Widger Kimberley, Vadeboncoeur Christina, Zelcer Shayna, Liu Ying, Kassam Alisha, Sutradhar Rinku, Rapoport Adam, Nelson Katherine, Wolfe Joanne, Earle Craig, Pole Jason D, Gupta Sumit
1 Paediatric Advanced Care Team, The Hospital for Sick Children , Toronto, Ontario, Canada .
2 Lawrence S. Bloomberg Faculty of Nursing, University of Toronto , Toronto, Ontario, Canada .
J Palliat Med. 2017 Nov;20(11):1210-1216. doi: 10.1089/jpm.2017.0028. Epub 2017 Jun 8.
Population-based research to identify underserviced populations and the impact of palliative care (PC) is limited as the validity of such data to identify PC services is largely unknown.
To determine the validity of using such data to identify the involvement of specialized pediatric PC teams among children with cancer.
Retrospective cohort.
Ontario children with cancer who died between 2000 and 2012, received care through a pediatric institution with a specialized PC team and a clinical PC database.
All patients in the clinical databases were linked to population-based health services administrative databases. Six algorithms were created to indicate the use of formal pediatric PC teams based on the record type (physician billings vs. inpatient records vs. both) and number of eligible codes required (≥1 vs. ≥2). Each was validated against the pediatric PC clinical databases.
The cohort comprised 572 children; 243 were in the clinical databases. Algorithms using only inpatient records had high specificity (80%-95%) but poor sensitivity (21%-56%). Including physician billings increased sensitivity but lowered specificity. The algorithm with overall best performance required ≥2 physician billing or inpatient diagnosis codes indicating PC [sensitivity 0.79 (95% CI 0.73-0.84), specificity 0.58 (95% CI 0.53-0.64)].
Health administrative data identifies involvement of specialized pediatric PC teams with good sensitivity but low specificity. Studies using such data alone to compare patients receiving and not receiving specialized pediatric PC are at significant risk of misclassification and potential bias. Population-based PC databases should be established to conduct rigorous population-based PC research.
基于人群的研究旨在确定服务不足的人群以及姑息治疗(PC)的影响,但由于此类数据用于识别PC服务的有效性很大程度上未知,此类研究受到限制。
确定使用此类数据识别癌症儿童中专业儿科PC团队参与情况的有效性。
回顾性队列研究。
2000年至2012年间在安大略省死亡、通过设有专业PC团队和临床PC数据库的儿科机构接受治疗的癌症儿童。
临床数据库中的所有患者均与基于人群的健康服务管理数据库相链接。创建了六种算法,根据记录类型(医生账单与住院记录与两者皆有)以及所需合格代码数量(≥1与≥2)来表明正式儿科PC团队的使用情况。每种算法均与儿科PC临床数据库进行验证。
该队列包括572名儿童;243名在临床数据库中。仅使用住院记录的算法具有较高的特异性(80%-95%),但敏感性较差(21%-56%)。纳入医生账单提高了敏感性,但降低了特异性。总体表现最佳的算法要求≥2个表明PC的医生账单或住院诊断代码[敏感性0.79(95%CI 0.73-0.84),特异性0.58(95%CI 0.53-0.64)]。
卫生行政数据能以良好的敏感性识别专业儿科PC团队的参与情况,但特异性较低。仅使用此类数据来比较接受和未接受专业儿科PC的患者的研究存在显著的错误分类风险和潜在偏差。应建立基于人群的PC数据库以开展严谨的基于人群的PC研究。