Special needs hurricane shelters and the ageing population: development of a methodology and a case study application

Navigating Aged Care Services with GIS: Trends, Developments, and Future Directions

BACKGROUND

With the growing challenge of an aging population, emerging technologies are increasingly being integrated into the production, organization, and delivery of aged care services. Geographic Information System (GIS), a computer-based tool for spatial information analysis and processing, has made significant strides in the allocation of care recources and service delivery for older adults, a notably vulnerable group. Despite its growing importance, cross-disciplinary literature reviews on this theme are scare. This scoping review was conducted to encapsulate the advancements and discern the future trajectory of GIS applications in aged care services.

METHODS

A comprehensive search across nine databases yielded 5941 articles. Adhering to specific inclusion and exclusion criteria, 61 articles were selected for a detailed analysis.

RESULTS

The 61 articles span from 2003 to 2022, with a notable increase in publications since 2018, comprising 41 articles (67% of the total) published between 2018-2022. Developed countries contributed 66% of the papers, with 45% focusing on accessibility issues. In the domain of aged care services, GIS has been predominantly utilized for model construction, mapping, and site selection, with a growing emphasis on addressing the unique needs of different subgroups of older adults.

CONCLUSION

The past two decades have seen substantial growth in the application of GIS in aged care services, reflecting its increasing importance in this field. This scoping review not only charts the historical development of GIS applications in aged care services but also underscores the need for innovative research approaches. Future directions should emphasize the integration of GIS with diverse methodologies to address the heterogeneous needs of older adults and improve the overall delivery of aged care services. Such advancements in GIS applications have the potential to significantly enhance the efficiency, accessibility, and quality of care for the aging population.

Location selection of field hospitals amid COVID-19 considering effectiveness and fairness: A case study of Florida

The world has experienced an unprecedented global health crisis since 2020, the COVID-19 pandemic, which inflicted massive burdens on countries' healthcare systems. During the peaks of the pandemic, the shortages of intensive care unit (ICU) beds illustrated a critical vulnerability in the fight. Many individuals suffering the effects of COVID-19 had difficulty accessing ICU beds due to insufficient capacity. Unfortunately, it has been observed that many hospitals do not have enough ICU beds, and the ones with ICU capacity might not be accessible to all population strata. To remedy this going forward, field hospitals could be established to provide additional capacity in helping emergency health situations such as pandemics; however, location selection is a crucial decision ultimately for this purpose. As such, we consider finding new field hospital locations to serve the demand within certain travel-time thresholds, while accounting for the presence of vulnerable populations. A multi-objective mathematical model is proposed in this paper that maximizes the minimum accessibility and minimizes the travel time by integrating the Enhanced 2-Step Floating Catchment Area (E2SFCA) method and travel-time-constrained capacitated p-median model. This is performed to decide on the locations of field hospitals, while a sensitivity analysis addresses hospital capacity, demand level, and the number of field hospital locations. Four counties in Florida are selected to implement the proposed approach. Findings can be used to identify the ideal location(s) of capacity expansions concerning the fair distribution of field hospitals in terms of accessibility with a specific focus on vulnerable strata of the population.

Demand-supply balance of disaster medical care in Osaka City based on damage estimation for a Nankai Trough megathrust earthquake: A geographic information system-based analysis

Aim

Studies have not fully examined whether the medical care system would be able to manage the high number of casualties due to a Nankai Trough earthquake, whose probability of recurrence in the next 30 years is ~70%. This study assessed the demand-supply balance of the disaster medical care system in Osaka city by integrating the data on damage estimation and disaster coping hospitals using a geographic information system.

Methods

We obtained data on the distribution of casualties in two cases, high and low rates of evacuation from the tsunami, and available beds in Osaka city calculated from operating data of each disaster coping hospital. We expanded these data on a geographic information system and investigated the balance of medical care.

Results

The total number of available beds in the disaster medical care facilities was 5,559, and the shortage with evacuation rates being either low or high, would be 47,631 and 1,487, respectively. With a low evacuation rate, bed shortage is a common occurrence in coastal areas. With a high evacuation rate, bed shortage decreases, and problems with medical care arise in the eastern area of Osaka city.

Discussion

In the case of a low evacuation rate, greater bed shortage was found along the coast of Osaka Bay where the probabilities of flooding are high. In the case of a high evacuation rate, however, the number of casualties was much lower. A shortage of medical care did not occur along the coast, but in the eastern part of Osaka city.

Assessment of an evacuation shelter program for people with access and functional needs in Monroe County, Florida during Hurricane Irma

This case study presents an evaluation framework to assess the facilitating and constraining factors that influenced the emergency response, operation, and management of a Special Needs Shelter Program in Monroe County, Florida during Hurricane Irma in 2017. A qualitative analysis of key documents and discussions with agencies directly involved in the Monroe County Special Needs Shelter Program was used to assess four major emergency response processes: notifications and communications, evacuation and transportation, sheltering, and interagency coordination. A critical cross-cutting theme emerged, which was a lack of a common definition for populations with "special needs" across different agencies resulting in uncertainties about who should be admitted to the Special Needs Shelter and have access to their services. We generate public health and emergency management lessons to inform future adaptation, preparedness, and response plans to extreme weather events for populations with access and functional needs in Monroe County and Florida's coastal communities more broadly.

Optimization of Shelter Location Based on a Combined Static/Dynamic Two-Stage Optimization Methodology: A Case Study in the Central Urban Area of Xinyi City, China

Determining how to reasonably allocate shelters in the central area of the city and improve evacuation efficiency are important issues in the field of urban disaster prevention. This paper introduces the methodology and mathematical model from the field of crowd emergency evacuation to shelter location optimization. Moreover, a shelter location optimization method based on the combination of static network analysis and dynamic evacuation simulation is proposed. The construction costs and evacuation times are taken as the objective functions. In the first stage, based on the static network analysis, a circular evacuation allocation rule based on the gravity model is proposed, and the genetic algorithm is then designed to solve the feasible schemes with the lowest shelter construction costs. In the second stage, the evacuation time is taken as the optimization objective. The age differences of refugees, the selection of evacuation routes, and the behavior of adults helping children and the elderly are simulated in a dynamic evacuation simulation model. The traditional social force model is improved to conduct a regional evacuation simulation and determine the optimal scheme with the shortest evacuation time. Finally, the central urban area of Xinyi City, Jiangsu Province, China, is taken as an empirical case.

Powerless in the Virgin Islands: Emergency Department and Wound Care Clinic Utilization Following the 2017 Hurricanes Irma and Maria on St. Thomas, U.S. Virgin Islands

OBJECTIVE

The main objective of this work was to characterize the prevalence of acute medical needs by examining emergency department (ED) and outpatient wound care clinic (WCC) visits before, during, and after the 2017 Hurricanes Irma and Maria, in St. Thomas, United States (U.S.) Virgin Islands.

METHODS

Descriptive statistics and logistic regression were used to assess associations between the occurrence of the storms and visits due to injuries and chronic conditions presented to the ED and WCC from September 1, 2016 to May 31, 2018.

RESULTS

ED visits increased and the rate of injury care was higher during the storms (12 patient visits per day) than before or after the storms (9 patient visits per day). WCC visits increased during (12%) and after the storms (45%), and were associated with patients 60 years and older. The odds of ED and WCC visits due to injury during the storms were significantly higher (odds ratio [OR]: 1.28, 95% confidence interval [CI]: 1.17, 1.40) than prior to the storms. The odds for visits due to injuries were 1.19 (95% CI: 1.12, 1.28) times higher after the storms.

CONCLUSIONS

Increases occurred in ED and WCC visits for injury care during the storms and in WCC visits after the storms. Public health preparedness mandates understanding how major hurricanes impact the prevalence of acute medical needs, and the factors that influence decisions to seek medical care, in their wake.

Methodology and Application of Spatial Vulnerability Assessment for Evacuation Shelters in Disaster Planning

Evacuation shelters are the most important means for safeguarding people in hazardous areas and situations, and thus minimizing losses, particularly those due to a disaster. Therefore, evacuation shelter assignment and evacuation planning are some of the critical factors for reducing vulnerability and increasing resilience in disaster risk reduction. However, an imbalance of shelter distribution and spatial heterogeneity of a population are the critical issues limiting the accessibility of evacuation shelters in real situations. In this study, we propose a methodology for spatial assessment to reduce vulnerability and evaluate the spatial distribution of both shelter demand and resources, considering spatial accessibility. The method was applied to the case study of Mabi, in the context of a disaster caused by the 2018 flooding. We applied this approach to evaluate the area and identified the vulnerability of the evacuation shelters and the residents. The proposed method revealed that 54.55% of the designated evacuation shelters and 59% of the total population were physically vulnerable to the flood. The results highlight, using GIS maps, that the total shelter capacity was significantly decreased to 43.86%. The outcome assessment addressed specific vulnerable shelters and the imbalance between the demand for and resources of each shelter. Accordingly, this study provides practical information and a valuable reference for supporting local governments and stakeholders to improve future disaster planning, prevention, and preparedness.

Evacuation Planning Optimization Based on a Multi-Objective Artificial Bee Colony Algorithm

Evacuation is an important activity for reducing the number of casualties and amount of damage in disaster management. Evacuation planning is tackled as a spatial optimization problem. The decision-making process for evacuation involves high uncertainty, conflicting objectives, and spatial constraints. This study presents a Multi-Objective Artificial Bee Colony (MOABC) algorithm, modified to provide a better solution to the evacuation problem. The new approach combines random swap and random insertion methods for neighborhood search, the two-point crossover operator, and the Pareto-based method. For evacuation planning, two objective functions were considered to minimize the total traveling distance from an affected area to shelters and to minimize the overload capacity of shelters. The developed model was tested on real data from the city of Kigali, Rwanda. From computational results, the proposed model obtained a minimum fitness value of 5.80 for capacity function and 8.72 × 108 for distance function, within 161 s of execution time. Additionally, in this research we compare the proposed algorithm with Non-Dominated Sorting Genetic Algorithm II and the existing Multi-Objective Artificial Bee Colony algorithm. The experimental results show that the proposed MOABC outperforms the current methods both in terms of computational time and better solutions with minimum fitness values. Therefore, developing MOABC is recommended for applications such as evacuation planning, where a fast-running and efficient model is needed.

Site Selection Models in Natural Disaster Shelters: A Review

Constructing natural disaster shelters is important for disaster emergency management, and site selection models provide a feasible technique and method. This paper presents site selection models for natural disaster shelters. A synthesis of the types, objectives, constraints, methods of solutions, targeted disasters and applications of different site selection models for natural disaster shelters is investigated. Shelter location models can be classified as single-objective models, multiobjective models and hierarchical models, according to the objective and hierarchy type. Minimizing the evacuation distance or time, shelter construction cost or number, and the total risk are the general objectives of the models. Intelligent optimization algorithms are widely used to solve the models, instead of the Geographic Information System (GIS) method, due to the complexity of the problem. The results indicate that the following should be the main focuses of future works: How to set a model that can be applied for determining the shelter locations of multiple disasters; how to consider the uncertainty in the models; how to improve the existing algorithms or models to solve large-scale location-allocation problems; and how to develop a new resource-saving model that is consistent with the concept of sustainable development, as advocated by shelter planners and policy makers, which can be applied in real situations. This study allows those undertaking shelter location research to situate their work within the context of shelter planning.