2009–2010
ERSTE Foundation Fellowship for Social Research Ensuring Income Security and Welfare in Old Age
Social Support Networks, Functional Abilities and Services Usage among Urban and Rural Elderly People in Poland Zyta Beata Wojszel
SOCIAL SUPPORT NETWORKS, FUNCTIONAL ABILITIES, AND SERVICE USAGE AMONG URBAN AND RURAL ELDERLY PERSONS IN POLAND Zyta Beata Wojszel, M.D., Ph.D. Department of Geriatrics Medical University of Bialystok Kilinski str. 1 15-089 Bialystok Tel.: (085)8694982 Fax : (085)8694974 E-mail: wojszel@umwb.edu.pl
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SUMMARY Aim of the study The purpose of this study was to examine the structure of social support networks for older people (75+ years old) in chosen urban and rural areas in Poland and the relationships between social support networks, functional abilities and service usage. Methods Two random-quota samples based on a gender and age structure of communitydwelling older people took part in the questionnaire study. The urban sample (N= 256) was selected from persons who lived in the city of Bialystok, and the rural sample (N=253) was selected from persons who lived in the neighbouring rural community of Sokolka. Data collected included: socio- demographic data; health outcomes (i.e., subjective well-being, functional disability level, cognitive ability, and emotional status); characteristics of social support networks (i.e. size, frequency of contact, and community ties – using the Practitioner Assessment of Network TypePANT); and usage of medical and social services during the last year. Data were collected in face-to-face interviews with elderly persons in their homes. A multivariate standard regression analysis was performed on functional disability levels and a logistic regression analysis was performed on the usage of formal and informal services supporting in ADL as outcomes with SPSS Package. Results The two most prevalent support network types for 75+-year-old people in both studied areas were “family dependent” and “locally integrated”. Men significantly more often than women were a part of a “local self-contained” network”, and women significantly more frequently were a part of “a locally integrated network”. 80+-yearold people significantly more often than younger people had a “family dependent” social support network. Respondents with the “local family dependent” support network, and to a lesser extend with the “private restricted” network, were less likely to be in good health and good functional ability compared with the other types of networks – first of all, with the “locally integrated” network. The higher functional disability level of the respondents in multivariate regression analysis was independently associated with age, living with others, cognitive impairment, being homebound, visual and hearing impairments as well as with having a “family dependent” social support network compared to other network types. Respondents with “the local family dependent” support network were more likely – and with “locally 2
integrated� less likely – to use formal and informal support in ADL and other social services compared with other types of networks, but in multivariate logistic regression analysis both of the above mentioned support networks predicted usage of these kinds of services, suggesting better capacities to cope with the frailties of old age than in other types of support networks. Conclusion: The study supports other authors’ findings that the network type is associated with health outcomes (i.e., subjective well-being, cognitive ability, depression, disability level) and resource usage (such as formal and informal services supporting in ADL/ other social services). Key words: social support networks; community-dwelling older people; health outcomes; services usage; urban- rural differences
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INTRODUCTION The projected increase in the number and proportion of older people in Poland [GUS, 2004; United Nations, 2003] has implications for both the family and our society as a whole, particularly with respect to long-term care. Older people in Poland are more likely to suffer from chronic conditions and to be limited in their daily activities [GUS, 2006]. The capacity that older people have to cope with life and its problems is related to the structure and content of their social networks [Wenger and Tucker, 2002]. Assessment of the strength of older people's support network has been recognised by policy-makers as important for predicting community care outcomes [Faber and Wasserman, 2002]. The social network of a given person is formed by all people with whom he/she is currently in some type of relationship, resulting in him/her becoming part of certain social groups as well as society as a whole. In the case of support networks, it is primarily those people who can be counted on when in need of help, support, and advice. These are people connected to the elderly person in a particular way – household members, companions, people providing emotional support, personal assistance, services, giving advice, as well as people receiving similar assistance from an older person (mutual assistance). Crucial features of a support network are: the people (i.e. what people constitute the network), the amount of members and the proximity of their places of residence. However, the people that are important are not only those residing the nearest, but also those that can quickly arrive with help, even through phone contact. It is known that the way a person needing help acts to a great extent depends upon the type of support network that surrounds him/her and which could possibly be used, if necessary. In recent national gerontological studies completed in the year 2000, some elements of the family situation and those characterising the social integration of older people – such as social contacts outside the family, leisure time activities and religious activity, the structure of caregivers taking care of elderly patients at home – had been assessed [Synak, 2002]. Unfortunately, each of these elements was analyzed separately and no attempt had been made to create a typology of support networks taking into account all the characteristics constituting them. As it turned out the family, despite changes occurring in it’s structure, is still a primary source of support for the disabled older people in Poland. It was shown that women more than men cultivate social contacts, although a stronger pro-societal orientation through 4
very active involvement was observed in the men. A large religious activity of older Poles was confirmed, especially in the rural environment. This was manifested, among others, through active participation in prayer groups. These are groups most frequently created at parishes, which on the one hand have religious purposes (common prayer and active participation in religious events) and on the other play an important role in supporting members of the group, when necessary. In Polish gerontological studies, to the knowledge of the author, no attempts have yet been undertaken to create a typology of support networks of older people, especially people of advanced age. There are various models and typologies of informal support networks. The PANT (Practitioner Assessment of Network Type) instrument developed by C. Wenger identifies the support network of an older person based on such elements as: having a relative within close proximity, the intensity of contacts with family, friends and neighbours, as well as interactions at wider social group levels including non-governmental organisations [Wenger, 1994b]. Using this tool in Polish studies will allow comparing the results obtained with the results of other countries. PURPOSE The purpose of this study was to examine the structure of social support networks of older people (75+ years old) in chosen urban and rural areas in postcommunist Poland, and the relationships between social support networks, the functional abilities and service usage among them. Consistent with the literature, it was hypothesized that network type will be associated with health outcomes (i.e., subjective well-being, cognitive ability, and disability level), and formal resource usage. The relationship between network types, health outcomes and service usage was to be examined, controlling for chosen characteristics of older people, such as age, gender, education and place of residence (urban- rural locations). METHOD The study design was a cross-sectional questionnaire study and survey performed at the end of 2007 and at the beginning of 2008. It was a repetition of the cross-sectional study carried out in 2000 [Bień et al., 2001], which was designed to evaluate trends in changes taking place in the situation of older people in the studied areas [Wojszel, 2009]. At the same time, it enabled taking on new research issues, 5
including the assessment of the typology of support networks of people of advanced age. The population studied lived in two chosen areas (urban - Bialystok city, and rural- Sokolka community) of the Podlaskie region in Poland. The Podlaskie region is one of the demographically oldest Polish provinces. Persons aged 65 years or more constitute 14.7% (12.1% in cities and 18.5% in villages), and over 75 years of age, 7.1% (5.2% in cities and 9.5% in villages) [GUS, 2008]. Bialystok is one of the largest urban agglomerations of north-eastern Poland with about 300,000 inhabitants. It is the administrative, economic, scientific and cultural centre of the Podlaskie region. Sokolka municipality is one of ten typical agricultural municipalities of Sokolski County. The findings of this research can be generalized for other areas with similar characteristics. The random-quota samples based on gender and age structure included community-dwelling older people aged 75 years and over. The face-to-face interviews were collected in older people’s homes by trained interviewers of the Research Institute of the Polish Sociological Association. When the eligible respondent could not be interviewed, often because of medical or cognitive problems, a proxy - the caregiver - was enlisted to answer questions for that person. The complementary questionnaire interviews were collected with carers of older people in the case of their disability. The survey tool included questions about: 1/ elderly persons’ sociodemographic characteristics (i.e., age, gender, marital status, place of residence: urban/rural, and way of living: alone/with others, level of education, self-rated economical status); 2/ health status related items (self-rated health, the occurrence of chronic illnesses of 16 listed, number of drugs taken every day for at least 3 months, visual and hearing impairment, mobility impairment), and a functional assessment of the respondent with geriatric functional ability scales; 3/ characteristics of social support networks (i.e., size, frequency of contact, and community ties - using C. Wenger’s network assessment instrument - PANT [Wenger, 1994b]); and 4/ questions about usage of chosen medical and social services during the last year. The following instruments were used to rate the respondent’s functional ability: • Assessment of activities of daily living (ADL) with a scale of the EASY-Care system [Philp I, 1996; Wojszel et al., 1999]- constructed on the basis of ten ADL activities of the Barthel scale [Mahoney and Barthel, 1965], combined with the six-item 6
instrumental activities of daily living (IADL) scale derived from the Duke OARS assessment [Fillenbaum and Smyer, 1981], plus an additional item on mobility. The “functional disability index” was formed on the basis of its items, which were all recoded (1=not able or able with help/ incontinent, where appropriate, versus 0=able without help/ continent, where appropriate; in case of sporadic urinary incontinence the respondent was classified as “continent”). The score on this index could range from 0 to 17 (the highest number of activities for which the elderly person needed help). • The mobility scale [Piotrowski, 1973] – placed the respondents into one of four groups. Group I – persons able to walk freely at home and outside the home; group II – persons walking freely around the home, but having difficulties moving outside the home; group III – persons able to walk around the home, but who cannot move outside the home; group IV – persons who are bedridden, in a wheelchair, or confined to an armchair. People belonging to the third and the fourth groups were classified as “home-bound”. • The short orientation-memory-concentration test- a questionnaire rating cognitive status [Katzman et al., 1983]. The test results were on the following scale: from 0 to 10 points showed a normal state or mild impairment of the respondent’s cognitive functions and above 10 points (maximum 28 points) - a moderate or a serious cognitive impairment. The complete test was performed on 196 (76.6%) urban and 204 (80.6%) rural respondents. In some cases, the results of the test, albeit incomplete, allowed to classify cognitive ability of the interviewed person. 48 of the respondents (9.4% of the total group) did not agree to perform it at all, and in 44 cases (8.6%) it was not performed because of the respondent’s poor health status. In the next 3 cases, the interviewed person was taking pro-cognitive drugs and these were considered dementia cases (diagnosed and treated). Respondents whose carers reported memory problems as one of the main reasons for caring for the older person, and with behavioural disturbances (3 or more points on the BISID scale [Keady and Nolan, 1996] performed during the interview with the carer) combined with serious ADL impairment (5 or more points on the functional disability index, as above), were also considered to have severe cognitive impairment. This situation was most often observed in respondents 80+ years old. In multivariate analyses of data, the corrected variable “dementia” - including the above mentioned cases - was used. 7
• The Geriatric Depression Scale (GDS) [Yesavage, 1988] – graded the emotional state of the respondent in two stages: from 0 to 5 points as a normal emotional state, and a suspected state of depression with a rising tendency from 6 to15 points. In 23 cases, the results of the test, albeit incomplete, allowed to classify the emotional status of the interviewed person. The corrected classification of the respondents was more often observed in rural respondents, having some difficulties answering certain GDS questions. In two cases, the respondents declared depression recognized by a doctor and in another one, the respondent - who did not complete the test - was taking antidepressants. These cases were also considered “depression”. In multivariate analyses of data, the corrected variable “depression” was used. The Practitioner Assessment of Network Type (PANT) was used to identify the social support network of older people [Wenger, 1994b]. It makes it possible to identify the type of network on the basis of responses to eight pre-coded questions. The typology of social support networks, developed by C. Wenger, has been shown to have a predictive value in identifying those who are more likely to be at risk [Wenger and Tucker, 2002]. The respondents were also asked about the usage of formal and informal services during the last 12 months. In the case of formal medical services, the usage of hospitalisation, home visits from a GP, home visits from a nurse, and usage of rehabilitation services were assessed. In the case of social services, the respondents were asked about the usage of services supporting in ADL and other social services. Statistical analysis of the data was conducted using the Statistical Package for the Social Sciences 12.0 [SPSS, 2003]. A Chi-square test of independence and the Mann-Whitney test (for continuous variables) was used to examine rural-urban differences in older person characteristics. Two tailed U-test for two frequencies was used to examine the differences in social support network typology share in the group of people without or with different characteristics. Differences in continuous variables and scale scores (such as Geriatric Depression Scale, cognitive impairment test, functional disability index, and number of chronic conditions) between groups could be not analysed using the t-test, since the scores were not normally distributed. The distribution of variables was tested using the Kolmogorow-Smirnow test. A multivariate standard regression analysis was performed on the “functional disability index” as outcome to assess the relationships among variables in the model, and to examine if the social support network types are independent variables predicting 8
functional disability. A direct logistic regression analysis was used to find independent variables influencing the “usage of formal and informal services supporting in ADL/other social services” in the group studied, and to assess the predictive value of the support network typology. For the regression, service usage was grouped into two categories: “no usage in the past year (=0)” and “usage in the past year (=1)”. For all tests of significance, a p-value of less than or equal to 0.05 was considered the minimum level of significance [Stanisz, 2001]. The study was approved by the Ethics Committee of the Medical University of Bialystok. During result analysis of the realized studies and while drawing conclusions, one should be aware of certain limitations associated with the research methods used. First and foremost, both the evaluation of efficiency and the occurrence of chronic illnesses were based on an interview with the older person or his/her guardian. In the case of occurrence of chronic illnesses, only people aware of their state (that is, those who have been diagnosed) could provide an answer in accordance with the actual state. Similarly, the appraisal of overall efficiency with the help of the utilized scales has its limitations. It should be noted that there is an occurrence of differences in this area between the declared and the observed during particular tasks being carried out [Kempen et al., 1996]. The fact that the realized research was cross-sectional, providing a statistical description of the situation, should be taken into consideration. Only longitudinal research can explain the causeand-effect relationship of influences and determinants. RESULTS 509 community dwelling older people aged 75 years and over (253 from a rural area and 256 from an urban area) took part in the study. Selected demographic and health-related characteristics of the respondents in the two areas studied are summarized in Table 1. Most of the respondents in both communities were women. A percentage of the older subgroup (80+-years-old) was higher in the rural area (57.7% versus 46.9% in the urban one), but the average age of the elderly examined in the urban and rural areas was similar, as well as gender and marital status structures. Differences in age structure between the male and female groups in the urban and rural areas were not statistically significant. There were significant differences observed between the urban and rural areas in the percentages of people living alone (37.9% in Bialystok city versus 28.4% in Sokolka community, p<0.05), with 9
secondary or higher level of education (44.5% in urban population and only 2.9% in rural population, p<0.0001), and people assessing their economic status as bad (19.5% in the city and 36.9% in the rural area, p<0.001). Respondents from the rural area, significantly more frequently rated their health status as bad (50.6% versus 28.4% in the urban sample, p<0.001), and reported visual and hearing impairments. The rural sample was also characterized by higher scores on the functional disability index and the EASY-Care scale, as well as significantly lower numbers of chronic conditions reported and the number of drugs taken every day. Rural respondents were also more often classified as depressed and as cognitively impaired. No differences between older people in the rural and urban areas were noted in the percentages of homebound people. The mean scores on the Geriatric Depression Scale and on the short cognitive impairment test were significantly higher in the rural sample. There were no significant differences between urban and rural areas in the frequency of reported hospitalization in the last 12 months. The average number of stays, estimated for the group of people who used them in the course of the past year (n=128), came to 1.51 (±0.8) and was similar for both areas. During the course of the last 12 months, 27.5% of the elderly people in the urban area and only 16.7% in the rural area (p<0.01) declared taking advantage of doctor home visits. The average number of doctor home visits, estimated for the group of people who took advantage of them in the course of the past year (n=122), came to 2.71 (±3.1), and in this case there where no significant differences between the places of residence. During the course of the last 12 months, 10.2% of the elderly people in the city and as many as 30.4% in the countryside (p<0.01) declared taking advantage of community nurse home visits. The average number of community nurse home visits, estimated for the group of people who took advantage of them in the course of the past year (n=103), came to 6.22 (±7.4) and was similar for both places of residence. The elderly people residing in the city significantly more often reported taking advantage of physiotherapy (8.6% versus 1.6% in the countryside, p<0.001). An insignificant percentage of survey respondents contacted a social service employee during the course of the last year – slightly more often for rural residents, but the difference was not statistically significant. The survey respondents were asked if in the course of last year they received help in the form of social services and services supporting in ADL, as well as about 10
the sources of received help. 54.7% of respondents in the city and 61.7% in the countryside declared receiving such help. For the most part, the help was provided by family members (90% of people in the city declared receiving help and 98.7% in the countryside, p<0.01), less often from friends/neighbours (15% of people declared receiving help in the city and 2.6% in the countryside, (p<0.001). There was insignificant assistance from governmental social aid institutions (1.4% in the city, 0% in the countryside), non-governmental organizations (1.4% urban, 0.6% rural) and other sources including privately financed help (2.9% in the city and 2.6% in the countryside). That is why in the analysis of determinants of taking advantage of these types of services, the total support received from various sources was included (in most cases, it was informal help â&#x20AC;&#x201C; from family neighbours and friends). Table 2 shows (in percentages) how the sample responded to each of the 8 PANT statements. The significant differences in the number of positive responses to some of the questions between rural and urban areas were noted. As can be seen, the elderly living in rural areas significantly more often had a family member living in close proximity. These differences also referred to having children, siblings or other family members living with the older person or not far away. As it might have been expected, the frequency of contact with family members was significantly greater in the case of the surveyed elderly people residing in the rural area. The informal support network, apart from family, is formed by acquaintances, friends, and neighbours. The older people in the rural area significantly more often than the elderly living in the city responded that they never see friends, because they do not have any. The survey respondents in the rural and urban areas answered alike to the question about the frequency of contact with their neighbours â&#x20AC;&#x201C; 6.7% older people in the city and 8.9% in the countryside were not in contact with their neighbours. However, about a quarter of them were in contact with their neighbours on a daily basis, and 50.8% of the people in the city and 42.7% in the countryside claimed such contacts at least once a week. Frequent sources of support for the elderly are religious groups. Older people in the city significantly more often claimed that they never take part in religious meetings such as oasis community meetings, rosary groups, prayer meetings etc. (mass attendance not included). More frequently, especially in the city, nonparticipation in such meetings was declared by the older representatives of the 80+year-old subgroup. In the case of the urban environment, this was declared by men 11
significantly more often. Older people in the countryside claimed participation in meetings of various social/neighbourhood groups significantly less often. In the city, attendance of such groups regularly was declared by 6.5% and occasionally by 8.9% of the surveyed. Significantly more frequently, such regular activity was claimed by women and people from the younger subgroup of old age. In the case of survey respondents in the countryside, only 4.1% claimed occasional participation in such meetings, more often people who belong to the younger group. Answers to all PANT questions were given by 466 respondents (91.6% of the whole group studied). A support network could be identified among 82.5% of them (Table 3). In the studied areas, two types of networks were found the most frequently: family dependent social support network (significantly more frequent in the countryside - 41% of cases versus 30.8% in the city, p<0.05), as well as locally integrated social support network (31.6% of cases in the city and 32.8% in the countryside). It is worth mentioning that significant differences between gender and age groups in the urban environment were observed (data not presented). Men significantly more often than women were a part of a local self-contained network, and women significantly more frequently were part of a locally integrated network. 80+-year-old people in the city significantly more often than younger people had a family dependent social support network, and people from the younger subgroup of the age - locally integrated network. In the case of the elderly in the countryside, no significant differences were observed between gender and age groups. A high percentage of so-called unclassified support networks (15.5%) was primarily connected with a high share of situations from the “borderline” of particular types of support networks. In 13.8% of cases, borderline between two category network types were identified (Table 4). This situation may reflect the fact that the network is shifting from one type to another, or it may just have characteristics of two types. The most frequent borderline types were “locally integrated- family dependent”, and “private- family dependent”. In 1.7% of the cases, the networks could not be classified because of contradictory data (inconclusive cases). The distribution of support networks in different subgroups of the studied individuals are presented in Table 5 (socio-demographic variables), as well as Tables 6 and 7 (health characteristics). The family dependent support network was observed more frequently in older (80+ years old), living in the rural area, living with others, and less educated (primary or lower level of education) subjects. They were more likely to 12
have different health problems and functional disabilities: rated their health status as poor, with cognitive impairment, with suspected depression, homebound, with visual impairment, and with urinary and bowel incontinence. Respondents with the locally integrated support network were more likely to be female and living alone. This type of network was observed more frequently in people assessing their health status as good or average, without cognitive impairment, without disturbances in emotional status, without mobility and visual impairment, continent. The local self-contained network was observed more frequently in male, married, and living alone respondents.
No
differences
between
the
analysed
socio-demographic
characteristics were observed in the case of a wider community focused network. This support network was observed more frequently in respondents declaring visual impairment. Private restricted network respondents were more likely to be living alone, rating their health as poor, with suspected depression, and homebound. Unclassified cases were more often married/ cohabiting, and no other differences were observed. The local family dependent and private restricted respondents were characterized by significantly higher scores on the functional disability index, suggesting a more severe dependence in activities of daily living, as well as the geriatric depression scale scores, suggesting more severe problems in mental health (Table 7). The multivariate regression analysis was used to find determinants of functional disability in community-dwelling older people. The total score of the functional disability index was used as a dependent variable and the 17 additional predictors were included in the model as independent variables. The results of the standard multivariate regression analysis of the model are presented in Table 8. Together, the independent variables included in the model accounted for 65.9% of total variance of the functional disability index (N=355, adjusted R2 = 0.659, SE 2.4, F = 41.445, p<0.001). A higher score on the index was significantly and independently associated with older age, not living alone, cognitive impairment, being homebound, visual and hearing impairment, as well as with having the local family dependent support network (compared to other types of networks). There were no differences between the analysed support network types in using different health and social services, apart from â&#x20AC;&#x153;home visits from a GPâ&#x20AC;?, which were significantly less often observed in the locally integrated support network respondents, as well as â&#x20AC;&#x153;formal and informal support in ADL and other social 13
services” was significantly more frequently observed in the family dependent support network and significantly less frequently observed in the locally integrated network (Table 9). In the bivariate logistic regression analyses, the family dependent support network was associated with a significantly higher and the locally integrated network with a significantly lower risk of using these services (Table 10). But in multivariate logistic regression analysis on the “usage of services supporting older people in ADL and other social services”, while controlling for socio-demographic and health variables, both of the above mentioned support networks predicted the usage of these services (Table 11). A direct logistic regression analysis was performed on the usage of services as outcome and 19 attitudinal predictors (5 demographic, 9 health status, and 5 social support network variables). Because of missing data, only 354 cases were available for analysis, 158 of “no usage of services” and 196 of “usage of services supporting in ADL/ other social services”. A test of the full model with all nineteen predictors against a constant-only model was statistically reliable, χ2 (19, N=354)=100.531, p<0.001, indicating that the predictors, as a set, reliably distinguished between subjects “using services supporting in ADL/ other social services” and “not using them”. The variance in usage of services, the status accounted for was not very impressive however, with Nagelkerke R2=0.331. On the basis of 19 variables, correction classification rates were 74.1% for not using services and 77.7% for using them, for an overall success rate of 76%. Table 11 shows regression coefficients, Wald statistics, odds ratios and 95% confidence intervals for odds ratios for each of the 19 predictors. According to the Wald criterion, only living alone, a higher disability index, not having depression and having a family dependent support network or a locally integrated network reliably predicted the usage of services supporting in ADL. DISCUSSION For a long time, gerontologists have been doing research on the topic of social networks and support amongst older adults and the impact of social support on their heath and well-being [Berkman, 1983; Bosworth and Schaie, 1997]. Social networks and social support can play a significant role in the maintenance of health and wellbeing of an older person. The effect of social support on psychological well-being has been demonstrated in many studies [Newsom and Schulz, 1996; Antonucci et al, 1997; Chi and Chou, 2001; Blazer, 2005; Chan and Lee, 2006], as well as, for 14
instance, its influence on mortality (general mortality [Hirdes and Forbes, 1992; Keller et al., 2003] or in specific conditions such as after myocardial infarction [Berkman et al., 1992]), on reducing the risk of ADL disability and enhancing recovery from ADL disability [Mendes de Leon et al., 1999], and on seeking residential admission [Bear,1989]. On the other hand, specific problems of elderly people, such as dementia or serious disability, cause that the support network structure is changing [Wenger, 1994 a]. The ability of networks to adopt to change is important for continued support. The robust support networks act as a buffer against the effects of increasing frailty [Wenger, 1997a]. An assessment of social networks and social support is recognized as an important component of a geriatric assessment protocol, since the fact that a given person has a network that can or does provide informational, affective social support and tangible help, can to a great extent affect the decisions made by the geriatric team caring for an older person [Kane, 1995]. Network type (its size and structure) is connected with the availability of support [Seemann and Berkman, 1988]. Therefore, its assessment would also be important at the level of community care practitioners and as part of hospital discharge assessment. The PANT instrument was constructed to be easy for practitioners to use, and was adopted and used in a range of countries [Wenger, 1994b]. This instrument makes it possible to identify one of the five network types, which differ first of all in the presence and availability of local close family, the frequency of interactions within the networks and the degree of involvement within the community. These are: 1/ local family dependent (focused on close family ties, few neighbours and peripheral friends); 2/ locally integrated (including close relations with local family, friends, and neighbours); 3/ local self-contained (characterised by arms-length relationships or infrequent contact with at least one relative, but the primary reliance is on neighbours); 4/ wider community focused (typified by an absence of nearby relatives but active relationships with geographically distant relatives, usually children, and a high salience of friends); and 5/ private restricted (associated with an absence of local kin, few nearby friends and low levels of community contacts or involvement). With each particular type of support network, there are different degrees of risks, such as: social isolation, hospital discharge outcomes, permanent admission to institutional care, risk connected with dementia and other mental illnesses [Wenger, 1997b]. 15
The results of the performed study support other authors’ findings that the network type is associated with socio-demographic characteristics of the elderly as well as with health outcomes (i.e., subjective well-being, cognitive ability, depression, disability level) [Wenger, 1994b]. As it was shown in the case of the communitydwelling oldest old people in the chosen urban and rural areas in Poland, the most prevalent support networks were “local family dependent” (41% in rural area and 30.8% in urban area; p<0.05) and “locally integrated” (32.8% in rural area and 31.6% in urban area; p-NS). These two types of social support networks are recognized as the most robust ones. The “local family dependent” support networks (type 1) are rather small in number, and more often concern older people from the older subgroups as well as those with worse health status (in comparison with other types of support groups). They are primarily based on good family relations and few friends/ neighbours. Often, an older person lives with an offspring (in most cases a daughter) or lives in the near vicinity. Almost all of the older person's needs are met by the family. Social involvement of this person is generally low. The second type of support network – “locally integrated” - is based on close relations with near-by family, friends and neighbours, with many friends also living in close proximity. They are slightly bigger than the other types of networks. Older people most often live in the same place for years, actively take part (or took part in the recent past) in social life, religious groups or volunteer groups [Wenger and Tucker, 2002]. The health and fitness state of elders whose support network was locally integrated was significantly better than in the case of other support groups. Even controlling for a participant’s socio-demographic and health status characteristics, the local family dependent support network was a significant and independent predictor of a higher total score on the functional disability index compared with the other types of support networks, especially the locally integrated network. This confirms the opinion that the “local family network” supports the most dependent older people at home. This type of support network was observed more frequently for instance in the case of dementia sufferers [Wenger, 1994a]. Other types of support networks, such as “local self-contained”, “wider community focused” and “private restricted”, were observed less frequently. One should be aware that these types of support networks have a lower ability to deal with disability and dependency in old age (particularly “local self-contained” and “private restricted”, in which family does not live in close proximity). They are less 16
efficient in the face of worsening health or the occurrence of other crisis situations. In any case, the most important thing is not the magnitude of the particular support group, but rather the type of connection with the elderly person supported by the group, as well as the culturally associated obligations and expectations towards members of the network. The more remote the connection, the more the expectations are symbolic; the closer the connections, the wider the scope of normative expectations. This is also influenced by the quality of former mutual ties, as well as the quality of help provided, geographic proximity, age, gender, and health status of network members. In a case when the criteria of support network inclusion to one of the above mentioned types are not met, the network is called an unclassifiable support network. A high percentage of the studied group of so-called unclassified support networks (15.5%) could indicate situations of "borderline" of particular types of support networks. In fact, in 13.8% of the cases, borderline between two categories of network types were identified. This situation may reflect the fact that the network is shifting from one type to another or it may just have the characteristics of two types. In the former case, most often, a crisis situation occurs, where the older person's health status worsens, or the older person or key network members migrate to another place of residence [Scharf, 1997]. It was assumed that the support network will determine the usage of formal support services. The study results did not confirm this hypothesis. No significant differences were found in the usage of hospitalisation, doctor or nurse home visits, or contact with a social worker. In the study of H. Litwin in Israel, the use of formal home-care services was significantly associated with the respondent’s age, gender, functional level and informal support network [Litwin, 2004]. Publicly-financed formal care services were utilized more frequently by older elderly, female, functionally impaired individuals, and people embedded in the “neighbour-focused” and “restricted networks” (and to a lesser degree in the “diverse” and “friend-focused” networks). In these cases, the social networks were characterized by fewer informal support resources at their disposal than in the other types. The informal sources of support had less capacity to provide ongoing informal care. But in the study by H. Stoddart and co-workers, social networks were not generally associated with the use of statutory and private home care services by older people living in the community [Stoddart et al., 2002]. In the presented research, the usage of services supporting in 17
ADL/other social services was practically limited to services rendered within an informal support network – two types of support networks, actually – “local family dependent” and “locally integrated” (controlling for the influence of different demographic and health status characteristics in multivariate logistic regression analysis) predicted usage of these kind of services, suggesting better capacities to cope with the frailties of old age than in other types of support networks. However, as C. Wagner demonstrated in her studies, the type of support network correlates with socio-demographic characteristics of the elderly (age, gender, migration, marital status, household structure), and also with service usage [Wenger, 1994b; 1997b]. Therefore, based on the structure of the support network of the elderly in a given population, the efficiency of informal caregivers as well as the demand for formal care services rendered by social service can be foreseen [Scharf, 1997]. This can have a practical usage in local planning of health and social services directed towards the elderly. In the case of typology based on PANT, support networks with a sole caregiver, that is private restricted and local self-contained networks, require special attention [Wenger and Tucker, 2002]. CONCLUSIONS •
The two most prevalent support network types for 75+-year-old people in the studied urban and rural areas in Poland are “local family dependent” and “locally integrated. The gender variation in support network formation is observed. Men significantly more often than women are a part of a “local selfcontained” network, and women significantly more frequently are part of a “locally integrated” network. 80+-year-old people significantly more often than younger people have a “family dependent” social support network.
•
Respondents in poor health/ functional ability are less likely to have the “local family dependent” support network, and to a lesser extend the „ private restricted” support network, then other types of networks – first of all “locally integrated” one.
•
Apart from “home visits from a GP” (significantly less often found in the “locally integrated” support network) and “formal and informal support in ADL/other social services”, no significant differences in social support network distribution was found.
18
•
Respondents with the “local family dependent” support network are more likely, and with “locally integrated” less likely, to use formal and informal support in ADL and other social services compared with other types of networks. In multivariate logistic regression analysis, both of the above mentioned support networks predicted usage of this kind of services suggesting better capacities to cope with the frailties of old age than in other types of support networks.
•
To the author’s knowledge, this is the first publication in the Polish literature using the social support network typology developed by C. Wenger in 75+year-old people based on the PANT instrument. Due to a documented prediction value and usefulness in everyday practice as well as a good acceptability among practitioners, this instrument of assessment of a support network could be used by policy makers in Poland during the phase of planning and organizing care for community dwelling older people.
ACKNOWLEDGEMENTS This paper was prepared thanks to the support of The ERSTE Stiftung. The empirical study was performed within the framework of the Ministry of Science and Higher Education in Poland (research project number: N404 045 32/1014).
19
Table 1. Rural-urban differences in characteristics of older people. URBAN AREA
RURAL AREA
Value
Value
%/ M±SD (N)
%/ M±SD (N)
80.5±4.7 (256)
81.1±4.8 (253)
NS
46.9 (256)
57.7 (253)
<0.05
Gender (male)
33.2 (256)
30.8 (253)
NS
Marital status (married/ cohabiting)
31.0 (255)
28.3 (251)
NS
Living alone
37.9 (256)
28.4 (250)
<0.05
Education (secondary or above)
44.5 (254)
2.9 (245)
<0.0001
Economic status ( bad)
19.5 (200)
36.9 (214)
<0.001
28.4 (254)
50.6 (253)
<0.0001
3.9±4.3 (246)
4.6±4.7 (226)
<0.1
35.1 (228)
44.2 (232)
<0.05
8.0±6.1 (196)
9.6±6.7 (204)
<0.01
40.1 (252)
51.9 (239)
<0.01
5.5±4.2 (249)
6.6±5.0 (224)
<0.1
Mobility impairment (homebound)
18.7 (256)
20.9 (253)
NS
Visual impairment
36.1 (255)
48.4 (252)
<0.01
Hearing impairment
31.0 (242)
40.3 (248)
<0.05
Number of drugs taken every day [0-15]
4.6±3.3 (256)
3.7±2.9 (253)
<0.01
Number of chronic conditions [0-16]
3.6±2.1 (253)
2.5±1.7 (248)
<0.001
Hospitalisation within past year
23.5 (255)
26.9 (253)
NS
Home visits from a GP
27.5 (255)
16.7 (252)
<0.01
Home visits from a nurse
10.2 (255)
30.4 (255)
<0.001
Rehabilitation services
8.6 (256)
1.6 (253)
<0.001
Contacts with a social worker
0.4 (256)
1.2 (253)
NS
Characteristics
p*
Demographic variables Age (years) 80+ years old
Health status and psycho-physical ability Subjective health status ( bad) Functional disability index (ADL) [0-17] Cognitive impairment Katzman’s scale score [0-28] Depression GDS score [0-15]
Service usage in the past 12 months
Formal and informal support in ADL (such as 54.7 (256) 61.7 (253) NS domestic chores etc.) and other social services Where: GDS- Geriatric Depression Scale; GP- general practitioner; ADL- Activities of Daily Living; M-mean, SD- standard deviation, N- number of cases; *- Chi square test of independence (two-tailed) or Mann-Whitney test in case of continuous variables. Continuous variables were presented as mean ± standard deviation.
20
Table 2. Networkâ&#x20AC;&#x2122;s defining characteristics in urban and rural samples [in %]. URBAN AREA RURAL AREA p Nearest child or other relative N=255 N=253 Same house/ within 1 mile 58.4 71.9 <0.05 1-15 miles 29.8 20.9 More than 15 miles 9.8 5.9 No relatives 2.0 1.3 Nearest child N=256 N=251 Same house/ within 1 mile 49.3 61.3 <0.05 1-15 miles 29.3 21.9 More than 15 miles 10.5 6.8 No children 10.9 10.0 Nearest sibling N=254 N=251 Same house/ within 1 mile 8.3 15.5 <0.01 1-15 miles 23.6 32.6 More than 15 miles 28.3 20.0 No sisters or brothers 39.8 31.9 Frequency of seeing child/ relative N=253 N=246 Daily 53.0 69.9 <0.01 At least weekly 26.0 17.9 Less often 17.8 10.2 Never/ no relative 3.2 2.0 Frequency of seeing/ chatting with a N=256 N=247 friend Daily 17.2 18.6 <0.001 At least weekly 44.5 38.9 Less often 29.3 19.8 Never/ no friends 9.0 22.7 Frequency of seeing/ chatting with a N=252 N=248 neighbour Daily 25.4 27.8 NS At least weekly 50.8 42.7 Less often 17.1 20.6 No contact with neighbours 6.7 8.9 Attends religious meetings N=253 N=251 Yes, regularly (at least one a month) 41.5 46.6 <0.01 Yes, occasionally 28.5 35.1 No 30.0 18.3 Attends community groups N=248 N=241 Yes, regularly (at least one a month) 6.5 <0.001 Yes, occasionally 8.9 2.1 No 84.6 97.9 Where: N- number of cases studied; p- two tailed Chi square test for urban- rural comparisons; NSnot significant.
21
Table 3. Social support network types in urban and rural samples [in % of people with a complete PANT test]. URBAN AREA 237 Family dependent 30.8 Locally integrated 31.6 Local self-contained 7.2 Wider community focused 3.4 Private restricted 7.6 Unclassified 19.4 Where: p-two- tailed U test for two frequencies; NS- not significant. N
RURAL AREA 229 41.0 32.8 5.7 2.2 3.5 14.8
p <0.05 NS NS NS NS NS
22
Table 4. Unclassified cases of support network [in %]. N
%
386
82.8
72
15.5
Family dependent- locally integrated
34
7.3
Family dependent- private-restricted
11
2.4
Family dependent- local self-contained
6
1.3
Locally integrated- local self-contained
9
1.9
Locally integrated- wider community focused
5
1.1
Locally integrated- private restricted
1
0.2
Local self-contained- wider community focused
3
0.6
Local self-contained- private restricted
1
0.2
Wider community focused-private restricted
2
0.4
8
1.7
Classified cases (as in Table 3) Unclassified cases
Inconclusive cases
Total 466 Where: N-number of cases- urban and rural respondents together.
100.0
23
Unclassified
Private restricted
Wider community focused
Local selfcontained
Locally integrated
Family dependent
Table 5. Demographic characteristics by social support network types [in %].
Gender female (n=312) 34.9 35.3 * 4.8 * 2.9 5.8 16.3 male (n=154) 37.7 26.0 9.7 2.6 5.2 18.8 Age Group 75-79 years old 30.5 * 36.3 6.2 2.7 5.3 19.0 (n=226) 80+ years old 40.8 28.3 6.7 2.9 5.8 15.4 (n=240) Marital status married/ cohabiting 34.3 26.4 10.0 * 2.1 4.3 22.9 * (n=140) not married (n=323) 36.5 35.0 5.0 3.1 6.2 14.2 Place of residence urban area (n=237) 30.8 * 31.6 7.2 3.4 7.6 * 19.4 rural area (n=229) 41.0 32.8 5.7 2.2 3.5 14.8 Living alone yes (n=153) 17.6 *** 38.6 * 10.5 * 4.6 9.8 ** 19.0 no (n=310) 44.8 29.0 4.5 1.9 3.2 16.5 Education secondary or above 22.4 ** 34.5 9.5 4.3 6.9 22.4 (n=116) primary or less 40.4 31.3 5.3 2.3 5.3 15.5 (n=342) Economic status good or average 34.3 35.4 5.6 2.6 5.6 16.4 (n=268) bad (n=112) 37.5 30.4 8.0 3.6 7.1 13.4 Where: *-p<0.05; **-p<0.01; ***-p<0.001- two tailed U test for two frequencies; only statistically significant differences were marked.
24
Self-rating of health status good/ average(n=275) 29.8 *** 42.5 *** 6.5 2.9 3.3 ** poor (n=189) 45.0 16.9 6.3 2.6 9.0 Cognitive impairment no (n=253) 27.3 *** 39.1 ** 6.7 3.2 5.1 yes (n=168) 43.5 24.4 7.1 3.0 6.0 Depression no (n=243) 25.1 *** 41.6 *** 7.4 4.1 3.3 * yes (n=209) 46.4 22.5 5.7 1.4 8.6 Mobility impairment (homebound) no (n=374) 30.5 *** 38.5 *** 7.2 2.7 4.0 ** yes (n=92) 57.6 6.5 3.3 3.3 12.0 Visual impairment no (n=266) 31.2 * 39.8 *** 7.1 1.1 ** 4.1 yes (n=198) 42.4 22.2 5.1 5.1 7.6 Hearing impairment no (n=291) 35.4 34.0 5.2 2.7 4.1 yes (n=158) 39.2 27.2 8.2 3.2 8.2 Urinary incontinence no (n=426) 34.0 ** 33.6 * 6.3 2.8 5.2 yes (n=38) 57.9 15.8 5.3 2.6 10.5 Bowel incontinence no (n=425) 34.4 * 33.6 * 6.6 2.8 5.4 yes (n=41) 51.2 17.1 4.9 2.4 7.3 Where: *-p<0.05; **-p<0.01; ***-p<0.001- two tailed U test for two frequencies; only statistically significant differences were marked.
Unclassified
Private restricted
Wider community focused
Local selfcontained
Locally integrated
Family dependent
Table 6. Health characteristics by social support network types [in %].
14.9 20.1 18.6 16.1 18.5 15.3 17.1 17.4 16.5 17.7 18.6 13.9 18.1 7.9 17.2 17.1
25
Family dependent
Locally integrated
Local selfcontained
Wider community focused
Private restricted
Unclassified
Table 7. Social support network of older people and mean values of chosen health status/ functional ability variables.
Age
81.1
80.0
80.5
80.5
80.4
80.7
NS
Functional disability index
6.3
2.2
2.9
2.8
5.0
4.3
***
Katzmanâ&#x20AC;&#x2122;s scale score
9.8
8.1
9.4
7.7
9.8
8.2
NS
GDS score
7.3
4.5
5.9
4.2
8.8
6.0
***
Number of chronic conditions
3.0
2.9
3.1
3.1
3.8
2.9
NS
a
Where: ANOVA test- ***- p<0.001; NS- not significant. GDS- Geriatric Depression Scale.
26
Table 8. Standard multivariate regression analysis of independent factors associated with the total score on the functional disability index.
Independent variable
B
Constant
S.E.
Ă&#x;
p
-5,787
2,55
<0.05
0,093
0,03
0,097
<0.01
-0,442
0,28
-0,052
NS NS
I. Socio- demographic characteristics Age (years) Gender (female) Place of residence (rural area)
0,291
0,32
0,035
Education (secondary or above)
-0,446
0,35
-0,050
NS
Living alone
-0,948
0,29
-0,108
<0.01
Number of chronic conditions
0,162
0,08
0,077
NS (0,05)
Number of drugs taken every day
0,098
0,05
0,073
NS (0,06)
Cognitive impairment
1,313
0,29
0,156
<0.001
Depression
0,615
0,33
0,074
NS (0.07)
Mobility impairment (homebound)
5,900
0,42
0,517
<0.001
Visual impairment
0,981
0,29
0,118
<0.01
0,571 0,29 III. Social support network type (versus- family dependent)
0,066
<0.05
II. Health status and geriatric syndromes
Hearing impairment Locally integrated
-1,023
0,34
-0,118
<0.01
Local self-contained
-1,259
0,55
-0,079
<0.05
Wider community focused
-1,862
0,72
-0,085
<0.05
Private restricted
-1,271
0,61
-0,070
<0.05
Unclassified
-0,220
0,39
-0,020
NS
2
N=355; adjusted R =0.659; SE=2.4; F=41.445; p<0.001 Where: adjusted R2- the adjusted squared multiple correlation; B- denotes the unstandardized coefficient; SE- the standard error of B; Ă&#x;- the standardized coefficient beta; p- the probability value; NS- not significant;
27
Wider community focused
Private restricted
Unclassified
Hospitalisation yes (n=116) 31.9 33.6 no (n=350) 37.1 31.7 Home visits from a GP yes (n=102) 42.2 19.6 ** no (n=362) 34.0 35.6 Home visits from a community nurse yes (n=91) 37.4 36.3 no (n=374) 35.3 31.3 Rehabilitation services yes (n=21) 38.1 23.8 no (n=442) 35.7 32.4 Formal and informal support in ADL/ other social services yes (n=280) 43.9 *** 27.5 ** no (n=186) 23.7 39.2 Where: *two tailed U test for two frequencies; NS- not significant.
Local selfcontained
Locally integrated
Family dependent
Table 9. Service usage within the past 12 months by social support network types [in %].
7.8 6.0
3.4 2.6
3.4 6.3
19.8 16.3
6.9 6.4
3.9 2.5
6.9 5.2
20.6 16.3
5.5 6.7
2.2 2.9
4.4 5.9
14.3 17.9
4.8 6.6
4.8 2.7
5.9
28.6 16.7
6.1 7.0
1.8 4.3
5.7 5.4
15.0 20.4
28
Table 10. Standard bivariate logistic regression analysis of independent factors associated with the usage of services supporting in ADL/ other social services. OR
95% CI
p
Nagelkerkeâ&#x20AC;&#x2122;s R2
Family dependent
2.53
1.67-3.82
<0.001
0.06
Locally integrated
0.59
0.4-0.87
<0.01
0.02
Local self-contained
0.86
0.41-1.82
NS
0.000
Wider community focused
0.41
0.13-1.26
NS
0.007
Private restricted
1.07
0.47-2.40
NS
0.000
Unclassified
0.69
0.42-1.12
NS
0.007
Independent variable
Where: OR- odds ratio; CI- confidence interval; p- the probability value; R2- the squared multiple correlation; NS- not significant.
29
Table 11. Summary results of the multivariate logistic regression analysis on usage of services supporting older people in ADL and other social services. 2
2
(Ď&#x2021; (19, N=354)=100.531, p<0.001; Nagelkerkeâ&#x20AC;&#x2122;s R =0.331)
Independent variable (predictor)
B (S.E.)
Wald
p
OR (95CI)
-1.991 (2.59)
0.590
NS
0.14
I. Socio- demographic characteristics 0.003 (0.03) Age (years)
0.007
NS
1.00 (0.94-1.07)
Gender (female)
-0.336 (0.28)
1.405
NS
0.71 (0.41-1.25)
Place of residence (rural area)
0.118 (0.32)
0.138
NS
1.12 (0.61-2.09)
Education (secondary or above)
0.111 (0.34)
0.105
NS
1.12 (0.57-2.18)
Living alone
0.823 (0.28)
8.349
<0.01
2.28 (1.30-3.98)
II. Health status and functional ability -0.037 (0.08) Number of chronic conditions
0.207
NS
0.96 (0.82-1.13)
Number of drugs taken every day
0.079 (0.05)
2.244
NS
1.08 (0.98-1.20)
Subjective health status (bed)
0.188 (0.33)
0.318
NS
1.21 (0.63-2.32)
Functional disability index
0.323 (0.06)
24.701
<0.00001
1.38 (1.22-1.57)
Cognitive impairment
0.010 (0.29)
0.001
NS
1.01 (0.57-1.79)
Depression
-0.813 (0.34)
5.612
<0.05
0.44 (0.23-0.87)
Mobility impairment (homebound)
0.006 (0.58)
0.00001
NS
1.01 (0.32-3.11)
Visual impairment
0.136 (0.29)
0.226
NS
1.15 (0.65-2.01)
Hearing impairment
0.462 (0.28)
2.649
NS
1.59 (0.91-2.77)
<0.01
2.82 (1.28-6.20)
Constant
III. Social support network type (versus- unclassified) 1.037 (0.40) 6.645 Family dependent Locally integrated
0.764 (0.39)
3.919
<0.05
2.15 (1.01-4.57)
Local self-contained
0.338 (0.55)
0.376
NS
1.40 (0.48-4.12)
Wider community focused
-0.276 (0.77)
0.128
NS
0.76 (0.17-3.43)
Private restricted
0.151 (0.66)
0.052
NS
1.16 (0.32-4.22)
Where: B- regression coefficient; SE- the standard error of B; p- the probability value; OR- odds ratio; CI- confidence interval.
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