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. Author manuscript; available in PMC: 2013 Jan 14.
Published in final edited form as: Int J Geriatr Psychiatry. 2009 May;24(5):472–478. doi: 10.1002/gps.2140

What cognitive functions are associated with passive suicidal ideation? Findings from a national sample of community dwelling Israelis

Liat Ayalon 1,*, Howard Litwin 2
PMCID: PMC3544336  NIHMSID: NIHMS431466  PMID: 18837056

SUMMARY

Purpose

To identify the specific cognitive domains associated with passive suicidal ideation (e.g. thoughts of being better off dead).

Methods

A cross sectional, national based study of 1,712 individuals over the age of 50. Outcome measure, passive suicidal ideation, was evaluated by the question, ‘in the past month, have you felt that you would rather be dead?’, taken from the Euro-D. Cognitive domains assessed were time orientation, verbal learning, verbal recall, word fluency, and arithmetic.

Results

After adjusting for demographic and clinical information, those reporting passive suicidal ideation were significantly more likely to have impaired performance on the time orientation task. None of the other cognitive domains were associated with passive suicidal ideation.

Conclusions

Clinicians working with older adults need to be aware not only of demographic and clinical information, but also of cognitive functioning and more specifically, time orientation, as a potential determinant of passive suicidal ideation. Possibly, cognitive domains that are less affected by education and prior learning (e.g. time orientation) have a unique association with passive suicidal ideation.

Keywords: suicidality, risk, death, epidemiology, affect

INTRODUCTION

Research has shown that older adults are at a substantially greater risk of dying by suicide than younger adults (Center for Disease Control, 2007), with the ratio of actual death as a result of suicide to attempted suicide being highest among this age group (Beck et al., 1999). Death wishes (i.e. both passive, such as thoughts of being better off dead and active, such as thoughts of taking one’s life), suicide attempts, and complete suicide often co-occur, but should not be equated (Paykel et al., 1974). Yet, death wishes are considered a red flag that necessitates clinical intervention. Hence, it is important to identify potential signs and indicators of death wishes in this high-risk population of older adults.

Common risk factors for suicidal ideation in late life include medical illness and disability (Hirsch et al., 2006a), affective illness (Barnow et al., 2004; Pfaff and Almeida, 2004), and past history of suicide attempts (Alexopoulos et al., 1999). Low future orientation and hopelessness have also been associated with suicidal ideation and past attempts (Rifai et al., 1994; Uncapher et al., 1998; Alexopoulos et al., 1999; Hirsch et al., 2006b).

Cognitive function

The literature on cognitive function and suicidal ideation is limited and the majority of research has focused on suicidal acts. It has been maintained that suicidal individuals suffer from cognitive rigidity that leads them to believe that suicide is their only option. This hypothesis has received support in several studies that found a link between cognitive functioning and suicide ideation or attempts (King et al., 2000; Kellip et al., 2001; Jollant et al., 2005; Ayalon et al., 2007). It was further argued that individuals who present with limited problem solving ability are also more likely to report a sense of hopelessness and dysphoria and, as a result, resort to suicidal ideation and attempts (Schotte and Clum 1987; Schotte et al., 1990; Dixon et al., 1994; Heisel et al., 2002). However, other studies have found no such a relationship between cognitive functioning and suicidality (Ellis et al., 1992; Potkin et al., 2003).

The present study

To date, most of the studies that have examined cognitive functioning as a predictor of suicidality have employed case control designs and were based on small or non-representative samples (Kelip et al., 2001; Heisel et al., 2002; Jollant et al., 2005). In addition, many of these studies employed cognitive screens that were not sufficiently sensitive to detect domain-specific cognitive functions that may indicate a specific suicide risk (Upadhyaya et al., 1999; Heisel et al., 2002; Ayalon et al., 2007). Finally, despite the well documented link between cognitive impairment and age (Lyketsos et al., 2002) and between suicidality and age (Center for Disease Control, 2007), only a small fraction of these studies has focused specifically on older adults (Upadhyaya et al., 1999; Heisel et al., 2002; Ayalon et al., 2007).

The present study adds to the current body of research by identifying those specific cognitive functions associated with passive suicidal ideation in a national sample of older adults. Based on past research, we expected more complex executive tasks, such as arithmetic ability and word fluency, to be associated with passive suicidal ideation, even after controlling for all other socio demographic and clinical characteristics. We had no specific hypotheses with regard to the role of the other cognitive tasks as predictors of passive suicidal ideation.

METHODS

The analysis uses data from SHARE-Israel, the Israeli component of the Survey of Health, Aging and Retirement in Europe (Börsch-Supan et al., 2005), which is a multidisciplinary study of people age 50 years and older that is based on the US Health and Retirement Study. The sample was a cluster sample of the Israeli population over the age of 50, stratified by ethnicity, religion, immigrant status and region. Within each stratum, the clustering was hierarchical: subjects within households within statistical regions within strata. Questionnaires were administered by trained interviewers in Hebrew, Arabic and Russian, according to the language preferences of participants. The main survey was executed by means of computer-assisted personal interviews. Specific training was given to interviewers regarding the administration of the cognitive measures. A supplementary drop off questionnaire was also completed and returned by respondents. The study was approved by the Institutional Review Board of the Hebrew University of Jerusalem. All participants gave informed consent prior to taking part in the interview. Participation was voluntary.

Overall, 1,774 households participated in the study (68.6% response rate), with responses received from 2,603 individuals. Because the primary sampling unit was households, the base sample included a small number of younger spouses. In the present study, we included only respondents who were 50 years and older at the time of the interview and who also completed the drop-off questionnaire portion of the survey (n = 1,712; 66.2% response rate to the drop off questionnaire). Of this sample, 46% were between 50–59, 26.9% were between 60–69 and 27% were 70 or older. The majority of the sample had a secondary level of education or less (76%) and women constituted 48.4% of the sample (Table 1).

Table 1.

Demographic and clinical characteristics of the sample by passive suicidal ideation (n=1,712)

Predictors Overall
Samplea 		No passive suicidal
ideation (1,481)		 Passive suicidal
ideation (134)		 Chi-square/t-test p-valueb
Genderc 5.6 0.22
 Female 726 (48.4%) 663 (47.7%) 63(57.4)
Education 123.9 <0.001
 Primary 411 (19.4%) 350 (17.1%) 61 (52.9%)
 Secondary 825 (56.5%) 773 (57.8%) 52 (37.5%)
 Tertiary 374 (24.0%) 354 (25.0%) 20 (9.4%)
Age 13.4 0.15
 50–59 687 (46.0%) 649 (46.4%) 38 (40.4%)
 60–69 493 (26.9%) 457 (27.3%) 36 (20.2%)
 70+ 435 (27.0%) 375 (26.2%) 60 (39.2%)
Marital status 21.9 0.02
 Married/living with a partner 1300 (75.8%) 1214 (76.9%) 86 (60.5%)
Hope (1–35) 25.4 (.25) 25.8 (.26) 22.1 (.69) −4.86 <0.001
Depression (0–42) 11.3 (.40) 10.6 (.43) 19.5 (.91) 7.45 <0.001
Medical Conditions (0–8) 1.15 (.07) 1.10 (.08) 1.83 (.24) 2.92 0.004
Activities of Daily Living (0–5) .26 (.04) 0.19 (.03) .97 (.21) 4.90 <0.001
Time Orientation (0–4) 3.70 (.03) 3.76 (.03) 3.21 (.14) −4.74 <0.001
Verbal Learning (0–10) 4.54 (.11) 4.61 (.12) 3.92 (.29) −2.13 0.03
Verbal Recall (0–10) 3.05 (.10) 3.11 (.11) 2.43 (.29) −2.10 0.03
Word Fluency (0–48) 18.56 (.34) 18.91 (.36) 14.91 (1.23) −2.64 0.008
Arithmetic (0–4) 2.49 (.06) 2.54 (.06) 1.94 (.15) −3.53 <0.001
Composite Cognitive Functioning
Score (0–76)		 32.41 (.50) 32.98 (.51) 26.78 (1.89) −2.96 0.003
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a

Results are presented as%for categorical variables and means (SD) for continuous variables. N varies between 1,712 to 1,537 due to missing values.

b

Chi square analyses were conducted for categorical variables and t-test analyses for continuous variables.

c

All analyses were weighted and stratified to account for complex sample design.

Dependent variable

Passive suicidal ideation

The passive suicidal ideation indicator is an item taken from the Euro-D measure, which is a face-to-face measure of depression (Prince et al., 1999): ‘in the past month, have you felt that you would rather be dead?’. Response options are: ‘yes’ (1)—participant has mentioned passive suicidal ideation vs ‘no’ (0)—no passive suicidal ideation mentioned. A similar question was used in past research as an indicator of passive suicidal ideation (Ayalon et al., 2007).

Independent variables

Cognitive functioning

As noted earlier, all cognitive measures were obtained in face-to-face interviews by surveyors specifically trained in their administration. These included time orientation, arithmetic ability, verbal learning and recall, and word fluency.

Time orientation

The SHARE questionnaire assesses participants’ knowledge of year, month, day of the month, and day of the week. To reflect overall time orientation, we summed up all accurate responses, with a higher score representing better performance (range 0–4).

Verbal learning

The SHARE questionnaire evaluates learning using the adapted Ten-Word Delay Recall Test. The measure has been previously used in several international studies (Prince et al., 2003). The list of ten words was read to participants and the number of spontaneously recalled words from the list following the first read was documented. Score range was 0–10 with a higher score representing better performance.

Verbal recall

Five minutes following the verbal learning task, participants were asked to spontaneously recall the list of words. Score range was 0–10 with a higher score representing better performance.

Word fluency

The SHARE questionnaire evaluates word fluency using the animal fluency task. Participants were asked to name all the animals they can think of within a one-minute trial. Participants received one point for each correct animal name. In the present study the range was 0–48.

Arithmetic

Arithmetic ability was evaluated using 4 questions. A question example is: ‘if the chance of catching a disease is 10%, how many people out of 1,000 are expected to catch the disease’. Response range was 0–4, with a higher score representing better performance.

Composite cognitive functioning

A composite score of all cognitive domains was calculated, ranging from 0–76, with a higher score representing better global cognitive functioning.

Covariates

Depression

We used the 14-item Center for Epidemiological Studies of Depression questionnaire (CES-D) as an indicator of overall depression level. The CES-D is a well-known measure frequently used to assess depressive symptomatology in the general population. Participants are asked to indicate the frequency with which they felt each of the symptoms within the past week. Scores range from 0–42 (Radloff, 1977).

The CES-D was used as an indicator of depression instead of the Euro-D score, in order to prevent multicollinearity between the suicide item taken from the Euro-D and overall depression. Relative to the Euro-D, the CES-D has the advantage of using a different response scale from the suicide item and of being administered in a paper-and-pencil format rather than an interview format.

Hope

The measure of hope was comprised of seven questions about one’s expectations and beliefs about the future and perceived ability to cope with the future. Questions originate from the Life Orientation Test—Revised (Scheier et al., 1994) and from the Hope scale (Snyder et al., 1996). Items are ranked on a five-item agree-disagree scale. The range of scores is 7–35, the higher the score, the greater one’s sense of hope.

Medical conditions

Respondents were asked whether a physician had told them they suffer from: arthritis, cancer, lung disease, diabetes, asthma, hypertension, stroke, or heart conditions. A composite score of overall number of medical conditions was constructed with a higher score representing more medical conditions (range 0–8).

Activities of Daily Living (ADL)

Respondents were asked whether they are limited in performing every day activities, such as dressing up, showering, using the restroom, eating, or getting in and out of bed. A composite score ranging from 0–5 was calculated with a higher score representing greater impairment.

Demographic information

Age, gender, marital status, and level of education were gathered based on self-report.

Analysis

We first ran univariate and bivariate analyses. We then assessed for multicollinearity. Last, we entered all cognitive predictors, controlling for age, gender, marital status, level of education, depression, hope, medical conditions, and ADL into the logistic regression, with passive suicidal ideation as the outcome variable. In an additional sensitivity analysis, we evaluated the relationship between the composite cognitive score and passive suicidal ideation, controlling for all other covariates.

RESULTS

Overall, 6.5% of the sample reported passive suicidal ideation. All cognitive domains, including the composite cognitive score had significant bivariate relationships with passive suicidal ideation; relative to those who did not report passive suicidal ideation, those who reported passive suicidal ideation had more impaired cognitive performance. As expected, those reporting passive suicidal ideation also had lower levels of hope and were more likely to be depressed than those who did not report passive suicidal ideation. In addition, those with passive suicidal ideation had more medical conditions and impaired ADL and were less likely to be married. They also had a lower level of education relative to those who did not report passive suicidal ideation (see Table 1).

Verbal learning and verbal recall were highly correlated (r = 0.73, p < 0.001). Hence, a composite score of verbal memory was calculated and entered into the multivariate regression analysis. We conducted a logistic regression analysis with all cognitive variables (e.g. time orientation, verbal learning, verbal recall, word fluency, and arithmetic ability), controlling for depression, age, gender, marital status, level of education, medical condition, and ADL. Table 2 summarizes the results. Those who reported passive suicidal ideation had a worse performance on the time orientation task than those who denied passive suicidal ideation. None of the other cognitive domains remained significant predictors of suicidal ideation in the fully adjusted model. As expected, those who reported greater depressive symptoms were significantly more likely to report passive suicidal ideation. Level of education was also a significant predictor, with those of tertiary level of education being less likely to report passive suicidal ideation than those of primary level of education. In an additional sensitivity analysis, the composite cognitive score [Odds Ratio (OR) = 0.94, 95% Confidence Intervals (CI): 0.90–0.99, p=0.03] was a significant correlate of passive suicidal ideation once we adjusted for age, gender, marital status, ADL, medical conditions, depression, and hope. However, in the fully adjusted model, once education was entered into the model, the composite cognitive score (OR = 0.97, 95%CI: 0.92– 1.02, p = 0.32) was no longer a significant correlate of passive suicidal ideation.

Table 2.

Multivariate logistic regression predicting passive suicidal ideation (n=1,537)a

OR 95% CI p
Gender
 Female 53 0.70–3.36 0.28
Education
 Primary (reference)
 Secondary 31 0.13–0.72 0.007
 Tertiary 17 0.05–0.51 0.002
Age
 50–59 (reference)
 60–69 0.72 0.30–1.68 0.45
 70+ 0.88 0.40–2.13 0.87
Marital Status
 Not Married (reference)
 Married/living with a partner 0.99 0.40–2.42 0.98
0.96 0.89–1.04 0.41
Hope (1–35)
Depression (0–42) 1.11 1.05–1.17 <0.001
Medical Conditions (0–8) 1.12 0.83–1.49 0.44
Activities of Daily Living (0–5) 1.23 0.95–1.58 0.10
Time Orientation (0–4) 0.65 0.44–0.95 0.02
Verbal Memory (0–20) 1.08 0.98–1.19 0.08
Word Fluency (0–48) 0.94 0.89–1.00 0.09
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a

N is reduced due to missing values on some of the variables. F(14,1091)=7.03, p<0.001.

DISCUSSION

The present study identified those specific cognitive domains that are associated with passive suicidal ideation. To our knowledge, this study is the first to evaluate this relationship using a variety of cognitive measures and a nationally representative sample. Overall, 6.5% of the sample reported passive suicidal ideation. This rate is only slightly higher than the rate of passive suicidal ideation reported for primary care older adults (Ayalon et al., 2007). The only cognitive domain that maintained a significant relationship with passive suicidal ideation in the fully adjusted model was orientation to time, with those presenting with a more impaired performance on the time orientation task being more likely to report passive suicidal ideation.

Contrary to a prior prevailing hypothesis according to which confusion serves as a protective mechanism against suicidality in older adults (Conwell, 1995), our findings show that there is a positive relationship between impaired orientation to time and passive suicidal ideation. This suggests that confusion is actually a risk factor for passive suicidal ideation. In support of this finding, we note that researchers have found that older adults who went on to develop clinical depression had an impaired orientation to time up to three years prior to their diagnosis of depression. These researchers suggested that impairment in orientation to time might be related to apathy or to lack of motivation (Berger et al., 1998). An alternative possibility is that impairment in time orientation is associated with confusion and as a result may cause frustration and lead to passive suicidal ideation, especially among those with better insight into their cognitive state. Time orientation may also be indicative of a greater and more pervasive cognitive impairment than the other cognitive domains evaluated in the present study, such as arithmetic or verbal learning.

Contrary to our hypothesis, arithmetic ability and word fluency were not significant predictors of passive suicidal ideation in the fully adjusted model. In addition, we found that although the overall cognitive score was a significant correlate of passive suicidal ideation even after we adjusted for age, gender, marital status, medical condition, ADL, depression, and hope, it was no longer a significant correlate once education was entered into the model. Whereas time orientation can be considered as a fluid ability that is less affected by education or prior learning, other abilities evaluated in this study, such as arithmetic or word fluency are more affected by prior learning and education. Hence, the present study suggests that impairments in fluid abilities have a more unique association with passive suicidal ideation than crystallized abilities that were acquired through prior learning and education.

Interestingly, the only demographic variable that was associated with passive suicidal ideation in the present study was level of education. As has been the case in previous research (Abel and Kruger, 1997), we found that those of higher levels of education were less likely to report passive suicidal ideation. These individuals likely have greater cognitive and financial resources that potentially allow them to consider alternatives to suicide at times of distress.

Our findings have several clinical implications; as already noted by past research, paying attention to depression as a potential sign of passive suicidal ideation is of utmost importance. Clinicians can also use information about impairment in time orientation as a red flag that indicates the necessity of careful suicidality assessment. However, in order to develop effective interventions on behalf of this population, further research is needed to identify the specific factors responsible for the association between orientation to time and suicidality.

Implications for future research

Future research that investigates the cognitive predictors of suicidal acts and completed suicide is much needed. A longitudinal study of cognitive functioning as a predictor of suicidal ideation and suicidality also is warranted. The use of cognitive measures of abilities that are less affected by educational level and prior learning in addition to more detailed measures of insight and confusion may assist in identifying the particular cognitive domains associated with suicidal ideation. Finally, evaluating structural changes in the brain can complement data from neuropsychological tests and improve our understanding of the relationship between cognition and suicidality.

The present study has a number of limitations that should be noted. First, we only evaluated passive suicidal ideation. Even though there is a link between passive suicidal ideation and acts, the two are not interchangeable and suicidal thoughts occur at a much higher frequency than the actual act (Paykel et al., 1974). Second, a comprehensive measure of suicidality was not incorporated in the present survey and thus, our ability to capture different aspects of suicidality was limited. Yet, including a small number of items on suicidal ideation is a common practice in epidemiological studies (Paykel et al., 1974; Ayalon et al., 2007). Third, the data come from the first wave of the SHARE survey. Given the cross-sectional nature of a single wave of data collection, we cannot infer about cause and effect. Finally, this study is focused on community dwelling older adults. Hence, individuals institutionalized due to severe impairments were excluded.

Nevertheless, the present study has a number of unique strengths that outweigh its limitations. First, the study presents findings obtained from a large population-based sample of people aged 50 years and older. To our knowledge, this is the only study to evaluate the relationship between an array of cognitive domains and passive suicidal ideation using a national based sample. Our findings suggest that level of education, depression, and orientation to time are all associated with passive suicidal ideation. Clinicians working with older adults need to be aware of sociodemographic and clinical characteristics as well as cognitive function as potential indicators of passive suicidal ideation. Attention has to be specifically focused on impairment in orientation to time as a potential predictor of suicidal ideation in older adults.

KEYPOINTS.

  • Old age is a major risk for suicide.

  • Old age is also a risk for cognitive impairment.

  • There is preliminary research demonstrating that cognitive impairment is associated with suicidal ideation.

  • This study demonstrates an association between a specific cognitive domain- time orientation- and passive suicidal ideation. Hence, clinicians working with older adults need to be aware not only of demographic and clinical information, but also of cognitive functioning and more specifically, time orientation, as a correlate of passive suicidal ideation.

ACKNOWLEDGEMENTS

This paper uses data from Release 1 of SHARE-Israel, which was funded by the U.S. National Institute on Aging (R21 AG025169), by the German-Israeli Foundation for Scientific Research and Development (G.I.F.) and by the National Insurance Institute of Israel. The overall SHARE framework was funded primarily by the European Commission through the 5th framework program (project QLK6-CT-2001-00360 in the thematic program Quality of Life). Additional funding for the European SHARE framework came from the U.S. National Institute on Aging (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, Y1-AG-4553-01 and OGHA 04-064).

SHARE-Israel is administered by the Gerontological Data Center, which was established by the funding from the Israeli Ministry of Science and Technology.

Footnotes

CONFLICT OF INTEREST None known.

REFERENCES

  1. Abel EL, Kruger ML. Educational attainment and suicide rates in the United States. Psychological Report. 1997;1:25–28. doi: 10.2466/pr0.97.1.25-28. [DOI] [PubMed] [Google Scholar]
  2. Alexopoulos GS, Bruce ML, Hull J, et al. Clinical determinants of suicidal ideation and behavior in geriatric depression. Arch Gen Psychiatry. 1999;56:1048–1053. doi: 10.1001/archpsyc.56.11.1048. [DOI] [PubMed] [Google Scholar]
  3. Ayalon L, Mackin S, Arean AP, et al. The role of cognitive functioning and distress in passive and active suicidal ideation among primary care patients. JAGS. 2007;55:1090–1094. doi: 10.1111/j.1532-5415.2007.01237.x. [DOI] [PubMed] [Google Scholar]
  4. Barnow S, Linden M, Freyberger HJ. The relationship between suicidal feelings and mental disorders in the elderly: results from the Berlin Aging Study (BASE) Psychol Med. 2004;34:741–746. doi: 10.1017/S0033291703008912. [DOI] [PubMed] [Google Scholar]
  5. Beck AT, Brown G, Steer RA, et al. Suicide ideation at its worst point: a predictor of eventual suicide in psychiatric out-patients. Suicide Life Threat Behav. 1999;29:1–9. [PubMed] [Google Scholar]
  6. Berger AK, Small BJ, Forsell Y, et al. Preclinical symptoms of major depression in very old age: A prospective longitudinal study. Am J Psychiatry. 1998;155:1039–1043. doi: 10.1176/ajp.155.8.1039. [DOI] [PubMed] [Google Scholar]
  7. Börsch-Supan A, Brugiavini A, Jürges H, et al. Health, Aging, and Retirement in Europe. First Results from the Survey of Health, Aging, and Retirement in Europe. MEA; Manheim: [Accessed online on 1 July 2007]. 2005. http://www.share-project.org/index.php?page=Documentation&menue=4& sub= [Google Scholar]
  8. Center for Disease Control [Accessed on 1 July 2007];Suicide Among Older Persons, United States 1980–1992. 2007 http://www.cdc.gov/epo/mmwr/preview/mmwrhtml/00039937.htm.
  9. Conwell Y. Dementia. Crisis. 1995;16:5–6. doi: 10.1027/0227-5910.16.1.5. [DOI] [PubMed] [Google Scholar]
  10. Dixon WA, Heppner P, Rudd D. Problem solving appraisal, hopelessness, and suicide ideation: evidence for a mediational model. J. Couns Psychol. 1994;41:91–98. [Google Scholar]
  11. Ellis TE, Berg RA, Franzen MD. Neuropsychological performance and suicidal behavior in adult psychiatric inpatients. Perception and Motor Skills. 1992;75:639–647. doi: 10.2466/pms.1992.75.2.639. [DOI] [PubMed] [Google Scholar]
  12. Heisel MJ, Flett GL, Besse A. Cognitive functioning and geriatric suicide ideation. Testing a mediational model. Am J Geriatr Psychiatry. 2002;10:428–436. [PubMed] [Google Scholar]
  13. Hirsch JK, Duberstein PR, Conner KR, et al. Future orientation moderates the relationship between functional status and suicide ideation in depressed adults. Depression Anxiety. 2006a;24:196–201. doi: 10.1002/da.20224. [DOI] [PubMed] [Google Scholar]
  14. Hirsch JK, Duberstein PR, Conner KR, et al. Future orientation and suicide ideation and attempts in depressed adults ages 50 and over. Am J Geriatr Psychiatry. 2006b;14:752–757. doi: 10.1097/01.JGP.0000209219.06017.62. [DOI] [PubMed] [Google Scholar]
  15. Jollant F, Bellivier F, Leboyer M, et al. Impaired decision making in suicide attempters. Am J Psychiatry. 2005;162:304–310. doi: 10.1176/appi.ajp.162.2.304. [DOI] [PubMed] [Google Scholar]
  16. Kelip JG, Sackeim HA, Brodsky BS, et al. Neuropsychological dysfunction in depressed suicide attempters. Am J Psychiatry. 2001;158:735–741. doi: 10.1176/appi.ajp.158.5.735. [DOI] [PubMed] [Google Scholar]
  17. King DA, Conwell Y, Cox C, et al. A neuropsychological comparison of depressed suicide attempters and nonattempters. J Neuropsychiatry Clin Neurosci. 2000;12:64–70. doi: 10.1176/jnp.12.1.64. [DOI] [PubMed] [Google Scholar]
  18. Lyketsos CG, Lopez O, Jones B, et al. Prevalence of neuropsychiatric symptoms in dementia and mild cognitive impairment: Results from the Cardiovascular Health Study. JAMA. 2002;288:1475–1483. doi: 10.1001/jama.288.12.1475. [DOI] [PubMed] [Google Scholar]
  19. Paykel ES, Myers JK, Lindenthal JJ, Tanner J. Suicidal feelings in the general population: a prevalence study. Br J Psychiatry. 1974;124:460–469. doi: 10.1192/bjp.124.5.460. [DOI] [PubMed] [Google Scholar]
  20. Pfaff JJ, Almeida OP. Identifying suicidal ideation among older adults in a general practice setting. J. Affect Disord. 2004;83:73–77. doi: 10.1016/j.jad.2004.03.006. [DOI] [PubMed] [Google Scholar]
  21. Potkin SG, Anand R, Alphs L, Fleming K. Neurocognitive performance does not correlate with suicidality in schizophrenic and schizoaffective patients at risk for suicde. Schizophrenia Res. 2003;59:59–66. doi: 10.1016/s0920-9964(02)00159-7. [DOI] [PubMed] [Google Scholar]
  22. Prince M, Acosta D, Chiu H, et al. Dementia diagnosis in developing countries: a cross-cultural validation study. Lancet. 2003;361:909–917. doi: 10.1016/S0140-6736(03)12772-9. [DOI] [PubMed] [Google Scholar]
  23. Prince MJ, Reischies F, Beekman AT, et al. Development of the Euro-D scale – a European Union initiative to compare symptoms of depression in 14 European countries. Br J Psychiatry. 1999;174:330–338. doi: 10.1192/bjp.174.4.330. [DOI] [PubMed] [Google Scholar]
  24. Radloff LS. The CES-D scale: A self-report depression scale for research in the general population. Appl Psychologic Measure. 1977;1:385–401. [Google Scholar]
  25. Rifai AH, George CJ, Stack JA, et al. Hopelessness in suicide attempters after acute treatment of major depression in late life. Am J Psychiatry. 1994;151:1687–1690. doi: 10.1176/ajp.151.11.1687. [DOI] [PubMed] [Google Scholar]
  26. Scheier MF, Carver CS, Bridges MW. Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): A reevaluation of the Life Orientation Test. J Pers Soc Psychol. 1994;67:1063–1078. doi: 10.1037//0022-3514.67.6.1063. [DOI] [PubMed] [Google Scholar]
  27. Schotte DE, Clum G. Problem solving skills in suicidal psychiatric patients. J Consult Clin Psychol. 1987;55:49–54. doi: 10.1037//0022-006x.55.1.49. [DOI] [PubMed] [Google Scholar]
  28. Schotte DE, Cools J, Payvar S. Problem-solving deficits in suicidal patients: trait vulnerability or state phenomenon? J Consult Clin Psychol. 1990;58:562–564. doi: 10.1037//0022-006x.58.5.562. [DOI] [PubMed] [Google Scholar]
  29. Snyder CR, Sympson SC, Ybasco FC, et al. Development and validation of the state hope scale. J Pers Social Psychol. 1996;70:321–335. doi: 10.1037//0022-3514.70.2.321. [DOI] [PubMed] [Google Scholar]
  30. Uncapher H, Gallagher-Thompson D, Osgood NJ, Bongar B. Hopelessness and suicidal ideation in older adults. Gerontologist. 1998;38:62–70. doi: 10.1093/geront/38.1.62. [DOI] [PubMed] [Google Scholar]
  31. Upadhyaya AK, Conwell Y, Duberstein PR, et al. Attempted suicide in older depressed patients. Effect of cognitive functioning. Am J Geriatr Psychiatry. 1999;7:317–320. [PubMed] [Google Scholar]

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