518066 research-article2013
JAHXXX10.1177/0898264313518066Journal of Aging and HealthHonigh-de Vlaming et al.
Article
Determinants of Trends in Loneliness Among Dutch Older People Over the Period 2005-2010
Journal of Aging and Health 2014, Vol. 26(3) 422–440 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0898264313518066 jah.sagepub.com
Rianne Honigh-de Vlaming, PhD1, Annemien Haveman-Nies, PhD1,2, Inge Bos-Oude Groeniger, MSc1, Lisette de Groot, PhD2, and Pieter van ’t Veer, PhD2
Abstract Objective: This study aims to investigate the influence of sociodemographic, health, and municipal characteristics on trends in loneliness among community-dwelling elderly people. Method: Data were gathered from 4,868 and 4,773 non-institutionalized elderly people aged 65 years and above in a health survey in 2005 and 2010, respectively. Crude and adjusted multilevel models were analyzed to study the independent associations of study year and socio-demographic, health, and municipal characteristics with loneliness. Results: Overall and across municipalities, loneliness estimates did not significantly differ between 2005 and 2010. However, among the sub-group with activity limitations, loneliness was higher in 2010 compared with 2005. Discussion: This study indicates a constant trend in loneliness in the total population and across sub-groups with the exception of participants with one or more activity limitations, where loneliness 1GGD
Noord- en Oost-Gelderland (Community Health Service), Apeldoorn, The Netherlands 2Wageningen University, Division of Human Nutrition, The Netherlands Corresponding Author: Annemien Haveman-Nies, PhD, Wageningen University, Division of Human Nutrition, P.O. Box 8129, 6700 EV, Wageningen, The Netherlands. Email: [email protected]
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increased. Individual socio-demographic and health characteristics were explanatory factors for variation in loneliness over time, whereas municipal characteristics were not. Keywords loneliness, elderly people, trends, activity limitations, public health
Introduction Societal changes, such as smaller family size, fewer people living in multigenerational households, more people never marrying, increasing divorce rates, and greater distance between the residences of family members (Dykstra, 2009; Victor et al., 2002), are raising public concern over an increasing prevalence of loneliness among elderly people. From the perspective of an aging society, especially in countries such as the Netherlands where the absolute number of elderly people is expected to increase from 2.4 million (16%) in 2010 to 4.6 million in 2040 (26%) (Van Duin & Garssen, 2010; Zantinge, van der Wilk, van Wieren, & Schoenmaker, 2011), these societal changes feed public concerns over an increasing prevalence of loneliness (Coalitie Erbij, 2013; Ministry of Health Welfare and Sport, 2011, 2012). Loneliness, however, is not a matter of age alone. Associations between age and loneliness can mainly be explained by age-related health problems and changes in social network ties (De Jong Gierveld, 1998; Dykstra, Van Tilburg, & de Jong Gierveld, 2005; Heylen, 2010; Jylhä, 2004; Pinquart & Sörensen, 2001; Tijhuis, De Jong Gierveld, Feskens, & Kromhout, 1999). Furthermore, marital status is clearly associated with loneliness; persons who live alone are at increased risk of loneliness compared with those who are married or live together (De Jong Gierveld, 1998; Dykstra & De Jong Gierveld, 1999; Dykstra et al., 2005; Heylen, 2010; Jylhä, 2004; Savikko, Routasalo, Tilvis, Strandberg, & Pitkälä, 2005; Tijhuis et al., 1999). The associations between gender and socio-economic status and loneliness are less consistent. In most cross-sectional studies, women appear lonelier than men. However, after adjustment for other socio-demographic and health variables, this gender effect mostly disappears (Golden et al., 2009; Heylen, 2010; Jylhä, 2004; Paúl & Ribeiro, 2009; Vanderleyden & Heylen, 2007). In some studies, loneliness is more common among less educated persons and those with a low income (Dykstra & De Jong Gierveld, 1999; Pinquart & Sörensen, 2001; Savikko et al., 2005; Victor, Scambler, Bowling, & Bond, 2005), but among others no association is found (Fry & Debats, 2002; Heylen, 2010; Stephens, Alpass, Towers, & Stevenson, 2011). With regard to health-related
Downloaded from jah.sagepub.com at WESTERN OREGON UNIVERSITY on May 31, 2015
424
Journal of Aging and Health 26(3)
factors that affect the ability of elderly people to sustain a good social network quality, issues such as functional mobility, chronic disease, and hearing and vision problems are each independently associated with higher loneliness scores or smaller social network sizes in some (Broese van Groenou, Hoogendijk, & van Tilburg, 2013; Cohen-Mansfield & Parpura-Gill, 2007; De Jong Gierveld, 1998; Dykstra & De Jong Gierveld, 1999; Dykstra et al., 2005; Heylen, 2010; Pronk et al., 2011; Savikko et al., 2005; Wenger, Davies, Shahtahmasebi, & Scott, 1996), but not all (Dykstra et al., 2005; Heylen, 2010; Jylhä, 2004; Savikko et al., 2005; Tijhuis et al., 1999) cross-sectional and longitudinal studies. Although societal changes point to an increasing trend in loneliness among elderly people, only a few determinants have been associated with loneliness. Furthermore, unfortunately, trend studies to date have not shown a clear enough picture to draw decisive conclusions thus far. In the Netherlands, data from the Longitudinal Aging Study Amsterdam (LASA) study over the period 1992-2006 showed that the percentages of persons categorizing themselves as lonely (sometimes, often, mostly, or always lonely) during the previous week increased from 16% to 21%, whereas the percentage of people classifying themselves in the last three categories (often, mostly, or always lonely) did not change significantly, 5% to 4% (Dykstra, 2009). In contrast, other studies in the Netherlands and neighboring countries did not support this result. No time trends were found in older men over the 10-year period 1985-1995 in the Zutphen Elderly Study (Tijhuis et al., 1999). Dykstra (2009) even reported a decreasing rather than an increasing trend in loneliness, at least in age-specific sub-samples of married persons, by comparing 30 crosssectional studies among 18- to 90-year-old adults in the period 1980-2005. Furthermore, a comparison of consecutive cross-sectional surveys in the United Kingdom (1945-1999) suggested that the percentage of lonely persons remained stable over the years (Victor et al., 2002), whereas in Belgium (1985-2001; Vanderleyden & Heylen, 2007) and Germany (1949-1995; Döring, 1997) a small decrease in the prevalence of loneliness was found. In addition, regional survey data of the community health services in the Netherlands report large variations in the prevalence of loneliness over the years on regional and municipal levels (GGD Gelre-IJssel, 2006, 2011; Van der Star & Soeterboek, 2007). This raises the question whether unique municipal characteristics, such as local facilities and policies, might further explain differences in loneliness across municipalities after consideration of individuallevel characteristics (Ministry of Health Welfare and Sport, 2011). Therefore, the aim of this study is to investigate the influence of the socio-demographic, health, and municipal characteristics on trends in loneliness among community-dwelling elderly people.
Downloaded from jah.sagepub.com at WESTERN OREGON UNIVERSITY on May 31, 2015
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Method Study Design and Study Population For this trend study, two independent cross-sectional surveys from 2005 and 2010 were used. The surveys were performed to measure determinants and outcomes of health and health care use among non-institutionalized Dutch elderly people aged 65 years or older living in the Northern and Eastern part of the province Gelderland, the Netherlands. For 15 municipalities, data were available in 2005 and 2010. Population size ranged between 21,179 and 155,962 inhabitants, and the proportion aged 65 years or older ranged between 15% and 23% on December 31, 2010 (Statistics Netherlands). In both studies, age-stratified random samples were taken from the municipal population registries. Study samples of 500 individuals in 2005 and 600 individuals in 2010 were randomly selected for each municipality on both occasions. People aged 75 years or older were oversampled to constitute half of the study population. Therefore, in the respective years, 250 and 300 persons aged 65 to 74 years and 250 and 300 persons aged 75 years or older were selected. For one larger city, the sample was raised to 2,500 and 3,500 persons in 2005 and 2010, respectively, again stratified by age (Figure 1). Data were collected using self-administered questionnaires. In 2005, the complete survey was conducted on paper. A questionnaire with a reply envelope was sent to the selected participants. After a period of 3 weeks and 6 weeks, the non-responders received a reminder by post. The second reminder also included a new copy of the questionnaire. In the 2010 survey, the first mailing was an invitation to conduct the survey online. After 2.5 weeks, a first hard copy of the questionnaire was sent to the non-responders. After an additional 3.5 weeks, another reminder was sent, this time without a copy of the questionnaire. The response rate was 77% in 2005 and 60% in 2010, of which 15% were online and 44% on paper. Data were available for 9,641 participants in total: 4,868 in 2005 and 4,773 in 2010.
Measures The main outcome loneliness was measured using the De Jong Gierveld loneliness scale. This scale is based on a cognitive approach to loneliness. According to this perspective, loneliness is caused by an imbalance between the standards and social support needs as felt by the individuals themselves on one hand and the actual existing social network on the other hand, rather than solely by the absence of specific relations per se (De Jong Gierveld & Kamphuis, 1985; De Jong Gierveld & Van Tilburg, 1999). The scale is composed of 11 questions, of which 5 are positively and 6 negatively formulated.
Downloaded from jah.sagepub.com at WESTERN OREGON UNIVERSITY on May 31, 2015
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Journal of Aging and Health 26(3)
Figure 1. Flowchart study participants in 2005 and 2010. aStatistics
Netherlands. 14 municipalities 250 persons aged 65 to 74 years and 250 persons aged 75+ years; 1 municipality 1,250 persons aged 65 to 74 years and 1,250 persons aged 75+ years. 2010: 14 municipalities 350 persons aged 65 to 74 years and 350 persons aged 75+; 1 municipality 1,500 persons 65 to 74 years and 1,500 persons 75+ years. cPercentage of study sample. b2005:
An example of a positively formulated item is: “There is always someone I can talk to about my day-to-day problems”; an example of a negatively formulated item is: “I miss having a really close friend.” Three answer categories were provided (yes, more or less, no). For the positive items, “no” and “more or less” answers were considered an indication of loneliness, whereas, for the negative items, “yes” and “more or less” were considered an indication of loneliness. A summed score of 0 to 2 corresponds to no loneliness, 3 to 8 to moderate loneliness, 9 to 10 to severe loneliness, and 11 to very severe loneliness. The scale permits one missing value per participant for which a score of 0 is given (De Jong Gierveld & Kamphuis, 1985; De Jong Gierveld & Van Tilburg, 1999; Van Tilburg & De Jong Gierveld, 1999). The internal consistency of the scale can be indicated as good and comparable with other studies (De Jong Gierveld & Van Tilburg, 1999; Van Tilburg & De Leeuw, 1991), with a Cronbach’s α of .84 and .86 in 2005 and 2010, respectively. The socio-demographic characteristics of age, sex, country of birth, marital status, education level, and income level were included as explanatory variables in the study. Country of birth was categorized as “the Netherlands” or “elsewhere”; marital status as married or living together, divorced or living
Downloaded from jah.sagepub.com at WESTERN OREGON UNIVERSITY on May 31, 2015
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separately, widowed, and single (never married or never lived with anyone); education as no or lower vocational education, intermediate vocational education, and higher vocational education or university; having difficulties with managing on income is classified as “having major or moderate difficulties” or “having no difficulties.” The self-perceived health of the participants and the presence of activity limitations or of chronic disease were all investigated as explanatory health characteristics. Self-perceived health was assessed using the question “How would you classify your health in general?” on a 5-point scale ranging from excellent to poor. Good self-perceived health was defined as having good, very good, or excellent health (Konig-Zahn, Furer, & Tax, 1993). Activity limitations were measured using the three items based on the Organisation for Economic Co-Operation and Development (OECD) disability indicator (McWhinnie, 1981): carrying 5 kg for 10 m, bending and picking something up from the floor, and walking 400 m continuously. Activity limitations were defined as having major difficulty with, or not able to do, one or more of these activities. For chronic disease, participants could indicate a list of 13 chronic diseases whether they had suffered from the disease during the past 12 months, either diagnosed by a physician or not. Suffering from chronic diseases was categorized as “suffering from one or more diseases” or “no diseases reported.”
Statistical Analyses Data description. Participants with missing data for loneliness, gender, age, marital status, level of education, managing on income, chronic disease, or activity limitations were excluded from the analyses (Figure 1). Prevalences and mean scores of the socio-demographic and health characteristics of the two study populations were presented and compared using the chi-square test and independent-samples t test for categorical and continuous variables, respectively. Data analysis. The loneliness score and its regression residuals were skewed distributions containing 32% of zeros. As this could not be resolved by transforming to Y = ln (score) because of the zeros, and transformation to Y = ln (score + 1) would leave regression coefficients uninterpretable, we conducted the analyses on the untransformed scores, thus maintaining comparability with the literature on loneliness (De Jong Gierveld & Dykstra, 2008; Dykstra et al., 2005; Fokkema & Van Tilburg, 2007; Stevens & Westerhof, 2006). Residuals of the regression models were plotted against the predicted values and showed roughly similar distributions, suggesting that the mean is a reasonable parameter for the location of the distribution. When the
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Figure 2. Mean loneliness scores for 15 municipalities in 2005 and 2010.
Note. The variance ± SE in mean loneliness scores was 0.01 ± 0.01 between municipalities in 2005 and 2010; and 7.84 ± 0.16 and 8.17 ± 0.17 within municipalities (or between individuals) in 2005 and 2010, respectively. Figure is available in full color in the online version at jah.sagepub.com.
transformation to Y = ln (score + 1) was used, the final model resulted in the same explanatory variables as the analysis with untransformed data. The data analysis was performed in two steps. First, the mean loneliness scores were obtained for each municipality in 2005 and 2010 (Figure 2). Differences within municipalities over time were tested with independentsample t tests. Similarly, changes in mean loneliness scores between 2005 and 2010, both for the total population and stratified for socio-demographic and health characteristics, were tested. Second, multilevel linear regression analyses were used to explain the change in loneliness in the total population using the individual socio-demographic, health, and municipal characteristics. For this purpose, the regression models included one level for the individual participants and a second level for the municipality. A forward modeling approach was followed. Model 1 included a constant with a random intercept for municipality only. In Model 2, a dummy variable for study year (reference year 2005) was included. To explore whether the change in mean loneliness scores differed between municipalities, a random slope for study year was added. However, this random slope did not
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improve the model fit (likelihood ratio test), meaning that municipality, defined here by the second level variable, explains only a negligible amount of variance in the change in loneliness. Therefore, study year was included as a fixed factor in Models 3 to 5, resulting in regular one-level regression models. In Model 3, age and gender were added as fixed effects, and Model 4 also included marital status, educational level, managing on income, activity limitations, and chronic disease. Finally, to study trends within sub-groups, the interaction with study year was explored for each socio-demographic and health determinant. Model 5 represents the final model, which also includes statistically significant interactions. For all models, unstandardized beta coefficients were reported and p values ≤ .05 were considered significant. In addition, the proportion of explained variance between municipalities and between participants within municipalities was calculated from the consecutive models. The multilevel analyses were conducted using the statistical software MLwiN 2.24 (Rashbash, Steele, Browne, & Goldstein, 2009).
Results Table 1 presents the socio-demographic and health characteristics of the study populations. Mean age (unweighted) was 73.8 years in both studies. The proportion of women, persons with no or lower vocational education, and solitary living persons was lower in 2010 than in 2005. With regard to health characteristics, no significant differences were seen in self-rated health. In contrast, the percentage of participants with activity limitations was significantly lower in 2010 than in 2005, whereas the percentage of participants with one or more chronic diseases was significantly higher in 2010. No significant differences were seen in loneliness prevalence and population average over time. In Table 2, mean loneliness scores are presented for the sub-groups of socio-demographic and health characteristics in 2005 and 2010. Participants with activity limitations were lonelier in 2010 than in 2005. Participants with intermediate vocational education and without problems managing on their income were less lonely in 2010. In Figure 2, the mean loneliness scores in 2005 and 2010 are presented as separate lines for each municipality. In 2005, the municipal average ranged between 2.22 (SD = 1.60) and 2.88 (SD = 1.72) and in 2010 between 2.09 (SD = 1.64) and 2.78 (SD = 1.72). Differences in loneliness scores between 2005 and 2010 within municipalities ranged from an increase of 0.29 (SE = 0.28) to a decrease of 0.33 (SE = 0.26). None of these differences were statistically significant. In addition, the within-municipality variance (i.e., variance between people and over time) was 7.84 ± 0.16 and 8.17 ± 0.17 in 2005 and 2010, respectively.
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Table 1. Socio-Demographic and Health Characteristics of the Study Populations in 2005 and 2010 (N = 9,641).
Socio-demographic characteristics Gender (%) Men Age (%) 65-74 75-84 85+ M (SD) Marital status (%) Married or living together Divorced, living separately Widowed Single, never married Education (%) No or low Intermediate High Manage on income (%) Moderate or major problems Country of birth (%) Netherlands Health characteristics Activity limitations (%) One or more limitations Chronic disease (%) One or more diseases Self-rated health (%) Fair or poor Loneliness (%) Not lonely Moderately lonely Severely lonely Very severely lonely M (SD)
2005 (n = 4,868)
2010 (n = 4,773)
46
50
.001
57 36 7 73.8 (6.5)
58 34 7 73.8 (6.5)
.120 .535
68 3 26 3
71 4 22 3
JAHXXX10.1177/0898264313518066Journal of Aging and HealthHonigh-de Vlaming et al.
Article
Determinants of Trends in Loneliness Among Dutch Older People Over the Period 2005-2010
Journal of Aging and Health 2014, Vol. 26(3) 422–440 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0898264313518066 jah.sagepub.com
Rianne Honigh-de Vlaming, PhD1, Annemien Haveman-Nies, PhD1,2, Inge Bos-Oude Groeniger, MSc1, Lisette de Groot, PhD2, and Pieter van ’t Veer, PhD2
Abstract Objective: This study aims to investigate the influence of sociodemographic, health, and municipal characteristics on trends in loneliness among community-dwelling elderly people. Method: Data were gathered from 4,868 and 4,773 non-institutionalized elderly people aged 65 years and above in a health survey in 2005 and 2010, respectively. Crude and adjusted multilevel models were analyzed to study the independent associations of study year and socio-demographic, health, and municipal characteristics with loneliness. Results: Overall and across municipalities, loneliness estimates did not significantly differ between 2005 and 2010. However, among the sub-group with activity limitations, loneliness was higher in 2010 compared with 2005. Discussion: This study indicates a constant trend in loneliness in the total population and across sub-groups with the exception of participants with one or more activity limitations, where loneliness 1GGD
Noord- en Oost-Gelderland (Community Health Service), Apeldoorn, The Netherlands 2Wageningen University, Division of Human Nutrition, The Netherlands Corresponding Author: Annemien Haveman-Nies, PhD, Wageningen University, Division of Human Nutrition, P.O. Box 8129, 6700 EV, Wageningen, The Netherlands. Email: [email protected]
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increased. Individual socio-demographic and health characteristics were explanatory factors for variation in loneliness over time, whereas municipal characteristics were not. Keywords loneliness, elderly people, trends, activity limitations, public health
Introduction Societal changes, such as smaller family size, fewer people living in multigenerational households, more people never marrying, increasing divorce rates, and greater distance between the residences of family members (Dykstra, 2009; Victor et al., 2002), are raising public concern over an increasing prevalence of loneliness among elderly people. From the perspective of an aging society, especially in countries such as the Netherlands where the absolute number of elderly people is expected to increase from 2.4 million (16%) in 2010 to 4.6 million in 2040 (26%) (Van Duin & Garssen, 2010; Zantinge, van der Wilk, van Wieren, & Schoenmaker, 2011), these societal changes feed public concerns over an increasing prevalence of loneliness (Coalitie Erbij, 2013; Ministry of Health Welfare and Sport, 2011, 2012). Loneliness, however, is not a matter of age alone. Associations between age and loneliness can mainly be explained by age-related health problems and changes in social network ties (De Jong Gierveld, 1998; Dykstra, Van Tilburg, & de Jong Gierveld, 2005; Heylen, 2010; Jylhä, 2004; Pinquart & Sörensen, 2001; Tijhuis, De Jong Gierveld, Feskens, & Kromhout, 1999). Furthermore, marital status is clearly associated with loneliness; persons who live alone are at increased risk of loneliness compared with those who are married or live together (De Jong Gierveld, 1998; Dykstra & De Jong Gierveld, 1999; Dykstra et al., 2005; Heylen, 2010; Jylhä, 2004; Savikko, Routasalo, Tilvis, Strandberg, & Pitkälä, 2005; Tijhuis et al., 1999). The associations between gender and socio-economic status and loneliness are less consistent. In most cross-sectional studies, women appear lonelier than men. However, after adjustment for other socio-demographic and health variables, this gender effect mostly disappears (Golden et al., 2009; Heylen, 2010; Jylhä, 2004; Paúl & Ribeiro, 2009; Vanderleyden & Heylen, 2007). In some studies, loneliness is more common among less educated persons and those with a low income (Dykstra & De Jong Gierveld, 1999; Pinquart & Sörensen, 2001; Savikko et al., 2005; Victor, Scambler, Bowling, & Bond, 2005), but among others no association is found (Fry & Debats, 2002; Heylen, 2010; Stephens, Alpass, Towers, & Stevenson, 2011). With regard to health-related
Downloaded from jah.sagepub.com at WESTERN OREGON UNIVERSITY on May 31, 2015
424
Journal of Aging and Health 26(3)
factors that affect the ability of elderly people to sustain a good social network quality, issues such as functional mobility, chronic disease, and hearing and vision problems are each independently associated with higher loneliness scores or smaller social network sizes in some (Broese van Groenou, Hoogendijk, & van Tilburg, 2013; Cohen-Mansfield & Parpura-Gill, 2007; De Jong Gierveld, 1998; Dykstra & De Jong Gierveld, 1999; Dykstra et al., 2005; Heylen, 2010; Pronk et al., 2011; Savikko et al., 2005; Wenger, Davies, Shahtahmasebi, & Scott, 1996), but not all (Dykstra et al., 2005; Heylen, 2010; Jylhä, 2004; Savikko et al., 2005; Tijhuis et al., 1999) cross-sectional and longitudinal studies. Although societal changes point to an increasing trend in loneliness among elderly people, only a few determinants have been associated with loneliness. Furthermore, unfortunately, trend studies to date have not shown a clear enough picture to draw decisive conclusions thus far. In the Netherlands, data from the Longitudinal Aging Study Amsterdam (LASA) study over the period 1992-2006 showed that the percentages of persons categorizing themselves as lonely (sometimes, often, mostly, or always lonely) during the previous week increased from 16% to 21%, whereas the percentage of people classifying themselves in the last three categories (often, mostly, or always lonely) did not change significantly, 5% to 4% (Dykstra, 2009). In contrast, other studies in the Netherlands and neighboring countries did not support this result. No time trends were found in older men over the 10-year period 1985-1995 in the Zutphen Elderly Study (Tijhuis et al., 1999). Dykstra (2009) even reported a decreasing rather than an increasing trend in loneliness, at least in age-specific sub-samples of married persons, by comparing 30 crosssectional studies among 18- to 90-year-old adults in the period 1980-2005. Furthermore, a comparison of consecutive cross-sectional surveys in the United Kingdom (1945-1999) suggested that the percentage of lonely persons remained stable over the years (Victor et al., 2002), whereas in Belgium (1985-2001; Vanderleyden & Heylen, 2007) and Germany (1949-1995; Döring, 1997) a small decrease in the prevalence of loneliness was found. In addition, regional survey data of the community health services in the Netherlands report large variations in the prevalence of loneliness over the years on regional and municipal levels (GGD Gelre-IJssel, 2006, 2011; Van der Star & Soeterboek, 2007). This raises the question whether unique municipal characteristics, such as local facilities and policies, might further explain differences in loneliness across municipalities after consideration of individuallevel characteristics (Ministry of Health Welfare and Sport, 2011). Therefore, the aim of this study is to investigate the influence of the socio-demographic, health, and municipal characteristics on trends in loneliness among community-dwelling elderly people.
Downloaded from jah.sagepub.com at WESTERN OREGON UNIVERSITY on May 31, 2015
Honigh-de Vlaming et al.
425
Method Study Design and Study Population For this trend study, two independent cross-sectional surveys from 2005 and 2010 were used. The surveys were performed to measure determinants and outcomes of health and health care use among non-institutionalized Dutch elderly people aged 65 years or older living in the Northern and Eastern part of the province Gelderland, the Netherlands. For 15 municipalities, data were available in 2005 and 2010. Population size ranged between 21,179 and 155,962 inhabitants, and the proportion aged 65 years or older ranged between 15% and 23% on December 31, 2010 (Statistics Netherlands). In both studies, age-stratified random samples were taken from the municipal population registries. Study samples of 500 individuals in 2005 and 600 individuals in 2010 were randomly selected for each municipality on both occasions. People aged 75 years or older were oversampled to constitute half of the study population. Therefore, in the respective years, 250 and 300 persons aged 65 to 74 years and 250 and 300 persons aged 75 years or older were selected. For one larger city, the sample was raised to 2,500 and 3,500 persons in 2005 and 2010, respectively, again stratified by age (Figure 1). Data were collected using self-administered questionnaires. In 2005, the complete survey was conducted on paper. A questionnaire with a reply envelope was sent to the selected participants. After a period of 3 weeks and 6 weeks, the non-responders received a reminder by post. The second reminder also included a new copy of the questionnaire. In the 2010 survey, the first mailing was an invitation to conduct the survey online. After 2.5 weeks, a first hard copy of the questionnaire was sent to the non-responders. After an additional 3.5 weeks, another reminder was sent, this time without a copy of the questionnaire. The response rate was 77% in 2005 and 60% in 2010, of which 15% were online and 44% on paper. Data were available for 9,641 participants in total: 4,868 in 2005 and 4,773 in 2010.
Measures The main outcome loneliness was measured using the De Jong Gierveld loneliness scale. This scale is based on a cognitive approach to loneliness. According to this perspective, loneliness is caused by an imbalance between the standards and social support needs as felt by the individuals themselves on one hand and the actual existing social network on the other hand, rather than solely by the absence of specific relations per se (De Jong Gierveld & Kamphuis, 1985; De Jong Gierveld & Van Tilburg, 1999). The scale is composed of 11 questions, of which 5 are positively and 6 negatively formulated.
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Journal of Aging and Health 26(3)
Figure 1. Flowchart study participants in 2005 and 2010. aStatistics
Netherlands. 14 municipalities 250 persons aged 65 to 74 years and 250 persons aged 75+ years; 1 municipality 1,250 persons aged 65 to 74 years and 1,250 persons aged 75+ years. 2010: 14 municipalities 350 persons aged 65 to 74 years and 350 persons aged 75+; 1 municipality 1,500 persons 65 to 74 years and 1,500 persons 75+ years. cPercentage of study sample. b2005:
An example of a positively formulated item is: “There is always someone I can talk to about my day-to-day problems”; an example of a negatively formulated item is: “I miss having a really close friend.” Three answer categories were provided (yes, more or less, no). For the positive items, “no” and “more or less” answers were considered an indication of loneliness, whereas, for the negative items, “yes” and “more or less” were considered an indication of loneliness. A summed score of 0 to 2 corresponds to no loneliness, 3 to 8 to moderate loneliness, 9 to 10 to severe loneliness, and 11 to very severe loneliness. The scale permits one missing value per participant for which a score of 0 is given (De Jong Gierveld & Kamphuis, 1985; De Jong Gierveld & Van Tilburg, 1999; Van Tilburg & De Jong Gierveld, 1999). The internal consistency of the scale can be indicated as good and comparable with other studies (De Jong Gierveld & Van Tilburg, 1999; Van Tilburg & De Leeuw, 1991), with a Cronbach’s α of .84 and .86 in 2005 and 2010, respectively. The socio-demographic characteristics of age, sex, country of birth, marital status, education level, and income level were included as explanatory variables in the study. Country of birth was categorized as “the Netherlands” or “elsewhere”; marital status as married or living together, divorced or living
Downloaded from jah.sagepub.com at WESTERN OREGON UNIVERSITY on May 31, 2015
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separately, widowed, and single (never married or never lived with anyone); education as no or lower vocational education, intermediate vocational education, and higher vocational education or university; having difficulties with managing on income is classified as “having major or moderate difficulties” or “having no difficulties.” The self-perceived health of the participants and the presence of activity limitations or of chronic disease were all investigated as explanatory health characteristics. Self-perceived health was assessed using the question “How would you classify your health in general?” on a 5-point scale ranging from excellent to poor. Good self-perceived health was defined as having good, very good, or excellent health (Konig-Zahn, Furer, & Tax, 1993). Activity limitations were measured using the three items based on the Organisation for Economic Co-Operation and Development (OECD) disability indicator (McWhinnie, 1981): carrying 5 kg for 10 m, bending and picking something up from the floor, and walking 400 m continuously. Activity limitations were defined as having major difficulty with, or not able to do, one or more of these activities. For chronic disease, participants could indicate a list of 13 chronic diseases whether they had suffered from the disease during the past 12 months, either diagnosed by a physician or not. Suffering from chronic diseases was categorized as “suffering from one or more diseases” or “no diseases reported.”
Statistical Analyses Data description. Participants with missing data for loneliness, gender, age, marital status, level of education, managing on income, chronic disease, or activity limitations were excluded from the analyses (Figure 1). Prevalences and mean scores of the socio-demographic and health characteristics of the two study populations were presented and compared using the chi-square test and independent-samples t test for categorical and continuous variables, respectively. Data analysis. The loneliness score and its regression residuals were skewed distributions containing 32% of zeros. As this could not be resolved by transforming to Y = ln (score) because of the zeros, and transformation to Y = ln (score + 1) would leave regression coefficients uninterpretable, we conducted the analyses on the untransformed scores, thus maintaining comparability with the literature on loneliness (De Jong Gierveld & Dykstra, 2008; Dykstra et al., 2005; Fokkema & Van Tilburg, 2007; Stevens & Westerhof, 2006). Residuals of the regression models were plotted against the predicted values and showed roughly similar distributions, suggesting that the mean is a reasonable parameter for the location of the distribution. When the
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Figure 2. Mean loneliness scores for 15 municipalities in 2005 and 2010.
Note. The variance ± SE in mean loneliness scores was 0.01 ± 0.01 between municipalities in 2005 and 2010; and 7.84 ± 0.16 and 8.17 ± 0.17 within municipalities (or between individuals) in 2005 and 2010, respectively. Figure is available in full color in the online version at jah.sagepub.com.
transformation to Y = ln (score + 1) was used, the final model resulted in the same explanatory variables as the analysis with untransformed data. The data analysis was performed in two steps. First, the mean loneliness scores were obtained for each municipality in 2005 and 2010 (Figure 2). Differences within municipalities over time were tested with independentsample t tests. Similarly, changes in mean loneliness scores between 2005 and 2010, both for the total population and stratified for socio-demographic and health characteristics, were tested. Second, multilevel linear regression analyses were used to explain the change in loneliness in the total population using the individual socio-demographic, health, and municipal characteristics. For this purpose, the regression models included one level for the individual participants and a second level for the municipality. A forward modeling approach was followed. Model 1 included a constant with a random intercept for municipality only. In Model 2, a dummy variable for study year (reference year 2005) was included. To explore whether the change in mean loneliness scores differed between municipalities, a random slope for study year was added. However, this random slope did not
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improve the model fit (likelihood ratio test), meaning that municipality, defined here by the second level variable, explains only a negligible amount of variance in the change in loneliness. Therefore, study year was included as a fixed factor in Models 3 to 5, resulting in regular one-level regression models. In Model 3, age and gender were added as fixed effects, and Model 4 also included marital status, educational level, managing on income, activity limitations, and chronic disease. Finally, to study trends within sub-groups, the interaction with study year was explored for each socio-demographic and health determinant. Model 5 represents the final model, which also includes statistically significant interactions. For all models, unstandardized beta coefficients were reported and p values ≤ .05 were considered significant. In addition, the proportion of explained variance between municipalities and between participants within municipalities was calculated from the consecutive models. The multilevel analyses were conducted using the statistical software MLwiN 2.24 (Rashbash, Steele, Browne, & Goldstein, 2009).
Results Table 1 presents the socio-demographic and health characteristics of the study populations. Mean age (unweighted) was 73.8 years in both studies. The proportion of women, persons with no or lower vocational education, and solitary living persons was lower in 2010 than in 2005. With regard to health characteristics, no significant differences were seen in self-rated health. In contrast, the percentage of participants with activity limitations was significantly lower in 2010 than in 2005, whereas the percentage of participants with one or more chronic diseases was significantly higher in 2010. No significant differences were seen in loneliness prevalence and population average over time. In Table 2, mean loneliness scores are presented for the sub-groups of socio-demographic and health characteristics in 2005 and 2010. Participants with activity limitations were lonelier in 2010 than in 2005. Participants with intermediate vocational education and without problems managing on their income were less lonely in 2010. In Figure 2, the mean loneliness scores in 2005 and 2010 are presented as separate lines for each municipality. In 2005, the municipal average ranged between 2.22 (SD = 1.60) and 2.88 (SD = 1.72) and in 2010 between 2.09 (SD = 1.64) and 2.78 (SD = 1.72). Differences in loneliness scores between 2005 and 2010 within municipalities ranged from an increase of 0.29 (SE = 0.28) to a decrease of 0.33 (SE = 0.26). None of these differences were statistically significant. In addition, the within-municipality variance (i.e., variance between people and over time) was 7.84 ± 0.16 and 8.17 ± 0.17 in 2005 and 2010, respectively.
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Table 1. Socio-Demographic and Health Characteristics of the Study Populations in 2005 and 2010 (N = 9,641).
Socio-demographic characteristics Gender (%) Men Age (%) 65-74 75-84 85+ M (SD) Marital status (%) Married or living together Divorced, living separately Widowed Single, never married Education (%) No or low Intermediate High Manage on income (%) Moderate or major problems Country of birth (%) Netherlands Health characteristics Activity limitations (%) One or more limitations Chronic disease (%) One or more diseases Self-rated health (%) Fair or poor Loneliness (%) Not lonely Moderately lonely Severely lonely Very severely lonely M (SD)
2005 (n = 4,868)
2010 (n = 4,773)
46
50
.001
57 36 7 73.8 (6.5)
58 34 7 73.8 (6.5)
.120 .535
68 3 26 3
71 4 22 3
