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Objective Integrating electronic health record (EHR) data with other resources is essential in rare disease research due to low disease prevalence. Such integration is dependent on the alignment of ontologies used for data annotation. The international classification of diseases (ICD) is used to annotate clinical diagnoses, while the human phenotype ontology (HPO) is used to annotate phenotypes. Although these ontologies overlap in the biomedical entities they describe, the extent to which they are interoperable is unknown. We investigate how well aligned these ontologies are and whether such alignments facilitate EHR data integration. Materials and Methods We conducted an empirical analysis of the coverage of mappings between ICD and HPO. We interpret this mapping coverage as a proxy for how easily clinical data can be integrated with research ontologies such as HPO. We quantify how exhaustively ICD codes are mapped to HPO by analyzing mappings in the unified medical language system (UMLS) Metathesaurus. We analyze the proportion of ICD codes mapped to HPO within a real-world EHR dataset. Results and Discussion Our analysis revealed that only 2.2% of ICD codes have direct mappings to HPO in UMLS. Within our EHR dataset, less than 50% of ICD codes have mappings to HPO terms. ICD codes that are used frequently in EHR data tend to have mappings to HPO; ICD codes that represent rarer medical conditions are seldom mapped. Conclusion We find that interoperability between ICD and HPO via UMLS is limited. While other mapping sources could be incorporated, there are no established conventions for what resources should be used to complement UMLS. Lay Summary We present a thorough empirical analysis of the compatibility between international classification of diseases (ICD) codes and human phenotype ontology (HPO) terms based on the unified medical language system (UMLS) Metathesaurus. ICD is used to annotate clinical diagnoses in EHR data, while HPO is used to annotate phenotypes in research databases. Bridging between the 2 artifacts is essential for health data integration and analysis. UMLS is a widely used source of cross-ontology mappings, and so it is important to quantitatively assess the extent to which ICD is mapped to HPO in the UMLS. The primary results from the paper include that a mere 2.2% of ICD codes in UMLS are directly linked to HPO. Furthermore, an analysis of our EHR dataset shows that less than half of the commonly used ICD codes can be mapped to HPO terms. Notably, commonly used ICD codes in EHR data tend to have corresponding mappings to HPO. In contrast, ICD codes representing rarer medical conditions are infrequently associated with HPO terms.

IntroductionNon-pharmaceutical interventions (NPIs) such as mask-wearing and social distancing, implemented as public health measures to slow COVID-19 transmission, had a major impact on the epidemiology of viral infections. However, little is known about their influence on bacterial infections in children.MethodsWe performed a multicentre observational study including eight hospitals in three countries (Spain, UK and USA). All hospitalisations in children under the age of 18 from January 2019 to February 2023 were included. Electronic health record data were used to assess changes in hospitalisations for bacterial infections in three different periods based on NPI stringency, classified as pre-NPI (January 2019 to February 2020), full NPI (March 2020 to February 2021) and partial NPI (March 2021 to February 2023). The primary outcomes were the counts of hospitalisations for invasive, respiratory and skin-associated bacterial infections. To identify changes in the monthly counts of bacterial infections in a data-driven manner, we used a multivariable quasi-Poisson regression model adjusting for important covariates with adaptive lasso penalty. We then assessed the statistical significance of the identified changes and examined the temporal trend before and after each change point.ResultsWe found that of the 508 585 paediatric hospitalisations, 41 076 (8.1%) were associated with any bacterial infection. 14 656 (35.7%) were invasive bacterial infections, 6763 (16.5%) were respiratory tract-associated and 7757 (18.9%) were skin-associated. Counts of bacterial infections decreased during the full-NPI period (average count 93.7 infections/month) compared with the pre-NPI period (average count 104.8 infections/month) and increased during the partial NPI period (average count 112.4 infections/month). A quasi-Poisson regression model showed a significant decrease in respiratory tract-associated bacterial infections after the start of the COVID-19 pandemic and a subsequent significant increase after the gradual lifting of NPIs, peaking during the winter of 2022–2023. No significant changes were observed over time for skin-associated and invasive bacterial infections.ConclusionsThe implementation of COVID-19 NPIs was significantly associated with changes in hospitalisations for respiratory associated-bacterial infections, but not invasive and skin-associated bacterial infections. These findings suggest that the impact of NPIs has been greatest for respiratory infections and indicate the potential of targeted NPIs to reduce these infections among children in the future.

Objectives To describe the implementation of the first phase of a regional perinatal data repository and to provide a roadmap for others to navigate technical, privacy, and data governance concerns in implementing similar resources. Methods Our implementation integrated regional physician billing records with maternal and infant electronic health records from an academic delivery hospital. These records, representing births during 2013–2015, constituted a data core supporting linkage to additional ancillary data sets. Measures obtained from pediatric follow-up, urgent care, emergency, and inpatient encounters were linked at the individual level as were measures obtained by home visitors during pre- and postnatal encounters. Residential addresses were geocoded supporting linkage to area-level measures. Results Integrated data contained regional billing records for 69,290 newborns representing approximately 81% of all regional live births and nearly 95% of live births in the region’s most populous county. Billing records linked to 7293 infant delivery hospital records and 7107 corresponding maternal hospital records. Manual review demonstrated 100% validity of matches among audited records. Additionally, 2430 home visiting records were linked to the data core as were pediatric primary care, urgent care, emergency department, and inpatient visits representing 42,541 children. More than 99% of the newborn billing records were geocoded and assigned a census tract identifier. Conclusions for Practice Our approach to methodological and regulatory challenges affords opportunities for expansion of systems to integrate electronic health records originating from additional medical centers as well as individual- and area-level linkage to additional data sets relevant to perinatal health.

The COVID-19 pandemic has been associated with an increase in mental health diagnoses among adolescents, though the extent of the increase, particularly for severe cases requiring hospitalization, has not been well characterized. Large-scale federated informatics approaches provide the ability to efficiently and securely query health care data sets to assess and monitor hospitalization patterns for mental health conditions among adolescents. To estimate changes in the proportion of hospitalizations associated with mental health conditions among adolescents following onset of the COVID-19 pandemic. This retrospective, multisite cohort study of adolescents 11 to 17 years of age who were hospitalized with at least 1 mental health condition diagnosis between February 1, 2019, and April 30, 2021, used patient-level data from electronic health records of 8 children's hospitals in the US and France. Change in the monthly proportion of mental health condition-associated hospitalizations between the prepandemic (February 1, 2019, to March 31, 2020) and pandemic (April 1, 2020, to April 30, 2021) periods using interrupted time series analysis. There were 9696 adolescents hospitalized with a mental health condition during the prepandemic period (5966 [61.5%] female) and 11 101 during the pandemic period (7603 [68.5%] female). The mean (SD) age in the prepandemic cohort was 14.6 (1.9) years and in the pandemic cohort, 14.7 (1.8) years. The most prevalent diagnoses during the pandemic were anxiety (6066 [57.4%]), depression (5065 [48.0%]), and suicidality or self-injury (4673 [44.2%]). There was an increase in the proportions of monthly hospitalizations during the pandemic for anxiety (0.55%; 95% CI, 0.26%-0.84%), depression (0.50%; 95% CI, 0.19%-0.79%), and suicidality or self-injury (0.38%; 95% CI, 0.08%-0.68%). There was an estimated 0.60% increase (95% CI, 0.31%-0.89%) overall in the monthly proportion of mental health-associated hospitalizations following onset of the pandemic compared with the prepandemic period. In this cohort study, onset of the COVID-19 pandemic was associated with increased hospitalizations with mental health diagnoses among adolescents. These findings support the need for greater resources within children's hospitals to care for adolescents with mental health conditions during the pandemic and beyond.