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改善骨质疏松症筛查的干预措施:爱荷华研究网络(IRENE)研究。

Interventions to improving osteoporosis screening: an Iowa Research Network (IRENE) study.

作者信息

Levy Barcey T, Hartz Arthur, Woodworth George, Xu Yinghui, Sinift Suzanne

机构信息

Department of Family Medicine, Roy J. and Lucille A. Carver College of Medicine, Iowa City. IA 52246, USA.

出版信息

J Am Board Fam Med. 2009 Jul-Aug;22(4):360-7. doi: 10.3122/jabfm.2009.04.080071.

Abstract

BACKGROUND

Primary care physicians often fail to diagnose low bone density. This pilot study assessed 2 interventions for their effect on bone mineral density testing.

METHODS

Five practices in the Iowa Research Network were randomized: 2 to chart reminder alone (CR), 2 to chart reminder plus mailed patient education (CR+PtEd), and one to usual care. A total of 204 women aged 65 years or older were recruited from within these practices. Bayesian hierarchical analyses were used instead of traditional statistical methods to take advantage of collateral data and to adjust for differences between clinics at baseline.

RESULTS

After the intervention, the rates of completed bone mineral density testing were 45.2% in the CR+PtEd group, 31.4% in the chart remainder only group, and 9.7% in the usual care practice. Bayesian analysis adjusted for patient and clinic characteristics, which made use of collateral data, gave an odds ratio of 5.47 for the effect of CR+PtEd group. The Bayesian P was .029 and the one-sided 95% credible interval for the odds ratio was greater than 1.2. The effect of CR+PtEd was confirmed by sensitivity analyses. Traditional hierarchical analysis adjusted for practice characteristics could not be used to estimate statistical significance because there were not enough clinics to accommodate a model that included all the important covariables.

CONCLUSIONS

Specific chart reminders to physicians combined with mailed patient education substantially increased the levels of bone density testing and could potentially be used to improve osteoporosis screening in primary care. Bayesian hierarchical analysis makes it possible to assess practice-level interventions when few practices are randomized.

摘要

背景

基层医疗医生常常无法诊断出低骨密度。这项初步研究评估了两种干预措施对骨密度检测的影响。

方法

爱荷华研究网络中的五个医疗机构被随机分组:两个仅采用图表提醒(CR),两个采用图表提醒加邮寄患者教育资料(CR + PtEd),一个采用常规护理。从这些医疗机构中总共招募了204名65岁及以上的女性。使用贝叶斯分层分析而非传统统计方法,以利用附带数据并调整基线时各诊所之间的差异。

结果

干预后,CR + PtEd组的骨密度检测完成率为45.2%,仅图表提醒组为31.4%,常规护理组为9.7%。经患者和诊所特征调整的贝叶斯分析利用了附带数据,得出CR + PtEd组效果的优势比为5.47。贝叶斯P值为0.029,优势比的单侧95%可信区间大于1.2。敏感性分析证实了CR + PtEd的效果。针对医疗机构特征进行调整的传统分层分析无法用于估计统计学意义,因为没有足够的诊所来纳入包含所有重要协变量的模型。

结论

向医生提供特定的图表提醒并结合邮寄患者教育资料可大幅提高骨密度检测水平,并有可能用于改善基层医疗中的骨质疏松症筛查。当随机分组的医疗机构较少时,贝叶斯分层分析能够评估医疗机构层面的干预措施。

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