The dynamics of food shopping behavior: Exploring travel patterns in low-income Detroit neighborhoods experiencing extreme disinvestment using agent-based modeling

Spatial accessibility and travel to pharmacy by type in the Detroit region

BACKGROUND

Community pharmacies are a critical part of the health care provision system. Yet less is understood about the spatial accessibility to pharmacies and how people travel to reach these services.

OBJECTIVES

This study compared spatial accessibility and actual travel to different types of pharmacies among selected neighborhoods in the Detroit region.

METHODS

Three types of neighborhoods were selected and compared, including two lower income Black urban neighborhoods of high-density and four upper income White suburbs (two of low density and two of high density). Spatial accessibility was computed by pharmacy type and compared among neighborhoods using ANOVA. Pharmacy trips reported in a travel survey were geocoded and linked with community pharmacies in a list generated from ReferenceUSA business data. Destination choices were mapped and the relationship between spatial accessibility and actual distance traveled was examined using ordinary least squares regressions.

RESULTS

On average, urban residents in Detroit had higher access to local independent pharmacies (0.74 miles to the nearest one) but relatively lower access to national chains (1.35 miles to the nearest one), which most residents relied on. Urban residents also tended to shop around more for services even among national chains. In fact, they bypassed nearby local independent pharmacies and traveled long distances to use farther pharmacies, primarily national chains. The average trip distance to pharmacy was 2.1 miles for urban residents, but only 1.1 miles and 1.5 miles for residents in high-density suburbs and low-density suburbs, respectively.

CONCLUSION

Supposedly good spatial access considering all pharmacies together may mask excessive burden in reaching the pharmacy services needed in low-income minority urban communities, as shown in the case of Detroit. Thus, when mapping pharmacy deserts, it is important to distinguish spatial accessibility among different pharmacy types.

Community Investments and Diet-Related Outcomes: A Longitudinal Study of Residents of Two Urban Neighborhoods

INTRODUCTION

Investments in historically oppressed neighborhoods through food retail, housing, and commercial development are hypothesized to improve residents' health, nutrition, and perceptions of their neighborhood as a place to live. Although place-based development (e.g., housing, retail, business assistance) is happening in many communities, there is little evidence of the long-term correlates of multiple investments such as health and nutrition among residents.

METHODS

A quasi-experimental longitudinal study was conducted using a cohort of randomly sampled households in two low-income, predominantly African American neighborhoods in Pittsburgh, PA, with surveys assessing residents' food insecurity, perception of their neighborhood as a place to live, perception of access to healthy foods, and dietary outcomes in 2011 and seven years later (2018), with an interim assessment in 2014. Analyses conducted in 2022 compared changes among residents of one neighborhood which had 2.6 times the investments over a 7-year period with changes among residents of a socio-demographically similar neighborhood that received fewer investments.

RESULTS

It was found that residents in the neighborhood receiving substantial investments demonstrated statistically significant improvements in neighborhood satisfaction (12.6% improvement compared with a 2.2% decrease) and perceived access to healthy food (52% improvement compared with 18.2% improvement), and marginally significant change in food security (14% compared with 4.8% improvement) compared with residents in the neighborhood receiving fewer investments.

CONCLUSIONS

Multiple place-based investments in neighborhoods can potentially induce positive change for residents in health and nutrition outcomes.

Changes of Grocery Shopping Frequencies and Associations with Food Deserts during the COVID-19 Pandemic in the United States

The COVID-19 pandemic has dramatically altered people's lives in multiple aspects, including grocery shopping behaviors. Yet, the changing trend of grocery shopping frequencies during the COVID-19 and its associations with food deserts remain unclear. We aimed to (1) examine variations of grocery shopping frequencies at county level in the USA during the COVID-19 pandemic from March 2020 to December 2021; (2) investigate associations between grocery shopping frequencies and food deserts during the COVID-19 pandemic; and (3) explore heterogeneity in grocery shopping frequencies-food desert associations across urban and rural areas. The county-level grocery shopping frequencies were derived from a grocery pattern dataset obtained from SafeGraph. We divided the 22-month period into 5 stages and employed the growth curve modeling to estimate the trajectories of grocery shopping frequencies and the associations between grocery shopping frequencies and food deserts in each stage, separately. Results revealed that grocery shopping frequencies experienced a "W-shaped" pattern from March 2020 to December 2021. Counties with the least percent of food deserts had slower decrease in grocery shopping frequencies at the initial stage and recovered more rapidly at later stages. Counties with the highest percent of food deserts were subject to deprivation amplification as a result of the pandemic. We also found differences existed in the grocery shopping frequencies-food desert associations between metropolitan counties and rural counties. Our findings suggest the impacts of COVID-19 on grocery shopping frequencies varied across different time periods, shedding light on designing different strategies to reduce the risk of contagion while shopping inside of grocery stores. Further, our findings highlight an urgent need to help people living in food deserts (especially in rural counties) to procure healthy foods safely during health emergencies like COVID-19 pandemic which disrupt mobility and social behaviors.

Designing a food supply chain strategy during COVID-19 pandemic using an integrated Agent-Based Modelling and Robust Optimization

Coronavirus disease (COVID-19) has spread for over a year and affected many aspects, including the food supply chain. One of the ways COVID-19 has impacted the food supply chain is the food production capacity reduction. It is necessary to develop the optimum food supply chain strategy by determining the optimum food hub location and food network to maintain food security which robust against disruptions and uncertainties. In this study, Robust Optimization (RO) is applied to handle the uncertainties. Nevertheless, the actual uncertain data might be hard to be collected or even unavailable at the moment. Therefore, an innovative framework is proposed to integrate RO with Agent-Based Modelling (ABM). ABM is used to simulate the upstream actor of the food supply chain and predict the uncertain food production capacity, which RO later handles. Particularly, this study focused on rice supply chain. The result shows that the framework is able to handle the uncertain rice supply chain problem, in which the actual uncertain data might be unavailable, and give the robust optimum food hub location and food network. The food hub location and food network are obtained by solving the Robust Counterpart (RC) model with respect to the uncertainty set obtained from the ABM simulation result.

Examining changes to food and beverage availability and marketing in a low-income community after the opening of a new supermarket

OBJECTIVE

In 2016, a Whole Foods Market© opened in the Englewood community of Chicago, IL - a predominately low-income African American community. This study aimed to examine changes to food and beverage availability and marketing in Englewood's existing food stores after the opening of this supermarket.

DESIGN

Quasi-experimental study.

SETTING

Two low-income African American communities in Chicago, IL.

PARTICIPANTS

Fieldworkers audited all small grocery and limited service stores (e.g. convenience stores, liquor stores and dollar/discount stores) located within one-square mile of the new supermarket and a one-square mile area of a demographically comparable community that also lacked a supermarket. Stores were audited before (2016) and after (2017 and 2018) the supermarket opened.

RESULTS

Of the 78 stores audited at baseline, 71·8 % were limited service stores and 85·9 % accepted Supplemental Nutrition Assistance Program (SNAP) benefits. Overall, the availability of healthy food and beverage options in nearby small stores was low at baseline and both follow-up periods. Difference-in-difference regression models revealed a significant increase in: (1) the percentage of stores in the intervention community (i.e. Englewood) offering regular cheese and promoting salty snacks at check-out from 2016 to 2017; and (2) the percentage of stores in the comparison community with interior store promotions for other sweetened beverages from 2016 to 2018.

CONCLUSIONS

Minimal changes in food and beverage availability and marketing occurred 1 and 2 years after the opening of a new supermarket. However, the wide range of staple food items offered by the supermarket expanded healthy food retail in Chicago's Englewood community.

Smart city lifestyle sensing, big data, geo-analytics and intelligence for smarter public health decision-making in overweight, obesity and type 2 diabetes prevention: the research we should be doing

The public health burden caused by overweight, obesity (OO) and type-2 diabetes (T2D) is very significant and continues to rise worldwide. The causation of OO and T2D is complex and highly multifactorial rather than a mere energy intake (food) and expenditure (exercise) imbalance. But previous research into food and physical activity (PA) neighbourhood environments has mainly focused on associating body mass index (BMI) with proximity to stores selling fresh fruits and vegetables or fast food restaurants and takeaways, or with neighbourhood walkability factors and access to green spaces or public gym facilities, making largely naive, crude and inconsistent assumptions and conclusions that are far from the spirit of 'precision and accuracy public health'. Different people and population groups respond differently to the same food and PA environments, due to a myriad of unique individual and population group factors (genetic/epigenetic, metabolic, dietary and lifestyle habits, health literacy profiles, screen viewing times, stress levels, sleep patterns, environmental air and noise pollution levels, etc.) and their complex interplays with each other and with local food and PA settings. Furthermore, the same food store or fast food outlet can often sell or serve both healthy and non-healthy options/portions, so a simple binary classification into 'good' or 'bad' store/outlet should be avoided. Moreover, appropriate physical exercise, whilst essential for good health and disease prevention, is not very effective for weight maintenance or loss (especially when solely relied upon), and cannot offset the effects of a bad diet. The research we should be doing in the third decade of the twenty-first century should use a systems thinking approach, helped by recent advances in sensors, big data and related technologies, to investigate and consider all these factors in our quest to design better targeted and more effective public health interventions for OO and T2D control and prevention.