Usher Institute, University of Edinburgh, Bioquarter 9, 9 Little France Road, Edinburgh, Scotland, EH16 4UX.
Asthma UK Centre for Applied Research, Bioquarter 9, 9 Little France Road, Edinburgh, Scotland, EH16 4UX.
BMC Med Res Methodol. 2020 Dec 10;20(1):303. doi: 10.1186/s12874-020-01184-8.
Records of medication prescriptions can be used in conjunction with pharmacy dispensing records to investigate the incidence of adherence, which is defined as observing the treatment plans agreed between a patient and their clinician. Using prescribing records alone fails to identify primary non-adherence; medications not being collected from the dispensary. Using dispensing records alone means that cases of conditions that resolve and/or treatments that are discontinued will be unaccounted for. While using a linked prescribing and dispensing dataset to measure medication non-adherence is optimal, this linkage is not routinely conducted. Furthermore, without a unique common event identifier, linkage between these two datasets is not straightforward.
We undertook a secondary analysis of the Salford Lung Study dataset. A novel probabilistic record linkage methodology was developed matching asthma medication pharmacy dispensing records and primary care prescribing records, using semantic (meaning) and syntactic (structure) harmonization, domain knowledge integration, and natural language feature extraction. Cox survival analysis was conducted to assess factors associated with the time to medication dispensing after the prescription was written. Finally, we used a simplified record linkage algorithm in which only identical records were matched, for a naïve benchmarking to compare against the results of our proposed methodology.
We matched 83% of pharmacy dispensing records to primary care prescribing records. Missing data were prevalent in the dispensing records which were not matched - approximately 60% for both medication strength and quantity. A naïve benchmarking approach, requiring perfect matching, identified one-quarter as many matching prescribing records as our methodology. Factors associated with delay (or failure) to collect the prescribed medication from a pharmacy included season, quantity of medication prescribed, previous dispensing history and class of medication. Our findings indicate that over 30% of prescriptions issued were not collected from a dispensary (primary non-adherence).
We have developed a probabilistic record linkage methodology matching a large percentage of pharmacy dispensing records with primary care prescribing records for asthma medications. This will allow researchers to link datasets in order to extract information about asthma medication non-adherence.
药物处方记录可与药房配药记录结合使用，以调查依从性的发生率，依从性定义为观察患者与临床医生商定的治疗计划。仅使用处方记录无法识别主要的不依从性；药物未从药房领取。仅使用配药记录意味着病情缓解和/或治疗停止的情况将无法被记录。虽然使用链接的处方和配药数据集来衡量药物不依从性是最佳的，但这种链接通常不会进行。此外，如果没有唯一的通用事件标识符，这两个数据集之间的链接就不简单。
我们对索尔福德肺研究数据集进行了二次分析。我们开发了一种新的概率记录链接方法，使用语义（意义）和句法（结构）协调、领域知识集成和自然语言特征提取，将哮喘药物药房配药记录与初级保健处方记录相匹配。我们进行了 Cox 生存分析，以评估与处方开具后药物配药时间相关的因素。最后，我们使用了一种简化的记录链接算法，其中仅匹配完全相同的记录，作为一种简单的基准测试，与我们提出的方法的结果进行比较。
我们将 83%的药房配药记录与初级保健处方记录相匹配。未匹配的配药记录中存在大量缺失数据——大约 60%的药物强度和数量都存在缺失。一种简单的基准测试方法，要求完全匹配，识别出的匹配处方记录数量仅为我们方法的四分之一。与从药房取药延迟（或失败）相关的因素包括季节、开方药物的数量、之前的配药记录和药物类别。我们的研究结果表明，超过 30%的处方未从药房领取（主要不依从）。
我们开发了一种概率记录链接方法，可将大部分药房配药记录与哮喘药物的初级保健处方记录相匹配。这将使研究人员能够链接数据集，以提取关于哮喘药物不依从性的信息。
