Parenting through grief: A cross-sectional study of recently bereaved adults with minor children

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Abstract

Background:

Grieving adults raising parentally-bereaved minor children experience persistently elevated symptoms of depression and grief. However, the factors associated with their mental health outcomes are not well understood.

Aim:

To investigate the psychosocial and demographic characteristics associated with grief distress and depressive symptom severity in bereaved adults with minor children.

Design:

Cross-sectional, web-based survey.

Setting/participants:

Eight hundred forty-five bereaved adults raising minor (age <18 years) children who had experienced the death of a co-parent. Primary outcomes were grief distress (Prolonged Grief Disorder-13), depressive symptoms (Patient-Reported Outcomes Measurement Information System-Depression), and widowed parenting self-efficacy (WPSES).

Results:

Mean grief scores were 33.5; mean depression scores were 58.3. Among the 690 individuals more than 6 months bereaved, 132 (19.3%) met criteria for prolonged grief disorder. In adjusted models, participants reporting higher grief scores were more recently bereaved, identified as mothers, non-Caucasian, had lower education and income, and had not anticipated their co-parent’s death. The statistical modeling results for depression scores were similar to grief scores except that depression was not associated with anticipation of co-parent death. Parents reporting lower WPSES scores had higher grief and depression scores. Retrospective assessments of more intense parenting worries at the time of co-parent death were also associated with higher grief and depression scores.

Conclusions:

For bereaved adults with minor children, unanticipated co-parent death was linked with higher grief distress but not symptoms of depression. Addressing parenting concerns may represent a common pathway for improving the mental health of parentally-bereaved families.

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Most cited references 48

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An interactive web-based dashboard to track COVID-19 in real time

Ensheng Dong, Hongru Du, Lauren Gardner (2020)

In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.

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Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS®): depression, anxiety, and anger.

, Steven P Reise, Nicholas Stover … (2011)

The authors report on the development and calibration of item banks for depression, anxiety, and anger as part of the Patient-Reported Outcomes Measurement Information System (PROMIS®). Comprehensive literature searches yielded an initial bank of 1,404 items from 305 instruments. After qualitative item analysis (including focus groups and cognitive interviewing), 168 items (56 for each construct) were written in a first person, past tense format with a 7-day time frame and five response options reflecting frequency. The calibration sample included nearly 15,000 respondents. Final banks of 28, 29, and 29 items were calibrated for depression, anxiety, and anger, respectively, using item response theory. Test information curves showed that the PROMIS item banks provided more information than conventional measures in a range of severity from approximately -1 to +3 standard deviations (with higher scores indicating greater distress). Short forms consisting of seven to eight items provided information comparable to legacy measures containing more items.

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Prolonged Grief Disorder: Psychometric Validation of Criteria Proposed for DSM-V and ICD-11

Holly G. Prigerson, Mardi J. Horowitz, Selby Jacobs … (2009)

Introduction Bereavement is a universal experience to which most individuals adequately adjust. Nevertheless, numerous studies have shown that bereaved individuals have higher rates of disability and medication use than their nonbereaved counterparts [1]–[7], and are themselves at heightened risk of death [8]–[11]. The excess morbidity and mortality is likely to be concentrated in a grief-stricken few. The challenge has been to identify vulnerable bereaved individuals so that interventions could reduce their risk of adverse outcomes. Following a major loss, such as the death of a spouse, a noteworthy minority of bereaved individuals experiences “a clinically significant behavioral or psychological syndrome or pattern that occurs in an individual and that is associated with present distress or disability” [12]. These are the requirements for meeting the definition of a mental disorder in the Diagnostic Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) [12]. Nevertheless, the DSM-IV excludes grief as a disorder on the grounds that it is “an expectable and culturally sanctioned response to a particular event” [12]. In the DSM-IV, bereavement is classified as a “V” code; that is, an “other condition that may be a focus of clinical attention” [12]. Similarly, in the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) [13], bereavement is classified as a “Z” code, which refers to “occasions when circumstances other than a disease or injury result in an encounter or are recorded by providers as problems or factors that influence care” [14]. The DSM-IV and ICD-10 focus on the distinction between “normal” grief and major depressive disorder (MDD), but neglect to acknowledge that grief, per se, may be pathological. Studies have shown that symptoms denoting complicated, or prolonged, grief are distinguishable from symptoms of uncomplicated grief [15]–[18] and that only the former are associated with significant impairment [15]–[25]. The aim of the present study is to validate criteria for prolonged grief disorder (PGD) proposed for inclusion in DSM-V and ICD-11. The justification for validating criteria for PGD and proposing inclusion in DSM-V and ICD-11 lies in the distinctive phenomenology, etiology, course, response to treatment, and adverse outcomes associated with PGD symptoms. PGD symptomatology—variously referred to as “complicated grief” (CG) [15],[17]–[20],[22],[25]–[28], “traumatic grief” (TG) [21],[23],[24],[29], and complicated grief disorder (CGD) [22]—have repeatedly been shown to be different from the symptoms of other DSM-IV and ICD-10 disorders (e.g., MDD). For example, in studies of bereaved individuals from a variety of different countries, yearning loads highly on the grief factor, but not on depression or anxiety factors, whereas sadness loads highly only on a depression factor, and feeling nervous and worried loads highly only on an anxiety factor [15],[19],[20],[26],[28]–[31]. A study of negative cognitions among bereaved individuals found that being overwhelmed by the loss (i.e., “If I would fully realize what the death of ___ meant, I would go crazy”) was a cognition specific to PGD, but not depression [32]. The distinction between the symptoms of grief and depressive symptoms found in bereaved individuals has also been shown in advanced cancer patients [30] and caregivers of nursing home residents with advanced dementia [31],[33]. The set of risk factors and clinical correlates of PGD includes a history of childhood separation anxiety [34], controlling parents [35], parental abuse or death [36], a close kinship relationship to the deceased (e.g., parents) [37],[38], insecure attachment styles [39], marital supportiveness and dependency [39],[40], and lack of preparation for the death [41],[42]—all suggesting that attachment issues are salient in creating a vulnerability to PGD. For example, we find that feelings of emotional dependency on the dying patient is associated with symptoms of grief, but not with symptoms of depression in patient caregivers [39] and recently bereaved persons [40]. We have also found that childhood separation anxiety uniquely predicts PGD, but not MDD, posttraumatic stress disorder (PTSD), or generalized anxiety disorder (GAD) following bereavement later in life [34]. The identified grief symptoms have been shown not to relate to the changes of electroencephalographic (EEG) sleep physiology associated with MDD [43]. Most recently, a functional magnetic resonance imaging (fMRI) study by O'Connor et al. [44] has demonstrated that only patients with complicated grief showed reward-related neural activity in the nucleus accumbens in response to reminders of the deceased. The nucleus accumbens cluster “was positively associated with yearning, but not with time since death, participant age, or positive/negative affect” [44]. Taken together, these findings suggest distinct clinical correlates of grief symptoms relative to depressive symptoms. PGD symptoms also demonstrate incremental validity in that they are associated with elevated rates of suicidal ideation and attempts, cancer, immunological dysfunction, hypertension, cardiac events, functional impairments, hospitalization, adverse health behaviors, and reduced quality of life in adults [19],[21],[24],[25],[30],[45] and in children and adolescents [46], after controlling for the effects of depression and/or anxiety. In a Swedish sample of bereaved parents 4–9 y after the death of their child from cancer, parents with unresolved grief were at risk for long-term mental and physical impairments, increased health service use, and increased sick leave over and above the effects of depression and anxiety [47]. These findings highlight the enduring nature of bereavement-related distress and disability, and the societal consequences of unresolved grief. The course and response to treatment of PGD differ from those of normal grief [48],[49] and depression [48]–[52]. Tricyclic antidepressants alone and with interpersonal psychotherapy have proven ineffective relative to placebo for the reduction of PGD symptoms [50]–[52]. By contrast, randomized, controlled trials of psychotherapy designed specifically for PGD have demonstrated efficacy for PGD symptom reduction [53],[54]. The efficacy of a PGD-specific treatment highlights the benefits of an accurate diagnosis. Although the results above suggest that symptoms of grief constitute a syndrome that operationally defines a mental disorder, no agreed upon and tested diagnostic algorithm for PGD exists. Psychiatrists such as Lindemann [55], Parkes [56], Raphael [57], Horowitz [22], and Jacobs [58] have noted the suffering associated with intense and/or chronic mourning. Nevertheless, no explicit criteria developed from a consensus process have been assessed and then tested in a community-based sample of bereaved individuals. The absence of explicit and agreed upon (i.e., standardized) criteria for PGD has hampered efforts for its inclusion in the DSM and ICD systems. The establishment of standardized criteria for PGD would enable researchers to investigate the prevalence, risk factors, outcomes, neurobiology, prevention, and treatment of this disorder. Such criteria would also assist clinicians in the accurate detection and treatment, as well as reimbursement for treatment, of PGD. As a first step toward the development of consensus criteria for PGD, we convened a group of experts in bereavement, mood and anxiety disorders, and psychiatric nosology to review the evidence justifying the development of diagnostic criteria [23]. Following the panel's conclusion that the evidence merited the development of a diagnostic algorithm for a grief disorder, they engaged in a 2-d workshop culminating in the formulation of consensus criteria. A preliminary testing of the criteria analyzed the most relevant, yet incomplete, data available at the time—data lacking the full set of proposed consensus criteria and an independent rating to diagnose PGD. Results of this preliminary report supported the sensitivity and specificity of the initially proposed algorithm [23]. Here, we report the results of a field trial designed specifically to develop and evaluate diagnostic algorithms for PGD based on symptoms proposed by the consensus panel. The aim of this study was to establish the psychometric validity of, and propose criteria for, a new syndrome, PGD. Methods Sample Data were obtained for the Yale Bereavement Study (YBS) (e.g., [25],[34]–[36],[40],[41],[45],[48]), a National Institute of Mental Health (NIMH)-funded (MH56529) investigation to conduct a field trial of consensus criteria [23] for PGD. The YBS was a longitudinal, interview-based study of community-dwelling bereaved individuals in Connecticut. The YBS was approved by the institutional review boards (IRBs) of all participating sites and was in accordance with Health Insurance Portability and Accountability Act (HIPAA) regulations. Recruitment involved locating family survivors bereaved 6 mo or less found on contact lists of the Greater Bridgeport/Fairfield American Association of Retired Persons (AARP) Widowed Persons Service (WPS), a community-based outreach program. The contact lists provided names of recently widowed persons who a volunteer widowed person would contact to describe the WPS program. Fewer than 5% of those contacted participated in any WPS program; the lists included those approached, but not necessarily actively involved in, the WPS. A comparison between vital records and the WPS contact list revealed that WPS listings provide an unbiased and comprehensive ascertainment of recently widowed people. Participants were also recruited from pastoral care offices in the New Haven area. Participants from this alternative source (117/317 = 37.0% of study participants) did not differ significantly from WPS participants (200/317 = 63.0% of study participants) on gender, income, education, race/ethnicity, or quality of life, but they were younger than WPS participants (p = 0.05) (mean age = 59.7 y, standard deviation [SD] = 16.4 versus 63.2 y, SD = 11.5 y, respectively). Of the 575 potential participants contacted, 317 (55.1%) agreed to participate. Reasons for nonparticipation included reluctance to participate in research (n = 11; 4.3%); being too busy (n = 46; 17.8%) or too upset (n = 27; 10.5%); “doing fine” (n = 23; 8.9%); “not interested” or “no reason” (n = 145; 56.2%); and “other” reasons (n = 6; 2.3%). Nonparticipants were more likely to be male (37.2% versus 25.9%, p 0.90; test-retest reliability coefficient = 0.80 [15]) and to possess criterion validity [15],[21],[24],[25],[45]. Based on prior work [23],[59], a symptom was considered present if rated 4 or 5, and absent if rated 1, 2, or 3, on its five-point scale. Interviewers were trained by project investigators (HGP, SJC) to provide a separate evaluation of whether or not the participant represented a current “case” of PGD. Assessment of Psychiatric Disorders Psychiatric disorders were assessed using the Structured Clinical Interview for DSM-IV (SCID Non-Patient Version) [60]. The rater-administered SCID assessed criteria for DSM-IV anxiety disorders (GAD, PTSD, and panic disorder [PD]) and mood disorders (MDD). Research has supported the reliability and validity of SCID diagnoses [61]. Assessment of Additional Outcomes Positive responses to one or more of the four Yale Evaluation of Suicidality [25] screening questions were categorized as having suicidal ideation. The Established Populations for Epidemiological Studies of the Elderly [62] measured performance of activities of daily living [63] and physical functioning [64]. Individuals with at least “some difficulty” with at least one of the 14 tasks (e.g., bathing) were considered functionally impaired in order to make the measure sensitive to impairment in a highly functioning sample. Scores less than 5 (below the lowest quartile) on the Medical Outcomes Short-Form [65] indicated inferior quality of life. Analyses and Results The psychometric validation of diagnostic criteria for PGD proceeded through a cumulative series of analyses, with each phase in the overall analysis having a distinct aim. In Phase 1 of the analysis, the aim was to limit the set of candidate symptoms for PGD to those that were informative and unbiased. In Phase 2 of the analysis, the goal was to construct an objective, reliable, valid symptom criterion standard for PGD by which to evaluate alternative diagnostic algorithms for meeting symptom criteria for PGD. The aim of Phase 3 of the analysis was to identify a specific, optimum diagnostic algorithm for meeting symptom criteria for PGD among a large set of candidate algorithms. Phase 4 of the analysis was designed to evaluate the predictive validity for temporal subtypes of meeting the optimal symptom criteria for PGD as an empirical means to inform the specification of a “timing criterion” for the diagnosis of PGD. In Phase 5, the goal was to propose a complete set of “DSM-style” diagnostic criteria based on results of the preceding phases in the analysis. Phase 6 of the analysis evaluated the predictive validity of the final, complete, proposed criteria for PGD. Phase 1: deriving a set of informative, unbiased symptoms of PGD IRT [66], item information function (IIF) analysis, and differential item functioning (DIF) analysis were used to evaluate candidate symptoms for PGD assessed 0–12 mo post-loss. IIF analysis was used to evaluate the amount of information about the prolonged grief (PG) “attribute” (underlying construct) provided by each of 22 dichotomous candidate symptoms for PGD. Consistent with the use of IRT to construct a one-dimensional scale for PG, Cattell's scree test [67] indicated that grief, as measured by these 22 symptoms, is one-dimensional. Figure 1 presents item information functions for these 22 symptoms derived from a two-parameter logistic (2-PL) item response model (IRM). Within the framework of IRT, information for a given value of the latent PG attribute is inversely related to its conditional standard error of measurement. Greater information implies lower measurement error, and greater measurement precision, for PG. Six symptoms with maximum “peak” information less than 20% of that of the most informative symptom were considered to be relatively uninformative and removed from further consideration as possible symptoms for assessing and diagnosing PGD. DIF analysis of between-group differences in item location parameters was used to evaluate potential biases in the assessment of the remaining 16 informative, candidate symptoms for PGD with respect to age (less than 65 y versus greater than or equal to 65 y), gender (male versus female), education (beyond versus not beyond high school), relationship to the deceased (spouse versus nonspouse), and time from loss (0–6 versus 6–12 mo post-loss). Figure 2 displays item characteristic curves by group, spouse, and nonspouse, for two items eliminated from consideration as possible symptoms for assessing and diagnosing PGD due to evidence of DIF using a 16-item 2-PL IRM. A total of four of 16 informative symptoms were found to be biased with respect to time from loss, gender, and/or relationship to the deceased. Table 1 provides a summary of results for these IRT IIF and DIF analyses of candidate symptoms for assessing and diagnosing PGD. 10.1371/journal.pmed.1000121.g001 Figure 1 Relative item information as a function of the prolonged grief attribute for 22 candidate symptoms for PGD. IRT IIF analysis of 22 binary candidate symptoms for PGD was performed using a 2-PL IRM. This figure displays item information as a function of the PG attribute for all 22 of these symptoms included in this IRM, relative to the maximum information for the most informative symptom, “inability to care about others since the death.” The horizontal line in the figure represents the standard used to discriminate between 16 informative candidate symptoms retained for further analysis, and six uninformative candidate symptoms excluded from further analysis (as indicated in Table 1). 10.1371/journal.pmed.1000121.g002 Figure 2 Differential item functioning for two biased symptoms. IRT DIF analysis of candidate symptoms for PGD was performed with respect to age (less than 65 y versus greater than or equal to 65 y), gender (male versus female), education (beyond versus not beyond high school), relationship to the deceased (spouse versus nonspouse), and time from loss (0–6 mo versus 6–12 mo post-loss). This figure displays IRT item characteristic curves (ICCs) for two symptoms found to differ with respect to relationship to the deceased (spouse versus nonspouse). The horizontal error bar associated with each ICC represents the standard error in the estimate of the location of the ICC with respect to the PG attribute. Of 16 informative symptoms examined, four symptoms displayed DIF and were excluded from further analysis (as indicated in Table 1). 10.1371/journal.pmed.1000121.t001 Table 1 Evaluation of candidate symptoms for PGD (n = 287). Candidate PGD Symptom Rate (%) IRT IIF Analysisa IRT DIF Analysisb I max c Θmax d Sex Spouse Time Inability to care about others since the death 6.6 1.00 1.70 Biased Yearning for, or preoccupation with, deceased 68.3 0.94 −0.53 Life empty, meaningless without deceased 34.8 0.93 0.46 Stunned, dazed, or shocked about the death 19.2 0.58 1.07 Trouble accepting the death 32.7 0.56 0.56 Feel part of you died along with the deceased 37.6 0.49 0.41 Difficulty moving on with life without deceased 18.1 0.46 1.17 Sense of numbness since the death 13.6 0.46 1.41 Future holds no meaning without the deceased 14.6 0.38 1.40 Biased Hard for you to trust others since the death 7.0 0.36 2.00 Avoid reminders of deceased 12.5 0.26 1.67 Survivor guilt 8.4 0.25 2.04 Loneliness as a result of the death 57.1 0.24 −0.26 Biased Biased Biased Lost sense of security since the death 23.3 0.23 1.09 Biased Bitterness or anger related to the death 25.1 0.23 1.01 On edge, jumpy since the death 11.5 0.20 1.88 Envious of others who have not lost someone close 7.0 0.16 2.51 Memories of the deceased upset you 22.6 0.14 1.31 Drawn to places, things associated with deceased 31.0 0.14 0.86 Disturbed sleep since the death 23.3 0.13 1.28 The death has shattered your world view 28.6 0.13 1.02 Lost sense of control since the death 16.4 0.10 1.93 Relatively uninformative (I max<0.20) or biased symptoms are displayed in bold font. All others are informative, unbiased symptoms and were retained for further analysis. a IRT IIF analysis was performed using all 22 symptoms, showing 16 to be informative (I max≥0.20). b IRT DIF analysis was restricted to 16 relatively informative symptoms, showing four to be biased. c I max represents maximum item information relative to that of “inability to care about others….” d Θmax represents location of I max for the item along the PG attribute scale. The following 12 informative, unbiased ICG-R symptoms were retained for consideration in a diagnostic algorithm: yearning; avoidance of reminders of the deceased; disbelief or trouble accepting the death; a perception that life is empty or meaningless without the deceased; bitterness or anger; emotional numbness or detachment from others; feeling stunned, dazed or shocked; feeling part of oneself died along with the deceased; difficulty trusting others; difficulty moving on with life; on edge or jumpy; survivor guilt (Cronbach's α = 0.82). Phase 2: deriving a criterion standard for assessing diagnostic algorithms for symptoms of PGD In the absence of an established, standard method for diagnosing PGD, there was a need to develop a criterion standard for “caseness” of PGD by which the performance of alternative algorithms for PGD could be evaluated. As a potential criterion standard for PGD, the rater determination of caseness of PGD had the advantage of reflecting experienced clinical judgment. However, rater assessments of PGD were subjective, were made without explicit reference to any established criteria, and were not always consistent with more objective, reliable assessments of PG as measured with IRM scores for the underlying PG attribute (i.e., raters assign PGD to some individuals with low scores, and not to others with high scores, on the PG attribute scale). It was decided that a criterion standard for caseness should be informed by clinical judgment, but should also be a function of PG symptom severity. Dichotomized IRM PG attribute scores, informed by rater assessments of PGD, would provide objective, reliable, valid criterion standard diagnoses for PGD. Scores from a 2-PL IRM for PG based on the 12 informative, unbiased symptoms were used to order individuals in terms of PG symptom severity. Agreement between rater and minimum-threshold PG attribute assessments of caseness of PGD was maximized to establish an optimum minimum threshold for caseness of PGD along this IRM scale. As illustrated in Figure 3, PGD “cases” determined by a minimum-threshold “cutoff” PG attribute score of approximately 1.0 had the greatest agreement (κ = 0.68) with cases determined by the rater assessments. An IRM PG attribute score above this minimum-threshold cutoff value became our criterion standard for PGD caseness. 10.1371/journal.pmed.1000121.g003 Figure 3 Agreement between rater diagnoses and dichotomized prolonged grief attribute score diagnoses of PGD as a function of cutoff PG attribute score for diagnosis. Dichotomized IRM PG attribute scores provide objective, reliable criterion standard diagnoses for PGD. This figure illustrates how rater diagnoses were used to establish a minimum-threshold cutoff PG attribute score for diagnosis of PGD (i.e., PG attribute score≥minimum-threshold cutoff PG attribute score). An optimal cutoff PG attribute score of 1 maximized agreement between rater diagnoses and dichotomized IRM PG attribute score diagnoses of PGD. Phase 3: identifying an optimal diagnostic algorithm for symptoms of PGD Based on consensus opinion of the previously mentioned expert panel [23], and confirmed by results showing yearning was the most common (68.3%) and most informative (I max = 0.94) of the 12 items and provided the maximum information and the lowest degree of severity (Θmax = −0.53), yearning was specified as a mandatory symptom. The analyses then sought to determine the number and combination of the remaining 11 (nonmandatory) symptoms in addition to yearning that would yield the most efficient (i.e., optimum balance between sensitive and specific) diagnosis for PGD with respect to our criterion standard. Combinatorics [68], the branch of mathematics that studies the number of different ways of arranging sets, was used to enumerate alternative sets of nonmandatory symptoms to construct alternative, candidate diagnostic algorithms for meeting the symptom criterion for PGD. Each of these diagnostic algorithms was specified in terms one common, mandatory symptom, yearning, a specific set of n other, nonmandatory symptoms, and some minimum number of nonmandatory symptoms within this set, k, which one must have to satisfy the symptom criterion for PGD. A total of 4,785 of these algorithms for meeting the symptom criterion for PGD were enumerated [i.e., the sum of (11 choose n×(n−3) for n = 5, 6, 7, 8, 9, where (11 choose n) represents the number of ways of choosing n of 11 nonmandatory symptoms, n ∈ {5, 6, 7, 8, 9}, and (n−3) represents the number of values of k ∈ {3, …, n−1}, considered for a given value of n] and subsequently evaluated with respect to the criterion standard. Algorithms requiring yearning and as few as three of five, and as many as eight of nine, additional symptoms were considered. Figure 4 displays results for a subset of the diagnostic algorithms considered. Each data point in Figure 4 represents the sensitivity and specificity of a unique diagnostic algorithm. The optimal, most efficient algorithm included yearning and at least five of the nine following symptoms: avoidance of reminders of the deceased; disbelief or trouble accepting the death; a perception that life is empty or meaningless without the deceased; bitterness or anger related to the loss; emotional numbness; feeling stunned, dazed, or shocked; feeling part of oneself had died along with the deceased; difficulty trusting others; difficulty moving on with life (sensitivity = 1.00; specificity = 0.99; positive predictive value = 0.94; negative predictive value = 1.00). The optimal algorithm displayed convergent validity with respect to the previously proposed diagnostic algorithm for PGD (κ = 0.68) and the rater diagnosis of PGD (κ = 0.68), and discriminant validity with respect to other mood and anxiety disorders (Φwith MDD = 0.36; PTSD = 0.31; GAD = 0.17). 10.1371/journal.pmed.1000121.g004 Figure 4 Alternative diagnostic algorithms for meeting symptom criteria for PGD. Each data point in this figure represents the performance, in terms of sensitivity and specificity with respect to a criterion standard for PGD, of a unique “DSM-style” diagnostic algorithm for meeting symptom criteria for PGD. Each algorithm is specified in terms of one common, mandatory symptom, yearning, a specific set of n other, nonmandatory symptoms, and some minimum number of nonmandatory symptoms within this set, k, which one must have to satisfy the symptom criterion for PGD. Based on the current data, the optimal, most efficient algorithm requires having yearning and at least five of the following nine symptoms: avoidance of reminders of the deceased; trouble accepting the death; a perception that life is empty or meaningless without the deceased; bitterness or anger related to the loss; emotional numbness; feeling stunned, dazed or shocked; feeling that part of oneself died along with the deceased; difficulty in trusting others; and difficulty moving on with life. Phase 4: evaluating the predictive validity for temporal subtypes of PGD Three subtypes of PGD were defined in terms of patterns of meeting diagnostic criteria for PGD at 0–6 and 6–12 mo post-loss: acute = meeting the symptom criteria for PGD at 0–6 mo, but not at 6–12 mo, post-loss; delayed = meeting the symptom criteria for PGD at 6–12 mo, but not at 0–6 mo, post-loss; and persistent = meeting the symptom criteria for PGD both at 0–6 and at 6–12 mo post-loss. In Table 2, we see that the acute temporal specification was not significantly associated with any of the examined outcomes evaluated 12–24 mo post-loss. Delayed was significantly (p<0.001) associated with suicidal ideation and poor quality of life. Persistent was significantly (p<0.01) associated with mental disorders (MDD, PTSD, or GAD), suicidal ideation, and poor quality of life. Delayed or persistent was significantly (p<0.05) associated with psychiatric disorders (MDD, PTSD, or GAD), suicidal ideation, functional disability, and poor quality of life. These results indicate that diagnoses of PGD before 6 mo post-loss do not effectively identify bereaved individuals at risk of long-term dysfunction, whereas delayed and persistent temporal subtypes do. 10.1371/journal.pmed.1000121.t002 Table 2 Mental health and functional consequences of meeting symptom criteria for PGD by temporal subtype. Outcome (12–24 Mo Post-Loss) Relative Risk for Outcome Associated with PGD Temporal Subtype: Acute (15/172 [8.7%])a Delayed (6/172 [3.5%])a Persistent (12/172 [7.0%])a Delayed or persistent (28/242 [11.6%])b RR 95% CI RR 95% CI RR 95% CI RR 95% CI MDD, PTSD, or GAD 1.54 (0.20–11.98) 3.86 (0.55–27.22) 11.58*** (4.41–30.43) 10.19*** (4.72–21.99) Suicidal ideation (n = 171:241)c 1.97 (0.64–6.09) 4.93*** (1.92–12.64) 3.29* (1.28–8.43) 4.44*** (2.62–7.53) Functional disability (n = 170:240)c 0.51 (0.18–1.45) 1.54 (0.73–3.25) 1.40 (0.79–2.50) 1.65** (1.16–2.34) Poor quality of life (n = 168:238)c 0.76 (0.20–2.89) 3.78*** (1.93–7.40) 2.58* (1.23–5.41) 3.17*** (2.03–4.95) Acute = meeting symptom criteria at 0–6 mo, but not at 6–12 mo, post-loss; Delayed = not meeting symptom criteria at 0–6 mo, but meeting symptom criteria at 6–12 mo post-loss; Persistent = meeting symptom criteria at 0–6 and 6–12 mo post-loss. a The denominator included those assessed at both 0–6 and 6–12 mo post-loss. b The denominator included all those assessed at 6–12 mo post-loss, regardless of the 0–6-mo post-loss assessment. c Sample sizes (n) varied due to missing data. The first number in the parenthesis represents n for those assessed at both 0–6 and 6–12 mo post-loss, the second number after the colon represents n for those assessed at 6–12 mo post-loss regardless of the 0–6-mo post-loss assessment. *p<0.05; **p<0.01; ***p<0.001. CI, confidence interval; RR, relative risk. Phase 5: proposing criteria for PGD To reduce further the likelihood of a false-positive diagnosis, a timing criterion (Criterion D) was added to specify that a diagnosis not be made until at least 6 mo have elapsed since the death. This would exclude the acute cases described above in which a person with initially high levels of grief in the first few months experiences declines in grief intensity at and beyond 6 mo post-loss. To be conservative in our diagnosis of PGD, we also added a requirement that the symptomatic distress be associated with functional impairment (Criterion E). The ultimate consensus criteria set for PGD proposed for DSM-V and ICD-11 appears in Table 3. Diagnoses of PDG based on these criteria demonstrated convergent validity with respect to the diagnostic algorithm proposed by Horowitz et al. [22] (κ = 0.69) and the rater diagnosis of PGD (κ = 0.52), and discriminant validity with respect to other mood and anxiety disorders (Φ with MDD = 0.48; PTSD = 0.23; GAD = 0.21). 10.1371/journal.pmed.1000121.t003 Table 3 Criteria for PGD proposed for DSM-V and ICD-11. Category Definition A. Event: Bereavement (loss of a significant other) B. Separation distress: The bereaved person experiences yearning (e.g., craving, pining, or longing for the deceased; physical or emotional suffering as a result of the desired, but unfulfilled, reunion with the deceased) daily or to a disabling degree. C. Cognitive, emotional, and behavioral symptoms: The bereaved person must have five (or more) of the following symptoms experienced daily or to a disabling degree: 1. Confusion about one’s role in life or diminished sense of self (i.e., feeling that a part of oneself has died) 2. Difficulty accepting the loss 3. Avoidance of reminders of the reality of the loss 4. Inability to trust others since the loss 5. Bitterness or anger related to the loss 6. Difficulty moving on with life (e.g., making new friends, pursuing interests) 7. Numbness (absence of emotion) since the loss 8. Feeling that life is unfulfilling, empty, or meaningless since the loss 9. Feeling stunned, dazed or shocked by the loss D. Timing: Diagnosis should not be made until at least six months have elapsed since the death. E. Impairment: The disturbance causes clinically significant impairment in social, occupational, or other important areas of functioning (e.g., domestic responsibilities). F. Relation to other mental disorders: The disturbance is not better accounted for by major depressive disorder, generalized anxiety disorder, or posttraumatic stress disorder. Phase 6: evaluating the predictive validity of the proposed criteria for PGD Among those not concurrently meeting DSM criteria for MDD, PTSD, or GAD (n = 215), PGD diagnoses assessed 6–12 mo post-loss (7/215 = 3.3%) were significantly (p<0.01) associated with psychiatric diagnoses (MDD, PTSD, or GAD), suicidal ideation, functional disability, and low quality of life 12–24 mo post-loss (see Table 4). Among those concurrently meeting DSM criteria for MDD, PTSD, or GAD (n = 27), PGD diagnoses 6–12 mo post-loss (10/27 = 37.0%) were significantly associated with psychiatric diagnoses (MDD, PTSD, or GAD) at 12–24 mo post-loss (relative risk = 2.38, p = 0.043). 10.1371/journal.pmed.1000121.t004 Table 4 Mental health and functional impairment at 12–24 mo post-loss associated with PGD among those not meeting DSM criteria for MDD, PTSD, or GAD at 6–12 mo post-loss (n = 215). Outcome (12–24 Mo Post-Loss) PGD Diagnosis (6–12 Mo Post-Loss) Yes (3.3%) No (96.7%) RR 95% CI MDD, PTSD, or GAD 28.6% 3.4% 8.49** (2.14–33.72) Suicidal ideationa (n = 214) 57.1% 10.1% 5.63*** (2.64–12.03) Functional disabilitya (n = 213) 71.4% 35.9% 1.99** (1.20–3.29) Poor quality of lifea (n = 210) 83.3% 14.7% 5.67*** (3.48–9.22) a Sample sizes (n) varied due to missing data. *p<0.05; **p<0.01; ***p<0.001. CI, confidence interval; RR, relative risk. Discussion Our results indicate that PGD meets DSM criteria for inclusion as a distinct mental disorder on the grounds that it is a clinically significant form of psychological distress associated with substantial disability. Findings from this field trial of consensus criteria for PGD confirm prior work demonstrating the distinctiveness of the symptoms of PGD (e.g., [15],[18]–[20],[22],[26],[27],[29]–[31]). The proposed diagnostic algorithm for PGD has quite incomplete overlap with established mental disorders commonly occurring among recently bereaved individuals (MDD, PTSD). Further, our results indicate that in the absence of mental disorders found in DSM-IV (e.g., MDD), the proposed algorithm for PGD predicts substantial dysfunction—impairment missed by the current psychiatric diagnostic system. Because standard treatments for depression have not always proven effective for the reduction of PGD [49]–[52], whereas psychotherapies designed specifically to ameliorate symptoms of PGD have demonstrated efficacy [53],[54], there exists a need for the accurate detection and specialized treatment of PGD. Although the YBS data may appear unrepresentative of the general US population, a comparison with US Census 2005 [48],[69],[70] data reveals similarities with the US widowed population. For example, the YBS sample was 73.7% female compared with 80.7% of the US widowed population and 95.3% white compared with 80.2% of the US widowed population. Like the population of US widowed individuals, the YBS sample is disproportionately female, white, and elderly. Compared with the US widowed population, however, the study participants were somewhat younger, more likely to be male, and a higher proportion was white and better educated. Future research should replicate the analyses in older, nonwhite, less-educated widowed samples. Although there is a need to confirm the results in nonwidowed bereaved persons, we consider widowhood following an older spouse's death from natural causes to be the prototypical case of bereavement. In the US, 84% of all deaths occur among individuals who are 65 y and over [71], and less than 7% of deaths are from unnatural causes (e.g., unintentional injuries, assault, suicide) [72]. Given that in later life one's spouse/partner is the person most likely to be adversely affected by the death, a sample of older widowed persons surviving the death of a spouse from natural causes provides an important sample in which to develop and test criteria for a bereavement-related mental disorder. In addition, the symptoms retained were only those proven to be invariant across gender, time from loss, and kinship groups (e.g., IRT DIF analysis removed items that performed differently based on whether or not the deceased was a spouse) and a distinct advantage of IRT is that it produces generalizable results regardless of sample characteristics [66]. Thus, the results are expected to be generalizable to most bereaved individuals. The generalizability of the results reported here is not intended to deny the value in further confirmation of the findings in nonwidowed, more traumatically bereaved, younger, less-educated, more male, and ethnically and geographically diverse samples, and the need to examine longer-term bereavement outcomes (e.g., 3, 5, and 10 y post-loss). Although the sample size may appear modest, the study was designed and appropriately powered to evaluate a wide range of potential diagnostic criteria (i.e., the first phases of the analyses used the full sample [n = 291]). The YBS PGD prevalence rate was obtained in a resilient community sample in which rates of mental illness were lower than those that have been reported in other bereavement studies (e.g., 9% for MDD compared with 22% in the first year of widowhood) [73]. The only analyses limited by statistical power would have been the predictive validity analyses. Here, we found large, statistically significant effects suggesting the conservative nature of our estimates of functional impairment associated with PGD. Study participants may have been less distressed than study nonparticipants. Given the relatively low rates of MDD in the YBS sample and that 10.5% refused participation in the YBS due to being “too upset,” the prevalence rate of PGD reported here may be an underestimate. In addition, our statistical power to detect significant effects of PGD on mental health and functional impairment outcomes would be lower than would have been the case if more distressed nonparticipants with PGD had been included in the study sample. Conclusion This report provides psychometric validation of a diagnostic algorithm for PGD. Although further validation work will, no doubt, be needed, we consider the evidence sufficient to justify PGD's serious consideration for inclusion in DSM-V and ICD-11. In light of the recent concerns about financial conflicts of interest in psychiatric research, especially that which involves pharmaceutical manufacturers, it is noteworthy that this study was federally funded by the US NIMH, and no part of this research was sponsored by producers of a potential therapeutic remedy for PGD. Although most bereaved individuals will eventually adapt to the loss of a significant other more or less successfully, a significant, identifiable minority will experience chronic and disabling grief. A PGD diagnosis has the potential to enhance the detection and effective treatment of a substantial cause of morbidity among persons who have experienced the loss of a significant other. The diagnosis and treatment of PGD offers the promise of reducing the personal and societal toll taken by prolonged grief.

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Journal

Journal ID (nlm-ta): Palliat Med

Journal ID (iso-abbrev): Palliat Med

Journal ID (publisher-id): PMJ

Journal ID (hwp): sppmj

Title: Palliative Medicine

Publisher: SAGE Publications (Sage UK: London, England )

ISSN (Print): 0269-2163

ISSN (Electronic): 1477-030X

Publication date (Electronic): 22 August 2021

Publication date (Print): December 2021

Volume: 35

Issue: 10

Pages: 1923-1932

Affiliations

[1 ]Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

[2 ]Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

[3 ]Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

[4 ]Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

[5 ]Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

[6 ]H. W. Odum Institute for Research in Social Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Author notes

[*]Eliza M Park, Departments of Psychiatry and Medicine, University of North Carolina at Chapel Hill, 170 Manning Drive, Campus Box #7305, Chapel Hill, NC 27599, USA. Email: leeza_park@ 123456med.unc.edu

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Eliza M Park https://orcid.org/0000-0002-2848-4989

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Publisher ID: 10.1177_02692163211040982

DOI: 10.1177/02692163211040982

PMC ID: 8637383

PubMed ID: 34423711

SO-VID: 57a5cd6f-6e32-495c-b5c7-baea3865f9f7

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