Abstract
This study examined the regional and geographical disparities in body mass index (BMI) among Chinese older adults. Using panel data from the Chinese Longitudinal Healthy Longevity Survey, participants included 3,740 older adults (age ≥ 65 years) who answered all three waves of the survey (2009–2014). Sex-stratified and multistate Cox regression was used to examine the disparities in BMI change. Results showed that both older males and older females who resided in the central-south had lower rates of weight change from nonobese to obese, compared with those from the east. Older females from urban regions had higher rate of weight change from nonobese to obese, compared with rural participants (hazard ratio [HR]: 1.35, 95% confidence interval [CI] = [1.13, 1.60]; p < .01). However, there were no disparities between urban and rural areas among older males (p > .05). These results provided practical implications for regional and geographical disparities in BMI among Chinese older adults.
Introduction
Since the early 21st century, China has experienced rapid urbanization. The dramatic urbanization is a key to China’s successful economy; nevertheless, this urbanization also has changed people’s lifestyle (B. Zhang et al., 2014). Many public health problems, such as obesity, hypertension, physical inactivity, change of dietary behavior, and other chronic conditions, have surfaced (Gong et al., 2012; Ng et al., 2014; Shen et al., 2019). With the escalating prevalence of chronic diseases in China, the burden of disease could challenge the health care system. For example, the estimated total losses associated with cardiovascular diseases, cancer, chronic respiratory diseases, diabetes, and mental health conditions in China are US$7.7 trillion between 2010 and 2030 (Bloom et al., 2018). Therefore, it is imperative to combat the public health challenges that have become prominent.
In addition to these public health challenges, another concern is related to the drastic increase of the older adult population in China. The proportion of the nation’s population comprised by older adults, 65 years or above, is predicted to triple, reaching 26.9% by 2050, from 8.2% in 2010 (Fang et al., 2015). An aging population goes hand-in-hand with chronic diseases. Chronic diseases account for an estimated 80% of total deaths in China and approximately 70% of the overall loss of disability-adjusted life years (DALYs) from sources including stroke, chronic obstructive pulmonary disease, and ischemic heart diseases, which were the leading causes of death in 2010 (Fang et al., 2015; L. Wang et al., 2005; Yang et al., 2013). Furthermore, there are more than 100 million adults with diabetes and 177 million with hypertension in China (Fang et al., 2015). The prevalence of prediabetes among Chinese middle-aged and older adults (40 years old and above) was 35.7% in 2013, much higher than 15.5% in 2008 (C. Hu & Jia, 2018). More than 50% of Chinese older adults have hypertension, a leading cause of stroke and myocardial infarction, and the prevalence reaches 90% for adults who are 80 years old or above (Hua et al., 2019).
China is facing an imminent obesity epidemic. The country certainly also is facing the growing prevalence of chronic diseases, and one of the leading risk factors associated with these chronic diseases is obesity. China has one of the largest prevalence rates of overweight or obese population worldwide (46%) (Y. Wang et al., 2019), and has an increasing trend of the incidence of obesity and chronic diseases (Y. Wang et al., 2017). Among the Chinese adult population, the prevalence of overweight tripled from 11.7% to 29.2% between 1991 and 2009, and urbanicity was positively associated with body mass index (BMI) (Gordon-Larsen et al., 2014). In a cross-sectional study, nearly 23.1% and 2.6% of community dwelling older adults were overweight and obese, respectively (You et al., 2018). The increasing trend of obesity and chronic diseases could make the older adult population more vulnerable, and the burden of disease among older adults will continue to grow. However, knowledge regarding the longitudinal trends of obesity among Chinese older adults remains limited. Further actions and research efforts targeting older adults are needed to combat obesity in China.
On top of these public health challenges regarding obesity, the growth of the older adult population, and prevalence of chronic diseases, China is a country with large regional and community disparities. In a cross-sectional study using the Chinese National Survey on Students’ Constitution and Health targeting Chinese children, Dong and colleagues (2019) observed that the prevalence of overweight or obese was higher in urban areas as compared with rural regions. In addition, the prevalence of overweight or obese was higher in eastern provinces but lower in western regions (Dong et al., 2019). Substantial adverse health outcomes were observed (Yiengprugsawan et al., 2019) in both well-developed areas (e.g., Shandong) and less-developed regions (e.g., Hubei and Jilin). A study focusing on geographic aspects of older adults’ health inequality in China found that older adults residing in the Yangtze River Delta region were significantly healthier than those who lived in Gansu and Hainan regions (Fan et al., 2019). The large social gaps that continue to exist between regions and between rural and urban areas might be associated with discrepancies in many health indicators (Meng et al., 2012). With such disparities, obesity also could be a critical health indicator affecting older adults’ health condition in general.
With the aforementioned findings, it is imperative to examine geographical and regional disparities of obesity among Chinese older adults, given that the currently available literature remains limited on this topic of interest. We used a publicly available data set, the Chinese Longitudinal Healthy Longevity Survey (CLHLS), to study the longitudinal trends of geographical and regional disparities among Chinese older adults. Public health practitioners, scholars, and social workers could use the results from this research for health promotion and obesity prevention among Chinese older adults.
Material and Method
Study Sample
Data were extracted from three waves of CLHLS: 2009 (collected between 2008 and 2009), 2012 (collected between 2011 and 2012), and 2014. The first wave of CLHLS was established in 1998. Data were collected every 2 or 3 years. CLHLS is a large and nationally representative database established by the Center for the Study of Aging and Human Development at Duke University with international collaborators from the China Social Sciences Foundation, the Max Planck Institute for Demographic Research, the Hong Kong Research Grants Council, and others. The CLHLS investigators conducted face-to-face interviews for data collection. The CLHLS investigators attempted to review centenarians in randomly selected half of the cities or counties in the surveyed provinces and mega cities. Nearby octogenarian, nonagenarian, and older adults between 65 and 79 years old, and predesignated sex and age, were selected for each interviewed centenarian.
Research participants include centenarians, nonagenarians, octogenarians, younger older adults 65 to 79 years old, and middle-age adults 35 to 64 years old. Participants were randomly selected from major provinces and mega cities. The study subjects of CLHLS cover a wide range of topics including substance use (smoking behavior and alcohol use), social policy, family relationships, mental health, disability, health status, dietary behaviors, and other related topics. The CLHLS investigators obtained informed consents from all research participants. As this present research uses only secondary data with de-identified information in the public domain, approvals from Institutional Review Boards were exempted from the authors’ institutions. Zeng (2012) provides further information about this data set.
To perform longitudinal analysis, we created two sex-stratified study samples with 1,815 male and 1,925 female participants who answered all questions of interest without missing values. Older adults, 65 years old or above, who were included in the study sample had to respond to all three survey waves in 2009, 2012, and 2014. The total study sample included 11,220 observations from 3,740 participants. Each participant provided three observations (n = 11,220).
Measurements
Primary predictors
There were two primary predictors in the data analysis. The first predictor was older adults’ provincial information, which indicated their geographical location (east/north/northeast/central-south/west). The geographical categories included the following provinces and mega cities: Jiangsu, Anhui, Zhejiang, Shandong, Fujian, Jiangxi, and Shanghai were coded as East; Hebei, Tianjin, Shanxi, and Beijing were coded as North; Shaanxi, Sichuan, and Chongqing were coded as West; Henan, Hubei, Hunan, Guangdong, Guangxi, and Hainan were coded as Central-South; and Heilongjiang, Jilin, and Liaoning were coded as Northeast. This categorization method was based on the classification of the CLHLS investigators (https://sites.duke.edu/centerforaging/programs/chinese-longitudinal-healthy-longevity-survey-clhls/project-goals/coverage-of-sampled-provinces/).
The second predictor was participants’ location of residence, which was dichotomized as rural and urban. Participants who lived in cities and towns were coded as urban, while older adults who resided in smaller units were coded as rural.
Outcome
In this research, older adults’ body max index, BMI = weight (kg)/height (m2), was used to examine participants’ level of obesity. Two categories of BMI were employed for data analysis: nonobese (BMI less than 25.0, including those who were underweight or normal) and obese (BMI higher than or equal to 25.0, including those who were overweight or obese). We coded BMI as a dichotomous variable, with 0 = nonobese, and 1 = obese. All BMI-related values were measured by the data collectors of CLHLS.
Covariates
Differences among age groups were incorporated into the analysis (65–80, 81–95, above 95). A set of socioeconomic variables were selected to represent older adults’ socioeconomic status: years of formal education (none, 1–5 years, 6–10 years, 11 years and above), and household income (1: between 0 and 5,000 RMB, 2: between 5,001 and 15,000 RMB, 3: between 15,001 and 35,000 RMB, 4: above 35,000 RMB, do not know). Household income was calculated based on the Chinese currency (RMB). Furthermore, marital status (married, others [including participants who were not married, widowed, separated, divorced, and in other nonmarried status]) and living arrangement (lived with household members, lived alone, lived in an institution) were selected as demographic measurements.
A set of variables was selected to capture older adults’ substance use status, physical activity, and health condition. These measurements included exercise status (0 = no, 1 = yes), alcohol consumption (0 = no, 1 = yes), smoking status (0 = no, 1 = yes), and chronic conditions that required inpatient treatments in the past 2 years (0 = no, 1 = yes).
Statistical Analyses
Sex-stratified models were estimated to examine the effect of rural-urban location and geographical regions on older adults’ BMI. The Cox regression was employed. We applied the multistate model for the Cox regression in our analyses. The advantage of using this approach is that it facilitates examination of differences in BMI change over time (Le-Rademacher et al., 2018; Meira-Machado et al., 2009). The investigated BMI change over time included two stages: (a) From nonobese to obese and (b) From obese to obese. Estimates from this model are reported as hazard ratios (HRs) with 95% confidence intervals (95% CI). All statistical tests were two-tailed with level of significance of .05 (p-value < .05). All multivariable models were controlled for the same set of predictors and covariates. We conducted statistical analyses using the package “survival” (Therneau, 2020) with R statistical software (version 3.6.2).
Results
Figure 1 is a line chart, which shows the trends of BMI among male and female older adults in the three survey waves. In both sex-stratified groups, the majority of older adults had normal BMI. For both male and female older adults, the prevalence of nonobese decreased over time, but obese increased, between 2009 and 2014.
Percentage of males and females at each BMI level between 2009 and 2014 waves: The Chinese Longitudinal Healthy Longevity Survey (CLHLS), 2009–2014.
Table 1 provides descriptive statistics for the sex groups in three waves of survey. Most male older adults resided in eastern and central-southern provinces. Approximately 51.5% lived in a rural community. Most participants were between 65 and 80 years old, received a lower level of education, were married, lived with household members, and had lower household income (third quartile or below). The majority of male older adults did not exercise, smoke, consume alcohol, or have any chronic conditions that required inpatient care in the past 2 years.
Descriptive Statistics of Final Study Sample: The Chinese Longitudinal Healthy Longevity Survey (CLHLS), 2009–2014 (n = 11,220).
Note. BMI = body mass index.
Most female older adults resided in eastern and central-southern provinces and approximately 52.7% lived in a rural community. Most participants were between 65 and 80 years old, received a lower level of education, lived with household members, and had lower household income (third quartile or below). However, most female older adults were not married. Similar to male participants, the majority of female older adults did not exercise, smoke, consume alcohol, or have any chronic conditions that required inpatient care in the past 2 years.
Table 2 shows the descriptive statistics of BMI change in different time periods among study participants. In both sexes, the proportion of participants maintaining nonobese condition declined. The condition of change from nonobese to obese also decreased in different time periods in both sexes. However, the condition of remaining obese increased in both sexes (Table 2).
Descriptive Statistics of Body Mass Index (BMI) Change in Different Time Periods Among Study Participants: The Chinese Longitudinal Healthy Longevity Survey (CLHLS), 2009–2014.
Tables 3 and 4 show the results of Cox regression models, estimating the associations of regional and geographical disparities with BMI among Chinese older adults. In Table 3, male older adults who lived in the central-south (HR = 0.67, 95% CI = [0.54, 0.85], p < .01) had lower propensity of reporting transition from nonobese to obese, compared with older adults living in the east. Participants’ region was not associated with older male adults’ BMI.
Results of Sex-Stratified (Male) Cox Regression Examining the Associations of Community and Geographical Regions With BMI Among Chinese Older Adults: CLHLS, 2009–2014 (n = 5,445).
Note. Reference level of each categorical or dichotomous variable: HR = 1.00. HR = hazard ratio; CI = confidence interval.
p <.05. **p <.01.
Results of Sex-Stratified (Female) Cox Regression Examining the Associations of Community and Geographical Regions With BMI Among Chinese Older Adults: CLHLS, 2009–2014 (n = 5,775).
Note. Reference level of each categorical or dichotomous variable: HR = 1.00. HR = hazard ratio; 95% CI = 95% confidence interval.
p < .05. **p < .01.
As shown in Table 4, female residents who lived in urban regions had 35% higher propensity of reporting transition from nonobese to obese (HR = 1.35, 95% CI = [1.13, 1.60], p < .01). Female older adults from the north had higher propensity of transition from nonobese to obese (HR = 1.57, 95% CI = [1.11, 2.23], p < .05) and higher propensity of maintaining obese status (HR = 1.69, 95% CI = [1.20, 2.40], p < .01). Female participants from the northeast had higher propensity of transitioning from nonobese to obese (HR = 1.43, 95% CI = [1.04, 1.97], p < .05), but those from the central-south regions were significantly less likely to report higher propensity (HR = 0.78, 95% CI = [0.64, 0.96], p < .05).
Discussion
To the best of the authors’ knowledge, this is the first research available using a Cox regression design to study community and geographical disparities in BMI among Chinese older adults, including a substantial amount of oldest-old (above 80 years old) respondents. Furthermore, adopting the multistate approach of the Cox regression to investigate this topic of interest also made it possible to identify the condition of weight change from nonobese to obese and those who remained obese.
The transition among older females from nonobese to obese in this research could be the results of rapid urbanization in China. In fact, the dramatic urbanization and economic growth in China in the past few decades are associated with several health risks including unhealthy lifestyles and life stresses (Chen et al., 2017). Among all public health challenges in urban areas, the escalating disease burden goes hand-in-hand with nutrition and lifestyle choices (Gong et al., 2012). Elevated blood pressure was greater for those who moved to urban areas compared with those who remained in rural villages (He et al., 1991). Urban environments and related lifestyles put Chinese residents in greater danger of obesity and hypertension, which conditions are the roots of many chronic diseases (Gong et al., 2012).
As urbanization in China has proceeded since the late 20th century, especially in mega cities there are more appealing yet unhealthy food choices. Unhealthy dietary behavior is one of the major public health challenges. The emergence of modern food system in urban areas, including the food chains in mega markets, has changed Chinese people’s dietary behaviors; frying foods, dining out, and snacking have become more prevalent (Zhai et al., 2014). These food choices could gradually affect Chinese’ residents’ health; nevertheless, people may not be aware of the negative effects from these food choices and may continue to pursue them. For example, despite previous research demonstrating the negative health effects of fast food on health (Dominguez et al., 2014; Pereira et al., 2005), a study using the China Health and Nutrition Survey (CHNS) observed that fast food preference was associated with poor psychological wellbeing measurements among Chinese middle-aged and older adults and with better psychological wellbeing measurements (Lee et al., 2018). Similar results related to sweetened beverage and salty snack preferences also were observed (Lee et al., 2018). The authors argued that these circumstances could be the results of “new choices” for middle-aged and older adults, given that these food choices, including fast food, salty snacks, and sweetened beverages, could be novel for older research participants (Lee et al., 2018). Kentucky Fried Chicken (KFC) and McDonalds opened their first stores in major Chinese cities in the late 1980s and early 1990s (Zhai et al., 2014), and the dietary landscape has shifted quickly since then, especially in urban areas. Further public health efforts should continue to investigate the effect of urbanization on older adults’ BMI, and more obesity prevention efforts are needed to lower chronic diseases.
Of note, it is important to mention one interesting finding regarding regional disparity in this research. The Cox regression models showed that the regional disparity was observed only among older females. In our analysis, the incidence of obesity varied greatly between males and females in rural and urban regions. For older males, the regional difference was less than 10%; however, the regional difference was almost 20% among older females. This sex-specific difference could indicate the need to investigate this topic of interest further and focus on the gap between older males and females, and the large gap between rural and urban regions among older females might increase the likelihood of the observed significant association. Furthermore, public health efforts should focus on urban older females regarding BMI control to lower the incidence of obesity.
This research observed the association of geographical variation with Chinese older adults’ BMI. In particular, older females from the north had a higher propensity of obese or remaining obese, compared with participants from the east. Older adults from the central-south were significantly less likely to report increasing BMI in both sex-stratified models. This finding regarding the higher incidence of obesity in the north also was clear among children (J. Zhang et al., 2018). One potential explanation of this finding is related to traditional northern dietary behavior, which includes high amounts of sodium and carbohydrate intake that are associated with hypertension and obesity (Song & Cho, 2017; D. Wang et al., 2011). Compared with the northern diet, southern food consumption behavior has a more balanced diet including high intake of vegetables, fruits, eggs, and meat that could help lower the risk of chronic conditions including obesity (Song & Cho, 2017). Because the higher incidence of obesity was observed in the northern region only among older females, further research is needed to examine the potential gap between males and females. Although the HRs were higher in northern regions among older males, the associations did not achieve the level of significance in this present research (all p > .05).
In addition, according to J. Zhang et al. (2018), northern China has a longer winter with lower temperatures (below 0°C on average), compared with the milder winter weather in southern China (above 0°C on average). Temperature might be an important environmental factor, the effect of which could be more evident in the older adult population because older adults lose body heat more easily leading to hypothermia (National Institute on Aging, 2018). To combat potential issues related to hypothermia or colder temperature, a higher level of food consumption might provide more calories and maintain body heat. For example, it has been demonstrated that individuals consume less food when the peripheral temperature is increased (Bernhard et al., 2015). With the higher incidence of hypothermia among older adults, more frequent food consumption might take place. It is critical to combat the cold winter in the north and maintain a healthy lifestyle among older adults. Public health endeavors and interventions are needed to target Chinese older adults in the north to reduce the prevalence of obesity through healthier lifestyle choices. Again, this regional disparity was observed only among older females. Henceforth, we should be careful not to make a conclusive claim regarding older males.
Finally, the prevalence of obesity has increased between 2009 and 2014, although the majority of participants had normal BMI. Providing sufficient public health assistance for tackling obesity among older adults could be a key to reducing chronic diseases in China. Lower prevalence of obesity could decrease the overall disease burden over time, especially for primary care providers (Batsis & Zagaria, 2018). However, in a study by Lv and colleagues (2018), the authors argued that higher BMI was associated with lower risks of disability in activities of daily living among the Chinese oldest-olds. This might suggest the need for further research to examine obesity and health outcomes among Chinese older adults.
A few limitations of this research should be pointed out. First, because this study uses observational data from a large longitudinal database, care should be taken not to draw causal conclusions. In addition, with a secondary data set like CLHLS, self-reported bias might occur. However, this is a common limitation for most survey-based research analyzing secondary data. Second, we used BMI to measure older adults’ level of obesity. BMI is a widely accepted measurement that has been used in other research associated with obesity among Chinese older adults (Lv et al., 2018), but other metrics should be considered to measure older adults’ BMI to avoid misclassification bias (F. Hu, 2008). Third, we included only older participants who answered all three survey waves because this research focused on individual trends and incidence of obesity over time. We excluded those who did not participate in all three surveys, so the results might reflect study bias. Thus, we can apply our study results only to those who answered all three waves of survey and the generalizability therefore is somewhat limited. Finally, the CLHLS questionnaire includes only self-reported consumption frequency measurements for some food items, and it was not possible to calculate caloric intake associated with BMI. Further research efforts could consider using dietary and caloric intake for a structural equation modeling (SEM) approach with longitudinal data to examine disparities in older adults’ BMI from a causal modeling perspective.
Conclusion
Despite these limitations, our research adds to the body of literature investigating regional and geographical disparities in BMI among Chinese older adults. We observed that the trends of obesity increased between 2009 and 2014 for both males and females, although the majority of study participants had normal BMI. The sex-stratified Cox models confirmed that urban female residents had higher propensity of reporting higher BMI, compared with rural female survey participants. This association was not observed among male participants. Compared with older females from the east, female participants from the north had higher propensity of reporting higher BMI; however, the association was not significant among older males. In addition, among both older males and females, participants from the south-central region had lower propensity to transition from nonobese to obese, compared with those who resided in the east.
This study provides practical implications for the field of gerontology related to the fact that obesity is a primary risk factor for many chronic diseases including diabetes and hypertension. Further research on public health efforts should take into account the specific needs of different regional and geographical areas. Additionally, the disparities observed in this study between older males and females should be considered in further research. Chinese public health practitioners also should help tackle obesity and implement weight control intervention targeting older females residing in urban areas, to address their higher propensity to transition from nonobese to obese. Environmental and societal factors, such as shift of dietary landscape and rapid urbanization, should be taken into consideration.
Footnotes
Acknowledgements
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 (NIA), the China Natural Science Foundation, the China Social Science Foundation, and the United Nations Population Fund.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Ethics of Clearance
The authors of this research used only secondary data set without identifiable personal information.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
