Living Arrangements and Sleep-Related Outcomes Among Older Adults in China

Abstract

It has been discussed previously that older adults’ living arrangements are associated with mortality. This study investigated the relationships between older adults’ living arrangements and sleep-related outcomes in China. The nationally representative sample included 4,731 participants who participated on two different occasions, with a total of 9,462 observations (2012 and 2014 waves). Panel logistic regression and panel ordinary least squares regression models were estimated with outcomes of sleep quality and average hours of sleep daily, respectively. Approximately 62% of individuals reported good quality of sleep. We observed that older adults who lived with family members had 17% greater odds of reporting good quality of sleep (adjusted odds ratio = 1.17, 95% confidence interval [1.03, 1.34], p < .05) and reported longer sleep duration daily (β = .334, standard error = .069, p < .01), compared with those who lived alone. Social support is needed to strengthen the residential relationship, especially with family members.

Keywords

older adults sleep quality sleep duration living arrangements China panel analysis

Introduction

Living arrangements among Chinese older adults have changed over the years with the aging of the overall population and decline in fertility (Lei, Strauss, Tian, & Zhao, 2015). Lack of a strong government support system like the social security system in the United States has left much of the responsibility of taking care of the older adults from a financial point of view to their families, especially in rural areas (Lei et al., 2015). Those living alone compared with living with family members such as children, despite lower activities of daily living (ADLs) disability, also self-rated their health more poorly and men in particular had increased mortality risks, compared with their counterparts with living arrangements that included their children (Li, Zhang, & Liang, 2009). Among older Chinese women, there was an association between living alone and depression (Chou, Ho, & Chi, 2006). Henceforth, living arrangement could be an important factor associated with health among older adults in general.

In a study of people over the age of 80 years in China, two of the most significant predictors of life satisfaction were health and economic status (Ng, Tey, & Asadullah, 2017). In China, it is reasonable to infer that those who live alone and face financial difficulty likely have lower satisfaction with their lives, and they likely experience more stress than those who have higher satisfaction with life, given that older adults’ loneliness is connected with lower income and never-married status in the United States (Anderson, 2010). A study by Feng, Falkingham, Liu, and Vlachantoni (2017) found differences in mortality based on living arrangements. For those who lived in institutions, or those moved into institutions after living with families, mortality was higher (Feng et al., 2017). Although traditional Chinese belief posits that family members should extend and provide care to older adults, this is no longer a long-term option for most Chinese to consider due to the rapid change in the Chinese social structure (Zhang & Goza, 2006). The disconnection between modern social structure and the traditional belief that family members should provide care to older adults has become challenges to Chinese society.

Other than living arrangements, Chinese older adults also suffer from increasing prevalence of sleep-related issues (Gu, Sautter, Pipkin, & Zeng, 2010). Sleep has been found to be associated with mortality, with both short and long sleep durations being predictors of death (Cappuccio, D’Elia, Strazzullo, & Miller, 2010). Among young adults, a robust association between loneliness and poor quality of sleep was observed (Matthews et al., 2017). A similar phenomenon is seen in the elderly population, with a study of Taiwanese older adults finding that social isolation has an adverse effect on quality of sleep (Yu, Steptoe, Niu, Ku, & Chen, 2018). With the gaps of knowledge, literature regarding living arrangements and sleep-related outcomes among older adults using a nationally representative study sample remains limited, in particular those residing in China.

Given the current living arrangements of many Chinese older adults due to the aforementioned environment that has been created, it is a topic worth exploring. With the association between social isolation and poorer sleep quality and the likelihoods of a suboptimal sleep pattern affecting mortality, the relationship between Chinese older adults' living arrangement and sleep-related outcomes should be examined. This relationship would suggest policy implications such as educating those in certain living arrangements that sleep hygiene indirectly is a risk factor for higher mortality. To address the research gap, we hypothesized that older adults’ living arrangements are associated with sleep-related outcomes in China. Further practical implications are discussed.

Materials and Methods

Data Source

We extracted longitudinal panel data from the 2012 (collected between 2011 and 2012) and 2014 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). CLHLS was established by international collaborators at the Center for the Study of Aging and Human Development at Duke University (Zeng, Vaupel, Xiao, Liu, & Zhang, 2017). The study subjects of CLHLS include a wide range of topics including substance use (such as cigarette and alcohol uses), dietary behavior, mental health, cognitive function, social policy (such as social health insurance), disease status, and others. The surveyed region of CLHLS covers approximately 85% of the populations in major provinces and mega cities, targeting a substantial amount of centenarians, nonagenarians, and octogenarians, with randomly selected study participants. Investigators of CLHLS conducted face-to-face interviews for data collection. Informed consents were provided by the study participants. Reliability, validity, and consistency of CLHLS measurements were quite high, with good quality data (Gu, 2008). Refer to Zeng (2012) for further information. Because CLHLS is a secondary and deidentifiable data set in the public domain, institutional review board approval was not required for this research.

Measurements and Variables

This research had two outcome variables. The first outcome was a self-assessed measurement to determine whether the study participant had good quality of sleep (coded as good or not good; a dichotomous variable). We classified participants who reported good and very good quality of sleep as good. Those who reported neutral, bad, and very bad quality of sleep were categorized as not good. This categorization was based on two previous studies (Gu et al., 2010; Lee, Chang, Liu, & Shelley, 2019). Dichotomizing the sleep quality variable helps reduce the potential modeling complications related to a skewed distribution for the outcome variable, given that the majority of participants reported good quality of sleep based on raw data. The second outcome variable was older adults’ average hours of sleep daily (a continuous variable, measured in hours). All sleep-related outcomes were self-reported by the participants through interviews with the CLHLS investigators.

The major predictor was older adults’ living arrangements, classified into three categories (a categorical variable): lived alone, lived with household members (i.e., older adults’ children or family relatives), and lived in an institution (i.e., nursing home).

We selected participants’ age in years (categorized as 65–80, 81–95, and above 95; a categorical variable) and sex (male and female; a dichotomous variable) as biological determinants. Socioeconomic determinants in this research included marital status (married and others [including not married, divorced, or widowed]; a dichotomous variable), years of formal education (none, 1–5, 6–10, and above 10; a categorical variable), and household income. We categorized older adults’ household income into six groups: first quintile (lower or equal to 10,000 RMB), second quintile (10,001–30,000 RMB), third quintile (30,001–50,000 RMB), fourth quintile (50,001–70,000 RMB), fifth quintile (higher than 70,001 RMB), and those who did not know their household income. We coded the household income as a categorical variable.

To provide participants’ basic regional information, community of residence and geographical regions were selected as surrogate variables. Community of residence was categorized as urban or rural (a dichotomous variable). Geographical regions included major megacities and provinces in China (a categorical variable): (a) North: Beijing, Hebei, Shanxi, and Tianjin; (b) Northeast: Liaoning, Jilin, and Heilongjiang; (c) East: Shanghai, Jiangsu, Anhui, Zhejiang, Jiangxi, Fujian, and Shandong; (d) Central–South: Henan, Hunan, Hubei, Guangdong, Guangxi, and Hainan; and (e) West: Chongqing, Sichuan, and Shaanxi.

To describe older adults’ physical and mental health status, we selected a total of five variables. Older adults’ current exercise status assessed whether the participant was physically active (no and yes; a dichotomous variable). Number of times suffering from chronic diseases that required inpatient treatments was selected to examine older adults’ overall chronic condition (none, 1–2 times, and above 2 times; a categorical variable). The term past two years refers to the 2 years prior to when older adults’ answered the questionnaire. Participants’ life satisfaction was categorized into four groups: good, neutral, bad, and not able to answer (a categorical variable). To determine whether an older adult suffered from depression, we selected a question about whether they felt sad or blue (no, yes, and not able to answer; a categorical variable). Disability with ADLs examines older adults’ disability status (not limited, strongly limited, and somewhat limited; a categorical variable). ADLs include bathing, dressing, eating, indoor transferring, toileting, and continence.

Statistical Analyses and Study Sample

A panel logistic regression model was employed to estimate older adults’ quality of sleep, and a panel ordinary least squares (OLS) regression model was used to estimate participants’ average hours of sleep daily. The panel analytic approach enables the researchers to examine changes in living arrangement over time by accounting for intraindividual variability, compared with the conventional cross-sectional study design (Hsiao, 2007). In addition to providing more robust conclusions, panel analysis heightens the capacity to account for the complexity of human behavior (Hsiao, 2007).

For statistical analyses, we used only unweighted measurements. Although sampling for the CLHLS data set is based on participants’ age, sex, and community of residence (Gu et al., 2010), the sampling scheme does not include important compositional variables that could be used for data weighting. Under such circumstance, weighted regression could inflate standard errors (SEs; Gu et al., 2010), making the regression estimates less accurate.

To perform longitudinal panel analyses, we selected a total of 6,009 older adults aged 65 years and above who answered survey questions on two different occasions—the 2012 and 2014 rounds of survey. Participants who responded to only one of the surveys or had missing information were excluded, with a total of 5,552 and 5,077 participants retained in the 2012 and 2014 waves, respectively. To avoid extreme values, permanently bedridden individuals were also deleted from the analyses. After we completed these steps, we merged two waves together with complete answers to questions of interests. The final study sample included 9,462 observations from a total of 4,731 participants, with 2,252 males and 2,479 females.

Adjusted odds ratios and 95% confidence intervals were reported for the panel logistic regression model. Standardized regression coefficients (β) and SEs were reported for the panel OLS regression model. All statistical interpretations were two-tailed using p < .05 to denote statistically significant results. We used R (version 3.4.3) and its package “pglm” for all statistical analyses (Croissant, 2017). All statistical models were controlled for the aforementioned major predictor and sociodemographic covariates.

Results

Figure 1 shows the observed prevalence of good quality of sleep and average hours of sleep daily within different age groups among the older adults in the study sample. Table 1 provides the descriptive statistics of all selected variables in the final study sample based on 9,462 responses from 4,731 participants. Approximately 62% of older adults reported good quality of sleep (Table 1). The mean of average sleep period daily was around 7.60 hours with standard deviation 2.33. Most older adults lived with household members (80.7%), fewer than 2% lived in an institution, and approximately 18% lived alone. A majority of the participants were at least 80 years old and not married. Most older adults had household income less than the third quintile and had lower levels of education. Most participants resided in the East, Central, and South regions. More than 60% of older adults did not exercise. Most older adults did not suffer from chronic conditions in the last 2 years, did not report depression-related symptoms, and did not have ADL disability. More than 60% of participants reported good quality of life (Table 1).

Figure 1.

Observed prevalence of good quality of sleep and average hours of sleep daily within different age groups: Chinese Longitudinal Healthy Longevity Survey, 2012 to 2014.

Table 1.

Descriptive Statistics of Variables in the Final Study Sample Based on 4,731 Participants (n = 9,462): Chinese Longitudinal Healthy Longevity Survey, 2012 to 2014.

Variables	Quality of sleep	Quality of sleep	Overall
	Not good (n = 3,615, 38.2%)	Good (n = 5,847, 61.8%)	(n = 9,462)
	n (%)	n (%)	n (%)
Major predictors
Living arrangements
Lived alone	730 (20.2)	971 (16.6)	1,701 (18.0)
Lived with family members	2,833 (78.4)	4,800 (82.1)	7,633 (80.7)
Lived in an institution	52 (1.4)	76 (1.3)	128 (1.4)
Control variables
Age (in years)
65–80	1,577 (43.6)	2,614 (44.7)	4,191 (44.3)
81–95	1,563 (43.2)	2,382 (40.7)	3,945 (41.7)
Above 95	475 (13.1)	851 (14.6)	1,326 (14.0)
Sex
Male	1,481 (41.0)	3,023 (51.7)	4,504 (47.6)
Female	2,134 (59.0)	2,824 (48.3)	4,958 (52.4)
Marital status
Married	1,496 (41.4)	2,739 (46.8)	4,235 (44.8)
Others	2,119 (58.6)	3,108 (53.2)	5,227 (55.2)
Household income
1	1,513 (41.9)	2,120 (36.3)	3,633 (38.4)
2	932 (25.8)	1,593 (27.2)	2,525 (26.7)
3	457 (12.6)	855 (14.6)	1,312 (13.9)
4	187 (5.2)	357 (6.1)	544 (5.7)
5	283 (7.8)	597 (10.2)	880 (9.3)
Do not know	243 (6.7)	325 (5.6)	568 (6.0)
Years of formal education received
None	2,087 (57.7)	2,977 (50.9)	5,064 (53.5)
1–5	903 (25.0)	1,559 (26.7)	2,462 (26.0)
6–10	507 (14.0)	1,088 (18.6)	1,595 (16.9)
Above 11	118 (3.3)	223 (3.8)	341 (3.6)
Community of residence
Urban	1,724 (47.7)	2,879 (49.2)	4,603 (48.6)
Rural	1,891 (52.3)	2,968 (50.8)	4,859 (51.4)
Geographical regions
North	116 (3.2)	238 (4.1)	354 (3.7)
Northeast	179 (5.0)	351 (6.0)	530 (5.6)
East	1,171 (32.4)	2,501 (42.8)	3,672 (38.8)
Central or South	1,607 (44.5)	2,057 (35.2)	3,664 (38.7)
West	542 (15.0)	700 (12.0)	1,242 (13.1)
Exercise status
No	2,473 (68.4)	3,684 (63.0)	6,157 (65.1)
Yes	1,142 (31.6)	2,163 (37.0)	3,305 (34.9)
Number of times suffering from chronic diseases that required inpatient treatments
None	2,622 (72.5)	4,678 (80.0)	7,300 (77.2)
1–2	825 (22.8)	1,033 (17.7)	1,858 (19.6)
Above 2	168 (4.6)	136 (2.3)	304 (3.2)
Life satisfaction
Good	1,873 (51.8)	3,981 (68.1)	5,854 (61.9)
Neutral	1,356 (37.5)	1,521 (26.0)	2,877 (30.4)
Bad	263 (7.3)	135 (2.3)	398 (4.2)
Not able to answer	123 (3.4)	210 (3.6)	333 (3.5)
Depression
No	2,448 (67.7)	4,720 (80.7)	7,168 (75.8)
Yes	661 (18.3)	506 (8.7)	1,167 (12.3)
Not able to answer	506 (14.0)	621 (10.6)	1,127 (11.9)
Disability with activity of daily living
Not limited	2,181 (60.3)	4,300 (73.5)	6,481 (68.5)
Strongly limited	443 (12.3)	437 (7.5)	880 (9.3)
Somewhat limited	991 (27.4)	1,110 (19.0)	2,101 (22.2)

Note. Mean of participants’ average daily hours of sleep: 7.60 hours (standard deviation: 2.33).

Table 2 provides the results of panel logistic and OLS regression models. Older adults who lived with household members had 17% greater odds of reporting good quality of sleep (adjusted odds ratios = 1.17, 95% confidence interval [1.03, 1.34], p < .05), compared with older adults who lived alone. Living in an institution was not associated with quality of sleep, although we also found higher odds of living in an institution and better quality of sleep, compared with older adults living alone. Participants who lived with household members (β = .334, SE = .069, p < .01) and lived in an institution (β = .430, SE = .211, p < .05) reported longer sleep durations than those who lived alone.

Table 2.

Panel Regression Models Examining the Associations Between Living Arrangements and Quality of Sleep and Between Living Arrangement and Daily Hours of Sleep: Chinese Longitudinal Healthy Longevity Survey, 2012 to 2014.

	Model 1: Quality of sleep (panel logistic regression)	Model 2: Daily hours of sleep (panel OLS regression)
	AOR [95% CI]	β, SE
Major predictors
Living arrangements
Lived alone	—^a,^b	—^a
Lived with family members	1.17 [1.03, 1.34]*	.334, .069**
Lived in an institution	1.15 [0.77, 1.73]	.430, .211*
Control variables
Age (in years)
65–80	—	—
81–95	1.02 [0.91, 1.13]	.182, .055**
Above 95	1.37 [1.16, 1.61]**	.828, .084**
Sex
Male	—	—
Female	0.68 [0.61, 0.76]**	−.415, .055**
Marital status
Married	—	—
Others	0.97 [0.87, 1.09]	.233, .059**
Household income
1	—	—
2	1.10 [0.98, 1.24]	.156, .062*
3	1.14 [0.98, 1.33]	.147, .078
4	1.12 [0.91, 1.39]	.140, .109
5	1.12 [0.94, 1.34]	.330, .091**
Do not know	1.08 [0.89, 1.32]	.148, .104
Years of formal education received
None	—	—
1–5	1.03 [0.91, 1.16]	.039, .062
6–10	1.14 [0.99, 1.32]	−.046, .075
Above 11	0.84 [0.64, 1.09]	−.466, .136**
Community of residence
Urban	—	—
Rural	1.02 [0.93, 1.13]	−.025, .050
Geographical regions
North	—	—
Northeast	1.04 [0.76, 1.43]	−.277, .160
East	1.15 [0.89, 1.49]	−.352, .131**
Central or South	0.68 [0.53, 0.89]**	−.465, .132**
West	0.69 [0.53, 0.92]**	−.455, .141**
Exercise status
No	—	—
Yes	1.12 [1.01, 1.25]*	−.063, .053
Number of times suffering from chronic diseases that required inpatient treatments
None	—	—
1–2	0.80 [0.71, 0.90]**	−.083, .060
Above 2	0.57 [0.44, 0.73]**	−.437, .135**
Life satisfaction
Good	—	—
Neutral	0.60 [0.55, 0.67]**	−.242, .054**
Bad	0.36 [0.28, 0.46]**	−.706, .123**
Not able to answer	1.32 [0.99, 1.75]	.602, .150**
Depression
No	—	—
Yes	0.55 [0.47, 0.63]**	−.373, .075**
Not able to answer	0.68 [0.58, 0.80]**	.075, .086
Disability with activity of daily living
Not limited	—	—
Strongly limited	0.60 [0.50, 0.70]**	.184, .087*
Somewhat limited	0.65 [0.58, 0.73]**	.002, .060

Note. SE = standard error; CI = confidence interval; OLS = ordinary least squares; AOR = adjusted odds ratio.

^aReference level (—).

^bReference level of panel logistic regression = 1.00.

*p < .05. **p < .01.

Discussion

This research investigated Chinese older adults’ living arrangements and sleeping patterns using a panel analysis with a large nationally representative sample, CLHLS. Model results demonstrated that older adults’ living arrangements were associated with quality of sleep and average sleep duration daily. In general, older adults who lived with household members reported better quality of sleep and had longer sleep duration, compared with those who lived alone. Older adults who lived in an institution also had longer sleep duration daily than participants who lived alone, but this significant association was not observed for quality of sleep. In previous research, sleep quality and sleep duration are equally important predictors of human health (Bin, 2016; Knutson, Ryden, Mander, & Van Cauter, 2006). In this research, the results of living with family members were all significantly and consistently associated with both sleep-related measurements. Therefore, living with family members might be a critical arrangement that helps older adults in general.

Our research findings regarding the association of living with household members and better quality of sleep among older adults are consistent with previous regional findings that older adults who lived alone had a greater chance of poor sleep quality in Shanghai (Luo et al., 2013). We also observed that lower life satisfaction or depression among older adults was associated with lower odds of good quality of sleep. Similarly, a literature review by Thapa, Visentin, Kornhaber, and Cleary (2018) concluded that older adults, who were left behind when their children migrated, experienced more problems related to depression and psychological health as compared to older adults who did not have migrant children. This pattern could be stronger in China, a country with strong emphasis on filial piety, a traditional belief of family care of older adults (Benjamin, Brandt, & Rozelle, 2000) to help ensure continuation of the family tree (Teon, 2016).

Family bonding plays a key role in Chinese society, and family relationship and well-being could be enhanced through multigenerational living along with the associations of sleeping patterns found in this research. It is imperative to maintain family bonding among older adults, given that mental health and sleep go hand in hand in the elderly population (Reid et al., 2006). With rapid changes in Chinese society, living with family members is not the only option to practice filial piety (Silverstein, Cong, & Li, 2006), but this living arrangement could be associated with better sleep among older adults. Further health promotional strategies should continue to strengthen the role of family relationship and related health outcomes such as sleeping patterns among Chinese older adults.

The importance of older adults’ living arrangements can also be found in other areas, in which case our study supplements the body of literature providing the evidence and importance of living with family members among Chinese older adults. Feng et al. (2017) found that living in an institution is associated with higher mortality rates for Chinese older adults, but living with family members is not associated with a dramatically increased mortality risk. In the same vein, living with family members is positively associated with life satisfaction among Chinese older adults (Ng et al., 2017; Sereny, 2006). A similar observation of living with household members and better life satisfaction has been found in the older adult population in Malaysia (Kooshiar, Yahaya, Hamid, Abu Samah, & Sedaghat Jou, 2012), another dynamic developing country with rapid urbanization.

Our study provides practical implications for further health promotion, given that sleep is an important resting behavior to improve well-being and human health. The Chinese central government should continue to promote the importance of living with family members and its effect on related health consequences including better sleep. Although living with family members is associated with better health, higher life satisfaction, and better sleep, Chinese families and older adults should also pay attention to other social issues, especially for older adults living alone.

In fact, the proportion of older adults living alone is increasing consistently (Lei et al., 2015). However, China does not have a robust public social security system for supporting older adults (Lei et al., 2015). Large social disparity still persists in the Chinese welfare system such as the public insurance plans or health-care utilization (Lee, Chiang, Shelley, & Liu, 2018; Yu, 2015). When family members do not live with older adults, the older adults may not have sufficient financial resources and related welfare to maintain good quality of life. In one case, preventative care utilization is strongly associated with better life satisfaction among Chinese older adults (Lee, Chang, & Liu, 2019), but household income is a strong factor accounting to the preventative care utilization in general (Liu et al., 2016). Accordingly, the Chinese central government should promote living arrangements with family members or residential support from family. Even with a relatively weak social system, the benefits of living with family members might reduce such concerns and decrease the burden of disease.

This research has several strengths, including a nationally representative sample and a panel analytic approach for studying older adults’ sleeping behavior in two dimensions. However, we should point out several study limitations. The sleep-related measurements in this research all were self-reported. The CLHLS investigators did not use sleep-related instruments, such as The Pittsburgh Sleep Quality Index, to assess older adults’ sleeping patterns. The self-reported measurements could induce bias. However, self-reported bias is a common limitation for most survey-based research. In fact, the previous literature has shown consistency between self-reported health outcomes and objective health conditions (Wu et al., 2013). In this case, the self-reported outcomes should provide sufficient information of participants’ quality of sleep and its duration. Furthermore, as the CLHLS data provide only basic information regarding older adults’ sleep, the CLHLS questionnaire did not ask about participants’ sleep-related problems such as insomnia or hypersomnia. Further research should attempt to resolve such limitations by studying sleep-related diseases.

On the other hand, our target population of this research was Chinese older adults, so we should be careful not to generalize our study findings to Western countries due to traditional Chinese characteristics of filial piety. In addition, a previous study has found positive associations between living with family members and life satisfaction among Malaysian older adults (Kooshiar et al., 2012). If this is unique to Eastern culture, we may generalize our research findings to other Asian countries. Further research is warranted to investigate similar topics of interest in the Western world. Finally, as the number of older adults living in an institution was relatively small in the study sample, we should be careful not to make a conclusive claim regarding this matter. Other investigators with similar research interests should examine the sleeping patterns among older adults who resided in institutions.

Conclusion

This research adds to the body of literature in the study of sleep by investigating the associations of older adults’ living arrangements with quality of sleep and average hours of sleep daily. Older adults who lived with family members had higher odds of reporting better quality of sleep and longer sleeping duration. The family bonding through living arrangements could help increase the odds of better quality of sleep among older adults. When older adults live with family members, family relationships can be enhanced and older adults could enjoy higher life satisfaction, in turn improving the quality of sleep. The Chinese central government has planned to launch reforms to promote healthier lifestyles in China. With increasing sleep-related problems in modern China (Gu et al., 2010), it is imperative for public health practitioners and strategists to increase Chinese older adults’ quality of sleep through appropriate family support.

Footnotes

Acknowledgments

Data used for this research were provided by the Chinese Longitudinal Healthy Longevity Survey (CLHLS) managed by the Center for Healthy Aging and Development Studies, Peking University. CLHLS is supported by funds from the U.S. National Institutes on Aging, China Natural Science Foundation, China Social Science Foundation, and United Nations Population Fund. The authors thank research participants and researchers for their efforts in collecting the CLHLS data.

Ethical Approval

The authors used only secondary data sets for this study and data analyses. Thus, approvals from Institutional Review Board were not required.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Yen-Han Lee

Mack Shelley

Author Biographies

Yen-Han Lee is a doctoral candidate at Indiana University Bloomington School of Public Health. His research interests are related to behavioral aspects of global health including substance use, lifestyle among older adults, and health policy.

Yen-Chang Chang is an associate professor and associate director of Center for General Education at National Tsing Hua University (Taiwan). His recent research interests are related to applied statistics in the public health field.

Timothy Chiang is a graduate student at Pennsylvania State University College of Medicine (Hershey). His interests are global health, healthcare, and social disparity of health.

Ching-Ti Liu is a professor of Biostatistics at Boston University School of Public Health. Dr. Liu's main area of research is statistical applications to the biomedical and public health research.

Mack Shelley is the Department Chair of Political Science and University Professor at Iowa State University. Dr. Shelley's research interests cover gerontology, public health, nutrition, and health policy.

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