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Eating out and getting fat? A comparative study between urban and rural China
Accepted Manuscript Eating out and getting fat? A comparative study between urban and rural China Qiyan Zeng, Yinchu Zeng PII:
S0195-6663(16)31021-2
DOI:
10.1016/j.appet.2017.09.027
Reference:
APPET 3629
To appear in:
Appetite
Received Date: 29 December 2016 Revised Date:
3 August 2017
Accepted Date: 27 September 2017
Please cite this article as: Zeng Q. & Zeng Y., Eating out and getting fat? A comparative study between urban and rural China, Appetite (2017), doi: 10.1016/j.appet.2017.09.027. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Eating out and getting fat? A comparative study between urban and rural China
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Qiyan Zeng, Yinchu Zeng* School of Agricultural Economics and Rural Development, Renmin University of China, Beijing, China
*
Corresponding author. School of Agricultural Economics and Rural Development, Renmin University of China,
Beijing, 100872, China, Email address: [email protected]
ACCEPTED MANUSCRIPT Eating out and getting fat? A comparative study between urban and rural China
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Abstract In parallel with the increased prevalence of overweight and obesity, the rate of food away from home (FAFH) consumption in China has increased notably in recent years. Under the long-term urban–rural dual structure in China, the purpose of this study was to investigate the impact of FAFH consumption on body mass index (BMI) by a comparative study between rural and urban areas, using 26,244 subjects from the 2004–2011 China Health and Nutrition Survey. The results indicated that urban residents have a higher rate of FAFH consumption than rural residents with the difference narrowing over time. The empirical results illustrated that the frequency of meals consumed away from home had a significantly positive effect on BMI in urban China, whereas no significant association was observed in rural China. The urban–rural difference resulted from different levels of surplus energy, which was mainly due to the different labor intensity among rural and urban residents. Keywords food away from home; overweight and obesity; urban and rural China; surplus energy
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1 Introduction The rate of overweight and obesity is a rapidly growing threat reaching epidemic proportions worldwide. China, the largest developing country, is no exception. The Chinese Residents Nutrition and Chronic Disease Status Report (2015) revealed that the rate of overweight adults aged 18 or older throughout the country was 30.15%, and the obesity rate was 11.9%, an increase of 7.3% and 4.8%, respectively from 2002. The prevalence of overweight and obesity has become not only a public health threat but also an economic problem in China. The direct economic burden attributable to overweight and obesity was estimated to be 90.77 billion Yuan (RMB) (~$12.97 billion), accounting for 42.9% of the total medical costs for major chronic diseases, or 4.5% of the national health expenditure in 2010 (Zhang et al., 2013). One factor that is largely held responsible for overweight and obesity is the transition in dietary intake and eating behaviors, especially when consumption of food away from home (FAFH) is concerned (Binkley, 2000). A large body of literature has shown that FAFH consumption is associated with higher intakes of energy and saturated fat, and low micronutrient intakes (Lin et al., 1999; Lachat et al., 2012; Nordström & Thunström, 2015), thus it may result in the energy imbalance that causes weight gain. In China, consumption of FAFH has become a continuously growing part of the Chinese dietary pattern and is expected to increase for the next couple of decades in both urban and rural areas (Gale, 2005; Ma et al., 2006). The share of urban food expenditure on FAFH raised from 7.9% in 1992 to 21.2% in 2010 (Zheng et al., 2015), and similarly the share of rural food expenditure on FAFH raised from 2.3% in 1990 to 13.3% in 2010 (Xu, 2011). A number of studies have already reported a positive relationship between FAFH consumption and body mass index (BMI) or risk of obesity, mainly because of the substantial contribution of FAFH to increased energy intake (Ayala et al., 2008; Bes-Rastrollo et al., 2010; Cai et al., 2008; Ko et al., 2007; Kyureghian et al., 2007; Ma et al., 2003; Mccrony et al., 1999). However, it is far from conclusive. Some studies have discovered no association between increased FAFH consumption and obesity risk (Burns et al., 2002; Marínguerrero et al., 2008;
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Orfanos et al., 2007; Simmons et al., 2005), whereas others have discovered an association that is gender specific, significant only to men or only to women (Bezerra & Sichieri, 2009; Bezerra et al., 2014; Drichoutis et al., 2012; Du et al., 2016; Kant & Graubard, 2004). Possible reasons for these various results are differences in energy intake due to the type of food facilities frequented or customer characteristics (Bezerra & Sichieri, 2009; Bezerra et al., 2014; Du et al., 2016; Marínguerrero et al., 2008). For example, women usually prefer to control weight and choose relatively healthy foods when dining out, thus FAFH consumption shows no effect on weight gain in women (Bezerra & Sichieri, 2009; Du et al., 2016). In conclusion, noticeable differences in the relationship between FAFH consumption and risk of overweight and obesity exist, and it is widely discussed that the difference in energy intake away from home is a major factor for the inconsistent results (Bezerra & Sichieri, 2009; Du et al., 2016; Marínguerrero et al., 2008). However, it should be noted that the fundamental crux of the obesity framework centers on the classic energy balance approach, where calorie intake exceeds calorie expenditure (Chou et al., 2004; Philipson & Posner, 2003; Rashad, 2006). Hence, high-calorie FAFH consumption could result in overweight and obesity only if total energy intake exceeds consumption; that is, the prerequisite for the effect of FAFH consumption on weight gain is that surplus energy reaches a certain level. Although differences in calorie intake are fully emphasized to explain the various results in the existing literature, calorie expenditure, another important part of energy balance, is often ignored. This can also help to explain disparities in the relationship between FAFH consumption and weight gain, especially in regions with significantly different levels of physical activity. In China, the urban–rural disparities in the relationship between FAFH consumption and risk of overweight and obesity are expected to be notable, especially when both energy intake and expenditure are taken into consideration. Due to the long-term urban-biased developing strategy towards industry and the Hukou system of household registration in China, there is an enormous urban–rural gap in economic and social development (Kanbur & Zhang, 2005; Yang, 1999). According to the National Bureau of Statistics of China, the per capita disposable income of urban residents reached 31,790 RMB in 2015, which is almost three times that of rural residents (10,772 RMB). Under the long-term urban–rural dual structure, urban–rural disparities in energy expenditure are considerable. Urban residents have a relatively low level of physical activity due to the increasingly sedentary nature of work and recreation. However, traditional agricultural production is still a basic part of most rural residents, and heavy physical activities, such as farm work, are universal. Also, studies have found that rural residents differ substantially from urban residents in food consumption structure (Meng et al., 2010; Zheng et al., 2015). Urban residents’ consumption on animal products is significantly larger than that of rural residents, while their grain consumption is smaller than that of rural residents (Meng et al., 2010). The urban–rural disparities in food consumption structure at home are expected to reflect that of FAFH consumption, which may contribute to differences in energy intake. Differences in energy intake and expenditure may lead to the urban–rural disparities in the effects of growing FAFH consumption on overweight and obesity. Unfortunately, these disparities have been neglected in the several studies that have analyzed these effects in China (Cao et al., 2014; Du et al., 2016). In this paper, we first explored the possible urban–rural disparities in associations between FAFH consumption and risks of overweight and obesity in China. We then analyzed the difference in the perspective of surplus energy, including both energy intake and
ACCEPTED MANUSCRIPT energy expenditure. The rest of the paper is organized as follows: Section 2 introduces the method and data; Section 3 presents the results of the association between FAFH and BMI; Section 4 discusses the mechanism of the empirical results; finally, the paper is concluded in Section 5.
2 Methods and Data 2.1 Data
2.2 Methods
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The data were drawn from the China Health and Nutrition Survey (CHNS), which is a longitudinal, household-based study that began in 1989. There have been nine surveys to-date, of which the latest was in 2011. The CHNS includes eight or nine diverse provinces that vary substantially in geography, economic development, public resources and health indicators; three additional megacities (Beijing, Shanghai and Chongqing) were added in the latest 2011 survey. A multistage cluster random sampling method was used to derive the original sample, and the related information was collected by questionnaire survey on the individual, household and community levels (Du et al., 2016). Therefore, demographics, socio-economics, physical activity and health data were collected in the CHNS. It also provides detailed individual food intake information during the surveyed 3 days, which fully supports our analysis. Given the drastic growth of the China’s catering industry since the beginning of the 21st century, our estimation was conducted for the latest four surveys (2004, 2006, 2009 and 2011). The analysis included non-pregnant individuals aged 18–60 years. Furthermore, given that the BMI factor of underweight malnourished populations is remarkably different from the general population (as a result of disease, excessive weight loss, etc.) and that the disabled and participants who report to have been diagnosed with non-communicable chronic diseases may have strict limitations on eating behaviors, they were excluded from the sample. A final sample size of 26,244 adults (9148 in urban and 17,096 in rural China) were included.
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According to previous studies, the prevalence of overweight and obesity is a result of multiple factors, including demographic, socio-economic and lifestyle variables, among which FAFH consumption is being paid more and more attention because of its high energy and fat content (Lin et al., 1999; Lachat et al., 2012). To formalize these interrelationships and to take into account the urban–rural difference, we set the multivariate linear model of the influence of FAFH consumption on overweight and obesity as follows: = + + + where indicates different individuals; represents the urban and rural difference; equals 1 if the respondent is in urban China and 2 if in rural China. The dependent variable BMI is the body mass index of individual in region . is the key variable, representing the consumption of food away from home of individual in region , and β shows the influence of FAFH on BMI . are controlled variables, and include demographic characteristics, socio-economic variables, lifestyle variables, year dummies and province dummies. β is the intercept term and μ is the error term. Definition of overweight and obesity BMI is defined as weight (kg) divided by the square of height (m²). Weight was measured to
ACCEPTED MANUSCRIPT the nearest 0.1 kg using an electronic scale, and height was measured to the nearest 0.1 cm using a stadiometer (Du et al., 2016). The cut-offs established by the Working Group On Obesity In China were used to classify the respondents: 24 kg/m² ≤BMI<28 kg/m² for overweight and BMI≥28 kg/m² for obesity.
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Assessment of food away from home consumption The CHNS provides detailed individual food intake information through in-person interviewer-administered 24 h recalls conducted by trained staff over 3 consecutive days, including 2 working days and 1 weekend day (Du et al., 2016). The frequency of FAFH consumption during the surveyed 3 day period was used to identify the degree of dining out. Each FAFH visit may be viewed as a substitute for a home-consumed meal, which permits a better picture of the trade-off between home-consumed meals and meals consumed away from home (Dong & Byrne, 2000). When identifying FAFH from food at home (FAH), we only focused on the place where people take food regarding breakfast, lunch and dinner, but did not decompose consumption behaviors by cooking place further. So FAFH was defined as all food consumption behaviors not taking place at home, including food at restaurants, workplace cafeterias, friends’ and relatives’ homes, etc. This definition is consistent with its use in other studies (Kearney et al., 2001; Marínguerrero et al., 2008; Orfanos et al., 2007; Zhou et al., 2011).
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Control variables Demographic, socio-economic and lifestyle variables were controlled in the model as covariates according to previous studies (Chou et al., 2004; Lakdawalla & Philipson, 2009; Philipson & Posner, 2003; Roemling & Qaim, 2012; Martín et al., 2008). Age (years), gender (male=0, female=1), education level (years of formal education) and marital status (married=1, otherwise=0) were chosen as individual demographic characteristics for controlling differences among individuals. Socio-economic variables included per capital annual household income (in logarithmic scale and inflated to 2011). Lifestyle variables include current smoking (no=0, yes=1), drinking alcohol at least once a month over last year (no=0, yes=1). In addition, year dummies and province dummies were also controlled for the effects of time and regional differences1.
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3 Results 3.1 Descriptive characteristics Descriptive analyses were conducted to investigate the distribution of main variables. Over the 7 year period, there was a notable increase in the consumption of FAFH (Table 1). The average rate of FAFH consumption2 among urban residents was consistently higher than that among rural residents (P<0.01), although the difference narrowed over time. Specifically, the rate in urban areas raised slightly from 16.03% to 18.30% and the rate in rural areas grew from 6.05% to 11.09% between 2004 and 2011. Table 1 Average rate of FAFH consumption in China between 2004–2011 (in %) 1
Regional differences are significant in China; for example, southerners prefer rice while northerners prefer food made from flour, which may show different effects on individual BMI. 2 The FAFH consumption rate was defined as the frequency of eating out divided by the total number of meals eaten during the surveyed 3 days.
ACCEPTED MANUSCRIPT Rural (n=17096) 6.05 7.06 8.46 11.09 +5.04
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Year Total (n=26244) Urban (n=9148) 2004 9.40 16.03 2006 9.89 15.74 2009 11.34 17.41 2011 13.95 18.30 Change 2004–2011 +4.55 +2.27 Note: Food intake information is from the surveyed 3 days of the CHNS
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We further investigated urban–rural disparities in the structure of FAFH consumption (Table 2). There was no significant difference in the away-from-home consumption of vegetables, red meat, poultry or aquatic products between urban and rural areas in 2011 (P>0.1). However, grain consumption in rural areas was considerably higher than that in urban areas (P<0.01). Egg and milk consumption in rural areas accounts for about half of that in urban areas in 2011. Furthermore, between 2004 and 2011, both urban and rural residents decreased their grain consumption (P<0.01) and increased their egg and milk consumption (P<0.01), while the consumption of red meat remained stable. There was also an increase in the amount of vegetables and white meat (including poultry and aquatic products) consumed away from home (P<0.01) in urban areas3. Table 2 The structure of FAFH consumption between 2004 and 2011 (g/meal)
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2011
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Urban Rural urban Rural Grain 126.86 157.88 102.58 111.53 Vegetables 54.04 73.11 72.82 69.46 Red meat 40.26 36.50 37.51 38.81 Poultry 7.35 12.65 11.34 11.10 Aquatic products 11.57 13.32 16.37 15.91 Milk and egg 13.31 5.44 17.01 8.54 Note: Food intake information is from the surveyed 3 days of the CHNS. The figures stand for the average amount of food (g) consumed away from home per person per meal.
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Furthermore, we compared the structure of food consumed at home and away from home in 2011 (Table 3). Compared to FAH, there was a considerable decrease in grain and vegetable consumption away from home in both urban and rural areas, whereas there was a noticeable increase in meat consumption (including red meat, poultry and aquatic products) when people dined out. These are in accordance with Du et al (2016) and Ma et al (2006). Moreover, the differences between FAFH and FAH were more significant in rural areas than in urban areas, 4
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Away-from-home grain consumption in rural and urban China fell continuously. Rural consumption of grains dropped from 157.88 g per person per meal in 2004 to 111.53 g in 2011, an average decline of 29.36%, and urban grain consumption dropped from 126.86 g in 2004 to 102.58 g in 2011, an average decline of 19.14%. As for meat consumption, red meat consumption in rural China increased from 36.50 g in 2004 to 38.81 g in 2011, and it has overtaken that in urban areas, which slightly decreased from 40.26 g in 2004 to 37.51 g in 2011. White meat consumption in urban areas increased noticeably, but it remained stable in rural areas. Additionally, vegetables eaten out declined slightly in rural China while an apparent increase can be seen in urban China, which reached 72.82 g, overtaking 69.46 g in rural areas in 2011. 4 This study takes 2011 as an example; similar conclusions were observed in 2004, 2006 and 2009.
ACCEPTED MANUSCRIPT especially for meat consumption. Therefore, it is expected that total grain and vegetable consumption will further decline and meat consumption will increase with the sustained growth of FAFH consumption in China, especially in rural areas. Table 3 The consumption structure of FAFH and FAH in 2011 (g/meal)
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FAFH FAH FAFH FAH Grain 102.58 114.35 111.53 147.40 Vegetables 72.82 107.87 69.46 107.84 Red meat 37.51 32.07 38.81 23.66 Poultry 11.34 8.88 11.10 6.04 Aquatic products 16.37 15.83 15.91 10.25 Milk and eggs 17.01 26.11 8.54 12.32 Note: Food intake information is from the surveyed 3 days of the CHNS. The figures stand for the average amount of food (g) consumed per person per meal.
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At the same time, the mean BMI in Chinese adults has significantly increased from 23.39 to 24.26 since 2004, which was in parallel with the increase in FAFH consumption. Rural residents had a lower mean BMI than urban residents (P<0.01), although the difference narrowed over time. In 2011, the mean BMI in urban and rural areas were exactly the same (24.26).
3.2 Empirical results
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Summary statistics of variables used in the empirical analysis are presented in Table 4. Table 4 Variable used in the analysis and summary statistics Total (n=26,244)
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SD
23.75 0.98 43.62 2013.43 0.53 8.59 0.88 0.29 0.31 8.90
3.37 1.84 10.53 879.56 0.50 3.81 0.32 0.45 0.46 1.08
23.86 1.47 43.79 2034.40 0.53 9.91 0.85 0.29 0.33 9.19
3.44 2.09 10.81 902.18 0.50 3.76 0.35 0.45 0.47 1.08
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BMI FAFH Age Age² Gender Educational level Marital status Smoking Alcohol drinking Income
Urban China (n=9148)
Rural China (n=17096) Mean 23.70 0.72 43.53 2002.21 0.52 7.89 0.90 0.29 0.30 8.75
SD 3.34 1.62 10.38 867.04 0.50 3.65 0.30 0.45 0.46 1.06
Table 5 presents the empirical results of the effects of FAFH consumption on BMI. Rural and urban China are estimated, respectively. The pooled cross-sectional regression model is mainly used to investigate the urban–rural disparities in the effects of FAFH consumption on overweight and obesity. We only included FAFH in the column of “Pooled OLS (Ordinary Least Square)1” to investigate its association with BMI after controlling for demographic characteristics, time and
ACCEPTED MANUSCRIPT regional differences. The column of “Pooled OLS2” additionally included lifestyle and socio-economic variables. The Random effect model was also selected to test the robustness of the results, as shown in the column “Random Effect”. All statistical analyses were performed by using the statistical software package STATA version 12.1.
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Table 5 Estimated results of pooled OLS and random effect for the association between FAFH and BMI Urban China Rural China Independent variable Pooled Pooled Random Pooled Pooled Random OLS1 OLS2 Effect OLS1 OLS2 Effect * * * FAFH 0.034 0.033 0.024 0.026 0.026 0.010 (0.018) (0.018) (0.015) (0.016) (0.016) (0.011) *** *** *** *** *** Age 0.185 0.194 0.192 0.238 0.242 0.233*** (0.028) (0.028) (0.029) (0.019) (0.020) (0.019) *** *** *** *** *** -0.002 -0.002 -0.002 -0.002 -0.002*** Age² -0.002 (0.0003) (0.0003) (0.0003) (0.0002) (0.0002) (0.0002) *** *** *** *** Gender -0.400 -0.637 -0.509 -0.040 -0.247 -0.248*** (0.070) (0.096) (0.109) (0.051) (0.070) (0.080) *** *** *** Educational level -0.071 -0.084 -0.056 0.011 0.005 0.005 (0.010) (0.011) (0.012) (0.008) (0.008) (0.008) Marital status -0.047 -0.063 -0.073 0.122 0.131 0.168** (0.119) (0.119) (0.123) (0.093) (0.093) (0.082) *** *** *** Smoking -0.601 -0.288 -0.450 -0.288*** (0.100) (0.100) (0.070) (0.061) ** Alcohol drinking 0.215 0.122 0.092 0.016 (0.089) (0.079) (0.067) (0.049) ** ** Income 0.076 0.037 0.053 -0.027 (0.036) (0.031) (0.026) (0.018) *** *** *** *** *** _cons 20.479 20.005 19.842 19.063 18.799 19.214*** (0.571) (0.641) (0.648) (0.417) (0.460) (0.442) Year dummy Yes Yes Yes Yes Yes Yes Province dummy Yes Yes Yes Yes Yes Yes Sample size 9148 9148 9148 17096 17096 17096 R-squared 0.079 0.083 0.081 0.088 0.091 0.090 Note: Figures in parentheses are robust standard errors. “***”, “**” and “*” denote significance at the 1%, 5% and 10% level, respectively. The frequency of FAFH consumption had a significant positive effect on BMI at the 10% level in urban China, which was consistent in three models showing good robustness. One unit increase in the frequency of FAFH consumption led to an increase in BMI of approximately 0.03. However, the frequency of meals consumed away from home in rural China had no significant effect on BMI, which was also consistent in all three models. The different effects of FAFH on BMI between urban and rural areas is very interesting, and is further analyzed in the discussion section.
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The results for control variables were in line with expectations. The relationship between age and BMI was an inverse U shape captured by the positive and negative significant coefficients of age and age-squared, respectively, which was in accordance with Ruhm (2007). Males tended to have a higher BMI than females for both urban and rural China. Better educated people were likely to have improved nutritional awareness and health knowledge, which contributed to a lower risk of overweight and obesity in urban China. Smoking had a negative effect on BMI among rural and urban residents, whereas alcohol drinking increased the risk of overweight and obesity in urban China. Higher income contributes to more food consumption and a higher dietary diversity in developing countries, thus it had a positive effect on BMI.
4 Discussion
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As mentioned previously, overweight and obesity are caused by an imbalance between energy intake and energy expenditure, where calorie intake exceeds calorie expenditure (Chou et al., 2004; Philipson and Posner, 2003; Rashad, 2006). Therefore, high-calorie FAFH consumption could result in overweight and obesity only if surplus energy reaches a certain level. Surplus energy here is defined as total energy intake minus total energy expenditure. Data for average daily energy intake (kcal/day) was directly provided by the CHNS, but there is no direct data for energy expenditure. In this study, energy expenditure was calculated based on the report of a joint FAO/WHO/UNU expert consultation. The average energy expenditure of a population can be estimated by multiplying the physical activity level (PAL) by the Basal Metabolic Rate (BMR) (FAO/ WHO/UNU,2001)5. To determine if the effect of FAFH consumption on BMI depends on the level of surplus energy for China’s current situation, we conducted an empirical regression analysis on the relationship between FAFH consumption and BMI in the distribution of daily surplus energy as shown in Table 6. The first and second columns include the samples with the 25% and 50% lowest surplus energy, respectively, while the third and fourth columns include the samples with the top 50% and 25% surplus energy, respectively. These results show that FAFH consumption could increase the risk of overweight and obesity only when the surplus energy reached a relatively high level in China, otherwise no significant effects would be found. Moreover, the results in the third and fourth columns indicate that the positive associations between FAFH and BMI show higher magnitude in the upper tail of surplus energy distribution, with the coefficient of FAFH increasing from 0.037 to 0.069.
Table 6 OLS regression estimate results for the association between FAFH and BMI in the distribution of surplus energy Independent variable (1) (2) (3) (4) ** FAFH 0.015 0.027 0.038 0.068***
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The amount of energy used for basal metabolism in a period of time is called the BMR and comprises a series of functions that are essential for life. BMR represents 45% to 70%of daily total energy expenditure, which is determined mainly by the individual’s age, gender, body size and body composition according to FAO/ WHO/UNU (2001). Physical activity is the most variable and, after BMR, the second largest component of daily energy expenditure. Activity levels are grouped into five types increasing from 1 to 5 in the CHNS. Energy expenditure of each physical activity is also assigned based on FAO/WHO/UNU (2001) as shown in Schedule 1. Multiplying the PAL by the BMR gives the actual energy expenditure, and then total energy intake minus total energy spent equals the level of daily surplus energy. Considering that the data for energy intake in the CHNS is lower than normal, and that most people in China get adequate calories for daily activities, we took 80% of the total energy consumption calculated.
ACCEPTED MANUSCRIPT (0.022) Yes Yes Yes Yes Yes 16.848*** (0.703) 6516 0.089 “*” denote
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(0.026) (0.017) (0.016) Demographic Variables Yes Yes Yes Socio-economic Variables Yes Yes Yes Lifestyle Variables Yes Yes Yes Year dummy Yes Yes Yes Province dummy Yes Yes Yes *** *** 19.318 17.032*** _cons 20.359 (0.840) (0.550) (0.502) Sample size 6517 13033 13034 R-squared 0.070 0.077 0.089 Note: Control variables are consistent with those in Table 5. “***”, “**” and significance at the 1%, 5% and 10% level, respectively.
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We next analyzed the level of daily surplus energy in rural and urban areas (Table 7). The daily surplus energy of urban residents reached 92.45 kcal, more than that of rural residents, which was 17.61 kcal (P<0.01). For urban residents, 52.45% belong to the group with the top 50% of surplus energy in China, but this was only 48.69% for rural residents. Therefore, although FAFH consumption leads to an increased intake of energy-dense food in both urban and rural areas (Table 3), the urban–rural disparities in the association between FAFH and BMI still exist due to the difference in surplus energy. Specifically, FAFH show no significant effects on BMI in rural China, mainly because surplus energy of most rural residents does not reach a certain level. To confirm this, we additionally conducted an empirical regression analysis on the relationship between FAFH consumption and BMI in rural areas in the distribution of surplus energy (Table 8). Column 1 includes whole rural samples, Columns 2 and 3 include the rural samples with the lowest 50% and the top 50% surplus energy in China, respectively. The results showed that FAFH had a significantly positive effect on BMI only for the 48.69% of rural subjects that belong to the group with the top 50% of surplus energy in China, but no significant effects were discovered for the other 51.31% of rural residents with less surplus. Hence, no significant association was found for rural residents as a whole. Similarly, as most urban residents belong to the group with the top 50% of surplus energy in China, the positive association between FAFH and BMI was discovered in urban China. Table 7 Daily surplus energy, energy intake and energy expenditure (kcal/day)
Urban China Rural China
Surplus energy Mean SE 92.45 16.19 17.61 7.37
Energy intake Mean SE 2078.00 16.09 2224.01 7.31
Energy expenditure Mean SE 1985.55 4.11 2206.40 3.14
Table 8 OLS regression estimated results for the association between FAFH and BMI in rural China in the distribution of surplus energy Independent variable (1) (2) (3) 0.026 FAFH -0.014 0.061*** (0.016) (0.023) (0.022) Demographic Variables Yes Yes Yes
ACCEPTED MANUSCRIPT Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes *** *** 18.799 19.039 17.199*** (0.460) (0.660) (0.676) 17096 8713 8269 Sample size 0.091 R-squared 0.087 0.092 Note: Control variables are consistent with those in Table 5. “***”, “**” and “*” denote significance at the 1%, 5% and 10% levels, respectively.
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Socio-economic Variables Lifestyle Variables Year dummy Province dummy _cons
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Furthermore, we analyzed the reasons why surplus energy in urban areas is more than that in rural areas. As shown in Table 7, the average daily energy intake of urban residents was 2078.00 kcal/day, less than that of rural residents, which reached 2224.01 kcal/day (P<0.01). This demonstrated that the relatively high level of surplus energy in urban residents does not result from energy intake, rather, the average daily energy expenditure of urban residents was 1985.55 kcal/day, considerably less than that of rural residents, 2206.40 kcal/day (P<0.01). Since BMR was comparable for urban and rural residents, PAL is the main factor for the difference in urban– rural energy expenditure. Table 9 shows the distribution of labor intensity in rural and urban China in detail. Under the long-term urban–rural dual structure, labor intensity is significantly heavier in rural areas, where heavy physical activity, such as farming, accounts for 46.51%; whereas, very light physical activity, such as office working, accounts for 69.52% in urban areas.
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Table 9 The distribution of labor intensity in rural and urban areas
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Very light physical activity Light physical activity Moderate physical activity Heavy physical activity Very heavy physical activity
Urban China 40.08% 29.44% 17.05% 13.01% 0.42%
Rural China 14.26% 20.33% 18.08% 46.51% 0.81%
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Therefore, the urban–rural disparities in associations between FAFH consumption and risk of overweight and obesity result from differences in surplus energy, which are mainly due to the different labor intensities among rural and urban residents. The remarkably higher level of physical activity among rural residents increases energy consumption, maintaining a negative balance between energy metabolism, this contributes to the insignificant relationship between FAFH and BMI. A positive association is observed in urban areas due to more surplus energy, which results from the predominance of sedentary work. Previous studies have shown a positive association between FAFH consumption and weight gain in many developed countries (Ayala et al., 2008; Bes-Rastrollo et al., 2010; Cai et al., 2008; Mccrory et al., 1999), which is consistent with our results in urban China. By contrast, Simmons et al (2005) also found that the choice of takeaway and restaurant foods was not related to the prevalence of adult obesity in rural communities in Australia, which is consistent with our results in rural China. In this paper, instead of focusing on energy intake, the level of surplus energy
ACCEPTED MANUSCRIPT including both energy intake and consumption was emphasized to analyze the association between dining out and weight gain. This is important, especially for countries or regions such as China, which are significantly disparate in physical activity levels.
5 Conclusions and implications
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In parallel with an increase in the prevalence of overweight and obesity, the rate of FAFH consumption in Chinese adults has increased notably in recent years. Urban residents have a higher FAFH rate than rural residents, but the difference has narrowed over time. There is no significant difference in the away-from-home consumption of vegetables or meat between urban and rural areas. Furthermore, FAFH consumption leads to an increased intake of energy-dense foods and the structural differences between FAFH and FAH are more significant in rural areas than in urban areas. Through a comparative study between rural and urban China, our empirical results showed that the frequency of meals consumed away from home had a significant positive effect on BMI in urban China, whereas no significant association was observed in rural China. These urban–rural differences result from different levels of surplus energy, which is mainly due to the difference in labor intensity among rural and urban residents. According to our results, to prevent the rapidly progressing obesity epidemic, public health policies should be implemented not only to help people make healthier food choices when dining out, but also to encourage people to do more physical activities, especially in urban China. This is important as high-energy FAFH consumption becomes an inevitable trend, and sedentary lifestyles are quite common. People can achieve energy balance by both eating less high-calorie foods when eating out and doing more physical exercise. Furthermore, decreasing agricultural employment, and increasing urbanization, the popularity of home appliances and a general trend towards a service sector economy imply lower physical activity in rural areas (Monda et al., 2007); thus, the positive effect of FAFH on the prevalence of overweight and obesity is expected to spread to rural China in the near future, which will also need prevention approaches.
Acknowledgements
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This work was supported by the National Natural Science Foundation of China (71633005). This research used data from the China Health and Nutrition Survey (CHNS). The authors thank the National Institute of Nutrition and Food Safety, the China Center for Disease Control and Prevention, the Carolina Population Center and the University of North Carolina at Chapel Hill. The authors also thank Dr. Li Chen for advice.
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S0195-6663(16)31021-2
DOI:
10.1016/j.appet.2017.09.027
Reference:
APPET 3629
To appear in:
Appetite
Received Date: 29 December 2016 Revised Date:
3 August 2017
Accepted Date: 27 September 2017
Please cite this article as: Zeng Q. & Zeng Y., Eating out and getting fat? A comparative study between urban and rural China, Appetite (2017), doi: 10.1016/j.appet.2017.09.027. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Eating out and getting fat? A comparative study between urban and rural China
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Qiyan Zeng, Yinchu Zeng* School of Agricultural Economics and Rural Development, Renmin University of China, Beijing, China
*
Corresponding author. School of Agricultural Economics and Rural Development, Renmin University of China,
Beijing, 100872, China, Email address: [email protected]
ACCEPTED MANUSCRIPT Eating out and getting fat? A comparative study between urban and rural China
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Abstract In parallel with the increased prevalence of overweight and obesity, the rate of food away from home (FAFH) consumption in China has increased notably in recent years. Under the long-term urban–rural dual structure in China, the purpose of this study was to investigate the impact of FAFH consumption on body mass index (BMI) by a comparative study between rural and urban areas, using 26,244 subjects from the 2004–2011 China Health and Nutrition Survey. The results indicated that urban residents have a higher rate of FAFH consumption than rural residents with the difference narrowing over time. The empirical results illustrated that the frequency of meals consumed away from home had a significantly positive effect on BMI in urban China, whereas no significant association was observed in rural China. The urban–rural difference resulted from different levels of surplus energy, which was mainly due to the different labor intensity among rural and urban residents. Keywords food away from home; overweight and obesity; urban and rural China; surplus energy
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1 Introduction The rate of overweight and obesity is a rapidly growing threat reaching epidemic proportions worldwide. China, the largest developing country, is no exception. The Chinese Residents Nutrition and Chronic Disease Status Report (2015) revealed that the rate of overweight adults aged 18 or older throughout the country was 30.15%, and the obesity rate was 11.9%, an increase of 7.3% and 4.8%, respectively from 2002. The prevalence of overweight and obesity has become not only a public health threat but also an economic problem in China. The direct economic burden attributable to overweight and obesity was estimated to be 90.77 billion Yuan (RMB) (~$12.97 billion), accounting for 42.9% of the total medical costs for major chronic diseases, or 4.5% of the national health expenditure in 2010 (Zhang et al., 2013). One factor that is largely held responsible for overweight and obesity is the transition in dietary intake and eating behaviors, especially when consumption of food away from home (FAFH) is concerned (Binkley, 2000). A large body of literature has shown that FAFH consumption is associated with higher intakes of energy and saturated fat, and low micronutrient intakes (Lin et al., 1999; Lachat et al., 2012; Nordström & Thunström, 2015), thus it may result in the energy imbalance that causes weight gain. In China, consumption of FAFH has become a continuously growing part of the Chinese dietary pattern and is expected to increase for the next couple of decades in both urban and rural areas (Gale, 2005; Ma et al., 2006). The share of urban food expenditure on FAFH raised from 7.9% in 1992 to 21.2% in 2010 (Zheng et al., 2015), and similarly the share of rural food expenditure on FAFH raised from 2.3% in 1990 to 13.3% in 2010 (Xu, 2011). A number of studies have already reported a positive relationship between FAFH consumption and body mass index (BMI) or risk of obesity, mainly because of the substantial contribution of FAFH to increased energy intake (Ayala et al., 2008; Bes-Rastrollo et al., 2010; Cai et al., 2008; Ko et al., 2007; Kyureghian et al., 2007; Ma et al., 2003; Mccrony et al., 1999). However, it is far from conclusive. Some studies have discovered no association between increased FAFH consumption and obesity risk (Burns et al., 2002; Marínguerrero et al., 2008;
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Orfanos et al., 2007; Simmons et al., 2005), whereas others have discovered an association that is gender specific, significant only to men or only to women (Bezerra & Sichieri, 2009; Bezerra et al., 2014; Drichoutis et al., 2012; Du et al., 2016; Kant & Graubard, 2004). Possible reasons for these various results are differences in energy intake due to the type of food facilities frequented or customer characteristics (Bezerra & Sichieri, 2009; Bezerra et al., 2014; Du et al., 2016; Marínguerrero et al., 2008). For example, women usually prefer to control weight and choose relatively healthy foods when dining out, thus FAFH consumption shows no effect on weight gain in women (Bezerra & Sichieri, 2009; Du et al., 2016). In conclusion, noticeable differences in the relationship between FAFH consumption and risk of overweight and obesity exist, and it is widely discussed that the difference in energy intake away from home is a major factor for the inconsistent results (Bezerra & Sichieri, 2009; Du et al., 2016; Marínguerrero et al., 2008). However, it should be noted that the fundamental crux of the obesity framework centers on the classic energy balance approach, where calorie intake exceeds calorie expenditure (Chou et al., 2004; Philipson & Posner, 2003; Rashad, 2006). Hence, high-calorie FAFH consumption could result in overweight and obesity only if total energy intake exceeds consumption; that is, the prerequisite for the effect of FAFH consumption on weight gain is that surplus energy reaches a certain level. Although differences in calorie intake are fully emphasized to explain the various results in the existing literature, calorie expenditure, another important part of energy balance, is often ignored. This can also help to explain disparities in the relationship between FAFH consumption and weight gain, especially in regions with significantly different levels of physical activity. In China, the urban–rural disparities in the relationship between FAFH consumption and risk of overweight and obesity are expected to be notable, especially when both energy intake and expenditure are taken into consideration. Due to the long-term urban-biased developing strategy towards industry and the Hukou system of household registration in China, there is an enormous urban–rural gap in economic and social development (Kanbur & Zhang, 2005; Yang, 1999). According to the National Bureau of Statistics of China, the per capita disposable income of urban residents reached 31,790 RMB in 2015, which is almost three times that of rural residents (10,772 RMB). Under the long-term urban–rural dual structure, urban–rural disparities in energy expenditure are considerable. Urban residents have a relatively low level of physical activity due to the increasingly sedentary nature of work and recreation. However, traditional agricultural production is still a basic part of most rural residents, and heavy physical activities, such as farm work, are universal. Also, studies have found that rural residents differ substantially from urban residents in food consumption structure (Meng et al., 2010; Zheng et al., 2015). Urban residents’ consumption on animal products is significantly larger than that of rural residents, while their grain consumption is smaller than that of rural residents (Meng et al., 2010). The urban–rural disparities in food consumption structure at home are expected to reflect that of FAFH consumption, which may contribute to differences in energy intake. Differences in energy intake and expenditure may lead to the urban–rural disparities in the effects of growing FAFH consumption on overweight and obesity. Unfortunately, these disparities have been neglected in the several studies that have analyzed these effects in China (Cao et al., 2014; Du et al., 2016). In this paper, we first explored the possible urban–rural disparities in associations between FAFH consumption and risks of overweight and obesity in China. We then analyzed the difference in the perspective of surplus energy, including both energy intake and
ACCEPTED MANUSCRIPT energy expenditure. The rest of the paper is organized as follows: Section 2 introduces the method and data; Section 3 presents the results of the association between FAFH and BMI; Section 4 discusses the mechanism of the empirical results; finally, the paper is concluded in Section 5.
2 Methods and Data 2.1 Data
2.2 Methods
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The data were drawn from the China Health and Nutrition Survey (CHNS), which is a longitudinal, household-based study that began in 1989. There have been nine surveys to-date, of which the latest was in 2011. The CHNS includes eight or nine diverse provinces that vary substantially in geography, economic development, public resources and health indicators; three additional megacities (Beijing, Shanghai and Chongqing) were added in the latest 2011 survey. A multistage cluster random sampling method was used to derive the original sample, and the related information was collected by questionnaire survey on the individual, household and community levels (Du et al., 2016). Therefore, demographics, socio-economics, physical activity and health data were collected in the CHNS. It also provides detailed individual food intake information during the surveyed 3 days, which fully supports our analysis. Given the drastic growth of the China’s catering industry since the beginning of the 21st century, our estimation was conducted for the latest four surveys (2004, 2006, 2009 and 2011). The analysis included non-pregnant individuals aged 18–60 years. Furthermore, given that the BMI factor of underweight malnourished populations is remarkably different from the general population (as a result of disease, excessive weight loss, etc.) and that the disabled and participants who report to have been diagnosed with non-communicable chronic diseases may have strict limitations on eating behaviors, they were excluded from the sample. A final sample size of 26,244 adults (9148 in urban and 17,096 in rural China) were included.
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According to previous studies, the prevalence of overweight and obesity is a result of multiple factors, including demographic, socio-economic and lifestyle variables, among which FAFH consumption is being paid more and more attention because of its high energy and fat content (Lin et al., 1999; Lachat et al., 2012). To formalize these interrelationships and to take into account the urban–rural difference, we set the multivariate linear model of the influence of FAFH consumption on overweight and obesity as follows: = + + + where indicates different individuals; represents the urban and rural difference; equals 1 if the respondent is in urban China and 2 if in rural China. The dependent variable BMI is the body mass index of individual in region . is the key variable, representing the consumption of food away from home of individual in region , and β shows the influence of FAFH on BMI . are controlled variables, and include demographic characteristics, socio-economic variables, lifestyle variables, year dummies and province dummies. β is the intercept term and μ is the error term. Definition of overweight and obesity BMI is defined as weight (kg) divided by the square of height (m²). Weight was measured to
ACCEPTED MANUSCRIPT the nearest 0.1 kg using an electronic scale, and height was measured to the nearest 0.1 cm using a stadiometer (Du et al., 2016). The cut-offs established by the Working Group On Obesity In China were used to classify the respondents: 24 kg/m² ≤BMI<28 kg/m² for overweight and BMI≥28 kg/m² for obesity.
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Assessment of food away from home consumption The CHNS provides detailed individual food intake information through in-person interviewer-administered 24 h recalls conducted by trained staff over 3 consecutive days, including 2 working days and 1 weekend day (Du et al., 2016). The frequency of FAFH consumption during the surveyed 3 day period was used to identify the degree of dining out. Each FAFH visit may be viewed as a substitute for a home-consumed meal, which permits a better picture of the trade-off between home-consumed meals and meals consumed away from home (Dong & Byrne, 2000). When identifying FAFH from food at home (FAH), we only focused on the place where people take food regarding breakfast, lunch and dinner, but did not decompose consumption behaviors by cooking place further. So FAFH was defined as all food consumption behaviors not taking place at home, including food at restaurants, workplace cafeterias, friends’ and relatives’ homes, etc. This definition is consistent with its use in other studies (Kearney et al., 2001; Marínguerrero et al., 2008; Orfanos et al., 2007; Zhou et al., 2011).
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Control variables Demographic, socio-economic and lifestyle variables were controlled in the model as covariates according to previous studies (Chou et al., 2004; Lakdawalla & Philipson, 2009; Philipson & Posner, 2003; Roemling & Qaim, 2012; Martín et al., 2008). Age (years), gender (male=0, female=1), education level (years of formal education) and marital status (married=1, otherwise=0) were chosen as individual demographic characteristics for controlling differences among individuals. Socio-economic variables included per capital annual household income (in logarithmic scale and inflated to 2011). Lifestyle variables include current smoking (no=0, yes=1), drinking alcohol at least once a month over last year (no=0, yes=1). In addition, year dummies and province dummies were also controlled for the effects of time and regional differences1.
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3 Results 3.1 Descriptive characteristics Descriptive analyses were conducted to investigate the distribution of main variables. Over the 7 year period, there was a notable increase in the consumption of FAFH (Table 1). The average rate of FAFH consumption2 among urban residents was consistently higher than that among rural residents (P<0.01), although the difference narrowed over time. Specifically, the rate in urban areas raised slightly from 16.03% to 18.30% and the rate in rural areas grew from 6.05% to 11.09% between 2004 and 2011. Table 1 Average rate of FAFH consumption in China between 2004–2011 (in %) 1
Regional differences are significant in China; for example, southerners prefer rice while northerners prefer food made from flour, which may show different effects on individual BMI. 2 The FAFH consumption rate was defined as the frequency of eating out divided by the total number of meals eaten during the surveyed 3 days.
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Year Total (n=26244) Urban (n=9148) 2004 9.40 16.03 2006 9.89 15.74 2009 11.34 17.41 2011 13.95 18.30 Change 2004–2011 +4.55 +2.27 Note: Food intake information is from the surveyed 3 days of the CHNS
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We further investigated urban–rural disparities in the structure of FAFH consumption (Table 2). There was no significant difference in the away-from-home consumption of vegetables, red meat, poultry or aquatic products between urban and rural areas in 2011 (P>0.1). However, grain consumption in rural areas was considerably higher than that in urban areas (P<0.01). Egg and milk consumption in rural areas accounts for about half of that in urban areas in 2011. Furthermore, between 2004 and 2011, both urban and rural residents decreased their grain consumption (P<0.01) and increased their egg and milk consumption (P<0.01), while the consumption of red meat remained stable. There was also an increase in the amount of vegetables and white meat (including poultry and aquatic products) consumed away from home (P<0.01) in urban areas3. Table 2 The structure of FAFH consumption between 2004 and 2011 (g/meal)
2004
2011
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Urban Rural urban Rural Grain 126.86 157.88 102.58 111.53 Vegetables 54.04 73.11 72.82 69.46 Red meat 40.26 36.50 37.51 38.81 Poultry 7.35 12.65 11.34 11.10 Aquatic products 11.57 13.32 16.37 15.91 Milk and egg 13.31 5.44 17.01 8.54 Note: Food intake information is from the surveyed 3 days of the CHNS. The figures stand for the average amount of food (g) consumed away from home per person per meal.
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Furthermore, we compared the structure of food consumed at home and away from home in 2011 (Table 3). Compared to FAH, there was a considerable decrease in grain and vegetable consumption away from home in both urban and rural areas, whereas there was a noticeable increase in meat consumption (including red meat, poultry and aquatic products) when people dined out. These are in accordance with Du et al (2016) and Ma et al (2006). Moreover, the differences between FAFH and FAH were more significant in rural areas than in urban areas, 4
3
Away-from-home grain consumption in rural and urban China fell continuously. Rural consumption of grains dropped from 157.88 g per person per meal in 2004 to 111.53 g in 2011, an average decline of 29.36%, and urban grain consumption dropped from 126.86 g in 2004 to 102.58 g in 2011, an average decline of 19.14%. As for meat consumption, red meat consumption in rural China increased from 36.50 g in 2004 to 38.81 g in 2011, and it has overtaken that in urban areas, which slightly decreased from 40.26 g in 2004 to 37.51 g in 2011. White meat consumption in urban areas increased noticeably, but it remained stable in rural areas. Additionally, vegetables eaten out declined slightly in rural China while an apparent increase can be seen in urban China, which reached 72.82 g, overtaking 69.46 g in rural areas in 2011. 4 This study takes 2011 as an example; similar conclusions were observed in 2004, 2006 and 2009.
ACCEPTED MANUSCRIPT especially for meat consumption. Therefore, it is expected that total grain and vegetable consumption will further decline and meat consumption will increase with the sustained growth of FAFH consumption in China, especially in rural areas. Table 3 The consumption structure of FAFH and FAH in 2011 (g/meal)
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FAFH FAH FAFH FAH Grain 102.58 114.35 111.53 147.40 Vegetables 72.82 107.87 69.46 107.84 Red meat 37.51 32.07 38.81 23.66 Poultry 11.34 8.88 11.10 6.04 Aquatic products 16.37 15.83 15.91 10.25 Milk and eggs 17.01 26.11 8.54 12.32 Note: Food intake information is from the surveyed 3 days of the CHNS. The figures stand for the average amount of food (g) consumed per person per meal.
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At the same time, the mean BMI in Chinese adults has significantly increased from 23.39 to 24.26 since 2004, which was in parallel with the increase in FAFH consumption. Rural residents had a lower mean BMI than urban residents (P<0.01), although the difference narrowed over time. In 2011, the mean BMI in urban and rural areas were exactly the same (24.26).
3.2 Empirical results
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Summary statistics of variables used in the empirical analysis are presented in Table 4. Table 4 Variable used in the analysis and summary statistics Total (n=26,244)
Variable
Mean
SD
Mean
SD
23.75 0.98 43.62 2013.43 0.53 8.59 0.88 0.29 0.31 8.90
3.37 1.84 10.53 879.56 0.50 3.81 0.32 0.45 0.46 1.08
23.86 1.47 43.79 2034.40 0.53 9.91 0.85 0.29 0.33 9.19
3.44 2.09 10.81 902.18 0.50 3.76 0.35 0.45 0.47 1.08
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BMI FAFH Age Age² Gender Educational level Marital status Smoking Alcohol drinking Income
Urban China (n=9148)
Rural China (n=17096) Mean 23.70 0.72 43.53 2002.21 0.52 7.89 0.90 0.29 0.30 8.75
SD 3.34 1.62 10.38 867.04 0.50 3.65 0.30 0.45 0.46 1.06
Table 5 presents the empirical results of the effects of FAFH consumption on BMI. Rural and urban China are estimated, respectively. The pooled cross-sectional regression model is mainly used to investigate the urban–rural disparities in the effects of FAFH consumption on overweight and obesity. We only included FAFH in the column of “Pooled OLS (Ordinary Least Square)1” to investigate its association with BMI after controlling for demographic characteristics, time and
ACCEPTED MANUSCRIPT regional differences. The column of “Pooled OLS2” additionally included lifestyle and socio-economic variables. The Random effect model was also selected to test the robustness of the results, as shown in the column “Random Effect”. All statistical analyses were performed by using the statistical software package STATA version 12.1.
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Table 5 Estimated results of pooled OLS and random effect for the association between FAFH and BMI Urban China Rural China Independent variable Pooled Pooled Random Pooled Pooled Random OLS1 OLS2 Effect OLS1 OLS2 Effect * * * FAFH 0.034 0.033 0.024 0.026 0.026 0.010 (0.018) (0.018) (0.015) (0.016) (0.016) (0.011) *** *** *** *** *** Age 0.185 0.194 0.192 0.238 0.242 0.233*** (0.028) (0.028) (0.029) (0.019) (0.020) (0.019) *** *** *** *** *** -0.002 -0.002 -0.002 -0.002 -0.002*** Age² -0.002 (0.0003) (0.0003) (0.0003) (0.0002) (0.0002) (0.0002) *** *** *** *** Gender -0.400 -0.637 -0.509 -0.040 -0.247 -0.248*** (0.070) (0.096) (0.109) (0.051) (0.070) (0.080) *** *** *** Educational level -0.071 -0.084 -0.056 0.011 0.005 0.005 (0.010) (0.011) (0.012) (0.008) (0.008) (0.008) Marital status -0.047 -0.063 -0.073 0.122 0.131 0.168** (0.119) (0.119) (0.123) (0.093) (0.093) (0.082) *** *** *** Smoking -0.601 -0.288 -0.450 -0.288*** (0.100) (0.100) (0.070) (0.061) ** Alcohol drinking 0.215 0.122 0.092 0.016 (0.089) (0.079) (0.067) (0.049) ** ** Income 0.076 0.037 0.053 -0.027 (0.036) (0.031) (0.026) (0.018) *** *** *** *** *** _cons 20.479 20.005 19.842 19.063 18.799 19.214*** (0.571) (0.641) (0.648) (0.417) (0.460) (0.442) Year dummy Yes Yes Yes Yes Yes Yes Province dummy Yes Yes Yes Yes Yes Yes Sample size 9148 9148 9148 17096 17096 17096 R-squared 0.079 0.083 0.081 0.088 0.091 0.090 Note: Figures in parentheses are robust standard errors. “***”, “**” and “*” denote significance at the 1%, 5% and 10% level, respectively. The frequency of FAFH consumption had a significant positive effect on BMI at the 10% level in urban China, which was consistent in three models showing good robustness. One unit increase in the frequency of FAFH consumption led to an increase in BMI of approximately 0.03. However, the frequency of meals consumed away from home in rural China had no significant effect on BMI, which was also consistent in all three models. The different effects of FAFH on BMI between urban and rural areas is very interesting, and is further analyzed in the discussion section.
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The results for control variables were in line with expectations. The relationship between age and BMI was an inverse U shape captured by the positive and negative significant coefficients of age and age-squared, respectively, which was in accordance with Ruhm (2007). Males tended to have a higher BMI than females for both urban and rural China. Better educated people were likely to have improved nutritional awareness and health knowledge, which contributed to a lower risk of overweight and obesity in urban China. Smoking had a negative effect on BMI among rural and urban residents, whereas alcohol drinking increased the risk of overweight and obesity in urban China. Higher income contributes to more food consumption and a higher dietary diversity in developing countries, thus it had a positive effect on BMI.
4 Discussion
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As mentioned previously, overweight and obesity are caused by an imbalance between energy intake and energy expenditure, where calorie intake exceeds calorie expenditure (Chou et al., 2004; Philipson and Posner, 2003; Rashad, 2006). Therefore, high-calorie FAFH consumption could result in overweight and obesity only if surplus energy reaches a certain level. Surplus energy here is defined as total energy intake minus total energy expenditure. Data for average daily energy intake (kcal/day) was directly provided by the CHNS, but there is no direct data for energy expenditure. In this study, energy expenditure was calculated based on the report of a joint FAO/WHO/UNU expert consultation. The average energy expenditure of a population can be estimated by multiplying the physical activity level (PAL) by the Basal Metabolic Rate (BMR) (FAO/ WHO/UNU,2001)5. To determine if the effect of FAFH consumption on BMI depends on the level of surplus energy for China’s current situation, we conducted an empirical regression analysis on the relationship between FAFH consumption and BMI in the distribution of daily surplus energy as shown in Table 6. The first and second columns include the samples with the 25% and 50% lowest surplus energy, respectively, while the third and fourth columns include the samples with the top 50% and 25% surplus energy, respectively. These results show that FAFH consumption could increase the risk of overweight and obesity only when the surplus energy reached a relatively high level in China, otherwise no significant effects would be found. Moreover, the results in the third and fourth columns indicate that the positive associations between FAFH and BMI show higher magnitude in the upper tail of surplus energy distribution, with the coefficient of FAFH increasing from 0.037 to 0.069.
Table 6 OLS regression estimate results for the association between FAFH and BMI in the distribution of surplus energy Independent variable (1) (2) (3) (4) ** FAFH 0.015 0.027 0.038 0.068***
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The amount of energy used for basal metabolism in a period of time is called the BMR and comprises a series of functions that are essential for life. BMR represents 45% to 70%of daily total energy expenditure, which is determined mainly by the individual’s age, gender, body size and body composition according to FAO/ WHO/UNU (2001). Physical activity is the most variable and, after BMR, the second largest component of daily energy expenditure. Activity levels are grouped into five types increasing from 1 to 5 in the CHNS. Energy expenditure of each physical activity is also assigned based on FAO/WHO/UNU (2001) as shown in Schedule 1. Multiplying the PAL by the BMR gives the actual energy expenditure, and then total energy intake minus total energy spent equals the level of daily surplus energy. Considering that the data for energy intake in the CHNS is lower than normal, and that most people in China get adequate calories for daily activities, we took 80% of the total energy consumption calculated.
ACCEPTED MANUSCRIPT (0.022) Yes Yes Yes Yes Yes 16.848*** (0.703) 6516 0.089 “*” denote
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(0.026) (0.017) (0.016) Demographic Variables Yes Yes Yes Socio-economic Variables Yes Yes Yes Lifestyle Variables Yes Yes Yes Year dummy Yes Yes Yes Province dummy Yes Yes Yes *** *** 19.318 17.032*** _cons 20.359 (0.840) (0.550) (0.502) Sample size 6517 13033 13034 R-squared 0.070 0.077 0.089 Note: Control variables are consistent with those in Table 5. “***”, “**” and significance at the 1%, 5% and 10% level, respectively.
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We next analyzed the level of daily surplus energy in rural and urban areas (Table 7). The daily surplus energy of urban residents reached 92.45 kcal, more than that of rural residents, which was 17.61 kcal (P<0.01). For urban residents, 52.45% belong to the group with the top 50% of surplus energy in China, but this was only 48.69% for rural residents. Therefore, although FAFH consumption leads to an increased intake of energy-dense food in both urban and rural areas (Table 3), the urban–rural disparities in the association between FAFH and BMI still exist due to the difference in surplus energy. Specifically, FAFH show no significant effects on BMI in rural China, mainly because surplus energy of most rural residents does not reach a certain level. To confirm this, we additionally conducted an empirical regression analysis on the relationship between FAFH consumption and BMI in rural areas in the distribution of surplus energy (Table 8). Column 1 includes whole rural samples, Columns 2 and 3 include the rural samples with the lowest 50% and the top 50% surplus energy in China, respectively. The results showed that FAFH had a significantly positive effect on BMI only for the 48.69% of rural subjects that belong to the group with the top 50% of surplus energy in China, but no significant effects were discovered for the other 51.31% of rural residents with less surplus. Hence, no significant association was found for rural residents as a whole. Similarly, as most urban residents belong to the group with the top 50% of surplus energy in China, the positive association between FAFH and BMI was discovered in urban China. Table 7 Daily surplus energy, energy intake and energy expenditure (kcal/day)
Urban China Rural China
Surplus energy Mean SE 92.45 16.19 17.61 7.37
Energy intake Mean SE 2078.00 16.09 2224.01 7.31
Energy expenditure Mean SE 1985.55 4.11 2206.40 3.14
Table 8 OLS regression estimated results for the association between FAFH and BMI in rural China in the distribution of surplus energy Independent variable (1) (2) (3) 0.026 FAFH -0.014 0.061*** (0.016) (0.023) (0.022) Demographic Variables Yes Yes Yes
ACCEPTED MANUSCRIPT Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes *** *** 18.799 19.039 17.199*** (0.460) (0.660) (0.676) 17096 8713 8269 Sample size 0.091 R-squared 0.087 0.092 Note: Control variables are consistent with those in Table 5. “***”, “**” and “*” denote significance at the 1%, 5% and 10% levels, respectively.
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Socio-economic Variables Lifestyle Variables Year dummy Province dummy _cons
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Furthermore, we analyzed the reasons why surplus energy in urban areas is more than that in rural areas. As shown in Table 7, the average daily energy intake of urban residents was 2078.00 kcal/day, less than that of rural residents, which reached 2224.01 kcal/day (P<0.01). This demonstrated that the relatively high level of surplus energy in urban residents does not result from energy intake, rather, the average daily energy expenditure of urban residents was 1985.55 kcal/day, considerably less than that of rural residents, 2206.40 kcal/day (P<0.01). Since BMR was comparable for urban and rural residents, PAL is the main factor for the difference in urban– rural energy expenditure. Table 9 shows the distribution of labor intensity in rural and urban China in detail. Under the long-term urban–rural dual structure, labor intensity is significantly heavier in rural areas, where heavy physical activity, such as farming, accounts for 46.51%; whereas, very light physical activity, such as office working, accounts for 69.52% in urban areas.
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Table 9 The distribution of labor intensity in rural and urban areas
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Very light physical activity Light physical activity Moderate physical activity Heavy physical activity Very heavy physical activity
Urban China 40.08% 29.44% 17.05% 13.01% 0.42%
Rural China 14.26% 20.33% 18.08% 46.51% 0.81%
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Therefore, the urban–rural disparities in associations between FAFH consumption and risk of overweight and obesity result from differences in surplus energy, which are mainly due to the different labor intensities among rural and urban residents. The remarkably higher level of physical activity among rural residents increases energy consumption, maintaining a negative balance between energy metabolism, this contributes to the insignificant relationship between FAFH and BMI. A positive association is observed in urban areas due to more surplus energy, which results from the predominance of sedentary work. Previous studies have shown a positive association between FAFH consumption and weight gain in many developed countries (Ayala et al., 2008; Bes-Rastrollo et al., 2010; Cai et al., 2008; Mccrory et al., 1999), which is consistent with our results in urban China. By contrast, Simmons et al (2005) also found that the choice of takeaway and restaurant foods was not related to the prevalence of adult obesity in rural communities in Australia, which is consistent with our results in rural China. In this paper, instead of focusing on energy intake, the level of surplus energy
ACCEPTED MANUSCRIPT including both energy intake and consumption was emphasized to analyze the association between dining out and weight gain. This is important, especially for countries or regions such as China, which are significantly disparate in physical activity levels.
5 Conclusions and implications
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In parallel with an increase in the prevalence of overweight and obesity, the rate of FAFH consumption in Chinese adults has increased notably in recent years. Urban residents have a higher FAFH rate than rural residents, but the difference has narrowed over time. There is no significant difference in the away-from-home consumption of vegetables or meat between urban and rural areas. Furthermore, FAFH consumption leads to an increased intake of energy-dense foods and the structural differences between FAFH and FAH are more significant in rural areas than in urban areas. Through a comparative study between rural and urban China, our empirical results showed that the frequency of meals consumed away from home had a significant positive effect on BMI in urban China, whereas no significant association was observed in rural China. These urban–rural differences result from different levels of surplus energy, which is mainly due to the difference in labor intensity among rural and urban residents. According to our results, to prevent the rapidly progressing obesity epidemic, public health policies should be implemented not only to help people make healthier food choices when dining out, but also to encourage people to do more physical activities, especially in urban China. This is important as high-energy FAFH consumption becomes an inevitable trend, and sedentary lifestyles are quite common. People can achieve energy balance by both eating less high-calorie foods when eating out and doing more physical exercise. Furthermore, decreasing agricultural employment, and increasing urbanization, the popularity of home appliances and a general trend towards a service sector economy imply lower physical activity in rural areas (Monda et al., 2007); thus, the positive effect of FAFH on the prevalence of overweight and obesity is expected to spread to rural China in the near future, which will also need prevention approaches.
Acknowledgements
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This work was supported by the National Natural Science Foundation of China (71633005). This research used data from the China Health and Nutrition Survey (CHNS). The authors thank the National Institute of Nutrition and Food Safety, the China Center for Disease Control and Prevention, the Carolina Population Center and the University of North Carolina at Chapel Hill. The authors also thank Dr. Li Chen for advice.
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