Change in determinants of use of physician services in Finland between 1987 and 1996

Social Science & Medicine 55 (2002) 1523–1537

Change in determinants of use of physician services in Finland between 1987 and 1996 Unto H.akkinen* National Research and Development Centre for Welfare and Health, Siltasaarenkatu 18 A, FIN-00531 Helsinki, Finland

Abstract The determinants of use of physician services in Finland in 1987 and 1996 were studied to evaluate how the utilisation altered over this period, which saw fairly radical changes in Finnish health care and the entire economy. We used econometric methods to describe the changes in structure and level of utilisation. The study was based on the Finnish Health Care Surveys of 1987 and 1996, which were nationally representative cross-sectional samples of the total non-institutionalised population. Visits to a doctor were analysed using a two-part model (logit +truncated negative binomial regression). Structural changes were tested by a Chow-type test and changes in utilisation level by a dummy variable indicating the year of study. Analyses were made separately for four different age groups: children aged 0–6 and 7–17, and adults aged 18–64 and over 64 years. The change in utilisation of physician services over the nine-year period was a product of both structural and level changes. Except in the youngest age group, both types of changes occurred in the second part of the utilisation model, which implies that they were more associated with supply side factors than demand factors. Among young children, the type of day-care seems to have been an important determinant of physician utilisation. Although its effect decreased considerably over the period, the total number of visits to a doctor in 1996 was still about 30% greater among children in nursery care than those at home. The rise in self-reported chronic illness was an important explanation of the increase in utilisation of doctors’ services, especially among children. Among adults aged 18–64, the most important structural change was an increase of the effect of self-rated health status variables on utilisation. Inequity in utilisation of services persisted with respect to income. In conclusion it can be stated that Finland’s tax-based and locally decentralised health care system adapted quite well to the radical changes experienced during the study period. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Doctor visits; Determinants of utilisation; Econometric model; Finland

Introduction Most empirical studies on health care utilisation have been cross-sectional analyses. The aim has usually been to determine the factors which affect utilisation and their relative effects. Some studies (e.g. Eyles, Birch, & Newbold, 1995) have addressed the effects of explanatory variables changing over time in order to evaluate the development of socio-economic equity in utilisation of or access to health care. Attempts to evaluate the effects more precisely by analysing separately the *Tel +358-9-39672327; fax: +358-9-39672485. E-mail address: unto.hakkinen@stakes.fi (U. H.akkinen).

changes in structure and level of utilisation have been rare (H.akkinen, Rosenqvist, & Aro, 1996). This information is useful for at least two reasons. Firstly, it is important to know whether the effect on utilisation of policy relevant variables such as socio-economic factors or availability of services has changed, and the impact this has had on overall utilisation. Secondly, for planning future health care services it is useful to know, more generally, the stability of the effects of factors related to both demand and supply of health care. This paper draws on the data gathered on determinants of use of physician services in Finland in 1987 and 1996. Over the period in question, Finnish health care and indeed the entire national economy, experienced a

0277-9536/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 2 7 7 - 9 5 3 6 ( 0 1 ) 0 0 2 8 5 - 4

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substantial upheaval. Unemployment mushroomed from 3% to 17% between 1990 and 1994, and was still quite high (15%) in 1996. During the first three years of the decade the gross domestic product per capita at constant prices declined by about 11%, and by 1996 was only 6% higher than in 1987. Health care expenditure in real terms decreased by about 15% between 1991 and 1994, and per capita spending on health care in real terms in 1996 was about the same level as in 1987. The economic depression was accompanied by far-reaching changes in Finnish health care. The most important development occurred in early 1993 as a part of a reform of the overall state subsidy system. The aim of the reform was to reduce central government power and increase the freedom of the local authorities (municipalities) to provide services (H.akkinen, 1999a). The weakened state of the economy at both central government and municipality level also justified an expansion of cost sharing in health care: user charges’ share of health care financing rose from 14% in 1987 to 20% in 1996. The primary aim of this paper was to evaluate how the utilisation was altered over the period 1987–1996. Using econometric methods, we set out to describe the changes in structure and level of utilisation. Here, changes in structure means changes in the model transmitting the effects of explanatory variables, independent of changes in these variables; changes in the level of utilisation means changes over time which are not explained by the factors included in the study nor by their transmission mechanism, i.e. changes of the type as a time trend. As an example, suppose that health care utilisation is dependent on an illness variable. By structural change we mean a change in the (coefficient) of an illness variable in explaining the utilisation. The change in level was evaluated in two ways. First, as a time trend, by investigating whether there was an independent effect of time after controlling other factors explaining utilisation. Second, as a change in the incidence of an illness variable itself. If utilisation was positively related to an illness variable, then the change in level would describe the effect of the change of the mean of the illness variable on utilisation between the two years.

The Finnish health care system In its institutional structure, financing and policy objectives of the Finnish health care system closely resembles those of other Nordic countries and Great Britain, in that it covers the whole population and services are mainly produced by the public sector and financed through general taxation. The local authorities (municipalities) are responsible for delivering health

services. Public health services are financed by municipal taxes, state subsidies and to some extent by user charges. Every municipality, alone or in federation with others, organises primary health care and specialised hospital care for its inhabitants. Primary health care is provided mainly by public health centres, which anyone can contact for outpatient services. To obtain specialised hospital services (except emergency) the patient is supposed to be referred from a health centre general practitioner or private physician. Employers are obligated to supplement the network of municipally owned health centres by providing certain preventive occupational services for their employees. In addition to the statutory occupational health care, employers may provide voluntary curative health care for employees, and until 1994 for their family members. These employers are refunded by National Sickness Insurance for the cost of occupational and other health care they provide. A part of the costs of medical expenses and private health services is also refunded to individuals by National Sickness Insurance, which covers, among other things, private sector examinations and treatments performed or prescribed by a physician. This paper analyses the entire scope of outpatient physician services in Finland. In 1996, 49% (in 1987, 46%) of all visits took place in health centres, 20% (in 1987, 18%) in outpatient departments of specialist hospitals, 18% (in 1987, 21%) in the private sector, and 13% (in 1987, 15%) in occupational care.

Theoretical and practical background The analysis of health care utilisation can be based on different theoretical rationales (Rosko & Broyles, 1988) such as the traditional theory of demand, the behavioural model of demand, the investment model of health, and the supplier-induced demand model. This study was mainly based on the traditional demand approach, in which health status is included as a need or taste variable. However, we used a two-part model describing (a) the incidence of visits, and (b) the number of visits by those making any visits. It was assumed that, initially, whether to visit a doctor or not was determined more by aspects of demand, whereas the number of visits after physician services that had already been used was expected to be governed more by supply side issues, such as doctors’ recommendations and decisions (Stoddardt & Barer, 1981). The changes in Finnish health care and in the entire economy are assumed to have had numerous and rather substantial structural as well as level effects on the utilisation of physician services. These can be separated into changes relating to demand or to supply of services.

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As in health care overall, cost sharing expanded in physician services, which would decrease the demand for services. In 1987, most visits to a doctor (e.g. at a health centre or within occupational health services) were free of charge, or subsidised more (visits to a public specialist) or less (to a private doctor). As a part of the state subsidy system, municipalities were given the right to decide whether or not to charge for services, and to set the level of these charges up to maximum limits decided by the government. By 1996, most municipalities levied charges on adults for visits to health centre doctors, although these doctors’ services were still given free to children. Between 1987 and 1996, the client’s cost share of visits to a public specialist also increased, but doctor services in occupational health care remained free in 1996.1 Overall user charges’ share of total expenditure in 1996 was slightly over 10% for outpatient services provided by municipal health centres and departments of public hospitals. It was about 60% for private physician services. The increase of cost sharing by clients can be assumed to have strengthened the effect of income in explaining demand among the adult population. Rising unemployment can be assumed to decrease utilisation, since becoming unemployed results in discontinuing the use of easily available occupational health services provided by employers. In addition to this change in the level of demand, a structural change can also be assumed, because the expansion of cost sharing in public doctors’ services may have particularly reduced their utilisation by the unemployed and their families. Considerable changes also occurred in the supply of public health services. As a consequence of the state subsidy, reform and cuts in state subsidies for health and social services local governments (municipalities) had, in addition to the increased freedom in provision of services, more economic responsibilities in providing the services. In this situation, the municipalities made acute health care their priority, which meant that cuts in health care were smaller than in other services they provided. During the recession, the municipalities or 1 Since 1993 a municipality has had two alternative methods of charging fees for visits to a health centre doctor. The first option is either an annual payment (maximum FIM 100) which covers all consultations during the year or a charge per consultation (maximum FIM 50) for those who do not want to pay this. The second option is to charge all patients a maximum of FIM 50 per consultation for their first three consultation in a calender year, with further consultations free. For public specialised care there is no annual payment system. Between 1987 and 1996, the user charges for specialist outpatient visits increased from FIM 40 to FIM 100. Finnish National Health Insurance refunds a part of the cost of private doctor services, but cost sharing is rather high: 63% in 1987 and 60% 1996.

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federations of municipalities (such as hospital districts providing specialist health care) did not decrease the number of permanent personnel (e.g. doctors). The savings in expenditure were achieved by decreasing the number of temporary and substitute personnel. Considerable improvement in productivity occurred simultaneously, both in health centres (Luoma, 2000) and somatic (non-psychiatric) hospitals (Linna, 1999). In hospitals, at least, much of the increased productivity was due to technological advances. Much of the care given earlier in the hospital inpatient departments is nowadays provided in outpatient facilities, which tends to increase the total number of doctor visits. Thus, cuts in expenditure tended to be over compensated by rising productivity: although health care resources measured as expenditure per capita at constant price did not increase between 1987 and 1996 there was a considerable rise in the number of services produced in public health services. According to our model the increase in the volume of services would largely affect the number of visits in the second part of the model. Moreover, the increase of volume may narrow the differences of utilisation between socio-economic groups if the increase concentrated on groups with the lowest relative utilisation. But decentralisation of responsibilities for provision and financing of health care to municipal level may tend to increase the regional variation of supply of services. Thus, there may be a potential for an increase in socioeconomic inequity if the supply of services at regional level is dependent on the economic situation (tax revenues) of the municipality. In addition, developments in other public social services affected the utilisation pattern of physician services among specific age groups. When considering utilisation of physician services among young children it should be noted that in Finland, as elsewhere in Scandinavia, women’s participation in work is publicly supported by the production and subsidy of day-care for children under school age. During the economic recession, child care in Finland was a priority area for the provision of public services and expenditure (Kautto, 2000). In addition, the structure of day-care moved towards public day-care rather than family care. This change may have had considerable effects on the utilisation of physician services among young children. A Finnish study in 1990 provided evidence that care in day centres is a determinant of acute respiratory infections in children under two years old, whereas family or home care does not essentially increase the risk of the disease (Louhiala, Jaakkola, Ruotsalainen, & Jaakkola, 1995). The rise in day-care can be assumed to have increased the demand for visits to a doctor and to have occurred mainly at the level of utilisation. Finally, a marked change also took place in care of the elderly. There was a move from institutional care

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towards more residence-based care, but this has been accompanied by a less efficient build-up of support services (Kautto, 2000). For example, home help services for elderly households decreased between 1987 and 1996. This may reflect an increased demand for physician services by non-institutionalised people and especially increase the effect of need variables explaining their use of services.

Data and methods This study was based on Finnish Health Care Surveys in 1987 and 1996, which were nationally representative cross-sectional samples of the total non-institutionalised population (Arinen et al., 1998). In both years, interviews were conducted face-to-face to ensure reliability of data and achieve a high response rate. For cost efficiency, all eligible household members (aged 15 or over) were interviewed personally and an adult (usually the mother) gave the data on children of 0–14 years of age. A major difference between the two surveys was that in 1987 the interviews were carried out by public health nurses and similar health care professionals, while in 1996 professional interviewers from Statistics Finland collected the data. A separate comparative survey (Lehtonen, 1996; Nieminen, 1997) was performed in conjunction with the main surveys to observe the effect, if any, this disparity had on the results. The findings indicated that it did not significantly affect the comparability of the data. The dependent variable measured the number of visits to a doctor during the recall period, i.e. the time between the first day of the year and the day of the interview (May–June). All visits to a doctor because of illness were analysed, including pregnancy and delivery. Because the dependent variable can only take nonnegative integer values a count data regression was appropriate. Moreover, a considerable proportion of individuals (varying from 63% to 36% depending on the age group and year) had made no visits during the recall period. We thus applied a two-part model 2 (logit and zero-truncated negative binomial model), which is nowadays regularly used for this type of data and can be supported by statistical and theoretical considerations (Grootendorst, 1995, H.akkinen et al., 1996, Gerdtham, 1997).3 The statistical analysis was performed stepwise. Firstly, explanatory variables were chosen according to 2 In count data literature, the two-part model and the hurdle model are usually treated as synonymous. 3 For this study, we did not make any extensive empirical comparison of alternative specifications (e.g. two-part versus zero inflated model). See Jones (1998) for a detailed discussion of alternative specifications.

theoretical considerations, experience from earlier Finnish studies, data availability and statistical criteria. Secondly, changes in the level of utilisation were estimated by a dummy variable indicating the year of study. Thirdly, structural changes were tested by a Chow-type test.4 Differences in specific variables were further examined with a dummy variable test (Maddala, 1992). If the overall model turned out to be different between the two years, it was estimated separately for each year on the basis of the results of the Chow-type test. If there were changes in the both structure as well as level of utilisation, then it was not possible to evaluate their relative importance since both occurred over the same period. However, the two changes could be described separately with the usual ceteris paribus assumptions. For example, the effect of a time trend was analysed by means of the coefficient of a dummy variable indicating the year of study, holding other variables at their mean values. Similarly, the structural changes could be described by means of changes in coefficients of variables estimated from different years, again holding other variables at their mean values. In our statistical model building, maximum likelihood was utilised, and it could be shown that the log likelihood for the whole model separated additively into one for the logit model and one for the zero-truncated negative binomial model. As shown by Grootendorst (1995), maximisation of these two components separately gives the maximum for the complete model. In the first part, it was possible to take into account the survey design by applying a survey estimation method to the logit regression.5 Although this has only minor effects on estimated coefficients and their standard errors, the result reported in the tables for the first part takes into account the study design. In the second part, it was not possible to make the adjustment since were are no easily available survey estimation methods for truncated negative binomial. The alternatives would be to use OLS for the second part, or non-truncated Poisson regression for the whole model to which survey estimation methods could easily be applied. The first alternative was rejected since OLS does not take account of the special character of the dependent variable. The second alternative was rejected since the two part model (logit+zero truncated Poisson regression) is well known to outperform the untruncated Poisson regression (Grootendorst, 1995). The effect of the study design was indirectly compared by fitting an OLS regression to the second part by taking and not taking into account 4

See H.akkinen et al. (1996) for an illustration of the test. Most of the statistical analyses were made using LIMDEP 7.0 software. For accounting the sample design STATA 6.0 software was used. Sampling weights and a clustering variable are reported in Arinen et al. (1998). 5

U. Hakkinen / Social Science & Medicine 55 (2002) 1523–1537 .

the sample design effect. Again the comparison indicated rather similar coefficients and p-values.6 It should be noted that the bias associated with the not taking into account the study design in the truncated negative binomial model can be assumed to be rather minor, because the variance of the negative binomial distribution includes a term for overdispersion. The tables do not present the estimated coefficients; instead they are illustrated by the elasticities for continuous variables. For dummy variables we calculated the percentage change, which indicates the way in which the change in the variable increased (or decreased) the use of physician services. Additional details concerning definitions, coefficients and calculation of elasticities and effects can be obtained from the author.

Explanatory variables The utilisation of physician services was hypothesised to depend on the need for them (health status), on demographic and socio-economic factors, and on factors related to the availability of services (Table 1). The theoretical and practical aspects associated with explanatory variables are discussed in more detail in our earlier studies (H.akkinen, 1991b; H.akkinen et al., 1996). The pattern and determinants (explanatory variables) of utilisation varied according to age. Some variables were neither available nor even sensible to be used in all age groups. Demographic changes occurred both between and within socio-economic groups. For example, the structure of income groups altered substantially over the period. The proportion of students in the lowest income quintile increased, whereas that of pensioners decreased. In order to avoid the problems associated with differences in explanatory variables and the change in socio-economic structure, the analyses were made separately for the four age groups: children aged 0–6, children aged 7–17, adults aged 18–64, and adults over 64. Within each age group the explanatory variables included the usual demographic factors of age and sex. 6 In the seven final second part models the maximum difference between the OLS coefficient estimates, taking and not taking into account the study design, was 0.13. Only for four coefficients this difference was greater than 0.1. As most variables are dichotomous this difference directly describes the number of visits, and this maximum is only about 4% evaluated at the mean number of visits to a doctor (2–3 visits, see Table 2). The differences in the p-values of the coefficient estimates were evaluated by looking at those coefficients whose significance changed using 0.05 as a cut-off point. A change in significance occurred in only eleven of the 139 coefficients. Among these 11 cases the highest difference in the p-value was 0.087 and only in five coefficients the difference was higher than 0.05.

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Measurement of need using various measures of health status is a central problem in modelling health care utilisation (Manning, Newhouse, & Ware, 1981). There is no gold standard for the measure of need, although self-rated health has been widely used because of its close correlation with many indicators of morbidity and mortality (Bjorner et al., 1996; Idler & Benyamini, 1997). In this study, the measurement of need was even more complicated, because between 1987 and 1996 the health status of Finns deteriorated according to some health variables (self-reported chronic illness, psychosomatic complaints), whereas others (self-rated health status among adults and activities of daily living among the elderly) show different results (Table 1, see also Arinen et al., 1998). In addition, a recent study also using the present data found that even the socioeconomic inequity in these health indicators with respect to income and education has developed differently (H.akkinen, 1999b). Here, we applied the usual approach of including many health status measures as explanatory variables in order to incorporate various dimensions of health (H.akkinen, 1991b; H.akkinen et al., 1996; Gerdtham, 1997). In all age groups, it was possible to use self-reported chronic illness as a need variable. In addition, for the adults we used self-rated health status and psychosomatic complaints, and for the elderly two measures of activities of daily living also. Since the total number of visits included those due to pregnancy and delivery, a specific dummy variable was used to take account of this effect. As stated before, income and unemployment were two of the variables of main interest. Income data were collected from register-based tax records maintained by the tax authorities, and merged with survey data by use of the unique personal identification number. We used net (after tax) family monthly income per household unit. Household units, according to the OECD scale, were 1 for the first adult, 0.7 for the second adult and 0.5 for each child. The income in 1987 was deflated to 1996 using the consumer price index. A log transformation was used to moderate the effects of extreme values. Among the working age population, unemployment was measured by a dummy variable indicating personal working status, whereas in the models describing children’s utilisation unemployment was measured by a dummy variable indicating whether at least one family member was unemployed. Education has often been hypothesised to have a negative impact on utilisation because, as more effective health producers, better educated individuals use less services than others (Grossman, 1972). However, an earlier Finnish study showed that the effect of education on health care utilisation was minimal, while among adults it was non-linear (H.akkinen et al., 1996). Therefore, in this study, education in the analysis describing

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Table 1 Description of variables and their means in 1987 and 1996 Variable

Children under 7 1987

Age Age 1, dummy variable, 1 if 1 year old (reference value=0 years) Age 2, dummy variable, 1 if 2 years old (reference value=0 years) Age 3, dummy variable, 1 if 3 years old (reference value=0 years) Age 4, dummy variable, 1 if 4 years old (reference value=0 years) Age 5, dummy variable, 1 if 5 years old (reference value=0 years) Age 6, dummy variable, 1 if 6 years old (reference value=0 years) Age 9–11, dummy variable, 1 if 9–11 years old (reference value=7–9 years) Age 12–14, dummy variable, 1 if 12–14 years old (reference value=7–9 years) Age 15–17, dummy variable, 1 if 15–17 years old (reference value=7–9 years) Age 25–34, dummy variable, 1 if 25–34 years old (reference value=18–24 years) Age 35–44, dummy variable, 1 if 35–44 years old (reference value=18–24 years) Age 45–54, dummy variable, 1 if 45–54 years old (reference value=18–24 years) Age 55–64, dummy variable, 1 if 55–64 years old (reference value=18–24 years) Age 75–84, dummy variable, 1 if 75–84 years old (reference value=65–74 years) Age over 84, dummy variable, 1 if over 84 years old (reference value=65–74 years) Sex, dummy variable, 1 if male Chronic illness, dummy variable, 1 if person is chronically ill Self-rated health status Good health, dummy variable, 1 if self-rated health is good (reference value=self-rated health medium) Rather good health, dummy variable, 1 if selfrated health is rather good (reference value =self-rated health medium) Rather poor health, dummy variable, 1 if selfrated health is rather poor(reference value =self-rated health medium) Poor health, dummy variable, 1 if self-rated health is poor (reference value =self-rated health medium) Overexertion, dummy variable,1 if person had overexertion Depression, dummy variable, 1 if person had depression or dejection Nervousness, dummy variable, 1 if person had nervousness or tension Fatigue, dummy variable, 1 if person had lack of fatigue or stamina

1996

0.149

0.141

0.139

0.149

0.155

0.143

0.163

0.145

0.136

0.151

0.135

0.144

0.509 0.090

0.516 0.203

Children 7–17 1987

Adults 18–64 1996

0.290

0.306

0.274

0.272

0.255

0.246

0.508 0.156

0.483 0.252

1987

Adults over 64 1996

0.244

0.195

0.260

0.238

0.177

0.253

0.164

0.189

1987

1996

0.335

0.285

0.057

0.041

0.497 0.346

0.454 0.457

0.362 0.820

0.376 0.835

0.466

0.468

0.092

0.100

0.247

0.257

0.182

0.196

0.059

0.057

0.207

0.180

0.017

0.021

0.081

0.092

0.209

0.333

0.167

0.169

0.136

0.252

0.260

0.275

0.172

0.277

0.210

0.208

0.242

0.428

0.480

0.469

U. Hakkinen / Social Science & Medicine 55 (2002) 1523–1537 .

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Table 1 (continued) Variable

Children under 7 1987

Sleeplessness, dummy variable, 1 if person had sleeplessness Walking, dummy variable, 0 if person could walk 400 m without aid or difficulty; otherwise 1 Cutting, dummy variable, 0 if person could cut his/her toenails without difficulty; otherwise 1 Pregnancy, dummy variable, 1 if pregnant Income, net (after tax) family monthly income 4.723 per household unita(deflated to 1996), 1000 FM, logb Education High family education; dummy variable, 1 if 0.407 at least one family member had more than 11 years education Low education, dummy variable 1 if person had less than 9 years education High education; dummy variable, 1 if person had more than 11 years education Unemployment: Family unemployment, dummy variable, 1 if at least one family member is unemployed Unemployment, dummy variable, 1 if person is unemployed Private insurance, dummy variable, 1 if private doctor visits are reimbursed (in addition to National Health Insurance) by private insurance Employer coverage; dummy variable, 1 if private doctor visits are reimbursed (in addition to National Health Insurance) by employers or voluntary social insurance Distance to doctor, distance (in km) from home to nearest doctor Distance to hospital, distance (in km) from the centre of the home municipality to the centre of the nearest municipality with an acute hospital

0.045

0.367

a b

1987

Adults 18–64 1996

1987 0.112

4.689

4.927

5.119

0.657

0.344

0.531

0.181

0.348

0.057

0.245

Adults over 64 1996

1987

1996

0.198

0.309

0.346

0.395

0.308

0.395

0.300

0.027 5.542

0.023 5.804

4.294

4.681

0.241

0.152

0.613

0.561

0.123

0.185

0.035

0.047

0.034

0.135

0.089

0.067

0.137

0.085

0.198

0.257

5.1

5.0

5.0

5.4

3.8

4.9

4.7

5.3

23.6

21.7

22.0

23.4

21.1

20.0

23.6

23.3


0.018


0.004


0.090

4722

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Type of children’s day care Nursery, dummy variable, 1 if children are at 0.143 nursery (reference value=care at home) Family care, dummy variable, 1 if children are 0.254 in family care (reference value=care at home) Time of interview, number of days between 0.006 the start of the year and the interview /145, log Number of observations

1996

Children 7–17

1359

0.225 0.138
0.009

832

0.008

2427

0.001

1178

OECD scale: 1 for the first adult, 0.7 for the second adult and 0.5 for each child. Figures in the table are not transformed to logs as in the statistical analysis.

0.006

9967

976

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adults’ utilisation was measured by two dummy variables, whereas for children’s utilisation we used only one variable describing whether at least one family member was well educated. The structure of a household might have an impact on utilisation and this may differ between age groups. For example, it can be assumed that children with only one parent use less services than those with both because of the demand on parents’ time. At the other end of the age range, elderly people living alone are less likely than others to receive informal care in case of sickness and functional incapacity, which may increase their demand for formal health services. The study data incorporated various measures of household structure (single parent family, family size, number of children, living alone). However, as these variables were insignificant in all models and did not affect the coefficients of other variables, they were excluded from the final models. Although the health care charges paid by households have risen over the past decade, most physician visits are still heavily subsidised. For this reason, time cost and the availability of services may be an even more important factor affecting utilisation than monetary cost. This effect was taken into account as the distance to the nearest doctor and distance to the nearest acute hospital. The latter was included because these hospitals provide public outpatient specialists. For those who were employed it was also possible to construct a measure of time cost indicating whether their income or salary would decrease if they visited a doctor during

working hours. As in our earlier study (H.akkinen, 1991b), this factor was insignificant among adults aged 18–64 and thus excluded from the final model. The monetary cost associated with the use of private doctor services was taken into account by two dummy variables indicating reimbursements from private insurance, and employers or voluntary social insurance in addition to National Health Insurance. Unfortunately, our data do not include information on supply and user charges of physician services. The effects of these factors were included in the dummy variable describing the year of study, which also took account of other factors changing over the study period. About 40% of children under 7 years were either in nursery or family day-care in both years. In order to evaluate this effect by the type of care, for young children we used variables describing the types of daycare (nursery or family day-care versus home care) as explanatory variables. Finally, we included in all models a variable for the date of interview in order to take into account differing recall periods of the dependent variable.

Results The total number of physician visits increased in all age groups (Table 2)Fthe most among children aged 7– 17 years (33%) and the least among the elderly (8%). This was largely due to greater number of visits by those who had at least one visit. Only among children under 7

Table 2 Proportion of study group with at least one physician visit, and the number of visits in different age groups during the five months in 1987 and 1996 Age group

1987

1996

Change (%)

Children aged 0–6 years Proportion (%) Number of visits by those with at least one visit Number of visits on average

0.57 2.72 1.55

0.63 2.92 1.84

10.5 7.3 18.7

Children aged 7–17 Proportion (%) Number of visits by those with at least one visit Number of visits on average

0.36 1.93 0.70

0.40 2.32 0.93

11.1 20.2 32.8

Adults aged 18–64 Proportion (%) Number of visits by those with at least one visit Number of visits on average

0.52 2.59 1.34

0.54 3.03 1.63

3.8 17.0 21.6

Adults over 64 Proportion (%) Number of visits by those with at least one visit Number of visits on average

0.64 2.57 1.64

0.64 2.77 1.77

0 7.8 7.8

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years did the probability of use of physician services increase somewhat more than the number of visits by those with at least one. The crude trends in utilisation were confirmed in the analyses of change in the level of utilisation (Table 3). Statistically significant changes were found among children under 7 years in the first part and in the other age groups in the second part. In addition, comparing Tables 2 and 3 indicates that the level of change measured by the time trend ‘‘explains’’ over half of the increase of total number of visits. The tests of structural changes suggested clear differences in the models between the two years among children under 7 years, adults aged 18–64, and adults over 65 (Table 4). Based on these results we constructed the final models (Tables 5–8), in which some models were estimated separately for both years.

effects of private insurance and unemployment on the probability of use also declined over time, although these changes were not confirmed by the dummy variable test. The total number of doctor visits in 1987 was 80% greater among children looked after in a nursery compared to those at home. There was a significant decrease of the effect of this variable in the second part, and the total effect of the variable was 32% in 1996. Utilisation also increased among those in family care but the total effect of this was smaller than that of nursery day-care, and the coefficients of the variable were insignificant in 1996. The truncated negative binomial model revealed a significant difference in the coefficient of distance to hospital variable. In 1996, the children living furthest from hospital had clearly fewer visits.

Children under 7 years

Children aged 7–17

In the youngest age group there were structural changes in both the parts describing utilisation. In both years, chronic illness was the most important factor explaining utilisation (Table 5). The dummy variable test revealed that differences between the logit models were mainly due to the change in the coefficient of family education. Thus, the increase in the mean level of parents’ education between 1987 and 1996 (Table 1) was associated with a decrease in its effect on utilisation. The

The increased utilisation among the children of school age was mainly due to changes in the level of utilisation. Since there were no structural changes, much of the change can be explained by the year dummy variable indicating a 24% increase. Again, chronic illness was the most important factor describing utilisation in both parts. The growing prevalence of private insurance increased the use of physician services by nearly 30% (Table 6).

Table 3 Change in the level of utilisation: calculated effects (change %) of dummy variable (1 if year=1996)a Age group

Children under 7 years Children aged 7–17 Adults 18–64 Adults over 64 a b

First part

Second part

Total effectb

Logit regression

Zero-truncated negative binomial regression

First and second parts

9.3* 4.8 0.0 4.7

3.7 18.1*** 11.2*** 16.1***

13.3 23.8 11.2 21.6

*po0:05; ** po0:01;***po0:001: Statistical significance is not calculated.

Table 4 Testing the stability of models by Chow-type test for samples in different age groupsa Age group

Logit regression

Zero-truncated negative binomial regression

Logit+truncated negative binomial regression

Children aged 0–6 Children aged 7–17 Adults aged 18–64 Adults over 64

p ¼ 0:016 p ¼ 0:797 p ¼ 0:236 p ¼ 0:343

p ¼ 0:036 p ¼ 0:026 po0:001 p ¼ 0:003

p ¼ 0:002 P ¼ 0:148 po0:001 p ¼ 0:007

a

The test is a log-likelihood ratio test applied for testing the equality of the coefficients between the two years.

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Table 5 Determinants of physician utilisation among children under 7 yearsa Variable

Age 1 Age 2 Age 3 Age 4 Age 5 Age 6 Sex Chronic illness Income High family education Family unemployment Private insurance Distance to doctor Distance to hospital Nursery Family care Time of interview

First part

Second part

Total effectb

Logit regression

Zero-truncated negative binomial regression

First and second parts

1987

1996

1987

1996

1987

1996

34.9*** 29.0** 13.3 9.3
2.3
6.5 2.6 49.5*** (0.102) 3.2# 20.3 19.9*** (0.015) (
0.019) 20.4* 23.1*** (0.821)

22.8* 11.2
8.9 1.1
7.2
12.4
4.6 29.9*** (0.043)
15.3**#
7.9 9.1 (
0.040) (0.024) 24.9** 11.6 (0.138)

33.3*
5.3
18.0
21.6*
21. 8*
32.4** 15.0* 76.6*** (
0.070)
7.5 5.9 9.8 (
0.005) (0.002)# 49.5***# 24.5** (0.674)

15.5
3.3
3.4
12.3
28.6*
30.7** 3.0 66.1*** (0.096) 4.9 10.0
7.5 (0.009) (
0.066)***# 5.5# 6.6 (0.854)

79.8 22.2
7.1
14.3
23.6
36.8 18.0 164.0 (0.025)
4.5 27.4 31.7 (0.010) (
0.017) 80.0 53.3 (2.048)

41.8 7.5
12.0
11.3
33.7
39.3
1.7 115.8 (0.143)
11.1 1.3 0.9 (
0.031) (
0.044) 31.8 19.0 (1.110)

a

Note: The estimated coefficients are illustrated by changes (%) for dummy variables and by elasticities for continuous variables (in parentheses). t-test of whether the coefficient is zero *po0:05; ** po0:01;***po0:001: Dummy variable test of stability of the coefficients between 1987 and 1996: # po0:05; ## po0:01; ### po0:001: b Statistical significance is not calculated.

Table 6 Determinants of physician utilisation among children aged 7–17 yearsa Variable

Age 9–11 Age 12–14 Age 15–17 Sex Chronic illness Income High family education Family unemployment Private insurance Distance to doctor Distance to hospital Time of interview Year 1996

First part

Second part

Total effectb

Logit regression

Zero-truncated negative binomial regression

First and second parts

1987 and 1996

1987 and 1996

1987 and 1996


21.9***
16.5*
3.8
1.1 69.5*** (0.087) 0.7 6.7 14.4** (
0.031) (
0.021) (0.185) 4.8


3.5 0.9 15.3*
0.7 37.2*** (0.100)
8.4* 8.8 12.2** (
0.012) (
0.023) (0.173) 18.1***


26.2
15.7
10.9
1.8 132.5 (0.195)
7.8 16.1 28.4 (
0.043) (
0.045) (0.391) 23.8

a Note: The estimated coefficients are illustrated by changes (%) for dummy variables and by elasticities for continuous variables (in parentheses). t-test of whether the coefficient is zero *po0:05; ** po0:01;***po0:001: b Statistical significance is not calculated.

Adults aged 18–64 At working age, almost all changes in utilisation occurred in the level and structure of persons who had at

least one visit. As in other age groups, and normally in this type of study, need variables (various measures of health status and pregnancy) were the most important factors explaining utilisation. The probability of utilisa-

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Table 7 Determinants of physician utilisation among adults aged 18–64a Variable

Age 25–34 Age 35–44 Age 45–54 Age 55–64 Sex Chronic illness Good Health Rather good health Rather poor health Poor health Overexertion Depression Nervousness Fatigue Sleeplessness Pregnancy Income Low education High education Unemployment Private insurance Employer coverage Distance to doctor Distance to hospital Time of interview Year 1996

First part Logit regression

Second part Zero-truncated negative binomial regression

Total effectb First and second parts

1987 and 1996

1987

1996

Second part 1987

Second part 1996


4.0
13.2***
12.7 ***
20.4***
17.6*** 45.6***
26.5***
12.1*** 28.6*** 36.1*** 3.2 11.6*** 2.8 2.2 8.3** 77.2*** (0.153)***
6.8**
4.5
8.6* 6.0* 12.7*** (
0.012) (
0.023)*** (0.546)*** 0.01


2.3
5.3
4.2
15.2***
7.6*** 30.5***
22.3***#
7.2*## 23.9*** 43.0***# 2.9 6.2 2.3 2.0 8.5* 73.7*** (0.078)**
3.2 2.6#
0.7 5.5 12.7*** (
0.003) (
0.018)* (0.001)


8.6
7.2
9.1
15.1**
6.8* 26.4***
31.8***#
19.0***## 26.4*** 76.0***#
5.8# 4.2 9.8* 11.1* 12.3** 88.1*** (0.056)
7.7
9.8*# 1.1 6.4 3.4 (
0.008) (
0.018) (0.528)***


6.2
17.8
16.4
32.5
23.9 90.0
42.9
18.4 59.3 94.6 6.2 18.5 5.2 4.2 17.5 207.8 (0.243)
9.8
2.0
9.2 11.8 27.0 (
0.015) (
0.041) (0.548)


12.3
19.5
20.6
32.4
23.2 84.1
49.9
28.8 61.6 137.5
2.8 16.3 12.9 13.5 21.6 233.3 (0.218)
14.0
13.9
7.6 12.8 16.5 (
0.020) (
0.041) (1.362)

a Note: The estimated coefficients are illustrated by changes (%) for dummy variables and by elasticities for continuous variables (in parentheses). t-test of whether the coefficient is zero *po0:05; ** po0:01;***po0:001: Dummy variable test of stability of the coefficients between 1987 and 1996: # po0:05; ## po0:01; ### po0:001: b Statistical significance is not calculated.

tion was also significantly associated with sex, education level, income, unemployment, and availability variables (employer coverage, distance to the nearest hospital). The structural changes in the second part seemed to be associated with the self-rated health variable, which had an even stronger effect on the number of visits in 1996 than in 1987. The change was significant both in those who reported good health and poor health, which indicates a trend of increasing importance of need variables in explaining utilisation. The coefficients of high education also changed significantly. Contrary to 1987, in 1996 higher education seems to have decreased utilisation among adults. This negative impact occurred in the second part of utilisation which accords with the idea that higher education decreases the utilisation initiated by a doctor (Rossiter & Wilensky, 1983). Thus, the results from 1996 tend to support the hypothesis that (Grossman, 1972) the welleducated are more effective health producers.

Adults over 64 Even among the elderly, the need variables were the prime determinants of utilisation. In addition, the probability of use was significantly positively associated with income. There were significant level and structural changes only in the second part of utilisation. The structural differences were mainly due to the change in the coefficients of the sex and walking variables. The year dummy variable had about the same impact as the change in the effect of the sex variable (see Table 3), which may indicate that much of the overall change in utilisation was due to the increased utilisation by males. Changes in explanatory variables Between 1987 and 1996 there were some changes in the mean values of explanatory variables (Table 1), the

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Table 8 Determinants of physician utilisation among adults over 65a Variable

Age 75–84 Age over 84 Sex Chronic illness Good health Rather good health Rather poor health Poor health Overexertion Depression Nervousness Fatigue Sleeplessness Walking Cutting Income Low education High education Distance to doctor Distance to hospital Time of interview Year 1996

First part Logit regression

Second part Zero-truncated negative binomial regression

Total effectb First and second parts

1987 and 1996

1987

1996

Second part 1987

Second part 1996


10.1**
11.6
2.8 48.4***
21.8***
9.9* 4.6 10.7 4.9 3.8 2.1 10.8** 7.9* 2.9 14.9*** (0.121)*
5.8 5.4 (
0.018) (
0.001) (0.558)** 4.7


16.3***
33.2**
5.6# 13.4
3.4
14.2* 14.7* 26.4** 10.7 15.8* 0.1 6.2 8.0
1.6# 0.5 (0.081)
2.0 12.1 (
0.009) (
0.024) (0.250)


15.5*
31.1 12.5# 4.7
28.1* 5.6 32.5** 58.0*** 14.8 28.2**
16.0* 6.5
0.8 18.0*#
7.0 (0.127)
5.9
13.8 (0.028) (
0.036) (0.715)*


24.7
41.1
8.4 68.3
26.3
22.7 20.0 39.9 16.1 20.2 2.2 17.7 16.5 1.3 15.5 (0.212)
7.7 18.2 (
0.027) (
0.025) (0.809)

24.0
39.2 9.4 55.4
45.1
4.9 38.6 74.9 20.4 33.1
14.2 10.0 7.0 21.4 6.9 (0.263)
11.4
9.2 (0.010) (
0.037) (1.277)

a Note: The estimated coefficients are illustrated by changes (%) for dummy variables and by elasticities for continuous variables (in parentheses). t-test of whether the coefficient is zero *po0:05; ** po0:01;***po0:001: Dummy variable test of stability of the coefficients between 1987 and 1996: # po0:05; ## po0:01; ### po0:001: b Statistical significance is not calculated.

most significant being the increase of chronic illness in all age groups except the elderly. Among children under seven, the use of public day-care increased at the expense of family care. The effect of these level changes is illustrated in Table 9. The calculations were made considering other variables at their mean values in 1987 and 1996 (figures in parentheses). For example, in the Table we can observe that the increase in the prevalence of chronic illness (from 9% to 20%) among children aged under 7 years was associated with an overall 9–13% increase in physician utilisation, which is roughly equivalent to the crude change in the level estimated by the time trend (Table 3) and about half of the total increase in utilisation (19%, Table 2). Among children aged 7–17 and adults aged 18–64 the calculated effect of increased chronic morbidity on utilisation was 6–10%. Among children, the effects of increase in day-care and decrease in family care seemed to be at the same level, and being opposite trends they diminished each other’s effects. Moreover, the effects are dependant on the year models that used in the calculations. Since the effect of these variables on utilisation decreased between the two years (Table 5) their impact is clearly lower when calculated using the 1996 models.

Apart from chronic illness and the type of children’s day-care, the mean values of some other variables, such as the level of education or unemployment, increased over the period. However, their impact on utilisation was less strong and the calculated effects of the change of other explanatory variables were more modest.

Conclusions Our findings indicate that the change in utilisation of physician services over the study period was a product of both structural and level changes. With the exception of the youngest age group, both types of change occurred in the second part of utilisation, which implies that they were more associated with supply factors than demand factors. Among the adult population, the structural changes were related to need factors which may point out the changes in clinical practices. For example, more curative facilities are allocated to those who have a greater need for services. Thus, the planning of future health services cannot be based solely on projected trends in demand factors.

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Table 9 Effect of the change in some explanatory variables (1987–1996) on the use of physician services (%) among children under 7 years, children aged 7–17 and adults aged 18–64a Explanatory variable/model Children under 7 Logit regression 1987 + negative binomial regression 1987 and 1996 Chronic illness (increase from 9.0% to 20.3%) Nursery (increase from 14.3% to 22.5%) Family care (decrease from 25.4% to 13.8%) Logit regression 1996 + negative binomial regression 1987 and 1996 Chronic illness (increase from 9.0% to 20.3%) Nursery (increase from 14.3% to 22.5%) Family care (decrease From 25.4% to 13.8%) Children aged 7–17 Logit regression 1987 and 1996+negative binomial regression 1987 and 1996 Chronic illness (increase from 15.6% to 25.2%)

Adults aged 18–65 Logit regression 1987 and 1996+negative binomial regression 1987 Chronic illness (increase from 34.8% to 45.7%)

Logit regression 1987 and 1996+negative binomial regression 1996 Chronic illness (increase from 34.8% to 45.7%)

First part

Second part

Total effect

6.2 (6.7) 1.7 (1.6)
2.6 (
2.3)

6.5 (6.4) 4.4 (4.3)
3.2 (
3.5)

13.1 (12.5) 6.2 (6.0)
7.7 (
5.7)

3.3 (3.6) 2.0 (2.1)
1.3 (
1.3)

5.8 (5.8) 0.6 (0.6)
0.9 (
1.0)

9.3 (9.6) 2.6 (2.7)
2.2 (
2.3)

5.9 (5.7)

3.6 (4.1)

9.7 (10.0)

4.4 (4.4)

2.3 (2.5)

6.7 (7.1)

4.4 (4.4)

2.0 (2.0)

6.5 (6.5)

a The calculations were made using the models in Tables 5, 6 and 7 and considering other explanatory variables at their mean value in 1987 and 1996 (in parentheses).

The increase in utilisation was highest among children aged 7–17, and associated totally with level changes. The increase in demand seems to have been related to a rising incidence of chronic illness, whereas in the second, part change in level was associated with time effect. Even in other age groups (except among the elderly) the results show that the increase in self-reported chronic illness was an important explanation of the rise in doctor visits. Among children, the rise in chronic illness is mainly due to more of asthma and allergies among the youngest members of the population (Arinen et al., 1998), which is also confirmed by the rise in drug reimbursements for asthma. However, although some of this increase is doubtless due to a ‘true’ rise in the prevalence of the disease, it might be that some of the increase in reported chronic disease (and also in drug utilisation for asthma) is due to an altered treatment approach: nowadays, asthma is dealt with earlier and more actively (Hermanson, Karvonen, & Sauli, 1998). It may also be that causality between chronic illness and

utilisation of physician services is two-directional: greater utilisation may increase self-reported chronic illness. Among young children, the type of day-care emerged as an important determinant of physician utilisation. Although this effect declined considerably between 1987 and 1996, the total number of visits to a doctor in 1996 was still about 30% greater among children in nursery day-care than in those at home. According to randomised controlled trials in the USA, day-care promotes children’s intelligence, development and school achievement. Long-term follow-up demonstrates increased employment, lower teenage pregnancy rates, higher socio-economic status and decreased criminal behaviour (Zoritch, Roberts, & Oakley, 1998). In Finland, daycare has recently been recommended on the basis of an economic analysis (Kajanoja, 1999). However, the effect of day-care on children’s health, health care utilisation, family well-being and use of time has so far not been taken into account in these considerations. Amongst

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other things, more attention should be focused on activities maintaining children’s health in day-care. In addition, the positive effect of day-care on utilisation may also be partly administrative: in Finland, a doctors’ certificate is needed for a parent’s absence from work because of a child’s sickness. The cuts in resources in institutionalised care as well as in home help services do not seem to reflect an increase in the demand for physician services among the elderly. Total utilisation of physician services increased more slowly among the elderly compared to other age groups. In fact, their demand in the first part for physician services did not increase even though the rise in real income was highest in just this age group.7 In addition, there were no structural or level changes in the first part of utilisation. Thus, the changes among the elderly were more supply driven indicating again that population crude trends in ageing may contribute much less to the growth of the health care sector than claimed by most observers (Zweifel, Felder, & Meiers, 1999). Although there were some changes in the structure and level of utilisation of physician services the changes in the effects of policy relevant variables on utilisation seemed to be rather small. The adverse effects of increase in cost sharing on the demand of utilisation were less than expected. One reason for this is that for public doctor services cost sharing is still rather modest: user charges amount to slightly over 10% of total expenditure. On the other hand, inequity in utilisation of services prevails with respect to income. The income variable was statistically significant among adults, and total income elasticity varied from 0.22 to 0.28. Utilisation was not related to income among the children, for whom public physician services are still free of charge. It should be noted that income may affect children’s utilisation via private insurance, since the prevalence of such an insurance increases with income (Arinen et al., 1998).8 7 Between the two years the mean real family monthly income per household unit increased by 9% among adults over 64, by 5% among adults aged 18–64, by 4% among families with children aged 7–17 and even decreased 1% among families with children under 7. 8 An additional analysis of the determinants of private insurance among the two child groups was made using a logit model and applying the same statistical approach and set of explanatory variables (except private insurance) as in Tables 5 and 6. According to the results there were structural changes but no changes in the level of insurance (i.e. the 1996 dummy was not statistically significant). However, in each of the four logit models estimated for the two age groups and the two years, income was the most important factor explaining the prevalence of private insurance. If private insurance is determined exogenously from utilisation (which in Finland can be assumed), then there is also a clear indirect effect of income via insurance on the utilisation of physician services.

According to a recent international comparison including Finland,9 a significant pro-rich inequity also prevails in physician contacts in four other countries (East Germany, the Netherlands, Sweden and the United States) of the ten included in the study (van Doorslaer et al., 2000). In Finland particularly, the private visits were concentrated in high-income groups. A reason for the effect of income that did not increase between the two years in Finland is that the share of private visits did not expand and even decreased somewhat. The direct effect of unemployment on utilisation was rather small. It did somewhat decrease the probability of using services among those of working age, but the assumed increase of the negative effect of unemployment on utilisation did not occur on a large scale. However, the effect of unemployment on utilisation may have largely occurred via its income effect. Between 1987 and 1996 average income increased, despite a significant increase in unemployment. But the effect of income did not change. The result of our earlier study indicates that income has a greater effect on utilisation among a low income than a high-income group (H.akkinen, 1991a). Finland’s rather comprehensive social security (unemployment benefits and other income maintenance transfers) ensured that income equality did not change over the period. Thus, the stability of the income effect on utilisation may have been a product of two contradictory effects: the increase of mean income, which may have decreased the total income elasticity, and the rise in unemployment, which can be assumed to have increased elasticity. In addition to comprehensive social security, the increase in productivity in public health services can also be assumed to have prevented a rise in socio-economic inequity in utilisation of physician services. In conclusion, it can be stated that the Finnish tax-based and locally decentralised health care system adapted quite well to the radical external shocks of this period. An analysis of trends in socio-economic inequity in hospital services also confirms this conclusion (Keskim.aki, 2000). In addition, according to the recent Eurobarometer Survey about 78% of Finns were satisfied with their health care system, which was the highest figure among all the countries of the European Union (Eurostat, 2000).

Acknowledgements The author thanks Gunnar Rosengvist, Risto Lehtonen, and two anonymous referees for useful comments and Richard Burton for checking the English language. 9 In the international comparison the Finnish data is the same as in this study.

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