Spatial patterns of natural hazards mortality in the United States
© Borden and Cutter; licensee BioMed Central Ltd. 2008
Received: 28 August 2008
Accepted: 17 December 2008
Published: 17 December 2008
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 geographic coverage. This makes it difficult to assess mortality from natural hazards in any systematic way. This paper examines the spatial patterns of natural hazard mortality at the county-level for the U.S. from 1970–2004 using a combination of geographical and epidemiological methods.
Chronic everyday hazards such as severe weather (summer and winter) and heat account for the majority of natural hazard fatalities. The regions most prone to deaths from natural hazards are the South and intermountain west, but sub-regional county-level mortality patterns show more variability. There is a distinct urban/rural component to the county patterns as well as a coastal trend. 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, Significant clusters of low mortality are in the Midwest and urbanized Northeast.
There is no consistent source of hazard mortality data, yet improvements in existing databases can produce quality data that can be incorporated into spatial epidemiological studies as demonstrated in this paper. It is important to view natural hazard mortality through a geographic lens so as to better inform the public living in such hazard prone areas, but more importantly to inform local emergency practitioners who must plan for and respond to disasters in their community.
Outcomes of natural hazard events can be grouped into two general categories; economic losses (including property, agricultural, direct, and indirect losses) and casualties (injuries and fatalities). Despite these two potential impacts on populations, contemporary hazards research in the United States focuses more on economic losses and loss reduction rather than examining casualties. This bias reflects the downward trend in casualties and the dramatic increases in hazard losses over time in the United States and other more developed countries [1, 2]. Despite the downward trend in human casualties, developed countries are still susceptible to significant losses of life from natural hazard events as shown by Hurricane Katrina, the 2003 European heat wave, and the 1995 Chicago heat wave.
Previous hazard mortality studies often lack a breadth of hazard types and utilize limited geographic scales. Notable exceptions include a global risk analysis that includes various hazard types , and a U.S. based historical analysis of hazard mortality . However, researchers often examine deaths for only one particular type of hazard such as floods [5, 6], earthquakes [7, 8], tornadoes , or heat . Although detailed examination of hazard related deaths for one hazard type is important, this fragmented and unitary approach leaves many unanswered questions about the geography of deaths from natural hazards as a whole. It also limits comparability between hazard event types. Certain hazard types (e.g., heat, floods) often are described as the number one cause of hazard related death without an appropriate multi-hazard study to substantiate such claims. Indeed, claims of examining "the deadliest hazard" often are used as justification for studying the mortality associated with a particular hazard type.
This paper examines the spatial patterns associated with hazard mortality at a sub-state level for the United States using a combination of geographical and epidemiological methods, and a sub-county georeferenced hazards events and losses database, SHELDUS. Two specific research questions are examined: 1) Which natural hazard contributes most to hazard-induced mortality, and 2) What is the spatial patterning of natural hazard mortality in the United States?
Studying death geographically
Studying any type of mortality is inherently geographical. Since the initial work of John Snow , countless mortality atlases have been produced such as those for, cancer , toxic hazards exposure , and all causes [14, 15]. Mortality mapping permits the exploration of spatial patterns [16, 17]; the development of more robust mortality mapping approaches [18, 19]; testing for statistically significant spatial clusters of mortality [20, 21]; and temporal analysis [22, 23].
Research that examines the spatial aspects of mortality has grown significantly over time, forming a niche in spatial epidemiology, which merges spatial analysis techniques from geography with mortality studies from public health/epidemiology [18, 24, 25]. Within spatial epidemiology, considerable research effort has focused on the computation of robust measures of mortality [26, 27], different clustering techniques to analyze spatial patterns [21, 28], and the creation of a reliable map of disease or mortality that is free of spurious statistical variation .
Natural hazard mortality
Despite the advancement of health geographics, the application of spatial epidemiological methods has not been applied systematically to deaths from natural hazards in the United States. Perhaps the biggest hindrance to conducting such broad spatial-analytical research on the geography of hazard deaths has been the lack of quality data. In order to explore hazard related deaths in a meaningful way, researchers need a large data repository that stores information on a variety of hazard types at a resolution fine enough to detect spatial patterns. A comprehensive, centralized, and reliable accounting of georeferenced natural hazard deaths has thus far been unavailable. Also the rarity of hazard deaths, especially in developed countries introduces the methodological issue of the "small number" problem resulting from calculating mortality rates for a rare cause of death in small areas.
Despite these limitations, there is an emerging literature on natural hazard mortality. For example, a number of studies have been conducted on the patterns of death in specific disaster events such as Hurricane Andrew [30, 31], the Northridge Earthquake [32, 33], and the Chicago heat wave . These types of studies are useful for determining specific causal mechanisms between hazards and death, but out of necessity, they are highly localized, and event specific. Other research has focused on more general causes and circumstances of hazard mortality from specific hazard types such as floods , or heat , and generalized effects of climate on mortality [36, 37]. Although informative and useful, the geography of hazard mortality is often an ancillary piece of the research, not the primary focus.
The spatial patterning of hazard mortality is less understood and studied. For example, Kalkstein and Davis  examined the effect of temperature on mortality using various cities throughout the United States as sample points, thereby providing a comparative regional analysis of urban areas. Two different studies examined tornado and flood deaths in the United States using spatially gridded data. A 40 km cell size was chosen to analyze flood deaths to approximate normal county size , yet a larger cell size was used for tornado deaths (60 km) without justification . Although an interesting approach, questions remain on the reasoning behind the choice of a particular pixel size, and the lack of size consistency for studying deaths from different hazards. Finally, Thacker et al.  was one of the first studies to examine multi-hazard mortality analysis using the CDC's Compressed Mortality File. In terms of encompassing a broad range of natural hazard types, their work most closely resembles the scope of this paper. However, Thacker et al.  cover a shorter time period (1979–2004) and fail to provide a strong spatial component to their research despite having county-level data. They offer a tabular analysis of mortality rates for various regions in the United States, but fail to provide a systematic spatial analysis.
A review of the literature shows that some studies contain a spatial-analytic component but not a range of hazard types, while other studies examine multiple hazards but use aspatial techniques. A natural hazard mortality study that combines spatial analysis at a fine resolution for a wide variety of natural hazards is missing. This paper improves the spatial resolution and analytic techniques of previous studies and includes a broader range of natural hazards in the analysis, thus providing a more complete picture of the geography of natural hazards mortality in the U.S.
The mortality data for this paper were culled from the Spatial Hazard Event and Loss Database for the United States (SHELDUS)(available at http://www.sheldus.org). This database provides hazard loss information (economic losses and casualties) from 1960 – 2005 for eighteen different hazard types at county level resolution  for all 50 states. To maintain consistency in the county level enumeration units and the quality of the mortality data, three adjustments were made. First, Alaska and Hawaii were excluded from the analysis. Second, to maintain consistent geographic units through time any changes in county boundaries were attributed to the original county for the entire time-period (this includes counties that were split or merged). Finally, all independent cities in Virginia, Maryland, and Missouri were absorbed into their respective counties. After these modifications, 3,070 county level enumeration units were used in this study.
Inconsistencies in the SHELDUS database were first addressed before any mortality measures were constructed. For the implementation of spatial epidemiological methods, two problems embedded in the design of SHELDUS warrant a brief discussion. These include event thresholds and geographic attribution of deaths.
NCDC damage categories
$50 – $500
$500 – $5,000
$5,000 – $50,000
$50,000 – $500,000
$500,000 – $5,000,000
$5,000,000 – $50,000,000
$50,000,000 – $500,000,000
$500,000,000 – $5,000,000,000
The second issue with the original SHELDUS data is the geographic attribution of deaths. When events affected multiple counties, and there was no information on the specific county where the fatality or the monetary losses occurred, all losses and casualties were evenly distributed across the affected counties, leading to fractional deaths and injuries . After 1995, however, Storm Data became more geographically precise in defining the locations of events, and the attribution of those losses and deaths to specific counties. Accordingly, SHELDUS was more deliberate in attributing deaths to their proper geographic location. This inconsistency necessitated a quality control analysis on SHELDUS data prior to 1995. From 1970 to 1995, every event in SHELDUS with a death was verified against Storm Data for geographic accuracy. In instances where the county of death was specified in Storm Data, SHELDUS was changed to reflect that information.
The simplest way to characterize mortality in the form of a rate is to map crude rates by dividing the number of deaths by the population at risk (usually the mid-year population) [41, 42]. Although crude rates can indicate where the magnitude of deaths is large , a major drawback is that they do not account for differences in the population structure of different areas . To account for varying age structures between counties, we employed indirect age standardization to our data and calculated standardized mortality ratios (SMRs) for each county. Indirect standardization was necessary for this analysis because SHELDUS data lacks the age at death for hazard fatalities. Although there is debate in the epidemiologic literature as to the utility of SMRs , they are a widely used and accepted measure of mortality in spatial epidemiology research [18, 27, 44].
Often referred to as the small number problem, spurious variation in rates can result from small denominator data (i.e. population) [45, 46]. Counties with small populations demonstrate extremely high mortality rates when, in fact, there are few actual recorded deaths. Furthermore, greater than expected fluctuation in mortality rates occurs with the addition of only one or two extra cases in low population counties. To adjust for spurious variation without compromising spatial resolution, mortality rates were transformed using an empirical-bayes operation to remove artificial extreme values, yet maintain the structure of broad spatial trends. This technique is commonly used with small-area rate data and is encouraged over regular SMRs .
To analyze the observed patterns spatially, we employed a local cluster analysis on the map of hazard mortality. The local Moran's I statistic  provided in the GeoDa 0.9.5-i5 software package  was used to reveal contiguous areas of elevated mortality. Such local statistics are useful to analyze the spatial variation of clusters that are not apparent in global measures.
Data classification changes
Generalized SHELDUS hazard types
Coastal (e.g. storm surge, rip currents, coastal erosion)
Flooding (e.g. flash, riverene)
Effect of fatality corrections in SHELDUS
Deadliest hazard types
Using the corrected SHELDUS data, natural hazard mortality was mapped to visually illustrate its geographic distribution. We first aggregated the data to a regional scale using the geographic divisions of the Federal Emergency Management Agency (FEMA), and then we produced a county-level map. Comparing county-level mortality maps to those at a higher level of spatial aggregation serves two analytical purposes. First, similar spatial trends between the county and the aggregated regional map (which provide stable mortality estimates) increase the confidence that stability was achieved in the transformed county-level maps. Second, similar patterns at different spatial scales support the notion that the observed county-level patterns are not a function of scale-dependent processes, thereby increasing the confidence that this is an accurate representation of hazard-induced mortality. These county-level maps not only provide a stable mortality map for reference purposes, but also present hazard mortality estimates at enumeration units that are relevant to local emergency managers and public health officials.
Mortality data were indirectly standardized to the year 2000 national hazard mortality rate using standardized mortality ratios. At the county level, adjusted SMRs were calculated using the empirical bayes procedure provided in GeoDa 0.9.5-i5  and log-transformed to achieve a normal distribution. The data were mapped using standard deviations from the mean.
The initial visual analysis is suggestive of a regional patterning of mortality from natural hazards. However, the identification of clusters of elevated mortality should be achieved through local spatial statistics rather than simple visual interpretation because size, shape, and potential for spurious rate variation of polygons can create the illusion of clusters that are not statistically significant .
To test for the presence of spatial clusters of hazard mortality, we employed spatial autocorrelation on the county level SMRs using both global and local indicators using the GeoDa. 0.9.5-i5 software package . A global Moran's I test was performed to assess whether the pattern of SMRs had an average tendency to cluster in space . Neighbors were designated based on first order queen contiguity . The likelihood of positive spatial autocorrelation in the dataset was confirmed with a global Moran's I coefficient of .30 (p < .001).
The local Moran's I and significance maps confirm our visual analysis of the geographic distribution of mortality from natural hazards. Those county level natural hazard mortality patterns most statistically relevant in terms of elevated mortality are found in the northen plains and southern Texas. Similarly, areas of important decreased mortality include the San Francisco Bay area and the urbanized Northeast.
The problems and corrections associated with SHELDUS data raise questions about our decision to use this data source over the CDC's Compressed Mortality File as was done by Thacker et al. . No dataset is perfect, and the Compressed 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 natural hazard events. Although both SHELDUS and the Compressed Mortality File likely suffer from undercounting hazard related deaths [4, 39], it is known that the only reason any of the deaths appear in Storm Data (and SHELDUS) is because of some natural event. In the CDC's Compressed Mortality File, deaths are interpreted from classifying the underlying cause listed on death certificates , whereas SHELDUS mortality is derived from Storm Data. NCDC, the parent source for Storm Data, uses death estimates for hazard events that may or may not be verified . The accuracy of these estimates is unknown, but some level of undercounting is almost certain. However, Storm Data remains the premier data source for weather hazard related losses and deaths .
Second, the coding system used by the CDC underwent a major revision after 1998, providing additional and more specific categories for deaths attributed to natural hazards. When undertaking a longitudinal study such as this, any new classification scheme creates analytical problems by introducing a change in the specificity of the data structure. This is shown in the work by Thacker et al.  as they were able to use only six types of natural hazards in their analysis because the 1979 – 1998 data were not as detailed as those from 1999 – 2004. Thacker et al.  also mentioned that apparent increases in the number of deaths for some natural hazard events might be a statistical artifact of this classification change rather than an actual increase in hazard deaths. Unlike the CDC data disparity, the inconsistencies in SHELDUS before and after 1995 were addressed in later versions of the database.
Matching hazard categories between SHELDUS and Thacker et al. (2008)
Hazard mortality data are fraught with inconsistencies across databases. Differences manifest themselves from the subjective nature of attributing any death to a hazard event. Because of the lack of a standardized death classification scheme , hazard deaths are not counted in the same way for any two databases. In fact, even within a national database (i.e. SHELDUS, Compressed Mortality File), hazard death attribution likely varies geographically. Therefore, we are cautious that the analyses and conclusions drawn from hazard mortality data are based on estimates of deaths from natural events.
There is considerable debate about which natural hazard is the most "deadly". According to our results, the answer is heat. But this finding could change depending on the data source, or how hazards within a data source are grouped, as we've shown here. Even if researchers could definitively assert the 'deadliest hazard,' a better issue to pose is where residents are more susceptible to fatalities from natural hazards within the United States.
The spatial patterns revealed in the results are not surprising – greater risk of death along the hurricane coasts, in rural areas, and in the South – all areas prone to natural hazards as well as significant population growth and expansion throughout the study period. However, the interpretation of these patterns reveals the problems associated with rare causes of death. Using this analysis as a blueprint for hazard mortality 'hot spots' supports justification for a more in-depth study of hazard- induced deaths in specific regions or communities. It is at this local scale where defining the deadliest hazard becomes important and emergency management officials can take action to try to reduce the number of future deaths.
There are limitations to this study (and others that study mortality from a rare cause of death) that are worth noting. First, we were able to visualize the spatial variation of hazard related deaths for our study period for the entire U.S., but in any given year or in any given county, very few if any deaths may occur. This rarity of occurrence prevented our analysis from providing detailed information on hazard-specific mortality rates or SMRs. Calculation of such measures would be highly unstable due to the minimal number of deaths in many counties [See ]. Second, there are limitations in the original data sources as noted earlier.
This paper provides the foundation of a solid understanding of the geography of hazard related mortality over time. Future research can use this information to study specific areas of elevated hazard mortality, and study its correlates in different areas. Ultimately, greater local knowledge about which types of hazards are deadliest in different geographic regions is useful information for strategies aimed at reducing the risk of death from natural hazards.
The over-arching contribution of this work is not to compare and contrast datasets, or even create the "best" possible map of hazard deaths. Rather, this work enables research and emergency management practitioners to examine hazard deaths through a geographic lens. Using this as a tool to identify areas with higher than average hazard deaths can justify allocation of resources to these areas with the goal of reducing hazard deaths. One logical avenue in achieving this goal is to assess the efficacy of information dissemination from emergency managers to the public. An important question is whether people in areas of high mortality know what to do (or what not to do) when a hazard event occurs. Improved understanding of how to react in a hazard event will contribute to reduced deaths from hazard events in high-mortality areas.
This 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. Cutter, Principal Investigator). Any opinions, findings, conclusions, or recommendations are those of the authors and do not represent the official views of the US DHS.
- Changnon SA, Pielke RA, Changnon D, Sylves RT, Pulwarty R: Human factors explain the increased losses from weather and climate extremes. Bulletin of the American Meteorological Society. 2000, 81 (3): 437-442. 10.1175/1520-0477(2000)081<0437:HFETIL>2.3.CO;2.View ArticleGoogle Scholar
- Cutter SL, Emrich CT: Are Natural Hazards and Disaster Losses in the U.S. Increasing?. EOS, Transactions, American Geophysical Union. 2005, 86:Google Scholar
- Dilley M, Chen RS, Deichmann U, Lerner-Lam AL, Arnold M, Agwe J, Buys P, Kjekstad O, Lyon B, Yetman G: Natural Disaster Hotspots: A Global Risk Analysis. 2005, Washington, D.C.: The International Bank for Reconstruction and DevelopmentView ArticleGoogle Scholar
- Thacker MTF, Lee R, Sabogal RI, Henderson A: Overview of deaths associated with natural events, United States, 1979–2004. Disasters. 2008, 32 (2): 303-315. 10.1111/j.1467-7717.2008.01041.x.PubMedView ArticleGoogle Scholar
- Ashley ST, Ashley WS: Flood Fatalities in the United States. Journal of Applied Meteorology and Climatology. 2008, 47 (3): 805-818. 10.1175/2007JAMC1611.1.View ArticleGoogle Scholar
- Jonkman SN: Global perspectives on loss of human life caused by floods. Natural Hazards. 2005, 34 (2): 151-175. 10.1007/s11069-004-8891-3.View ArticleGoogle Scholar
- Chou YJ, Huang N, Lee CH, Tsai SL, Chen LS, Chang HJ: Who is at risk of death in an earthquake?. American Journal of Epidemiology. 2004, 160 (7): 688-695. 10.1093/aje/kwh270.PubMedView ArticleGoogle Scholar
- Malilay J, Elias IF, Olson D, Sinks T, Noji E: Mortality and Morbidity Patterns Associated with the October 12, Egypt Earthquake. Earthquake Spectra. 1992, 11 (3): 457-476. 10.1193/1.1585823.View ArticleGoogle Scholar
- Ashley WS: Spatial and Temporal Analysis of Tornado Fatalities in the United States: 1880–2005. Weather and Forecasting. 2007, 22 (6): 1214-1228. 10.1175/2007WAF2007004.1.View ArticleGoogle Scholar
- Klinenberg E: Heat Wave: A Social Autopsy of Disaster in Chicago. 2002, Chicago: The University of Chicago PressView ArticleGoogle Scholar
- Snow J: On the Mode of Communication of Cholera. 1855, New York: The Commonwealth Fund, 2Google Scholar
- Devesa SS, Grauman DJ, Blot WJ, Pennello GA, Hoover RN, Fraumeni JFJ: Atlas of Cancer Mortality in the United States. 1999, National Institutes of Health, National Cancer InstituteGoogle Scholar
- Goldman BA: The Truth About Where You Live: An Atlas for Action on Toxins and Mortality. 1991, New York: Random House IncGoogle Scholar
- Pickle LW, Mungiole M, Jones GK, White AA: Atlas of United States Mortality. 1996, Hyattsville, MD: National Center for Health StatisticsGoogle Scholar
- Zarate AO: International Mortality Chartbook: Levels and Trends, 1955 – 91. 1994, Hyattsville, MD: Public Health ServiceGoogle Scholar
- Benach J, Yasui Y, Borrell C, Rosa E, Pasarin MI, Benach N, Espanol E, Martinez JM, Daponte A: Examining geographic patterns of mortality. European Journal of Public Health. 2003, 13 (2): 115-123. 10.1093/eurpub/13.2.115.PubMedView ArticleGoogle Scholar
- James WL, Cossman RE, Cossman JS, Campbell C, Blanchard T: A brief visual primer for the mapping of mortality trend data. International Journal of Health Geographics. 2004, 3 (7): 1-17.Google Scholar
- Lawson AB: Tutorial in Biostatistics: Disease map reconstruction. Statistics in Medicine. 2001, 20 (14): 2183-2204. 10.1002/sim.933.PubMedView ArticleGoogle Scholar
- Pickle LW: Exploring spatio-temporal patterns of mortality using mixed effects models. Statistics in Medicine. 2000, 19 (17–18): 2251-+. 10.1002/1097-0258(20000915/30)19:17/18<2251::AID-SIM567>3.0.CO;2-M.PubMedView ArticleGoogle Scholar
- Jemal A, Kulldorff M, Devesa SS, Hayes RB, Fraumeni JF: A geographic analysis of prostate cancer mortality in the United States, 1970–89. International Journal of Cancer. 2002, 101 (2): 168-174. 10.1002/ijc.10594.View ArticleGoogle Scholar
- Munasinghe RL, Morris RD: Localization of disease clusters using regional measures of spatial autocorrelation. Statistics in Medicine. 1996, 15 (7–9): 893-905. 10.1002/(SICI)1097-0258(19960415)15:7/9<893::AID-SIM258>3.0.CO;2-M.PubMedView ArticleGoogle Scholar
- Lipfert FW, Morris SC: Temporal and spatial relations between age specific mortality and ambient air quality in the United States: regression results for counties, 1960–97. Occupational and Environmental Medicine. 2002, 59 (3): 156-174. 10.1136/oem.59.3.156.PubMedPubMed CentralView ArticleGoogle Scholar
- Nkhoma ET, Hsu CE, Hunt VI, Harris AM: Detecting spatiotemporal clusters of accidental poisoning mortality among Texas counties, U.S., 1980 – 2001. International Journal of Health Geographics. 2004, 3 (25): 1-13.Google Scholar
- Elliott P, Wakefield JC, Best NG, Briggs DJ: Spatial epidemiology: methods and applications. Spatial Epidemiology: Methods and Applications. Edited by: Elliott P, Wakefield JC, Best NG, Briggs DJ. 2000, New York: Oxford University Press, 3-14.Google Scholar
- Rushton G: Public health, GIS, and spatial analytic tools. Annual Review of Public Health. 2003, 24: 43-56. 10.1146/annurev.publhealth.24.012902.140843.PubMedView ArticleGoogle Scholar
- Bithell JF: A classification of disease mapping methods. Statistics in Medicine. 2000, 19 (17–18): 2203-2215. 10.1002/1097-0258(20000915/30)19:17/18<2203::AID-SIM564>3.0.CO;2-U.PubMedView ArticleGoogle Scholar
- Goldman DA, Brender JD: Are standardized mortality ratios valid for public health data analysis?. Statistics in Medicine. 2000, 19 (8): 1081-1088. 10.1002/(SICI)1097-0258(20000430)19:8<1081::AID-SIM406>3.0.CO;2-A.PubMedView ArticleGoogle Scholar
- Assuncão RM, Reis EA: A New Proposal to Adjust Moran's I for Population Density. Statistics in Medicine. 1999, 18 (16): 2147-2162. 10.1002/(SICI)1097-0258(19990830)18:16<2147::AID-SIM179>3.0.CO;2-I.PubMedView ArticleGoogle Scholar
- Wakefield J, Elliott P: Issues in the statistical analysis of small area health data. Statistics in Medicine. 1999, 18 (17–18): 2377-2399. 10.1002/(SICI)1097-0258(19990915/30)18:17/18<2377::AID-SIM263>3.0.CO;2-G.PubMedView ArticleGoogle Scholar
- Combs DL, Parrish RG, McNabb SJN, Davis JH: Deaths related to Hurricane Andrew in Florida and Louisiana, 1992. International Journal of Epidemiology. 1996, 25 (3): 537-544. 10.1093/ije/25.3.537.PubMedView ArticleGoogle Scholar
- Lew EO, Wetli CV: Mortality from Hurricane Andrew. Journal of Forensic Sciences. 1996, 41 (3): 449-452.PubMedView ArticleGoogle Scholar
- Peek-Asa C, Kraus JF, Bourque LB, Vimalachandra D, Yu J, Abrams J: Fatal and hospitalized injuries resulting from the 1994 Northridge earthquake. International Journal of Epidemiology. 1998, 27 (3): 459-465. 10.1093/ije/27.3.459.PubMedView ArticleGoogle Scholar
- Peek-Asa C, Ramirez MR, Shoaf K, Seligson H, Kraus JF: GIS mapping of earthquake-related deaths and hospital admissions from the 1994 Northridge, California, earthquake. Annals of Epidemiology. 2000, 10 (1): 5-13. 10.1016/S1047-2797(99)00058-7.PubMedView ArticleGoogle Scholar
- Jonkman SN, Kelman I: An analysis of the causes and circumstances of flood disaster deaths. Disasters. 2005, 29 (1): 75-97. 10.1111/j.0361-3666.2005.00275.x.PubMedView ArticleGoogle Scholar
- Basu R, Samet JM: Relation between elevated ambient temperature and mortality: A review of the epidemiologic evidence. Epidemiologic Reviews. 2002, 24 (2): 190-202. 10.1093/epirev/mxf007.PubMedView ArticleGoogle Scholar
- Kalkstein LS: A New Approach to Evaluate the Impact of Climate on Human Mortality. Environmental Health Perspectives. 1991, 96: 145-150. 10.2307/3431223.PubMedPubMed CentralView ArticleGoogle Scholar
- Kalkstein LS: Climate and Human Mortality: Relationships and Mitigating Measures. Advances in Bioclimatology: Human Bioclimatology. Edited by: Auliciems A. 1998, Verlag: Springer, 5: 161-177.View ArticleGoogle Scholar
- Kalkstein LS, Davis RE: Weather and Human Mortality: An Evaluation of Demographic and Interregional Responses in the United States. Annals of the Association of American Geographers. 1989, 79 (1): 44-64. 10.1111/j.1467-8306.1989.tb00249.x.View ArticleGoogle Scholar
- Hazards & Vulnerability Research Institute: The Spatial Hazard Events and Losses Database for the United States, Version 5.1 [Online Database]. 2007, Columbia, SC: University of South Carolina,http://www.sheldus.orgGoogle Scholar
- NCDC: Storm Data. 2007, National Oceanic and Atmospheric Administration (NOAA),http://www.ncdc.noaa.gov/oa/climate/sd/Google Scholar
- Anselin L, Lozano N, Koschinsky J: Rate Transformations and Smoothing. 2006, Spatial Analysis Laboratory Department of Geography University of Illinois Urbana-Champaign, Accessed 11 May 2008,http://www.sal.uiuc.eduGoogle Scholar
- Buescher PA: Age-Adjusted Death Rates. Statistical Primer No 13. 1998, Raleigh, NC: North Carolina Department of Health and Human ServicesGoogle Scholar
- Wilson JL, Buescher PA: Mapping Mortality and Morbidity Rates. Statistical Primer No 15. 2002, Raleigh, NC: North Carolina Department of Health and Human Services, 2008:Google Scholar
- Julious SA, Nicholl J, George S: Why do we continue to use standardized mortality ratios for small area comparisons?. J Public Health Med. 2001, 23 (1): 40-46. 10.1093/pubmed/23.1.40.PubMedView ArticleGoogle Scholar
- Haining R: Spatial Data Analysis: Theory and Practice. 2003, Cambridge: Cambridge University PressView ArticleGoogle Scholar
- Wojdyla D, Poletto L, Cuesta C, Badler C, Passamonti ME: Cluster Analysis with Constraints: Its Use with Breast Cancer Mortality Rates in Argentina. Statistics in Medicine. 1996, 15 (7–9): 741-746. 10.1002/(SICI)1097-0258(19960415)15:7/9<741::AID-SIM245>3.0.CO;2-9.PubMedView ArticleGoogle Scholar
- Anselin L: Local Indicators of Spatial Association – LISA. Geographical Analysis. 1995, 27 (2): 93-115.View ArticleGoogle Scholar
- Anselin L: GeoDa 0.9 User's Guide. 2003, Spatial Analysis Laboratory, University of Illinois, Urbana-Champaign, ILGoogle Scholar
- Osnes K: Iterative random aggregation of small units using regional measures of spatial autocorrelation for cluster localization. Statistics in Medicine. 1999, 18 (6): 707-725. 10.1002/(SICI)1097-0258(19990330)18:6<707::AID-SIM73>3.0.CO;2-1.PubMedView ArticleGoogle Scholar
- Anselin L, Syabri I, Kho Y: GeoDa: An introduction to spatial data analysis. Geographical Analysis. 2006, 38 (1): 5-22. 10.1111/j.0016-7363.2005.00671.x.View ArticleGoogle Scholar
- NCDC: Storm Data Preparation: National Weather Service Instruction 10–1605. 2007, 94-Google Scholar
- Ashley WS, Black AW: Fatalities Associated with Nonconvective High-wind Events in the United States. Journal of Applied Meteorology and Climatology. 2008, 47 (2): 717-725. 10.1175/2007JAMC1689.1.View ArticleGoogle Scholar
- Combs DL, Quenemoen LE, Parrish RG, Davis JH: Assessing disaster-attributed mortality: development and application of a definition and classification matrix. International Journal of Epidemiology. 1999, 28 (6): 1124-1129. 10.1093/ije/28.6.1124.PubMedView ArticleGoogle Scholar
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