A new method to evaluate the completeness of case ascertainment by a cancer registry
A new method to evaluate the completeness of case ascertainment by a cancer registry
复制标题
DOI：
10.1007/s10552-008-9114-0
复制
发表时间：
2008-06-01
影响因子：
2.3
通讯作者：
Pickle, Linda W.
中科院分区：
文献类型：
--
作者：
Das, Barnali;Clegg, Limin X.;Pickle, Linda W.
背景对癌症的流行病学研究以及随后减少人群癌症负担的决策依赖于可用数据的质量。数据越可靠，我们就越有信心相信，所做的决定将在人群中产生预期的效果。北美中央癌症登记协会(NAACCR)对基于人群的癌症登记进行认证，以确保数据质量的一致性。癌症病例确证的完整性指数是对登记质量的一个重要评估。NAACCR目前计算这一指数时假设癌症发病率与癌症死亡率的比率在癌症地点、性别和种族组内的地理区域是恒定的。NAACCR没有将这一指数的变异性纳入认证过程。方法我们提出了一种改进的计算该指数的方法，该方法基于美国国家癌症研究所开发的统计模型，该模型使用人口统计学和生活方式数据预测预期发病率。结果我们根据所有现有的登记处数据，使用发病率模型预测每个登记区的新发病例数。然后，我们调整用于报告延迟和数据更正的特定于注册表的预期数字。拟议的完整性指数是每个登记处的观测数字与调整后的预测数字之比。我们计算了新指数的方差，并提出了一种简单的方法将这种变异纳入认证过程。结论更好的建模减少了具有不切实际的高完备性指数的注册数量。我们通过将可变性纳入认证过程，提供了关于登记处业绩的更全面情况。
Background Epidemiologic research into cancer and subsequent decision making to reduce the cancer burden in the population are dependent on the quality of available data. The more reliable the data, the more confident we can be that the decisions made would have the desired effect in the population. The North American Association of Central Cancer Registries (NAACCR) certifies population-based cancer registries, ensuring uniformity of data quality. An important assessment of registry quality is provided by the index of completeness of cancer case ascertainment. NAACCR currently computes this index assuming that the ratio of cancer incidence rates to cancer mortality rates is constant across geographic areas within cancer site, gender, and race groups. NAACCR does not incorporate the variability of this index into the certification process.Methods We propose an improved method for calculating this index based on a statistical model developed at the National Cancer Institute to predict expected incidence using demographic and lifestyle data. We calculate the variance of our index using statistical approximation.Results We use the incidence model to predict the number of new incident cases in each registry area, based on all available registry data. Then we adjust the registry-specific expected numbers for reporting delay and data corrections. The proposed completeness index is the ratio of the observed number to the adjusted prediction for each registry. We calculate the variance of the new index and propose a simple method of incorporating this variability into the certification process.Conclusions Better modeling reduces the number of registries with unrealistically high completeness indices. We provide a fuller picture of registry performance by incorporating variability into the certification process.