Berke Ethan M, Smith Tenbroeck, Song Yunjie, Halpern Michael T, Goodman David C
The Center for Healthcare Research and Reform, The Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth Medical School, Hanover, New Hampshire, USA.
J Palliat Med. 2009 Feb;12(2):128-32. doi: 10.1089/jpm.2008.0239.
End-of-life care is increasingly recognized as an important part of cancer management for many patients. Current methods to measure end-of-life care are limited by difficulties in identifying cancer cohorts with administrative data. We examined several techniques of identifying end-of-life cancer cohorts with claims data that is population-based, geographically scalable, and amenable to routine updating.
Using Medicare claims for patients 65 years of age and older, four techniques for identifying end-of-life cancer cohorts were compared; one based on Part A data using a broad primary or narrow secondary diagnosis of cancer, two based on Part B data, and one combining the Part A and B methods. We tested the performance of each definition to ascertain an appropriate end-of-life cancer population.
The combined Part A and B definition using a primary or secondary diagnosis of cancer within a window of 180 days prior to death appears to be the most accurate and inclusive in ascertaining an end-of-life cohort (78.7% attainment).
Combining inpatient and outpatient claims data, and identifying cases based upon a broad primary or a narrow secondary cancer definition is the most accurate and inclusive in ascertaining an end-of-life cohort.
临终关怀日益被视为许多癌症患者治疗的重要组成部分。目前衡量临终关怀的方法因难以利用行政数据识别癌症队列而受到限制。我们研究了几种利用基于人群、地理上可扩展且便于定期更新的索赔数据来识别临终癌症队列的技术。
利用65岁及以上患者的医疗保险索赔数据,比较了四种识别临终癌症队列的技术;一种基于A部分数据,采用广泛的癌症原发诊断或狭义的继发诊断,两种基于B部分数据,一种结合了A部分和B部分的方法。我们测试了每个定义的性能,以确定合适的临终癌症人群。
在死亡前180天的窗口期内,结合使用癌症原发或继发诊断的A部分和B部分联合定义,在确定临终队列方面似乎是最准确和最具包容性的(达到率为78.7%)。
结合住院和门诊索赔数据,并基于广泛的原发或狭义的继发癌症定义来识别病例,在确定临终队列方面是最准确和最具包容性的。