The injury burden of the 2010 Haiti earthquake: A stratified cluster survey

Injury, Int. J. Care Injured 44 (2013) 842–847

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The injury burden of the 2010 Haiti earthquake: A stratified cluster survey Shannon Doocy a,*, Gabrielle Jacquet a,b, Megan Cherewick a, Thomas D. Kirsch a,b a b

Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA Johns Hopkins School of Medicine, Baltimore, MD, USA

A R T I C L E I N F O

A B S T R A C T

Article history: Accepted 27 January 2013

Introduction: On January 12, 2010, a 7.0 magnitude earthquake devastated metropolitan Port au Prince and surrounding areas and resulted in widespread injury, mortality and displacement. This study aimed to estimate the injury rate among the affected population and the resulting demand of emergency medical care in the aftermath of the earthquake. Methods: In January 2011, a cross-sectional stratified cluster (60  20 household) survey of the earthquake-affected population in metropolitan Port au Prince was conducted to assess their well-being, unmet needs and perceptions of humanitarian assistance one year post-earthquake. Mixed effects simple and multiple logistic regressions were used to measure the total unadjusted and adjusted odds of injury. Results: A total of 261 injuries were reported in the pre-earthquake population of 6489 individuals with reported injury status. The overall earthquake injury rate was estimated at 40.2 injuries/1000 (CI: 35.6– 45.3). Individual characteristics such as age, gender, and education status were not significantly associated with risk of injury. Elevated injury rates were observed among households residing in camps at 46.7/1000 (CI: 39.7–54.5) as compared to those in neighbourhoods where the injury rate was 33.7/ 1000 (CI: 27.8–40.5) (p = 0.018). Extrapolation of the survey injury rate to the affected population yields an estimated 124,577 earthquake injuries (range 110,048–140,033) which is substantially lower than the 300,000 reported injuries. Conclusions: Estimates of the injury burden in disasters in lower- and middle-income countries is essential for disaster preparedness and response planning in future natural disasters. Given the difficulties in reporting injuries in emergencies, including both challenges of aggregating information and lack of standardized definitions and inclusion/exclusion criteria for injuries that are not severe, ascertaining the injury burden of disasters will be a persistent challenge. ß 2013 Elsevier Ltd. All rights reserved.

Keywords: Earthquake Injury Haiti Humanitarian emergency Natural disaster

Introduction Earthquakes were responsible for an estimated 1.87 million deaths in the 20th century1 and the injury burden is likely even greater. Substantial challenges in estimating earthquake injury burden persist because of variation in definitions, classification and reporting procedures, underreporting due to lack of data collection, and failure to aggregate and maintain centralized reporting in emergency contexts. A recent review of significant earthquakes found that injuries were reported in only 56% of earthquakes with mortality that occurred between 1960 and 2009 and that 2 million earthquake injuries were reported worldwide in this period.2 Building design, geography and development

* Corresponding author at: Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St., Ste E8132, Baltimore, MD 21205, USA. Tel.: +1 410 502 2628; fax: +1 410 614 1419. E-mail address: [email protected] (S. Doocy). 0020–1383/$ – see front matter ß 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.injury.2013.01.035

indicators are important factors in earthquake vulnerability. There is substantial variation in the human impacts of earthquakes, and low levels of economic development have been associated with higher earthquake morbidity and mortality suggesting that poorer countries face increased risk due to a variety of characteristics of the built environment.2–4 On January 12, 2010, a 7.0 magnitude earthquake struck western Haiti, approximately 25 kilometres from the capital. The earthquake devastated metropolitan Port au Prince and surrounding areas with a reported 222,750 deaths, 300,000 injured, 1.5 million displaced, and more than 3 million affected. Relief and recovery operations were historic: by October 2010, the response was one of the largest in history with an estimated cost of $4.5 billion.5 As is common in many disaster settings, the majority of injury information is from hospitals and focuses on characterization of presenting injuries, clinical trauma management, and outcomes at the facility. Population-based surveys that aim to characterize injury and estimate disaster injury burden are less frequent. This study sought to characterize injury in the 2010 Haiti

S. Doocy et al. / Injury, Int. J. Care Injured 44 (2013) 842–847

earthquake and identify risk factors for injury in major earthquakes in low income settings. Methods A cross-sectional cluster survey of the earthquake-affected population in metropolitan Port au Prince was conducted in January 2011 to assess the impact of the event on health and livelihood. A stratified cluster survey with 60 clusters of 20 households including 30 camp and 30 neighbourhood clusters was used to enable comparison between camp and non-camp (neighbourhood) populations. The proportion of affected populations in camps and neighbourhoods could not be accurately estimated from available government and United Nations (UN) information so an equal distribution was chosen as best estimate. Households were included in the survey if their dwelling had been damaged, their income or livelihood affected, or a household member was injured or died as a result of the earthquake. Sample size was based on the study objectives to detect a 10% difference in the prevalence of living conditions and economic impact (based on a conservative prevalence of 50%) between camp and neighbourhood-based populations with 80% power, alpha = 0.05 and an anticipated cluster sample design effect of 1.5. The survey instrument was developed in English, translated into Creole, and conducted by trained Haitian nationals. Clusters were assigned using probability proportional to size sampling. Camps were sampled from a list of planned and spontaneous settlements obtained from the camp coordination and management cluster.6 The starting point in each cluster was a randomly selected intersection; every 3rd shelter was sampled until 20 households were completed. For neighbourhoods, cluster allocation used remote sensing building damage assessment based on the assumption that the number of moderately and heavily damaged residential structures was directly proportional to the earthquake affected population of that area.7 Communes were chosen proportionally based on the number of moderately and heavily damaged residential structures. Within communes, clusters were assigned proportionally based on 2009 population estimates.8 In each section geographic coordinates were randomly selected and the nearest intersection was used as the cluster start. Then a randomly selected number and direction were generated, among the streets or pathways meeting the intersection, to select one for the survey start location. From there, every 3rd residential entrance was sampled; in buildings with multiple households, one household was randomly sampled. Data was collected using questionnaire-based interviews by a team of local Haitian interviewers from Port au Prince. Data analysis was performed with Stata version 12 (College Station, TX) using simple logistic regression, multivariable logistic regression and mixed effects logistic modelling methods as well as chi-squared and t-tests. The design effect was calculated at 1.21; consequently, odds of injury and related confidence intervals were adjusted to reflect the cluster survey design. Mixed effects simple and multiple logistic regressions were used to measure the total unadjusted and adjusted odds of injury. Multiple variable logistic regressions were conducted with a mixed-effect model (xtmelogit command in Stata). Random effects were structured within the cluster and household variables to adjust for the study design. The study was certified exempt by ‘‘the Johns Hopkins Bloomberg School of Public Health’’ IRB and approved by the Haitian Ministry of Public Health and Population. Results In January 2011, when the survey was conducted, the 1197 households surveyed had a combined population of 6696

843

individuals, of which 6547 (97.9%) were reported as household members on the day of the earthquake. A total of 149 individuals were born or moved into households after January 12, 2010 and were not included in the earthquake-exposed population that served as a denominator for injury calculations. Descriptive characteristics of the sample population are summarized in Table 1. The average household size was 5.3 in both neighbourhoods and camps. The sample population in camps had a significantly higher proportion of females, was significantly younger and lower educational attainment as compared to the sample population in neighbourhoods (Table 1). Camp residents were also significantly less likely to own their own home (53.2% vs. 22.7%, p < 0.001) and more likely to live in crowded conditions (24.9% vs. 39.1%, p < 0.001, defined as 4 people/room) prior to the earthquake, suggesting that households of lower socioeconomic status were more likely to be displaced. A total of 261 injuries were reported in the pre-earthquake population between January 12, 2010 earthquake and January 2011 survey (of 6489 individuals with reported injury status). The overall earthquake injury rate is estimated to be 40.2 injuries/1000 (CI: 35.6–45.3) (Table 2). Injuries were more common in members of households residing in camps at 46.7 injuries/1000 (CI: 39.7– 54.5) as compared to those in neighbourhoods where the injury rate was 33.7/1000 (CI: 27.8–40.5) (p = 0.018). No significant difference in injury rates was observed between males and females (p = 0.848). When assessed by age, injury rates were as follows: children 0–17 years, 38.5 injuries/1000 (CI: 31.0–47.3); adults 18– 59 years, 42.2 injuries/1000 (CI: 36.2–48.9); and older adults 60+ years, 26.5 injuries/1000 (CI: 9.8–56.9). Age-specific mortality and injury rates are presented in Fig. 1. In general, a slight statistically insignificant increase in both mortality and injury rates were observed until age 40. In the 40–49 year age group, the lowest mortality rate and highest injury rates were observed. Among adults ages 50 and above, there was elevated mortality and fewer injuries as compared to other age groups, presumably because fewer of the critically injured survived. Variables related to the built environment and socioeconomic status were examined to assess their relationship with injury risk. No significant difference in injury rates was observed by household education level (highest education attained by any family member (p = 0.952)). However crowding had a significant impact on rate of injury (p = 0.014), with households with between 2.0 and 2.9 individuals per room experienced significantly higher injury rates than those with fewer than 2.0 people per room and more than 3.0 people per room. With respect to built environment, there was no significant difference in injury rates between residents of single vs. multi-level homes (p = 0.934) however extent of damage to the home was associated with injury. The lowest injury rate was observed in moderately damage homes at 30.0/1000 as compared to injury rates of 47.6/1000 in homes with no damage or minor damage and 51.0/1000 in severely damage and destroyed homes (p < 0.001). The population residing in camps had a significantly higher injury rate than those in neighbourhoods at 46.7/1000 as compared to 33.7/1000 (p = 0.018). In multiple linear regression the only characteristics associated with injury risk were crowding (pre-earthquake) and displacement in camps at one year postearthquake. Individuals from households with 2.0–2.9 people per room were 1.44 (CI: 1.03–2.03, p = 0.03) more likely to experience injury than those with < 2.0 people per room, and residents of camps were 1.44 (CI: 1.06–1.95, p = 0.02) times more likely to experience injury than neighbourhood residents. No significant differences in injury circumstances, characteristics, care seeking or outcomes were observed between neighbourhood and camp populations allowing for presentation of results from the entire survey population without disaggregation. Most injuries (69.0%) occurred at home, though a minority

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Table 1 Comparison of baseline characteristics by post-earthquake residence location. Neighbourhoods (N = 3261) Baseline characteristics of the sample population n Age 0–17 1010 18–59 2096 60+ 143 Mean (SD) 27.1(17.0) Sex Males 1567 Females 1632 Household education None 76 Primary school or less (%) 428 Secondary school (%) 1764 Higher education 976 Pre-earthquake living conditions Type of house Detached single family 1232 Attached single family 1497 Apartment/multiple dwelling 525 Multi-level building Single level 2099 Multiple levels 1039 Wall material Concrete or brick 3072 Other 166 Roof Material Cement/concrete 2014 Metal sheeting 1193 Plastic/thatch 54 Home and land occupancy Own home w/title to land 1731 Own home w/o title to land 274 Rent 1244 Other 8 Crowding Category (people/rm) <2.0 1248 2.0–2.9 825 3.0–3.9 376 4.0+ 812 a b

Camps (N = 3286)

Total (N = 6547)

p-Value

(%) 31.1 64.5 4.4 23.6 (16.2)

n 1293 1894 87 25.4(16.7)

(%) 39.5 57.9 2.7

n 2303 3990 230

(%) 35.3 61.2 3.5

49.0 51.0

1490 1748

46.0 54.0

3057 3380

47.5 52.5

2.3 13.2 54.4 30.1

217 811 1925 298

6.7 25.0 59.2 9.2

293 1239 3689 1274

4.5 19.1 56.8 19.6

37.9 46.0 16.1

1233 1463 590

37.5 44.5 18.0

2465 2960 1115

37.7 45.3 17.1

66.9 33.1

1901 1002

65.5 34.5

4000 2041

66.2 33.8

94.9 5.1

3136 150

95.4 4.6

6208 316

95.2 4.8

61.8 36.6 1.7

2014 1272 0

61.3 38.7 0.0

4028 2465 54

61.5 37.7 0.8

53.2 8.4 38.2 0.3

741 182 2322 25

22.7 5.6 71.0 0.8

2472 456 3566 33

37.9 7.0 54.6 0.5

38.3 25.3 11.5 24.9

679 744 577 1286

20.7 22.6 17.6 39.1

1927 1569 953 2098

29.4 24.0 14.6 32.1

<0.001a

0.017a

<0.001b

0.219b

0.249a

0.291a

0.342b

<0.001b

<0.001b

Calculated using the t test Calculated using analysis of variance.

occurred while the individual was outdoors (20.3%) or in another indoor location (6.5%). The majority of injuries (75.5%) were serious enough to require medical care with the most commonly reported types of injuries as follows: laceration or bleeding wound, 33.1%; broken bone or fracture, 18.4%; internal head injury, 18.5%; internal organ injury, 13.3% and neck or back injury, 8.8%. Of the injured individuals in the survey population, 71.8% (CI: 65.8–77.2) received medical care. The median number of healthcare visits per injury was 8.5 (mean = 7.0, range = 0–60). The most common locations where care was sought included NGO or international hospitals or clinics (42.1%) followed by government health facilities (37.6%) and private hospitals or clinics (12.9%). Wound care and suturing accounted for 43.2% and 29.0%, respectively, of injury treatments; an additional 17.6% of the injured had surgeries including 5.1% with amputations. Of the injured, 25.7% were hospitalized with a median in-patient stay of 12 days (mean = 23.5, range 1–150). Full recovery was reported by 84.5% (CI: 79.4–88.7) of the injured; partial and full loss of use with lasting disability were reported by 8.8% (CI: 5.6–13.0) and 3.2% (CI: 1.4–6.2) respectively; 3.2% (CI: 1.4–6.2) of injuries resulted in death. The total burden of earthquake injuries, excluding injury related deaths, is summarized in Table 3. Based on extrapolation of survey rates to the exposed population there were 124,577 earthquake injuries (range 110,048–140,033). The burden on the health system included an estimated 69,310 care seekers (range 59,725–80,125), 31,840 hospitalizations (range 24,730–40,495)

and 21,330 surgeries (range 15,765–28,748) including 6812 amputations (range 3400–10,510). An estimated 14,220 permanent disabilities (range 9583–20,402) resulted from the earthquake including 3709 severe disabilities (range 1546–7419). Discussion The 2010 Haiti earthquake was one of the most devastating seismic events in recent history. The magnitude of resulting mortality and injury were widespread and the event is thought to be one of the deadliest natural disasters in recent history.9 However, the true extent of the human costs of the Haitian earthquake will remain unquantified. Accurate reporting of mortality and injury in emergency situations is often a challenge and in the case of Haiti, the magnitude of devastation, limited infrastructure and poor coordination in the initial phases of the response meant that reliable and comprehensive reporting systems were not established. In the aftermath of such events, population based surveys are good means of estimating the mortality and injury burden. This study aimed to document the injury burden of the 2010 Haitian earthquake and risk factors for injury. The crude injury rate in the survey population was 40.2(CI: 35.6–45.3) injuries per 1000 exposed. Lacerations and fractures were the most common types of injuries observed which is consistent with other studies of earthquake injuries.2,10–15 Studies of patients hospitalized for injuries in the Haitian earthquake

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Table 2 Injury estimates and odds of injury by baseline characteristics.

Overall Sex Male Female Age category 0–17 18–59 60+ Education level None Primary Secondary Higher Ed Crowding <2.0 2.0–2.9 3.0–3.9 4.0+ Location Neighbourhood Camp Multilevel 1 level >1 level Damage level Green Yellow Red a b

Injury rate (per 1000)

Odds of injurya

Rate

95 CI

Odds Ratio

40.2

35.6–45.3

–

120 136

39.7 40.4

33.0–47.3 34.0–47.7

Reference 1.03

0.80–1.32

.848

2283 3957 226

88 167 6

38.5 42.2 26.5

31.0–47.3 36.2–48.9 9.8–56.9

Reference 1.10 0.68

0.85–1.44 0.30–1.59

.435b .461 .378

289 1227 3661 1260

12 51 149 47

41.5 41.6 40.7 37.3

21.6–71.4 31.1–54.3 34.5–47.6 27.5–49.3

Reference 1.00 0.98 0.89

0.52–1.92 0.53–1.81 0.45–1.74

.952b 1.000 .947 .743

1910 1562 935 2082

69 85 33 74

36.1 54.4 35.3 35.5

28.2–45.5 43.7–66.9 24.4–49.2 28.0–44.4

Reference 1.51 0.92 0.96

1.08–2.10 0.60–1.42 0.68–1.35

.014b .015 .706 .810

3233 3256

109 152

33.7 46.7

27.8–40.5 39.7–54.5

Reference 1.40

1.06–1.86

.018

3979 2005

157 84

39.5 41.9

33.6–46.0 33.6–51.6

Reference 1.01

0.76–1.35

.934

525 3203 2747

25 96 140

47.6 30.0 51.0

31.1–69.5 24.3–36.5 43.0–59.9

Reference 0.61 1.05

0.39–0.97 0.67–1.64

<.001b .036 .841

Total exposed

Total injuries

6489

261

3025 3363

95 CI –

p-Value –

Mixed effect logistic modelling was used to obtain OR and CI estimates accounting for cluster design. Indicates overall p-value for the categorical variable calculated using the likelihood ratio test.

indicate that fractures/dislocations, wound infections and injuries requiring amputations were the most common24,26 which is consistent with findings of the present study given that most soft tissue injuries can be treated on an outpatient basis.

Neither age nor sex was significantly associated with injury risk in our survey. The association between age and earthquake injury risk has varied greatly in prior studies and included decreased risk among children,16,17 and increased risk among young and/or

Table 3 Earthquake Injury Projections. Extrapolation of survey rates to the exposed populationa

Number of injuries (n = 261) Survey population rate (per 1000 exposed) Metropolitan Port au Prince All affected areas Number of people seeking care for injuries (n = 183) Survey population rate (per 1000 exposed) Metropolitan Port au Prince All affected areas Number of hospitalizations (n = 45) Survey population rate (per 1000 exposed) Metropolitan Port au Prince All affected areas Number of surgeries (n = 45) Survey population rate (per 1000 exposed) Metropolitan Port au Prince All affected areas Number of amputations (n = 13) Survey population rate (per 1000 exposed) Metropolitan Port au Prince All affected areas Number of injury related disabilities (all) (n = 30) Survey population rate (per 1000 exposed) Metropolitan Port au Prince All affected areas Number of injury related disabilities (severe) (n = 8) Survey population rate (per 1000 exposed) Metropolitan Port au Prince All affected areas

Best estimate

Low estimate

High estimate

40.3 99,050 124,577

35.6 87,498 110,048

45.3 111,339 140,033

28.2 69,310 87,173

24.3 59,725 75,117

32.6 80,125 100,774

10.3 25,315 31,840

8.0 19,662 24,730

13.1 32,197 40,495

6.9 16,959 21,330

5.1 12,535 15,765

9.3 22,858 28,748

2.0 4916 6182

1.1 2704 3400

3.4 8357 10,510

4.6 11,306 14,220

3.1 7619 9583

6.6 16,222 20,402

1.2 2949 3709

0.5 1229 1546

2.4 5899 7419

a Based on 261 injured and 255 injured with descriptive injury and care seeking data reported in the survey population of 6481 with injury status reported. Extrapolated estimated based on 2009 population figures of 2,457,807 for Metropolitan Port au Prince and 3,091,236 for all affected areas.8

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846

Crude Rate (events per 1000)

80 70 60 50 40

Injury

30

Mortality

20 10 0 0-9

10-19

20-29

30-39

40-49

50-59

60+

Age Category Fig. 1. Age specific injury and mortality rates.

working age adults,13,18,19 the elderly,17 and with increasing age.12,20–23 Another study from the 2010 Haiti earthquake found that young adults (15–24 year olds) were the largest proportion hospitalized for earthquake injuries, accounting for 22% of patients.24 However, this age group accounts for approximately 21% of the total population which suggests that injury rates in this age group were not elevated.25 Another study found that 65% of those hospitalized for injuries were between 18–59 years of age, while this age group accounts for 49% of the population.26 This suggests that working age adults were at increased risk for injury, but the report did not differentiate between young or older age groups within this population. When interpreted in conjunction with the observation that younger working age adults did not account for a disproportionately large proportion of the injured,24 this may suggests that injury rates were in fact high among older working age adults which is consistent with the age-specific injury patterns observed in this study. Our study did not find gender to be a predictor of injury. This is consistent with another study of hospitalized patients with injuries following the Haitian earthquake25,26 and the broader literature.2,27 The only household level characteristics significantly associated with injury risk in was crowding, a common proxy of socioeconomic status and displacement into a camp. Lower socioeconomic status has previously been identified as a predictor of earthquake-related injuries.28 Our results suggested that households with moderate damage levels and intermediate levels of crowding faced an increased injury risk. This is consistent with anecdotal reports that the middle class was the most affected by the earthquake because the upper class benefited from better quality housing construction and the lower classes lived in shanty towns which were less likely to produce serious injuries because of the lighter weight construction materials. Another important factor which cannot be accounted in interpreting injury rates is behavioural patterns of the different populations and their exposure to different built environments outside the home at the time of the earthquake. The limited health infrastructure and surgical capacity in Port au Prince was unquestionably overwhelmed in the immediate aftermath of the earthquake. Foreign medical teams and the establishment of field hospitals helped to address the short and medium needs of the injured following the earthquake however this decentralized and uncoordinated approach made it impossible to collect consolidated information on the extent of earthquake related injuries. However, this information is important for response planning for future earthquakes affecting urban areas in lower and middle income countries. The extent of injuries and mortality are mediated by the built environment, rescue and recovery efforts and emergency medical response. Extrapolation of

the survey injury rate to the affected population yields an estimated 124,577 earthquake injuries (range 110,048–140,033) which is substantially lower than the 300,000 reported injuries.5 Another approach to estimating injury burden is based on the injury to death ratio. Analysis of high mortality earthquakes (>20,000 deaths) in the past 50 years (n = 9)29 where both mortality and injury are reported yield a median injury:death ratio of 1.58 and a mean injury:death ratio of 2.66. Using the survey population mortality rate of 24.0 deaths/1000 and the resulting extrapolated mortality estimate of 74,190,30 there were an estimated 117,220–197,345 injuries in the Haiti earthquake which is consistent with estimates produced by direct extrapolation. Data on injury outcomes from our study suggest that there were 14,220 permanent disabilities (range 9583–20,402) caused by the earthquake, including 6182 amputations (range 3400–10,510). This increased amputation rate may be explained by the need to manage crush injuries emergently in an environment with no capacity to manage more complicated limb-saving procedures or complications thereof. Limitations A significant limitation of cluster survey designs in earthquake settings is that areas where casualties are concentrated, such as collapsed apartment buildings, are likely to be missed which could result in the under-estimation of injury. Accurate reporting of information on injured household members other than the respondent and the relatively long recall period both posed concerns for the reliability of information reported. Another data collection challenge was developing clear metrics for reporting severity of injuries and their outcomes; it is possible that misclassification resulted in the over or underestimation of injury. Better supervision during the data collection process would have resulted in better quality data and more confidence in the descriptive information relating to injury treatment and outcomes. With respect to assessing risk factors for injuries, improved descriptions of buildings where injuries occurred and location of individuals at the time of the earthquake would have been useful in analyzing the built environment’s mediating effect on injury outcomes. However, the long recall period, potential inaccuracies in reporting relevant environmental details for the location of all household members, and the inability to systematically confirm reported building material data for the majority of individuals prohibited the collection of more detailed information on the built environment and subsequent risk analysis. Conclusions Population-based surveys of injury prevalence in the aftermath of disasters are an alternative approach to estimating the injury of these events and provide information on risk that is seldom captured via surveillance systems and facility data. In this study an earthquake-related injury rate of 40.2/1000 was observed among the sample population and extrapolation to the affected population yielded an estimated 124,577 earthquake injuries. This is substantially lower than official reports of the number of injured, however, it is consistent with findings of other population based surveys of mortality which suggest that the mortality burden was also overestimated.30–32 The methods by which the official estimates were obtained are undocumented and their accuracy has been questioned with some critics suggesting they are inflated, in particular given the substantially lower casualty reports in the immediate aftermath of the event.32 Given the difficulties in reporting injuries in emergencies, including both challenges of aggregating information and lack of standardized definitions and inclusion/exclusion criteria for

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injuries that are not severe, ascertaining the injury burden of disasters will be a persistent challenge. This is particularly in large scale and high impact events such as the 2010 Haitian earthquake that occur in lower and middle income countries where reporting systems are weak or non-existent. Improvements in reporting, including standardization of methodologies and definitions and coordination between responding organizations may help to improve initial estimates of injury and mortality in emergencies. Strengthening the health system response to emergencies, which can improve both case management and injury outcomes, should be a long-term objective of disaster preparedness programmes in all settings, in particular disaster-prone resource poor countries where the majority of the disaster injury burden is concentrated. Conflict of interest statement The authors declare no conflict of interest. Acknowledgements Special thanks to the field team and staff at IDEJEN, a Haitian non-profit organization, for their support and dedication and to Project Concern International, Sally Graglia and Paul Perrin for helping to facilitate the study. We would also like to express our gratitude to the Biostatistics Faculty at Johns Hopkins Bloomberg School of Public Health, in particular Dr. John McGready, for assistance with data analysis. This study was funded by the Johns Hopkins Centre for Refugee and Disaster Response and the Johnson and Johnson Foundation. References 1. Wisner B, Blaikie P, Cannon T, Davis I. At risk: natural hazards, people’s vulnerability and disasters. 2nd ed. New York: Routledge; 2008. 275. 2. Doocy S, Daniels A, Packer C, Dick A, Kirsch T. The human impacts of earthquakes: a review. Manuscript submitted to PLoS Currents: Disasters. 3. Gutierrez E, Taucer F, De Groeve T, Al-Khudhairy DHA, Zaldivar JM. Analysis of worldwide earthquake mortality using multivariate demographic and seismic data. Am J Epidemiol 2005;161(12):1151–8. 4. Spence R. Saving lives in earthquakes: successes and failures in seismic protection since 1960. Bull Earthquake Eng 2007;5(2):139–251. 5. Bhattacharjee A, and Lossio R. (2011). Evaluation of OCHA response to the Haiti earthquake: final report. Available at: http://ochanet.unocha.org/p/Documents/Evaluation%20of%20OCHA%20Response%20to%20the%20Haiti%20 Earthquake.pdf. (accessed 10.06.11). 6. Camp Coordination and Camp Management (CCCM) Cluster Haiti. Updated list of IDP sites of the DTM, November 2010 [Data file]. URL:http://groups.google.com/group/cccmhaiti?pli=1; (accessed 5.01.11). 7. United Nations Institute for Training and Research (Unitar). (2010). Commune comparative graph illustrations of Building Damage Assessment. Building Damage Assessment In support to Post Disaster Needs Assessment and Recovery Framework, April 2010. URL: http://www.unitar.org/unosat/node/44/1442; (accessed 5.01.11). 8. Republique d’Haiti, Ministere de l’Economie et des Finances Institut, Haitien de Statistique et d’Informatique. Population Totale, Population de 18 ans et plus me´nages et densities estimes en 2009. URL: http://www.ihsi.ht/pdf/projection/ POPTOTAL&MENAGDENS_ESTIM2009.pdf; (accessed 5.05.11). 9. EM-DAT the International Disaster Database. Center for Research and Epidemiology in Disasters. Brussels, Belgium: Universite Catholique du Louvain; 2011. URL: www.emdat.be (accessed 18.07.12).

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