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Open AccessArticle

Adaptation and Validation of the Chinese Version of the Nutrition Environment Measurement Tool for Stores

1
Institute of Health Sciences, China Medical University, Shenyang 110122, China
2
Department of International Health, Center for Human Nutrition, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205-2179, USA
*
Author to whom correspondence should be addressed.
Yang Liu and Shenzhi Song contributed equally to the paper.
Int. J. Environ. Res. Public Health 2019, 16(5), 782; https://doi.org/10.3390/ijerph16050782
Received: 16 November 2018 / Revised: 17 January 2019 / Accepted: 27 February 2019 / Published: 4 March 2019
(This article belongs to the Special Issue Built Environments, Food Environments, and Public Health)

Abstract

Changes in lifestyle and food environment have created a heavy burden of obesity and chronic disease in China. However, measurements of the food environment have been rarely reported in China or other countries with similar food cultures; this measurement shortage is partially due to the lack of valid and reliable measurement tools. The aim of the present study was to adapt and validate a Chinese version of the Nutritional Environment Measurement Survey for Stores (C-NEMS-S). Categories and items of the NEMS-S were culturally adapted to fit the Chinese population and included grains, dry beans, starchy tubers, vegetables, fruits, seafood, meat and poultry, dietary oils, milk, bread, instant noodles, and beverages. A scoring sheet for each food category was created to measure availability, quality, and pricing. Then, the C-NEMS-S was validated in 10 large-sized supermarkets and 10 convenience stores in Shenyang, China. Two trained raters performed their evaluations separately at the same store. The intra-class correlation coefficient (ICC) of the availability composite score was 0.98. All food measures had a moderate or good ICC (0.41 to 1.00). The kappa for each food measure ranged from 0.52 to 1.00. C-NEMS-S was able to show the difference in healthy food availability between large-sized supermarkets and convenience stores, as well as the price differences between healthier options and regular options. Large-sized supermarkets had a significantly higher total score (p < 0.001) and healthier option availability for all food measures (all items were statistically significant (p < 0.05), except sugar-free beverages). Healthier options cost more than regular options for grains, milk, bread, and instant noodles (from 4% to 153%). The adapted C-NEMS-S can be used to measure the consumer food environment in stores in China.
Keywords: food environment; China; Nutritional Environment Measurement Survey-Stores (NEMS-S) food environment; China; Nutritional Environment Measurement Survey-Stores (NEMS-S)

1. Introduction

The food environment, which could also be called a nutrition environment, refers to the physical presence of food that affects a person’s diet, a person’s proximity to food store locations, the distribution of food stores, food service, any physical entity by which food may be obtained, or a connected system that allows access to food [1]. The food environment plays an important role in food choice, eating patterns, and energy intake [2]. It is widely reported that the food environment is associated with the increasing epidemic of childhood and adult obesity [3,4,5,6]. Recent efforts on obesity prevention have partly focused on studying the role of environmental factors, and national level policies on the food environment have been implemented in many countries [7,8,9].
It was reported that the food environment within stores, as it relates to healthy food availability, quality, and price, may contribute to the dietary intake disparities in many countries [10,11,12]. Specifically, higher quality food stores, such as specialized fruit and vegetable (F&V) markets, were associated with greater F&V intake after controlling for individual-level characteristics, according to a study conducted in a Brazilian city [10]. The proportion of in-store shelf space for skim, 1% fat, and 2% fat milk was reported by a study conducted in the United States to be associated with low-fat milk consumption [11]. Besides price and convenience, purity, freshness, association with specific places, and ‘Pakistani-ness’ were reported as the bases for making decisions about ‘good food’ in Pakistan [12].
Over the past several decades, China has been undergoing economic development and urbanization at an accelerated rate [13]. In 1950, 13% of people in China lived in cities. By 2010, the urban share of the population had grown to 45%; this is projected to reach 60% by 2030 [14]. Meanwhile, the total energy intake of Chinese people increased by 80%, from 1635 kcal/capita/d in 1961 to 2943 kcal/capita/d in 2003. By 2006, less than 1 percent of people were getting 10 percent of their energy from fat, 44 percent were getting 30 percent of their energy from fat, and nearly two-thirds were getting more than 10 percent of their energy from animal products [15]. The prevalence of overweight and obese children increased from less than 2% in 1985 to 15% in 2010, and the overall rate of overweight and obese people in major cities is over 20% [16,17,18]. However, the food environment after such huge societal change in China, and its contribution to the obesity prevalence in China, has yet to be described. Answers these questions would help provide evidence for food environment improvement and potentially limit the obesity epidemic in China.
Lack of valid, reliable measures of food environments hinders food environment studies in China and other Asian countries. Many food environment measurement tools exist for other countries [19,20]. Among these, the Nutritional Environment Measures Survey in Stores (NEMS-S) is an observational measure to assess the food environment within retail food stores [21,22]. The NEMS-S measures the availability of healthier options, price, and quality for 10 categories of food products. The NEMS-S is characterized by its relative ease of use and ability to adapt to different settings and populations [23,24,25,26,27]. The 10 categories of food products included in the NEMS-S were based on the types of food products that contributed the most fat and calories, which are different from those in China, to the American diet. So, before using the NEMS-S in China, food products were culturally adapted in this study to fit the Chinese food culture. This study aimed to adapt and validate a Chinese version of the Nutritional Environment Measurement Survey for Stores (C-NEMS-S) in order to provide a tool for measuring the food environment in China for future studies.

2. Methods

2.1. Adaptation of Food Measures and Healthier Options

The basic principle of the NEMS-S is to gather information on comparable categories and species of food across stores, which includes the availability, price, and quality of healthy options for a particular food. Three steps were conducted to adapt the NEMS-S to the C-NEMS-S: selecting food measures, defining healthy food or healthier options, and adapting scores for availability, price, and quality.

2.1.1. Selection of Food Measures

Food measures were selected based on the foods that contribute the most energy to the Chinese diet, as listed in the China Food Composition handbook developed by The Institute of Nutrition and Food Safety at the National Centers for Disease Control and Prevention [28] and in the Chinese Dietary Guidelines developed by the Chinese Nutrition Society, Ministry of Health, Ministry of Agriculture, General Administration of Sport, and National Centers for Disease Control and Prevention [29].
Chinese people divided formal meals into two general parts: “fan” and “cai” [2]. “Fan” is the staple food for every Chinese meal, accounts for a main part of Chinese dietary energy intake, and comprises grains (such as rice, wheat, maize, millet, or sorghum), dry beans, starchy tubers, or root vegetables (such as potatoes, cassava, yams, or taro) [3,29]. It was reported that, by 2015, annual consumption of grains in China had risen to 534 kg per person [30]. Hence, grains, dry beans, and starchy tubers were listed into the C-NEMS-S as the first three food categories. “Cai” could be made of various kinds of vegetables, meat and poultry, and dietary oil. Eight subcategories of vegetables (root vegetables; fresh beans; solanaceous fruit vegetables; bulb vegetables; tender stems, leaves and cauliflower vegetables; aquatic vegetables; wild vegetables; bacteria and algae vegetables) were included into the C-NEMS-S, according to the China Food Composition handbook, which covers all kinds of vegetables eaten by the Chinese people [28]. Meat and poultry that could be cooked as dishes for meals in China included poultry and livestock (pork, beef, mutton, chicken, duck, etc.) and seafood (fish, shrimp, crab, shellfish: freshwater or saltwater). Another important ingredient in Chinese meals was dietary oil, of which Chinese residents consume an average of 42.1 kg per year per person; dietary oil was reported to be related to nutrition-related non-communicable diseases (NR-NCDs) among the poor [31].
Breads and instant noodles are the foods most often consumed as instant meals, according to the China Food Composition handbook and were included in the C-NEMS-S.
Milk is being consumed more frequently in China, with the rise of household income [32]. Milk was recommended by the Chinese Food Guide as a healthy food item. Milk’s measurements and scoring in the C-NEMS-S were kept the same as in the original NEMS-S.
Six subcategories of fruits (pip fruits, stone fruits, soft fruits, citrus fruits, subtropical and tropical fruits, and melons) were included into the C-NEMS-S and cover all kinds of fruits consumed in China [28].
Six subcategories of beverages (lactic acid drinks, milk beverages, juices, teas, plant protein beverages, apple vinegar) were included into the C-NEMS-S and cover all kinds of beverages consumed in China [28].

2.1.2. Definition of Healthy Foods or Healthier Options

An important principle of the NEMS-S was to measure availability, price, and quality of “healthier” options (such as low-fat milk for the milk measure and diet coke for the beverage measure) or healthy foods (such as fruits or vegetables). Healthier options and healthy foods are defined and presented in Table 1.

2.1.3. Adaptation of Scores for Availability, Price, and Quality

Raters evaluated and scored each food measure based on its availability, price, and quality. Firstly, raters looked for healthier options for each food measure, as listed in Table 1. Then, they judged whether the healthier options were available and compared the price with regular options. Scores were given to each food measure based on availability of their healthier options and price, as listed in Table 2. Quality was only evaluated for fresh food, which included vegetables, fruits, meat and poultry, and seafood. Raters judged whether the quality of these fresh foods were acceptable or unacceptable. “Acceptable” (A) referred to peak condition, top quality, good color, and being fresh, firm, and clean. “Unacceptable” (UA) referred to being bruised, old looking, mushy, dry, or overripe, having dark sunken spots in irregular patches or cracked or broken surfaces, and signs of shriveling, mold or excessive softening. The rating was based on the majority (>50%) of vegetables, fruits, meat and poultry, or seafood. If it was difficult to decide whether to mark “A” or “UA”, “UA” was used, and a description was included in the comments.

2.2. Measurement Study

The adapted C-NEMS-S was tested in environment audits in 10 large-sized supermarkets (independent or chain, with areas more than 6000 m2) and 10 convenience stores (areas no more than 6000 m2) in February 2017. To assess inter-rater reliability, two raters entered the same store at the same time, with the store owners’ permission, and did the evaluations separately. The Research Ethics Committee of China Medical University approved the study ((2017) 055).
Two undergraduate students from China Medical University were recruited as raters. They were both junior students from preventive medicine and have taken courses on nutrition. Recruitment posters were posted on campus, and seven students signed up. These students took a test in nutrition, statistics, epidemiology, and environmental sciences, and the top two students were selected. Training for raters included taking the NEMS online courses [33], discussing with researchers about each food category and item in the C-NEMS-S, and studying a detailed operation guide on the C-NEMS-S.

2.3. Data Analysis

Both the intra-class correlation coefficient (ICC) and kappa coefficient were used to determine inter-rater reliability. The ICC was calculated based on composite score and availability score for each food category and item. An ICC less than 0.4 indicated poor inter-rater reliability and and ICC greater than 0.75 indicated good inter-rater reliability [34]. Kappa was calculated using the availability of the food item (yes/no). Kappa less than 0.4 indicated poor inter-rater reliability, a range between 0.4 to 0.6 represented middle inter-rater reliability, a range between 0.6 to 0.8 represented good inter-rater reliability, and greater than 0.8 indicated excellent inter-rater reliability [35].
The known-groups comparison method was used to determine construct validity [26]. If the scale was valid, the scores of the two groups with known differences would differ significantly. According to previous studies and the original NEMS-S, supermarkets and convenience stores were two groups with known differences: supermarkets were healthier than convenience stores [21,26,36,37]; healthier options and regular options were two groups with known differences with regard to price [21]. Therefore, we used scores acquired from the C-NEMS-S to test whether this instrument could also identify these differences. The Student’s t test was used to compare the total score between supermarkets and convenience stores. A nonparametric test was used to compare the availability between supermarkets and convenience stores and to compare prices between healthier options and regular options for each food measure. All data analyses were completed using SPSS Statistics version 20.0 (IBM Corporation, New York, NY, USA).

3. Results

3.1. The Adapted C-NEMS-S

Twelve measures were included in the C-NEMS-S (Supplementary Table S1: C-NEMS-S Questionnaire): grains, dry beans, starchy tubers, vegetables, fruits, seafood, meat and poultry, dietary oils, milk, bread, instant noodles, and beverages. Table 3 shows the food measurements and score dimensions.

3.2. Reliability

Table 4 shows the inter-rater reliability for the availability of each food measure. The intra-class correlation coefficient of availability had a composite score of 0.98. All food measures obtained a moderate or good ICC (ranging from 0.41 to 1.00). The kappa for each food measure ranged from 0.52 to 1.00. For price score and quality score, agreement between the two raters was very high, as shown in Supplement Table S2 and Supplement Table S3. Due to the low variance between different stores, ICC statistics were either too low or could not be calculated for price and quality.

3.3. Comparison between Chinese Supermarkets and Convenience Stores

As shown in Table 5, supermarkets scored significantly higher in total than convenience stores (p < 0.001). Table 6 shows the proportion of the food stores that provided healthier options for each food measure. Large-sized supermarkets had a significantly greater amount of healthier options available for all food measures (all 12 items were statistically significant (p < 0.05), except for sugar-free beverages). The results were consistent between rater 1 and rater 2.

3.4. Price Comparison between Healthier Options and Regular Options

Table 7 shows the price comparison between healthier options and regular options for each food measure. Healthier options cost more than regular options for grains, milk, bread, and instant noodles. The results were consistent between rater 1 and rater 2.

4. Discussion

The present study adapted and tested the first tool to measure the retail food environment in China. The original NEMS-S was adapted to the Chinese culture. The C-NEMS-S adapted in this study had high inter-rater reliability and was able to display the differences in healthy food availability between large-sized supermarkets and convenience stores, as well as the price differences between healthier options and regular options.
The NEMS-S is regarded as an observational measure or environmental auditing tool. The auditing ability of raters is critical to obtaining high reliability observations. For the original NEMS-S, the inter-rater kappa coefficient ranged from 0.83 to 1.00 [26]. For the Brazilian version of the NEMS-S, the inter-rater kappa coefficient ranged from 0.69 to 1.00, and the ICC raged from 0.75 to 1.00 [21]. The inter-rater reliability of the C-NEMS-S in the present study was only slightly lower than that of the original and the Brazilian version. Nonetheless, both the ICC and kappa coefficient were acceptable, ranging from moderate to high (0.41 to 1.00 for the ICC, 0.52 to 1.00 for the kappa coefficient) [34,35].
Compared with other food categories, milk, bread, instant noodles, and beverages had a lower ICC or kappa. This may be because packaged food categories occupied larger proportions of shelf space. In order to determine healthier options for these food categories, careful reading of food labels and packaging information is needed. Some raters may, therefore, fail to find healthier options for these food categories during this process. Though the ICC and kappa for these food categories were acceptable, future training programs, to teach raters how to measure in-store food environments, should pay more attention to these packaged food categories.
According to the results, large-sized supermarkets had a significantly greater amount of healthier options available for all food measures (all items were statistically significant (p < 0.05), except for sugar-free beverages) compared with convenience stores. The result was consistent with previous studies on differences between supermarkets and convenience stores. According to previous studies, supermarkets could provide healthier foods at reasonable prices [36,37]. For the original NEMS-S in the United States and the adapted NEMS-S in Brazil [21,26], supermarkets obtained higher scores on healthier food availability. Hence, supermarkets were hypothesized to score high in the C-NEMS-S, as well. According to the results in the present study, the C-NEMS-S obtained higher availability for all food measures in large-sized supermarkets. These findings in the present study support the ability of the C-NEMS-S to differentiate between supermarkets and convenience stores.
Except for meat and poultry, all healthier options cost more than regular options. This result indicates the ability of the measurement scoring to identify the differences between healthier options and regular options. In addition, this result also suggests that access to various food outlets might not be a problem for the food environment in China, but more attention should be paid to the consumer environment. The price discrepancy between healthier options and regular options may become a target for future policies aimed at improving the food environment in China.
There were several limitations in the present study. Firstly, dietary habit is different between northern China and southern China. The current validation survey of the C-NEMS-S was only conducted in Shenyang, a city in northern China. In addition, the field work happened in winter, so the seasonality of the food offerings could have played a role in influencing reliability testing and findings. However, the C-NEMS-S was developed based on guidelines suitable for the whole of China by referencing the Chinese Dietary Guidelines (2016) [29] and Chinese food categories in the China Food Composition handbook [28]. Food categories and species were selected based on a nation-wide perspective and were not limited to northern Chinese cuisine or limited by seasonality. With further validation surveys in cities in southern China and during different times of the year, the C-NEMS-S may be verified to be suitable for the whole of China. Secondly, a small sample size was used to conduct the environment audit in stores and to test the reliability of the C-NEMS-S. To cover more areas of China, a larger sample size is warranted in future validation studies. Thirdly, snacks were not included in the final version of the C-NEMS-S as a food measure. During evaluations, it was too difficult to determine low fat or low sugar healthier snacks from the large variety of snacks. This is a problem to be amended in updated versions of the C-NEMS-S in our future studies.

5. Conclusions

A Chinese version of the NEMS-S, which allowed for assessment of the food environment in China and potentially other Asian countries with similar food culture, was adapted and evaluated for China in the present study. The present study also implies that supermarkets could provide healthier food options compared to convenience stores in China. However, healthier options cost more than regular options, which may hinder the choice to purchase healthy foods and should be taken into consideration in future intervention studies or government food environment policies.

Supplementary Materials

The following are available online at https://www.mdpi.com/1660-4601/16/5/782/s1, Table S1: Table s1. The C-NEMS-S. Table S2: Price scores by two raters. Table S3: Quality scores by two raters.

Author Contributions

Conceptualization, Y.L., S.S. and D.W.; Data curation, J.H.; Formal analysis, Y.L. and S.S.; Investigation, S.S.; Methodology, Y.L. and Y.M.; Validation, J.G. and N.J.; Writing – original draft, Y.L. and S.S.; Writing – review & editing, J.G., N.J., J.H., Y.M. and D.W.

Funding

This research was funded by National Natural Science Foundation of China, grant number 71774173.

Acknowledgments

We thank Karen Glanz and Margaret Clawson for permission to use and adapt the original NEMS-S for China and for kindly providing information on the development of the original NEMS-S.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. General Food Environment Resources. Available online: https://www.cdc.gov/healthyplaces/healthtopics/healthyfood/general.htm (accessed on 18 December 2018).
  2. Mattes, R.; Foster, G.D. Food environment and obesity. Obesity 2014, 22, 2459–2461. [Google Scholar] [PubMed]
  3. Gordon-Larsen, P. Food availability/convenience and obesity. Adv. Nutr. 2014, 5, 809–817. [Google Scholar] [CrossRef] [PubMed]
  4. Gamba, R.J.; Schuchter, J.; Rutt, C.; Seto, E.Y. Measuring the food environment and its effects on obesity in the United States: A systematic review of methods and results. J. Community Health 2015, 40, 464–475. [Google Scholar] [CrossRef] [PubMed]
  5. Holsten, J.E. Obesity and the community food environment: A systematic review. Public Health Nutr. 2009, 12, 397–405. [Google Scholar] [CrossRef] [PubMed]
  6. Williams, J.; Scarborough, P.; Matthews, A.; Cowburn, G.; Foster, C.; Roberts, N.; Rayner, M. A systematic review of the influence of the retail food environment around schools on obesity-related outcomes. Obes. Rev. 2014, 15, 359–374. [Google Scholar] [CrossRef] [PubMed]
  7. Gittelsohn, J.; Trude, A. Environmental Interventions for Obesity and Chronic Disease Prevention. J. Nutr. Sci. Vitaminol. 2015, 61, S15–S16. [Google Scholar] [CrossRef] [PubMed]
  8. Anderson, S.E.; Martins, P.A.; Gittelsohn, J. Changing the Food Environment for Obesity Prevention: Key Gaps and Future Directions. Curr. Obes. Rep. 2014, 3, 451–458. [Google Scholar] [CrossRef] [PubMed]
  9. Odoms-Young, A.; Sing, C.R.; Springfield, S.; McNabb, L.; Thompson, T. Retail Environments as a Venue for Obesity Prevention. Curr. Obes. Rep. 2016, 5, 8. [Google Scholar] [CrossRef] [PubMed]
  10. De Menezes, M.C.; Victoria, D.R.A.; Lima, C.B.V.D.; Lopes, A.C.S. Individual and food environmental factors: Association with diet. Public Health Nutr. 2018, 21, 2782–2792. [Google Scholar] [CrossRef] [PubMed]
  11. Cheadle, A.; Psaty, B.M.; Curry, S.; Wagner, E.; Diehr, P.; Koepsell, T.; Kristal, A. Community-level comparisons between the grocery store environment and individual dietary practices. Prev. Med. 1991, 20, 250–261. [Google Scholar] [CrossRef]
  12. Hasnain, S. “Everyone just ate good food”: ‘Good food’ in Islamabad, Pakistan. Appetite 2018, 127, 1–9. [Google Scholar] [CrossRef] [PubMed]
  13. Wu, Y.; Xue, H.; Wang, H.; Su, C.; Du, S.; Wang, Y. The impact of urbanization on the community food environment in China. Asia Pac. J. Clin. Nutr. 2017, 26, 504–513. [Google Scholar] [PubMed]
  14. What Should We Understand about Urbanization in China? Available online: https://insights.som.yale.edu/insights/what-should-we-understand-about-urbanization-in-china (accessed on 22 December 2018).
  15. Popkin, B.M.; Du, S.F. Dynamics of the Nutrition Transition toward the Animal Foods Sector in China and its Implications: A Worried Perspective. J. Nutr. 2003, 133, 3898S–3906S. [Google Scholar] [CrossRef] [PubMed]
  16. Ji, C.Y.; Chen, T.J. Empirical changes in the prevalence of overweight and obesity among Chinese students from 1985 to 2010 and corresponding preventive strategies. Biomed. Environ. Sci. 2013, 26, 12. [Google Scholar]
  17. Shan, X.Y.; Xi, B.; Cheng, H.; Hou, D.Q.; Wang, Y.; Mi, J. Prevalence and behavioral risk factors of overweight and obesity among children aged 2–18 in Beijing, China. Int. J. Pediatr. Obes. 2010, 5, 7. [Google Scholar] [CrossRef] [PubMed]
  18. Xue, H.; Wu, Y.; Wang, X.Y.; Wang, Y. Time Trends in Fast Food Consumption and Its Association with Obesity among Children in China. PLoS ONE 2016, 11, e0151141. [Google Scholar] [CrossRef] [PubMed]
  19. Ohri-Vachaspati, P.; Leviton, L.C. Measuring food environments: A guide to available instruments. Am. J. Health Promot. 2010, 24, 410–426. [Google Scholar] [CrossRef] [PubMed]
  20. Gustafson, A.; Hankins, S.; Jilcott, S. Measures of the consumer food store environment: A systematic review of the evidence 2000–2011. J. Community Health 2012, 37, 897–911. [Google Scholar] [CrossRef] [PubMed]
  21. Glanz, K.; Sallis, J.F.; Saelens, B.E.; Frank, L.D. Nutrition Environment Measures Survey in stores (NEMS-S): Development and evaluation. Am. J. Prev. Med. 2007, 32, 282–289. [Google Scholar] [CrossRef] [PubMed]
  22. Saelens, B.E.; Glanz, K.; Sallis, J.F.; Frank, L.D. Nutrition Environment Measures Study in restaurants (NEMS-R): Development and evaluation. Am. J. Prev. Med. 2007, 32, 273–281. [Google Scholar] [CrossRef] [PubMed]
  23. Lytle, L.A.; Sokol, R.L. Measures of the food environment: A systematic review of the field, 2007–2015. Health Place 2017, 44, 18–34. [Google Scholar] [CrossRef] [PubMed]
  24. Cavanaugh, E.; Green, S.; Mallya, G.; Tierney, A.; Brensinger, C.; Glanz, K. Changes in food and beverage environments after an urban corner store intervention. Prev. Med. 2014, 65, 7–12. [Google Scholar] [CrossRef] [PubMed]
  25. Cavanaugh, E.; Mallya, G.; Brensinger, C.; Tierney, A.; Glanz, K. Nutrition environments in corner stores in Philadelphia. Prev. Med. 2013, 56, 149–151. [Google Scholar] [CrossRef] [PubMed]
  26. Martins, P.A.; Cremm, E.C.; Leite, F.H.; Maron, L.R.; Scagliusi, F.B.; Oliveira, M.A. Validation of an adapted version of the nutrition environment measurement tool for stores (NEMS-S) in an urban area of Brazil. J. Nutr. Educ. Behav. 2013, 45, 785–792. [Google Scholar] [CrossRef] [PubMed]
  27. Nution Environment Survey. Available online: http://www.med.upenn.edu/nems/publications.shtml (accessed on 27 February 2018).
  28. Yang, Y.X.; Wang, G.Y.; Pang, X.C. China Food Composition, 2nd ed.; Peking University Medical Press: Beijing, China, 2009; pp. 3–38. ISBN 978-781-116-727-6. [Google Scholar]
  29. Chinese Nutrition Society. The Chinese Dietary Guidelines, 4th ed.; People’s Health Publishing House: Beijing, China, 2016; pp. 1–298. ISBN 978-711-722-214-3. [Google Scholar]
  30. Zhan, S.H.; Huang, L.L. Internal spatial fix: China’s geographical solution to food supply and its limits. Geoforum 2017, 85, 13. [Google Scholar] [CrossRef]
  31. Ng, S.W.; Zhai, F.Y.; Popkin, B.M. Impacts of China’s edible oil pricing policy on nutrition. Soc. Sci. Med. 2008, 66, 414–426. [Google Scholar] [CrossRef] [PubMed]
  32. Issa, L.A.; Peng, Y.H.; Fan, S.Y.; Tahir, H. Economical Evaluation of Milk Consumption in China. 2006. Available online: https://ssrn.com/abstract=948199 (accessed on 5 December2018).
  33. Nutrition Environment Measure Survey. Available online: https://www.med.upenn.edu/nems/ (accessed on 27 February 2018).
  34. Bartko, J.J. The intraclass correlation coefficient as a measure of reliability. Psychol. Rep. 1966, 19, 3–14. [Google Scholar] [CrossRef] [PubMed]
  35. Landis, J.R.; Koch, G.G. The meastirement of obsereer agreement for categorical data. Biometrics 1977, 33, 16. [Google Scholar] [CrossRef]
  36. Wang, Y.F.; Wang, L.; Xue, H.; Qu, W. A Review of the Growth of the Fast Food Industry in China and Its Potential Impact on Obesity. Int. J. Environ. Res. Public Health 2016, 13, 1112. [Google Scholar] [CrossRef] [PubMed]
  37. Wall, M.M.; Larson, N.I.; Forsyth, A.; Van Riper, D.C.; Graham, D.J.; Story, M.T.; Neumark-Sztainer, D. Patterns of obesogenic neighborhood features and adolescent overweight: A comparison of statistical approaches. Am. J. Prev. Med. 2012, 42, e65–e75. [Google Scholar] [CrossRef] [PubMed]
Table 1. Definition of healthy foods and healthier options.
Table 1. Definition of healthy foods and healthier options.
Food MeasureDefinition of Healthier Option
GrainsHealthier option: Whole grains. Whole grains were selected as healthier options for grains due to their low glycemic index (GI) as recommended by the Chinese Dietary Guidelines and previous research [29].
Dry beansHealthy food, as recommended by the Chinese Dietary Guidelines [29].
Starchy TubersHealthy food, as recommended by the Chinese Dietary Guidelines [29].
VegetablesHealthy food, as recommended by the Chinese Dietary Guidelines [29].
FruitsHealthy food, as recommended by the Chinese Dietary Guidelines [29].
SeafoodHealthy food, as recommended by the Chinese Dietary Guidelines [29].
Meat and PoultryHealthier option: Lean meat options (tenderloin). Tenderloin contains less than 10% fat.
Dietary oilsHealthier option: Plant-based oils. Plant-based oils are recommended by the Chinese Dietary Guidelines as it is rich in Vitamin E, while animal oil has a higher proportion of saturated fat and cholesterol [29].
MilkHealthier option: Low-fat milk. Packages are labeled as “low-fat”, which indicates fat content lower than 1.5 g/100 mL, and “no-fat”, which indicates fat content lower than 0.5%.
BreadHealthier option: Whole wheat bread.
Instant noodlesHealthier option: Non-fried instant noodles
BeveragesHealthier option: Sugar-free beverages. Packages are labeled as “no-sugar”, which indicates sugar content lower than 1.5 g/100 g or 1.5 g/100 mL.
Table 2. Scoring Sheet for the Chinese version of the Nutritional Environment Measurement Survey (NEMS) for Stores.
Table 2. Scoring Sheet for the Chinese version of the Nutritional Environment Measurement Survey (NEMS) for Stores.
ItemAvailability of Healthier OptionPriceQuality
GrainsYES whole grains = 2 pts
2–3 species = 1 pt
>3 species = 2 pts
Lower for whole grains = 2 pts
Higher for whole grains = −1 pt
Not Applicable
Dry Beans0 specie = 0 pt
1 species = 1 pt
2–3 species = 2 pts
≥4 species = 3 pts
Not ApplicableNot Applicable
Starchy Tubers0 specie = 0 pt
1 species = 1 pt
2–4 species = 2 pts
5 species = 3 pts
Not ApplicableNot Applicable
Vegetables0 specie = 0 pt
1–4 species = 1 pt
5–9 species = 2 pts
≥10 species = 3 pts
Not Applicable25–49% acceptable = 1 pt
50–74% acceptable = 2 pts
75% + acceptable = 3 pts
Fruits0 specie = 0 pt
1–4 species = 1 pt
5–9 species = 2 pts
≥10 species = 3 pts
Not Applicable25–49% acceptable = 1 pt
50–74% acceptable = 2 pts
75% + acceptable = 3 pts
Seafood0 specie = 0 pt
1–4 species = 1 pt
5–9 species = 2 pts
≥10 species = 3 pts
Not Applicable25–49% acceptable = 1 pt
50–74% acceptable = 2 pts
75% + acceptable = 3 pts
Meat and PoultryYES meat with less than 10% fat = 2 pts
2–3 species = 1 pt
>3 species = 2 pts
Lower for lean meat = 2 pts
Higher for lean meat = −1 pt
Not Applicable
Dietary oilsYES plant-based oils = 2 pts
2–3 species = 1 pt
>3 species = 2 pts
Lower for plant-based oils = 2 pts
Higher for plant-based oils = −1 pt
Not Applicable
MilkYES low-fat milk = 2 pts
Proportion (lowest fat to whole) >50% = 1 pt
Lower for low-fat milk = 2 pts
Same for both = 1 pt
Higher for low-fat milk = −1 pt
Not Applicable
BreadYES whole wheat bread = 2 pts
>2 species = 1 pt
Lower for whole wheat bread = 2 pts
Higher for whole wheat bread = −1 pt
Not Applicable
Instant NoodlesYES non-fried instant noodles = 2 pts
>2 species = 1 pt
Lower for non-fried instant noodles = 2 pts
Higher for non-fried instant noodles = −1 pt
Not Applicable
BeveragesYES sugar-free carbonated beverages = 1 pt
YES sugar-free lactose beverages = 1 pt
YES sugar-free fruit juice = 1 pt
YES sugar-free tea beverages = 1 pt
YES sugar-free vegetable protein beverages = 1 pt
YES sugar-free apple cider vinegar = 1 pt
Lower for sugar-free carbonated beverages = 2 pts
Higher for sugar-free carbonated beverages = −1 pt
Lower for sugar-free lactose beverages = 2 pts
Higher for sugar-free lactose beverages = −1 pt
Lower for sugar-free fruit juice = 2 pts
Higher for sugar-free fruit juice = −1 pt
Lower for sugar-free tea beverages = 2 pts
Higher for sugar-free tea beverages = −1 pt
Lower for sugar-free vegetable protein beverages = 2 pts
Higher for sugar-free vegetable protein beverages = −1 pt
Lower for sugar-free apple cider vinegar = 2 pts
Higher for sugar-free apple cider vinegar = −1 pt
Not Applicable
Availability Subtotal =Price Subtotal =Quality Subtotal =
Total Chinese version of the Nutritional Environment Measurement Survey (C-NEMS) Stores Score =
POINT RANGES: Availability Subtotal: 0 to 48; Price Subtotal: −14 to 26; Quality Subtotal: 0 to 9; TOTAL NEMS SCORE RANGE: from −14 to 83.
Table 3. Food measures and score dimensions in the Chinese version of the Nutritional Environment Measurement Survey for Stores (C-NEMS-S).
Table 3. Food measures and score dimensions in the Chinese version of the Nutritional Environment Measurement Survey for Stores (C-NEMS-S).
Category of FoodAvailability of Healthier OptionsPriceQuality
Healthier OptionAvailability
GrainsWhole GrainsXX
Dry beans 1-X
Starchy Tubers 1-X
Vegetables 1-X X
Fruits 1-X X
Seafood 1-X X
Meat and PoultryLean meat optionsXXX
Dietary oilsPlant-based oilsXX
MilkLow-fat milkXX
BreadWhole wheat breadXX
Instant noodlesNon-fried instant noodlesXX
BeveragesSugar-free beveragesXX
1 Healthier food; no need for listing of healthier options. X Score dimensions.
Table 4. Inter-rater reliability of each food category based on availability.
Table 4. Inter-rater reliability of each food category based on availability.
Category of FoodICC aAvailability (Kappa) b
Grains0.991.00
Dry beans0.991.00
Starchy Tubers1.001.00
Vegetables0.981.00
Fruits0.910.80
Seafood0.960.89
Meat and poultry0.911.00
Dietary oils0.930.54
Milk0.630.52
Bread0.750.60
Instant noodles0.690.52
Beverages0.410.60
Total0.98-
a Intra-class correlation coefficient (ICC) was calculated based on availability score. b Kappa was calculated based on the availability of the food item (yes/no).
Table 5. C-NEMS-S total score by store type.
Table 5. C-NEMS-S total score by store type.
RaterSupermarketConvenience Storep
M aSD bM aSD b
Rater 131.802.441.902.28<0.001
Rater 228.504.202.203.68<0.001
a M means the mean. b SD means the standard deviation.
Table 6. Availability (%) of healthier options by store type.
Table 6. Availability (%) of healthier options by store type.
Category of FoodLarge-Sized Supermarkets (N = 10)Convenience Stores (N = 10)p
Rater 1
Whole grains10010<0.001
Dry beans90100.002
Starchy Tubers900<0.001
Vegetables1000<0.001
Fruits10010<0.001
Seafood8000.002
Lean meat options1000<0.001
Plant-based oils100200.002
Low-fat milk80200.023
Whole wheat bread90300.007
Non-fried instant noodles6000.023
Sugar-free beverages80300.063
Rater 2
Whole grains10010<0.001
Dry beans90100.002
Starchy Tubers1000<0.001
Vegetables1000<0.001
Fruits10010<0.001
Seafood1000<0.001
Lean meat options8000.002
Plant-based oils100200.002
Low-fat milk6000.023
Whole wheat bread90300.007
Non-fried instant noodles6000.023
Sugar-free beverages80300.063
Table 7. Price comparison between healthier option and regular option for each food measure.
Table 7. Price comparison between healthier option and regular option for each food measure.
Category of FoodHealthier OptionRegular Optionp
M aSD bM aSD b
Rater 1
Grains c6.192.272.450.09<0.001
Meat and poultry c28.4814.4314.500.000.18
Milk d3.280.273.140.320.05
Bread e7.071.136.800.480.01
Instant noodles f3.370.252.500.15<0.001
Rater 2
Grains c5.942.462.620.32<0.001
Meat and poultry c20.156.5017.505.400.14
Milk d4.671.213.140.160.02
Bread e7.420.156.690.34<0.001
Instant noodles f2.950.362.500.140.01
a M means the mean. b SD means the standard deviation. c The unit is RMB/0.5 kg d. The unit is RMB/250 mL. e The unit is RMB/ loaf. f The unit is RMB/ packet.
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