Competing definitions of contextual environments

BACKGROUND: The growing interest in the effects of contextual environments on health outcomes has focused attention on the strengths and weaknesses of alternate contextual unit definitions for use in multilevel analysis. The present research examined three methods to define contextual units for a sample of children already enrolled in a respiratory health study. The Inclusive Equal Weights Method (M1) and Inclusive Sample Weighted Method (M2) defined communities using the boundaries of the census blocks that incorporated the residences of the CHS participants, except that the former estimated socio-demographic variables by averaging the census block data within each community, while the latter used weighted proportion of CHS participants per block. The Minimum Bounding Rectangle Method (M3) generated minimum bounding rectangles that included 95% of the CHS participants and produced estimates of census variables using the weighted proportion of each block within these rectangles. GIS was used to map the locations of study participants, define the boundaries of the communities where study participants reside, and compute estimates of socio-demographic variables. The sensitivity of census variable estimates to the choice of community boundaries and weights was assessed using standard tests of significance. RESULTS: The estimates of contextual variables vary significantly depending on the choice of neighborhood boundaries and weights. The choice of boundaries therefore shapes the community profile and the relationships between its components (variables). CONCLUSION: Multilevel analysis concerned with the effects of contextual environments on health requires careful consideration of what constitutes a contextual unit for a given study sample, because the alternate definitions may have differential impact on the results. The three alternative methods used in this research all carry some subjectivity, which is embedded in the decision as to what constitutes the boundaries of the communities. The Minimum Bounding Rectangle was preferred because it focused attention on the most frequently used spaces and it controlled potential aggregation problems. There is a need to further examine the validity of different methods proposed here. Given that no method is likely to capture the full complexity of human-environment interactions, we would need baseline data describing people's daily activity patterns along with expert knowledge of the area to evaluate our neighborhood units

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)

Associations between Urban Sprawl and Life Expectancy in the United States

In recent years, the United States has had a relatively poor performance with respect to life expectancy compared to the other developed nations. Urban sprawl is one of the potential causes of the high rate of mortality in the United States. This study investigated cross-sectional associations between sprawl and life expectancy for metropolitan counties in the United States in 2010. In this study, the measure of life expectancy in 2010 came from a recently released dataset of life expectancies by county. This study modeled average life expectancy with a structural equation model that included five mediators: annual vehicle miles traveled (VMT) per household, average body mass index, crime rate, and air quality index as mediators of sprawl, as well as percentage of smokers as a mediator of socioeconomic status. After controlling for sociodemographic characteristics, this study found that life expectancy was significantly higher in compact counties than in sprawling counties. Compactness affects mortality directly, but the causal mechanism is unclear. For example, it may be that sprawling areas have higher traffic speeds and longer emergency response times, lower quality and less accessible health care facilities, or less availability of healthy foods. Compactness affects mortality indirectly through vehicle miles traveled, which is a contributor to traffic fatalities, and through body mass index, which is a contributor to many chronic diseases. This study identified significant direct and indirect associations between urban sprawl and life expectancy. These findings support further research and practice aimed at identifying and implementing changes to urban planning designed to support health and healthy behaviors

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

The association between contextual socioeconomic factors and prevalent asthma in a cohort of Southern California school children

Spatial variation in childhood asthma and a recent increase in prevalence indicate that environmental factors play a significant role in the etiology of this important disease. Socioeconomic position (SEP) has been associated inversely and positively with childhood asthma. These contradictory results indicate a need for systematic research about SEP and asthma. Pathways have been suggested for effects of SEP on asthma at both the individual and community level. We examined the relationship of prevalent asthma to community-level indicators of SEP among 5762 children in 12 Southern California communities, using a multilevel random effects model. Estimates of community-level SEP were derived by summarizing census block group-level data using a novel method of weighting by the proportion of the block groups included in a community-specific bounding rectangle that contained 95% of local study subjects. Community characteristics included measures of male unemployment, household income, low education (i.e., no high school diploma) and poverty. There was a consistent inverse association between male unemployment and asthma across the inter-quartile range of community unemployment rates, indicating that asthma rates increase as community SEP increases. The results were robust to individual-level confounding, methods for summarizing census block group data to the community level, scale of analysis (i.e., community-level vs. neighborhood-level) and the modeling algorithm. The positive association between SEP and prevalent childhood asthma might be explained by differential access to medical care that remains unmeasured, by the hygiene hypothesis (e.g., lower SES may associate with higher protective exposures to endotoxin in early life), or by SEP acting as a proxy for unmeasured neighborhood characteristics.USA Neighborhood Childhood asthma Multilevel modeling Socioeconomic position Contextual factor

Geographic variation of intrahepatic cholangiocarcinoma, extrahepatic cholangiocarcinoma, and hepatocellular carcinoma in the United States.

Intrahepatic (ICC) and extrahepatic cholangiocarcinomas (ECC) are tumors that arise from cholangiocytes in the bile duct, but ICCs are coded as primary liver cancers while ECCs are coded as biliary tract cancers. The etiology of these tumors is not well understood. It has been suggested that the etiology of ICC is more similar to that of another type of liver cancer, hepatocellular carcinoma (HCC), than to the etiology of ECC. If this is true, geographic incidence patterns and trends in ICC incidence should be more similar to that of HCC than ECC.To examine this hypothesis, data from the North American Association of Central Cancer Registries Cancer in North America data file were analyzed. Incidence rates and joinpoint trends were calculated by demographic subgroup. County-level incidence rates were mapped.Overall incidence rates, racial distribution, male:female ratio, and peak ages were more similar between ICC and ECC than with HCC. During 2000-2009, average annual incidence rates of ECC increased. During 2005-2009, average annual ICC incidence rates also increased. High rates for all three cancer sites were found in the Pacific region, particularly Hawaii and Alaska. Rates of ICC and ECC were also high in the Northeast and the upper Midwest, while rates of HCC were high in the South.Demographic patterns and geographical variation were more closely related between ICC and ECC than HCC, suggesting that the etiology of ICC and ECC may be similar. Increasing rates of both tumors suggest that further etiology studies are warranted

Modeled United States county-level hepatocellular carcinoma incidence rates, 2000–2009.

<p>Modeled United States county-level hepatocellular carcinoma incidence rates, 2000–2009.</p