Who Is at Risk of Death in an Earthquake?

Yiing-Jenq Chou1 , Nicole Huang2, Cheng-Hua Lee3, Shu-Ling Tsai4, Long-Shen Chen1 and Hong-Jen Chang4

1 Department of Social Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan.
2 Department of Health Policy, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
3 Institute of Health Care and Hospital Administration, National Yang-Ming University, Taipei, Taiwan.
4 Bureau of National Health Insurance, Taipei, Taiwan.

Received for publication October 18, 2002; accepted for publication May 4, 2004.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Although, theoretically, the impacts of a disaster are not randomly distributed across health and socioeconomic classes, empirical evidence of this claim is scarce. In a population-based cohort study, the authors identified risk factors for mortality from the September 21, 1999, Taiwan earthquake, which occurred in the middle of the night. Among 297,047 earthquake victims in central Taiwan who experienced partial or complete dwelling damage, 295,437 (noncases) survived the earthquake and 1,610 (cases) died between September 21 and October 31, 1999. Odds ratios were adjusted for both micro-level individual variables and macro-level neighborhood variables. People with mental disorders (odds ratio (OR) = 2.0, 95% confidence interval (CI): 1.1, 3.5), people with moderate physical disabilities (OR = 1.7, 95% CI: 1.2, 2.3), and people who had been hospitalized just prior to the earthquake (OR = 1.4, 95% CI: 1.2, 1.7) were the most vulnerable. The degree of vulnerability increased with decreasing monthly wage (measured in New Taiwanese dollars (NT$)) (NT$20,000~NT$39,999: OR = 1.5, 95% CI: 1.1, 2.1; <NT$20,000: OR = 2.2, 95% CI: 1.6, 3.0). The significant associations of both prequake health status and socioeconomic status with earthquake death suggest that earthquake death did not occur randomly. These results might help to guide allocation of public resources for reducing casualties.

health status; mortality; natural disasters; social class; socioeconomic status

Abbreviations: Abbreviations: CI, confidence interval; ICD-9-CM, International Classification of Diseases, Ninth Revision, Clinical Modification; NT$, New Taiwanese dollars; OR, odds ratio; SES, socioeconomic status.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
At 1:47 a.m. on September 21, 1999, a devastating earthquake hit the Sun Moon Lake region in central Taiwan. Scoring 7.3 on the Richter scale, the earthquake left more than 2,400 people dead, 11,000 people injured, 105,000 homes destroyed, and hundreds of medical facilities damaged (1). The disaster literature suggests that the impact of a disaster is not random but is influenced by the effects of social interaction and organization patterns (2, 3). Research on disaster vulnerability has uncovered many observable risk factors for earthquake death, such as demographic characteristics, physical disability, structural factors, and seismic features (417), but has still left some uncertainty. Although, theoretically, health status and socioeconomic status (SES) could be two important determinants of earthquake vulnerability, little is known about the relation of these two risk factors to earthquake-related death (2, 3, 18).

The literature suggests that physical and mental health limitations can affect disaster response (2, 3). Physically or mentally fragile persons may be less able to respond quickly or to take proper protective action during earthquakes and hence may be more likely to die (2, 3, 17). In addition to health status, SES could play an important role. The literature suggests that people with poorer SES are more likely to be exposed to environmental risk factors, such as lower housing quality, residential crowding, and unfavorable neighborhood conditions (1921), and that type of dwelling, structure use, age of dwelling, and residential crowding are strong predictors of casualties (12, 13, 22, 23). Thus, these people may have a higher risk of earthquake mortality.

More importantly, because of the ubiquitous relation between health status and SES (24, 25), making an unconfounded estimate of the association between each of these factors and earthquake mortality requires inclusion of both predictors in the regression models. To our knowledge, no study has systematically documented the relation of two key determinants (health status and SES) to earthquake-related death, because the availability of such data after a large-scale earthquake has been limited. The availability of comprehensive National Health Insurance data on prequake SES and detailed information on health status allowed us to conduct a population-based cohort study to ascertain the relation of prequake health status and SES to earthquake death in the 1999 Taiwan earthquake.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Setting
The devastating 1999 Taiwan earthquake took place in the middle of the night in the Taichung region, which is the largest metropolitan area in central Taiwan. It consists of Taichung City, Taichung County, Chan-Hua County, and Nan-Tou County. The earthquake mainly struck the 22 municipalities on the eastern side of this metropolitan area. The central government granted federal disaster assistance to those 22 municipalities, which are referred to in this article as the "affected area."

Study population
A population-based cohort study was designed and conducted in the affected area. At the time of the earthquake, this affected area had approximately 1.43 million residents, as identified from the government-maintained Family Registration Database. The Family Registration Database is managed by the Ministry of the Interior. It is updated regularly and provides relatively accurate demographic information on residents, such as age, gender, and address. Since detailed information on health status and SES was available for only the 1,202,002 residents (84 percent) of the earthquake-affected municipalities who were also enrolled in the National Health Insurance program through the central branch of the Bureau of National Health Insurance in September 1999, the earthquake-affected residents who were enrolled in the National Health Insurance program through other branches were excluded.

Outcome
These 1,202,002 residents were followed for mortality between September 21, 1999, and October 30, 1999. A government reporting system was set up in the affected area to identify persons who had died from the earthquake during the study period. For study purposes, 1,610 cases met the eligibility criteria, and it was confirmed that their deaths resulted from the destruction of a dwelling during the earthquake. Unnatural deaths, such as deaths related to accidents and disasters, that occur in Taiwan must be jointly confirmed by a prosecutor and a forensic specialist (or coroner). We confirmed earthquake deaths on the basis of death certificates (International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code E909: "Cataclysmic earth surface movements and eruptions"). The 295,437 persons in the affected area who were injured or experienced property damage are referred to in this article as "survivors." The dwellings of both cases and survivors were destroyed (partially or completely) during the earthquake. These survivors were issued "quake cards" that allowed them to obtain health care without paying the cost-sharing amount required under the National Health Insurance program. The Victims’ Data File, managed by the Bureau of National Health Insurance, tracks basic information on cardholders and allowed us to identify victims from the overall population.

Study variables
SES variable
The National Health Insurance Enrollment Database, managed by the Bureau of National Health Insurance, was used to obtain information on prequake SES and disability status for the study subjects. Enrollment in the National Health Insurance program is done mainly through employers. The National Health Insurance program offers universal and comprehensive coverage financed by payroll taxes on people with a well-defined monthly wage and head taxes on people without a well-defined monthly wage. People with a well-defined monthly wage (measured in New Taiwanese dollars (NT$)) were classified into one of three categories: ≥NT$40,000, NT$20,000~NT$39,999, or <NT$20,000. People without a well-defined monthly wage can enroll in the program either through associations, such as farmers’ associations, or through local government offices. People without a well-defined monthly wage were categorized as either farmers who had enrolled through farmers’ associations or "others," including veterans, corner-store owners, or low-income people, who had enrolled through local government offices. Thus, this prequake SES variable had five categories in total. Dependents of the insured were classified into the same five categories.

Physical disability
As part of the welfare program in Taiwan, the government provides premium subsidies to persons with physical disabilities. We were able to identify each subject’s prequake physical disability status from the National Health Insurance Enrollment Database. Criteria for physical disability status (mild, moderate, or severe) are jointly defined by expert panels and the Department of Health. Four physical disability categories were used: none, mild, moderate, and severe. For example, mild hearing loss is defined as hearing, on average, the most quiet sounds with the better ear at 55–69 dB. Moderate hearing loss is defined as hearing, on average, the most quiet sounds with the better ear at 70–89 dB. Severe hearing loss is defined as hearing, on average, the most quiet sounds with the better ear at >90 dB (26).

Major disease status
The Major Diseases Database, which is managed by the Bureau of National Health Insurance, was used to identify persons who had major diseases or injuries before the earthquake. In Taiwan, people with specific major disease or injury diagnoses from medical doctors can apply for a "major disease/injury card." Cardholders are exempted from the cost-sharing required under the National Health Insurance program. On the basis of the Injury Severity Index, the National Health Insurance major disease list includes 30 types of major diseases or injuries, such as cancer, end-stage renal disease, chronic psychotic disorder, cirrhosis of the liver, and acquired immunodeficiency syndrome (27). We used the possession of a major disease/injury card to represent another aspect of the subject’s prequake health status. The three major disease status categories used were: no major disease/injury, major mental disorder, and major nonmental disease.

Hospitalization status
National Health Insurance inpatient claims data were used to obtain information on prequake hospitalization (September 1998–August 1999) for all subjects. We included two variables in the study to assess whether earthquake-related mortality varied between short-term health status and long-term health status. The variables were nonchildbirth hospitalization status 1–6 months before the earthquake and nonchildbirth hospitalization status 7–12 months before the earthquake. Childbirth hospitalizations were excluded because they would not be likely to indicate overall frailty.

Area characteristics
Although our study focused on 22 municipalities officially affected by the earthquake, socioeconomic environment and the strength of the earthquake might still have varied among those 22 municipalities. Therefore, to adjust for area-level SES and degree of devastation, we incorporated two municipality-level variables: 1) average per-capita taxable income and 2) percentage of completely collapsed homes among all partially or completely collapsed homes. We obtained data on the total taxable income of each municipality from the Ministry of Finance. Then we divided total income by the total population in each municipality to estimate average per-capita taxable income at the municipality level. Average per-capita taxable income for each municipality was used to represent each municipality’s neighborhood socioeconomic environment. Although this was not a perfect measure of neighborhood socioeconomic environment, it served as a proxy.

Statistical analysis
The characteristics of the study subjects in September 1999 (SES, possession of a major disease card, severity of physical disability, and hospitalization status) were used to represent their prequake characteristics. Linkage of data sets was conducted by the Bureau of National Health Insurance using personal identification numbers and birthdays. Each subject’s personal identification number was encrypted in the analytical file, and no information in the final data set would allow us to identify the study subject in real life. We abided by the Bureau’s strict regulations regarding data release and the protection of privacy and confidentiality. The data were structured so that there was one record per individual. Associations between independent variables and the outcome (mortality vs. survival) were modeled using multiple logistic regression. On the basis of previous studies (16, 17) and the results of univariate analysis, age and gender were included in the multivariate model as possible confounding factors. Results were expressed as odds ratios and 95 percent confidence intervals. Sensitivity analyses were conducted for various model specifications and case definitions. The final results remained the same and suggested that the results were robust.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Table 1 presents the characteristics of the persons who died (cases) and the survivors (controls). The case group contained a slightly greater percentage of females than the survivor group. The distributions of data on age, SES, and health status variables differed between the two groups. The case group included a greater percentage of elderly people (age >65 years) (31.9 percent of the cases vs. 10.4 percent of the survivors). Farmers constituted a large proportion of the study population (45.3 percent of the cases vs. 34.6 percent of the survivors).


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TABLE 1. Characteristics of persons who died of earthquake-related injury (cases) and survivors (controls) prior to the 1999 earthquake in central Taiwan*
 
Table 2 lists both univariate and multivariate effects of demographic, SES, health status, and area characteristics on vulnerability to earthquake death. Consistent with past studies (16, 17), demographic characteristics were strongly associated with earthquake-related death, and the association remained equally significant after adjustment for SES, health status, and area characteristics. Women had a greater risk than men after adjustment. Consistent with previous findings (16, 17), mortality risk increased with age for the adult population and increased with decreasing age for children under age 16 years, after adjustment for gender, SES, health status, and area characteristics.


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TABLE 2. Risk factors for earthquake-related death in the 1999 earthquake in central Taiwan, according to prequake characteristics, September 21, 1999–October 31, 1999
 
SES was a significant determinant of earthquake-related death in the 1999 Taiwan earthquake. For people with a well-defined monthly wage, the risk of earthquake death gradually increased with decreasing monthly wage after results were controlled for demographic, health status, and area characteristics. Persons with a monthly wage of <NT$20,000 had the greatest risk of death (adjusted odds ratio (OR) = 2.2, 95 percent confidence interval (CI): 1.6, 3.0), followed by persons with a monthly wage between NT$20,000 and NT$39,999 (adjusted OR = 1.5, 95 percent CI: 1.1, 2.1). People without a well-defined monthly wage, such as farmers, veterans, and others, are traditionally more socially and economically disadvantaged. They were also more vulnerable to the effects of the earthquake than those in the highest monthly wage group (≥NT$40,000). Persons enrolled in the National Health Insurance program through local government offices had the greatest risk of death: After adjustment for all other variables, the adjusted odds ratio was 2.5 (95 percent CI: 1.8, 3.4). Interestingly, the odds ratio for farmers fell from 2.7 (95 percent CI: 2.0, 3.7) to 1.8 (95 percent CI: 1.3, 2.4) after adjustment for demographic, health status, and area characteristics. This change was caused mainly by the age adjustment factor. The farmers remained at increased risk of earthquake mortality after adjustment. The results imply that, given that their dwellings were either partially or completely collapsed, people with poorer SES were at increased risk of earthquake mortality after adjustment for health status and other factors.

Major disease status, severity of physical disability, and prequake hospitalization were used to represent various aspects of individual health status. Multivariate analysis showed that, after adjustment for SES and other variables, having a major mental disorder significantly increased the risk of earthquake death (OR = 2.0, 95 percent CI: 1.1, 3.5), followed by major nonmental diseases (OR = 1.5, 95 percent CI: 1.1, 2.0). In univariate analysis, all persons with physical disability had a significantly greater risk than persons without physical disability. However, after adjustment for demographic factors, SES, major disease status, hospitalization status, and area characteristics, only persons with moderate physical disability continued to have a significantly greater risk (OR = 1.7, 95 percent CI: 1.2, 2.3). The results were similar to the findings of Osaki and Minowa (16) in Japan. Furthermore, after adjustment for demographic factors, SES, disability, major disease status, and area characteristics, persons who had been hospitalized during the 6 months prior to the earthquake were at significantly greater risk (adjusted OR = 1.4, 95 percent CI: 1.2, 1.7) than persons who had been hospitalized 7–12 months prior to the earthquake (adjusted OR = 1.0, 95 percent CI: 0.8, 1.3).

Finally, area characteristics, such as degree of devastation and average taxable income, were significantly associated with earthquake mortality. Degree of devastation, as measured by the percentage of completely collapsed houses, was significantly associated with earthquake mortality after adjustment for demographic factors, SES, health status, and average taxable income (OR = 1.5, 95 percent CI: 1.4, 1.5). Furthermore, given that the dwelling of the study subject was either partially or completely collapsed, the earthquake caused more severe casualties in higher-income municipalities than in lower-income municipalities after adjustment for demographic factors, SES, health status, and degree of devastation. For every NT$100,000 increase in average annual per-capita taxable income, 1.2 times’ higher mortality was observed at the municipality level (95 percent CI: 1.0, 1.3).


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The multivariate results of this population-based cohort study suggest that health status and SES are two probable key predictors of earthquake death. Indeed, lower SES, major disease status, and moderate physical disability status increased the risk of earthquake death 1.5- to 2.5-fold.

Several of our findings were notable. First, although the risk for moderately disabled people in Taiwan was slightly lower than the risk observed for people with physical disability in Japan (OR = 1.9, 95 percent CI: 1.0, 3.4) (16), our categorical physical disability measure provided more information than the binary measure used in Osaki and Minowa’s study, and the estimate was more precise, with a smaller standard error. Second, consistent with previous findings for other natural disasters or accident-related deaths (2830), psychiatric illness was a significant risk factor for earthquake-related death. Persons with mental illness might be less capable of taking prompt protective action at the time of an earthquake and less able to withstand the pain of injuries until rescue (18). The results suggest that people with mental illness or their caretakers would very much benefit from specifically targeted training and intervention. In a publicly released data set containing data on a nationally representative sample of 200,000 Taiwanese residents, in 2002, of the persons falling into the broad mental illness category used in this study, 58.1 percent had schizophrenic disorders (ICD-9-CM code 295.XX) and 23.7 percent had affective psychoses (ICD-9-CM code 296.XX) (31).

Furthermore, the earthquake caused significantly more severe casualties in higher-income municipalities than in lower-income municipalities. There are two plausible explanations for this. One is that geographically, the richer municipalities were coincidently located near the epicenter or at both ends of the Chelungpu thrust, where the earthquake and the cluster of numerous aftershocks occurred (32, 33). A surface rupture of 80 km in length along the Chelungpu thrust resulted in relatively more severe causalities at both ends of the thrust than at the epicenter (32, 34). The other explanation is that richer municipalities are more likely to be commercial centers and have more high-rise buildings (23). When the earthquake hit, it could have resulted in more severe casualties because of the collapse of those commercial high-rise buildings.

This study had several strengths. It was unique in that the scale of earthquake damage is rarely as large as that reported here. In addition, earthquake-prone areas around the world (even in developed countries like the United States and Japan) rarely have population-based databases such as that of the National Health Insurance program in Taiwan, which was used to systematically trace the prequake characteristics of every individual in the earthquake-affected area. Second, because of the ubiquitous relation between SES and health status, the study took both health status and SES into consideration simultaneously. Hence, the relation found between either determinant and earthquake death would not be confounded by the other. Although the issue is discussed theoretically and descriptively elsewhere, our study systematically demonstrated an independent and significant association between prequake health status and earthquake death. Third, the study incorporated individual-level SES in the analysis of risk factors for earthquake death. In contrast to past ecologic studies (9, 10), this study differentiated between persons in different socioeconomic classes on the basis of their individual-level characteristics, and hence provided a different perspective in determining the influence of SES on an individual’s vulnerability to earthquake death.

Fourth, we were able to include relatively comprehensive data on health status variables provided by the National Health Insurance program for each member of the study population. In contrast with past studies, in this study we incorporated more aspects of individual-level health status to present a more complete picture of the relation between health status and earthquake death (717). We used the National Health Insurance files to identify whether vulnerability to earthquake death differed between mentally ill patients and physically ill patients, between people with different levels of severity of physical disability, and between people hospitalized at different times prior to the earthquake. Fifth, given that the 1999 Taiwan earthquake struck after midnight when the majority of people were asleep, the observed effects of SES and health status on vulnerability to earthquake death are unlikely to have been confounded by factors such as a person’s location (indoors vs. outdoors). The timing of the earthquake and the availability of individual-level data on SES offered us a unique opportunity to study the effects of an individual’s health status on his or her chances of survival in a major earthquake. It is reasonable to believe that one major difference between the people who died (the cases) and those who survived (the controls) might well be whether health status at the time of the earthquake hindered people from exiting their dwellings or taking other protective action. The generalizability of the study findings was high, since many earthquakes occur at night, and nighttime earthquakes tend to result in more significant human casualties than daytime earthquakes (10).

Despite these strengths, several limitations should be noted. First, there was limited statistical power with regard to some health status factors because of the small size of the case group. Second, the accuracy of prequake hospitalization status designations might be a concern, since some members of the study population hospitalized prior to the earthquake might have been misclassified as having no prequake hospitalization if they were admitted to hospitals outside the jurisdiction of the central branch of the Bureau of National Health Insurance. The association between health status and earthquake death could be weaker or stronger than the one we observed (35, 36). Third, the use of a single measure (i.e., the National Health Insurance program’s payroll and occupation-based categories) to analyze the association between individual SES and earthquake death did not allow us to explore the association fully. However, to our knowledge, this study is the first to have incorporated individual-level SES in earthquake epidemiologic analysis. More importantly, the independent and significant association found demonstrates that income and occupation may be key determinants of vulnerability to earthquake death. Future research may help to advance our knowledge of this issue by using a more comprehensive set of SES measures. Fourth, because of the lack of individual-level information on housing quality and degree of dwelling damage (partially destroyed vs. completely destroyed), we were unable to examine the causal pathways leading from poorer SES to increased risk of earthquake mortality through housing quality and degree of dwelling damage. Fifth, although the variable on percentage of completely collapsed houses was included in the analysis as an aggregate measure of degree of devastation, residual confounding from other unobservable seismic features might still have been present.

These results have important policy implications. First, this study empirically demonstrated that the impacts of a disaster are not random: The 1999 Taiwan earthquake disproportionately affected sick, moderately disabled, and poorer people. These results may help to guide the allocation of public resources to reduce casualties in future disasters. For example, governments could provide vulnerable populations with better housing through existing welfare programs or upgrade the quality of public housing. Second, the findings suggest that people with the most severe disabilities are not necessarily the most vulnerable. One plausible explanation is that because of their level of dependence, severely disabled people might be more likely than moderately disabled people to be cared for in nursing homes or long-term-care facilities, which have more strict housing construction codes and are under the constant supervision of the Social Welfare Bureau (37, 38). For preventive purposes, the government could offer better training to caregivers of moderately disabled or sick people in private homes on evacuation procedures and on relocating beds to a more protected area in the dwelling. Third, the study results provide essential information to governments and the medical community on how to devise more efficient emergency evacuation procedures and how to better anticipate medical service needs after major earthquakes. Efforts focusing on vulnerable groups rather than on the general population may be more effective in reducing casualties in earthquake-prone areas. The significant associations between health status and SES and earthquake death illustrated in this study might be a useful reference for officials in many countries where catastrophic disasters occur.


    ACKNOWLEDGMENTS
 
This study was supported by grants from the Taiwanese Department of Health (grant DOH 89-NH-052) and the National Science Council of Taiwan (grant NSC 90-2415-H-010-001).


    NOTES
 
Reprint requests to Dr. Yiing-Jenq Chou, Department of Social Medicine, School of Medicine, National Yang-Ming University, 155 Li-Nong Street, Taipei, Taiwan 112 (e-mail: yjchou{at}ym.edu.tw). Back


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 DISCUSSION
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