PDF(Pubmed)
Abstract：
Patients with mental and physical health conditions are complex to treat and often use multiple medications. It is unclear how adherence to one medication predicts adherence to others. A predictive relationship could permit less expensive adherence monitoring if overall adherence could be predicted through tracking a single medication.
To test this hypothesis, we examined whether patients with multiple mental and physical illnesses have similar adherence trajectories across medications. Specifically, we conducted a retrospective cohort analysis using health insurance claims data for enrollees who were diagnosed with a serious mental illness, initiated an atypical antipsychotic, as well as an SSRI (to treat serious mental illness), biguanides (to treat type 2 diabetes), or an ACE inhibitor (to treat hypertension). Using group-based trajectory modeling, we estimated adherence patterns based on monthly estimates of the proportion of days covered with each medication. We measured the predictive value of the atypical antipsychotic trajectories to adherence predictions based on patient characteristics and assessed their relative strength with the R-squared goodness of fit metric.
Within our sample of 431,591 patients, four trajectory groups were observed: non-adherent, gradual discontinuation, stop-start, and adherent. The accuracy of atypical antipsychotic adherence for predicting adherence to ACE inhibitors, biguanides, and SSRIs was 44.5, 44.5, and 49.6%, respectively (all p < 0.001 vs. random). We also found that information on patient adherence patterns to atypical antipsychotics was a better predictor of patient adherence to these three medications than would be the case using patient demographic and clinical characteristics alone.
Among patients with multiple chronic mental and physical illnesses, patterns of atypical antipsychotic adherence were useful predictors of adherence patterns to a patient\'s adherence to ACE inhibitors, biguanides, and SSRIs.
Otsuka Pharmaceutical Development & Commercialization, Inc.
To test this hypothesis, we examined whether patients with multiple mental and physical illnesses have similar adherence trajectories across medications. Specifically, we conducted a retrospective cohort analysis using health insurance claims data for enrollees who were diagnosed with a serious mental illness, initiated an atypical antipsychotic, as well as an SSRI (to treat serious mental illness), biguanides (to treat type 2 diabetes), or an ACE inhibitor (to treat hypertension). Using group-based trajectory modeling, we estimated adherence patterns based on monthly estimates of the proportion of days covered with each medication. We measured the predictive value of the atypical antipsychotic trajectories to adherence predictions based on patient characteristics and assessed their relative strength with the R-squared goodness of fit metric.
Within our sample of 431,591 patients, four trajectory groups were observed: non-adherent, gradual discontinuation, stop-start, and adherent. The accuracy of atypical antipsychotic adherence for predicting adherence to ACE inhibitors, biguanides, and SSRIs was 44.5, 44.5, and 49.6%, respectively (all p < 0.001 vs. random). We also found that information on patient adherence patterns to atypical antipsychotics was a better predictor of patient adherence to these three medications than would be the case using patient demographic and clinical characteristics alone.
Among patients with multiple chronic mental and physical illnesses, patterns of atypical antipsychotic adherence were useful predictors of adherence patterns to a patient\'s adherence to ACE inhibitors, biguanides, and SSRIs.
Otsuka Pharmaceutical Development & Commercialization, Inc.
摘要：
患有精神和身体健康状况的患者治疗复杂，经常使用多种药物。目前尚不清楚坚持一种药物如何预测坚持其他药物。如果可以通过跟踪单一药物来预测总体依从性，则预测关系可以允许较便宜的依从性监测。
为了检验这一假设，我们检查了患有多种精神和身体疾病的患者在不同药物治疗中是否有相似的依从性轨迹.具体来说,我们使用被诊断患有严重精神疾病的参保人的健康保险索赔数据进行了回顾性队列分析，启动了一种非典型的抗精神病药物，以及SSRI（治疗严重精神疾病），双胍（治疗2型糖尿病），或ACE抑制剂（治疗高血压）。使用基于组的轨迹建模，我们根据每月估计的每种药物覆盖天数的比例估算了依从性模式.我们根据患者特征测量了非典型抗精神病药轨迹对依从性预测的预测值，并用R平方拟合优度度量评估了它们的相对强度。
在我们的431,591名患者样本中,观察到四个轨迹组：非粘附，逐渐停止,停止-启动,和坚持。非典型抗精神病药物依从性预测ACE抑制剂依从性的准确性，双胍,SSRIs分别为44.5、44.5和49.6%，分别(所有p<0.001与random).我们还发现，与仅使用患者人口统计学和临床特征的情况相比，有关患者对非典型抗精神病药的依从性模式的信息更好地预测了患者对这三种药物的依从性。
在患有多种慢性精神和身体疾病的患者中，非典型抗精神病药物依从性的模式是患者对ACE抑制剂依从性的有效预测指标，双胍,和SSRIs。
Otsuka制药开发与商业化，Inc.
为了检验这一假设，我们检查了患有多种精神和身体疾病的患者在不同药物治疗中是否有相似的依从性轨迹.具体来说,我们使用被诊断患有严重精神疾病的参保人的健康保险索赔数据进行了回顾性队列分析，启动了一种非典型的抗精神病药物，以及SSRI（治疗严重精神疾病），双胍（治疗2型糖尿病），或ACE抑制剂（治疗高血压）。使用基于组的轨迹建模，我们根据每月估计的每种药物覆盖天数的比例估算了依从性模式.我们根据患者特征测量了非典型抗精神病药轨迹对依从性预测的预测值，并用R平方拟合优度度量评估了它们的相对强度。
在我们的431,591名患者样本中,观察到四个轨迹组：非粘附，逐渐停止,停止-启动,和坚持。非典型抗精神病药物依从性预测ACE抑制剂依从性的准确性，双胍,SSRIs分别为44.5、44.5和49.6%，分别(所有p<0.001与random).我们还发现，与仅使用患者人口统计学和临床特征的情况相比，有关患者对非典型抗精神病药的依从性模式的信息更好地预测了患者对这三种药物的依从性。
在患有多种慢性精神和身体疾病的患者中，非典型抗精神病药物依从性的模式是患者对ACE抑制剂依从性的有效预测指标，双胍,和SSRIs。
Otsuka制药开发与商业化，Inc.
