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A proposed approach in defining population-based rates of major injury from a trauma registry dataset: Delineation of hospital catchment areas (I)

机译:从创伤登记数据集中定义基于人群的主要伤害发生率的拟议方法:医院服务区的划分(I)

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Background Determining population-based rates for major injury poses methodological challenges. We used hospital discharge data over a 10-year period (1996–2005) from a national trauma registry, the Trauma Audit and Research Network (TARN) Manchester, to construct valid numerators and denominators so that we can calculate population-based rates of major injury in the future. Methods We examined data from all hospitals reporting to TARN for continuity of numerator reporting; rates of completeness for patient postcodes, and clear denominator populations. We defined local market areas (>70% of patients originating from the same postcode district as the hospital). For relevant hospitals we assessed data quality: consistency of reporting, completeness of patient postcodes and for one selected hospital, North Staffordshire Royal Infirmary (NSRI), the capture rate of numerator data reporting. We used an established method based on patient flow to delineate market areas from hospitals discharges. We then assessed the potential competitors, and characterized these denominator areas. Finally we performed a denominator sensitivity analysis using a patient origin matrix based on Hospital Episodes Statistics (HES) to validate our approach. Results Sixteen hospitals met the data quality and patient flow criteria for numerator and denominator data, representing 12 hospital catchment areas across England. Data quality issues included fluctuations numbers of reported cases and poor completion of postcodes for some years. We found an overall numerator capture rate of 83.5% for the NSRI. In total we used 40,543 admissions to delineate hospital catchment areas. An average of 3.5 potential hospital competitors and 15.2 postcode districts per area were obtained. The patient origin matrix for NSRI confirmed the accuracy of the denominator/hospital catchment area from the patient flow analysis. Conclusion Large national trauma registries, including TARN, hold suitable data for determining population-based injury rates. Patient postcodes from hospital discharge allow identification of denominator populations using a market area approach.
机译:背景技术确定基于人群的重大伤害率构成了方法上的挑战。我们使用了十年(1996-2005年)国家创伤登记中心曼彻斯特创伤审计与研究网络(TARN)的出院数据来构建有效的分子和分母,以便我们可以计算出基于人群的主要人群出院率将来受伤。方法我们检查了所有报告给TARN的医院的数据,以确保分子报告的连续性。患者邮政编码的完整率和明确的分母总数。我们定义了本地市场区域(> 70%的患者来自与医院相同的邮政编码区域)。对于相关医院,我们评估了数据质量:报告的一致性,患者邮政编码的完整性,以及所选的一家北斯塔福德郡皇家医院(NSRI)医院的分子数据报告捕获率。我们使用了一种基于患者流量的既定方法来确定医院出院的市场区域。然后,我们评估了潜在的竞争对手,并确定了这些分母领域。最后,我们使用基于医院发作统计数据(HES)的患者来源矩阵进行了分母敏感性分析,以验证我们的方法。结果十六家医院符合分子和分母数据的数据质量和患者流标准,代表了英格兰的12个医院服务区域。数据质量问题包括报告病例的数量波动和几年来邮政编码的不完善。我们发现NSRI的分子总捕获率为83.5%。我们总共使用了40543个入院图来描述医院的服务区域。平均每个区域获得了3.5个潜在的医院竞争者和15.2个邮政编码区域。 NSRI的患者来源矩阵通过患者流量分析确认了分母/医院集水区的准确性。结论包括TARN在内的大型国家创伤登记处拥有确定基于人群的伤害率的合适数据。来自医院出院的患者邮政编码可以使用市场区域方法来识别分母。

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