Characterizing Fit-for-Purpose Real-World Data: An Assessment of a Mother–Infant Linkage in the Japan Medical Data Center Claims Database

IF 3.4 2区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Clinical Epidemiology Pub Date : 2024-01-31 DOI:10.2147/clep.s429246

Julie Barberio, Rohini K Hernandez, Ashley I Naimi, Rachel E Patzer, Christopher Kim, Timothy L Lash

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Abstract

Purpose: Observational postapproval safety studies are needed to inform medication safety during pregnancy. Real-world databases can be valuable for supporting such research, but fitness for regulatory purpose must first be vetted. Here, we demonstrate a fit-for-purpose assessment of the Japan Medical Data Center (JMDC) claims database for pregnancy safety regulatory decision-making.
Patients and Methods: The Duke-Margolis framework considers a database’s fitness for regulatory purpose based on relevancy (capacity to answer the research question based on variable availability and a sufficiently sized, representative population) and quality (ability to validly answer the research question based on data completeness and accuracy). To assess these considerations, we examined descriptive characteristics of infants and pregnancies among females ages 12– 55 years in the JMDC between January 2005 and March 2022.
Results: For relevancy, we determined that critical data fields (maternal medications, infant major congenital malformations, covariates) are available. Family identification codes permitted linkage of 385,295 total mother–infant pairs, 57% of which were continuously enrolled during pregnancy. The prevalence of specific congenital malformation subcategories and maternal medical conditions were representative of the general population, but preterm births were below expectations (3.6% versus 5.6%) in this population. For quality, our methods are expected to accurately identify the complete set of mothers and infants with a shared health insurance plan. However, validity of gestational age information was limited given the high proportion (60%) of missing live birth delivery codes coupled with suppression of infant birth dates and inaccessibility of disease codes with gestational week information.
Conclusion: The JMDC may be well suited for descriptive studies of pregnant people in Japan (eg, comorbidities, medication usage). More work is needed to identify a method to assign pregnancy onset and delivery dates so that in utero medication exposure windows can be defined more precisely as needed for many regulatory postapproval pregnancy safety studies.

Keywords: routine health care data, international databases, database evaluation

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描述现实世界数据的适用性：对日本医疗数据中心索赔数据库中母婴关联的评估

目的：需要进行批准后安全性观察研究，以了解孕期用药的安全性。真实世界的数据库对支持此类研究很有价值，但必须首先审查其是否适合监管目的。在此，我们展示了日本医疗数据中心（JMDC）索赔数据库在妊娠安全监管决策中的适用性评估：Duke-Margolis 框架根据相关性（根据变量可用性和足够规模的代表性人群回答研究问题的能力）和质量（根据数据完整性和准确性有效回答研究问题的能力）来考虑数据库是否适合监管目的。为了评估这些考虑因素，我们研究了 2005 年 1 月至 2022 年 3 月期间 JMDC 中 12-55 岁女性的婴儿和孕妇的描述性特征：就相关性而言，我们确定关键数据字段（产妇用药、婴儿主要先天畸形、协变量）可用。通过家庭识别码可以连接到 385,295 对母婴，其中 57% 的母婴在怀孕期间连续登记。特定先天性畸形亚类和孕产妇病症的发生率在一般人群中具有代表性，但该人群中的早产率低于预期（3.6% 对 5.6%）。就质量而言，我们的方法有望准确识别出共享医疗保险计划的全套母婴。然而，由于活产分娩代码缺失的比例较高（60%），再加上婴儿出生日期被压制以及无法获得带有孕周信息的疾病代码，孕周信息的有效性受到了限制：结论：JMDC 可能非常适合对日本孕妇进行描述性研究（如合并症、用药情况）。还需要做更多的工作来确定分配妊娠开始和分娩日期的方法，以便更精确地定义子宫内药物暴露窗口，这也是许多法规批准后妊娠安全性研究的需要。

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来源期刊

Clinical Epidemiology Medicine-Epidemiology

CiteScore

6.30

自引率

5.10%

发文量

169

审稿时长

16 weeks

期刊介绍： Clinical Epidemiology is an international, peer reviewed, open access journal. Clinical Epidemiology focuses on the application of epidemiological principles and questions relating to patients and clinical care in terms of prevention, diagnosis, prognosis, and treatment. Clinical Epidemiology welcomes papers covering these topics in form of original research and systematic reviews. Clinical Epidemiology has a special interest in international electronic medical patient records and other routine health care data, especially as applied to safety of medical interventions, clinical utility of diagnostic procedures, understanding short- and long-term clinical course of diseases, clinical epidemiological and biostatistical methods, and systematic reviews. When considering submission of a paper utilizing publicly-available data, authors should ensure that such studies add significantly to the body of knowledge and that they use appropriate validated methods for identifying health outcomes. The journal has launched special series describing existing data sources for clinical epidemiology, international health care systems and validation studies of algorithms based on databases and registries.