Langner Ingo, Ohlmeier Christoph, Haug Ulrike, Hense Hans Werner, Czwikla Jonas, Zeeb Hajo
Clinical Epidemiology, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany.
Health Services Research, IGES Institut GmbH, Berlin, Germany.
BMJ Open. 2019 Jul 26;9(7):e026834. doi: 10.1136/bmjopen-2018-026834.
To adapt a Canadian algorithm for the identification of female cases of breast cancer (BC) deaths to German health insurance claims data and to test and validate the algorithm by comparing results with official cause of death (CoD) data on the individual and the population level.
Validation study, secondary data, medical claims.
Claims data of two statutory health insurance providers (SHIs) for inpatient and outpatient care, CoD added via record linkage with epidemiological cancer registry (ECR).All women insured with the two SHIs and who deceased in the period 2006-2013, were residents of North Rhine Westphalia (NRW) and were linked with ECR data: n=22 413.
Based on inpatient and outpatient diagnoses in the year before death, six algorithms were derived and the accordance of the algorithm-based CoD with the official CoD was evaluated calculating specificity, sensitivity, negative and positive predictive values (NPV, PPV). Furthermore, algorithm-based age-specific BC mortality rates covering several calendar years were calculated for the entire insured female population and compared with official national rates.
Our final algorithm, derived from the NRW subsample, comprised codes indicating the presence of BC, metastases, a terminal illness phase and the absence of codes for other tumours. Overall, specificity, sensitivity, NPV and PPV of this algorithm were 97.4%, 91.3%, 98.9% and 81.7%, respectively. In the age range 40-80 years, sensitivity and PPV slightly decreased with increasing age. Algorithm-based age-specific BC mortality rates agreed well with official rates except for the age group 85 years and older.
The algorithm-based identification of BC deaths in German claims data is feasible and valid, except for higher ages. The algorithm to ascertain BC mortality rates in an epidemiological study seems applicable when information on the official CoD is not available in the original database.
将一种用于识别女性乳腺癌（BC）死亡病例的加拿大算法应用于德国医疗保险理赔数据，并通过在个体和人群层面将结果与官方死因（CoD）数据进行比较来测试和验证该算法。
验证研究，二次数据，医疗理赔。
两家法定医疗保险机构（SHIs）的住院和门诊护理理赔数据，通过与癌症流行病学登记处（ECR）的记录链接添加CoD。所有在2006 - 2013年期间参保且去世的、居住在北莱茵 - 威斯特法伦州（NRW）的两家SHIs的女性，与ECR数据相链接：n = 22413。
根据死亡前一年的住院和门诊诊断，推导六种算法，并通过计算特异性、敏感性、阴性和阳性预测值（NPV、PPV）来评估基于算法的CoD与官方CoD的一致性。此外，计算了整个参保女性人群涵盖多个日历年的基于算法的特定年龄BC死亡率，并与官方全国率进行比较。
我们从NRW子样本中得出的最终算法包括表明存在BC、转移、终末期疾病阶段以及不存在其他肿瘤代码的编码。总体而言，该算法的特异性、敏感性、NPV和PPV分别为97.4%、91.3%、98.9%和81.7%。在40 - 80岁年龄范围内，敏感性和PPV随年龄增长略有下降。除85岁及以上年龄组外，基于算法的特定年龄BC死亡率与官方率吻合良好。
在德国理赔数据中基于算法识别BC死亡是可行且有效的，但高龄情况除外。当原始数据库中没有官方CoD信息时，该算法在流行病学研究中确定BC死亡率似乎是适用的。
