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

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

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)

Intracellular lumen extension requires ERM-1-dependent apical membrane expansion and AQP-8-mediated flux

SUMMARY Many unicellular tubes such as capillaries form lumens intracellularly, a process that is not well understood. Here we show that the cortical membrane organizer ERM-1 is required to expand the intracellular apical/lumenal membrane and its actin undercoat during single-cell C.elegans excretory canal morphogenesis. We characterize AQP-8, identified in an ERM-1 overexpression (ERM-1[++]) suppressor screen, as a canalicular aquaporin that interacts with ERM-1 in lumen extension in a mercury-sensitive manner, implicating water-channel activity. AQP-8 is transiently recruited to the lumen by ERM-1, co-localizing in peri-lumenal cuffs interspaced along expanding canals. An ERM-1[++]-mediated increase in the number of lumen-associated canaliculi is reversed by AQP-8 depletion. We propose that the ERM-1-AQP-8 interaction propels lumen extension by translumenal flux, suggesting a direct morphogenetic effect of water-channel-regulated fluid pressure

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

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. Conclusions 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

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