Division of Pharmaceutical Outcomes and Policy, UNC Eshelman School of Pharmacy, CB 7573, 2204 Kerr Hall, University of North Carolina, Chapel Hill, NC 27599-7573, USA.
Res Social Adm Pharm. 2013 May-Jun;9(3):263-75. doi: 10.1016/j.sapharm.2009.07.001. Epub 2009 Sep 18.
Given the importance of pharmacological treatment in mental disorders, it is important to have a thorough understanding of predictors and variations in antipsychotic use.
To provide a description of patient characteristics associated with antipsychotic use and to examine predictors of atypical antipsychotic use among antipsychotic users.
Data were obtained from the 2004 and 2005 Medical Expenditure Panel Survey. Dependent variables were annual, self-reported, atypical and typical antipsychotic use. Independent variables included predisposing, enabling, and need characteristics according to Andersen's Behavioral Model. In addition to descriptive statistics, logistic regression analyses were performed to examine the determinants of antipsychotic use.
Patients aged 65 and older were 0.63 times as likely to use antipsychotics as patients aged 26-45. Poor and near-poor patients were 1.55 and 1.37 times as likely to use antipsychotics as middle- to high-income patients, respectively. The odds of antipsychotic use were 2.95 and 1.99 times for patients with public and prescription insurance coverage, respectively. Patients with a usual source of health care were 1.51 times as likely to use antipsychotics as those without. Compared with typical antipsychotic use, patients aged 25 and younger were 3.88 times as likely to use atypical antipsychotics as patients aged between 26 and 45. Urban residents were 1.87 times as likely as rural residents to use atypical antipsychotics. The odds of antipsychotic and atypical antipsychotic use for the poor mental health population were 8.73 and 3.87 times as patients with good to excellent mental health status.
Predisposing and need factors play important roles in determining the use of antipsychotics. However, among antipsychotic users, the use of atypical versus typical antipsychotics appears to have been influenced primarily by need. These findings should be useful to clinicians and policy makers in directing antipsychotic treatments to patients in need.
鉴于药物治疗在精神障碍中的重要性，深入了解抗精神病药物使用的预测因素和变化非常重要。
描述与抗精神病药物使用相关的患者特征，并研究抗精神病药物使用者中使用非典型抗精神病药物的预测因素。
数据来自 2004 年和 2005 年的医疗支出面板调查。因变量为每年自我报告的典型和非典型抗精神病药物使用情况。自变量包括根据安德森行为模型的倾向因素、促成因素和需要特征。除了描述性统计分析外，还进行了逻辑回归分析，以研究抗精神病药物使用的决定因素。
65 岁及以上的患者使用抗精神病药物的可能性是 26-45 岁患者的 0.63 倍。贫困和接近贫困的患者使用抗精神病药物的可能性分别是中等收入至高收入患者的 1.55 倍和 1.37 倍。有公共和处方保险的患者使用抗精神病药物的可能性分别是没有保险的患者的 2.95 倍和 1.99 倍。有常规医疗来源的患者使用抗精神病药物的可能性是没有常规医疗来源的患者的 1.51 倍。与使用典型抗精神病药物相比，年龄在 25 岁及以下的患者使用非典型抗精神病药物的可能性是 26-45 岁患者的 3.88 倍。城市居民使用非典型抗精神病药物的可能性是农村居民的 1.87 倍。精神健康状况较差的人群使用抗精神病药物和非典型抗精神病药物的可能性分别是精神健康状况良好至极佳的人群的 8.73 倍和 3.87 倍。
倾向因素和需要因素在确定抗精神病药物的使用方面发挥着重要作用。然而，在使用抗精神病药物的患者中，使用非典型抗精神病药物而非典型抗精神病药物的情况似乎主要受需要因素的影响。这些发现对抗精神病药物治疗的临床医生和决策者将抗精神病药物用于有需要的患者具有重要意义。
