Department of Orthopedic Surgery, Orthopedic and Arthritis Center for Outcomes Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Med Care. 2012 Jan;50(1):99-106. doi: 10.1097/MLR.0b013e31822944d1.
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.
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.
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.
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.
医疗保健的空间可达性可以通过患者居住地与卫生服务之间的接近程度来衡量，通常以驾驶距离或驾驶时间来衡量。精确的驾驶距离和时间很少可用。尽管邮政编码中心之间的直线距离和精确地址位置之间的直线距离被用作护理距离的替代测量指标，但这些测量指标的准确性尚未得到广泛研究。
在一组 Medicare 受益人群中，从商业软件（MapQuest）获得患者居住地和诊所之间的实际驾驶距离和时间。我们使用拆分样本设计构建和验证线性回归模型，从邮政编码中心之间的估计距离以及精确的住宅和医院位置之间的距离预测实际驾驶距离和时间，同时调整城市/郊区/农村居住状态。
平均而言，预测的驾驶距离和时间大于实际值。邮政编码中心距离单独预测的驾驶距离大于观察值：农村地区 +19%（3.2 英里），郊区地区 +23%（3.7 英里），城市地区 +27%（2.0 英里）。农村地区预测时间比观察值长 36%（9.4 分钟），郊区地区长 32%（6.8 分钟），城市地区长 38%（4.7 分钟）。包括居住的城市/郊区/农村分类可提高郊区和城市地区预测驾驶距离和时间的准确性，但降低农村地区的准确性。从精确位置估计的距离也观察到类似的趋势。
邮政编码中心和精确住宅/医院位置之间的距离为流行病学研究提供了驾驶距离和时间的合理估计。当数据通过城市分类进行扩充时，郊区和城市住宅的估计值会得到改善。
