Environmental Justice and the Food Environment in Prince George’s County, Maryland: Assessment of Three Communities
Bibliographic record
Abstract
Lack of access to a health-promoting food environment can lead to poor health outcomes including obesity which is a problem for African-Americans in Prince George’s County, Maryland. Previous research examined the quality of the food environment at the regional level but did not consider local level indicators. In this study, we utilized an environmental justice framework to examine the local food environment in the County. We collected data from 127 food outlets, (convenience stores, grocery stores, and supermarkets), in three racially and socioeconomically diverse communities – Bladensburg (predominantly African American/ Black, with the lowest median household income); Greenbelt (similar percentage of non-white persons as Hyattsville, with the highest median household income); and Hyattsville (dominated by a Hispanic population). We examined the availability, quality, and accessibility of food within each community, using a modified version of the Johns Hopkins Center for a Livable Future (CLF) healthy food availability index (HFAI).We also used ArcMap 10.6 to examine the spatial distribution of stores in relation to sociodemographic factors and generate descriptive statistics to examine HFAI score differences across the communities, sociodemographic composition, and store types at the block group level. Mean HFAI scores were 7.76, 10.75, and 9.60 for Bladensburg, Greenbelt, and Hyattsville, respectively suggesting a relative disparity in access to diverse healthy and good quality food sources for these communities although these differences were not statistically significant (p=0.79). Statistically significant differences between the communities were found with respect to ethnic stores, stores that sold fresh vegetables (p=0.047), and stores that sold fresh fruits (p=0.012). Getis-Ord Gi Hot Spot Analysis revealed one statistically significant cold spot at 95% confidence, and two others at 90% confidence in Hyattsville, indicating a cluster of low-scoring stores. The results indicate a potential need for expanded food infrastructure in these communities to improve public health. We also identified the need for culturally appropriate foods and proposed ethnic stores as potential salutogens to improve the food environment in culturally diverse neighborhoods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".