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

ABSTRACT: Only a handful of studies have leveraged agent-based models (ABMs) to examine public health outcomes and policy interventions associated with uneven urban food environments. While providing keen insights about the role of ABMs in studying urban food environments, these studies underutilize real-world data on individual behavior in their models. This study provides a unique contribution to the ABM and food access literature by utilizing survey data to develop an empirically-rich spatially-explicit ABM of food access. This model is used to simulate and scrutinize individual travel behavior associated with accessing food in low-income neighborhoods experiencing disinvestment in Detroit (Michigan), U.S. In particular, the relationship between trip frequencies, mode of travel, store choice, and distances traveled among individuals grouped into strata based on selected sociodemographic characteristics, including household income and age, is examined. Results reveal a diversified picture of not only how income and age shape food shopping travel but also the different thresholds of tolerance for non-motorized travel to stores. Younger and poorer population subgroups have a higher propensity to utilize non-motorized travel for shopping than older and wealthier subgroups. While all groups tend to travel considerable distances outside their immediate local food environment, different sociodemographic groups maintain unique spatial patterns of grocery-shopping behavior throughout the city and the suburbs. Overall, these results challenge foundational tenets in urban planning and design, regarding the specific characteristics necessary in the built environment to facilitate accessibility to urban amenities, such as grocery stores. In neighborhoods experiencing disinvestment, sociodemographic conditions play a more important role than the built environment in shaping food accessibility and ultimately travel behavior.

SUBMITTER: Vojnovic I

PROVIDER: S-EPMC7751856 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Vojnovic Igor I Ligmann-Zielinska Arika A LeDoux Timothy F TF

PloS one 20201221 12

Only a handful of studies have leveraged agent-based models (ABMs) to examine public health outcomes and policy interventions associated with uneven urban food environments. While providing keen insights about the role of ABMs in studying urban food environments, these studies underutilize real-world data on individual behavior in their models. This study provides a unique contribution to the ABM and food access literature by utilizing survey data to develop an empirically-rich spatially-explici ...[more]

PMID: 33347464

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Project description:IntroductionIndividuals living in low-income neighborhoods have disproportionately high rates of obesity, Type-2 diabetes, and cardiometabolic conditions. Perceived safety in one's neighborhood may influence stress and physical activity, with cascading effects on cardiometabolic health.MethodsIn this study, we examined relationships among feelings of safety while walking during the day and mental health [perceived stress (PSS), depression score], moderate-to-vigorous physical activity (PA), Body Mass Index (BMI), and hemoglobin A1C (A1C) in low-income, high-vacancy neighborhoods in Detroit, Michigan. We recruited 69 adults who wore accelerometers for one week and completed a survey on demographics, mental health, and neighborhood perceptions. Anthropometrics were collected and A1C was measured using A1CNow test strips. We compiled spatial data on vacant buildings and lots across the city. We fitted conventional and multilevel regression models to predict each outcome, using perceived safety during daytime walking as the independent variable of interest and individual or both individual and neighborhood-level covariates (e.g., number of vacant lots). Last, we examined trends in neighborhood features according to perceived safety.ResultsIn this predominantly African American sample (91%), 47% felt unsafe during daytime walking. Feelings of perceived safety significantly predicted PSS (β = - 2.34, p = 0.017), depression scores (β = - 4.22, p = 0.006), and BMI (β = - 2.87, p = 0.01), after full adjustment. For PA, we detected a significant association for sex only. For A1C we detected significant associations with blighted lots near the home. Those feeling unsafe lived in neighborhoods with higher park area and number of blighted lots.ConclusionFuture research is needed to assess a critical pathway through which neighborhood features, including vacant or poor-quality green spaces, may affect obesity-via stress reduction and concomitant effects on cardiometabolic health.

Project description:Backgroundthe incidence of novel coronavirus disease (COVID19) is elevated in areas with heightened socioeconomic vulnerability. Early reports from US hospitals also implicated social disadvantage and chronic disease history as COVID19 mortality risk factors. However, the relationship between race and COVID19 mortality remains unclear.Methodswe examined in-hospital COVID19 mortality risk factors in a multi-hospital tertiary health care system that serves greater Detroit, Michigan, a predominantly African American city with high rates of poverty and chronic disease. Consecutive adult patients who presented to emergency departments and tested positive for COVID19 from 3/11/2020 through 4/18/2020 were included. Using log-binomial regression, we assessed the relationship between in-hospital mortality and residence in census tracts that were flagged for extreme socioeconomic vulnerability, patient-level demographics, and clinical comorbidities.Findingsa total of 1,015 adults tested positive for COVID19 during the study period; 80% identified as Black people, 52% were male and 53% were ≥ 65 years of age. The median body mass index was 30•4 and the median Charlson Comorbidity Index score was 4. Patients from census tracts that were flagged for vulnerability related to socioeconomic status had a higher mortality rate than their peers who resided in less vulnerable census tracts (β 0.26, standard error (SE) 0.11, degrees of freedom (df) 378, t-value (t) 2.27, exp(β) 1.29, p-value 0.02). Adjustment for age category, Black race, sex and/or the Charlson Comorbidity Index score category reduced the magnitude of association by less than 10% [exp(β) 1.29 vs. 1.21]. Black race [p = 0.38] and sex [p = 0.62] were not associated with mortality in this sample.Interpretationpeople who lived in areas flagged for extreme socioeconomic vulnerability had elevated mortality risk in our predominantly African-American cohort of COVID19 patients who were able to seek hospital care during the so-called 'first wave' of the pandemic. By contrast, Black race was not associated with mortality in our sample.

Dataset Information

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

Publications

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

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