Estimating Proximity to Care
Are Straight Line and Zipcode Centroid Distances Acceptable Proxy Measures
Bibliographic Data
| ID | 9099062 |
|---|---|
| Authors | Robin L Bliss, Robin Bliss (Brigham and Women's Hospital, corresponding author), Jeffrey N Katz (0000-0003-2104-4670, Brigham and Women's Hospital, corresponding author), ELIZABETH A WRIGHT (corresponding author), Elena Losina (0000-0002-3424-0892, Boston University, corresponding author) |
| Year | 2012 |
| Volume | 50 |
| Issue | 1 |
| Pages | 99-106 |
| Publication date | 2012-01-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Care (JOURNAL) |
| Journal identifiers | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/mlr.0b013e31822944d1 |
| PMID | 22167065 |
| PMCID | PMC3240808 |
| OpenAlex | W2024710344 |
| Language | EN |
| Citations received | 9 |
| References cited | 12 |
BACKGROUND: Spatial accessibility of healthcare may be measured by proximity of patient residence to health services, typically in driving distance or driving time. Precise driving distances and times are rarely available. Although straight line distances between zipcode centroids and between precise address locations are used as proxy measures for distance to care, the accuracy of these measures has received little study. METHODS: Among a cohort of Medicare beneficiaries, actual driving distances and times between patient residence and clinic were obtained from commercial software (MapQuest). We used a split-sample design to build and validate linear regression models that predict actual driving distances and times from estimated distances between zipcode centroids and between precise residential and hospital locations, adjusting for urban/suburban/rural residential status. RESULTS: On average, predicted driving distances and times were larger than actual values. Zipcode centroid distances alone predicted longer driving distances than observed values: rural +19% (3.2 miles), suburban +23% (3.7 miles), and urban +27% (2.0 miles). Predicted time was 36% (9.4 min) longer in rural, 32% (6.8 min) longer in suburban, and 38% (4.7 min) longer in urban areas than observed values. Including urban/suburban/rural categorization of residence improved the accuracy of predicted driving distance and time for suburban and urban areas but diminished accuracy for rural areas. Similar trends were observed for distance estimates from precise locations. CONCLUSIONS: Distances between zipcode centroids and precise residential/hospital locations provide reasonable estimates of driving distance and time for epidemiologic research. Estimates are improved for suburban and urban residences when data are augmented by urban categorization
Centroid · Distance decay · Econometrics · Economic geography · Geography · Proxy (statistics) · Residence · Rural area · Sample (material) · Sociology · Statistics · Transport engineering · Computer Science · Demography · Environmental Science · Global Health Workforce Issues · Mathematics · Medicine · Older Adults Driving Studies · Urban Transport and Accessibility
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Proximity Does Not Equal Access
| Unique citing works | 9 |
|---|---|
| Citations per year | 0,64 |
| Citation span | 2012 - 2022 (11) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 7 |