Evaluation of Food Labeling Policy in Korea: Analyzing the Community Health Survey 2014-2017

National prevalence and trends in food labeling awareness, comprehension, usage, and COVID-19 pandemic-related factors in South Korea, 2014-2022

Although food labeling on food packages is crucial for promoting a healthy diet, limited research has been conducted on how the COVID-19 pandemic (hereinafter "the pandemic") has affected food labeling awareness. Therefore, this study aims to analyze the changes in trends in food labeling awareness, comprehension, and usage in South Korea during the pandemic. We utilized a nationwide, large-scale, and long-term dataset provided by the Korea Community Health Survey (KCHS) from 2014 to 2022 (total = 1,756,847 participants). This allowed the researchers to assess the long-term trends in the prevalence of food labeling awareness, comprehension, and usage. Furthermore, we investigated the factors associated with awareness specifically related to the pandemic. In total, 1,756,847 adults (54.19% women) participated in this study. The upward slope in overall food labeling awareness became less pronounced and even exhibited a downward slope during the pandemic (βdiff - 1.759; 95% CI - 1.874 to - 1.644). The upward slope in food labeling comprehension and usage became more pronounced during the pandemic (comprehension: βdiff 0.535; 95% CI 0.436-0.634; usage: βdiff 0.693; 95% CI 0.601-0.785). The vulnerability factors associated with lower food labeling awareness during the pandemic included older age, male, obesity, residing in rural areas, lower household income, lower educational level, smoking, and increased alcohol consumption. This study analyzed the 9-year trend in the prevalence of food labeling awareness, comprehension, and usage based on nationally representative data of adults in South Korea from 2014 to 2022. Our findings suggest that personalized nutrition strategies are needed to recognize vulnerable groups with risk factors and improve food labeling awareness among Korean adults during the pandemic.

The application of artificial intelligence in health policy: a scoping review

BACKGROUND

Policymakers require precise and in-time information to make informed decisions in complex environments such as health systems. Artificial intelligence (AI) is a novel approach that makes collecting and analyzing data in complex systems more accessible. This study highlights recent research on AI's application and capabilities in health policymaking.

METHODS

We searched PubMed, Scopus, and the Web of Science databases to find relevant studies from 2000 to 2023, using the keywords "artificial intelligence" and "policymaking." We used Walt and Gilson's policy triangle framework for charting the data.

RESULTS

The results revealed that using AI in health policy paved the way for novel analyses and innovative solutions for intelligent decision-making and data collection, potentially enhancing policymaking capacities, particularly in the evaluation phase. It can also be employed to create innovative agendas with fewer political constraints and greater rationality, resulting in evidence-based policies. By creating new platforms and toolkits, AI also offers the chance to make judgments based on solid facts. The majority of the proposed AI solutions for health policy aim to improve decision-making rather than replace experts.

CONCLUSION

Numerous approaches exist for AI to influence the health policymaking process. Health systems can benefit from AI's potential to foster the meaningful use of evidence-based policymaking.

Utilization of nutrition labels and related factors among patients with diabetes in Korea

BACKGROUND/OBJECTIVES

The prevalence of diabetes has continued to increase globally. Changes in eating habits, lack of exercise, increased stress, and aging are major contributors. Glycemic control is the key strategy of diabetes management. The purpose of this study was to analyze the utilization of nutrition labels and related factors among patients with diabetes.

MATERIALS/METHODS

Data from the 7th Korea National Health and Nutrition Examination Survey were used. General, health-related, diabetes-related characteristics from 1,587 adults with diabetes history were included. Nutrition label utilization was assessed with awareness and use of nutrition labels and effects on food choice. For statistical analyses, chi-square test and multiple logistic regression analysis were performed.

RESULTS

The prevalence of awareness, use, and effects of nutrition labels on food choice among diabetic patients were 48.8%, 11.4%, and 9.6%, respectively. High monthly income, walking frequency, family history of diabetes, younger age at diagnosis, and shorter duration of diabetes were associated with higher nutrition label awareness. Nutrition label use and effect on food choice were higher in women, those with high monthly income, those diagnosed at younger than 45 yrs, those with diabetes for less than 10 yrs, those with meal therapy, or patients who had undergone a fundus examination.

CONCLUSIONS

Nutrition label utilization status was low in Korean patients with diabetes. Strategies are needed to promote nutrition label use as a diet management tool for patients with diabetes.

Development and psychometric analysis of a new tool to assess food literacy in diabetic patients

Background

One of the factors affecting self-care in diabetic patients is food literacy, which helps said patients in following a healthy diet. Thus, it is crucial to analyze food literacy in diabetic patients through suitable and reliable instruments. 

Objective

The current study aimed to design a questionnaire for food literacy assessment in diabetic patients and analyze its psychometric features.

Method

The present study was a cross-sectional descriptive analysis carried out in 2021. Firstly, the concepts of food literacy in diabetic patients were identified and the questionnaire was deigned based on them. Secondly, its face and content validities and its reliability were analyzed. Finally, the construct validity was analyzed by exploratory factor analysis. The study was carried out on 300 diabetic participants chosen at random via stratified cluster sampling from Health service centers. The exploratory factor analysis was carried out by extracting the main factors and using varimax rotation with eigenvalue values more than 1.

Results

A five-pronged structure accounted for 52.745% of food literacy variance. This included the ability to read food facts, practical ability to group foods, the ability to identify the caloric content of different foods, the ability to understand the effect of food on health, and the ability to prepare a healthy meal. Items with an impact score below 1.5 were discarded. Additionally, items with CVR scores below 0.62 and CVI scores below 0.79 were deleted too. The Kaiser-Meyer-Okin measurement was 0.836 (p < 0.001). Alpha Cronbach Scale dimension was 0.610–0.951.

Conclusion

The results of this study showed that the exploratory dimensions of the current study were consistent with health literacy measurements, such as functional, interactive, and critical food literacy. This scale has acceptable reliability and validity. Health professionals can use this scale to analyze and improve food literacy in diabetic patients. This is a new instrument and thus far no questionnaire has been made to evaluate food literacy in diabetic patients.

Depression and Related Factors in Korean Adults During the Coronavirus Disease 2019 Outbreak

OBJECTIVE

We aimed to determine the status of depression and its related factors among adult Koreans during the coronavirus disease 2019 (COVID-19) outbreak.

METHODS

We used data from the 2020 Korea Community Health Survey (KCHS). We assessed depressive feelings and symptoms using the Patient Health Questionnaire-9 (PHQ-9 ≥10). In addition, we assessed general and COVID-19-related characteristics, including isolation due to and concerns about COVID-19. We analyzed the data using chi-square tests and multiple logistic regression analyses.

RESULTS

The rates of depressive feelings and symptoms were 5.9% and 2.9%, respectively. Of the adult respondents, 68.5% were concerned about COVID-19, while 75.9% were concerned about economic harm due to COVID-19. The adjusted odds ratios for depressive symptoms assessed using the PHQ-9 were significantly high among women responders, adults aged 19-44 years, low-income households, those who experienced COVID-19-related symptoms, and those concerned about death due to COVID-19 and economic harm due to COVID-19. Similar results were obtained for depressive feeling.

CONCLUSION

Concerns related to COVID-19 infection are related to depression. This suggests that COVID-19 significantly affects mental health. Therefore, during public health crises, such as new communicable diseases, mental health and the incidence of the infectious disease require assessment and monitoring.

Food, Big Data, and Decision-making: a Scoping Review-the 3-D Commission

Food is an important determinant of health, featuring prominently in the Sustainable Development Goals. The term "big data" is seldom used in relation to food, partly because food data are scattered across different sectors. The increasing availability of food-related data presents an opportunity to glean new insights on food and food systems. These insights may enhance the quality of products and services and improve decision-making on optimizing food availability, all to the end of producing better health. Yet, knowledge gaps remain about the unique opportunities and challenges linked to big data on food and their use in decision-making. This scoping review explored the available literature linking food with big data and decision-making, using the following research question: What is the current literature on data about food, and how are these data used in decision-making? We searched PubMed until 29 February 2020 and Embase, Web of Sciences, and the Cochrane Database of Systematic Reviews until 8 March 2020. We included studies written in English and conducted narrative analyses to identify relevant themes from included studies. Sixteen studies fulfilled our eligibility criteria, including big data analyses, modelling studies, and reviews. These studies described the added value of using big data and how evidence from big data had or can be used for decision-making, as well as challenges and opportunities for such use. The majority of the included studies examined the link between food and big data, while hypothesizing of how these insights could inform decision-making, including policies, interventions, programs, and financing. There were only two examples wherein big data on food informed decision-making directly. The review highlights several false dichotomies in how the subject is approached in the literature and the importance of context, both between and within countries, in shaping the availability and types of data that can be used as meaningful evidence to inform decision-making. This review shows the paucity of research around the intersection of food, big data, and decision-making, as well as the potential in using big data on food systems to the end of informing decisions to improve the health of populations. Future research and decision-making around health systems can benefit from examining the full spectrum of perspectives on the subject. Future research and decision-making around health systems can also employ the steadfast embrace of technology, which will potentially reduce disparities in big data availability, to the end of improving the health of populations.