chronic disease as an evacuation impediment: using a geographic information system and 911 call data...

Risk, Hazards & Crisis in Public Policy

www.psocommons.org/rhcpp

Vol. 1: Iss. 3, Article 5 (2010)

Chronic Disease as an Evacuation Impediment: Using a Geographic

Information System and 911 Call Data after Katrina to Determine Neighborhood Scale Health

Vulnerability Andrew Curtis, University of Southern California

Abstract

This paper will add to the health and disasters literature by considering the spatial patterns of a factor not commonly associated with evacuation decisions, the existing chronic health burden of the population. 911 calls for the period from August 29 to September 8, 2005, were mapped using a Geographic Information System for 15 neighborhoods of New Orleans. For each neighborhood the total number of calls was reduced to unique residences, and any mention of a health-related problem was recorded. By using single locations and then reinterpreting medical attributes as a percentage of all such locations, or as a percentage of those mentioning medical information, an approach is presented to spatially standardize knowledge from these data. The results show that a sizable portion of those who did not evacuate from Katrina before landfall were suffering from some ailment, especially chronic diseases commonly associated with the urban poor. Of particular note is the diabetes rate, with six different neighborhoods having approximately 10% of their 911 calls mentioning the disease. The paper concludes with suggestions as to how this research should progress, with fine spatial scale geographic analysis of health data becoming more common in understanding evacuation impediments and the post-disaster landscape in general.

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© 2010 Policy Studies Organization

Keywords: evacuation, 911 calls, GIS, Hurricane Katrina, health vulnerability

Author Notes: The author would like to thank all of his ongoing Katrina collaborators, but especially William Fagan, Barrett Kennedy, and Jacqueline Mills. Contact: Andrew Curtis, Department of American Studies & Ethnicity, College of Letters, Arts and Sciences, University of Southern California, [email protected].

Recommended Citation: Curtis, Andrew (2010) “Chronic Disease as an Evacuation Impediment: Using a Geographic Information System and 911 Call Data after Katrina to Determine Neighborhood Scale Health Vulnerability,” Risk, Hazards & Crisis in Public Policy:Vol. 1: Iss. 3, Article 5. DOI: 10.2202/1944-4079.1027 http://www.psocommons.org/rhcpp/vol1/iss3/art5

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Risk, Hazards & Crisis in Public Policy, Vol. 1 [2010], Iss. 3, Art. 5

http://www.psocommons.org/rhcpp/vol1/iss3/art5DOI: 10.2202/1944-4079.1027

Introduction

The Intergovernmental Panel on Climate Change (IPCC) released a report in 2007 entitled “Climate Change 2007: Impacts, Adaptation and Vulnerability” (Field et al. 2007). Chapter 14 of the report focused on the particular vulnerabilities of North America. Not surprisingly, in the wake of Hurricane Katrina (Katrina from this point on) coastal areas, and the Gulf Coast in particular, were geographies of concern. What was also of interest was the emphasis placed on the disproportionate impact any disaster would likely have on different cohorts in society, in terms of their ability to mitigate (adapt) and cope with the resulting exposure. Specifically mentioned was the role of health in vulnerability outcomes. As the events of Katrina have demonstrated, a socially vulnerable population suffered the disproportionate brunt of the disaster, in terms of not evacuating, suffering through the storm and the prolonged rescue, and a protracted move to recovery (Hartman and Squires 2006; Penner and Ferdinand 2009). Unfortunately for many living in this region, there was already a heavy health burden which Katrina only exacerbated.

Although there are many questions that are still to be explored about this disaster, one which has received reasonable attention is why did so many people stay behind? This paper will add to this literature by considering the spatial patterns of a factor not commonly associated with evacuation decisions, the existing chronic health burden of the population. Whether these health patterns are merely associated with other evacuation impediments (such as poverty) or are causations in themselves is beyond the scope of this paper. Instead, we intend to show whether a sizable portion of those who stayed in New Orleans were suffering from some ailment, especially chronic diseases commonly associated with the urban poor.

A Vulnerable Population

It is widely accepted that the same disaster will not be experienced equally by all people. Some cohorts are less well equipped to avoid, adapt to, and recover from exposure to an event. Traditional cohorts that fall into this category of social vulnerability include those with lower incomes, the young and elderly, the disabled, female-headed households, and ethnic minorities (Bolin 1986; Cutter, Boruff, and Shirley 2003; Fothergill, Maestas, and Darlington 1999; Mileti 1999; Morrow 1999; Phillips 1993). Part of this disproportionate impact is the ability to evacuate and therefore limit

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Curtis: Chronic Disease as an Evacuation Impediment


exposure (Dow and Cutter 1998; 2000; 2002). The social factors identified as being impediments to evacuation range from not wanting to abandon pets (Heath et al. 2001; Nolen and Rezendes 2006), not appreciating or understanding the risk involved (Arlikatti et al. 2006), to not wanting to undertake long and arduous journeys (Wolshon et al. 2005). Possibly the cohort with the largest number of evacuation challenges are minorities living in poverty (Riad, Norris, and Ruback 1999). The reasons why financial resources influence evacuation decision making are numerous, including not owning a vehicle, not having the ability to pay for fuel or an extended stay in a motel, to not wanting to lose possessions and property to potential looters (Bourque et al. 2006; Eisenman et al. 2007). Compounding these impediments are associated factors of understanding the level of risk—which can be a combination of educational awareness (including an effective and culturally sensitive means of disseminating risk), previous experience, a faith in the offices of society providing adequate protection (for New Orleans these most notably being the levees), or competent evacuation plans (Arlikatti et al. 2006; Cova and Church 1997; Litman 2006). Unfortunately the stresses in many neighborhoods of New Orleans were extreme, including deficiencies in the educational and political systems, with high rates of poverty, crime, and chronic and infectious disease (Bullard and Wright 2009). It is hardly surprising, therefore, that research into evacuation decision making before Katrina found that many from the region would not evacuate (Howell and Bonner 2005; Travis 2005).

Investigations after Katrina, many aimed at evacuees in shelters, have sought to clarify why people stayed behind (Eisenman et al. 2007; Elder et al. 2007). A survey at one Texas shelter found that 61% did not evacuate before the storm, 29% because they underestimated the risk involved and 36% because they had no means to evacuate (Bourque et al. 2006). Similar surveys by Eisenman et al. (2007) and Elder et al. (2007) found that as well as “classic” evacuation impediments such as inadequate information dissemination (especially about where to go), the role of social networks such as the extended family in group decision making was also critical in the decision-making process. For many Gulf Coast communities living at or below the poverty line, neighborhood support networks help reduce the impact of the day-to-day stressors (Eisenman et al. 2007); and the thought of leaving this security behind would likely have entered into the evacuation decision-making process (Fordham 1998; Penner and Ferdinand 2009).

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Post-Disaster Health Data

Although the link between health and disasters has been well studied, especially for health vulnerable cohorts such as the elderly, disabled, pregnant, or those in hospital care (Bourque et al. 2006; Curtis 2008; Curtis and Leitner 2006; Curtis, Mills, and Leitner 2007), the link between the health burden typically associated with poor minority urban populations acting as an impediment to evacuation is less well developed (Phillips and Morrow 2007). In the Eisenman et al. study (2007) only 29 out of 1,182 statements were categorized as containing health impediments (including the health of family members), for example with distance from a provider being specifically mentioned in regard to a diabetic. Other health situations in their paper fell into other categories, such as the health of an elderly relative affecting a family’s decision to evacuate, or the lack of mobility preventing possible public transportation options.

However, if the individual is in pain, suffers from a debilitating illness, or lacks mobility, the prospect of a prolonged evacuation and temporary shelter existence may certainly influence an evacuation decision. Many chronic diseases, such as diabetes, hypertension, and even obesity, disproportionately impact people living in poverty, and especially non-white populations. In other words the very population “left behind” in New Orleans (Atkins and Moy 2005; Satcher et al. 2005). Indeed, surveys conducted seven weeks following Katrina found that 55.7% of returnee households carried at least one chronic illness (2006a). Many of these health conditions are likely to be exacerbated by the stress of disaster exposure. Diabetes, pregnancy, and heart conditions can all worsen in high stress situations. Postoperative patients, those requiring respiratory apparatus, and hypertensive patients (to name but three) are all reliant on medication or medical apparatus, and it is likely the typical family living in or close to poverty in New Orleans would only have limited supplies. Finally, people who were suffering from prior mental health problems are more likely to suffer disaster exposure-related psychopathologies. We are therefore left with a situation where, whether as a cause or consequence, a cohort which is less likely to evacuate will also be the most likely to suffer health problems, which may then worsen through exposure. From a disaster preparedness perspective it would seem prudent to gain as much information as possible regarding the pre-event spatial health vulnerability map. However, acquiring health data at a suitable spatial scale for neighborhood analysis is problematic for a number of reasons. Firstly, data might not exist, and if it does, it may be housed in multiple locations (clinics, hospitals, etc.), making data coordination problematic. Secondly, confidentiality is

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necessarily strict, meaning that it is hard to acquire fine scale data because of the risk of reengineering information back to the individual (Curtis, Mills, and Leitner 2006a).

This paper utilizes a post-event data analysis approach to identify patterns in health-related evacuation decisions. Health data are collected in different forms after disasters. For example, syndromic surveillance was attempted after Katrina using either the existing Louisiana Department of Health or Hospitals Office of Public Health (LAOPH) Internet-based Reportable Disease Database or the CDC’s Early Aberration Reporting System (EARS) (2006b). Although there is useful information to be gleaned from these records, it is important to understand the limitations of such data as medical reporting in the post-event phase of a disaster is often chaotic and record keeping problematic, including the misclassification of illnesses based only on symptom diagnosis, a lack of denominators meaning disease rates cannot be calculated, and no health baseline against which comparisons can be made (2006c). A final problem, and one that is crucial to an understanding of the underlying neighborhood health risk, is the lack of a fine scale spatial location with many of the records.

This paper investigates a previously underused health data source, 911 calls for the period following a disaster. The activities of the Geographic Information System (GIS) desk in the Louisiana State Emergency Operation Center (EOC) during the response are well documented (Curtis et al. 2006b). One of the tasks was to map 911 call data at regular intervals for search and rescue operations. These calls varied in content, but many included a written description of the need associated with a specific location. Some of these descriptions tally with traditional known impediments, for example:

“can’t make my parents understand the seriousness of the situation”

“she won’t evacuate because of her pets”

“this elderly lady is stubborn and has not been out of her house for five years before the storm”

“will not leave his home to looters”

However, for the purposes of this paper, only medical references will be used to illustrate the neighborhood health patterns of those who remained behind. One advantage of these data is that each record contains a location that can be mapped, which in turn facilitates an investigation into the geographic variation of health vulnerability in the underlying non-evacuated population, especially if we treat these calls as a spatial sample of the

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immediate post-Katrina situation. Obviously a limitation with these data is the lack of clinically diagnosed conditions, so it is assumed a diabetic in need will self-report correctly.

The hypothesis for this paper is that there is a spatial variation in 911 calls recording chronic diseases in the days following Katrina, meaning that these conditions, when taken in aggregate by neighborhood, are not randomly distributed across the map. For this paper, this hypothesis was investigated using a selection of 15 neighborhoods with different income and racial backgrounds. Several of these neighborhoods were also chosen because of ongoing recovery investigations in the city by the author (especially Holy Cross, Hollygrove, Lower Ninth Ward, St. Bernard Area). The locations of these neighborhoods can be seen in Figure 1, with a selection of basic socioeconomic information presented in Table 1.

The neighborhood summary in Table 1 contains information relevant for assessing the social and health vulnerability of a neighborhood. Firstly measures of the wealth of the area, expressed as the average household income and the percentage of people living in poverty, are included. Also, a classic measure of social vulnerability, the proportion of those families living in poverty who have a female head of household with children in their care, is shown. The range of wealth extends from an average household income of $111,664 (Lakeshore) to a low of $19,564 (Treme). Lakeshore also has the lowest number of people living in poverty (2.7%) while Treme and St. Bernard Area both have more than half of their population living in poverty (56.9 and 66%, respectively). The overall disparity between Lakeshore and the French Quarter, and the other socially vulnerable neighborhoods in terms of the percentage of female-headed families living in poverty is stark, with every one of the African American neighborhoods (defined as the dominant proportion of people residing there) exceeding 60%.

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Figure 1. The 15 Neighborhoods of Orleans Parish Selected for This Study

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Data source: Greater New Orleans Community Data Center (http://www.gnocdc.org/)

BW = Bywater, ER = East Riverside, FQ = French Quarter, HG = Hollygrove, HC = Holy Cross, LS = Lakeshore/Lake Vista, L9 – Lower Ninth Ward, MG = Marigny, E(LW) New Orleans East – Little Woods, E(PV) New Orleans East – Pine Village, 7W = Seventh Ward, StB = St. Bernard Area, StC = St. Claude, StR = St. Roch, Tr = Treme, WR = West Riverside.

BW ER FQ HG HC LS L9 MG E(LW) E(PV) 7W StB StC StR Tr WR

Total households 2,263 1,386 2,908 2,655 1,982 1,577 4,820 1,960 15,761 1,699 6,489 2,020 4,114 4,336 3,429 2,635

Average household income 27,+ 31,+ 58,+ 30,+ 32,+ 111,+ 27,+ 35,+ 43,+ 43,+ 26,+ 19,+ 29,+ 28,+ 19,+ 48,+

People living in poverty (%) 38.6 36.9 10.8 28.4 29.4 2.7 36.4 24.1 17.4 18.3 38 66 39 37.1 56.9 18.1

Female head of household with children 62.7 66.41 0 61 70.6 0 60.8 56.2 64.3 81.5 63 84.2 67.2 66.9 74.5 57.7

Reporting disabled 26.8 25.1 14.6 29.5 28.6 15.6 30.9 26.2 18.2 18.8 30.2 25.7 25.9 27.3 29.5 21

Physical disability (% over 65) 30.7 34.8 10.3 36.5 42.3 25.5 39.1 43.3 28.7 25.8 43.2 53.3 41.7 38.7 38.7 25.9

Mental disability (% over 65) 14.6 17.1 3 19.3 23.5 9.6 18.9 19.2 13.1 10.5 14.3 16.5 16.6 20.5 22.1 14.8

Proportion white 32.4 30.9 89.7 2.6 9.4 93.8 0.5 72.7 9.8 9.7 3 1.1 6.9 3.9 4.9 56.9

Proportion over 65 10.3 10 15.6 15.4 11.3 25.5 14 14.2 7.3 8.2 13.9 7.6 9.9 10.8 9.7 13.3

Table 1. Social Profile of the 15 Neighborhoods

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The second category of socioeconomic measures involves the proportion of disabled in each neighborhood. Although not a perfect measure, this is used as a surrogate for the total health burden, and as such the neighborhoods with the lowest proportions are again Lakeshore and French Quarter, while the Seventh Ward and the Lower Ninth Ward have the highest proportion. More detail is added by considering those over the age of 65, and the proportion of this total that face either physical or mental disabilities. Obviously for neighborhoods with a high number of elderly, high proportions for either of these health conditions might be an impediment to evacuate. Therefore, although Lakeshore has the highest proportion of elderly, they appear to be much healthier than elderly populations in Holy Cross and Marigny.

The data to investigate the health surface of the immediate post-Katrina neighborhoods is a subset of all 911 calls for Orleans Parish for the period August 29 to September 8. These data should be seen as a spatial sample of events happening for this period. However, over-interpretation of results should be guarded against because of the following reasons: call source—in normal situations a victim or a witness makes an emergency call. During Katrina, mainly due to circuits being overloaded, many victims had to have their messages relayed back from outside the area. For example, during a presentation by the author about the GIS response during late 2005, a member of the audience described how he had relayed several calls back from St. Bernard Parish (his previous home) as the victims could only make contact with a telephone in Kentucky. Similar call pathways can be seen within some of the 911 messages themselves:

“victim left message on Pittsburgh friends answering machine—trapped in home.”

Secondly, there is a bias towards individuals having access to a phone. Although this might prove problematic at the individual residential level due to financial constraints, a further limit was suffering severe damage, especially for sections of the Lower Ninth Ward where buildings were literally swept aside by the force of water breaking through the Industrial Canal (Figure 2). Thirdly, the number of calls does not represent the total number of people in need. Sometimes multiple calls were made regarding the same family, either during the same day or spread over several days. In many instances a single call was made for several people in a family, several families, or sometimes an entire building:

“6 people stuck in the attic”

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“Approximately fifteen people are on top of the Jax brewery”

“50–100 stranded in the building—out of food, water and medication”

Figure 2. Building Footprints from both the Lower Ninth Ward and Holy Cross

(inset).

The images in Figure 2 were digitized from aerial photography in the days immediately following Hurricane Katrina. The main image shows a zoom into the area next to the two main breaks in the Industrial Canal. The pattern of buildings being washed away from these breaks is clearly evident.

As a result the following strategy was employed. The number of calls for each neighborhood was “cleaned” by removing all multiple calls and obvious outsider enquiries. The remaining calls (and all attribute information) were then tied to a single residence. This residence could be a

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single family home, an apartment complex, or a nursing home. Although not perfect, these data can then be interpreted as the total number of locations about which 911 calls were made, and the different health problems faced at that location.

By considering the total number of households in each neighborhood, and the racial profile, it is expected that the Lower Ninth Ward, New Orleans East, Seventh Ward, St. Claude, and St. Roch will generate the highest number of single residence 911 calls. By following the assumption that social and health vulnerabilities are closely linked, one would assume that diseases associated with urban poverty would be high in many of these neighborhoods, but especially in St. Bernard Area and Treme, while they would be lowest in Lakeshore and French Quarter. Also referring back to Table 1, one would expect the highest number of disabled references to originate in the Seventh and Lower Ninth Ward, while the least would occur in Lakeshore and French Quarter. Method Emergency 911 calls initially geocoded for use in the EOC during the response to Katrina were mapped in a GIS (Arc Map 9.3) (Curtis et al. 2006b). Calls were selected that fell within the boundary of the 15 neighborhoods comprising the study region, and up to a 50 m buffer around the edge of each. This was to help prevent artificial boundary effects whereby people living on either side of the same street would not be compared together if a neighborhood boundary followed the street center line. This meant that some addresses would be double counted though this is not a problem as smoothing approaches are commonly applied in GIS-supported local area analysis (Rushton et al. 2004).

For each neighborhood, the total number of calls was reduced to unique residences—in this way if multiple calls existed for the same address, either because of nonresponse to a call meaning the victims sheltered in place for multiple days, or because the same call entered the system from multiple sources, only the one location would be included in the analysis. This reduced set of calls was then investigated for any mention of a medical problem, or direct reference to age or “elderly.” For the purposes of this paper, the minimum age to be classed as elderly was 60, allowing more flexibility than the more standard definition of 65. This lower number was chosen due to the author’s familiarity with the social conditions of many communities in Orleans Parish with this age being deemed to be more consistent with the spirit of an “elderly” vulnerable cohort.

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Each mention of a health-related problem was recorded, whether these referred to an existing condition that would either affect rescue (for example mobility), or necessitate rescue (no medications), as well as hurricane-related trauma (of which there were relatively few mentions in the calls). A content analysis was performed on the health descriptions, with the following categories being recorded:

• Diabetes (self-explanatory, though mention of insulin only would also be recorded)

• Hypertension (frequently referred to as “high blood pressure”)

• Kidney problem (also any mention of dialysis)

• Diabetes/hypertension/kidney problems (a compound measure of the above three, but with no double counting to the same address if multiple conditions existed)

• Heart disease

• Cancer (also including the names of specific cancers)

• Post-operation (any mention of an individual who had recently received serious medical treatment, especially recovery from an operation)

• Mobility (a broad category that covered descriptions of the ambulatory condition of a victim, for example cannot walk or bedridden, or comments on mobility devices, for example wheelchairs or walkers, and associated conditions such as being eight months pregnant)

• Respiratory problems (a broad category that covered descriptions of disease, for example emphysema, or physical breathing apparatus such as oxygen tanks).

• Mental state (a broad category that covered diagnosed conditions, such as schizophrenia, or a described state of mind, for example “emotionally dangerous,” and also potential cognitive hindrances such as Alzheimer’s or epilepsy)

• Medication (any message where mention is made as to the need for medication or medical technology for the continuing health of the

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victim, with “out of medication” being the most common description)

These categories were chosen both because of their association with chronic disease often suffered by the urban poor (especially diabetes, hypertension, kidney disease), conditions that might have played significant roles in the decisions not to evacuate (especially heart, cancer, postoperation, and mobility), and because of implications for a prolonged negative health legacy (most of the categories but especially mental health). It should be noted that several other categories are not reported in this paper, such as “blindness.” It is possible that sight problems, as with mobility issues, may also be symptoms of diabetes even if this illness is not mentioned specifically. However, this linkage cannot be verified. It should also be noted that many victims suffered from multiple conditions (diabetes, hypertension, and heart disease often being three symptoms common to the same person). Results All 911 calls for the period August 29 to September 8, 2005, for Orleans Parish were mapped in a GIS (Arc Map 9.3). Those calls falling inside each of the 15 named neighborhoods (and a 50 m buffer around the outside) were extracted as separate maps. Table 2 displays the total number of calls for each neighborhood broken into each day. Interestingly the mode for calls varied between neighborhoods, with highs ranging from August 30 to September 1. Of more value in terms of comparison is the reduced call set to unique residences (removing all multiple call locations). The three neighborhoods with the highest number of unique locations are New Orleans East (198), Seventh Ward (175), and St. Claude (152). The ranking of these three neighborhoods in terms of total households (compared to all 15 neighborhoods) is 1, 2, and 5, respectively. Both the Lower Ninth Ward and St. Roch have more households, though St. Roch is close in number to St. Claude for both single 911 call locations and households. The Lower Ninth Ward has far lower call numbers than expected, which might in part be due to the devastation experienced in this area, with many victims not having the ability to call from severely damaged buildings (see Figure 2). The lowest number of single residence calls originates in St. Bernard Area (28), Lakeshore (29), and East Riverside (31). The ranking (from the lowest end) of these three neighborhoods in terms of total households is 5, 2, and 1, respectively. Although not a sophisticated analysis, the number of 911 calls

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approximates the ranking of total households in each neighborhood, suggesting little spatial variation in evacuation decision at this level of aggregation.

Table 2 also displays the percentage of all single location 911 calls that contain a medical or elderly reference. The range of medical references has a low of 17.4% (Lower Ninth Ward) to a high of 48.4% (East Riverside). Both of these neighborhoods, however, should be interpreted carefully due to the previously described devastation in the Lower Ninth Ward and a possible small number effect in East Riverside (the low number of calls is more prone to the variability of a single attribute). If we concentrate on only those neighborhoods with at least 50 single residence calls, then the highest is West Riverside (suggesting that the East Riverside result may also have validity) and the lowest is New Orleans East. The remainder displays a degree of stability, with approximately 30% of all calls containing some medical reference. If we take the same approach with calls mentioning the elderly, again only using neighborhoods with more than 50 calls, far more variation exists, ranging from a high of 35.3% and 33.9% (West Riverside and St. Roch) to a low of 9.6% and 11.6% (New Orleans East and the Lower Ninth Ward).

A content analysis was also performed on the messages accompanying each 911 call, with the medical reference being broken into 10 “conditions” and one composite of chronic urban health (diabetes, hypertension, and kidney problems). Tables 3 and 4 display the results in two forms, firstly as a percentage of the total single location calls mentioning that condition, and secondly as a percentage of all single location calls mentioning any medical condition. In this way Table 3 shows the general impact of this condition in the neighborhood, while Table 4 displays spatial variation in the medical condition as part of each neighborhood’s overall health burden.

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29-Aug 30-Aug 31-Aug 1-Sep 2-Sep 3-Sep 4-Sep 5-Sep 6-Sep 7-Sep 8-Sep Total Single %Med %Eld

Bywater 3 9 13 24 15 9 19 6 6 6 0 110 89 32.6 15.7

East Riverside 1 2 5 7 10 4 5 1 5 3 1 44 31 48.4 22.6

French Quarter 0 6 20 18 15 17 10 8 5 5 3 107 73 34.2 19.2

Hollygrove 1 23 29 24 15 3 11 8 4 6 1 125 87 32.2 21.8

Holy Cross 2 7 15 9 8 7 7 5 4 1 1 66 39 35.9 20.5

Lakeshore 2 6 12 10 1 1 3 2 1 2 0 40 29 20.7 17.2

Lower Ninth 14 7 16 15 11 3 4 6 4 1 1 82 69 17.4 11.6

Marigny 0 6 11 14 13 19 11 8 5 6 2 95 67 29.9 25.4

New Orleans East 44 49 71 51 24 13 21 15 6 11 1 306 198 21.2 9.6

Seventh Ward 5 25 36 50 41 23 19 19 11 10 0 239 175 29.1 17.7

St. Bernard Area 1 5 11 6 2 1 1 3 3 1 0 34 28 32.1 17.9

St. Claude 7 25 31 27 18 16 19 15 14 13 1 186 152 34.9 21.1

St. Roch 10 22 22 38 21 20 12 7 13 6 2 173 127 31.5 33.9

Table 2. Summary of 911 Calls for the Period August 29 to September 8

Treme 1 11 37 37 26 15 8 13 5 7 0 160 118 35.6 20.3

West Riverside 0 5 16 17 12 5 9 4 2 4 0 74 51 47.1 35.3

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% 911 Calls Diabetes Hypertension Kidney DB&HT&DY Heart Cancer Post-Op Mobility Respiratory Medication Mental

Bywater 7.9 1.1 0.0 7.9 3.4 1.1 2.2 6.7 2.2 7.9 9.0

East Riverside 9.7 6.5 9.7 22.6 9.7 6.5 0.0 3.2 3.2 16.1 6.5

French Quarter 1.4 1.4 1.4 4.1 6.8 0.0 1.4 9.6 2.7 1.4 1.4

Hollygrove 6.9 5.7 0.0 11.5 4.6 1.1 1.1 9.2 1.1 5.7 5.7

Holy Cross 12.8 5.1 0.0 17.9 5.1 0.0 0.0 2.6 7.7 0.0 2.6

Lakeshore 3.4 0.0 0.0 3.4 0.0 0.0 0.0 0.0 0.0 6.9 6.9

Lower Ninth 4.3 1.4 1.4 7.2 7.2 1.4 0.0 4.3 0.0 1.4 0.0

Marigny 10.4 1.5 0.0 10.4 6.0 1.5 0.0 6.0 3.0 11.9 7.5

New Orleans East 6.1 0.5 0.0 6.6 5.1 2.0 0.0 4.0 1.0 5.1 2.5

Seventh Ward 8.6 3.4 0.6 10.9 6.3 1.1 0.0 5.7 1.1 1.7 2.3

St. Bernard Area 10.7 10.7 0.0 14.3 10.7 3.6 0.0 7.1 0.0 0.0 7.1

St. Claude 5.9 3.3 1.3 9.9 5.9 2.6 2.6 11.8 2.6 3.9 2.0

St. Roch 11.0 1.6 0.8 13.4 2.4 1.6 0.0 6.3 3.1 7.1 1.6

Treme 7.6 2.5 3.4 11.9 0.8 0.0 0.0 10.2 0.8 6.8 3.4

West Riverside 11.8 7.8 3.9 17.6 3.9 2.0 0.0 15.7 3.9 9.8 5.9

Table 3. Percentage of 911 Calls Mentioning Each Condition by Neighborhood

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% Med 911 Calls Diabetes Hypertension Kidney DB&HT&DY Heart Cancer Post-Op Mobility Respiratory Medication Mental

Bywater 24.1 3.4 0.0 24.1 10.3 3.4 6.9 20.7 6.9 24.1 27.6

East Riverside 20.0 13.3 20.0 46.7 20.0 13.3 0.0 6.7 6.7 33.3 13.3

French Quarter 4.0 4.0 4.0 12.0 20.0 0.0 4.0 28.0 8.0 4.0 4.0

Hollygrove 21.4 17.9 0.0 35.7 14.3 3.6 3.6 28.6 3.6 17.9 17.9

Holy Cross 35.7 14.3 0.0 50.0 14.3 0.0 0.0 7.1 21.4 0.0 7.1

Lakeshore 16.7 0.0 0.0 16.7 0.0 0.0 0.0 0.0 0.0 33.3 33.3

Lower Ninth 25.0 8.3 8.3 41.7 41.7 8.3 0.0 25.0 0.0 8.3 0.0

Marigny 35.0 5.0 0.0 35.0 20.0 5.0 0.0 20.0 10.0 40.0 25.0

New Orleans East 28.6 2.4 0.0 31.0 23.8 9.5 0.0 19.0 4.8 23.8 11.9

Seventh Ward 29.4 11.8 2.0 37.3 21.6 3.9 0.0 19.6 3.9 5.9 7.8

St. Bernard Area 33.3 33.3 0.0 44.4 33.3 11.1 0.0 22.2 0.0 0.0 22.2

St. Claude 17.0 9.4 3.8 28.3 17.0 7.5 7.5 34.0 7.5 11.3 5.7

St. Roch 35.0 5.0 2.5 42.5 7.5 5.0 0.0 20.0 10.0 22.5 5.0

Treme 21.4 7.1 9.5 33.3 2.4 0.0 0.0 28.6 2.4 19.0 9.5

West Riverside 25.0 16.7 8.3 37.5 8.3 4.2 0.0 33.3 8.3 20.8 12.5

Table 4. 911 Calls Mentioning Each Condition as a Percentage of all Calls Mentioning Any Medical Situation

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If we concentrate on Table 3, and focus on those neighborhoods where at least 10% of calls mention a specific medical condition, we can see that five neighborhoods exceed the threshold for diabetes (Holy Cross 12.8, West Riverside 11.8, St. Roch 11, St. Bernard Area 10.7, and Marigny 10.4), only one neighborhood (St. Bernard Area 10.7) exceeds the threshold for hypertension, and none for kidney. However, if we combine these three chronic conditions, East Riverside, Hollygrove, the Seventh Ward, and Treme all exceed the threshold, with three neighborhoods, East Riverside, Holy Cross, and West Riverside, having approximately 20% of all residences making a 911 call mentioning at least one of the three diseases. No neighborhood exceeds the 10% threshold for respiratory problems, cancer, or postoperative conditions, and only St. Bernard Area (10.7) does so for heart conditions. Of particular importance for traditional evacuation impediments, people with mobility problems, three neighborhoods exceed the 10% threshold (West Riverside 15.7, St. Claude 11.8, Treme 10.2%) though two others are worth noting (French Quarter 9.6, Hollygrove 6.9). East Riverside and Marigny also exceed the 10% threshold for calls mentioning medication need. Finally, and with implications for future health impacts associated with both the stress of the immediate disaster and the prolonged recovery period, although none exceed the 10% threshold, Bywater (9), Marigny (7.5), and St. Bernard Area (7.1) all have a high proportion of calls with some mental health reference. This is worrying because there is reason to believe that post-event problems in mental health are related to both exposure and the previous mental health state of the individual (Caldera et al. 2001). These callers have experienced both.

Table 4 displays the same categories of health data extracted from the 911 calls, but this time expressed as a percentage of all calls mentioning any medical situation. By using a 25% threshold (meaning one in four calls mentioning a medical problem), eight different neighborhoods exceed this level for diabetes, with Holy Cross (35.7), Marigny (35), St. Roch (35), and St. Bernard Area all being greater than 30%. Only one neighborhood exceeds the threshold for hypertension (St. Bernard Area) and none for kidney. The compound variable results in only three neighborhoods being lower than the 25% threshold (French Quarter 12, Lakeshore 16.7, and Bywater 24.1). Interestingly both French Quarter and Lakeshore are the most affluent and with the highest proportion of whites. The Lower Ninth Ward and St. Bernard Area both exceed the 25% threshold for heart-related mentions (41.7 and 33.3, respectively), while no neighborhood reaches the level for respiratory problems, cancer, or postoperative conditions. Six neighborhoods exceed the threshold for mobility, though a more interesting way to consider this condition is to look at the lowest three, Lakeshore (0),

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East Riverside (6.7), and Holy Cross (7.1)—all other neighborhoods were approximately 20% or higher. Three neighborhoods exceed the threshold for medication needs and mental health (Marigny 40, East Riverside 33.3, and Lakeshore 33.3 for medication, and Lakeshore 33.3, Bywater 27.6, and Marigny 25 for mental health). Discussion Disasters can exacerbate existing disparities (Norris, Friedman, and Watson 2002a; Norris et al. 2002b); indeed this very point is emphasized throughout the IPCC report for North America mentioned at the beginning of this paper. In the United States the same cohorts that are often classified as being socially vulnerable are also those carrying the greatest health burden, especially with regard to chronic diseases such as diabetes, hypertension, and obesity. It is therefore expected that the population most affected by Katrina is also likely to have been suffering from these diseases. This was borne out in surveys of both shelter populations and in returnees to post-Katrina New Orleans (Berggren and Curiel 2006). Whether these health problems are just associated with other measures of social vulnerability or are an impediment in themselves is beyond the scope of this paper. In order to initiate this dialogue a first step must be to understand the spatial pattern of the neighborhood health surface for New Orleans. By using 911 call data for the days following the storm’s landfall, spatial patterns of health problems deemed serious enough to be mentioned within the call have been mapped. By using single call locations, an approach is presented to spatially standardize health information from these data.

We can return to the paper’s hypothesis and the other expectations with regard to the different neighborhood social characteristics presented in Table 1. By comparing the total number of households in each neighborhood, ceteris paribus we expected the Lower Ninth Ward, New Orleans East, Seventh Ward, St. Claude, and St. Roch to generate the highest number of single residence 911 calls. This is true except for the Lower Ninth Ward—which may have lower numbers due to the increased damage experienced there. If we also include the social characteristics of these neighborhoods, especially levels of poverty and proportion of non-whites, we expected diseases associated with urban poverty to be high in several of the neighborhoods, but especially in the St. Bernard Area and Treme, while they would be lowest in Lake Shore and French Quarter. In actuality St. Bernard Area and Treme vary in terms of diabetes, hypertension, and kidney problems percentages. Of more consistency is the reverse of this pattern, that

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the more affluent neighborhoods have few chronic disease mentions. For example, Lakeshore has one of the lowest mentions of medical conditions in the calls. However, it does have the highest grouping for medication needs and mental health problems in Table 4, though this is in part due to the lack of the other chronic conditions in the neighborhood exaggerating the proportion.

The overall pattern of diabetes across the poorer neighborhoods is alarming. Six different neighborhoods have approximately 10% of their 911 calls mentioning diabetes. If hypertension and kidney problems are also added into a combination variable associated with urban poor health, then the number of neighborhoods of concern reaches 9 (in Table 3) or 13 (in Table 4). Equally of interest here are the neighborhoods with the lowest percentages—which under Table 4 are the French Quarter and Lakeshore—the two most affluent and largely white neighborhoods. These results are in keeping with surveillance in the shelters (2006b) which found that chronic diseases comprised 31% of interactions with medical personnel, with diabetes being the highest.

Again, referring back to Table 1, we expected the highest number of mobility calls in the Seventh and Lower Ninth Wards, while the least would occur in Lake Shore and French Quarter. Interestingly, neither the Seventh Ward nor the Lower Ninth Ward appear in the top five, though this might in part be explained by our measure of “mobility” capturing more than just disability. Lake Shore does indeed have the lowest number of mobility-related calls, though the French Quarter actually has the fourth largest number (by Table 3).

Our central hypothesis that there is a spatial variation in the health burden across the different neighborhoods has been supported. Basic socioeconomic characteristics can be used to identify which neighborhoods are least likely to carry a high chronic health burden, and to a lesser degree which have the highest. However, the spatial variation between neighborhoods suggests that future analysis needs to be conducted on real health data rather than rely on the surrogate of census information. Further, more detailed insight can be gained if finer scale analyses are conducted at the sub-neighborhood level. As an example, Figure 3 shows the results of a kernel density analysis of all 911 calls mentioning diabetes, hypertension, or kidney problems falling inside the 10 contiguous neighborhoods of central New Orleans. A kernel density surface smoothes spatial events (the locations for each 911 call), creating a finer scale spatial impression that is not broken by artificial neighborhood boundaries. Although we again have to be careful about the overinterpretation of small numbers, the contoured islands show sections of the neighborhoods where these chronic diseases were highest.

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Such an approach could be used to guide survey teams to further understand the health burden of these areas and the impact these conditions might have as an impediment to evacuation.

Figure 3. Results from a Kernel Density Analysis for the Contiguous Test Neighborhoods Seen in Figure 1

Note: Areas with a contour floating above them have the highest concentration of the three chronic diseases (diabetes, hypertension, and kidney problems).

The purpose of using Katrina 911 call data was to help show that there is a health dimension to the non-evacuated population. Whether this is a byproduct of other impediments or not, the problem exists. The pre-Katrina diabetes rate of Orleans and Jefferson Parishes was approximately 11% (Cefalu et al. 2006), which is similar to many of the neighborhoods investigated in this paper. Unfortunately many shelters were not adequately prepared for sufferers of diabetes, with inadequate medication, syringe supplies, and methods of syringe disposal, and poor nutrition and exercise potential. For diabetics without adequate medication, hyperglycemia could

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result in secondary health problems including skin infections, a common outcome reported in shelter surveillance tools. What this paper has shown is that there is neighborhood variation in these disease rates, and spatially targeting resources according to results such as these go some way to addressing the cultural sensitivity that many evacuation commentators often lament as missing (Madrid et al. 2006; Phillips and Morrow 2007).

The next step is to use better (and more contemporary) health data. For example, diagnosed patients with diabetes are recorded. However, even if a central database exists for all patients, arguably the biggest limitation to conducting further spatial study of the health burden, and its impact on evacuation decisions, is confidentiality. The lack of guidelines for data release from medical data providers to data users, especially at scales below the zip code aggregation, limits the willingness to share information ( Curtis, Mills, and Leitner 2006a). More work is needed on mechanisms for data sharing that are useful to end users, while limiting risk for patients and liability for all parties.

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chronic disease as an evacuation impediment: using a geographic information system and 911 call data...

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