Statistics for Spatial Data.

The science of the total environment

https://globalhealth.duke.edu/sites/default/files/event-attachments/2.18_meara.pdf

Global Surgery 2030: evidence and solutions for achieving health, welfare, and economic development

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(14)62114-0.pdf

Mortality risk attributable to high and low ambient temperature: a multicountry observational study

Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd preventing chronic diseases a vital investment to read.

Preventing Chronic Diseases: A Vital Investment

This Special Issue on GIS and public health is the result of a highly selective process, which saw the participation of some 20 expert peer-reviewers and led to the acceptance of one half of the high-quality submissions received over the past year. Many threads link these papers to each other and, indeed, to our original call for papers, but the element that most clearly emerges from these works is the inextricable connection between public health and the environment. Indeed, GIS analysis of public health simply cannot disregard the geospatial dimension of environmental resources and risks. What consistently emerges from these analyses is that current geospatial research can only scratch the surface of the complex interactions of spatial resources, risks, and public health. In today’s world, or at least in the developed world, researchers and practitioners can count on virtually endless data, on inexpensive computational power, and on seamless connectivity. In this research environment, these papers point to the need for improved analytical tools, covering concepts, representation, modeling and reliability. These works are important contributions that help us to identify what advances in geospatial analysis can better address the complex interactions of public health with our physical and cultural environment, and bridge research and practice, so that geospatial analyses can inform public health policy making. [...]

/pdf/gis-and-public-health-238u4eymed.pdf

GIS and Public Health

Several methodological approaches have been used to estimate distance in health service research. In this study, focusing on cardiac catheterization services, Euclidean, Manhattan, and the less widely known Minkowski distance metrics are used to estimate distances from patient residence to hospital. Distance metrics typically produce less accurate estimates than actual measurements, but each metric provides a single model of travel over a given network. Therefore, distance metrics, unlike actual measurements, can be directly used in spatial analytical modeling. Euclidean distance is most often used, but unlikely the most appropriate metric. Minkowski distance is a more promising method. Distances estimated with each metric are contrasted with road distance and travel time measurements, and an optimized Minkowski distance is implemented in spatial analytical modeling. Road distance and travel time are calculated from the postal code of residence of each patient undergoing cardiac catheterization to the pertinent hospital. The Minkowski metric is optimized, to approximate travel time and road distance, respectively. Distance estimates and distance measurements are then compared using descriptive statistics and visual mapping methods. The optimized Minkowski metric is implemented, via the spatial weight matrix, in a spatial regression model identifying socio-economic factors significantly associated with cardiac catheterization. The Minkowski coefficient that best approximates road distance is 1.54; 1.31 best approximates travel time. The latter is also a good predictor of road distance, thus providing the best single model of travel from patient's residence to hospital. The Euclidean metric and the optimal Minkowski metric are alternatively implemented in the regression model, and the results compared. The Minkowski method produces more reliable results than the traditional Euclidean metric. Road distance and travel time measurements are the most accurate estimates, but cannot be directly implemented in spatial analytical modeling. Euclidean distance tends to underestimate road distance and travel time; Manhattan distance tends to overestimate both. The optimized Minkowski distance partially overcomes their shortcomings; it provides a single model of travel over the network. The method is flexible, suitable for analytical modeling, and more accurate than the traditional metrics; its use ultimately increases the reliability of spatial analytical models.

/pdf/comparison-of-distance-measures-in-spatial-analytical-5els7rvc39.pdf

Comparison of distance measures in spatial analytical modeling for health service planning

A GIS implementation of a model of systemic vulnerability assessment in urbanized areas exposed to combined risk of landslide and flood

/pdf/geographic-information-analysis-for-sustainable-development-2x5sl3qhiu.pdf

Geographic Information Analysis for Sustainable Development and Economic Planning: New Technologies

Background: In a universal, public health care system, access to kidney transplantation should not be influenced by residence location. We determined the likelihood of kidney transplantation from deceased donors among Canadian dialysis patients living in 7 geographic regions. Within each region we also determined whether distance from the closest transplant centre was associated with the likelihood of transplantation. Methods: A random sample of 7034 subjects initiating dialysis in Canada between 1996 and 2000 was studied. We used Cox proportional hazards models to examine the relation between residence location and the likelihood of kidney transplantation from deceased donors over a median period of 2.4 years. Results: There were significant differences in the likelihood of kidney transplantation from deceased donors and predicted waiting times between the different geographic regions. For example, the adjusted relative likelihood of transplantation in Alberta was 3.74 (95% confidence interval [CI] 2.95–4.76) compared with the likelihood in Ontario ( p Interpretation: The adjusted likelihood of undergoing a kidney transplant from a deceased donor varied substantially between geographic regions in Canada. In contrast, the likelihood of transplantation within regions was not affected by distance from the closest transplant centre.

/pdf/residence-location-and-likelihood-of-kidney-transplantation-39oa406m00.pdf

Stefania Bertazzon

Papers

GIS and Public Health

Comparison of distance measures in spatial analytical modeling for health service planning

Geographic Information Analysis for Sustainable Development and Economic Planning: New Technologies

Residence location and likelihood of kidney transplantation

Accounting for spatial effects in land use regression for urban air pollution modeling