Development and evaluation of an algorithm to link mothers and infants in two US commercial healthcare claims databases for pharmacoepidemiology research

We developed and implemented an algorithm to infer mother-infant links in two large US commercial healthcare databases that exhibited high linkage coverage and similar characteristics across linked vs. non-linked persons. This signifies generalizability of linked mother-infant pairs to commercially insured source populations, which facilitates large-scale research on prenatal exposures and infant outcomes. This constitutes novel research by virtue of our emphasis on linked vs. non-liked characterization comparisons to support generalizability. Similarity of measured characteristics in linked vs. non-linked mother and infant records is supporting evidence that results produced by analyzing linked cohorts will generalize to the underlying source population, in this case commercially insured pregnant people and their infants. Our assessment of average linked-infant follow-up time (Clinformatics®: 855 days, CCAE: 1060 days) allows their inclusion in perinatal-exposure studies where outcomes of interested are not birth outcomes per se but longer-term infant conditions. Further, our linkage algorithm was implemented in the OMOP CDM, and the source code is publicly available. The utility of using standardized analytic routines against a standard data representation allows for transportable, complex algorithms to be implemented in other claims databases formatted to the OMOP CDM with no loss of fidelity [39].

Our algorithm identified > 3.4 million linked mothers and > 4.1 million linked infants. Access to large, linked populations makes feasible the study of a wide range of prescription drug exposures, maternal and neonatal outcomes, and subgroups that are often unavailable in smaller linked populations [40, 41] and registries [18, 42, 43]. This approach requires fewer study resources compared to studies that require primary data collection [44].

Across databases, linked mothers comprised 73.6% of all mothers with live births. In Clinformatics®, 77.3% of mothers were successfully linked to infants, which is lower but comparable to the 84% reported in a recent study using data from the same source with fewer linkage restrictions [19]. Despite similar methods, other linkage studies have reported mixed linkage coverage, suggesting that differences are due to data accuracy and/or availability variation across sources. Palmsten et al. linked Medicaid-enrolled mothers and infants and reported linkage coverage of 55.6% for inpatient deliveries, although with considerable variation by state (0–96%) [23], which the authors attributed to varying family identifier quality and use. A study in TRICARE enrollees in the Military Health System reported 90% of pregnancies ending in live births were linked with infants [24], which may be attributable to lower insurance coverage churn.

In our study, linked infants comprised 49.1% of all infants defined as persons 0 years of age at their observation period start. Contextualizing our linked infant coverage is difficult because most studies only report the proportion of linked pregnancies [19, 23]. However, Garbe et al. conducted a study using the German Pharmacoepidemiological Research Database (GePaRD), a claims database from four statutory health insurance providers, and reported that 77.3% of newborns were linked with mothers [45]. Additionally, a study among Medicaid enrollees in Tennessee reported 97% of infants were linked with a delivery, however such high coverage is likely explained by the use of vital record data with identifying information [41].

While our primary analysis used a ± 60-day window between infant DOB and mothers’ pregnancy episode end to identify candidate links, in sensitivity analyses, we observed high correspondence at 7, 14, and 30 days, including same-day correspondence of 31.3% in CCAE and 67.4% in Clinformatics®. Overall correspondence was greater in Clinformatics®, which may be due to more accurate and specific DOB information. Increasing the correspondence window to 90 days increased the proportion of linked infants by only 1.5% in CCAE and 0.2% in Clinformatics®, which we do not interpret as material because most of the correspondence occurred within ± 30 days.

Characteristic comparisons between linked and non-linked mothers revealed similar demographic, clinical, and healthcare utilization profiles. Our linkage evaluation largely supports the generalizability of the linked mother population, having compared thousands of covariates between linked and non-linked mother cohorts and observing few differences. Of note, two of the differences we found in both CCAE and Optum were also detected in a recent study using the Sentinel network: non-linked mothers were younger and had shorter gestations than linked mothers [46]. It is possible that these consistent differences are due to unmeasured factors associated with mother and infant not sharing the same insurance policy. Despite this, we found that SMDs were < 0.1 for nearly all observed characteristics, suggesting that in a prenatal exposure study on a small, exposed subset of the linked mother population, systematic differences between the study sample and non-linked mothers to whom the results will apply will be minimal.

Despite substantial similarity between linked and non-linked infants, we observed more differences than when comparing mothers. Of note, linked infants had greater total healthcare utilization and prevalence of individual clinical events, including birth and infant-care related claims. Because our algorithm linked mothers to infants by a shared insurance ID within a defined temporal interval, candidate infants whose inferred DOB fell outside of that interval would be non-linked and less likely to have their clinical events captured in the database. This suggests that some billing records may be attributed to family members on other insurance plans among the non-linked populations. Still, this evaluation supports the generalizability of the linked infant population. In cases of multiple pregnancies, linkages are made between maternal records and all live births from that pregnancy. In the event of a multifetal pregnancy ending in both a live birth and a stillbirth, codes associated with the still born infant may be observed on the live born infant record or on that of the linked mother. The occurrence of this scenario is expected to be very rare, with less than 0.5% of multifetal pregnancies experiencing a stillbirth in one study [47].

Despite CCAE and Clinformatics® both consisting of administrative claims data from large US commercial insurance plans, data content heterogeneity between them still exists, which could contribute to results differences between them. In Clinformatics®, we observed more situations where multiple women of child-bearing age were associated with one candidate infant. This suggests that more extended family members may be included on the same insurance plan in Clinformatics® than in CCAE, which would increase the situations where one infant is associated with > 1 candidate mother on the same health plan. Regarding selection bias, by excluding multiple women of child-bearing age on the same insurance plan, we may be selectively decreasing representation of large, varied families covered in the Clinformatics® database.

We note that several recent studies have established mother-infant linkage algorithms in claims databases with similar methods to the one described in this paper [23, 24, 34, 45, 48]. Specifically, linkage algorithms in the Clinformatics® [34] and in the CCAE [48] used infant dates of birth, maternal delivery dates, and family insurance IDs to link delivering mothers with infants. The algorithm used in CCAE captured slightly more links because it did not restrict to live births initially, had a wider correspondence window allowance, and when multiple mothers or pregnancies were associated with a single infant, it selected the earliest whereas our approach excluded those ambiguous links. While other studies have used related methods successfully [23, 24, 34, 45, 48], we show that our standardized approach works across multiple databases. The algorithm presented in this study offers a reproducible framework that can be implemented across different databases, particularly those transformed to the OMOP CDM. Further, we have characterized the populations of linked and unlinked mothers and children, which aids in contextualizing the output of these linkages and implications for their use in future research.

A strength of our study is the rigorous linkage approach utilizing insurance ID in addition to delivery and birth procedure dates in large US claims databases representing the commercially insured population. Further, we provide open-source code and a web-application to interactively review characterization results, which provides valuable context for the external validity of future studies among linked populations. Lastly, developing a reproducible mother-child linkage algorithm in large administrative databases facilitates evidence generation in pregnant populations with improved rigor by avoiding recall, referral, and self-selection biases inherent to registry or other primary data collection studies of prenatal medication use [18].

Using administrative healthcare claims databases in pharmacoepidemiologic research has limitations. Erroneously coded or missing diagnostic, procedure, and drug dispensing records results in misclassification which may under- or over-estimate exposures, covariates, health outcomes, other clinical events, and healthcare utilization [49]. Subsequent information bias that can result from misclassification is underappreciated [50] and could bias findings of future drug safety studies. Further, because the data do not provide exact date of birth information for non-linked infants, estimating event prevalence during 365-days post-birth is imprecise. This may result in misclassification by failing to capture events specifically related to the birth encounter itself. We observed these birth-related conditions and procedures as imbalanced in Fig. 2 and the infants tab of the web application.

Still, developing reliable mother-infant linkages in large healthcare databases has increased the capacity to examine associations between rare prenatal drug exposures and infant outcomes with sufficient power. For example, prenatal use of antidepressants, stimulants, antihypertensive medications, and sulfonamides have been studied in relation to validated congenital anomalies [51‐55]. This has yielded needed real-world evidence on the safety of prenatal exposures.

While we found few differences between linked and non-linked populations suggestive of high internal validity to the underlying commercially insured US population, our results do not necessarily ensure external validity to those covered under other types of insurance or lacking coverage. The data in this study are representative of people with US-based, employer-sponsored health insurance, indicative of a higher socioeconomic status population. Given the established association between wealth and health [56, 57], care should be taken not to assume that linked vs. non-linked similarity we observed is consistent across other socioeconomic demographics. Further, administrative healthcare databases include detailed outpatient drug dispensing records but provide fewer details on inpatient dispensing records, prescriptions, or administrations typically available in electronic medical records. Additionally, we note that pregnancy episode length was slightly shorter in non-linked pregnancies (Table 1), but we do not believe this difference could substantially influence observed linked vs. non-linked maternal covariate differences in the year before birth, which were few. Lastly, our study has not been validated. However validation of a similar algorithm developed in claims data among Medicaid beneficiaries showed high positive predictive value [58].