A Survey Study of Public Health Nurses’ Knowledge in Disaster Management in Indonesia

AIM: This descriptive survey study examines the level of public health nurses’ (PHNs) knowledge regarding disaster management. The knowledge was examined according to three disaster phases, including preparedness, response, and recovery phase. MATERIALS & METHODS: A stratified proportionate random sampling method was employed to recruit 252 PHNs of Aceh Province, Indonesia. The data were collected during November and December of 2010 by using the questionnaire developed by the researchers. They were analyzed using frequencies, percentages, means, standard deviations, and minimum and maximum scores. Additional analyses were performed to identify potential contributing factors to the PHNs' knowledge using the Spearman rank correlation (rs) and the Mann-Whitney U test. RESULTS: The finding showed that PHNs' knowledge in disaster management was moderate (M=70.73%, SD=8.41), and nearly half of the subjects (42.5%) were categorized in this level. The lowest mean score was found in the response phase (64.75%), and four items with the lowest percentage of correct answers were also found in this phase. CONCLUSION: The low level of knowledge for the response phase can be used to flag health policymakers and public health centers (PHC) to develop appropriate educational training and disaster drills for PHNs in collaboration with stakeholders in the community.

Mitigating interorganizational conflicts in humanitarian logistics collaboration: the roles of contractual agreements, trust and post-disaster environmental uncertainty phases

PurposeGovernance is the key to establishing effective collaboration among humanitarian logistics partners addressing an ongoing relief work. With a focus on humanitarian interorganizational collaboration, this research draws on governance theories to investigate how conflicts can be mitigated in this challenging setting.Design/methodology/approachThe focus on governance extends attention to the frontiers of contractual agreement, trust and environmental uncertainty to be applied in the humanitarian setting. To develop perspectives, an online survey of 289 field executives working in humanitarian organizations across the globe is conducted. The findings are based on hierarchical regressions.FindingsEnvironmental uncertainty, in humanitarian logistics, is not straightforward, but wields distinctive challenges in the response phase (immediate to the disaster) as well as the recovery phase (beginning of build back) – to loom prospects of conflict between partners. Findings outline that contractual agreement can increase conflict during the response phase (high environmental uncertainty), but mitigate it during the recovery phase (low environmental uncertainty). Furthermore, contractual agreement interactively strengthens the ability of trust to reduce conflict. Yet, trust acting alone shows best outcome to mitigate conflict.Research limitations/implicationsContrary to the established understanding in traditional logistics suggesting the vitality of contracts to easily mitigate challenges posed by environmental uncertainty, the humanitarian setting extends a unique outset for interorganizational governance based on the temporality of response and recovery phases.Originality/valueThis research pioneers to quantitatively examine the setting of humanitarian logistics based on survey. Given the difficulty of data acquisition, the extant research has largely relied on qualitative investigations when considering the agenda of governance.

Relevant Indicators of Social Vulnerability during Response Phase Context of Flooding inTebingTinggi City, North Sumatra Indonesia

Indicators to reflect vulnerability to flood vary among scholars. These variations tend to be developed primarily due to heavy dependency on the statistics data availability. Unless employing such predetermined indicators to assess the states of social vulnerability, scholars were less attracted to study what relevant indicators should be in assessing such social vulnerability. Apart from this gap, majority of the studies have overlooked the significance of time-frame contexts of flooding in determining the situation of social vulnerability. Consequently, many conducted the investigations without questioning the relevance of indicators in terms of time-frame contexts. This current study, accordingly, attempts to investigate relevant indicators of social vulnerability to flood with respect to response phase context. With the study area of Tebing Tinggi in North Sumatra, 76 indicators are tested using Likert scale of survey and Principal Components Analysis (PCA). As a result, 6 retained components produce 12 relevant indicators to reflect social vulnerability to flood during response phase; they are: Elderly, Female, Single parent families, Household or per capita income, Children, Poverty and deprivation, Dependency, Informal sector/day labourer, Slum dwellers, Male, Renters, and Unemployment.

Compassionate Use of GC5131 (Hyperimmunoglobulin) Therapy in Critically Ill Patients Diagnosed with COVID-19: A Case Series and Review of Literature

Presently, the use of convalescent plasma and hyperimmunoglobulin obtained from individuals who have recovered from coronavirus disease 2019 (COVID-19) has proved to potentially provide passive antibody-based immunity, thereby leading to several clinical trials to develop an immune-based COVID-19 treatment. However, the therapeutic efficacy of hyperimmunoglobulin in critically ill patients with COVID-19 remains unknown. On 23 October 2020, we first administered GC5131 in a compassionate-use program to critically ill patients at the Kyungpook National University, Chilgok Hospital, Korea. Since then, five more critically ill patients were treated with GC5131 in this compassionate-use program in our hospital up until 17 December 2020. We retrospectively reviewed the clinical responses of six critically ill patients diagnosed with COVID-19 who received the hyperimmunoglobulin concentrate, GC5131, which was produced by the Green Cross Corporation. After the administration of GC5131, five patients died due to an exacerbation of COVID-19 pneumonia. GC5131 was ineffective when administered to critically ill patients with COVID-19. Nevertheless, we propose that to expect a therapeutic effect from GC5131, it should be administered as early as possible to avoid the excessive inflammatory response phase in patients with severe and advanced COVID-19 infection. This step was difficult to achieve in the real world due to the time required for decision making and the process of the compassionate-use program.

Detection of Collapsed Bridges from Multi-Temporal SAR Intensity Images by Machine Learning Techniques

Bridges are an important part of road networks in an emergency period, as well as in ordinary times. Bridge collapses have occurred as a result of many recent disasters. Synthetic aperture radar (SAR), which can acquire images under any weather or sunlight conditions, has been shown to be effective in assessing the damage situation of structures in the emergency response phase. We investigate the backscattering characteristics of washed-away or collapsed bridges from the multi-temporal high-resolution SAR intensity imagery introduced in our previous studies. In this study, we address the challenge of building a model to identify collapsed bridges using five change features obtained from multi-temporal SAR intensity images. Forty-four bridges affected by the 2011 Tohoku-oki earthquake, in Japan, and forty-four bridges affected by the 2020 July floods, also in Japan, including a total of 21 collapsed bridges, were divided into training, test, and validation sets. Twelve models were trained, using different numbers of features as input in random forest and logistic regression methods. Comparing the accuracies of the validation sets, the random forest model trained with the two mixed events using all the features showed the highest capability to extract collapsed bridges. After improvement by introducing an oversampling technique, the F-score for collapsed bridges was 0.87 and the kappa coefficient was 0.82, showing highly accurate agreement.

Application of Boyd’s OODA loop to emergency response

Emergency management is a dynamic task that requires constant evaluation and research so as to improve processes and systems. Most importantly, the rise in emergencies has led to increased costs of managing them. These cost increases are happening at a time when US government agencies are experiencing budget-cuts. Therefore, a search for solutions to improve emergency management is critical, especially in the emergency response phase. The emergency response phase is particularly important because studies show that due to the confusion and panic caused when a disaster strikes, failures in command and control often lead to delays in action and increased operation costs.This article argues that Boyd’s OODA loop, a command and control concept used by the US military for several years, presents opportunities as an off-the-shelf resource that can be adapted by nonmilitary response organizations at federal, state, local, and agency level. The study is an important contribution to the ongoing discourse on emergency management policy and practice in the United States.

A methodology for managing public spaces to increase access to essential goods and services by vulnerable populations during the COVID-19 pandemic

PurposeThe purpose of this paper is to present a spatial decision support system (SDSS) to be used by the local authorities of a city in the planning and response phase of a disaster. The SDSS focuses on the management of public spaces as a resource to increase a vulnerable population’s accessibility to essential goods and services. Using a web-based platform, the SDSS would support data-driven decisions, especially for cases such as the COVID-19 pandemic which requires special care in quarantine situations (which imply walking access instead of by other means of transport).Design/methodology/approachThis paper proposes a methodology to create a web-SDSS to manage public spaces in the planning and response phase of a disaster to increase the access to essential goods and services. Using a regular polygon grid, a city is partitioned into spatial units that aggregate spatial data from open and proprietary sources. The polygon grid is then used to compute accessibility, vulnerability and population density indicators using spatial analysis. Finally, a facility location problem is formulated and solved to provide decision-makers with an adaptive selection of public spaces given their indicators of choice.FindingsThe design and implementation of the methodology resulted in a granular representation of the city of Lima, Peru, in terms of population density, accessibility and vulnerability. Using these indicators, the SDSS was deployed as a web application that allowed decision-makers to explore different solutions to a facility location model within their districts, as well as visualizing the indicators computed for the hexagons that covered the district’s area. By performing tests with different local authorities, improvements were suggested to support a more general set of decisions and the key indicators to use in the SDSS were determined.Originality/valueThis paper, following the literature gap, is the first of its kind that presents an SDSS focused on increasing access to essential goods and services using public spaces and has had a successful response from local authorities with different backgrounds regarding the integration into their decision-making process.

Factors Associated With Relief Overdemanding in The Disaster Response Phase: A Qualitative Content Analysis

Demand for relief increases after disasters. Some research suggests that the number of relief supplies required to satisfy the relief demanding after disasters is significantly higher than preliminary estimates, especially in low and lower-middle-income countries. So, this study was aimed to explore the reasons for relief overdemanding in the disaster response phase. In this qualitative content analysis study the managers of the National Disaster Management Organization (NDMO), the Iranian Medical Emergency and Accident Management Center, Tehran Disaster Mitigation and Management Organization (TDMMO) and the Iranian Red Crescent Society (IRCS) were purposively sampled and invited to interview. The unstructured face-to-face interviews were recorded, transcribed, and analyzed using "constant comparison" and "microanalysis" methods. 21 agreed to participate and were interviewed. The interviews uncovered affected people-level factors such as the react to fear and anxiety and the unsatisfied demand due to improper distribution of facilities and crisis managers-level issues (e.g. facing people's dissatisfaction, incorrect or insufficient information and considering disaster as the opportunity to raise resources) as well as officials-level determinants (e.g. advertising and excitement, the partisanship of officials and low confidence on accident managers). Several factors influencing the relief overdemanding exist in disaster management in Iran. Strengthening local management when responding to disasters and conducting efficient disaster need assessment can reduce relief overdemanding and vastly prevent wasting surplus resources in the affected area.

Increased Risk of Death Triggered by Domestic Violence, Hunger, Suicide, Exhausted Health System during COVID-19 Pandemic: Why, How and Solutions

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, just like many other public health emergencies, is a well-established global health burden that has resulted in several changes in routines and lifestyles of people globally. The coronavirus disease (COVID-19) pandemic, caused by SARS-CoV-2, has directly or indirectly involved in the loss of lives of more than 3.24 million as of 6th May, 2021. The increasing threats posed by this pandemic were subsided by the swift and drastic measures put in place by different countries. As other causes of death before the emergence of COVID-19 still exist, the pandemic has further worsened their impact. The increased risks of COVID-19 deaths are not only due to the health burden it possesses, but also due to some other factors. These factors include domestic violence that becomes rampant, especially during lockdowns; hunger due to low economic development, unemployment, and loss of jobs; suicide due to depression; exhausted health system due to high level of COVID-19 cases and inability to contain it. As we move from the response phase into recovery, the pandemic’s direct and broader impacts on individuals, households, and communities will influence the capacity to recover. An understanding of these impacts is therefore required to develop priorities to support recovery. This paper identifies other causes of death amidst the pandemic, such as domestic violence, hunger, suicide, and exhausted health system, and how to minimize their effects.