Composite major congenital malformation (MCM) outcomes are commonly used to assess teratogenic effects of prenatal medication exposure, but this approach dilutes effect estimates when the risk is confined to a specific MCM. Tree-based scan statistics address this by screening outcomes using a hierarchical tree, enabling detection of specific risks without predefined hypotheses. To apply this method across ICD-9-CM and ICD-10-CM eras, we developed a unified hierarchical outcomes tree for MCM. We selected ICD-9-CM and ICD-10-CM codes classified as congenital anomalies, removing minor malformations, chromosomal anomalies, and single-gene conditions. A multilevel tree was built based on the Multi-level Clinical Classification Software, General Equivalence Mappings, and expert review. We validated the tree using birth cohorts from MarketScan and Medicaid databases (2011-2013; 2016-2018), assessing the balance of MCM prevalences within 1 year of birth via standardized mean differences (SMDs). The final tree included 1023 codes, organized into 244 clinical MCM groups at the most granular level. We identified 572 107 (2011-2013) and 360 167 infants (2016-2018) in MarketScan and 362 820 and 3 500 589 infants in Medicaid. All SMDs were below 0.1, indicating consistency across coding eras. This hierarchical MCM tree bridges ICD-9-CM and ICD-10-CM, enabling consistent outcome definitions and enhancing the detection of specific teratogenic risks.

Preventing fetal exposure to teratogenic medications is a public health priority. The Teratogenic Risk Impact Mitigation (TRIM) tool has been developed to support regulatory decisions regarding risk mitigation programs. One of the explicit TRIM criteria requires medication-specific quantification of fetal exposure risk. To develop and compare population-based estimates of fetal exposure risk for medications with known teratogenicity. This retrospective US population-based cohort study used claims data from Medicaid and MarketScan® databases from 01/2014 to 12/2018. Data were analyzed from 12/2023 to 12/2024. We included female patients aged 12–55 years with ≥ 1 teratogenic medication prescription. A validated algorithm identified pregnancies that ended with a live or non-live outcome among medication users. We estimated the fetal exposure rate as the number of pregnancies per at-risk medication exposure time, accounting for teratogenic risk profiles (e.g., ACE inhibitors exert risk in 2nd/3rd trimesters). Rates were averaged across databases to generate tertiles of high/medium/low fetal exposure risk. Among 59,815,213 female enrollees (31,905,714 Medicaid; 27,909,499 MarketScan), 12,819,073 used ≥ 1 teratogenic medication. Fetal exposure rates ranged from 0–1512 per 1000 user-years (tertiles: 2.9 and 11.5), with higher rates observed in Medicaid. Medications that exhibited high rates included fluconazole (1512/1000 user-years, 95

Medical chart review is commonly used in epidemiologic research to evaluate health outcomes, refine measurement algorithms, and identify potential study biases. In pharmacoepidemiologic research, administrative claims and other structured healthcare databases provide longitudinal detail suitable for similar purposes but are typically presented in tabular formats that are cumbersome to interpret. This study introduces PEPRVision, a visualization framework that transforms structured longitudinal healthcare data into intuitive graphics to support patient profile review in pharmacoepidemiology research. PEPRVision was developed based on principles of preattentive visual processing, leveraging visual attributes such as color, spatial position, shape, and length to facilitate rapid pattern recognition. We demonstrated PEPRVision using US administrative claims data through four case studies: (1) evaluating a claims-based algorithm for measurement of congenital hearing loss, (2) enhancing study design decisions by identifying reverse causation in a drug safety study, (3) assessing the face validity of signals of potential teratogenicity detected through data mining for triage for advancement to causal inference studies, and (4) identifying mechanisms of inappropriate prenatal exposure to teratogenic medications to inform improvement of risk mitigation strategies. As a complement to population-based causal inference studies, PEPRVision facilitates review of patient-level variation, enhancing the validity and clinical relevance of pharmacoepidemiologic research.

Preventing fetal exposure to teratogenic medications is an important target for risk mitigation efforts. Decisions about risk mitigation efforts specific to teratogenic medications are complex. The Teratogenic Risk Impact and Mitigation (TRIM) tool was developed as an innovative decision support tool to facilitate prioritization of teratogenic medications for risk mitigation strategies. We employed a modified Delphi study design involving experts across teratology, obstetrics/gynecology, and medication safety. Panelists proposed decision criteria in three focus groups, followed by e-Delphi rounds to reach a consensus on criteria regarding three dimensions: (1) completeness; (2) relevance; and (3) distinctiveness. Aggregated feedback from each round was used to inform revision of the criteria in subsequent rounds. A total of 33 candidate criteria proposed by 32 focus group participants were organized into ten distinct criteria for the Delphi process. Consensus (defined as > 85

Importance Continuation of biologics in patients with an autoimmune condition who become pregnant involves weighing consequences of pregnancy-related changes in disease severity and potential teratogenic effects of medications. Characterization of biologic treatment patterns during pregnancy may provide insight into maternal and fetal risks and benefits. Objective To describe the utilization pattern of biologics in pregnant individuals with autoimmune conditions. Design, Setting, and Participants This cohort study used data from Merative MarketScan Research Databases, which contain administrative claims of commercially insured individuals in the US. Pregnant patients aged 16 to 55 years with an autoimmune condition and biologic use 6 months before conception between January 1, 2011, and December 31, 2022, were included. The data were analyzed between October 15, 2024, and February 28, 2025. Exposure Use of biologics for autoimmune disease after conception. Main Outcomes and Measures The proportion of patients who used biologics for Crohn disease, ulcerative colitis, psoriasis or psoriatic arthritis, rheumatoid arthritis, ankylosing spondylitis, systemic lupus erythematosus, and multiple sclerosis was assessed, and the association between underlying autoimmune disease and use of biologics during pregnancy was measured using multivariable logistic regression. Results A total of 6131 pregnant patients (median [IQR] age, 32 [29-36] years) with an autoimmune condition were included. The most prevalent conditions were Crohn disease (1372 patients [25.6%]) and rheumatoid arthritis (1295 patients [24.1%]). Of all patients, 4393 (71.6%; 95% CI, 70.5%-72.8%) used biologics at least once during pregnancy. Among pregnancies with live birth outcomes, biologic use declined throughout gestation, with 2981 patients (68.6% [95% CI, 67.2%-70.0%]), 2555 patients (58.8% [95% CI, 57.3%-60.3%]), and 2113 patients (48.6% [95% CI, 47.1%-50.1%]) using biologics during the first, second, and third trimesters, respectively, and 3350 patients (77.1% [95% CI, 75.8%-78.3%]) using them post partum. Compared with pregnant patients with rheumatoid arthritis, those with Crohn disease (odds ratio [OR], 7.88 [95% CI, 5.93-10.47]) and ulcerative colitis (OR, 5.35 [95% CI, 3.73-7.66]) were more likely to use biologics, while those with psoriasis or psoriatic arthritis (OR, 0.65 [95% CI, 0.52-0.80]) were less likely. Conclusions and Relevance In this cohort study, a decline in the use of biologics for autoimmune disease was observed during the pregnancy period that rebounded only partially thereafter. Notable variations in use across autoimmune conditions suggest that indication-specific risk-benefit assessments of biologic use are needed.

OBJECTIVES:This study aimed to estimate the incidence of respiratory syncytial virus (RSV) infections in US inpatient and outpatient settings. METHODS:We established national cohorts of privately insured children < 5 years (2011-2019) to estimate annual and seasonal incidences of lower respiratory tract infection (LRTI), RSV-LRTI, and RSV acute respiratory infection (RSV-ARI), stratified by age and high-risk conditions per American Academy of Pediatrics definitions. Sensitivity analyses varied episode definitions and assessed the impact of immunoprophylaxis and RSV under-ascertainment. RESULTS:Among 6,767,107 children, annual RSV-LRTI rates dropped with increasing age in both inpatient (7.9 for age < 1 year to 0.2 for age 4 per 1000 person-years) and outpatient settings (48.3 to 1.6). Most RSV-ARI (~80%-90%) was RSV-LRTI. RSV-LRTI accounted for > half of LRTI hospitalizations among infants (7.9 RSV-LRTI versus 14.7 LRTI) and for ~20% outpatient LRTI (48.3 versus 250.3), but this contribution declined with older age. Outpatient RSV-LRTI was > 5 times inpatient rates. Inpatient RSV-LRTI rates dropped consistently with increasing gestational age (GA) (35.6 for GA < 29 weeks versus 7.6 for term infants), while outpatient rates were similar across GA groups (54.0 versus 51.6). Infants with Down syndrome had the highest RSV-LRTI rates, and any high-risk group had rates >2 times higher than healthy term infants. Across all strata, seasonal rates were > 2 annual rates. Modeling suggested that claims data captured 42% of all RSV episodes. CONCLUSION:This study provides national, population-based estimates of medically attended RSV infections across age groups and high-risk strata. Results allow granular assessments of disease burden to guide recommendations for new RSV prevention strategies.

The COVID-19 pandemic has impacted healthcare utilization and, consequently, real-world data. In this study, we used analytical and data visualization approaches to untangle effects on condition measurement and true shifts in the patient population seeking healthcare. We used Merative MarketScan 2018-2020 commercial claims data to develop 24 monthly cohorts of patients aged ≥18 years with 12 months baseline enrollment and an encounter for diabetes, cancer, hypertension, depression, myocardial infarction, atrial fibrillation, or urinary tract infections as the index condition in a given month. We compared monthly prevalence of each condition in 2020 versus 2019. We then imposed 3-, 6-, and 12-month look-back periods (LBP) to capture comorbidities grouped by Clinical Classifications Software Refined (CCSR) or summarized in the Charleson Comorbidity Index (CCI) and conducted similar 2020 versus 2019 prevalence comparisons. Changes in condition prevalence varied across conditions with strongest declines for cancer in April 2020 (−57.4%) and strongest increases for depression in December 2020 (+11.8%). The mean CCI was higher for most conditions during the spring of 2020, and this difference was accentuated by applying a longer LBP. Similar trends were found regarding the number of CCSR categories. Pandemic-related changes in condition capture were complex, involving both increases and decreases in encounters for specific conditions and in comorbidities, along with variations in comorbidity capture dependent on LBP. We provided a practical approach to untangle these phenomena along with open-source algorithms and visualization tools to assess these changes and inform study design and analysis.