Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.
The George Institute for Global Health, UNSW, Sydney, New South Wales, Australia.
PLoS One. 2021 Nov 8;16(11):e0259667. doi: 10.1371/journal.pone.0259667. eCollection 2021.
Correct certification of cause of death by physicians (i.e. completing the medical certificate of cause of death or MCCOD) and correct coding according to International Classification of Diseases (ICD) rules are essential to produce quality mortality statistics to inform health policy. Despite clear guidelines, errors in medical certification are common. This study objectively measures the impact of different medical certification errors upon the selection of the underlying cause of death.
A sample of 1592 error-free MCCODs were selected from the 2017 United States multiple cause of death data. The ten most common types of errors in completing the MCCOD (according to published studies) were individually simulated on the error-free MCCODs. After each simulation, the MCCODs were coded using Iris automated mortality coding software. Chance-corrected concordance (CCC) was used to measure the impact of certification errors on the underlying cause of death. Weights for each error type and Socio-demographic Index (SDI) group (representing different mortality conditions) were calculated from the CCC and categorised (very high, high, medium and low) to describe their effect on cause of death accuracy.
The only very high impact error type was reporting an ill-defined condition as the underlying cause of death. High impact errors were found to be reporting competing causes in Part 1 [of the death certificate] and illegibility, with medium impact errors being reporting underlying cause in Part 2 [of the death certificate], incorrect or absent time intervals and reporting contributory causes in Part 1, and low impact errors comprising multiple causes per line and incorrect sequence. There was only small difference in error importance between SDI groups.
Reporting an ill-defined condition as the underlying cause of death can seriously affect the coding outcome, while other certification errors were mitigated through the correct application of mortality coding rules. Training of physicians in not reporting ill-defined conditions on the MCCOD and mortality coders in correct coding practices and using Iris should be important components of national strategies to improve cause of death data quality.
医生正确认证死因（即填写死因医学证明或 MCCOD）并根据国际疾病分类（ICD）规则正确编码对于生成质量死因统计数据以告知卫生政策至关重要。尽管有明确的准则，但医学认证中的错误仍然很常见。本研究客观地衡量了不同的医学认证错误对选择根本死因的影响。
从 2017 年美国多死因数据中选择了 1592 份无错误的 MCCOD 样本。根据已发表的研究，在无错误的 MCCOD 上分别模拟了完成 MCCOD 时的十种最常见错误类型。在每次模拟后，使用 Iris 自动死亡率编码软件对 MCCOD 进行编码。使用机会校正一致性（CCC）来衡量认证错误对根本死因的影响。根据 CCC 计算了每种错误类型和社会人口指数（SDI）组（代表不同的死亡率条件）的权重，并进行分类（非常高、高、中、低），以描述它们对死因准确性的影响。
唯一的非常高影响错误类型是将不确定的情况报告为根本死因。发现高影响错误是在死亡证明第一部分报告竞争原因和难以辨认，中影响错误是在死亡证明第二部分报告根本原因、不正确或缺少时间间隔以及在第一部分报告促成原因，低影响错误包括每行多个原因和不正确的顺序。SDI 组之间的错误重要性差异很小。
将不确定的情况报告为根本死因可能会严重影响编码结果，而其他认证错误通过正确应用死亡率编码规则得到缓解。在 MCCOD 上不报告不确定情况的医师培训以及死亡率编码员的正确编码实践和使用 Iris 应该是提高死因数据质量的国家战略的重要组成部分。
