Predicting access to healthful food retailers with machine learning

Unraveling the Channels of Food Security of the Households in Northern Kenya: Evidence from an Exclusive Dataset

Background

Most of the 10 million Kenyans lacking food security lived in the arid and semi-arid northern part of the country in a climatic condition of high temperatures and very little rainfall throughout the year. Frequent droughts had devastating effects on the livelihoods and food availability of the population.

Objectives

The objective of this study was to assess the food security status of the households in Northern Kenya and examine the factors contributing to their food security.

Methods

De-identified secondary data were used from the 2015 Feed the Future household survey conducted in 9 counties of Northern Kenya. The experience-based indicator of food security was derived from the 6-item Household Food Security Survey Module (HFSSM), which categorized sample households into 3 groups: food secure, having low food security, and having very low food security. An ordered probit model and machine learning algorithm, namely ordered random forest, were used to find the most important determinants of food security.

Results

Findings suggest that the daily per capita food expenditure, level of education of the household head, and durable asset ownership are the key predictors of food security. Households living in rural areas were likely to have low food security, but their probability of being food secure increased with at least primary education and livestock ownership, thus reflecting the importance of education and livestock production among rural communities in Northern Kenya. Access to improved water and participation in food security programs were found to be more important for food security among rural households than they were for urban households.

Conclusions

These results implied that long-term policies on improving access to education, livestock ownership, and improved water may shape the food security status of rural households in Northern Kenya.

Unraveling the Role of Objective Food Environment in Chinese Elderly's Diet-Related Diseases Epidemic: Considering Both Healthy Food Accessibility and Diversity

The essential role of the objective food environment in achieving healthy aging has been widely recognized worldwide. However, the existing empirical evidence is mostly based on Western cases, and how the objective food environment associates with health outcomes among Chinese elderly remains poorly understood. By merging nationally representative micro survey data with Baidu-based spatial data on the location of food outlets, this study develops accessibility and diversity indicators to explore the relationship between food environment and diet-related diseases among Chinese elderly and investigates how healthy lifestyles moderate this relationship. The results show that improvement in healthy food accessibility and diversity decreases both the probability and the number of diet-related diseases that the elderly suffer. Having more healthy lifestyle factors is associated with a lower risk of suffering from diet-related diseases and strengthens the negative effect of healthy food environment on suffered diet-related diseases. Heterogeneity effect analysis suggests that the relationship between objective food environment and diet-related diseases differs by city scale and income level. The findings of this study shed light on designing tailor-made policies for non-Western countries to promote healthy aging.

Premiums for Residing in Unfavorable Food Environments: Are People Rational?

The most extensive research areas in the food environment literature include identifying vulnerable dietary environments and studying how these environments affect eating behaviors and health. So far, research on people’s willingness to pay (WTP) for residing in different types of food environments is limited. Therefore, this study aims to estimate WTP for different types of food environments by using spatial hedonic pricing models. The empirical application applies to the Canadian city of Edmonton. The results show that people are willing to pay a premium to live in neighborhoods with poor access to supermarkets and grocery stores (food-desert type) and neighborhoods with excessive access to fast-food restaurants and convenience stores (food-swamp type). Why do rational people prefer to live in disadvantaged food environments? The seemingly counter-intuitive result has its rationality. The premium paid to live in food-desert type environment may reflect people’s dislike of noise, traffic jams, and potential safety issues brought by supermarkets and grocery stores. The WTP for living in food-swamp type environment may reflect people’s preference for convenience and time-saving brought by fast-food consumption in modern urban society. Additionally, the inability of low-income families to afford healthy food may be a deeper reason for choosing to live in neighborhoods with excess access to fast food. To improve the eating environment and encourage healthy lifestyles, the government can encourage healthier fast-food restaurants, provide grocery shopping vouchers, and promote community garden projects.

Food Insecurity Reduces the Chance of Following a Nutrient-Dense Dietary Pattern by Brazilian Adults: Insights from a Nationwide Cross-Sectional Survey

Dietary patterns derived by data-driven techniques are still scarce in the food security context and may be a useful tool to inform policymakers to promote adequate and healthy diets for vulnerable populations. We investigated the association between food security status and dietary patterns among 28,127 Brazilian adults using data from the 2017–2018 Household Budget Survey. Food security status was measured by the Brazilian Food Insecurity Scale. Food items reported in two 24 h dietary recalls were combined into food groups. Dietary patterns were derived from exploratory factor analysis, and participants were grouped according to their factor scores by K-means cluster analysis. Multiple logistic regression models were fitted to investigate the association between food security status and clusters of adherence to dietary patterns. Four dietary patterns were derived: ‘Brazilian breakfast style’ (cluster 3), ‘Brazilian Traditional staple foods’ (cluster 2), ‘Beverages, ready-to-eat and convenience foods’ (cluster 4), and ‘Fruits, vegetables, and whole grains’ (cluster 1). After adjustments, food insecurity was inversely associated with adherence to the ‘Fruits vegetables, and whole grains’ pattern (OR = 0.75, 95% CI: 0.63–0.89). Findings suggest food insecurity negatively impact the adoption of a nutrient-dense dietary pattern and highlight the critical role of policy actions in a scenario of increasing food insecurity.

Geospatial and Machine Learning Regression Techniques for Analyzing Food Access Impact on Health Issues in Sustainable Communities

Food access is a major key component in food security, as it is every individual’s right to proper access to a nutritious and affordable food supply. Low access to healthy food sources influences people’s diet and activity habits. Guilford County in North Carolina has a high ranking in low food security and a high rate of health issues such as high blood pressure, high cholesterol, and obesity. Therefore, the primary objective of this study was to investigate the geospatial correlation between health issues and food access areas. The secondary objective was to quantitatively compare food access areas and heath issues’ descriptive statistics. The tertiary objective was to compare several machine learning techniques and find the best model that fit health issues against various food access variables with the highest performance accuracy. In this study, we adopted a food-access perspective to show that communities that have residents who have equitable access to healthy food options are typically less vulnerable to health-related disasters. We propose a methodology to help policymakers lower the number of health issues in Guilford County by analyzing such issues via correlation with respect to food access. Specifically, we conducted a geographic information system mapping methodology to examine how access to healthy food options influenced health and mortality outcomes in one of the largest counties in the state of North Carolina. We created geospatial maps representing food deserts—areas with scarce access to nutritious food; food swamps—areas with more availability of unhealthy food options compared to healthy food options; and food oases—areas with a relatively higher availability of healthy food options than unhealthy options. Our results presented a positive correlation coefficient of R2 = 0.819 among obesity and the independent variables of transportation access, and population. The correlation coefficient matrix analysis helped to identify a strong negative correlation between obesity and median income. Overall, this study offers valuable insights that can help health authorities develop preemptive preparedness for healthcare disasters.

Modelling Techniques to Improve the Quality of Food Using Artificial Intelligence

Artificial intelligence (AI), or AI/machine vision, is assuming an overwhelming part in the realm of food handling and quality affirmation. As indicated by Mordor Intelligence, AI in the food and refreshments market is required to enlist a CAGR of 28.64%, during the conjecture time frame 2018–2023. Artificial intelligence makes it workable for PCs to gain as a matter of fact, investigate information from the two data sources and yields, and perform most human assignments with an improved level of accuracy and proficiency. Here is a concise gander at how AI is expanding sanitation and quality activities. This exploration has along these lines tried to furnish policymakers with a way to assess new and existing strategies, while likewise offering a reasonable premise through which food chains orders can be made stronger through the thought of the executive’s practices and strategy choices. This survey centers on the AI applications according to four mainstays of food security that is food accessibility, food availability, food use, and strength.

Social and financial impacts of food allergy on the economically disadvantaged and advantaged families: A qualitative interview study

BACKGROUND

Differences in the qualitatively-perceived burden of food allergy based on economic disadvantages have not been previously described.

OBJECTIVE

To describe the perceptions of food allergy-related social and financial issues of families who have a child with food allergy, considering the similarities and differences between economically advantaged and disadvantaged families.

METHODS

From March to July 2019, we interviewed parents of children being followed up for food allergy at a tertiary allergy clinic in central Canada. Interviews were recorded, transcribed, and analyzed thematically. Economic groups were categorized on the basis of the national cutoffs for low-income after-tax dollars.

RESULTS

We interviewed 18 parents (17 [94.4% mothers]) of whom 6 (33.3%) were economically disadvantaged, and who represented 25 children (10 [40.0%] girls) with food allergy. We identified 3 common themes: food allergy had (1) resulted in the need to teach others in addition to contributing to tensions in relationships; (2) increased time costs for food shopping and preparation; and (3) contributed to concerns securing qualified child care. We also identified 2 divergent themes that underscored how this burden was perceived between economic groups: (1) medications and medical appointments are costly or inconvenient; and (2) allergy-friendly food choices: single vs several sources and servings.

CONCLUSION

Despite some qualitative similarities in the social and financial burdens of food allergy, there are some notable differences between economic groups that underscore the need for further discussions surrounding the related policy.