People and places shaping food procurement among recipients of Supplemental Nutrition Assistance Program (SNAP)

Applications of Complex Systems Models to Improve Retail Food Environments for Population Health: A Scoping Review

Retail food environments (RFEs) are complex systems with important implications for population health. Studying the complexity within RFEs comes with challenges. Complex systems models are computational tools that can help. We performed a systematic scoping review of studies that used complex systems models to study RFEs for population health. We examined the purpose for using the model, RFE features represented, extent to which the complex systems approach was maximized, and quality and transparency of methods employed. The PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines were followed. Studies using agent-based modeling, system dynamics, discrete event simulations, networks, hybrid, or microsimulation models were identified from 7 multidisciplinary databases. Fifty-six studies met the inclusion criteria, including 23 microsimulation, 13 agent-based, 10 hybrid, 4 system dynamics, 4 network, and 2 discrete event simulation models. Most studies (n = 45) used models for experimental purposes and evaluated effects of simulated RFE policies and interventions. RFE characteristics simulated in models were diverse, and included the features (e.g., prices) customers encounter when shopping (n = 55), the settings (e.g., restaurants, supermarkets) where customers purchase food and beverages (n = 30), and the actors (e.g., store managers, suppliers) who make decisions that influence RFEs (n = 25). All models incorporated characteristics of complexity (e.g., feedbacks, conceptual representation of multiple levels), but these were captured to varying degrees across model types. The quality of methods was adequate overall; however, few studies engaged stakeholders (n = 10) or provided sufficient transparency to verify the model (n = 12). Complex systems models are increasingly utilized to study RFEs and their contributions to public health. Opportunities to advance the use of these approaches remain, and areas to improve future research are discussed. This comprehensive review provides the first marker of the utility of leveraging these approaches to address RFEs for population health.

Aiming for better use of convenience food: an analysis based on meal production functions at home

BACKGROUND

In recent years, the evaluation of convenience food has changed. It came to be considered not to have a negative effect on health and is now positioned as a tool to support dietary habits of elderly and other people. In advanced countries where the population is aging, convenience foods are expected to improve the eating habits of the elderly.

METHODS

We defined the indicators of cooking effort and usage intensity of convenience food and presented a model wherein a "meal" is home-produced. In the model, a home cook decides the optimal cooking effort to apply for a given usage intensity of convenience food. Using an empirical form of the proposed model, we performed a multiple regression analysis and calculated "the elasticity of cooking effort with respect to the usage intensity of convenience food" for home cooks, with each attribute defined by a combination of different personality and demographic factors, using the estimated coefficients.

RESULTS

Regression analysis results revealed a negative correlation between cooking effort and the usage intensity of convenience food, which is consistent with our theoretical model of home meal production. The results showed that home cooks who have special food preferences may not be satisfied with accepting convenience foods purchased from the market as they are and that these home cooks will require a higher cooking effort to obtain higher satisfaction. The elasticity of elderly home cooks was low, implying that they are not flexible enough to accept convenience food.

CONCLUSIONS

The results revealed that existing convenience foods do not have the same impact on home cooks with attributes. This problem can be solved with smart food systems that utilize information and communication technology, which allow home cooks to explore information on convenience foods that match their preferences and enable food providers to offer food that matches the specific tastes of home cooks. The regression results suggest this possibility.

Socioecological Path Analytic Model of Diet Quality among Residents in Two Urban Food Deserts

BACKGROUND

Diet is critical to chronic disease prevention, yet there are persistent disparities in diet quality among Americans. The socioecological model suggests multiple factors, operating at multiple levels, influence diet quality.

OBJECTIVE

The goal was to model direct and indirect relationships among healthy eating identity, perceived control of healthy eating, social support for healthy eating, food retail choice block scores, perceptions of healthy food availability, and food shopping behaviors and diet quality measured using Healthy Eating Index-2010 scores (HEI-2010) for residents living in two urban communities defined as food deserts.

DESIGN

A cross-sectional design was used including data collected via self-reported surveys, 24-dietary recalls, and through objective observations of food retail environments.

PARTICIPANTS/SETTING

Data collection occurred in 2015-2016 in two low-income communities in Cleveland (n=243) and Columbus (n=244), OH.

MAIN OUTCOME MEASURE

HEI-2010 scores were calculated based on the average of three 24-hour dietary recalls using the Nutrition Data System for Research.

ANALYSIS

Separate path models, controlled for income, were run for each community. Analysis was guided by a conceptual model with 15 hypothesized direct and indirect effects on HEI-2010 scores. Associations were considered statistically significant at P<0.05 and P<0.10 because of modest sample sizes in each community.

RESULTS

Across both models, significant direct effects on HEI-2010 scores included healthy eating identity (β=.295, Cleveland; β=.297, Columbus, P<0.05) and distance traveled to primary food store (β=.111, Cleveland, P<0.10; β=.175, Columbus, P<0.05). Perceptions of healthy food availability had a significant, inverse effect in the Columbus model (β=-.125, P<0.05). The models explained greater variance in HEI-2010 scores for the Columbus community compared with Cleveland (R²=.282 and R²=.152, respectively).

CONCLUSIONS

Findings highlight the need for tailored dietary intervention approaches even within demographically comparable communities. Interventions aimed at improving diet quality among residents living in food deserts may need to focus on enhancing healthy eating identity using culturally relevant approaches while at the same time addressing the need for transportation supports to access healthy food retailers located farther away.