Demographics and Future Needs for Public Long Term Care and Services

Demographics and Future Needs for
Public Long Term Care and Services
among the Elderly in Sweden
- The Need for Planning
Ilija Batljan
Stockholm Studies in Social Work 24 • 2007
Department of Social Work
Stockholm University
Doctoral thesis
Department of Social Work
Stockholm University
© Ilija Batljan
ISBN: 978-91-7155-428-4
ISSN: 0281-2851
Graphic design: Ingrid Tinglöf
US-AB Print Center, Stockholm 2007
CONTENTS
CONTENTS ________________________________________ 3
ABSTRACT ________________________________________ 5
SAMMANFATTNING _________________________________ 8
LIST OF ORIGINAL PUBLICATIONS ____________________ 11
ABBREVIATIONS __________________________________ 12
FIGURES ________________________________________ 13
1. INTRODUCTION _________________________________ 15
1.1. Population ageing ............................................................................................... 15
1.2 Demographic changes and needs for long term care for the elderly .................... 18
1.3 Aims of the study ................................................................................................ 19
2. BACKGROUND - FROM DEMOGRAPHIC PROJECTIONS TO
THE NEEDS FOR LTCAS ______________________________ 21
2.1 LTCaS and planning............................................................................................ 21
2. 1.1. Long-term care and services for the elderly in Sweden ............................................ 21
2.1.2 The welfare state and LTCaS ................................................................................. 24
2.1.3 Tools for planning for future LTCaS needs ............................................................. 25
2.2. Demographic projections ................................................................................... 28
2.2.1. Gender disparities (faster increase in the number of the elderly men) ...................... 28
2.2.2. Underestimation of the future number of older people ............................................... 29
2.2.3. Increasing life expectancy....................................................................................... 29
2.2.4. Limits to life expectancy ........................................................................................ 31
2.3 From need to use of formal LTCaS ..................................................................... 33
2.3.1 Association between use of LTCaS and ADL limitations ............................................... 33
2.3.2 Needs – Demand - Use .......................................................................................... 37
2.4 Mortality – Disability/Morbidity ....................................................................... 40
2.4.1. Morbidity and mortality – different hypotheses....................................................... 40
2.5. Socio-economic composition .............................................................................. 44
2.5.1 Education as indicator of socio-economic position among the elderly ......................... 45
2.5.2. From education to mortality .................................................................................. 46
2.6 Data - indicators, time periods and survey non-response ................................... 49
2.7 Implications from previous research for the present study ................................. 52
3. MATERIALS AND METHODS ________________________ 53
3.1. Subjects and procedures ...................................................................................... 53
3.1.1. National population registers ................................................................................. 53
3.1.2. Skåne data ........................................................................................................... 54
3.1.3. Swedish Survey of Living Conditions ..................................................................... 55
3.1.4. Non response rate in SNSLC ................................................................................. 55
3.2. Study variables.................................................................................................... 56
3.2.1. Future volume of hours worked ............................................................................. 56
3.2.2 Number of older people .......................................................................................... 57
3.2.3 Health indicators ................................................................................................... 57
3.2.4 Socio-demographic factors ...................................................................................... 58
3.2.5 Costs ..................................................................................................................... 59
3.3. Statistical methods .............................................................................................. 60
3.3.1 Scenarios, proportions and descriptive statistics ........................................................ 60
3.3.2 Estimation of mortality .......................................................................................... 60
3.3.3 Demographic projections.........................................................................................61
4. RESULTS _______________________________________ 63
4.1. Impact of inclusion of health status .................................................................... 63
4.2. Demographic projections and the impact of inclusion of educational status...... 65
4.3. The impact of taking into account educational mortality and morbidity
differentials as well as changes in educational composition of the population .......... 67
4.4. Health development ........................................................................................... 71
4.5. Mortality ............................................................................................................ 72
4.6 The connection between mortality and morbidity/disability ............................. 72
4.6.1 More people – fewer suffering ill-health ...................................................................72
4.6.2 Acute health care costs are concentrated to the last year of life ...................................73
5. DISCUSSION____________________________________ 75
5.1. Main findings ...................................................................................................... 75
5.2. Discussion of the findings................................................................................... 76
5.2.1 Morbidity/disability – mortality ...................................................................... 76
5.2.2. Demographic projections and educational health inequalities ......................... 80
5.3 Methodological considerations ............................................................................ 85
5.4. Policy options..................................................................................................... 87
5.4.1 Improved planning.................................................................................................87
5.4.2 Case for prevention.................................................................................................88
5.4.3 Research and statistics .............................................................................................89
5.4.4 Some insights concerning EU countries ....................................................................89
5.5 The new paradigm: The elderly = 75 and older? ............................................... 90
6. CONCLUSIONS __________________________________ 93
ACKNOWLEDGEMENTS ______________________________ 95
REFERENCES _____________________________________ 97
ABSTRACT
Long term care and social services (LTCaS) for older people are an important part of the Scandinavian welfare state. The fast growing number of elderly people in Sweden has caused many concerns about increases in future needs (and particularly costs) of age-related social
programs such as LTCaS.
The general aim of this dissertation is to examine how projected
demographic changes may affect future needs for long-term care and
services in Sweden assuming different trends in morbidity and mortality. The following data sources are used: national population registers,
register data on inpatient/outpatient health care from region Skåne, the
Swedish National Survey on Living Conditions (SNSLC) for the period
1975-1999.
In this thesis we have presented three alternative methods (studies I,
II and IV) to inform simple demographic extrapolations of needs for
health and social care for the elderly. We have also developed a new
method for demographic projections (study III) that will further improve the demographic base for our methods. This new method has
also been combined with the method in study II in order to enhance
alternative methods of demographic extrapolations of future needs for
LTCaS with a socio-economic dimension (study IV).
According to our studies II and IV, the health of older people
(measured as the prevalence of severe ill-health) has improved during
the study period. Furthermore, we show that, around 6% of the total
population with six or less remaining years of life accounts for nearly
37% of total costs for inpatient health care. Developments in morbidity
and disability prevalence in recent years, and the question whether its
decline will continue, have enormous implications for the future needs
for LTCaS and for social policies in general. Policy response may include both changes to the LTCaS system and investments in public
health in older people.
Taking into account health status, when projecting future needs for
LTCaS, will result in a fairly substantial reduction of the rate of the
demographically influenced increase in projected LTCaS needs. However, the size of the reduction is dependent on which health indicators
are used and which time period is used as a foundation for data for
health status.
The changes in population composition (that have already occurred
or are projected) regarding education and mortality differentials per
educational level may have a significant impact on the number of the
elderly in the future. The population projection, where those factors
are taken into account, results in a higher number of older people (ca
15% by year 2035) and a longer life expectancy than projected in official statistics.
We show that the projected increase in the number of older people
suffering from severe ill-health, as a consequence of population ageing,
may be counterbalanced to a large extent by changes in the educational
composition towards a higher proportion of the population having a
high educational level and lower prevalence of severe ill-health. On the
other hand, a higher than projected increase in the number of older
people could strain the pension system and will, according to a large
majority of our scenarios, probably lead to increased needs for LTCaS.
Population projections by age, gender and educational level should
be used when assessing how demographic changes affect needs for
LTCaS. This may also hopefully influence discussions of alternative
population projections in order to balance the systematic underestimation of the number of older persons, which has been more of a
rule than an exception, when projecting the future number of older
people in many countries including Sweden.
Improved methods of demographic extrapolations are important for
both improving planning within LTCaS, and enhancing human resources policies for long term care. Those methods are furthermore
crucial in order to design public policies that address the needs of aging
populations, including planning, financing, and public health programs.
The value of scenarios and projections of this kind does not lie in
their perfect match with the future. The fact is that forecasts most
often are proven to be wrong. Nevertheless, it is crucial to know what
will happen if the development continues in the same direction, or
alternatively what may happen if the preconditions are changed.
Future developments in mortality rates, morbidity/disability rates,
changes in population composition as regards to gender, age and socioeconomic status are inevitably uncertain. There is also great uncertainty in regard to how the relationship between education, mortality
and morbidity will develop. However, future needs are not only uncertain, but also affected by today’s actions. We need to improve our planning tools in order to support policy-makers to plan for uncertainty
concerning future needs and demand for LTCaS.
There are few countries in the world that have as developed statistical registers as Sweden has. However, also as regards to statistics, there
are some areas that need improvement. Lack of individual-based statistics concerning older people’s use of LTCaS and their health status and
functional ability is a problem for monitoring and planning within the
LTCaS system.
SAMMANFATTNING
Äldreomsorg är en viktig del av den skandinaviska välfärdsstaten. Det
snabbt ökande antalet äldre personer och det faktum att vårdbehoven
inte är jämnt fördelade på åldersgrupperna i befolkningen har lett till en
del oro och funderingar hur framtida ökningen av behoven av äldreomsorg (och framförallt kostnaderna för densamma) kommer att påverka
välfärdsstaten.
Syftet med denna avhandling är att presentera utvecklade metoder
för demografiska framskrivningar av framtida behov av vård och omsorg samt att analysera hur de förväntade (prognostiserade) demografiska förändringarna kommer att påverka framtida behov av äldreomsorg. Scenarier med olika antaganden gällande utveckling av dödlighet och sjuklighet analyseras. Avhandlingen bygger på data från olika
datakällor: nationella befolkningsregister, registerdata som omfattar
individrelaterade kostnadsdata för den öppna (för år 1997) och den
slutna (för åren 1991-1997) hälso- och sjukvården i Region Skåne och
data från Statistiska Centralbyråns undersökningar av levnadsförhållanden (ULF) 1975-1999.
I avhandlingen presenteras tre alternativa metoder (artiklarna I, II
och IV) för att utveckla den enkla demografiska framskrivningen av
framtida behov av vård och omsorg. Dessutom presenteras en ny metod för befolkningsprognoser där prognosen baseras på antaganden
gällande inte bara kön och ålder som i officiella befolkningsprognoser,
utan också utbildningsnivå (artikel III) som stöd för vidare utveckling
av våra framskrivningar.
Utvecklingen när det gäller dödlighet och sjuklighet under de senaste decennierna samt frågan om hur utvecklingen kommer att se ut
framöver kan ha mycket stora konsekvenser för både framtida behov
av vård och omsorg och hela välfärdspolitiken. Äldres hälsa mätt som
andel personer med svår ohälsa har förbättrats under perioden 19751999 (artikel II och IV). I artikel I visas att ca 6% av den totala befolkningen som förväntas leva 6 år eller kortare svarar för ca 37% av de
totala slutenvårdskostnaderna inom hälso- och sjukvården. Samhällets
svar på de förväntade förändringarna när det gäller den åldrande befolkningen och dess betydelse för framtida behov av vård och omsorg
kan handla om allt från förändringar inom ramen för äldreomsorg, via
stark fokusering på ökade insatser av den arbetsaktiva befolkningen, till
folkhälsosatsningar. Därför är det viktigt att ha så bra bild av den framtida utvecklingen som möjligt.
Befolkningsprognoserna baseras vanligtvis på antagandet om att
dödligheten fortsätter sjunka. Om vi i våra framskrivningar av framtida
behov också tar hänsyn till den trendmässiga utvecklingen av hälsan,
resulterar detta i relativ stark reduktion av ökningen av framtida behov
av äldreomsorg. Den här typen av framskrivningar är dock känsliga för
val av hälsoindikator. Framskrivningarna är också känsliga för vilken
period som hälsotrenden baseras på, i och med att hälsoutvecklingen
varierat kraftigt sedan början på 1990-talet.
Förändringar i populationssammansättningen när det gäller andelen
av äldre med hög utbildning och dödlighetsskillnader beroende på utbildningsnivå kan påverka prognoserna rörande antalet äldre personer i
befolkningen. Om vi i våra befolkningsprognoser tar hänsyn till dessa
förändringar resulterar detta i både ett högre antal äldre personer och
högre förväntad medellivslängd än prognostiserat i enlighet med de
officiella befolkningsprognoserna.
Vi visar att den prognostiserade ökningen av antalet äldre personer
med svår ohälsa som resultat av ökat antal äldre personer kan dock i stor
omfattning balanseras av förändringar i populationssammansättningen
med ökad andel äldre personer med högre utbildning och lägre prevalens
av svår ohälsa. Å andra sidan skulle en snabbare ökning av antalet äldre
personer än prognostiserad leda till ett ansträngt pensionssystem.
Befolkningsprognoser per kön, ålder och utbildningsnivå skulle
kunna användas i de demografiska framskrivningarna av framtida behov av äldreomsorg. Detta skulle förhoppningsvis stimulera diskussionen om behov av att ta fram alternativa befolkningsprognoser för att
någorlunda balansera de ständiga underskattningarna av antalet äldre
som varit mer regel än undantag under de senaste decennierna när det
gäller både de svenska och många andra länders befolkningsprognoser.
Värdet av att ta fram och presentera olika scenarier som de som presenteras i avhandlingen är inte att försöka fånga upp den exakta utvecklingen i framtiden. Faktum är att de flesta prognoser träffar fel. Värdet av
scenarierna är framförallt att kunna visa vad som kan komma att hända
om en viss utveckling fortsätter eller om förutsättningarna förändras.
Antaganden om den framtida utvecklingen av dödlighet, sjuklighet
och förändringar i populationssammansättningen per kön, ålder och
utbildningsnivå är osäkra. Det finns bl.a. stora osäkerheter angående
hur sambandet mellan utbildningsnivå, dödlighet och sjuklighet kommer
att utvecklas. Trots detta är det viktigt att lyfta fram att framtida behov
av äldreomsorg är inte bara osäkra, utan de påverkas också av vad som
görs idag. Därför behöver utvecklas olika typer av planeringsstöd för
att understödja planering och beslut när det gäller den framtida utvecklingen av äldreomsorg.
LIST OF ORIGINAL PUBLICATIONS
I
Batljan I. and Lagergren M. (2004). Inpatient/Outpatient
Healthcare Costs and Remaining Years of Life – Effect of
Decreasing Mortality on Future Acute Healthcare Demand. Social Science & Medicine 59(12):2459-66.
II
Batljan I. and Lagergren M. (2005). Future Demand for
Formal Long-term Care in Sweden. European Journal of
Ageing 2(3):216-224.
III
Batljan I, and Thorslund M. (2007). Population aging: the
effect of change in educational composition.
SUBMITTED.
IV
Batljan I, Lagergren M, Thorslund M. (2007). Population
aging in Sweden: the effect of change in educational
composition on the future number of older people suffering severe ill-health. SUBMITTED.
The papers are reproduced with permission from the publishers:
Elsevier Science (Ireland) (I), Springer-Verlag (II).
ABBREVIATIONS
ADL
LTCaS
OECD
SNSLC
Activities of daily living, the persons having limitations in
those activities need help to get up or go to bed, or to get
dressed and undressed, or help with bathing or toileting.
The Centre for Epidemiology (EpC) is a part of the Swedish
National Board of Health and Welfare.
European Union
Gross National Product
Instrumental activities of daily living, the persons having
limitations in those activities need help with cooking or
cleaning or doing laundry or buying food.
Long-term care and services for the elderly
Organisation for Economic Co-operation and Development
Swedish National Survey of Living Conditions
WHO
World Health Organization
EpC
EU
GDP
IADL
US
United States of America,
FIGURES
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
A. EU, population pyramid, 2004
B. EU, population pyramid, 2050
A. Demographic projections, increase in number of older people,
women
B. Men
Life expectancy at birth, Sweden, 1841/1850-1991/2000.
Life expectancy at 65 years, Sweden, 1841/1850-2001/2005.
A. Age and gender distribution of use of LTCaS at aggregate level
compared to share of older people reporting having functional
limitations in IADL, year 2003, men
B. Women.
A. Age and gender distribution of people living in special housing accommodation at aggregate level compared to share of older
people reporting having functional limitations in ADL, year
2003, men.
B. Women.
A care consumption model.
Hypotheses concerning relation between mortality and morbidity/disability
A. The prevalence of poor health, men and women aged 65 - 84
years between 1980/81 and 2005.
B. Between 1980/81 and 1996/97, and 1998/99 and 2005.
Swedish population aged 35 – 85 by age and educational level by
the year 1999.
Volume-index trends for inpatient/outpatient health care demand in Sweden, 2000-2030.
Volume-index trend for LTCaS demand in Sweden, 2000–2030.
Projected increase in number of elderly people in Sweden (65+)
2000–2035 according to alternative projections (index 2000=100).
Development in the projected number of older people suffering
ill-health 2000-2035, scenarios based on population projections
with constant mortality.
Development in the projected number of older people suffering
ill-health 2000-2035, scenarios based on population projections
with decreasing mortality
1. INTRODUCTION
1.1. Population ageing
The world’s elderly population aged 65 and over is growing by 10
million per year (Kinsella and Welkoff, 2001). An increase in the number of people that live longer combined with a decrease in fertility rates
will result in an ageing population in both developed and developing
countries. Population ageing is a global phenomenon, with developing
countries projected to age very rapidly (Kalache, Barreto and Keller,
2005). However, the population living in the European Union (EU) is
also ageing. The number of people aged 65 and over is expected to
increase by 70 percent between the year 2000 and 2050 (Eurostat, 2005).
According to projections, the increase is going to be particularly rapid
among those aged 80 years and over. Thus the number of persons aged
80 years and over in the European Union countries is projected to
almost triple by year 2050 (Eurostat, 2005). The rapid population
ageing in EU is illustrated by figure 1 A and B.
Figure 1 A. EU, population pyramid, 2004
Age
89
85
81
77
73
69
65
61
57
53
49
45
41
37
33
29
25
21
17
13
9
5
1
4000
European Union (25 countries), Population (thousends of persons)
(2004)
3000
2000
1000
Males
0
1000
2000
3000
4000
Females
15
Figure 1 B. EU, population pyramid, 2050
Age
89
85
81
77
73
69
65
61
57
53
49
45
41
37
33
29
25
21
17
13
9
5
1
European Union (25 countries), Population (thousends of persons)
(2050)
90+ = 9176
90+ = 5004
4000
3000
2000
1000
Males
0
1000
2000
3000
4000
Females
Source: Eurostat (2005).
Europe has the highest proportion of elderly population, and Sweden is
one of the countries in the world with the highest proportion of its
population older than 65 years. For many years Sweden had the highest
proportion, but by year 2000, Italy became the demographically oldest
of the world’s major nations - over 18 percent of all Italians are aged 65
or over (Kinsella and Welkoff, 2001). Around 17% of the Swedish
population is 65 years and older and more than 5% are 80 years and
older. Currently, Sweden and Italy have the highest proportion of people 80 years and older in the world (United Nations, 2007).
16
Figure 2 A. Demographic projections, increase in number of older
people, women
400
350
Index 2006=100
300
250
200
150
100
50
0
2006 2010 2015 2020 2025 2030 2035 2040 2045 2050
65-74
75-84
85-94
95+
Source: Statistics Sweden (2006a).
As in the other European nations, the number of elderly people in
Sweden is set to rise sharply in the decades ahead (Figure 2 A-B). When
the generation born in the 1940s passes retirement age there will, initially, be a rapid increase in the number of people aged 65–74. A decade
later, a large increase in the number of people aged 75-84 will ensue.
And finally just about one decade later the number of people aged 8594 will start to increase. There is an age group that is increasing very
fast over the next 40 years. The number of people 95 years and older is
projected to almost triple, from 15,000 to 40,000!
Population development is projected to differ between women and
men. The number of women in the ages up to 95 years will increase by
50 percent in the next 45 years, being relatively stable after 2035
(Figure 2 A). The corresponding development for men will be much
steeper. In age groups 75-84 and 85-94 the number of men is projected
17
to increase by 92% and 152% (Figure 2B), compared to 51% and 70%
for women respectively (Figure 2 A).
Figure 2 B. Demographic projections, increase in number of older
people, men
400
350
Index 2006=100
300
250
200
150
100
50
0
2006 2010 2015 2020 2025 2030 2035 2040 2045 2050
65-74
75-84
85-94
95+
Source: Statistics Sweden (2006a).
Due to the very fast increase in the number of elderly men, the gender
ratio (the number of women 65 years and older per 100 men) is projected to decrease from 130 by 2006 to 114 by 2025 and 111 by 2050.
1.2 Demographic changes and needs for long
term care for the elderly
Aging populations combined with the fact that the number of very old
people is set to rise so sharply in the decades ahead is often identified as
one of the greatest societal challenges in the next 50 years. There are
18
many concerns about the increase in future needs (and particularly
costs) of age-related social programs for health and social care for the
elderly (OECD, 1998; Jacobzone, Cambois and Robine, 2000;
Jackson and Howe, 2003; Cotis, 2003; Regling and Costello, 2003; Economic Policy committee 2001, 2006). Within the EU, the projected
increase in the expenditure for those programs and its effects on public
budgets is often described as the priority issue to be dealt with (Economic policy committee, 2001).
The primary reason why the change in the population’s age composition affects costs of health and social care is, of course, that care consumption is not evenly divided among age groups. It seems pretty obvious that information on the projected size of the future population
by sex and individual age groups is essential, but not sufficient to
understand the influence of the demographic shifts on future demand
for LTCaS for the elderly. However it is exactly this rather narrow
approach, where authors assume a direct relationship between the number of older people in population and demand for LTCaS that is often
used when projecting future needs and demand for LTCaS (SOU
1996:163, Fölster, 1998). On the contrary, credible scenarios of future
demand for LTCaS for the elderly or health services require a range of
issues to be taken into account (Madden and Goss, 1998).
Developing methods for planning will hopefully stimulate decision
making processes and give more stable conditions to further development of care. Ageing populations require all countries to estimate the
future costs for long-term care for the elderly services at the population
level. Currently many countries lack this kind of information and it is
not available to policy makers. There is also a lack of a scientificallybased methodology for executing forecasts on future costs for longterm care. As mentioned above, in many cases only a direct (simple)
demographic extrapolation is used.
1.3 Aims of the study
The main aim of this study is to examine how projected demographic
changes may affect future needs for long-term care and services in
Sweden assuming different trends in morbidity and mortality.
19
The specific purposes of the study are as follows:
1. To introduce and describe, using empirical data from Sweden,
methods that can be used to estimate how demographic changes including mortality and morbidity changes can affect future needs for
inpatient/outpatient health care and LTCaS for older people
(studies I, II and IV).
2. To present projections of future demand for inpatient/outpatient
health care and LTCaS for older people showing how projected
demographic development may influence health and social care
needs in Sweden in the period 2000–2030 (studies I and II).
3. To describe the connection between health care costs and remaining
years of life in Sweden (study I).
4. To present trends (1975–1999) concerning the proportion of persons
with severe ill-health in the 65–84 age group, divided by gender, age
and educational level (studies II and IV). Those trends are compared
with the development of mortality during the same period (study II).
5. To estimate the impact educational mortality risk differentials may
have on the future size and educational composition of the elderly
population in Sweden (study III).
6. To present projections of future needs for LTCaS using information
on educational mortality and morbidity differentials as well as the
projected changes in educational composition of the population
2000–2035 (study IV).
20
2. BACKGROUND - FROM
DEMOGRAPHIC PROJECTIONS TO
THE NEEDS FOR LTCaS
In this chapter the background of the thesis is described. A discussion
of why tools for planning are needed is presented in chapter 2.1. In
chapter 2.2 it is shown that population projections have systematically
underestimated the number of older people. However, it is not only
the number of older people that is uncertain; there is also a lack of information about the needs for LTCaS. Therefore it is discussed in chapter 2.3 how some approximation about the needs for LTCaS can be
obtained. Demographic projections and needs for LTCaS both have in
common that they build on health development. The mortality – morbidity/disability relationship is discussed in chapter 2.4. Chapter 2.5
emphasizes the importance of socio-economic status for mortality and
its effect on population projections.
Mortality in Sweden is relatively easy to measure. On the other hand,
the information on morbidity and disability heavily depends on the
quality of the measures. In chapter 2.6 caveats are discussed concerning
choice of indicator, study periods and issues concerning non-response
when getting information on morbidity/disability from surveys.
2.1 LTCaS and planning
2. 1.1. Long-term care and services for the elderly in Sweden
Long-term care and services for the elderly is regarded as an important
part of the Swedish welfare system. Long term care and social services
(LTCaS) is the term that will be used in this thesis when discussing
Swedish care for the elderly. Sweden has a long history of delivering
different care services to the elderly (Edebalk, 1990, 1991). The main
objective as regards to long-term care and social services for the elderly
defined by Riksdagen (the Swedish Parliament) is that older persons
shall have access to good health care and social services (Government
Bill 1997/98:113). The responsibility for achieving this objective is
divided between three levels of government. At the national level, the
21
Parliament and the Government sets out policy aims and directives by
means of legislation and economic steering measures. At the regional
level, 21 county councils are responsible for the provision of health and
medical care. Finally, at the local level, since 1 January 1992, Sweden’s
around 290 municipalities are comprehensively responsible for longterm service and care for the elderly and people with disabilities.
The present Swedish system for LTCaS can be divided into:
- special housing accommodation (institutional care)
- home care (services and personal care)
- support programmes for family caregivers (respite and relief services, support and educational groups for carers and economic
support for caring)
- provision of assistive devices according to the needs
- other services like: special transport services to persons that are unable to use public transport because of disability, rehabilitation, prescription drugs within special housing accommodation,…
Municipal expenditure on LTCaS for the elderly in 2005 in Sweden is
estimated at upwards of SEK 80 billion (Swedish National Board of
Health and Welfare, 2007). Care in special housing accommodations
amounts to 64%, and care and services in ordinary housing are 34% of
the total costs. According to the Swedish National Board of Health and
Welfare, health care for older people, provided by County Councils,
amount to SEK 81 billion.
LTCaS in Sweden are mainly financed by municipal taxes
(Bergmark, Thorslund and Lindberg, 2000). In 2004, municipal taxes
covered around 85% of expenditure on LTCaS, compared to 4 percent
that was financed by fees. A smaller part of the elderly care is financed
by state grants directed to the municipalities (Swedish Association of
Local Authorities and Regions, 2006). The new rules (the maximum
amount and the mandatory amount a care recipient should have to live
on before starting to pay for services) introduced in 2002 resulted in an
increased number of care recipients who do not pay any fees at all from 14% in May 2002 to 33% in September 2004 (Swedish Association
of Local Authorities and Regions, 2006).
In 2005, 6.4 per cent of people 65 years and older, and 25.2 per cent
among those 85 years and older, were living permanently in special
forms of housing accommodation (Swedish National Board of Health
and Welfare, 2006). The majority of the population of elderly patients
22
in institutions comprises those with cognitive impairment (i.e. dementia) (Wimo and Jonsson, 2001).
Home help services (help with daily activities and personal care)
provided by the municipality under the Social Services Act (2001:453,
substitutes act 1980:620) constitute crucial services for making it possible for the elderly to continue living in their own homes as long as
possible. In 2005, 8.6 per cent of the population (65 years and older)
and 27.4 per cent among those 85 years and older, received home help
services. Of those receiving home help services in 2005, almost half
(46%) also received home health care1 (Swedish National Board of
Health and Welfare, 2006). Home health care is today in many cases
highly specialised and includes qualified medical care, as well as palliative care (Lagergren, 2002). The proportion of older people receiving
LTCaS has decreased during last 15-20 years (Trydegård, 2000;
Szebehely, 2002; Larsson, 2004; Thorslund, 2005).
Informal care
Concerning support programmes for family caregivers, which still are a
very small part of Swedish LTCaS, Jegermalm (2005, p. 51) points out
that “the Swedish welfare state seems to be following an international
trend by taking informal caregivers into consideration and investing in
support services for them”. Those new trends in Sweden may also be
seen in the light of the fact that in the late nineties an increasingly clear
priority has been given to single people in the allocation of home help
(Szebehely, 1998; Larsson and Thorslund, 2002; Larsson, Thorslund
and Silverstein, 2005). A substantial share of informal care is provided
by spouses for each other. It seems to be understood, that for couples,
the spouse (foremost elderly wives) should provide the care (Szebehely,
2002). It has been argued that in Sweden, during the last years, the increase in LTCaS provided by families (most has been provided by
spouses and daughters) partly match the decline of public services
(Johansson, Sundström and Hassing, 2003).
In an international comparison Sweden is still a country with universal and extensive long-term care and services for the elderly
(LTCaS). By the year 2000, Denmark (3.0% of GDP), Sweden (2.6% of
GDP) and Netherlands (2.5% of GDP) were the EU countries with
1
Home health care is acute health care provided at home. The definition of what is
home healthcare varies between municipalities. 145 municipalities have provided
information to the Swedish National Board of Health and Welfare (SNBHW).
23
highest public expenditure for LTCaS (Economic Policy Committee
2001). All other countries had expenditures around 1% of GDP. However, international comparisons of public expenditures for LTCaS are
rather difficult. For instance, it should be noted that in Sweden, LTCaS
also contain a large part of long-term nursing home care for the elderly,
which in other countries is a part of the health care system.
2.1.2 The welfare state and LTCaS
Welfare production may be organized in different ways. Organizations
differ according to how much responsibility for the welfare production
that is taken by market, family or the state. In the terms concerning the
care for the elderly this may be described as how the responsibility for
providing care is shared between formal, informal (family and friends)
and private providers.
During the decades following the Second World War Sweden developed into a universal welfare state according to the Nordic model
(based on public, mandatory social insurance and heavily subsidized
health and social care services covering the whole population). Pointing
out that redistribution is basis for the welfare state, Palme (2006) emphasized following goals for the modern welfare state: poverty reduction, reducing overall inequalities, providing social insurance and services of different kinds.
Esping-Anderson’s classic work concerning the three worlds of welfare capitalism grouped the Nordic welfare states in the social-democratic regime-type (Esping-Anderson, 1990) as compared to the conservative (e.g. Austria, France, Germany, and Italy) and the liberal (e.g.
United States, Canada and Australia) welfare state regime-type. The
indicators, chosen by Esping-Anderson when clustering different
OECD countries, focus on social insurance schemes and health care.
Social services (both child care and care for the elderly) have been emphasized as an important part of Nordic welfare in analyses of welfare
states by Sipilä (1997). However, it should be pointed out that the expansion of services came at a later stage of Nordic welfare state development, compared to the social insurance and was partly related to the
growing quest for gender equality, increasing female labour force population and the increasing number of older people (Palme, 2006).
If the welfare state is a way to manage the social risk, then the risks
(old age infirmity) pooled by public LTCaS are “democratic risks” because they will afflict us all (Esping-Andersen, 1999). It is furthermore
24
very difficult to project how long the exposure (the time during which
an old person needs LTCaS) will be and when it will start. Such risks
are difficult to manage and that is why there are no existing whollyprivate financed market solutions for LTCaS on a larger scale in the
world. Financing LTCaS is particularly dependent on pooling the risk
over the course of a life. Here, the welfare state, defined as the way to
affect outcomes of, and conditions for market distribution by political
decisions (Korpi and Palme, 2003), may be the needed solution. From
this point of view it is very important to have LTCaS incorporated as a
crucial part of welfare state.
Formal LTCaS is also important as regards contribution to gender
equality. The introduction of social services in the welfare state (child care
and LTCaS for elderly) is the result of active policy decisions committed
to lessening the caring burdens of the family. It is also why, this kind of
welfare state has been characterized as female-friendly (Hernes, 1987).
The increasing number of older people (and increasing older people’s share of the total population) poses challenges for social science as
part of our obligation to analyse what will happen in the future (Lindh,
Malmberg and Palme, 2005). Projections are important for planning
purposes. As pointed out above, Sweden is an archetype of the Nordic
welfare state with high levels of public spending and based on a high
level of redistribution of financial resources amongst a large majority of
the population, as well as different groups of the population that use
different services during the course of their lives (Korpi and Palme,
1998). That means planning may be particularly important in Nordic
countries where a large amount of resources pass through public budgets.
Not only public finances are calling for planning within the welfare
state. Even access to human resources within Swedish long-term care
and services (LTCaS) is crucial for the delivery of services and the welfare state’s ability to meet the needs of the elderly population.
(Swedish Ministry of Health and Social Affairs, 1997 and 2000). Given
the extent of redistribution between the groups within the welfare state
and its reference to redistribution over a lifetime, many people feel that
it is the welfare state’s duty to ensure that elderly in need of care or
social services receive help of high quality (Svallfors, 2002).
2.1.3 Tools for planning for future LTCaS needs
Public policies are always prepared under uncertainty related to the
future environment they will operate within. That means there is a
25
need to make a choice, when preparing public policies, which description of the future they will be based on. Public policies may be based
on non-use of knowledge (i.e. naive extrapolation or ad hoc assumptions) or on use of knowledge (i.e. on elaborate extrapolations, behavioural models or simulation models). If the term “forecast” is used
for a description of the future based on knowledge, then “forecasts” is
what distinguishes reasoned planning from blind action” (Aaron, 2000).
Projections and forecasts are useful tools for the development of public
policies. That is particularly true when focusing on dynamic changes in
demographics, as described above, and their relation to health care and
long term care and services.
There is a need for comprehensive projections that combine demographics and health trends, because making projections of future mortality and disability is a useful aid in decisions on priorities for health
research, capital investment, and training (Murray and Lopez, 1997).
The value of scenarios and projections of this kind does not lie in
their perfect match with the future. The fact is that forecasts most often are proven to be wrong (Aaron, 2000). The strength of the forecasting approach, however, lies in the fact that different scenarios for
future health trends that “are likely, or probable, or merely possible
can have an important role in shaping public-health policy” (Murray
and Lopez, 1997). Given the strong impact changes in the number of
elderly may have on long term care needs, this is particularly true for
policies on long term care services. It is important to know what will
happen if the development will continue in the same direction, or alternatively what may happen if the preconditions are changed (Thorslund
and Larsson, 2002). Models and scenarios are important tools for the
design of public policies for LTCaS. The models may further be used
when planning for future allocation of resources in order to meet the
need for long-term care. In that case, these models constitute basic data
for decision making concerning the future of public finances (Economic policy committee, 2006). Using models as a tool for planning
avoids actual experiments that are too costly, time consuming or risky
from a quality point of view (Lagergren, 1998).
Monitoring is another important area where models may contribute
to an increased knowledge base. The use of models for monitoring
may influence policy debate concerning the allocation of resources.
Within the LTCaS, human resources, their quantity and qualifications,
are the central resources (Edebalk, 2002). Also here, modelling (e.g, by
26
using different demographic extrapolations as a tool) the future demand
for human resources may be crucial.
Long term care and services planners need tools for planning. Planning, which takes into account demographic changes as well as other
changes in society that impact on the need for care in the population,
will improve and refine health human resource policies (Birch, 2002).
Projections and simulations based on developments concerning older
people’s functional ability as well as scenarios of the future development
of economic resources are vital for development of LTCaS (Thorslund
and Larsson, 2002).
A growing literature in Sweden shows that during the late 1980’s
and the 1990’s the proportion of people using LTCaS has decreased
considerably (Thorslund and Parker, 1997; Thorslund and Bergmark,
2000; Lagergren, 2002; Szhebehey, 2002). Turbulences in the Swedish
system of care for the elderly have affected the organisation as well as
the resources (Szebehely, 2000). The development of the care for elderly and the relatively huge differences between municipalities
(Trydegård, 2000) shows a historical lack of central planning according
to needs, but also illustrates the independence the local authorities have
in planning and delivering LTCaS. Given the fact that the number of
elderly people and need for long-term care vary from municipality to
municipality (Swedish Association of Local Authorities, 2002), it is
essential to develop planning according to needs at the local level. Planning is also emphasized in the Social Services Act which stipulates that
the municipalities shall plan services for the older people.
Modelling the need for formal LTCaS gives us an opportunity to
monitor if decreasing resources in LTCaS are the result of a decreasing
share of the elderly population needing LTCaS or if it is the result of
constraints in public economy and cutbacks in the supply of formal
LTCaS. Batljan and Lagergren (2000) show that the decreasing proportion of LTCaS users (as a share of elderly populations) between the
middle of the 1980’s and the late 1990’s hardly can be explained by a
decrease in the number of those in need of LTCaS. Larsson (2005) arrives at the same conclusions regarding the development of the proportion of people reporting having home care and the share of the elderly
suffering from functional limitations in IADL.
Using different models for policy prediction and planning may also
influence our understanding of the role of different public policies
within often complex and dynamic social systems (Levy, Bauer and
Lee, 2006). Models are one important component for improvement in
27
planning and monitoring. The other is the data. As pointed out by
Manton (1988) there is a need to have nationally representative studies
with a long timeframe in order to have a better basis for planning
policies for elderly people.
2.2. Demographic projections
One of the central points for our understanding of the future development of LTCaS is demographics and in particular demographic projections. It follows that demographic projections are given great importance in decision making processes. Importance and use of demographic
projections has increased rapidly during the past decades. Population
projections, or forecasts are drawn up every three years in Sweden,
relate to long periods (usually several decades) and contain information
on the size of the population by sex and individual age groups. There is
also an annual revision of the projections taking into account population changes during the year before the original projections year (Statistics Sweden, 2005a). In Sweden like in most OECD countries, three
different alternative population projections are estimated on each forecasting occasion: a basic forecast, accompanied by low and high alternatives. Nevertheless, it is the basic alternative that attracts most attention. As shown above, according to the last projections (basic alternative)
Sweden expects a rapid increase in the number of elderly in the future.
2.2.1. Gender disparities
(faster increase in the number of the elderly men)
Development for men and women is projected separately (see figure 2
A-B above). The number of men is projected to increase faster than the
number of women during the nearest decades. This is a result of a decreasing gender gap in life expectancy. The gender gap in life expectancy has been relatively large since the Second World War. On the
other hand, the gender gap has decreased in many developed countries
over the last 15-20 years. In France, England & Wales, Sweden,
Switzerland, and Italy, the decrease in the gender gap is mainly related
to the decrease in cardiovascular mortality (Mesle, 2004). The Swedish
gender mortality differential has narrowed since 1980, mainly as a result from larger than expected reductions in male mortality due to
heart disease, mortality from accidents and violence, lung cancer and
28
"other" cancers (Trovato and Heyen, 2003). Smoking plays an important role in the size of the gender gap in mortality (Bobak, 2003). However, as shown below in figure 3, the gender gap in life expectancy has
been established since at least 160 years ago in Sweden.
2.2.2. Underestimation of the future number of older people
It seems it should be relatively easy to calculate the correct future
number of older persons, since almost all these already live in Sweden
today. Despite that, until now, the forecasts of the number of older
people in Sweden done during the last twenty five years, have one
thing in common – a systematic underestimation of the number of
older people (Batljan and Lagergren, 2000). The main reason for this is
the fact that mortality for older people has decreased considerably during the last decades of the 20th century, especially among the men and
the oldest - in a way that demographers couldn’t or did not dare to
imagine. This is no specific Swedish phenomenon, but a general international observation. Also in Australia, population projections have
systematically underestimated the reductions in mortality among
women and those 85 years and older (Booth and Tickle, 2003). Kielman
(1997) found the same underestimation pattern in analysing population
projections for the United Kingdom, Netherlands, Denmark, Canada
and Norway. One of the reasons for this is probably to be found in
different attempts by different researchers to launch a paradigm on the
biological maximum length of life (see the discussion below). Thus,
given the systematic underestimation of the projected number of older
people, the number of older people may increase quicker than forecasted during the nearest 25 years.
2.2.3. Increasing life expectancy
Life expectancy in Sweden was 82.8 years for women and 78.4 years for
men in 2005 (Statistics Sweden, 2006b) and has never been higher than
today in Sweden (Swedish National board of health care and welfare/EpC, 2005). Compared to the year 2000 life expectancy has increased by more than a year for men (1.04) and three quarters of a year
for women. Increased life expectancy must be seen as a great success.
Life expectancy at birth for Swedish men ranks second in the world,
behind Japan. In 2004 life expectancy at birth in Japan was 78.6 years
for men and 85.6 years for women. Swedish women share with other
29
countries places 6th to 8th among OECD countries as regards to life expectancy at birth (OECD, 2006).
85
80
75
70
65
60
55
50
45
40
35
18
4
18 1-1
5 85
18 1-1 0
6 86
18 1-1 0
7 8
18 1-1 70
8 88
18 1-1 0
9 89
19 1-1 0
0 9
19 1-1 00
1 91
19 1-1 0
2 92
19 1-1 0
3 9
19 1-1 30
4 94
19 1-1 0
5 95
19 1-1 0
6 9
19 1-1 60
7 97
19 1-1 0
8 98
19 1-1 0
91 99
-2 0
00
0
at birth
Figure 3. Life expectancy at birth, Sweden, 1841/1850-1991/2000.
Men
Women
Source: Batljan (2005).
The success in increasing life expectancy among both men and women
in Sweden is illustrated by the figure above (figure 3). As shown in
figure 3, this almost perfectly linear increase in life expectancy among
both men and women during the last 160 years doesn’t show any signs
of stagnation.
The development shown in figure 3 has been repeated with some
differences in timing in all developed countries like France, England or
the United States where life expectancy at birth increased from 47 years
in 1900 to 78 years today (Cutler, Deaton and Lleras-Muney, 2005).
30
2.2.4. Limits to life expectancy
There are no indications that we are approaching any biological limit
to life expectancy in the decades studied here. Rather the opposite
seems to be the case. White (2002) shows a steady increase in life expectancy for 21 OECD countries during the period 1955-1995, with an
increase of 0.21 years of life per calendar year. Even more striking,
Oeppen and Vaupel (2002) found that during the last 160 years life expectancy for the countries studied has increased steadily at an almost
constant pace (compare with figure 3). Wilmoth and Lundström (1996)
showed that the maximum age at death has increased for 5 countries
analysed. Further as stated in Wilmoth et al. (2000) “National demographic statistics suggest that the maximum age at death has been rising
steadily in industrialized countries for more than 100 years”. Wilmoth
et al also show that in Sweden “more than 70 percent of the rise in the
maximum age at death from 1861 to 1999 is attributable to reductions
in death rates above age 70” and that “the more rapid rise in the maximum age since 1969 is due to the faster pace of old-age mortality decline during recent decades”. Using mortality data from Swedish population registers, Vaupel and Lundström (1994) have shown that mortality in Sweden has fallen steadily over the past 50 years in all age groups,
including the very oldest of all, aged 100 and over. Grundy (1997)
reaches the same conclusion on the basis of a summary of research findings from several countries — findings that do not support the notion
of a biological fixed limit. Also Vaupel (1998) shows strong life expectancy improvements for the elderly (however, to a somewhat lesser
extent for females). Based upon that, life expectancy is expected to continue to increase in Sweden (as in other OECD countries).
Discussion about the limits to life expectancy is important for how
demographic projections are done and which assumptions are used. In
that sense this discussion also has direct consequences for the assessment of future needs for health and long term care.
There are many cases of scientific articles pointing out some value as
a limit to life expectancy, only to find that, just a few years after the
articles were published, life expectancy in some country has passed the
proposed limit. For instance Wilmoth (1998) compares an article from
Bourgeois-Pichat (1978), where Bourgeois-Pichat argued that the biological limit to life expectancy was 73.8 years for men and 80.3 years
for women, with the fact that Japanese men’s life expectancy passed the
limit by 1982 and Japanese women by 1985. Comparisons with Sweden
31
show that Swedish men passed the limit in 1984 and Swedish women in
1989. Another example, also emphasized by Wilmoth (1998), concerns
a study (Olshansky et al, 1991) where 35 years was pointed out as a
biological limit for further life expectancy at 50. Also this limit was
passed only 6 years later in 1996 by Japanese women. One important
message from those stories is that the burden of evidence speaks against
those advocating that we are approaching the biological limits to life
expectancy. However, one explanation behind these stories, despite the
clear development of life expectancy at birth shown in figure 3, may be
found in that increases in life expectancy during some periods of time
used to slow down (in some cases even showing signs of stabilising at a
certain level (see figure 4).
Figure 4. Life expectancy at 65 years of age, Sweden, 1841/18502001/2005.
21
at 65 years
19
17
15
13
11
18
4
18 1-1
5 85
18 1-1 0
6 86
18 1-1 0
7 8
18 1-1 70
8 8
18 1-1 80
9 89
19 1-1 0
0 90
19 1-1 0
1 9
19 1-1 10
2 9
19 1-1 20
3 9
19 1-1 30
4 94
19 1-1 0
5 95
19 1-1 0
6 9
19 1-1 60
7 9
19 1-1 70
8 98
19 1-1 0
9 99
20 1-2 0
01 00
-2 0
00
5
9
Men
Women
Source: Own calculations and Statistics Sweden (2006b).
From figure 4, we may also observe that the gender gap increased very
fast after the Second World War and the following 30 years.
32
2.3 From need to use of formal LTCaS
In Sweden there is no official statistical information about the needs for
formal LTCaS. Information about the needs of those using LTCaS is
also lacking. The statistical information concerning older people’s use
of LTCaS is only available in age and gender group terms. The fact that
the question: “Who gets what care? “ cannot be answered by the official
statistics, reflects the lack of basic information concerning LTCaS
(Lagergren, 2005a). Neither do we have any information concerning
the educational level or household composition (married or single-living persons) for users of LTCaS.
2.3.1 Association between use of LTCaS and ADL limitations
The increase in the number of older people is expected to result in an
increase in the number of people that will need LTCaS. Nevertheless,
ageing in itself is not a disease and what matters is to what extent older
persons need long term care, support and assistance. The concept of
needs is particularly important for Swedish public LTCaS. According
to the Swedish Social Services Act, any person who is unable to provide for his or her needs or to obtain provision for them in any other
way is entitled to assistance for their livelihood and for their living in
general. In Sweden, the municipalities are responsible for the needs
assessment and to provide social services and care for older people according to their needs. Under the Social Services Act, home care and
services cover personal care (including assistance with eating and drinking, getting dressed and undressed and personal hygiene) and home help
services (including e.g. cleaning and doing laundry, help with shopping,
post office and bank errands and preparation of meals etc.). The Social
Services Act also requires the municipalities to establish special forms
of housing accommodation with service and care for older persons in
need of special support round the clock. The needs of older people for
LTCaS are not easy to define. The person in need, the relative, the assistance assessing person, the doctor, the nurse, the care assistant; all
can experience or see various needs in the same person (Rothman et al,
1991; Rubenstein et al, 1984; Magaziner et al, 1988; Thorslund and
Wärneryd, 1990). In Sweden, the municipalities are responsible for the
individual needs assessment (done by social workers).
As pointed out above, Swedish statistics on LTCaS focus on older
people as a group not on individuals, and we do not have data on
33
health status and functional ability among LTCaS recipients. Also
other essential information such as if home care recipients have other
services, their social network and family circumstances, is lacking
(Trydegård, 2000). Lack of individual-based statistics is a problem for
both monitoring and planning within LTCaS system.
Indicators on functional limitations in activities of daily living (Katz
et al, 1963), often defined as functional limitations in activities of daily
life – ADL (need help to get up or go to bed, or to get dressed and undressed, or help with bathing etc.) and functional limitations in instrumental activities of daily life – IADL (need help with cooking or cleaning or doing laundry or buying food), have been pointed out as suitable
for assessment of needs for LTCaS (Robine, 2003). As emphasized
above care and help with the tasks mentioned here are also specified in
the Social Services Act.
Figure 5. Age and gender distribution of use of LTCaS compared to
share of older people reporting having functional limitations in
IADL, year 2003.
A. Men.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
65-69
70-74
75-79
80-84
LTCaS
34
85-89
IADL
90-
Figure 5 B. Women.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
65-69
70-74
75-79
80-84
LTCaS
85-89
90-
Totalt
IADL
Source: Batljan (2005).
In figure 5 and 6, the data from aggregate statistics on LTCaS recipients
are combined with data from the Swedish survey of living conditions in
order to compare age and gender distribution of use of LTCaS with
functional limitations variables.
Figure 5 shows the relationship between use of LTCaS2 and the
share of population reporting having difficulties with IADL. There is
also, according to figure 6, an apparent relationship between the share
of the population reporting ADL limitations and proportion of people
living in special housing accommodation (receiving institutional care).
The differences between the share of population that receives
LTCaS and the share of the population that reports IADL limitations
may be a result of the development emphasized by the Swedish study
2
Here, it should be emphasized that we are comparing only share of population
using LTCaS, and not how many hours of home help services, those using LTCaS,
have received.
35
that showed that the number of people in Sweden receiving home care
and services has declined very fast during the period 1988-2003, and
that this was only partly explained by improvements in functional ability as measured by IADL (Larsson, 2005). Futhermore, it should be
pointed out here that the relationship between the share of population
that were recipients of LTCaS and the share of population reporting
IADL is age and gender dependent. Given the same level of ill-health,
very old persons (i.e. persons 85 years and older) and older women
have more functional limitations than old people younger than 85
years and older men (Lagergren, 2004).
Figure 6. Age and gender distribution of people living in special
housing accommodation compared to share of older people reporting
having functional limitations in ADL, year 2003.
A. Men.
50%
45%
% of population
40%
35%
30%
25%
20%
15%
10%
5%
0%
65-69
70-74
75-79
80-84
Institutional care
36
85-89
ADL
90-
65+
Figure 6 B. Women.
50%
45%
% of population
40%
35%
30%
25%
20%
15%
10%
5%
0%
65-69
70-74
75-79
80-84
Institutional care
85-89
90-
65+
ADL
Source: Batljan (2005).
2.3.2 Needs – Demand - Use
In order to project future needs for LTCaS and due to lack of direct
information about the needs it is important to discuss the relationship
between terms as needs, demand and use (or utilization or consumption). Starting with the concept of need, which is central for this thesis,
it should be pointed out that often, also given the discussion above, the
basis on which needs are assessed is a perception of health and functional ability — subjectively experienced or objectively established.
Furthermore, the fact that persons with the same functional limitations
may be assessed to have or even feel different needs, emphasizes that
the concept of need is more of a relative rather than an absolute concept (Thorslund and Larsson, 2002).
According to Bradshaw (1972) there are four main categories of
need. Felt need is need which people feel - that is, need from the perspective of the people who have it. Those needs do not fully result in
demand. Expressed need is the need which people say they have. Peo37
ple can feel need which they do not express and they can express needs
they do not feel. Normative need is need which is identified according
to a norm (or set standard); such norms are generally set by experts.
Benefit levels, for example, or standards of unfitness in houses, have to
be determined according to some criterion. Comparative need concerns problems which emerge by comparison with others who are not
in need. One of the most common uses of this approach has been the
comparison of social problems in different areas in order to determine
which areas are most deprived.
Which need category is assessed in Swedish LTCaS? Concerning
Swedish LTCaS, the pathway from having for example functional limitations to having your needs assessed as needs includes probably all
four categories. Often the process starts with a person (or often a relative to that person) experiencing (feeling) the need. In the next step the
need is expressed to the municipal needs assessment person. This expressed need may also be seen as “demand” for LTCaS. An assessment
person then assesses the need using standards and norms as comparisons. The standards and norms may be affected by available resources.
Assessed needs often do not result immediately in LTCaS use. Often
the older persons have to wait some months before getting access to a
place within special accommodation. Finally the assessed need results in
use of LTCaS .
38
Figure 7. A care consumption model.
Economics
Public finances
Age,
Gender,
Household
composition
Needs
Informal care
Resources
Supply
Demand
Formal care
utilization
Education level
Health care
Technical aids and environment
Social Networks
Source: Partly based on Thorslund and Larsson (2002) and Smedby et al. (1977).
Figure 7 above presents a schematic conceptual model of care consumption – a model partly published by Thorslund and Larsson (2002) - and
different factors affecting needs for care (and use of LTCaS). The model
shows the needs in relation to other factors helping us to distinguish and
understand how different parts of the model are affecting each other.
Larsson (2004) found the following factors as predictors of LTCaS
use: age, functional limitations, household composition (civil status),
education, psychiatric health and social networks. Given our model
those factors may affect both need and demand for LTCaS. On the
other hand as illustrated in Figure 7 and pointed out by Batljan and
Lagergren (2000) among others – available resources affect care consumption (even more than projected needs in population do). Thus it
should be emphasized that LTCaS supply is an important predictor of
demand (Miller et al, 2005). Many people do not demand what they
realise they are not able to obtain. However there are also other supply
mechanisms like high charges or low quality of the services that do not
affect the need, but will reduce demand. In a study of elderly people
who had withdrawn from the home-help service, the National Board of
Health and Welfare found that, in two out of five cases, the cause was
dissatisfaction with charges or quality (Swedish National Board of
39
Health and Welfare, 1998). In summary, given the complexity of the
concept of need, and the way needs assessment is done in Sweden, it is
difficult to distinguish in practice between need and demand. In this
thesis, bearing this discussion in mind, we use health indicators as an
approximation of needs.
2.4 Mortality – Disability/Morbidity
Future needs for LTCaS for older people depend on old people’s health
and their functional ability. Thus, mortality development, disability
development and the relation between those two are among crucial
issues for projections of future needs for LTCaS. The question then
becomes how is the observed decrease in mortality associated with
older people’s state of health and functional ability, in age-group terms.
This has been a highly controversial question for several years.
2.4.1. Morbidity and mortality – different hypotheses
Several hypotheses (Figure 8) concerning the relation between mortality and elderly people’s state of health and functional ability have figured in the international literature (Robine and Michel, 2004):
- compression of morbidity
- expansion of morbidity
- dynamic equilibrium (even described as postponement of severe
morbidity).
One central issue involved in all these hypotheses is the trend in the
number of years of healthy life expectancy in relation to total life expectancy (Robine, Romieu and Michel, 2003). These hypotheses point
out the importance of including disability (or chronic morbidity) in the
analysis of future needs, emphasizing the connections between mortality and disability/morbidity. Furthermore, given the fact that population projections are often considered as given, the hypotheses remind
us that there are assumptions behind this, assumptions that need to be
discussed and analysed. Following just the existence of three hypotheses, no matter which development they emphasize, contributes to a
base for analyses of the demographic impact on future needs for LTC.
40
Figure 8. Hypotheses concerning relation between mortality and
morbidity/disability
Years without morbidity/disability
Years with morbidity/disability
Year 2000
Year 2030
compression of morbidity/disability (Fries 1980)
Year 2030
compression of morbidity/disability (Fries 2003)
Year 2030
expansion of morbidity/disability
Year 2030 dynamic equilibrium/postponement of severe morbidity
According to the hypothesis of compression of morbidity, originally
propounded by James Fries (1980; 1983; 1986), improved living conditions and healthier ways of life cause the onset of chronic illnesses, such
as cardiovascular diseases, cancer, etc to be postponed to an increasingly
high age. In addition, also the fact that more and more chronic diseases
may be dealt with successfully cause the onset of disability to be postponed to an increasingly higher age. According to this hypothesis,
humankind has a genetically determined — albeit individually variable
— maximum age. The hypothesis therefore entails the assumption that
chronic morbidity/disability is “compressed” into the last years of life.
Fries (1980) cited 85 years as the mean biological maximum age and,
accordingly, considered this the theoretical limit for the possible increase in mean life expectancy. The final result of a trend complying
with this hypothesis would be that everyone “died healthy” or following a very short period of illness at an advanced age; in other words, the
number of years in health would tend to become the same as the number of years in life, and the average number of years in poor health
would decline towards zero.
On the other hand, in a modern definition of the compression of morbidity (Fries, 2003), there is no assumption on life expectancy approaching
the natural limit. In this definition, the hypothesis is presented as a positive
concept, where healthy life expectancy grows faster than total life
expectancy and the number of years spent in bad health decreases.
41
Some empirical support for the compression hypothesis has been
provided by Stout and Crawford (1988), who analysed admissions to a
geriatric unit in the years 1954–86 and found that the patients’ average
age when they became heavily dependent on care had risen, and also
that their active-life expectancy had increased. But they also found a
rise in the proportion of years of life spent in geriatric care, which conflicts with Fries’ hypothesis. A similar study by Henderson, Goldacre
and Griffith (1990), nevertheless, showed that the rise in the number of
life years had not resulted in any increase in the time spent in hospital
during the last years of life. The Swedish H70 study was able to demonstrate substantial improvements in health for 70-year-olds over a tenyear period in the 1970’s, but no major differences for people aged 80
and over (Svanborg, 1984). Using data on disability-free life expectancy
and comparing them with life expectancy data for men and women in
France between 1981 and 1991, Robine, Mormiche and Sermet (1998)
provide additional empirical evidence for the modified compression of
disability hypothesis. Healthy lifestyle is correlated both with increasing survival and compression of the disability into a few years at the
end of life (Vita et al, 1998; Ferrucci et al, 1999; Nusselder et al, 2000;
Hubert et al, 2002). Doblhammer and Kytir (2001) provided empirical
evidence for the compression of morbidity hypothesis in their study on
trends in healthy life expectancy in Austria.
A directly opposed hypothesis, expansion of morbidity, was proposed by Olshansky et al. (1991). Their argument was that medical
inputs for the elderly result in a higher proportion of people with
health problems surviving to an advanced age. Age-related morbidity
thus increases: severely ill old people no longer disappear by death.
This is also the content of the “medical paradox” that the more people
whose lives are saved, the more health problems the health-care services
must subsequently deal with. Broadly the same argument had previously been put forward by Gruenberg (1977). Gruenberg emphasized
that the new potential for arresting infectious diseases with antibiotics
was most significant when it came to saving the lives of the chronically
ill, but that it did not cure their chronic illnesses. Thus, in its pure
form, the hypothesis postulates that active life expectancy is unchanged
despite increased life expectancy and what increases, instead, is the
number of years of ill-health.
Support for the expansion hypothesis has been provided by various
researchers, including Guralnik (1991) and Kaplan (1991). In a review
of the literature on compression and expansion of morbidity, Hum and
42
Simpson (2002) conclude that additional years of life will probably
occur in a moderately impaired health state. Some relatively new
Swedish studies (Rosén and Haglund, 2005; Parker, Ahacic and
Thorslund, 2005; Thorslund et al, 2004) support the expansion of morbidity hypothesis.
The last hypothesis, dynamic equilibrium (also referred to as postponement of severe morbidity), was launched by Manton (1982). This
hypothesis states that the time spent with severe morbidity and disability remains approximately constant when life expectancy increases.
This is due to the fact that medical treatments and improvement in
lifestyles reduce the rate of progression of chronic diseases. It thus assumes that the postponement of death to higher ages due to falling
mortality is accompanied by a parallel postponement of severe morbidity and/or disability. The hypothesis was built on evidence showing
that the prevalence of several chronic diseases given age group declined
in the USA in the 1980s. The diseases that have become less prevalent
given age and gender include dementia, stroke and circulatory disorders. On the other hand, hip fractures and Parkinson’s disease have
become more common. These trends are explained by improvements
in underlying factors, such as higher educational level, better nutrition,
increased physical activity, etc. Since these factors continue to develop
favourably, Manton, Stallard and Corder (1995) found it reasonable to
assume that the elderly population’s state of health will continue to
improve and active life expectancy would rise.
According to the “postponement of morbidity” hypothesis, the agespecific prevalence of ill-health and functional disabilities in old people
would decline and, at the same time, overall health and social care consumption would remain unchanged.
Survey results reported by Manton, Stallard and Corder (1997) and
Manton and Gu (2001), based on the National Long-Term Care Survey
(NLTCS) showed a clear, significant decrease in the age-standardised
proportion of functionally disabled older people in the USA in 1982–
1999. Similar results have been reported in the work of Crimmins et al.
(1997) and Freedman and Martin (1998). In an overview of international trends of elderly people’s functional ability, Waidman and
Manton (1998) conclude that the proportion of functionally disabled
old people is decreasing in most industrialised countries. Empirical evidence for the dynamic equilibrium hypothesis has also been provided
in different studies from the Nordic countries (Batljan and Lagergren,
43
2000; Hagen, Botten and Waaler, 2002; Malmberg et al, 2002) and New
Zealand (Graham et al, 2004).
One crucial point in the discussion concerning these three hypotheses has been the epidemiological background to the observed changes in
the prevalence of morbidity and functional disabilities. During recent
years the discussions concerning the postponement of severe morbidity
and compression of disability have been focused more on measures of
disability. It is important to distinguish between morbidity and disability when discussing different hypotheses (Parker and Thorslund,
2007). In a systematic review of disability trends among older adults
during the late 1980s and 1990s in the USA, Freedman et al. (2002)
show consistent evidence of a decline in IADL disability and conflicting evidence of decline in basic ADL disability. Crimmins (2004)
emphasises that the effect of increased survival may be balanced by the
reduction in the incidence of disease, and by the fact that having a
disease appears to be less disabling than in the past. Concerning IADLdisability, less-disabling diseases may be the result of environmental
improvements and particularly access to assistive devices (Spillman,
2004; Freedman et al, 2004).
2.5. Socio-economic composition
Mortality rates have declined at relatively constant rates during the last
160 years in Sweden as shown above. At the same time, disentangling
the role of the different factors behind the reduced mortality is a difficult task. Cutler, Deaton and Lleras-Muney (2005) emphasized the following factors as crucial in reduced mortality: nutrition, public health
interventions, health insurance programs, income changes, social policies and medical care. One other important force affecting demographic
changes is probably changes in the socio-economic composition of the
populations (Lutz, Goujon and Doblhammer-Reiter, 1999). Effects
from such changes on the population projections are seldom analysed
because of lack of data. Education is the indicator that has been most
often studied in this context. The association between low educational
level and morbidity (Winkelby et al, 1992; Parker et al, 1996; Karp et
al, 2004) and mortality (Kitagawa and Hauser, 1973; Valkonen, 1989;
Pappas et al, 1993; Vågerö and Lundberg, 1995; Elo and Preston, 1996;
Kunst, 1997; Mackenbach et al, 1997) is well established. Consequently,
changes in the educational composition of the population have been
44
pointed out affecting both prevalence of disability and mortality levels
(Waidmann and Liu, 2000; Fredman and Martin, 1999; Manton and
Vaupel, 1995; Preston, 1992). In a Canadian study where one year
health status changes were assessed, Buckley et al. (2004) show that for
elderly who are initially in good health, the probability of remaining in
good health was lower for the low-educated than for the high-educated.
The probabilities of remaining in good health decline with age at almost the same pace among different educational levels.
2.5.1 Education as indicator of socio-economic position
among the elderly
Winkleby et al. (1992) argues that education, rather than income or
occupation, is the best socio-economic indicator for the health of the
elderly. Education has even been pointed out as the most frequently
used socio-economic indicator in health research (Miech and Hauser,
2001). However, it should be pointed out that there are different traditions in different parts of the world as regards to the use of different
indicators of socio-economic position. In Sweden and Europe social
class, based upon occupational status, is used more often than education
as an indicator of socio-economic composition, contrary to the USA,
where education has been the main indicator of socio-economic composition (Lahelma et al, 2004).
A weakness of education as indicator of socio-economic position
among the elderly is that the majority of the elderly in many countries
used to receive only compulsory schooling when they were young
(Valkonen, 2001). Another weakness may be convergence in the rates
of mortality (Elo and Preston, 1996; House et al, 1994) and health
status (Thorslund and Lundberg, 1994).
All socio-economic indicators are, furthermore composite indicators
where “parts of the effects of each socio-economic indicator can be
either explained by or mediated through other socio-economic indicators” and those “socio-economic indicators are not interchangeable but
partially independent and partially inter-dependent determinants of
health”. (Lahelma et al, 2004). Nevertheless, it is worth emphasizing,
that different socio-economic indicators measure different phenomena
and that their association with health status is the result of different
causal mechanisms (Geyer et al, 2006). Still, education is acquired relatively early in life and is not likely to change in older ages, which has
45
the clear advantage of income and social class for prediction and planning purposes.
2.5.2. From education to mortality
Several hypotheses regarding the nature of the relationship between
education and mortality have been presented. One way to structure
different hypotheses is to start with the fact that, as pointed out by
Goldman (2001), there are three overall categories of explanations for
the association between socio-economic position and health. Causal
mechanisms are those “through which socio-economic status and social
relationships potentially affect health status and the risk of dying”. Selection or reverse causation “refers to a set of pathways whereby unhealthy individuals may reduce their social position or become socially
more isolated as a consequence of their inferior health status”. Artefactual mechanisms represent measurement errors. However as pointed
out by Cutler and Lleras-Muney (2006), there may be also a fourth factor that may affect both education and health. This factor may be seen
as an indirect selection, where the socio-economic position is not selected by health in itself but, rather by determinants of health. “Genetic determinants of personal attributes (cognitive ability, personality,
bodily and mental fitness, …) that influence educational and occupational achievement, and also determine adult health, either directly or
through health-related behaviours” (Mackenbach, 2005) may represent
one possible pathway concerning indirect selection. Goldman (2001)
emphasizes that there seems to be consensus among researchers from
different disciplines that the observed inequalities in health are driven
largely (although not entirely) by a complex set of causal processes,
rather than by direct selections or artefactual mechanisms. LlerasMuney (2002) also shows clearly the causal effect of education on mortality. In an attempt to structure different explanations regarding the
relationship between education and health, Mackenbach (2005) labelled
material, psychosocial and stress-related, and behavioural factors as
belonging to the ‘‘specific determinants’’ perspective. This perspective
is one of the three complementary perspectives Mackenbash (2005)
points out as explanations behind socio-economic health inequalities.
The other two are selection perspectives (both direct and indirect) and
the “life course perspective”.
The life course perspective focuses on time dependency. There are at
least three different models explaining the effect of education on health
46
within the life course perspective. The first model is mainly a biological, critical period model, or the fetal origins hypothesis of adult diseases, and it emphasizes exposure to health risks during the critical time
period early in life. According to this model discrete events in early life
affect both health and educational attainment in later life. A second
model - the pathway model, predominantly social, may be that negative social factors in the early-life environment set individuals onto life
trajectories that negatively affect their later health. Early life exposures
to socio-economic disadvantage or specific health determinants to later
life health, may affect both school performance and create “the unhealthy life career”- unemployment, poor economic conditions, poor
housing, environmental and occupational toxins, risky jobs, risky behaviour, may be important to explain health inequalities (Lundberg,
1993; Kåreholt, 2001; Hayward, 2004). Consequently, the health inequalities partly result from the accumulation of exposure to hazards
over the life course. The socio-economic circumstances in early life
may have a particular influence on the risk of cardiovascular disease
and mortality from cardiovascular causes (Davey Smith et al, 1998). On
the other side it should be pointed out that “the characteristics on
which indirect selection occurs could either be innate characteristics
that are independent from the circumstances in which individuals grow
up, or factors that are largely determined by socio-economic and other
circumstances in early life (Mackenbach, 2005).
A third model within life course perspective focuses on the accumulation of repeated exposure to negative factors adversely affecting
health status either through a biological process or through a social
process.
Within the specific determinants perspective, there are three important causal pathways from level of education to mortality (or ill health)
that have been emphasized by Ross and Wu (1995) and Van Oort, van
Lenthe and Mackenbach (2005). Those mechanisms work through
material factors, psychosocial or stress-related factors, and behavioural
factors. Material factors may affect mortality either directly or indirectly, via behavioural and via psychosocial factors. Psychosocial factors may also exert a direct and an indirect effect, through behavioural
factors.
Comparing material factors, (type of health insurance, financial
problems, and housing tenure), psychosocial factors (life events and
external locus of control), and behavioural factors (smoking habits and
physical activity) Van Oort, van Lenthe and Mackenbach (2005) found
47
that material factors contributed most to the educational differentials in
mortality in the Netherlands.
Psychosocial factors (life events, job strain, lack of control over living and working conditions, lack of social support, …) concern a person’s relative position in a society. Relative position or rank within the
social distribution have been found to affect health in animals
(Sapolsky, 1998) and in humans (Marmot, 2002).The exact mechanisms
by which psychosocial factors related to education are transformed into
morbidity and mortality via biological processes or organ dysfunctions
are still unclear. However, those mechanisms have been explained by
the fact that “individuals at the lower end of the hierarchy have lower
control over their lives and are constantly subjected to arbitrary demands by others, causing increases in stress and subsequently resulting
in stress-related diseases” (Cutler and Lleras-Muney, 2006). This means
that different stressors such as lack of self-efficacy, competence and lack
of control (or a sense of control) of own life circumstances in general
(both at work and at home) could lead to increased vulnerability with a
negative impact on health status (Swedish Council for Social Research,
1998). Increased vulnerability may be even higher for people with lower
education who are also exposed to other risk factors (Ericsson, 2001).
Behavioural factors (smoking, diet, alcohol misuse, lack of physical
exercise, …) are commonly emphasized as an important link between
education and health. High-educated people have a better ability to
receive information about health-promoting behaviour and adopt
healthier lifestyles. Smoking-related diseases have been found to contribute significantly to the total educational level disparity in potential
life-years lost (Wong et al, 2002). Nevertheless, Lantz et al. (1998),
found that four important behavioural risk factors (cigarette smoking,
alcohol drinking, sedentary lifestyle, and relative body weight) explain
a rather modest proportion of the association between socio-economic
factors (income and education) and mortality.
Finally, there are two other hypotheses that may be complementary
to the above. Grossman (1975) argued that education makes people
better decision-makers and Fuchs (1982) argued that people who are
interested in benefits on a long term basis invest both in education and
health. Although it is not established which of the pathways discussed
in different hypotheses “matter more for health, they each are likely to
contribute to the overall pattern of higher years of schooling being
associated with better health status” (Hernandez and Blazer, 2006, p. 28).
48
2.6 Data - indicators, time periods
and survey non-response
There are many problems when trying to move further in the discussion related to hypotheses and theories about the connection between
mortality and morbidity and summarizing findings from different
countries. One problem is the use of different health indicators (different health dimensions). Brønnum-Hansen (2005) argues that trends in
healthy life expectancy at 65 years depend on the choice of health indicator. The importance of focusing on “health indicators” when assessing health trends among the elderly and particularly comparing different studies has also been emphasized by Parker and Thorslund (2007).
The fact that different studies cover different periods of time and often include only two time-points may also affect our chances to select
and understand the actual trends. The following figures may give different pictures despite that they are based on the same data and show
development for the same indicator, with the only difference that the
figures cover different periods of time (Figure 9 A, B). Figure 9 A
shows that the prevalence of poor health has decreased for both men
and women aged 65-84 years between 1980/81 and 2005. Focusing on
the period 1980/81 – 1996/97 (Figure 9 B) shows an even stronger decrease in the prevalence of poor health, among the 75-84 age group.
However, focusing only on the period 1998/99-2005 presented in
the figure 9 B gives us a completely different picture concerning the age
group 75-84 years. Instead of a strong decrease in the prevalence an
increase in the prevalence of poor health was observed among both
men and women. It should be observed that despite the shorter period
analysed in the figure 9 B, this time series is still longer than those used
in many other studies.
49
Figure 9 A. The prevalence of poor health, men and women aged 65 84 years between 1980/81 and 2005.
% of population (age group)
25
20
15
10
5
0
3 4 5
1 3 5 7 9 1 3 5 7 9 1
-8 -8 -8 -8 -8 -9 -9 -9 -9 -9 -0 -0 0 0 0 0
80 9 82 9 84 9 86 9 88 9 90 9 92 9 94 9 96 998 000 0 02 2 2
9
1 1 1 1 1 1 1 1 1 1 2 2
Men, 65-74 years
Women, 65-74 years
Men, 75-84 years
Women, 75-84 years
Linjär (Women, 65-74 years)
Linjär (Men, 65-74 years)
Linjär (Men, 75-84 years)
Linjär (Women, 75-84 years)
Source: Statistics Sweden (2006c).
Furthermore, what is obvious from the figures is that the estimated
prevalence rates have large fluctuations between the years. Both
Swedish (Lagergren, 2004) and American studies (Crimmins, Saito and
Reynolds, 1997) have emphasized sensitivity in conclusions about
trends from only two time points.
50
9 B. The prevalence of poor health, men and women aged 65 - 84
years between 1980/81 and 1996/97, and 1998/99 and 2005.
25
% of population (age group)
20
15
10
5
0
5 7 9
3 4 5
7 9 1 3
1 3 5
1
0-8 82-8 84-8 86-8 88-8 90-9 92-9 94-9 96-9 98 -9 00 -0 02-0 20 0 20 0
8
19 19 19 19 19 19 19 19 19 19 20 20
Men, 75-84 years (1980-1997)
Women, 75-84 years (1980-1997)
Men, 75-84 years (1998-2005)
Women, 75-84 years (1998-2005)
Linjär (Men, 75-84 years (1980-1997))
Linjär (Women, 75-84 years (1980-1997))
Linjär (Women, 75-84 years (1998-2005))
Linjär (Men, 75-84 years (1998-2005))
Source: Statistics Sweden (2006c).
The quality of data often emerges as the most important problem. Wen
(2004) emphasized, for example, that changes in the wording of questions and changes in the type of survey (face to face, telephone) can
influence the trend results.
51
Increasing non-response rates have been pointed out as a possible explanation behind different health trends within the elderly population
observed in Swedish studies (Parker, Ahacic and Thorslund, 2005).
Also Van Loon et al. (2003) pointed out that non-response may lead to
a bias in estimates of the prevalence of morbidity. In a study on Iowa
women aged 55-69 years with only 43 percent response rate, non-respondents were found to have higher rates of myocardial infarction,
substantially higher attack rates for lung cancer, slightly higher attack
rates for all-site cancer and higher all-cause mortality than respondents
(Bisgard et al, 1994). Non-respondents older than 75 years have been
found to have a higher prevalence of ill-health than respondents
(Rockwood et al, 1989). Analyses of data for 27 countries from the
World Health Organization (WHO) MONICA (multinational MONItoring of trends and determinants in CArdiovascular disease) study
show that there is a risk that estimates of population health trends
based on respondent data may be biased (Tolonen et al, 2005). Nonresponse has also been found to affect the associations between disease
and functional status and self-rated health (Hoeymans et al, 1998), as
well as estimates of registered health care utilization (Reijneveld and
Stronks, 1999). Also Ives et al. (1994) found that the estimates are affected by non-response and they further point out that different causes
behind the non-response may have different effects on the estimates.
2.7 Implications from previous research for the
present study
Previous research has emphasized the important influence demographic
changes will have on future needs for long term care and services. But
there is also a need for alternative demographic projections. Furthermore, the impact of socio-economic inequalities in health and changes
in population composition need to be addressed when exploring how
demographic changes will affect the need for LTCaS in the future. This
study will further develop some aspects already pointed out earlier in
this chapter. The focus is on the deepening of our knowledge related to
mortality, morbidity, last years of life and health care costs, educational
mortality and morbidity differentials, connections between mortality
and morbidity and finally on the question of how demographic changes
will affect needs for LTCaS during the next two - three decades in Sweden.
52
3. MATERIALS AND METHODS
3.1. Subjects and procedures
In this thesis, the following available Swedish data are used: national
population registers, register data on inpatient/outpatient health care
from region Skåne and Swedish National Survey on Living Conditions
for the period 1975-1999.
3.1.1. National population registers
Sweden has a long tradition of population statistics (Statistics Sweden,
2000). The Swedish population registers contain accurate demographic
data (stock and flows) for the whole population based on the use of
personal identity numbers for all citizens in Sweden. The registers are
continuously updated with such events as births, deaths, immigration
and emigration. The quality of Swedish population registers is good,
even though there is some overcoverage because of the fact that the
population register is not always informed about departures from
Sweden (Statistics Sweden, 2003).
For the purpose of our study III, population registers were linked
with a national educational register (Statistics Sweden, 2004) containing
educational data for the population aged 16–74 from 1985 and onwards.
The educational register is based on the population register and
• self-reported educational level in the censuses of 1970 and 1990
• reports of degrees conferred from the main Swedish educational
system from 1970
• self-reported educational level collected intermittently by surveys
from immigrated people.
Furthermore, using cohort information, the educational register has
been enlarged by adding one-year age group for each calendar year
from 1986 for the purpose of the study III. That means, that in the year
1986 we have educational data for the population aged 16–75, in 1987
the data for the population is 16–76 years, and so on. In 1999 the age
interval has been extended to 16–88 in the enlarged educational register.
53
3.1.2. Skåne data
The tradition of using total registers has also been developed in the
field of health care (Swedish National Board of Health and Welfare/EpC, 2003). However, individual-related health care utilisation
data covering all population and going back a number of years are available in Sweden at present only from the Skåne region (formerly
Malmöhus County Council). This is also why Skåne data are widely
used as a source of data in Swedish studies concerning health care utilisation (Merlo et al, 2003; Swedish National Board of Health and Welfare, 2002; SOU 1996:163) and why we use those data in our study I.
The fact that all citizens in Sweden have a special personal identification number makes it possible to connect information on health care
utilisation with death register data and in that way create data on costs
per number of remaining years of life. Costs included in our analysis
are total costs for inpatient and outpatient health care. We have had
access to individual-related cost data for inpatient care in the years
1992–1997, while for outpatient care the corresponding figures have
been available only for the year 1997. Individual-related cost data were
calculated as a function of the patient’s own health care utilization, and
almost every health care contact has been linked to a specific cost. It
has thus been possible to link 95% of the total costs for health care to a
specific individual.
Skåne data do not include the information on costs of prescription
drugs (drugs used in inpatient care are included). Nor have costs of
long-term nursing home care been included. In Sweden most of the
long-term nursing home care was transferred to municipal elderly care
in 1992 and is since that time regarded as a part of social care for the
elderly (Lagergren, 2002). Through this reform, around 10% of total
health care costs were transferred. Taking that into account and considering out-patient care costs for pharmaceutical agents, the health care
costs covered by our study constitute around 70% of total health care
costs in Sweden as reported by OECD (OECD, 2003).
The population of the Skåne region accounts for 13% of the
Swedish population. Skåne region’s population distribution and distribution of inpatient/outpatient health care costs has been assessed as
fairly representative of Sweden as a whole by the 1996 Swedish commission on funding and organisation of health services and medical care
(SOU 1996:163).
54
3.1.3. Swedish Survey of Living Conditions
The data used in study II and IV are based on the Swedish National
Survey of Living Conditions (SNSLC or in Swedish: ULF). The
SNSLC is annual, a nationally representative sample and has been conducted by Statistics Sweden every year since 1974. The SNSLC was
established by the Swedish parliament as a continuous cross-sectional
survey with the purpose of social monitoring and using many indicators. Since 1986, there is also a panel section (25%) that is repeated
every 8 years. When focusing on health, the SNSLC included questions
concerning health conditions — individuals’ self-assessments of their
own general state of health, ADL, IADL, the prevalence of chronic
illness, ailments and other forms of infirmity, and impaired mobility,
need for assistance etc.
The sample frame for the SNSLC covers the whole population living in Sweden, both community-living and institutionalized persons.
Given the existence of population registers, described above, the sample
universe is well known and undercoverage and/or overcoverage are
probably very small problems for this survey. Concerning older people, unfortunately, during almost all years the SNSLC has had an upper
age limit. Between 1975 and 1979 the upper age limit was 75 years.
Between 1980 and 2001 the corresponding limit was 85 years. However, surveys done in 2002-2005 as well as a special survey from
1988/1989 include all ages above 64.
Data used in study II and IV were taken from the annual surveys
carried out in the period 1975–1999 and covering in total 32,502 older
people aged 65-84 years. That means the number of yearly observations
is on average 1300. To provide a more stable selection and in particular
to give the opportunity to analyse different age groups by gender, the
surveys have been collated in 5-year intervals in study II and IV.
Ethical considerations
All participating interview subjects were informed that the register of
individuals would be complemented with certain information including
data from the death register. SNSLC is since 1995, also regulated by law.
3.1.4. Non response rate in SNSLC
Concerning our study II and IV, the non-response rate varied between
18.5% in 1975–1979, 20.0% in 1985–1989 and 22.0% in 1995–1999. The
non-response rate varied between men and women, between different
55
age groups and for different causes (data not shown). The non-response
rate was lower among men than women. Also, the increase in non-response rate over time was lower among men than among women.
Among persons aged 65-74 years the increase in non-response rate was
moderate compared to the relatively large increase among those 80
years and older. The reasons behind non-response are possible to decipher according to three different types: refusers, non-contacts and the
persons unable to participate because of sickness. The main reason for
an increasing non-response rate was an increased number of people not
available for interviews. The proportion of those that were sick or institutionalised has been found to be relatively stable over time, with a
minor increase during the last years (Johansson et al, 2006).
Adjustment for non response rate in SNSLC
The problem of non-response is dealt with differently in different surveys. Adjustment for the non-response rate in SNSLC is done by poststratification by sex, age, civil status and H-region (regional distribution
in Sweden). After adjustment the data used represents the total population in these regards.
3.2. Study variables
3.2.1. Future volume of hours worked
The main outcome measure in studies I and II is demographically determined demand in terms of volume of required number of service
hours. The assumption is that the current gender and age group utilisation profiles3—as expressed in cost terms—are a good approximation of
the average number (by gender and age group) of hours worked required by the public LTCaS for older people.
The volume of hours worked can easily be transformed to assess future demand for human resources for LTCaS given provider mix and
average number of working hours per provider.
It should be emphasized that gender and age group utilisation profiles can be expressed in different ways, e.g. by average costs or average
number of service hours or average number of nurses, assistant nurses,
aides and home helpers currently giving services per capita for each
3
56
Utilisation profiles represent consumption per capita by gender and age groups.
gender and age group. Gender and age group utilisation profiles are
approximations of how resources in the form of working hours or
number of nurses, assistant nurses, aides and home helpers are allocated
between gender and age groups. This method is independent of
measurement unit. In our studies, we are using cost per capita for each
gender and age group (II) or number of remaining years of life (I) as
weights when expressing current utilisation of services. Gender and age
group utilisation profiles are used practically as weights, when making
demographic extrapolations of future requirements on human resources.
3.2.2 Number of older people
The number of older people is an important variable in all the studies.
Older people or elderly are in Sweden often defined as people 65 years
and older. The number of older people is taken from Swedish population statistics. Swedish official population projections are used concerning the future number of older people per gender and age group in
study I and II. However in study III, we generated our own projections
of the future number of older people per gender and age group and for
each of the three educational level categories. Population projection
scenarios by age, gender and educational level are our main outcome
measure in study III. Those new population projections have then been
used in study IV.
3.2.3 Health indicators
Severe ill-health according to the Statistics Sweden’s health index was used
in study II and IV. The future number of older people (65 years and
older) suffering severe ill-health is the main outcome in study IV.
Statistics Sweden’s health index is a composite index based on questions concerning individuals’ self-assessments of their own general state
of health, the prevalence of chronic illness, ailments and other forms of
infirmity, and impaired mobility and need for assistance. Combining
the answers to the questions covering those dimensions, a health index
with four degrees was constructed—full health, slight ill-health, moderate ill-health and severe ill-health. A detailed description of Statistics
Sweden’s health index can be found elsewhere (Statistics Sweden, 1989;
Swedish National Board of Health and Welfare, 1997; Bostrom and
Persson, 2001).
57
Self-reported health as one of the dimensions in the health index is
an indicator of overall health status reflecting many aspects of health
not captured in other measures. Global self-perceived health is a strong
predictor of mortality (Idler and Benyamini, 1997; Sundquist and
Johansson, 1997). The relationship between global self-perceived health
and mortality is strong even when controlling for "objective health
status" derived from physician and self-reported conditions and health
service utilization data (Mossey and Shapiro, 1982). There is also a doseresponse association between self-perceived health and mortality (Idler,
Kasl and Lemke, 1990; Burström and Fredlund, 2001).
3.2.4 Socio-demographic factors
Information on socio-economic status is presented in study III, where
educational level is used as a main variable for mortality analyses and
study IV, where educational mortality and morbidity differentials as
well as changes in educational composition of population were used.
The education level used covers three categories, indicating the extent
of school attendance: low: <=9 years (i.e. comprehensive school), medium: 10 and 11 years, and high: >=12 years. A similar categorization
of educational level has been used in other epidemiological studies
(Iglesias et al, 2003; Sundquist et al, 2004) building on Swedish data.
The distribution of the Swedish population by age and educational
level in 1999 is presented in Figure 10.
58
Figure 10. Swedish population aged 35 – 85 by age and educational
level by the year 1999.
80%
70%
60%
50%
Low
40%
Medium
High
30%
Missing
20%
10%
0%
35
40
45
50
55
60 65
Age
70
75
80
85
Source: Extended data from the educational register (Statistics Sweden, 2004).
Proportion of missing data on education is low, only 0.5 – 2.0 per cent
in the ages 35–73 and 2.5 – 4.5 in the ages of 74 to 85. In the population
census of 1990 all persons born 1926–1974 received an extract from the
educational register. If the information in the register was wrong or
missing each person had the opportunity to correct the information in
the register. People of age 65 or older (born 1925 and before) have not
been updated in the register. That is the reason for the higher level of
missing data in ages over 73 in 1999. The persons with missing data on
education were coded as having a low education level. Tests have
shown that those persons had a similar mortality pattern to those having a low level of education.
3.2.5 Costs
Costs included in our analysis in study I are total costs for inpatient
and outpatient health care. We have had access to individual-related
cost data for inpatient care in the years 1992–1997, while for outpatient
care the corresponding figures have been available only for the year 1997.
LTCaS costs used in study II are estimated by the Swedish Association of Local Authorities (2002) using survey information from 10-15
59
municipalities and official statistics on the total number of hours and total
number of persons receiving LTCaS per November 1st for a given year.
3.3. Statistical methods
3.3.1 Scenarios, proportions and descriptive statistics
In studies I, II, and IV we use descriptive statistics. In study II and IV
we use data based on respondents. Those data are prepared for descriptive statistics by post stratification of the observations according to age,
gender, civil status and H-region (regional distribution of Sweden)
based on register data of the Swedish population.
Scenario technique (Schwartz, 1996; Royston, 1997) is used in all
studies. To model prevalence for future years, it is assumed that population prevalence of severe ill-health (as an indicator for need of care) in
studies I and II was associated with both the demographic profile and
mortality. In study I, assumed mortality development is also used as a
health indicator. In study IV, different assumptions concerning development of severe ill-health by gender, age group and educational
level were analysed. In study II and IV, the above described data from
SNSLC 1975–1999 were utilized to model the logarithmic extrapolation of observed health trends related to the proportion of old people
with severe ill-health. Because the SNSLC has an upper age limit (85
years), prevalence of severe ill-health among age groups 85-89 and 90+
was extrapolated using the assumptions that improvement in terms of
prevalence of severe ill-health in the 85–89 age group is half of that in the
80–84 group and that prevalence rate in the 90+ age group is constant.
In our scenarios we combine assumptions on mortality and morbidity development with and without taking into account socio-economic
health inequalities. Furthermore, we present own population projections in scenarios from study III.
3.3.2 Estimation of mortality
Standard procedures were used for estimation of mortality rates
(Shryock et al, 1973):
60
D
m = n x
n x
R
n x
,
D is the number of deaths, x is the initial age group, n
n x
R is the
(=1) is the number of years in the age group, and
n x
where
corresponding exposure time. The mortality rates were transformed
into probabilities to die by assuming piecewise constant intensities.
The estimates on mortality for men and women by educational level
in study III was done for aggregated age groups (5-year intervals) in
order to arrive at more robust estimates.
3.3.3 Demographic projections
Demographic projections presented in study III were performed by
projecting the population at the end of 2000 into the future. The projection was done without external migration. The projection method is
the standard cohort-component method (Statistics Sweden, 2000).
The population was calculated for a one-year period by,
Pxt++11 = Pxt ⋅ (1 − q xt +1 )
Pxt = Population aged x at the end of calendar year t
q xt = Probability to die for a person x years of age during calendar
year t
t = Time from the end of the year 2000 to the end of 2035
The population projections in study III were made for a period of 35
years by means of assumed probabilities of death. Changes in fertility
rates do not have an impact on our projections, because we are only
studying the size of the elderly population for a limited period in this
study. The population projections presented in study III were used in
study IV.
61
4. RESULTS
4.1. Impact of inclusion of health status
In study I and II we compare simple (direct) demographic extrapolation
of future number of working hours within LTCaS with extrapolations
according to our methods (Figure 11 and 12). Our methods take into
account not only changes in the number of older people in different
age group but also health changes (measured by health care cost by
number of remaining years of life in study I and changes in proportion
of people suffering severe ill-health in study II).
Index 2000 = 100
Figure 11. Volume-index trends for inpatient/outpatient health care
demand in Sweden, 2000-2030.
190
180
170
160
150
140
130
120
110
100
90
80
70
2000
2005
2010
2015
2020
2025
2030
Simple demographic extrapolation
Mortality adjusted demographic extra polation
Source: study I
63
Note: Simple demographic extrapolation: health care cost per capita for each
gender and 5-years age group are multiplied with the projected number of older
persons per gender and age group. Mortality-adjusted demographic extrapolations: health care cost by gender and number of remaining years of life are multiplied by the projected number of persons per gender and number of remaining
years of life.
Figure 12. Volume-index trend for LTCaS demand in Sweden, 2000–
2030.
190
180
170
Index 2000 = 100
160
150
140
130
120
110
100
90
80
70
2000
2005
2010
2015
2020
2025
2030
Simple demographic extrapolation
Extrapolation incl. health trends acc. SNSLC* 75/79-95/99
Source: study II.
Note: Simple demographic extrapolation: LTCaS cost per capita for each gender
and 5-years age group are multiplied by the projected number of older persons
per gender and age group. In revised extrapolation simple demographic extrapolation is adjusted by multiplication by trend index according to data on health
changes per gender and age groups from SNSLC.
The first observation is that no matter which method is used there is a
projected demographically influenced increase in both health care and
LTCaS demand. Increase is modest for health care compared to LTCaS.
64
One reason for this is that, as pointed out above, in Sweden most long
term nursing homes are a part of Swedish LTCaS. Long term care is
more heavily concentrated on the very old than acute health care. Thus
the remaining acute care has a less pronounced age profile. The result
of our estimates of the growth in volume of working hours presented
in study II, obtained from direct demographic extrapolation assuming
unchanged needs per age group and gender and the corresponding
growth given expected health improvements, show that the average
yearly increase drops from 1.5% to 0.9% or, altogether for the period
2000–2030, from 58% to 30%. Thus the projected increase in the volume of demand for LTCaS for older people in Sweden by year 2030
will be almost halved, compared with the increase as calculated assuming unchanged age/gender-specific health and functional ability.
Extrapolation of health care costs assuming a constant cost per remaining years of life, age and sex also results in a fairly substantial reduction of the rate of the demographically influenced increase in health
care demand in Sweden compared to the results from direct demographic extrapolation method. The average yearly increase drops from
0.6% to 0.4% or, altogether for the period 2000–2030, from 18% to
11%. The increase in health care demand in the period 2000–2030 arrived at, by means of our method, will be around 37% lower than estimates done with a direct demographical extrapolation, which does not
take the decreasing mortality pattern into account.
4.2. Demographic projections and the impact of
inclusion of educational status
In study I and II, we use official population projections when comparing direct demographic extrapolation with extrapolations used by our
method. However taking demographic projections as given may affect
projected demand. As we pointed out in chapter 2, official demographic projections have for a long time underestimated development
in the number of older people. In study III we show that changes in
population composition regarding education and mortality differentials
per educational level have a significant impact on the projected number
of the elderly in the future. Four different scenarios have been presented. In scenario 4 (assumed mortality decline and educational mortality differentials taken into account), the number of elderly (65+) in
Sweden will increase by 62% during the period 2000–2035, compared
65
to a projected increase of 54% in scenario 3 (assuming the same mortality decline as in scenario 4) where educational mortality differentials
are not taken into account (figure 13).
Index 2000 = 100
Figure 13. Projected increase in number of elderly people in Sweden
(65+) 2000–2035 according to alternative projections (index
2000=100).
190
180
170
160
150
140
130
120
110
100
90
80
70
2000 2005 2010 2015
2020 2025 2030 2035
Scenario 1
Scenario 2
Scenario 3
Scenario 4
Statistics Sweden 2000
Source: study III.
Note: Population projections in scenarios 1 and 2 are based on unchanged mortality rates by gender, age groups (in scenarios 1 and 2), and educational level
(scenario 2). In scenarios 3 and 4, population projections are based on declining
mortality by gender, age groups (in scenarios 3 and 4), and educational level
(scenario 4).
Even assuming unchanged mortality as in scenarios 1 and 2, the number of elderly people will most probably still continue to increase over
the next 35 years. The increase of 32 percent between the year 2000 and
66
2035 is a result of changes in the size of cohorts and reduced mortality
at all ages in the past (which results in a growing number in a cohort
surviving to an advanced age). The inclusion of educational level in our
analyses in scenario 2 has significance for the development among both
gender and different age groups (results not shown), as well as for the
total number of older people in the future that is projected to increase
by 38 percent by year 2035 (figure 13).
Life expectancy varies widely by gender and educational level. The
inclusion of educational level mortality differentials in our projections
results in a strong increase in life expectancy at 65. An increase in life
expectancy at 65 years for men during the next 35 years is projected to
5.1 years in scenario 4 compared to 4.1 in scenario 3. The corresponding increase for women is 3.6 and 2 years.
Life expectancy will increase at a different speed for women and men
with different educational levels, which further widens the gap between
the educational groups. Furthermore, given assumed mortality reductions per age, sex and educational level, socio-economic differentials in
life expectancy will increase both for men and women. For instance, the
average highly-educated female person by 2035 is in scenario 4 projected
to have a life expectancy at birth of 88.6 years, compared to 83.7 years
for an average low-educated female. This result may be compared with
84.1 years and 81.0 years of life expectancy at birth for an average
highly-educated and average low-educated female in 2000 respectively.
According to scenario 4, there will be a dramatic shift in the socioeconomic structure of the elderly population during the next 35 years.
Today 4 out of 5 women older than 80 years have a low level of education. In scenario 4, by year 2035 only 1 out of 5 women will have a low
level of education. The change in socio-economic structure is similar
for older men (from 7 of 10 in the year 2000 to 1 of 4 with a low level
of education by the year 2035).
4.3. The impact of taking into account educational mortality and morbidity differentials as
well as changes in educational composition of
the population
In our study IV, we present 8 different scenarios where educational
mortality and morbidity differentials as well as changes in the educa-
67
tional composition of the population were taken into account. We
group our scenarios into 2 groups as regards the assumptions on mortality development by gender, age and educational level. The first
group of scenarios that we analyze are those where the population projection is based on the assumptions on constant mortality rate (the corresponding increase in number of older people is according to scenario
2 from study III) and those are presented in figure 14.
Index 2000 = 100
Figure 14. Development in the projected number of older people suffering ill-health 2000-2035, scenarios based on population projections
with constant mortality.
190
180
170
160
150
140
130
120
110
100
90
80
70
2000 2005 2010 2015 2020 2025 2030 2035
Const.edu/Const.mortality
Trend.edu/Const.mortality
Simple/const.mortality
Source: study IV.
Note: In scenarios “Simple/const.mortality” and “Const.edu/Const.mortality”
the number of older persons suffering severe ill-health by gender, 5-years age
group (“Simple/const.mortality” and “Const.edu/Const.mortality”) and educational level (“Const.edu/Const.mortality”) is multiplied with the projected
number of older persons per gender and age group. Scenario
“Trend.edu/Const.mortality” is based on scenario “Const.edu/Const.mortality”
that is adjusted by multiplication by trend index according to data on health
changes per gender, age groups and educational level from SNSLC.
68
The assumed decreasing prevalence of severe ill-health following the
observed trend and given the assumption of constant mortality will
result in an 11% decrease in the number of persons suffering ill-health
in scenario “Trend.edu/Const.mortality” (Figure 14), despite a 37%
projected increase in the number of older persons (the corresponding
increase according to the simple demographic extrapolation was 40% in
scenario “Simple/const.mortality”).
Figure 14 shows, furthermore, that according to scenario
“Const.edu/Const.mortality” (constant morbidity rates during the next
35 years) the inclusion of educational mortality and morbidity differentials together with projected changes in population composition by
educational level result in a more than halved increase (18% compared
to 40%) in the number of persons suffering SIH compared to the corresponding increase as a result from the scenario based on simple demographic extrapolation.
Results from the second group of scenarios, where the population
projection is based on the assumptions of decreasing mortality rate (the
corresponding increase in number of older people is according to scenario 4 from study III), are presented in figure 15.
69
Index 2000 = 100
Figure 15. Development in the projected number of older people suffering ill-health 2000-2035, scenarios based on population projections
with decreasing mortality
190
180
170
160
150
140
130
120
110
100
90
80
70
2000 2005 2010
2015 2020
2025 2030 2035
Const.edu/Trend.mortality
Trend.edu/Trend.mortality
Simple/trend.mortality
Converg.low_edu/Trend.mortality
Converg.high_edu/Trend.mortality
Source: study IV.
Note: Scenarios “Simple/trend.mortality”, “Const.edu/Trend.mortality” and
“Trend.edu/Trend.mortality” are similar to scenarios presented in the figure 14.
The only difference is that the population projection used is based on the assumptions on declining mortality rates. Scenarios “Converg.low_edu/ Trend.
mortality” and “Converg.high_edu/Trend.mortality” are based on scenario “Const.edu/Trend.mortality” that is adjusted by multiplication by assumed health
changes per gender, age groups and educational level. In the “Converg.low_edu/Trend.mortality” we assume increasing prevalence of SIH among those
having medium and high educational level. Among those who have medium
educational level the prevalence of SIH is assumed to increase by 1.5 percent
yearly until arriving at the same prevalence of SIH as among those with low
educational level. Equivalently, those having high educational level are assumed
to arrive at the same prevalence as those having medium educational level. In the
“Converg.high_edu/Trend.mortality” we assume in a corresponding way the
decreasing prevalence of SIH among those having low and medium educational
level, until they arrive at the same prevalence level as the above educated group.
70
The number of elderly suffering SIH in Sweden will increase according
to our scenario “Trend.edu/Trend.mortality” (following observed
trends on mortality and morbidity in the different educational groups)
by 14% during the period 2000-2035, compared to the 73-percentage
increase in the scenario “Simple/trend.mortality”.
Taking educational morbidity differentials into account counterbalances the increases in the prevalence of ill-health by more than half,
even in our scenario “Converg.high_edu/Trend.mortality” compared
to “Simple/trend.mortality” (Figure 15).
Despite assumptions of dramatically reversed health trends (worsening of health status, see note under figure 15 above) during the next 35
years compared to the period 1975-1999, used in our scenario “Converg.low_edu/Trend.mortality”, the future number of persons suffering SIH is not projected to increase more than in the scenario “Simple/trend.mortality” based on simple demographic extrapolation
(Figure 15), because of the dramatic changes in the population composition by educational level.
4.4. Health development
According to analyses presented in the study II and IV, the prevalence
of severe ill-health among the elderly in age group 65–84 years declined
from 22% in 1975/1979 to 17% in 1995/1999. The improvement in
health status was most pronounced in the 65–74 age group among
women, and in the 65–79 age group among men. The improvement
was greater for men than for women. The proportion of women aged
65–84 suffering severe ill-health decreased by 1.4% per year (regardless
of the analysed period being 1975/1979–1995/1999 or 1985/89–
1995/1999). For men, this proportion decreased by 2.8% per year for
the period 1975/79–1995/1999, and by 2.5% per year for the period
1985/89–1995/1999.
Concerning socio-economic inequalities, we show in our study IV
some increase in the health gap between people having different educational levels. However, the prevalence of severe ill-health has decreased
considerably among all educational categories during the period
1975/79-1995/99.
71
4.5. Mortality
During the period 1975/79–1995/1999, mortality among Swedish
women aged 65–84, decreased by 1.4% per year (the same decrease was
observed during the period 1985/89–1995/1999). Among Swedish men
in age group 65–84, the mortality rate decreased by 1.2% per year for
the period 1975/1979–1995/1999, and by 1.6% per year for the period
1985/89–1995/1999. In other words the mortality rate has been falling
faster among men than women in the latest time period. Concerning
the oldest old people, the decrease in mortality rate during the period
1975/1979–1995/1999 in the 85–89 age group was 0.8% among men
and 1.2% among women. The mortality rate decrease in the openended interval age group 90+ has been about 0.3% per year for both
men and women during the period 1975/1979–2000/2004 as pointed
out in study II.
Our estimates of (yearly) changes in the mortality rate for men and
women aged 35–79 by educational level used in study III show that
mortality has declined among all educational categories for the population studied during the period 1992–1995 to 1996–1999. Analyses concerning people up to 74 years older show the same development for the
extended period 1985-1999.
We are assuming that mortality will continue to decline in two of
our four scenarios in study III. It should be pointed out that official
Swedish projections are also based on the assumption that the mortality
rate will continue to decrease, but at a slower rate than the trend. For
example as emphasized in our study I, over the next 30 years, the probability of an 80-year old man dying within five years is expected to fall
from 43% to 36%, while the expected decrease for a 65-year-old is relatively even larger—from 11% to 7%, or more than one-third.
4.6 The connection between mortality and
morbidity/disability
4.6.1 More people – fewer suffering ill-health
The connection between declining mortality and morbidity during the
period 1975/1979-1995/1999 is apparent from the results in our studies
II and IV, where we show that despite an increase in the population
aged 65–84 years from about 670,000 (average per year) in 1975/1979 to
72
750,000 in 1995/1999, the number of people suffering from severe illhealth decreased from about 150,000 to 130,000 persons.
4.6.2 Acute health care costs are concentrated to the last
year of life
The connection between mortality and severe morbidity may also be
analysed by the association between high health care costs as an indicator of severe morbidity and the number of remaining years of life.
According to the estimates in study I, less than 1% of the population
that died during the last calendar year (having zero remaining years of
life) accounted for circa 11% of the total annual expenditure for inpatient health care. This group with zero remaining years of life thus
has 14 times higher share of the total annual expenditure for inpatient
health care than their share of the population. Furthermore, around 6%
of the total population with six or less remaining years of life accounts
for nearly 37% of total costs for inpatient health care. Total per capita
inpatient and outpatient health care costs are 11 times higher for deceased (during a calendar year) than for survivors.
There are clear differences between men and women in the distribution of health care costs in terms of remaining years of life. Over the
whole life span women have higher per capita costs than men for both
inpatient and outpatient health care. Nevertheless, men have between
11% (outpatient) and 13% (inpatient) higher per capita health care costs
during the last year of life (calendar year).
73
5. DISCUSSION
5.1. Main findings
The main findings from this thesis can be summarized in the following
points:
Alternative methods
- We have developed three alternative methods to improve direct
demographic extrapolations of need for health care and LTCaS
for the elderly: 1) Taking into account the distribution of cost
by number of remaining years of life, 2) Including not only
changes in the number of older people in different age groups
but also health changes by gender and age group, 3) Taking into
account educational mortality and morbidity differentials and
changes in the educational composition of population.
- Our methods emphasize the importance of including indicators
of health status when projecting future needs of care.
- We present new alternative methods for population projections
taking into account socio-economic mortality differentials.
Health development
- Health care consumption is, to a substantial extent, concentrated to the final phase of life.
- The proportion of older people with severe ill-health among
people aged 65-84 years decreased steadily in Sweden during the
period 1975/1979–1995/1999 in all age groups, for both genders—male and female, and for all educational levels.
Population composition
- The socio-economic composition of the elderly population will
change significantly during the next decades.
- Changes in the composition of the population (that have already occurred or are projected) regarding education and mortality differentials per educational level have a significant impact on the projected number of the elderly in the future. This
leads to a higher number of older people and a longer life expectancy than projected in official statistics.
- The projected increase in the number of older people suffering
severe ill-health, as a consequence of population ageing, may be
75
counterbalanced to a large extent by changes in the educational
composition towards a higher proportion of the population
having a high educational level (and low prevalence of severe
ill-health).
5.2. Discussion of the findings
The knowledge about demographic changes is crucial in order to plan
and prepare for ageing population (Economic policy committee, 2006).
Our methods emphasize the importance of including indicators of
health status when projecting future needs of care. In that way we acknowledge that the prevalence of ill-health is significant in modelling
and not just the number of people per age group. The inclusion of
health status in our model may also emphasize the value of health interventions at population level over a long time, giving arguments to
advocates of public health interventions.
5.2.1 Morbidity/disability – mortality
The key issue of how population ageing will affect future needs for
LTCaS is the morbidity/disability – mortality relationship, as discussed
in chapter 2. Given our results that show decreasing mortality among
both men and women, and across socio-economic groups, the empirical
evidence relating the development of morbidity/disability should be
taken into account. The analysis presented in our studies I (high health
care costs as proxy for severe morbidity) and II and IV (prevalence of
severe ill-health as an indicator of severe morbidity) supports the hypothesis about postponement of severe morbidity. The fact that health
care consumption is directly connected to the number of remaining
years of life may be explained by the relationship between changes in
severe morbidity and changes in mortality. The strong connection between the number of remaining years of life and health care costs have
been shown from studies done in different countries and covering
different time periods (Roos, Montgomery and Roos, 1987; Lubitz and
Riley, 1993; Zweifel, Felder and Meiers, 1999; Cutler and Meara, 1999;
McGrail et al, 2000; Hogan et al, 2001; Batljan and Lagergren, 2004).
Even more striking and relevant for our use of the indicator (health
care costs by number of remaining years of life) and for connections
between severe morbidity and mortality is the fact that the share of
76
health care costs concerning the last years of life in relation to total
health care costs has been found to be stable over time (Lubitz and
Riley, 1993), for the period 1976-1988 and Garber, MaCurdy, and
McClellan (1998) for the period 1988-1995, and Hogan et al. 2001). As
pointed out by Miller (2000) “Despite changes over the last two decades
in medical technology, in the Medicare program itself, and in the characteristics of Medicare enrollees (age and disability), time-until-death
has remained a consistent indicator of costs.”
The results from our studies I, II and IV are based upon on the period from the middle of the 1970s to the late 1990s. However, the picture seems more mixed when looking at other studies (Johansson et al,
2006; Parker, Ahacic and Thorslund, 2005) which also cover the first
years of the new Millennium and include different health indicators –
not only Statistics Sweden’s health index. As shown in figure 9 B health
development varied a lot also during the 1990’s.
So, which conclusions may be drawn from the observed health
trends in study II and study IV? As pointed out in the chapter 2, the
choice of indicator (meaning which dimension of health is studied) is
crucial for the understanding of the development.
The development described in study II and IV is measured by an indicator of severe ill-health. This indicator - health index by Statistics
Sweden - is a composite measure that combines both measures of disability as well as measures of self-perceived health status (as a part of
global health measures). According to study II and IV, the prevalence
of severe ill-health has declined during the period 1975/79-1995/99.
Behind this decline there is a different development for measures of
disability (decline) and the global health measure (unchanged to small
decline). There are different developments for the different dimensions
of health also when comparing the prevalence of disability which has
decreased and the prevalence of chronic diseases (e.g. high blood pressure and diabetes) which has increased (Johansson et al, 2006).
The similar trends related to prevalence of disease (increase), global
health measure (small decline) and prevalence of a disability (relatively
large decrease) have been shown in a study of healthy life expectancy
based on Danish data, covering the period 1987-2000 (BrønnumHansen, 2005). Some evidence from the USA is also pointing in the
same direction concerning the decrease in disability rates among the
elderly (Crimmins, 2004; Freedman, Martin and Schoeni, 2002; Cutler,
2001; Manton and Gu, 2001; Crimmins and Saito, 2000) and increase in
prevalence of chronic diseases (Crimmins and Saito, 2000; Freedman
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and Martin, 2000). The trends from US data, show particularly a
strong decline in IADLs (Spillman, 2004). There is also some decline in
the prevalence of ADLs, and this trend is also close to the observations
from another US study (Freedman et al, 2004). Thus the answers to
questions about the health development of older people are strongly
dependent on the choice of health dimensions studied.
How to understand different developments for different health indicators?
Following a discussion related to different hypotheses - compression,
expansion, and postponement of severe morbidity, we could draw the
conclusion based on the study by Johansson et al. (2006) that, regarding
disease, we are observing an expansion of chronic morbidity. However,
there also seems to be an opposite compression of disability. To explain
these two, at first glance seemingly contradictory conclusions, is important in order to understand the causes and relations of the two
trends. The fact that the expansion of chronic morbidity does not lead
to an expansion of disability may be the result of effective health care
technologies including (new drugs) and environmental changes. Due to
these developments, having a disease may have become less disabling
(Spilman, 2004; Freedman et al, 2004; Costa, 2000). The physical
environment in which older people live has changed and may have
affected disability rates.
On the other side, it is possible that the increasing prevalence of disease is a result of changes in diagnostics and changed threshold for what
is considered a disease. It is well known that the threshold for hypertension has changed the last thirty years. Also the threshold for diabetes has been lowered the last decade (Freedman et al, 2006). Furthermore, it is plausible that attitudes and lower educational levels could
have affected reporting of diseases in the past.
Higher educated persons may be better at verbalizing their symptoms than lower educated persons (Larsson, 2004). Changes in attitudes
and higher educational levels may then have resulted in relatively
higher prevalence of diseases than there would have been otherwise
with the old perception of disease. If changes in diagnostics and
changed threshold indeed explain increasing chronic morbidity, then it
is possible that the association between disease and disability may not
have changed over time. Improved diagnosis of symptoms, conditions
and diseases as well as diagnosis at an earlier stage may also affect disability rates by way of early detection preventing development of a
78
severe disability. No matter which explanation seems credible, it is
only by empirical data that we can get the answer.
Usually, we compare prevalence of disease and disability at the same
point of time. Nevertheless, diseases observed in the past may cause
today’s disability, as a consequence of the disablement process. Thus it
is possible that there is a time lag effect that should be analysed. If the
time lag is playing a role (meaning that today’s disease may cause disability in the future), and the changes in diagnostics and changed threshold have not affected the prevalence of disease, then the disability
prevalence may change in the future in the same direction as disease
prevalence during the last years. Time dimensions are also important
with regard to the fact that disability is not a permanent status (a certain
proportion is moving in and out see: Wolf, Mendes de Leon and Glass,
2007; Cai, Schenker and Lubitz, 2006).
Disability reflects both presence and – particularly - severity of
chronic disease. However, it is important to distinguish between diseases or health conditions that cause disability from those that co-occur
(comorbidity) with a disability (Freedman et al, 2006) or those that
have been disability-induced. It should be pointed out here that we lack
studies concerning incidence of disability.
The other dimension that needs to be dealt with concerns which age
groups that should be taken into consideration. In our studies II and IV
we have analysed older people 65-84 years old. Different trends may be
observed for different health indicators in different age groups, at different points of time (Parker and Thorslund, 2007).
The importance of different indicators for assessment of needs for
LTCaS, or needs for inpatient/outpatient health care varies. Concerning LTCaS, the most suitable indicators are likely functional limitations in ADL respectively IADL. The information about ADL and
IADL has not been available for all years during the period 1975-1999.
Severe ill-health according to Statistics Sweden’s health index is an
alternative indicator (available for the study period) that has been used
in our studies II and IV as well as elsewhere (Lagergren, 2005a;
Lagergren, 2005b). Unfortunately, we do not have information on
health status or/and functional ability for older people that use LTCaS
(except than in new special studies as for example SNAC study
(Lagergren et al, 2004)). Lack of individual-based statistics concerning
older people’s use of LTCaS and their health status and functional ability
is a problem for both monitoring and planning within LTCaS system.
79
The planning of the Swedish public LTCaS is dependent on an improved information base. Furthermore, the strong association between
inpatient/outpatient health care costs and number of remaining years
of life needs to be further analysed. This analyses needs to be expanded
to include costs for LTCaS and also be traced over time. In summary,
analyses of morbidity trends should be combined with analyses of mortality trends.
5.2.2. Demographic projections and educational health inequalities
In chapter 2 we pointed out the fact that demographic projections up
till now have underestimated the future number of older people. One
of the explanations behind this underestimation lies in the ever ongoing
debate about limits to life expectancy. The demographers, as well as
people in general, had difficulty to imagine that the tremendous success
regarding increasing life expectancy could continue at the same “speed”.
In the future, changes in population composition regarding education
and mortality differentials per educational level may lead to further
underestimation of the future number of older people. In study III a
new alternative method for population projections taking into account
socio-economic mortality differentials was presented. This new method
resulted in much higher numbers of older people in the future than
projected by official projections. Higher numbers of older people, as a
result of faster increases in life expectancy than projected in official
statistics, may also have a significant impact on future needs for LTCaS
at population level.
An answer to the central question concerning limits to life expectancy and the continuing mortality decline is provided by the conclusion from an international comparisons study of the annual average
reduction in the mortality rates in advanced ages for 27 developed
countries (Kannisto et al, 1994). Kanisto et al. pointed out that life expectancy wasn’t approaching a limit. Reductions in the mortality rates
in advanced ages have accelerated since the years 1950, the pace of decline of the mortality rates in countries with low mortality has been on
average as high as the reduction in countries with high mortality levels,
and the mortality rates between different countries have not been converging over time.
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We showed (study III) that mortality rates among all educational
and demographic groups declined between 1985 and 1999. We have also
showed that prevalence of severe ill-health among all educational, gender and age groups declined between 1975/79 and 1995/99 (study IV).
Similar trends, concerning disability rates among socio-economic
groups, were shown by Schoeni et al. (2005) based on US data. Another
similarity between our results concerning educational mortality and
morbidity differentials and the result of Schoeni et al. (2005) is that the
rate of decline (for both mortality and disability trends) was larger for
older people having more years of education. As a consequence, increasing disparities between socio-economic groups develop. Furthermore, a large US study covering more than 300 000 people aged 55
years and older (Minkler, Fuller-Thomson and Guralnik, 2006) has
shown that disability rates given level of education, measured as longlasting functional limitation in walking, climbing stairs, reaching,
lifting, or carrying, have decreased among all age groups.
Demographic extrapolation by age, gender and educational level, as
well as information on disability rates by age, gender and educational
level can thus be used for assessing the effects demographic changes
may have for the needs for LTCaS.
In summary, understanding underlying trends is not only important
for a discussion related to the three hypotheses concerning relationship
mortality – morbidity/disability, but also in order to understand how
the changing educational composition of the elderly population will
influence both morbidity/disability and mortality development. Good
planning and monitoring requires alternative demographic projections
that allow for even faster increases in life expectancy than the established methods. Introducing educational level mortality differentials
seems to be one step in improving the field of demographic projections.
The causal relationship between education and health?
Taking into account educational mortality differentials may help to
understand some underlying trends behind demographic changes.
However, the association between education and mortality is not
simple and straightforward, but rather a complex one. Different causal
mechanisms behind the association may have different effects on future
mortality and the number of older people. The effect of education on
mortality in the coming decades will depend on the nature of the association between education and mortality. Consequently, the next important step concerning this thesis, is to discuss the future association
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between educational level, mortality and functional limitations. Will
projected future changes in education composition affect both mortality and morbidity in the same way? Will these associations change when
the number of highly educated people surges during the coming decades?
Given different possible causal mechanisms, we can expect different
results concerning how changes in educational composition may affect
the future number of older people. In chapter 2, we discussed different
hypotheses (according to life-course perspective, selection – both direct
and indirect – perspective and specific determinant’s perspective) regarding the association between educational level and mortality. We
also argued that there is evidence for a causal effect of education on
mortality in accordance with results from several studies (Cutler and
Lleras-Muney, 2006). Possible explanations behind this causality could
be found using both the life-course perspective (Ben-Shlomo and Kuh,
2002) and different material, psychosocial and behavioural factors (Van
Oort, van Lenthe and Mackenbach, 2005) – as parts of the specific
determinant’s perspective (Mackenbach, 2005). Below we will discuss
how these different explanations may affect our results.
Material and behavioural explanation will probably have a direct effect from increasing educational level to lower mortality and better
health. In Sweden, better educated older people have greater material
resources (higher pension income) than those with primary education
(Alm Stenflo, 2002). Middle-aged people with higher levels of education
in Sweden (future elderly) smoke less, eat better, and exercise more
than those with less education (Swedish National Board of Health and
Welfare/EpC, 2005).
The relationship is much more complicated regarding the psychosocial explanation. The status syndrome (capturing psychological experience of inequality, but also adverse conditions at work, in residential areas, and in general, to lack of empowerment and lack of control
over own life) has been shown to be a persistent factor behind inequalities in health (Marmot, 2004). However, this status syndrome may be
affected in at least two different ways when a large share of the population is highly educated. Firstly, those without a higher educational
level may tend to be even more marginalized in a society where a relatively big share of the population is highly educated. Secondly, the perception of “belonging to the privileged part of the society” may be
weaker when a majority of the population is highly educated. Not only
the perception, but also the direct benefits of “belonging to the privileged part of the society” may be constrained by competition for scarce
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resources (no matter if the resource is new treatment, good working
conditions or a good environment). Therefore, there is a possibility that
signs of decreasing marginal utility of the effect of education on health
may appear. At the same time, the status syndrome has been shown to
be a persistent factor behind inequalities in health (Marmot, 2004).
Concerning the life course perspective it seems that our result will
be relatively stable given the development observed after Second World
War, in which all new generations have enjoyed better living conditions as children than their predecessors. In a cohort study with data
from seven European countries, Janssen and Kunst (2005) found that
factors such as living conditions in childhood and smoking in adulthood may be an important driving force in determining the recent
trends in mortality among the elderly.
As pointed out by Marmot (2004), there is evidence for three broad
conceptual models of life course perspective: the latent effects, pathway
model, and cumulative life course models and their effect on health.
The results by Seeman et al. (2004) from the study done on the
MacArthur Successful Aging cohort (initially selected to represent the top
third of those aged 70–79 in terms of physical and cognitive functioning)
clearly support both the life course perspective as well as our assumptions
that education is a persistent significant predictor of differential mortality and morbidity risks in cohorts of older men and women.
Life course perspective may be an explanation behind the fact that
new cohorts have lower mortality by gender, age group and educational level than older cohorts. For example even though the share of
people in age group 70-74 having high education almost tripled between 1985 and 2002 in Sweden for both men and women, mortality
decline shows no signs of decline in marginal utility (Statistics Sweden,
2005b). The same development among middle aged may be illustrated
by the development for women in age group 50-54. The share of
women having post secondary education in the age group 50-54 years
increased from around 13% to around 34 percent between 1985 and
2002. During the same period the mortality declined by 1.1% per year
in this age group (Statistics Sweden, 2005b, author’s own calculations).
Those highly educated women that were 50-54 years in 2002 will be 8388 years in 2035 and it seems plausible they will have better health and
lower mortality than today’s elderly women in the same age group.
On the other hand, mortality and morbidity may be affected in a
negative way by the obesity epidemic among young adults, the re-emergence of infectious diseases, wars, and health effects of ecological
83
changes (Olshansky et al, 2005). At the same time, increased investments in the appropriate preventive measures and the new health technologies may alleviate at least some of those risks. Finally, better educated people act faster as regards to adapting to new technologies, are
better prepared to comply with treatments (Goldman and Smith, 2002),
and are able to manage chronic conditions better (Goldman and
Lakdawalla, 2001). Therefore, all other things being equal, the dramatic
changes in educational composition of the elderly populations will
probably have a strong effect on mortality and morbidity in the future.
To sum up, there is an abundance of evidence about socio-economic
gradients in mortality and morbidity (using different indicators of
socio-economic position) among men and women, among children,
adults and elderly, for different causes of death, from different periods,
countries and populations and those gradients have been long-lasting
during long periods of time (United States National Research Council,
2001; Swedish Council for Social Research, 1998). Nevertheless, we
would like to underline that an overall understanding of the causes to
educational level mortality and morbidity differentials is far from clear.
The future and further research will give us more answers.
Education and demand for services
Education may also have a direct impact on demand for a service and
then the use of it. Elderly people’s expectations and attitudes towards
applying for help vary widely from one person to another. Having a
high educational level often means having a good knowledge about
different types of care and knowing how to use resources in order to
obtain formal care. Portrait, Lindeboom and Deeg (2000) show using
Dutch data that higher education levels increased the probability of
obtaining formal in-home care, at the expense of informal care. A
higher percentage of people having higher educational level may lead to
an increase in LTCaS demand, no matter how the needs for care are
changed. Swedish studies do not support Portrait, Lindeboom and
Deeg (2000), with regard to the use of public home help nor use of institutional care (Palme et al., 2003). However, Szebehely (2002) has
shown that cutbacks in formal LTCaS during the 1990’s in Sweden
have been partially compensated by an increased reliance on relatives
among low-educated, as well as an increase in the use of private market
services among high-educated people.
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5.3 Methodological considerations
Mechanistic extrapolations will always be mechanistic no matter the
level of sophistication of methods used in order to get a good input for
simulations. On the other hand, when a mechanistic extrapolation is
based on a sound knowledge that takes different possibilities into account, policy makers will be given an opportunity to get a broader picture of possible outcomes. That is also why we often use the term “scenario” throughout this thesis. This means that we focus on the question
“what if?” in order to understand possible directions for the future.
As pointed out above, we do not have information about the distribution of needs for LTCaS. We therefore used consumption data as a
starting point for our extrapolations. It is possible that today’s consumption of care may not be answering the actual needs. Formal rejections of applications for LTCaS as well as appeals against negative decisions are (relatively) rare in Sweden (Swedish National Board of Health
and Welfare, 2000). On the other hand, the relevance of a demographic
extrapolation may also be affected by changes in people’s propensity to
seek care and assistance, given the degree of ill-health and impairment
of functional ability. Changes in this propensity may be based on
changes in attitudes, revised expectations or new opportunities. The
need a person experiences is never an absolute quantity. The experienced need is influenced both by concrete, material circumstances, and
by more subjective variables, such as attitudes and expectations. People
do not demand what they believe they cannot, in any case, obtain. Refraining from applying for assistance may be caused by excessively high
charges, or because one considers the offered quality of the services to
be too low. In a study of elderly people who had withdrawn from the
home help service, the National Board of Health and Welfare found
that, in two out of five cases, the cause was dissatisfaction with charges
or quality (Swedish National Board of Health and Welfare, 1998).
One issue related to the assessment of future health trends is which
indicators should be used. The use of Statistics Sweden’s health index in
study II and IV, as a composite measure, complicates the analysis and
our chances to understand concepts behind the observed changes.
However, Statistics Sweden’s health index has been found to be associated with LTCaS use and is used as an indicator in the Swedish system
of municipality cost equalisation (Lagergren, 2003). As pointed out by
Robine (2003) the indicators of functional limitations are often seen as
more suitable for assessment of needs for LTCaS. On the other hand,
85
already today, cognitive impairment is the main reason for people moving to around-the clock-care in special accommodation. This may be
even more true in the future. Nevertheless, in the assessment made by
the municipal decision makers (needs assessment persons) it is still most
common to measure particular physical impairments.
One of the limitations concerning this thesis is that we do not analyse
household composition despite that this has been shown to be an important predictor of use of LTCaS (Larsson, 2004). This socio-demographic
factor is not analysed directly in study II. In that study, we extrapolate
trends in health using trend extrapolations of the observed proportions
with severe ill-health by age and by gender group. Mortality development, as emphasized in chapter 2, with a decreasing gender gap may result in more people cohabitating. At the same time, the increasing number of divorces in the last decades may to some extent counterbalance
the higher number of men surviving to higher ages. On the other hand,
the decreasing gender gap in life expectancy should also lead to more
men being available for the divorced women and the widows.
There is a need for making a number of underlying assumptions
based on behavioural modelling, in order to capture new trends among
people 65 years and older. However, other studies have modelled marital status. Lagergren (2005b) has presented a model, where age groups,
gender, marital status, degree of ill-health or disability is used in connection to LTCaS use according to four different levels. Our study II,
without taking into account marital status yields similar results to
Lagergren (2005b).
Concerning study I and the conclusions related to the distribution
of cost of health care on the remaining years of life, it should be
pointed out that data on costs of prescription drugs have not been included in our study. That may, to some extent, affect results due to the
fact that public spending on prescription drugs in Sweden has been
increasing rapidly during last years and now amounts to circa 10 percent of the total health care costs.
Also, in our population projections, we have not taken into account
the fact that some individuals may have raised their educational level
after the age of 35. Another limitation in our projections is that we
have not analysed the convergence in rates of mortality by educational
level among higher age groups, older than 80.
As we see it, these limitations cannot influence our projections in
any significant way. The main limitation, as pointed above, is the uncertainty regarding whether any association between educational status
86
and mortality will persist in the same way as it has over the last decades. This question needs to be further analysed.
Finally, the focus in this thesis lies on formal care. Future demand
and need for LTCaS will also be highly affected by the development of
informal care. Our projections assume that informal care will develop
in the same way as formal LTCaS. Assuming that informal care will be
unchanged at today’s absolute level may affect our results significantly
(Thorslund and Larsson, 2002). Sensitivity analyses concerning other
countries have shown that the effect of shifts from informal care to
formal care depends on which sorts of formal care that may substitute
informal care (home help services or institutions) and on the starting
point for scenarios as regards how much a country relies on informal
care (Comas-Herrera et al, 2006).
It follows that there is a need to further develop methods that take
into account and quantify informal care that is provided to older people.
Furthermore, in this thesis, despite the fact that information about
both men and women has been used as an input in analyses, the main
results are presented and discussed in aggregate form. That may have
resulted in hiding gender differences. This is particularly important to
be aware of when we know that women are both main users and main
carers within LTCaS. On the other hand, the careful planning may
from that perspective be particularly important for women. It is also
why we need more research concerning gender differences as regards to
both health trends and LTCaS needs.
The question whether the same health indicators should be used for
men and women when assessing future needs for LTCaS is also an issue
that should be further analysed.
5.4. Policy options
5.4.1 Improved planning
The methods we are discussing in this thesis may help the further development of social policies in Sweden. Improved assessment of future
needs and demand for LTCaS is one contribution. A second contribution is improved monitoring. Yet another contribution is improved
methods for population projections. Estimates of future needs and demand for LTCaS are crucial preconditions for pro-active, and not simply responsive, planning for LTCaS needs for tomorrow’s older
87
population (Jagger, 2000). The instruments presented here may then
help the development of a better quality of LTCaS and more stable
preconditions for ordinary people’s expectations for the future. The
population projections are furthermore essential for the development
of policies concerning the pension system. The methods presented here
and the trends that have been discussed may further contribute to more
reliable input to projections of future requirements on public finances.
5.4.2 Case for prevention
Including health changes when modelling demographically determined
needs for LTCaS, make a case for prevention. This kind of model may
then be used as advocacy for the development of different health interventions. Understanding the driving forces behind different trends and
the association between morbidity and mortality may help in addressing
causes behind disease and disability, giving us a better base for advocating for public health interventions. Furthermore, policy interventions
that address the higher prevalence of health problems in the lower
socio-economic groups and incorporate strategies to reduce health inequalities may contribute to minimising the negative consequences of
health disparities and ageing in European countries during future decades (Avendano, Aro and Mackenbach, 2005).
As pointed out above, one explanation behind the different trends
for diseases and disability may be changes in physical environments.
That means the investments in improving physical environments for
older people both inside and outside the home may affect disability
levels and, as a consequence, the need for LTCaS.
One other perspective is interventions within health care that may
affect needs for LTCaS. While much of today’s discussion concerns
survival interventions within health care, it is possible that investments
within health care may affect needs for LTCaS, through successful
treatment of ill-health and functional impairment. High quality rehabilitation and aftercare, new medical technologies and new drugs may
have a positive impact on LTCaS needs by curbing the trend towards
increased care dependency. A breakthrough in the treatment of dementia would, for example, be immensely significant in economic terms, as
well as in terms of human suffering.
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5.4.3 Research and statistics
This thesis points out some directions for further research that may
have important influence on social policies and public finances. It
should be a policy concern for decision makers at national level to
stimulate research concerning future studies and planning, demographic
changes, connections between mortality and morbidity, as well as
socio-economic differentials.
Concerning access to data and statistical development, there is a
growing need, as shown in this study, to have access to better statistical
data. Lack of individual-based data concerning use of LTCaS and health
care and their direct connection to health status, disability and living
arrangements is a vital policy concern in order to make better informed
decisions.
5.4.4 Some insights concerning EU countries
The Swedish case can for different reasons be interesting for other (particularly EU) countries from more than only the methodological perspective. Many factors exist that show that there is a large need for
more formal long-term care in European countries. Trends towards an
increasing number of women in paid work, is one. The low fertility rate
experienced by many European countries during the last decades, with
the result that the number of children per elderly person in need for
care - often considered as potential caregivers - will decrease, is another.
Moreover, as European countries become more global and developed, children are more likely to live further away from their parents,
resulting in a reduced number of available potential caregivers. To some
extent Sweden has gone through some of these situations already. Since
the late 1980s Sweden is one of the countries with the highest labour
force participation among women in the OECD countries (OECD,
2000). Furthermore, a great majority of older persons in Sweden live
alone or with their spouses. Only about 2% live with their children.
Finally Sweden has already gone through a rapid demographic transition. For the last 40 years, the proportion of people older than 80
years has increased dramatically, compared to an almost “zero development” for the next coming decade. Sweden is also among the countries with highest life expectancy (as pointed out in chapter 2) in the
world. Sweden's experience in moving through different stages of the
demographic transition is being repeated in other industrial countries,
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and this is projected to occur almost throughout the entire world in the
next 50 years (Thorslund, 2004).
5.5 The new paradigm: The elderly
= 75 and older?
This thesis is done within an empirical tradition (Sohlberg and
Sohlberg, 2002 ) and is quantitatively oriented and focuses on statistical
associations. The theoretical base discussed here is a number of different hypotheses concerning the mortality and morbidity/disability relationship, given the definition that older people are people 65 years and
older. Our research question is then closely related to the question:
“How old are elderly people?”. This question opens up alternative
methods for our analyses. Below, we will attempt to provide a short
discussion and point out some important trends in the construction of
models for analyzing older people and their needs.
In the research as well as in public debate in Sweden and other
OECD countries we assume that people 65 and older constitute older
people. Accordingly, the increasing number of older people means that
the number of persons older than some age, often 65 years and older,
will increase. However, the categories “older people” and/or ”old age”
are social constructs (Achenbaum, 2005). This social construction correlates to conditions in, and dimensions of, different parts of society.
Furthermore, social constructions change over time. The change of
social construction in the term “to be old” may be illustrated by data
from Norway showing that a majority of people surveyed in the year
1969 characterized age 72-73 as “to be old” compared to 77 years among
those surveyed from the year 1993 (Daatland, 1994).
The social construct of older people, in today’s developed countries,
is connected to regulations within two vital areas related to people’s
participation in labour market. Firstly, the mandatory retirement age
has been the age of 65 during many decades both in Sweden (since
1976) and in other western countries. The retirement age was 67 years
in Sweden between 1913 and 1976 despite an increase in life expectancy
at birth from 58 years for women and 56 years for men to 77 years for
women and 72 years for men (Statistics Sweden, 1999). The mandatory
retirement age was abolished in 2003 in Sweden when the new pension
system with flexible retirement age was introduced (Holzmann and
Palmer, 2006). Nevertheless, in Sweden the right to get the basic
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pension is still connected to 65 years of age (Swedish Social Insurance
Agency, 2005). Secondly, according to international standards (ILO
conventions) 64 years of age is emphasized as an upper age limit for the
labour force. Usually many people leave the labour force 4-5 years before the age of 65 in OECD countries (Andersson, 2003). However,
during the last years we have found evidence of a new development in
Sweden. According to Batljan (2005), the employment rate has increased substantially among both men and women in the age group 6064. Furthermore, more and more people are continuing working after
the age of 65 (Nygren, 2005).
According to our data concerning 1975/1979, the proportion of
people that reported severe ill-health in the age group 65-69 years was
around 15 percent. The same proportion is today found among the age
group 75-79 years (SNSLC data concerning 2002/2003). Analyses of
SNSLC data show that it is difficult to find significant differences in selfperceived health between the age groups 65-74 and 55-64 (Batljan, 2005).
Finally, the use of different definitions over time also underlines the
overall picture of social construction of the categories “older people”
and ”old age”. For example Shryock and Siegel (1976) pointed out that
a population is considered “old” if it has a median age of 30 years or
over. That should be compared with the fact that already in the year
2000 the median age in the world was 26 years (United Nations, 2002).
The United Nations uses 10% or more of the population beyond the
age of 60 or 65 years as the measure of an aged population allowing
different thresholds for developing and developed countries (McPherson,
1990). In different documents related to developing countries the terms
’older people’ and ’elderly persons’ often refers to people aged 60 years
and older (ECLAC, 2004). In developed countries we normally refer to
people aged 65 years and over as “older people” and “elderly persons”.
Because the nature of aging is an individual process, it is practically
impossible to have a precise definition related to specific age. It is also
well known that a 65 year old low-educated person in many cases has
worse health status and higher mortality risk (as pointed out in study
III) than an older high-educated person (see e.g. Elo and Preston (1996) ).
As mentioned above, the SNSLC has had an upper age limit for
many years. This upper age limit was as low as 75 years until 1980. The
increase to 85 years in 1980, and the fact that the upper age limit was
banned until recent years, may also be an indication of the changing
picture of older people and the social construction of ageing.
91
Given the discussion above, it looks like it is time to reconsider the
use of the term “older people” and “old age” in Sweden for the people
65 years and older. The threshold age could be increased to 70 years
already today in order to be replaced by another threshold in 30-35
years going forward. In the science tradition within which this thesis is
written, we could show then that increase in number of older people
(using different threshold ages for different years) between 2000 and
2035 will only be 9 percent (compared to the usual assumption where
the number of older people, 65 years and older increases by 57 percent), from 1 153 000 (70 years and older assumed older people by
2000) to 1 253 000 (75 years and older assumed older people by year
2035). According to the scenario methodology that has been used in
this thesis, we could then do comparisons between two scenarios: 1)
unchanged social construction of the older people and 2) changed social
construction of the older people to be defined as 70 years and older by
year 2000 and (assumed changed social construction of the older people) 75 years and older by year 2035. Given that “older people” need
LTCaS, projections of future needs will be considerably different if the
definition of “older people” is people 65 years and older, or if the definition is changed according to the discussion and as shown above.
The other connection to the discussion in this thesis may be by stating that the increasing threshold age is in some sense supporting the
postponement of morbidity hypothesis. This is due to the use of the
term “older people” or “old age” in the context that these terms in fact
often are used: persons with worsening health and increased morbidity
and disability. However this relationship is not straightforward, given
that there are some signs that health among those 80-85 years and older is
not improving, rather there are some tendencies towards worsening health
status during the last 5-10 years (Parker, Ahacic and Thorslund, 2005).
92
6. CONCLUSIONS
In this thesis we have presented three alternative methods (studies I, II
and IV) to improve direct demographic extrapolations of needs for
health and social care for the elderly. We have also developed a new
method for improvement of demographic projections (study III) that
will further improve the demographic base for our methods. This new
method has also been combined with the method in study II (could be
applied on study I too) in order to enhance alternative methods of
demographic extrapolations of future needs for LTCaS with socio-economic dimensions (study IV).
Population projections by age, gender and educational level should
be used when assessing how demographic changes affect needs for
LTCaS. This may also hopefully influence discussions of alternative
population projections in order to balance the systematic underestimation of the number of older persons that has been more of a rule
than exception in many countries including Sweden.
Information on the future size (the ageing of the population) and
socio-economic composition of the elderly population, as well as assumed changes in the prevalence of functional limitations by age, gender and socio-economic status have substantial implications for future
needs. Thus, this information should be used in LTCaS for elderly
planning, for monitoring purposes or when assessing financial sustainability of long-term care in the future. We show that the projected increase in the number of older people suffering severe ill-health, as a
consequence of population ageing, may be counterbalanced to a large
extent by changes in the educational composition towards a higher
proportion of the population having a high educational level and lower
prevalence of severe ill-health. On the other side, an increase in the
number of older people that is much higher than projected will strain
different pension systems.
It is crucial for today’s policy makers, planners and researchers to
have access to high quality information on population development,
public health trends and health status among older people. According
to our studies II and IV older people’s health (measured as the prevalence of severe ill-health) has improved during the study period.
Developments in morbidity and disability prevalence in recent years
and the question whether its decline will continue has enormous implications for the future needs for LTCaS and for social policies in gen93
eral. Policy response may include changes of the LTCaS system,
measures to increase employment and investments in public health
among older people.
Improved methods of demographic extrapolations are important for
both improving planning within LTCaS, and enhancing health and
long term care human resources policies. Those improved methods are
furthermore crucial in order to design public policies that address the
needs of aging populations, including planning, financing, and public
health programs.
The value of scenarios and projections of this kind does not lie in
their perfect match with the future. The fact is that forecasts most often are proven to be wrong. Nevertheless, it is important to analyse
what will happen if the development continues in the same direction or
what may happen if the preconditions are changed.
Future developments in mortality rates, morbidity/disability rates,
changes in population composition as regards to gender, age and socioeconomic status are inevitably uncertain. There is also great uncertainty how the association between education, mortality and morbidity
will develop. On the other hand, future needs are not only uncertain,
but also affected by today’s actions. We need to improve our planning
tools in order to support policy-makers to plan for uncertainty concerning future needs and demand for LTCaS.
There are few countries in the world that have as extensive statistical registers as Sweden. However, also as regards to statistics there are
some areas that need improvements. Lack of individual statistics concerning older people’s use of LTCaS and their health status and functional ability is a problem for both monitoring and planning within
LTCaS system.
94
ACKNOWLEDGEMENTS
My first and foremost thanks go to my supervisor Mats Thorslund and
co-supervisor Mårten Lagergren. Thank you for reading and discussing
numerous versions of the manuscript, I would never have reached the
end without your support and help. Mats Thorslund, was the first to
encourage me to start my PhD studies and contact with him and Department of Social Work at Stockholm University gave me a fantastic
opportunity to work on this thesis and to develop not only my research, but also on a personal level. Mats is always accessible and supportive, and he opened many new doors to the up-to-date research on
ageing to me. Furthermore, I will always consider my studies at Department of Social Work as an enlightening experience in favour of the
knowledge that there is some other reality outside, not only quantitatively described.
Mårten Lagergren and I published in early 2000 a report with the
title “Will there be a helping hand?” (Batljan and Lagergren, 2000). At
that time, it was still more coincidence than rule, to question the demographic projections, as a single determinant of future cost for long term
care. Health trends and their importance were often omitted. In that
sense working with the dissertation has also been a journey were I have
had the opportunity to follow the increase in interest on the health of
older peoples, also as a factor that will play a role for the future of
public finances.
I would like to thank Olle Lundberg for constructive and valuable
comments on my final manuscript. Thanks to Ingrid Tinglöf for help
with the layout and to Lotten Riese Cederholm for all administrative
help. I am also grateful to Chris Mortimer for his important help as a
language advisor. Working at the Swedish ministry of health and social
affairs gave me an opportunity to cooperate with probably the best
statistical office in the world, Statistics Sweden (SCB). SCB, thank you
very much. Special thanks goes to Sven-Eric Johansson, Åke Nilsson,
Hans Lundström, Jan Quist, and Gun Alm Stenflo. I would also like to
thank the Swedish Ministry of Health and Social Affairs and Ministry
of Finance as well as Thor Litman from Skåne region for their support
with the study I. Thanks also to the European Commission DG
ECOFIN for inviting me to a Visiting Fellowship.
95
When working with modelling, simulation and research in general,
academics are many times wondering if their work is going to be used
in policy making. In my career I have had the opportunity to operate
both supporting policy makers at central level within the ministry, and
at an international level within the Inter-American Development Bank
and OECD, and as a policy maker at national and local level. However,
despite hands-on experience, I am not able to reveal the secret and answer this question. As a researcher I do believe that every step forward
counts and that is why I enjoyed working long nights with this thesis. I
do believe that science may improve our world to the better. My hope
is that this thesis may be found useful when planning for the future of
long term care.
Thank you Sanja for your love and your support. Thank you for
having patience with a long term student, always curious enough to
jump from the sofa in the middle of watching a movie, in order to
check something from the article he was reading before, and then coming back to the sofa when the movie was finished. The examples are
many of this. To make it short, thank you for everything.
In a first draft of this thesis I wrote “Thank you Mia (my daughter)
for eating biscuits and drinking milk by my side when I was working
with this thesis during the Easter of 2006”. Since then I have many
times considered deleting that sentence, nevertheless the sentence tells
the reader something that has been significant for the work behind this
dissertation and that is that I and my family have lived with it in a very
special way (during the evenings, nights, weekends and summer vacations). During all the years I have had at least one full-time job and my
PhD studies plus my curiosity to know more about ageing, demographics and public health.
My father, my mother, my mother-in-law, my father-in-law just like
my two brothers have all, as personalities and in their interest for
knowledge and science, also influenced me in the way that I hope I can
pass on to my daughter Mia.
Finally, Sanja, Mia, Mårten and Mats I will always be in debt to
you, thank you very much. We did it.
96
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