Psychiatr Q DOI 10.1007/s11126-014-9327-1 ORIGINAL PAPER
Predictors in Geriatric Psychiatry Hospital Length of Stay Elizabeth A. DiNapoli • Natalie Regier • Jesse McPherron • Michael J. Mundy • Sabin Sabastian • Jerry Doss • Patricia A. Parmelee
Ó Springer Science+Business Media New York 2014
Abstract This paper examined predictors of length of stay in a freestanding geriatric psychiatry hospital. Data on patient and treatment characteristics of geriatric inpatients (N = 1,593) were extracted from an archival administrative tracking database from Mary Starke Geriatric Harper Center. Five independent variables (length of time between last discharge and most recent admission, number of previous admissions, number of assaults, co-morbid medical condition, and admitting psychiatric diagnosis) were entered into a hierarchical regression model as potential predictors of length of stay in a geriatric psychiatry hospital. Number of assaults committed by the patient was the only significant predictor of length of stay, such that patients that had a greater number of assaults were more likely to have longer lengths of stay than those with fewer assaults. These findings highlight the importance of identifying patients at risk for assaultive behavior and developing effective interventions for aggression in geriatric psychiatry hospitals. Keywords Geriatric inpatient psychiatry facility Length of stay Predictors Hospitalization Psychiatric disorder Introduction Approximately 20 % of older adults (55 years of age or older) experience mental health disorders that are outside the realm of normal aging [1]. The most common mental health diagnoses in older adults are anxiety disorders (15.3 %), mood disorders (11.9 %), and
E. A. DiNapoli (&) N. Regier J. McPherron P. A. Parmelee Department of Psychology, The University of Alabama, Box 870348, Tuscaloosa, AL 35487, USA e-mail: [email protected] M. J. Mundy S. Sabastian J. Doss Mary Starke Harper Geriatric Psychiatry Center, Tuscaloosa, AL, USA P. A. Parmelee Center for Mental Health and Aging, Tuscaloosa, AL, USA
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severe cognitive impairments (13.9 %) [2, 3]. These diagnoses are associated with poor health outcomes, higher health care utilization, increased disability and impairment, increased mortality, and higher risk of suicide [4]. This burden is expected to increase substantially because the older adult segment of the U.S. population is the fastest growing demographic [5]. In fact, the number of older adults age 65 and older with psychiatric disorders in the U.S. is expected to increase to 15 million by 2030 [6]. Along with a burgeoning elder population comes increasing numbers of older patients with co-morbid medical and psychiatric needs. In a sample of 95 geriatric psychiatry patients, 92 % had one or more significant medical problems with an average of 1.9 medical conditions [7]. Such medical and psychiatric co-morbidities in older adults pose challenges in their psychiatric care. Thus, geriatric psychiatry facilities have arisen out of the need to provide services for older adult patients who need both psychiatric and medical inpatient care [8]. In comparison to general psychiatry inpatient treatment, geriatric psychiatry subspecialty inpatient care appears to be associated with distinct clinically relevant assessment and treatment advantages [9]. Unfortunately, geriatric psychiatry facilities are a limited and expensive resource. Therefore, research on the optimal use of geriatric psychiatry facilities should be a high priority, particularly as the need for services will increase with the growing number of older adults. Currently, there is a lack of data that highlight the use of geriatric psychiatric services (see review by George et al. 2011). More importantly, there is a need for research that identifies the predictors of geriatric psychiatry hospital length of stay (LOS). Such information is essential in order to improve interventions, design, and use of geriatric psychiatry hospitals to reduce LOS. Recently, Ismail and colleagues [10] identified factors that predicted longer hospital LOS in geriatric patients with mood disorders. Such factors included: living alone, number of recent psychiatric admissions, and involuntary admission. Additionally, receiving electroconvulsive therapy (ECT), falling, pharmacology complications, consultation delays and primary psychiatric diagnosis have also been associated with longer LOS in inpatient geropsychiatric facilities [11, 12]. Furthermore, older adults who are discharged to adult homes or nursing homes are likely to stay twice as long as patients who return to home [13]. Current Study Unique to the literature, this study examined data from a freestanding geriatric psychiatry facility as compared to geropsychiatry units within a larger hospital. The present study uses an archival administrative tracking database from Mary Starke Geriatric Harper Center, dating back to 2004, to determine predictors of geriatric psychiatry hospital length of stay (LOS). Predictor variables of interest include: length of time between last discharge and most recent admission, number of previous admissions, number of assaults, co-morbid medical conditions, and admitting psychiatric diagnosis. No a priori hypotheses are offered regarding specific relations.
Methods Setting Mary Starke Harper Geriatric Psychiatry Facility (Harper Center) is a state-supported facility for residents age 65 and over. The facility contains four units, each with approximately 25
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residents. Residents have been committed and ordered by the probate court to obtain treatment at this facility. In most cases, the commitments are involuntary and involve a prior incident in the community, such as inappropriate public behavior, neglecting one’s residence, or disturbing the peace. Patients at the Harper Center are usually treated with pharmacotherapy as well as casework, group therapy, and recreational therapy. Database Creation A database describing 1,602 patients was created from archival electronic tracking files from Harper Center administrative records dating from 2004 to 2010. These electronic files, which were managed and maintained by Harper Center personnel, contain information that can be used to characterize patients, document clinical assessments and treatment, and track discharge and re-entry. Data were amassed and completely de-identified by a clinical geropsychology graduate student that was placed at the Harper Center for clinical practicum experience. All patients were assigned a unique but nonmeaningful numeric identifier and password-protected files were transferred to University of Alabama’s Center for Mental Health and Aging. The University of Alabama Institutional Review Board approved this study. Variables Demographic information included age, sex, and race of the participant. Nine participants (N = 1,593) were excluded from the analyses because their race was classified as ‘‘other.’’ Diagnostic variables included primary admitting psychiatric diagnoses, coded according to Diagnostic and Statistical Manual, Fourth Edition (DSM-IV) [14] criteria into five broad categories (dementias, schizophrenias, bipolar, depressive and other disorders). Identified physical health problems were coded from primary admitting diagnoses, and categorized on the basis of the Cumulative Illness Rating Scale—Geriatric (CIRS-G) [15, 16] into 13 major organ systems (e.g., cardiac, renal, neurological, musculoskeletal). An assessment of behavioral symptoms included the total number of assaults displayed by each patient. Lastly, discharge variables included length of stay, number of prior admissions to the Harper Center, and lag time between the last discharge and most recent admission. Data Analyses Hierarchical linear regression analysis was used to examine predictors of length of stay. Demographic variables (age, sex, and race) were entered as covariates on Step 1. Predictive variables (number of assaults, medical conditions, psychiatric diagnosis, number of previous admissions, and length of time between last discharge and most recent admission) were entered as independent variables on Step 2. The significance of independent associations of each predictor with length of stay was tested with a = .05.
Results Descriptive Analysis The mean age of this sample was 73.36 (SD = 6.38, Range = 61–95), which consisted of 839 (52.7 %) females and 754 (47.3 %) males. The majority of the sample identified as
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White (67.5 %), whereas 32.5 % identified as Black. Primary psychiatric diagnoses include the dementias (48.5 %), schizophrenia spectrum (29.9 %), bipolar (9.2 %) and major depressive disorders (4.2 %). Medical co-morbidities were common, the most frequent being vascular (35.9 %), neurological (30.4 %) and metabolic-endocrine or vitamin (16.7 %) conditions. The number of assaults by a patient ranged from none to 57 (M = .70, SD = 2.94). The average length of stay was 135.11 days (SD = 239.31 days), with a median (Range) of 71 days (0–3,088 days). Patients had an average of .27 (SD = .72, Range = 0–9) total number of prior admissions at the Harper Center. For those that had a prior admission (N = 277; 17.39 %), there was an average lag time of 416.48 days (SD = 462.76, Range = 7–3,041 days) between the last discharge and most recent admission. Predictive Analysis Five independent variables were entered into the regression model as potential predictors of length of stay in a geriatric psychiatry hospital. Table 1 presents the results of this analysis. After controlling for covariates (age, sex, and race), number of assaults committed by the patient was the only significant predictor (p \ .0001) of length of stay. As such, patients that had a greater number of assaults were more likely to have longer lengths of stay than those with fewer assaults. The final model included only number of assaults as the predictor variable, which was found to explain 10.1 % of the variance in length of stay (F = 168.53; p \ .001). Exploratory Analysis There was a significant main effect for race, which accounts for .8 % of the variance in length of stay, F(1, 1496) = 12.03, p = .001. Black patients were found to have longer length of stay (M = 166.14, SD = 314.12) in the geriatric psychiatry facility than White patients (M = 120.40, SD = 192.44). However, the race by number of assaults interaction was not significant (p = .15). Pearson’s Chi square tests of proportional difference and independent sample t tests were used to note statistical differences between Black and White patients. As summarized in Table 2, significant differences were found between Black and White patients on background characteristics. More specifically, Black patients were significantly younger (72.75 ± 6.47 vs. 73.63 ± 6.34) and had greater number of assaults (1.01 ± 4.11 vs. .55 ± 2.15) than White patients. In addition, Black and White patients differed in terms of primary psychological and medical diagnoses.
Discussion To our knowledge, although there has been previous work with inpatients in subunits of larger facilities, these are the first published data characterizing freestanding geriatric psychiatry hospital patients and assessing predictors of their LOS. We found the mean LOS for freestanding geriatric psychiatry hospitals (135.11 days) to be nearly four times longer than the reported LOS in geriatric psychiatric inpatient wards (14.1–36.4 days) [10–12]. Of the five predictor variables we tested, number of assaults by a patient was the only significant predictor of LOS in a geriatric psychiatry hospital. More specifically, patients that
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Psychiatr Q Table 1 Hierarchical linear regression results for length of stay (N = 1,593) Independent variables
Coefficient (B)
Std. error
p value
Step 1—Covariates Age
-7.18
3.96
.07
Sex
-23.41
46.31
.61
94.01
46.00
.04
Race Step 2—Clinical Predictors
-26.78
24.77
.28
Primary medical diagnosis
Primary psychological diagnosis
-5.68
8.22
.49
Number of assaults by patient
\.0001
31.29
7.19
Total number of prior admissions
6.56
22.92
Number of days between last discharge and most recent admission
-.015
.78
.042
.73
Statistics
df
p value
Model summary: F = 4.03, R2 = .173, p \ .0001
Table 2 Sample characteristics by race (N = 1,593) Variable
Race Black (518; 32.5 %)
White (1,075; 67.5 %)
72.75 ± 6.47
73.63 ± 6.34
t = 2.038
1,031
.042
Men
247
507
v2 = .038
1
.845
Women
271
568
Cardiac
11
41
v2 = 33.94
13
.001
Vascular
168
239
Metabolic-endocrine or vitamin
62
127
Pulmonary
9
28
Neurological
94
251
Musculoskeletalrheumatological
13
26
Other
23
42
Dementia
235
538
Schizophrenias
217
260
Bipolar
24
123
Depressive
6
61
Other
5
16
Number of assaults
1.01 ± 4.11
.55 ± 2.15
t = -2.94
1,591
.003
Total number of prior admissions
.31 ± .69
.25 ± .74
t = -1.70
1,528
.08
Age (Mean ± SD) Sex
Primary medical diagnosis
Primary psychology diagnosis v2 = 73.04
4 \.0001
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displayed greater assaultive behavior had longer LOS than less assaultive patients. We also found that Black patients had longer geriatric psychiatry hospital LOS than White patients. Racial differences in LOS may be partially explained by the findings that Black patients were more assaultive than White patients. Therefore, assaultive behavior is an important barrier to discharge. Our results that assaultive behavior is a predictor of LOS seem intuitive, since such behaviors often complicate discharge planning. Many explanations may be offered in attempt to understand racial disparities in assaultive behavior. Racial minority groups, especially Blacks, are often perceived as more aggressive and potentially more dangerous than Whites [17, 18]. Such racial misconceptions and attitudes may influence commitment decisions by the probate court. For instance, it is possible that older Black adults that display assaultive behaviors may be more likely to be involuntarily committed to geriatric psychiatry hospitals than assaultive White older adults. Grossman and colleagues [19] found patients from racial minority groups were more likely than White patients to have been arrested for a violent crime. Another explanation for our findings is that segregation and/or racial biases may in turn differentially expose Black older adults to key risk factors that induce violence (e.g., neighborhood social context, marital status of parents) [20]. Clinical Implications Study findings highlight the importance of identifying at-risk assaulters and developing effective interventions for aggression (e.g., antipsychotic medication, risk management strategies) in geriatric psychiatry hospitals. Research that characterizes psychiatric inpatients with assaultive behaviors has found that assaults are related to positive psychotic symptoms, older adults, males, organic brain syndrome, personality disorders, and a history of violence [21, 22]. Admission assessments should begin to assess for predictive factors of assaultive behavior and include a standard objective assessment for violence. Since admission data can be used to predict hospital LOS, a thorough assessment at the time of admission is essential. In addition, geriatric psychiatry hospitals should begin to use administrative data to rapidly identify highrisk patients of assaultive behavior, as well as educate staff about predictors of assault. Study Limitations Although the results of the current study provide some insight into predictors of LOS in geriatric psychiatry hospitals, it should be acknowledged that there were several limitations. First, the data were drawn from an administrative database and entirely retrospective. As such, our analyses were limited to only the data available. Therefore, many other potential variables of interest were excluded from our analyses, such as socioeconomic status, marital status, education, falls and incidents other than assaults, and functional status. Second, the analyses are based upon a group of Black and White patients that were committed to a freestanding geriatric psychiatry hospital in the south. Thus, our findings may not be generalizable to individuals from diverse racial/ethnic backgrounds, other hospital settings, or different geographical areas. Future Directions These data may reveal additional strategies for improving efficiency of geriatric psychiatry hospitals. Future studies should (a) continue to examine assaultive behavior as a predictive
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factor of LOS, (b) build upon the research using administrative data to predict LOS, (c) replicate the current study using a more diverse sample of patients, (d) elucidate explanatory factors for racial disparities in assaultive behaviors in psychiatric inpatients, (e) examine the direct medical costs associated with assaultive behaviors and LOS, and (f) investigate different methods of reducing assaultive behavior in geriatric psychiatry hospitals.
Summary and Conclusions This retrospective analysis demonstrated that administrative data can be effectively used to examine predictors of geriatric psychiatry hospital LOS. In combination with the scarce resource of geriatric psychiatry hospitals and the substantial growth of the population of older adults with co-morbid psychiatric and medical disorders, this investigation provides further justification for determining the nature of the relations between patient characteristics and LOS. Understanding the factors that predict hospital LOS may allow for prompt, targeted interventions that reduce LOS and improve overall access to geriatric psychiatry hospitals. Conflict of interest The authors have no disclosures or conflicts of interest to report. Human and Animal Rights No animal or humans studies were carried out by the authors of this article.
References 1. American Association of Geriatric Psychiatry. Geriatrics and mental health—the facts, 2008. http:// www.aapgonline.org/prof/facts_mh.asp. Accessed 10 Mar 2013. 2. Kessler RC, Berglund P, Demler O, et al.: Lifetime prevalence and age-of-onset distributions of DSMIV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry 62:593–602, 2005. 3. Plassman BL, Langa KM, Fisher GG, et al.: Prevalence of Dementia in the United States: The aging, demographics, and memory study. Neuroepidemiology 29:125–32, 2007. 4. Bartels SJ, Blow FC, Brockmann LM, et al.: Substance abuse and mental health among older Americans: The state of the knowledge and future directions. In: Older American Substance Abuse and Mental Health Technical Assistance Center, 2005. http://www.samhasa.gov/OlderAdultsTAC/. Accessed 2 Sep 2014. 5. United States Census Bureau News. Unprecedented global aging examined in New Census Bureau Report commissioned by the National Institute on Aging (Issued brief No. CB09-108). Washington, DC: Bernstein, R., & Cire, B. 2009. http://www.census.gov/PressRelease/www/releases/archives/aging_ population/013988.html. Accessed 18 Feb 2013. 6. Jeste DV, Alexopoulous GS, Bartels SJ, et al. Consensus statement on the upcoming crisis in geriatric mental health: research agenda for the next 2 decades. Archives of General Psychiatry 56:848–53, 1999. 7. Sheline Y. High prevalence of physical illness in a geriatric psychiatric inpatient population. General Hospital Psychiatry 12:396–400, 1990. 8. George J, Adamson J, Woodford, H.: Joint geriatric and psychiatric wards: a review of the literature. Age and Ageing 40:543–8, 2011. 9. Yazgan IC, Greenwald BS, Kremen NJ, et al.: Geriatric psychiatry versus general psychiatry inpatient treatment of the elderly. The American Journal of Psychiatry 161:353–5, 2004. 10. Ismail Z, Arenovich T, Grieve C, et al.: Predicting hospital length of stay for geriatric patients with mood disorders. Can J Psychiatry. 57:696–703, 2012. 11. Blank K, Hixon L, Gruman C, et al.: Determinants of geropsychiatric inpatient length of stay. Psychiatric Quarterly 76:195–212, 2005. 12. Draper B, Luscombe G.: Quantification of factors contributing to length of stay in an acute geropsychiatric ward. International Journal of Geriatric Psychiatry 13:1–7, 1998.
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Psychiatr Q 13. Aisen PS, Giblin KE, Packer LS, et al.: Determinants of length of stay in geropsychiatry. The American Journal of Geriatric Psychiatry 2:165–8, 1994. 14. American Psychiatric Association: Diagnostic and statistical manual of mental health disorders. 4th edn., Washington DC, 1994. 15. Linn BS, Linn MW, Gurel L.: Cumulative Illness Rating Scale. Journal of the American Geriatrics Society 16:622–6, 1968. 16. Parmelee PA, Katz IR, Lawton MP.: The relation of pain to depression among institutionalized age. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences 46:15–21, 1991. 17. Prince TB, David B, Otis D.: The use of restraint and seclusion in different racial groups in an inpatient forensic setting. The Journal of the American Academy of Psychiatry and the Law 32:163–8, 2004. 18. Rossi AM, Jacobs M, Monteleone M, et al.: Characteristics of psychiatric patients who engage in assaultive or other fear-inducing behaviors. The Journal of Nervous and Mental Disease 174:154–60, 1986. 19. Grossman LS, Haywood TW, Cavanaugh JL, et al.: State psychiatric hospital patients with past arrests for violent crimes. Psychiatric Services 46:790–5, 1995. 20. Sampson R J, Morenoff JD, Raudenbush S.: Social anatomy of racial and ethnic disparities in violence. American Journal of Public Health 95:224–32, 2005. 21. Davis S.: Violence by psychiatric-inpatients – a review. Hospital and Community Psychiatry 42:585–92, 1991. 22. Nolan KA, Czobor P, Roy BB, et al.: Characteristics of assaultive behavior among psychiatric inpatients. Psychiatric Services 54:1012–16, 2003.
Elizabeth A DiNapoli, PhD received her doctorate in clinical psychology from The University of Alabama. She is currently a postdoctoral fellow at the Mental Health Research, Education and Clinical Center (MIRECC) in the VA Pittsburgh Healthcare System. Dr. DiNapoli’s research interests include psychosocial interventions for older adults, late-life depression, and mental health and aging. Natalie Regier, PhD is currently working as a Postdoctoral Research Fellow for the Johns Hopkins University Center for Innovative Care in Aging under the directorship of Dr. Laura Gitlin. Dr. Regier received her Ph.D in Clinical Psychology from the University of Alabama’s Geropsychology Program, where she conducted research in Dr. Patricia Parmelee’s laboratory within the Center for Mental Health and Aging. Prior to earning her doctorate, she worked as a clinical research assistant for nearly 5 years under Dr. Jiska Cohen-Mansfield on studies of nonpharmacological interventions for disengagement and agitation in nursing home residents with major neurocognitive disorders. Most recently, she completed a clinical internship at the Milwaukee VA Medical Center, followed by a clinical fellowship at the VA Boston Healthcare System. Jesse McPherron is pursuing graduate studies in clinical psychology at the University of Alabama and is on internship at the South Texas Veterans Medical Center enroute to completing a PhD. He has research interests related to older adults including depression, pain, disability, long-term care, cognitive impairment, and age-related decreases in hemispheric asymmetry. Michael J. Mundy, PhD received his doctorate in experimental psychology from Auburn University, with clinical training from The University of Alabama, Birmingham. Since 1972, he has worked in community mental health centers and state hospitals with both administrative and clinical roles. Sabin Sabastian, MD has been serving as the Clinical Director of the Mary Starke Harper Geriatric Psychiatry Center for over 10 years. He supervises the clinical management of geriatric patients admitted to the facility. He is a Geriatric Psychiatrist, having completed his training at the University of Pittsburgh Medical Center. He previously worked in the community setting providing psychiatric treatment in nursing homes, inpatient and outpatient treatment settings. Jerry Doss, BS received his degree in nursing from The University of Alabama, Capstone College of Nursing. He has 32 years of experience in medical/surgical services, dialysis, home health, and currently mental health care.
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Psychiatr Q Patricia A. Parmelee, PhD is Director of the Center for Mental Health and Aging and Professor of Psychology at the University of Alabama. A social psychologist by training, she has been active in research and services for the elderly for nearly 30 years, and is nationally known for her work on quality of life and quality of care for chronically ill older persons. Prior to joining the UA faculty in 2008, Dr. Parmelee held positions at the Emory University School of Medicine, the Atlanta Veterans Affairs Medical Center, and the Birmingham/Atlanta Geriatric Research, Education and Clinical Center. She previously served as Vice President for Outcomes Management at Genesis Health Ventures, a Pennsylvania-based provider of longterm care; as Associate Director of Research and Senior Research Psychologist at the Philadelphia Geriatric Center, and as Associate Professor of Clinical Epidemiology at the University of Pennsylvania School of Medicine. She is an elected Fellow of both the American Psychological Association and the Gerontological Society of America.
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Predictors in Geriatric Psychiatry Hospital Length of Stay Elizabeth A. DiNapoli • Natalie Regier • Jesse McPherron • Michael J. Mundy • Sabin Sabastian • Jerry Doss • Patricia A. Parmelee
Ó Springer Science+Business Media New York 2014
Abstract This paper examined predictors of length of stay in a freestanding geriatric psychiatry hospital. Data on patient and treatment characteristics of geriatric inpatients (N = 1,593) were extracted from an archival administrative tracking database from Mary Starke Geriatric Harper Center. Five independent variables (length of time between last discharge and most recent admission, number of previous admissions, number of assaults, co-morbid medical condition, and admitting psychiatric diagnosis) were entered into a hierarchical regression model as potential predictors of length of stay in a geriatric psychiatry hospital. Number of assaults committed by the patient was the only significant predictor of length of stay, such that patients that had a greater number of assaults were more likely to have longer lengths of stay than those with fewer assaults. These findings highlight the importance of identifying patients at risk for assaultive behavior and developing effective interventions for aggression in geriatric psychiatry hospitals. Keywords Geriatric inpatient psychiatry facility Length of stay Predictors Hospitalization Psychiatric disorder Introduction Approximately 20 % of older adults (55 years of age or older) experience mental health disorders that are outside the realm of normal aging [1]. The most common mental health diagnoses in older adults are anxiety disorders (15.3 %), mood disorders (11.9 %), and
E. A. DiNapoli (&) N. Regier J. McPherron P. A. Parmelee Department of Psychology, The University of Alabama, Box 870348, Tuscaloosa, AL 35487, USA e-mail: [email protected] M. J. Mundy S. Sabastian J. Doss Mary Starke Harper Geriatric Psychiatry Center, Tuscaloosa, AL, USA P. A. Parmelee Center for Mental Health and Aging, Tuscaloosa, AL, USA
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Psychiatr Q
severe cognitive impairments (13.9 %) [2, 3]. These diagnoses are associated with poor health outcomes, higher health care utilization, increased disability and impairment, increased mortality, and higher risk of suicide [4]. This burden is expected to increase substantially because the older adult segment of the U.S. population is the fastest growing demographic [5]. In fact, the number of older adults age 65 and older with psychiatric disorders in the U.S. is expected to increase to 15 million by 2030 [6]. Along with a burgeoning elder population comes increasing numbers of older patients with co-morbid medical and psychiatric needs. In a sample of 95 geriatric psychiatry patients, 92 % had one or more significant medical problems with an average of 1.9 medical conditions [7]. Such medical and psychiatric co-morbidities in older adults pose challenges in their psychiatric care. Thus, geriatric psychiatry facilities have arisen out of the need to provide services for older adult patients who need both psychiatric and medical inpatient care [8]. In comparison to general psychiatry inpatient treatment, geriatric psychiatry subspecialty inpatient care appears to be associated with distinct clinically relevant assessment and treatment advantages [9]. Unfortunately, geriatric psychiatry facilities are a limited and expensive resource. Therefore, research on the optimal use of geriatric psychiatry facilities should be a high priority, particularly as the need for services will increase with the growing number of older adults. Currently, there is a lack of data that highlight the use of geriatric psychiatric services (see review by George et al. 2011). More importantly, there is a need for research that identifies the predictors of geriatric psychiatry hospital length of stay (LOS). Such information is essential in order to improve interventions, design, and use of geriatric psychiatry hospitals to reduce LOS. Recently, Ismail and colleagues [10] identified factors that predicted longer hospital LOS in geriatric patients with mood disorders. Such factors included: living alone, number of recent psychiatric admissions, and involuntary admission. Additionally, receiving electroconvulsive therapy (ECT), falling, pharmacology complications, consultation delays and primary psychiatric diagnosis have also been associated with longer LOS in inpatient geropsychiatric facilities [11, 12]. Furthermore, older adults who are discharged to adult homes or nursing homes are likely to stay twice as long as patients who return to home [13]. Current Study Unique to the literature, this study examined data from a freestanding geriatric psychiatry facility as compared to geropsychiatry units within a larger hospital. The present study uses an archival administrative tracking database from Mary Starke Geriatric Harper Center, dating back to 2004, to determine predictors of geriatric psychiatry hospital length of stay (LOS). Predictor variables of interest include: length of time between last discharge and most recent admission, number of previous admissions, number of assaults, co-morbid medical conditions, and admitting psychiatric diagnosis. No a priori hypotheses are offered regarding specific relations.
Methods Setting Mary Starke Harper Geriatric Psychiatry Facility (Harper Center) is a state-supported facility for residents age 65 and over. The facility contains four units, each with approximately 25
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residents. Residents have been committed and ordered by the probate court to obtain treatment at this facility. In most cases, the commitments are involuntary and involve a prior incident in the community, such as inappropriate public behavior, neglecting one’s residence, or disturbing the peace. Patients at the Harper Center are usually treated with pharmacotherapy as well as casework, group therapy, and recreational therapy. Database Creation A database describing 1,602 patients was created from archival electronic tracking files from Harper Center administrative records dating from 2004 to 2010. These electronic files, which were managed and maintained by Harper Center personnel, contain information that can be used to characterize patients, document clinical assessments and treatment, and track discharge and re-entry. Data were amassed and completely de-identified by a clinical geropsychology graduate student that was placed at the Harper Center for clinical practicum experience. All patients were assigned a unique but nonmeaningful numeric identifier and password-protected files were transferred to University of Alabama’s Center for Mental Health and Aging. The University of Alabama Institutional Review Board approved this study. Variables Demographic information included age, sex, and race of the participant. Nine participants (N = 1,593) were excluded from the analyses because their race was classified as ‘‘other.’’ Diagnostic variables included primary admitting psychiatric diagnoses, coded according to Diagnostic and Statistical Manual, Fourth Edition (DSM-IV) [14] criteria into five broad categories (dementias, schizophrenias, bipolar, depressive and other disorders). Identified physical health problems were coded from primary admitting diagnoses, and categorized on the basis of the Cumulative Illness Rating Scale—Geriatric (CIRS-G) [15, 16] into 13 major organ systems (e.g., cardiac, renal, neurological, musculoskeletal). An assessment of behavioral symptoms included the total number of assaults displayed by each patient. Lastly, discharge variables included length of stay, number of prior admissions to the Harper Center, and lag time between the last discharge and most recent admission. Data Analyses Hierarchical linear regression analysis was used to examine predictors of length of stay. Demographic variables (age, sex, and race) were entered as covariates on Step 1. Predictive variables (number of assaults, medical conditions, psychiatric diagnosis, number of previous admissions, and length of time between last discharge and most recent admission) were entered as independent variables on Step 2. The significance of independent associations of each predictor with length of stay was tested with a = .05.
Results Descriptive Analysis The mean age of this sample was 73.36 (SD = 6.38, Range = 61–95), which consisted of 839 (52.7 %) females and 754 (47.3 %) males. The majority of the sample identified as
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White (67.5 %), whereas 32.5 % identified as Black. Primary psychiatric diagnoses include the dementias (48.5 %), schizophrenia spectrum (29.9 %), bipolar (9.2 %) and major depressive disorders (4.2 %). Medical co-morbidities were common, the most frequent being vascular (35.9 %), neurological (30.4 %) and metabolic-endocrine or vitamin (16.7 %) conditions. The number of assaults by a patient ranged from none to 57 (M = .70, SD = 2.94). The average length of stay was 135.11 days (SD = 239.31 days), with a median (Range) of 71 days (0–3,088 days). Patients had an average of .27 (SD = .72, Range = 0–9) total number of prior admissions at the Harper Center. For those that had a prior admission (N = 277; 17.39 %), there was an average lag time of 416.48 days (SD = 462.76, Range = 7–3,041 days) between the last discharge and most recent admission. Predictive Analysis Five independent variables were entered into the regression model as potential predictors of length of stay in a geriatric psychiatry hospital. Table 1 presents the results of this analysis. After controlling for covariates (age, sex, and race), number of assaults committed by the patient was the only significant predictor (p \ .0001) of length of stay. As such, patients that had a greater number of assaults were more likely to have longer lengths of stay than those with fewer assaults. The final model included only number of assaults as the predictor variable, which was found to explain 10.1 % of the variance in length of stay (F = 168.53; p \ .001). Exploratory Analysis There was a significant main effect for race, which accounts for .8 % of the variance in length of stay, F(1, 1496) = 12.03, p = .001. Black patients were found to have longer length of stay (M = 166.14, SD = 314.12) in the geriatric psychiatry facility than White patients (M = 120.40, SD = 192.44). However, the race by number of assaults interaction was not significant (p = .15). Pearson’s Chi square tests of proportional difference and independent sample t tests were used to note statistical differences between Black and White patients. As summarized in Table 2, significant differences were found between Black and White patients on background characteristics. More specifically, Black patients were significantly younger (72.75 ± 6.47 vs. 73.63 ± 6.34) and had greater number of assaults (1.01 ± 4.11 vs. .55 ± 2.15) than White patients. In addition, Black and White patients differed in terms of primary psychological and medical diagnoses.
Discussion To our knowledge, although there has been previous work with inpatients in subunits of larger facilities, these are the first published data characterizing freestanding geriatric psychiatry hospital patients and assessing predictors of their LOS. We found the mean LOS for freestanding geriatric psychiatry hospitals (135.11 days) to be nearly four times longer than the reported LOS in geriatric psychiatric inpatient wards (14.1–36.4 days) [10–12]. Of the five predictor variables we tested, number of assaults by a patient was the only significant predictor of LOS in a geriatric psychiatry hospital. More specifically, patients that
123
Psychiatr Q Table 1 Hierarchical linear regression results for length of stay (N = 1,593) Independent variables
Coefficient (B)
Std. error
p value
Step 1—Covariates Age
-7.18
3.96
.07
Sex
-23.41
46.31
.61
94.01
46.00
.04
Race Step 2—Clinical Predictors
-26.78
24.77
.28
Primary medical diagnosis
Primary psychological diagnosis
-5.68
8.22
.49
Number of assaults by patient
\.0001
31.29
7.19
Total number of prior admissions
6.56
22.92
Number of days between last discharge and most recent admission
-.015
.78
.042
.73
Statistics
df
p value
Model summary: F = 4.03, R2 = .173, p \ .0001
Table 2 Sample characteristics by race (N = 1,593) Variable
Race Black (518; 32.5 %)
White (1,075; 67.5 %)
72.75 ± 6.47
73.63 ± 6.34
t = 2.038
1,031
.042
Men
247
507
v2 = .038
1
.845
Women
271
568
Cardiac
11
41
v2 = 33.94
13
.001
Vascular
168
239
Metabolic-endocrine or vitamin
62
127
Pulmonary
9
28
Neurological
94
251
Musculoskeletalrheumatological
13
26
Other
23
42
Dementia
235
538
Schizophrenias
217
260
Bipolar
24
123
Depressive
6
61
Other
5
16
Number of assaults
1.01 ± 4.11
.55 ± 2.15
t = -2.94
1,591
.003
Total number of prior admissions
.31 ± .69
.25 ± .74
t = -1.70
1,528
.08
Age (Mean ± SD) Sex
Primary medical diagnosis
Primary psychology diagnosis v2 = 73.04
4 \.0001
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displayed greater assaultive behavior had longer LOS than less assaultive patients. We also found that Black patients had longer geriatric psychiatry hospital LOS than White patients. Racial differences in LOS may be partially explained by the findings that Black patients were more assaultive than White patients. Therefore, assaultive behavior is an important barrier to discharge. Our results that assaultive behavior is a predictor of LOS seem intuitive, since such behaviors often complicate discharge planning. Many explanations may be offered in attempt to understand racial disparities in assaultive behavior. Racial minority groups, especially Blacks, are often perceived as more aggressive and potentially more dangerous than Whites [17, 18]. Such racial misconceptions and attitudes may influence commitment decisions by the probate court. For instance, it is possible that older Black adults that display assaultive behaviors may be more likely to be involuntarily committed to geriatric psychiatry hospitals than assaultive White older adults. Grossman and colleagues [19] found patients from racial minority groups were more likely than White patients to have been arrested for a violent crime. Another explanation for our findings is that segregation and/or racial biases may in turn differentially expose Black older adults to key risk factors that induce violence (e.g., neighborhood social context, marital status of parents) [20]. Clinical Implications Study findings highlight the importance of identifying at-risk assaulters and developing effective interventions for aggression (e.g., antipsychotic medication, risk management strategies) in geriatric psychiatry hospitals. Research that characterizes psychiatric inpatients with assaultive behaviors has found that assaults are related to positive psychotic symptoms, older adults, males, organic brain syndrome, personality disorders, and a history of violence [21, 22]. Admission assessments should begin to assess for predictive factors of assaultive behavior and include a standard objective assessment for violence. Since admission data can be used to predict hospital LOS, a thorough assessment at the time of admission is essential. In addition, geriatric psychiatry hospitals should begin to use administrative data to rapidly identify highrisk patients of assaultive behavior, as well as educate staff about predictors of assault. Study Limitations Although the results of the current study provide some insight into predictors of LOS in geriatric psychiatry hospitals, it should be acknowledged that there were several limitations. First, the data were drawn from an administrative database and entirely retrospective. As such, our analyses were limited to only the data available. Therefore, many other potential variables of interest were excluded from our analyses, such as socioeconomic status, marital status, education, falls and incidents other than assaults, and functional status. Second, the analyses are based upon a group of Black and White patients that were committed to a freestanding geriatric psychiatry hospital in the south. Thus, our findings may not be generalizable to individuals from diverse racial/ethnic backgrounds, other hospital settings, or different geographical areas. Future Directions These data may reveal additional strategies for improving efficiency of geriatric psychiatry hospitals. Future studies should (a) continue to examine assaultive behavior as a predictive
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factor of LOS, (b) build upon the research using administrative data to predict LOS, (c) replicate the current study using a more diverse sample of patients, (d) elucidate explanatory factors for racial disparities in assaultive behaviors in psychiatric inpatients, (e) examine the direct medical costs associated with assaultive behaviors and LOS, and (f) investigate different methods of reducing assaultive behavior in geriatric psychiatry hospitals.
Summary and Conclusions This retrospective analysis demonstrated that administrative data can be effectively used to examine predictors of geriatric psychiatry hospital LOS. In combination with the scarce resource of geriatric psychiatry hospitals and the substantial growth of the population of older adults with co-morbid psychiatric and medical disorders, this investigation provides further justification for determining the nature of the relations between patient characteristics and LOS. Understanding the factors that predict hospital LOS may allow for prompt, targeted interventions that reduce LOS and improve overall access to geriatric psychiatry hospitals. Conflict of interest The authors have no disclosures or conflicts of interest to report. Human and Animal Rights No animal or humans studies were carried out by the authors of this article.
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Elizabeth A DiNapoli, PhD received her doctorate in clinical psychology from The University of Alabama. She is currently a postdoctoral fellow at the Mental Health Research, Education and Clinical Center (MIRECC) in the VA Pittsburgh Healthcare System. Dr. DiNapoli’s research interests include psychosocial interventions for older adults, late-life depression, and mental health and aging. Natalie Regier, PhD is currently working as a Postdoctoral Research Fellow for the Johns Hopkins University Center for Innovative Care in Aging under the directorship of Dr. Laura Gitlin. Dr. Regier received her Ph.D in Clinical Psychology from the University of Alabama’s Geropsychology Program, where she conducted research in Dr. Patricia Parmelee’s laboratory within the Center for Mental Health and Aging. Prior to earning her doctorate, she worked as a clinical research assistant for nearly 5 years under Dr. Jiska Cohen-Mansfield on studies of nonpharmacological interventions for disengagement and agitation in nursing home residents with major neurocognitive disorders. Most recently, she completed a clinical internship at the Milwaukee VA Medical Center, followed by a clinical fellowship at the VA Boston Healthcare System. Jesse McPherron is pursuing graduate studies in clinical psychology at the University of Alabama and is on internship at the South Texas Veterans Medical Center enroute to completing a PhD. He has research interests related to older adults including depression, pain, disability, long-term care, cognitive impairment, and age-related decreases in hemispheric asymmetry. Michael J. Mundy, PhD received his doctorate in experimental psychology from Auburn University, with clinical training from The University of Alabama, Birmingham. Since 1972, he has worked in community mental health centers and state hospitals with both administrative and clinical roles. Sabin Sabastian, MD has been serving as the Clinical Director of the Mary Starke Harper Geriatric Psychiatry Center for over 10 years. He supervises the clinical management of geriatric patients admitted to the facility. He is a Geriatric Psychiatrist, having completed his training at the University of Pittsburgh Medical Center. He previously worked in the community setting providing psychiatric treatment in nursing homes, inpatient and outpatient treatment settings. Jerry Doss, BS received his degree in nursing from The University of Alabama, Capstone College of Nursing. He has 32 years of experience in medical/surgical services, dialysis, home health, and currently mental health care.
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Psychiatr Q Patricia A. Parmelee, PhD is Director of the Center for Mental Health and Aging and Professor of Psychology at the University of Alabama. A social psychologist by training, she has been active in research and services for the elderly for nearly 30 years, and is nationally known for her work on quality of life and quality of care for chronically ill older persons. Prior to joining the UA faculty in 2008, Dr. Parmelee held positions at the Emory University School of Medicine, the Atlanta Veterans Affairs Medical Center, and the Birmingham/Atlanta Geriatric Research, Education and Clinical Center. She previously served as Vice President for Outcomes Management at Genesis Health Ventures, a Pennsylvania-based provider of longterm care; as Associate Director of Research and Senior Research Psychologist at the Philadelphia Geriatric Center, and as Associate Professor of Clinical Epidemiology at the University of Pennsylvania School of Medicine. She is an elected Fellow of both the American Psychological Association and the Gerontological Society of America.
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