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| Journal of the New Zealand Medical Association,
26-October-2007, Vol 120 No 1264
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Trends in hospital bed utilisation in New Zealand
1989 to 2006: more or less beds in the future?
Laurence Malcolm
Hospital beds are an important and costly resource for all
health systems. Attempts to contain health expenditure for the past 2–3
decades have focused strongly upon reducing both bed numbers and utilisation
through reducing length of stay, admission gate-keeping processes, and the
provision of alternative community-based
services1. As a consequence, there has been a
progressive reduction in bed rate availability and utilisation in almost all
health systems.
District health boards (DHBs), with their integrated
hospital/secondary and primary care/community-based services, provide an
opportunity open to few other health systems to develop alternatives to
expensive hospital-based care. With increasing pressures upon DHB resources and
the emergence of an organized primary health care system, several initiatives
have been established through DHBs in conjunction with independent practice
associations (IPAs) and more recently primary health organisations (PHOs) to
develop more substantial community-based alternatives to hospital care.
In New Zealand, downward trends in bed availability and bed
days have been apparent for at least two decades in hospital boards and more
recently in DHBs. This has been promoted by population-based
funding.2 Although the Ministry of Health for
nearly two decades has published data on hospital utilisation/throughput, there
has been very little analysis of the trends from these
data.3
This paper therefore has the following aims: to analyse
trends in hospital bed utilisation for bed availability, bed days, discharge
rates, and trends in day patients from 1988 to 2006; to examine variation
between DHBs in utilisation; to consider factors which may explain the trends
observed; to compare the results with international trends in bed utilisation
(particularly Australia, England, and the US), and to discuss future need for
beds and factors affecting this need, including the integrated DHB system.
MethodsThe following sources of data and information were used
to prepare this paper.
Ministry of Health Hospital Throughput reports were
obtained from 1988/89 to 2005/06.3 Full details
of how the data are prepared for comparison are presented in the Appendices to
these reports. For the most part, these reports are on the Ministry of Health
website but for the 3 years ending June 2006 were obtained directly from
Ministry staff. These reports present data on hospital utilisation in detail and
trend data. The data were used to analyse trends in discharges/1000 population,
day patients, bed days, and average length of stay (ALOS) and bed availability.
Allowance for bed days and numbers was made for day patients by allocating one
bed day per day patient.
Ministry of Health data were sought for numbers of
licensed beds, both public and private. However the latest available figures are
only for 2001. Hence current bed availability was calculated assuming 85%
occupancy. New Zealand Private Surgical Hospitals Association provided private
hospital data from reports from its membership for the 2006/07. Data for some
previous years was derived from reports and surveys of the Private Hospitals
Association.
New Zealand Hospital Throughput data have been case
weighted, ‘filtered’, and ‘truncated’ to ensure as far
as possible comparability between DHBs and their predecessors and trends over
time. Filtering removes certain DRG categories such as well babies, short-stay
observations, endoscopies, some mental health events, and other categories
totalling about 26. Truncation removes bed days above the 97th percentile for
each DRG. The overall effect is to reduce discharge numbers by about 3% and bed
days by 6%.
Comparison between years and a DHBs were based upon
standardised data.3 Two methods were used by
the MOH. For comparison by DHB regions, age/socioeconomic deprivation/ethnic
standardised nation was employed. For national comparisons over time, and
comparisons between socioeconomic deprivation groups, age/gender
standardisation was used.3
Casemix adjustment takes into account changes in the
types of treatment that patients receive which vary in complexity and cost.
Comparisons between DHBs and over time were based upon national hospital
reference prices.3 This takes into account the
change in ICD coding from July 2000 and other changes, e.g. in data
quality.
International sources included data from the Australian
Institute of Health and Welfare4 and a
comparison of hospital utilisation in the UK NHS and the Californian-based HMO
Kaiser Permanente by Feachem et al.5
ResultsTrends in bed availability and utilisation in New ZealandTable 1 presents calculated results
of rates of bed availability and utilisation annually for the period 1988/89 to
2005/06. Trends in these indicators are also presented in Figures 1–4. The
following findings should be noted from Table 1 and the Figures.
Discharge rates—Total discharge
rates, and especially standardised discharge rates, have steady increased until
2000/01 but since then the rate has flattened off. However this increase is
almost entirely due to the rate of increase in day patients with no increase
over this period in inpatient rates. The larger increase in standardised rates
indicates an increase in acuity of patients receiving hospital care.
Average length of stay (ALOS)—Figure
2 shows at a continuing steady decline over the period in both inpatient and
total ALOS. In 1988/89, the inpatient ALOS was 6.67 and the total 6.13 days. In
2005/2006 these figures had declined to 3.90 and 2.81 days respectively i.e. a
more than 50% drop in the total figure.
Rate of bed days utilisation—Figure 3
shows an associated decline in inpatient bed days. As described above these are
truncated for the purposes of comparison between years. Table 1 also shows an
increasing number of the day patients in which one-bed day was allocated for
each day patient.
Rate of bed availability—Figure 4
shows the calculated availability of beds in both public and private sectors. As
indicated above data are not available from the Ministry of Health for public
bed availability since 2000. In any case the figures provided in the past by the
MOH have been based upon licensed beds and have not excluded beds used for
longer stay patients, including mental patients. Hence they cannot be
appropriately compared for trends with the truncated and filtered Hospital
Throughput data. The trends of Table 1 and Figure 4 show a decline in public bed
availability to a current 1.56/1000 with some increase in the private sector to
0.4 giving a total of nearly 2.0/1000 population.
Variation between DHBs—Figure 5 shows
the variation between DHBs in discharge ratios standardised for age, gender, and
casemix for the 2005/2006 year. There is obviously a wide variation between DHBs
from a low of 0.84 in Capital and Coast DHB to a high of 1.20 in Tairawhiti and
Wairarapa DHBs—i.e. a ratio of 43%. In other words even after allowing for
need factors some DHBs have very much lower and higher discharge rates than
others.
Ministry of Health calculations indicate that variations of
more than about 4% from the mean of 1.00 are statistically highly significant at
99% confidence limits. Examination of the equivalent data for 2001/2002 shows
that this variation has markedly increased.
International comparisonsTable 2 compares the most recent New Zealand data Australian
sources for 2005 (Australian Institute of Health and
Welfare)4 and data from a comparative study of
the UK NHS with the California-based HMO Kaiser Permanente for the year
2000.5,6 Although not specifically stated it
appears that this comparison includes only inpatients and excludes day
patients.
Table 2. Comparison of rates/1000 population
for bed utilisation in New Zealand (2005/06), Australia (2005), NHS (2000), and
US Kaiser Permanente (2000)
Given the different years of comparison and trends over
previous years it is likely that both the NHS and Kaiser figures for ALOS and
acute bed days and probably bed availability are likely to be even less than
those presented.
Although there are uncertainties in these comparisons, with
doubts about what is included and excluded, they clearly indicate that New
Zealand has a much lower rate of hospital utilisation than Australia and the NHS
but a much higher rate than Kaiser Permanente. The latter figures clearly
indicate that the much lower rate of bed utilization, less than 50%, of the
figures for New Zealand is due to much lower discharge rates than the
ALOS.
Figure 1. Trends in rates of throughput
(discharges) per 1000 population of public and total daypatients and inpatients,
inclusive of daypatients from 1998/89 to 2005/06
 Figure 2. Trends in average length of stay of
inpatients (truncated) and total patients including day patients from 1998/89 to
2005/06
 Figure 3. Trends in rates for New Zealand per
1000 population of public bed days, inpatient, and day patient, from 1998/89 to
2005/06
 Figure 4. Trends in rates of availability per 1000
population of public and private beds from 1998/89 to 2005/06
 Figure 5. Variation between DHBs in discharge
ratios standardised for age, gender, and casemix 2005/06
 DiscussionLimitations of the data—The New
Zealand data series provides reasonably reliable comparisons over the period
given the filtering, truncation, and standardisation process to which the data
is subjected. This ensures that, as far as possible, there is appropriate
comparison between years and between DHBs. The principal uncertainties relate to
bed availability given the lack of recent data and the inclusion in previous
data of beds used for a variety of purposes additional to acute short-term
stays. There are also serious uncertainties about private-sector data. None is
currently available regarding utilisation. The data, both public and private, is
relatively limited compared with other countries, e.g. Australia.
There are also uncertainties about the international
comparisons including comparability for population needs, inclusion or exclusion
of certain categories of patients excluded from the New Zealand data, e.g.
admissions for endoscopies. Despite this the differences are sufficiently marked
to indicate that progress in reducing bed utilisation in New Zealand is well
ahead of Australia but falls well short of some situations in the US such as
Kaiser Permanente.5 This organisation is widely
regarded as a benchmark for international studies but its utilisation figures
are reasonably typical of those of US-managed care
markets.5
Changes in bed utilisation in New
Zealand—The New Zealand data clearly points to consistent
progress in reducing bed utilisation. Although there has been a continuing
increase in discharge rates, almost entirely related to the increase in day
patients, there has been a marked fall in the rate of bed days explained almost
entirely by the dramatic fall in the ALOS. This is related to a range of factors
including an increasing percentage of day surgery, better management of the
inpatient episode, new technologies (e.g. endoscopic surgery), and increasing
provision of alternative community-based services to enable earlier discharge.
This decrease is in the face of increasing demand for
hospital-based procedures including new technologies and drugs and an aging
population. The findings clearly indicate that the there has been an increase in
cost weighted discharges related to increasing complexity of patients admitted
for hospital care. It is almost certain that, despite factors leading to a
reducing ALOS, a limit may soon be reached in achieving further reductions in
bed utilisation.
Previous studies—The findings of this
study are reasonably consistent with previous work in this area. For example,
Pool et al reported on a detailed study (age and gender standardised) of
hospital bed days over the period 1985–87 to
1999–01.7 Overall rates/1000 reduced from
890 to 520 for males and 720 to 350 for females. Allowing for truncation of the
MOH data these rates and trends are similar to this study. However their study
did not include trends in rates of discharges and
ALOS.7
Katzenellenbogen et al undertook a detailed study of
hospital discharge data over a similar period. They concluded that trends in
hospital days, appropriately filtered, provided a more reliable picture of
utilisation related to health needs than hospital
discharges.9 However their work was largely
focused on the concept of health expectancy rather than an analysis of trends as
such.9
A study of public hospital bed numbers was undertaken by
Jackson in 2006.10 He reported a total of 1.66
beds/1000 population, a very similar figure to the calculated 1.56 beds/1000
days in this study.
A study of variation in hospital board utilisation by
Barnett reported in 1984 showed that a key factor in hospital utilisation in the
past and variation between population areas was highly correlated with bed
availability as expressed by Roemer’s
Law.11 Malcolm found in 1983 that this
relationship was especially marked in mental health
services.12
Mansoor studied variation in bed availability and
utilisation in the early days of area health boards in
1990.13 He again found wide variation between
boards in both rates of admission and bed days. The overwhelming factor
explaining this variation, especially in bed days, was bed availability. Need,
as measured by the Health and Equity Score used at that time, was not an
explanatory factor.
Pool et al also found a wide variation in rates of bed day
utilisation between the regional councils used for population comparisons in
their study to 2001.7 This variation had
markedly decreased over the 15 years of the study period and was very much less
that reported in the Barnett and Mansoor
studies.11,13 It is almost certain that this is
related to a reduction in number of beds in previously overbedded districts and
hence the variation in bed availability—a key factor in utilisation
variation in the past.
International comparisons—The most
obvious explanation for the differences between the four countries/settings
presented in Table 2 is the level of integration in health systems especially
between primary and secondary care. The DHB system in New Zealand is probably
the most integrated of any comparable country perhaps even more than Kaiser in
the range of services included. The data from Kaiser Permanente, where the ALOS
is similar to that of New Zealand, indicates that much better prospects for
further reductions in New Zealand can be achieved by reducing admissions to
hospital.
Variation between DHBs—The findings
above indicate that even after standardisation for age, gender, and casemix
there is still a marked variation between DHBs in discharge ratios in 2006. The
highest DHBs have a 36% higher ratio than the lowest. However this variation is
very much less than in the past, especially in the days of hospital and area
health boards as presented above.
However other factors not included in standardization may be
significant. These include deprivation, ethnicity, and rurality as possible
explanations for the much higher ratios in Tairawhiti and the West Coast DHBs.
However a correlation analysis shows that there are only a small and
non-significant relationships between DHB-standardised discharge ratios and mean
deprivation score (0.29), % Māori (0.28), and rural status (0.32).
The 16% lower than the mean standardised discharge ratios
for Capital and Coast and Mid Central DHBs suggest possible benchmarks for other
DHBs to achieve in reducing their discharge rates. It would seem that further
research to examine these differences—especially by DHBs with
above-average ratios and struggling to keep within their population-based
funding, e.g. Canterbury—would be well worth undertaking.
Will we need more or less beds in the
future?—It would appear that the main target for reducing bed
utilisation and bed numbers in the future is to firmly focus upon reducing
hospital admissions. There is no doubt that further gains can be made in
increasing the proportion of day patients and reducing ALOS. However the Kaiser
data point to the prospects of lower if not very much lower rates of discharges.
Several initiatives have been developed in recent years by
IPAs/PHOs to provide alternative community services for potentially admissible
patients.14 In general these have been
reasonably successful although limited. However there have been few published
analyses or even descriptions of these initiatives. A much more comprehensive
approach is needed. For example Canterbury DHB is implementing a comprehensive
acute demand programme which could have a major impact upon reducing the future
need for beds, containing costs, and freeing up resources including for elective
surgical services.
Other developing initiatives include PHOs targeting
identified patients with chronic diseases such as cardiovascular disease and
diabetes.15 A Christchurch study examining the
relationship between primary and secondary in 1996/97, on patients registered
with the Christchurch South Health Centre showed that patients with chronic
diseases and having high use health cards (HUHCs), constituted 8.6% of the
population but generated 42.4% of hospital bed
days.16
Furthermore, the Christchurch study demonstrated the
importance of primary care factors in the utilisation of secondary care
especially acute hospitalisation in older patients and the potential for
targeting patients in primary care settings to contain hospital
admissions.
Given past trends in hospital utilisation and potential for
further reductions, Figure 6 shows a possible future for bed availability and
utilisation with fuller integration of primary and secondary care. There are
questions about the organisational arrangements within DHBs to support this
integration.
Figure 6 Possible future trends in bed
availability and utilisation in New Zealand with full integration of primary and
secondary care
 A key problem is the continuing focus upon the hospital as
an organisation. The downside of this is that some of those working within this
organisation have a strong hospital focused culture that may be threatened by
primary and community care alternatives. Despite this, the DHB system has both
the potential as well as incentives to realign hospital and community cultures
into a more integrated patient focused approach to care.
Competing interests: Elected member of
Canterbury District Health Board.
Author information: Laurence Malcolm,
Professor Emeritus and Consultant, Aotearoa Health, Lyttelton—and CDHBoard
Member, Christchurch
Acknowledgment: I am grateful to Ministry
of Health staff for the provision of recent as-yet-unpublished draft data on
Hospital Throughput and the Private Surgical Hospitals Association for provision
of bed numbers.
Correspondence: Laurence Malcolm, Aotearoa
Health, RD1, Lyttelton, North Canterbury. Fax: (03) 329 9084; email: lm@cyberxpress.co.nz
References:
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