Analysis of Use and Need of Domiciliary Care Services in Taiwan

A Research of Public Domiciliary Care Services for Elderly People Living Alone --the Case of Taiwan Song-Lin Huang*, Michael Black** Yu-Chia Chen***

Abstract

The policy of community care has been put into practice for more than a decade in Taiwan. Its policy has been focused on domiciliary care services (DCS) mainly. This research deals with these especially on DCS in almost the nationwide (except two metropolitan cities) after the policy being implemented 6 years. The database of this research consists of replies from 28542 elderly people who were living alone in Taiwan to answer questions about the need and use of the DCS in 2000. The relationship between need and use of the DCS, and the differences between these periods amongst elderly people living alone will be revealed. Although there were some limitation in the survey by phones, the paper seeks to analyse the use of domiciliary care services (DCSs) amongst elderly living alone in Taiwan. More specifically, it addresses the influence of an elderly person’s personal characteristics on the use of the service among those elderly people living alone from time to time. In addition, some differences in mean need and use of DCS between different basic characteristics of respondents will be evident in this research. Moreover, based on the logistic regression analyses, it may reveal the existence and the change of a substitute effect or a supplementary effect between the formal sector and the informal sector in terms of DCS in Taiwan.

Keywords: formal domiciliary care service (DCS)；elderly people living alone.

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*Song-Lin Huang, Assistant Professor at Meiho Institute of Technology. He may be contacted at [email protected]. **Michael Black, Assistant Professor at Meiho Institute of Technology. ***Yu-Chia Chen, Associate Professor at Meiho Institute of Technology.

1 A Research of Domiciliary Care Services for Elderly People Living Alone --the Case of Taiwan

1.0 Introduction The policy of community care has been put into practice for more than a decade in Taiwan. The policy has been focused on domiciliary care services (DCS) mainly. This paper seeks to analyse the use of DCS in Taiwan (except two metropolitans—Taipei and Kaohsiung cities) after the policy being implemented 6 years and relates this to various ways of defining the need for DCS. More specifically, it addresses the influence of an elderly person’s personal characteristics on the use of the service among elderly people living alone. The first section looks at the literature and methodology of this research. The second section looks at the frequency and percentage of use of DCS according to different basic characteristics. The third sections provide a cross- tabulation of use according to different independent variables relating to income, family and social issues, and location. The fourth section applies logistic regression analysis to users of DCS. Finally, the paper offers a discussion and conclusions.

1.1 Literature review Formal DCS from public sector The types of formal domiciliary care services provided vary from one local authority to another. In Taiwan what constitutes the content of domiciliary care are as follows: telephone contact, home visits, help with shopping, accompanying to hospitals, transport, meals-on-wheels services, day-care services, emergency assistance, help with housework or home help, and luncheon services. Although day care nursing and home-based nursing care are included in domiciliary care, all the public DCS are provided by the Social Affairs Departments of the local authorities (Social Affairs Dept. TPG, 1990, 1999; Social Affairs Dept., MOI, 2001).

In 2002, a regulation—Guideline of Care Service Workers Training which mixed the personal care services together with both of the Health Department and Social Affair Department in Central Government was enacted. A DCS worker was renamed as ‘Care Service Worker’. According to this regulation, care service workers are trained to be able to provide personal services for either of elderly people who are in hospital or in their own homes. They are entitled to serve people who are with high functional

2 disability (ADLs and IADLs)1. However, local authorities do not generally supply personal nursing care services to the elderly people living alone in their own home (Interior of Ministry, 2004), because most of the DCS care workers prefer to take care of people in hospitals or in institutions in order to get more reasonable money. The situation for elderly people living their own home alone remains almost the same as it was when this survey was conducted in 1999 and 2000. The items of day care nursing and home nursing care are not included in this research.

The trend for the use of DCS in the survey since 1989 is presented in Table 1.1. Although local authorities claim to have started it before then, a survey carried out in 1989 shows no evidence of this. Only 0.66 per cent of elderly people used DCS in 1991 according to the general survey, while 2.10 per cent and 2.18 per cent of elderly people used DCS in 2000 and 2002 (Ministry of the Interior, 2000, 2002). In 1998, a very important project, ‘Project for Strengthening Services for the Care of Elderly People’, was proposed (Ministry of Interior, 1999). Then, a ‘Three-year Plan for the Long-term Care of Elderly People’ (Health Dept., Executive Yuen, ROC, 2000) and Program of Care Services Industrialization were issued (CEPD, 2002). In both of these proposals the main policy objective set out was to establish a system of community care and privatization policy. Again, the main service for elderly people was clearly focused on DCS, family support, and social participation (Sun, et al., 2000, p.135-137). The top priority of these policies for DCS was to be care for elderly people living alone whose income was under 2.5 times the poverty line. Most of these public domiciliary care services were provided by either the local authorities or the voluntary sector initially (Social Services Dept. TPG, 1990, 1998). In 2003, the Program of Care Services Industrialization policy extended DCS to those elderly people over 2.5 times the poverty line. DCS may be booming recently. The author’s another research has delt with it this year.

1 ADLs are Activities of Daily Living. They include, for example, going outside; walking; climbing stairs; transferring; wheeling; baths/showers; toileting; dressing; grooming; eating; range of motion on 17 items including left and right digits, wrist, elbow, shoulder, ankle, knee, and hip, head and trunk; 8 items on strength, balance and coordination (such as grasping, rolling) (Kane et al., 1987). IADLs are Instrumental Activities of Daily Living. they include, for example, using the telephone; shopping for groceries or clothes; getting transport to places out of walking distance; preparing meals; doing housework; taking medicine; handling own money (Kane et al., 1987). 3 Table 1.1 Trends in DCS use by elderly people in Taiwan Years Total population Percentage of DCS users in total surveyed population surveyed 1989 1188 - 1991 1359 0.66 1997 1689 3.79 2000 2807 2.10 2002 2276 2.18 Sources: Ministry of Interior, 1989, 1991, 1997a, 2000, 2002.

Objective needs for DCS If it is an externally observable condition which constitutes a need of DCS, it tends to be regarded as a normative need. Under care or case management, needs assessment is objective, but assessing the different elements is complicated. The Practitioner’s Guide in the UK (Meredith, 1996, pp. 58–59) provides a comprehensive assessment guideline, including a range of information which could be gathered over time in a complex situation. In Taiwan, although there are not so many details available, similar criteria have been trained for needs assessments and may be conducted by social workers. However, the need assessments are not really applied in this research, because the only criterion of giving the DCSs is the income level— those whose income under 2.5 poverty line are the main focuses of the public DCS before 2002 in Taiwan.

Subjective needs for DCS Subjective need is a person’s own valuation of his/her need for DCS, and it tends to be regarded as the same as expressed need. It has been suggested that females are more willing to express need than males (Hill & Stull, 1987; Balswick, 1988; Basow, 1992). Much of the research in Taiwan to measure the needs of elderly people has used the subjective point of view of elderly people (Ministry of Interior, 1993, 1997, 2000, 2002; Yang and Hwang, 1998; Chang, 1998; Wueng, 1998; Wang, 1998; Lin, 1998; Chen, 1999; Song, 1999; Liu, 2001), not only because this emphasises that services are for clients, but because the new community care policy is influenced by ideas of consumerism and user-orientation. According to these researches (Yang and Hwang, 1998; Chang, 1998; Wueng, 1998; Wang, 1998; Lin, 1998; Chen, 1999; Song, 1999; Chao & Huang, 2000; Lyu, 2001, 2002), in Taiwan elderly people prefer to rely on family support when they need help. Elderly people believe that to rely on the support of the government is a disgrace. Although there has been a slight change

4 in the last ten years, this idea is still deep in the heart of many elderly people in Taiwan.

Use of DCS Some researchers who have sought to incorporate a user dimension into performance monitoring have made use of satisfaction surveys (Nocon et al., 1996, p. 36). Satisfaction is seen as a measure of outcome in its own right, being related to improvements in the users’ well-being and thus related to the effectiveness of services (Locker and Dunt, 1978; Fitzpatrick, 1991; Donabedian, 1992). In addition, a person’s satisfaction only comes from the use of a service. People can use services only when these services are provided. Moreover, how many services are provided is an indication of the degree of implementation by local authorities.

Anderson and Newman (1973, pp. 95-124) have proposed that the numbers of services used stem from an interaction of a person’s predisposition to use services, conditions enabling them to access needed services, and perceived needs (Schofield, 1998, pp. 132–133). These three factors can be described more fully as follows:  Predisposing factors include socio-demographic variables, such as age, sex, relationships, household composition and ethnicity, as well as attitudes towards health and formal services.  Enabling factors are the conditions that must exist to obtain services, regardless of a person’s predisposition or need. They include community resources, especially the availability of services, referral pathways, geographic location and family finances, social support, and knowledge of services.  Need factors, viewed as the most immediate cause of using services, are typically measured by the care-recipient’s health and cognitive impairment, as well as the carer’s stress, burden and intensity of care. In addition, care- recipients’ needs have been reported to be strongly correlated to the use of formal services. In the UK, research has found (Perring et al., 1990; Perry and Felce, 1995) that high cognitive impairment is linked to low use of home care services, while high functional disability is linked to the use of personal care, home nursing and therapeutic services.

All of these show not only that there is a significant connection between use of DCS and need, but also a connection between health care and social care in the formal care services and connections between elderly people characteristics or between their environments. Because there were very few outcomes of the home nursing care and therapeutic services under the National Health Insurance in

5 Taiwan2 and the connection between health care and social care was not included in the study, the questionnaire mainly focused on the correlation between needs and use of DCS in the social care field.

1.2 Methodology

This research is using the data of General Survey in Taiwan Province which was conducted from the September 1999 to the January of 2000. Because the use of DCS is the most important focus in this paper, not only is the use of DCS calculated according to total scores but users also are categorised into two levels: non-users, i.e. those who use none of the services, and users, i.e. those who use at least one item. These categories are respectively denominated 0 and 13. Elderly people who have used one item are in a significantly different category from those who have used none.

In addition, need for DCS is the second one that should be explored. An expressed need is a need that elderly people indicated that they believed they had. People are more likely to be defined as in need if asked to express it in this subjective way. It is a criterion that shows a real need at some level. In order to measure this subjective DCS need, each item of expressed need (Huang, 2003) scored as one point then all were calculated into sum or three levels. All of these were used to show differences and relationships between independent and dependent variables by cross-tabulation.

Moreover, one of the aims of this research was to examine the factors that affect the uses of DCS policy within community care policy. Logistic regression analysis provided a mean of tracing which factors affected the use of DCS. Two categories of use of DCS was subjected to logistic regression analysis in this paper.

2.0 Basic characteristics and need and use of DCS The use of DCS is the main focus of this section. Because expressed need is an important factor affecting the use of DCS, the frequency and the percentage of both expressed need and use of DCS are also presented. The variables of gender, age, and health were dealt with in the cross-tabulations in order to provide a complete picture of

2 In 1999, it has just been released by the National Health Insurance Bureau that the home nursing care and home therapeutic services were allowed to be covered by the NHI in Taiwan (National Health Insurance Bureau, ROC, 1999). This is why cases on public nursing services were very few by the time of conducting the survey of this research. 3 In this paper, in order to easily recognise who had been cared for by the formal sector, use of DCS was categorised into two. It might not reveal the situation, but it made it easier for us to conduct a cross-tabulation analysis and to be the dependent variable of the logistic analysis.

6 DCS use in Taiwan. Use of DCS and Need forDCS Generally speaking, most respondents (85.9%) used no formal domiciliary services. Relating to their uses, the range of services elderly people required was gauged from their answers to ten questions relating to the following specific services mentioned above (see Section 1.1). This shows that although many local authorities have declared that they put a lot of effort into social care and implement DCS policy well, in practice only a few respondents living alone have received services. Expressed need is a very important interval variable for people using services. Although it is essential to look at the dependent variable, the question of how many needs have been met is also very important. The survey shows that more than 36.7 per cent of respondents indicated a high level of need.

Basic characteristics and expressed need, use of DCS As expected, the cross-tabulation shows that people of different ages had different degrees of expressed need. The older the respondents were, the more need they indicated. Although this tendency was not very pronounced, it was statistically significant. In addition, more female than male respondents indicated that they had high need (Huang, 2003). If we look at the connection between health and expressed need we certainly find that respondents in ‘bad’ or ‘very bad’ health or ‘immobile’ also tended to express high levels of need. One interesting finding was that a slightly smaller proportion of respondents who were ‘immobile’ indicated that they were in high need than of those in ‘very bad health’ (58.8%, compared to 54.8%). The reason could be that they have already received more family support than those in ‘very bad’ health (Huang, 2003).

The basic characteristics variables give a good general profile of needs of DCS. It shows that the older, the less healthy and the females tend to have a larger proportion of high need. However, this difference is not reflected when actual service is examined. Although users were more likely to be the older respondents, there was not a larger proportion of females than males, nor of respondents in poor health than good. It is often argued that younger people who are in good health should not be the target group of DCS, owing to the financial limitations of local government. However, the data on use show that the proportion of users who were younger, male and in ‘good’ health was still quite large (see Table 2.1). Although DCS initially tried to help people who were in poor health, which is an objective criterion and easily defined by social workers in their assessments, the focus of DCS policy has been distorted in Taiwan.

7 Table 2.1 Basic characteristics and use of DCS (n=28542) Variables User or non-user CHI-SQUARE Age Non-user User P value (% within row) (% within row) Under 75 12362(86.9) 1859(13.1) 35.486*** P <=.000 75–84 10155(85.2) 1765(14.8) Over 84 1988(82.8) 413(17.2) Gender 1.225 Males 15256(86.0) 2476(14.0) P =.263 Female 9249(85.6) 1561(14.4) Health 6.284 Good 10607(85.9) 1737(14.1) P =.099 Bad 10858(86.2) 1744(13.8) Very bad 2370(84.4) 438(15.6) Immobile 422(87.0) 63(13.0) Note: * P< 0.05, ** P< 0.01, *** P< 0.001

An examination of use by respondents aged 85 and over who were not former soldiers shows that respondents in this group who were in ‘bad’ health or worse were a smaller proportion of DCS users than those in good health. The cross-tabulation even shows that the more frail they were the less they used DCS. The fact is, people in bad health do not have significantly more services at their disposal. This shows that the Central Government’s DCS policy in Central Government is distorted in implementation.

Whether they were users or non-users, females tended to express a higher level of need than males (37.9% of female non-users and 56.0% of female users had high needs, as against only 31.9% of male non-users and 49.2% of male users). It is reassuring that a higher proportion of users indicated high needs compared to non-users. When I conducted a further analysis of the group of non-soldiers aged 85 and over the same pattern emerged.

Those who are in good health are not in such great need and should be given a lower priority, given the financial restraints on social welfare in Taiwan. However, the percentage of respondents in good health who used the services (5.9%) was as much or more than the percentage of users who were in bad or very bad health, or who were

8 immobile (respectively 3.3%, 4.2% and 4.4%). It should also be noted that users of services in good health indicated a higher level of need than non-users in good health. As mentioned previously, the reason those in good health are users may be that they alone and without help, and need someone to care about them. On the other hand it may be that those who use services have a greater tendency to express their need, since for them the fact that they have got help is evidence that they need it.

However the survey clearly shows that the poorer a person’s health, the more need he or she expressed. Respondents whose health was ‘bad’ or ‘very bad’ or who were ‘immobile’ expressed high DCS needs (40.3%, 55.4%, 52.9% respectively) but made little use of services. However, as Table 2.2 shows, the relationship between felt needs (or expressed needs), objective needs and use is a complex one. However, taken together these indices provide evidence on the extent to which service provision is ineffective. Table 2.2 Health, expressed need and use (n=28542) LEVEL OF NEEDS CHI-SQUARE USE HEALTH No need Low need High need P value (%within row) (%within row) (%within row) Non-user Good 4493 (41.4) 3879 (35.8) 2468(22.8) 1444.492*** Bad (Needs 3062(27.5) 3586(32.2) 4488(40.3) P <=.000 occasional care) Very bad (Needs 530(21.7) 558(22.9) 1354(55.4) regular care) Immobile (Needs 136(31.1) 70(16.0) 231(52.9) intensive care) User Good 104(5.9) 914(51.7) 749(42.4) 194.246*** Bad (Needs 59(3.3) 771(43.2) 953(53.4) P <=.000 occasional care) Very bad (Needs 19(4.2) 89(19.7) 344(76.1) regular care) Immobile (Needs 3(4.4) 18(26.5) 47(69.1) intensive care) Notes: 1. * P< 0.05, ** P< 0.01, *** P< 0.001.

3.0 Family support, expressed need and use of DCS The family social situation may be the most important issues for older people living alone in traditional Taiwanese society. Elderly people expect to be respected in society

9 and to be cared for in the community, but, if they live alone, they also want the support of their children. In Taiwan the idea of being dependent on the next generation is still deeply ingrained in the minds of many elderly people. Many elderly people have borne children in order to make their old age secure. Within their peer group they regard being dependent on their children with a kind of pride. Most people in Taiwan who have children retain this feeling about dependency (Hu 1997, p. 27). If in their old age they do not enjoy the support of their children, they feel disgraced.

Location of children in relation to expressed need, use The survey shows that the location of children had very little impact upon either their propensity to express high need or upon their use of services. However, a slightly higher proportion of those without children indicated that they used DCS (15.5%).

An analysis of the group with high need shows clearly that a smaller proportion of people with children living in the same town have high need than other groups, whereas a larger proportion of both users and non-users with no children have high need than other groups, although there are still some differences between non-users and users (users tend to show their need highly). It is to be noted that a very large proportion of those respondents indicating high need had no children, and that even though those without children are the main target of DCS they get little use of DCS (43.3%) (see Table 3.1). It shows that given poor targeting and only small proportion of elderly people getting services there is bound to be a lot of unmet need. The distortion of DCS policy has clearly gone too far.

10 Table 3.1 Location of children and use of DCS among those expressing high- need (n =10232) Variables Location of Chi-square children User or Living in the same Living in different No children P value not towns towns (%within row) (%within row) (%within row) Non-users 1334 3316 3549 46.487*** (16.3) (40.4) (43.3) P <=.000 Users 287 695 1051 (14.1) (34.2) (51.7) Note: * P< 0.05, ** P< 0.01, *** P< 0.001.

Identity of primary carer in relation to use What kind of primary carer an older person has will influence their level of use of DCS. People who have no primary carer were a relatively small proportion of those indicating high need. This should be because a higher proportion of older people who have no primary carer are in better health. Not surprisingly, a high percentage of respondents whose carers are neighbours or friends indicated high need for public DCS. By contrast, those cared for by their own children constituted the highest percentage of respondents with no need. This indicates that where there is a support from children, there is less need for public domiciliary care services.

Basically, in the cross-tabulation of identity of primary carer to use of DCS, respondents with no primary carers showed the highest proportion of users, whereas those with children as their primary carers had the smallest proportion of users. As expected, those with friends and neighbours as their primary carers showed a moderate use of DCS. It is to be noted that although there is a slight tendency for those respondents with no carer to be more likely to get DCS (14.9%), a large proportion (85.1%) of those with no carer are still non-users.

Further analysis of the cross-tabulation of ‘identity of primary carer’ to use shows that amongst those who indicated they had high need, 21.9% of those with no carer get help, 18.1% of those with friends get help, 17% of those with neighbours get help, and 15.4% with children as carers get help. Evidently the DCS has had some success in order to have achieved this variation in the take up of its services.

11 Do those with friends and neighbours as their primary carers have a larger proportion with high need? As I conducted further analysis using cross-tabulation between ‘identity of primary carer’ and use of DCS in the group of ‘non-soldiers aged 85 and over with high need’, although the number of cases was not very great (n=889), 28.7% of those with no carers were users. In addition, 23.3% of those with neighbours as their primary carers and 19.3% of those with friends as their primary carers were users, but 17.1% of those with children as their primary carers were users. This shows a reasonable result although it is a very small difference.

In a further analysis of non-users in very bad health or immobile, only 521 cases were analysed, but it shows clearly that a larger proportion of respondents with no primary carer, and with friends or neighbours as their primary carers indicated high need than other groups. This showed that those non-users with no primary carer were basically healthier (as I conducted a further F-test analysis of health level among those non-users with no primary carer), yet if they become ill their DCS needs will tend to be higher. People in this category should be given a higher priority in the provision of DCS.

Different levels of social contact may also influence the degree of need. People lacking social contacts more often have higher levels of need. The survey showed that, no matter whether they used the services or not, those who indicated social contacts ‘very often’ were less likely to say they were in ‘high need’ as compared with all other responses. It is understandable that people who are not healthy might find it difficult to have social contact outside their homes, and indeed a slightly larger percentage of them use DCS than other groups. However, those non-users who had very few social contacts and few social contacts indicated that they were in high need (34.2% and 37.8% respectively), and 35.9% of those with ‘often’ social contacts indicated that they were in high need. People in these categories should be given a higher priority in the provision of DCS.

A hypothesis generally held regarding community care policy is that older people like to stay in their own community as long as possible rather than enter a home. This is certainly the case in Taiwan. In the 2000 survey (MOI, 2000, p.112), only 2.60 per cent of the population over 50 years old said they would like to enter a home after their retirement. Basically, high need leads to greater interest in entering a home than low or no need. A higher percentage of those with high need than with no need or low need were willing to enter a home. There are some different institutional needs between users and non-users of the service, as already mentioned, but both may need residential care, particularly as a result of physical disability. DCS users may need more help,

12 including residential care services; on the other hand, non-users may not have care available, in which case they may also need residential care services. In this survey, no matter whether they were users or non-users, most of the people surveyed were unwilling to enter homes. Moreover, there was a tendency for a slightly greater proportion of users than non-users to be willing to enter a home. In other words, contrary to the hypothesis that help reduces the wish to be institutionalised, there is little difference between users and non-users in the high need group. If anything, the results show that if there is high need the current DCS do not reduce elderly people’s desire to enter a home, but rather the opposite.

Use, expressed need, identity of primary carer and health Although respondents generally tended to indicate that they had some needs when they were visited, more respondents in bad or very bad health or immobile than those in good health said they were in need of DCS. If those in poor health were cared for by friends, neighbours or others but did not see anyone as their ‘primary carer’ their need for DCS was higher still. Only 15.3 per cent of those who were ill indicated that they used DCS, which is only 0.9 per cent more than healthy respondents. Again, this indicates that DCS policy has been distorted in some way. The government has used the poverty line as a criterion of need, whereas in fact health is a more important indicator. In addition, there were very large numbers in poor health expressing need who got nothing, regardless of their support system. That is of course a fundamental point running through all this part of the analysis. This analysis shows that some respondents who were in good health accessed DCS, but most who were in poor health did not. (see Table 3.2)

13 Table 3.2 Use and need to identity of primary carer among respondents with poor health (n=15675) User or not CHI- Need or not CHI- SQUARE SQUARE Identity of Non- Users P value No Need P value primary carer users need Nobody 6729(84.7) 1219(15.3) 75.303 1932(24.3) 6016(75.7) 74.430 *** *** Spouse 102(88.7) 13(11.3) P <= 33(28.7) 82(71.3) P <= .000 .000 Children 2027(89.7) 232(10.3) 630(27.9) 1629(72.1) Friends 1755(86.5) 275(13.5) 418(20.6) 1612(79.4)

Neighbours 2449(87.8) 341(12.2) 523(18.7) 2267(81.3) Other 414(77.7) 119(22.3) 122(22.9) 411(77.1) Notes: 1. Need includes those with low need and high need; 2. * P< 0.05, ** P< 0.01, *** P< 0.001

4.0 Logistic regression analysis of DCS use However, the question remains: how exactly do the independent factors predict the use of DCS? A further analysis using the logistic regression model helps to clarify the situation. In the last parts of this paper, the use of limited two or three dimensional analysis techniques has shown that no variables have a very marked impact upon use. There remains therefore a question whether a cluster of variables may not be seen to be more significant when taken in combination. A logistic regression analysis of use DCS was then conducted in this section.

The analysis in the model In order to assess the relationship between independent variables and dependent variables, all nineteen factors – including basic characteristics (Gender, age and health), income status, social and family support, and location factors – were subjected to a logistic regression model analysis. As a result of this analysis, in which the stepwise method of assessing the relationship between independent variables and dependent variables in the SPSS was used, the coefficient of R-Square in the model was 0.050. Nine variables were found to be significant in the regression model. Tables 4.1 and 4.2 present the results discussed in this section.

14 Table 4.1 Regression model of public care services used Model unstandardised coefficients Wald Sig. Exp(B) R-square B S.E. 1 Expressed need 2.742 .088 969.397 .000 15.519 .158 (yes=1, else=0) Constant -4.542 .085 2858.683 .000 .011 2 Expressed need 2.805 .088 1009.088 .000 16.524 .176 (yes=1, else=0) Urban or rural .703 .046 233.718 .000 2.020 (urban=1,else=0) Constant -4.974 .091 3002.052 .000 .007 3 Expressed need 2.779 .088 988.846 .000 16.104 .180 (yes=1, else=0) Urban or rural .706 .046 234.679 .000 2.025 (urban=1, else=0) Income status(from .324 .048 45.975 .000 1.383 government=1, else=0) Constant -5.159 .095 2924.207 .000 .006 4 Expressed need 2.779 .088 988.260 .000 16.096 .181 (yes=1, else=0) Age (85+=1, else=0) .279 .073 14.468 .000 1.322 Urban or rural .705 .046 233.891 .000 2.023 (urban=1, else=0) Income status(from .318 .048 44.181 .000 1.375 government=1, else=0) Constant -5.181 .096 2932.894 .000 .006 5 Expressed need 2.785 .088 992.392 .000 16.206 .181 (yes=1, else=0) Age (85+=1, else=0) .291 .074 15.623 .000 1.337 Urban or rural .696 .046 227.473 .000 2.006 (urban=1, else=0) Income status(from .315 .048 43.322 .000 1.371 government=1, else=0) Informal carer (yes=1, -.153 .046 11.074 .001 .858 else=0) Constant -5.116 .097 2760.201 .000 .006 6 Expressed need 2.779 .088 986.658 .000 16.096 .182 (yes=1, else=0) Gender (male=1, else=0) -.109 .046 5.539 .019 .897 Age (85+=1, else=0) .282 .074 14.710 .000 1.326 Urban or rural .696 .046 227.505 .000 2.006 (urban=1, else=0) Income status(from .318 .048 43.941 .000 1.374 government=1, else=0) Informal carer (yes=1, -.165 .046 12.688 .000 .848 else=0) Constant -5.042 .102 2432.540 .000 .006 Note: The dependent variable is use of DCS.

15 Table 4.1 Regression model of public care services used (continued) Model unstandardised coefficients Wald Sig. Exp(B) R-square B S.E. 7 Expressed need 2.776 .088 984.397 .000 16.050 .182 (yes=1, else=0) Gender (male=1, else=0) -.135 .047 8.177 .004 .874 Age (85+=1, else=0) .293 .074 15.801 .000 1.341 Urban or rural .699 .046 228.760 .000 2.011 (urban=1, else=0) Location of children (near -.175 .067 6.906 .009 .839 by=1, else=0) Income status(from .315 .048 43.128 .000 1.370 government=1, else=0) Informal carer (yes=1, -.149 .047 10.162 .001 .862 else=0) Constant -5.005 .103 2358.618 .000 .007 8 Expressed need 2.781 .088 987.383 .000 16.132 .183 (yes=1, else=0) Gender (male=1, else=0) -.132 .047 7.843 .005 .876 Age (85+=1, else=0) .297 .074 16.249 .000 1.346 Urban or rural .699 .046 229.144 .000 2.012 (urban=1, else=0) Location of children (near -.178 .067 7.150 .007 .837 by=1, else=0) Income status(from .316 .048 43.512 .000 1.372 government=1, else=0) Enter a home (yes=1, -.199 .086 5.390 .020 .820 else=0) Informal carer (yes=1, -.153 .047 10.637 .001 .858 else=0) Constant -4.995 .103 2347.130 .000 .007 9 Expressed need 2.785 .089 989.425 .000 16.196 .183 (yes=1, else=0) Gender (male=1, else=0) -.173 .052 11.199 .001 .841 Age (85+=1, else=0) .303 .074 16.835 .000 1.354 Urban or rural .691 .046 222.196 .000 1.996 (urban=1, else=0) No children (no children=1, .107 .055 3.822 .051 1.113 else=0) Location of children (near -.134 .071 3.619 .057 .874 by=1, else=0) Income status(from .303 .048 39.242 .000 1.354 government=1, else=0) Enter a home (yes=1, -.206 .086 5.783 .016 .814 else=0) Informal carer (yes=1, -.151 .047 10.456 .001 .860 else=0) Constant -5.018 .104 2333.581 .000 .007 Note: The dependent variable is use of DCS.

16 The following Table 4.2 shows the full regression model, fourteen factors were taken into account, but only nine factors were clearly significant.

Table 4.2 Full regression policy model of dependent DCS use Positive or negative Wald value and sig. Model effects X1 Basic characteristics X11Ages(85+=1, else=0) Positive 16.835*** X12 Gender (male=1, female=0) Negative 11.199*** X13 Health (healthier=1, else=0) X2 Income issues X21 Income sources X22 Income status (Government income Positive 39.242*** support=1, else=0) X 3 family and social contacts X31.1Location of children（same town=1, Negative 3.619* else=0） X31.2Location of children（different town=1, other=0） X31.3 No children (no children=1, else=0) Positive 3.822* X32.1 No carer=1, carer=0 X32.2 Informal carer (yes=1, else=0) Negative 10.456*** X33 Social contacts (very often=1, very few=4) X34 Enter a home (yes=1, no=0) Negative 5.783** X4 Location issues X41 Urban or rural (urban=1, else=0) Positive 222.196*** I1 Expressed need Positive 989.425*** Note: * P< 0.05, ** P< 0.01, *** P< 0.001. Discussion about the logistic regression model analysis As mentioned previously, by looking at the cross-tabulation between variables, I found that many independent variables made a significant difference to dependent variables and between variables. However, there is no evidence that the independent variables had any effect on the dependent variable, use of DCS. Therefore, I decided to conduct a logistic regression model analysis not only to analyse the correlation between independent variables and dependent variables, but also to show how many factors

17 affected the dependent variable, use of DCS, and how strongly they did so. This is a model designed by myself in which I use standard common-sense criteria to suggest what should be the key variables if there were a good fit to the use of DCS. Having analysed all these data of the 28542 cases to show the effects of different factors or independent variables, the logistic regression model produced a very successful and provable model showing which factors were most important. A complete picture emerged and I was able to discover the practical outcome of DCS policy in terms of users. The evidence that DCS policy had not only been ineffective but also distorted had been more precisely and significantly confirmed.

The variables selected in the logistic regression model Basically, a logistic regression model analysis is intended to show how many independent variables affect the dependent variable. The use of DCS is the dependent variable in the present analysis. It was illustrated as follows:

1. The first variable identified by the analysis was the degree of the elderly person’s expressed need. The need for DCS of older people had the greatest positive effect in the model. It contributed up to a 0.158 coefficient of R-Square. This indicates that the more need users had, the more DCS they used, and it also shows that although a very small proportion of elderly people used the care services, the care services supplied roughly reflected this need.

2. The second variable – urban or rural – also significantly affected the dependent variable. It brought the coefficient of R-Square up to 0.176 positively. Although local governments emphasised that they supplied greater care services to rural areas than others, this figure still shows that elderly people living alone in urban areas generally got more public care services than those living in rural areas.

3. The third variable – the main income status – was selected and made the coefficient of R-Square 0.180 positively. The main income source for 47.13 per cent of the elderly population was that derived from their children (Ministry of the Interior, 2000, p. 33). However, the figure was very different for the elderly population living alone. Only 6.7 per cent of the total elderly population surveyed were regularly supported by their children, while 12.1 per cent were supported irregularly. This positive correlation perhaps suggests that those people whose main income was from a job, a retirement lump sum, a pension or a savings account received less care services, while those whose main income was supplied by friends or the government got more. In addition, this might demonstrate that the wealthier

18 they were, the less they used the public care services, no matter how much they objectively needed them. 4. The fourth variable selected was age (age 85 and over), which brought the R-Square coefficient up to 0.181 positively. This shows that the older people (age 85 and over) used more DCS. Although health had not been taken into account in forming DCS policy, age was something that had been considered in the delivery of a public DCS system, for example, the older elderly people—age 85 and over were to be supplied special money and care in some counties and cities in Taiwan.

5. The fifth variable selected was informal carer, which kept the R-Square Coefficient at 0.181 negatively. This shows that those who had informal carers (for example their family members or friends) used less DCS.

6. The sixth selected was gender, which made the R-Square coefficient 0.182. Because they were negative correlation, it was revealed that those who were males got less DCS. The result indicates that although more males were surveyed than females, less of them used the public care services. Females used the DCS more because they were more inclined to reveal their difficulties and get help from their families.

7. The sixth variable selected was location of children (children living in the same town). It brought the R-Square coefficient up to the significant figure of 0.183 negatively. Generally speaking, the nearer elderly people lived to their children; the less they used the public DCS because of this family support. It might show that ‘primary groups such as children and relatives supply resources that are more available and acceptable than those of friends or neighbours’, or that ‘the nature of task relates to the structural characteristics of the family, friendship, neighbour groups and the formal care services’. 8. The eighth variable selected was enter a home. It raised the R-Square coefficient to 0.183 negatively. This shows that people who were willing to enter a home used less DCS. The emphasis of community care policy is on helping elderly people to stay in their own communities as long as possible. Those who indicated they were willing to enter a home should be given more public care services. Contrary to expectation, they used less DCS oppositely. It shows that the policy does not fit the need of old people who are living alone, because its focus is on income status. 9. The ninth variable selected was no child (no child=1, else=0). It made the R-Square coefficient slightly higher positively. This shows that people who had no children got more DCS. This indicates that the DCS policy reflect this situation basically,.

19 In the analysis, independent variables – health, employment, identity of primary carer (no carer=1, else=0), and social contacts – had no significant impact on whether or not they got DCS. A service effectively targetted towards need should surely show a very positive impact for poor health and for the absence of a carer or social contacts and negative impact for employment.

First priorities in an ideal model The priorities that I am offering derive from my literature review and work experiences. The first priorities in the ideal model would be health, age, enter a home, income status, and expressed need of DCS. The most important of these is health because the main purpose of the DCS is to serve people in frail health. Elderly people whose health is poor are in objective need. These are the people the DCS was designed for. The second most important priority is age. Basically, the older an elderly person is, the more frail he or she is likely to be. It can be said that age is important because of its effect on health. The third priority is the willingness to enter an institution. Elderly people who express a need to enter a home tend to be in a worse state than others, because in Taiwan to enter a home is generally considered a disgrace. (Although this is certainly the case in the traditional southern parts of Taiwan, the subject is a controversial issue among researchers) (Chao, et al. 2000; Hu, 1997; Ministry of Interior, 2000). They must find that, because of their physical situation, they cannot cope with their daily lives any more. The fourth priority should be income status, because the public DCS ought to provide the services to the most disadvantaged groups. The fifth one should be an expressed need of DCS, because the DCS policy implementation affects people in need whether they used DCS or not. If the willingness to be served were not there, people would refuse any services from government even though they were really very frail and in serious need. Elderly people in Taiwan have difficulty in trusting strangers, even though DCS workers show their identification. They tend to be suspicious of workers until they see them for the second or third time. These five variables are the most important factors in ensuring the best use of public DCS.

Results of the output of DCS in the model The regression model analysis shows that only two variables within my list of criteria for high priority - expressed need, income status (government support=1, else=0) and ages (age 85+=1, else=0) were included in the DCS use policy model. It shows that DCS policy hitherto has been distorted, except in its emphasis on expressed need, which might not reflect objective need (see Table 4.3).

20 Table 4.3 Research results of hypotheses test relating to the dependent variable, use of DCS Independent variables Priorities of Sig. of evidence Slope posited Sig. of evidence ind. var. in an after testing in hypotheses after testing ideal model in hypotheses Basic characteristics X1 X11 Age First 4 Positive Positive X12 Gender (male=1) 6 Positive or Negative negative X13 Health First Positive No significant Difference

X2 Income issues X21 Income sources Negative or No significant Positive Difference X22 Income status First 3 Positive Positive (governments supports=1) X23 Employment Negative No significant Difference Family and social X3 contacts No child (yes=1, Positive Positive X311 9 else=0) X312 Location of children 7 Negative Negative (same town=1) X313 Location of children (in Positive No significant different town=1) Difference X321 Identity of carer 5 Negative Negative (informal carer=1) X322 Identity of carer (no Positive No significant carer=1) Difference X33 Social contact (very Negative No significant often=1) Difference X34 Institutionalisation First 8 Negative Positive (yes=1)

21 Location issues X4 X41 Urban or rural (urban 2 Negative Positive =1) First Positive Positive I1 Expressed need of 1 DCS

Although a very small proportion of the elderly population used care services, the care services supplied roughly reflected their expressed need. Expressed need, however, is only subjective, and people’s real need is related to their health rather than their income levels or subjective needs. Clearly, those respondents who had health problems, and who had no primary carer were in objective need. However, the regression analysis shows that these have no impact upon the likelihood of obtaining services. These variables were excluded in the contemporary DCS delivery care policy model, especially the most important one – health – which had not been selected in the regression model. This demonstrates the mistakes that have been made in policy.

5.0 Conclusions The study has shown that only a small proportion of elderly people living alone use public DCS, and that many of those who are most in need are not benefiting from the service after five years of the community care policy being implemented since 1995. It is noteworthy that although women enjoy certain advantages over men, such as more often having children nearby and more care support from children, a larger proportion of them belong to a 'disadvantaged group' defined by low income, poor health, an age over 84, and the lack of a primary carer. The needs of elderly females will inevitably increase over the next few decades, and government policy will need to take them into account.

While a rough correspondence was found between elderly people's expressed need and their use of DCS, there are many with objective needs who are being overlooked. These objective needs should be assessed on the basis of the age of the lone elderly, the presence or otherwise of a primary carer in their family or local community, the extent of their social contacts, their willingness to enter an institution, their income status and the state of their health.

Owing to financial constraints, local authorities too often restrict eligibility for DCS to those elderly people who are currently receiving government subsidies. Important though their income level is, an effective DCS delivery system must take into account

22 other measures of need, particularly health. A more complete picture that reflects on the effectiveness and efficiency of the local authorities emerged through the process of adding the regression model analysis. I was able to discover the practical outcome of DCS policy in respect of the needs of users. The evidence that DCS policy had not only been ineffective but also distorted had been more precisely and significantly confirmed.

Recently, Taiwan Central Government has set up a subsidy system to help people above 2.5 times of the poverty line. According to this document, these elderly people living alone are entitled to obtain the DCS for 7 days or 14 days for free. All these expenses are granted by Central Government. It basically reflects this distorted policy. However, this is only for those people whose income over 2.5 times of poverty line but not for those income under 2.5 times of poverty line. It has been a great burden for local authorities to provide DCS to those people whose income under 2.5 times of poverty line since Central Government decided not to subsidise local governments after 2000. Although Central Government has amended the Law of Financial Distribution between Central and Local Governments that made some of Central Government budget could be distributed to local authorities, many local authorities still have to try to cope with it by reducing the percentage of subsidies to those people whose income between 2.5 times and 1.5 times of poverty line (i.e. paying only 2 in 3 in order to save money). The problem is that under the shortage of budget in local authorities, what should be done with these people? In addition, what should be done with people above 2.5 times of the poverty line and what a disaster if Central Government decides not to subsidise this budget?

In this research, the author suggests that all the top priority variables - health, age, income status, expressed need of DCS and focusing on the target groups—the elderly people under 2.5 times of poverty line, but not those people above 2.5 times, should be taken into account in the future DCS policy.

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