Department of Epidemiology and Biostatistics, McGill University, 1020 Pine Avenue West, Montreal, QC, H3A 1A2, Canada.
BMC Public Health. 2012 Mar 8;12:166. doi: 10.1186/1471-2458-12-166.
Syndromic surveillance systems are plagued by high false-positive rates. In chronic disease monitoring, investigators have identified several factors that predict the accuracy of case definitions based on diagnoses in administrative data, and some have even incorporated these predictors into novel case detection methods, resulting in a significant improvement in case definition accuracy. Based on findings from these studies, we sought to identify physician, patient, encounter, and billing characteristics associated with the positive predictive value (PPV) of case definitions for 5 syndromes (fever, gastrointestinal, neurological, rash, and respiratory (including influenza-like illness)).
The study sample comprised 4,330 syndrome-positive visits from the claims of 1,098 randomly-selected physicians working in Quebec, Canada in 2005-2007. For each visit, physician-facilitated chart review was used to assess whether the same syndrome was present in the medical chart (gold standard). We used multivariate logistic regression analyses to estimate the association between claim-chart agreement about the presence of a syndrome and physician, patient, encounter, and billing characteristics.
The likelihood of the medical chart agreeing with the physician claim about the presence of a syndrome was higher when the treating physician had billed many visits for the same syndrome recently (ORper 10 visit, 1.05; 95% CI, 1.01-1.08), had a lower workload (ORper 10 claims, 0.93; 95% CI, 0.90-0.97), and when the patient was younger (ORper 5 years of age, 0.96; 95% CI, 0.94-0.97), and less socially deprived (ORmost versus least deprived, 0.76; 95% CI, 0.60-0.95).
Many physician, patient, encounter, and billing characteristics associated with the PPV of surveillance case definition are accessible to public health, and could be used to reduce false-positive alerts by surveillance systems, either by focusing on the data most likely to be accurate, or by adjusting the observed data for known biases in diagnosis reporting and performing surveillance using the adjusted values.
综合征监测系统存在高假阳性率的问题。在慢性病监测中，研究人员已经确定了一些预测基于行政数据诊断的病例定义准确性的因素，其中一些甚至将这些预测因素纳入了新的病例检测方法中，从而显著提高了病例定义的准确性。基于这些研究的结果，我们试图确定与五种综合征（发热、胃肠道、神经、皮疹和呼吸道（包括流感样疾病））的病例定义阳性预测值（PPV）相关的医生、患者、就诊和计费特征。
研究样本包括 2005 年至 2007 年期间在加拿大魁北克随机选择的 1,098 名医生的就诊记录中的 4,330 例综合征阳性就诊记录。对于每次就诊，我们使用医生协助的病历审查来评估同一综合征是否存在于病历中（金标准）。我们使用多变量逻辑回归分析来估计病例记录中与综合征存在的一致程度与医生、患者、就诊和计费特征之间的关系。
当治疗医生最近为同一综合征开具了多次就诊记录（每增加 10 次就诊，OR 值为 1.05；95%置信区间，1.01-1.08）、工作量较低（每增加 10 次就诊，OR 值为 0.93；95%置信区间，0.90-0.97）以及患者年龄较小时（每增加 5 岁，OR 值为 0.96；95%置信区间，0.94-0.97），以及社会资源较不匮乏时（最富裕与最贫困相比，OR 值为 0.76；95%置信区间，0.60-0.95），则医生记录与医生声称存在综合征的病例记录更一致。
许多与监测病例定义的 PPV 相关的医生、患者、就诊和计费特征都可以为公共卫生所利用，并且可以通过以下方式来减少监测系统的假阳性警报：专注于最有可能准确的数据，或者通过调整已知诊断报告偏差的数据来进行监测，或者使用调整后的数值进行监测。
