Overview of global real-world data sources for pediatric pharmacoepidemiologic research.

PURPOSE: Given limited information available on real-world data (RWD) sources with pediatric populations, this study describes features of globally available RWD sources for pediatric pharmacoepidemiologic research. METHODS: An online questionnaire about pediatric RWD sources and their attributes and capabilities was completed by members and affiliates of the International Society for Pharmacoepidemiology and representatives of nominated databases. All responses were verified by database representatives and summarized. RESULTS: Of 93 RWD sources identified, 55 unique pediatric RWD sources were verified, including data from Europe (47%), United States (38%), multiregion (7%), Asia-Pacific (5%), and South America (2%). Most databases had nationwide coverage (82%), contained electronic health/medical records (47%) and/or administrative claims data (42%) and were linkable to other databases (65%). Most (71%) had limited outside access (e.g., by approval or through local collaborators); only 10 (18%) databases were publicly available. Six databases (11%) reported having >20 million pediatric observations. Most (91%) included children of all ages (birth until 18th birthday) and contained outpatient medication data (93%), while half (49%) contained inpatient medication data. Many databases captured vaccine information for children (71%), and one-third had regularly updated data on pediatric height (31%) and weight (33%). Other pediatric data attributes captured include diagnoses and comorbidities (89%), lab results (58%), vital signs (55%), devices (55%), imaging results (42%), narrative patient histories (35%), and genetic/biomarker data (22%). CONCLUSIONS: This study provides an overview with key details about diverse databases that allow researchers to identify fit-for-purpose RWD sources suitable for pediatric pharmacoepidemiologic research.

Geographic Trends in Pediatric Psychotropic Medication Dispensing Before and After the Start of the COVID-19 Pandemic.

OBJECTIVE: This study examined trends and geographic variability in dispensing of prescription psychotropic medications to U.S. youths before and after the start of the COVID-19 pandemic. METHODS: Using national data on prescription medication dispensing, the authors performed a cross-sectional study examining the monthly percent change in psychotropic medications dispensed (total N=95,639,975) to youths (ages 5-18 years) in 2020 versus 2019, across medication classes and geographic regions. RESULTS: For many medications, more were dispensed in March 2020 than in March 2019 and fewer in April-May 2020 versus April-May 2019. Stimulants had the largest decline: -26.4% in May 2020 versus May 2019. The magnitude of the monthly percent change varied by region. CONCLUSIONS: Fewer psychotropic medications were dispensed to U.S. youths after the start of the COVID-19 pandemic compared with 2019. Although some medication classes rebounded to prepandemic dispensing levels by September 2020, dispensing varied by class and region.

Comparison of Rates of Type 2 Diabetes in Adults and Children Treated With Anticonvulsant Mood Stabilizers.

Importance: Anticonvulsant mood stabilizer treatment is associated with an increased risk of weight gain, but little is known about the risk of developing type 2 diabetes (T2D). Objective: To evaluate the comparative safety of anticonvulsant mood stabilizers on risk of T2D in adults and children by emulating a target trial. Design, Setting, and Participants: This observational cohort study used data from IBM MarketScan (2010-2019), with a 5-year follow-up period. The nationwide sample of US commercially insured patients included children (aged 10-19 years) and adults (aged 20-65 years) who initiated anticonvulsant mood stabilizer treatment. Data were analyzed from August 2020 to May 2021. Exposures: Initiation and continuation of carbamazepine, lamotrigine, oxcarbazepine, or valproate. Main Outcomes and Measures: Onset of T2D during follow-up. Weighted pooled logistic regression was used to estimate the association of initiation and continuation of carbamazepine, lamotrigine, oxcarbazepine, or valproate with the risk of developing T2D. Inverse probability weights were used to control for confounding and loss to follow-up by measured baseline and time-varying covariates. Results: The analysis included 274 206 adults (159 428 women [58%]; mean [SD] age, 39.9 [13.2] years) and 74 005 children (38 672 girls [52%]; mean [SD] age, 15.6 [2.6] years) who initiated an anticonvulsant mood stabilizer. In adults, initiation of valproate was associated with an increased risk of developing T2D compared with initiation of lamotrigine (5-year risk difference [RD], 1.17%; 95% CI, 0.66% to 1.76%). The number needed to harm was 87 patients initiating valproate for 1 patient to develop T2D within 5 years compared with initiation of lamotrigine. Point estimates were similar when evaluating the association of treatment continuation (5-year RD, 1.99%; 95% CI, -0.64% to 5.31%). The estimated association was smaller and more variable comparing carbamazepine and oxcarbazepine to lamotrigine. In children, RDs were much smaller and more variable (5-year RD for initiation of oxcarbazepine vs lamotrigine, 0.29%; 95% CI, -0.12% to 0.69%; 5-year RD for initiation of valproate vs lamotrigine, 0.18%; 95% CI, -0.09% to 0.49%). Conclusions and Relevance: In this cohort study, valproate was associated with the highest risk of developing T2D in adults. The comparative safety was generally similar in children, but estimates were small and variable. In the absence of randomized trials, emulating target trials within health care databases can generate the age-specific drug safety data needed to inform treatment decision-making.

Use of Linked Databases for Improved Confounding Control: Considerations for Potential Selection Bias.

Pharmacoepidemiologic studies are increasingly conducted within linked databases, often to obtain richer confounder data. However, the potential for selection bias is frequently overlooked when linked data is available only for a subset of patients. We highlight the importance of accounting for potential selection bias by evaluating the association between antipsychotics and type 2 diabetes in youths within a claims database linked to a smaller laboratory database. We used inverse probability of treatment weights (IPTW) to control for confounding. In analyses restricted to the linked cohorts, we applied inverse probability of selection weights (IPSW) to create a population representative of the full cohort. We used pooled logistic regression weighted by IPTW only or IPTW and IPSW to estimate treatment effects. Metabolic conditions were more prevalent in linked cohorts compared with the full cohort. Within the full cohort, the confounding-adjusted hazard ratio was 2.26 (95% CI: 2.07, 2.49) comparing initiation of antipsychotics with initiation of control medications. Within the linked cohorts, a different magnitude of association was obtained without adjustment for selection, whereas applying IPSW resulted in point estimates similar to the full cohort's (e.g., an adjusted hazard ratio of 1.63 became 2.12). Linked database studies may generate biased estimates without proper adjustment for potential selection bias.

Association of Selective Serotonin Reuptake Inhibitors With the Risk of Type 2 Diabetes in Children and Adolescents.

Importance: Concerns exist that use of selective serotonin reuptake inhibitors (SSRIs) increases the risk of developing type 2 diabetes (T2D) in adults, but evidence in children and adolescents is limited. In the absence of a randomized clinical trial, evidence must be generated using real-world data. Objective: To evaluate the safety of SSRI use in children and adolescents with respect to the associated risk of T2D. Design, Setting, and Participants: This cohort study of patients aged 10 to 19 years with a diagnosis for an SSRI treatment indication was conducted within the nationwide Medicaid Analytic eXtract (MAX; January 1, 2000, to December 31, 2014) and the IBM MarketScan (January 1, 2003, to September 30, 2015) databases. Data were analyzed from November 1, 2018, to December 6, 2019. Exposures: New users of an SSRI medication and comparator groups with no known metabolic adverse effects (no antidepressant exposure, bupropion hydrochloride exposure, or psychotherapy exposure). Within-class individual SSRI medications were compared with fluoxetine hydrochloride. Main Outcomes and Measures: Incident T2D during follow-up. Intention-to-treat effects were estimated using Cox proportional hazards regression models, adjusting for confounding through propensity score stratification. As-treated effects to account for continuous treatment were estimated using inverse probability weighting and marginal structural models. Results: A total of 1 582 914 patients were included in the analysis (58.3% female; mean [SD] age, 15.1 [2.3] years). The SSRI-treated group included 316 178 patients in the MAX database (publicly insured; mean [SD] age, 14.7 [2.1] years; 62.2% female) and 211 460 in the MarketScan database (privately insured; mean [SD] age, 15.8 [2.3] years; 63.9% female) with at least 2 SSRI prescriptions filled, followed up for a mean (SD) of 2.3 (2.0) and 2.2 (1.9) years, respectively. In publicly insured patients, initiation of SSRI treatment was associated with a 13% increased hazard of T2DM (intention-to-treat adjusted hazard ratio [aHR], 1.13; 95% CI, 1.04-1.22) compared with untreated patients. The association strengthened for continuous SSRI treatment (as-treated aHR, 1.33; 95% CI, 1.21-1.47), corresponding to 6.6 (95% CI, 4.2-10.4) additional cases of T2D per 10 000 patients treated for at least 2 years. Adjusted HRs were lower in privately insured patients (intention-to-treat aHR, 1.01 [95% CI, 0.84-1.23]; as-treated aHR, 1.10 [95% CI, 0.88-1.36]). Findings were similar when comparing SSRI treatment with psychotherapy (publicly insured as-treated aHR, 1.44 [95% CI, 1.25-1.65]; privately insured as-treated aHR, 1.21 [95% CI, 0.93-1.57]), whereas no increased risk was observed compared with bupropion treatment publicly insured as-treated aHR, 1.01 [95% CI, 0.79-1.29]; privately insured as-treated aHR, 0.87 [95% CI, 0.44-1.70]). For the within-class analysis, no medication had an increased hazard of T2D compared with fluoxetine. Conclusions and Relevance: These findings suggest that children and adolescents initiating SSRI treatment may be at a small increased risk of developing T2D, particularly publicly insured patients. The magnitude of association was more modest than previously reported, and the absolute risk was small. The potential small risk should be viewed in relation to the efficacy of SSRIs for its major indications in young patients.

Development and Validation of a Pediatric Comorbidity Index.

Comorbidity scores are widely used to help address confounding bias in nonrandomized studies conducted within health-care databases, but existing scores were developed to predict all-cause mortality in adults and might not be appropriate for use in pediatric studies. We developed and validated a pediatric comorbidity index, using health-care utilization data from the tenth revision of the International Classification of Diseases. Within the MarketScan database of US commercial claims data, pediatric patients (aged ≤18 years) continuously enrolled between October 1, 2015, and September 30, 2017, were identified. Logistic regression was used to predict the 1-year risk of hospitalization based on 27 predefined conditions and empirically identified conditions derived from the most prevalent diagnoses among patients with the outcome. A single numerical index was created by assigning weights to each condition based on its ß coefficient. We conducted internal validation of the index and compared its performance with existing adult scores. The pediatric comorbidity index consisted of 24 conditions and achieved a C statistic of 0.718 (95% confidence interval (CI): 0.714, 0.723). The index outperformed existing adult scores in a pediatric population (C statistics ranging from 0.522 to 0.640). The pediatric comorbidity index provides a summary measure of disease burden and can be used for risk adjustment in epidemiologic studies of pediatric patients.

Antidiabetic medication use in commercially insured children and adolescents in the United States from 2004 to 2019.

AIM: To describe the patterns of non-insulin antidiabetic medication use, initiation and adherence in the paediatric population. METHODS: We conducted a descriptive study of non-insulin antidiabetic medication use in children and adolescents (aged 10-18 years) using real-world data from a nationwide US commercial claims database (January 2004-September 2019). Trends in the prevalence of non-insulin antidiabetic medication use overall and by class were evaluated. Among new users of non-insulin antidiabetic agents, medication adherence was examined using group-based trajectory models. RESULTS: In a cohort of more than 1 million paediatric patients, the prevalence of any non-insulin antidiabetic medication use was 75.7 per 100 000 patients in 2004 and more than doubled to 162.0 per 100 000 in 2019. Biguanides (metformin) was by far the most widely used medication class. The use of newer classes was low (<10 per 100 000), but there was an uptake in the use of glucagon-like peptide-1 receptor agonists after liraglutide received paediatric approval in 2019. Medication adherence was poor during the 18 months after treatment initiation: 79.6% of initiators experienced an early treatment interruption (median time to interruption: 90 days among metformin monotherapy initiators) and 21% of initiators did not return for a prescription refill after the first month. CONCLUSIONS: There was a substantial increase in non-insulin antidiabetic medication use among commercially insured paediatric patients from 2004 to 2019. Nearly all patients were treated with metformin, while the use of newer agents remained low. Despite the increase in medication use, short treatment episodes were observed, even among patients with a diagnosis of type 2 diabetes, raising concern over poor adherence.

Predicting overdose among individuals prescribed opioids using routinely collected healthcare utilization data.

INTRODUCTION: With increasing rates of opioid overdoses in the US, a surveillance tool to identify high-risk patients may help facilitate early intervention. OBJECTIVE: To develop an algorithm to predict overdose using routinely-collected healthcare databases. METHODS: Within a US commercial claims database (2011-2015), patients with ≥1 opioid prescription were identified. Patients were randomly allocated into the training (50%), validation (25%), or test set (25%). For each month of follow-up, pooled logistic regression was used to predict the odds of incident overdose in the next month based on patient history from the preceding 3-6 months (time-updated), using elastic net for variable selection. As secondary analyses, we explored whether using simpler models (few predictors, baseline only) or different analytic methods (random forest, traditional regression) influenced performance. RESULTS: We identified 5,293,880 individuals prescribed opioids; 2,682 patients (0.05%) had an overdose during follow-up (mean: 17.1 months). On average, patients who overdosed were younger and had more diagnoses and prescriptions. The elastic net model achieved good performance (c-statistic 0.887, 95% CI 0.872-0.902; sensitivity 80.2, specificity 80.1, PPV 0.21, NPV 99.9 at optimal cutpoint). It outperformed simpler models based on few predictors (c-statistic 0.825, 95% CI 0.808-0.843) and baseline predictors only (c-statistic 0.806, 95% CI 0.787-0.26). Different analytic techniques did not substantially influence performance. In the final algorithm based on elastic net, the strongest predictors were age 18-25 years (OR: 2.21), prior suicide attempt (OR: 3.68), opioid dependence (OR: 3.14). CONCLUSIONS: We demonstrate that sophisticated algorithms using healthcare databases can be predictive of overdose, creating opportunities for active monitoring and early intervention.

Assessing the impact of the new ICD-10-CM coding system on pharmacoepidemiologic studies-An application to the known association between angiotensin-converting enzyme inhibitors and angioedema.

PURPOSE: To replicate the well-established association between angiotensin-converting enzyme inhibitors versus beta blockers and angioedema in the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) era. METHODS: We conducted a retrospective, inception cohort study in a large insurance database formatted to the Sentinel Common Data Model. We defined study periods spanning the ICD-9-CM era only, ICD-10-CM era only, and ICD-9-CM and ICD-10-CM era and conducted simple-forward mapping (SFM), simple-backward mapping (SBM), and forward-backward mapping (FBM) referencing the General Equivalence Mappings to translate the outcome (angioedema) and covariates from ICD-9-CM to ICD-10-CM. We performed propensity score (PS)-matched and PS-stratified Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: In the ICD-9-CM and ICD-10-CM eras spanning April 1 to September 30 of 2015 and 2016, there were 152 017 and 145 232 angiotensin-converting enzyme inhibitor initiators and 115 073 and 116 652 beta-blocker initiators, respectively. The PS-matched HR was 4.19 (95% CI, 2.82-6.23) in the ICD-9-CM era, 4.37 (2.92-6.52) in the ICD-10-CM era using SFM, and 4.64 (3.05-7.07) in the ICD-10-CM era using SBM and FBM. The PS-matched HRs from the mixed ICD-9-CM and ICD-10-CM eras ranged from 3.91 (2.69-5.68) to 4.35 (3.33-5.70). CONCLUSION: The adjusted HRs across different diagnostic coding eras and the use of SFM versus SBM and FBM produced numerically different but clinically similar results. Additional investigations as ICD-10-CM data accumulate are warranted.

Adaptation and Validation of the Combined Comorbidity Score for ICD-10-CM.

BACKGROUND: The combined comorbidity score, which merges the Charlson and Elixhauser comorbidity indices, uses the ninth revision of the International Classification of Diseases, Clinical Modification (ICD-9-CM). In October 2015, the United States adopted the 10th revision (ICD-10-CM). OBJECTIVE: The objective of this study is to examine different coding algorithms for the ICD-10-CM combined comorbidity score and compare their performance to the original ICD-9-CM score. METHODS: Four ICD-10-CM coding algorithms were defined: 2 using General Equivalence Mappings (GEMs), one based on ICD-10-CA (Canadian modification) codes for Charlson and Elixhauser measures, and one including codes from all 3 algorithms. We used claims data from the Clinfomatics Data Mart to identify 2 cohorts. The ICD-10-CM cohort comprised patients who had a hospitalization between January 1, 2016 and March 1, 2016. The ICD-9-CM cohort comprised patients who had a hospitalization between January 1, 2015 and March 1, 2015. We used logistic regression models to predict 30-day hospital readmission for the original score in the ICD-9-CM cohort and for each ICD-10-CM algorithm in the ICD-10-CM cohort. RESULTS: Distributions of each version of the score were similar. The algorithm based on ICD-10-CA codes [c-statistic, 0.646; 95% confidence interval (CI), 0.640-0.653] had the most similar discrimination for readmission to the ICD-9-CM version (c, 0.646; 95% CI, 0.639-0.653), but combining all identified ICD-10-CM codes had the highest c-statistic (c, 0.651; 95% CI, 0.644-0.657). CONCLUSIONS: We propose an ICD-10-CM version of the combined comorbidity score that includes codes identified by ICD-10-CA and GEMs. Compared with the original score, it has similar performance in predicting readmission in a population of United States commercially insured individuals.

Zidovudine use in pregnancy and congenital malformations.

OBJECTIVE: There is inconsistent evidence that zidovudine use during pregnancy increases overall, cardiac, and male genital malformations. DESIGN: We conducted a systematic review and meta-analysis of zidovudine use and malformations and, using Bayesian methods, combined it with data from a cohort study of mother-infant pairs in the nationwide Medicaid Analytic eXtract (MAX). METHODS: Using MAX data (2000-2010), we identified pregnant women with HIV treated with antiretroviral therapy (ART). Women with at least one zidovudine dispensing during the first trimester were compared to women receiving ART without zidovudine in the first trimester. Malformation outcomes were defined using diagnosis/procedure codes. To adjust for confounding, we performed 1 : 1 propensity score matching. Bayesian methods require specification of a prior, which we developed in the meta-analysis. Logistic regression models combined MAX data with the prior, estimating odds ratios (ORs) and 95% credible intervals. RESULTS: Fourteen articles contributed information on overall malformations, seven on cardiac malformations, and five on male genital malformations. In MAX, matching led to a sample of 735 women each in the zidovudine and comparator groups. When comparing first trimester zidovudine use to other ART, the Bayesian procedure yielded OR estimates slightly above the null for overall [OR = 1.11, 95% credible interval (0.80-1.55)] and cardiac [OR = 1.30 (0.63-2.71)] malformations. There were no zidovudine-exposed cases of male genital malformations in MAX, but the meta-analysis yielded elevated OR estimates [OR = 2.57 (1.26-5.24)]. CONCLUSION: For most malformations, first-trimester zidovudine was not associated with increased risk. The potential increase in male genital malformations was small in absolute terms, and should be evaluated further.