household characteristics (e.g., uninsured) and behaviors (e.g., mobility), and independent of social safety-net system engagement. Social observatory datasets facilitate assessment of geospatial influences on cancer prevention and control behaviors. Future research is needed to characterize geospatial measurement error.
Time: 3:45 - 4:00 p.m.
Colorectal cancer (CRC) mortality is higher among persons diagnosed with late-stage CRC; therefore it is likely that areas with high CRC mortality rates will also have high rates of late-stage CRC. We tested whether the proportion of late-stage CRC cases among all CRC cases was a better predictor of CRC mortality than was the rate of late-state cases among the population at risk for CRC. We matched individual-level cancer incidence data from the Iowa Cancer Registry to mortality data from the Iowa Department of Health, for Iowans aged 50 years and older. In this population from 1999 – 2010 there were 21,756 persons diagnosed with CRC, of whom 14,435 were late-stage, and 7,950 deaths from CRC. Incidence, mortality and population data for ZIP Code tabulation areas were aggregated to 3,423 small areas, which each contained at least 30 expected deaths. Indirectly age-sex adjusted measures, reported as standardized ratios, were calculated for each area. Adjusted R-squares from ordinary least squares regression quantified the correlation between these measures. In Iowa, between 1999 and 2010, the late-stage incidence rates per 100,000 person-years ranged from 114.9 to 243.6, the late-stage proportions per 100 diagnosed cases ranged from 52.9 to 77.1, and the mortality rates per 100,000 person-years ranged from 41.6 to 96.5. The correlation between late-stage incidence rates and mortality rates (r2 = 0.411, p < 0.001) was nearly 400 times higher than the correlation between the proportion of cases that were late and mortality rates (r2 = 0.003, p < 0.001). The late-stage incidence rate is a better predictor of CRC mortality in Iowa than the proportion of late-stage cases. Impact: State CRC prevention programs should consider using the late-stage incidence rate as a surveillance measure and for program planning. Disclaimer: The findings and conclusions in the report are those of the author and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
Time: 4:00 - 4:15 p.m.
While many cancer studies have examined how distance to the nearest facility may affect cancer treatment decisions and outcomes, little research has been undertaken on what factors affect the actual distance patients travel and what this means in terms of the types of patients different cancer facilities see. We use 2010-2014 Pennsylvania Cancer Registry data and a Stata user-provided open-source routing machine program to first calculate patients' driving time to the hospital where they receive treatment as well as the driving time to the nearest hospital. We then examine patient (age, sex, race, and insurance type, disease (primary site, stage), and facility characteristics that are associated with distance traveled to the treating hospital. For all analyses we estimate linear regression models with standard errors clustered at the county level. Preliminary results suggest that younger age, being insured, VA/Tricare insurance, a higher US News hospital quality score, hospital volume, and distance to nearest hospital were significantly associated with greater travel time. In addition, we found significantly greater travel times for prostate, cervical/uterine, and ovarian cancers but significantly shorter times for colorectal, breast, and lung cancers as well as cancers at a distant stage. Patient, hospital, and disease characteristics are all associated with significant differences in cancer care travel times. Understanding what factors are associated with patient cancer care location choices is critical as policymakers and payers attempt to encourage patients to seek care at higher quality facilities and will be particularly critical to understanding the potential future impact of alternative care model, particularly in oncology.
Session 13: Geoinformatics
Time: 2:45 - 3:15 p.m.
It is well known that the accuracy of input data used for scientific investigations can affect the results generated from experiments, as well as the validity and power of the conclusions that can be drawn from them. These facts are particularly relevant when considering the spatial accuracy of data used in health research. For many years, qualitative metrics have been used to define the accuracy of geocoded data, which have in turn been used to judge the appropriateness of input data for epidemiological and disease surveillance and control purposes in public health. The existing measures used to indicate the spatial accuracy of geocoded data in health research are inadequate, sometimes misleading to research end-users, and are difficult to integrate into accuracy assessments in meaningful ways. Given prior results from large national-scale health investigations, it is possible to predict in advance what level of spatial accuracy can be obtained for different localities. Further, these same approaches can be used to re-assess the spatial accuracy of the data which underlie prior investigations to evaluate if inaccuracies impacted the validity of findings. This talk will examine the sources, magnitudes, and implications of error sources in the geocoding process, as well propose alternative spatial accuracy assessments that could and should be used in place of qualitative measures and hierarchical coding schemes that may imply false precision. Geocoding is an important first step in the health research process. It usually works well, but when it does not – it matters – and researchers must be provided with understandable and actionable measures to evaluate just how poorly it went.
Better Practices for Spatial Energetics Data Processing
Time: 3:15 - 3:30 p.m.
Spatial energetics research relies on processing of large amounts of sensor and GIS data. Correct data handling and processing is critical. The Transportation and Community Study (TRAC), focusing on longitudinal effects of the new light rail system in Seattle, WA, collected global positioning systems (GPS; Qstarz BTQ1000XT), accelerometry (Actigraph GT3X), and travel diary data from hundreds of adults for one week over three measurement waves. In the course of data processing and analysis we identified several technical problems induced by the device vendors' proprietary software (QTravel, Actilife), as well as commonly used analytic software (MS Excel and Access). We also identified issues surrounding the use of geographic information systems (GIS) software (ArcGIS) for geocoding and network routing. We present errors in time stamp handling related to time zones and daylight savings time in common software used for handling device data spatial energetics research. We also present some problems encountered integrating instrument data with GIS data. Furthermore, we present solutions to these problems. Results: Incorrectly accounting for standard/daylight savings time can induce time shifts of plus or minus one hour in GPS and accelerometry data. This can affect time synchronization among time stamped data tables, which can have downstream effects in estimating when and where physical activity takes place. Such locational problems may affect measurement of the built environment characteristics of physical activity bout locations. Likewise, problems with GIS data, software, and processing during geocoding and network routing can induce similar errors affecting estimation of built environment variable values at geocoded locations, and of the distances required to travel between known points. The large amount of data required for spatial energetics research precludes detailed review of all data, thus unforeseen errors may arise without analysts' knowledge. Data and software used in spatial energetics research may contain errors or omissions that compromise analysis and findings. More attention needs to be paid to these issues, with researchers sharing information and website and refereed journals reporting on the limitations of both data and software used. Solutions to identified problems may help other researchers in improving data processing and analysis.
Time: 3:30 - 3:45 p.m.
There is a great deal of interest in the way exposure to risk and protective factors within neighborhoods affects the health of people who traverse their streets. Real-time exposure estimates derived from actual mobility patterns are inherently more specific than residential approximations, but empirical methods for contrasting the predictive power of residential versus real-time exposure estimates are not well established. A nationwide density surface of convenience and related retail outlet locations was generated using kernel density estimation (KDE). This surface was linked to participants' (N=363) residential location, as well as their real-time geographic locations, recorded every 10-minutes over 180-days. Hourly mobility (N=1,567,156 total hours) was characterized by radius of gyration and associated contact with the retail density surface. Overall, 61.3% of real-time, hourly exposures were of relatively low intensity, and after controlling for temporal and seasonal variation, 72.8% of these low-level exposures was accounted for by residence in one of the two lowest residential density quintiles. Residence in the two highest residential density quintiles accounted for approximately 50% of extreme exposure levels, but extreme levels of exposure were rare, constituting about 1% of the data. Altogether 55.2% of real-time exposures were not explained by participants' residential density level, and of the unexplained exposures, 38.5% fell in the central, moderate range of the distribution. Results document the degree that residential exposure estimates may lead to misclassification, suggesting that a substantial proportion of real-time exposures will be misleadingly ignored, especially among people residing in areas characterized by moderate levels of residential density. This paper describes the development of an analytic framework for direct comparison of estimates based on residential versus real-time exposure to the landscape of retail outlets across the US, an industry vector for distribution of products known to heavily influence a range of health-related behaviors and clinical outcomes.
Time: 3:45 - 4:00 p.m.
Vehicle exhaust contains a mixture of gases and particulate matter with known mutagenic and carcinogenic effects. Using three complementary exposure methods, we examined the association between NOx, a marker of traffic-related air pollutants, and breast cancer risk among female Multiethnic Cohort participants, residing predominately in Los Angeles County. We estimated average NOx exposure for 57,530 females, using 93,148 residential addresses over a 17 year period of follow-up by: 1) temporally adjusted land use regression (LUR; high spatial and high temporal resolution); 2) California line source dispersion model, version 4 (CALINE4; high spatial and moderate temporal resolution); 3) Bayesian kriging interpolation of routine monitoring station data (low spatial and high temporal resolution). Cox proportional hazard regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI). Stratified analyses were conducted by race/ethnicity, moving status, and distance to monitoring stations. Breast cancer risk was significantly associated with an increase in the interquartile range in NOx exposure in African Americans (LUR: HR=1.24; 95% CI: 1.01-1.53) and Japanese Americans (LUR: HR=1.39; 95% CI: 1.05-1.83 and CALINE4: HR=1.13; 95% CI: 1.00-1.27). For the LUR model, stronger significant associations were observed in African Americans and Japanese Americans among non-movers (single residential address) and those living closer to monitoring stations ( < median distance=5 miles). No significant associations were identified by kriging interpolation. Exposure assessment with high spatial and high/moderate temporal resolutions identified long-term effects of NOx on breast cancer risk. Future analyses will examine associations by neighborhood- and individual-level factors.
Time: 4:00 - 4:15 p.m.
Planners and public health officials seek to prioritize built environment investments which contribute to health behaviors important for chronic disease and cancer prevention. Despite this relevance, there is no widely available, nationally applicable decision-support planning tool to quantify health impacts at fine-grained geographies. This study creates resources to measure and optimize health impacts of transport and land use investments: 1) a national, consistent built, natural and social environment database at the Census block group level, and 2) a web-based health module to enhance the functionality of multiple existing scenario planning software tools. The health module contains statistical models of health behaviors and outcomes developed using participant address level data from the California's Health Interview and Household Travel Surveys. The models show sensitivity of health behaviors and outcomes to several useful environment variables, such as residential density and tree canopy. Using the health module, communities can estimate health behaviors and outcomes using the national database (existing conditions) and custom scenarios that reflect their land use and transport planning investment decisions. Successful health interventions may be implemented through strategies using the health module to enhance scenario planning tools' functionality to estimate health outcomes, including cancer risk factors. These data, models and tools allow more jurisdictions, even those which are challenged by insufficient data, staff and resources, to more easily include health impacts in planning decisions.
Session 14: Confidentiality and Defining Place Without Compromising Confidentiality
Session Chair: Douglas Richardson, Ph.D., American Association of Geographers
Opening Remarks from Session Chair
Time: 2:45 - 3:00 p.m.
An Adaptive Geomasking Method for Health Data Dissemination
Time: 3:00 - 3:15 p.m.
To protect patients' confidentiality, the data provider often employs the geomasking technique to de-identify individuals. Geomasking moves a point from its original location to a random location that is within a certain spatial extent. The main concern in geomasking is the balance between de-identifying individuals and preserving the spatial pattern determined by the individual locations. So far there is a lack of method for the data provider to evaluate this balance, and often geomasking in practice is performed in a subjective or even arbitrary way. Based on the understanding that as long as the de-identification can be achieved, the moving distance should always be minimized, an adaptive geomaksing process is proposed, which adjusts the moving distance according to the local population density. To achieve this adaption, instead of specifying a geographic distance as the maximum moving distance, a safe size of population is specified, which eventually has more explicit statistical and epidemiological meanings than a subjectively chosen distance. Furthermore, when choosing a random location, a location that has a population density similar to that of the original location has a greater chance to be selected. An experiment with a simulated dataset demonstrates that the adaptive geomasking is more advantageous in both preserving the original spatial pattern and evaluating (and thus controlling) the balance between confidentiality and fidelity. The adaptive geomasking improves the conventional geomasking that is based on specified geographic distances. The process should be able to facilitate dissemination of health data for research purposes.
Time: 3:15 - 3:30 p.m.
Physical activity prevents several cancers, yet less that 50% of U.S. adults meet physical activity recommendations. There is a growing interest in using urban design to foster physical activity. These analyses investigate the associations between physical activity among residents of NYC and residential neighborhood walkability and access to green space. Sociodemographic information and data on physical activity measured by accelerometers were collected from 803 residents of NYC between November 2010 and November 2011. Physical activity was measured as minutes of moderate intensity equivalent activity per week ((minutes of moderate activity + (2 x minutes of vigorous activity)). The residential neighborhood was defined as a 1km radial buffer around the home. Neighborhood walkability was measured using a scale that combines data on residential density, land use mix, intersection density, retail space configuration and subway stop count. Access to green space was measured as the proportion of residential neighborhood covered with large parks spaces (park areas>6 acres) and small park spaces and the number of street trees per km of neighborhood area. After adjustment for individual and neighborhood sociodemographic characteristics, total weekly minutes of moderate intensity equivalent physical activity were significantly associated with neighborhood walkability (+26% across the interquartile range (IQR) of walkability, p<0.01), access to small and large park spaces (+12%, P<0.05, +11%, p=0.09 respectively, across the IQR of park access) and street tree density (+15% across the IQR of tree density, p<0.05). Physical activity among NYC residents is associated with neighborhood walkability and some measures of green space access.
Time: 3:30 - 3:45 p.m.
Due to concerns about confidentiality, cancer patients' information is censored and any residential information is typically replaced by reference to coarse areal units (e.g., ZIP code). Censoring does not necessarily preserve the relative spatial associations in the data, leading to an empirical question of whether researchers can obtain meaningful statistical inference with censored geospatial data. We develop an approach based on Kullback-Leibler (KL) divergence (NSF, 1228669) to address this problem. We compute a distribution of areas where respondents may reside and sample from this distribution to generate an imputed dataset. To obtain this distribution, we combine hierarchical Census data with censored geospatial data to estimate a probability distribution over areas with multiple imputation. We offer the KL primer by applying the method to a large dataset (over 700,000 geocoded observations) to the study of housing in Arkansas, and discuss extensions in cancer research. Our approach facilitates the use of spatial analytic tools to censored geospatial data. Geographically weighted regression was used on the imputed data and we found that coefficient estimates fall within the 95% confidence intervals of the true parameters (drawn from the geocoded data). The same patterns hold for other spatial clustering measures (e.g., Moran's I). The results indicate that our approach can help researchers make meaningful statistical inference with censored geospatial data. Our approach serves as a primer on handling restricted cancer patient data as it allows researchers to obtain preliminary findings about the spatial associations with public data, facilitating the decision to apply for restricted data.
Time: 3:45 - 4:00 p.m.
Activity Space (AS) research offers a promising approach to understanding environmental influences on cancer-risk behaviors and outcomes. An objective of the ASCMEE measurement grant (5R21CA195543) is to develop tools to examine the joint spatial and temporal stability of AS. ASCMEE included the collection of 28-day GPS data (97 adults, Chicago); generating a geospatial database of 3+ million data points. We focus on the application of a new, flexible data aggregation tool to describe and synthesize the spatiotemporal patterns generated from GPS data. We introduce general and conditioned AS profile graphs to both represent accumulated exposure to places across time and to identify places where participants spend most time. We compare data for different sequences and durations of days across the 28-day period; and demonstrate the robustness of results through sensitivity analysis and the examination of specific cases. Results demonstrate heterogeneity in the size and complexity of individual AS over time, reveal different subgroups (rapid vs. slow accumulators) and the role of specific days or journeys in accumulated exposure. This method development scratches the surface of what is possible in AS research. The new ASCMEE tools are fast and efficient (do not require GIS), parameters options control spatial and temporal metrics and output can easily generate AS profile graphs. These methods/results have conceptual and substantive implications for the design of AS studies vis-à-vis the duration of data collection. This new tool can provide more accurate measures of spatiotemporal environmental exposures and ultimately a better understanding of environmental contributions to cancer risk behaviors.
Time: 4:00 - 4:15 p.m.
|4:15 p.m. – 5:30 p.m.||
View agenda for Wednesday, September 14
|8:00 a.m. - 8:15 a.m.||Registration|
|8:15 a.m. – 8:30 a.m.||
Welcome Back and Overview of Conference Day 3
David Berrigan, Ph.D., M.P.H.
|8:30 a.m. – 9:30 a.m.||
Zaria Tatalovich, Ph.D. (Moderator)
Spatial Turn in Health Research and its Implications for Cancer Control
Invited Speaker: Douglas Richardson, Ph.D., Association of American Geographers
Advances in real-time spatiotemporal data generation technologies, mobile sensors, and analytical methods have created a rapidly evolving landscape of new health research opportunities and challenges. This presentation draws on three inter-related American Association of Geographers (AAG) research initiatives which explore key elements of this new health and cancer research landscape: 1) the AAG Initiative for an NIH-wide Geospatial Infrastructure (funded by NIH and AAG); 2) Geospatial Frontiers of Health and Social Environments (funded by NIH); and 3) Addressing Challenges For Geospatial Data-Intensive Research Communities: Research on Unique Confidentiality Risks & Geospatial Data Sharing within a Virtual Data Enclave (funded by NSF). These AAG initiatives present linked and interactive health and geospatial data research agendas in the domains of spatial technologies, spatiotemporal data infrastructure, and health research methods relevant to cancer control. They also are creating an increased awareness by health and biomedical researchers of the core role that geography, GIScience, and spatial analysis play in addressing cancer research and treatment needs. Technical and institutional obstacles to implementing geospatial approaches to cancer control, such as standards, interoperability, common terminology, and data confidentiality are also identified and addressed, and future research directions for advancing this field are proposed.
Emerging Technologies to Measure Neighborhood Conditions in Cancer Research
Invited Speaker: Mario Schootman, Ph.D., Saint Louis University
Adverse neighborhood conditions play an important role beyond individual characteristics. There is increasing interest in identifying specific characteristics of the social and built environments adversely affecting cancer risk and outcomes. To-date, most studies have been limited by: 1) a focus on residence only when most people spend one third of their time elsewhere; 2) failure to consider cumulative exposures over time (e.g., residential history); and 3) use of arbitrary administrative units (county, census tract, or zip code) to infer neighborhood risks. This study describes the utility, validity and reliability of selected emerging technologies (Google Street View, webcams, crowdsourcing, remote sensing, social media, and lifespace) to measure neighborhood conditions in cancer research. It also describes next steps for future research and opportunities for location-based interventions. Emerging technologies such as Google Street View, social media, webcams, and crowdsourcing may serve as effective and inexpensive tools to measure the ever-changing environment. Georeferenced social media responses may help identify where to target intervention activities, but also to passively evaluate their effectiveness. Future studies should measure exposure across key time points during the life-course as part of the exposome paradigm and integrate different types of data sources to measure neighborhood contexts. By harnessing these technologies, cancer research can not only monitor exposure of populations to the environment and move from place-based to people-based exposure, but also intervene using novel strategies to improve cancer-related outcomes.
|9:30 a.m. – 10:30 a.m.||
Stephen Taplin, M.D., M.P.H. (Moderator)
Charting a Path Toward Improved Data Collection and Methods for Measuring Geographic Accessibility to Cancer Prevention and Care
Invited Speaker: Kevin A. Henry, Ph.D., Fox Chase Cancer Center and Temple University
Access to health care is multifaceted, including availability, acceptability, and financial and geographic accessibility of services. Geographic accessibility measures how physically accessible resources are for the population, while availability reflects what resources are available and in what amount to meet the demand. Barriers such as a deficit or maldistribution of health services or inadequate transportation often results in long travel distances or times. In the past decade, there has been an increase in the number of studies that have focused on geographical access to health services, only made possible through the widespread availability and affordability of software and data for geocoding and calculating travel distance/time; and advancements in the methods to measure geographic accessibility. In this presentation I will briefly describe the most popular approaches and methods used to measure geographic accessibility to health services and their limitations. I will follow with a description of a research agenda, offering suggestions of how to advance research on geographic accessibility to cancer prevention and care services. This research agenda focuses on absolute and relative measures of travel time/distance based on multiple transportation modes, time constraints based variations in time-use and mobility patterns, and improving our understanding of how geographical accessibility to health care is experienced by different populations. I will discuss short-term solutions such as leveraging underutilized datasets; and, long-term solutions involving the collection of new data from surveys and improving the collection of relevant data from cancer registries.
Cancer Control and Populations from a Geospatial Perspective: A Focus on NCI-Cancer Center Catchments
Invited Speaker: Tracy Onega, Ph.D., M.A., M.S. Dartmouth College
NCI-designated Cancer Centers have a charge to serve the cancer control needs of their catchment areas. Refined approaches to measure catchments, align services with needs, and assess population impact, are needed to best serve that mission. Over the past decade, NCI-Cancer Centers have expanded their presence within regions through satellite facilities intended to reach particular communities and/or provide specific services other than at the parent location. There has been no assessment of how satellite facilities change the geographic footprints of NCI-Cancer Centers, or how further geographic 'reach' of NCI-Cancer Centers may impact cancer control efforts, particularly for vulnerable populations.
NCI-designated Cancer Centers (N=62) in 2014 were geocoded and defined as "parent" or "satellite", based on web searches and phone interviews. We ascertained 76 and 211 geographically-unique parent and satellite facilities, respectively. Closest facility was calculated between block group centroids and facilities, measured as travel time (minutes). Population attributes for census tracts (age, race/ethnicity, median income, rurality, and education), were summarized by median for travel time. Travel time savings by adding satellites were quantified.
For the total U.S. population of 279,540,000, median travel time savings was 72 min.(IQR;27-172). Savings (median;IQR in minutes) were greatest for Native American (155;62-308), followed by White and Hispanic (86;33-174 and 86;23-229), Black (69;16-193), and least for Asian populations (28;15-66). Rural gradient showed expected savings, ranging from 57 minutes in urban areas to 180 in small town/isolated rural. Greatest savings by income was found in the lowest quartile (141;24-256 min.). Travel time savings across education levels were not notable. Mapping of NCI Cancer Center catchments based on travel time, reveals a range of increase in spatial footprint when considering satellite locations; more urban cancer centers did not expand their geographic extent much, while those in less urban areas did.
Satellite facilities improve geographic access to NCI Cancer Centers, with impacts on reduced travel time greatest for vulnerable populations. Regional expansion of NCI Cancer Center footprints may improve access and utilization of specialized cancer care. This work provides a 'first step' in measurement of catchments, but must be followed with strategies to fully characterize heterogeneous populations within the catchment in terms of prevention, treatment, and survivorship needs, resources, and outcomes.
|10:30 a.m. – 10:45 a.m.||Break|
|10:45 a.m. – 11:45 a.m.||
Closing Panel Session
David Berrigan, Ph.D., M.P.H. (Moderator)
Geospatial and Contextual Approaches to Cancer Control and Population Sciences: Next Steps
Linda W. Pickle, Ph.D.
Kathy J. Helzlsouer, M.D., M.H.S.
|11:45 a.m. – 12:00 p.m.||
Closing Remarks and Next Steps
Gary L. Ellison, Ph.D., M.P.H.
- Geographic Information Systems and Science for Cancer Control
This resource center allows cancer researchers, cancer control planners, cancer advocacy groups and public health officials to interactively map, explore and download geographically-based cancer related information.
- David Berrigan, Ph.D., M.P.H., Program Director, Health Behaviors Research Branch, Behavioral Research Program, DCCPS, NCI
- Gary L. Ellison, Ph.D., M.P.H., Chief, Environmental Epidemiology Branch, Epidemiology and Genomics Research Program, DCCPS, NCI
- Scarlett Lin Gomez, Ph.D., Research Scientist III, Cancer Prevention Institute of California and Consulting Associate Professor, Department of Health Research and Policy, Stanford University School of Medicine
- Kevin A. Henry, Ph.D., Assistant Professor, Geography and Urban Studies and Fox Chase Cancer Center, Temple University
- April Oh, Ph.D., M.P.H., Program Director, Health Communication and Informatics Research Branch, Behavioral Research Program, DCCPS, NCI
- Timothy R. Rebbeck, Ph.D., Professor of Cancer Epidemiology, Harvard TH Chan School of Public Health and Dana Farber Cancer Institute
- Mario Schootman, Ph.D., Associate Dean for Research, James R. Kimmey Endowed Chair in Public Health, Professor of Epidemiology and of Health Services Research and of Medicine, and co-Director of Doctoral Program, Saint Louis University
- Stephen Taplin, M.D., M.P.H., Deputy Associate Director, Healthcare Delivery Research Program, DCCPS, NCI
- Zaria Tatalovich, Ph.D., Program Director, Statistical Research and Application Branch, Surveillance Research Program, DCCPS, NCI
- For questions about conference logistics, contact Rob Watson.