Geographic Variation in Outpatient Health Care Service Utilization After Spinal Cord Injury

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• 作者：Nathaniel Bell ; PhD^a ; ^{Nathaniel@sc.edu} ; Tsion Kidanie ; MSc^b ; Bo Cai ; PhD^b ; James S. Krause ; PhD^c
• 关键词：Geographic information systems ; Health services accessibility ; Rehabilitation ; Spinal cord injuries
• 刊名：Archives of Physical Medicine and Rehabilitation
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：98
• 期：2
• 页码：341-346
• 全文大小：214 K
• 卷排序：98

文摘

To assess whether travel distances between patients and providers predict the frequency in which outpatient health care services are accessed after spinal cord injury (SCI).DesignA retrospective cohort study was carried out in South Carolina among employee health plan and Medicaid insurance recipients.SettingResearch center.ParticipantsTwo years of outpatient claimant data were evaluated from patients (N=243) aged ≥18 years who were hospitalized between 2010 and 2012.InterventionsTravel distances were estimated by geocoding provider and patient address information onto street network files.Main Outcome MeasuresVariation in service utilization use was assessed using negative binomial regression. Outpatient visits for physical medicine and rehabilitation, physician and specialty clinic, radiology, internal medicine, behavioral mental health, and “other” were evaluated.ResultsLonger travel distances were statistically significant predictors of decreased physician/specialty clinic (relative risk [RR]=.87; 95% confidence interval [CI], .79–.96) and physiotherapy (RR=.57; 95% CI, .46–.71) utilization, with mixed findings for other providers. Secondary analyses in which differences in service use were analyzed using census-defined classifications of urban and rural status did not demonstrate any geographic pattern.ConclusionsThere are significant geographic variations in the use of select outpatient services among SCI populations across the state that are related to longer travel distances. That these patterns were only visible when using travel distance models as opposed to census-based classifications of urban and rural status adds support to augmenting routine data collection and surveillance with spatial analytical models.