Evaluating perinatal health in Europe: a comparison of routine population birth data sources

Context: International comparisons of the health of mothers and babies provide essential benchmarks for guiding health practice and policy, but statistics are not routinely compiled in a comparable way. These data are especially critical during health emergencies, such as the coronavirus disease (COVID-19) pandemic. The Population Health Information Research Infrastructure (PHIRI) project aimed to promote the exchange of population data in Europe and included a Use Case on perinatal health. Objective: To develop and test a protocol for federated analysis of population birth data in Europe. Methods: The Euro-Peristat network with participants from 31 countries developed a Common Data Model (CDM) and R scripts to exchange and analyse aggregated data on perinatal indicators. Building on recommended Euro-Peristat indicators, complemented by a three-round consensus process, the network specified variables for a CDM and common outputs. The protocol was tested using routine birth data for 2015 to 2020; a survey was conducted assessing data provider experiences and opinions. Results: The CDM included 17 core data items for the testing phase and 18 for a future expanded phase. 28 countries and the four UK nations created individual person-level databases and ran R scripts to produce anonymous aggregate tables. Seven had all core items, 17 had 13-16, while eight had ≤12. Limitations were not having all items in the same database, required for this protocol. Infant death and mode of birth were most frequently missing. Countries took from under a day to several weeks to set up the CDM, after which the protocol was easy and quick to use. Conclusion: This open-source protocol enables rapid production and analysis of perinatal indicators and constitutes a roadmap for a sustainable European information system. It also provides minimum standards for improving national data systems and can be used in other countries to facilitate comparison of perinatal indicators.

Background and Objectives: Peritoneal dialysis (PD) requires well-functioning medical devices (MDs). PD complications can result in significant adverse events, including the discontinuation of PD, hospitalization, and death. This study aimed to evaluate the feasibility of detecting various PD complications and data related to MDs. Materials and Methods: A retrospective study was conducted on patients who received PD catheter insertions between January 2001 and March 2021 to evaluate PD-related complications. PD complications were evaluated through diagnostic, procedural, and MD codes using a common data model (CDM) and were compared with those from electronic health records (EHRs). The results from one CDM database were compared with those from another CDM database. Results: A total of 342 patients were enrolled. One hundred and ninety-five patients experienced PD complications more than once. Nineteen prescription codes and twenty diagnostic codes from the EHR were identified, covering 11 procedures, three MDs, and seven complications related to PD. Infectious complications were detected using the CDM, whereas mechanical complications were missed. Although data on PD catheters and adaptors were available in the EHR, they were not detected via the CDM. Some infectious and mechanical complications were identified via the CDM in the other database. After implementing amended matching, these data were detected. Conclusions: While some PD-related medical data recorded in EHRs were misrepresented or omitted during the CDM database extraction, transformation, and loading process, the CDM shows potential to serve as a source of real-world data for active surveillance.

ObjectiveThe objective was to work with local services in Bradford to improve the quality of routine perinatal mental health data for research and service planning. This stemmed from analysis of locally linked routine data, which identified significant gaps and issues, and the motivation of services to better use their data. MethodsBorn in Bradford for All (BiB4All) is an electronic data linkage cohort of mothers and their children, which aims to use routinely collected data for research and to inform local policy and practice. To achieve this aim, we established links with relevant stakeholder groups in Bradford, to understand their current priorities. Through attending stakeholder meetings, we identified a common goal of improving routine perinatal mental health data across services in Bradford, to enable a better understanding of inequalities and unmet need. We co-produced a research question and analysis of BiB4All data to explore the extent of the current data issues. ResultsEstablishing a trusted relationship between local services recording routine data and researchers using these data enabled the co-design of a workshop aiming to develop an action plan to address identified data issues. The workshop (planned for April 2025) is underpinned by Ketso principles and will bring together local maternity, health visiting and specialist perinatal mental health services in Bradford to discuss: Their current data assets, based on insights from the BiB4All data analysisHow to improve their dataBarriers to improving their dataNext steps for implementing changes to data collection This collaboration has developed understanding among perinatal mental health services about how routine data can be used beyond individual clinical care for research and service planning. ConclusionBuilding capacity for routine data to be used for research and service improvements is an important step to more equitable access and uptake to perinatal mental health services. We are planning an evaluation to understand the impact of this work on data quality, service access and inclusion.

Migration and perinatal health surveillance: An international Delphi survey

Objectives: Electronic health records (EHRs) have increasingly emerged as a powerful source of clinical data that can be leveraged for reuse in research and in modular health apps that integrate into diverse health information technologies. A key challenge to these use cases is representing the knowledge contained within data from different EHR systems in a uniform fashion. Method: We reviewed several recent studies covering the knowledge representation in the common data models for the Observational Medical Outcomes Partnership (OMOP) and its Observational Health Data Sciences and Informatics program, and the United States Patient Centered Outcomes Research Network (PCORNet). We also reviewed the Health Level 7 Fast Healthcare Interoperability Resource standard supporting app-like programs that can be used across multiple EHR and research systems. Results: There has been a recent growth in high-impact efforts to support quality-assured and standardized clinical data sharing across different institutions and EHR systems. We focused on three major efforts as part of a larger landscape moving towards shareable, transportable, and computable clinical data. Conclusion: The growth in approaches to developing common data models to support interoperable knowledge representation portends an increasing availability of high-quality clinical data in support of research. Building on these efforts will allow a future whereby significant portions of the populations in the world may be able to share their data for research.

Perinatal health and malocclusions in preschool children: Findings from a cohort of adolescent mothers in Southern Brazil.

BackgroundImproved data access and funding for health services research have promoted the application of routine data to measure costs and effects of interventions within the German health care system. Following the trend towards real world evidence, this review aims to evaluate the status and quality of health economic evaluations based on routine data in Germany.MethodsTo identify relevant economic evaluations, a systematic literature search in the databases PubMed and EMBASE was complemented by a manual search. The included studies had to be full economic evaluations using German routine data to measure either costs, effects, or both. Study characteristics were assessed with a structured template. Additionally, the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) were used to measure quality of reporting.ResultsIn total, 912 records were identified and 35 studies were included in the further analysis. The majority of these studies was published in the past 5 years (n = 27, 77.1%) and used insurance claims data as a source of routine data (n = 30, 85.7%). The most common method used for handling selection bias was propensity score matching. With regard to the reporting quality, 42.9% (n = 15) of the studies satisfied at least 80% of the criteria on the CHEERS checklist.ConclusionsThis review confirms that routine data has become an increasingly common data source for health economic evaluations in Germany. While most studies addressed the application of routine data, this analysis reveals deficits in considering methodological particularities and in reporting quality of economic evaluations based on routine data. Nevertheless, this review demonstrates the overall potential of routine data for economic evaluations.

Background:Use of routine data sources within clinical research is increasing and is endorsed by the National Institute for Health Research to increase trial efficiencies; however there is limited evidence for its use in clinical trials, especially in relation to self-harm. One source of routine data, Hospital Episode Statistics, is collated and distributed by NHS Digital and contains details of admissions, outpatient, and Accident and Emergency attendances provided periodically by English National Health Service hospitals. We explored the reliability and accuracy of Hospital Episode Statistics, compared to data collected directly from hospital records, to assess whether it would provide complete, accurate, and reliable means of acquiring hospital attendances for self-harm – the primary outcome for the SHIFT (Self-Harm Intervention: Family Therapy) trial evaluating Family Therapy for adolescents following self-harm.Methods:Participant identifiers were linked to Hospital Episode Statistics Accident and Emergency, and Admissions data, and episodes combined to describe participants’ complete hospital attendance. Attendance data were initially compared to data previously gathered by trial researchers from pre-identified hospitals. Final comparison was conducted of subsequent attendances collected through Hospital Episode Statistics and researcher follow-up. Consideration was given to linkage rates; number and proportion of attendances retrieved; reliability of Accident and Emergency, and Admissions data; percentage of self-harm episodes recorded and coded appropriately; and percentage of required data items retrieved.Results:Participants were first linked to Hospital Episode Statistics with an acceptable match rate of 95%, identifying a total of 341 complete hospital attendances, compared to 139 reported by the researchers at the time. More than double the proportion of Hospital Episode Statistics Accident and Emergency episodes could not be classified in relation to self-harm (75%) compared to 34.9% of admitted episodes, and of overall attendances, 18% were classified as self-harm related and 20% not related, while ambiguity or insufficient information meant 62% were unclassified. Of 39 self-harm-related attendances reported by the researchers, Hospital Episode Statistics identified 24 (62%) as self-harm related while 15 (38%) were unclassified. Based on final data received, 1490 complete hospital attendances were identified and comparison to researcher follow-up found Hospital Episode Statistics underestimated the number of self-harm attendances by 37.2% (95% confidence interval 32.6%–41.9%).Conclusion:Advantages of routine data collection via NHS Digital included the acquisition of more comprehensive and timely trial outcome data, identifying more than double the number of hospital attendances than researchers. Disadvantages included ambiguity in the classification of self-harm relatedness. Our resulting primary outcome data collection strategy used routine data to identify hospital attendances supplemented by targeted researcher data collection for attendances requiring further self-harm classification.

Objective This study aims to compare data availability and analytic results for patients using matched data items from a dedicated disease registry versus data extracted directly from an electronic patient record (EPR) system and a trusted research environment (TRE). Methods Data from patients enrolled in the National JDM Cohort and Biomarker Study (JDCBS) was compared with routine EPR data from the same patients attending a specialist children’s hospital between 2019-2021. Data from both sources were extracted, de-identified, and analysed within a trusted research environment adhering to NHS security standards. Descriptive statistics, visualizations, and statistical comparisons were performed. Results Of the 688 registry patients in total, 270 attended one specialist hospital with EPR data available. The EPR system yielded 328,527 data points on these patients compared to 40,673 from the registry, including 2-10 fold more data items across data categories. Diagnoses were more numerous in the EPR data, while registry data captured more comprehensive medication records. Laboratory test results were 10 times more frequent in EPR data, including a broader range of test types. Despite higher data volume in EPR, the clinical significance of the additional data points remains uncertain. Conclusion Routine EPR data can effectively replicate much disease registry data with a larger volume of data points, potentially offering additional analytical possibilities. However, specific targeted registry data collection remains valuable for certain data elements. A hybrid approach, utilizing both routine EPR data and focused registry collection, could optimise healthcare research by reducing costs and avoiding duplication.

Background:The reuse of data from electronic health records (EHRs) for research purposes promises to improve the data foundation for clinical trials and may even support to enable them. Nevertheless, EHRs are characterized by both, heterogeneous structure and semantics. To standardize this data for research, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standard has recently seen an increase in use. However, the conversion of these EHRs into the OMOP CDM requires complex and resource intensive Extract Transform and Load (ETL) processes. This hampers the reuse of clinical data for research. To solve the issues of heterogeneity of EHRs and the lack of semantic precision on the care site, the openEHR standard has recently seen wider adoption. A standardized process to integrate openEHR records into the CDM potentially lowers the barriers of making EHRs accessible for research. Yet, a comprehensive approach about the integration of openEHR records into the OMOP CDM has not yet been made. Methods:We analyzed both standards and compared their models to identify possible mappings. Based on this, we defined the necessary processes to transform openEHR records into CDM tables. We also discuss the limitation of openEHR with its unspecific demographics model and propose two possible solutions. Results:We developed the OMOP Conversion Language (OMOCL) which enabled us to define a declarative openEHR archetype-to-CDM mapping language. Using OMOCL, it was possible to define a set of mappings. As a proof-of-concept, we implemented the Eos tool, which uses the OMOCL-files to successfully automatize the ETL from real-world and sample EHRs into the OMOP CDM. Discussion:Both Eos and OMOCL provide a way to define generic mappings for an integration of openEHR records into OMOP. Thus, it represents a significant step towards achieving interoperability between the clinical and the research data domains. However, the transformation of openEHR data into the less expressive OMOP CDM leads to a loss of semantics.

OBJECTIVES: The objectives of this study were to present maternal and perinatal health indicators for the years 2019–2022. METHODOLOGY: This is a cross-sectional review that analyzed data on maternal and perinatal health indicators, generated from submissions of POGS-accredited hospitals (training and service) from January 2019 to December 2022. The data were compared to the national data obtained from official public documents published by the Philippine Statistics Authority in 2022 and 2023. RESULTS: The number of member hospitals has steadily increased over the years, and compliance rates have been consistently over 85%. The total number of registered cases and live births declined in 2020 and 2021, but has increased to prepandemic levels in 2022. The primary cesarean section (CS) rates remained above 20%, with the highest rate noted in 2021 (25.5%). The top three-most common indications for primary CS were dysfunctional labor, fetal distress, and malpresentation. Stillbirth, perinatal, and maternal mortality rates showed an increase from 2019 to 2022, with peak rates registered for the year 2021. The top five causes of maternal death are: medical complications, hemorrhage, hypertension, infection, and others (unspecified). The case fatality rate among pregnant patients with confirmed COVID-19 infection was 1.18%. CONCLUSION: From 2019 to 2021, there was a decline in the number of registered cases and live births, and an increase in the primary CS rates, maternal mortality ratio, perinatal death rates, and stillbirth rates. This may be explained by the prevailing social, health, and economic impact of the COVID-19 pandemic during these years.

ObjectiveThe rapidly evolving COVID-19 pandemic has created a need for timely data from the healthcare systems for research. To meet this need, several large new data consortia have been developed that require frequent updating and sharing of electronic health record (EHR) data in different common data models (CDMs) to create multi-institutional databases for research. Traditionally, each CDM has had a custom pipeline for extract, transform, and load operations for production and incremental updates of data feeds to the networks from raw EHR data. However, the demands of COVID-19 research for timely data are far higher, and the requirements for updating faster than previous collaborative research using national data networks have increased. New approaches need to be developed to address these demands.MethodsIn this article, we describe the use of the Fast Healthcare Interoperability Resource (FHIR) data model as a canonical data model and the automated transformation of clinical data to the Patient-Centered Outcomes Research Network (PCORnet) and Observational Medical Outcomes Partnership (OMOP) CDMs for data sharing and research collaboration on COVID-19.ResultsFHIR data resources could be transformed to operational PCORnet and OMOP CDMs with minimal production delays through a combination of real-time and postprocessing steps, leveraging the FHIR data subscription feature.ConclusionsThe approach leverages evolving standards for the availability of EHR data developed to facilitate data exchange under the 21st Century Cures Act and could greatly enhance the availability of standardized datasets for research.

BACKGROUND Inflammatory Bowel Disease (IBD) is an increasingly prevalent condition that results in weight loss, bloody diarrhea, and poor quality of life. Traditional population-based research on patients with IBD has involved use of relatively shallow claims data that is deficient in precise demographics. IBD research using richly detailed electronic health record (EHR) data has been difficult due to the need for laborious manual chart review to extract information, but new methods and technology allow for rapid EHR data extraction. We present here the results of the extraction of IBD patient data from our EHR into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). METHODS This study is an observational cohort study using patient health care data from our EHR extracted into the OMOP CDM. Transforming data into the standard CDM format allows for information from different databases and coding schema to be compared and aggregated. We used a de-identified dataset of all patients seen at Johns Hopkins Medicine since 2016. Our cohort of IBD, Crohn’s disease (CD), and Ulcerative Colitis (UC) patients were stratified by age, sex, race, weight, and pregnancy status. Within the cohorts, we obtained demographics, comorbidities, symptoms, labs, medications, and other data. We validated the extraction by comparison with EHR queries using Slicer/Dicer (SD). RESULTS There were 11,357 IBD patients including 6,512 CD patients and 4,224 UC patients. There were more female patients for both CD and UC (56.4% v 57.5%) and more white patients for both CD and UC (55.4% v 56.7%) compared to black (23.5% v 22.6%) and Asian patients (5.6% v 4.9%). CD and UC patients were treated with systemic steroids (49.1% v 51.5%), 5-ASA (24.9% v 47.2%) anti-TNF inhibitors (24.0% v 12%), IL12/23 inhibitors (8.4% v 2.7%), and JAK inhibitors (0.3% v 1.8%). Using SD queries, the percent differences were determined between the OMOP and EHR data, revealing a 0.54% difference in the number of CD patients and 10.36% difference in the number of UC patients (Table 1). CONCLUSION Using OMOP CDM to analyze clinical characteristics and outcomes for patients with IBD is a valuable method for obtaining de-identified patient data, reducing patient risk, and facilitating rapid creation of on-demand cohorts for multi-institutional research studies. This advantage is balanced by the lack of access to row-level data to validate cohorts. Local validation of data extraction through SD queries can allow for rapid creation of cohorts for big data research. The most significant differences between SD queries and OMOP CDM was in the calculation of prevalence of medications and race, suggesting that differing data extraction coding methods could result in queries that underestimate rates of treatment due to proprietary local coding schema.

BackgroundAcute blood glucose (BG) decompensations (hypoglycemia and hyperglycemia) represent a frequent and significant risk for inpatients and adversely affect patient outcomes and safety. The increasing need for BG management in inpatients poses a high demand on clinical staff and health care systems in addition.ObjectiveThis study aimed to generate a broadly applicable multiclass classification model for predicting BG decompensation events from patients’ electronic health records to indicate where adjustments in patient monitoring and therapeutic interventions are required. This should allow for taking proactive measures before BG levels are derailed.MethodsA retrospective cohort study was conducted on patients who were hospitalized at a tertiary hospital in Bern, Switzerland. Using patient details and routine data from electronic health records, a multiclass prediction model for BG decompensation events (<3.9 mmol/L [hypoglycemia] or >10, >13.9, or >16.7 mmol/L [representing different degrees of hyperglycemia]) was generated based on a second-level ensemble of gradient-boosted binary trees.ResultsA total of 63,579 hospital admissions of 38,250 patients were included in this study. The multiclass prediction model reached specificities of 93.7%, 98.9%, and 93.9% and sensitivities of 67.1%, 59%, and 63.6% for the main categories of interest, which were nondecompensated cases, hypoglycemia, or hyperglycemia, respectively. The median prediction horizon was 7 hours and 4 hours for hypoglycemia and hyperglycemia, respectively.ConclusionsElectronic health records have the potential to reliably predict all types of BG decompensation. Readily available patient details and routine laboratory data can support the decisions for proactive interventions and thus help to reduce the detrimental health effects of hypoglycemia and hyperglycemia.

There is wide recognition that, with the rapid implementation of electronic health records (EHRs), large data sets are available for research. However, essential standardized nursing data are seldom integrated into EHRs and clinical data repositories. There are many diverse activities that exist to implement standardized nursing languages in EHRs; however, these activities are not coordinated, resulting in duplicate efforts rather than building a shared learning environment and resources. The purpose of this paper is to describe the historical context of nursing terminologies, challenges to the use of nursing data for purposes other than documentation of care, and a national action plan for implementing and using sharable and comparable nursing data for quality reporting and translational research. In 2013 and 2014, the University of Minnesota School of Nursing hosted a diverse group of nurses to participate in the Nursing Knowledge: Big Data and Science to Transform Health Care consensus conferences. This consensus conference was held to develop a national action plan and harmonize existing and new efforts of multiple individuals and organizations to expedite integration of standardized nursing data within EHRs and ensure their availability in clinical data repositories for secondary use. This harmonization will address the implementation of standardized nursing terminologies and subsequent access to and use of clinical nursing data. Foundational to integrating nursing data into clinical data repositories for big data and science, is the implementation of standardized nursing terminologies, common data models, and information structures within EHRs. The 2014 National Action Plan for Sharable and Comparable Nursing Data for Transforming Health and Healthcare builds on and leverages existing, but separate long standing efforts of many individuals and organizations. The plan is action focused, with accountability for coordinating and tracking progress designated.