Big Data in the Assessment of Pediatric Medication Safety

Pediatric Pharmacology for the Primary Care Provider: Advances and Limitations

Despite >1 in 5 children taking prescription drugs in the United States, off-label drug use is common. To increase the study of drugs in children, regulatory bodies have enacted legislation to incentivize and require pediatric drug studies. As a result of this legislation, novel trial approaches, and an increase in personnel with pediatric expertise, there have been numerous advancements in pediatric drug development. With this review, we aim to highlight developments in pediatric pharmacology over the past 6 years for the most common disease processes that may be treated pharmacologically by the pediatric primary care provider. Using information extracted from label changes between 2018 and 2023, the published literature, and Clinicaltrials.gov, we discuss advances across multiple therapeutic areas relevant to the pediatric primary care provider, including asthma, obesity and related disorders, mental health disorders, infections, and dermatologic conditions. We highlight instances in which new drugs have been developed on the basis of a deeper mechanistic understanding of illness and instances in which labels have been expanded in older drugs on the basis of newly available data. We then consider additional factors that affect pediatric drug use, including cost and nonpharmacologic therapies. Although there is work to be done, efforts focused on pediatric-specific drug development will increase the availability of evidence-based, labeled guidance for commonly prescribed drugs and improve outcomes through the safe and effective use of drugs in children.

Improving child health through Big Data and data science

Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. IMPACT: Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research.

Challenges of Utilizing Medical Big Data in Reproductive Health Research

In the background of the “Three-Child Policy” introduced by the Chinese government, reproductive health has become one of the most important public health issues. With the promotion of digitization management of medical care institutions for women and children in the country, there will be chances to acquire medical big data of obstetrics and pediatrics. Here the authors are presenting their opinions on the challenges of the management and utilization of reproductive big data.

Off-label drug use in neonates and infants in Spain: A five-year observational study

OBJECTIVES

To provide information about the off-label rate of all drug prescriptions in neonates and infants up to 1 year in Spain. Also, to analyse the off-label prescription of medicines under current practice in this age group according to different evidence sources.

STUDY DESIGN

A five-year (2015-2019) exploratory observational study about off-label prescription in neonates and infants (0 to 1 year) at primary health care in Spain. All drug prescriptions in this age group were analysed and classified according to their labelling in off-label or on-label. The drugs prescribed off-label were subsequently reviewed in national formularies and other databases to assess its evidence of use beyond what is recommended in the Summary of Product Characteristics (SmPC).

RESULTS

On average 34.50% of total prescriptions were prescribed off-label according to the SmPC. 17.93% of total prescriptions in neonates and infants up to 1 year old were not based on clinical evidence from SmPC, Pediamécum, BNF or DailyMed. In more than 88% of cases, off-label use was related to the posology section of the SmPC, followed by the therapeutic indications and contraindications sections, in 35.20% and 24.10% of cases, respectively. Almost 13% of off-label drugs were over-the-counter. Salbutamol followed by topical tobramycin and colecalciferol were the drugs most prescribed off-label.

CONCLUSIONS

Off-label use of drugs remains as an important public health concern, especially for neonates and infants up to 1 year, who receive the greatest proportion of off-label prescriptions. The evidence-based off-label prescription is a widespread practice that has shown a stable trend during the 5-year study period providing also a certain extent of flexibility to paediatricians in some therapeutic decisions.

International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries

IMPORTANCE

Additional sources of pediatric epidemiological and clinical data are needed to efficiently study COVID-19 in children and youth and inform infection prevention and clinical treatment of pediatric patients.

OBJECTIVE

To describe international hospitalization trends and key epidemiological and clinical features of children and youth with COVID-19.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study included pediatric patients hospitalized between February 2 and October 10, 2020. Patient-level electronic health record (EHR) data were collected across 27 hospitals in France, Germany, Spain, Singapore, the UK, and the US. Patients younger than 21 years who tested positive for COVID-19 and were hospitalized at an institution participating in the Consortium for Clinical Characterization of COVID-19 by EHR were included in the study.

MAIN OUTCOMES AND MEASURES

Patient characteristics, clinical features, and medication use.

RESULTS

There were 347 males (52%; 95% CI, 48.5-55.3) and 324 females (48%; 95% CI, 44.4-51.3) in this study's cohort. There was a bimodal age distribution, with the greatest proportion of patients in the 0- to 2-year (199 patients [30%]) and 12- to 17-year (170 patients [25%]) age range. Trends in hospitalizations for 671 children and youth found discrete surges with variable timing across 6 countries. Data from this cohort mirrored national-level pediatric hospitalization trends for most countries with available data, with peaks in hospitalizations during the initial spring surge occurring within 23 days in the national-level and 4CE data. A total of 27 364 laboratory values for 16 laboratory tests were analyzed, with mean values indicating elevations in markers of inflammation (C-reactive protein, 83 mg/L; 95% CI, 53-112 mg/L; ferritin, 417 ng/mL; 95% CI, 228-607 ng/mL; and procalcitonin, 1.45 ng/mL; 95% CI, 0.13-2.77 ng/mL). Abnormalities in coagulation were also evident (D-dimer, 0.78 ug/mL; 95% CI, 0.35-1.21 ug/mL; and fibrinogen, 477 mg/dL; 95% CI, 385-569 mg/dL). Cardiac troponin, when checked (n = 59), was elevated (0.032 ng/mL; 95% CI, 0.000-0.080 ng/mL). Common complications included cardiac arrhythmias (15.0%; 95% CI, 8.1%-21.7%), viral pneumonia (13.3%; 95% CI, 6.5%-20.1%), and respiratory failure (10.5%; 95% CI, 5.8%-15.3%). Few children were treated with COVID-19-directed medications.

CONCLUSIONS AND RELEVANCE

This study of EHRs of children and youth hospitalized for COVID-19 in 6 countries demonstrated variability in hospitalization trends across countries and identified common complications and laboratory abnormalities in children and youth with COVID-19 infection. Large-scale informatics-based approaches to integrate and analyze data across health care systems complement methods of disease surveillance and advance understanding of epidemiological and clinical features associated with COVID-19 in children and youth.

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 β 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.