Impact of the built, social, and food environment on long-term weight loss within a behavioral weight loss intervention.

Background: Behavioral weight loss interventions can lead to an average weight loss of 5%-10% of initial body weight, however there is wide individual variability in treatment response. Although built, social, and community food environments can have potential direct and indirect influences on body weight (through their influence on physical activity and energy intake), these environmental factors are rarely considered as predictors of variation in weight loss. Objective: Evaluate the association between built, social, and community food environments and changes in weight, moderate-to-vigorous physical activity (MVPA), and dietary intake among adults who completed an 18-month behavioral weight loss intervention. Methods: Participants included 93 adults (mean ± SD; 41.5 ± 8.3 years, 34.4 ± 4.2 kg/m2, 82% female, 75% white). Environmental variables included urbanicity, walkability, crime, Neighborhood Deprivation Index (includes 13 social economic status factors), and density of convenience stores, grocery stores, and limited-service restaurants at the tract level. Linear regressions examined associations between environment and changes in body weight, waist circumference (WC), MVPA (SenseWear device), and dietary intake (3-day diet records) from baseline to 18 months. Results: Grocery store density was inversely associated with change in weight (ß = -0.95; p = 0.02; R 2 = 0.062) and WC (ß = -1.23; p < 0.01; R 2 = 0.109). Participants living in tracts with lower walkability demonstrated lower baseline MVPA and greater increases in MVPA versus participants with higher walkability (interaction p = 0.03). Participants living in tracts with the most deprivation demonstrated greater increases in average daily steps (ß = 2048.27; p = 0.02; R 2 = 0.039) versus participants with the least deprivation. Limited-service restaurant density was associated with change in % protein intake (ß = 0.39; p = 0.046; R 2 = 0.051). Conclusion: Environmental factors accounted for some of the variability (<11%) in response to a behavioral weight loss intervention. Grocery store density was positively associated with weight loss at 18 months. Additional studies and/or pooled analyses, encompassing greater environmental variation, are required to further evaluate whether environment contributes to weight loss variability.

Developing Geographic Areas for Cancer Reporting Using Automated Zone Design.

The reporting and analysis of population-based cancer statistics in the United States has traditionally been done for counties. However, counties are not ideal for analysis of cancer rates, due to wide variation in population size, with larger counties having considerable sociodemographic variation within their borders and sparsely populated counties having less reliable estimates of cancer rates that are often suppressed due to confidentiality concerns. There is a need and an opportunity to utilize zone design procedures in the context of cancer surveillance to generate coherent, statistically stable geographic units that are more optimal for cancer reporting and analysis than counties. To achieve this goal, we sought to create areas within each US state that are: 1) similar in population size and large enough to minimize rate suppression; 2) sociodemographically homogeneous; 3) compact; and 4) custom crafted to represent areas that are meaningful to cancer registries and stakeholders. The resulting geographic units reveal the heterogeneity of rates that are hidden when reported at the county-level while substantially reducing the need to suppress data. We believe this effort will facilitate more meaningful comparative analysis of cancer rates for small geographic areas and will advance the understanding of cancer burden in the United States.

Environmental data and methods from the Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) core measures environmental working group.

This article describes geospatial datasets and exemplary data across five environmental domains (walkability, socioeconomic deprivation, urbanicity, personal safety, and food outlet accessibility). The environmental domain is one of four domains (behavioral, biological, environmental and psychosocial) in which the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project suggested measures to help explain variation in responses to weight loss interventions. These data are intended to facilitate additional research on potential environmental moderators of responses to weight loss, physical activity, or diet related interventions. These data represent a mix of publicly and commercially available pre-existing data that were downloaded, cleaned, restructured and analyzed to create datasets at the United States (U.S.) block group and/or census tract level for the five domains. Additionally, the resource includes detailed methods for obtaining, cleaning and summarizing two datasets concerning safety and the food environment that are only available commercially. Across the five domains considered, we include component as well as derived variables for three of the five domains. There are two versions of the National Walkability Index Dataset (one based on 2013 data and one on 2019 data) consisting of 15 variables. The Neighborhood Deprivation Index dataset contains 18 variables and is based on the US Census Bureau's 5-year American Community Survey (ACS) data for 2013-2017. The urbanicity dataset contains 11 variables and is based on USDA rural-urban commuting (RUCA) codes and Census Bureau urban/rural population data from 2010. Personal safety and food outlet accessibility data were purchased through commercial vendors and are not in the public domain. Thus, only exemplary figures and detailed instructions are provided. The website housing these datasets and examples should serve as a valuable resource for researchers who wish to examine potential environmental moderators of responses to weight loss and related interventions in the U.S.

Assessment of Interstate Residential Mobility of SEER Patients: SEER and LexisNexis Residential Address Linkage.

The National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) program is continuously exploring opportunities to augment its already extensive collection of data, enhance the quality of reported cancer information, and contribute to more comprehensive analyses of cancer burden. This manuscript describes a recent linkage of the LexisNexis longitudinal residential history data with 11 SEER registries and provides estimates of the inter-state mobility of SEER cancer patients. To identify mobility from one state to another, we used state postal abbreviations to generate state-level residential histories. From this, we determined how often cancer patients moved from state-to-state. The results in this paper provide information on the linkage with LexisNexis data and useful information on state-to-state residential mobility patterns of a large portion of US cancer patients for the most recent 1-, 2-, 3-, 4-, and 5-year periods. We show that mobility patterns vary by geographic area, race/ethnicity and age, and cancer patients tend to move less than the general population.

Effects of Buffer Size and Shape on the Association of Neighborhood SES and Adult Fruit and Vegetable Consumption.

Introduction: Neighborhood environment factors are relevant for dietary behaviors, but associations between home neighborhood context and disease prevention behaviors vary depending on the definition of neighborhood. The present study uses a publicly available dataset to examine whether associations between neighborhood socioeconomic status (NSES) and fruit/vegetable (FV) consumption vary when NSES is defined by different neighborhood sizes and shapes. Methods: We analyzed data from 1,736 adults with data in GeoFLASHE, a geospatial extension of the National Cancer Institute's Family Life, Activity, Sun, Health, and Eating Study (FLASHE). We examined correlations of NSES values across neighborhood buffer shapes (circular or street network) and sizes (ranging from 400 to 1,200 m) and ran weighted simple and multivariable regressions modeling frequency of FV consumption by NSES for each neighborhood definition. Regressions were also stratified by gender. Results: NSES measures were highly correlated across various neighborhood buffer definitions. In models adjusted for socio-demographics, circular buffers of all sizes and street buffers 750 m and larger were significantly associated with FV consumption frequency for women only. Conclusion: NSES may be particularly relevant for women's FV consumption, and further research can examine whether these associations are explained by access to food stores, food shopping behavior, and/or psychosocial variables. Although different NSES buffers are highly correlated, researchers should conceptually determine spatial areas a priori.

A national examination of neighborhood socio-economic disparities in built environment correlates of youth physical activity.

Adolescents in the U.S. do not meet current physical activity guidelines. Ecological models of physical activity posit that factors across multiple levels may support physical activity by promoting walkability, such as the neighborhood built environment and neighborhood socioeconomic status (nSES). We examined associations between neighborhood built environment factors and adolescent moderate-to-vigorous physical activity (MVPA), and whether nSES moderated associations. Data were drawn from a national sample of adolescents (12-17 years, N = 1295) surveyed in 2014. MVPA (minutes/week) were estimated from self-report validated by accelerometer data. Adolescents' home addresses were geocoded and linked to Census data from which a nSES Index and home neighborhood factors were derived using factor analysis (high density, older homes, short auto commutes). Multiple linear regression models examined associations between neighborhood factors and MVPA, and tested interactions between quintiles of nSES and each neighborhood factor, adjusting for socio-demographics. Living in higher density neighborhoods (B(SE): 9.22 (2.78), p = 0.001) and neighborhoods with more older homes (4.42 (1.85), p = 0.02) were positively associated with adolescent MVPA. Living in neighborhoods with shorter commute times was negatively associated with MVPA (-5.11 (2.34), p = 0.03). Positive associations were found between MVPA and the high density and older homes neighborhood factors, though associations were not consistent across quintiles. In conclusion, living in neighborhoods with walkable attributes was associated with greater adolescent MVPA, though the effects were not distributed equally across nSES. Adolescents living in lower SES neighborhoods may benefit more from physical activity interventions and environmental supports that provide opportunities to be active beyond neighborhood walkability.

Providing Higher Resolution Indicators of Rurality in the Surveillance, Epidemiology, and End Results (SEER) Database: Implications for Patient Privacy and Research.

BACKGROUND: The burden of cancer is higher in rural areas than urban areas. The NCI's Surveillance, Epidemiology, and End Results (SEER) database currently provides county-level information on rurality for cancer patients in its catchment area, but more nuanced measures of rurality would improve etiologic and surveillance studies. METHODS: We analyzed disclosure risk and conducted a sample utility analysis of census tract-level measures of rurality, using (1) U.S. Department of Agriculture's Rural Urban Commuting Area (RUCA) codes and (2) U.S. Census data on percentage of the population living in nonurban areas. We evaluated the risk of disclosure by calculating the percentage of census tracts and cancer cases that would be uniquely identified by a combination of these two rurality measures with a census tract-level socioeconomic status (SES) variable. The utility analyses examined SES disparities across levels of rurality for lung and breast cancer incidence and relative survival. RESULTS: Risk of disclosure was quite low: <0.03% of census tracts and <0.03% of cancer cases were uniquely identified. Utility analyses demonstrated an SES gradient in lung and breast cancer incidence and survival, with relatively similar patterns across rurality variables. CONCLUSIONS: The RUCA and Census rurality measures have been added to a specialized SEER 18 database. Interested researchers can request access to this database to perform analyses of urban/rural differences in cancer incidence and survival. IMPACT: Such studies can provide important research support for future interventions to improve cancer prevention and control.

GIScience and cancer: State of the art and trends for cancer surveillance and epidemiology.

Maps are well recognized as an effective means of presenting and communicating health data, such as cancer incidence and mortality rates. These data can be linked to geographic features like counties or census tracts and their associated attributes for mapping and analysis. Such visualization and analysis provide insights regarding the geographic distribution of cancer and can be important for advancing effective cancer prevention and control programs. Applying a spatial approach allows users to identify location-based patterns and trends related to risk factors, health outcomes, and population health. Geographic information science (GIScience) is the discipline that applies Geographic Information Systems (GIS) and other spatial concepts and methods in research. This review explores the current state and evolution of GIScience in cancer research by addressing fundamental topics and issues regarding spatial data and analysis that need to be considered. GIScience, along with its health-specific application in the spatial epidemiology of cancer, incorporates multiple geographic perspectives pertaining to the individual, the health care infrastructure, and the environment. Challenges addressing these perspectives and the synergies among them can be explored through GIScience methods and associated technologies as integral parts of epidemiologic research, analysis efforts, and solutions. The authors suggest GIScience is a powerful tool for cancer research, bringing additional context to cancer data analysis and potentially informing decision-making and policy, ultimately aimed at reducing the burden of cancer.

A geo-view into historical patterns of smoke-free policy coverage in the USA.

INTRODUCTION: Ample evidence shows that implementation of smoke-free policies can significantly reduce tobacco use. The indoor smoke-free policy coverage in the U.S. increased over the past 25 years. This study synthesized the available historical smoke-free policy data and achieved two complementary goals: 1) reconstructed historical patterns of indoor smoke-free policy coverage in the U.S., and 2) developed a web-based interactive tool for visualization and download of the U.S. historical smoke-free policy data for research. METHODS: Historical information on local and regional smoke-free policy was downloaded from the American Nonsmokers Rights Foundation (ANRF). Subsequent methodological processes included: geo-referencing of smoke-free policy data, spatial-temporal data linkage, spatial pattern analysis, data visualization, and the development of an interactive tool. RESULTS: The percentage of population covered by the smoke-free policies varies across the different geographic locations, scales, and over time. On average, the percentage of people covered by the smoke-free laws in the U.S. increased substantially in the recent decade. The Tobacco-Policy-Viewer reveals geographic patterns of increase in smoke-free policy adoption by cities, counties, and States over time. CONCLUSION: The utility of visualizing the historical patterns of smoke-free policy coverage in the U.S. is to understand where and for how long smoke-free policies were in place for indoor facilities and to inform planning for education and interventions in the areas of need. The benefit of data provided for download, via the Tobacco-Policy-Viewer, is to catalyze future research on the impacts of historical smoke-free policy coverage on reduction in secondhand-smoke exposures, tobacco use, and tobacco related diseases.

Using Gini coefficient to determining optimal cluster reporting sizes for spatial scan statistics.

BACKGROUND: Spatial and space-time scan statistics are widely used in disease surveillance to identify geographical areas of elevated disease risk and for the early detection of disease outbreaks. With a scan statistic, a scanning window of variable location and size moves across the map to evaluate thousands of overlapping windows as potential clusters, adjusting for the multiple testing. Almost always, the method will find many very similar overlapping clusters, and it is not useful to report all of them. This paper proposes to use the Gini coefficient to help select which of the many overlapping clusters to report. METHODS: The Gini coefficient provides a quick and intuitive way to evaluate the degree of the heterogeneity of the collection of clusters, which is useful to explain how well the cluster collection reveal the underlying true cluster patterns. Using simulation studies and real cancer mortality data, it is compared with the traditional approach for reporting non-overlapping clusters. RESULTS: The Gini coefficient can identify a more refined collection of non-overlapping clusters to report. For example, it is able to determine when it makes more sense to report a collection of smaller non-overlapping clusters versus a single large cluster containing all of them. It also fulfils a set of desirable theoretical properties, such as being invariant under a uniform multiplication of the population numbers by the same constant. CONCLUSIONS: The Gini coefficient can be used to determine which set of non-overlapping clusters to report. It has been implemented in the free SaTScan™ software version 9.3 ( www.satscan.org ).

The relationship between area poverty rate and site-specific cancer incidence in the United States.

BACKGROUND: The relationship between socioeconomic status and cancer incidence in the United States has not traditionally been a focus of population-based cancer surveillance systems. METHODS: Nearly 3 million tumors diagnosed between 2005 and 2009 from 16 states plus Los Angeles were assigned into 1 of 4 groupings based on the poverty rate of the residential census tract at time of diagnosis. The sex-specific risk ratio of the highest-to-lowest poverty category was measured using Poisson regression, adjusting for age and race, for 39 cancer sites. RESULTS: For all sites combined, there was a negligible association between cancer incidence and poverty; however, 32 of 39 cancer sites showed a significant association with poverty (14 positively associated and 18 negatively associated). Nineteen of these sites had monotonic increases or decreases in risk across all 4 poverty categories. The sites most strongly associated with higher poverty were Kaposi sarcoma, larynx, cervix, penis, and liver; those most strongly associated with lower poverty were melanoma, thyroid, other nonepithelial skin, and testis. Sites associated with higher poverty had lower incidence and higher mortality than those associated with lower poverty. CONCLUSIONS: These findings demonstrate the importance and relevance of including a measure of socioeconomic status in national cancer surveillance. Cancer 2014;120:2191-2198. © 2014 The Authors. Cancer published by Wiley Periodicals, Inc. on behalf of American Cancer Society.

Confidence intervals for ranks of age-adjusted rates across states or counties.

Health indices provide information to the general public on the health condition of the community. They can also be used to inform the government's policy making, to evaluate the effect of a current policy or healthcare program, or for program planning and priority setting. It is a common practice that the health indices across different geographic units are ranked and the ranks are reported as fixed values. We argue that the ranks should be viewed as random and hence should be accompanied by an indication of precision (i.e., the confidence intervals). A technical difficulty in doing so is how to account for the dependence among the ranks in the construction of confidence intervals. In this paper, we propose a novel Monte Carlo method for constructing the individual and simultaneous confidence intervals of ranks for age-adjusted rates. The proposed method uses as input age-specific counts (of cases of disease or deaths) and their associated populations. We have further extended it to the case in which only the age-adjusted rates and confidence intervals are available. Finally, we demonstrate the proposed method to analyze US age-adjusted cancer incidence rates and mortality rates for cancer and other diseases by states and counties within a state using a website that will be publicly available. The results show that for rare or relatively rare disease (especially at the county level), ranks are essentially meaningless because of their large variability, while for more common disease in larger geographic units, ranks can be effectively utilized.

Multilevel predictors of colorectal cancer screening use in California.

BACKGROUND: Screening can detect colorectal cancer (CRC) early, yet its uptake needs to be improved. Social determinants of health (SDOH) may be linked to CRC screening use but are not well understood. OBJECTIVES: To examine geographic variation in CRC screening and the extent to which multilevel SDOH explain its use in California, the most populous and racially/ethnically diverse state in the United States. STUDY DESIGN: Analysis of individual and neighborhood data on 20,626 adult respondents aged >50 years from the 2005 California Health Interview Survey. METHODS: We used multilevel logistic regression models to estimate the effects of individual characteristics and area-level segregation, socioeconomic status (SES), and healthcare resources at 2 different geographic levels on CRC screening use. RESULTS: We confirmed that individual-level factors (eg, race/ethnicity, income, insurance) were strong predictors and found that area-level healthcare resources were associated with CRC screening. Primary care shortage in the Medical Service Study Area was associated with CRC screening for any modality (odds ratio [OR] = 0.89; 95% confidence interval [CI], 0.80-1.00). County-level HMO penetration (OR = 1.85; 95% CI, 1.47-2.33) and primary care shortage (OR = 0.73; 95% CI, 0.53-0.99) were associated with CRC screening with flexible sigmoidoscopy. CONCLUSIONS: Contextual factors including locality, primary care resources, and HMO membership are important determinants of CRC screening uptake; SES and segregation did not explain variation in screening behavior. More studies of contextual factors and varying geographic scales are needed to further elucidate their impact on CRC screening uptake.

U.S. geographic distribution of prevaccine era cervical cancer screening, incidence, stage, and mortality.

BACKGROUND: Cervical cancer prevention programs are being reconfigured to incorporate human papillomavirus (HPV) testing and vaccination. To define priority areas for prevention efforts, we examined the geographic distribution of cervical cancer screening, incidence, stage, and mortality in the United States, prior to the introduction of HPV-based prevention technologies. METHODS: County-level cervical cancer incidence data from 37 central registries were obtained from Surveillance, Epidemiology, and End Results and North American Association of Central Cancer Registries. A spatial-temporal model that accounted for demographic and behavioral attributes was used to generate a complete view of county-level incidence from 1995 to 2004, including counties with missing data. Distribution of stage at diagnosis was examined by registry. Counties with high mortality and infrequent screening were identified using vital statistics and newly available county-level screening estimates. RESULTS: Compared with non-Hispanic whites and Asian and Pacific Islanders, incidence rates were higher among non-Hispanic black, American Indian and Alaska Native, and Hispanic women. Counties with infrequent screening often experienced elevated incidence and mortality rates and were located in states with suboptimal stage at diagnosis profiles. Affected areas included Appalachia, the southeastern Atlantic states, and the lower Mississippi Valley. Elevated death rates were experienced in central counties of large metropolitan areas. CONCLUSIONS: Geographic and racial/ethnic variability were evident in cervical cancer incidence and mortality. Women living in areas with endemic poverty would benefit from access to HPV-based prevention technologies. IMPACT: These findings provide a baseline for monitoring progress in cervical cancer control in the era of HPV-based prevention.

Identifying clusters of active transportation using spatial scan statistics.

BACKGROUND: There is an intense interest in the possibility that neighborhood characteristics influence active transportation such as walking or biking. The purpose of this paper is to illustrate how a spatial cluster identification method can evaluate the geographic variation of active transportation and identify neighborhoods with unusually high/low levels of active transportation. METHODS: Self-reported walking/biking prevalence, demographic characteristics, street connectivity variables, and neighborhood socioeconomic data were collected from respondents to the 2001 California Health Interview Survey (CHIS; N=10,688) in Los Angeles County (LAC) and San Diego County (SDC). Spatial scan statistics were used to identify clusters of high or low prevalence (with and without age-adjustment) and the quantity of time spent walking and biking. The data, a subset from the 2001 CHIS, were analyzed in 2007-2008. RESULTS: Geographic clusters of significantly high or low prevalence of walking and biking were detected in LAC and SDC. Structural variables such as street connectivity and shorter block lengths are consistently associated with higher levels of active transportation, but associations between active transportation and socioeconomic variables at the individual and neighborhood levels are mixed. Only one cluster with less time spent walking and biking among walkers/bikers was detected in LAC, and this was of borderline significance. Age-adjustment affects the clustering pattern of walking/biking prevalence in LAC, but not in SDC. CONCLUSIONS: The use of spatial scan statistics to identify significant clustering of health behaviors such as active transportation adds to the more traditional regression analysis that examines associations between behavior and environmental factors by identifying specific geographic areas with unusual levels of the behavior independent of predefined administrative units.

The impact of underreported Veterans Affairs data on national cancer statistics: analysis using population-based SEER registries.

Reduced cancer reporting by the US Department of Veterans Affairs (VA) hospitals in 2007 (for patients diagnosed through 2005) impacted the most recent US cancer surveillance data. To quantify the impact of the reduced VA reporting on cancer incidence and trends produced by the Surveillance, Epidemiology, and End Results Program, we estimated numbers of missing VA patients in 2005 by sex, age, race, selected cancer sites, and registry and calculated adjustment factors to correct for the 2005 incidence rates and trends. Based on our adjustment factors, we estimated that as a result of the underreporting, the overall cancer burden was underestimated by 1.6% for males and 0.05% for females. For males, the percentage of patients missing ranged from 2.5% for liver cancer to 0.4% for melanoma of the skin. For age-adjusted male overall cancer incidence rates, the adjustment factors were 1.015, 1.012, and 1.035 for all races, white males, and black males, respectively. Modest changes in long-term incidence trends were observed, particularly in black males.

Cervical cancer incidence in the United States in the US-Mexico border region, 1998-2003.

BACKGROUND: Cervical cancer mortality rates have declined in the United States, primarily because of Papanicolaou testing. However, limited information is available about the incidence of the disease in the US-Mexico border region, where some of the poorest counties in the United States are located. This study was undertaken to help compare the patterns of cervical cancer incidence among women in the US-Mexico border region and other parts of the United States. METHODS: Age-adjusted cervical cancer incidence rates for border counties in the states bordering Mexico (California, Arizona, New Mexico, Texas) for the years 1998 to 2003 were compared with the rates for nonborder counties of the border states and with those of nonborder states. Differences were examined by age, race, ethnicity, rural residence, educational attainment, poverty, migration, stage of disease, and histology. RESULTS: Overall, Hispanic women had almost twice the cervical cancer incidence of non-Hispanic women in border counties, and Hispanic women in the border states had higher rates than did non-Hispanic women in nonborder states. In contrast, cervical cancer incidence rates among black women in the border counties were lower than those among black women in the nonborder states. Among white women, however, incidence rates were higher among those in nonborder states. Differences in cervical cancer incidence rates by geographic locality were also evident by age, urban/rural residence, migration from outside the United States, and stage of disease. CONCLUSIONS: Disparities in cervical cancer incidence in the US-Mexico border counties, when the incidence is compared with that of other counties and geographic regions, are evident. Of particular concern are the higher rates of late-stage cervical cancer diagnosed among women in the border states, especially because such cervical cancer is preventable.

Detection of spatial clusters: application to cancer survival as a continuous outcome.

In this article, we develop the first detailed illustration of the use of a cluster detection method using a spatial scan statistic based on an exponential survival model. We use this approach to study the spatial patterns of survival of patients with stage III or stage IV colorectal cancer or with stage I/II, stage III, or stage IV lung cancer in the State of California and the County of Los Angeles (LA) diagnosed during 1988 through 2002. We present the location of the detected clusters of short survival or long survival and compute nonparametric estimates of survival inside and outside of those detected clusters confirming the survival pattern detected by the spatial scan statistic in both areas. In LA County, we investigate the possible relationship between the cluster locations and race, sex, and histology using nonparametric methods, and we compare socioeconomic factors such as education, employment, income, and health insurance inside and outside of the detected clusters. Finally, we evaluate the effect of related covariates on statistically significant long and short survival clusters detected in LA County using logistic regression models. This article illustrates a new way to understand survival patterns that may point to health disparities in terms of diagnosis and treatment patterns.

The crossroads of GIS and health information: a workshop on developing a research agenda to improve cancer control.

Cancer control researchers seek to reduce the burden of cancer by studying interventions, their impact in defined populations, and the means by which they can be better used. The first step in cancer control is identifying where the cancer burden is elevated, which suggests locations where interventions are needed. Geographic information systems (GIS) and other spatial analytic methods provide such a solution and thus can play a major role in cancer control. This report presents findings from a workshop held June 16-17, 2005, to bring together experts and stakeholders to address current issues in GIScience and cancer control. A broad range of areas of expertise and interest was represented, including epidemiology, geography, statistics, environmental health, social science, cancer control, cancer registry operations, and cancer advocacy. The goals of this workshop were to build consensus on important policy and research questions, identify roadblocks to future progress in this field, and provide recommendations to overcome these roadblocks.

Maternal exposure to arsenic, cadmium, lead, and mercury and neural tube defects in offspring.

Arsenic, cadmium, lead, and mercury are neurotoxins, and some studies suggest that these elements might also be teratogens. Using a case-control study design, we investigated the relation between exposure to these heavy metals and neural tube defects (NTDs) in offspring of Mexican-American women living in 1 of the 14 Texas counties bordering Mexico. A total of 184 case-women with NTD-affected pregnancies and 225 control-women with normal live births were interviewed about their environmental and occupational exposures during the periconceptional period. Biologic samples for blood lead and urinary arsenic, cadmium, and mercury were also obtained for a subset of these women. Overall, the median levels of these biomarkers for heavy metal exposure did not differ significantly (P > 0.05) between case- and control-women. However, among women in the highest income group, case-women were nine times more likely (95% confidence interval (CI) 1.4-57) than control-women to have a urinary mercury 5.62 microg/L. Case-women were 4.2 times more likely (95% CI 1.1-16) to report burning treated wood during the periconceptional period than control-women. Elevated odds ratios (ORs) were observed for maternal and paternal occupational exposures to arsenic and mercury, but the 95% CIs were consistent with unity. The 95% CIs of the ORs were also consistent with unity for higher levels of arsenic, cadmium, lead, and mercury in drinking water and among women who lived within 2 miles at the time of conception to industrial facilities with reported emissions of any of these heavy metals. Our findings suggest that maternal exposures to arsenic, cadmium, or lead are probably not significant risk factors for NTDs in offspring. However, the elevated urinary mercury levels found in this population and exposures to the combustion of treated wood may warrant further investigation.