BACKGROUND:Somatizing patients have maladaptive and increased rates of medical care utilization. If there were a way of routinely identifying such patients, one that did not require intensive, case-by-case review, they could be targeted for specific interventions to improve their use of medical care. OBJECTIVE:We sought to identify patterns of medical care utilization that would distinguish somatizing and nonsomatizing medical outpatients with acceptable sensitivity and specificity. DESIGN:Subjects completed questionnaires assessing somatization and sociodemographic characteristics. Their medical care utilization was obtained for the 12 months preceding the index visit. We then used multivariable logistic regression and recursive partitioning to identify patients with a provisional diagnosis of somatoform disorder. These exploratory models used various patterns of medical care utilization and sociodemographic characteristics as the independent variables. SUBJECTS:We studied consecutive adults attending 2 primary care practices on randomly chosen days. MEASURES:The provisional diagnosis of a somatoform disorder was assessed with a 15-item self-report questionnaire. The number of primary care visits, specialty visits, mental health visits, emergency visits, and inpatient and outpatient costs were obtained for the 12 months preceding the index visit from our hospital's automated medical records, which also provided a rating of aggregate medical morbidity. Self-reported utilization outside our hospital system was obtained from a subsample of patients. RESULTS:Complete data were obtained on 1440 patients. Somatizing patients had more specialty care than primary care visits, higher outpatient than inpatient costs, and more emergency visits than nonsomatizing patients. A regression model containing 7 measures of utilization and 4 sociodemographic characteristics distinguished somatizing and nonsomatizing patients with a c-statistic = 0.73. Recursive partitioning identified 10 terminal nodes with a very high specificity (99%) but a very low sensitivity (15%). CONCLUSIONS:We identified 7 discrete patterns of medical care utilization that distinguished somatizing and nonsomatizing patients. However, they did so with only modest specificity and sensitivity. This algorithm might be used effectively as the first step in a 2-step screening procedure whose second step would entail more intensive screening or individual, case-by-case review to identify somatizing patients in primary care practice.

译文

背景:躯体化患者适应不良,医疗利用率更高。如果有一种常规识别此类患者的方法,而无需进行深入的个案审查,则可以针对他们采取特定的干预措施,以改善他们对医疗的使用。
目的:我们试图确定可以利用可接受的敏感性和特异性来区分躯体化和非躯体化门诊患者的医疗利用模式。
设计:受试者完成问卷调查,评估躯体化和社会人口统计学特征。在索引访问之前的12个月中获得了他们的医疗保健利用率。然后,我们使用多变量logistic回归和递归分区来确定患有躯体形式疾病的临时诊断的患者。这些探索性模型使用了各种医疗保健利用模式和社会人口统计学特征作为自变量。
受试者:我们研究了在随机选择的日子里连续参加2种初级保健实践的成年人。
措施:用15个项目的自我报告调查表对躯体形式障碍的临时诊断进行了评估。在索引访问之前的12个月中,从我们医院的自动医疗记录中获得了初级保健就诊,专科就诊,心理健康就诊,急诊就诊以及住院和门诊费用的数量,还提供了总的发病率。我们从患者子样本中获得了我们医院系统外部的自我报告利用率。
结果:获得了1440例患者的完整数据。躯体化患者比初级保健就诊者拥有更多的专科护理,门诊患者比住院费用更高,并且急诊就诊者比非躯体化患者更多。包含7个使用率度量标准和4个社会人口统计学特征的回归模型以c统计量= 0.73区分了躯体化和非躯体化的患者。递归分区确定了10个终端节点,它们的特异性很高(99%),但敏感性很低(15%)。
结论:我们确定了7种离散的医疗服务利用模式,分别区分了躯体化和非躯体化的患者。但是,他们这样做只是出于适度的特异性和敏感性。该算法可以有效地用作两步筛查程序中的第一步,该步骤的第二步将需要进行更深入的筛查,或者进行逐项个案审查,以识别初级保健实践中的躯体化患者。

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