Outline of talk 1. Start with a brief recounting of my personal, intellectual journey as a physician doing disaster research and introduce the concept of social vulnerability to disasters that will be familiar to disparities researchers but has only recently emerged in the disaster research literature. 2. Discuss our research on Hurricane Katrina. 3. Suggestions for responses to reviewer criticisms.
Hurricane Katrina demonstrated this concept of social vulnerability for everyone. A large segment of American society lives without the social and economic resources to protect themselves during disasters. Storm damage disproportionately affected the vulnerable of New Orleans: 1. Vulnerable elderly were over-represented among the fatalities
1. Damaged areas were 75% black compared to 46% black in undamaged areas 2. Recovery appears to be occurring more rapidly among communities with higher incomes.
100-120,000 persons did not evacuate. Most were poor, African American, and representative of those who are more vulnerable to disasters. Research on the association of poverty and race/ethnicity with evacuation decisions mostly confirms the pattern seen with Katrina: impoverished and minority communities are less likely to evacuate. Factors operating within minority groups unknown. Suburban rather than urban centers, where social vulnerability is worse. Major reasons were poverty, lack of transportation or shelter, and historical experience with riding out hurricanes. (Blendon AJPH) Social psychological theory predicts that decision-making is complex, multifactorial and socially embedded. Results invite questions about the circumstances and complex interrelationships of the reasons offered for not evacuating and potentially others (e.g., family and social networks) that were not included in the survey. Qualitative research can give detailed, in-depth accounting of the cultural context, social environment, and individual cognitions
Will try to demonstrate that participants…
Peculiarities of the large-scale evacuation from impoverished sections of New Orleans provided a skewed population for study, which was further compounded by the rapidly changing occupancy of the shelters. Attempted to reduce bias in sample selection strategy called a convenience sample with respect to those who may have been evacuated to these shelters and who were left on Sept 9-12, 2005.
Study participants were randomly selected. Inside the centers, we randomly selected cots; outside the centers, we randomly selected from lines against the building.
With the exception of the demographic components, all interviews were conducted in a semi-structured format.
Fifty-eight of 75 persons approached (77%) participated Sample comparable to the AJPH study sample. Disproportionately African American (67% NO; 33% Louisiana ).
Disproportionately poor and low education (23% NO & LA with less than 20K/year; 75% NO & LA are HS graduates)
We used an inductive analysis strategy to interpret and structure our data. Using a grounded theory approach distinct codes or domains of interest emerged and were identified during team discussions. Once a coding schema was devised, each transcript was reviewed by two of three “coders” who independently applied the codes to classify text based interview data using Atlas.ti version 4.2 software. Differences in coding were resolved by consensus agreement. Coding was completed when all portions of the interview materials were coded, domains were “saturated” and common themes emerged.
1194 statements were identified and coded Results reported and representative quotations are the most salient domains of meaning discovered.
One car for the entire family was not enough; sometimes other family members had already evacuated with it or the family was too large for a single car.
Extended family outside of New Orleans who provided “an open invitation” to participants as facilitating evacuation and the absence of friends and family outside of New Orleans as hindering evacuation. No specific destinations prescribed in the evacuation orders.
Almost 45% of the sample reported they owned or had access to a car, but many still did not have money for gas, hotels, or food.
The health of their extended families influenced evacuation.
A major theme was the importance of social networks (the web of relationships that surround individuals) as obstacles or facilitators to evacuation. Many statements overlapped with statements about transportation, shelter and health. Here a participant describes how she, as extended family, was a resource that enabled others to evacuate
interactions with friends, neighbors and church members influenced evacuation. (Implication: Church opportunity)
Obligations to the elderly also strongly influenced participant’s evacuation. THIS woman was already ON THE ROAD! (Implication: Opportunity for church groups to be involved)
The prior experiences of the elderly effected decisions to evacuate.
Comparable in age, gender, income, and education to AJPH sample concurrent in the shelters.
The health of interviewees themselves influenced evacuation.
An individual’s evacuation decision began a chain reaction that impacted an entire family.
Not personalizing the risk
Underestimate risk of a flood (educational opportunity)
Evacuation as riskier than staying
Historical experience of hurricanes was not influenced by this event
Distrust of authorities fueled participant’s belief that the flooding was not due to the hurricane. Participants thought the levees had been deliberately “blown” to save wealthy neighborhoods and businesses at the expense of poor, black neighborhoods, and blamed local, state, and federal officials for negligence.
1. Disasters, social vulnerability, and evacuation from Hurricane Katrina <ul><li>David Eisenman, MD MSHS </li></ul><ul><li>Division of GIM/HSR </li></ul><ul><li>May 5, 2006 </li></ul>
2. Co-authors <ul><li>Kristina Cordasco, MD MPH </li></ul><ul><li>Steve Asch, MD MPH </li></ul><ul><li>Deborah Glik, ScD </li></ul><ul><li>Joya Golden, BA </li></ul>We gratefully acknowledge the participants of this study who were willing to participate during a time of intense personal difficulty. Special thanks to Michele Allen, M.D., M.S. This study was funded by the Quick Response Research program of National Hazards Research and Applications Information Center and Grant No. 1 K01 CD000049-01 from the Centers for Disease Control and Prevention.
5. Reasons for non-evacuation <ul><li>Little known about low-income, urban, minority communities </li></ul><ul><li>Transportation, shelter, historical experience cited in surveys </li></ul><ul><li>Decision-making is multifactorial and socially embedded; surveys don’t address this. </li></ul><ul><li>Qualitative research is needed </li></ul>
6. Purpose <ul><li>To study the experience of Hurricane Katrina evacuees to understand evacuation decision-making in impoverished, urban, mainly minority communities. </li></ul><ul><li>Participants describe factors affecting evacuation that are more complex than previously reported, interacted with one another, and were socially influenced. </li></ul>
8. Study Recruitment <ul><li>Adult evacuees residing in major centers </li></ul><ul><li>Random selection </li></ul><ul><li>September 9-12, 2005 </li></ul>
10. Data Collection <ul><li>Semi-structured </li></ul><ul><li>Sources and understanding of information prior to the hurricane </li></ul><ul><li>Knowledge, perceptions and resources that influenced evacuation </li></ul><ul><li>Recorded, professionally transcribed </li></ul>
11. Sample questions <ul><li>Were you aware of the recommendations to evacuate? </li></ul><ul><li>When did you learn this information? From what source? </li></ul><ul><li>Did you consider leaving? Did you want to leave? </li></ul><ul><li>What made evacuating easy/hard for you? </li></ul>
12. Socio-demographic Characteristics of Study Participants (N=58) New Orleans resident 95% Gender 52% Male Ethnicity African American White Latino Asian/PI 81% 10% 5% 3% Age 18-34 years old 35-54 years old 55-74 years old 75+ years old Missing 16% 46% 31% 5% 2%
13. Socio-demographic Characteristics of Study Participants (N=58) Income < $20, 000 $20,000-30,000 30,000 - 40,000 40,000-50,000 50,000 + Refused 50% 31% 9% 2% 5% 3% Education < High School High School > High School 45% 40% 10%
14. Data Analysis <ul><li>Grounded theory approach </li></ul><ul><li>In-vivo & theoretical coding </li></ul><ul><li>2 of 3 ‘coders’ independently applied codes and resolved differences by consensus </li></ul><ul><li>Atlas.ti software </li></ul>
16. Results: Major Themes 1194 statements coded Message understanding Health Transportation Shelter Trust Money, jobs, property Risk perception Social network
17. Transportation <ul><li>I mean, if you've got 20 people trying to get in one car it's not going to happen. So some people, you just stay because you have to. </li></ul>
18. Shelter <ul><li>Really truly, we had cars, but we didn't know anybody to go to. </li></ul><ul><li>They said go to Texas but I didn't know anybody in Texas. </li></ul>
19. Money , property, jobs <ul><li>You have to be able to feed your children when you leave. You have to be able to have a place to stay, you have to have gas money, you have to have rental car money. I couldn't afford to do that. You need at least $500/$600, and that's the least amount of money. </li></ul><ul><li>Discussing clients from HIV/AIDS group home: </li></ul><ul><li>“ We had five of them placed, two of them were not placed, so that means when we had to evacuate…I had to take them with me.” </li></ul>
20. Money, property , jobs <ul><li>They were already robbing. And my dad, he had to stay behind because we had a lot of tools and belongings there. </li></ul>
21. Money, property, jobs <ul><li>‘ If you don't come around then, you know, I'll just see you when I see you.’…That means when I see you you're going to be fired. </li></ul>
22. Health <ul><li>I could have made it on my own, but it was just my aunt and my uncle. Every few steps he made…she forgot his walker…every few steps he made he was falling down. </li></ul>
23. Social networks <ul><li>I started making phone calls to my children warning them to get out. And after that, my sister, she had called me. So I went to pick her and her children up, and grand children, and we just started driving, heading toward Florida. </li></ul>
24. Social networks <ul><li>“ So our clinical manager called back. She says, ‘Stella, the Lord said get out of that house.’ I said, ‘We're on our way out now if you would hang up.’” </li></ul>
25. Social networks <ul><li>My plans were to leave. Unfortunately we received a call and we had to come back home. My mother-in-law had called for us to come back…. You know when they get a certain age they get confused. </li></ul>
26. Social networks <ul><li>Like my Mom said, she's been through Betsy, Camille, all the hurricanes, the major hurricanes and she just wasn't evacuating. So I wasn't going to leave my Mom to stay there by herself. </li></ul><ul><li>I had a 90 year old mother that I was taking care of and she would not leave that house for hell or high water. </li></ul>
28. The influence of social networks: conclusions and recommendations <ul><li>Broad networks hindered and facilitated evacuation </li></ul><ul><ul><li>Stretched limited resources </li></ul></ul><ul><ul><li>Obligations to extended family, especially elderly who resisted evacuation or were frail, inhibited individuals and nuclear families </li></ul></ul><ul><ul><li>Counterpart to Drabek’s finding “ families move as units and remain together, even at the cost of overriding dissenting opinions.” </li></ul></ul><ul><li>Disaster research and programs must address social units (households, extended families, neighborhoods) and institutions (churches) not just individuals. </li></ul>
29. Limitations Strengths <ul><li>Social response bias </li></ul><ul><li>Specific urban community </li></ul><ul><li>Convenience sample? </li></ul>• Adds to understanding of the influence of social networks on decisions and behavior • Evaluating interactions between factors influencing evacuation • Comparable to concurrent study samples
31. Health <ul><li>… because I'm a diabetic and I have to be close by to get to doctors and hospital... </li></ul><ul><li>I no healthy to drive too far. </li></ul><ul><li>I take so much medication by that time I was like groggy </li></ul>
32. Social networks <ul><li>My mother-in-law wouldn't leave the house. My husband wouldn't leave her and I'm not going to leave him. </li></ul>
33. Risk perception <ul><li>I know it’s a flooding city but the street I live on does not flood </li></ul>
34. Risk perception <ul><li>Flooding became dangerous to one person only “when it got up to my neck… I'm an excellent swimmer.” </li></ul>
35. Risk perception <ul><li>The last storm we had there, it was more people got hurt on the highway traveling away from the storm, running out of gas, accidents, than it would have been if they stayed home. </li></ul>
36. Risk perception <ul><li>“ I probably would ride another one out….I mean, even though it was a category 5, all it did was tore the roof off my house.” </li></ul>
37. Trust <ul><li>It was from them opening flood gates, telling lies about the levee breaking and stuff...I believe they do these things intentionally...so they can flood out those black neighborhoods. </li></ul>