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Identifying food deserts and swamps based on relative healthy food access: a spatio-temporal Bayesian approach.

Luan H, Law J, Quick M - Int J Health Geogr (2015)

Bottom Line: For the study region, a significant decreasing trend in RHFA is observed (-0.024), suggesting that food swamps have become more prevalent during the study period.For small-areas, significant decreasing temporal trends in RHFA were observed for all small-areas.Results suggest that food swamps are more prevalent than food deserts in the Region of Waterloo.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Environment, School of Planning, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada. h3luan@uwaterloo.ca.

ABSTRACT

Background: Obesity and other adverse health outcomes are influenced by individual- and neighbourhood-scale risk factors, including the food environment. At the small-area scale, past research has analysed spatial patterns of food environments for one time period, overlooking how food environments change over time. Further, past research has infrequently analysed relative healthy food access (RHFA), a measure that is more representative of food purchasing and consumption behaviours than absolute outlet density.

Methods: This research applies a Bayesian hierarchical model to analyse the spatio-temporal patterns of RHFA in the Region of Waterloo, Canada, from 2011 to 2014 at the small-area level. RHFA is calculated as the proportion of healthy food outlets (healthy outlets/healthy + unhealthy outlets) within 4-km from each small-area. This model measures spatial autocorrelation of RHFA, temporal trend of RHFA for the study region, and spatio-temporal trends of RHFA for small-areas.

Results: For the study region, a significant decreasing trend in RHFA is observed (-0.024), suggesting that food swamps have become more prevalent during the study period. For small-areas, significant decreasing temporal trends in RHFA were observed for all small-areas. Specific small-areas located in south Waterloo, north Kitchener, and southeast Cambridge exhibited the steepest decreasing spatio-temporal trends and are classified as spatio-temporal food swamps.

Conclusions: This research demonstrates a Bayesian spatio-temporal modelling approach to analyse RHFA at the small-area scale. Results suggest that food swamps are more prevalent than food deserts in the Region of Waterloo. Analysing spatio-temporal trends of RHFA improves understanding of local food environment, highlighting specific small-areas where policies should be targeted to increase RHFA and reduce risk factors of adverse health outcomes such as obesity.

No MeSH data available.


Related in: MedlinePlus

a RHFA in 2014 and b estimated RHFA in 2014 [pi4 in Model (2)]
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Fig4: a RHFA in 2014 and b estimated RHFA in 2014 [pi4 in Model (2)]

Mentions: As mentioned, estimated RHFA is different from calculated RHFA. Calculated RHFA is simply the number of healthy food outlets divided by the sum of healthy and unhealthy food outlets. Estimated RHFA is the probability of a food outlet being healthy [pij in Model (2)] and is based on calculated RHFA in a given DA and the average of calculated RHFA’s in adjacent areas [via the spatial random effects in Model (2)]. In this case, estimated RHFA helps to account for the realistic assumption that people could travel beyond DA or buffering zone boundaries to procure food; therefore, the RHFA value is smoothed (Fig. 4b). In contrast, calculated RHFA constraints food access within the DA or buffering zones. Two DAs with the same calculated RHFA could have varied estimated RHFA if the averages of calculated RHFA’s in their adjacent areas are different. To exemplify the difference between calculated RHFA and estimated RHFA, we selected two pairs of DAs (highlighted in Fig. 4) with the same calculated RHFA but differing estimated RHFA in 2014: one pair are food deserts (Areas 1 and 2 have calculated RHFA = 0 %) and the other pair are food swamps (Areas 3 and 4 have calculated RHFA = 4.76 %). Area 1 has a higher average of calculated RHFA’s among adjacent areas (3.58 %) compared to Area 2 (2.08 %), leading to Area 1 having a greater estimated RHFA. Similarly, Area 3 has adjacent areas with a higher average of calculated RHFA’s than Area 4, leading to Area 3 having a greater estimated RHFA. Practically, these results suggest that Area 2 is a more serious food desert than Area 1 and that Area 4 is a more serious food swamp than Area 3. When identifying small-areas for food policy interventions, this information helps to continuously categorize food deserts and food swamps, suggesting that Area 2 should be prioritized first because it has the lowest estimated RHFA, followed by Area 1, Area 4, and Area 3 (Table 3).Fig. 4


Identifying food deserts and swamps based on relative healthy food access: a spatio-temporal Bayesian approach.

Luan H, Law J, Quick M - Int J Health Geogr (2015)

a RHFA in 2014 and b estimated RHFA in 2014 [pi4 in Model (2)]
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4696295&req=5

Fig4: a RHFA in 2014 and b estimated RHFA in 2014 [pi4 in Model (2)]
Mentions: As mentioned, estimated RHFA is different from calculated RHFA. Calculated RHFA is simply the number of healthy food outlets divided by the sum of healthy and unhealthy food outlets. Estimated RHFA is the probability of a food outlet being healthy [pij in Model (2)] and is based on calculated RHFA in a given DA and the average of calculated RHFA’s in adjacent areas [via the spatial random effects in Model (2)]. In this case, estimated RHFA helps to account for the realistic assumption that people could travel beyond DA or buffering zone boundaries to procure food; therefore, the RHFA value is smoothed (Fig. 4b). In contrast, calculated RHFA constraints food access within the DA or buffering zones. Two DAs with the same calculated RHFA could have varied estimated RHFA if the averages of calculated RHFA’s in their adjacent areas are different. To exemplify the difference between calculated RHFA and estimated RHFA, we selected two pairs of DAs (highlighted in Fig. 4) with the same calculated RHFA but differing estimated RHFA in 2014: one pair are food deserts (Areas 1 and 2 have calculated RHFA = 0 %) and the other pair are food swamps (Areas 3 and 4 have calculated RHFA = 4.76 %). Area 1 has a higher average of calculated RHFA’s among adjacent areas (3.58 %) compared to Area 2 (2.08 %), leading to Area 1 having a greater estimated RHFA. Similarly, Area 3 has adjacent areas with a higher average of calculated RHFA’s than Area 4, leading to Area 3 having a greater estimated RHFA. Practically, these results suggest that Area 2 is a more serious food desert than Area 1 and that Area 4 is a more serious food swamp than Area 3. When identifying small-areas for food policy interventions, this information helps to continuously categorize food deserts and food swamps, suggesting that Area 2 should be prioritized first because it has the lowest estimated RHFA, followed by Area 1, Area 4, and Area 3 (Table 3).Fig. 4

Bottom Line: For the study region, a significant decreasing trend in RHFA is observed (-0.024), suggesting that food swamps have become more prevalent during the study period.For small-areas, significant decreasing temporal trends in RHFA were observed for all small-areas.Results suggest that food swamps are more prevalent than food deserts in the Region of Waterloo.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Environment, School of Planning, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada. h3luan@uwaterloo.ca.

ABSTRACT

Background: Obesity and other adverse health outcomes are influenced by individual- and neighbourhood-scale risk factors, including the food environment. At the small-area scale, past research has analysed spatial patterns of food environments for one time period, overlooking how food environments change over time. Further, past research has infrequently analysed relative healthy food access (RHFA), a measure that is more representative of food purchasing and consumption behaviours than absolute outlet density.

Methods: This research applies a Bayesian hierarchical model to analyse the spatio-temporal patterns of RHFA in the Region of Waterloo, Canada, from 2011 to 2014 at the small-area level. RHFA is calculated as the proportion of healthy food outlets (healthy outlets/healthy + unhealthy outlets) within 4-km from each small-area. This model measures spatial autocorrelation of RHFA, temporal trend of RHFA for the study region, and spatio-temporal trends of RHFA for small-areas.

Results: For the study region, a significant decreasing trend in RHFA is observed (-0.024), suggesting that food swamps have become more prevalent during the study period. For small-areas, significant decreasing temporal trends in RHFA were observed for all small-areas. Specific small-areas located in south Waterloo, north Kitchener, and southeast Cambridge exhibited the steepest decreasing spatio-temporal trends and are classified as spatio-temporal food swamps.

Conclusions: This research demonstrates a Bayesian spatio-temporal modelling approach to analyse RHFA at the small-area scale. Results suggest that food swamps are more prevalent than food deserts in the Region of Waterloo. Analysing spatio-temporal trends of RHFA improves understanding of local food environment, highlighting specific small-areas where policies should be targeted to increase RHFA and reduce risk factors of adverse health outcomes such as obesity.

No MeSH data available.


Related in: MedlinePlus