Brief Report

Prescription use and survival among nonagenarians presenting to the ED^?

Rebecca L. Rao MD?, Raquel M. Schears MD, MPH

Department of Emergency Medicine, Mayo Clinic, Rochester, MN 55905, USA

Received 22 July 2008; revised 10 December 2008; accepted 17 December 2008

Abstract To characterize prescription medication use and survival effect among nonagenarians with an emergency department (ED) visit, we performed a retrospective chart review for all nonagenarians presenting to the ED in 2002. Data were collected on medication number and category and on survival after discharge. At admission, patients were taking no medications (3.2%), 1 to 4 medications (35%), 5 to 9 medications (51.9%), or at least 10 medications (9.9%); the median number increased by 2 at discharge (P b .001). Among 565 patients dismissed, 6-month survival was 77.8% and 1-Year survival was 65.6%. Patients discharged with prescriptions for opioids or other analgesics were more likely to die within 12 months than those discharged without these medications. Patients taking aspirin had a 40% lower mortality compared with those not taking aspirin (P = .004). Patients discharged with medication in other categories had no excess mortality.

(C) 2010

Introduction

Persons 85 years and older account for the fastest- growing segment in the United States [1], resulting in physicians caring for increasing numbers of extremely elderly patients. Overall, the elderly constitute the largest resource allocation for emergency department (ED) care, and this population is projected to increase by 104% between 2000 and 2030 [2]. Inappropriate medication use is a patient safety concern identified among younger elders (age N65 years) [3] but is relatively unaddressed among patients older than 90 years. Inappropriate medications are agents that are ineffective or pose unnecessarily high risk despite avail- ability of safer alternatives [3,4]. According to the Beers

^? Portions of this article were previously published in abstract form in Mooney RL, Schears RM, Weaver AL. Ann Emerg Med 2006;48 Suppl:S74.

* Corresponding author.

E-mail address: [email protected] (R.L. Rao).

criteria [3], 12.6% of elderly patients have received at least 1 inappropriate medication in the ED [5]. Gaddis et al [6] found that medications prescribed in the ED cause Drug interactions less frequently (5%) than those identified on presentation to the ED (25%).

Although investigators have addressed potentially inap- propriate medication use [7-9], we present the first cohort investigation of nonagenarians with an initial ED presenta- tion for Acute medical care. Our goal was to describe the use of prescription medications and measure their association with survival among this subset. We hypothesized that narcotic and Psychotropic medications prescribed to non- agenarians are likely to limit survival.

Methods

Medical records of all nonagenarians who presented to a large, Urban academic ED in 2002 were reviewed. Patients

0735-6757/$ - see front matter (C) 2010 doi:10.1016/j.ajem.2008.12.020

who were 90 years and older at the time of ED triage in the 2002 calendar year were identified from a hospital admin- istrative database for inclusion in the study. Institutional review board approval was obtained.

Data were obtained from the individual patient clinical records. The data on the ED visits were from written charts and electronic notes transcribed from supervisory-level clinician dictations. Emergency department-basED pharmacists ver- ified the patient-provided Medication lists incorporated into the visit record by nursing colleagues after triage of patients and ED room assignment. For admitted patients, hospital discharge pharmacy orders, which were cross-correlated with the hospital discharge summary, were used to track changes from the admission baseline for the primary statistical analyses. For ED-dismissed patients, the home-going revi- sions were tracked through chart notes and checked by adjustments made in formal prescriptions written by physician providers. These data were abstracted by the investigators (RR and RS) and a trained research assistant with use of a standard abstraction form, and information was manually transferred into the study database. Abstractors were trained in counting and categorizing for study purposes and were monitored on a daily basis, but specific hypothesis blinding was impractical. Initial interobserver reproducibility for 5 items (ED visit date, hospital admission as yes or no, discharge date, date- confirmed medication total count, and category number), coded by 2 investigators for a comparison sample of 21 patient charts, had a ? statistic of 0.94 (range, 0.82-1.00) for medication category and 1.00 for the remaining items, which showed excellent agreement. We did not measure lack of reproducibility because of erroneous data entry nor did we include double entry to reduce transcription errors.

Demographic information and data on medications at admission and discharge were collected. Most nonprescrip- tion agents, which are available over the counter without a physician’s prescription, were excluded from analysis. Supplements, multiple meter-dosed inhalers, prescription eye drops, and combination medications (eg, hydrochlor- othiazide-triamterene) each counted as 1 medication. The following over-the-counter medications were included: acetaminophen, diphenhydramine hydrochloride, guaifene- sin with dextromethorphan, and nonsteroidal anti-inflamma- tory drugs (NSAIDs). Particular medications were categorized for survival analysis, using the Tarascon Pocket Pharmacopoeia [10]. Five medication categories were used: analgesics, antidepressants, antipsychotics, anxiolytics, and mood stabilizers. Further subcategories were created for analgesics (opioids, NSAIDs [excluding aspirin], aspirin, and other), antipsychotics (typical and atypical), and anxiolytics (benzodiazepine and nonbenzodiazepine).

Vital status at last follow-up or death was abstracted from all clinical records. In addition, date and cause of death were ascertained from the state death certificates index.

The main outcome measures were number and category of medications at admission and discharge and survival calculated from the discharge date. Discharge was defined as

hospital discharge if the patient was hospitalized and as ED discharge if the patient was not hospitalized. If patients were hospitalized more than once, only the first hospitalization was used for medication analysis. Among patients with acute visits to the ED not resulting in hospitalization, the last ED visit of the calendar year was used for study comparisons.

Data analysis

The difference in the median number of medications at admission between patient subgroups was evaluated using the Wilcoxon rank sum test. The difference in the median number of medications at admission vs discharge was evaluated with the Wilcoxon Signed Rank Test. For each medication category, the proportion of patients taking that medication was compared between presentation at the ED and discharge using the McNemar test for comparing correlated propor- tions. For patients who died before discharge (n = 32), medications at death were considered as a surrogate for medications at discharge, except for standard advanced cardiac life support drugs used in attempted resuscitation at the end of life. Survival analysis was limited to patients who were alive at discharge after the index ED evaluation. The primary outcome measure was survival at 1 year after discharge. For patients who died within 1 year, the duration of follow-up was calculated from ED admission to death. The follow-up duration of all other patients was censored at 366 days. A secondary analysis of survival was performed to try to minimize the effect of opioids being prescribed in conditions with limited expected survival or conditions that would heavily weigh the likelihood of being prescribed analgesics. Patients who died of the following conditions were excluded from the secondary analysis: cancer, dys- rhythmia, pulmonary embolus, musculoskeletal fracture, and unknown causes. Survival estimates were obtained with the Kaplan-Meier method. Associations with death within 12 months were evaluated based on fitting Cox proportional hazards regression models. The following associations were included: age (per 1-year change), sex (male vs female), mode of arrival (medical vs private transport), number of difficulties with activities of daily living (0, 1-3, 4-6, or 7-

9), self-rated exercise capacity (0, 1-30, 31-60, 61-120, or N120 minutes), do not resuscitate/do not intubate (DNR/DNI) status (yes vs no), and weighted Charlson Comorbidity Index (per 1-unit change). Associations were summarized by calculating Hazard ratios and corresponding 95% confidence intervals. Calculated P values were 2-sided, and P b .05 was considered statistically significant. Analyses were performed with SAS software (SAS Institute, Cary, NC).

Results

During the study period, 597 nonagenarians visited the ED, accounting for 960 ED visits and 716 hospitalizations. Most of the patients evaluated were women (72.9%), and one

half of the patients arrived by emergency medical system transport (50.9%). Patients presented from community residences (54.3%), nursing homes (30.8%), or assisted- living settings (14.9%). The study cohort characteristics are listed in Table 1.

Number of medications recorded at ED admission is summarized in Fig. 1 (median, 5). Number and type of

Table 1 Summary of the characteristics of the 597 study patients

Characteristic Patients

No. ^a %

Fig. 1 Number of prescription medications at ED presentation. Nonprescription medications included in the analysis: acetamino- phen, NSAIDs including aspirin, diphenhydramine, and guaifene- sin with dextromethorphan.

Age, y (mean +- SD, 93.3 +- 2.7; range, 90-102) Older than 95 171 28.6 Sex Female 435 72.9 Male 162 27.1 Mode of arrival at ED EMS 303 50.9 Private 292 49.1 Living arrangement House/apartment (H/A) 324 54.3 Nursing home (NH) 184 30.8 Assisted living (AL) 89 14.9 Self-rated difficulty with ADLs None 129 22.2 1-3 196 33.7 4-6 164 28.2 7-9 92 15.8 Self-rated exercise capacity (min/wk) None 420 73.0 1-30 78 13.6 31-60 33 5.7 61-120 19 3.3 N2 h 25 4.3 BMI (mean +- SD, 24.8 +- 4.5; range, 13.9-43.8) Normal (b25) 192 54.6 Overweight (25-30) 119 33.8 Obese (N30) 41 11.6 No. of ED admissions per patient per year 1-2 519 86.9 3-5 73 12.2 N5 5 0.8 Hospitalized in 2002 No 136 22.8 Yes 461 77.2 DNR/DNI status No 327 54.8 Yes 270 45.2 Weighted Charlson Comorbidity Index [11] 0 73 12.2 1-2 175 29.3 3-4 153 25.6 >=5 196 32.8

ADLs indicates activities of daily living; BMI, body mass index; EMS, emergency medical system. a All totals do not sum to 597 because of missing values for some characteristics.

medications at admission and discharge are summarized in Table 2. Patients living in the community were taking significantly fewer medications at admission than those in nursing homes (P b .015) or assisted living (P b .001). Overall, the median number of medications increased by 2 at discharge (P b .001) and was unrelated to sex or residence. The proportion of patients discharged with analgesics increased by 8.5% (P b .001) compared with the proportion at admission; the increase was less than 3% for antipsycho- tics (P = .029), anxiolytics (P = .006), and mood stabilizers (P = .035). There was no significant change for antidepres-

sants (Table 2).

Table 2 Number and type of medications at admission to ED and at discharge

Characteristic	At admission	At discharge	P
No. of medications, mean +- SD (median)
Patients
Overall (n = 597) 5.6 +- 3.0 (5) 6.7 +- 3.3 (7) b.001 a
Living arrangement
(H/A) (n = 324) 5.2 +- 2.8 (5) 6.2 +- 3.2 (6) b.001 a
NH (n = 184) 6.2 +- 3.0 (6) 7.1 +- 3.5 (7) b.001 a
AL (n = 89) 6.1 +- 3.1 (6) 7.3 +- 3.3 (7) b.001 a
Sex
Male (n = 162) 5.4 +- 3.0 (5) 6.3 +- 3.3 (6) b.001 a
Female (n = 435) 5.7 +- 2.9 (6) 6.8 +- 3.3 (7) b.001 a
No. of patients (%)
Medication type
Analgesics	388 (65.0)	439 (73.5)	.001 b
Antidepressants	138 (23.1)	143 (24.0)	.34 b
Antipsychotics	29 (4.9)	38 (6.4)	.029 b
Anxiolytics	61 (10.2)	75 (12.6)	.006 b
Mood stabilizers	24 (4.0)	31 (5.2)	.035 b
a Wilcoxon signed rank test. b McNemar test.

Among the 597 patients, 32 patients died before discharge and another 176 died within 12 months after discharge. Among the 258 patients who were alive at latest follow-up, the median duration of follow-up was 2.3 years (range, 0.6-2.7 years). Estimated survival rates at 6 and 12 months after discharge were 77.8% and 65.6%, respectively. Several potential confounding factors were considered for their univariate association with survival at 1 year (Table 3).

Table 4 and Fig. 2 summarize patient survival separately, depending on the medication status at discharge and excluding 9 who were discharged without medications.

Patients discharged with opioids were 1.5 times more likely to die in the next 12 months than those not discharged with opioids (P = .04), and patients discharged with other analgesics were 2.2 times more likely to die in the next 12 months than those not discharged with these other analgesics (P b .001). Of the 115 patients discharged with opioids, 52 (45.2%) had DNR/DNI status, and 194 (43.1%) of the 450 patients not discharged with opioids also had DNR/DNI status. In the secondary analysis, which excluded patients who died of cancer (8 patients), dysrhythmia (5 patients), pulmonary embolus (1 patient), musculoskeletal fracture (3 patients), and unknown causes (15 patients), patients discharged with opioids were 1.7 times more likely to die in the next 12 months than those

Factor HR (95% CI) P

Age (per 1-year change) 1.1 (1.0-1.1) .008 Male sex (vs female) 1.1 (0.8-1.6) .50 Medical mode of 1.5 (1.1-2.0) .008 arrival (vs private) Living arrangement House/Apartment R Nursing home 2.1 (1.5-2.9) b.001 Assisted living 1.3 (0.9-2.1) .019 No. of difficulties with ADLs None R 1-3 1.0 (0.6-1.5) .96 4-6 1.3 (0.8-1.9) .32 7-9 1.8 (1.1-2.8) .017 Self-rated exercise capacity (min/wk) None R 1-30 6.0 (1.5-24.1) .012 31-60 3.7 (0.8-15.7) .082 61-120 3.8 (0.8-18.1) .098 N2 h 0.7 (0.1-8.1) .81 DNR/DNI status (yes vs no) 1.6 (1.2-2.2) .001 Weighted Charlson 1.0 (1.0-1.1) .13 Comorbidity Index (per 1-unit change) BMI (per 1-unit change) 1.0 (0.9-1.0) .064

CI indicates confidence interval; HR, hazard ratio; R, reference.

not discharged with opioids (P = .008). Aspirin was associated with a protective effect, with a 30% lower mortality rate in the next 12 months for those taking aspirin at both admission and discharge compared with those not discharged with aspirin (hazard ratio, 0.7; P = .015). The other NSAIDs did not appear to be associated with survival. The results were similar after adjusting for confounding factors. After adjustment for entry into hospice, patients in the combined group receiving opioids and other analgesics were 1.6 times more likely to die within the next 12 months (P = .007).

Discussion

Many previous studies of medication use in elderly patients have included those 65 years and older [3,4,12-19]. However, to our knowledge, Harris et al [20] reported the only study that has characterized medication use in nonagenarians. Their retrospective review of 214 nonagen- arians found that the mean number of medications at admission and discharge was 4.6 and 4.7, respectively, with a statistically significant increase only for community- dwelling residents. The number of psychotropic medications was unaffected by hospitalization.

In comparison, approximately half of our 597 patients were taking more than 5 medications on admission, with the mean number of medications increasing from 5.6 to 6.7 (median increase, 2). There was no relation to living independently or gender. The number taking analgesics was significantly more at discharge.

Table 3 Univariate associations with survival at 1 year after discharge

Although nearly all medications have potential for adverse effects, age-related pharmacologic changes may make nonagenarians particularly sensitive to the well- known adverse effects of opioids, including respiratory depression [21]. In addition, cognitive decline has been reported in elderly patients who take opiate pain and psychotropic medications [16,22,23], which in turn has been linked with increased morbidity and mortality [17,24,25].

Life expectancy generally is limited for nonagenarians. For our study cohort, the 6-month overall survival rate of 77.8% decreased to 65.6% at 1 year. We found an increase in mortality associated with treatment with opioids and other analgesics and a decrease in mortality with treatment with aspirin. A clear mechanism for the increased mortality rate associated with opioids and other analgesics is not evident. Several hypotheses seem reasonable. The cumula- tive effects of Advancing age, multiple medications, and limitations of metabolic pathways in nonagenarians may promote greater central activity. Drug-induced Altered level of consciousness or cognition may accelerate death through secondary processes such as Aspiration pneumonia, falls, and accidents. Despite the association with Bleeding complications, gastrointestinal ulcers, impaired kidney function, and cardiovascular morbidity, NSAID use does

Aspirin	-	-	-	-	-	-
Neither (n = 252)	74.6	61.4	Referent	-	Referent	-
Admission only (n = 33)	60.3	41.7	1.8 (1.04-3.2)	.034	1.6 (0.9-3.0)	.10
Discharge only (n = 52)	73.6	69.1	0.8 (0.5-1.4)	.48	0.8 (0.5-1.4)	.49
Both (n = 228)	84.3	72.1	0.7 (0.5-0.9)	.015	0.6 (0.4-0.9)	.006
NSAIDs c	-	-	-	-	-	-
Neither (n = 491)	77.9	65.4	Referent	-	Referent	-
Admission only (n = 26)	70.8	70.8	0.9 (0.4-1.9)	.80	0.9 (0.4-1.9)	.72
Discharge only (n = 14)	64.3	50.0	1.7 (0.8-3.5)	.19	1.5 (0.7-3.4)	.35
Both (n = 34)	87.6	71.4	0.8 (0.4-1.6)	.52	0.9 (0.5-1.8)	.76
Opioids	-	-	-	-	-	-
Neither (n = 437)	80.8	68.3	Referent	-	Referent	-
Admission only (n = 13)	58.7	58.7	1.7 (0.7-4.1)	.26	2.3 (0.9-5.7)	.077
Discharge only (n = 70)	66.4	59.9	1.5 (1.02-2.3)	.040	1.6 (1.1-2.5)	.026
Both (n = 45)	72.5	49.3	1.7 (1.1-2.8)	.024	1.5 (0.9-2.5)	.14
Other analgesics d	-	-	-	-	-	-
Neither (n = 309)	84.9	75.5	Referent	-	Referent	-
Admission only (n = 17)	60.5	40.3	3.3 (1.6-6.5)	b.001	2.9 (1.4-6.0)	.004
Discharge only (n = 94)	66.5	55.2	2.2 (1.5-3.3)	b.001	2.1 (1.4-3.1)	b.001
Both (n = 145)	72.1	54.7	2.1 (1.5-3.0)	b.001	1.8 (1.2-2.6)	.002
a Categories were indexed according to Tarascon Pocket Pharmacopoeia [10]. b Adjusted for age, sex, mode of arrival, number of difficulties with activities of daily living, self-rated exercise capacity, DNR/DNI status, and weighted Charlson Comorbidity Index. c Does not include aspirin. d Includes tramadol, lidocaine, and acetaminophen.

not appear to be associated with survival. In addition, the protective effects of aspirin were observed, which may reflect that the antiplatelet properties outweigh the adverse effects in this population.

Table 4 Survival according to medication category at discharge

Medication category and status ^a

Survival after ED dismissal (%)

Unadjusted analysis

Adjusted analysis ^b

6 mo 12 mo HR (95% CI) P HR (95% CI) P

Although a clear cause-and-effect relation is difficult to establish, one might argue that confounding factors bias the observed association of an increase in mortality associated with opioids and other analgesics. For example, patients near the end of life are commonly prescribed narcotics. However, only a small portion of our patient population (32/597) died in the hospital. Secondary survival analysis, excluding patients dying of conditions with limited expected survival or conditions that would heavily weigh the likelihood of being prescribed analgesics, continued to show an association with increased mortality, as did adjusting for hospice entry. This suggests that end-of-life Narcotic use is unlikely to account for the associations observed. Also, we attempted to adjust for several confounding factors in fitting the final model for multi- variate analysis (Table 4).

Although the results of our study are informative and additive, there are several limitations. The retrospective design did not allow direct follow-up of patients after medications were added. Prescription alone may not reliably correlate with Patient adherence. Despite attempts to account for confounding factors, they possibly were still present.

Atypical and typical antipsychotics could not be analyzed separately owing to small numbers. The results may not apply to the general population or to a more urban setting. It would be advantageous to perform large prospective studies

Fig. 2 Kaplan-Meier curves depicting patient survival by analgesic status at discharge: no analgesics (n = 126), aspirin (ASA) only (n = 129), opioids only (n = 19), other analgesics only (n = 85). (NSAIDs only [n = 10], ASA and another analgesic [n = 151], and other combinations excluding ASA [n = 45] are not shown.)

that could validate our results and evaluate the possibility of a direct cause-and-effect relationship.

Owing to the present trend toward undertreating pain in the elderly, our results need to be validated before becoming part of accepted clinical practice.

Conclusions

In summary, our results show that, overall, the number of prescription medications for nonagenarians increased from baseline, after an acute care episode initiated in the ED. A significant increase was noted in the number of analgesics prescribed. In addition, opioids and other analgesics were associated with a significant decrease in survival of these elderly patients, but aspirin appeared to have a protective relationship. Further study is needed to validate these results.

References

1. Bell FC, Wade AH, Goss SC. Life tables for the United States Social Security Area 1900-2080. Baltimore: US Department of Health and Human Services, Social Security Administration, Office of the Actuary; 1992. Actuarial Study No. 107. Publication No. 11-11536.
2. Wilber ST, Gerson LW, Terrell KM, Carpenter CR, Shah MN, Heard K, et al. Geriatric EMergency medicine and the 2006 Institute of Medicine reports from the Committee on the Future of Emergency Care in the U.S. health system. Acad Emerg Med 2006;13(12): 1345-51 Electronic publication 2006 Oct 27.
3. Beers MH. Explicit criteria for determining potentially inappropriate medication use by the elderly: an update. Arch Intern Med 1997;157 (14):1531-6.
4. Fick DM, Cooper JW, Wade WE, Waller JL, Maclean JR, Beers MH. Updating the Beers criteria for potentially inappropriate medication use in older adults: results of a US consensus panel of experts. [published correction appears in Arch Intern Med. 2004;164(3):298] Arch Intern Med 2003;163(22):2716-24.
5. Caterino JM, Emond JA, Camargo Jr CA. Inappropriate medication administration to the acutely ill elderly: a nationwide emergency department study, 1992-2000. J Am Geriatr Soc 2004;52(11):1847-55.
6. Gaddis GM, Holt TR, Woods M. drug interactions in at-risk emergency department patients. Acad Emerg Med 2002;9(11):1162-7.
7. Fialova D, Topinkova E, Gambassi G, Finne-Soveri H, Jonsson PV, Carpenter I, et al, AdHOC Project Research Group. Potentially inappropriate medication use among elderly home care patients in Europe. JAMA 2005;293(11):1348-58.
8. Beers MH, Ouslander JG, Rollingher I, Reuben DB, Brooks J, Beck JC. Explicit criteria for determining inappropriate medication use in

nursing home residents: UCLA Division of geriatric medicine. Arch Intern Med 1991;151(9):1825-32.

1. McLeod PJ, Huang AR, Tamblyn RM, Gayton DC. Defining inappropriate practices in prescribing for Elderly people: a national consensus panel. CMAJ 1997;156(3):385-91.
2. Tarascon Pocket Pharmacopoeia. Lompoc, CA: Tarascon Publishing; 2005.
3. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: develop- ment and validation. J Chronic Dis 1987;40(5):373-83.
4. Hanlon JT, Fillenbaum GG, Kuchibhatla M, Artz MB, Boult C, Gross CR, et al. Impact of inappropriate drug use on mortality and functional status in representative community dwelling elders. Med Care 2002;40 (2):166-76.
5. Klarin I, Wimo A, Fastbom J. The association of inappropriate drug use with hospitalisation and mortality: a population-based study of the very old. Drugs Aging 2005;22(1):69-82.
6. Olsen RB, Gydesen SU, Kristensen M, Eschen FT, Sorensen C, Hasle H, et al. Psychotropic medication in the elderly: a survey of prescribing and clinical outcome. Dan Med Bull 1990;37(5):455-9.
7. Gill SS, Rochon PA, Herrmann N, Lee PE, Sykora K, Gunraj N, et al. Atypical antipsychotic drugs and risk of ischaemic stroke: population based retrospective cohort study. BMJ 2005;330(7489):445 Electronic publication 2005 Jan 24.
8. Dealberto MJ, Mcavay GJ, Seeman T, Berkman L. Psychotropic drug use and cognitive decline among older men and women. Int J Geriatr Psychiatry 1997;12(5):567-74.
9. Guo Z, Wills P, Viitanen M, Fastbom J, Winblad B. Cognitive impairment, drug use, and the risk of Hip fracture in persons over 75 years old: a community-based prospective study. Am J Epidemiol 1998;148(9):887-92.
10. Gray SL, Penninx BW, Blough DK, Artz MB, Guralnik JM, Wallace RB, et al. Benzodiazepine use and physical performance in commu- nity-dwelling older women. J Am Geriatr Soc 2003;51(11):1563-70.
11. Wang PS, Bohn RL, Glynn RJ, Mogun H, Avorn J. Zolpidem use and hip fractures in older people. J Am Geriatr Soc 2001;49(12):1685-90.
12. Harris J, Finucane P, Healy D, Bakarich A. Medication use in hospitalized nonagenarians. Intern Med J 2002;32(3):104-7.
13. Cepeda MS, Farrar JT, Baumgarten M, Boston R, Carr DB, Strom BL. side effects of opioids during short-term administration: effect of age, sex, and race. Clin Pharmacol Ther 2003;74(2):102-12.
14. Fraser GE, Singh PN, Bennett H. Variables associated with Cognitive function in elderly California Seventh-day Adventists. Am J Epidemiol 1996;143(12):1181-90.
15. Wadsworth EJ, Moss SC, Simpson SA, Smith AP. Preliminary investigation of the association between psychotropic medication use and accidents, minor injuries and cognitive failures. Hum Psycho- pharmacol 2003;18(7):535-40.
16. Andersen K, Nybo H, Gaist D, Petersen HC, McGue M, Jeune B, et al. Cognitive impairment and mortality among nonagenarians: the Danish 1905 cohort survey. Dement Geriatr Cogn Disord 2002;13(3): 156-63.
17. Nybo H, Petersen HC, Gaist D, Jeune B, Andersen K, McGue M, et al. Predictors of mortality in 2,249 nonagenarians: the Danish 1905- Cohort Survey. J Am Geriatr Soc 2003;51(10):1365-73.