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Informal and formal care: substitutes or complements in care for people with dementia? Empirical evidence for 8 European countries

Patrick Bremer1, David Challis2, Ingalill Rahm Hallberg3, Helena Leino-Kilpi4, Kai Saks5, Bruno Vellas6, Sandra M.G. Zwakhalen7, Dirk Sauerland8 on behalf of the RightTimePlace-

Care Consortium

Abstract

Background: In order to contain public health care spending, European countries attempt to

promote informal caregiving. However, such a cost reducing strategy will only be successful if

informal caregiving is a substitute for formal health care services. We therefore analyze the ef-

fect of informal caregiving for people with dementia on the use of several formal health care

services.

Study Design: The empirical analysis is based on primary data generated by the EU-project

´RightTimePlaceCare` which is conducted in 8 European countries. 1223 people with demen-

tia receiving informal care at home were included in the study.

Methods: Using a regression framework we analyze the relationship between informal care

and three different formal health care services: the receipt of professional home care, the num-

ber of nurse visits and the number of outpatient visits.

Results: The relationship between formal and informal care depends on the specific type of

formal care analyzed. For example, a higher amount of informal caregiving goes along with a

lower demand for home care services and nurse visits but a higher number of outpatient visits.

Conclusion: Increased informal caregiving effectively reduces public health care spending by

reducing the amount of formal home care services. However, these effects differ between

countries.

Keywords: Formal Care, Informal Care, Dementia, Substitute, Count Data Model, Europe

1 corresponding author, Witten/Herdecke University, Alfred-Herrhausen-Straße 50, 58448 Witten, Germany, Phone: +49 2302 926-579, [email protected] University of Manchester3 Lund University4 University of Turku5 University of Tartu6 University of Toulouse7 Maastricht University8 University of Witten/Herdecke

mailto:[email protected]

1. Introduction

The simultaneous decline in mortality and birth rates which cause Europe´s population to

grow older, leads to an increase in the proportion of frail older people with extensive care

needs. Estimates indicate that the number of dependent individuals will nearly triple from

around 101 million in 2010 to 277 million in 2050 [1]. At the same time, around 50 percent of

those people in need of care are estimated to suffer from dementia [1]. Dementia is a complex

neurodegenerative disorder that affects cognitive, mental and functional capabilities and re-

sults in a high dependency on care and assistance of those affected by the disease. Given the

fact that the prevalence of dementia increases almost exponentially with older age, European

health care systems will face a considerable higher demand for long-term care during the next

decades [1].

Although, rising health care costs are an issue that policy makers have debated for more than

two decades, the tendency described above goes along with a growing concern of expanding

public expenditures on long-term care services in the future [2]. Therefore, in some European

countries policy has encouraged informal caregiving in order to reduce public health care

spending [3, 4, 5]. However, it needs to be taken into account that the strategy of enhanced

incentives for informal caregiving in order to ease the financial pressure upon the public purse

is feasible only if the assumption holds that informal caregiving is an effective substitute for

formal care. This indicates that to formulate adequate long-term care policies a clear under-

standing is required as to how the provision of informal care affects the use of formal care

services.

Comprehensive literature documents that two different relationships between formal and in-

formal care can be distinguished. Both types of care can either substitute or complement each

other. The empirical analysis of the relationship between formal and informal care is not

straightforward to assess as the provision of care by family members might be endogenous to

the care recipients´ (CR) formal health care use. Because of the complex nature of both types

of care, previous studies do not definitively establish whether formal and informal care substi-

tute or complement each other. E.g. Van Houtven and Norton [6], Bonsang [7] and Bolin et

al. [8] found that informal care is a substitute for formal home care and paid domestic help

respectively, whereas Liu et al. [9] and Langa et al. [10] ascertained that the increased paid

home care in the US primarily went to people who were already receiving a greater amount of

informal care from their adult children. Mixed evidence also was found regarding doctor and

hospital visits in general: Some studies indicate that informal care is a complement for both

[e.g. 8] and others found a negative relationship between informal caregiving and both doctor

and hospital visits. On the other hand, for outpatient surgery and nursing home entry, the rela-

tionship is unambiguous: previous findings clearly suggest that informal care complements

outpatient surgery [6, 11] and substitutes for (or at least delays) nursing home entry [12, 13].

However, considerably less is known about the relationship of both types of care in the spe-

cific case of dementia caregiving. In general, previous studies indicate that both formal and

informal care increases with dementia severity and that once a certain degree of severity is

reached, informal care is replaced more and more by formal care services [14]. Furthermore,

analyzing data from patients with dementia in Spain and Sweden respectively, Peña-Longob-

ardo/Oliva-Moreno and Wimo et al. found that informal care substitutes for paid domestic

help and complements the utilization of day care facilities [15, 16].

Although the studies mentioned above delivered consistent results for two different European

countries, there is substantial evidence of cultural and hence institutional differences between

Northern and Southern European countries, which cause the outcomes of healthcare provision

for people with dementia (PwD) to differ tremendously across Europe [17, 18, 19, 20]. Partic-

ularly with regard to informal dementia caregiving, previous studies suggest a significant

north-south gradient within Europe. Bremer et al. for example, found a negative association

between the amount of informal caregiving and caregivers´ health status in northern and cen-

tral parts of Europe, whereas no such relationship was observed in Southern Europe [21].

Moreover, a higher preference for receiving informal care was reported in southern regions of

Europe and variations were found in the relationship between informal care provision and

labor market participation in different European countries [22]. Generally, countries in the

south of Europe are commonly described as countries with stronger family ties, suggesting

that norms regarding family responsibilities and inter-generational support are more pro-

nounced compared to more northern or central European countries – which the contrary often

are referred to as having less distinct family ties [23].

In the specific case of informal dementia caregiving, such a comparison between several Eu-

ropean countries is to our best known not available. Therefore, in this paper we analyze the

effect of informal dementia caregiving on the use of several formal health care services (the

receipt of professional home care, the number of nurse visits and the number of outpatient

visits), with special emphasis on differences between European countries.

The remainder of the paper is structured as follows: section two gives a brief overview of the

data source, discusses some econometric issues regarding the relationship between formal and

informal care, and describes the empirical strategy. Section three presents the empirical re-

sults, section four discusses these findings and section five offers some conclusions.

2. Data and methods

2.1 Data source

The empirical analysis is based on primary data obtained from a large European research

project called `RightTimePlaceCare´ (RTPC). RTPC is a prospective cohort study conducted

in eight European countries which can be assigned to more northerly (Estonia, Finland and

Sweden), the southern (Spain), and more central areas of Europe (France, Germany, the

Netherlands and England). Data were collected by face-to-face interviews between November

2010 and April 2012. The dataset contains comprehensive information about PwD and their

informal caregivers. A detailed description of the study’s rationale and its methodology can

be found in Verbeek et al. [24].

The current study focuses on PwD receiving professional home care who were at risk for in-

stitutionalization and their informal caregivers. General criteria for PwD to be eligible for

RTPC were: 1) a formal diagnosis of dementia recorded in the clinical record; 2) a maximum

Mini Mental State Examination (MMSE) score of 24 in order to exclude mild cases; and 3)

the presence of an informal caregiver who visits at least twice a month.

From the original sample of 1.223 respondents, 198 (16.1 %) observations were excluded

because of missing values in one or more variables.

2.2 Variables

Formal health care services

As dependent variables we analyze the utilization of three different formal health care ser-

vices: the number of professional home care visits, the number of nurse visits and the number

of outpatient visits (visits to a GP, a specialist or a physiotherapist). Resource utilization was

assessed using the Resource Utilization in Dementia (RUD) questionnaire [25]. The RUD is a

comprehensive and validated standard tool to measure resource utilization in units defined as

relevant during the month before the interview. With respect to formal health care utilization,

respondents had to specify the total number of outpatient visits, the number of times they re-

ceived professional home care, and the number of visits from a district nurse, a community

nurse or another nurse within the last month.

Informal caregiving

The intensity of informal caregiving was assessed by the total numbers of hours per day care-

givers spent assisting their relatives with activities of daily living (ADL) and instrumental

activities of daily living (IADL). ADL include tasks which are necessary for fundamental

functioning (e.g. eating and bathing) whereas IADL comprise tasks that allow individuals to

live independently (e.g. housework and shopping). We restricted total hours of informal care-

giving to a maximum of 16 hours per day as we assumed a daily minimum of 8 hours non-

caregiving time including sleeping time. In the multivariate analysis, the intensity of informal

caregiving was included as continuous variable and for the descriptive statistics (Table 1) time

spent caregiving was divided into three groups: a low level of care was coded for 0 – 90 hours

of monthly care; a medium level for 91 – 240 hours; and a high level of care for more than

240 hours of care per month.

Covariates

When assessing the impact of informal caregiving on the utilization of formal health care ser-

vices, it is of utmost importance to take a broad range of PwD´s health characteristics into

account. As the provision of informal care might be endogenously determined through unob-

served factors which independently raise the use of formal health care services, it is likely that

unobserved health impairments influence both the amount of formal care as well as the

amount of informal care. If this assumption holds, the coefficient of informal caregiving in-

tensity would pick up the effect of unobserved health characteristics and may bias upward the

impact of informal caregiving intensity on formal health care use. This might lead to the erro-

neous conclusion that informal care is positively related to several formal health care services

when in fact both types substitute each other.

Previous studies which relied on data with insufficient information about PwD´s health char-

acteristics often used an instrumental variable approach to account for the endogeneity of

caregiving decisions [6, 8]. In this paper however we are taking advantage of the unique pri-

mary RTPC-dataset which contains comprehensive information about PwD´s mental and

physical health conditions.

PwD´s level of cognitive impairment was assessed by the Standardized Mini-Mental State

Examination (S-MMSE) which consists of 20 items and covers e.g. subjects like orientation in

time and space, short term memory and some short tasks on language, calculation and co-

ordination. The S-MMSE score ranges from 0 to 30 whereby a higher score indicates less

severe cognitive impairment [26]. Physical impairment was measured with the Katz Index of

Independence in Activities of Daily Living. The Katz Index ranks adequacy of performance in

the six functions of bathing, dressing, toileting transferring, continence and feeding. The total

score runs from 0 to 6, whereby a lower value indicates a lower degree of independence [27].

The NeuroPsychiatric Inventory-Questionnaire (NPI-Q) is applied as an instrument to identify

behavioral and neuropsychiatric problems. The NPI-Q is composed of 12 items which can be

distinguished in two neurovegetative and ten behavioral areas [28]. From these items, a sever-

ity and a distress score were calculated. In this analysis we use the severity score which runs

from 0 to 36 where lower values represent a lower degree of neuropsychiatric and behavioral

problems. The presence of comorbidities was assessed by the Charlson Comorbidity Index

which covers 19 possible comorbidities. The total score ranges from 0 to 37 with a higher

score indicating a greater number of comorbidities [29]. Other health indicators include binary

variables for the type of dementia and whether PwD had at least one fall during the last 30

days. Socio-demographic variables such as age, gender, marital status and living arrangement

(alone vs not alone) were also included.

2.3 Empirical Strategy

The conceptual framework suggested by Van Houtven and Norton [6] serves as theoretical

model in the following analysis. Van Houtven and Norton extended Grossman´s classic model

of health and health care demand [30] by including informal care as determinant for care re-

cipients´ formal health care use. According to the model, both caregivers and care recipients

aim to maximize their utility function whereas care recipients choose their amount of formal

health care use given the amount of informal care provided by informal caregivers. Reflecting

this theoretical framework, we estimate the use of three different formal health care services

as a function of informal caregiving intensity and further explanatory variables.

First, we estimated pooled models which include the whole sample and subsequently models

for each country separately. The focus of the analysis lies on the impact of caregiving inten-

sity on PwD´s formal health care use. As the amount of informal care largely depends on

PwD´s need for care, which is determined by disease severity, we stepwise included different

indicators for patients’ illness characteristics in the pooled models. This enables us to ascer-

tain how the relation between informal caregiving intensity and CR´s formal health care use is

moderated through different disease characteristics. Accordingly, the basic pooled model is

given by:

Yk = β0 + β1CareInt + β2X + β3C + αk + εk

where k = 1, 2 and 3 respectively, stands for the three different types of formal health care use

(home help visit, nurse visit and outpatient visits). The parameter β1 provides information on

the effect of informal caregiving intensity on formal health care use, whereas the vector X

includes socio-demographic factors and vector C captures country dummies using England as

reference country. Furthermore, all regression models include an overdispersion parameter α

(see the following section) and an error term ε. Subsequently, we estimate the following

model:

Yk = β0 + β1CareInt + β2X + β3C + β4Z + αk + εk

which additionally comprises the effect β4 of vector Z which accounts for all illness character-

istics apart from ADL-independency. Finally, by including the degree of ADL-independency

(KATZ), the full specified model

Yk = β0 + β1CareInt + β2X + β3C + β4Z + β5KATZ + αk + εk

controls for all illness characteristics simultaneously. When estimating the country-specific

models, we only distinguish between the basic model and the full specified model.

A high proportion of the sample had none or only a few outpatient visits, nurse visits or pro-

fessional home help visits respectively. In addition, only a small proportion reported a higher

level of utilization. Therefore, the distribution of all outcome variables is right skewed. For

this kind of distribution, count-data models should be applied as a linear regression model

would yield biased and sometimes negative coefficients [31].

Count-data models are appropriate, when the outcome of interest is a non-negative integer and

the distribution is right skewed. In the present dataset, for all outcome variables, the equidis-

persion property is violated, meaning that the (conditional) variance exceeds the (conditional)

mean and therefore overdispersion is present. Given the presence of considerable overdisper-

sion in our data, we apply negative binomial models, which account for the additional disper-

sion by introducing a constant overdispersion parameter α. For all models, we use the NB2-

option which assumes a quadratic variance function [32].

For all models a test of the null hypothesis of α = 0 was conducted whose results can be found

at the end of each Table. The parameter α can be interpreted as a measure of the variance of

heterogeneity and its range lies in all models between 0.61 and 0.72. The Likelihood-ratio test

statistics are highly significant and hence the null hypotheses are conclusively rejected in each

model. This confirms the presence of considerable overdispersion in the data and justifies the

application of the NegBin model.

Furthermore, we tested for multicollinearity between all independent variables and performed

several robustness checks (i.e. we used alternative estimation strategies and applied a different

coding for several variables of interest) for all models. These modifications only had a minor

impact on the results and the key findings remained the same. Further information about the

robustness checkscan be found in the supplementary material .

3. Results

3.1 Descriptive Statistics

The majority of care recipients were female (63.2 %) with an average age of 82.3 years. The

mean sample S-MMSE score was 14.3 index points, indicating the presence of moderate de-

mentia on average (cp. Alzheimer´s Society 2013). ADL-independence and Charlson Comor-

bidity Index scores averaged 3.5 and 2.1 index points respectively. On average, care recipients

had 20 nurse visits, 17.5 professional home care visits and 2.1 outpatient visits within the last

30 days prior to the interview.

With increasing caregiving intensity, care recipients displayed more comorbidities as well as

lower ADL-independence and S-MMSE scores. Regarding the outcomes of interest, the fol-

lowing patterns are evident: care recipients, who received the highest amount of informal

care, had a lower number of nurse visits and home help visits (9 and 4 visits respectively). By

comparison, for PwD receiving the smallest amount of informal care the corresponding num-

bers are 28 and 26 respectively. Regarding outpatient visits, no difference occurred in relation

to the informal caregiving intensity.

Comparing different countries, the negative relationship between informal caregiving intens-

ity and the number of nurse and home help visits was found in Finland, France, Germany,

Sweden and the Netherlands. A positive relationship between outpatient visits and caregiving

intensity was detected in Germany and Sweden. Further descriptive statistics can be found in

the appendix.

3.2 Regression Results

For the pooled sample, the regression results for home help visits (in the first column of each

model), nurse visits (second column) and outpatient visits (third column) are presented in Ta-

ble 1. Regarding the number of home help visits, receiving more informal care was signifi-

cantly associated with fewer visits in all models, indicating a substitutional relation between

the amount of informal care and the use of professional home help on average.

Table I here

All coefficients are displayed in an exponentiated way (incident rate ratios) that can be given

a multiplicative interpretation. Thus, values above 1 indicate a positive association between

the dependent and the explanatory variable and values below 1 imply a negative relationship

between both variables. In model 1, where merely demographic factors are controlled for, one

additional hour of informal caregiving per month is associated with home help visits decreas-

ing by the multiple of 0.998. In model 2, the magnitude of the caregiving coefficient does not

change substantially. Among the disease characteristics, the degree of ADL-independence is

the strongest predictor for the number of home help visits, whereas the other disease severity

measures do not have any significant impact. In model 3, which controls for all disease char-

acteristics simultaneously, one additional KATZ-index point decreases the number of home

help visits by the multiple of 0.87.

The relationship between informal caregiving intensity and home help visits within the differ-

ent countries is illustrated in Table 2 where only the coefficients of caregiving hours and three

disease severity measures are reported. After considering missing values in the multivariate

regressions, the English sample reduces to n = 58. As this sample size hinders us from draw-

ing any reliable inferences, the English subgroup is not examined in the county-specific

analyses.

Table II here

From the isolated model (column 1) a negative and significant association is evident between

the amount of informal care and home help visits in all countries except for Spain and Esto-

nia. When turning to the full specified model (column 2), the caregiving estimate remains

significant and its magnitude only reduces slightly in Finland, France, Germany and Sweden,

whereas in the Netherlands the relevance of informal caregiving intensity disappears once

controlling for disease severity. We therefore conclude that informal care is a substitute for

home help visits in four out of seven countries.

The regression results for the number of nurse visits for the whole sample are illustrated in the

second columns of Table 1. It is clear in all models that in the pooled sample the amount of

informal caregiving has no impact on the number of nurse visits.

Table 3 presents the country-specific results for the total number of nurse visits within the last

month. In the full specified model (column 2), apart from Spain, all caregiving estimates have

values below one, which points to a substitutive relationship between informal caregiving

and nurse visits in these countries.

Table III here

However this relationship is significant only in France, the Netherlands and Sweden. Just as

in the pooled sample, in some countries the fact of cohabiting with somebody is a significant

predictor for less nurse visits whereas suffering from several diseases is related to a higher

number of nurse visits. For example, those living with others in Estonia, Finland and the

Netherlands have 0.23 times, 0.2 times and 0.1 times the amount of nurse visits compared to

individuals living alone. With respect to comorbidities, it shows that more illnesses are associ-

ated with approximately 1.6 times the amount of nurse visits in Spain and Sweden.

Examining the relationship between caregiving intensity and the total number of outpatient

visits on average (right columns of Table 1), one can see that the caregiving estimate is above

one in all models, which implies a complementary relationship between informal care and

outpatient visits. The coefficient for informal caregiving is the highest in model 1 (β = 1.002)

and reduces slightly in model 2 (β = 1.001). In model 3 which controls for all disease charac-

teristics simultaneously, the quantity of informal care has no longer a significant impact on

the number of outpatient visits. The total number of outpatient visits is rather influenced by

factors that indicate a higher degree of health problems.

With respect to the country-specific results (Table 4), the positive relationship between infor-

mal caregiving and outpatient visits which was observed for the full sample only holds for

Germany whereas in the remaining countries this association is not significant. In Germany,

one extra hour of informal caregiving translates into 1.004 times more outpatient visits per

month.

Table IV here

4. Discussion

On average we found that informal caregiving substitutes for home help and nurse visits

whereas we identified a (less distinct) complementary relationship between informal care and

total outpatient visits. When looking at the country-specific results, the negative association

between informal caregiving on the one hand and home help visits and nurse visits on the

other hand was present in Finland, France, Germany, Sweden, France, and the Netherlands

whereas the positive relation between informal care and total outpatient visits only holds for

Germany.

The identification of a negative relationship between informal caregiving and formal home

help is generally in line with several previous studies which predominantly revealed a substi-

tutional relationship between both types of care [e.g. 6, 7]. With respect to outpatient visits,

existing results have been ambiguous: Some studies indicate that informal care is a comple-

ment [e.g. 8] and others found a negative relationship between informal caregiving and outpa-

tient visits. With regard to nurse visits, we could not find any former studies investigating the

effects of informal caregiving on the number of nurse visits.

In general, these results emphasize that it is crucial to distinguish different types of formal

health care services when investigating the relationship between informal and formal care.

Our findings that informal care and formal home care are substitutes, while informal care and

outpatient visits complement each other are consistent with Bolin et al. who state that the rela-

tionship between both types of care depends on the question whether the purpose of informal

care is to restore or to maintain CR´s health status [8]. In the case of restorative care, which

requires highly specialized health care professionals such as general practitioners (GPs) or

specialists, informal caregivers might act as better informed agents for their impaired relatives

and therefore determine the access to health care professionals. They might also increase ad-

herence, perhaps being more likely to ensure that outpatient appointments are kept. In such

circumstances, a complementary relationship between both types of care seems to be more

likely. Contrary to this, when it comes to the maintenance of CR´s health and well-being by

assistance with activities of daily living which normally does not require such skilled profes-

sionals, it seems to be more realistic that informal- and formal home help substitute for each

other.

This study extends previous research on the association between formal and informal health

care utilization in several ways: First, in contrast to past research, which mainly focused on

individuals from a single country and therefore exacerbated comparisons between countries

due to methodological differences, we made use of an unique primary dataset which was ob-

tained from eight different countries from all over Europe in a consistent manner. This en-

abled us to compare country-specific results directly with each other. Second, using specific

inclusion criteria, the RTPC-study is the first that focused on people with dementia at risk of

institutionalization. This focus on individuals who are at the margins of long-term care admis-

sion is of particular importance in terms of ongoing long-term care policy debates. Third, the

empirical analysis benefits from the fact that the RTPC-dataset was developed especially for

patients with dementia receiving professional long-term care and provides accurate measures

of mental and physical health status. This allowed us to address the potential endogeneity be-

tween both types of care in the analysis.

Nonetheless, the results and their implications must be viewed in the light of some limitations.

First, the empirical analysis is based on retrospective self-reported data on resource utiliza-

tion. Like all self-reported data, these might be prone to inaccuracies. In the specific case of

the total duration of informal caregiving, other studies indicate that caregivers tend to overes-

timate their duration of caring [33]. However, studies which validated the RUD-instrument

are generally encouraging [34]. Second, due to the specific focus of the RTPC-study, the sam-

ple consists of people with dementia being at risk of institutionalization, which means that the

country-specific samples are not representative to the average population of people with de-

mentia. Hence, the external validity of our results might be challenged. Finally, the study’s

cross-sectional design hinders us to test causal relationships between both types of care.

Despite these limitations, the current study generates important new insights regarding the

association between formal and informal care in eight European countries. Given the huge

societal challenge caused by the expected rise of the share of frail older people in general and

the increasing dementia prevalence in particular, these findings should be considered when

formulating long-term care policies in Europe.

5. Conclusion

What policy implications can be derived from these findings? As mentioned before, nowadays

many European governments provide incentives for informal caregiving, presumably with the

intention of cost savings and the expectation that increased informal caregiving can substitute

for more costly formal health care services. However, in order to quantify the potential net

savings on formal care expenditures, allowance has to be made for the costs of informal care-

giving which may at least partly offset the gross savings. These costs typically arise for sev-

eral reasons: First, informal caregiving incurs opportunity costs by reducing labor supply and

forgone wages [35]; second, greater informal caregiving can result in adverse health conse-

quences and increased health care utilization among caregivers [21, 36]; thirdly, there are

costs paid to carers through the public purse such as tax relief and carer’s allowances.

The empirical results in this study suggest a possible net cost-saving effect through a substitu-

tional relationship for both health care services provided in the household (home help and

nurse visits). These resultant cost-savings are unlikely to be negated by the effect of modest

increases in outpatient visits. However, these analyses do not take the labor market effects

into account. Furthermore, it should be kept in mind that the net effect of increased outpatient

visits on long-term expenditures is not straightforward to assess as some of the extra visits an

informal caregiver generates might be of a preventive nature, which again might lead to a cost

reduction in the long run.

However, it is essential to take into account that the relationships between both types of care

seem to differ within various countries. In Finland, France, Germany, Sweden and the Nether-

lands we identified a substitutional relationship with respect to at least one of the health care

services provided at home whereas no such relationship was observed in Estonia and Spain.

At first glance these results seem to be contradictory to the findings of Bolin et al. who found

further evidence for a north-south gradient by revealing that the negative effect of informal

caregiving on formal home care use was more distinct in central and northern Europe com-

pared to southern Europe [8]. This, according to the authors, indicates that in countries with

stronger family ties, informal care substitutes to a greater extent for formal health care ser-

vices.

One possible explanation for the lack of a substitutional association between formal and infor-

mal care in Estonia and particularly Spain (as representative of the strong family-tie countries)

might be the fact that in these countries the level of formal health care utilization is already

quite low whereas the level of informal care is distinctly above the European average (see

Table VI, Appendix). Therefore, one might assume that in these countries, the amount of for-

mal care provided at home is already at a minimum and that this small quantity is not influ-

enced by the amount of informal care.

Furthermore, it should be considered that the majority of people with dementia will be admit-

ted to a nursing home at some point, since their care needs cannot be met adequately by fam-

ily caregivers anymore [37]. However, the choice of the right time to move PwD from their

homes to an institutional care facility is a complex one as it requires comprehensive knowl-

edge of outcomes and benefits for both caregivers and patients (e.g. quality of life and quality

of care) in the home care and the institutional setting.

In addition to these aspects, for policy makers it is crucial to understand which costs arise in

both settings and how these costs vary with disease severity and type of care (informal/ for-

mal). In this analysis we adopted an economic perspective and focused on the latter point by

analyzing the relationship between formal and informal care for PwD being at risk of institu-

tionalization. As already mentioned, our main findings indicate that on average increased in-

formal caregiving effectively reduces the amount of formal home care services. European

governments have already recognized this ensuing potential for reducing public health care

spending and therefor providing financial incentives for informal caregiving has become more

common over the last decades [38]. However, the specific measures differ between countries.

For example in Spain, informal caregivers receive tax credits, in France and Sweden social

security allowances and grants from social care budgets are provided, whereas caregivers in

Germany and the UK benefit from long-term care insurance and individual budgets respec-

tively [38].

Finally, previous research has revealed that especially family caregivers in central northern of

Europe often face a harmful double obligation caused by labour market participation on the

one side and caring duties on the other side [39]. To address this, governments should estab-

lish the legal framework for more employment-friendly working conditions. In practical terms

these policies should involve the simplification of paid leave and enhanced flexibility regard-

ing working time arrangements through telecommuting for example.

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Table I: Determinants of PwDs´ formal health care use (Pooled Model)Model 1 Model 2 Model 3

Home Care Nurse Visits

Outpat. Visits Home Care Nurse Visits Outpat. Visits Home Care

Nurse Visits Outpat. Visits

Age 1.033***(0.013)

1.022**(0.010)

0.984*(0.009)

1.024*(0.013)

1.011(0.010)

0.990(0.09)

1.021*(0.013)

1.010(0.010)

0.986(0.090)

Female 1.290(0.201)

1.393**(0.180)

1.112(0.140)

1.121(0.189)

1.448***(0.205)

1.308**(0.178)

1.138(0.190)

1.500***(0.213)

1.223(0.165)

Caregiving hours 0.998***(0.001)

0.999(0.001)

1.002***(0.001)

0.998***(0.001)

0.999(0.001)

1.001*(0.001)

0.998***(0.001)

0.999(0.001)

1.001(0.001)

Living with Caregiver - - - 0.610*(0.156)

0.471***(0.091)

0.895(0.165)

0.540**(0.140)

0.445***(0.090)

0.731*(0.135)

Living with sb. else - - - 0.900(0.240)

0.515***(0.105)

0.683**(0.131)

0.860(0.225)

0.506(0.110)

0.584***(0.112)

Married - - - 0.720(0.160)

1.105(0.191)

1.430**(0.231)

0.744(0.160)

1.104***(0.188)

1.500**(0.240)

ADL-independency - - - - - - 0.880***(0.041)

0.922**(0.034)

0.822***(0.029)

S-MMSE - - - 0.980*(0.013)

0.992(0.010)

1.003(0.010)

0.992(0.014)

1.000(0.011)

1.028***(0.010)

NPI-severity - - - 0.993(0.012)

1.009(0.010)

1.023**(0.010)

0.989(0.012)

1.010(0.010)

1.025**(0.010)

Alzheimer Disease - - - 0.713*(0.125)

0.861(0.126)

0.630***(0.082)

0.761(0.134)

0.912(0.140)

0.702***(0.090)

VD - - - 1.021(0.244)

1.271(0.245)

0.686**(0.122)

1.038(0.249)

1.271(0.245)

0.720*(0.128)

Comorbidities - - - 1.077(0.065)

1.261***(0.071)

1.190***(0.060)

1.057(0.064)

1.245***(0.071)

1.144***(0.054)

Fall - - - 1.270(0.230)

1.156(0.172)

1.670***(0.224)

1.200(0.220)

1.130(0.168)

1.620***(0.214)

Estonia1 0.230***(0.081)

4.865***(1.543)

0.763(0.217)

0.190***(0.720)

4.927***(1.621)

0.556**(0.162)

0.180***(0.070)

4.852***(1.588)

0.649(0.190)

Finland 0.842(0.302)

9.929***(3.282)

1.100(0.310)

1.030(0.382)

12.155***(4.155)

0.903(0.260)

0.940(0.350)

11.688***(4.000)

0.926(0.260)

France 0.823(0.290)

51.292***(16.867)

5.231***(1.460)

1.050(0.400)

75.882***(26.388)

5.011***(1.400)

1.005(0.374

74.444***(25.800)

5.780***(1.601)

Germany 0.143*** 50.515*** 1.937** 0.134*** 51.283*** 1.284 0.110*** 48.442*** 1.284

(0.060) (17.328) (0.580) (0.060) (18.247) (0.386) (0.050) (17.180) (0.380)Netherlands 1.440

(0.510)31.936***(10.548)

3.533***(0.986)

1.790(0.660)

42.329***(14.503)

2.732***(0.764)

1.570(0.580)

38.861***(13.321)

2.804***(0.771)

Spain 0.372***(0.136)

1.989**(0.651)

1.060(0.301)

0.434**(0.170)

2.982***(1.063)

1.190(0.354)

0.390**(0.152)

2.816***(1.000)

1.223(0.360)

Sweden 1.913*(0.700)

1.303(0.449)

0.411***(0.139)

2.332**(0.870)

1.461(0.532)

0.275***(0.087)

2.121**(0.793)

1.449(0.525)

0.275***(0.087)

Constant 1.111(1.200)

0.101*(0.921)

2.750(2.100)

4.800(5.670)

0.173*(0.165)

1.162(0.970)

9.120*(10.924)

0.256(0.247)

2.294(1.930)

Apha 5.064(0.282)

3.437(0.186)

2.422(0.165)

4.892(0.275)

3.217(0.177)

2.201(0.152)

4.832(0.272)

3.200(0.176)

2.100(0.146)

N 1025 1025 1025 1025 1025 1025 1025 1025 1025Chibar2(1) 2224.46 3777.12 3014.10 2994.06 4494.16 2531.76 2167.96 3414.66 2454.56Prob≥chibar2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Reference country: England

*** p<0.01, ** p<0.05, * p<0.1

Table II: Country-specific determinants for the number of professional home help visitsCaregiving hours

isolated1Fully specified model2

Caregiving hours ADL-independency S-MMSE Comorbidities

Estonia 0.999(0.002)

0.999(0.004)

1.095(0.220)

1.001(0.070)

0.665(0.235)

Finland 0.991***(0.001)

0.992***(0.002)

0.840(0.120)

0.960(0.037)

0.980(0.162)

France 0.997**(0.001)

0.996**(0.001)

0.963(0.089)

0.965(0.027)

0.505***(0.0865)

Germany 0.992***(0.003)

0.986(0.005)

0.608*(0.154)

0.986(0.060)

1.371(0.328)

Netherlands 0.996**(0.002)

0.997(0.002)

0.728***(0.066)

1.015(0.024)

1.116(0.107)

Spain 1.001(0.002)

0.997(0.002)

0.873(0.179)

0.919(0.076)

0.934(0.367)

Sweden 0.996**(0.002)

0.995**(0.002)

0.637***(0.078)

0.966(0.032)

0.965(0.129)

The model includes age and gender.2 The model includes age, gender, living situation and all disease severity measures simultaneously.

*** p<0.01, ** p<0.05, * p<0.1

Table III: Country-specific determinants for the number of nurse visitsCaregiving hours



Estonia 1.000(0.001)

0.999(0.001)

0.913(0.078)

0.999(0.030)

1.220(0.185)

Finland 0.995***(0.001)

0.998(0.002)

0.785**(0.082)

0.995(0.025)

0.989(0.135)

France 0.998(0.001)

0.996*(0.002)

0.875(0.092)

0.986(0.031)

1.346(0.267)

Germany 0.999(0.001)

0.999(0.001)

1.027(0.115)

0.986(0.025)

1.123(0.125)

Netherlands 0.994***(0.002)

0.995**(0.002)

0.737***(0.077)

0.991(0.027)

1.030(0.120)

Spain 1.002(0.001)

1.002(0.001)

0.926(0.052)

1.034(0.044)

1.604*(0.394)

Sweden 0.998(0.003)

0.994*(0.089)

0.952(0.160)

0.995(0.053)

1.570**(0.272)


*** p<0.01, ** p<0.05, * p<0.1

Table IV: Country-specific determinants for the number of outpatient visitsCaregiving hours



Estonia 1.001(0.001)

1.000(0.002)

0.862(0.102)

1.024(0.035)

1.323(0.237)

Finland 1.001(0.002)

1.000(0.002)

0.958(0.145)

1.029(0.045)

1.150(0.186)

France 1.001(0.001)

1.000(0.001)

0.882(0.062)

1.014(0.023)

0.980(0.096)

Germany 1.003***(0.001)

1.004***(0.001)

0.879(0.077)

1.011(0.019)

0.982(0.092)

Netherlands 0.999(0.002)

0.999(0.002)

0.864(0.081)

1.027(0.024)

1.156(0.127)

Spain 1.004(0.001)

1.001(0.001)

0.782**(0.092)

1.036(0.039)

0.910(0.156)

Sweden 1.003(0.002)

1.001(0.002)

0.637***(0.080)

1.087**(0.036)

1.318**(0.148)


*** p<0.01, ** p<0.05, * p<0.1

Appendix

Acknowledgement

The RightTimePlaceCare study is supported by a grant from the European Commission within the 7th Framework Program (project 242153)

The RightTimePlaceCare Consortium partners are as follows:

Coordinator:

University of Witten/Herdecke (DE): Gabriele Meyer PhD, RN, professor (scientific coordinator, WP 1 leader), Astrid Stephan MScN, RN, Anna

Renom Guiteras, geriatrician, Dirk Sauerland Dr.rer.pol., professor (WP 4 & 6 leader), Dr. Ansgar Wübker, Dr. Patrick Bremer.

Consortium Members:

Maastricht University (NL): Jan P.H. Hamers PhD, RN, professor (WP 3 leader); Basema Afram MSc, Hanneke C. Beerens MSc, RN, Michel H.C.

Bleijlevens, PhD, PT; Hilde Verbeek, PhD; Sandra M.G. Zwakhalen, PhD, RN; Dirk Ruwaard, MD, professor.

Lund University (SE): Ingalill Rahm Hallberg, professor (WP 2 leader); Ulla Melin Emilsson, professor; Staffan Karlsson, PhD, Senior Lecturer,

RN; Connie Lethin PhD-student, MSc, RN; Christina Bökberg, PhD-student, MSc, RN.

University of Manchester (UK): David Challis, professor; Caroline Sutcliffe; Dr David Jolley; Sue Tucker; Ian Bowns; Brenda Roe, professor; Alis-

tair Burns, professor

University of Turku (FI): Helena Leino-Kilpi, PhD, RN, professor; Jaana Koskenniemi, MNSc, RN, researcher; Riitta Suhonen, PhD, RN, profes-

sor; Matti Viitanen, MD, PhD, professor; Seija Arve, PhD, RN, adj professor; Minna Stolt, MNSc, PhD; Maija Hupli, PhD, RN.

University of Tartu (EE): Kai Saks, MD, PhD, professor (WP 5 leader); Ene-Margit Tiit, PhD, professor; Jelena Leibur, MD, MBA; Katrin Raamat,

MA; Angelika Armolik, MA; Teija Tuula Marjatta Toivari, RN;

Fundació Privada Clinic per la Recerca Biomedica, Hospital Clinic of Barcelona (ES): Adelaida Zabalegui PhD, RN (WP 5 leader); Montserrat

Navarro PhD, RN; Esther Cabrera PhD, RN (Tecnocampus Mataró), Ester Risco MNSc, RN.

Gerontôpole, University of Toulouse (FR): Dr Maria Soto; Agathe Milhet; Dr Sandrine Sourdet; Sophie Gillette; Bruno Vellas, professor.

Descriptive Statistics

Table V: Mean Values and standard deviations for the whole sampleVariables Description ØDependent variablesHome help visits Number of professional home care visits in the last month 17.5 (30.6)Nurse Visits Number of nurse visits in the last month 20.0 (36.5)Outpatient Visits Number of outpatient visits in the last month 2.1 (5.0)Independent variablesCaregiving hours Total numbers of hours per day caregivers spent assisting their rela-

tives with ADL or IADL4.6 (4.5)

Socio-demographic factorsAge PwD´s age in years 82.3 (6.4)Female Gender: female = 1; otherwise = 0 0.63Living with Caregiver Living with Caregiver = 1; otherwise = 0 0.5Living with sb. else Living with sb. else = 1; otherwise = 0 0.21Married If PwD is married = 1; otherwise = 0 049Health characteristicsKATZ Katz Index of Independence in ADL (0-6) 3.5 (1.8)S-MMSE Standardized Mini-Mental State Examination (0-30) 14.3 (6.6)NPI-severity NeuroPsychiatric Inventory-Questionnaire (0-36) 9.3 (6.3)Comorbidities Charlson Comorbidity Index (0-37) 2.1 (1.3)Fall If PwD had at least one fall during the last month = 1; otherwise = 0 0.22Type of dementiaAlzheimer Disease If yes = 1; otherwise = 0 0.54Vascular dementia (VD) If yes = 1; otherwise = 0 0.16Other type of dementia If yes = 1; otherwise = 0 0.3

Table VI: Descriptive statistics of patients with dementia in 8 European countriesCountry Variables ADL low

(≤ 90 h)ADL med

(91-240 h)ADL high(> 240 h)

Ø

England(N=58)

CR´s demogr. characterist.Age 85.0 (5.9) 81.2 (6.0) 78.9 (5.3) 82.0 (6.2)

Gender: female in % 57.1 61.9 68.8 62.1CR´s Health status

KATZ 4.1 (1.8) 3.7 (1.7) 3.0 (1.8) 3.7 (1.8)S-MMSE 14.7 (6.5) 13.8 (6.9) 12.4 (7.7 ) 13.7 (6.9)

Comorbidities 2.0 (0.9) 1.9 (0.8) 2.1 (1.4) 2.0 (0.9)CR´s Health Care Use

Home help visits 23.3 (34.8) 21.2 (39.4) 3.3 (9.0) 17.0 (32.6)Nurse visits 0.1 (0.4) 1.9 (6.7) 1.1 (2.5) 1.0 (4.2)

Outpatient visits 1.2 (2.6) 0.6 (0.8) 2.1 (1.5) 1.2 (2.9)Estonia(N=159)



KATZ 4.1 (1.7) 3.2 (1.8) 2.1 (2.2) 3.1 (2.1)S-MMSE 12.7 (6.0) 11.9 (6.4) 7.2 (5.9) 10.6 (6.6)



Outpatient visits 0.8 (2.7) 0.9 (2,7) 1.1 (3.8) 1.0 (3.1)Finland(N=159)



KATZ 4.6 (1.4) 3.3 (1.5) 2.9 (1.5) 4.0 (1.6)S-MMSE 17.8 (4.4) 16.2 (6.5) 11.9 (6.6) 16.6 (5.7)



Outpatient visits 1.0 (3.8) 1.7 (8.1) 0.8 (1.0) 1.2 (5.0)France(N=143)


Gender: female in % 81.4 43.8 44.4 66.4

CR´s Health statusKATZ 4.5 (1.7) 3.7 (1.8) 2.0 (2.3) 4.1 (1.9)

S-MMSE 15.5 (5.0) 13.1 (6.6) 11.6 (5.1) 14.5 (5.7)Comorbidities 1.6 (1.1) 1.5 (0.7) 2.2 (1.9) 1.6 (1.1)

CR´s Health Care UseHome help visits 19.8 (25.3) 12.7 (12.0) 8.4 (12.3) 16.6 (21.3)

Nurse visits 66.8 (51.9) 37.8 (47.1) 30.0 (42.4) 54.8 (51.7)Outpatient visits 4.7 (5.6) 6.4 (7.8) 3.9 (4.1) 5.2 (6.4)

Germany(N=90)



KATZ 3.4 (1.9) 2.4 (1.6) 1.7 (1.3) 2.6 (1.8)S-MMSE 14.3 (7.5) 11.9 (8.6) 7.7 (8.1) 11.8 (8.4)



Outpatient visits 1.2 (1.9) 2.3 (3.0) 4.2 (5.0) 2.4 (3.4)Netherlands(N=156)



KATZ 3.7 (1.7) 2.9 (1.4) 2.3 (1.5) 3.4 (1.7)S-MMSE 16.1 (6.0) 12.6 (6.9) 9.0 (9.1) 14.9 (6.5)



Outpatient visits 3.5 (7.7) 3.3 (5.2) 2.7 (2.5) 3.4 (7.0)Spain(N=136)



KATZ 4.4 (1.7) 3.6 (1.6) 2.8 (1.9) 3.6 (1.8)S-MMSE 17.1 (4.5) 16.3 (5.7) 14.6 (5.5) 16.1 (5.4)


Home help visits 4.4 (8.8) 4.9 (9.7) 6.1 (10.1) 5.1 (9.5)

Nurse visits 1.1 (3.0) 2.0 (6.7) 2.5 (6.5) 1.8 (5.6)Outpatient visits 0.7 (1.1) 1.6 (4.9) 2.0 (5.4) 1.4 (4.1)

Sweden(N=124)



KATZ 4.2 (1.6) 3.2 (1.5) 3.0 (1.5) 3.7 (1.7)S-MMSE 17.6 (5.1) 13.9 (6.5) 13.0 (6.8) 15.6 (6.2)



Outpatient visits 0.2 (0.5) 0.6 (1.2) 0.7 (1.6) 0.4 (1.0)Average(N=1025)



KATZ 4.2 (1.7) 3.2 (1.7) 2.4 (1.9) 3.5 (1.8)S-MMSE 16.1 (5.6) 13.6 (6.9) 10.6 (7.0) 14.3 (6.6)



Outpatient visits 2.1 (5.0) 2.2 (5.3) 1.9 (4.2) 2.1 (5.0)

Robustness checks

1. Alternative estimation strategy: Results from the Poisson model

Table VII: Determinants of PwDs´ formal health care use (Pooled Model)Model 1 Model 2 Model 3

Home Care Nurse Visits

Outpat. Visits Home Care Nurse Visits Outpat. Visits Home Care

Nurse Visits Outpat. Visits

Age 1.043***(0.223)

1.017***(0.010)

0.986**(0.009)

1.034**(0.223)

1.012**(0.019)

0.988(0.091)

1.028*(0.233)

1.001**(0.011)

0.985(0.096)

Female 1.513(0.234)

1.273***(0.180)

1.109*(0.143)

1.310(0.289)

1.150***(0.212)

1.214**(0.198)

1.304(0.195)

1.144***(0.298)

1.191**(0.223)

Caregiving hours 0.996***(0.001)

0.998(0.001)

1.001***(0.001)

0.998***(0.001)

0.999*(0.001)

1.001**(0.002)

0.997***(0.002)

0.999(0.011)

1.001(0.003)

Living with Caregiver - - - 0.499**(0.837)

0.529***(0.121)

0.899(0.225)

0.458**(0.178)

0.504**(0.091)

0.990(0.139)

Living with sb else - - - 0.996(0.267)

0.803***(0.200)

0.774**(0.141)

0.919(0.325)

0.767**(0.150)

0.685***(0.167)

Married - - - 0.946*(0.122)

1.050*(0.185)

1.270**(0.431)

0.7961(0.231)

1.021(0.238)

1.269**(0.267)


0.889***(0.134)

0.864***(0.029)

S-MMSE - - - 0.974*(0.113)

0.983(0.008)

1.005(0.011)

0.988(0.023)

0.995(0.131)

1.020***(0.013)

NPI-severity - - - 0.999(0.092)

0.994(0.001)

1.005*(0.113)

0.998(0.102)

0.990(0.122)

1.000(0.119)

Alzheimer Disease - - - 0.846**(0.321)

0.880(0.196)

0.733***(0.121)

0.904(0.254)

0.944(0.240)

0.773***(0.190)

VD - - - 1.045(0.254)

1.486(0.975)

0.918(0.132)

1.056(0.298)

1.494(0.311)

0.890(0.138)

Comorbidities - - - 1.052*(0.123)

1.091***(0.011)

1.095**(0.161)

1.036(0.124)

1.078***(0.161)

1.083***(0.154)

Fall - - - 1.270(0.232)

1.090(0.198)

1.644***(0.344)

1.043(0.450)

1.052(0.176)

1.517***(0.234)

Estonia1 0.176***(0.121)

4.845***(1.793)

0.774(0.227)

0.179***(0.733)

3.912***(0.921)

0.792(0.169)

0.139***(0.190)

3.840***(1.618)

0.785(0.198)

Finland 0.941(0.454)

8.401***(3.567)

1.087(0.334)

1.021(0.243)

9.855***(6.175)

1.224(0.340)

0.955(0.367)

9.201***(4.097)

1.127(0.287)

France 0.706***(0.311)

44.447***(12.897)

5.025***(1.460)

1.013*(0.433)

52.745***(23.444)

5.226***(1.456)

1.005(0.394)

53.000***(27.765)

5.058***(1.639)

Germany 0.156***(0.011)

46.991***(19.112)

2.054***(0.513)

0.129***(0.160)

42.430***(22.315)

1.889***(0.311)

0.113***(0.130)

38.100***(19.180)

1.284(0.380)

Netherlands 1.370***(0.544)

27.814***(11.863)

3.317***(0.906)

1.509*(0.634)

32.807***(16.503)

3.217***(0.798)

1.311(0.345)

29.539***(13.987)

2.804***(0.771)

Spain 0.319***(0.156)

1.869**(0.678)

1.151*(0.431)

0.300**(0.121)

2.148***(1.163)

1.600*(0.409)

0.275**(0.159)

1.988***(1.986)

1.512*(0.430)

Sweden 2.005**(0.545)

1.138(0.973)

0.405***(0.349)

2.319**(0.892)

1.211(0.885)

0.360***(0.167)

2.220**(0.873)

1.444(0.785)

0.338***(0.187)

Constant 0.437(1.200)

0.221**(0.876)

3.7216(1.122)

1.934(2.453)

0.483**(0.085)

1.230(0.998)

5.414*(11.574)

1.187(0.247)

2.797(1.876)

N 1025 1025 1025 1025 1025 1025 1025 1025 1025Prob≥chibar2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Reference country: England

*** p<0.01, ** p<0.05, * p<0.1

Table VIII: Country-specific determinants for the number of professional home help visitsCaregiving hours



Estonia 0.999(0.011)

0.999(0.013)

0.883(0.128)

1.299(0.130)

0.897(0.131)

Finland 0.990***(0.012)

0.993***(0.021)

0.864**(0.132)

0.963(0.175)

0.987(0.235)

France 0.996*(0.005)

0.996**(0.003)

0.894(0.112)

0.984(0.131)

0.670**(0.224)

Germany 0.986**(0.006)

0.985(0.008)

0.702*(0.134)

0.860(0.115)

1.271(0.225)

Netherlands 0.996*(0.009)

0.998(0.008)

0.790***(0.187)

1.011(0.127)

1.137(0.230)

Spain 1.001(0.011)

0.999(0.0012)

0.912(0.152)

0.999(0.111)

0.995*(0.444)

Sweden 0.996**(0.023)

0.998*(0.119)

0.800**(0.230)

0.973(0.123)

0.934(0.272)


*** p<0.01, ** p<0.05, * p<0.1

Table IX: Country-specific determinants for the number of nurse visitsCaregiving hours



Estonia 0.999(0.002)

0.999(0.007)

0986(0.270)

0.987(0.070)

1.165*(0.335)

Finland 0.994***(0.001)

0.997*(0.001)

0.902**(0.128)

0.963***(0.006)

0.993(0.187)

France 0.997*(0.001)

0.997*(0.002)

0.907(0.111)

0.985*(0.117)

1.387*(0.965)

Germany 0.999(0.003)

0.989(0.015)

1.190(0.187)

0.990*(0.189)

1.059(0.511)

Netherlands 0.994**(0.007)

0.996*(0.003)

0.800***(0.866)

0.900(0.654)

1.071*(0.107)

Spain 1.002(0.001)

1.002(0.012)

0.957(0.239)

1.269*(0.126)

1.389*(0.446)

Sweden 0.997**(0.003)

0.994**(0.002)

0.915(0.059)

0.990(0.017)

1.426***(0.187)


*** p<0.01, ** p<0.05, * p<0.1

Table X: Country-specific determinants for the number of outpatient visitsCaregiving hours



Estonia 1.001(0.001)

1.000(0.003)

0.885**(0.122)

1.026(0.065)

1.211(0.307)

Finland 1.000(0.003)

1.000(0.002)

0.765*(0.245)

1.129(0.112)

1.180(0.206)

France 1.000(0.001)

1.000(0.001)

0.905(0.112)

0.999(0.123)

0.995(0.126)

Germany 1.003***(0.001)

1.004***(0.001)

0.866(0.127)

1.021(0.118)

0.993(0.122)


0.996(0.002)

0.838(0.121)

1.041*(0.109)

1.100(0.129)

Spain 1.003(0.001)

1.001(0.002)

0.854***(0.112)

1.037(0.087)

0.787(0.134)

Sweden 1.003(0.003)

1.002(0.005)

0.651***(0.089)

1.084**(0.065)

1.292**(0.156)


*** p<0.01, ** p<0.05, * p<0.1

2. Alternative coding for the variable ´Informal Caregiving Intensity` (NegBin Model)

Table XI: Determinants of PwDs´ formal health care use (Pooled Model)Model 1 Model 2 Model 3

Home Care

Nurse Visits Outpat. Visits Home Care Nurse Visits Outpat. Visits Home Care Nurse Visits Outpat. Visits

Age 1.033***(0.013)

1.023**(0.001)

0.984*(0.009)

1.023*(0.013)

1.013(0.001)

0.987(0.09)

1.021*(0.013)

1.010(0.010)

0.986(0.009)

Female 1.290(0.201)

1.303*(0.169)

1.130(0.143)

1.109(0.185)

1.391**(0.199)

1.316**(0.180)

1.103(0.186)

1.435**(0.206)

1.231(0.167)

Medium level of Care2 0.571***(0.105)

0.583***(0.085)

1.415***(0.187)

0.405**(0.109)

0.607***(0.096)

1.265(0.183)

0.633**(0.122)

0.591***(0.092)

1.140(0.164)

High level of Care2 0.377***(0.092)

0.766(0.150)

1.705***(0.303)

0.403***(0.122)

0.825(0.178)

1.421*(0.277)

0.406***(0.109)

0.807(0.174)

1.279(0.247)

Living with Caregiver - - - 0.624*(0.159)

0.540***(0.105)

0.878(0.164)

0.550**(0.139)

0.506***(0.099)

0.726*(0.136)

Living with sb else - - - 0.896(0.238)

0.526***(0.107)

0.683**(0.132)

0.856(0.225)

0.504***(0.101)

0.582***(0.112)

Married - - - 0.711(0.156)

1.097(0.188)

1.420**(0.231)

0.747(0.162)

1.094(0.186)

1.498**(0.241)


0.914**(0.034)

0.822***(0.029)

S-MMSE - - - 0.975*(0.013)

0.999(0.001)

1.003(0.009)

0.989(0.014)

0.999(0.010)

1.027***(0.010)

NPI-severity - - - 0.992(0.012)

1.012(0.010)

1.024**(0.010)

0.988(0.012)

1.012(0.010)

1.026**(0.010)

Alzheimer Disease - - - 0.706**(0.123)

0.850(0.124)

0.632***(0.082)

0.753(0.132)

0.903(0.133)

0.702***(0.090)

VD - - - 1.061(0.253)

1.356(0.262)

0.685**(0.122)

1.081(0.259)

1.352(0.261)

0.719*(0.128)

Comorbidities - - - 1.081(0.064)

1.243***(0.070)

1.192***(0.057)

1.060(0.064)

1.125***(0.070)

1.145***(0.054)

Fall - - - 1.289(0.232)

1.191(0.177)

1.690***(0.228)

1.211(0.217)

1.160(0.173)

1.626***(0.216)

Estonia1 0.221***(0.079)

4.086***(1.103)

0.762(0.217)

0.191***(0.072)

4.365***(1.435)

0.550**(0.161)

0.183***(0.069)

4.285***(1.402)

0.649(0.188)

Finland 0.811 7.978*** 1.052 1.028 11.253*** 0.886 0.930 10.676*** 0.919

(0.293) (2.616) (0.298) (0.383) (3.793) (0.251) (0.344) (3.595) (0.258)France 0.800

(0.284)44.062***(14.867)

5.027***(1.460)

1.049(0.391)

71.282***(24.566)

4.909***(1.373)

1.006(0.373)

69.437***(23.834)

5.729***(1.593)

Germany 0.138***(0.055)

46.834***(15.903)

1.858**(0.555)

0.132***(0.055)

51.741***(18.265)

1.240(0.375)

0.106***(0.045)

48.140***(16.940)

1.264(0.375)


26.168***(8.577)

3.431***(0.960)

1.743(0.640)

37.436***(12.703)

2.729***(0.764)

1.514(0.588)

33.980***(11.535)

2.803***(0.772)

Spain 0.355***(0.130)

1.852*(0.604)

1.066(0.304)

0.445**(0.175)

3.064***(1.088)

1.179(0.353)

0.396**(0.155)

2.838***(1.004)

1.231(0.363)

Sweden 1.913*(0.695)

1.157(0.394)

0.382***(0.120)

2.362**(0.877)

1.351(0.487)

0.264***(0.084)

2.125**(0.788)

1.332(0.478)

0.270***(0.085)

Constant 1.473(1.544)

0.135*(0.121)

2.795(2.133)

5.662(6.620)

0.185*(0.175)

1.206(1.004)

11.071*(13.172)

0.287(0.278)

2.303(1.941)

Apha 5.011(0.322)

3.948(0.987)

2.574(0.234)

4.783(0.295)

3.112(0.198)

2.298(0.142)

4.987(0.298)

3.301(0.201)

2.099(0.206)

N 1025 1025 1025 1025 1025 1025 1025 1025 1025Chibar2(1) 22354.11 3546.92 3556.106 2994.06 4948.35 2532.69 2254.01 3998.66 2987.12Prob≥chibar2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Reference country: England2 Reference category: Low level of Care

*** p<0.01, ** p<0.05, * p<0.1

Table XII: Country-specific determinants for the number of professional home help visitsCaregiving hours isolated1 Fully specified model2

High level of Care Med. level of Care High level of Care Med. level of Care ADL-independency S-MMSE ComorbiditiesEstonia 1.475

(1.231)2.940

(2.552)3.835

(4.404)4.013

(6.193)1.040

(0.225)1.031

(0.077)0.715

(0.260)Finland 0.100***

(0.053)0.200***(0.086)

0.134***(0.086)

0.380*(0.201)

0.844(0.122)

0.968(0.038)

0.954(0.163)

France 0.408(0.223)

0.622*(0.176)

0.191***(0.119)

0.660(0.206)

0.958(0.089)

0.961(0.028)

0.485***(0.085)

Germany 0.172*(0.179)

0.140***(0.093)

0.032**(0.046)

0.020***(0.022)

0.599*(0.169)

0.982(0.061)

1.135(0.287)

Netherlands 0.333*(0.307)

0.636*(0.181)

0.686(0.638)

0.660(0.202)

0.732***(0.066)

1.015(0.024)

1.136(0.111)

Spain 1.351(0.028)

1.233(0.953)

0.270(0.252)

0.436(0.388)

0.857(0.181)

0.935(0.082)

1.014(0.401)

Sweden 0.331*(0.212)

0.553*(0.193)

0.262*(0.185)

0.564(0.254)

0.638***(0.080)

0.977(0.032)

0.959(0.131)


*** p<0.01, ** p<0.05, * p<0.1

Table XIII: Country-specific determinants for the number of nurse visitsCaregiving hours isolated1 Fully specified model2


(0.292)0.255***(0.098)

0.458*(0.216)

0.228***(0.098)

0.973(0.084)

0.994(0.028)

1.151(0.170)

Finland 0.333**(0.139)

0.295***(0.101)

0.845(0.533)

0.987(0.576)

0.795**(0.085)

0.998(0.045)

0.986(0.136)

France 0.499(0.334)

0.589(0.206)

0.228(0.208)

0.459*(0.205)

0.875(0.093)

0.981(0.031)

1.305(0.272)

Germany 0.767(0.341)

0.742(0.248)

0.826(0.433)

0.935(0.322)

1.024(0.118)

0.985(0.025)

1.122(0.128)


0.512**(0.167)

0.411**(0.232)

0.532*(0.189)

0.728***(0.075)

0.992(0.027)

1.061(0.122)

Spain 1.332(0.121)

1.704(0.913)

1.324(0.940)

0.756(0.388)

0.910(0.117)

1.038(0.045)

1.557*(0.391)

Sweden 0.140*(0.150)

0.603(0.293)

0.167(0.189)

0.319*(0.209)

0.957(0.160)

0.998(0.058)

1.500**(0.265)


*** p<0.01, ** p<0.05, * p<0.1

Table XIV: Country-specific determinants for the number of outpatient visitsCaregiving hours isolated1 Fully specified model2


(0.763)1.105

(0.573)1.509

(0.011)1.679

(1.056)0.880

(0.107)1.017

(0.036)1.324

(0.234)Finland 1.301

(0.574)1.509

(0.729)1.191

(0.584)1.223

(0.300)0.937

(0.145)1.025

(0.043)1.098

(0.182)France 1.121

(0.343)1.272

(0.284)1.119

(0.341)1.167

(0.342)0.885**(0.062)

1.018(0.023)

0.988(0.095)

Germany 3.647***(1.435)

1.943**(0.626)

4.764***(1.916)

2.426***(0.809)

0.866(0.061)

1.017(0.023)

1.018(0.098)


0.884(0.266)

0.715(0.680)

0.731(0.239)

0.865(0.081)

1.025(0.024)

1.173(0.129)

Spain 3.296***(0.145)

2.773**(0.210)

1.121(0.475)

1.153(0.489)

0.778**(0.095)

1.025(0.038)

0.911(0.158)

Sweden 2.041(1.457)

2.292*(1.002)

2.228(1.560)

2.993**(1.569)

0.602***(0.079)

1.102***(0.036)

1.288**(0.137)


*** p<0.01, ** p<0.05, * p<0.1

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