Combining Straight‐Line and Map‐Based Distances to Investigate the Connection Between Proximity to Healthy Foods and Disease
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| Formato: | Artículo Open Access |
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Wiley
2025
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| _version_ | 1867021737169780736 |
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| author | Sarah C. Lotspeich Ashley E. Mullan Lucy D'Agostino McGowan Staci A. Hepler |
| author_facet | Sarah C. Lotspeich Ashley E. Mullan Lucy D'Agostino McGowan Staci A. Hepler Sarah C. Lotspeich Ashley E. Mullan Lucy D'Agostino McGowan Staci A. Hepler |
| collection | Wiley Open Access |
| contents | Combining Straight‐Line and Map‐Based Distances to Investigate the Connection Between Proximity to Healthy Foods and Disease Sarah C. Lotspeich Ashley E. Mullan Lucy D'Agostino McGowan Staci A. Hepler Statistics in Medicine ABSTRACT Healthy foods are essential for a healthy life, but accessing healthy food can be more challenging for some people than others. This disparity in food access may lead to disparities in well‐being, potentially with disproportionate rates of diseases in communities that face more challenges in accessing healthy food (i.e., low‐access communities). Identifying low‐access, high‐risk communities for targeted interventions is a public health priority, but current methods to quantify food access rely on distance measures that are either computationally simple (like the length of the shortest straight‐line route) or accurate (like the length of the shortest map‐based driving route), but not both. We propose a multiple imputation approach to combine these distance measures, allowing researchers to harness the computational ease of one with the accuracy of the other. The approach incorporates straight‐line distances for all neighborhoods and map‐based distances for just a subset, offering comparable estimates to the “gold standard” model using map‐based distances for all neighborhoods and improved efficiency over the “complete case” model using map‐based distances for just the subset. Through the adoption of a measurement error framework, information from the straight‐line distances can be leveraged to compute informative placeholders (i.e., impute) for any neighborhoods without map‐based distances. Using simulations and data for the Piedmont Triad region of North Carolina, we quantify and compare the associations between two health outcomes (diabetes and obesity) and neighborhood‐level access to healthy foods. The imputation procedure also makes it possible to predict the full landscape of food access in an area without requiring map‐based measurements for all neighborhoods. 10.1002/sim.70054 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1002/sim.70054 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_70054 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Combining Straight‐Line and Map‐Based Distances to Investigate the Connection Between Proximity to Healthy Foods and Disease Sarah C. Lotspeich Ashley E. Mullan Lucy D'Agostino McGowan Staci A. Hepler Statistics in Medicine Combining Straight‐Line and Map‐Based Distances to Investigate the Connection Between Proximity to Healthy Foods and Disease Sarah C. Lotspeich Ashley E. Mullan Lucy D'Agostino McGowan Staci A. Hepler Statistics in Medicine ABSTRACT Healthy foods are essential for a healthy life, but accessing healthy food can be more challenging for some people than others. This disparity in food access may lead to disparities in well‐being, potentially with disproportionate rates of diseases in communities that face more challenges in accessing healthy food (i.e., low‐access communities). Identifying low‐access, high‐risk communities for targeted interventions is a public health priority, but current methods to quantify food access rely on distance measures that are either computationally simple (like the length of the shortest straight‐line route) or accurate (like the length of the shortest map‐based driving route), but not both. We propose a multiple imputation approach to combine these distance measures, allowing researchers to harness the computational ease of one with the accuracy of the other. The approach incorporates straight‐line distances for all neighborhoods and map‐based distances for just a subset, offering comparable estimates to the “gold standard” model using map‐based distances for all neighborhoods and improved efficiency over the “complete case” model using map‐based distances for just the subset. Through the adoption of a measurement error framework, information from the straight‐line distances can be leveraged to compute informative placeholders (i.e., impute) for any neighborhoods without map‐based distances. Using simulations and data for the Piedmont Triad region of North Carolina, we quantify and compare the associations between two health outcomes (diabetes and obesity) and neighborhood‐level access to healthy foods. The imputation procedure also makes it possible to predict the full landscape of food access in an area without requiring map‐based measurements for all neighborhoods. 10.1002/sim.70054 http://creativecommons.org/licenses/by/4.0/ |
| title | Combining Straight‐Line and Map‐Based Distances to Investigate the Connection Between Proximity to Healthy Foods and Disease |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.70054 |