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Prediction of posttraumatic stress disorder among adults in flood district

  • Peng Huang1, 2,
  • Hongzhuan Tan1Email author,
  • Aizhong Liu1,
  • Shuidong Feng3 and
  • Mengshi Chen1
BMC Public Health201010:207

DOI: 10.1186/1471-2458-10-207

Received: 11 May 2009

Accepted: 26 April 2010

Published: 26 April 2010

Abstract

Background

Flood is one of the most common and severe forms of natural disasters. Posttraumatic stress disorder (PTSD) is a common disorder among victims of various disasters including flood. Early prediction for PTSD could benefit the prevention and treatment of PTSD. This study aimed to establish a prediction model for the occurrence of PTSD among adults in flood districts.

Methods

A cross-sectional survey was carried out in 2000 among individuals who were affected by the 1998 floods in Hunan, China. Multi-stage sampling was used to select subjects from the flood-affected areas. Data was collected through face-to-face interviews using a questionnaire. PTSD was diagnosed according to DSM-IV criteria. Study subjects were randomly divided into two groups: group 1 was used to establish the prediction model and group 2 was used to validate the model. We first used the logistic regression analysis to select predictive variables and then established a risk score predictive model. The validity of model was evaluated by using the model in group 2 and in all subjects. The area under the receiver operation characteristic (ROC) curve was calculated to evaluate the accuracy of the prediction model.

Results

A total of 2336 (9.2%) subjects were diagnosed as probable PTSD-positive individuals among a total of 25,478 study subjects. Seven independent predictive factors (age, gender, education, type of flood, severity of flood, flood experience, and the mental status before flood) were identified as key variables in a risk score model. The area under the ROC curve for the model was 0.853 in the validation data. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of this risk score model were 84.0%, 72.2%, 23.4%, and 97.8%, respectively, at a cut-off value of 67.5 in the validation data.

Conclusions

A simple risk score model can be used to predict PTSD among victims of flood.

Background

Flood is one of the most common and severe forms of natural disasters. It can result in direct economic and property losses, physical injuries, deaths, and psychological injuries. Posttraumatic stress disorder (PTSD) is a common disorder among victims of various disasters such as traffic accidents[1, 2], violent crimes[3], hurricanes[4], earthquakes[5, 6], and floods [710]. PTSD is also a severe and complex disorder precipitated by exposure to psychologically distressing events, and it is characterized by persistent intrusive memories about the traumatic event, persistent avoidance of stimuli associated with the trauma, and persistent symptoms of increased arousal[11].

Floods occur frequently in China. A severe flood that struck China's Hunan province in 1998 left hundreds of thousands of residents homeless, and damaged many infrastructural and agricultural projects. It is of great importance to find ways of promptly identifying flood victims who are likely to develop PTSD to enable the government take timely measures to protect the health of such victims. Currently, there are no PTSD prediction models that can be applied to flood victims. The aim of this study was therefore to identify determinants of PTSD and to develop a risk score model to predict PTSD among flood victims.

Methods

Study area and population

The 1998 floods in China affected over 180 million people. It is estimated that the flood displaced 18.393 million people; destroyed 6.85 million houses; caused 4,150 deaths; and yielded a direct economic loss of about $32 billion (New Report from Ministry of Health, China, 1999). Hunan was the most severely affected province. Victims who had been directly exposed to the 1998 flood in Hunan formed our target population. The study area covered the catchment area of the Dongting Lake (north of Hunan) and the west of Hunan.

The catchment area of the Dongting Lake is located south of the middle reaches of the Yangzi River in southern China. It is usually warm, humid, and rainy during summer. The area, which is flood-prone, experienced soaked and collapsed floods in 1998. It consists of 31 counties; covers an area of 31,000 km2; and has an estimated human population of 11.3 million. Residents who live in this area share similar natural conditions and socio-economic and health status. The majority of them are farmers with low levels of education. The area within the west of Hunan covers 7 counties affected by the flash floods of 1998. These counties also share similar socio-demographic characteristics.

We used a multi-stage stratified and cluster sampling method to select study subjects. Firstly, we randomly selected 7 counties from 31 counties that suffered soaked and collapsed floods (Yueyang, Lingxiang, Huarong, Qianlianghu, Ziyang, Anxiang, Datonghu) and 1 county from 7 counties that experienced flash flood (Longshan). Then, by a systematic sampling approach, we randomly sampled 50% of townships in the selected counties, 50% of villages in the selected townships, and 50% of households in the selected villages. All family members in the selected households aged 16 years and older; experienced the flood; and willing to be interviewed were invited to participate in our study.

Flood type and severity

Flood was classified into 3 types: soaked flood, collapsed embankment flood, and flash flood. Soaked floods are also called drainage-problem floods, occurred as a result of regular drainage systems not able to handle high precipitation levels. Collapsed embankment floods, which are also called river flood, are caused by flooding of the river outside its regular boundaries, often as a result of high precipitation levels. Flash floods usually occur as a result of local rainfalls with high intensity[12].

The severity of flood suffered was also divided into 3 categories: mild (affected area <50%), intermediate (affected area 50%-75%), and severe (affected area >75%), according to the standard setup by the Chinese flood management authority.

Data collection

The survey was conducted between January and May 2000. 40 trained interviewers, who worked at the local Centres for Disease Control and Prevention and had a bachelor's degree or higher, carried out face-to-face interviews using a questionnaire to obtain demographic data, to ascertain PTSD, and to measure personality and psychological characteristics. The interviewers received on-site supervision from psychologists. The project was approved by the Research Ethics Board of Central South University, and all subjects agreed to participate in the investigation. All interviews occurred in the study subjects own home, in a private room with no other person present. Interviews lasted for about 20 minutes. To facilitate the study, we contacted the study subject by telephone before the interview. If the scheduled time was not convenient for him/her, we changed it.

The diagnosis of PTSD was made according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria[11], which included 17 symptoms scored as 0 = none, 1 = slight, 2 = moderate, 3 = severe, and 4 = extreme. Symptoms with scores = 2 were defined as positive. The 17 symptoms of PTSD were further divided into 3 groups, representing 3 diagnostic criteria B, C, and D. Criterion B symptoms represented the re-experiencing cluster, and subjects were defined as B symptom positive if they showed one or more positive items in the B group. Criterion C symptoms represented the avoidance cluster, and subjects were defined as C symptom positive if they showed 3 or more positive items in the C group. Criterion D symptoms represented the hyperarousal cluster, and subjects were defined as D symptom positive if they showed two or more positive items in the D group. In addition, there were criteria A and E for the diagnosis of PTSD. Criterion A represented exposure to an extreme traumatic stressor involving direct personal experience of an event, witnessing an event, learning about unexpected or violent death, serious harm, or threat of death or injury experienced by a family member or another close associate (A1); and the person's response to the event must involve intense fear, helplessness, or horror (A2). All subjects in our study witnessed the 1998 flood and experienced the threat of death or injury from the flood. Also, all the probable PTSD-positive subjects met the A2 criterion. Criterion E represented the disturbance lasting more than 1 month. Subjects were diagnosed as having PTSD if Criteria A, B, C, D, and E symptoms were all positive. We assessed all symptoms, including the time and duration of occurrence. The questionnaire for PTSD had been tested in Chinese populations and had been proved to be valid and reproducible [8].

All interviewers participated in a 2-day training program, which focused on the questionnaires. A working manual was provided to ensure that all interviewers had the same understanding for the questionnaire. The completed questionnaires were checked by the coordinator (one coordinator in each county) of the study. If a questionnaire was found to be incomplete or inconsistent, the interview was repeated for the same subject to reduce missing data as much as possible.

Statistical analysis

We randomly divided the data sets into two groups, one group (group 1: approximately 70% of the samples) for the creation of the prediction model and the other group (group 2: approximately 30% of the samples) for the validation of the prediction model.

We first used stepwise forward logistic regression analysis to select the predictive variables. The dependent variable was PTSD (yes = 1, no = 0). Based on professional judgement and literature [1316], we selected the following potential predicting variables into the initial regression model: age (x1,16~ = 1; 35~ = 2; 55~ = 3), gender (x2, male = 0; female = 1), education (x3, illiterate = 3; elementary school = 2; high school or higher = 1), occupation (x4, farmer = 1; nonfarmer = 0), type of flood,(x5, soaked = 1; collapsed = 2; flash flood = 3), severity of flood (X6, mild = 1; moderate = 2; severe = 3), flood experience (X7 = X7.1+ X7.2+ X7.3+ X7.4+ X7.5+ X7.6. X7.1: were you trapped and waited for rescue during the flood, yes = 1, no = 0; X7.2: were you seriously injured during the flood, yes = 1, no = 0; X7.3: were your relatives or friends seriously injured during the flood, yes = 1, no = 0; X7.4: did you witness others drowning during the flood, yes = 1, no = 0; X7.5: was this flood your first experience of floods, yes = 1, no = 0; X7.6: was your house damaged by the flood, yes = 1, no = 0), and mental status before flood (X8 = X8.1+ X8.2+ X8.3+ X8.4. X8.1: would you consider yourself tensed or highly strung-up, yes = 1, no = 0; X8.2: do you often feel life is very boring, yes = 1, no = 0; X8.3: do you often feel lonely, yes = 1, no = 0; X8.4: are you easily hurt when people find faults with you or your work, yes = 1, no = 0). All potential predicting variables were valued according to the levels of PTSD prevalence.

To develop a simple risk score, the risk factors identified through multivariate logistic regression were assigned an integer coefficient. Integers were chosen to be approximately proportional to the estimated continuous coefficients from the logistic model. Assignment of points to risk factors was based on a linear transformation of the corresponding β regression coefficient. The coefficient of each variable was divided by the lowest β value and rounded to the nearest integer[17]. The final value of risk score predictive model was the sum of the risk scores mentioned above. Group 2 was used to confirm the accuracy of the risk score model by calculating the area under ROC curve. We then assessed the sensitivity, specificity, crude agreement (CA), positive predictive value (PPV) and negative predictive value (NPV) of the risk score model at different cut-off values for subjects in group 2 and for all subjects, using the diagnostic result of DSM-IV criteria as the gold standard. The CA was obtained by the sum of true positive and true negative divided by total number of subjects. The CA assumed that a prediction model had no diagnostic value if CA = 0, and that a model was invariably correct if CA = 1. SPSS 13.0 was used for all the data analysis.

Results

A total of 8 counties, 40 towns, 310 villages, 13,450 households, and 29,285 individuals aged 16 years and older were selected for the study. Among the 29,285 subjects 1,128 (3.9%) refused to participate, 1,035 (3.5%) had not been interviewed, 1,644 (5.6%) had incomplete data, and 25,478 had complete data, yielding a response rate of 87.0%. A total of 2,336 subjects were probable PTSD positive, yielding a probable positive rate of 9.2%. For the 25,478 subjects in the final analysis, 17,846 (70%) were randomly selected to group 1 and 7,632 (30%) to group 2. There was no significant difference in baseline characteristics and probable PTSD rates (P > 0.05) between the two study groups (Table 1).
Table 1

Sample distribution and probable PTSD-positive rates in 2 groups

Variables

Group 1 (N = 17846)

Group 2 (N = 7632)

Total (N = 25478)

 

N

%

N

%

N

%

Probable positive rate of PTSD (%)

x1 age

   16~

8025

45.0

3364

44.1

11389

44.7

8.4

   35~

7019

39.3

3046

39.9

10065

39.5

9.2

   55~

2802

15.7

1222

16.0

4024

15.8

11.3

x2 gender

   male

8441

47.3

3557

46.6

11998

47.1

8.2

   female

9405

52.7

4075

53.4

13480

52.9

10.2

x3 education

   high school or higher

1749

9.8

733

9.6

2482

9.7

3.1

   elementary school

14360

80.5

6168

80.8

20528

80.6

8.0

   illiterate

1737

9.7

731

9.6

2468

9.7

24.9

x4 occupation

   nonfarmer

1534

8.6

678

8.9

2212

8.7

6.2

   farmer

16312

91.4

6954

91.1

23266

91.3

9.4

x5 type of flood

   soaked

9166

51.4

3936

51.6

13102

51.4

2.9

   collapsed

6527

36.6

2799

36.7

9326

36.6

12.9

   flash flood

2153

12.1

897

11.8

3050

12.0

24.9

x6 severity of flood

   mild

7984

44.7

3384

44.3

11368

44.6

1.7

   moderate

3965

22.2

1720

22.5

5685

22.3

13.9

   severe

5897

33.0

2528

33.1

8425

33.1

16.1

x7.1 were you trapped and waited for rescue during the flood?

   no

17364

97.3

7437

97.4

24801

97.3

8.5

   yes

482

2.7

195

2.6

677

2.7

32.2

x7.2 were you seriously injured during the flood?

   no

17717

99.3

7585

99.4

25302

99.3

9.0

   yes

129

0.7

47

0.6

176

0.7

32.4

x7.3 were your relatives or friends seriously injured during the flood?

   no

17654

98.9

7547

98.9

25201

98.9

9.0

   yes

192

1.1

85

1.1

277

1.1

28.5

x7.4 did you witness others drowning during the flood?

   no

17753

99.5

7579

99.3

25332

99.4

9.0

   yes

93

0.5

53

0.7

146

0.6

33.6

x7.5 was this flood your first experience of floods?

   no

7440

41.7

3169

41.5

10609

41.6

2.7

   yes

10406

58.3

4463

58.5

14869

58.4

13.8

x7.6 was your house damaged by the flood?

   no

12109

67.9

5243

68.7

17352

68.1

4.4

   yes

5737

32.1

2389

31.3

8126

31.9

19.3

x8.1 would you consider yourself tensed or highly strung-up?

   no

15078

84.5

6406

83.9

21484

84.3

7.5

   yes

2768

15.5

1226

16.1

3994

15.7

18.1

x8.2 do you often feel life is very boring?

   no

15766

88.3

6707

87.9

22473

88.2

7.6

   yes

2080

11.7

925

12.1

3005

11.8

20.7

x8.3 do you often feel lonely?

   no

16217

90.9

6901

90.4

23118

90.7

7.8

   yes

1629

9.1

731

9.6

2360

9.3

22.6

x8.4 are you easily hurt when people find faults with you or your work?

   No

15081

84.5

6428

84.2

21509

84.4

7.2

   Yes

2765

15.5

1204

15.8

3969

15.6

19.8

y PTSD

   No

16209

90.8

6933

90.8

23142

90.8

 

   Yes

1637

9.2

699

9.2

2336

9.2

 

Note: Chi-square tests of all variables showed no significant differences between the two groups (P > 0.05).

Table 2 shows results of the stepwise logistic regression analysis among group 1 subjects. There were 7 variables entered into the prediction model, namely, age (X1), gender (X2), education (X3), type of flood (x5), severity of flood (X6), flood experience (X7), and mental status before flood (X8). The Logistic probability model was as follows:
Table 2

Significant PTSD predictive variables included in the logistic model

    

95.0% C.I. for OR

 

Variable

β

Sig.

Odds ratio

Lower

Upper

risk score (points)

x1(age)

0.085

0.031

1.089

1.008

1.176

1

X2(gender)

0.213

0.000

1.237

1.104

1.386

3

X3(education)

1.088

0.000

2.970

2.625

3.360

13

X5(type of flood)

0.689

0.000

1.991

1.811

2.189

8

X6(Severity of flood)

0.327

0.000

1.387

1.275

1.510

4

X7(Flood experience)

0.672

0.000

1.957

1.807

2.120

8

X8(mental status)

0.519

0.000

1.680

1.601

1.763

6

Constant

-8.091

0.000

0.000

   
https://static-content.springer.com/image/art%3A10.1186%2F1471-2458-10-207/MediaObjects/12889_2009_Article_2118_Equa_HTML.gif
Based on β regression coefficient of each variable, risk score was calculated with Singh' method[17]. The final risk score model is as follow:
https://static-content.springer.com/image/art%3A10.1186%2F1471-2458-10-207/MediaObjects/12889_2009_Article_2118_Equb_HTML.gif
For example, if a subject is 38 years old (X1 = 2); male (X2 = 0); illiterate(X3 = 3); experienced moderate flood (X6 = 2); suffered flash flood(X5 = 3); and with the scores of 2 and 3 for flood experience (X7)and mental status before flood (X8) respectively, his total risk score will be as follows:
https://static-content.springer.com/image/art%3A10.1186%2F1471-2458-10-207/MediaObjects/12889_2009_Article_2118_Equc_HTML.gif
The area under ROC curve for both the logistic probability model and the risk score model for group 2 were 0.853 (Figure 1). This means that the risk score model, which is much simpler and easier to use, could yield similar results as the logistic probability model.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2458-10-207/MediaObjects/12889_2009_Article_2118_Fig1_HTML.jpg
Figure 1

The ROC curve of logistic probability model and risk score model for group 2 subjects.

Table 3 compares the validity of the risk score model under different cut-off values (based on individual total risk scores) in different groups. It appears risk score 63.5 ~ 69.5 may be an acceptable cut-off value yielding a sensitivity of 80.5% ~ 89.4%, specificity of 63.4% ~ 75.0%, CA of 65.8% ~ 75.5%, PPV of 19.8% ~ 24.5%, and NPV of 97.4% ~ 98.3% in group 2 (Table 3).
Table 3

The validity of predictive model under different cut-off value in different populations (%)

 

Group 2

All subjects

Cutoff value

Sen

Spe

CA

PPV

NPV

Sen

Spe

CA

PPV

NPV

25.0

100.0

0.0

9.2

9.2

 

100.0

0.0

9.2

9.2

 

39.5

99.9

8.2

16.6

9.9

99.9

99.9

8.4

16.8

9.9

99.9

57.5

95.3

51.2

55.2

16.5

99.1

94.6

51.4

55.4

16.4

99.0

62.5

90.6

60.4

63.2

18.7

98.5

90.3

60.6

63.3

18.8

98.4

63.5

89.4

63.4

65.8

19.8

98.3

88.8

63.3

65.6

19.6

98.2

64.5

88.0

65.9

67.9

20.6

98.2

87.4

65.8

67.8

20.5

98.1

65.5

86.8

67.4

69.2

21.2

98.1

86.5

67.3

69.1

21.1

98.0

66.5

85.1

69.6

71.0

22.0

97.9

85.3

69.4

70.9

22.0

97.9

67.5

84.0

72.2

73.3

23.4

97.8

83.3

71.8

72.9

23.0

97.7

68.5

82.3

73.9

74.7

24.1

97.6

81.5

73.4

74.1

23.6

97.5

69.5

80.5

75.0

75.5

24.5

97.4

80.2

74.5

75.0

24.1

97.4

70.5

78.8

76.4

76.6

25.2

97.3

78.8

75.9

76.2

24.8

97.3

71.5

76.4

79.4

79.1

27.2

97.1

76.0

79.0

78.7

26.8

97.0

73.5

71.1

83.3

82.2

30.0

96.6

69.9

83.0

81.8

29.3

96.5

78.5

49.6

89.3

85.7

31.9

94.6

51.8

89.6

86.1

33.5

94.8

97.5

10.9

98.8

90.7

47.8

91.7

11.6

98.8

90.8

49.4

91.7

130.0

0.0

100.0

90.8

 

90.8

0.0

100.0

90.8

 

90.8

Based on individual risk scores calculated from our risk score model, we can predict the probability of PTSD occurrence. For the case mentioned above (risk score = 107), if 65 is selected as cut-off value, we will consider this individual to be at a high risk of developing PTSD. The higher the risk score is, the greater the probability of the individual developing PTSD.

Discussion

PTSD is a common psychological disorder in disaster-affected populations. It has been widely used to evaluate the psychological impact of natural disasters and accidents[1, 2, 610]. To our knowledge this is the first study to explore the prediction of PTSD by risk score model among flood victims in a large population. The method of risk score has been widely used for prediction or screening of disease because of its simplicity and ease of interpretation [1721]. In our study, a risk score model was established according to β regression coefficient from logistic regression analysis, which included 7 predictive variables. These variables included demographic characteristics (x1, x2, x3), type of flood (x5), severity of flood (x6), flood experience(x7) and mental status before flood (x8). In order to make the prediction model simpler and easier to understand, we combined X7.1-X7.6 into X7 and X8.1-X8.4 into X8, with the cumulative score as their score. The area under ROC curve for the logistic probability and risk score models were very similar but since the risk score model is much simpler and easier to use, we recommend its use in PTSD prediction among flood victims. The suitability of the risk score model is further supported by a sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 84.0%, 72.2%, 23.4%, and 97.8%, respectively at the cut-off value of 67.5 in group 2.

To make the model have better predictive value, we used 4 mental status-related variables (x8.1-x8.4) as potential predictors representing one's mental status before flood, in addition to age, gender, education, and severity of flood suffered, which were important predictors from previous studies [13, 14, 16]. Although there have been different findings about the strength of association between mental health status before trauma and PTSD [13, 14, 16], coupled with the fact that retrospective reports may be influenced by current symptoms, our study results still yielded valuable information. Mental status prior to flood was an effective predictor of PTSD in our study.

In view of the fact that our study focused on the prediction of PTSD, rather than screening, only demographic characteristic(x1, x2, x3), type of flood (x5), severity of flood (x6), flood experience(x7) and mental status before flood (x8) were included in our predictive model(early symptoms of PTSD were not included). We listed sensitivity, specificity, PPV, and NPV at different cut-off values so allow public health workers choose the appropriate cut-off value for their PTSD predictions. For example, if the goal is to find as many PTSD cases as possible, one could use 39.5 as the cut-off value, and this will raise the sensitivity level to 99.9%. If the goal is to reduce the false positives, one could use 97.5 as the cut-off value to yield a specificity level of 98.8%.

Our PTSD prediction model was validated with a separate sample, which showed its true and reliable performance when applied on other populations. All the predictive variables included in the model could be easily obtained through a simple questionnaire after a flood. Compared with other PTSD screening models, which included some PTSD early symptoms as predictors [1316, 22], our model showed lower sensitivity and specificity. However, it could be used to predict the possible occurrence of PTSD immediately after a flood. Our model, therefore, is significant in public health programs.

Our study used a retrospective survey method to investigate the impacts of flood. As a result, recall bias and information bias could occur. However, because interviewers did not know who had PTSD or who had not at the time of interviewing, the recall bias and information bias, if any, may have occurred randomly.

Another limitation of our study is the fact that the diagnosis of PTSD was made using a questionnaire administered by interviewers. Although the interviewers received on-site supervision from psychologists, the diagnosis of PTSD may not be accurate. In view of this, all suspected cases were diagnosed as 'probable PTSD'.

The potential predictive variable considered in our model was selected based on professional judgement as well as literature. Other variables, such as economic loss, property damage, and family history of mental illness were considered in some preliminary analyses of our data. However, since those variables were not found to be statistical significant in univariate analyses, we did not include them in the multivariable logistic regression analysis. Although our model has not been proved to be an optimal one, it is a practical and useful model for PTSD prediction at least for now. Its performance in other populations needs to be further investigated.

Conclusions

The risk score based predictive model for PTSD developed in this study has an acceptable predictive value with favourable applicability, and can be used to identify persons at risk of PTSD during floods.

List of abbreviations

PTSD: 

Posttraumatic stress disorder

CA: 

crude agreement

PPV: 

positive predictive value

NPV: 

negative predictive value

ROC: 

receiver operating curve.

Declarations

Acknowledgements

We wish to thank the Directors and staff of the Centres for Disease Control and Prevention (CDC) in Ziyang, Datonghu, Anxiang, Yueyang, and Xiangxi districts in Hunan for their support in the conduct of the field investigations.

Authors’ Affiliations

(1)
School of Public Health, Central South University
(2)
School of Public Health, Nanchang University
(3)
School of Public Health, University of South China

References

  1. Ameratunga S, Tin Tin S, Coverdale J, Connor J, Norton R: Posttraumatic stress among hospitalized and nonhospitalized survivors of serious car crashes: a population-based study. Psychiatr Serv. 2009, 60 (3): 402-404. 10.1176/appi.ps.60.3.402.View ArticlePubMedGoogle Scholar
  2. Hashemi B, Shaw RJ, Hong DS, Hall R, Nelson K, Steiner H: Posttraumatic stress disorder following traumatic injury: narratives as unconscious indicators of psychopathology. Bull Menninger Clin. 2008, 72 (3): 179-190. 10.1521/bumc.2008.72.3.179.View ArticlePubMedGoogle Scholar
  3. Brewin CR, Andrews B, Rose S, Kirk M: Acute Stress Disorder and Posttraumatic Stress Disorder in Victims of Violent Crime. Am J Psychiatry. 1999, 156 (3): 360-366.PubMedGoogle Scholar
  4. Goenjian AK, Molina L, Steinberg AM, Fairbanks LA, Alvarez ML, Goenjian HA, Pynoos RS: Posttraumatic Stress and Depressive Reactions Among Nicaraguan Adolescents After Hurricane Mitch. Am J Psychiatry. 2001, 158 (5): 788-794. 10.1176/appi.ajp.158.5.788.View ArticlePubMedGoogle Scholar
  5. Lai TJ, Chang CM, Connor KM, Lee LC, Davidson JRT: Full and partial PTSD among earthquake survivors in rural Taiwan. J Psychiatric Res. 2004, 38 (3): 313-322. 10.1016/j.jpsychires.2003.08.005.View ArticleGoogle Scholar
  6. Priebe S, Grappasonni I, Mari M, Dewey M, Petrelli F, Costa A: Posttraumatic stress disorder six months after an earthquake: Findings from a community sample in a rural region in Italy. Soc Psychiatry Psychiatr Epidemiol. 2009, 44 (5): 393-397. 10.1007/s00127-008-0441-y.View ArticlePubMedGoogle Scholar
  7. Feng S, Tan H, Benjamin A, Wen S, Liu A, Zhou J, Li S, Yang T, Zhang Y, Li X, et al: Social Support and Posttraumatic Stress Disorder among Flood Victims in Hunan, China. Annals of Epidemiology. 2007, 17 (10): 827-833. 10.1016/j.annepidem.2007.04.002.View ArticlePubMedGoogle Scholar
  8. Liu A, Tan H, Zhou J, Li S, Yang T, Wang J, Liu J, Tang X, Sun Z, Wen S: An Epidemiologic Study of Posttraumatic Stress Disorder in Flood Victims in Hunan China. Can J Psychiatry. 2006, 51: 350-354.PubMedGoogle Scholar
  9. Norris FH, Murphy AD, Baker CK, Perilla JL: Postdisaster PTSD Over Four Waves of a Panel Study of Mexico's 1999 Flood. J Trauma Stress. 2004, 17 (4): 283-292. 10.1023/B:JOTS.0000038476.87634.9b.View ArticlePubMedGoogle Scholar
  10. Stepien A, Kantorska-Janiec M: [PTSD as result of the 1997 flood--occurrence and display of distemper]. Psychiatr Pol. 2005, 39 (1): 103-114.PubMedGoogle Scholar
  11. American Psychiatric Association (4th Ed). Diagnostic and statistical manual of mental disorders. 1994, Washington, DC: American Psychiatric Association
  12. Jonkman S: 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
  13. Brewin CR, Andrews B, Valentine JD: Meta-Analysis of Risk Factors for Posttraumatic Stress Disorder in Trauma-Exposed Adults. Journal of Consulting and Clinical Psychology. 2000, 68 (5): 748-766. 10.1037/0022-006X.68.5.748.View ArticlePubMedGoogle Scholar
  14. Brewin CR: Systematic review of screening instruments for adults at risk of PTSD. Journal of Traumatic Stress. 2005, 18 (1): 53-62. 10.1002/jts.20007.View ArticlePubMedGoogle Scholar
  15. O'Donnell ML, Creamer MC, Parslow R, Elliott P, Holmes ACN, Ellen S, Judson R, McFarlane AC, Silove D, Bryant RA: A Predictive Screening Index for Posttraumatic Stress Disorder and Depression Following Traumatic Injury. Journal of Consulting and Clinical Psychology. 2008, 76 (6): 923-932. 10.1037/a0012918.View ArticlePubMedGoogle Scholar
  16. Ozer EJ, Best SR, Lipsey TL, Weiss DS: Predictors of Posttraumatic Stress Disorder and Symptoms in Adults: A Meta-Analysis. Psychological Bulletin. 2003, 129 (1): 52-73. 10.1037/0033-2909.129.1.52.View ArticlePubMedGoogle Scholar
  17. Singh M, Lennon RJ, Holmes DR, Bell MR, Rihal CS: Correlates of procedural complicationsand a simple integer risk scorefor percutaneous coronary intervention. Journal of the American College of Cardiology. 2002, 40 (3): 387-393. 10.1016/S0735-1097(02)01980-0.View ArticlePubMedGoogle Scholar
  18. Mehran R, Aymong ED, Nikolsky E, Lasic Z, Iakovou I, Fahy M, Mintz GS, Lansky AJ, Moses JW, Stone GW, et al: A simple risk score for prediction of contrast-induced nephropathy after percutaneous coronary intervention: Development and initial validation. Journal of the American College of Cardiology. 2004, 44 (7): 1393-1399.PubMedGoogle Scholar
  19. Rassi A, Rassi A, Little WC, Xavier SS, Rassi SG, Rassi AG, Rassi GG, Hasslocher-Moreno A, Sousa AS, Scanavacca MI: Development and Validation of a Risk Score for Predicting Death in Chagas' Heart Disease. N Engl J Med. 2006, 355 (8): 799-808. 10.1056/NEJMoa053241.View ArticlePubMedGoogle Scholar
  20. Sullivan LM, Massaro JM, D'Agostino RB: Tutorial in biostatistics: presentation of multivariate data for clinical use: the Framingham Study risk score functions. Stat Med. 2004, 23: 1631-1660. 10.1002/sim.1742.View ArticlePubMedGoogle Scholar
  21. Wu C, Hannan EL, Walford G, Ambrose JA, Holmes DR, King SB, Clark LT, Katz S, Sharma S, Jones RH: A risk score to predict in-hospital mortality for percutaneous coronary interventions. Journal of the American College of Cardiology. 2006, 47 (3): 654-660. 10.1016/j.jacc.2005.09.071.View ArticlePubMedGoogle Scholar
  22. Ehring T, Kleim B, Clark DM, Foa EB, Ehlers A: Screening for Posttraumatic Stress Disorder. The Journal of Nervous and Mental Disease. 2007, 195 (12): 1004-1012. 10.1097/NMD.0b013e31815c1999.View ArticlePubMedGoogle Scholar
  23. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/10/207/prepub

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

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