594
LETTERS TO THE EDITOR
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JAGS
not associated with clinical outcomes, perhaps because of the small sample size. The EHR underestimated the prevalence of dementia, and hence there is a lack of association with the EHR delirium marker. In the future, the development of an automated EHR measure of delirium should include efforts that will improve its sensitivity to make it more clinically useful. First, there is a need for continued education of interdisciplinary healthcare teams to improve recognition.9 Second, there is a need to improve the effectiveness of the tool. Two components of the EHR delirium marker (physical restraints and medications) are more likely to identify hyperactive delirium, whereas the third (nurses’ assessment) allows documentation of hypoactive delirium. The nurses’ electronic documentation includes attention, level of consciousness, thought process, and motor behavior (agitation or retarded behavior level). Nurses’ documentation of each parameter may be appropriate but not trigger the delirium marker. The nurses do not have to complete all parameters at each assessment. Further work to validate each nursing assessment and develop an appropriate trigger system based on a larger sample size would be appropriate. Finally, consideration may be given to use of alternative screening tools.10 The findings should be considered in the context of several limitations. The ACE Tracker tool updates data at midnight. The researchers examined participants several hours later (between 8 a.m. and noon), and there may have been a change in their mental status during that time. The researchers examined participants once during the hospital stay, which may have contributed to underestimation of prevalence of delirium. Data were not collected on individuals who refused to participate, limiting knowledge of these nonparticipants and possibly introducing bias. Despite its limitations, the EHR delirium marker is an inexpensive innovation that can be used to alert healthcare professionals to the potential diagnosis of delirium when it is positive. In its current form, it is not a good screening tool because of low sensitivity. Further work should be directed at improving its performance to increase its clinical usefulness.
Conflict of Interest: None. Author Contributions: Khan A., Simpson M., Singh M., Hook M., Geng Y., Malone M.L.: study concept and design, acquisition of data, analysis and interpretation of data, preparation of manuscript, final approval. Sponsor’s Role: None.
Ariba Khan, MBBS, MPH Aurora Health Care, Milwaukee, Wisconsin University of Wisconsin, School of Medicine and Public Health, Milwaukee, Wisconsin
To the Editor: The primary goal of the Program of AllInclusive Care for the Elderly (PACE) is to provide a costeffective, patient-centered, community-based alternative to nursing home care (NH) for a NH-eligible population.1–4 PACE programs are fully capitated and are therefore responsible for all costs associated with health care.2,3,5 Safe and supportive housing can be a critical component of achieving that goal. When care needs for an individual are high, PACE programs might consider assisted living facilities (ALFs) and must weigh the advantages and disadvantages of ALFs against the capabilities of providing care at home (with additional support from PACE) or in a NH. Cost of care is an important consideration. In the United States in 2010, the yearly cost of ALFs was $37,572, and the NH cost was $79,935 for a private room.6,7 ALFs vary in size and staffing model, but provide a monitored and supportive environment that is generally more homelike than NHs, although the skills and expertise typically available in ALFs have been questioned.8,9 The comprehensive care oversight may mitigate this, and the case management that
Michelle Simpson, PhD, RN Maharaj Singh, PhD Mary Hook, PhD, RN-BC Yan Geng, MD Aurora Health Care, Milwaukee, Wisconsin Michael L. Malone, MD Aurora Health Care, Milwaukee, Wisconsin University of Wisconsin, School of Medicine and Public Health, Milwaukee, Wisconsin
ACKNOWLEDGMENTS The authors gratefully acknowledge staff scientific writer and editor Katie Klein of Aurora Research Institute for editorial preparation of the manuscript.
REFERENCES 1. Malone ML, Vollbrecht M, Stephenson J et al. Acute Care for Elders (ACE) tracker and e-Geriatrician: Methods to disseminate ACE concepts to hospitals with no geriatricians on staff. J Am Geriatr Soc 2010;58: 161–167. 2. Rubin FH, Williams JT, Lescisin DA et al. Replicating the Hospital Elder Life Program in a community hospital and demonstrating effectiveness using quality improvement methodology. J Am Geriatr Soc 2006;54: 969–974. 3. Inouye SK, van Dyck CH, Alessi CA et al. Clarifying confusion: The Confusion Assessment Method. A new method for detection of delirium. Ann Intern Med 1990;113:941–948. 4. Inouye SK. The Confusion Assessment Method (CAM): Short CAM Training Manual and Coding Guide. Boston, MA: Hospital Elder Life Program, LLC, 2014. 5. Fick DM, Cooper JW, Wade WE et al. Updating the Beers criteria for potentially inappropriate medication use in older adults: Results of a US consensus panel of experts. Arch Intern Med 2003;163:2716–2724. Erratum: Arch Intern Med 2004;164:298. 6. Morse JM. Preventing Patient Falls. Thousand Oaks, CA: Sage Publications, 1996. 7. Katz S, Ford AB, Moskowitz RW et al. Studies of illness in the aged. The index of ADL: A standardized measure of biological and psychosocial function. JAMA 1963;185:914–919. 8. Straus SE, Richardson WS, Glasziou P et al. Evidence-Based Medicine: How to Practice and Teach EBM, 3rd Ed. Edinburgh: Churchill Livingstone, 2005. 9. Schuurmans MJ, Duursma SA, Shortridge-Baggett LM. Early recognition of delirium: A review of the literature. J Clin Nurs 2001;10: 721–729. 10. LaMantia MA, Messina FC, Hobgood CD et al. Screening for delirium in the emergency department: A systematic review. Ann Emerg Med 2014;63:551–560.
ASSISTED LIVING FACILITY USE BY THE PROGRAM OF ALL-INCLUSIVE CARE FOR THE ELDERLY
JAGS
MARCH 2015–VOL. 63, NO. 3
LETTERS TO THE EDITOR
595
Table 1. Characteristics of Program of All-Inclusive Care for the Elderly (PACE) Participants According to Living Situation*
Characteristic
Age, mean SDa Female, % Black, % Number of medical diagnoses, mean SD Number of total medications, mean SD Number of activity of daily living dependencies, mean SDa Bladder incontinence, % Bowel incontinence, % Dementia diagnosis, % Depression diagnosis, % Presence of other psychiatric conditions, % Number of hospital days in past 6 months, mean SD Months in program, mean SDa Scheduled attendance at PACE, d/wk, mean SD Scheduled home care visits, d/wk, mean SDa
Total, N = 126
78.2 80 62 11.4 12.2 3.0 76 46 47 40 55 1.1 37.9 2.4 0.5
9.7
3.7 5.9 2.4
3.3 31.7 1.4 1.3
Home Alone, n = 27
74.2 74 70 10.7 13.4 0.8 52 19 22 30 37 0.8 28.6 2.4 1.1
10.9
4.0 6.3 1.3
2.7 30.1 1.1 2.1
Home with Caregiver, n = 61
76.7 87 63 11.3 12.7 2.9 74 38 46 38 57 1.0 26.2 2.7 0.6
9.6
5.4 5.4 2.3
3.0 20.5 1.5 1.2
Assisted Living Facility, n = 29
84.1 72 55 11.7 10.2 4.5 93 72 68 41 62 1.8 54.3 2.3 0
6.4
3.9 5.6 1.9
4.7 28.2 1.4
Nursing Home, n = 9
80.8 78 56 13.2 10.8 5.9 100 89 56 56 67 1.3 88.8 1.0 0
5.6
5.0 7.6 0.3
2.0 39.1 1.9
*At one PACE site - Hopkins ElderPlus, Baltimore, MD. One-way analysis of variance was used to test for differences between living arrangements; aP < .001. SD = standard deviation.
PACE provides can offload tasks from and provide assistance to ALFs in managing PACE enrollees who reside there. The aim of this study was to identify factors associated with ALF use by one PACE program and to characterize potential advantages and disadvantages of ALF use within the PACE model. This study was conducted at one PACE site—Hopkins Elder Plus (HEP)—in two parts: cross-sectional examination of patient-level data abstracted from charts at HEP to compare characteristics of PACE enrollees who resided in different settings and qualitative content analysis of three PACE staff focus group discussions to improve understanding of factors associated with the use of ALFs for PACE enrollees. One hundred and twenty-six charts (100% of enrollees in November 2010) were abstracted, and participants were categorized according to four residential types: home alone, home with family, ALF, and NH (Table 1). This analysis showed that participants residing in ALFs and NHs were similar to those living at home except that they were older and more functionally dependent, had been enrolled in PACE longer, and as expected, received no home care. The three focus groups consisted of clinical (n = 7), social work and administrative (n = 8), and recreational and transport (n = 7) staff members who had been working at PACE for an average of 8.2 years. Content analysis revealed that the primary determinants of ALF use were safety concerns for participants and impaired health and limited capabilities of home caregivers. PACE staff members cited several specific examples of individuals for whom these concerns were the primary drivers of ALF use. Cost was also mentioned as an important consideration when making housing recommendations (e.g., ALF vs NH), because PACE programs are typically responsible for much of the cost. Staff members noted that they were aware of the cost differences and were careful to ensure
that higher levels of care (such as NH) were truly necessary. Discussions about trade-offs (autonomy and quality of life vs safety and assistance) were also part of these discussions, as well as the “home-like” atmosphere of ALFs. ALFs were also being used frequently for a temporary, monitored living situation to meet a short-term need. This happens “a few times per month,” according to focus group members. Factors such as geographic proximity to family also prompted ALF use (over NH) for short-term needs. This mixed-methods study is the first to provide data on issues related to the use of ALFs by a PACE program. Although older and more functionally dependent, participants living in ALFs were otherwise similar to those living at home, suggesting that other factors influence the decision to use ALFs. The quantity and types of services that PACE provides (home care, visits to day care centers) to certain participants could have influenced who was able to stay at home and who needed ALF or NH. The potential advantages of collaboration between ALFs and PACE include the clinical support that PACE provides, which can fill gaps in the capacity of ALFs to care for frail and medically complex residents whom they would otherwise be unable to manage.8–10 Short-term usage for observation and supervised care (with PACE support) is another novel opportunity with potential advantages to the resident, ALFs, and PACE. In summary, ALFs offer the additional resource of continuously supervised residential care, which can work synergistically with the clinical support and care direction of PACE toward the goal of extending care outside of NHs, meeting individual care needs, and providing high-value care. Preeti Kohli, BA School of Medicine, Stony Brook University, Stony Brook, New York
596
LETTERS TO THE EDITOR
Alicia I. Arbaje, MD, MPH Bruce Leff, MD Deborah Statom, BS Matthew McNabney, MD Division of Geriatric Medicine and Gerontology, School of Medicine, Johns Hopkins University, Baltimore, Maryland
ACKNOWLEDGMENTS Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper. Author Contributions: Kohli, Arbaje, Leff, Statom, McNabney: study concept and design, acquisition of subjects and data, analysis and interpretation of data, preparation of manuscript. Sponsor’s Role: None.
REFERENCES 1. Eng C, Pedulla J, Eleazer GP et al. Program of All-Inclusive Care for the Elderly (PACE): An innovative model of integrated geriatric care and financing. J Am Geriatr Soc 1997;45:223–232. 2. Gross DL, Temkin-Greener H, Kunitz S et al. The growing pains of integrated health care for the elderly: Lessons from the expansion of PACE. Milbank Q 2004;82:257–282. 3. Irvin CV, Massey S, Dorsey T. Determinants of enrollment among applicants to PACE. Health Care Financ Rev 1997;19:135–153. 4. Boult C, Wieland GD. Comprehensive primary care for older patients with multiple chronic conditions: “Nobody rushes you through”. JAMA 2010;304:1936–1943. 5. Meret-Hanke LA. Effects of the program of all-inclusive care for the Elderly on hospital use. Gerontologist 2011;51:774–785. 6. Bowblis JR. Nursing home prices and market structure: The effect of assisted living industry expansion. Health Econ Policy Law 2014;9:95–112. 7. Grabowski DC, Stevenson DG, Cornell PY. Assisted living expansion and the market for nursing home care. Health Serv Res 2012;47:2296–2315. 8. Morgan LA, Rubinstein RL, Frankowski AC et al. The facade of stability in assisted living. J Gerontol B Psychol Sci Soc Sci 2014;69B:431–441. 9. Sloane PD, Zimmerman S, Brown LC et al. Inappropriate medication prescribing in residential care/assisted living facilities. J Am Geriatr Soc 2002;50:1001–1011. 10. Zimmerman S, Gruber-Baldini AL, Sloane PD et al. Assisted living and nursing homes: Apples and oranges? Gerontologist 2003;43:107–117.
RELIABILITY AND VALIDITY OF THE PERSIAN VERSION OF THE FALLS EFFICACY SCALE— INTERNATIONAL 2
To the Editor: With the increase in life expectancy, the number of adults aged 60 and older will increase to 2 billion by 2050, 80% of whom will be living in developing countries.1 Falling is one of the main health problems of older adults, with approximately one-third of individuals aged 65 and older experiencing falls.2 Falls and fear of falls are related to one another, with each being a risk factor for the other.3 Fear of falls may result in avoidance of daily activities and reduction in the older adult’s quality of life.4,5 The Falls Efficacy Scale—International (FES-I) is an instrument that Prevention of Falls Network Europe (ProFaNE) designed to investigate fear of falls in older adults.6 The present study was conducted to assess the reliability
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JAGS
and validity of a Persian version of the FES-I in Iranian older adults.
METHODS Individuals aged 60 and older from a retirement center in Tabriz (Iran) who were able to speak, comprehend, read, and write Persian and were living independently in the community participated (n = 200). The study was performed between October 2012 and March 2013 after approval of the ethics committee of Tabriz University of Medical Sciences. The FES-I is a self-report questionnaire with 16 items assessed on a four-item Likert scale (not at all concerned to very concerned).6 Cronbach alpha (internal consistency) and Spearman-Brown correlation coefficients (test–retest) were used to investigate reliability; values greater than 0.7 indicated good reliability, and values less than 0.5 indicated unacceptable reliability.7 To determine the validity of the construct, exploratory and confirmatory factor analysis were considered. Correlation matrix, principal axis factoring, varimax rotation, and the Kaiser-Meyer-Olkin measure of sampling adequacy (KMO) were used for exploratory factor analysis.1 To evaluate the structure of the factors of exploratory factor analysis, goodness of fit of confirmatory factor analysis was conducted based on chi-square degrees of freedom (v2/df) less than 5, goodness-of fit index (GFI), adjusted goodness-of-fit index (AGFI) greater than 0.9, root mean square residual (RMSR) less than 0.1, root mean square error of approximation (RMSEA) less than 0.08, comparative fit index (CFI) greater than 0.9, normed fit index (NFI) greater than 0.9, non-normed fit index (NNFI) greater than 0.9, incremental fit index (IFI) greater than 0.9, relative fit index (RFI).7 Data analysis was performed using SPSS version 11.5 (SPSS Inc., Chicago, IL). In all analyses, P < .05 was considered statistically significant.
RESULTS Cronbach alpha was 0.90 to 0.95 and Spearman-Brown correlation coefficients were 0.82 to 0.84 for the factors and total instrument. The adequacy of the factor analysis model was confirmed (KMO = 0.936 and for Bartlett test, v2 of Bartlett test was 2,505.781, df 120, P < .05). In exploratory factor analysis with varimax rotation, two factors were extracted. Items 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, and 16 from the questionnaire were loaded in the first factor, and items 11, 13, 14, and 15 were loaded in the second factor. Two extracted factors determined 67.55% of total variance changes.1 In confirmatory factor analysis, based on the comparison of the data of goodness of fit for two- and one-factor analysis and theoretical basic (v2/df < 5, RMSR < 0.1, NFI > 0.9, NNFI > 0.9, CFI > 0.9, IFI > 0.91) and the closeness of GFI and AGFI to 0.9, there was no considerable difference in these values for three models. All relationships between the items and factors were significant (P < .05). Thus, based on this model, the structure of exploratory factor analysis for this questionnaire was confirmed in two factors (Table 1).
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LETTERS TO THE EDITOR
MARCH 2015–VOL. 63, NO. 3
JAGS
not associated with clinical outcomes, perhaps because of the small sample size. The EHR underestimated the prevalence of dementia, and hence there is a lack of association with the EHR delirium marker. In the future, the development of an automated EHR measure of delirium should include efforts that will improve its sensitivity to make it more clinically useful. First, there is a need for continued education of interdisciplinary healthcare teams to improve recognition.9 Second, there is a need to improve the effectiveness of the tool. Two components of the EHR delirium marker (physical restraints and medications) are more likely to identify hyperactive delirium, whereas the third (nurses’ assessment) allows documentation of hypoactive delirium. The nurses’ electronic documentation includes attention, level of consciousness, thought process, and motor behavior (agitation or retarded behavior level). Nurses’ documentation of each parameter may be appropriate but not trigger the delirium marker. The nurses do not have to complete all parameters at each assessment. Further work to validate each nursing assessment and develop an appropriate trigger system based on a larger sample size would be appropriate. Finally, consideration may be given to use of alternative screening tools.10 The findings should be considered in the context of several limitations. The ACE Tracker tool updates data at midnight. The researchers examined participants several hours later (between 8 a.m. and noon), and there may have been a change in their mental status during that time. The researchers examined participants once during the hospital stay, which may have contributed to underestimation of prevalence of delirium. Data were not collected on individuals who refused to participate, limiting knowledge of these nonparticipants and possibly introducing bias. Despite its limitations, the EHR delirium marker is an inexpensive innovation that can be used to alert healthcare professionals to the potential diagnosis of delirium when it is positive. In its current form, it is not a good screening tool because of low sensitivity. Further work should be directed at improving its performance to increase its clinical usefulness.
Conflict of Interest: None. Author Contributions: Khan A., Simpson M., Singh M., Hook M., Geng Y., Malone M.L.: study concept and design, acquisition of data, analysis and interpretation of data, preparation of manuscript, final approval. Sponsor’s Role: None.
Ariba Khan, MBBS, MPH Aurora Health Care, Milwaukee, Wisconsin University of Wisconsin, School of Medicine and Public Health, Milwaukee, Wisconsin
To the Editor: The primary goal of the Program of AllInclusive Care for the Elderly (PACE) is to provide a costeffective, patient-centered, community-based alternative to nursing home care (NH) for a NH-eligible population.1–4 PACE programs are fully capitated and are therefore responsible for all costs associated with health care.2,3,5 Safe and supportive housing can be a critical component of achieving that goal. When care needs for an individual are high, PACE programs might consider assisted living facilities (ALFs) and must weigh the advantages and disadvantages of ALFs against the capabilities of providing care at home (with additional support from PACE) or in a NH. Cost of care is an important consideration. In the United States in 2010, the yearly cost of ALFs was $37,572, and the NH cost was $79,935 for a private room.6,7 ALFs vary in size and staffing model, but provide a monitored and supportive environment that is generally more homelike than NHs, although the skills and expertise typically available in ALFs have been questioned.8,9 The comprehensive care oversight may mitigate this, and the case management that
Michelle Simpson, PhD, RN Maharaj Singh, PhD Mary Hook, PhD, RN-BC Yan Geng, MD Aurora Health Care, Milwaukee, Wisconsin Michael L. Malone, MD Aurora Health Care, Milwaukee, Wisconsin University of Wisconsin, School of Medicine and Public Health, Milwaukee, Wisconsin
ACKNOWLEDGMENTS The authors gratefully acknowledge staff scientific writer and editor Katie Klein of Aurora Research Institute for editorial preparation of the manuscript.
REFERENCES 1. Malone ML, Vollbrecht M, Stephenson J et al. Acute Care for Elders (ACE) tracker and e-Geriatrician: Methods to disseminate ACE concepts to hospitals with no geriatricians on staff. J Am Geriatr Soc 2010;58: 161–167. 2. Rubin FH, Williams JT, Lescisin DA et al. Replicating the Hospital Elder Life Program in a community hospital and demonstrating effectiveness using quality improvement methodology. J Am Geriatr Soc 2006;54: 969–974. 3. Inouye SK, van Dyck CH, Alessi CA et al. Clarifying confusion: The Confusion Assessment Method. A new method for detection of delirium. Ann Intern Med 1990;113:941–948. 4. Inouye SK. The Confusion Assessment Method (CAM): Short CAM Training Manual and Coding Guide. Boston, MA: Hospital Elder Life Program, LLC, 2014. 5. Fick DM, Cooper JW, Wade WE et al. Updating the Beers criteria for potentially inappropriate medication use in older adults: Results of a US consensus panel of experts. Arch Intern Med 2003;163:2716–2724. Erratum: Arch Intern Med 2004;164:298. 6. Morse JM. Preventing Patient Falls. Thousand Oaks, CA: Sage Publications, 1996. 7. Katz S, Ford AB, Moskowitz RW et al. Studies of illness in the aged. The index of ADL: A standardized measure of biological and psychosocial function. JAMA 1963;185:914–919. 8. Straus SE, Richardson WS, Glasziou P et al. Evidence-Based Medicine: How to Practice and Teach EBM, 3rd Ed. Edinburgh: Churchill Livingstone, 2005. 9. Schuurmans MJ, Duursma SA, Shortridge-Baggett LM. Early recognition of delirium: A review of the literature. J Clin Nurs 2001;10: 721–729. 10. LaMantia MA, Messina FC, Hobgood CD et al. Screening for delirium in the emergency department: A systematic review. Ann Emerg Med 2014;63:551–560.
ASSISTED LIVING FACILITY USE BY THE PROGRAM OF ALL-INCLUSIVE CARE FOR THE ELDERLY
JAGS
MARCH 2015–VOL. 63, NO. 3
LETTERS TO THE EDITOR
595
Table 1. Characteristics of Program of All-Inclusive Care for the Elderly (PACE) Participants According to Living Situation*
Characteristic
Age, mean SDa Female, % Black, % Number of medical diagnoses, mean SD Number of total medications, mean SD Number of activity of daily living dependencies, mean SDa Bladder incontinence, % Bowel incontinence, % Dementia diagnosis, % Depression diagnosis, % Presence of other psychiatric conditions, % Number of hospital days in past 6 months, mean SD Months in program, mean SDa Scheduled attendance at PACE, d/wk, mean SD Scheduled home care visits, d/wk, mean SDa
Total, N = 126
78.2 80 62 11.4 12.2 3.0 76 46 47 40 55 1.1 37.9 2.4 0.5
9.7
3.7 5.9 2.4
3.3 31.7 1.4 1.3
Home Alone, n = 27
74.2 74 70 10.7 13.4 0.8 52 19 22 30 37 0.8 28.6 2.4 1.1
10.9
4.0 6.3 1.3
2.7 30.1 1.1 2.1
Home with Caregiver, n = 61
76.7 87 63 11.3 12.7 2.9 74 38 46 38 57 1.0 26.2 2.7 0.6
9.6
5.4 5.4 2.3
3.0 20.5 1.5 1.2
Assisted Living Facility, n = 29
84.1 72 55 11.7 10.2 4.5 93 72 68 41 62 1.8 54.3 2.3 0
6.4
3.9 5.6 1.9
4.7 28.2 1.4
Nursing Home, n = 9
80.8 78 56 13.2 10.8 5.9 100 89 56 56 67 1.3 88.8 1.0 0
5.6
5.0 7.6 0.3
2.0 39.1 1.9
*At one PACE site - Hopkins ElderPlus, Baltimore, MD. One-way analysis of variance was used to test for differences between living arrangements; aP < .001. SD = standard deviation.
PACE provides can offload tasks from and provide assistance to ALFs in managing PACE enrollees who reside there. The aim of this study was to identify factors associated with ALF use by one PACE program and to characterize potential advantages and disadvantages of ALF use within the PACE model. This study was conducted at one PACE site—Hopkins Elder Plus (HEP)—in two parts: cross-sectional examination of patient-level data abstracted from charts at HEP to compare characteristics of PACE enrollees who resided in different settings and qualitative content analysis of three PACE staff focus group discussions to improve understanding of factors associated with the use of ALFs for PACE enrollees. One hundred and twenty-six charts (100% of enrollees in November 2010) were abstracted, and participants were categorized according to four residential types: home alone, home with family, ALF, and NH (Table 1). This analysis showed that participants residing in ALFs and NHs were similar to those living at home except that they were older and more functionally dependent, had been enrolled in PACE longer, and as expected, received no home care. The three focus groups consisted of clinical (n = 7), social work and administrative (n = 8), and recreational and transport (n = 7) staff members who had been working at PACE for an average of 8.2 years. Content analysis revealed that the primary determinants of ALF use were safety concerns for participants and impaired health and limited capabilities of home caregivers. PACE staff members cited several specific examples of individuals for whom these concerns were the primary drivers of ALF use. Cost was also mentioned as an important consideration when making housing recommendations (e.g., ALF vs NH), because PACE programs are typically responsible for much of the cost. Staff members noted that they were aware of the cost differences and were careful to ensure
that higher levels of care (such as NH) were truly necessary. Discussions about trade-offs (autonomy and quality of life vs safety and assistance) were also part of these discussions, as well as the “home-like” atmosphere of ALFs. ALFs were also being used frequently for a temporary, monitored living situation to meet a short-term need. This happens “a few times per month,” according to focus group members. Factors such as geographic proximity to family also prompted ALF use (over NH) for short-term needs. This mixed-methods study is the first to provide data on issues related to the use of ALFs by a PACE program. Although older and more functionally dependent, participants living in ALFs were otherwise similar to those living at home, suggesting that other factors influence the decision to use ALFs. The quantity and types of services that PACE provides (home care, visits to day care centers) to certain participants could have influenced who was able to stay at home and who needed ALF or NH. The potential advantages of collaboration between ALFs and PACE include the clinical support that PACE provides, which can fill gaps in the capacity of ALFs to care for frail and medically complex residents whom they would otherwise be unable to manage.8–10 Short-term usage for observation and supervised care (with PACE support) is another novel opportunity with potential advantages to the resident, ALFs, and PACE. In summary, ALFs offer the additional resource of continuously supervised residential care, which can work synergistically with the clinical support and care direction of PACE toward the goal of extending care outside of NHs, meeting individual care needs, and providing high-value care. Preeti Kohli, BA School of Medicine, Stony Brook University, Stony Brook, New York
596
LETTERS TO THE EDITOR
Alicia I. Arbaje, MD, MPH Bruce Leff, MD Deborah Statom, BS Matthew McNabney, MD Division of Geriatric Medicine and Gerontology, School of Medicine, Johns Hopkins University, Baltimore, Maryland
ACKNOWLEDGMENTS Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper. Author Contributions: Kohli, Arbaje, Leff, Statom, McNabney: study concept and design, acquisition of subjects and data, analysis and interpretation of data, preparation of manuscript. Sponsor’s Role: None.
REFERENCES 1. Eng C, Pedulla J, Eleazer GP et al. Program of All-Inclusive Care for the Elderly (PACE): An innovative model of integrated geriatric care and financing. J Am Geriatr Soc 1997;45:223–232. 2. Gross DL, Temkin-Greener H, Kunitz S et al. The growing pains of integrated health care for the elderly: Lessons from the expansion of PACE. Milbank Q 2004;82:257–282. 3. Irvin CV, Massey S, Dorsey T. Determinants of enrollment among applicants to PACE. Health Care Financ Rev 1997;19:135–153. 4. Boult C, Wieland GD. Comprehensive primary care for older patients with multiple chronic conditions: “Nobody rushes you through”. JAMA 2010;304:1936–1943. 5. Meret-Hanke LA. Effects of the program of all-inclusive care for the Elderly on hospital use. Gerontologist 2011;51:774–785. 6. Bowblis JR. Nursing home prices and market structure: The effect of assisted living industry expansion. Health Econ Policy Law 2014;9:95–112. 7. Grabowski DC, Stevenson DG, Cornell PY. Assisted living expansion and the market for nursing home care. Health Serv Res 2012;47:2296–2315. 8. Morgan LA, Rubinstein RL, Frankowski AC et al. The facade of stability in assisted living. J Gerontol B Psychol Sci Soc Sci 2014;69B:431–441. 9. Sloane PD, Zimmerman S, Brown LC et al. Inappropriate medication prescribing in residential care/assisted living facilities. J Am Geriatr Soc 2002;50:1001–1011. 10. Zimmerman S, Gruber-Baldini AL, Sloane PD et al. Assisted living and nursing homes: Apples and oranges? Gerontologist 2003;43:107–117.
RELIABILITY AND VALIDITY OF THE PERSIAN VERSION OF THE FALLS EFFICACY SCALE— INTERNATIONAL 2
To the Editor: With the increase in life expectancy, the number of adults aged 60 and older will increase to 2 billion by 2050, 80% of whom will be living in developing countries.1 Falling is one of the main health problems of older adults, with approximately one-third of individuals aged 65 and older experiencing falls.2 Falls and fear of falls are related to one another, with each being a risk factor for the other.3 Fear of falls may result in avoidance of daily activities and reduction in the older adult’s quality of life.4,5 The Falls Efficacy Scale—International (FES-I) is an instrument that Prevention of Falls Network Europe (ProFaNE) designed to investigate fear of falls in older adults.6 The present study was conducted to assess the reliability
MARCH 2015–VOL. 63, NO. 3
JAGS
and validity of a Persian version of the FES-I in Iranian older adults.
METHODS Individuals aged 60 and older from a retirement center in Tabriz (Iran) who were able to speak, comprehend, read, and write Persian and were living independently in the community participated (n = 200). The study was performed between October 2012 and March 2013 after approval of the ethics committee of Tabriz University of Medical Sciences. The FES-I is a self-report questionnaire with 16 items assessed on a four-item Likert scale (not at all concerned to very concerned).6 Cronbach alpha (internal consistency) and Spearman-Brown correlation coefficients (test–retest) were used to investigate reliability; values greater than 0.7 indicated good reliability, and values less than 0.5 indicated unacceptable reliability.7 To determine the validity of the construct, exploratory and confirmatory factor analysis were considered. Correlation matrix, principal axis factoring, varimax rotation, and the Kaiser-Meyer-Olkin measure of sampling adequacy (KMO) were used for exploratory factor analysis.1 To evaluate the structure of the factors of exploratory factor analysis, goodness of fit of confirmatory factor analysis was conducted based on chi-square degrees of freedom (v2/df) less than 5, goodness-of fit index (GFI), adjusted goodness-of-fit index (AGFI) greater than 0.9, root mean square residual (RMSR) less than 0.1, root mean square error of approximation (RMSEA) less than 0.08, comparative fit index (CFI) greater than 0.9, normed fit index (NFI) greater than 0.9, non-normed fit index (NNFI) greater than 0.9, incremental fit index (IFI) greater than 0.9, relative fit index (RFI).7 Data analysis was performed using SPSS version 11.5 (SPSS Inc., Chicago, IL). In all analyses, P < .05 was considered statistically significant.
RESULTS Cronbach alpha was 0.90 to 0.95 and Spearman-Brown correlation coefficients were 0.82 to 0.84 for the factors and total instrument. The adequacy of the factor analysis model was confirmed (KMO = 0.936 and for Bartlett test, v2 of Bartlett test was 2,505.781, df 120, P < .05). In exploratory factor analysis with varimax rotation, two factors were extracted. Items 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, and 16 from the questionnaire were loaded in the first factor, and items 11, 13, 14, and 15 were loaded in the second factor. Two extracted factors determined 67.55% of total variance changes.1 In confirmatory factor analysis, based on the comparison of the data of goodness of fit for two- and one-factor analysis and theoretical basic (v2/df < 5, RMSR < 0.1, NFI > 0.9, NNFI > 0.9, CFI > 0.9, IFI > 0.91) and the closeness of GFI and AGFI to 0.9, there was no considerable difference in these values for three models. All relationships between the items and factors were significant (P < .05). Thus, based on this model, the structure of exploratory factor analysis for this questionnaire was confirmed in two factors (Table 1).
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