Adapting Global Dietary Recommendation Indices to Assess Retail Food Environment Quality: Spatial Insights from Rural...

Rapid nutrition transitions in low- and middle-income countries are increasingly shaped by retail food environments, influencing dietary choices and contributing to rising burdens of nutrition-related noncommunicable diseases (NCDs). However, standardized and scalable metrics for characterizing retail food environment quality in relation to dietary recommendations and NCD risk remain limited.

This study aimed to adapt Global Dietary Recommendation (GDR) indices to assess retail food environment quality by quantifying the availability of food groups associated with NCD risk and protection.

A cross-sectional market survey was conducted among food retailers in one rural and one urban administrative ward in Kenya. Data were collected on food groups sold, retail outlet typology, retailer gender, and geographic coordinates. Three indices were constructed: the Healthy Retail Food Environment Score (HRFES), the Unhealthy Retail Food Environment Score (URFES), and the Retail Food Environment Quality Index (RFEQI). Fixed-effect regression models examined associations between retail typology and index scores, whereas spatial autocorrelation analyses identified clustering patterns of healthy and unhealthy food availability.

Stall and tabletop retailers were strongly associated with higher HRFES [incidence rate ratio (IRR): 3.15; 95% confidence interval (CI): 2.61, 3.85; P < 0.001] and higher overall food environment quality [RFEQI; IRR: 1.19; 95% CI: 1.12, 1.27; P < 0.001]. In contrast, supermarkets were most strongly associated with higher URFES [IRR: 11.30; 95% CI: 6.96, 18.5; P < 0.001] and substantially lower RFEQI [IRR: 0.62; 95% CI: 0.41, 0.88; P < 0.05]. Spatial analyses showed more pronounced clustering of unhealthy food groups than healthy food groups across both rural and urban settings.

Adapted GDR-based indices offer a robust and policy-relevant approach for assessing retail food environment quality and its spatial distribution. These tools support monitoring, cross-context comparisons, and targeted interventions to improve diet quality and reduce NCD risk in rapidly transforming food systems.