Who Gets Help and Help from Whom? Received Support and Later-Life - - PDF document

Who Gets Help and Help from Whom? Received Support and Later-Life Functional Limitations Among the Elderly in the U.S. and China

Zhangjun Zhou Department of Sociology Population Research Institute Pennsylvania State University University Park, PA U.S.A.

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Introduction The expected percentage of aging population in East Asia is projected to reach 25% by 2050, which brings challenges in understanding how different types of social support work effectively for elderly care and elderly’s health (United Nations, 2015). While a large body of research linking social support and mental and physical health are based upon the Western cultural context, less attention has been drawn to understanding how the relationship might vary in other social and cultural context. Another body of research provides important insights into understanding cultural differences in people’s perceptions and behaviors in seeking social support for help between the Western and Asian cultural contexts. Such differences also lead to cultural and societal differences in the availability and supply of social support among the

• elderly. The present study steps into that intersection by studying between-culture differences in

the supply and receipt of social support among the elderly, and the differential roles played by the received support in later-life functional limitations among the elderly in the U.S. and China. Background Received Social Support and Physical Health The functional aspect of social support (Cohen and Wills, 1985) are usually organized along two dimensions: what support is perceived to be available, and what support is actually received or provided by others (Uchino, 2004). The actual receipt of social support is grounded in behavioral transactions occurring over a set period of time (Dunkel-Schetter and Bennett, 1990). Received support is not related to the perceived availability of support in a straightforward manner (Dunkel-Schetter and Bennett, 1990). Studies have found that received support is not as highly related to successful coping compared to the perceived availability of support (Barrera, 2000; Lehman, Ellard, and Wortman, 1986). The theoretical models linking the functional aspects of social support and health are the stress-related model and buffering model. The stress-related model emphasizes the role of social support in the stress-related processes. Social network members may provide with the resources to avoid or reduce the exposure to some types of negative life events (Gore, 1981; LaRocco, House, and French, 1980). The buffering model argues that social support is beneficial because it decreases the negative effects of stress on both mental and physical health (Cobb, 1976; Cohen and Wills, 1985; Cohen and Herbert, 1996). While the association between social support and mental health is particularly strong, studies also consistently found that the lack of social contacts, connections, and support is strongly associated with increased mortality risks (Berkman, 1984; House, Umberson, and Landis, 1988; Israel and Rounds, 1987). The receipt of instrumental and functional support are consistently found to be associated with less functional limitations and slower functional declines (Demange et al. 2004; Seeman et al. 1996; Travis et al. 2004; Choi and Wodarski 1996; Bierman and Statland 2010; Unger et al. 1999).

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Cultural Differences in Support-seeking Behaviors and Receipt of Support Studies have found variations in people’s perceptions and behaviors on social support across different cultures, specifically in people’s behaviors in calling on social support networks and coping with stressors between Western and Asian cultures (Taylor et al., 2004). Studies from a more general sense of cultural differences on social relationships have summarized that Westerns tend to view a person as independent and separate from other people, while Asians tend to view a person as fundamentally connected with others (Markus and Kitayama, 1991; Shweder and Bourne, 1984; Triandis, 1989). Such findings may lead to the assumption that Asians would be more likely to seek social support for help because they place emphasis on their interconnectedness with the social group. However, researchers have shown a different result. In an individual cultural context, Westerners take actions that are oriented toward the expression of their opinions and beliefs, the realization of their rights, and the achievement of their goals (Fiske, Kitayama, Markus, and Nisbett, 1998; Kim and Markus, 1999). In such context with individualist values, relationships may be seen as means for promoting individual goals, and as such, one may recruit explicit help or aid from those in one’s social networks in order to achieve

• ne’s personal goals (Taylor et al., 2004). On the contrary, Asians tend to view a person as

primarily a relational entity that is interdependent with others. In a collectivist cultural context, social relationships, roles, norms, and group solidarity typically are more fundamental to social behavior than an individual’s needs (Taylor et al., 2004). This interdependent view of the self holds that a person should conform to social norms and respond to group goals by seeking consensus and compromise; as such, personal beliefs and needs are secondary to social norms and relationships (Fiske et al., 1998; Kim and Markus, 1999). Thus, in the Asian cultural context, with maintaining group harmony as the priority, any effort to bring personal problems to the attention of others or enlist their help may risk undermining harmony and/or making inappropriate demands on the group (Taylor et al., 2004). A typical example would be caring for an elderly parent (Ng, 2002). In Asian cultural context, elderly parents are more commonly relying on family-based care instead of institutional or community-based care, largely because elderly care is more regarded as a personal issue to many Asian people. Bringing such a personal problem to the attention of social group for help may make people feel embarrassed, or even risky of undermining the interconnectedness with the social group. Researchers have attributed such differences in people’s perceptions and behaviors on social support to differences between the Western individualist cultures and the Asian collectivist

• cultures. For example, European Americans are more likely to report needing and receiving

social support than are Asians and Asian Americans (Hsieh, 2000; Shin, 2002; Wellisch et al., 1999). Received social support may have negative buffering effects for Asians, because it made Asians more stressed (Liang and Bogat, 1994). One study using open-ended questionnaire and standardized coping measure for assessing coping has found that Asians and Asian Americans report less and rely on social support less than European Americans when coping with stressful events (Taylor et al., 2004). To further explain, Taylor has concluded that Asians and Asian Americans are less likely to seek social support because they are concerned about the possible relational ramification of seeking support, such as disturbing the harmony of the group, losing face, receiving criticism, and making the situation worse.

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While the existing literature has demonstrated strong association between received instrumental support and functional limitations, less attention has been drawn to comparing the cultural differences in people’s receipt of support as a result of the availability of support and support-seeking behaviors, and how these differences might lead to the different impacts of instrumental support on individuals’ physical health. This study plans to focus on the functional perspective of received social support and its impact on functional health outcomes, with a particular focus on the elderly in later life stages. It also attempts to incorporate the cultural perspective of social support and investigates the cultural differences in the impact of received functional support among the elderly. Data This study chooses the U.S. as an example of society in the Western cultural context, and China as an example of society in the Asian cultural context. The U.S. is also a good representative of individualist cultural value while China as a good representative of collectivist cultural value. I pooled data from two nationally representative datasets, Health and Retirement Study (HRS), and China Health and Retirement Longitudinal Study (CHARLS), and harmonized them into one dataset for its analysis. HRS is a long-term panel study of approximately 28,000 Americans over the age of 50 and born before 1959 with information on the economic, social, and health status of

• respondents. It starts in 1992 and collects information every two years. It also collects refreshing

samples every 6 years to make the overall sample nationally representative of the population aged 50 and above (Sonnega et al. 2014). Its most recent sample with national representativeness is from wave 2010. CHARLS is modeled after HRS as a nationally representative sample of 17,500 Chinese residents aged 45 and older. Its baseline wave is fielded in 2011 and also collects follow-up information every two years (Zhao et al. 2014). The structure and content of CHARLS make it possible for its integration and comparison with HRS. For comparability, this study constrains both HRS and CHARLS sample to respondents aged 50 and above, and includes two waves from each dataset (for HRS, wave 2010 and 2012; for CHARLS, wave 2011 and 2013). The 2010 wave for HRS and 2011 wave for CHARLS will be considered as the “baseline wave” for this study. “Wave 2” of this study will refer to wave 2012 in HRS and wave 2013 in

• CHARLS. This study draws upon raw data from both the HRS and CHARLS, as well as

harmonized versions of data from RAND (RAND, 2015) and the Gateway to Global Aging Data1. Measurements Functional limitations are measured by activities of daily living indices (ADL) and instrumental activities of daily living (IADL). Both ADL and IADL are based on a series of common physical tasks in HRS and CHARLS that asked respondents if they had any difficulty finishing (RAND

1 Harmonized CHARLS dataset and Codebook, Version B (November, 2015) developed by the Gateway to Global

Aging Data, funded by the National Institute on Ageing (R01 AG030153, RC2 AG 036619, 1R03AG043052). For more information, please refer to www.g2aging.org.

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Center for the Study of Aging, 2015). Tasks of ADL include bathing, eating, dressing, walking across a room, and getting in or out of bed. Tasks of IADL include taking medication, handling money, shopping, and preparing meals (the fifth commonly-used item in IADL of “using a telephone” is excluded from this study since the CHARLS baseline does not have this item). Both ADL and IADL limitations were measured at baseline wave and wave 2. After constructing a summary measure of number of ADL at baseline and at wave 2, a dichotomous variable deterioration in ADL is created with 1 indicating respondents having more number of ADL limitations at wave 2 than at baseline and 0 indicating otherwise. Another dichotomous variable deterioration in IADL is created similarly to represent the functional decline measured be increase in number of IADL limitations. Received support are measured by different types/sources of help that respondents received for their daily life and physical activities. In both HRS and CHARLS, respondents are asked to identify who helped them most often with daily activities from a list of choices including their close family members (such as spouse, parents, children and children-in-laws, grandchildren and grandchildren-in-laws, and siblings), other relatives, paid helpers (such as nanny), and volunteer

• r employee of facility. Five dichotomous variables are created to identify respondents’ received

help including help from spouse, help from children, help from other relatives, help from other paid/unpaid helpers, and receiving no help at all. Another dichotomous variable any helper if respondents have reported receiving any help from spouse, children, relatives, and other helpers. For both HRS and CHARLS, questions on functional helpers are asked only when respondents reported that they had at least one ADL or one IADL limitation. Thus, the following analysis will exclude those without any functional limitation at baseline. Other covariates include respondents’ sociodemographic status from baseline wave including age, gender, educational attainment (harmonized between two datasets using ISCED codes with “1” as primary education and “5” as first stage of tertiary education, Chien et al. 2015), and whether married or not. Health-related control variables include respondents’ self- reported health (based on a 1-5 scale from excellent to poor), whether respondents ever smoke and ever drink, and respondents’ BMI index constructed with height and weight. Respondents’ psychological distress (depressed) are also controlled using harmonized CESD measures (Radloff, 1977) from both datasets. In addition, I controlled for the cognition ability and severity

• f comorbidity of respondents. The cognitive ability is measured by a total word recall score

(ranged 0 to 20) that sums the immediate and delayed word recall scores. The severity of chronic comorbidity is measured by the total number of chronic conditions including diabetes, stroke, hypertension, cancer, lung disease, and heart problems. Three dichotomous variables are

• constructed. No chronic condition equals to 1 when respondents have no chronic conditions and

0 otherwise. One chronic condition equals to 1 when respondents have only one chronic conditions and 0 otherwise. Two plus chronic conditions equals to 1 when respondents have two

• r more chronic conditions and 0 otherwise.

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Preliminary Analysis After using a consistent approach to construct and standardize variables of interest in both HRS and CHARLS, I pooled two datasets and merged them as one cross-national dataset with a country identifier. I constrained my sample to respondents aged 50 and above, and those who have at least one ADL or one IADL limitation at baseline. I first used listwise deletion on variables of interest for the preliminary analysis. Summary statistics are shown in Table 1. The HRS sample from the U.S. are older, with a higher proportion of females, and with higher educational attainment than the CHARLS sample from China. This is consistent with the pattern that life expectancy at birth in the U.S. is approximately 3 years longer than that in China (World Bank, 2015). The Chinese aging cohorts have a much higher proportion of being married at baseline (78%) while this percentage is only 44% among the U.S. sample, mainly because the U.S. sample are older with more female respondents. Thus, it is not surprising to observe that 33% of the Chinese elderly received help from their spouses for their functional limitations, while only 27% of the U.S. elderly got the same help. As for other types of received help, the

• lder adults in the U.S. have a higher rate of receiving help from their children (31%), family

members and relatives (10%), and other helpers such as nanny and employee of facility (20%). Among these respondents with functional limitation in both countries, 65% of the U.S. sample received any type of functional help while only half of the Chinese sample (50%) did. The HRS sample has a higher proportion of respondents who have ADL limitations at baseline (77%) and at wave 2 (58%). The CHARLS sample has a higher prevalence of IADL limitations at baseline (78%) but the prevalence is lower at wave 2 (41%) than that of the HRS at wave 2 (53%). In terms of functional decline, the HRS sample has a higher proportion of respondents who have experienced deterioration in ADL (24%) and in IADL (21%) at later-life than the CHARLS sample does. As for other health status measures, the CHARLS sample has healthier lifestyles measured by lower rates of those who ever drink and ever smoke, and lower mean BMI score, and are less severe in chronic conditions. However, the CHARLS sample has a higher mean score in self-reported health (i.e. poorer self-reported health), a substantially higher prevalence rate of depression, and substantially lower score of cognitive measure. [Table 1 here] Table 2 represents a series of logistic models comparing country differences between the U.S. and China in the probability of receiving particular types of functional help at baseline wave, conditioning on the health status among the elderly. The odds ratio of variable “China” represents the odds ratio of Chinese respondents in receiving a particular type of help referenced to U.S. respondents, controlling for the covariates. Chinese elderly have a lower probability in receiving functional help across all types of helpers, ranging from spouse, children, relatives and

• ther helpers. Chinese elderly are also less likely to receive any help than the U.S. elderly do.

Interestingly, even though the Chinese respondents have a higher prevalence of being married and getting help from spouse, their odds of receiving spousal help are still 57.4% lower than their U.S. counterparts, controlling for their sociodemographic status, health behaviors, and psychological and physical health conditions. Similarly, Chinese elderly have 61.5% lower odds

• f receiving help from their children, 72.1% lower odds of getting help from their relatives, and

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87% lower odds of receiving help from other helpers, compared to the U.S. elderly. Overall, the results from table 2 shows substantial country differences in the supply and receipt of functional help among the elderly between the U.S. and China. Chinese elderly with functional limitations are much more disadvantageous in receipt of functional help. [Table 2 here] Based on models from table 2, I also tested for country*covariate interactions and investigated what factors might have worked differently between the U.S. and China in predicting the receipt of functional help among their elderly. Figure 1 shows that as the sample respondents have more limitations in ADL, the probability of receiving any functional help goes up in both the U.S. and China. However, the U.S. sample consistently has a higher predicted probability, and the gap of predicted probability between the U.S. and China widens as respondents have more severe functional limitations. Figure 2 shows that the predicted probability of receiving functional help from children increases among older age groups. U.S. respondents still are more likely to receive children’s help, but the gap between the U.S. and China narrows among older respondents. [Figure 1 and Figure 2 here] To analyze the effect of received help on later-life functional limitations, I used logistic regressions with the dichotomously defined variable deterioration as the predicted outcome. For the preliminary analysis, I tested for the effect of received functional help at baseline on deterioration in ADL at wave 2. To explore the varying effect of the receipt of help between the U.S. and China, a “helper X China” interaction was included to capture the country differences in the effect of receiving a particular type of help. The full models controlled for respondents’ baseline sociodemographic status, health behaviors, depression, cognitive ability, comorbidity of chronic conditions, and IADL limitations to capture the underlying social and health factors that might confound the relationship between received help and later-life functional limitations. [Table 3 here] Table 3 shows the differential effect of receiving spousal help on later-life deterioration in ADL between the U.S. and China. The significant odds ratio of “Spouse Helper X China” interaction term represents the significant country differences in the effect of receiving spousal help between two countries. In model 2, compared to those without spousal help, U.S. elderly with spousal help increases their odds of deterioration in ADL at wave 2 (odds ratio: 1.543). However, for Chinese older adults, the receipt of spousal help lowers their odds of deterioration in ADL at wave 2 by 5.4% (odds ratio: 1.543*0.613=0.946) than their counterparts without spousal help. While the observed positive relationship between receipt of spousal help and later ADL limitations for U.S. older adults might be confounded by unobserved factors, receiving spousal help is clearly protective against functional decline for Chinese older adults.

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Discussion and Planned Analysis The preliminary analysis shows that there are considerable country differences between the U.S. and China in the likelihood of receiving different types of functional help among the elderly with functional limitations. Conditioning on the same level of sociodemographic and health status, Chinese older adults are consistently less likely than U.S. older adults in receiving functional help from their children, relatives, spouse, and other helpers. The effect of a particular type of functional help on later-life functional limitations also varies between the U.S. and China. For example, even though Chinese older adults are less likely to receive spousal help, the receipt of spousal help turns out to be more protective against their functional decline at later life. For next steps, I plan to extend the current analysis in a couple ways. First, I will examine the effect of other types of functional help (such as children’s help, relatives’ help, and other helper) on the later-life deterioration in ADL, and whether such effect varies by country context. Second, I will also investigate the other outcome of later-life deterioration in IADL. We might expect the effect of functional help on IADL to work differently, since IADL limitations require more instrumental and intensive present of help. Additionally, I also will test each item in ADL and IADL measures as the outcome separately, and see whether the effect of receipt of particular help matters differently for specific functional outcomes. Finally, I will investigate more on why the country differences exist (if any) in the effect of received help, for what groups of elderly these differences are larger (e.g. cohort effect, effect of rural/urban residence), and look for pathways through which the received help works differently between the two countries. I will also use the imputed version of dataset and test whether the current results remain constant. I will also conduct robustness tests and formal tests on model specification to test the observed relationships.

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Table 1. Descriptive Statistics for Study Variables: HRS and CHARLS Percent or Mean (SD) HRS1 CHARLS2 Age 70.6 (12.74)* 65.57 (9.56)* Female (%) 62* 59* Married (%) 44* 78* Education (ISCED) 2.8 (1.41)* 1.4 (0.70)* Health Status Ever drink (%) 40* 36* Ever smoke (%) 60* 37* Self-reported health 3.7 (1.01)* 4.4 (0.79)* BMI (kg/m2) 29.4 (7.62)* 23.0 (4.00)* Depressed (%) 36* 56* No chronic condition (%) 12* 45* 1 chronic condition (%) 25* 32* 2 or 2+ chronic condition (%) 63* 23* Total word recall score 8.16 (3.45)* 5.93 (3.15)* Received Help (%) from Spouse 27* 33* Children 31* 17* Relatives 10* 3.7* Other helper 20* 1.4* Any helper 65* 50* Functional Limitations (%) Any ADL difficulty (baseline) 77* 61* Any ADL difficulty (wave 2) 58* 38* Any IADL difficulty (baseline) 68* 78* Any IADL difficulty (wave 2) 53* 41* Deterioration in ADL at wave 2 24* 17* Deterioration in IADL at wave 2 21* 16* N 4,925 3,961

Notes: ADL = Activities of Daily Living; IADL = Instrumental Activities of Daily Living. Sample restricted to those with at least one ADL or IADL limitation at baseline with no more than 2 types of received functional help.

• 1. Health and Retirement Study (2010-2012).
• 2. China Health and Retirement Longitudinal Study (2011-2013).

*Statistically significant differences between HRS and CHARLS, two-tailed test

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Table 2. Odds Ratios of Predicting the Receipt of Functional Help at Baseline Wave Any Helper Child Helper Spouse Helper Relatives Helper Other Helper China 0.294*** 0.385*** 0.426*** 0.279*** 0.130*** (0.0304) (0.0480) (0.0495) (0.0586) (0.0356) Age 1.025*** 1.024*** 0.997 0.990 1.024*** (0.0035) (0.0038) (0.0041) (0.0052) (0.0050) Female 1.136 2.372*** 0.675*** 1.278 1.048 (0.0848) (0.2176) (0.0584) (0.1679) (0.1296) Married 1.919*** 0.407*** 36.06*** 0.305*** 0.226*** (0.1423) (0.0335) (4.4144) (0.0402) (0.0298) Education (ISCED) 0.901*** 0.842*** 1.034 0.918* 1.136** (0.0253) (0.0263) (0.0342) (0.0397) (0.0456) Self-reported health 1.240*** 1.127** 1.201*** 1.045 1.087 (0.0473) (0.0501) (0.0539) (0.0665) (0.0656) BMI 1.025*** 1.026*** 1.010 1.002 0.996 (0.0056) (0.0057) (0.0064) (0.0075) (0.0072) Ever smoke 1.150 1.073 1.111 1.130 0.954 (0.0835) (0.0890) (0.0944) (0.1342) (0.1065) Ever drink 0.835** 0.867 0.928 0.758* 0.979 (0.0581) (0.0709) (0.0760) (0.0907) (0.1102) Depressed 0.860* 0.982 0.880 1.154 0.947 (0.0613) (0.0800) (0.0733) (0.1339) (0.1075) Total word recall score 0.969** 0.942*** 1.003 0.987 0.977 (0.0105) (0.0120) (0.0127) (0.0178) (0.0167) Any ADL limitations 2.236*** 1.478*** 1.682*** 1.580*** 2.854*** (0.1818) (0.1307) (0.1567) (0.2082) (0.3826) Any IADL limitations 13.88*** 6.442*** 7.174*** 5.548*** 7.375*** (1.2063) (0.7026) (0.7429) (0.9668) (1.1793) Any chronic conditions 1.174 0.959 1.290** 0.931 1.346 (0.0968) (0.1028) (0.1221) (0.1513) (0.2429) N 5418 5418 5418 5418 5418

Exponentiated coefficients; Data pooled from HRS (2010-2012) and CHARLS (2011-2013); *p<0.05, **p<0.01, ***p<0.001; Model constants not shown here.

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Table 3. Odds Ratios of Predicting Deterioration in ADL at Wave 2 with Received Functional Help from Spouse at Baseline Model 1 Model2 Deterioration in ADL Spouse Helper 1.394*** 1.543*** (0.1296) (0.1825) China 0.717*** 0.758* (0.0716) (0.1060) Spouse Helper X China 0.725 0.613** (0.1301) (0.1142) Age 1.022*** (0.0040) Female 1.178 (0.1040) Married 0.813* (0.0814) Education 0.977 (0.0310) BMI 1.017** (0.0059) Ever drink 1.043 (0.0851) Ever smoke 1.014 (0.0847) Self-reported health 1.251*** (0.0567) Depression 1.093 (0.0894) Total word recall score 0.945*** (0.0120) 1 chronic condition 1.171 (0.1363) 2 or 2+ chronic conditions 1.297* (0.1492) Any IADL limitations 1.561*** (0.1379) Constant 0.260*** 0.0143*** (0.0133) (0.0067) N 4,666 4666

Exponentiated coefficients; Standard errors in parentheses Data pooled from HRS (2010-2012) and CHARLS (2011-2013).

* p < 0.05, ** p < 0.01, *** p < 0.001

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Figure 1. Predicted Probability of Receiving Any Type of Functional Help by Summary of ADL Limitations, Baseline, HRS and CHARLS

Note: Data pooled from HRS (2010-2012) and CHARLS (2011-2013). ADL = Activities of Daily Living. Model controlled for sociodemographic status, health status, and baseline IADL limitation. SUM_ADL is the summary score of ADL limitation items ranged from 0 to 5. Country*SUM_ADL is statistically significant at the .05 level.

.2 .4 .6 .8 1 Pr(Anyhelp) 1 2 3 4 5 SUM_ADL HRS CHARLS

Predicted Probability of Receiving Any Help at Baseline

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Figure 2. Predicted Probability of Receiving Functional Help from Children by Age, Baseline, HRS and CHARLS

Note: Data pooled from HRS (2010-2012) and CHARLS (2011-2013). Model controlled for sociodemographic status, health status, and baseline ADL and IADL limitation. Country*age is statistically significant at the .001 level.

.2 .4 .6 Pr(Childhelp) 50 55 60 65 70 75 80 85 90 95 100 105 Age at Baseline HRS CHARLS

Predicted Probability of Receiving Children's Help at Baseline