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Associations between Exposure to Unhealthy Food Outlets Within Residential District and Obesity: Using Data from 2013 Census on Establishments and 2013-2014 Korea National Health and Nutrition Examination Survey


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Associations between Exposure to Unhealthy Food Outlets Within Residential District and Obesity: Using Data from 2013 Census on Establishments and 2013-2014 Korea National Health and Nutrition Examination Survey

Article information

Korean J Community Nutr. 2016;21(5):463-476
Publication date (electronic) : 2016 October 31
doi : https://doi.org/10.5720/kjcn.2016.21.5.463
Yoonjung Kim1),2), Sung Nim Han2),3)orcid_icon
1)Division of Health and Nutrition Survey, Centers for Disease Control and Prevention, Cheongju-si, Korea.
2)Department of Food and Nutrition, College of Human Ecology, Seoul National University, Seoul, Korea.
3)Research institute of Human Ecology, College of Human Ecology, Seoul National University, Seoul, Korea.
Corresponding author: Sung Nim Han. Department of Food and Nutrition, College of Human Ecology, Seoul National University, Seoul 08826, Korea. Tel: (02) 880-6836, Fax: (02) 884-0305, snhan@snu.ac.kr
Received 2016 October 11; Revised 2016 October 17; Accepted 2016 October 17.

Abstract

Objectives

Environmental, social and personal factors influence eating patterns. This study aimed to investigate the relationship between unhealthy food outlets within a residential area and obesity using nationally representative Korean survey data and data from the Census on Establishments.

Methods

Data on the food intakes and socioeconomic variables of a total of 9,978 adults aged ≥ 19 years were obtained from the 2013-2014 Korea National Health and Nutrition Examination Survey. Geographic locations of restaurants were obtained from the 2013 Census on Establishments in Korea. Administrative area was categorized into tertiles of count of unhealthy food outlets based on the distribution of number of unhealthy food outlets among all urban (Dong) and rural (Eup or Myun) administrative districts in Korea. Multilevel logistic regressions model were used to assess the association between the number of unhealthy food outlets and obesity.

Results

People living in the district with the highest count of unhealthy food outlets had higher intakes of fat (45.8 vs. 44.4 g/day), sodium (4,142.6 vs. 3,949.8 mg/day), and vitamin A (753.7 vs. 631.6 µgRE/day) compared to those living in the district with the lowest count of unhealthy food outlets. A higher count of unhealthy food outlets was positively associated with frequent consumption of instant noodles, pizza, hamburgers and sandwiches, sweets and sour pork or pork cutlets, fried chicken, snacks, and cookies. Higher exposure to unhealthy food outlets was associated with increased odds of obesity (1st vs. 3rd tertile; OR 1.689; 95% CI 1.098-2.599).

Conclusions

A high count of unhealthy food outlets within a residential area is positively associated with the prevalence of obesity in Korea. The results suggest that food environmental factors affects the health outcomes and interventions aiming to restrict the availability of unhealthy food outlets in local neighborhoods may be a useful obesity prevention strategy.

Keywords: Unhealthy food outlets; food environment; obesity; Korea National Health and Nutrition Examination Survey; Census on Establishments

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Article information Continued

Copyright © 2016 The Korean Society of Community Nutrition

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Table 1

Distribution of tertile groups according to the count of unhealthy food outlets

Table 1

Table 2

Characteristics of tertile groups according to the count of unhealthy food outlets

Table 2

Data are expressed as unweighted frequency and weighted percentage or mean.

p-value was obtained from the Rao-Scott χ2 test for categorical variables and Bonferroni correction of multiple comparison for continuous variables.

Table 3

Daily nutrient intake across tertile groups according to the count of unhealthy food outlets

Table 3

1) The p-values for differences across groups were calculated according to Bonferroni correction of multiple comparisons at alpha=0.05 using PROC SURVEYREG (*: p<0.05; **: p<0.01).

2) The p for trend obtained to trend as the levels of the predictor variable increase.

3) The means of daily nutrient intake were analyzed after adjusting for gender (male, female), age (19 − 39, 40 − 59, ≥60), education level (less than elementary school, middle school, high school, university or above), employment status (non-manual, manual, service, not working), household monthly income (quartile), BMI (<18.5, 18.5 − 24.9, ≥25), current diet control (yes, no), urban-rural status (urban, rural).

Table 4

Insufficient or excessive nutrient intakes across tertile groups according to the count of unhealthy food outlets

Table 4

1) The p-values for differences across groups were calculated according to Bonferroni correction of multiple comparisons at alpha=0.05 using PROC SURVEYREG (*: p<0.05; **: p<0.01).

2) The p for trend obtained to trend as the levels of the predictor variable increase.

3) The percentage were analyzed after adjusting for gender (male, female), age (19 − 39, 40 − 59, ≥60), education level (less than elementary school, middle school, high school, university or above), employment status (non-manual, manual, service, not working), household monthly income (quartile), BMI (<18.5, 18.5 − 24.9, ≥25), current diet control (yes, no), urban-rural status (urban, rural).

Table 5

Weekly food consumption frequency across tertile groups according to the count of unhealthy food outlets

Table 5

1) The p-values for differences across groups were calculated according to Bonferroni correction of multiple comparisons at alpha=0.05 using PROC SURVEYREG (*: p<0.05; **: p<0.01; ***: p<0.001).

2) The p for trend obtained to trend as the levels of the predictor variable increase.

3) The means of food consumption frequency were analyzed after adjusting for gender (male, female), age (19 − 39, 40 − 59, ≥60), education level (less than elementary school, middle school, high school, university or above), employment status (non-manual, manual, service, not working), household monthly income (quartile), BMI (<18.5, 18.5 −24.9, ≥25), current diet control (yes, no), urban-rural status (urban, rural).

Table 6

Associations between unhealthy food outlets and obesity (n=8,912)

Table 6

1) Model 2: Adjusted for individual level variable; Model 3: Adjusted for Model 2+local level variable

2) OR; Odds Ratio, 95%

3) CI; 95% Confidence Interval

*: p<0.05, **: p<0.01, ***: p<0.001