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Open AcceResearchSpatial patterns of natural hazards mortality in the United StatesKevin A Borden and Susan L Cutter*
Address: Hazards and Vulnerability Research Institute, Department of Geography, University of South Carolina, Columbia, SC 29208, USA

Email: Kevin A Borden - [email protected]; Susan L Cutter* - [email protected]

* Corresponding author

AbstractBackground: Studies on natural hazard mortality are most often hazard-specific (e.g. floods,earthquakes, heat), event specific (e.g. Hurricane Katrina), or lack adequate temporal or geographiccoverage. This makes it difficult to assess mortality from natural hazards in any systematic way. Thispaper examines the spatial patterns of natural hazard mortality at the county-level for the U.S. from1970–2004 using a combination of geographical and epidemiological methods.

Results: Chronic everyday hazards such as severe weather (summer and winter) and heat accountfor the majority of natural hazard fatalities. The regions most prone to deaths from natural hazardsare the South and intermountain west, but sub-regional county-level mortality patterns show morevariability. There is a distinct urban/rural component to the county patterns as well as a coastaltrend. Significant clusters of high mortality are in the lower Mississippi Valley, upper Great Plains,and Mountain West, with additional areas in west Texas, and the panhandle of Florida, Significantclusters of low mortality are in the Midwest and urbanized Northeast.

Conclusion: There is no consistent source of hazard mortality data, yet improvements in existingdatabases can produce quality data that can be incorporated into spatial epidemiological studies asdemonstrated in this paper. It is important to view natural hazard mortality through a geographiclens so as to better inform the public living in such hazard prone areas, but more importantly toinform local emergency practitioners who must plan for and respond to disasters in theircommunity.

BackgroundOutcomes of natural hazard events can be grouped intotwo general categories; economic losses (including prop-erty, agricultural, direct, and indirect losses) and casualties(injuries and fatalities). Despite these two potentialimpacts on populations, contemporary hazards researchin the United States focuses more on economic losses andloss reduction rather than examining casualties. This biasreflects the downward trend in casualties and the dramaticincreases in hazard losses over time in the United Statesand other more developed countries [1,2]. Despite the

downward trend in human casualties, developed coun-tries are still susceptible to significant losses of life fromnatural hazard events as shown by Hurricane Katrina, the2003 European heat wave, and the 1995 Chicago heatwave.

Previous hazard mortality studies often lack a breadth ofhazard types and utilize limited geographic scales. Nota-ble exceptions include a global risk analysis that includesvarious hazard types [3], and a U.S. based historical anal-ysis of hazard mortality [4]. However, researchers often

Published: 17 December 2008

International Journal of Health Geographics 2008, 7:64 doi:10.1186/1476-072X-7-64

Received: 28 August 2008Accepted: 17 December 2008

This article is available from: http://www.ij-healthgeographics.com/content/7/1/64

© 2008 Borden and Cutter; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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examine deaths for only one particular type of hazardsuch as floods [5,6], earthquakes [7,8], tornadoes [9], orheat [10]. Although detailed examination of hazardrelated deaths for one hazard type is important, this frag-mented and unitary approach leaves many unansweredquestions about the geography of deaths from naturalhazards as a whole. It also limits comparability betweenhazard event types. Certain hazard types (e.g., heat,floods) often are described as the number one cause ofhazard related death without an appropriate multi-hazardstudy to substantiate such claims. Indeed, claims of exam-ining "the deadliest hazard" often are used as justificationfor studying the mortality associated with a particular haz-ard type.

This paper examines the spatial patterns associated withhazard mortality at a sub-state level for the United Statesusing a combination of geographical and epidemiologicalmethods, and a sub-county georeferenced hazards eventsand losses database, SHELDUS. Two specific researchquestions are examined: 1) Which natural hazard contrib-utes most to hazard-induced mortality, and 2) What is thespatial patterning of natural hazard mortality in theUnited States?

Studying death geographicallyStudying any type of mortality is inherently geographical.Since the initial work of John Snow [11], countless mor-tality atlases have been produced such as those for, cancer[12], toxic hazards exposure [13], and all causes [14,15].Mortality mapping permits the exploration of spatial pat-terns [16,17]; the development of more robust mortalitymapping approaches [18,19]; testing for statistically sig-nificant spatial clusters of mortality [20,21]; and temporalanalysis [22,23].

Research that examines the spatial aspects of mortality hasgrown significantly over time, forming a niche in spatialepidemiology, which merges spatial analysis techniquesfrom geography with mortality studies from publichealth/epidemiology [18,24,25]. Within spatial epidemi-ology, considerable research effort has focused on thecomputation of robust measures of mortality [26,27], dif-ferent clustering techniques to analyze spatial patterns[21,28], and the creation of a reliable map of disease ormortality that is free of spurious statistical variation [29].

Natural hazard mortalityDespite the advancement of health geographics, the appli-cation of spatial epidemiological methods has not beenapplied systematically to deaths from natural hazards inthe United States. Perhaps the biggest hindrance to con-ducting such broad spatial-analytical research on thegeography of hazard deaths has been the lack of qualitydata. In order to explore hazard related deaths in a mean-

ingful way, researchers need a large data repository thatstores information on a variety of hazard types at a resolu-tion fine enough to detect spatial patterns. A comprehen-sive, centralized, and reliable accounting of georeferencednatural hazard deaths has thus far been unavailable. Alsothe rarity of hazard deaths, especially in developed coun-tries introduces the methodological issue of the "smallnumber" problem resulting from calculating mortalityrates for a rare cause of death in small areas.

Despite these limitations, there is an emerging literatureon natural hazard mortality. For example, a number ofstudies have been conducted on the patterns of death inspecific disaster events such as Hurricane Andrew [30,31],the Northridge Earthquake [32,33], and the Chicago heatwave [10]. These types of studies are useful for determin-ing specific causal mechanisms between hazards anddeath, but out of necessity, they are highly localized, andevent specific. Other research has focused on more generalcauses and circumstances of hazard mortality from spe-cific hazard types such as floods [34], or heat [35], andgeneralized effects of climate on mortality [36,37].Although informative and useful, the geography of hazardmortality is often an ancillary piece of the research, notthe primary focus.

The spatial patterning of hazard mortality is less under-stood and studied. For example, Kalkstein and Davis [38]examined the effect of temperature on mortality using var-ious cities throughout the United States as sample points,thereby providing a comparative regional analysis ofurban areas. Two different studies examined tornado andflood deaths in the United States using spatially griddeddata. A 40 km cell size was chosen to analyze flood deathsto approximate normal county size [5], yet a larger cellsize was used for tornado deaths (60 km) without justifi-cation [9]. Although an interesting approach, questionsremain on the reasoning behind the choice of a particularpixel size, and the lack of size consistency for studyingdeaths from different hazards. Finally, Thacker et al. [4]was one of the first studies to examine multi-hazard mor-tality analysis using the CDC's Compressed Mortality File. Interms of encompassing a broad range of natural hazardtypes, their work most closely resembles the scope of thispaper. However, Thacker et al. [4] cover a shorter timeperiod (1979–2004) and fail to provide a strong spatialcomponent to their research despite having county-leveldata. They offer a tabular analysis of mortality rates forvarious regions in the United States, but fail to provide asystematic spatial analysis.

A review of the literature shows that some studies containa spatial-analytic component but not a range of hazardtypes, while other studies examine multiple hazards butuse aspatial techniques. A natural hazard mortality study



that combines spatial analysis at a fine resolution for awide variety of natural hazards is missing. This paperimproves the spatial resolution and analytic techniques ofprevious studies and includes a broader range of naturalhazards in the analysis, thus providing a more completepicture of the geography of natural hazards mortality inthe U.S.

MethodsDataThe mortality data for this paper were culled from the Spa-tial Hazard Event and Loss Database for the United States(SHELDUS)(available at http://www.sheldus.org). Thisdatabase provides hazard loss information (economiclosses and casualties) from 1960 – 2005 for eighteen dif-ferent hazard types at county level resolution [39] for all50 states. To maintain consistency in the county level enu-meration units and the quality of the mortality data, threeadjustments were made. First, Alaska and Hawaii wereexcluded from the analysis. Second, to maintain consist-ent geographic units through time any changes in countyboundaries were attributed to the original county for theentire time-period (this includes counties that were splitor merged). Finally, all independent cities in Virginia,Maryland, and Missouri were absorbed into their respec-tive counties. After these modifications, 3,070 countylevel enumeration units were used in this study.

Inconsistencies in the SHELDUS database were firstaddressed before any mortality measures were con-structed. For the implementation of spatial epidemiologi-cal methods, two problems embedded in the design ofSHELDUS warrant a brief discussion. These include eventthresholds and geographic attribution of deaths.

These event thresholds were due to the reliance inSHELDUS on its primary data source, NCDC's Storm Data,which reported events on a categorical damage scale(Table 1) [39,40]. Prior to 1995, only events that gener-ated at least $50,000 (Category 5) in economic damagewere included in the SHELDUS database. SHELDUS usedthe lower bound of each damage category when reporting

losses to maintain the most conservative estimate of lossespossible. After 1995, however, NCDC increased the preci-sion of loss information for hazard events, reportinglosses as exact dollar amounts rather than logarithmic cat-egories. Thus, SHELDUS contained two time-periods withmarkedly different standards regarding which events wereincluded in the database. For example, prior to 1995,mortality from events that failed to reach the monetarythreshold (such as lightning) would be excluded. Toadjust for this problem, any event in Storm Data thatcaused a death and less than $50,000 in economic dam-ages was added to the SHELDUS database for the period1970–1995. Correcting these problems for 1960–1969 iscurrently underway by the SHELDUS developers and wasunavailable for use in this paper.

The second issue with the original SHELDUS data is thegeographic attribution of deaths. When events affectedmultiple counties, and there was no information on thespecific county where the fatality or the monetary lossesoccurred, all losses and casualties were evenly distributedacross the affected counties, leading to fractional deathsand injuries [39]. After 1995, however, Storm Data becamemore geographically precise in defining the locations ofevents, and the attribution of those losses and deaths tospecific counties. Accordingly, SHELDUS was more delib-erate in attributing deaths to their proper geographic loca-tion. This inconsistency necessitated a quality controlanalysis on SHELDUS data prior to 1995. From 1970 to1995, every event in SHELDUS with a death was verifiedagainst Storm Data for geographic accuracy. In instanceswhere the county of death was specified in Storm Data,SHELDUS was changed to reflect that information.

MappingThe simplest way to characterize mortality in the form ofa rate is to map crude rates by dividing the number ofdeaths by the population at risk (usually the mid-yearpopulation) [41,42]. Although crude rates can indicatewhere the magnitude of deaths is large [43], a major draw-back is that they do not account for differences in the pop-ulation structure of different areas [43]. To account forvarying age structures between counties, we employedindirect age standardization to our data and calculatedstandardized mortality ratios (SMRs) for each county.Indirect standardization was necessary for this analysisbecause SHELDUS data lacks the age at death for hazardfatalities. Although there is debate in the epidemiologicliterature as to the utility of SMRs [44], they are a widelyused and accepted measure of mortality in spatial epide-miology research [18,27,44].

Often referred to as the small number problem, spuriousvariation in rates can result from small denominator data(i.e. population) [45,46]. Counties with small popula-

Table 1: NCDC damage categories

Category Damage Range

1 < $502 $50 – $5003 $500 – $5,0004 $5,000 – $50,0005 $50,000 – $500,0006 $500,000 – $5,000,0007 $5,000,000 – $50,000,0008 $50,000,000 – $500,000,0009 $500,000,000 – $5,000,000,000


http://www.sheldus.org


tions demonstrate extremely high mortality rates when, infact, there are few actual recorded deaths. Furthermore,greater than expected fluctuation in mortality rates occurswith the addition of only one or two extra cases in lowpopulation counties. To adjust for spurious variationwithout compromising spatial resolution, mortality rateswere transformed using an empirical-bayes operation toremove artificial extreme values, yet maintain the struc-ture of broad spatial trends. This technique is commonlyused with small-area rate data and is encouraged over reg-ular SMRs [18].

To analyze the observed patterns spatially, we employed alocal cluster analysis on the map of hazard mortality. Thelocal Moran's I statistic [47] provided in the GeoDa 0.9.5-i5 software package [48] was used to reveal contiguousareas of elevated mortality. Such local statistics are usefulto analyze the spatial variation of clusters that are notapparent in global measures.

Data classification changesTo better interpret SHELDUS deaths across hazard types,the 18 original hazards were generalized into 11 distinctcategories (Table 2). For the purposes of analyzing deathsacross hazard categories, each event, regardless of howmany hazards were involved was assigned into one andonly one category. For example, an event with multiplecauses (e.g. hail, wind, lightning, and rain) was catego-rized as Severe Weather. Even though lightning is associ-ated with severe weather, it has its own category due to thenumber of fatality inducing events reported with light-ning as the only hazard.

As a result of adding deaths from events that generatedless than $50,000 in damages, the total number of fatali-ties from 1970 – 2004 increased by 31% (Table 3). Asexpected, lightning was the most affected category basedon the new corrections, increasing its number of fatalitiesby 77%. This clearly demonstrates databases that recordhazard losses based solely on economic damages may notbe the most appropriate ones to use when studying hazardmortality without first correcting for errors of omission.

ResultsDeadliest hazard typesFigure 1 shows the distribution of deaths for 11 hazardcategories as a percent of total hazard deaths from 1970 –2004. Heat/drought ranks highest among these hazardcategories causing 19.6% of total deaths, closely followedby severe summer weather (18.8%) and winter weather(18.1%). Geophysical events (such as earthquakes), wild-fires, and hurricanes are responsible for less than 5% oftotal hazard deaths combined. What is noteworthy here isthat over time, highly destructive, highly publicized, oftencatastrophic singular events such as hurricanes and earth-quakes are responsible for relatively few deaths whencompared to the more frequent, less catastrophic eventssuch as heat waves, and severe weather (summer or win-ter).

Spatial distributionUsing the corrected SHELDUS data, natural hazard mor-tality was mapped to visually illustrate its geographic dis-tribution. We first aggregated the data to a regional scaleusing the geographic divisions of the Federal EmergencyManagement Agency (FEMA), and then we produced acounty-level map. Comparing county-level mortality

Table 2: Generalized SHELDUS hazard types

SHELDUS Category Generalized Category

Coastal Coastal (e.g. storm surge, rip currents, coastal erosion)Flooding Flooding (e.g. flash, riverene)Earthquake GeophysicalTsunami/SeicheVolcanoDrought Heat/DroughtHeatHurricane/Tropical Storm Hurricane/Tropical StormLightning LightningAvalanche Mass MovementLandslideFog Severe WeatherHailSevere Storm/ThunderstormWindTornado TornadoWildfire WildfireWinter Weather Winter Weather



maps to those at a higher level of spatial aggregationserves two analytical purposes. First, similar spatial trendsbetween the county and the aggregated regional map(which provide stable mortality estimates) increase theconfidence that stability was achieved in the transformedcounty-level maps. Second, similar patterns at differentspatial scales support the notion that the observed county-level patterns are not a function of scale-dependent proc-esses, thereby increasing the confidence that this is anaccurate representation of hazard-induced mortality.These county-level maps not only provide a stable mortal-ity map for reference purposes, but also present hazardmortality estimates at enumeration units that are relevantto local emergency managers and public health officials.

Mortality data were indirectly standardized to the year2000 national hazard mortality rate using standardizedmortality ratios. At the county level, adjusted SMRs werecalculated using the empirical bayes procedure providedin GeoDa 0.9.5-i5 [48] and log-transformed to achieve anormal distribution. The data were mapped using stand-ard deviations from the mean.

Hazard mortality is most prominent in the South (FEMAregions IV and VI) (Figure 2). While FEMA region VIIIappears to have the highest risk level based on SMRs, thisfinding must be interpreted with caution, and is likely afunction of the small population size within the region.Although the SMRs were stabilized with the empiricalbayes procedure, hazard deaths are so rare that the smallnumber problem cannot be totally removed. Theseregional patterns are due to the occurrence of varioussevere weather hazards and tornadoes (region IV), winterweather (region VIII), and floods and tornadoes (regionVI) as the primary causal mortality agents. Pie chartslocated in each region show the proportion of the topthree causes of hazard deaths. The fourth section in eachpie chart, labeled "other", includes any of the generalizedevent types listed in Table 2. The utility of this classifica-

tion approach is to visualize the relative significance of thetop three causes of death in each FEMA Region. A FEMARegion with a very small "other" category as in Region 1(Figure 2) suggests that the top three causes of death arequite important to the overall impact of hazard deaths.However, a large "other" category shows places such asRegion 4 (Figure 2), where deaths are more evenly distrib-uted by hazard type. Although potentially useful at thenational emergency management level for assessing areasof higher mortality, the regions are so large that they donot show spatial variability in hazard mortality. While,the South shows elevated mortality, the higher mortalityrates may be clustered in a few high- risk areas, not uni-formly distributed throughout the region as the map sug-gests.

County level hazard-induced mortality for the contiguousUnited States shows more spatial variability than theregional map (Figure 3). For instance, the highest valuesin the South are along the Atlantic and Gulf Coasts, espe-cially in the Florida panhandle, and along the Carolinas'coast. Elevated mortality in southwestern Texas andthroughout Arkansas contributes to higher mortality forFEMA region VI.

The initial visual analysis is suggestive of a regional pat-terning of mortality from natural hazards. However, theidentification of clusters of elevated mortality should beachieved through local spatial statistics rather than simplevisual interpretation because size, shape, and potential forspurious rate variation of polygons can create the illusionof clusters that are not statistically significant [49].

Cluster analysisTo test for the presence of spatial clusters of hazard mor-tality, we employed spatial autocorrelation on the countylevel SMRs using both global and local indicators usingthe GeoDa. 0.9.5-i5 software package [48]. A globalMoran's I test was performed to assess whether the pattern

Table 3: Effect of fatality corrections in SHELDUS

Event Type SHELDUS Mortality Database Total Increase

Lightning 517 1,744 2,261 77%Winter Weather 2,306 1,306 3,612 36%Severe Weather 2,402 1,360 3,762 36%Coastal 357 99 456 22%Flooding 2,188 600 2,788 22%Heat/Drought 3,227 679 3,906 17%Tornado 2,006 308 2,314 13%Mass Movement 154 16 170 9%Hurricane/Trop. St. 279 25 304 8%Geophysical 302 0 302 0%Wildfire 84 0 84 0%Totals 13,821 6,137 19,958 31%




Natural hazard deaths by event typeFigure 1Natural hazard deaths by event type.


of SMRs had an average tendency to cluster in space [50].Neighbors were designated based on first order queencontiguity [48]. The likelihood of positive spatial autocor-relation in the dataset was confirmed with a globalMoran's I coefficient of .30 (p < .001).

With a tendency for similar SMR values to cluster estab-lished, a local indicator of spatial association was used toidentify the location of clusters. Local pockets of positivespatial autocorrelation (areas where similar values areclustered in space) for county level SMR data appearthroughout the continental United States) (Figure 4a).Areas of high SMR values occur through the northernPlains, mountain west, and South, particularly the Floridapanhandle, the Carolinas, lower Mississippi River, andRocky Mountain west. Other noticeable trends include

the tendency for large urban centers to demonstrate clus-ters of low SMR values (e.g. Atlanta, San Francisco, andNew York). Low SMRs in urban areas do not mean thatthere is less overall risk, but instead less risk of dying onan individual basis since there are more people. Clustersof low SMR values generally occur in the Midwest andNortheast coastal corridor. All local clusters shown are sta-tistically significant at 95% confidence. However, a confi-dence map shows variation in significance values toidentify clusters that exceed 95% confidence (Figure 4b).

The local Moran's I and significance maps confirm our vis-ual analysis of the geographic distribution of mortalityfrom natural hazards. Those county level natural hazardmortality patterns most statistically relevant in terms ofelevated mortality are found in the northen plains and

Hazard induced mortality by FEMA region 1970 – 2004Figure 2Hazard induced mortality by FEMA region 1970 – 2004. *SMRs use Year 2000 as Standard Population.



southern Texas. Similarly, areas of important decreasedmortality include the San Francisco Bay area and theurbanized Northeast.

DiscussionThe problems and corrections associated with SHELDUSdata raise questions about our decision to use this datasource over the CDC's Compressed Mortality File as wasdone by Thacker et al. [4]. No dataset is perfect, and theCompressed Mortality File, also has its share of problems.First, unlike Storm Data (upon which SHELDUS is based),the Compressed Mortality File is not solely focused on natu-ral hazard events. Although both SHELDUS and the Com-pressed Mortality File likely suffer from undercountinghazard related deaths [4,39], it is known that the only rea-son any of the deaths appear in Storm Data (and

SHELDUS) is because of some natural event. In the CDC'sCompressed Mortality File, deaths are interpreted from clas-sifying the underlying cause listed on death certificates[4], whereas SHELDUS mortality is derived from StormData. NCDC, the parent source for Storm Data, uses deathestimates for hazard events that may or may not be veri-fied [51]. The accuracy of these estimates is unknown, butsome level of undercounting is almost certain. However,Storm Data remains the premier data source for weatherhazard related losses and deaths [52].

Second, the coding system used by the CDC underwent amajor revision after 1998, providing additional and morespecific categories for deaths attributed to natural hazards.When undertaking a longitudinal study such as this, anynew classification scheme creates analytical problems by

County-level hazard induced mortality 1970 – 2004Figure 3County-level hazard induced mortality 1970 – 2004. *SMRs use Year 2000 as Standard Population. **SMRs calculated using empirical bayes procedure and log transformed. Map colors based on http://www.ColorBrewer.org, by Cynthia A. Brewer, Penn State.


http://www.ColorBrewer.org



County-level SMR clusters (A) and significance levels (B) from hazard induced mortality 1970 – 2004Figure 4County-level SMR clusters (A) and significance levels (B) from hazard induced mortality 1970 – 2004. Map colors based on http://www.ColorBrewer.org, by Cynthia A. Brewer, Penn State.

http://www.ColorBrewer.org


introducing a change in the specificity of the data struc-ture. This is shown in the work by Thacker et al. [4] as theywere able to use only six types of natural hazards in theiranalysis because the 1979 – 1998 data were not asdetailed as those from 1999 – 2004. Thacker et al. [4] alsomentioned that apparent increases in the number ofdeaths for some natural hazard events might be a statisti-cal artifact of this classification change rather than anactual increase in hazard deaths. Unlike the CDC data dis-parity, the inconsistencies in SHELDUS before and after1995 were addressed in later versions of the database.

To create a more meaningful comparison between the twodatasets, SHELDUS data were modified temporally andcategorically to mimic the Thacker et al. [4] study. Weextracted a subset of the SHELDUS data for 1979 – 2004,and grouped hazards according to the categories used byThacker et al. [4] (Table 4). A discrepancy between data-bases lies in pairing "cold" and "storms/floods" fromThacker et al. [4] with matching categories fromSHELDUS. For Thacker et al.'s [4] "cold," the SHELDUScategory "winter weather" was used, which includes coldrelated and other winter weather deaths such as thosefrom blizzards and winter storms. These winter stormswould technically be categorized under "storms/floods"in Thacker et al. [4]. In SHELDUS, however, cold and win-ter storm deaths all fall under "winter weather." The pro-portions of deaths by hazard type seem quite different,with over half of Thacker et al.'s [4] deaths attributed tocold, and the majority of SHELDUS deaths in storms/floods (Figure 5). The proportional differences betweenThacker et al. [4] and our data by individual causal agentare statistically significant (p < .01) with the exception ofearth movements which is statistically significant at p <0.05.

Hazard mortality data are fraught with inconsistenciesacross databases. Differences manifest themselves fromthe subjective nature of attributing any death to a hazard

event. Because of the lack of a standardized death classifi-cation scheme [53], hazard deaths are not counted in thesame way for any two databases. In fact, even within anational database (i.e. SHELDUS, Compressed MortalityFile), hazard death attribution likely varies geographically.Therefore, we are cautious that the analyses and conclu-sions drawn from hazard mortality data are based on esti-mates of deaths from natural events.

ConclusionThere is considerable debate about which natural hazardis the most "deadly". According to our results, the answeris heat. But this finding could change depending on thedata source, or how hazards within a data source aregrouped, as we've shown here. Even if researchers coulddefinitively assert the 'deadliest hazard,' a better issue topose is where residents are more susceptible to fatalitiesfrom natural hazards within the United States.

The spatial patterns revealed in the results are not surpris-ing – greater risk of death along the hurricane coasts, inrural areas, and in the South – all areas prone to naturalhazards as well as significant population growth andexpansion throughout the study period. However, theinterpretation of these patterns reveals the problems asso-ciated with rare causes of death. Using this analysis as ablueprint for hazard mortality 'hot spots' supports justifi-cation for a more in-depth study of hazard- induceddeaths in specific regions or communities. It is at this localscale where defining the deadliest hazard becomes impor-tant and emergency management officials can take actionto try to reduce the number of future deaths.

There are limitations to this study (and others that studymortality from a rare cause of death) that are worth not-ing. First, we were able to visualize the spatial variation ofhazard related deaths for our study period for the entireU.S., but in any given year or in any given county, very fewif any deaths may occur. This rarity of occurrence pre-vented our analysis from providing detailed informationon hazard-specific mortality rates or SMRs. Calculation ofsuch measures would be highly unstable due to the mini-mal number of deaths in many counties [See [4]]. Second,there are limitations in the original data sources as notedearlier.

This paper provides the foundation of a solid understand-ing of the geography of hazard related mortality over time.Future research can use this information to study specificareas of elevated hazard mortality, and study its correlatesin different areas. Ultimately, greater local knowledgeabout which types of hazards are deadliest in differentgeographic regions is useful information for strategiesaimed at reducing the risk of death from natural hazards.

Table 4: Matching hazard categories between SHELDUS and Thacker et al. (2008)

Thacker Category SHELDUS Category

Cold Winter WeatherHeat HeatLightning LightingStorms/floods Coastal

FloodingHurricane/Tropical StormSevere Storm/ThunderstormTornado

Earth Movements AvalancheEarthquakeLandslideVolcano




Comparison of deaths by hazard type using Thacker et al. 2008 (A) and SHELDUS (B)Figure 5Comparison of deaths by hazard type using Thacker et al. 2008 (A) and SHELDUS (B).


The over-arching contribution of this work is not to com-pare and contrast datasets, or even create the "best" possi-ble map of hazard deaths. Rather, this work enablesresearch and emergency management practitioners toexamine hazard deaths through a geographic lens. Usingthis as a tool to identify areas with higher than averagehazard deaths can justify allocation of resources to theseareas with the goal of reducing hazard deaths. One logicalavenue in achieving this goal is to assess the efficacy ofinformation dissemination from emergency managers tothe public. An important question is whether people inareas of high mortality know what to do (or what not todo) when a hazard event occurs. Improved understandingof how to react in a hazard event will contribute toreduced deaths from hazard events in high-mortalityareas.

Competing interestsThe authors declare that they have no competing interests.

Authors' contributionsSLC conceived of the study, while KAB conducted theresearch. SLC and KAB wrote the manuscript and the revi-sion. Both authors approved the final manuscript.

AcknowledgementsThis research was supported by the U.S. Department of Homeland Security (DHS) through the National Consortium for the Study of Terrorism and Responses to Terrorism (START), grant number N00140510629 (S.L. Cut-ter, Principal Investigator). Any opinions, findings, conclusions, or recom-mendations are those of the authors and do not represent the official views of the US DHS.

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