Abstract
Introduction
Ensuring medication safety during pregnancy is crucial for protecting maternal and fetal health. However, fragmented data sources and the lack of comprehensive databases present substantial barriers to effective pharmacovigilance. The Japan Drug Information Institute in Pregnancy (JDIIP) database, which contains data on drug treatment counseling for pregnant women, is expected to help address the lack of comprehensive databases for pregnancy pharmacovigilance (PregPV).
Objective
We evaluated the quality and utility of the JDIIP database for PregPV activities, particularly its ability to consolidate and utilize drug-exposure data among pregnant women in Japan.
Methods
To assess the quality and utility of the JDIIP database for PregPV, we examined its alignment with 48 core data elements (CDEs) considered critical for PregPV, as recently proposed by a European Union consortium through the ConcePTION Project. We performed a detailed mapping of each CDE definition—including maternal lifestyle factors, drug exposure, and pregnancy outcomes—against the corresponding data elements captured in the JDIIP database.
Results
The JDIIP database either directly collected or could derive 38 of the 48 specific items (79%) recommended by the ConcePTION Project. At the category level, the JDIIP database aligned closely with the CDE requirements for database management details, pregnancy details, maternal medical history, pregnancy medication exposure, live/stillborn birth outcomes, and malformation details, achieving coverage of over 80% of the necessary variables in each category. Some categories, such as maternal medical conditions arising during pregnancy and infant complications within the first year of life, showed less alignment, with coverage rates below 50%. Although the JDIIP database provides comprehensive coverage of critical pharmacovigilance elements, data collection for specific variables and categories that better align with the CDE framework can be enhanced to improve alignment with the CDE framework and strengthen pharmacovigilance capabilities.
Conclusions
Our findings highlight the potential of the JDIIP database as a valuable resource for advancing PregPV research. Although the collection of certain maternal and infant data elements could be improved, the substantial alignment of the database with established CDEs positions it as a promising tool for advancing PregPV initiatives in Japan.
Key Points
| The Japan Drug Information Institute in Pregnancy consultation database is essential for monitoring drug safety during pregnancy. |
| The database includes critical details regarding maternal characteristics, medication exposure, and pregnancy outcomes. |
| With the information available in the database, researchers and healthcare professionals can gain insights into drug safety profiles, explore potential risk factors, and contribute to improving the quality of care for pregnant women and their infants. |
Introduction
The use of drug treatments during pregnancy is common, with more than 70% of pregnant women using at least one medication, including supplements [1, 2]. Pharmacotherapy is essential for managing preexisting or pregnancy-related conditions, making it crucial to understand the risks and benefits of medication use during pregnancy. In particular, the importance of pregnancy pharmacovigilance (PregPV) was tragically underscored by the thalidomide disaster of the late 1950s and early 1960s [3]. Despite advancements since then, the lack of information regarding the risks of drug therapy during pregnancy affects both clinicians and pregnant women. Although reproductive toxicology assessments are part of new drug development, animal studies often have limited value due to species differences [4]. For example, aspirin induces cardiac malformations in some animal species but not in humans [5]. Ethical concerns further complicate conducting clinical trials to determine the effects of drugs on fetuses and newborns, thereby limiting direct human evidence [6, 7]. Consequently, post-marketing data collection is vital for PregPV [8].
The US Food and Drug Administration (FDA) has established a guideline for post-approval pregnancy safety studies and good pharmacovigilance practices in the USA, which emphasizes the importance of post-marketing surveillance and pregnancy registries [9]. This guideline highlights the importance of monitoring and evaluating drug safety in pregnant women through comprehensive data collection systems. Additionally, adverse event reporting systems play a crucial role in identifying potential safety issues related to drugs used during pregnancy, although they face challenges regarding data completeness [10–12]. In Europe, the European Medicines Agency’s guidelines and initiatives— such as the European Network of Teratology Information Services, the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance, and the ConcePTION Project—support harmonized data collection and risk assessment [13–16]. These initiatives aim to establish a more coordinated approach to PregPV by standardizing data elements and methodologies.
However, Japan lacks specific guidelines for collecting information on pregnant women and instead relies on a system of spontaneous reporting by healthcare professionals or pharmaceutical companies, which is insufficient for comprehensive data collection [17]. Although health insurance claims databases are increasingly used in Japan for pregnancy-related pharmacoepidemiologic research, they often lack clinical details on gestational age, pregnancy outcomes, and congenital anomalies—information essential for PregPV [18–20]. Therefore, complementary resources that capture more granular clinical information are needed to supplement claims-based analyses. The Japan Drug Information Institute in Pregnancy (JDIIP) was established in 2005 under the Ministry of Health, Labour and Welfare program to address these challenges. The JDIIP provides consultation services to pregnant women regarding medication use and collects detailed information in a comprehensive database. This database provides an opportunity to analyze real-world evidence and understand the impact of medications on pregnancy outcomes [21–23]. Unlike existing spontaneous reporting systems, which suffer from severe underreporting and often lack essential information, the JDIIP database comprehensively captures consultation cases, including those without adverse pregnancy outcomes.
Despite its potential, no studies to date have evaluated the quality and utility of the JDIIP database for PregPV. Therefore, in this study, we aimed to address this gap by conducting a comprehensive assessment of the quality and utility of the JDIIP database to guide improvements in PregPV practices in Japan. Additionally, we explored the strengths and limitations of the JDIIP database to inform clinical and regulatory decision-making.
Methods
Data Source
This study used data from the JDIIP database at the National Center for Child Health and Development, which was established in October 2005 under the Ministry of Health, Labour and Welfare program. At the time of this report, the JDIIP program collaborated with approximately 60 perinatal centers distributed across all 47 prefectures of Japan. These facilities primarily consist of regional perinatal centers and referral hospitals, forming a nationwide consultation and data collection network. This study received ethical approval from the ethics committee of the National Center for Child Health and Development under ethical review number 2020-005. This study strictly adhered to the principles outlined in the Declaration of Helsinki. Participants provided written informed consent for their childbirth outcomes to be tracked. Pregnant women who consented to participate in the study, had known pregnancy outcomes, and had taken at least one drug or supplement during pregnancy were included in the analysis.
Pregnant women typically learn about the JDIIP service through referrals from healthcare providers (e.g., obstetricians or pharmacists), informational leaflets distributed at maternity clinics, or internet searches. Consultations are initiated by the women themselves, who call the JDIIP office to schedule an outpatient appointment. During the consultation, information is provided primarily on the safety of medications, including prescription drugs, over-the-counter medicines, supplements, and vaccines. Broader health-related concerns may also be discussed depending on the case, but the JDIIP does not replace clinical services or provide diagnoses.
Individuals who wished to schedule a counseling appointment were required to complete a predefined questionnaire and return it by mail. The predefined questionnaire included questions on age, drug use during pregnancy, current illnesses, medical history, alcohol consumption, smoking, and folic acid intake. Approximately 1 month after their expected delivery date, participants who provided informed consent received a postcard questionnaire about pregnancy outcomes and the results of their infant’s 1-month checkup. In cases where pregnancy outcomes were unclear, doctors contacted the women’s attending obstetricians by telephone to confirm details of the baby’s outcomes. The survey concluded that information on pregnancy outcomes was obtained. An administrative summary of the consultation case database is presented in Table 1. According to a previous report [24], the median gestational age at the time of consultation was 10 weeks, indicating that most consultations occurred during the first trimester. Additional details have been reported previously [25].
Table 1.
Information on data source
| Characteristic | Details |
|---|---|
| Institution | The National Center for Child Health and Development |
| Name | The Japan Drug Information Institute in Pregnancy (JDIIP) consultation case database |
| Governance | Public |
| Website (English version) | https://www.ncchd.go.jp/en/center/activity/JDIIP/ |
| The initial role of the study | Clinical research |
| Beginning of data collection | 2005 |
| End of data collection | Ongoing |
| Primary reporter | Both pregnant women and non-pregnant women who wish to receive consultation |
| Case enrollment | Upon signature of informed consent by a pregnant woman |
| Follow-up time | Pregnant woman: at enrollment and end of pregnancy. Infant: at 1-month checkup. |
| Type of data | Prospective cases |
| Exposure type | All exposures |
Evaluation of Data Characteristics from the PregPV Perspective
To evaluate the effectiveness of the JDIIP database for PregPV, we conducted a thorough review of the data entries by aligning them with the core data elements (CDEs) for primary data collection in PregPV, as recommended by the ConcePTION project [26]. The ConcePTION project, launched by the Innovative Medicines Initiative, aims to develop a comprehensive framework for monitoring and assessing medication safety during pregnancy. Our analysis used the most up-to-date CDE standards from the European Network of Teratology Information Services website (March 27, 2024). We focused on 48 CDEs classified as “Essential to collect when studying pregnancy and infant outcomes” according to the ConcePTION CDE framework.
Based on a previous study, we adopted a classification system to map the data elements in the JDIIP database to ConcePTION CDEs for PregPVs [27]. Each CDE was categorized into one of the following four categories, reflecting its level of alignment with the CDE criteria.
Directly obtained: This category includes CDEs for which the JDIIP database provides complete and direct information that aligns with the ConcePTION CDE criteria, without requiring interpretation or modification. These CDEs reflected a high degree of compatibility with the ConcePTION CDEs and indicated that the JDIIP database could directly support comprehensive risk assessments of these elements.
Derived: This category includes CDEs not directly provided by the JDIIP database but could be inferred or calculated from available data. This indicates that, although the JDIIP database does not explicitly contain these elements in the same manner, it holds enough information to derive them, primarily to support risk assessment needs.
Divergent: This category includes CDEs for which the JDIIP database provides information, but the data diverge significantly from the criteria due to differences in definitions, scope, or detail levels. These discrepancies may limit the direct applicability of the data for risk assessment as defined by the ConcePTION CDE criteria.
Not available: This category includes CDEs for which the JDIIP database provides no information, indicating a gap in the ability of the database to support risk assessments related to these elements. This lack of data highlights areas where the JDIIP database may require enhancement to fully meet the comprehensive PregPV requirements.
Statistical Analysis
We conducted a descriptive analysis of the JDIIP database to assess the quality of the captured information. The following variables were analyzed: age at consultation (years), mother’s height (cm), mother’s weight (kg), body mass index (kg/m2), pregnancy planning status (planned or unplanned), pregnancy history, folic acid and alcohol use, smoking status, past or present medical history, pregnancy medication exposure details, pregnancy outcomes, and delivery details. Continuous variables are reported as medians and interquartile ranges, and categorical variables are presented as frequencies and percentages. All statistical analyses were performed using R (version 4.0.3; The R Foundation for Statistical Computing, Vienna, Austria).
Results
Evaluation of Data Characteristics from the PregPV Perspective
To understand the capability of the JDIIP database for PregPV, we assessed its alignment with 48 specific items recommended by the CDE framework. Among these, 38 (79%) were either directly collected in the database or derived from multiple existing variables within the database (Table 2). At the category level, the database management details, pregnancy details, maternal medical history, pregnancy medication exposure, live/stillborn birth outcomes, and malformation details were closely aligned with the CDE requirements, with more than 80% of the necessary variables successfully covered in each category.
Table 2.
Alignment of Japan Drug Information Institute in Pregnancy (JDIIP) data with the core data elements (CDEs) of the ConcePTION project
| CDE items | Covering status (%)a | JDIIP database details |
|---|---|---|
| Database management details | ||
| Pregnancy exposure | 5/5 (100) | 1. Directly obtained |
| Primary reporter type | 1. Directly obtained | |
| Primary reporter contact details | 1. Directly obtained | |
| Initial report date | 1. Directly obtained | |
| Prospective status | 1. Directly obtained | |
| Maternal details | ||
| Maternal date of birth | 2/3 (67) | 1. Directly obtained |
| Maternal age at last menstrual period | 2. Derived | |
| Maternal BMI pre-pregnancy | 3. Divergent | |
| Pregnancy details | ||
| Date of LMP | 4/5 (80) | 1. Directly obtained |
| EDD | 1. Directly obtained | |
| Source of directly reported EDD | 1. Directly obtained | |
| Plurality | 1. Directly obtained | |
| Prenatal test(s) | 4. Not available | |
| Maternal medical history details | ||
| Maternal pre-pregnancy medical conditions (history) | 1/1 (100) | 1. Directly obtained |
| Pregnancy medication exposure details | ||
| Drug name(s) | 10/11 (91) | 1. Directly obtained |
| Drug start date | 1. Directly obtained | |
| Drug stop date | 1. Directly obtained | |
| Drug indication(s) | 4. Not available | |
| Peri-LMP exposure | 1. Directly obtained | |
| Trimester 1 exposure | 1. Directly obtained | |
| Trimester 2 exposure | 1. Directly obtained | |
| Trimester 3 exposure | 1. Directly obtained | |
| Route of exposure | 1. Directly obtained | |
| Dose per use | 1. Directly obtained | |
| Frequency of use | 1. Directly obtained | |
| Maternal illness and obstetric complication details | ||
| Maternal medical conditions arising in pregnancy | 0/2 (0) | 3. Divergent |
| Maternal death | 4. Not available | |
| Pregnancy outcome details | ||
| Pregnancy outcome collection status | 7/10 (70) | 1. Directly obtained |
| Date of end of pregnancy | 1. Directly obtained | |
| Gestational age at the end of pregnancy | 1. Directly obtained | |
| Induced termination | 1. Directly obtained | |
| Ectopic pregnancy | 4. Not available | |
| Stillbirth | 1. Directly obtained | |
| Spontaneous abortion | 1. Directly obtained | |
| Molar pregnancy | 3. Divergent | |
| Blighted ovum | 4. Not available | |
| Live birth | 1. Directly obtained | |
| Live/stillborn birth outcome details | ||
| Gestational timing of live/stillborn offspring | 6/6 (100) | 1. Directly obtained |
| Infant birth weight | 1. Directly obtained | |
| Infant sex | 1. Directly obtained | |
| Infant head circumference | 1. Directly obtained | |
| Small for gestational age at delivery | 2. Derived | |
| Large for gestational age at delivery | 2. Derived | |
| Live-born neonatal/infant outcome details | ||
| Complications in the first year of life | 0/2 (0) | 4. Not available |
| Postnatal death of live-born infant | 3. Divergent | |
| Malformation details | ||
| Congenital anomaly | 3/3 (100) | 1. Directly obtained |
| Details of all congenital anomaly(ies) | 1. Directly obtained | |
| Infant malformation case classification | 1. Directly obtained | |
aProportion of “1. Directly obtained” OR “2. Derived” (%)
BMI body mass index, EDD expected date of delivery, LMP last menstrual period.
In contrast, a small subset of variables did not align adequately with the previously proposed CDE framework due to differing definitions (4 of 48 items; 8%) or because they were not collected (6 of 48 items; 13%).
Some categories, such as maternal medical conditions arising during pregnancy and infant complications within the first year of life, were less aligned with the CDE framework, with coverage rates falling below 50%. Regarding maternal medical conditions that arose during pregnancy, data were initially collected from patients at the time of consultation. However, subsequent changes in these conditions often went unrecorded unless patients returned to the consultation office on their initiative. Pregnancy outcomes were primarily surveyed using postcards sent approximately 1 month after the expected delivery date. Consequently, this method primarily captured postnatal fatalities of live-born infants identified at the 1-month checkup and may have omitted other significant outcomes.
Evaluation of Data Quality of the JDIIP database
Building on the initial assessment of the alignment of the JDIIP database with the ConcePTION CDE framework, we conducted a descriptive analysis to evaluate the quality of information captured within the database. The process of selecting the data for this analysis is illustrated in Fig. 1. Among the 12,971 pregnant women who contacted the JDIIP between October 2005 and December 2017, a total of 1333 (10.3%) did not provide informed consent for follow-up or research. Although no formal comparative analysis was conducted, internal assessments did not identify any clear differences in maternal age or medication category between the consenting and non-consenting groups. A total of 7291 women provided written informed consent at the time of consultation. Among these, 5840 had confirmed pregnancy outcomes and were included in this analysis, resulting in a follow-up completion rate of 80%.
Fig. 1.
Data processing flowchart
Figure 1 illustrates the data processing steps of the JDIIP consultation database and describes the filtering steps used to exclude incomplete or unconfirmed cases, resulting in the final dataset used for the study analysis. “Insufficient information” refers to cases in which a response to the follow-up postcard survey was received but the pregnancy outcome could not be determined. This includes responses in which the outcome section was left blank or the information provided was ambiguous or incomplete, making it insufficient to classify the pregnancy outcome. Because the primary analysis required confirmed outcomes, these cases were excluded to avoid potential misclassification
As shown in Table 3, the median age of the women was 32 years. Necessary CDE items, including maternal height, weight, body mass index, pregnancy and childbirth history, lifestyle factors, and past medical history, were recorded. Over half of the pregnancies were unplanned (3544/5840; 60.7%). Lifestyle factors, including alcohol consumption and smoking, were successfully identified in over 99% of the pregnant women. We also confirmed that detailed records of the quantity and frequency of smoking and alcohol consumption were obtained and analyzed as necessary. At the time of consultation, the most common history or complication was psychiatric disorders, including depression (1917/5840; 32.8%).
Table 3.
Descriptive data summary of Japan Drug Information Institute in Pregnancy (JDIIP) database
| Characteristics | Pregnant women (n = 5840) |
|---|---|
| Age at consultation, years | 32.0 (29.0–35.0) |
| Mother’s height, cm | 158.0 (155.0–162.0) |
| Mother’s weight, kg | 50.3 (47.0–56.0) |
| Body mass index, kg/m2 | 20.1 (18.7–22.0) |
| Planning of this pregnancy | |
| Planned pregnancy | |
| Spontaneous | 1710 (29.2) |
| Fertility treatment | 567 (9.7) |
| Unplanned pregnancy | |
| Wish to continue the pregnancy | 3092 (52.9) |
| Did not wish to become pregnant | 452 (7.7) |
| Unknown | 19 (0.3) |
| Pregnancy history | |
| Prior pregnancy | |
| None | 2606 (44.6) |
| Present | 3230 (55.3) |
| Unknown | 4 (0.1) |
| Prior delivery | |
| None | 3359 (57.5) |
| Present | 2478 (42.4) |
| Unknown | 3 (0.1) |
| Prior elective abortion | |
| None | 5011 (85.8) |
| Present | 829 (14.2) |
| Unknown | 0 (0) |
| Alcohol use | |
| No | 2852 (48.8) |
| Yes | |
| Until pregnancy was known | 2884 (49.4) |
| Even after becoming pregnant | 102 (1.7) |
| Unknown | 2 (0.0) |
| Smoking | |
| No | 4594 (78.7) |
| Yes | |
| Until pregnancy was known | 994 (17.0) |
| Even after becoming pregnant | 251 (4.3) |
| Unknown | 1 (0.0) |
| Folic acid use | |
| No | 3179 (54.4) |
| Yes | 2606 (44.6) |
| When to start folic acid | |
| Before conception | 885 (34.0) |
| After pregnancy was known | 1710 (65.6) |
| Unknown | 11 (0.4) |
| Unknown | 55 (0.9) |
| Past or present medical history | |
| Malignant tumor disease | 70 (1.2) |
| Cardiovascular disease | 107 (1.8) |
| Hematological disease | 50 (0.9) |
| Nervous system disease | 86 (1.5) |
| Psychiatric disorder | 1917 (32.8) |
| Diabetes | 89 (1.5) |
| Thyroid disease | 224 (3.9) |
| Hypertension | 112 (1.9) |
| Renal disease | 132 (2.3) |
| Epilepsy | 166 (2.8) |
Data are presented as median (interquartile range) or n (%) unless otherwise indicated
Additionally, data collection on folic acid supplementation, which is not mandated but is strongly recommended in the CDE framework, was nearly comprehensive. Specifically, data revealed that 2606 patients (44.6%) commenced folic acid supplementation at their initial consultation. Among these, 34% had started taking folic acid supplements before pregnancy, suggesting proactive health measures.
Regarding pregnancy medication exposure details, the median number of drugs used per woman was 5.7 (interquartile range 2.0–8.0). The most frequently used drugs belonged to the nervous system (N), alimentary tract and metabolism (A), and respiratory system (R) categories, according to the Anatomical Therapeutic Chemical (ATC) classification system (Fig. 2). At the third level of the ATC system, the most frequently reported drug categories based on the number of unique patients were anxiolytics (N05B, n = 1471), drugs for peptic ulcer and gastroesophageal reflux disease (A02B, n = 1252), antidepressants (N06A, n = 1161), and nonsteroidal anti-inflammatory and antirheumatic products (M01A, n = 977). Over-the-counter drugs, such as nonsteroidal anti-inflammatory drugs, cold remedies, influenza vaccines, and herbal medicines, were commonly used among medications without ATC codes.
Fig. 2.
Number of unique patients for drug classes ATC Anatomical Therapeutic Chemical
Figure 2 shows the number and percentage of unique patients for each drug class, based on the ATC classification system. Each patient was counted only once per ATC major category, even if they had been prescribed multiple drugs within the same class. The x-axis represents the number of unique patients who used drugs within each class during pregnancy, and the y-axis represents each drug class.
Among 5840 confirmed pregnancy outcome cases, 5793 singleton cases were analyzed for pregnancy outcomes and delivery details (Table 4). Among these, 5186 cases resulted in live births, and their delivery details were further analyzed. Pregnancy outcomes included live births in most cases, followed by miscarriages, elective abortions, and stillbirths. Details on malformations were also gathered, including pediatrician observations during the 1-month postnatal checkup. Approximately 2.9% (151/5186 singleton live births) of all the examined cases exhibited malformations. Major malformations were documented in 105 patients, accounting for 2.0% of all cases. Within this group, ventricular septal defects were the most common, identified in 28 patients (25% of major malformations). Other major malformations included patent foramen ovale (nine cases), hydrocele testis (six cases), patent ductus arteriosus (five cases), cleft lip and palate (five cases), and atrial septal defects (five cases).
Table 4.
Pregnancy outcomes in singleton cases in Japan Drug Information Institute in Pregnancy (JDIIP) database
| Outcome | Singleton (n = 5793) | Singleton live birth (n = 5186) |
|---|---|---|
| Pregnancy outcome | ||
| Live birth | 5186 (89.5) | |
| Stillbirth | 23 (0.4) | |
| Miscarriage | 431 (7.4) | |
| Elective abortions | 153 (2.6) | |
| Sex of the child | ||
| Male | 2628 (50.7) | |
| Female | 2547 (49.1) | |
| Unknown | 11 (0.2) | |
| Gestational age at birth, weeks) | 39.2 (38.3–40.1) | |
| Preterm birth, < 37 weeks | 372 (7.2) | |
| Child weight, g | 2966 (2684–3240) | |
| Child height, cm | 49.0 (47.2–50.0) | |
| Child head, cm | 33.0 (32.0–34.0) | |
| Child chest, cm | 31.8 (30.5–33.0) | |
| Any malformations | 151 (2.9) | |
| Major malformations | 105 (2.0) |
Data are presented as median (interquartile range) or n (%) unless otherwise indicated.
Discussion
This study demonstrated the quality and utility of the JDIIP database for PregPV. Our assessment revealed that a substantial proportion of the ConcePTION CDEs in the framework established by the ConcePTION Project were either directly collected or derivable from existing variables within the JDIIP database. This strong alignment with established standards underscored the quality and comprehensiveness of the data captured during the consultation process.
The JDIIP database showed an 80% follow-up rate for pregnancy outcomes, which is noteworthy for PregPV. A previous questionnaire survey of pharmaceutical companies in Japan revealed that they struggled with data collection on drug exposure during pregnancy, with 60% of companies reporting that they could collect data for less than 25% of all followed-up cases [28]. This indicates that, under the current voluntary reporting system for adverse events in Japan, obtaining comprehensive data on pregnant women is challenging. Additionally, cases without abnormal outcomes are often underreported in voluntary reporting systems [10, 29]. However, the JDIIP database uniquely facilitates data collection for all consultation cases with confirmed drug exposure, including those without abnormal outcomes, thus enabling a comprehensive examination of patient characteristics and real-world drug use patterns.
The JDIIP database was highly effective in capturing details related to database management, pregnancy specificity, maternal medical history, medication exposure during pregnancy, live or stillborn birth outcomes, and malformations (Table 2). These categories exhibited over 80% coverage of the necessary variables, highlighting the strength of the database in capturing critical information relevant to PregPV. However, certain areas, such as maternal medical conditions arising during pregnancy and infant complications within the first year of life, showed less alignment with the CDE framework. This discrepancy can be attributed to the nature of data collection, which primarily relied on spontaneous return visits by patients and postcard surveys conducted approximately 1 month after the expected delivery date. Consequently, dynamic changes in maternal conditions or long-term infant outcomes may have been underreported. To address these data gaps, enhancements to the JDIIP data collection are being considered. For example, data on infant outcomes up to 1 year of age have recently been added to the follow-up protocol, and this will help capture infant complications within the first year of life. Furthermore, integrating the JDIIP database with hospital electronic medical records could supplement currently missing clinical details (such as prenatal test results, specific drug indications, maternal death, or ectopic pregnancy) that are not collected in the current consultation-based system. In the future, linkage with structured public health data sources related to maternal and child health could also be explored to improve the completeness of long-term follow-up, although such integration is not yet in place. For data elements where the JDIIP provides information that differs from standard definitions (the “divergent” CDE category), efforts to harmonize these variables with internationally accepted definitions (e.g., the ConcePTION core data set) would enhance the comparability and utility of the data.
Despite these limitations, our descriptive analysis demonstrated the ability of the database to provide valuable insights into real-world scenarios. Key maternal characteristics, including age, pregnancy planning, medical history, and lifestyle factors, are well documented in the JDIIP database. Furthermore, this database offers comprehensive details on medication exposure during pregnancy, enabling the identification of frequently used drug classes and the timing of exposure. Notably, some variables with high clinical relevance, such as gestational age at the end of pregnancy and congenital abnormalities, were successfully collected from the database (Table 4). This is noteworthy because these data are not available in ICH E2B(R3), the standardized reporting format for spontaneous adverse event reporting systems [27], and so the JDIIP database potentially enables a more sophisticated risk assessment. The comprehensive nature of the database could represent the basis for facilitating risk evaluation by stakeholders, including marketing authorization holders and regulatory authorities, as well as primary reporters for pregnancy exposure reports.
Nonetheless, it is important to acknowledge the inherent limitations of the JDIIP database. First, as a consultation-based database, the JDIIP database may be subject to selection bias, because only women who seek consultation are included. Women who do not use any medications during pregnancy generally have less incentive to seek consultation; even if they do, their pregnancy outcomes are not followed up in the current system. As a result, the JDIIP cohort does not include an internal control group of unexposed pregnancies. Including follow-up data from pregnant women who did not use medications could serve as a valuable internal control group, thereby improving the validity of comparative risk assessments. However, expanding the follow-up system to include non-medication users would require additional procedural steps, including obtaining informed consent and securing ethical approval. Moreover, substantial logistical and human resources would be required to support follow-up of patients who are not part of the follow-up system. These considerations highlight the logistical and ethical challenges that must be addressed before expanding the follow-up system. It is also noteworthy that approximately one-third of participants in the JDIIP database were reported to have psychiatric disorders, including depressive and anxiety disorders. This relatively high prevalence may partly reflect our broad definition of psychiatric disorders, which encompasses a wide spectrum of conditions, and partly the characteristics of women more likely to seek consultation—particularly those undergoing psychotropic treatment and concerned about medication use during pregnancy. Some of these women may not have had severe underlying conditions, but rather sought reassurance due to heightened anxiety or uncertainty. In such cases, access to individualized consultation may have contributed to reduced anxiety and the successful continuation of the pregnancy, ultimately leading to healthy birth outcomes. This supportive role of the consultation service should be recognized as part of its broader contribution to PregPV. Nonetheless, such overrepresentation should be considered when interpreting findings related to psychiatric conditions or psychotropic drug exposure. Second, the follow-up period is relatively short, primarily focusing on pregnancy outcomes and early infancy, which limits the ability to assess long-term effects on child development. Third, the database may not capture all relevant confounding factors, such as genetic factors that could influence pregnancy outcomes, potentially limiting the ability to establish causal relationships between drug exposures and observed outcomes. Fourth, due to the absence of a nationwide registry linking pregnancy and birth outcomes in Japan, it is not currently possible to validate JDIIP data at the individual level using patient or birth records. To evaluate the representativeness of the JDIIP population in terms of key maternal and pregnancy characteristics, we compared selected JDIIP variables with recent national statistics. The median maternal age at childbirth in the JDIIP cohort was 32.0 years, comparable to the national average of 32.2 years in 2022, steadily rising from 31.9 years in 2018 [30]. The preterm birth rate (< 37 weeks) among singleton live births in the JDIIP dataset was 7.2%, which aligns with national estimates of approximately 5–6%. These findings suggest that the JDIIP cohort demonstrates reasonable epidemiological representativeness for use in PregPV research. Finally, since no patient residence data (e.g., region or urban/rural status) are collected, we could not directly evaluate regional representativeness. Nonetheless, the JDIIP consultation network spans all 47 prefectures of Japan, likely capturing a geographically diverse sample and thereby reducing geographic selection bias.
To fully leverage the JDIIP database in assessing the effects of drug exposure on pregnant women and fetal outcomes, further accumulation of cases is needed. Specifically, the relatively small sample size might make it challenging to explore rare events adequately. Additionally, the data acquisition timing was skewed toward when consultations occurred, primarily during early pregnancy, potentially affecting the representation of various pregnancy stages, and later pregnancy exposures or outcomes. Gestational age at the time of consultation is an important contextual factor that affects the scope and reliability of data captured. As previously reported [24], the median gestational age at the time of consultation was 10 weeks. This indicates that most consultations occurred during the first trimester. Accordingly, drug exposure data during early pregnancy are well represented, whereas exposures or complications arising later in pregnancy may be underreported, particularly among those who did not return for follow-up. Recent system improvements have enabled the collection of updated information on medication changes during mid to late pregnancy to mitigate this limitation. Although restricting analyses to women who sought consultation after week 12 might reduce uncertainty regarding first-trimester exposures in some cases, such an approach may also introduce greater recall bias due to the longer time lapse between exposure and reporting. Moreover, it would limit the generalizability of the findings in early pregnancy safety studies, which are often the primary focus of PregPV. Therefore, we retained all cases in the present analysis and acknowledged this limitation. Future studies may benefit from sensitivity analyses stratified by gestational age at consultation.
Another important consideration in evaluating the completeness of pregnancy outcome data is the observed miscarriage rate. The miscarriage rate observed in the JDIIP database (7.4%) appears lower than the commonly reported rate of approximately 10–15% in clinically recognized pregnancies internationally [31]. In Japan, a recent analysis of the Japan Environment and Children’s Study cohort indicated that 15.3% of pregnant women had a history of miscarriage or stillbirth [32]. This discrepancy likely reflects the timing and structure of data capture in the JDIIP system. The median gestational age at the time of consultation is 10 weeks [24], whereas most miscarriages occur in the early first trimester, particularly before 10 weeks of gestation [33]. As such, early pregnancy losses may not be captured if consultation has not yet occurred. In addition, women may be more likely to seek consultation when the pregnancy is ongoing, leading to underrepresentation of early losses. These factors likely contribute to the lower miscarriage rate observed in this cohort. These limitations underscore the need to interpret the results carefully and to recognize the inherent constraints of the data. However, collecting all necessary information within a single database system poses a challenge. Therefore, obtaining the required data through appropriate collaboration with multiple sources should be considered [34]. From a collaborative perspective, the current database, which primarily relies on voluntary patient consultations, may not include information on drugs with high data evaluation demands, such as newly marketed drugs or those associated with certain safety concerns. Promoting awareness and recognition of the database registration system through collaboration with healthcare professionals and academic societies is essential for addressing this issue. Considering the potential use of the database for PregPV, pharmaceutical companies, and industry groups could also help promote awareness of these data within the medical community. From a regulatory perspective, authorities could consider conducting safety evaluations and implementing measures, such as revising package inserts using data from the JDIIP database. For pharmaceutical companies, effective use of the JDIIP database may help overcome data collection challenges through an adverse event spontaneous reporting system, which could be integrated with routine pharmacovigilance activities or serve as a partial alternative approach, particularly in response to specific safety concerns. Sustainable data collection strategies to achieve larger sample sizes, diverse data collection timeframes, and extended observation periods may help mitigate the current limitations of PregPV and provide a more comprehensive understanding of the effects of drug exposure in pregnant women and their offspring.
This study had some limitations. First, the latest version of the CDE was used to ensure the objectivity of the database’s value assessment. However, the CDE is scheduled for continuous review, and parts of the initial version included in this study may differ from the latest online version. Second, data utility in the JDIIP database was evaluated based on the presence of relevant items and whether the data format was analyzable. The actual analyzability, including the adequacy of the data volume, depends on the specific analysis objectives.
Conclusion
The JDIIP consultation database is invaluable for monitoring drug safety during pregnancy, as it provides a rich collection of real-world data that closely adheres to established standards. The strengths of the database lie in its ability to capture critical details regarding maternal characteristics, medication exposure, and pregnancy outcomes. By leveraging this comprehensive data source, researchers and healthcare professionals can gain insights into drug safety profiles, explore potential risk factors, and ultimately help improve the quality of care for pregnant women and their offspring. Continued efforts to enhance data collection and integrate it with other data sources will further solidify the role of this database as a powerful tool for advancing pharmacovigilance and promoting safer medication use during pregnancy.
Acknowledgements
The authors thank all the pregnant women who voluntarily reported their cases to the JDIIP consultation database. Their participation and willingness to share valuable information enabled this pharmacovigilance study. We thank Editage for the English language editing.
Declarations
Funding
This research was supported by a grant from the Institute of Statistical Mathematics.
Conflicts of Interest
Shinichi Matsuda is employed by Chugai Pharmaceutical Co., Ltd. Manabu Akazawa has received consulting fees and honoraria from Astellas Pharma Inc., Janssen Pharmaceutical K.K., Shionogi & Co., Ltd, GSK, and Mitsubishi Tanabe Pharma Corporation. Mihoko Ota was employed by Takeda Pharmaceutical Co., Ltd before the submission of this work. Hiroaki Oka is employed by Shionogi & Co., Ltd. Naoki Nitani is employed by CMIC HealthCare Institute Co., Ltd. Naho Yakuwa, Mikako Goto, Kunihiko Takahashi, Tatsuhiko Anzai, Sachi Koinuma, Izumi Fujioka, Yoriko Miura, Tomiko Tawaragi, and Atsuko Murashima have no relevant financial or non-financial interests to disclose.
Ethics Approval
This study received ethical approval from the ethics committee of the National Center for Child Health and Development under ethical review number 2020–005.
Consent to Participate
Written informed consent was obtained from all individual participants involved in the study.
Consent for Publication
Not applicable.
Availability of Data and Material
The data supporting this study’s findings are not openly available due to sensitivity reasons but are available from the corresponding author upon reasonable request.
Code Availability
Not applicable.
Author Contributions
All authors contributed to the study conceptualization and design. Naho Yakuwa, Mikako Goto, Sachi Koinuma, Izumi Fujioka, Yoriko Miura, and Atsuko Murashima contributed to the data acquisition. Shinichi Matsuda, Naho Yakuwa, Mikako Goto, Manabu Akazawa, Kunihiko Takahashi, Tatsuhiko Anzai, and Tomiko Tawaragi performed the data analysis. All authors contributed to the data interpretation, drafting, and revision of the manuscript. All authors have read and approved the final version of the manuscript.
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