In recent years, researchers have begun to use clustered health lifestyles to explain the health disparities among individuals [1-4]. The benefit of this perspective is that it has extended the scopes of existing analyses on individual health behaviors to classified health lifestyles. Individual or single health behaviors that have been commonly used in prior analyses included poor dietary habits, cigarette smoking, excessive alcohol consumption et al. [5-9]. Scholars who promoted the health lifestyle approach argued that health behaviors tend to cluster in ways that reflect the social and structural contexts of individuals, which in turn affects individual health status [10]. This is because behaviors are not isolative, but co-occur with another [4]. Health lifestyle theories therefore contended that concentrating on single behaviors or small subsets of risky behaviors provides limited insight into health behavior patterns [11]. Thus, considering multiple behaviors simultaneously is a more appropriate strategy that creates larger and more enduring behavior change to improve individual health [12].
As far as studies on health status of Chinese elders, abundant analyses have documented a strong link between health behaviors and health outcomes among Chinese older adults [13-16]. Nevertheless, as studies focusing on other social contexts, most research on Chinese oldest-old also focused on single health behaviors. Since the sub-population of oldest-old is growing at extraordinary speed in China, it is important to explore potential factors that may improve the oldest-old’s health status to alleviate the burden of the society as well as the family caregivers. Under such a proceeding, this research intended to take the health lifestyle approach, i.e., a combination of multiple health-related behaviors, to attain a better understanding of health-related practices and their relationship with Chinese oldest-old’s health outcomes. Relying on latent class analysis strategy, the study used 2014 wave of the Chinese Longitudinal Health and Longevity Survey (CLHLS 2011), a nationally presentative data, to include health behaviors from multiple domains to present a relatively more comprehensive picture of health behaviors among Chinese oldest-old. It also aimed to elucidate how health lifestyles have shaped Chinese oldest-old’s health outcomes. Findings based on analyzing nationally representative data in China are valuable to address disease prevention and health promotion related issues among the oldest-old in world countries. Exploring how clustered health behaviors influence the oldest-old’s health outcomes can also expand theories explaining health disparities among elders in general.
The health lifestyle approach and prior literature
The health lifestyle approach can be considered as a theoretical development in research of health disparities. The concept of health lifestyle was derived from Weber’s idea of lifestyles as the interaction of life choices and life chances. Weber reasoned that lifestyles are not associated with individuals but groups of people with similar social status and backgrounds. Such a definition has been further expanded to include factors such as understandings of what good health means, health norms, policy environments et al. (Krueger et al., 2009). Bourdieu [17] further treated health lifestyles as broad and potentially unobservable orientations that organize patterns of behaviors. Health lifestyle perspectives emphasized more on patterns of behaviors rather than single behaviors. The perspectives highlighted social, cultural and economic forces on individual choices of health behaviors [10].
Some pioneer studies using the health lifestyle approach have been conducted to examine the general population. These studies can be classified as the following groups: First, linking personal characteristics, such as gender and age, to individual health lifestyles [10, 18]. Second, demonstrating a strong positive association between SES and clustered health behaviors among adults in different social contexts [19-22]. Third, exploring determinants of health lifestyle behaviors in adolescence and revealing how early age health lifestyle behaviors had imprints on one’s health behaviors in adulthood [23, 24]. Fourth, documenting significant influence of health lifestyles on individual health outcomes, including mental health, self-rated health (SRH) and alike [2, 25, 26] and underlining the positive effects of health lifestyle behaviors on diseases prevention [27, 28].
Health lifestyle approach has also been found useful in epidemiological studies examining health and mortality among older adults in a variety of countries. By operationalizing healthy lifestyle behaviors as physical activities, consumption of fruits and vegetables, and whether smoking, Martin-Maria and colleagues’ [29] study showed significantly positive effect of healthy lifestyle behaviors on subjective well-being among Spain sample aged 65 and over. Through studying multiple lifestyle behaviors of older persons in Korea and Amsterdam, scholars highlighted that participation in healthy lifestyles contributed to the maintenance of functional independence (measured as ADL and IADL) and cognitive function in later life [30, 31]. The study based on examining lifestyle behaviors including non-smoking and physical activity among elders in Sweden revealed that a low risk health behavior profile could add five years to women's lives and six years to men's after age 75 [32].
The above reviewed analyses have provided guidance to this current research investigating the link between health lifestyles to Chinese oldest-old’s health outcomes. The selection of health lifestyles as well as health status measures was based on the commonly used measures in previous studies. The analysis answered two main questions: First, what are predominant health lifestyles of the Chinese oldest-old? Second, how have these main health lifestyles shaped Chinese oldest-old’s health outcomes? Findings of this study were expected to fill the voids of prior literature by investigating Chinese oldest-old’s health disparities from single health behaviors. Results based on analyzing the China data were also supposed to enrich health lifestyle theories on the whole. Below the paper moved to an introduction of data, measures and methods used in the study.
Data, measures and methods
Data
Data came from the 2014 Chinese Longitudinal Healthy Longevity Survey (CLHLS) which was conducted in randomly selected half of the counties/cities in 22 provinces of China. Until now, 7 waves (1998, 2000, 2002, 2005, 2008, 2011-12, and 2014) of survey data have been collected. The survey was initially launched to meet the needs for scientific research on the oldest-old. Thus, the dataset provided an excellent source for studying the oldest-old in China. Previous literature showed that persons who reported age 106 or higher were considered as invalid cases [33]. Therefore, persons aged 106 and higher were excluded from this study due to insufficient information to validate their reported extremely high age. The study eventually obtained 3,416 oldest-old aged 85 to 105, with 2,025 males and 1,391 females.
Measures
Health lifestyle indicators
Health lifestyles measures used in previous analyses can be classified as the following categories: (1) dietary patterns (including eating fruits, vegetables, breakfast et al.), (2) smoking, alcohol consumption, (3) sleep, (4) obesity and physical activity, (5) seat belt wearing and media use, (6) body mass index (BMI), and (7) regular physical examination [34-41]. The selection of health lifestyle indicators in this research has been largely guided by prior studies and four key domains were applied, including dietary behaviors, smoking and alcohol use, sleep, and physical and leisure activities.
The first domain was dietary behaviors. In the CLHLS survey, the respondent was asked the frequency of eating or drinking fresh fruit, fresh vegetables and tea. The study coded these three variables as dichotomous ones with labeling respondents answering “almost everyday” and as “1” and “0” if otherwise. Tea consumption was considered because previous research pointed out that tea drinking related to longevity and reduced risk of mortality and death from cardiovascular diseases [42]. Tea consumption was thus used as an important health lifestyle behavior in this study.
The second domain related to smoking and alcohol use. Since the variables measuring the respondent’s exact amount of cigarette or alcohol consumption had an extremely large amount of missing values with responding rates lower than 20.0% of the total sample, the research applied other measures. Those measures relied on CLHLS survey questions asking the respondent whether he or she smoked or drank alcohol “in the past” and “at present”. The respondent who never smoked in the past or at present was coded as “0” and “1” if otherwise. It was assumed that for those individuals who smoked in the past and was still smoking when the survey was conducted was a heavy smoker; the same rationale and coding strategy were also applied to the alcohol consumption variable.
Sleep was the third domain which was represented by two indicators: sleeping duration and sleep quality. The study dichotomized the sleep duration variable as “1” indicating having 8 hours or more sleep each day and “0” as having less than 8 hours sleep. The sleep quality variable was dichotomized with those who reported their sleep quality as “good” and “very good” as “1” and poor sleep quality as ”0” (including the categories that were originally coded in the survey as ‘so so’, ‘bad’ and ‘very bad’).
The fourth domain was physical and leisure activities. The research relied on two survey questions asking whether the respondent exercised regularly in the past and at present to determine if he or she was physically active. Those who exercised regularly both at present and during the past were coded as “1”, and “0” if otherwise. The research also classified leisure activities into sedentary actives and active activities. Sedentary activities were such as reading newspapers/books, playing cards and /or mah-jong, and watching TV and/or listening to radio. Active activities included raising domestic animals, doing gardening work et al. For those who participated in leisure activities almost everyday were coded as “1” and “0” if otherwise.
Health outcome measures
The health outcome measures used in this research were consistent with measures used in previous research, including self-rated health (SRH) [43, 44], cognitive function [45-47], chronic diseases [13, 48] and activity of daily living (ADL) [49, 50]. The respondent’s SRH was coded as a continuous variable (1=very bad, 5=very good). Chronic disease variable was measured by whether the respondent reported any chronic diseases (1=yes, 0=no). The CLHLS survey asked the respondent whether he or she was suffering from 24 types of chronic diseases, including: hypertension, diabetes, heart disease, stroke/ cerebrovascular disease, bronchitis/emphysema/asthma/pneumonia, pulmonary tuberculosis, cataracts, glaucoma, cancer, prostate tumor, gastric or duodenal, Parkinson’s disease, bedsore, arthritis, dementia, epilepsy, cholecystitis/cholelith disease, blood disease, rheumatism or rheumatoid disease, chronic nephritis, galactophore disease, uterine tumor, hyperplasia of prostate, and hepatitis. Since the missing values for prostate tumor, chronic nephritis, galactophore disease and hyperplasia of prostate exceeded half of the respondents, these 4 types of chronic diseases were dropped from the analysis. As a result, the study included the rest 20 types of chronic diseases. If the respondent answered he or she was suffering from at least one type of the 20 types of chronic diseases, then the respondent was coded as “1” for the chronic disease variable, and “0” if otherwise. Cognitive function of the respondent was measured by using the Chinese version of the Mini-Mental State Examination (MMSE). The MMSE was adapted from Folstein, Folstein, and McHugh [51] and tested four aspects of cognitive functioning: orientation, calculation, recall, and language. The total possible score on the MMSE is 30, with lower scores indicating poor cognitive ability. Based on recommendations in the literature, responses of ‘‘unable to answer’’ were coded as incorrect answers [52]. Activity of daily living (ADL) disability was defined as self-reported difficulty with any of the following ADLs items: (a) bathing, (b) dressing, (c) eating, (d) indoor transferring, (e) toileting, and (f) continence. To avoid problems of complications and small sub-sample sizes in model estimation, the ADL functional capacity was dichotomized into “0” (meaning no ADL limitation) and “1” (meaning at least one ADL limitation).
Control variables
The analysis also controlled for the respondent’s demographic characteristics such as age, gender, rural and urban residence. Respondents who lived in cities and towns were classified as urban residents. The respondent’s socioeconomic status was also controlled, including years of schooling, per capita household income, and occupation before age 60. The occupation variable was coded as “1” if the respondent held a professional or administrative occupation and “0” if otherwise. Since socioeconomic condition in early childhood has been documented to have a cumulative effect on one’s later life health status and mortality [53, 54], the early childhood (or parental) SES was controlled as well. These measures included whether the respondent frequently went to bed hungry as a child, education of the respondent’s father and the respondent’s father’s occupation before age 60 (1=professional or administrative job, 0=otherwise). Although the percentages of respondents and respondents’ fathers who held professional or administrative jobs were low, the occupation measure has been repeatedly used as indicators of one’s SES [55-57]. Thus, the validity of the occupation measure representing SES has been proved by previous analyses. Table 1 showed descriptive statistics for all variables.