Intention to Comply With Mandatory Hurricane Evacuation Orders Among Persons Living Along a Coastal Area

Reininger, Belinda M.; Raja, Sartaj Alam; Carrasco, Ana Sanchez; Chen, Zhongxue; Adams, Barbara; McCormick, Joseph B.; Rahbar, Mohammad H.

doi:10.1001/dmp.2012.57

Cited by 37 publications

(34 citation statements)

References 32 publications

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“…Trust and fairness are two social capital factors imbedded in relationships between individuals and society and the outcome expectations associated with social interactions. These factors influence the bonding among network members (Putnam, 1995) and are particularly important in light of data collected from this sample and reported elsewhere that show 46% of respondents indicate that their household would rely on other people from another household to evacuate them (Reininger et al, 2008; Reininger et al, 2012). Trust and fairness foster collaboration and have been shown to overcome the hardships faced in disasters.…”

Section: Discussionmentioning

confidence: 84%

“…No monetary incentives were provided to the participants. (Reininger et al, 2008 ; Reininger et al, 2012)…”

Section: Methodsmentioning

confidence: 99%

“…As a full description of the study design and methods for this study have been reported (Reininger et al, 2008; Reininger et al, 2012) we will summarize the parent study design and methods here. A stratified, two-stage cluster sampling methodology was used.…”

Section: Methodsmentioning

confidence: 99%

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Social capital and disaster preparedness among low income Mexican Americans in a disaster prone area

Reininger¹,

Rahbar

Lee

et al. 2013

Social Science & Medicine

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158

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Examination of social capital and its relationship to disaster preparedness has grown in prominence partially due to world-wide need to effectively respond to terrorist attacks, viral epidemics, or natural disasters. Recent studies suggested that social capital may be related to a community’s ability to plan for and respond to such disasters. Few studies, however, have examined social capital constructs among low income populations living in disaster prone areas and accounted for the influence of social capital at the individual and community level. We examined social capital as measured by perceived fairness, perceived civic trust, perceived reciprocity and group membership. We undertook a multistage random cluster survey in three coastal counties in Texas (U.S.) noted for their high levels of poverty. Individuals from 3088 households provided data on social capital, socioeconomic and demographic characteristics, and self-reported level of preparedness for a hurricane. We used multivariable logistic regression to test potential associations between social capital measures and disaster preparedness. After adjusting for age, gender, marital status, ethnicity, education, employment, household income, acculturation, self-reported health, special needs persons in household, household size, and distance to the shore we found a higher prevalence of preparedness among individuals who reported the highest perception of fairness [AOR=3.12, 95% CI: (1.86, 5.21)] compared to those individuals who reported lowest perceptions of fairness. We also found a higher prevalence of preparedness [AOR= 2.06; 95% CI: (1.17, 3.62)] among individuals who reported highest perceptions of trust compared to individuals who reported lowest perceptions of trust. Perceived reciprocity and group membership were not associated with preparedness. These results extend previous findings on social capital and disaster preparedness and further characterize social capital’s presence among a low income population living in a hurricane prone area.

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Section: Discussionmentioning

confidence: 84%

“…No monetary incentives were provided to the participants. (Reininger et al, 2008 ; Reininger et al, 2012)…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Social capital and disaster preparedness among low income Mexican Americans in a disaster prone area

Reininger¹,

Rahbar

Lee

et al. 2013

Social Science & Medicine

Self Cite

158

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“…These are counties located on or near the Texas Coastline, an area prone to hurricanes. The methods for data collection have previously been reported (12, 23). …”

Section: Methodsmentioning

confidence: 99%

Barriers to Disaster Preparedness among Medical Special Needs Populations

Meyer¹,

Vatcheva²,

Castellanos³

et al. 2015

Front. Public Health

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A medical special needs (MSN) assessment was conducted among 3088 respondents in a hurricane prone area. The sample was female (51.7%), Hispanic (92.9%), aged >45 years (51%), not insured for health (59.2%), and with an MSN (33.2%). Barriers to preparedness were characterized for all households, including those with inhabitants reporting MSN ranging from level 0 (mild) to level 4 (most severe). Multivariable logistic regression tested associations between hurricane preparedness and barriers to evacuation by level of MSN. A significant interaction effect between number of evacuation barriers and MSN was found. Among households that reported individuals with level 0 MSN, the odds of being unprepared increased 18% for each additional evacuation barrier [OR = 1.18, 95% CI (1.08, 1.30)]. Among households that reported individuals with level 1 MSN, the odds of being unprepared increased 29% for each additional evacuation barrier [OR = 1.29, 95% CI (1.11, 1.51)]. Among households that reported individuals with level 3 MSN, the odds of being unprepared increased 68% for each additional evacuation barrier [OR = 1.68, 95% CI (1.21, 1.32)]. MSN alone did not explain the probability of unpreparedness, but rather MSN in the presence of barriers helped explain unpreparedness.

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“…Thus far, aside from the vast research on evacuation issues (Cova and Johnson ; Chen ; Reininger et al. ) there has been a recent, growing body of work on goods pre‐placement and distribution with respect to hurricanes and other disasters (Chang et al. ; Galindo and Batta ), which has roots in prior literature (Haghani and Oh ; Barbarosoglu and Arda ; Altay and Green III ; Sheu ).…”

Section: Introductionmentioning

confidence: 99%

Positioning Disaster Relief Teams Given Dynamic Service Demand: A Hybrid Agent‐Based and Spatial Optimization Approach

Widener

Horner

2014

Transactions in GIS

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During extreme events, like hurricanes, it is essential to position relief services for non-evacuees throughout the affected region in optimal locations. While previous research has explored a variety of spatial optimization models to accomplish such a task, most work assumes that the population with demand is relatively static. However, this assumption neglects to account for potential feedbacks in the relief distribution system. For example, a population's behavior can both affect and be affected by the placement of relief services, resulting in a dynamic spatial distribution of demand. This article presents a hybrid modeling approach that utilizes GIS data with agent-based and spatial optimization models to position emergency relief teams in Bay County, Florida during a hurricane event. Non-evacuating household agents choose to remain at home or seek shelter, while relief team agents are periodically repositioned to account for changes in the spatial distribution of household agents. A total of 220 simulations are run to explore a variety of scenarios. Results show different repositioning timing strategies and the magnitude of the feedback effect drastically changes the level of access households have to relief teams. Ultimately, this work demonstrates the importance of accounting for behavioral and spatial dynamics in disaster relief distribution systems.

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Intention to Comply With Mandatory Hurricane Evacuation Orders Among Persons Living Along a Coastal Area

Cited by 37 publications

References 32 publications

Social capital and disaster preparedness among low income Mexican Americans in a disaster prone area

Social capital and disaster preparedness among low income Mexican Americans in a disaster prone area

Barriers to Disaster Preparedness among Medical Special Needs Populations

Positioning Disaster Relief Teams Given Dynamic Service Demand: A Hybrid Agent‐Based and Spatial Optimization Approach

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