Dr Foster Unit at Imperial, Department of Primary Care and Public Health, Imperial College London, London.
Br J Gen Pract. 2013 Aug;63(613):e534-42. doi: 10.3399/bjgp13X670660.
More accurate and recent estimates of adverse events in primary care are necessary to assign resources for improvement of patient safety, while predictors must be identified to ameliorate patient risk.
To determine the incidence of recorded iatrogenic harm in general practice and identify risk factors for these adverse events.
Cross-sectional sample of 74,763 patients at 457 English general practices between 1 January 1999 and 31 December 2008, obtained from the General Practice Research Database.
Patient age at study entry, sex, ethnicity, deprivation, practice region, duration registered at practice, continuity of care, comorbidities, and health service use were extracted from the data. Adverse events were defined by Read Codes for complications of care (Chapters S, T, and U). Crude and adjusted analyses were performed by Poisson regression, using generalised estimating equations.
The incidence was 6.0 adverse events per 1000 person-years (95% confidence interval [CI] = 5.74 to 6.27), equivalent to eight adverse events per 10,000 consultations (n = 2,540,877). After adjustment, patients aged 65-84 years (risk ratio [RR] = 5.62, 95% CI = 4.58 to 6.91; P<0.001), with the most consultations (RR = 2.14, 95% CI = 1.60 to 2.86; P<0.001), five or more emergency admissions (RR = 2.08, 95% CI = 1.66 to 2.60; P<0.001), or the most diseases according to expanded diagnosis clusters (RR = 8.46, 95% CI = 5.68 to 12.6; P<0.001) were at greater risk of adverse events. Patients registered at their practice for the longest periods of time were less at risk of an adverse event (RR = 0.40, 95% CI = 0.35 to 0.47; P<0.001).
The low incidence of recorded adverse events is comparable with other studies. Temporal sequencing of risk factors and case ascertainment would benefit from data triangulation. Future studies may explore whether first adverse events predict future incidents.
为了合理分配资源以提高患者安全水平，我们需要更准确、更贴近现实的初级保健不良事件评估结果，同时也需要识别出预测不良事件的因素。
旨在明确一般实践中记录的医源性伤害发生率，并确定这些不良事件的危险因素。
本研究是一项横断性样本研究，共纳入了 1999 年 1 月 1 日至 2008 年 12 月 31 日期间在英格兰 457 家普通诊所登记的 74763 名患者，患者数据来自普通实践研究数据库。
从数据中提取患者入组时的年龄、性别、种族、贫困程度、所在地区、在诊所的登记年限、连续性护理、合并症和卫生服务使用情况。使用 Read 编码将医疗差错并发症（章节 S、T 和 U）定义为不良事件。采用广义估计方程进行泊松回归分析进行粗分析和调整分析。
发生率为每 1000 人年发生 6.0 次不良事件（95%置信区间[CI]为 5.74 至 6.27），相当于每 10000 次就诊发生 8 次不良事件（n = 2540877）。经过调整后，年龄在 65-84 岁的患者（风险比[RR] = 5.62，95%CI = 4.58 至 6.91；P<0.001）、就诊次数最多的患者（RR = 2.14，95%CI = 1.60 至 2.86；P<0.001）、急诊住院 5 次及以上的患者（RR = 2.08，95%CI = 1.66 至 2.60；P<0.001）或根据扩展诊断群确定的疾病最多的患者（RR = 8.46，95%CI = 5.68 至 12.6；P<0.001）发生不良事件的风险更高。在诊所登记时间最长的患者发生不良事件的风险较低（RR = 0.40，95%CI = 0.35 至 0.47；P<0.001）。
记录不良事件的发生率较低，与其他研究结果相似。风险因素的时间序列和病例确定将受益于数据三角剖分。未来的研究可能会探索首次不良事件是否可预测未来的不良事件。
