Posttraumatic stress disorder associated with unexpected death of a loved one: Cross-

national findings from the World Mental Health Surveys

Short title: PTSD and unexpected death of a loved one

Keywords: PTSD/Posttraumatic Stress Disorder, epidemiology, life events/stress, trauma,

crossnational, international

Lukoye Atwoli1,2, Dan J. Stein2, Andrew King3, Maria Petukhova3, Sergio Aguilar-Gaxiola4,

Jordi Alonso5, Evelyn J. Bromet6, Giovanni de Girolamo7, Koen Demyttenaere8, Silvia Florescu9,

Josep Maria Haro10, Elie G. Karam11, Norito Kawakami12, Sing Lee13, Jean-Pierre Lepine14,

Fernando Navarro-Mateu15, Siobhan O’Neill16, Beth-Ellen Pennell17, Marina Piazza18, Jose

Posada-Villa19, Nancy A. Sampson3, Margreet ten Have20, Alan M. Zaslavsky3, Ronald C.

Kessler3, on behalf of the WHO World Mental Health Survey Collaborators

1Department of Mental Health, Moi University School of Medicine, Eldoret, Kenya

2Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Republic

of South Africa

3Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA

4Center for Reducing Health Disparities, UC Davis Health System, Sacramento, California, USA

5IMIM-Hospital del Mar Research Institute, Parc de Salut Mar; Pompeu Fabra University (UPF);

and CIBER en Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain

6Department of Psychiatry, Stony Brook University School of Medicine, Stony Brook, New

York, USA

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7IRCCS St John of God Clinical Research Centre // IRCCS Centro S. Giovanni di Dio

Fatebenefratelli, Brescia, Italy

8Department of Psychiatry, University Hospital Gasthuisberg, Katholieke Universiteit Leuven,

Leuven, Belgium

9National School of Public Health, Management and Professional Development, Bucharest,

Romania

10Parc Sanitari Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain

11Department of Psychiatry and Clinical Psychology, Faculty of Medicine, Balamand University;

Department of Psychiatry and Clinical Psychology, St George Hospital University Medical

Center; and Institute for Development Research Advocacy and Applied Care (IDRAAC), Beirut,

Lebanon

12Department of Mental Health, School of Public Health, The University of Tokyo, Tokyo, Japan

13Department of Psychiatry, Chinese University of Hong Kong, Tai Po, Hong Kong

14Hôpital Lariboisière Fernand Widal, Assistance Publique Hôpitaux de Paris INSERM UMR-S

1144, University Paris Descartes – Paris Diderot, France

15IMIB-Arrixaca, CIBERESP-Murcia, Subdirección General de Salud Mental y Asistencia

Psiquiátrica, Servicio Murciano de Salud, El Palmar (Murcia), Murcia, Spain

16School of Psychology, University of Ulster, Londonderry, United Kingdom

17Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor,

Michigan, USA

18Universidad Peruana Cayetano Heredia; and National Institute of Health, Lima, Peru

19Colegio Mayor de Cundinamarca University, Bogota, Colombia

20Trimbos-Instituut, Netherlands Institute of Mental Health and Addiction, Utrecht, Netherlands

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Corresponding author: Lukoye Atwoli, Department of Mental Health, Moi University School

of Medicine, PO Box 4606, Eldoret 30100, Kenya; Tel: +254 53 2060958/9 Ext 3230; Fax: +254

53 206 1749; lukoye.atwoli@mu.ac.ke

Conflict of interest disclosures:

Dr. Stein has received research grants and/or consultancy honoraria from Abbott, AstraZeneca,

Eli-Lilly, GlaxoSmithKline, Jazz Pharmaceuticals, Johnson & Johnson, Lundbeck, Orion, Pfizer,

Pharmacia, Roche, Servier, Solvay, Sumitomo, Sun, Takeda, Tikvah, and Wyeth. Dr.

Demyttenaere has served as a consultant with Servier, Lundbeck, Lundbeck Institute,

AstraZeneca and Naurex. In the past three years, Dr. Kessler received support for his

epidemiological studies from Sanofi Aventis, was a consultant for Johnson & Johnson Wellness

and Prevention, and served on an advisory board for the Johnson & Johnson Services Inc. Lake

Nona Life Project. Dr. Kessler is a co-owner of DataStat, Inc., a market research firm that carries

out healthcare research. The other authors report no biomedical financial interests or potential

conflicts of interest relevant to this manuscript.

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ABSTRACT

Background: Unexpected death of a loved one (UD) is the most commonly reported traumatic

experience in cross-national surveys. However, much remains to be learned about PTSD after

this experience. The WHO World Mental Health (WMH) Survey Initiative provides a unique

opportunity to address these issues.

Methods: Data from 19 WMH surveys (n=78,023; 70.1% weighted response rate) were collated.

Potential predictors of PTSD (respondent socio-demographics, characteristics of the death,

history of prior trauma exposure, history of prior mental disorders) after a representative sample

of UDs were examined using logistic regression. Simulation was used to estimate overall model

strength in targeting individuals at highest PTSD risk.

Results: PTSD prevalence after UD averaged 5.2% across surveys and did not differ

significantly between high and low-middle income countries. Significant multivariate predictors

included: the deceased being a spouse or child; the respondent being female and believing they

could have done something to prevent the death; prior trauma exposure; and history of prior

mental disorders. The final model was strongly predictive of PTSD, with the 5% of respondents

having highest estimated risk including 30.6% of all cases of PTSD. Positive predictive value

(i.e., the proportion of high-risk individuals who actually developed PTSD) among the 5% of

respondents with highest predicted risk was 25.3%.

Conclusions: The high prevalence and meaningful risk of PTSD make UD a major public health

issue. This study provides novel insights into predictors of PTSD after this experience and

suggests that screening assessments might be useful in identifying high-risk individuals for

preventive interventions.

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INTRODUCTION

Unexpected death of a loved one (UD) is the most commonly reported traumatic

experience in community epidemiological surveys across the world (Benjet et al., 2016). It is

also one of the traumatic experiences associated with the highest number of cases of post-

traumatic stress disorder (PTSD) in country-specific community surveys (Atwoli et al., 2013;

Breslau et al., 1998; Carmassi et al., 2014; Olaya et al., 2014) and is also associated with

significantly elevated risk of first onset of other mental disorders (Keyes et al., 2014). Awareness

that PTSD occurs in the wake of unexpected death is relatively recent (Zisook, Chentsova-

Dutton, & Shuchter, 1998), though, and raises questions about the prevalence and correlates of

PTSD associated with this experience. Few community epidemiological surveys have

specifically addressed these questions. The WHO World Mental Health (WMH) Surveys

(Kessler & Ustun, 2008) provide a unique opportunity to do so by assessing prevalence and

predictors of UD-related PTSD in general population samples across the globe. Here we focus on

prevalence and predictors of UD-related DSM-IV PTSD. The predictors considered are those

found to be significant in previous studies of more general PTSD (DiGangi et al., 2013; Ferry et

al., 2014) as well as those significant in previous studies of bereavement and complicated grief

(Kristensen et al., 2012; Lobb et al., 2010), including respondent socio-demographics,

characteristics of the death, respondent childhood adversities, history of prior traumatic

experiences, and history of prior psychopathology.

Consistent with previous community epidemiological surveys of PTSD, WMH

respondents were asked to complete a checklist of lifetime exposures to a wide variety of

traumatic experiences (TEs). Given that some people are exposed to a large number of different

TEs in their lifetime, it is impossible to assess PTSD separately for each of these occurrences.

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The standard approach to this problem is to ask each respondent to select the one or two lifetime

TE occurrences they consider to be their “worst” (or the ones associated with the most

psychological distress) and to assess PTSD after those events (Breslau et al., 1998). But that

approach leads to upwardly-biased estimates of conditional PTSD risk after TE exposure

(Atwoli, Stein, Koenen, & McLaughlin, 2015). WMH addressed this problem by using

probability sampling methods to select one lifetime occurrence of one TE for each respondent as

that respondent’s “random TE,” obtaining information about the circumstances around that

occurrence that could influence PTSD risk, and then retrospectively assessing symptoms of

PTSD after that occurrence. We focus here on the random TEs involving unexpected death of a

loved one and their associated UD-related PTSD.

MATERIALS AND METHODS

Samples

The WMH surveys are a coordinated set of community epidemiological surveys of the

prevalence and correlates of common mental disorders carried out in nationally or regionally

representative household samples in countries throughout the world (Kessler & Ustun, 2008).

The data reported here come from the subset of 19 WMH surveys that used an expanded PTSD

assessment to determine PTSD prevalence associated with random TEs as defined above. (Table

1) These surveys included 10 in countries classified by the World Bank (World Bank) as high

income countries and 9 in countries classified as low or middle income countries. Each survey

was based on a probability sample of household residents in the target population using a multi-

stage clustered area probability sample design. Total sample size across surveys was 78,023,

although we focus here on the 2,813 respondents with UD selected as their random TEs. A more

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complete description of WMH sampling procedures is available elsewhere (Heeringa et al.,

2008).

(Table 1 about here)

Field procedures

After obtaining informed consent, interviews were administered face-to-face in

respondent homes in compliance with the Declaration of Helsinki and with approval from local

IRBs. The interview schedule was developed in English and translated into other languages using

a standardized WHO protocol (Harkness et al., 2008). Bilingual survey supervisors in

participating countries were trained and supervised by centralized WMH field staff and

interviewers were monitored using procedures described elsewhere (Pennell et al., 2008) to

guarantee cross-national consistency in data quality.

Measures

Traumatic experiences: Respondents were asked about lifetime exposure to each of 27

different types of traumatic experiences (TEs) and 2 open-ended questions about exposure to

“any other” TE and to a private TE the respondent did not want to name. Positive responses

were probed for number of lifetime occurrences of each TE type and age at exposure to the first

occurrence of each TE type. In the case of the random TEs, we also included questions about age

of exposure and the context surrounding the TE (see below for UD). As noted above, the random

TE for each respondent was selected using a probability sampling scheme from the full list of all

lifetime TE types and occurrences reported by the respondent.

Unexpected death of a loved one (UD): Reports of unexpected deaths were elicited by

asking “Did someone very close to you ever die unexpectedly; for example, they were killed in

an auto accident, murdered, committed suicide, or had a fatal heart attack at an early age?” In

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cases where a UD was the random TE, the respondent’s age at the time of the UD was recorded

along with responses to five questions about the experience: the respondent’s relationship to the

deceased (spouse, parent, child, sibling, other relative, or nonrelative); the cause of death

(homicide, suicide, accident/medical error, or illness); length of illness if the death was due to

illness; the age of the deceased at the time of death; and the respondent’s perception of whether

they could have prevented the death assessed as a yes-no answer to the question: “Looking back

on it now, is there any way you could have prevented the death from happening?”

PTSD: DSM-IV mental disorders were assessed with the Composite International

Diagnostic Interview (CIDI) (Kessler & Ustun, 2004). As detailed elsewhere (Haro et al., 2006),

blinded clinical reappraisal interviews with the Structured Clinical Interview

for DSM-IV (SCID) found CIDI-SCID concordance for PTSD to be moderate (AUC=.69)

(Landis & Koch, 1977). Sensitivity and specificity were .38 and .99, respectively, resulting in a

likelihood ratio positive (LR+) of 42.0, which is well above the threshold of 10 typically used to

consider a screening scale diagnosis definitive (Gardner & Altman, 2000). Consistent with the

high LR+, the proportion of CIDI cases confirmed by the SCID was 86.1%, suggesting that the

vast majority of CIDI/DSM-IV PTSD cases would independently be judged to have DSM-IV

PTSD by a trained clinician.

Other mental disorders: The CIDI also assessed 14 prior (to respondent’s age of

exposure to the random TE) lifetime DSM-IV mental disorders. These included mood disorders,

anxiety disorders, disruptive behavior disorders, and substance disorders. Age-of-onset (AOO) of

each disorder was assessed using special probing techniques shown experimentally to improve

recall accuracy (Knäuper, Cannell, Schwarz, Bruce, & Kessler, 1999). This allowed us to

determine based on retrospective AOO reports whether each respondent had a history of each

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disorder prior to the age of occurrence of the random TE. DSM-IV organic exclusion rules and

diagnostic hierarchy rules were used (other than for oppositional defiant disorder, which was

defined with or without conduct disorder, and substance abuse, which was defined with or

without dependence). Agoraphobia was combined with panic disorder because of low

prevalence. Dysthymic disorder was combined with major depressive disorder for the same

reason.

Other PTSD predictors: We examined six classes of predictors. The first two were

described above: characteristics of the death and the respondent’s history of prior mental

disorders. The third class was socio-demographics: age, education, and marital status (each as of

the time of the death), and sex. Age was coded in quartiles. Given the wide variation in education

levels across countries, education was classified as low, low-average, high-average, or high

(coded as a continuous 1-4 score) according to within-country norms (Scott et al., 2014). The

next three classes of predictors assessed the respondent’s history of exposure to stressful

experiences prior to the random UD: previous experience of UD; exposure to each of the other

28 lifetime TEs; and exposure to each of 12 childhood family adversities (CAs). Consistent with

prior WMH research on CAs (Kessler et al., 2010), we distinguished between CAs in a highly-

correlated set of seven that we labeled Maladaptive Family Functioning CAs (parental mental

disorder, parental substance abuse, parental criminality, family violence, physical abuse, sexual

abuse, neglect) and other CAs (parental divorce, parental death, other parental loss, serious

physical illness, family economic adversity).

Analysis Methods

In addition to the sample weight, each respondent reporting a TE was weighted by the

inverse of the probability of selection of the random TE occurrence. For example, a respondent

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who reported three TE types and two occurrences of the randomly-selected type would receive a

TE weight of 6.0 for the selected random TE. The product of the sample weight with the TE

weight was used in analyses of the random TEs, yielding a sample that is representative of all

lifetime TEs occurring to all respondents. The sum of the consolidated weights across

respondents with a randomly selected UD was standardized in each survey for purposes of

pooled cross-national analysis to equal the observed number of respondents with this TE in the

sample.

Prevalence of PTSD associated with randomly selected UDs was estimated using cross-

tabulations. Logistic regression was then used to examine predictors of PTSD pooled across

surveys. Predictors were entered in blocks, beginning with socio-demographics, followed

sequentially by characteristics of the death, prior TE and CA exposure, and prior mental

disorders. All models included dummy control variables for surveys, meaning that the reported

coefficients represent pooled within-survey coefficients. Logistic regression coefficients and

standard errors were exponentiated and are reported as odds-ratios (ORs) with 95% confidence

intervals (CIs), with statistical significance evaluated using .05-level two-sided tests.

The design-based Taylor series method (Wolter, 1985) implemented in the SAS software

system (SAS Institute Inc., 2008) was used to adjust for the weighting and clustering of

observations. Design-based F tests were used to evaluate significance of each block of predictor,

with numerator degrees of freedom equal to number of predictors and denominator degrees of

freedom equal to number of geographically-clustered sampling error calculation units containing

random UDs across surveys (n=1,062) minus the sum of primary sample units from which these

sampling error calculation units were selected (n=569) and one less than the number of variables

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in the predictor set (Reed III, 2007), resulting in 493 denominator degrees of freedom in

evaluating bivariate associations and fewer in evaluating multivariate associations.

Once the final model was estimated, a predicted probability of PTSD was generated for

each respondent from model coefficients. A receiver operating characteristic (ROC) curve was

then calculated from this summary predicted probability (Zou, O'Malley, & Mauri, 2007). Area

under the ROC curve (AUC) was calculated to quantify overall prediction accuracy of the model

(Hanley & McNeil, 1983). We also evaluated concentration of risk of PTSD among the 5% of

respondents with highest predicted risk of PTSD based on the final model, which we defined as

the proportion of all observed cases of PTSD that was found among this 5% of respondents. This

was done to determine how well subsequent PTSD could have been predicted in the immediate

aftermath of the death using our model. We also calculated positive predictive value, the

proportion of the 5% of respondents with highest predicted risk that actually developed PTSD.

Given that a number of different predictors were examined, the possibility of false

positives and over-fitting was taken into consideration in two ways. First, as noted above, we

evaluated simultaneous significance of predictor blocks and interpreted individually significant

coefficients only when the overall block was significant. Second, we used the method of

replicated 10-fold cross-validation with 20 replicates (i.e., 200 separate estimates of model

coefficients) to correct for the over-estimation of overall model prediction accuracy when

estimating AUC, concentration of risk, and positive predictive value (Smith, Seaman, Wood,

Royston, & White, 2014).

RESULTS

Prevalence of UD and association with PTSD

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Prevalence of UD was 30.2% (2,813 respondents) across surveys (Interquartile range,

IQR, 24.4-33.0%), with an average 1.6 lifetime occurrences per respondent with any and

representing 16.4% of all TEs in the population (IQR 15.3-17.5% across surveys). (Detailed

results are available upon request.) PTSD prevalence associated with random UDs averaged

5.2% across surveys and was comparable in high versus low/middle income countries (4.8%

versus 5.9%; 21=0.6, p=.45). (Table 1) However, prevalence differed significantly across all

surveys (218=35.4, p=.010) and among surveys in high income countries (2

9=19.0, p=.030) but

not among surveys in low/middle income countries (28=15.3, p=.06).

(Table 1 about here)

Predictors of PTSD associated with UD

Respondents who were in the oldest age quartile (35+) at the time they experienced the

UD had significantly elevated univariate PTSD odds compared to those in the youngest quartile

(ages 1-17) (OR 2.5; 95% CI 1.1-5.9). (Table 2) PTSD was also significantly more common

among women than men (OR 3.0; 95% CI 1.5-6.0) and among the currently (at the time of the

death) married (OR 2.1; 95% CI 1.3-3.6) and previously married (OR 3.2; 95% CI 1.3-7.7) than

the never married in univariate models, but was not significantly associated with respondent

education.

Model 1: However, sex was the only socio-demographic that remained significant in a

multivariate model that included all the socio-demographics (Table 2, Model 1). We

subsequently elaborated that model to include a methodological control for number of years

between respondent age at the time of unexpected death and age at interview to investigate the

possibility of time-related recall bias, but that association was non-significant (OR 1.1; 95% CI

0.9-1.3).

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Model 2: The respondent’s relationship to the deceased was a significant predictor of

PTSD (F4,490=12.6, p<.001) in the model that added characteristics of the death to the socio-

demographic predictors (Table 2, Model 2), with highest odds of PTSD associated with death of

the respondent’s spouse (OR 9.6; 95% CI 4.1-22.3) or son or daughter (OR 8.7; 95% CI 4.2-

18.0) followed by death of any other child (OR 4.2; 95% CI 1.7-10.2) and of the respondent’s

parent (OR 2.2; 95% CI 1.1-4.4) compared to others. Cause of death was not a significant

predictor (F3,491=0.8, p=.49). The respondent’s perception that he/she could have done something

to prevent the death was also a significant predictor (OR 2.8; 95% CI 1.2-6.6).

(Table 2 about here)

Model 3: Preliminary analysis found that prior lifetime exposure to TEs predicted PTSD

significantly, but that this association was mainly due to TEs involving interpersonal violence or

man-made disasters (detailed results are available on request), which were found to be

significantly inter-correlated in an exploratory factor analysis reported elsewhere (Benjet et al.,

2016). Multivariate analysis showed that those reporting these TEs had significantly increased

odds of PTSD after the UD (OR 2.6; 95% CI 1.2-5.9 per TE in the range 0-3). (Table 2, Model

3) Preliminary analysis also showed that Maladaptive Family Functioning CAs predicted PTSD

related to unexpected death (detailed results are available on request), while further analysis

showed that these gross associations were due to three particular CAs --parental mental illness,

parental alcohol abuse, sexual abuse (OR 2.8; 95% CI 1.7-4.8 per TE in the range 0-2). The

respondent’s perception that he/she could have done something to prevent the death was non-

significant in Model 3.

Model 4: Preliminary analysis showed that each of the 14 temporally primary lifetime

DSM-IV/CIDI disorders assessed in the surveys had an elevated OR (10 of them significant at

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the .05 level) when considered one at a time, but that few remained significant in a multivariate

model due to high comorbidity among the disorders. Further analysis (Table 2, Model 4) then

showed that the most parsimonious characterization of these joint associations was provided by a

composite variable that summed the number of anxiety disorders (0-3+), ADHD, and number of

substance disorders (0-2) (OR 1.8; 95% CI 1.5-2.3 per disorder in the range 0-8).

Strength and consistency of overall model predictions

Estimated AUC based on 20 replicates of 10-fold cross-validated predictions (as

described in the Methods) was .80 in the total sample and .74-.86 in subsamples defined by

respondent sex, age, and education. (Figure 1) The 5% of respondents with highest predicted risk

included 30.6% of all cases of UD-related PTSD. This is six times the proportion expected by

chance. (Table 3) Subgroup values of this concentration of risk ranged from 36.8% among those

with high/high-average education to 14.7% among men. Positive predictive value among the 5%

of respondents with highest predicted risk was 25.3% in the total sample and ranged from 36.6%

among respondents from low or middle income countries to 18.2% among respondents from high

income countries.

(Figure 1 and Table 3 about here)

DISCUSSION

The study has a number of limitations. First, although prospective evidence suggests that

retrospective reports of TEs are valid (Dohrenwend et al., 2006), respondents with PTSD may

have been biased towards higher recall of prior lifetime TE exposures or mental disorders

(Roemer, Litz, Orsillo, Ehlich, & Friedman, 1998; Zoellner, Foa, Brigidi, & Przeworski, 2000).

Second, PTSD might have led to respondent perceptions that they could have done something to

prevent the death, inducing the significant positive association between that “predictor” and

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PTSD. Third, diagnoses were based on a fully structured lay-administered interview rather than a

semi-structured clinical interview. While the WMH clinical appraisal data are reassuring (Haro

et al., 2006), only a small number of countries carried out clinical reappraisal studies, potentially

limiting generalizability. Fourth, although the combined sample size of the WMH surveys is

large, the number of respondents selected for in-depth UD assessment was relatively small,

reducing statistical power to carry out subtle analyses. In particular, with only 252 respondents

meeting criteria for PTSD and 20 predictors, the resulting 12.6 events-per-variable (EPV) ratio,

well above the 10.0 EPV recommended to avoid biased OR estimates in an additive model

(Peduzzi, Concato, Kemper, Holford, & Feinstein, 1996), did not allow us to consider

interactions of trauma characteristics with pre-existing vulnerabilities or other interactions. Fifth,

the WMH interview schedule was developed before DSM-5 criteria for persistent complex

bereavement disorder (PCBD; American Psychiatric Association, 2013) were codified. As a

result, no information was obtained in the surveys on PCBD or other complicated grief

syndromes (Cozza et al., 2016), making it impossible for us to evaluate the extent to which our

results would be changed if they were adjusted for comorbidity or confounding of our PTSD

diagnoses with these syndromes (Maercker & Znoj, 2010).

Despite these limitations, the present study makes several significant contributions to

knowledge on the sequelae of UD. First, no previous cross-national study has reported on the

prevalence of PTSD after UD. We found this to average 5.2%, which is somewhat higher than

the 4.0% mean prevalence for any randomly selected TE across the WMH surveys (Kessler et

al., 2014), although the prevalence of UD-related PTSD varied widely across surveys. It is

unclear why this variation exists, but the higher mean prevalence than for other TEs emphasizes

the public health importance of UD-related PTSD (Atwoli et al., 2013; Breslau et al., 1998;

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Carmassi et al., 2014; Ferry et al., 2014; Kawakami, Tsuchiya, Umeda, Koenen, & Kessler,

2014; Keyes et al., 2014; Olaya et al., 2014).

Second, we found a number of significant predictors of UD-related PTSD. While the

literature on predictors of UD-related PTSD is sparse, our results are consistent with evidence

about the predictors of PTSD after other types of TEs (Brewin, Andrews, & Valentine, 2000;

DiGangi et al., 2013; Ferry et al., 2014; Ozer, Best, Lipsey, & Weiss, 2003), and the findings

about relationship with the deceased, earlier lifetime traumatic events, and history of mental

disorders are consistent with prior studies of complicated grief, including work on bereavement

symptoms after loss of a spouse or child (Kristensen et al., 2012; Lobb et al., 2010). Overlap of

predictors of UD-related PTSD with the predictors found in studies of complicated grief

highlights important commonalities, supports inclusion in the same chapter of the psychiatric

nosology (Maercker & Znoj, 2010), but again raises concerns about our lack of knowledge about

how our results would have changed if data had been available in the WMH surveys to

distinguish UD-related PTSD from PCBD.

Third, the lack of association between cause of death and PTSD is relevant to a key

debate about the DSM-5 diagnostic criteria for PTSD. While DSM-IV (American Psychiatric

Association, 2000) permitted unexpected death to qualify as a potentially traumatic event for

PTSD, DSM-5 (American Psychiatric Association, 2013) developed a more stringent threshold

for criterion A1, requiring that in cases of actual or threatened death of a family member or

friend, the event(s) must have been directly witnessed, violent, or accidental. The WMH

interview did not enquire about the respondent witnessing the death, making it impossible for us

to know if the UD qualified as a DSM-5 TE. However, PTSD symptoms can occur after non-

violent/non-witnessed death (Zisook et al., 1998) and this narrowing of the definition of

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qualifying death in DSM-5 has been questioned (Friedman, 2013; Keyes et al., 2014; Larsen &

Pacella, 2016). It is relevant to this debate that our analysis found that specific manner of death

of a loved one has little impact on the risk of subsequent DSM-IV PTSD. This is true,

furthermore, even though some of the deaths reported were not “unexpected” in the sense that

they were reportedly due to physical illnesses of some duration, although the exact time of death

might have been unexpected (e.g., a relative known to have only a relatively short time to live

but seemingly in stable condition suddenly dropping dead at a holiday dinner).

Perhaps the most striking result in our study was that 30.6% of people who experienced

UD-related PTSD were among the 5% of respondents with highest predicted risk scores in our

cross-validated model. This result is broadly consistent with other recent studies showing that

PTSD can be predicted with good accuracy using predictor data collected in the immediate

aftermath of trauma (Galatzer-Levy, Karstoft, Statnikov, & Shalev, 2014; Karstoft et al., 2015;

Kessler et al., 2014). It is noteworthy that the high concentration of risk of PTSD we found was

based on a replicated cross-validated simulation designed to adjust for over-fitting. Our results

provide strong suggestive evidence that useful models could be developed in future prospective

studies to target prevention and treatment of UD-related PTSD (Endo, Yonemoto, & Yamada,

2015; Maercker & Znoj, 2010; Simon, 2013).

CONCLUSION

Unexpected death of a loved one is a highly prevalent TE associated with a somewhat

higher prevalence of PTSD than other TEs. Predictors of UD-related PTSD appear to be

consistent with other PTSD. Preliminary evidence suggests that UD-related PTSD could be

predicted with good accuracy from data available shortly after the death, although this evidence

is based on retrospective data and needs to be confirmed prospectively. These findings

Atwoli 17

emphasize that UD is a major public health issue and suggest that screening assessments might

be useful in identifying high-risk individuals for early interventions.

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ACKNOWLEDGMENTS

The World Health Organization World Mental Health Survey collaborators are Tomasz

Adamowski, PhD, MD, Sergio Aguilar-Gaxiola, MD, PhD, Ali Al-Hamzawi, MD, Mohammad

Al-Kaisy, MD, Abdullah Al Subaie, MBBS, FRCP, Jordi Alonso, MD, PhD , Yasmin Altwaijri,

MS, PhD, Laura Helena Andrade, MD, PhD, Lukoye Atwoli, MD, PhD, Randy P. Auerbach,

PhD, William G. Axinn, PhD, Corina Benjet, PhD, Guilherme Borges, ScD, Robert M. Bossarte,

PhD, Evelyn J. Bromet, PhD, Ronny Bruffaerts, PhD, Brendan Bunting, PhD, Ernesto Caffo,

MD, Jose Miguel Caldas de Almeida, MD, PhD, Graca Cardoso, MD, PhD, Alfredo H. Cia, MD,

Stephanie Chardoul, Somnath Chatterji, MD, Alexandre Chiavegatto Filho, PhD, Pim Cuijpers,

PhD, Louisa Degenhardt, PhD, Giovanni de Girolamo, MD, Ron de Graaf, MS, PhD, Peter de

Jonge, PhD, Koen Demyttenaere, MD, PhD, David D. Ebert, PhD, Sara Evans-Lacko, PhD, John

Fayyad, MD, Fabian Fiestas, MD, PhD, Silvia Florescu, MD, PhD, Barbara Forresi, PhD, Sandro

Galea, DrPH, MD, MPH, Laura Germine, PhD, Stephen E. Gilman, ScD, Dirgha J. Ghimire,

PhD, Meyer D. Glantz, PhD, Oye Gureje, PhD, DSc, FRCPsych, Josep Maria Haro, MD, MPH,

PhD, Yanling He, MD, Hristo Hinkov, MD, Chi-yi Hu, PhD, MD, Yueqin Huang, MD, MPH,

PhD, Aimee Nasser Karam, PhD, Elie G. Karam, MD, Norito Kawakami, MD, DMSc, Ronald

C. Kessler, PhD, Andrzej Kiejna, MD, PhD, Karestan C. Koenen, PhD, Viviane Kovess-

Masfety, MSc, MD, PhD, Carmen Lara, MD, PhD, Sing Lee, PhD, Jean-Pierre Lepine, MD,

Itzhak Levav, MD, Daphna Levinson, PhD, Zhaorui Liu, MD, MPH, Silvia S. Martins, MD,

PhD, Herbert Matschinger, PhD, John J. McGrath, PhD, Katie A. McLaughlin, PhD, Maria

Elena Medina-Mora, PhD, Zeina Mneimneh, PhD, MPH, Jacek Moskalewicz, DrPH, Samuel D.

Murphy, DrPH, Fernando Navarro-Mateu, MD, PhD, Matthew K. Nock, PhD, Siobhan O'Neill,

PhD, Mark Oakley-Browne, MB, ChB, PhD, J. Hans Ormel, PhD, Beth-Ellen Pennell, MA,

Atwoli 19

Marina Piazza, MPH, ScD, Stephanie Pinder-Amaker, PhD, Patryk Piotrowski, MD, PhD, Jose

Posada-Villa, MD, Ayelet M. Ruscio, PhD, Kate M. Scott, PhD, Vicki Shahly, PhD, Tim Slade,

PhD, Jordan W. Smoller, ScD, MD, Juan Carlos Stagnaro, MD, PhD, Dan J. Stein, MBA, MSc,

PhD, Amy E. Street, PhD, Hisateru Tachimori, PhD, Nezar Taib, MS, Margreet ten Have, PhD,

Graham Thornicroft, PhD, Yolanda Torres, MPH, Maria Carmen Viana, MD, PhD, Gemma

Vilagut, MS, Elisabeth Wells, PhD, Harvey Whiteford, PhD, David R. Williams, MPH, PhD,

Michelle A. Williams, ScD, Bogdan Wojtyniak, ScD, Alan M. Zaslavsky, PhD.

The World Health Organization World Mental Health (WMH) Survey Initiative is

supported by the National Institute of Mental Health (NIMH; R01 MH070884 and R01

MH093612-01), the John D. and Catherine T. MacArthur Foundation, the Pfizer Foundation, the

US Public Health Service (R13-MH066849, R01-MH069864, and R01 DA016558), the Fogarty

International Center (FIRCA R03-TW006481), the Pan American Health Organization, Eli Lilly

and Company, Ortho-McNeil Pharmaceutical, Inc., GlaxoSmithKline, and Bristol-Myers Squibb.

We thank the staff of the WMH Data Collection and Data Analysis Coordination Centres for

assistance with instrumentation, fieldwork, and consultation on data analysis.

The São Paulo Megacity Mental Health Survey is supported by the State of São Paulo

Research Foundation (FAPESP) Thematic Project Grant 03/00204-3. The Bulgarian

Epidemiological Study of common mental disorders EPIBUL is supported by the Ministry of

Health and the National Center for Public Health Protection. The Colombian National Study of

Mental Health (NSMH) is supported by the Ministry of Social Protection. The Mental Health

Study Medellín – Colombia was carried out and supported jointly by the Center for Excellence

on Research in Mental Health (CES University) and the Secretary of Health of Medellín. The

ESEMeD project is funded by the European Commission (Contracts QLG5-1999-01042;

Atwoli 20

SANCO 2004123, and EAHC 20081308), (the Piedmont Region (Italy)), Fondo de Investigación

Sanitaria, Instituto de Salud Carlos III, Spain (FIS 00/0028), Ministerio de Ciencia y Tecnología,

Spain (SAF 2000-158-CE), Departament de Salut, Generalitat de Catalunya, Spain, Instituto de

Salud Carlos III (CIBER CB06/02/0046, RETICS RD06/0011 REM-TAP), and other local

agencies and by an unrestricted educational grant from GlaxoSmithKline. The World Mental

Health Japan (WMHJ) Survey is supported by the Grant for Research on Psychiatric and

Neurological Diseases and Mental Health (H13-SHOGAI-023, H14-TOKUBETSU-026, H16-

KOKORO-013) from the Japan Ministry of Health, Labour and Welfare. The Lebanese

Evaluation of the Burden of Ailments and Needs Of the Nation (L.E.B.A.N.O.N.) is supported

by the Lebanese Ministry of Public Health, the WHO (Lebanon), National Institute of Health /

Fogarty International Center (R03 TW006481-01), anonymous private donations to IDRAAC,

Lebanon, and unrestricted grants from, Algorithm, AstraZeneca, Benta, Bella Pharma, Eli Lilly,

Glaxo Smith Kline, Lundbeck, Novartis, Servier, Phenicia, UPO. The Northern Ireland Study of

Mental Health was funded by the Health & Social Care Research & Development Division of the

Public Health Agency. The Peruvian World Mental Health Study was funded by the National

Institute of Health of the Ministry of Health of Peru. The Romania WMH study projects

"Policies in Mental Health Area" and "National Study regarding Mental Health and Services

Use" were carried out by National School of Public Health & Health Services Management

(former National Institute for Research & Development in Health), with technical support of

Metro Media Transilvania, the National Institute of Statistics-National Centre for Training in

Statistics, SC. Cheyenne Services SRL, Statistics Netherlands and were funded by Ministry of

Public Health (former Ministry of Health) with supplemental support of Eli Lilly Romania SRL.

The South Africa Stress and Health Study (SASH) is supported by the US National Institute of

Atwoli 21

Mental Health (R01-MH059575) and National Institute of Drug Abuse with supplemental

funding from the South African Department of Health and the University of Michigan. Dr. Stein

is supported by the Medical Research Council of South Africa (MRC). The Psychiatric Enquiry

to General Population in Southeast Spain – Murcia (PEGASUS-Murcia) Project has been

financed by the Regional Health Authorities of Murcia (Servicio Murciano de Salud and

Consejería de Sanidad y Política Social) and Fundación para la Formación e Investigación

Sanitarias (FFIS) of Murcia. The Ukraine Comorbid Mental Disorders during Periods of Social

Disruption (CMDPSD) study is funded by the US National Institute of Mental Health (RO1-

MH61905). The US National Comorbidity Survey Replication (NCS-R) is supported by the

National Institute of Mental Health (NIMH; U01-MH60220) with supplemental support from the

National Institute of Drug Abuse (NIDA), the Substance Abuse and Mental Health Services

Administration (SAMHSA), the Robert Wood Johnson Foundation (RWJF; Grant 044708), and

the John W. Alden Trust.

None of the funders had any role in the design, analysis, interpretation of results, or

preparation of this paper. The views and opinions expressed in this report are those of the authors

and should not be construed to represent the views of the sponsoring organizations, agencies, or

governments.

A complete list of all within-country and cross-national WMH publications can be found

at http://www.hcp.med.harvard.edu/wmh/.

Atwoli 22

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Table 1. Prevalence of DSM-IV/CIDI PTSD associated with unexpected death of a loved one (UD) among respondents for whom UD was their randomly selected traumatic event by survey (n=2,813)a

 %

PTSDb (95% CI)c

Number with

PTSDb

Total sample

sizeb

I. High income countriesBelgium 6.8 (2.2-19.3) (6) (74)

France 2.7 (0.8-4.6) (14) (107)

Germany 8.1 (2.5-23.4) (7) (73)

Italy 5.3 (3.0-7.6) (12) (104)

Japan 1.4 (0.1-2.6) (8) (114)

Netherlands 3.8 (1.3-6.2) (8) (82)

Northern Ireland 12.6 (3.7-21.5) (27) (139)

Spain 4.1 (1.2-7.0) (18) (172)

Spain - Murcia 1.7 (0.5-5.4) (8) (202)

United States 4.5 (1.3-7.7) (50) (516)

Total 4.8 (3.3-6.2) (158) (1,583)

29 19.0*

II. Low or middle income countriesBrazil 7.1 (2.3-11.9) (10) (85)

Bulgaria 13.8 (4.0-38.0) (15) (72)

Colombia 0.7 (0.1-4.4) (4) (121)

Colombia - Medellín 11.7 (4.0-29.5) (21) (162)

Lebanon 4.0 (1.3-11.6) (6) (68)

Peru 1.4 (0.3-3.1) (4) (92)

Romania 3.3 (0.9-7.8) (6) (92)

South Africa 3.3 (0.2-6.4) (8) (374)

Ukraine 10.4 (3.1-17.7) (20) (164)

Total 5.9 (3.3-8.4) (94) (1,230)

28 15.3

III. Total 5.2 (3.9-6.6) (252) (2,813)

Overall between country difference 218 35.4*

High vs low or middle difference 21 0.6

*Significant at the .05 level, two-sided test.

aEach respondent who reported lifetime exposure to one or more Traumatic Events (TEs) had one occurrence of

one such experience selected at random for detailed assessment. Each of these randomly selected TEs was

weighted by the inverse of its probability of selection at the respondent level to create a weighted sample of TEs

that was representative of all TEs in the population. The randomly selected “deaths of a loved one” were the subset

of these randomly selected TEs involving “death of a loved one”. The sum of weights of the randomly selected

“deaths of a loved one” was standardized within surveys to sum to the observed number of respondents whose

randomly selected TE was “death of a loved one”. The n reported in the last column of this table represents that

number of respondents. The results reported here are for the surveys where at least one respondent with a

randomly selected “death of a loved one” met DSM-IV/CIDI criteria for PTSD related to that TE. Two surveys were

Atwoli 30

excluded for the following reasons: Mexico for low frequency of outcome (n=94) and Israel for having no

respondents experiencing “death of a loved one” as a TE (n=0).

bThe reported sample sizes are unweighted. The unweighted proportions of respondents with PTSD do not match

the prevalence estimates in the first column because the latter were based on weighted data.

cConfidence intervals that include 0.0% as the lower bound were estimated using the Wilson-score method (Reed

III, 2007). This method was used for the following countries: Belgium, Germany, Spain - Murcia, Bulgaria, Colombia,

Colombia - Medellín, Lebanon, Peru, and Romania.

dThe Wilson interval method (Reed III, 2007) was used to calculate confidence intervals when the lower bound of

1.96 times the standard error was less than 0.0.

Atwoli 31

Table 2. Associations of socio-demographics, trauma characteristics, and prior stressors with PTSD after randomly selected unexpected death of a loved one (n=2,813)a

Univariate model Model 1 Model 2 Model 3 Model 4OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)

I. Socio-demographics at time of traumatic eventRespondent age at TE exposure (vs. 1-17 years)

Upper middle-older age (35+) 2.5* (1.1-5.9) 1.7 (0.5-6.2) 1.2 (0.4-3.9) 1.6 (0.5-5.3) 0.9 (0.2-3.1)

Lower middle age (25-34) 1.4 (0.5-3.8) 1.1 (0.3-3.9) 1.1 (0.4-3.3) 1.2 (0.4-3.7) 0.7 (0.2-2.3)

Young adult (18-24) 0.7 (0.3-1.9) 0.7 (0.2-2.1) 0.8 (0.3-2.1) 0.9 (0.3-2.5) 0.6 (0.2-1.5)

F3,491 5.1* p=.002 1.5 p=.21 0.4 p=.76 0.5 p=.70 0.6 p=.60

Female gender (vs. male) 3.0* (1.5-6.0) 2.7* (1.3-5.6) 2.1* (1.0-4.3) 1.9* (1.1-3.5) 2.2* (1.2-3.9)

Education 1.0 (0.7-1.3) 1.0 (0.7-1.5) 1.0 (0.7-1.4) 1.0 (0.7-1.3) 1.0 (0.8-1.4)

Marital history (vs. never married)

Currently married 2.1* (1.3-3.6) 1.4 (0.6-3.1) 1.1 (0.5-2.4) 1.1 (0.5-2.5) 1.5 (0.6-3.9)

Previously married 3.2* (1.3-7.7) 1.7 (0.5-5.4) 2.2 (0.6-7.5) 1.7 (0.5-5.2) 0.8 (0.5-6.2)

F2,492 5.3* p=.005 0.4 p=.65 0.9 p=.39 0.5 p=.59 0.5 p=.63

II. Trauma characteristicsWho died (vs. other relative or non-family member)

Spouse 12.3* (5.6-27.0) -- -- 9.6* (4.1-22.3) 10.3* (4.5-23.6) 13.0* (5.3-31.9)

Son or daughter 12.1* (5.8-25.3) -- -- 8.7* (4.2-18.0) 11.7* (1.4-6.7) 15.1* (7.2-31.5)

Some other child (0-12 years old) 5.9* (1.5-22.2) -- -- 4.2* (1.7-10.2) 3.1* (1.4-6.7) 2.0* (1.1-3.9)

Parent 2.3* (1.2-4.3) -- -- 2.2* (1.1-4.4) 2.5* (1.3-4.9) 3.3* (1.7-6.6)

F4,490 15.7* p<.001 -- -- 12.6* p<.001 17.1* p<.001 15.4* p<.001

Cause of death (vs. illness or other)

Homicide 0.7 (0.2-2.6) -- -- 1.3 (0.5-3.5) 1.7 (0.6-4.5) 2.1 (0.8-5.4)

Suicide 0.4 (0.1-1.3) -- -- 0.5 (0.2-1.4) 0.5 (0.2-1.4) 0.4 (0.1-1.5)

Accident, natural disaster, or medical mishap 0.7 (0.4-1.3) -- -- 1.0 (0.6-1.8) 1.1 (0.6-2.0) 1.4 (0.7-2.5)

F3,491 0.9 p=.46 -- -- 0.8 p=.49 1.0 p=.37 1.9 p=.14

III. Perceived preventabilityR could have prevented death 3.4* (1.2-10.2) -- -- 2.8* (1.2-6.6) 1.9 (0.7-4.9) 1.5 (0.5-4.0)

IV. Prior vulnerability factorsPrior stresses

Prior exposure to any traumatic event (0-3)b 2.5* (1.4-4.5) -- -- -- -- 2.6* (1.2-5.9) 1.7 (1.0-3.1)

Maladaptive Family Functioning CAs (0-2)c 3.5* (2.2-5.6) -- -- -- -- 2.8* (1.7-4.8) 2.2* (1.3-3.8)

Prior mental disorders (0-8)d 1.8* (1.5-2.2) -- -- -- -- -- -- 1.8* (1.5-2.3)

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F(7,487), (15,479), (17,477), (18,476)e 5.6* p<.001 7.6* p<.001 11.4* p<.001 11.1* p<.001

*Significant at the .05 level, two-sided test.

aModels were based on weighted data. See the text for details. Each model included dummy variable controls for WMH survey.

bNumber of prior traumatic events (values=0-3+) was calculated as the sum of 4 individual prior TEs (beaten by caregiver, beaten by someone else, witnessed physical fight at home, and man-

made disaster) from Appendix Table 4.

cNumber of Maladaptive Family Functioning Childhood Adversities (MFF CAs) (values=0-2+) was calculated as the sum of 3 significant individual MFF CA's (parental mental, parental substance

misuse, and sexual abuse) from Appendix Table 5.

dNumber of mental disorders was calculated as the weighted sum of ADHD, drug abuse/dependence, and alcohol abuse/dependence from Appendix Table 6.

eDesign-based F tests were used to evaluate significance of predictor sets, with numerator degrees of freedom equal to number of predictors and denominator degrees of freedom equal to

number of geographically-clustered sampling error calculation units containing randomly selected deaths of a loved one across surveys (n=1,062) minus the sum of primary sample units from

which these sampling error calculation units were selected (n=569) and one less than the number of variables in the predictor set (Reed III, 2007), resulting in 493 denominator degrees of

freedom in evaluating bivariate associations and fewer in evaluating multivariate associations.

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Table 3. Concentration of risk and positive predictive value of observed PTSD among the 5% of respondents assessed for PTSD after randomly selected unexpected death of a loved one with highest predicted risk of PTSD in the total sample and stratified by subgroups

Simulated samplea (n = 56,260) Observed sampleb (n = 2,813)Concentration of risk Positive Predictive Value Concentration of risk Positive Predictive Value% PTSD (SE) % PTSD (SE) % PTSD (SE) % PTSD (SE)

I. Total 30.6 (6.2) 25.3 (5.3) 53.7 (6.5) 37.2 (5.9)

II. Country incomeHigh 26.7 (4.3) 18.2 (3.2) 50.5 (7.8) 37.7 (7.6)

Low or middle 34.6 (11.4) 36.6 (11.1) 57.0 (10.3) 36.8 (8.9)

III. Age 30+ years old 35.7 (6.5) 22.0 (3.2) 61.1 (8.2) 35.5 (6.1)

< 30 years old 25.0 (12.0) 32.8 (14.8) 45.6 (10.6) 40.0 (10.7)

IV. GenderMale 14.7 (4.0) 22.6 (9.7) 48.2 (15.0) 42.5 (15.2)

Female 35.2 (7.6) 25.6 (5.8) 55.3 (7.2) 36.1 (6.1)

V. EducationLow or low-average 24.6 (5.4) 22.9 (5.6) 45.0 (9.2) 27.5 (7.1)

High or high-average 36.8 (10.7) 27.2 (8.3) 62.7 (8.3) 50.5 (8.6)

aEstimates calculated from 20 replicates of 10-fold cross-validation of the final model.

bEstimates calculated from the final model.

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Figure title: Figure 1. AUC of PTSD model, total sample and by selected sub-groups,

"Unexpected death of a loved one", weighted analysis

Figure footnote: Note. "Older (top half of age range)" = 30+ years old; "Younger

(bottom half of age range)" < 30 years old. "Higher education" = high and high-average;

"Lower education" = low and low-average.

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Appendix Table 1. WMH sample characteristics by World Bank income categoriesa

Sample sizes

Country by income category Surveyb Sample characteristicsc Field dates Age range

Response rated Part 1 Part 2

Reported any TE

Reported unexpected death of a loved one

Unexpected death of a loved one was the

randomly-selected TE

I. High income countries

Belgium ESEMeDNationally representative. The sample was selected from a national register of Belgium residents 2001-2 18-95 50.6 2,419 1,043 710 284 74

France ESEMeDNationally representative. The sample was selected from a national list of households with listed telephone numbers. 2001-2 18-97 45.9 2,894 1,436 1,071 440 107

Germany ESEMeD Nationally representative. 2002-3 19-95 57.8 3,555 1,323 928 387 73

Italy ESEMeDNationally representative. The sample was selected from municipality resident registries. 2001-2 18-100 71.3 4,712 1,779 1,064 400 104

JapanWMHJ 2002-

2006 Eleven metropolitan areas. 2002-6 20-98 55.1 4,129 1,682 1,138 460 114

Netherlands ESEMeDNationally representative. Sample selected from municipal postal registries. 2002-3 18-95 56.4 2,372 1,094 788 336 82

Northern Ireland NISHS Nationally representative. 2004-7 18-97 68.4 4,340 1,986 1,287 591 139

Spain ESEMeD Nationally representative. 2001-2 18-98 78.6 5,473 2,121 1,284 496 172

Spain – MurciaPEGASUS-

Murcia Murcia region. 2010-12 18-96 67.4 2,621 1,459 942 430 202

United States NCS-R Nationally representative. 2002-3 18-99 70.9 9,282 5,692 4,954 2,644 516

Total 63.5 (41,797) (19,615) (14,166) (6,468) (1,583)

II. Low or middle income countries

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Brazil - São PauloSão Paulo Megacity São Paulo metropolitan area. 2005-7 18-93 81.3 5,037 2,942 2,330 1,085 85

Bulgaria NSHS Nationally representative. 2003-7 18-98 72.0 5,318 2,233 776 328 72

Colombia NSMHAll urban areas of the country (approximately 73% of the total national population) 2003 18-65 87.7 4,426 2,381 2,068 1,062 121

Colombia – Medellín e,f MMHHS Medellín metropolitan area. 2011-12 19-65 97.2 3,261 1,673 1,387 697 162

Lebanon LEBANON Nationally representative. 2002-3 18-94 70.0 2,857 1,031 873 445 69

Peru EMSMP Nationally representative. 2004-5 18-65 90.2 3,930 1,801 1,530 609 92

Romania RMHS Nationally representative. 2005-6 18-96 70.9 2,357 2,357 997 255 92

South Africae SASH Nationally representative. 2003-4 18-92 87.1 4,315 4,315 3,085 1,667 374

Ukraine CMDPSD Nationally representative. 2002 18-91 78.3 4,725 1,720 1,545 773 164

Total 81.0 (36,226) (20,453) (14,591) (6,921) (1,230)

III. Total 70.1 (78,023) (40,068) (28,757) (13,389) (2,813)

aThe World Bank (2012) Data. Accessed May 12, 2012 at: http://data.worldbank.org/country. Some of the WMH countries have moved into new income categories since the surveys were conducted. The income groupings above reflect the status of each country at the time of data collection. The current income category of each country is available at the preceding URL.

bESEMeD (The European Study Of The Epidemiology Of Mental Disorders); WMHJ2002-2006 (World Mental Health Japan Survey); NISHS (Northern Ireland Study of Health and Stress); PEGASUS-Murcia (Psychiatric Enquiry to General Population in Southeast Spain-Murcia); NCS-R (The US National Comorbidity Survey Replication); NSHS (Bulgaria National Survey of Health and Stress); NSMH (The Colombian National Study of Mental Health); LEBANON (Lebanese Evaluation of the Burden of Ailments and Needs of the Nation); MMHHS (Medellín Mental Health Household Study); EMSMP (La Encuesta Mundial de Salud Mental en el Peru); RMHS (Romania Mental Health Survey); SASH (South Africa Health Survey); CMDPSD (Comorbid Mental Disorders during Periods of Social Disruption).

cMost WMH surveys are based on stratified multistage clustered area probability household samples in which samples of areas equivalent to counties or municipalities in the US were selected in the first stage followed by one or more subsequent stages of geographic sampling (e.g., towns within counties, blocks within towns, households within blocks) to arrive at a sample of households, in each of which a listing of household members was created and one or two people were selected from this listing to be interviewed. No substitution was allowed when the originally sampled household resident could not be interviewed. These household samples were selected from Census area data in all countries other than France (where telephone directories were used to select households) and the Netherlands (where postal registries were used to select households). Several WMH surveys (Belgium, Germany, Italy) used municipal resident registries to select respondents without listing households. The Japanese sample is the only totally un-clustered sample, with households randomly selected in each of the 11 metropolitan areas and one random respondent selected in each sample household. 14 of the 19 surveys are based on nationally representative household samples.

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dThe response rate is calculated as the ratio of the number of households in which an interview was completed to the number of households originally sampled, excluding from the denominator households known not to be eligible either because of being vacant at the time of initial contact or because the residents were unable to speak the designated languages of the survey. The weighted average response rate is 70.1%.

eFor the purposes of cross-national comparisons we limit the sample to those 18+.

fColombia moved from the "lower and lower-middle income" to the "upper-middle income" category between 2003 (when the Colombian National Study of Mental Health was conducted) and 2010 (when the Medellín Mental Health Household Study was conducted), hence Colombia's appearance in both income categories. For more information, please see footnote a.

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Appendix Table 2. Distribution of lifetime exposure to traumatic experiences (TEs) and death of a loved one in the participating WMH surveys (n=40,067)a

Proportion of respondents exposed

to any lifetime TE

Proportion of respondents exposed to any lifetime death

of a loved one

Mean number of rapes/ sexual

assaults among those exposed to

any death of a loved one

Rapes/sexual assaults as a

proportion of all lifetime TEs

(n) of randomly selected

death of a loved one% (SE) % (SE) Mean (SE) % (SE)

I. High income

Belgium 65.8 (3.1) 24.4 (2.2) 1.5 (0.1) 28.1 (2.6) (74)

France 72.7 (2.3) 28.2 (1.9) 1.5 (0.1) 26.5 (2.1) (107)

Germany 67.3 (2.2) 27.2 (2.0) 1.6 (0.1) 29.0 (2.1) (73)

Italy 56.1 (2.2) 20.4 (1.2) 1.5 (0.0) 23.8 (1.4) (104)

Japan 60.7 (1.7) 23.7 (1.4) 1.6 (0.1) 31.5 (2.0) (114)

The Netherlands 65.6 (2.8) 25.4 (2.1) 1.6 (0.1) 31.3 (2.2) (82)

Northern Ireland 60.6 (1.7) 26.7 (1.2) 1.6 (0.1) 30.5 (1.3) (139)

Spain 54.0 (1.7) 20.6 (1.4) 1.4 (0.1) 32.2 (1.9) (172)

Spain - Murcia 62.4 (1.9) 29.6 (2.3) 1.3 (0.1) 40.2 (1.5) (202)

United States 82.7 (0.9) 41.9 (1.1) 2.0 (0.1) 31.9 (0.7) (516)

Total 67.9 (0.6) 29.8 (0.6) 1.7 (0.0) 30.8 (0.5) (1,583)

II. Low or middle income countries

Brazil 73.8 (1.5) 31.0 (1.1) 1.5 (0.0) 25.2 (0.9) (85)

Bulgaria 28.6 (1.3) 11.0 (1.0) 1.4 (0.1) 35.0 (3.9) (72)

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Colombia 82.7 (1.4) 39.7 (1.5) 1.8 (0.1) 27.6 (1.2) (121)

Colombia - Medellín 75.1 (2.6) 33.0 (2.3) 2.0 (0.1) 29.6 (1.5) (162)

Lebanon 81.1 (2.7) 36.6 (2.5) 1.6 (0.1) 27.0 (1.4) (68)

Peru 83.1 (0.8) 31.6 (1.0) 1.4 (0.1) 22.2 (1.2) (92)

Romania 41.5 (1.1) 10.5 (0.7) 1.4 (0.0) 21.0 (1.3) (92)

South Africa 73.8 (1.2) 39.2 (1.2) 1.6 (0.0) 33.2 (0.8) (374)

Ukraine 84.6 (1.7) 41.0 (2.1) 1.4 (0.1) 27.5 (1.3) (164)

Total 68.4 (0.6) 30.5 (0.5) 1.6 (0.0) 28.0 (0.4) (1,230)

III. Total 68.2 (0.4) 30.2 (0.4) 1.6 (0.0) 29.3 (0.3) (2,813)

aTwo surveys were excluded for the following reasons: Mexico for low frequency of outcome (n=94) and Israel for having no respondents experiencing “death of a loved one” as a TE (n=0).

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Appendix Table 3. Sample distribution and prevalence of DSM-IV/CIDI PTSD associated with randomly-selected unexpected death of loved on by selected respondent characteristics (n=2,813)a

  Sample Distribution PTSD Prevalence

  % (SE) % (95% CI) (n)b

I. Socio-demographics at time of traumatic event

Age

Older (35+) 28.1 (1.7) 8.3 (5.3-11.4) (1,003)

Lower middle age (25-34) 22.6 (1.7) 4.9 (2.8-7.0) (605)

Young adult (18-24) 23.9 (1.8) 2.8 (1.4-4.2) (621)

Children and adolescents (1-17) 25.4 (2.1) 4.4 (1.1-7.7) (584)

χ24 -- -- 15.3 p=.002 --

Gender

Female 58.2 (2.3) 7.0 (4.9-9.0) (1,980)

Male 41.8 (2.3) 2.8 (1.3-4.3) (833)

χ21 -- -- 9.9 p=.002 --

Education

Low 24.9 (1.5) 5.7 (3.0-8.5) (711)

Low-average 31.9 (2.0) 3.9 (2.1-5.7) (840)

High-average 32.2 (1.8) 6.3 (3.3-9.2) (947)

High 11.1 (1.3) 4.9 (1.9-8.0) (315)

χ23 -- -- 4.5 p=.21 --

Marital status

Currently married 31.1 (1.8) 7.0 (4.5-9.5) (1,028)

Previously married 6.5 (0.9) 9.9 (2.7-17.1) (198)

Never married 62.4 (1.8) 3.9 (2.3-5.5) (1,587)

χ22 -- -- 10.6 p=.005 --

II. Trauma characteristics

Who died

Spouse 4.8 (0.7) 24.0 (12.6-35.3) (254)

Son or daughter 5.4 (0.7) 17.5 (8.9-26.2) (232)

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Some other child (ages 0-12) 4.1 (0.7) 11.9 (0.0-28.3) (115)

Parent 19.9 (1.4) 5.2 (3.2-7.2) (583)

Other relative or non-family member 65.7 (1.8) 2.5 (1.4-3.5) (1,629)

χ24 -- -- 62.5 p<.001 --

Cause of death

Homicide 10.9 (1.2) 6.1 (2.8-9.5) (295)

Suicide 9.9 (1.1) 2.5 (0.0-4.9) (257)

Accident, natural disaster, or medical mishap 35.2 (2.1) 4.8 (2.6-6.9) (952)

Illness or other 44.8 (2.1) 6.0 (3.8-8.3) (1,309)

χ23 -- -- 2.6 p=.46 --

III. Perceived preventability

R could have prevented death

Yes 7.1 (1.1) 11.4 (0.9-21.9) (202)

No 92.9 (1.1) 4.8 (3.5-6.0) (2,611)

χ21 -- -- 5.0 p=.026 --

IV. Prior vulnerability factors

Number of prior exposure to any traumatic eventsc

0 77.5 (1.9) 4.5 (3.3-5.7) (2,514)

1 17.1 (1.7) 5.2 (2.4-8.1) (236)

2 4.8 (1.0) 12.2 (0.0-27.0) (59)

3+ 0.6 (0.3) 46.3 (0.0-100.0) (4)

χ23 -- -- 13.7 p=.003 --

Number of maladaptive family functioning childhood adversitiesd

0 84.1 (1.5) 4.0 (2.8-5.1) (2,464)

1 13.8 (1.4) 8.4 (4.6-12.3) (302)

2+ 2.1 (0.5) 35.4 (9.8-61.0) (47)

χ22 -- -- 29.4 p<.001 --

Number of prior mental disorderse

0 73.3 (1.6) 4.0 (2.7-5.3) (2080)

1 13.6 (1.3) 4.1 (2.2-6.0) (410)

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2 6.1 (0.8) 5.6 (1.4-9.7) (177)

3 3.1 (0.6) 16.9 (6.4-27.4) (87)

4 0.9 (0.2) 10.8 (1.7-19.8) (31)

5 2.2 (0.1) 4.3 (0.0-8.9) (10)

6 0.5 (0.4) 74.5 (30.8-100.0) (9)

7 0.4 (0.2) 73.8 (38.4-100.0) (8)

8 0.0 (0.0) 100.0 (100.0-100.0) (1)

χ28 -- -- 35.8 p<.001 --

V. Total -- -- 5.2 (3.9-6.6) (2,813)

aPrevalence estimates are based on weighted data. See the text for a description of the weighting procedures. Significance tests are design-based tests of univariate associations of each characteristic with PTSD based on logistic regression models with dummy controls for survey. Estimates were calculated from weighted data. Wald chi-square statistics and p-values were estimated while controlling for country.

bReported sample sizes are unweighted.

cTraumatic experiences include those involving interpersonal violence and manmade disasters (beaten by caregiver, beaten by someone else, witnessed physical fight at home, and man-made disaster), identified as significant from Appendix Table 4.

dMaladaptive family functioning childhood adversities include parental mental illness, parental substance disorder, and sexual abuse, identified as significant from Appendix Table 5.

eMental disorders include anxiety disorders, ADHD, drug abuse/dependence, and alcohol abuse/dependence.

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Appendix Table 4. Associations of prior lifetime traumatic experiences with DSM-IV/CIDI PTSD associated with unexpected death of a loved one (n=2,813)a

Univariate Model Multivariate Model 1b Multivariate Model 2 Multivariate Model 3 Multivariate Model 4 Multivariate Model 5

OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)

I. Exposure to sectarian violence

Relief worker in war zone 0.0 (0.0-0.0) -- -- -- -- -- -- -- -- -- --

Civilian in war zone 0.4 (0.1-2.2) 0.3 (0.0-2.3) -- -- -- -- -- -- -- --

Civilian in region of terror 1.1 (0.4-2.7) 0.8 (0.2-2.9) -- -- -- -- -- -- -- --

Refugee 2.4 (0.8-7.1) 2.0 (0.5-8.4) -- -- -- -- -- -- -- --

Kidnapped 1.9 (0.2-16.1) 0.3 (0.0-2.3) -- -- -- -- -- -- -- --

Total count (0-2) 0.9 (0.5-1.7) -- -- 0.6 (0.3-1.2) -- -- -- -- -- --

Ftotal -- -- F4,490=0.9 p=.46 -- -- -- -- -- -- -- --

Fbetween category -- -- F3,491=1.1 p=.35 -- -- -- -- -- -- -- --

II. Participation in sectarian violence

Combat experience 1.0 (0.1-8.9) 1.1 (0.1-22.1) -- -- -- -- -- -- -- --

Witnessed death 2.0 (0.9-4.2) 1.5 (0.7-3.2) -- -- -- -- -- -- -- --

Accidentally cause injury/death 17.0* (2.2-131.2) 5.4* (1.3-22.0) -- -- -- -- -- -- -- --

Purposefully injured/killed someone 65.2* (4.7-912.7) 12.9* (1.6-104.3) -- -- -- -- -- -- -- --

Saw atrocities 0.8 (0.1-6.9) 0.7 (0.0-15.3) -- -- -- -- -- -- -- --

Total count (0-2) 1.9 (1.0-3.8) -- -- 1.6 (0.9-3.0) -- -- -- -- -- --

Ftotal -- -- F5,489=3.1 p=.009 -- -- -- -- -- -- -- --

Fbetween category -- -- F4,490=1.7 p=.15 -- -- -- -- -- -- -- --

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III. Interpersonal violence

Beaten by caregiver 4.1* (1.4-12.6) 2.3* (1.0-5.1) -- -- -- -- -- -- -- --

Beaten by someone else 3.3 (0.4-25.3) 0.8 (0.2-2.9) -- -- -- -- -- -- -- --

Witnessed physical fight at home 3.2* (1.4-7.2) 2.6 (0.9-7.9) -- -- -- -- -- -- -- --

Total count (0-2)c 3.2* (1.4-7.1) -- -- 2.4* (1.3-4.6) 2.8* (1.5-5.2) -- -- 0.9 (0.2-4.3)

Ftotal -- -- F3,491=2.9 p=.036 -- -- -- -- -- -- -- --

Fbetween category -- -- F2,492=1.5 p=.24 -- -- -- -- -- -- -- --

IV. Exposure to sexual violence

Beaten by spouse/partner 2.5* (1.0-5.8) 2.7* (1.2-6.4) -- -- -- -- -- -- -- --

Raped 3.4* (1.3-8.8) 2.3 (0.7-7.2) -- -- -- -- -- -- -- --

Sexually assaulted 2.9* (1.3-6.6) 2.1 (0.9-5.1) -- -- -- -- -- -- -- --

Stalked 0.9 (0.2-4.6) 0.7 (0.1-5.0) -- -- -- -- -- -- -- --

Traumatic event to loved one 2.0 (0.6-6.1) 1.9 (0.5-7.0) -- -- -- -- -- -- -- --

Some other event 2.7 (0.8-8.5) 1.8 (0.5-7.0) -- -- -- -- -- -- -- --

Private event 0.3 (0.1-1.1) 0.2* (0.0-0.7) -- -- -- -- -- -- -- --

Total count (0-5) 1.6* (1.1-2.2) -- -- 1.4 (1.0-2.1) -- -- -- -- -- --

Ftotal -- -- F7,487=2.1 p=.043 -- -- -- -- -- -- -- --

Fbetween category -- -- F6,488=1.6 p=.14 -- -- -- -- -- -- -- --

V. Accident/injuries

Toxic chemical exposure 6.7* (1.0-44.5) 1.2 (0.4-4.3) -- -- -- -- -- -- -- --

Automobile accident 2.3 (0.9-5.5) 2.1 (0.9-4.8) -- -- -- -- -- -- -- --

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Other life threatening accident 1.8 (0.2-12.7) 0.3 (0.1-1.1) -- -- -- -- -- -- -- --

Natural disaster 0.5 (0.1-1.5) 0.3 (0.1-1.2) -- -- -- -- -- -- -- --

Life-threatening illness 2.7 (0.9-8.3) 1.2 (0.5-3.1) -- -- -- -- -- -- -- --

Child with serious illness 1.1 (0.4-3.5) 1.0 (0.3-3.2) -- -- -- -- -- -- -- --

Total count (0-3) 1.6 (0.9-2.7) -- -- 1.1 (0.7-1.6) -- -- -- -- -- --

Ftotal -- -- F6,488=1.4 p=.20 -- -- -- -- -- -- -- --

Fbetween category -- -- F5,489=1.7 p=.13 -- -- -- -- -- -- -- --

VI. Other trauma

Man-made disaster 7.0* (1.7-28.8) 4.2* (1.3-13.3) 4.9* (1.6-15.3) 5.2* (1.8-15.0) -- -- 1.8 (0.6-5.5)

Mugged or threatened with a weapon 1.8 (0.6-5.6) 0.8 (0.3-2.2) -- -- -- -- -- -- -- --

Unexpected death of a loved one 1.4 (0.8-2.5) 1.0 (0.6-1.8) -- -- -- -- -- -- -- --

Total count (0-3) 1.7 (0.9-3.3) -- -- -- -- -- -- -- -- -- --

Ftotal -- -- F3,491=2.9 p=.035 -- -- -- -- -- -- -- --

Fbetween category -- -- F2,492=3.9 p=.021 -- -- -- -- -- -- -- --

VII. Total traumatic experiences

Ftotal -- -- F28,466=2.9 p<.001 F5,489=4.0 p=.002 -- -- -- -- -- --

Fbetween category -- -- F27,467=2.3 p<.001 F4,490=2.6 p=.034 -- -- -- -- F2,492=0.6 p=.52

VIII. Composite of Interpersonal violence and man-made disaster

Total count (0-3) 3.3* (1.7-6.2) -- -- -- -- -- -- 3.3* (1.7-6.2) 2.9* (1.1-7.2)

IX. Any traumatic event

Total count (0-3) 1.4* (1.2-1.6) -- -- -- -- -- -- -- --

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Exactly 1 prior TE 1.4 (0.8-2.5) -- -- -- -- -- -- -- --

Exactly 2 prior TEs 1.2 (0.6-2.4) -- -- -- -- -- -- -- --

3+ prior TEs 2.4* (1.2-4.6) -- -- -- -- -- -- -- --

Ftotal F3,491=3.1 p=.026 -- -- -- -- -- -- -- --

*Significant at the .05 level, two-sided design-based test.

aAll results are based on weighted data. See the text for a description of the weighting procedures. Coefficients come from logistic regression models controlling for the socio-demographic variables and characteristics of the unexpected death in Table 2, Model 2 and dummy variables for survey predicting PTSD.

bOnly the traumatic experiences associated with at least 1 case of PTSD were carried forward into the multivariate models.

cThe odds ratio of 0.9 (0.2-4.3) in multivariate model 5 comes from a dummy variable for exactly 2 of the count of the three interpersonal violence traumas and man-made disaster.

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Appendix Table 5. Associations of childhood adversities with DSM-IV/CIDI PTSD associated with unexpected death of a loved one (n=2,813)a

Univariate Model Multivariate Model 1 Multivariate Model 2 Multivariate Model 3 Multivariate Model 4 Multivariate Model 5

OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)

I. Maladaptive family functioning (MFF) childhood functioning (CA)

Parental mental illness 4.0* (2.2-7.2) 3.2* (1.5-6.7) 3.0* (1.4-6.4) 3.6* (1.9-6.7) -- -- 0.8 (0.3-2.1)

Parental substance misuse 3.4* (1.4-8.1) 2.7* (1.0-7.2) 2.8* (1.1-7.2) 2.5* (1.1-5.7) -- -- 0.6 (0.2-1.7)

Parental criminality 1.2 (0.5-2.9) 0.5 (0.2-1.3) 0.5 (0.2-1.3) -- -- -- -- -- --

Family violence 2.4* (1.1-5.1) 1.1 (0.3-3.6) 1.0 (0.3-3.4) -- -- -- -- -- --

Physical abuse 3.3* (1.3-8.3) 2.2 (0.7-6.4) 2.3 (0.7-7.7) -- -- -- -- -- --

Sexual abuse 6.1* (2.4-15.3) 5.0* (2.1-11.9) 4.7* (1.9-11.4) 4.6* (1.9-11.2) -- -- -- --

Neglect 3.1* (1.4-7.1) 1.0 (0.3-3.2) 1.2 (0.5-3.1) -- -- -- -- -- --

Ftotal -- -- F7,487=6.5* p<.001 F7,487=7.2* p<.001 F3,491=11.9* p<.001 -- -- F2,492=0.6 p=.58

Fbetween category b -- -- F6,488=3.0* p=.007 F6,488=3.0* p=.007 -- -- -- -- -- --

Count of selected MFF CAs (0-2) 3.6* (2.4-5.3) -- -- -- -- -- -- 3.6* (2.4-5.3) 4.9* (2.0-12.1)

II. Other CAs

Parental death 0.9 (0.5-1.7) 0.8 (0.4-1.5) -- -- -- -- -- -- -- --

Parental divorce 1.6 (0.6-4.5) 1.1 (0.5-2.7) -- -- -- -- -- -- -- --

Other parental loss 2.7 (0.7-11.1) 2.4 (0.5-10.4) -- -- -- -- -- -- -- --

Serious physical illness 0.4 (0.1-1.6) 0.2 (0.0-1.1) -- -- -- -- -- -- -- --

Family economic adversity 1.3 (0.3-5.3) 0.8 (0.2-2.8) -- -- -- -- -- -- -- --

Ftotal -- -- F5,489=1.1 p=.40 -- -- -- -- -- -- -- --

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Fbetween category -- -- F4,490=1.3 p=.28 -- -- -- -- -- -- -- --

III. Total CAs (MFF and other)

Ftotal -- -- F12,482=4.1* p<.001 -- -- -- -- -- -- -- --

Fbetween category -- -- F11,483=2.2* p=.012 -- -- -- -- -- -- -- --

IV. Count of MFF CAs

Exactly 1 4.1* (1.9-8.8) -- -- -- -- -- -- -- -- -- --

2+ 4.3* (2.3-8.3) -- -- -- -- -- -- -- -- -- --

Ftotal F2,492=12.0 p<.001 -- -- -- -- -- -- -- -- -- --

V. Count of other CAs

Exactly 1 1.2 (0.7-2.0) -- -- -- -- -- -- -- -- -- --

2+ 1.3 (0.2-9.6) -- -- -- -- -- -- -- -- -- --

Ftotal F2,492=0.3 p=.74 -- -- -- -- -- -- -- -- -- --

*Significant at the .05 level, two-sided design-based test.

aAll results are based on weighted data. See the text for a description of the weighting procedures. Coefficients come from logistic regression models controlling for the socio-demographic variables and characteristics of the unexpected death in Table 2, Model 2 and dummy variables for survey predicting PTSD.

bThis test evaluates the significance of differences among ORs among the 7 MFF CAs or ORs among the 5 other CAs.

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Appendix Table 6. Associations of prior lifetime DSM-IV/CIDI mental disorders with DSM-IV/CIDI PTSD associated with unexpected death of a loved one (n=2,813)a

Univariate Multivariate Model 1 Multivariate Model 2 Multivariate Model 3 Multivariate Model 4OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)

I. Mood disordersMDE or dysthymia 2.1* (1.1-3.8) 1.1 (0.6-2.2) -- -- -- -- -- --

BPD 10.1* (1.7-58.5) 2.3 (0.5-10.4) -- -- -- -- -- --

Total count (0-2) 2.4* (1.3-4.1) -- -- 1.4 (0.8-2.4) -- -- -- --

Ftotal -- -- F2,492=0.6 p=.55 -- -- -- -- -- --

Fbetween category -- -- F1,493=0.7 p=.40 -- -- -- -- -- --

II. Anxiety disordersAgoraphobia/panic 1.3 (0.5-3.6) 0.6 (0.2-1.7) -- -- -- -- -- --

GAD 3.6* (1.5-8.5) 2.6* (1.1-6.0) -- -- -- -- -- --

PTSD 3.2* (1.1-9.2) 1.8 (0.5-6.5) -- -- -- -- -- --

Social phobia 3.3* (1.5-7.2) 2.9* (1.3-6.7) -- -- -- -- -- --

Specific phobia 1.4 (0.8-2.7) 0.9 (0.5-1.7) -- -- -- -- -- --

SAD 3.7* (1.3-10.5) 1.5 (0.6-3.8) -- -- -- -- -- --

Total count (0-3) 1.8* (1.4-2.4) -- -- 1.5* (1.1-2.1) 5.1 (0.5-51.9) -- --

Ftotal -- -- F6,488=2.5 p=.023 -- -- -- -- -- --

Fbetween category -- -- F5,489=1.7 p=.12 -- -- -- -- -- --

III. Behavior disordersADHD 6.1* (2.5-14.4) 6.7* (2.3-19.7) 6.6* (2.5-17.1) 21.4* (1.6-294.9) 1.5 (0.4-5.4)

Conduct 1.4 (0.2-8.4) 0.3 (0.0-2.2) -- -- -- -- -- --

IED 1.1 (0.1-9.4) 0.9 (0.1-12.3) -- -- -- -- -- --

ODD 4.3* (1.5-12.5) 3.2 (0.8-12.9) -- -- -- -- -- --

Total count (0-3) 2.1* (1.2-3.8) -- -- -- -- -- -- -- --

Ftotal -- -- F4,490=4.6 p=.001 -- -- -- -- -- --

Fbetween category -- -- F3,491=3.2 p=.024 -- -- -- -- -- --

IV. Substance disordersDrug 18.1* (4.3-75.1) 7.9* (1.3-47.2) -- -- -- -- -- --

Alcohol 5.7* (2.3-14.6) 2.4 (0.7-8.4) -- -- -- -- -- --

Total count (0-2) 4.7* (2.2-9.9) -- -- 3.9* (1.8-8.3) 10.3* (1.3-84.5) 1.5 (0.6-4.0)

Ftotal -- -- F2,492=5.6 p=.004 -- -- -- -- F2,492=0.4 p=.67

Fbetween category -- -- F1,493=0.8 p=.36 -- -- -- -- -- --

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Ftotal -- -- F14,480=3.1 p<.001 -- -- -- -- -- --

Fbetween disorders -- -- F13,481=1.9 p=.030 -- -- -- -- -- --

VI. Composite of anxiety, ADHD, and substance disordersScore (0-8) 1.8* (1.5-2.3) -- -- -- -- -- -- 1.6* (1.2-2.2)

VII. TotalAny disorder 2.3* (1.4-3.8) -- -- -- -- -- -- -- --

Exactly 1 0.9 (0.5-1.6) -- -- -- -- -- -- -- --

Exactly 2 2.3 (0.9-5.7) -- -- -- -- -- -- -- --

Exactly 3 5.2* (1.9-13.9) -- -- -- -- -- -- -- --

4+ 7.4* (2.8-19.1) -- -- -- -- -- -- -- --

Ftotal F4,490=7.1* p<.001 -- -- -- -- -- -- -- --

*Significant at the .05 level, two-sided design-based test.

aAll results are based on weighted data. See the text for a description of the weighting procedures. Coefficients come from logistic regression models controlling for the socio-demographic variables, characteristics of the unexpected death, and prior stressors in Table 2, Model 3 and dummy variables for survey predicting PTSD.

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