An evaluation of edge effects in nutritional accessibility and availability measures: a simulation study

<p>Abstract</p> <p>Background</p> <p>This paper addresses the statistical use of accessibility and availability indices and the effect of study boundaries on these measures. The measures are evaluated via an extensive simulation based on cluster models for local outlet density. We define outlet to mean either food retail store (convenience store, supermarket, gas station) or restaurant (limited service or full service restaurants). We designed a simulation whereby a cluster outlet model is assumed in a large study window and an internal subset of that window is constructed. We performed simulations on various criteria including one scenario representing an urban area with 2000 outlets as well as a non-urban area simulated with only 300 outlets. A comparison is made between estimates obtained with the full study area and estimates using only the subset area. This allows the study of the effect of edge censoring on accessibility measures.</p> <p>Results</p> <p>The results suggest that considerable bias is found at the edges of study regions in particular for accessibility measures. Edge effects are smaller for availability measures (when not smoothed) and also for short range accessibility</p> <p>Conclusions</p> <p>It is recommended that any study utilizing these measures should correct for edge effects. The use of edge correction via guard areas is recommended and the avoidance of large range distance-based accessibility measures is also proposed.</p

Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance:a case study of national dengue surveillance in Thailand

BACKGROUND: Dengue is a mosquito-borne disease that causes over 300 million infections worldwide each year with no specific treatment available. Effective surveillance systems are needed for outbreak detection and resource allocation. Spatial cluster detection methods are commonly used, but no general guidance exists on the most appropriate method for dengue surveillance. Therefore, a comprehensive study is needed to assess different methods and provide guidance for dengue surveillance programs.METHODS: To evaluate the effectiveness of different cluster detection methods for dengue surveillance, we selected and assessed commonly used methods: Getis Ord [Formula: see text], Local Moran, SaTScan, and Bayesian modeling. We conducted a simulation study to compare their performance in detecting clusters, and applied all methods to a case study of dengue surveillance in Thailand in 2019 to further evaluate their practical utility.RESULTS: In the simulation study, Getis Ord [Formula: see text] and Local Moran had similar performance, with most misdetections occurring at cluster boundaries and isolated hotspots. SaTScan showed better precision but was less effective at detecting inner outliers, although it performed well on large outbreaks. Bayesian convolution modeling had the highest overall precision in the simulation study. In the dengue case study in Thailand, Getis Ord [Formula: see text] and Local Moran missed most disease clusters, while SaTScan was mostly able to detect a large cluster. Bayesian disease mapping seemed to be the most effective, with adaptive detection of irregularly shaped disease anomalies.CONCLUSIONS: Bayesian modeling showed to be the most effective method, demonstrating the best accuracy in adaptively identifying irregularly shaped disease anomalies. In contrast, SaTScan excelled in detecting large outbreaks and regular forms. This study provides empirical evidence for the selection of appropriate tools for dengue surveillance in Thailand, with potential applicability to other disease control programs in similar settings.</p

Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand

Background: Dengue is a mosquito-borne disease that causes over 300 million infections worldwide each year with no specific treatment available. Effective surveillance systems are needed for outbreak detection and resource allocation. Spatial cluster detection methods are commonly used, but no general guidance exists on the most appropriate method for dengue surveillance. Therefore, a comprehensive study is needed to assess different methods and provide guidance for dengue surveillance programs. Methods: To evaluate the effectiveness of different cluster detection methods for dengue surveillance, we selected and assessed commonly used methods: Getis Ord Gi∗, Local Moran, SaTScan, and Bayesian modeling. We conducted a simulation study to compare their performance in detecting clusters, and applied all methods to a case study of dengue surveillance in Thailand in 2019 to further evaluate their practical utility. Results: In the simulation study, Getis Ord Gi∗ and Local Moran had similar performance, with most misdetections occurring at cluster boundaries and isolated hotspots. SaTScan showed better precision but was less effective at detecting inner outliers, although it performed well on large outbreaks. Bayesian convolution modeling had the highest overall precision in the simulation study. In the dengue case study in Thailand, Getis Ord Gi∗ and Local Moran missed most disease clusters, while SaTScan was mostly able to detect a large cluster. Bayesian disease mapping seemed to be the most effective, with adaptive detection of irregularly shaped disease anomalies. Conclusions: Bayesian modeling showed to be the most effective method, demonstrating the best accuracy in adaptively identifying irregularly shaped disease anomalies. In contrast, SaTScan excelled in detecting large outbreaks and regular forms. This study provides empirical evidence for the selection of appropriate tools for dengue surveillance in Thailand, with potential applicability to other disease control programs in similar settings

Environmental and reproductive health: A spatial analysis of adverse birth outcomes and environmental contaminants in British Columbia.

Exposure to contaminants during pregnancy is associated with certain adverse birth outcomes that require further investigation. Community reproductive and environmental health risk maps were produced utilizing birth data obtained from the B.C. Perinatal Health Program and environmental contaminant data from the National Pollutant Release inventory and other national and provincial sources. Geographical information systems (GIS) were utilized to spatially relate perinatal and environmental hazard data, and the risk of adverse birth outcomes was tested using watersheds as the ecological aggregation unit adjusting for individual-level risk factors. The perinatal data included birth outcomes (low birth weight, prematurity, inter-uterine growth restriction, congenital anomalies, stillbirths) and numerous maternal and antenatal risk factor data for all singleton births in B.C. from 2001 to 2006. Small but significant increased risks of adverse birth outcomes were found in high and intermediate hazard watersheds compared to low hazard watersheds. This suggests a possible environmental effect on these reproductive outcomes, however, further studies are needed to corroborate these results. --P.ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b165168

Assessing vulnerability and modelling assistance: using demographic indicators of vulnerability and agent-based modelling to explore emergency flooding relief response

Flooding is a significant concern for much of the UK and is recognised as a primary threat by most local councils. Those in society most often deemed vulnerable: the elderly, poor or sick, for example, often see their level of vulnerability increase during hazard events. A greater knowledge of the spatial distribution of vulnerability within communities is key to understanding how a population may be impacted by a hazard event. Vulnerability indices are regularly used – in conjunction with needs assessments and on-the-ground research – to target service provision and justify resource allocation. Past work on measuring and mapping vulnerability has been limited by a focus on income-related indicators, a lack of consideration of accessibility, and the reliance on proprietary data. The Open Source Vulnerability Index (OSVI) encompasses an extensive range of vulnerability indicators supported by the wider literature and expert validation and provides data at a sufficiently fine resolution that can identify vulnerable populations. Findings of the OSVI demonstrate the potential cascading impact of a flood hazard as it impacts an already vulnerable population: exacerbating pre-existing vulnerabilities, limiting capabilities and restricting accessibility and access to key services. The OSVI feeds into an agent-based model (ABM) that explores the capacity of the British Red Cross (BRC) to distribute relief during flood emergencies using strategies based upon the OSVI. A participatory modelling approach was utilised whereby the BRC were included in all aspects of the model development. The major contribution of this work is the novel synthesis of demographics analysis, vulnerability mapping and geospatial simulation. The project contributes to the growing understanding of vulnerability and response management within the NGO sector. It is hoped that the index and model produced will allow responder organisations to run simulations of similar emergency events and adjust strategic response plans accordingly