eMJA: Wang et al, Incidence of nursing home placement in a defined community

Healthcare

Incidence of nursing home placement in a defined community

Jie Jin Wang, Paul Mitchell, Wayne Smith, Robert G Cumming and Stephen R Leeder

MJA 2001; 174: 271-275
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Abstract - Methods - Results - Discussion - Acknowledgements - References - Authors' details
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Abstract

Objective: To assess cumulative incidence and non-cognitive factors predicting nursing home placement in a defined older population.
Design and setting: Six-year follow-up of a population-based cohort living west of Sydney.
Participants: 3654 non-institutionalised residents aged 49 years or older (82.4% of those eligible) participated in baseline examinations during 1992 to 1994.
Main outcome measures: Permanent nursing home admission for long-term institutionalised aged care in New South Wales, confirmed by records of approvals by the regional Aged Care Assessment Team and subsidy payments by government.
Results: After excluding 384 participants who moved from the area or were lost to follow-up, 162 participants (5.0%) had been admitted to nursing homes on a permanent basis by October 1999. Of participants who died since baseline, 20% had been admitted to a nursing home before death. Of those alive, 1.6% were current nursing home residents. Six-year cumulative incidence rates for nursing home placement were 0.7%, 1.1%, 2.4%, 3.9%, 9.0%, 18.3% and 34.9% for people aged 55-59, 60-64, 65-69, 70-74, 75-79, 80-84 and 85 years or older, respectively. Non-cognitive factors at baseline predicting subsequent nursing home admission included each additional year of age (risk ratio [RR], 1.14), fair or poor compared with excellent self-rated health (RR, 2.9, 3.6), walking difficulty (RR, 3.6) and current smoking (RR, 1.9). People owning their homes had a decreased likelihood of nursing home placement (RR, 0.6).
Conclusions: Incidence rates of institutional aged care doubled for each five-year interval from the age of 60 years. A range of non-cognitive factors predict nursing home placement.

Although there is a rich literature describing risk factors for nursing home placement,^1-5 few studies have reported its incidence in a defined older general population.^2,5-9 Most cohort studies of nursing home placement have been conducted in samples of at-risk older persons, and mostly in the United States.^{1-3,5-7,10,11}

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.

Methods

Participants

We used participants in the Blue Mountains Eye Study, a population-based cohort study of vision and health parameters among non-institutionalised residents, initially aged 49 years and over, living in a defined area west of Sydney. The study area includes two postcode areas (2780, 2782), which comprise the suburbs Katoomba, Leura, Medlow Bath and Wentworth Falls. The baseline study population was fairly representative of Australia's ethnic mix, but was older and had slightly higher socioeconomic status than the State population for this age.¹⁵

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

Australians requiring institutional aged care need prior assessment and approval from a regional hospital Aged Care Assessment Team (ACAT) to determine whether they need respite or permanent care, and low-level or high-level care. People who need low-level care receive approval for hostel admission and those needing high-level care receive approval for nursing home admission.

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

Baseline characteristics were collected during face-to-face interviews with all participants using a standardised questionnaire. Participants were asked whether they regularly used community support services, including Meals-on-Wheels, Home Care or community nurse visits. They were asked whether they owned their home, or were renting or living in a relative's home, and whether they lived alone, with a spouse or with others. Occupational prestige was assessed using the Daniel Occupational Prestige Scale.¹⁷ A detailed medical history was taken, including history of angina, heart attack, stroke, hypertension, diabetes, arthritis or gout. There were questions about smoking, alcohol consumption, falls, regular exercise and self-reported hearing loss. Participants in whom the examiner noted difficulty in walking or use of a cane, walker or wheelchair were categorised as having a "walking disability". Global self-rated health was assessed by asking, "For someone of your age, how would you rate your overall health? Would you say it is excellent, good, fair, or poor?"

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

Age-standardised incidence rates for nursing home placement in the study population were calculated by direct standardisation to the 2001 Australian projected population (series II);¹⁸ 95% confidence intervals of the standardised rates were calculated using the Breslow method.¹⁹ We excluded the 49-year-old participants to compute incidence rates for persons aged 50 years or older. For those who had died since being admitted, the duration (in days) of nursing home residence was calculated from the date of admission to date of death.

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.

Results

The study population comprised 3654 participants aged 49-97 years. By the time of the follow-up examinations in 1997-1999, 384 of the 3654 baseline participants (10.5%) had moved away from the study area and were excluded from this study. Comparison of baseline characteristics for the remaining 3270 participants and the 384 who moved away showed that those who had moved out of the area were younger (40% were aged < 60 years, compared with 26% in the remaining participants), slightly less likely to own their home (81% v. 89%), to have had cancer (5.7% v. 8.8%), and were more likely to report diabetes (9.9% v. 6.6%).

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

To compute incidence rates for nursing home admission for people aged 50 years or older, 17 people aged 49 years were excluded. Box 1 shows six-year cumulative crude age-specific and sex-specific incidence of nursing home placement in five-year age groups. From age 60 years, incidence rates for increasing five-year age intervals doubled. The age-standardised six-year incidence rate was 4.55% (95% CI, 3.83%-5.26%) for people aged 50 years or older, 5.77% (95% CI, 4.63%-6.91%) for women and 3.25% (95% CI, 2.40%-4.10%) for men.

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

Baseline non-cognitive predisposing factors for nursing home placement (age, marital status, living status), enabling factors (home ownership, job prestige), need factors (disabilities, diseases, impairments), and health-risk behaviours (smoking, alcohol consumption, exercise) were all significantly associated with subsequent nursing home admission, after adjusting for age (Box 2). Some significant findings could have arisen by chance as a result of the multiple comparisons made. In the multivariate Cox regression model, age, lack of home ownership, reduced (fair or poor) self-rated health, presence of a walking disability and current smoking at the baseline interview were significantly associated with an increased risk of subsequent nursing home placement (Box 3, Model 1). People who regularly consumed alcohol at baseline had a reduced risk of subsequent nursing home placement. For each one-line reduction in best-corrected visual acuity at baseline, there was a borderline association with subsequent nursing home placement (Box 3, Model 2).

Discussion

Blue Mountains Eye Study participants were drawn from a defined suburban area with well established community support services. We were able to identify baseline study participants who received ACAT approval for admission to a nursing home and could also confirm their placement in permanent nursing home care, as well as its duration since admission.

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.

Acknowledgements

We greatly appreciate assistance from ACAT staff at the Blue Mountains District Hospital, Wentworth Area Health Service and from staff in the Aged Care and Planning Branch, New South Wales State Office, Commonwealth Department of Health and Aged Care, for their help in confirming nursing home placement. This study was supported by the Australian Department of Health and Family Services and the Save Sight Institute, University of Sydney. Dr Wang held a National Health and Medical Research Council Public Health Postgraduate Research Scholarship (No. 987445) when this study was conducted.

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(Received 19 Jun, accepted 5 Oct, 2000)

Authors' details

University of Sydney, NSW.
Jie Jin Wang, MB BS, MMed(Clin Epi), Epidemiologist, Department of Ophthalmology, Save Sight Institute;
Paul Mitchell, MD, PhD, Associate Professor, Department of Ophthalmology, Save Sight Institute;
Robert G Cumming, MB BS, PhD, Associate Professor, Department of Public Health;
Stephen R Leeder, MB BS, PhD, Professor and Dean, Faculty of Medicine.

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