eMJA     The Medical Journal of Australia

Home | Issues | eMJA shop | Classifieds | Contact | More... | Topics | Search | Login | Buy full access   

Abstract

In Australian communities, the Dubbo Study reported a 1.7% cumulative incidence of nursing home admission during a 4.2-year follow-up of 1237 men and 1568 women aged 60 years and over.^8,12 A population-based cohort study of hip fracture and risk of institutionalisation in western Sydney⁹ reported a 5% incidence of nursing home placement among 160 control patients (without hip fracture) aged 65 years and over during 14 months.

Although the Australian Institute of Health and Welfare has estimated the lifetime probability of nursing home use by age and sex,¹³ no Australian reports have provided age-specific and sex-specific incidence or predictors of nursing home placement from a defined general older population. As the proportion of frail older people living in the community increases,¹⁴ studies of the incidence of and predictions of nursing home placement could assist in making accurate projections of future demands for nursing homes.

The purpose of this report is to describe age-specific and sex-specific six-year cumulative incidence of nursing home placement in a defined, representative older Australian urban population. We also aimed to assess baseline non-cognitive factors associated with placement.

Participants

This project was approved by the Western Sydney Area Human Research Ethics Committee and written, informed consent was obtained from all 3654 participants. Baseline examinations were performed from 14 January 1992 to 10 January 1994, with a participation rate of 82.4% overall. Baseline non-participants had a slightly higher proportion of persons aged 80 years and over (10% in participants v. 16.8% in non-participants).¹⁶ All participants were invited to attend five-year follow-up examinations from 29 May 1997 to 2 December 1999. Deaths were identified by cross-matching study participants with Australian National Death Index data.

Assessing nursing home admissions

In assessing cumulative incidence of nursing home admission, we included participants who had been given permanent placement in nursing homes in NSW only; those admitted to hostels were excluded. We identified participants living in nursing homes during the follow-up period from information provided by family members, relatives or neighbours. Records of participants who had died since baseline or with whom we lost contact were cross-checked with ACAT records at the Blue Mountains District Hospital. This allowed us to identify people for whom permanent nursing home placement had been approved since their baseline examination to December 1999. This list was further cross-checked against records of subsidy payments by staff in the Aged Care and Planning Branch at the New South Wales State Office of the Commonwealth Department of Health and Aged Care (people admitted to nursing homes receive government subsidy payments that are paid directly to the nursing home).

Assessing non-cognitive factors

Visual acuity was measured while participants wore their current glasses, by means of a LogMAR chart, and was followed by a standardised subjective refraction.¹⁶ Visual acuity in each eye was recorded as the number of letters read correctly (from 0 [< 6/60] to 70 [6/3], after refraction).

Statistical analysis

² Statistics were used to compare the characteristics of participants who were followed (ie, those who attended follow-up interviews, those who had died, and those admitted to nursing homes) with those who had moved. Cox proportional hazard regression analyses were performed with SAS.²⁰ Our model was based on the conceptual framework for studies of determinants of medical care use described by Andersen and Newman.^3,21 This includes three study factor types: predisposing factors (eg, age, education, living status [eg, living alone]), enabling factors (eg, financial status or paying resource for use of services), and need factors (eg, disabilities, impairments, health status). We also assessed health-risk behaviours (eg, smoking, alcohol consumption) in relation to nursing home placement. Nursing home placement was the hazard event, and time to event was calculated as days since baseline examination to nursing home admission. Age was included in the model as a continuous variable. Sociodemographic measures and presence of diseases or impairments were defined dichotomously. Each study factor was assessed initially by age-adjusted Cox regression. Final multivariate models were constructed to assess the risk ratio of each factor at baseline to subsequent nursing home placement, while adjusting for other covariables, including a multivariate Cox regression model without the vision variable (Model 1) and a Cox model with the vision variable (Model 2). Interactions between each study variable and time to event were checked, with no significant interaction found. Risk ratios (RR) and 95% confidence intervals (95% CI) are presented.

The two people admitted to hostels (rather than nursing homes) were also excluded, leaving 3268 participants. These comprised 1846 women (56.5%) and 1422 men (43.5%). By October 1999, 162 participants (5.0%) were confirmed to have been admitted for permanent nursing home care in NSW, including 104 women (5.6%) and 58 men (4.1%). Of the 604 participants who died since the baseline examination, 120 (19.9%) were admitted to a nursing home before death. Among the remaining 2664 study participants still alive, 42 (1.6%) were current nursing home residents in December 1999. An additional 26 people (0.8%) had received local ACAT approval for nursing home admission, but, as we could not confirm that they had ever received subsidy payments, these people were included in our analyses as not having been admitted to a nursing home.

Incidence of nursing home placement

The median duration of nursing home residence until death among 120 nursing home residents who died was 145 days, ranging from one day to 5.4 years (lower quartile, 35 days; upper quartile, 1.4 years).

Non-cognitive factors predicting nursing home placement

Our study has a number of limitations. Firstly, we could have underestimated or overestimated the number of people placed in nursing homes if those who had moved out of the area were more or less likely to be admitted. As this group had a slightly younger mean age, we may have slightly overestimated the incidence of nursing home placement. A potential underestimate could also have arisen as (i) participants were slightly less likely to be aged 80 or more years than non-participants,¹⁶ and (ii) 26 participants given approval for nursing home admission, but for whom admission was not confirmed, were excluded.

Secondly, although cognitive impairment is the most frequent disability leading to nursing home placement, we did not assess cognitive status at baseline, so could not include this among factors predicting admission. The result of measuring cognitive status and incorporating these data into the predictive model might have either (i) caused the effect of non-cognitive factors to disappear or be reduced in magnitude, or (ii) continued to show associations between non-cognitive factors and admission. At baseline, the study population included non-institutionalised residents aged 49-97 years, and virtually all participants (98%) had visual acuity from both eyes measured reliably, so we feel it is unlikely that many participants had significant dementia or cognitive impairment at that time. The US Long-Term Care Channeling Demonstration found that effects on the use of aged-care facilities of self-rated health, home ownership and physical impairment were independent of cognitive impairment.³ Further, as some non-cognitive factors are modifiable, it is important to recognise their effects on outcomes if these effects are independent of cognitive impairment. Although we did not specifically assess activities of daily living (ADL), we did assess whether participants had difficulty in walking, which may be a marker for ADL limitation.

Despite these reservations, to our knowledge this is the first population-based study to provide age-specific and sex-specific incidence for nursing home admission in an Australian community. Our six-year crude incidence of nursing home placement (5.0% among people aged 50 or more years) was substantially higher than the overall rate reported from the Dubbo Study (1.7% over 4.2 years among people aged 60 or more years),^8,12 but lower than for a group of normal control subjects (5% over 1.2 years).⁹

The Dubbo Study reported higher nursing home admission rates in women than in men.⁸ Although we found no overall difference between the sexes, a substantially higher incidence was found for women aged 80 years or older. This finding is in keeping with an Australian Institute of Health and Welfare report, which provided age-sex profiles of 73 552 nursing home residents in 1994.²² This report shows similar proportions for women and men up to age 75, but three times as many female as male Australian nursing home residents above this age.

The Dubbo Study also reported that age and disability were significant factors in a multivariate model predicting nursing home admission.⁸ The likelihood of nursing home admission in the Dubbo Study increased by 13% for each year of age; this is identical to the age effect in our multivariate Cox regression model (13%-14% per year).

Among relatively old and frail US Medicare beneficiaries, those who owned their homes were reported to have 30% fewer nursing home admissions;³ this finding was attributed to the strong sense of attachment and place associated with home ownership. In our population, people owning their home at baseline were 60% less likely to have subsequent nursing home admission. We classified lack of home ownership as an enabling factor, as people in this group are more likely to be dependent on pensions. Nursing home admission for these people may not result in additional costs over and above their pension, while means testing may require home owners to pay a substantial sum up-front for nursing home placement. Home owners might be able or may prefer to "buy in" support services to assist them in staying at home longer. They may also have a stronger emotional attachment to a house they have lived in for decades.

The multivariate model derived from our data contained five significant predictive factors and one protective factor. These five factors represent all three categories in Andersen and Newman's conceptual framework for determinants of use of medical care,²¹ and so may be applicable in describing determinants of institutional aged-care use in Australia as well as in the US.

As shown in previous reports,^23,24 our findings also suggested that reduced vision might be a marker or an independent predictor of nursing home admission. Our study showed that there was a substantially higher prevalence of visual impairment among nursing home residents than people of similar age living in the community.²⁵ This finding highlights the need for older persons being assessed for nursing home placement to have their vision and hearing tested for treatable causes of sensory impairment.

1. Kane RA, Kane RL. Evidence about the need for care. Long-term care: Principles, programs and policies. New York: Springer, 1987: 25-32.
2. Jette AM, Branch LG, Sleeper LA, et al. High-risk profiles for nursing home admission. Gerontologist 1992; 32: 634-640.
3. Greene VL, Ondrich JI. Risk factors for nursing home admissions and exits: a discrete-time hazard function approach. J Gerontol 1990; 45 (6 Suppl): S250-S258.
4. Murtaugh CM, Kemper P, Spillman BC. The risk of nursing home use in later life. Med Care 1990; 28: 952-962.
5. Wolinsky FD, Callahan CM, Fitzgerald JF, Johnson RJ. The risk of nursing home placement and subsequent death among older adults. J Gerontol 1992; 47 (4 Suppl): S173-S182.
6. Dwyer JW, Barton AJ, Vogel WB. Area of residence and the risk of institutionalization. J Gerontol 1994; 49 (2 Suppl): S75-S84.
7. Tinetti ME, Williams CS. Falls, injuries due to falls, and the risk of admission to a nursing home. N Engl J Med 1997; 337: 1279-1284.
8. McCallum J, Simons L, Simons J, Wilson J. Best Practice Paper 6. Hospital and home: a longitudinal study of hospital, residential and community service use by older people living in Dubbo, NSW. Sydney, NSW Office on Ageing, Social Policy Directorate, 1994: 1-35.
9. Cumming RG, Klineberg R, Katelaris A. Cohort study of risk of institutionalisation after hip fracture. Aust N Z J Public Health 1996; 20: 579-582.
10. Cohen MA, Tell EJ, Wallack SS. The risk factors of nursing home entry among residents of six continuing care retirement communities. J Gerontol 1988; 43 (1 Suppl): S15-S21.
11. Lord SR. Predictors of nursing home placement and mortality of residents in intermediate care. Age Ageing 1994; 23(6): 499-504.
12. McCallum J, Simons L, Simons J, et al. The continuum of care for older people. Aust Health Rev 1995; 18: 40-55.
13. Liu Z. The probability of nursing home use over a lifetime. Canberra: Australian Institute of Health and Welfare, 1998: 1-21. (Welfare Division Working Paper No. 16.)
14. Australian Institute of Health and Welfare. Aged and respite care in Australia: extracts from recent publications. Canberra: AIHW, 1997: 1-102.
15. Wang JJ, Mitchell P, Smith W, Leeder SR. Factors associated with use of community support services in an older Australian population. Aust N Z J Public Health 1999; 23: 147-153.
16. Attebo K, Mitchell P, Smith W. Visual acuity and the causes of visual loss in Australia. The Blue Mountains Eye Study. Ophthalmology 1996; 103: 357-364.
17. Congalton AA. Status and prestige in Australia. Melbourne: F W Cheshire, 1969: 1-160.
18. McLennan WAS. Population projections: 1997 to 2051. Canberra: Australian Bureau of Statistics, 1998: 1-130.
19. Breslow NE. Rates and rate standardisation. Statistical methods in cancer research. Vol. II. Lyon: International Agency for Research on Cancer, 1987: 48-79.
20. SAS [computer program]. Version 6.12. Cary, NC: SAS Institute Inc, 1995.
21. Andersen R, Newman JF. Societal and individual determinants of medical care utilization in the United States. Milbank Mem Fund Q Health Soc 1973; 51: 95-124.
22. Jenkins A. Client profiles for aged care services in Australia: Canberra, Australian Institute of Health and Welfare, 1996: 1-58. (Welfare Division Working Paper No. 11.)
23. Peterson R, Kirchner C. Prevalence of blindness and visual impairment among institutional residents. Statistical brief No. 11. J Vis Impairm Blindness October 1980: 323-326.
24. Tielsch JM, Javitt JC, Coleman A, et al. The prevalence of blindness and visual impairment among nursing home residents in Baltimore. N Engl J Med 1995; 332: 1205-1209.
25. Mitchell P, Hayes P, Wang JJ. Visual impairment in nursing home residents: the Blue Mountains Eye Study. Med J Aust 1997; 166: 73-76.

Authors' details

National Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT.
Wayne Smith, BMed, PhD, Senior Fellow.

Reprints will not be available from the authors.
Correspondence: Associate Professor Paul Mitchell, Department of Ophthalmology, University of Sydney, Eye Clinic, Westmead Hospital, Hawkesbury Road, Westmead, NSW, Australia, 2145.
paulmiATwestgate.wh.usyd.edu.au

©MJA 2001
Make a comment