Pharmacoepidemiological Research Research Articles

Assessing agreement between population-level administrative pharmaceutical databases and patient-reported medication dispensation in cardiac rehabilitation patients

BackgroundPharmacoepidemiology has emerged as a crucial field in evaluating the use and effects of medications in large populations to ensure their safe and effective use. This study aimed to assess the agreement of cardiac medication use between a provincial medication database, the Pharmaceutical Information Network (PIN), and reconciled medication data from confirmation through patient interviews for patients referred to cardiac rehabilitation. MethodsThe study included data from patients referred to the TotalCardiology Rehabilitation CR program, and medication data was available in both TotalCardiology Rehabilitation charts and PIN. The accuracy of medication data obtained from patient interviews was compared to that obtained from PIN with proportions and kappa statistics to evaluate the reliability of PIN data in assessing medication use. ResultsPatient-reported usage was higher for statins (41.6 %) vs. 38.4 %), ACE/ARB, beta-blockers (75.7 %) vs. 73.7 %), DOAC (3.5 %) vs. 2.6 %), and ADP-receptor antagonists (71.0 %) vs. 68.1 %) than if PIN was used. Patient-reported usage data was lower for Ezetimibe (4.7 vs. 4.8 %), Aldosterone antagonists (5.4 %) vs. 5.5 %), digoxin (0.9 %) vs. 1.0 %), calcium channel blockers (19.2 vs. 19.9 %) and warfarin (7.2 %) vs. 8.1 %). The results indicated that the differences between the two sources were very small, with an average agreement of 95.3 % and a kappa of 0.70. ConclusionThe studyʼs results, which show a high level of agreement between PIN and patient self-reporting, affirm the reliability of PIN data as a source for obtaining an accurate assessment of medication use. This finding is crucial in the context of pharmacoepidemiology research, where the accuracy of data is paramount. Further research to explore the complementary use of both data sources will be valuable.

Identifying Dementia Severity Among People Living With Dementia Using Administrative Claims Data

ObjectivesThere is currently no reliable tool for classifying dementia severity level based on administrative claims data. We aimed to develop a claims-based model to identify patients with severe dementia among a cohort of patients with dementia. DesignRetrospective cohort study. Setting and ParticipantsWe identified people living with dementia (PLWD) in US Medicare claims data linked with the Minimum Data Set (MDS) and Outcome and Assessment Information Set (OASIS). MethodsSevere dementia was defined based on cognitive and functional status data available in the MDS and OASIS. The dataset was randomly divided into training (70%) and validation (30%) sets, and a logistic regression model was developed to predict severe dementia using baseline (assessed in the prior year) features selected by generalized linear mixed models (GLMMs) with least absolute shrinkage and selection operator (LASSO) regression. We assessed model performance by area under the receiver operating characteristic curve (AUROC), area under precision-recall curve (AUPRC), and precision and recall at various cutoff points, including Youden Index. We compared the model performance with and without using Synthetic Minority Oversampling Technique (SMOTE) to reduce the imbalance of the dataset. ResultsOur study cohort included 254,410 PLWD with 17,907 (7.0%) classified as having severe dementia. The AUROC of our primary model, without SMOTE, was 0.81 in the training and 0.80 in the validation set. In the validation set at the optimized Youden Index, the model had a sensitivity of 0.77 and specificity of 0.70. Using a SMOTE-balanced validation set, the model had an AUROC of 0.83, AUPRC of 0.80, sensitivity of 0.79, specificity of 0.74, positive predictive value of 0.75, and negative predictive value of 0.78 when at the optimized Youden Index. Conclusions and ImplicationsOur claims-based algorithm to identify patients living with severe dementia can be useful for claims-based pharmacoepidemiologic and health services research.

Risk of agranulocytosis with metamizole in comparison with alternative medications based on health records in Spain.

We aimed to estimate the absolute (incidence) and relative (hazard ratio; HR) risk of agranulocytosis associated with metamizole in comparison with non-steroidal antiinflammatory drugs (NSAIDs). A cohort study of new users of metamizole versus NSAIDs was performed with BIFAP (Pharmacoepidemiologic Research Database in Public Health Systems; Spain). Patients aged ≥ 2years in 2005-2022 were followed up from the day after their first metamizole or NSAID dispensation till the end of the treatment period to identify patients hospitalized due to idiosyncratic agranulocytosis. Incidence rate (IR) and adjusted HR of agranulocytosis with metamizole versus NSAID were estimated assuming the onset date of agranulocytosis was the date of hospitalization sensitivity analysis or 7days before (main analysis). In secondary analyses, we used (1) opioids-paracetamol as negative control and (2) any hospitalized neutropenia as outcome (assuming the onset was 7days before). The cohorts included 444,972 new users of metamizole, 3,814,367 NSAID, and 3,129,221 opioids-paracetamol on continuous treatment during a median of 37-40days. Overall, 26 hospitalized agranulocytosis occurred, 5 in the first week (and so removed in main analysis) and 21 thereafter. IR of agranulocytosis was 14.20 (N = 5 cases) and 8.52 (N = 3), 1.95 (N = 6) and 1.62 (N = 5), and 4.29 (N = 15) and 3.72 (N = 13)/107 person-weeks of continuous treatment using the date of hospitalization or 7days before, respectively. Two, 0 and 2 of cases identified in both analyses had neoplasia in every cohort, respectively. HR of agranulocytosis associated with metamizole was 7.20 [95% CI: 1.92-26.99] and 4.40 [0.90-21.57] versus NSAID, and3.31 [1.17-9.34] and 2.45 [0.68-8.83] versus opioid-paracetamol, respectively. HR of neutropenia with metamizole was 2.98 [1.57-5.65] versus NSAID. Agranulocytosis was very rare but more common (above 4 times more) with metamizole than other analgesics. The impact of the drug-induced agranulocytosis was less precise with metamizole than the comparators due to its lower use, which precluded to find statistical differences in main analysis. The increased risk of hospitalized neutropenias with metamizole supports the link with its severity although triggers unavailable during the follow-up (ex. cytotoxic medication) can not be discarded.

Medication management in long-term care: using evidence generated from real-world data to effect policy change in the Australian setting.

Older individuals residing in long-term care facilities (LTCFs) are often living with multimorbidity and exposed to polypharmacy, and many experience medication-related problems. Because randomized controlled trials seldom include individuals in LTCFs, pharmacoepidemiological studies using real-world data are essential sources of new knowledge on the utilization, safety and effectiveness of pharmacotherapies and related health outcomes in this population. In this commentary, we discuss recent pharmacoepidemiological research undertaken to support the investigations and recommendations of a landmark public inquiry into the quality and safety of care provided in the approximately 3,000 Australian LTCFs which house over 240,000 residents annually and informed subsequent national medication-related policy reforms. Suitable sources of real-world data for pharmacoepidemiological studies in long-term care cohorts and methodological considerations are also discussed.

Building transparency and reproducibility into the practice of pharmacoepidemiology and outcomes research.

Real-world evidence (RWE) studies are increasingly used to inform policy and clinical decisions. However, there remain concerns about the credibility and reproducibility of RWE studies. Observational researchers should highlight the level of transparency of their studies by providing a succinct statement addressing study transparency with the publication of every paper, poster, or presentation that reports on a RWE study. In this paper, we propose a framework for an explicit transparency statement that declares the level of transparency a given RWE study has achieved across five key domains: 1) protocol, 2) pre-registration, 3) data, 4) code sharing, and 5) reporting checklists.

Coding of Childhood Psychiatric and Neurodevelopmental Disorders in Electronic Health Records of a Large Integrated Health Care System: Validation Study.

Mental, emotional, and behavioral disorders are chronic pediatric conditions, and their prevalence has been on the rise over recent decades. Affected children have long-term health sequelae and a decline in health-related quality of life. Due to the lack of a validated database for pharmacoepidemiological research on selected mental, emotional, and behavioral disorders, there is uncertainty in their reported prevalence in the literature. We aimed to evaluate the accuracy of coding related to pediatric mental, emotional, and behavioral disorders in a large integrated health care system's electronic health records (EHRs) and compare the coding quality before and after the implementation of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding as well as before and after the COVID-19 pandemic. Medical records of 1200 member children aged 2-17 years with at least 1 clinical visit before the COVID-19 pandemic (January 1, 2012, to December 31, 2014, the ICD-9-CM coding period; and January 1, 2017, to December 31, 2019, the ICD-10-CM coding period) and after the COVID-19 pandemic (January 1, 2021, to December 31, 2022) were selected with stratified random sampling from EHRs for chart review. Two trained research associates reviewed the EHRs for all potential cases of autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), major depression disorder (MDD), anxiety disorder (AD), and disruptive behavior disorders (DBD) in children during the study period. Children were considered cases only if there was a mention of any one of the conditions (yes for diagnosis) in the electronic chart during the corresponding time period. The validity of diagnosis codes was evaluated by directly comparing them with the gold standard of chart abstraction using sensitivity, specificity, positive predictive value, negative predictive value, the summary statistics of the F-score, and Youden J statistic. κ statistic for interrater reliability among the 2 abstractors was calculated. The overall agreement between the identification of mental, behavioral, and emotional conditions using diagnosis codes compared to medical record abstraction was strong and similar across the ICD-9-CM and ICD-10-CM coding periods as well as during the prepandemic and pandemic time periods. The performance of AD coding, while strong, was relatively lower compared to the other conditions. The weighted sensitivity, specificity, positive predictive value, and negative predictive value for each of the 5 conditions were as follows: 100%, 100%, 99.2%, and 100%, respectively, for ASD; 100%, 99.9%, 99.2%, and 100%, respectively, for ADHD; 100%, 100%, 100%, and 100%, respectively for DBD; 87.7%, 100%, 100%, and 99.2%, respectively, for AD; and 100%, 100%, 99.2%, and 100%, respectively, for MDD. The F-score and Youden J statistic ranged between 87.7% and 100%. The overall agreement between abstractors was almost perfect (κ=95%). Diagnostic codes are quite reliable for identifying selected childhood mental, behavioral, and emotional conditions. The findings remained similar during the pandemic and after the implementation of the ICD-10-CM coding in the EHR system.

Incident HIV-Associated Wasting/Low Weight Is Associated with Nearly Doubled Mortality Risk in the Modern ART Era.

HIV-associated wasting (HIVAW) is an underappreciated AIDS-defining illness, despite highly effective antiretroviral therapy (ART). We (a) assessed the association between incident HIVAW/low weight and all-cause mortality and (b) described virologic outcomes after people with HIV (PWH) experienced HIVAW/low weight while on ART. In the Observational Pharmaco-Epidemiology Research & Analysis (OPERA®) cohort, PWH without prior HIVAW/low weight who were active in care in 2016-2020 were followed through the first of the following censoring events: death, loss to follow-up, or study end (October 31, 2021). HIVAW/low weight was a diagnosis of wasting or low body mass index (BMI)/underweight or a BMI measurement <20 kg/m2. Hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between time-dependent HIVAW/low weight and mortality were estimated with extended Cox regression models. Over a median follow-up of 45 months (interquartile range: 27, 65), there were 4,755 (8%) cases of HIVAW/low weight and 1,354 (2%) deaths among 62,314 PWH. PWH who experienced HIVAW/low weight had a significantly higher risk of death than those who did not (HR: 1.96; 95% CI: 1.68, 2.27) after adjusting for age, race, ethnicity, and changes in viral load (VL) and Veterans Aging Cohort Study Mortality Index scores over follow-up. Among 4,572 PWH on ART at HIVAW/low weight, 68% were suppressed (VL of <200 copies/mL); subsequent virologic failure was uncommon (7%). Among viremic PWH, 70% and 60% achieved suppression and undetectability (VL of <50 copies/mL), respectively, over follow-up. HIVAW remains a challenge for some PWH. Particular attention needs to be paid to HIVAW/low weight and virologic control to restore health and potentially reduce the risk of death.

Characterisation and validation of lactation information from structured electronic health records for use in pharmacoepidemiological studies.

Breastfeeding information stored within electronic health records (EHR) has recently been used for pharmacoepidemiological research, however the data are primarily collected for clinical care. To characterise breastfeeding information recorded in structured fields in EHR during infant and postpartum health care visits, and to assess the validity of lactation status based on EHR data versus maternal report at research study visits. We assessed breastfeeding information recorded in structured fields in EHR from one health system for a subset of 211 patients who were also enrolled in a study on breast milk composition between 2014 and 2017 that required participants to exclusively breastfeed their infants until at least 1 month of age. We assessed the frequency of breastfeeding information in EHR during the first 12 months of age and compared lactation status based on EHR with maternal report at 1 and 6-month study visits (reference standard). The median number of breastfeeding records in the EHR per infant was six (interquartile range 3) with most observations clustering in the first few weeks of life and around well-infant visits. At the 6-month study visit, 93.8% of participants were breastfeeding and 80.1% were exclusively breastfeeding according to maternal report. Sensitivity of EHR data for identifying ever breastfeeding was at or near 100%, and sensitivity for identifying ever exclusive breastfeeding was 98.0% (95% CI: 95.0%, 99.2%). Sensitivities were 97.3% (95% CI: 93.9%, 98.9%) for identifying any breastfeeding and 94.4% (95% CI: 89.7%, 97.0%) for exclusive breastfeeding, and positive predictive values were 99.5% (95% CI: 97.0%, 99.9%) for any breastfeeding and 95.0% (95% CI: 90.4%, 97.4%) for exclusive breastfeeding. Breastfeeding information in structured EHR fields have the potential to accurately classify lactation status. The validity of these data should be assessed in populations with a lower breastfeeding prevalence.

Standardizing to specific target populations in distributed networks and multisite pharmacoepidemiologic studies.

Distributed network studies and multisite studies assess drug safety and effectiveness in diverse populations by pooling information. Targeting groups of clinical or policy interest (including specific sites or site combinations) and applying weights based on effect measure modifiers (EMMs) prior to pooling estimates within multisite studies may increase interpretability and improve precision. We simulated a 4-site study, standardized each site using inverse odds weights (IOWs) to resemble the 3 smallest sites or the smallest site, estimated IOW-weighted risk differences (RDs), and combined estimates with inverse variance weights (IVWs). We also created an artificial distributed network in the Clinical Practice Research Datalink (CPRD) Aurum consisting of 1 site for each geographic region. We compared metformin and sulfonylurea initiators with respect to mortality, targeting the smallest region. In the simulation, IOWs reduced differences between estimates and increased precision when targeting the 3 smallest sites or the smallest site. In the CPRD Aurum study, the IOW+IVW estimate was also more precise (smallest region: RD = 5.41% [95% CI, 1.03-9.79]; IOW+IVW estimate: RD = 3.25% [95% CI, 3.07-3.43]). When performing pharmacoepidemiologic research in distributed networks or multisite studies in the presence of EMMs, designation of target populations has the potential to improve estimate precision and interpretability. This article is part of a Special Collection on Pharmacoepidemiology.

Use of isotretinoin among girls and women of childbearing age and occurrence of isotretinoin-exposed pregnancies in Germany: A population-based study.

Exposure to isotretinoin during pregnancy must be avoided due to its teratogenicity, but real-world data on its use are scarce. We aimed to describe (i) isotretinoin use in women of childbearing age in Germany; (ii) the occurrence of isotretinoin-exposed pregnancies; and (iii) malformations among children exposed in utero. Using observational data from the German Pharmacoepidemiological Research Database (GePaRD, claims data from approximately 20% of the German population), we conducted annual cross-sectional analyses to determine age-standardized prevalence of isotretinoin use between 2004 and 2019 among girls and women aged 13 to 49 years. In cohort analyses, we estimated the number of exposed pregnancies by assessing whether there was prescription supply overlapping the beginning of pregnancy (estimated supply was varied in sensitivity analyses) or a dispensation within the first 8 weeks of pregnancy. Data of live-born children classified as exposed in a critical period according to these criteria were reviewed to assess the presence of congenital malformations. The age-standardized prevalence of isotretinoin use per 1,000 girls and women increased from 1.20 (95% confidence interval [CI]: 1.16, 1.24) in 2004 to 1.96 (95% CI: 1.92, 2.01) in 2019. In the base case analysis, we identified 178 pregnancies exposed to isotretinoin, with the number per year doubling during the study period, and at least 45% of exposed pregnancies ended in an induced abortion. In sensitivity analyses, the number of exposed pregnancies ranged between 172 and 375. Among live-born children, 6 had major congenital malformations. The main limitation of this study was the lack of information on the prescribed dose, i.e., the supply had to be estimated based on the dispensed amount of isotretinoin. Isotretinoin use among girls and women of childbearing age increased in Germany between 2004 and 2019, and there was a considerable number of pregnancies likely exposed to isotretinoin in a critical period. This highlights the importance of monitoring compliance with the existing risk minimization measures for isotretinoin in Germany.

Risk of dementia among antidepressant elderly users: A population-based cohort analysis in Spain

The use of antidepressants with anticholinergic effects has been associated with an increased risk of dementia. However, the results published are contradictory. The aim of the study is to compare the risk of developing dementia in elderly who were prescribed tricyclic antidepressants (TCA) versus those who were prescribed selective serotonin reuptake inhibitors (SSRIs) and other antidepressants (OA). A prospective population-based cohort study was performed using the Spanish Database for Pharmacoepidemiological Research in Primary Care (BIFAP) data (from 2005 to 2018). The cohort study included 62,928 patients age ≥ 60 without dementia and with antidepressant long-term monotherapy. Patients were divided into exposure antidepressant groups based on ATC system [TCA, SSRIs users and OAs users]. The risk of dementia was calculated by Cox regression models, providing hazard ratios (HR) and 95 % confidence intervals. The Kaplan-Meier model was used for survival analysis. Chi2 test was used as association test. The results showed SSRI users had higher dementia risk than TCA users (HR = 1.864; 95%CI = 1.624–2.140). Moreover, OA users had also significant risk of dementia (HR = 2.103; 95%CI = 1.818–2.431). Several limitations are the variation of the trend in the prescription of antidepressants, the small number of patients that use some antidepressants, the lack of information related to the dose, or socioeconomic characteristics, the use of antidepressant drugs for other indications, or the therapeutic compliance.Our findings showed that older users of SSRI and OA have more risk of developing dementia than TCA elderly users. However, additional studies would be needed.

Extracting forced vital capacity from the electronic health record through natural language processing in rheumatoid arthritis-associated interstitial lung disease.

To develop a natural language processing (NLP) tool to extract forced vital capacity (FVC) values from electronic health record (EHR) notes in patients with rheumatoid arthritis-interstitial lung disease (RA-ILD). We selected RA-ILD patients (n = 7485) in the Veterans Health Administration (VA) between 2000 and 2020 using validated ICD-9/10 codes. We identified numeric values in proximity to FVC string patterns from clinical notes in the EHR. Subsequently, we performed processing steps to account for variability in note structure, related pulmonary function test (PFT) output, and values copied across notes, then assigned dates from linked administrative procedure records. NLP-derived FVC values were compared to values recorded directly from PFT equipment available on a subset of patients. We identified 5911 FVC values (n = 1844 patients) from PFT equipment and 15 383 values (n = 4982 patients) by NLP. Among 2610 date-matched FVC values from NLP and PFT equipment, 95.8% of values were within 5% predicted. The mean (SD) difference was 0.09% (5.9), and values strongly correlated (r = 0.94, p < 0.001), with a precision of 0.87 (95% CI 0.86, 0.88). NLP captured more patients with longitudinal FVC values (n = 3069 vs. n = 1164). Mean (SD) change in FVC %-predicted per year was similar between sources (-1.5 [30.0] NLP vs. -0.9 [16.6] PFT equipment; standardized response mean = 0.05 for both). NLP of EHR notes increases the capture of accurate, longitudinal FVC values by three-fold over PFT equipment. Use of this NLP tool can facilitate pharmacoepidemiologic research in RA-ILD and other lung diseases by capturing this critical measure of disease severity.

Successful implementation of a stakeholder engagement program for pharmacoepidemiologic research.

Rigorously conducted pharmacoepidemiologic research requires methodologically complex study designs and analysis yet evaluates problems of high importance to patients and clinicians. Despite this, participation in and mechanisms for stakeholder engagement in pharmacoepidemiologic research are not well-described. Here, we describe our approach and lessons learned from engaging stakeholders, of varying familiarity with research methods, in a rigorous multi-year pharmacoepidemiologic research program evaluating the comparative effectiveness of diabetes medications. We recruited 5 patient and 4 clinician stakeholders; each was compensated for their time. Stakeholders received initial formal training in observational research and pharmacoepidemiologic methods sufficient to enable contribution to the research project. After onboarding, stakeholder engagement meetings were held virtually, in the evening, 2-3 times annually. Each was approximately 90 min and focused on 1-2 specific questions about the project, with preparatory materials sent in advance. Stakeholder meeting attendance was high (89%-100%), and all stakeholders engaged with the research project, both during and between meetings. Stakeholders reported positive experiences with meetings, satisfaction, and interest in the research project and its findings, and dedication to the success of the project's goals. They affirmed the value of receiving materials to review in advance and the effectiveness of a virtual platform. Their contributions included prioritizing and suggesting research questions, optimizing written evidence briefs for a lay audience, and guidance on broader topics such as research audience and methods of dissemination. Stakeholder engagement in pharmacoepidemiologic research using complex study designs and analysis is feasible, acceptable, and positively impacts the research project.

German longitudinal prescription database (LRx): Description ofcharacteristics, use in pharmacoepidemiological research, and limitations .

Large databases have played a critical role in pharmacoepidemiological research in the last decade, with this role likely to gain further importance in the future. The aim of the present paper is to describe the characteristics, the recent use, and the limitations of the German longitudinal prescription (LRx) database. The LRx database contains patient-level data on prescriptions collected in retail pharmacies, corresponding to ~80% of prescriptions reimbursed by statutory health insurance funds in Germany. The LRx database includes a higher proportion of older adults and women compared to the overall German population with statutory health insurance. Coverage per family of drugs ranges from 71.8% for antiepileptics to 94.7% for urological agents. Multiple pharmacoepidemiological studies based on the data from the German LRx database have been published in the last years on topics such as patterns of prescription and treatment adherence and persistence. A large number of disorders have been investigated in this research (e.g., type 2 diabetes, inflammatory diseases, and psychiatric conditions). The major limitations of the LRx database are the lack of formal diagnoses and the absence of hospital data. In conclusion, the German LRx database could be a key source of data for future pharmacoepidemiological studies.

Pharmacoepidemiology: An Overview.

The aims of this review are to provide a comprehensive overview of the definition and scope of pharmacoepidemiology, to summarize the study designs and methodologies used in the field, to discuss the future trends in the field and new methodologies to address bias and confounding, and finally to give some recommendations to clinicians interested in pharmacoepidemiologic research. Because drug efficacy and safety from randomized clinical trials do not reflect the real-world situation, pharmacoepidemiological studies on drug safety monitoring and drug effectiveness in large numbers of people are needed by healthcare professionals and regulatory institutions. We aim to highlight the importance of pharmacoepidemiologic research in informing evidence-based medicine and public health policy. The development of new designs and methodologies for the generation of valid evidence, as well as new initiatives to provide guidance and recommendations on how to incorporate real-world evidence into the drug development process, are reported on. In addition, we have touched on the implication of artificial intelligence in the management of real-world data. This overview aims to summarize all important aspects to consider when conducting or interpreting a pharmacoepidemiologic study.

Validation of the "Indication for Use" (INDO) Variable in the Danish National Prescription Registry.

Despite its potential value in register-based pharmacoepidemiologic research, recorded information on "indication for use" (INDO) in the Danish National Prescription Registry has rarely been used, likely because of questions about the variable's validity, which to our knowledge no study has systematically assessed. We extracted data on 80,814 prescriptions from the software systems (PharmaNet and C2) of five Danish community pharmacies filled between 4 and 16 February 2019 and 2020. Using the indication information recorded in the pharmacy software systems as the gold standard, we evaluated the extent and quality of the corresponding information from the Prescription Registry. Of all prescriptions identified, we captured >99% in the Prescription Registry. The proportion of prescriptions with recorded indication codes in the Prescription Registry was 82% (n = 66,164) but was lower for C2 than PharmaNet. Correcting for the overrepresentation of C2 data in our sample, the estimated proportion of registration was ≈88%. Almost 100% (66,158 of 66,164) of the prescriptions with recorded indication codes in the Prescription Registry had correctly recorded indication codes. Nonspecific indication codes were present in 5.6%-36% of selected drugs and drug classes. Prescriptions filled at Danish community pharmacies are accurately captured by the Danish National Prescription Registry, and the recorded information on indication is generally valid and usable in research. However, minor concerns remain about missingness, nonspecific recorded indication codes, and lower validity, and a higher proportion of missingness of recorded indication codes is expected before 2017.A supplementary digital video by the article's co-author, Hanin Harbi, is available at http://links.lww.com/EDE/C89.

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

BackgroundAdministrative healthcare claims databases are used in drug safety research but are limited for investigating the impacts of prenatal exposures on neonatal and pediatric outcomes without mother-infant pair identification. Further, existing algorithms are not transportable across data sources. We developed a transportable mother-infant linkage algorithm and evaluated it in two, large US commercially insured populations.MethodsWe used two US commercial health insurance claims databases during the years 2000 to 2021. Mother-infant links were constructed where persons of female sex 12–55 years of age with a pregnancy episode ending in live birth were associated with a person who was 0 years of age at database entry, who shared a common insurance plan ID, had overlapping insurance coverage time, and whose date of birth was within ± 60-days of the mother’s pregnancy episode live birth date. We compared the characteristics of linked vs. non-linked mothers and infants to assess similarity.ResultsThe algorithm linked 3,477,960 mothers to 4,160,284 infants in the two databases. Linked mothers and linked infants comprised 73.6% of all mothers and 49.1% of all infants, respectively. 94.9% of linked infants’ dates of birth were within ± 30-days of the associated mother’s pregnancy episode end dates. Characteristics were largely similar in linked vs. non-linked mothers and infants. Differences included that linked mothers were older, had longer pregnancy episodes, and had greater post-pregnancy observation time than mothers with live births who were not linked. Linked infants had less observation time and greater healthcare utilization than non-linked infants.ConclusionsWe developed a mother-infant linkage algorithm and applied it to two US commercial healthcare claims databases that achieved a high linkage proportion and demonstrated that linked and non-linked mother and infant cohorts were similar. Transparent, reusable algorithms applied to large databases enable large-scale research on exposures during pregnancy and pediatric outcomes with relevance to drug safety. These features suggest studies using this algorithm can produce valid and generalizable evidence to inform clinical, policy, and regulatory decisions.

Detection of major congenital malformations depends on length of follow-up in Swedish National Health Register Data: Implications for pharmacoepidemiological research on medication safety in pregnancy.

In observational medication pregnancy safety studies, children are often followed from birth to 1 year of age. However, some major congenital malformations (MCM) may take longer to diagnose. We aimed to investigate the proportion of children with detected MCMs at different lengths of follow-up and compare them to the proportion detected at 1 year after birth. This population-based register study included all singleton children liveborn in Sweden from 2006 to 2016. MCM were identified by ICD-10 codes in the Medical Birth Register and National Patient Register, aligned to the EUROCAT classification system. Cumulative proportion of children with detected MCM at birth, 90 days, 1, 2, and 3 years was calculated and compared between children born preterm and at term. In 1,138,113 liveborn children, the cumulative proportion of children with a detected MCM increased from 1.9% at birth to 3.1%, 3.9%, 4.4% and 4.7% at 90 days, 1, 2, and 3 years after birth, respectively, and varied by MCM subgroup. MCMs of the eye, ear-face-neck, nervous system and genitals were detected with the longest delay, with 31%-59% more detected at 3- versus 1-year follow-up. Compared to children born at term, the proportion of children with any MCM was 2.5 times higher amongst preterm children, with a higher proportion detected over the first 90 days for most MCM subgroups. The proportion of children with a detected MCM varied by MCM subgroup and follow-up time. In pharmacoepidemiology studies of medication safety in pregnancy using Swedish national data, the length of child follow-up should be chosen in accordance with the expected age at detectionif a specific subgroup of MCM is under investigation, for example, eye and genital MCM require longer follow-up for detection than abdominal wall and digestive system MCM. However, in most circumstances, 1 year of follow-up is sufficient.

Core concepts in pharmacoepidemiology: Measurement of medication exposure in routinely collected healthcare data for causal inference studies in pharmacoepidemiology.

Observational designs can complement evidence from randomized controlled trials not only in situations when randomization is not feasible, but also by evaluating drug effects in real-world, considering a broader spectrum of users and clinical scenarios. However, use of such real-world scenarios captured in routinely collected clinical or administrative data also comes with specific challenges. Unlike in trials, medication use is not protocol based. Instead, exposure is determined by a multitude of factors involving patients, providers, healthcare access, and other policies. Accurate measurement of medication exposure relies on a similar broad set of factors which, if not understood and appropriately addressed, can lead to exposure misclassification and bias. To describe core considerations for measurement of medication exposure in routinely collected healthcare data. We describe the strengths and weaknesses of the two main types of routinely collected healthcare data (electronic health records and administrative claims) used in pharmacoepidemiologic research. We introduce key elements in those data sources and issues in the curation process that should be considered when developing exposure definitions. We present challenges in exposure measurement such as the appropriate determination of exposure time windows or the delineation of concomitant medication use versus switching of therapy, and related implications for bias. We note that true exposure patterns are typically unknown when using routinely collected healthcare data and that an in-depth understanding of healthcare delivery, patient and provider decision-making, data documentation and governance, as well as pharmacology are needed to ensure unbiased approaches to measuring exposure. Various assumptions are made with the goal that the chosen exposure definition can approximate true exposure. However, the possibility of exposure misclassification remains, and sensitivity analyses that can test the impact of such assumptions on the robustness of estimated medication effects are necessary to support causal inferences.

Measuring the Uptake of Pharmacoepidemiologic Research Through Qualitative Analysis of Citations: A Case Study from a Canadian Network of Researchers

ABSTRACT Health research, including drug safety research, is communicated, in part, by being cited; understanding these citations can help determine its reach and impact. We analyzed the uptake of a Canadian Network for Observational Drug Effect Studies study of the heart failure risk of incretin-based drugs using quantitative and qualitative bibliometric approaches. A Scopus® search (2016–2020) returned 127 citing articles, mostly single studies and review articles. Many were also high impact journals, with intended audiences of other researchers, policy makers, and practitioners. Using the Becker Model, 93% contributed to “advancing knowledge.” Research impact can be difficult to establish. We have demonstrated one comprehensive approach that can be further adapted and automated; researchers and funders need to determine the best indicators, tools, and frameworks to use that are feasible and context relevant.