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Background:
Environments conducive to walking may help people avoid sedentary lifestyles and associated diseases. Recent studies developed walkability models combining several built environment characteristics to optimally predict walking. Developing and testing such models with the same data could lead to overestimating one's ability to predict walking in an independent sample of the population. More accurate estimates of model fit can be obtained by splitting a single study population into training and validation sets (holdout approach) or through developing and evaluating models in different populations. We used these two approaches to test whether built environment characteristics near the home predict walking for exercise. Study participants lived in western Washington State and were adult members of a health maintenance organization. The physical activity data used in this study were collected by telephone interview and were selected for their relevance to cardiovascular disease. In order to limit confounding by prior health conditions, the sample was restricted to participants in good self-reported health and without a documented history of cardiovascular disease.
Results:
For 1,608 participants meeting the inclusion criteria, the mean age was 64 years, 90 percent were white, 37 percent had a college degree, and 62 percent of participants reported that they walked for exercise. Single built environment characteristics, such as residential density or connectivity, did not significantly predict walking for exercise. Regression models using multiple built environment characteristics to predict walking were not successful at predicting walking for exercise in an independent population sample. In the validation set, none of the logistic models had a C-statistic confidence interval excluding the null value of 0.5, and none of the linear models explained more than one percent of the variance in time spent walking for exercise. We did not detect significant differences in walking for exercise among census areas or postal codes, which were used as proxies for neighborhoods.
Conclusion:
None of the built environment characteristics significantly predicted walking for exercise, nor did combinations of these characteristics predict walking for exercise when tested using a holdout approach. These results reflect a lack of neighborhood-level variation in walking for exercise for the population studied.
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Background:
Although malaria disappeared from southern France more than 60 years ago, suspicions of recent autochthonous transmission in the French Mediterranean coast support the idea that the area could still be subject to malaria transmission. The main potential vector of malaria in the Camargue area, the largest river delta in southern France, is the mosquito Anopheles hyrcanus (Diptera: Culicidae). In the context of recent climatic and landscape changes, the evaluation of the risk of emergence or re-emergence of such a major disease is of great importance in Europe. When assessing the risk of emergence of vector-borne diseases, it is crucial to be able to characterize the arthropod vector's spatial distribution. Given that remote sensing techniques can describe some of the environmental parameters which drive this distribution, satellite imagery or aerial photographs could be used for vector mapping.
Results:
In this study, we propose a method to map larval and adult populations of An. hyrcanus based on environmental indices derived from high spatial resolution imagery. The analysis of the link between entomological field data on An. hyrcanus larvae and environmental indices (biotopes, distance to the nearest main productive breeding sites of this species i.e., rice fields) led to the definition of a larval index, defined as the probability of observing An. hyrcanus larvae in a given site at least once over a year. Independent accuracy assessments showed a good agreement between observed and predicted values (sensitivity and specificity of the logistic regression model being 0.76 and 0.78, respectively). An adult index was derived from the larval index by averaging the larval index within a buffer around the trap location. This index was highly correlated with observed adult abundance values (Pearson r = 0.97, p
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Background:
Disease data sharing is important for the collaborative preparation, response, and recovery stages of disease control. Disease phenomena are strongly associated with spatial and temporal factors. Web-based Geographical Information Systems provide a real-time and dynamic way to represent disease information on maps. However, data heterogeneities, integration, interoperability, and cartographical representation are still major challenges in the health geographic fields. These challenges cause barriers in extensively sharing health data and restrain the effectiveness in understanding and responding to disease outbreaks. To overcome these challenges in disease data mapping and sharing, the senior authors have designed an interoperable service oriented architecture based on Open Geospatial Consortium specifications to share the spatio-temporal disease information.
Results:
A case study of infectious disease mapping across New Brunswick (Canada) and Maine (USA) was carried out to evaluate the proposed architecture, which uses standard Web Map Service, Styled Layer Descriptor and Web Map Context specifications. The case study shows the effectiveness of an infectious disease surveillance system and enables cross-border visualization, analysis, and sharing of infectious disease information through interactive maps and/or animation in collaboration with multiple partners via a distributed network. It enables data sharing and users' collaboration in an open and interactive manner.
Conclusion:
In this project, we develop a service oriented architecture for online disease mapping that is distributed, loosely coupled, and interoperable. An implementation of this architecture has been applied to the New Brunswick and Maine infectious disease studies. We have shown that the development of standard health services and spatial data infrastructure can enhance the efficiency and effectiveness of public health surveillance.
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Background:
Researchers and public health officials in Canada, the United States and Australia have for some time noted broader geographic accessibility to gambling establishments, above all in socioeconomically underprivileged communities. This increase in availability could lead to more and more gambling problems. This article focuses, in an ecological perspective, in particular on a spatial analysis of the geographic accessibility of sites possessing a VLT permit in the Montréal area, i.e. Montréal Island, the South Shore and Laval, from the standpoint of the development of an indicator of the vulnerability (socioeconomic components and demographic components) to gambling of populations at the level of certain neighbourhood units (dissemination areas). With the recent development of geographic information systems (GIS), it is now possible to ascertain accessibility to services much more accurately, for example by taking into account the configuration of the road network.
Results:
The findings of our analysis reveal widespread geographic accessibility to sites possessing a VLT permit in the downtown area and in pericentral districts. In some neighbourhood units, a site possessing a VLT permit may be within a three-minute walk. In the region studied overall, average walking time to a VLT site is nine minutes. Access to this type of service on foot is usually limited in the outskirts. However, a number of groups of sites possessing VLT permits are found along certain axial highways. According to local spatial self-correlation analyses, the findings suggest a significant link between walking accessibility to sites possessing VLT permits and the vulnerability of the communities. In a number of neighbourhood units with ready access to VLT's the populations display high vulnerability.
Conclusion:
These findings reveal that accessibility to sites possessing a VLT permit is often linked to the vulnerability (socioeconomic and demographic components) of communities. Reliance in our analyses on neighbourhood units with fairly small areas enabled us to emphasize the rectilinear dimension of the spatial distribution of sites possessing VLT permits. This is a significant link that public health officials must consider when elaborating programs to combat pathological gambling.
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Background:
Over the past two decades, geographical accessibility of urban resources for population living in residential areas has received an increased focus in urban health studies. Operationalising and computing geographical accessibility measures depend on a set of four parameters, namely definition of residential areas, a method of aggregation, a measure of accessibility, and a type of distance. Yet, the choice of these parameters may potentially generate different results leading to significant measurement errors.The aim of this paper is to compare discrepancies in results for geographical accessibility of selected health care services for residential areas (i.e. census tracts) computed using different distance types and aggregation methods.
Results:
First, the comparison of distance types demonstrates that Cartesian distances (Euclidean and Manhattan distances) are strongly correlated with more accurate network distances (shortest network and shortest network time distances) across the metropolitan area (Pearson correlation greater than 0.95). However, important local variations in correlation between Cartesian and network distances were observed notably in suburban areas where Cartesian distances were less precise.Second, the choice of the aggregation method is also important: in comparison to the most accurate aggregation method (population-weighted mean of the accessibility measure for census blocks within census tracts), accessibility measures computed from census tract centroids, though not inaccurate, yield important measurement errors for 5% to 10% of census tracts.
Conclusion:
Although errors associated to the choice of distance types and aggregation method are only important for about 10% of census tracts located mainly in suburban areas, we should not avoid using the best estimation method possible for evaluating geographical accessibility. This is especially so if these measures are to be included as a dimension of the built environment in studies investigating residential area effects on health. If these measures are not sufficiently precise, this could lead to errors or lack of precision in the estimation of residential area effects on health.
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Background:
Geostatistical techniques are now available to account for spatially varying population sizes and spatial patterns in the mapping of disease rates. At first glance, Poisson kriging represents an attractive alternative to increasingly popular Bayesian spatial models in that: 1) it is easier to implement and less CPU intensive, and 2) it accounts for the size and shape of geographical units, avoiding the limitations of conditional auto-regressive (CAR) models commonly used in Bayesian algorithms while allowing for the creation of isopleth risk maps. Both approaches, however, have never been compared in simulation studies, and there is a need to better understand their merits in terms of accuracy and precision of disease risk estimates.
Results:
Besag, York and Mollie's (BYM) model and Poisson kriging (point and area-to-area implementations) were applied to age-adjusted lung and cervix cancer mortality rates recorded for white females in two contrasted county geographies: 1) state of Indiana that consists of 92 counties of fairly similar size and shape, and 2) four states in the Western US (Arizona, California, Nevada and Utah) forming a set of 118 counties that are vastly different geographical units. The spatial support (i.e. point versus area) has a much smaller impact on the results than the statistical methodology (i.e. geostatistical versus Bayesian models). Differences between methods are particularly pronounced in the Western US dataset: BYM model yields smoother risk surface and prediction variance that changes mainly as a function of the predicted risk, while the Poisson kriging variance increases in large sparsely populated counties. Simulation studies showed that the geostatistical approach yields smaller prediction errors, more precise and accurate probability intervals, and allows a better discrimination between counties with high and low mortality risks. The benefit of area-to-area Poisson kriging increases as the county geography becomes more heterogeneous and when data beyond the adjacent counties are used in the estimation. The trade-off cost for the easier implementation of point Poisson kriging is slightly larger kriging variances, which reduces the precision of the model of uncertainty.
Conclusion:
Bayesian spatial models are increasingly used by public health officials to map mortality risk from observed rates, a preliminary step towards the identification of areas of excess. More attention should however be paid to the spatial and distributional assumptions underlying the popular BYM model. Poisson kriging offers more flexibility in modeling the spatial structure of the risk and generates less smoothing, reducing the likelihood of missing areas of high risk.
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