Correlates of Healthy Eating in Urban Food Desert Communities

The dynamics in food selection stemming from price awareness and perceived income adequacy: a cross-sectional study using 1-year loyalty card data

BACKGROUND

Higher cost of healthy foods may explain unhealthy dietary patterns in lower-income households. Unfortunately, combining food selection and nutrient intake data to price and expenditure is challenging. Food retailer's customer loyalty card data, linked to nutrient composition database, is a novel method for simultaneous exploration of food purchases, price, and nutrition.

OBJECTIVES

We studied the associations between perceived income adequacy (PIA) as a grouping variable with price (per kilogram or megajoule) and the volume of purchases (percentage of expenditure or energy) simultaneously as outcome variables for 17 most purchased food groups.

METHODS

We used 1-year (2018) loyalty card data from the largest grocery chain in Finland. Participants were 28,783 loyalty cardholders who made ≥41% of food purchases from the retailer and answered an online questionnaire at the midpoint of data collection. The 5-level PIA described the perceived financial situation in the household. Energy and nutrient content of foods purchased were from the Finnish Food Composition Database Fineli. We calculated the Nutrient Rich Food Index per 100 g food using 11 nutrients. Trends in prices and expenditures between PIA levels were analyzed using 2-sided Jonckheere-Terpstra tests, with false discovery rate control (Benjamini-Hochberg method) and confounder adjustments (inverse probability weighting).

RESULTS

Lower PIA participants selected cheaper foods per kilogram and megajoule within most food groups. They also favored unhealthy food groups cheap in energy [<1 € (USD 1.18)/MJ]. Despite lower purchase price, the expenditure (%) among lower PIA was higher on alcohol, snacks, sugar-sweetened beverages, and sweets and chocolates.

CONCLUSIONS

Participants with lower PIA showed stronger price awareness. It is crucial to consider the pricing of competing alternative food groups, when steering toward environmentally sustainable and healthier food purchases. Package labeling might also direct the selection of healthier choices among the less expensive items within a food group.

Adult food choices in association with the local retail food environment and food access in resource-poor communities: a scoping review

BACKGROUND

There is a growing body of research on local retail food environments globally in both urban and rural settings. Despite this, little research has been conducted on adult food choices, local retail environments, and healthy food access in resource-poor communities. The purpose of this study is therefore to provide an overview of the evidence on adult food choices (measured as dietary intake) in association with the local retail food environment and food access in resource-poor communities (defined as low-income communities and/or households).

METHODS

We searched nine databases for studies published from July 2005 to March 2022 and identified 2426 records in the primary and updated search. Observational studies, empirical and theoretical studies, focused on adults ≤ 65 years, published in English peer-reviewed journals, examining local retail food environments and food access, were included. Two independent reviewers screened identified articles using the selection criteria and data extraction form. Study characteristics and findings were summarized for all studies and relevant themes summarized for qualitative and mixed methods studies.

RESULTS

A total of 47 studies were included in this review. Most studies were cross sectional (93.6%) and conducted in the United States of America (70%). Nineteen (40.4%) studies assessed the association between food choice outcomes and local retail food environment exposures, and evidence on these associations are inconclusive. Associations of certain food choice outcomes with healthy food retail environments were positive for healthy foods (in 11 studies) and unhealthy foods (in 3 studies). Associations of certain food choice outcomes with unhealthy retail food environment exposures were positive for unhealthy foods in 1 study and negative for healthy foods in 3 studies. In 9 studies, some of the food choice outcomes were not associated with retail food environment exposures. A healthy food store type and lower food prices were found to be major facilitators for healthy food access in resource-poor communities, while cost and transportation were the main barriers.

CONCLUSIONS

More research is needed on the local retail food environment in communities in low- and middle-income countries to develop better interventions to improve food choices and access to healthy foods in resource-poor communities.

Food Acquisition during the COVID-19 Lockdown and Its Associations with the Physical-Digital Integrated Community Food Environment: A Case Study of Nanjing, China

The COVID-19 pandemic and measures such as lockdowns affect food access, dietary choices, and food security. We conducted an online survey among 517 respondents during early 2020 in Nanjing, China to explore respondents' food acquisition behaviors before and during the pandemic and associations with the community food environment. Using geographic analysis and binary logistic models, we revealed that despite inconvenience regarding food acquisition, no food security issues occurred during lockdown in Nanjing. The pandemic changed the access and frequency of obtaining food; meanwhile, pre-pandemic habits had a strong impact on food acquisition behavior. Online and in-store food acquisition showed a substitution relationship, with online food access playing a crucial role in food acquisition. Physical and digit food outlets are highly integrated in Chinese urban communities, and both objectively measured and perceived accessibility of these food outlets had a significant association with the food acquisition methods and transportation mode chosen by people during this public health crisis.

How to preserve healthy aging through nutritional strategies: The new approach of the Food Social Network (Food NET) project

BACKGROUND: Aging is a process that does not refer only to the accumulation of damage in a human being over time but rather to individual changes determined by genetic, lifestyle, social and environmental factors. Aging is one of the greatest known risk factors for most human diseases. An older person in good health has a good level of independence, weighs less on the national health system and plays a productive and active role in his/her community; thus, the concept of “healthy aging”, reflecting older adult-environment fit should be promoted. The interactions between lifestyle, including nutrition, and health play a fundamental role in the aging process; eating habits and eating behaviours are recognized as important modifiable factors potentially leading to a healthy “phenotype”. OBJECTIVE AND METHODS: A multidisciplinary consortium with three Italian universities (the University of Milano-Bicocca, University of Pavia and University of Calabria) and Italian Small-Medium Enterprises proposed the Food Social Network (Food NET) project. Food NET overall outcome is to achieve target-specific guidelines and exact technologies for accessible functional foods, aimed at improving the quality of life and nutritional status of citizens (aged>65) of the Metropolitan City of Milan (Italy). CONCLUSIONS: This project is part of the “Smart Living” and food-related strategies aimed at responding to the needs of this target population, developing new food products, appropriate to meet the specific requirements and ensuring and promoting sustainable diets for healthy aging by effective food policy approaches.

Dietary Patterns and Risk of a New Carious Lesion Postpartum: A Cohort Study

Dental caries (cavities), one of the most common infectious diseases, is caused by a number of factors. Oral microbes, dietary practices, sociodemographic factors, and dental hygiene all inform caries risk. Assessing the impact of diet is complicated as individuals eat foods in combinations, and the interactions among the foods may alter caries risk. Our study aimed to prospectively assess the association between dietary patterns and caries risk in the postpartum period, a potentially sensitive period for caries development. We analyzed in-person dental assessments and telephone food frequency questionnaires (FFQs) from 879 Caucasian women participating in the Center for Oral Health Research in Appalachia Cohort 2 (COHRA2) that were collected biannually for up to 6 y. One-week recall of food intake frequency was assessed using a Likert scale. We used principal component analysis to summarize the FFQ data; the top 2 components described 15% and 12% of the variance in FFQ data. The first component was characterized by high consumption of fruits and vegetables, while the second component was heavily influenced by desserts and crackers. We used a modified Poisson model to predict the risk of an increase in the number of decayed, missing, and filled teeth in the postpartum period by 1) dietary patterns and 2) individual foods and beverages at the previous study visit, after controlling for other known risk factors, including history of carious lesions. Eating a dietary pattern high in desserts and crackers was associated with a 20% increase in the number of decayed, missing, and filled teeth in the postpartum period (95% confidence interval, 1.03-1.39). However, this effect was attenuated among those who also consumed a dietary pattern high in fruits and vegetables. Dietary patterns should be considered when devising interventions aimed at preventing dental caries.

Finite Mixture Modeling for Program Evaluation: Resampling and Pre-processing Approaches

BACKGROUND

Finite mixture models cluster individuals into latent subgroups based on observed traits. However, inaccurate enumeration of clusters can have lasting implications on policy decisions and allocations of resources. Applied and methodological researchers accept no obvious best model fit statistic, and different measures could suggest different numbers of latent clusters.

OBJECTIVES

The purpose of this article is to evaluate and compare different cluster enumeration techniques.

RESEARCH DESIGN

Study I demonstrates how recently proposed resampling methods result in no precise number of clusters on which all fit statistics agree. We recommend the pre-processing method in Study II as an alternative. Both studies used nationally representative data on working memory, cognitive flexibility, and inhibitory control.

CONCLUSIONS

The data plus priors method shows promise to address inconsistencies among fit measures and help applied researchers using finite mixture models in the future.

Neural Networks to Estimate Generalized Propensity Scores for Continuous Treatment Doses

BACKGROUND

The generalized propensity score (GPS) addresses selection bias due to observed confounding variables and provides a means to demonstrate causality of continuous treatment doses with propensity score analyses. Estimating the GPS with parametric models obliges researchers to meet improbable conditions such as correct model specification, normal distribution of variables, and large sample sizes.

OBJECTIVES

The purpose of this Monte Carlo simulation study is to examine the performance of neural networks as compared to full factorial regression models to estimate GPS in the presence of Gaussian and skewed treatment doses and small to moderate sample sizes.

RESEARCH DESIGN

A detailed conceptual introduction of neural networks is provided, as well as an illustration of selection of hyperparameters to estimate GPS. An example from public health and nutrition literature uses residential distance as a treatment variable to illustrate how neural networks can be used in a propensity score analysis to estimate a dose-response function of grocery spending behaviors.

RESULTS

We found substantially higher correlations and lower mean squared error values after comparing true GPS with the scores estimated by neural networks. The implication is that more selection bias was removed using GPS estimated with neural networks than using GPS estimated with classical regression.

CONCLUSIONS

This study proposes a new methodological procedure, neural networks, to estimate GPS. Neural networks are not sensitive to the assumptions of linear regression and other parametric models and have been shown to be a contender against parametric approaches to estimate propensity scores for continuous treatments.