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.

Health Care Team Interventions to Reduce Distress Behaviors in Older Adults: A Systematic Review.

OBJECTIVES: This review examines health care team-focused interventions on managing persistent or recurrent distress behaviors among older adults in long-term residential or inpatient health care settings. METHODS: We searched interventions addressing health care worker (HCW) knowledge and skills related to distress behavior management using Ovid MEDLINE, Elsevier Embase, and Ovid PsycINFO from December 2002 through December 2022. RESULTS: We screened 6,582 articles; 29 randomized trials met inclusion criteria. Three studies on patient-facing HCW interactions (e.g. medication management, diagnosing distress) showed mixed results on agitation; one study found no effect on quality of life. Six HCW-focused studies suggested short-term reduction in distress behaviors. Quality-of-life improvement or decreased antipsychotic use was not evidenced. Among 17 interventions combining HCW-focused and patient-facing activities, 0 showed significant distress reduction, 8 showed significant antipsychotic reduction (OR = 0.79, 95%CI [0.69, 0.91]) and 9 showed quality of life improvements (SMD = 0.71, 95%CI [0.39, 1.04]). One study evaluating HCW, patient-, and environmental-focused intervention activities showed short-term improvement in agitation. CONCLUSIONS AND CLINICAL IMPLICATIONS: Novel health care models combining HCW training and patient management improve patient quality of life, reduce antipsychotic use, and may reduce distress behaviors. Evaluation of intervention's effects on staff burnout and utilization is needed.

Fitness of real-world data for clinical trial data collection: Results and lessons from a HARMONY Outcomes ancillary study.

BACKGROUND: Despite the extensive use of real-world data for retrospective, observational clinical research, our understanding of how real-world data might increase the efficiency of data collection in patient-level randomized clinical trials is largely unknown. The structure of real-world data is inherently heterogeneous, with each source electronic health record and claims database different from the next. Their fitness-for-use as data sources for multisite trials in the United States has not been established. METHODS: For a subset of participants in the HARMONY Outcomes Trial, we obtained electronic health record data from recruiting sites or Medicare claims data from the Centers for Medicare & Medicaid Services. For baseline characteristics and follow-up events, we assessed the level of agreement between these real-world data and data documented in the trial database. RESULTS: Real-world data-derived demographic information tended to agree with trial-reported demographic information, although real-world data were less accurate in identifying medical history. The ability of real-world data to identify baseline medication usage differed by real-world data source, with claims data demonstrating substantially better performance than electronic health record data. The limited number of lab results in the collected electronic health record data matched closely with values in the trial database. There were not enough follow-up events in the ancillary study population to draw meaningful conclusions about the performance of real-world data for identification of events. Based on the conduct of this ancillary study, the challenges and opportunities of using real-world data within clinical trials are discussed. CONCLUSION: Based on a subset of participants from the HARMONY Outcomes Trial, our results suggest that electronic health record or claims data, as currently available, are unlikely to be a complete substitute for trial data collection of medical history or baseline lab results, but that Medicare claims were able to identify most medications. The limited size of the study population prevents us from drawing strong conclusions based on these results, and other studies are clearly needed to confirm or refute these findings.

Healthcare Utilization for Medicaid-Insured Children with Medical Complexity: Differences by Sociodemographic Characteristics.

OBJECTIVE: To compare differences in healthcare utilization and costs for Medicaid-insured children with medical complexity (CMC) by race/ethnicity and rurality. METHODS: Retrospective cohort of North Carolina (NC) Medicaid claims for children 3-20 years old with 3 years continuous Medicaid coverage (10/1/2015-9/30/2018). Exposures were medical complexity, race/ethnicity, and rurality. Three medical complexity levels were: without chronic disease, non-complex chronic disease, and complex chronic disease; the latter were defined as CMC. Race/ethnicity was self-reported in claims; we defined rurality by home residence ZIP codes. Utilization and costs were summarized for 1 year (10/1/2018-9/30/2019) by complexity level classification and categorized as acute care (hospitalization, emergency [ED]), outpatient care (primary, specialty, allied health), and pharmacy. Per-complexity group utilization rates (per 1000 person-years) by race/ethnicity and rurality were compared using adjusted rate ratios (ARR). RESULTS: Among 859,166 Medicaid-insured children, 118,210 (13.8%) were CMC. Among CMC, 36% were categorized as Black non-Hispanic, 42.7% White non-Hispanic, 14.3% Hispanic, and 35% rural. Compared to White non-Hispanic CMC, Black non-Hispanic CMC had higher hospitalization (ARR = 1.12; confidence interval, CI 1.08-1.17) and ED visit (ARR = 1.17; CI 1.16-1.19) rates; Hispanic CMC had lower ED visit (ARR = 0.77; CI 0.75-0.78) and hospitalization rates (ARR = 0.79; CI 0.73-0.84). Black non-Hispanic and Hispanic CMC had lower outpatient visit rates than White non-Hispanic CMC. Rural CMC had higher ED (ARR = 1.13; CI 1.11-1.15) and lower primary care utilization rates (ARR = 0.87; CI 0.86-0.88) than urban CMC. DISCUSSION: Healthcare utilization varied by race/ethnicity and rurality for Medicaid-insured CMC. Further studies should investigate mechanisms for these variations and expand higher value, equitable care delivery for CMC.

Long-term opioid therapy definitions and predictors: A systematic review.

PURPOSE: This review sought to (a) describe definitions of long-term opioid therapy (LTOT) outcome measures, and (b) identify the predictors associated with the transition from short-term opioid use to LTOT for opioid-naïve individuals. METHODS: We conducted a systematic review of the peer-reviewed literature (January 2007 to July 2018). We included studies examining opioid use for more than 30 days. We classified operationalization of LTOT based on criteria used in the definitions. We extracted LTOT predictors from multivariate models in studies of opioid-naïve individuals. RESULTS: The search retrieved 5,221 studies, and 34 studies were included. We extracted 41 unique variations of LTOT definitions. About 36% of definitions required a cumulative duration of opioid use of 3 months. Only 17% of definitions considered consecutive observation periods, 27% used days' supply, and no definitions considered dose. We extracted 76 unique predictors of LTOT from seven studies of opioid-naïve patients. Common predictors included pre-existing comorbidities (21.1%), non-opioid prescription medication use (13.2%), substance use disorders (10.5%), and mental health disorders (10.5%). CONCLUSIONS: Most LTOT definitions aligned with the chronic pain definition (pain more than 3 months), and used cumulative duration of opioid use as a criterion, although most did not account for consistent use. Definitions were varied and rarely accounted for prescription characteristics, such as days' supply. Predictors of LTOT were similar to known risk factors of opioid abuse, misuse, and overdose. As LTOT becomes a central component of quality improvement efforts, researchers should incorporate criteria to identify consistent opioid use to build the evidence for safe and appropriate use of prescription opioids.

Data linkage in pharmacoepidemiology: A call for rigorous evaluation and reporting.

PURPOSE: The purpose of this paper is to provide guidance on the evaluation of data linkage quality through the development of a checklist for reporting key elements of the linkage process. METHODS: Responding to a call for manuscripts from the International Society for Pharmacoepidemiology (ISPE), a working group including international representation from the academic, industry, and contract research, and regulatory sectors was formed to develop a checklist for evaluation of data linkage performance and reporting data linkage specifically for pharmacoepidemiologic research. This checklist expands on the reporting of studies conducted using observational routinely collected health data specific to pharmacoepidemiology (RECORD-PE) guidelines. RESULTS: A key aspect of data linkage evaluation for pharmacoepidemiology is to articulate how a linkage process was performed and its accuracy in terms of validation and verification of the resulting linked data. This study generates a checklist, which covers domains including data sources, linkage variables, linkage methods, linkage results, and linkage evaluation. For each domain, specific recommendations provide a clear and transparent assessment of the linkage process. CONCLUSIONS: Linking data sources can help to enrich analytic databases to more accurately define study populations, enable adjustment for confounding, and improve the capture of health outcomes. Clear and transparent reporting of data linkage processes will help to increase confidence in the evidence generated from these data by allowing researchers and end users to critically assess the potential for bias owing to the data linkage process.

Linking electronic health data in pharmacoepidemiology: Appropriateness and feasibility.

PURPOSE: To provide guidance on data linkage appropriateness and feasibility to plan purposeful and sustainable new linkages that advance pharmacoepidemiology and healthcare research. Planning a new data linkage requires careful evaluation to weigh the resources required with the potential overall benefits. METHODS: In response to an International Society for Pharmacoepidemiology (ISPE) call for manuscripts, a working group comprised of members from academic, industry, and government determined priority content areas; appropriateness and feasibility of data linkage was selected. Within this topic, scientific and operational considerations were determined, reviewed, and formulated into key areas, and translated into 12 consensus recommendations. RESULTS: Guidance for feasibility assessment was categorized into five key areas: (1) research objectives and justification; (2) data quality and completeness; (3) the linkage process; (4) data ownership and governance; and (5) overall value added by linkage. Within these key areas, recommendations to consider prior to initiation were developed to evaluate suitability of the linkage to meet research objectives, assess source data completeness and population coverage, and ensure well-defined data governance standards and protections. When creating novel linked datasets, researchers must assess the feasibility of both scientific (data quality and linkage methods) and operational (access, data use and transfer, governance, and cost) aspects. CONCLUSIONS: The data linkage feasibility assessment considerations outlined can be used as a guide when designing sustainable linked data resources to generate actionable evidence in healthcare research. These recommendations were constructed for wide applicability and can be adapted depending on the geographic, structural, and data components of the linkage.

BMC health services research title: the 2020 blast in the port of Beirut: can the Lebanese health system "build back better"?

The August 2020 explosion in Lebanon resulted in casualties, injuries, and a great number of internally displaced persons. The blast occurred during an economically and politically complex time in the country. Given multiple and competing post-explosion reconstruction priorities, in ths editorial we briefly examine the requirements for a build back better scenario.

Correction to: The 2020 blast in the Port of Beirut: can the Lebanese health system "build back better"?

An amendment to this paper has been published and can be accessed via the original article.

Leveraging electronic health records for clinical research.

Electronic health records (EHRs) can be a major tool in the quest to decrease costs and timelines of clinical trial research, generate better evidence for clinical decision making, and advance health care. Over the past decade, EHRs have increasingly offered opportunities to speed up, streamline, and enhance clinical research. EHRs offer a wide range of possible uses in clinical trials, including assisting with prestudy feasibility assessment, patient recruitment, and data capture in care delivery. To fully appreciate these opportunities, health care stakeholders must come together to face critical challenges in leveraging EHR data, including data quality/completeness, information security, stakeholder engagement, and increasing the scale of research infrastructure and related governance. Leaders from academia, government, industry, and professional societies representing patient, provider, researcher, industry, and regulator perspectives convened the Leveraging EHR for Clinical Research Now! Think Tank in Washington, DC (February 18-19, 2016), to identify barriers to using EHRs in clinical research and to generate potential solutions. Think tank members identified a broad range of issues surrounding the use of EHRs in research and proposed a variety of solutions. Recognizing the challenges, the participants identified the urgent need to look more deeply at previous efforts to use these data, share lessons learned, and develop a multidisciplinary agenda for best practices for using EHRs in clinical research. We report the proceedings from this think tank meeting in the following paper.

One-step in situ biosynthesis of graphene oxide-bacterial cellulose nanocomposite hydrogels.

Graphene oxide-bacterial cellulose (GO/BC) nanocomposite hydrogels with well-dispersed GO in the network of BC are successfully developed using a facile one-step in situ biosynthesis by adding GO suspension into the culture medium of BC. During the biosynthesis process, the crystallinity index of BC decreases and GO is partially reduced. The experimental results indicate that GO nanosheets are uniformly dispersed and well-bound to the BC matrix and that the 3D porous structure of BC is sustained. This is responsible for efficient load transfer between the GO reinforcement and BC matrix. Compared with the pure BC, the tensile strength and Young's modulus of the GO/BC nanocomposite hydrogel containing 0.48 wt% GO are significantly improved by about 38 and 120%, respectively. The GO/BC nanocomposite hydrogels are promising as a new material for tissue engineering scaffolds.

Variations in antibiotic prescribing among children enrolled in North Carolina Medicaid, 2013-2019.

PURPOSE: The majority of pediatric antibiotic prescribing occurs in the outpatient setting and inappropriate use contributes to antimicrobial resistance. There are regional variations in outpatient antibiotic use with the highest rates occurring in the Southern states, including in Appalachia. The purpose of this study was to describe the rates and risk factors for inappropriate antibiotic prescription among pediatric patients enrolled in North Carolina (NC) Medicaid. METHODS: We used Medicaid prescription claims data from 2013 to 2019 to describe patterns of pediatric antibiotic prescription in NC. We assessed patient and provider factors to identify variations in prescribing. FINDINGS: Children who were less than 2 years of age, non-Hispanic White, and living in a rural area had the highest overall rates of antibiotic prescription. Compared to pediatricians, the risk of inappropriate antibiotic prescription was highest among other specialists and general practioners and lowest among nurse practitioners. Rural areas of NC had the highest rates of inappropriate antibiotic prescribing, and the risk for non-Hispanic Black children compared to children of other races/ethnicities was compounded by rurality. CONCLUSIONS: Prescribing practices in NC differ compared to neighboring states with a lower overall risk of inappropriate prescription in Appalachian regions; however, disparities by race and rurality exist. Outpatient stewardship efforts in NC should focus on ensuring health equity by appreciating racial and geographic variations in prescribing patterns and providing education to all health care providers.

Underimmunization of very low birth weight infants at discharge from the neonatal intensive care unit.

OBJECTIVE: To quantify immunization status among premature infants discharged from neonatal intensive care units (NICUs), and identify risk factors for underimmunization. STUDY DESIGN: We performed a cohort study of infants <33 weeks gestation discharged home between 2011 and 2020 from 241 NICUs. Using multivariable logistic regression, we examined the association between risk factors and underimmunization at discharge, defined as <1 dose of 5 vaccine types when discharged at 60-119 days of age and <2 doses when discharged at 120-179 days of age. RESULTS: Of 30,766 infants discharged at 60-119 days of age, 14% were underimmunized. Among 4358 infants discharged at 120-179 days of age, 53% were underimmunized. For infants discharged at 60-119 days of age, ventilator support within 30 days of discharge was associated with underimmunization. Having a surgical procedure was associated with underimmunization in both groups. CONCLUSION: A large proportion of premature infants discharged from the NICU are underimmunized.

smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies.

Objectives: Partially observed confounder data pose a major challenge in statistical analyses aimed to inform causal inference using electronic health records (EHRs). While analytic approaches such as imputation are available, assumptions on underlying missingness patterns and mechanisms must be verified. We aimed to develop a toolkit to streamline missing data diagnostics to guide choice of analytic approaches based on meeting necessary assumptions. Materials and methods: We developed the smdi (structural missing data investigations) R package based on results of a previous simulation study which considered structural assumptions of common missing data mechanisms in EHR. Results: smdi enables users to run principled missing data investigations on partially observed confounders and implement functions to visualize, describe, and infer potential missingness patterns and mechanisms based on observed data. Conclusions: The smdi R package is freely available on CRAN and can provide valuable insights into underlying missingness patterns and mechanisms and thereby help improve the robustness of real-world evidence studies.

A Principled Approach to Characterize and Analyze Partially Observed Confounder Data from Electronic Health Records.

Objective: Partially observed confounder data pose challenges to the statistical analysis of electronic health records (EHR) and systematic assessments of potentially underlying missingness mechanisms are lacking. We aimed to provide a principled approach to empirically characterize missing data processes and investigate performance of analytic methods. Methods: Three empirical sub-cohorts of diabetic SGLT2 or DPP4-inhibitor initiators with complete information on HbA1c, BMI and smoking as confounders of interest (COI) formed the basis of data simulation under a plasmode framework. A true null treatment effect, including the COI in the outcome generation model, and four missingness mechanisms for the COI were simulated: completely at random (MCAR), at random (MAR), and two not at random (MNAR) mechanisms, where missingness was dependent on an unmeasured confounder and on the value of the COI itself. We evaluated the ability of three groups of diagnostics to differentiate between mechanisms: 1)-differences in characteristics between patients with or without the observed COI (using averaged standardized mean differences [ASMD]), 2)-predictive ability of the missingness indicator based on observed covariates, and 3)-association of the missingness indicator with the outcome. We then compared analytic methods including "complete case", inverse probability weighting, single and multiple imputation in their ability to recover true treatment effects. Results: The diagnostics successfully identified characteristic patterns of simulated missingness mechanisms. For MAR, but not MCAR, the patient characteristics showed substantial differences (median ASMD 0.20 vs 0.05) and consequently, discrimination of the prediction models for missingness was also higher (0.59 vs 0.50). For MNAR, but not MAR or MCAR, missingness was significantly associated with the outcome even in models adjusting for other observed covariates. Comparing analytic methods, multiple imputation using a random forest algorithm resulted in the lowest root-mean-squared-error. Conclusion: Principled diagnostics provided reliable insights into missingness mechanisms. When assumptions allow, multiple imputation with nonparametric models could help reduce bias.

Genomic classifiers and prognosis of localized prostate cancer: a systematic review.

BACKGROUND: Refinement of the risk classification for localized prostate cancer is warranted to aid in clinical decision making. A systematic analysis was undertaken to evaluate the prognostic ability of three genomic classifiers, Decipher, GPS, and Prolaris, for biochemical recurrence, development of metastases and prostate cancer-specific mortality in patients with localized prostate cancer. METHODS: Data sources: MEDLINE, Embase, and Web of Science were queried for reports published from January 2010 to April 2022. STUDY SELECTION: prospective or retrospective studies reporting prognosis for patients with localized prostate cancer. DATA EXTRACTION: relevant data were extracted into a customized database by one researcher with a second overreading. Risk of bias was assessed using a validated tool for prognostic studies, Quality in Prognosis Studies (QUIPS). Disagreements were resolved by consensus or by input from a third reviewer. We assessed the certainty of evidence by GRADE incorporating adaptation for prognostic studies. RESULTS: Data synthesis: a total of 39 studies (37 retrospective) involving over 10,000 patients were identified. Twenty-two assessed Decipher, 5 GPS, and 14 Prolaris. Thirty-four studies included patients who underwent prostatectomy. Based on very low to low certainty of evidence, each of the three genomic classifiers modestly improved upon the prognostic ability for biochemical recurrence, development of metastases, and prostate cancer-specific mortality compared to standard clinical risk-classification schemes. LIMITATIONS: downgrading of confidence in the evidence stemmed largely from bias due to the retrospective nature of the studies, heterogeneity in treatment received, and era in which patients were treated (i.e., prior to the 2000s). CONCLUSIONS: Genomic classifiers provide a small but consistent improvement upon the prognostic ability of clinical classification schemes, which may be helpful when treatment decisions are uncertain. However, evidence from current management-era data and of the predictive ability of these tests is needed.

Stimulant treatment and injury among children with attention deficit hyperactivity disorder: an application of the self-controlled case series study design.

OBJECTIVE: The aim of the present work was to assess the short-term effects of stimulant medication use on risk of injury among children diagnosed as having attention deficit hyperactivity disorder (ADHD). METHODS: The study group for this self-controlled case series study was children aged 1-18 years old diagnosed as having ADHD who experienced an incident medically-attended injury event and received at least one prescription for stimulant medication between 1993 and 2008 (n=328), identified from The Health Improvement Network primary care database from the UK. Conditional Poisson regression was used to estimate incident rate ratios (IRR) and 95% CIs for injury comparing periods of time exposed to stimulant medication to unexposed periods. RESULTS: Among children with ADHD prescribed stimulant medication, the rate of medically-attended injury was decreased during periods of stimulant medication use as compared to unexposed periods (IRR 0.68, 95% CI 0.50 to 0.91). There was evidence of a protective association among males and among children aged 10-14 years. This effect did not change over time on treatment. CONCLUSIONS: Stimulant medication use may decrease the risk of injury among children treated for ADHD, although unmeasured time varying confounding may be an alternative explanation. Injury risk may be considered during the decision-making process with regard to medication continuation among children with ADHD.

Impact of state legislation and institutional protocols on opioid prescribing practices following pediatric tonsillectomy.

Objectives: Tonsillectomy is a common pediatric surgery, and pain is an important consideration in recovery. Due to the opioid epidemic, individual states, medical societies, and institutions have all taken steps to limit postoperative opioids, yet few studies have examined the effect of these interventions on pediatric otolaryngology practices. The primary aim of this study was to characterize opioid prescribing practices following North Carolina state opioid legislation and targeted institutional changes. Methods: This single center retrospective cohort study included 1552 pediatric tonsillectomy patient records from 2014 to 2021. The primary outcome was number of oxycodone doses per prescription. This outcome was assessed over three time periods: (1) Before 2018 North Carolina opioid legislation. (2) Following legislation, before institutional changes. (3) After institutional opioid-specific protocols. Results: The mean (± standard deviation) number of doses per prescription in Periods 1, 2, and 3 was: 58 ± 53, range 4-493; 28 ± 36, range 3-488; and 23 ± 17, range 1-139, respectively. In the adjusted model, Periods 2 and 3 had lower doses by -41% (95% CI -49%, -32%) and -40% (95% CI -55%, -19%) compared to Period 1. After 2018 North Carolina legislation, dosage decreased by -9% (95% CI -13%, -5%) per year. Despite interventions, ongoing variability in prescription regimens remained in all periods. Conclusion: Legislative and institution specific opioid interventions was associated with a 40% decrease in oxycodone doses per prescription following pediatric tonsillectomy. While variability in opioid practices decreased post-interventions, it was not eliminated. Level of evidence: 3.