Observational intensity bias associated with illness adjustment: cross sectional analysis of insurance claims.
Observational intensity bias associated with illness adjustment: cross sectional analysis of insurance claims.
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DOI：
10.1136/bmj.f549
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发表时间：
2013-02-21
期刊：
影响因子：
--
通讯作者：
Welch HG
中科院分区：
文献类型：
--
作者：
Wennberg JE;Staiger DO;Sharp SM;Gottlieb DJ;Bevan G;McPherson K;Welch HG
目的利用管理数据库中记录的诊断，确定医生在调整疾病后的就诊频率相关偏倚。 设置索赔数据从美国医疗保险计划提供的服务，在2007年之间的306个美国医院转诊地区。设计横截面分析。 参与者2007年居住在美国的医疗保险受益人服务费的20%样本（n=5 153 877）。主要结果测量疾病调整对地区死亡率和使用标准和访问校正疾病方法进行调整的费用率的影响。标准方法根据行政数据库中列出的诊断使用合并症指标进行调整;修改后的方法根据医生就诊频率纠正了这些指标。测量共病的三种惯例是：Charlson共病指数，Iezzoni慢性疾病和分层疾病类别风险评分。 结果与标准指数相比，访视校正的Charlson合并症指数更能解释306个医院转诊地区的年龄、性别和种族死亡率的差异（R2= 0.21v0.11，P<0.001），与性别和种族校正的死亡率相比，降低了区域差异，而使用标准Charlson共病指数校正则增加了区域差异。在就诊率最高和最低的五分之一的医院转诊地区，性别和种族调整的死亡率相似，使用标准指数调整后，最高五分之一的死亡率降低了18%（46.4 vs56.3/1000，P<0.001）。年龄，性别和种族调整后的支出以及访问校正的支出是超过30%以上的最高的五分之一的访问比最低的五分之一，但只有12%的调整后，使用标准指数。使用Iezzoni和分层条件类别惯例测量合并症获得了类似的结果。结论：当使用基于医疗保险管理数据库中记录的诊断数量的共病指标对区域死亡率和支出率进行疾病调整时，医生的就诊率会产生实质性偏差。在不对就诊率的区域差异进行校正的情况下进行调整，往往会使就诊率高的区域似乎死亡率和费用较低，反之亦然。与观察指标相比，访视校正的合并症指标更好地解释了年龄、性别和种族死亡率的变化，并减少了观察强度偏倚。
Objective To determine the bias associated with frequency of visits by physicians in adjusting for illness, using diagnoses recorded in administrative databases. Setting Claims data from the US Medicare program for services provided in 2007 among 306 US hospital referral regions. Design Cross sectional analysis. Participants 20% sample of fee for service Medicare beneficiaries residing in the United States in 2007 (n=5 153 877). Main outcome measures The effect of illness adjustment on regional mortality and spending rates using standard and visit corrected illness methods for adjustment. The standard method adjusts using comorbidity measures based on diagnoses listed in administrative databases; the modified method corrects these measures for the frequency of visits by physicians. Three conventions for measuring comorbidity are used: the Charlson comorbidity index, Iezzoni chronic conditions, and hierarchical condition categories risk scores. Results The visit corrected Charlson comorbidity index explained more of the variation in age, sex, and race mortality across the 306 hospital referral regions than did the standard index (R2=0.21 v 0.11, P<0.001) and, compared with sex and race adjusted mortality, reduced regional variation, whereas adjustment using the standard Charlson comorbidity index increased it. Although visit corrected and age, sex, and race adjusted mortality rates were similar in hospital referral regions with the highest and lowest fifths of visits, adjustment using the standard index resulted in a rate that was 18% lower in the highest fifth (46.4 v 56.3 deaths per 1000, P<0.001). Age, sex, and race adjusted spending as well as visit corrected spending was more than 30% greater in the highest fifth of visits than in the lowest fifth, but only 12% greater after adjustment using the standard index. Similar results were obtained using the Iezzoni and the hierarchical condition categories conventions for measuring comorbidity. Conclusion The rates of visits by physicians introduce substantial bias when regional mortality and spending rates are adjusted for illness using comorbidity measures based on the observed number of diagnoses recorded in Medicare’s administrative database. Adjusting without correction for regional variation in visit rates tends to make regions with high rates of visits seem to have lower mortality and lower costs, and vice versa. Visit corrected comorbidity measures better explain variation in age, sex, and race mortality than observed measures, and reduce observational intensity bias.
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影响因子：
2.8
作者：
Heijink, Richard;Koolman, Xander;Westert, Gert
通讯作者：
Westert, Gert
影响因子：
2.2
作者：
Bevan, Gwyn
通讯作者：
Bevan, Gwyn
影响因子：
105.7
作者：
Schmidt, Morten;Jacobsen, Jacob Bonde;Sorensen, Henrik Toft
通讯作者：
Sorensen, Henrik Toft
影响因子：
9.7
作者：
Baker, Laurence C.;Johnson, Scott J.;Birnbaum, Howard
通讯作者：
Birnbaum, Howard
DOI：
10.1161/circoutcomes.108.825612
发表时间：
2009-11
期刊：
Circulation. Cardiovascular quality and outcomes
影响因子：
--
作者：
Ong MK;Mangione CM;Romano PS;Zhou Q;Auerbach AD;Chun A;Davidson B;Ganiats TG;Greenfield S;Gropper MA;Malik S;Rosenthal JT;Escarce JJ
通讯作者：
Escarce JJ