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Social support and the self-rated health of older people

View Article: PubMed Central - PubMed

ABSTRACT

The lack of social support in elderly populations incurs real societal costs and can lead to their poor health. The aim of this study is to investigate the self-rated health (SRH) and social support among older people as well as its associated factors.

We conducted a cross-sectional study among 312 urban community-dwelling elderly aged 65 to 90 years in Tainan Taiwan and Fuzhou Fujian Province from March 2012 to October 2012. A Spearson correlation test, independent t test, a Pearson χ2 test, a linear regression analysis, and a multiple-level model were performed to analyze the results.

The participants identified children as the most important source of objective and subjective support, followed by spouse and relatives. Tainan's elderly received more daily life assistance and emotional support, showed stronger awareness of the need to seek help, and maintained a higher frequency of social interactions compared with the elderly in Fuzhou. The mean objective support, subjective support, and support utilization scores as well as the overall social support among Tainan's elderly were significantly high compared with the scores among Fuzhou's elderly. Further, Tainan's elderly rated better SRH than Fuzhou's elderly. Correlation analysis showed that social support was significantly correlated with city, age, living conditions, marital status, and SRH. Multiple linear regression analysis, with social support as a dependent variable, retained the following independent predictors in the final regression model: city (4.792, 95% confidence interval [CI]: 3.068–6.516, P = 0.000), age (−0.805, 95% CI: −1.394 to −0.135, P = 0.013), marital status (−1.260, 95% CI: −1.891 to −0.629, P = 0.000), living conditions (4.069, 95% CI: 3.022–5.116, P = 0.000), and SRH −1.941, 95% CI: −3.194 to −0.688, P = 0.003). The multiple-level model showed that city would impact older people's social support (χ2 = 5.103, P < 0.001). Marital status (−2.133, 95% CI: −2.768 to −1.499, P = 0.000), education (1.697, 95% CI: 0.589–2.805 P = 0.003), living conditions (4.20, 95% CI: 1.762–6.638, P = 0.000), and SRH (−3.144, 95% CI: −4.502 to −1.727, P = 0.000) were the associated factors. Thus, city, age, marital status, education, living conditions, and SRH might be the associated factors for social support among older people.

This study presents some feasible implications for social support improvement in China and in other nations worldwide.

No MeSH data available.


Related in: MedlinePlus

Multiple linear regression analysis of associated factors for social support in Tainan's older people. Social support as a dependent variable, the following independent predictors retained in the final regression model: marital status, living condition, and SRH.
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Figure 3: Multiple linear regression analysis of associated factors for social support in Tainan's older people. Social support as a dependent variable, the following independent predictors retained in the final regression model: marital status, living condition, and SRH.

Mentions: First, the Spearson correlation analysis revealed that social support was significantly correlated with city (r = 0.215, P < 0.001), age (r = −0.233, P < 0.001), living conditions (r = 0.362, P < 0.001), marital status (r = −0.326, P < 0.001), and SRH (r = −0.285, P < 0.001). Meanwhile, the univariate analysis indicated that overall social support was significantly different by city group, age group, marital status group, living condition group, and SRH group (Table 6). Second, a stepwise multiple linear regression analysis was conducted. City (4.792, 95% confidence interval [CI]: 3.068–6.516, P = 0.000), age (−0.805, 95% CI: −1.394 to −0.135, P = 0.013), marital status (−1.260, 95% CI: −1.891 to −0.629, P = 0.000), living conditions (4.069, 95% CI: 3.022–5.116, P = 0.000), and SRH (−1.941, CI: −3.194 to −0.688, P = 0.003) were each independent correlates of overall social support. The variance inflation factor (VIF) of all sociodemographic variables was <2.0, which indicated that there was not significant collinearity among sociodemographic variables. Thus, the equation for social support was built using multiple linear regression: social support = 20.07 + 4.792 City-0.805 Age-1.260 Marital status + 4.096 Living conditions-1.955 SRH. R = 0.697 (Table 7 and Fig. 2). Third, according to the city classification, we conducted linear regression analyses of social support to find the differences in associated factors for the 2 cities. The results showed that marital status (−1.012, 95% CI: −2.017 to −0.008, P = 0.000), living conditions (4.324, 95% CI: 3.067–5.518, P = 0.000), and SRH (−2.128, 95% CI: −4.087 to −0.170, P = 0.000) were the associated factors of social support among Tainan's older people (Table 8 and Fig. 3). Age (−1.335, 95% CI: −2.121 to −0.588, P = 0.001), marital status (−1.246, CI: −2.008 to −0.484, P = 0.001), and SRH (−1.74, CI: −3.337 to −0.144, P = 0.033) influenced the social support of Fuzhou's older people (Table 8 and Fig. 4).


Social support and the self-rated health of older people
Multiple linear regression analysis of associated factors for social support in Tainan's older people. Social support as a dependent variable, the following independent predictors retained in the final regression model: marital status, living condition, and SRH.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4998465&req=5

Figure 3: Multiple linear regression analysis of associated factors for social support in Tainan's older people. Social support as a dependent variable, the following independent predictors retained in the final regression model: marital status, living condition, and SRH.
Mentions: First, the Spearson correlation analysis revealed that social support was significantly correlated with city (r = 0.215, P < 0.001), age (r = −0.233, P < 0.001), living conditions (r = 0.362, P < 0.001), marital status (r = −0.326, P < 0.001), and SRH (r = −0.285, P < 0.001). Meanwhile, the univariate analysis indicated that overall social support was significantly different by city group, age group, marital status group, living condition group, and SRH group (Table 6). Second, a stepwise multiple linear regression analysis was conducted. City (4.792, 95% confidence interval [CI]: 3.068–6.516, P = 0.000), age (−0.805, 95% CI: −1.394 to −0.135, P = 0.013), marital status (−1.260, 95% CI: −1.891 to −0.629, P = 0.000), living conditions (4.069, 95% CI: 3.022–5.116, P = 0.000), and SRH (−1.941, CI: −3.194 to −0.688, P = 0.003) were each independent correlates of overall social support. The variance inflation factor (VIF) of all sociodemographic variables was <2.0, which indicated that there was not significant collinearity among sociodemographic variables. Thus, the equation for social support was built using multiple linear regression: social support = 20.07 + 4.792 City-0.805 Age-1.260 Marital status + 4.096 Living conditions-1.955 SRH. R = 0.697 (Table 7 and Fig. 2). Third, according to the city classification, we conducted linear regression analyses of social support to find the differences in associated factors for the 2 cities. The results showed that marital status (−1.012, 95% CI: −2.017 to −0.008, P = 0.000), living conditions (4.324, 95% CI: 3.067–5.518, P = 0.000), and SRH (−2.128, 95% CI: −4.087 to −0.170, P = 0.000) were the associated factors of social support among Tainan's older people (Table 8 and Fig. 3). Age (−1.335, 95% CI: −2.121 to −0.588, P = 0.001), marital status (−1.246, CI: −2.008 to −0.484, P = 0.001), and SRH (−1.74, CI: −3.337 to −0.144, P = 0.033) influenced the social support of Fuzhou's older people (Table 8 and Fig. 4).

View Article: PubMed Central - PubMed

ABSTRACT

The lack of social support in elderly populations incurs real societal costs and can lead to their poor health. The aim of this study is to investigate the self-rated health (SRH) and social support among older people as well as its associated factors.

We conducted a cross-sectional study among 312 urban community-dwelling elderly aged 65 to 90 years in Tainan Taiwan and Fuzhou Fujian Province from March 2012 to October 2012. A Spearson correlation test, independent t test, a Pearson &chi;2 test, a linear regression analysis, and a multiple-level model were performed to analyze the results.

The participants identified children as the most important source of objective and subjective support, followed by spouse and relatives. Tainan's elderly received more daily life assistance and emotional support, showed stronger awareness of the need to seek help, and maintained a higher frequency of social interactions compared with the elderly in Fuzhou. The mean objective support, subjective support, and support utilization scores as well as the overall social support among Tainan's elderly were significantly high compared with the scores among Fuzhou's elderly. Further, Tainan's elderly rated better SRH than Fuzhou's elderly. Correlation analysis showed that social support was significantly correlated with city, age, living conditions, marital status, and SRH. Multiple linear regression analysis, with social support as a dependent variable, retained the following independent predictors in the final regression model: city (4.792, 95% confidence interval [CI]: 3.068&ndash;6.516, P = 0.000), age (&minus;0.805, 95% CI: &minus;1.394 to &minus;0.135, P = 0.013), marital status (&minus;1.260, 95% CI: &minus;1.891 to &minus;0.629, P = 0.000), living conditions (4.069, 95% CI: 3.022&ndash;5.116, P = 0.000), and SRH &minus;1.941, 95% CI: &minus;3.194 to &minus;0.688, P = 0.003). The multiple-level model showed that city would impact older people's social support (&chi;2 = 5.103, P&#8202;&lt;&#8202;0.001). Marital status (&minus;2.133, 95% CI: &minus;2.768 to &minus;1.499, P = 0.000), education (1.697, 95% CI: 0.589&ndash;2.805 P = 0.003), living conditions (4.20, 95% CI: 1.762&ndash;6.638, P = 0.000), and SRH (&minus;3.144, 95% CI: &minus;4.502 to &minus;1.727, P = 0.000) were the associated factors. Thus, city, age, marital status, education, living conditions, and SRH might be the associated factors for social support among older people.

This study presents some feasible implications for social support improvement in China and in other nations worldwide.

No MeSH data available.


Related in: MedlinePlus