Can You Get Ptsd From a Death in the Family

Depress Anxiety. Author manuscript; available in PMC 2022 Oct 30.

Published in final edited form equally:

PMCID: PMC5661943

NIHMSID: NIHMS915062

Posttraumatic stress disorder associated with unexpected death of a loved i: Cross-national findings from the World Mental Health Surveys

Lukoye Atwoli,^{one, 2} Dan J. Stein,² Andrew King,³ Maria Petukhova,³ Sergio Aguilar-Gaxiola,^four Jordi Alonso,^five Evelyn J. Bromet,⁶ Giovanni de Girolamo,⁷ Koen Demyttenaere,⁸ Silvia Florescu,⁹ Josep Maria Haro,¹⁰ Elie G. Karam,¹¹ Norito Kawakami,¹² Sing Lee,¹³ Jean-Pierre Lepine,^xiv Fernando Navarro-Mateu,¹⁵ Siobhan O'Neill,¹⁶ Beth-Ellen Pennell,¹⁷ Marina Piazza,¹⁸ Jose Posada-Villa,^nineteen Nancy A. Sampson,ⁱⁱⁱ Margreet x Have,²⁰ Alan M. Zaslavsky,³ and Ronald C. Kessler³, on behalf of the WHO Globe Mental Health Survey Collaborators

Lukoye Atwoli

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

²Department of Psychiatry and Mental Wellness, University of Greatcoat Town, Greatcoat Town, Republic of Southward Africa

Dan J. Stein

^twoDepartment of Psychiatry and Mental Health, University of Greatcoat Town, Cape Town, Republic of Due south Africa

Andrew Rex

^threeDepartment of Health Intendance Policy, Harvard Medical School, Boston, Massachusetts, U.s.a.

Maria Petukhova

³Section of Health Intendance Policy, Harvard Medical Schoolhouse, Boston, Massachusetts, USA

Sergio Aguilar-Gaxiola

⁴Heart for Reducing Health Disparities, UC Davis Health Organization, Sacramento, California, USA

Jordi Alonso

⁵IMIM-Hospital del Mar Research Constitute, Parc de Salut Mar; Pompeu Fabra University (UPF); and CIBER en Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain

Evelyn J. Bromet

^half-dozenSection of Psychiatry, Stony Beck University School of Medicine, Stony Brook, New York, Us

Giovanni de Girolamo

⁷IRCCS St John of God Clinical Research Heart//IRCCS Centro Southward. Giovanni di Dio Fatebenefratelli, Brescia, Italy

Koen Demyttenaere

⁸Section of Psychiatry, University Infirmary Gasthuisberg, Katholieke Universiteit Leuven, Leuven, Kingdom of belgium

Silvia Florescu

⁹National School of Public Health, Direction and Professional Development, Bucharest, Romania

Josep Maria Haro

^xParc Sanitari Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain

Elie One thousand. Karam

¹¹Department of Psychiatry and Clinical Psychology, Faculty of Medicine, Balamand University; Department of Psychiatry and Clinical Psychology, St George Hospital University Medical Heart; and Institute for Development Research Advocacy and Applied Care (IDRAAC), Beirut, Lebanon

Norito Kawakami

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

Sing Lee

^thirteenDepartment of Psychiatry, Chinese University of Hong Kong, Tai Po, Hong Kong

Jean-Pierre Lepine

¹⁴Hôpital Lariboisière Fernand Widal, Assistance Publique Hôpitaux de Paris INSERM UMR-S 1144, Academy Paris Descartes – Paris Diderot, France

Fernando Navarro-Mateu

¹⁵IMIB-Arrixaca, CIBERESP-Murcia, Subdirección General de Salud Mental y Asistencia Psiquiátrica, Servicio Murciano de Salud, El Palmar (Murcia), Murcia, Espana

Siobhan O'Neill

¹⁶School of Psychology, University of Ulster, Londonderry, United kingdom of great britain and northern ireland

Beth-Ellen Pennell

¹⁷Survey Research Eye, Institute for Social Inquiry, Academy of Michigan, Ann Arbor, Michigan, USA

Marina Piazza

^eighteenUniversidad Peruana Cayetano Heredia; and National Institute of Wellness, Lima, Peru

Jose Posada-Villa

¹⁹Colegio Mayor de Cundinamarca University, Bogota, Republic of colombia

Nancy A. Sampson

³Department of Wellness Care Policy, Harvard Medical Schoolhouse, Boston, Massachusetts, United states

Margreet ten Have

²⁰Trimbos-Instituut, Netherlands Plant of Mental Wellness and Addiction, Utrecht, Netherlands

Alan Thou. Zaslavsky

^threeDepartment of Health Care Policy, Harvard Medical Schoolhouse, Boston, Massachusetts, United states of america

Ronald C. Kessler

³Department of Wellness Care Policy, Harvard Medical Schoolhouse, Boston, Massachusetts, United states

Abstract

Background

Unexpected death of a loved one (UD) is the most commonly reported traumatic feel in cross-national surveys. However, much remains to be learned about PTSD after this experience. The WHO World Mental Wellness (WMH) Survey Initiative provides a unique opportunity to address these issues.

Methods

Data from 19 WMH surveys (northward=78,023; 70.1% weighted response charge per unit) were collated. Potential predictors of PTSD (respondent socio-demographics, characteristics of the death, history of prior trauma exposure, history of prior mental disorders) afterward a representative sample of UDs were examined using logistic regression. Simulation was used to gauge overall model strength in targeting individuals at highest PTSD risk.

Results

PTSD prevalence after UD averaged v.2% across surveys and did not differ significantly between loftier and depression-middle income countries. Pregnant multivariate predictors included: the deceased beingness a spouse or child; the respondent existence female and believing they could have washed something to forestall the death; prior trauma exposure; and history of prior mental disorders. The final model was strongly predictive of PTSD, with the v% of respondents having highest estimated risk including 30.vi% of all cases of PTSD. Positive predictive value (i.eastward., the proportion of high-take a chance individuals who actually adult PTSD) among the 5% of respondents with highest predicted risk was 25.three%.

Conclusions

The high prevalence and meaningful risk of PTSD make UD a major public wellness event. This study provides novel insights into predictors of PTSD afterwards this experience and suggests that screening assessments might exist useful in identifying high-risk individuals for preventive interventions.

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

INTRODUCTION

Unexpected decease of a loved one (UD) is the about commonly reported traumatic experience in customs epidemiological surveys across the earth (Benjet et al., 2016). It is as well ane of the traumatic experiences associated with the highest number of cases of mail service-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 kickoff 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 customs epidemiological surveys have specifically addressed these questions. The WHO Earth Mental Health (WMH) Surveys (Kessler & Ustun, 2008) provide a unique opportunity to do so past assessing prevalence and predictors of UD-related PTSD in full general population samples across the globe. Hither we focus on prevalence and predictors of UD-related DSM-IV PTSD. The predictors considered are those found to exist significant in previous studies of more general PTSD (DiGangi et al., 2013; Ferry et al., 2014) as well every bit those meaning 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 consummate 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. The standard approach to this problem is to ask each respondent to select the ane or two lifetime TE occurrences they consider to be their "worst" (or the ones associated with the near psychological distress) and to assess PTSD later those events (Breslau et al., 1998). Just that approach leads to upwardly-biased estimates of provisional PTSD risk afterwards TE exposure (Atwoli, Stein, Koenen, & McLaughlin, 2015). WMH addressed this problem past using probability sampling methods to select 1 lifetime occurrence of one TE for each respondent equally that respondent's "random TE," obtaining information about the circumstances around that occurrence that could influence PTSD run a risk, and then retrospectively assessing symptoms of PTSD subsequently that occurrence. We focus hither on the random TEs involving unexpected death of a loved ane and their associated UD-related PTSD.

MATERIALS AND METHODS

Samples

The WMH surveys are a coordinated gear up 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 in a higher place. (Tabular array 1) These surveys included ten in countries classified past the World Banking concern (Earth Bank) equally loftier 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-phase clustered area probability sample design. Total sample size beyond surveys was 78,023, although nosotros focus here on the 2,813 respondents with UD selected as their random TEs. A more than complete description of WMH sampling procedures is available elsewhere (Heeringa et al., 2008).

Table ane

Prevalence of DSM-IV/CIDI PTSD associated with unexpected decease of a loved one (UD) amid respondents for whom UD was their randomly selected traumatic effect by survey (n=two,813)^a

	% PTSDb	(95% CI)c	Number with PTSDb	Total sample sizeb
I. High income countries
Kingdom of belgium	half-dozen.8	(ii.two–19.3)	(half dozen)	(74)
France	2.7	(0.8–4.6)	(14)	(107)
Federal republic of germany	8.ane	(2.5–23.four)	(7)	(73)
Italian republic	5.iii	(3.0–7.6)	(12)	(104)
Nippon	i.4	(0.1–2.half dozen)	(8)	(114)
Netherlands	3.8	(1.3–6.2)	(eight)	(82)
Northern Ireland	12.6	(three.seven–21.5)	(27)	(139)
Spain	4.1	(i.two–seven.0)	(xviii)	(172)
Spain - Murcia	i.7	(0.v–v.four)	(viii)	(202)
Us	4.v	(i.3–7.7)	(l)	(516)
Total	four.8	(3.3–6.2)	(158)	(1,583)
χ2 ix	nineteen.0*
II. Low or middle income countries
Brazil	7.1	(two.3–xi.9)	(10)	(85)
Republic of bulgaria	13.8	(4.0–38.0)	(15)	(72)
Colombia	0.7	(0.ane–iv.iv)	(4)	(121)
Colombia - Medellín	11.7	(4.0–29.v)	(21)	(162)
Lebanese republic	iv.0	(1.3–11.6)	(vi)	(68)
Peru	ane.4	(0.3–3.1)	(4)	(92)
Romania	3.3	(0.9–vii.8)	(6)	(92)
S Africa	iii.three	(0.ii–six.4)	(viii)	(374)
Ukraine	10.iv	(3.ane–17.seven)	(20)	(164)
Total	5.9	(three.three–viii.4)	(94)	(1,230)
χ2 8	15.3
III. Total	5.two	(3.9–six.6)	(252)	(two,813)
Overall betwixt land divergence χ2 eighteen	35.4*
High vs low or heart difference χii 1	0.6

Field procedures

After obtaining informed consent, interviews were administered contiguous in respondent homes in compliance with the Proclamation of Helsinki and with blessing from local IRBs. The interview schedule was adult 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 nearly lifetime exposure to each of 27 different types of traumatic experiences (TEs) and ii open-ended questions about exposure to "whatsoever other" TE and to a private TE the respondent did not want to proper name. Positive responses were probed for number of lifetime occurrences of each TE type and age at exposure to the beginning occurrence of each TE type. In the case of the random TEs, nosotros 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 expiry of a loved one (UD)

Reports of unexpected deaths were elicited past asking "Did someone very close to you ever dice unexpectedly; for example, they were killed in an auto accident, murdered, committed suicide, or had a fatal heart assail at an early age?" In cases where a UD was the random TE, the respondent's age at the time of the UD was recorded along with responses to v questions most the feel: the respondent's relationship to the deceased (spouse, parent, child, sibling, other relative, or nonrelative); the cause of death (homicide, suicide, blow/medical fault, 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'south perception of whether they could take prevented the death assessed as a yes-no answer to the question: "Looking back on it now, is in that location any way you lot could have prevented the death from happening?"

PTSD

DSM-Four mental disorders were assessed with the Composite International Diagnostic Interview (CIDI) (Kessler & Ustun, 2004). Every bit detailed elsewhere (Haro et al., 2006), blinded clinical reappraisal interviews with the Structured Clinical Interview for DSM-IV (SCID) found CIDI-SCID cyclopedia for PTSD to exist 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 higher up 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-4 PTSD past a trained clinician.

Other mental disorders

The CIDI as well assessed 14 prior (to respondent's age of exposure to the random TE) lifetime DSM-IV mental disorders. These included mood disorders, anxiety disorders, confusing behavior disorders, and substance disorders. Historic period-of-onset (AOO) of each disorder was assessed using special probing techniques shown experimentally to better call up accuracy (Knäuper, Cannell, Schwarz, Bruce, & Kessler, 1999). This allowed us to make up one's mind based on retrospective AOO reports whether each respondent had a history of each disorder prior to the age of occurrence of the random TE. DSM-IV organic exclusion rules and diagnostic bureaucracy rules were used (other than for oppositional defiant disorder, which was defined with or without conduct disorder, and substance abuse, which was divers with or without dependence). Agoraphobia was combined with panic disorder because of depression prevalence. Dysthymic disorder was combined with major depressive disorder for the aforementioned 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 3rd class was socio-demographics: historic period, instruction, and marital status (each equally of the fourth dimension of the decease), and sex. Age was coded in quartiles. Given the wide variation in pedagogy levels across countries, education was classified as depression, low-average, high-average, or high (coded as a continuous 1–iv score) according to inside-country norms (Scott et al., 2014). The adjacent 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 decease, other parental loss, serious physical disease, family economic adversity).

Analysis Methods

In improver to the sample weight, each respondent reporting a TE was weighted by the inverse of the probability of pick of the random TE occurrence. For example, a respondent who reported iii 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 cantankerous-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 decease, prior TE and CA exposure, and prior mental disorders. All models included dummy control variables for surveys, meaning that the reported coefficients correspond 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 Constitute Inc., 2008) was used to accommodate for the weighting and clustering of observations. Pattern-based F tests were used to evaluate significance of each block of predictor, with numerator degrees of liberty equal to number of predictors and denominator degrees of freedom equal to number of geographically-amassed sampling fault calculation units containing random UDs across surveys (n=1,062) minus the sum of main sample units from which these sampling error calculation units were selected (due north=569) and one less than the number of variables in the predictor gear up (Reed Iii, 2007), resulting in 493 denominator degrees of freedom in evaluating bivariate associations and fewer in evaluating multivariate associations.

One time 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). Expanse under the ROC curve (AUC) was calculated to quantify overall prediction accurateness 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 concluding model, which we divers 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 firsthand backwash of the death using our model. Nosotros also calculated positive predictive value, the proportion of the v% of respondents with highest predicted risk that actually developed PTSD.

Given that a number of unlike predictors were examined, the possibility of false positives and over-fitting was taken into consideration in 2 ways. First, equally noted above, nosotros evaluated simultaneous significance of predictor blocks and interpreted individually significant coefficients only when the overall block was significant. 2d, we used the method of replicated x-fold cross-validation with xx replicates (i.e., 200 separate estimates of model coefficients) to correct for the over-estimation of overall model prediction accurateness when estimating AUC, concentration of risk, and positive predictive value (Smith, Seaman, Wood, Royston, & White, 2014).

RESULTS

Prevalence of UD and association with PTSD

Prevalence of UD was thirty.2% (ii,813 respondents) across surveys (Interquartile range, IQR, 24.iv–33.0%), with an boilerplate 1.half dozen 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 bachelor upon asking.) PTSD prevalence associated with random UDs averaged v.two% beyond surveys and was comparable in high versus low/middle income countries (4.8% versus 5.9%; χ² _i=0.6, p=.45). (Table 1) Nevertheless, prevalence differed significantly across all surveys (χ² ₁₈=35.4, p=.010) and amidst surveys in loftier income countries (χ^two ₉=19.0, p=.030) but not among surveys in low/middle income countries (χ^two ₈=fifteen.three, p=.06).

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 i.1–5.ix). (Table 2) PTSD was likewise significantly more common among women than men (OR 3.0; 95% CI i.five–6.0) and among the currently (at the time of the death) married (OR ii.i; 95% CI i.three–three.vi) and previously married (OR iii.ii; 95% CI 1.3–7.7) than the never married in univariate models, but was not significantly associated with respondent education.

Table ii

Associations of socio-demographics, trauma characteristics, and prior stressors with PTSD subsequently randomly selected unexpected death of a loved one (north=two,813)^a

	Univariate model		Model i		Model two		Model three		Model 4
	OR	(95% CI)	OR	(95% CI)	OR	(95% CI)	OR	(95% CI)	OR	(95% CI)
I. Socio-demographics at time of traumatic event
Respondent age at TE exposure (vs. i–17 years)
Upper middle-older historic period (35+)	2.5*	(1.one–5.9)	i.vii	(0.5–half-dozen.two)	1.ii	(0.4–3.9)	1.half dozen	(0.five–5.3)	0.nine	(0.2–iii.1)
Lower eye historic period (25–34)	1.4	(0.5–iii.eight)	one.1	(0.3–3.9)	1.ane	(0.4–iii.3)	1.2	(0.4–3.7)	0.7	(0.ii–2.3)
Young adult (18–24)	0.seven	(0.3–one.9)	0.7	(0.two–2.1)	0.viii	(0.three–two.i)	0.nine	(0.3–ii.v)	0.half-dozen	(0.2–1.v)
F3,491	five.i*	p=.002	1.5	p=.21	0.4	p=.76	0.5	p=.70	0.six	p=.lx
Female gender (vs. male person)	3.0*	(i.five–6.0)	two.7*	(one.3–v.6)	2.ane*	(1.0–4.3)	ane.9*	(1.1–three.5)	2.2*	(1.2–3.9)
Instruction	1.0	(0.7–one.3)	1.0	(0.7–1.5)	1.0	(0.7–1.4)	1.0	(0.7–i.3)	i.0	(0.eight–1.iv)
Marital history (vs. never married)
Currently married	two.1*	(1.3–3.6)	1.4	(0.6–three.1)	one.1	(0.v–2.4)	i.ane	(0.5–2.5)	i.five	(0.six–three.ix)
Previously married	iii.two*	(i.iii–seven.7)	1.seven	(0.five–5.4)	2.ii	(0.vi–seven.v)	1.7	(0.5–five.2)	0.viii	(0.v–half-dozen.ii)
F2,492	5.3*	p=.005	0.4	p=.65	0.9	p=.39	0.5	p=.59	0.5	p=.63
II. Trauma characteristics
Who died (vs. other relative or non-family fellow member)
Spouse	12.3*	(5.6–27.0)	–	–	9.6*	(4.ane–22.3)	10.3*	(four.5–23.6)	13.0*	(5.3–31.9)
Son or daughter	12.1*	(5.8–25.3)	–	–	8.7*	(4.ii–18.0)	eleven.vii*	(1.iv–6.vii)	fifteen.1*	(7.two–31.5)
Some other child (0–12 years old)	5.ix*	(one.five–22.2)	–	–	4.2*	(ane.7–ten.2)	3.ane*	(1.iv–6.7)	2.0*	(1.i–3.9)
Parent	2.3*	(i.2–four.3)	–	–	two.2*	(1.i–iv.4)	2.five*	(i.three–iv.9)	iii.3*	(ane.vii–six.6)
F4,490	15.7*	p<.001	–	–	12.vi*	p<.001	17.1*	p<.001	15.4*	p<.001
Cause of death (vs. illness or other)
Homicide	0.seven	(0.2–2.six)	–	–	1.3	(0.5–3.5)	1.vii	(0.vi–4.5)	2.1	(0.8–v.4)
Suicide	0.4	(0.1–1.3)	–	–	0.5	(0.two–1.4)	0.5	(0.two–1.iv)	0.4	(0.i–1.v)
Accident, natural disaster, or medical mishap	0.7	(0.4–1.3)	–	–	1.0	(0.half-dozen–1.8)	1.1	(0.vi–two.0)	one.4	(0.seven–2.v)
F3,491	0.9	p=.46	–	–	0.8	p=.49	1.0	p=.37	one.9	p=.14
III. Perceived preventability
R could have prevented expiry	3.4*	(one.2–ten.ii)	–	–	2.8*	(1.ii–half dozen.6)	1.9	(0.7–four.9)	1.5	(0.5–iv.0)
IV. Prior vulnerability factors
Prior stresses
Prior exposure to any traumatic event (0–3)b	2.five*	(1.four–4.5)	–	–	–	–	2.half-dozen*	(ane.two–v.9)	i.7	(ane.0–three.one)
Maladaptive Family Functioning CAs (0–ii)c	iii.five*	(ii.2–5.vi)	–	–	–	–	two.8*	(i.vii–4.eight)	two.2*	(ane.3–three.8)
Prior mental disorders (0–eight)d	i.8*	(ane.5–2.ii)	–	–	–	–	–	–	1.viii*	(1.5–2.3)
F(7,487), (fifteen,479), (17,477), (18,476) e			v.half-dozen*	p<.001	seven.6*	p<.001	11.4*	p<.001	11.1*	p<.001

Model i

Notwithstanding, sex was the merely socio-demographic that remained meaning in a multivariate model that included all the socio-demographics (Table 2, Model i). Nosotros after elaborated that model to include a methodological control for number of years between respondent age at the time of unexpected decease and age at interview to investigate the possibility of time-related retrieve bias, but that association was non-significant (OR 1.1; 95% CI 0.9–1.3).

Model 2

The respondent's relationship to the deceased was a significant predictor of PTSD (F_iv,490=12.half dozen, p<.001) in the model that added characteristics of the decease to the socio-demographic predictors (Tabular array 2, Model two), with highest odds of PTSD associated with death of the respondent'southward spouse (OR nine.half dozen; 95% CI iv.1–22.3) or son or daughter (OR 8.7; 95% CI 4.ii–18.0) followed by death of whatsoever other child (OR iv.2; 95% CI 1.7–10.2) and of the respondent's parent (OR 2.ii; 95% CI 1.1–iv.4) compared to others. Crusade of decease was non a significant predictor (F_3,491=0.8, p=.49). The respondent's perception that he/she could have washed something to prevent the death was besides a significant predictor (OR 2.8; 95% CI 1.2–6.6).

Model three

Preliminary assay constitute 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 plant to be significantly inter-correlated in an exploratory cistron 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.vi; 95% CI 1.two–v.9 per TE in the range 0–3). (Tabular array 2, Model 3) Preliminary analysis besides showed that Maladaptive Family Functioning CAs predicted PTSD related to unexpected death (detailed results are bachelor on asking), while further assay showed that these gross associations were due to iii particular CAs –parental mental illness, parental alcohol abuse, sexual abuse (OR two.8; 95% CI 1.seven–4.eight per TE in the range 0–2). The respondent'due south perception that he/she could have washed something to prevent the decease was not-pregnant 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 (ten of them significant at the .05 level) when considered one at a time, but that few remained pregnant in a multivariate model due to high comorbidity among the disorders. Farther analysis (Tabular array 2, Model four) then showed that the most parsimonious characterization of these articulation associations was provided by a blended variable that summed the number of feet disorders (0–3+), ADHD, and number of substance disorders (0–two) (OR 1.eight; 95% CI 1.5–2.3 per disorder in the range 0–eight).

Forcefulness and consistency of overall model predictions

Estimated AUC based on twenty replicates of 10-fold cross-validated predictions (equally described in the Methods) was .eighty in the total sample and .74–.86 in subsamples defined past respondent sex, age, and education. (Figure 1) The v% of respondents with highest predicted hazard included 30.6% of all cases of UD-related PTSD. This is six times the proportion expected past hazard. (Tabular array 3) Subgroup values of this concentration of risk ranged from 36.viii% among those with high/high-boilerplate didactics to xiv.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.half dozen% among respondents from low or middle income countries to 18.2% among respondents from high income countries.

AUC of PTSD model, full sample and by selected sub-groups, "Unexpected decease of a loved 1", weighted analysis

Notation. "Older (top one-half of age range)" = 30+ years old; "Younger (bottom half of age range)" < xxx years old. "Higher education" = high and loftier-average; "Lower education" = low and low-average.

Table three

Concentration of chance and positive predictive value of observed PTSD among the 5% of respondents assessed for PTSD after randomly selected unexpected death of a loved 1 with highest predicted risk of PTSD in the total sample and stratified by subgroups

	Faux samplea (n = 56,260)				Observed sampleb (north = 2,813)
	Concentration of risk		Positive Predictive Value		Concentration of risk		Positive Predictive Value
	% PTSD	(SE)	% PTSD	(SE)	% PTSD	(SE)	% PTSD	(SE)
I. Total	thirty.half dozen	(6.two)	25.3	(5.3)	53.vii	(6.v)	37.2	(five.9)
II. State income
Loftier	26.7	(iv.3)	xviii.2	(iii.2)	50.5	(vii.8)	37.vii	(7.6)
Low or heart	34.vi	(xi.four)	36.vi	(11.1)	57.0	(10.3)	36.8	(8.9)
III. Historic period
30+ years old	35.vii	(6.5)	22.0	(3.two)	61.1	(viii.two)	35.five	(half-dozen.1)
< xxx years old	25.0	(12.0)	32.8	(14.8)	45.half dozen	(10.6)	40.0	(ten.7)
Iv. Gender
Male	14.vii	(4.0)	22.half-dozen	(ix.7)	48.2	(xv.0)	42.5	(15.2)
Female	35.2	(7.six)	25.6	(5.8)	55.three	(seven.two)	36.1	(six.i)
V. Educational activity
Depression or low-average	24.6	(5.4)	22.9	(five.six)	45.0	(9.two)	27.5	(vii.1)
High or high-average	36.8	(10.7)	27.ii	(8.iii)	62.7	(viii.iii)	fifty.5	(viii.half-dozen)

DISCUSSION

The study has a number of limitations. Outset, although prospective evidence suggests that retrospective reports of TEs are valid (Dohrenwend et al., 2006), respondents with PTSD may have been biased towards higher remember 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 forestall the death, inducing the significant positive association betwixt that "predictor" and 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), merely 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 pocket-size, reducing statistical power to comport out subtle analyses. In particular, with only 252 respondents meeting criteria for PTSD and twenty predictors, the resulting 12.half-dozen events-per-variable (EPV) ratio, well in a higher place the 10.0 EPV recommended to avert biased OR estimates in an condiment model (Peduzzi, Concato, Kemper, Holford, & Feinstein, 1996), did not permit us to consider interactions of trauma characteristics with pre-existing vulnerabilities or other interactions. Fifth, the WMH interview schedule was adult earlier DSM-5 criteria for persistent complex bereavement disorder (PCBD; American Psychiatric Association, 2013) were codification. 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 report 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 plant this to average 5.2%, which is somewhat higher than the 4.0% hateful 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, just the higher hateful prevalence than for other TEs emphasizes the public health importance of UD-related PTSD (Atwoli et al., 2013; Breslau et al., 1998; 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 prove well-nigh 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 human 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 well-nigh our lack of knowledge well-nigh 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 Clan, 2000) permitted unexpected death to authorize every bit a potentially traumatic event for PTSD, DSM-5 (American Psychiatric Clan, 2013) developed a more than stringent threshold for criterion A1, requiring that in cases of bodily or threatened death of a family member or friend, the effect(s) must have been straight witnessed, violent, or adventitious. The WMH interview did not inquire about the respondent witnessing the decease, making it impossible for us to know if the UD qualified as a DSM-5 TE. All the same, PTSD symptoms can occur after non-violent/non-witnessed decease (Zisook et al., 1998) and this narrowing of the definition of qualifying death in DSM-v has been questioned (Friedman, 2013; Keyes et al., 2014; Larsen & Pacella, 2016). It is relevant to this fence that our analysis found that specific manner of death of a loved one has piddling touch on the gamble of subsequent DSM-IV PTSD. This is true, furthermore, fifty-fifty though some of the deaths reported were non "unexpected" in the sense that they were reportedly due to physical illnesses of some elapsing, although the verbal time of death might have been unexpected (eastward.thou., a relative known to have merely a relatively brusk time to alive only seemingly in stable status of a sudden dropping expressionless at a holiday dinner).

Perhaps the about striking result in our report was that xxx.vi% of people who experienced UD-related PTSD were among the 5% of respondents with highest predicted hazard scores in our cross-validated model. This upshot is broadly consequent with other contempo studies showing that PTSD tin be predicted with skilful accuracy using predictor data collected in the immediate backwash of trauma (Galatzer-Levy, Karstoft, Statnikov, & Shalev, 2014; Karstoft et al., 2015; Kessler et al., 2014). Information technology is noteworthy that the high concentration of take chances of PTSD we found was based on a replicated cross-validated simulation designed to accommodate 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).

Determination

Unexpected expiry 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 bear witness suggests that UD-related PTSD could be predicted with expert accuracy from data bachelor presently after the death, although this show is based on retrospective information and needs to be confirmed prospectively. These findings emphasize that UD is a major public health effect and suggest that screening assessments might be useful in identifying high-take chances individuals for early interventions.

Supplementary Fabric

appendix tables

Acknowledgments

The World Health System Globe Mental Health Survey collaborators are Tomasz Adamowski, PhD, Physician, Sergio Aguilar-Gaxiola, MD, PhD, Ali Al-Hamzawi, MD, Mohammad Al-Kaisy, MD, Abdullah Al Subaie, MBBS, FRCP, Jordi Alonso, Doctor, PhD, Yasmin Altwaijri, MS, PhD, Laura Helena Andrade, Md, PhD, Lukoye Atwoli, Dr., 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, Medico, PhD, Graca Cardoso, MD, PhD, Alfredo H. Cia, Physician, 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, Physician, PhD, David D. Ebert, PhD, Sara Evans-Lacko, PhD, John Fayyad, MD, Fabian Fiestas, MD, PhD, Silvia Florescu, Medico, PhD, Barbara Forresi, PhD, Sandro Galea, DrPH, Dr., MPH, Laura Germine, PhD, Stephen East. Gilman, ScD, Dirgha J. Ghimire, PhD, Meyer D. Glantz, PhD, Oye Gureje, PhD, DSc, FRCPsych, Josep Maria Haro, Doctor, MPH, PhD, Yanling He, Md, Hristo Hinkov, Physician, Chi-yi Hu, PhD, MD, Yueqin Huang, Doctor, MPH, PhD, Aimee Nasser Karam, PhD, Elie G. Karam, Doctor, Norito Kawakami, MD, DMSc, Ronald C. Kessler, PhD, Andrzej Kiejna, Dr., PhD, Karestan C. Koenen, PhD, Viviane Kovess-Masfety, MSc, MD, PhD, Carmen Lara, Medico, PhD, Sing Lee, PhD, Jean-Pierre Lepine, Doc, Itzhak Levav, MD, Daphna Levinson, PhD, Zhaorui Liu, MD, MPH, Silvia S. Martins, Doc, 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. White potato, DrPH, Fernando Navarro-Mateu, MD, PhD, Matthew Chiliad. Nock, PhD, Siobhan O'Neill, PhD, Mark Oakley-Browne, MB, ChB, PhD, J. Hans Ormel, PhD, Beth-Ellen Pennell, MA, Marina Piazza, MPH, ScD, Stephanie Pinder-Amaker, PhD, Patryk Piotrowski, MD, PhD, Jose Posada-Villa, MD, Ayelet 1000. Ruscio, PhD, Kate M. Scott, PhD, Vicki Shahly, PhD, Tim Slade, PhD, Hashemite kingdom of jordan Westward. Smoller, ScD, Doc, Juan Carlos Stagnaro, Dr., 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 Establish 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. Nosotros thank the staff of the WMH Data Collection and Information Analysis Coordination Centres for assist with instrumentation, fieldwork, and consultation on data analysis.

The São Paulo Megacity Mental Wellness Survey is supported by the State of São Paulo Research Foundation (FAPESP) Thematic Project Grant 03/00204-3. The Bulgarian Epidemiological Report of mutual mental disorders EPIBUL is supported past the Ministry building 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 past the Eye 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; 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 Inquiry on Psychiatric and Neurological Diseases and Mental Health (H13-SHOGAI-023, H14-TOKUBETSU-026, H16-KOKORO-013) from the Nihon Ministry of Health, Labour and Welfare. The Lebanese Evaluation of the Burden of Ailments and Needs Of the Nation (L.E.B.A.North.O.Due north.) is supported by the Lebanese Ministry building of Public Health, the WHO (Lebanon), National Institute of Wellness/Fogarty International Center (R03 TW006481-01), anonymous individual 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 Republic of ireland Study of Mental Health was funded by the Wellness & 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 written report projects "Policies in Mental Wellness Area" and "National Study regarding Mental Health and Services Use" were carried out by National School of Public Health & Wellness Services Management (quondam National Institute for Enquiry & Development in Wellness), with technical support of Metro Media Transilvania, the National Plant of Statistics-National Centre for Training in Statistics, SC. Cheyenne Services SRL, Statistics Netherlands and were funded past Ministry building of Public Wellness (one-time Ministry of Health) with supplemental back up of Eli Lilly Romania SRL. The South Africa Stress and Health Written report (SASH) is supported by the US National Establish of Mental Health (R01-MH059575) and National Institute of Drug Abuse with supplemental funding from the South African Department of Health and the Academy of Michigan. Dr. Stein is supported by the Medical Research Council of South Africa (MRC). The Psychiatric Research to Full 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 east Investigación Sanitarias (FFIS) of Murcia. The Ukraine Comorbid Mental Disorders during Periods of Social Disruption (CMDPSD) study is funded by the US National Constitute of Mental Health (RO1-MH61905). The Usa 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 Assistants (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, assay, estimation of results, or preparation of this paper. The views and opinions expressed in this written report are those of the authors and should not be construed to correspond the views of the sponsoring organizations, agencies, or governments.

A complete listing of all within-country and cross-national WMH publications can be found at http://www.hcp.med.harvard.edu/wmh/.

Footnotes

Disharmonize 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 every bit a consultant with Servier, Lundbeck, Lundbeck Found, AstraZeneca and Naurex. In the by three years, Dr. Kessler received back up for his epidemiological studies from Sanofi Aventis, was a consultant for Johnson & Johnson Health and Prevention, and served on an advisory board for the Johnson & Johnson Services Inc. Lake Nona Life Projection. Dr. Kessler is a co-possessor of DataStat, Inc., a market inquiry business firm that carries out healthcare research. The other authors written report no biomedical financial interests or potential conflicts of interest relevant to this manuscript.

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5661943/

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