Evaluating information management system in epidemic infectious diseases in Iran.

Health information management is a vital and constructive component of the health system, refers to the process of producing and collecting, organising and storing, analysing, disseminating and using information. The aim of this study was to evaluate the strengths and weaknesses of the information management system in epidemic infectious diseases in Iran, specifically focusing on the registration, reporting, quality, confidentiality, and security of infectious disease data. This assessment was conducted from the perspective of policymakers and experts responsible for data registration and reporting. After examining the processes of registering and reporting infectious disease data and interviewing experts, a researcher-designed questionnaire was prepared to evaluate the infectious disease information management system. To assess the content validity of the Content Validity Index and Content Validity Ratio Index, a questionnaire was utilized. The reliability of the questionnaire was confirmed using Cronbach's alpha. By employing purposeful sampling and adhering to the inclusion criteria, 150 participants were included in the study. Questionnaires were distributed via email, WhatsApp, or Telegram to employees at various levels of Iran's health and treatment systems who were responsible for registering and reporting infectious disease data. The study encompassed 100 participants who successfully concluded the research. The results highlight that the key strength of healthcare data registration lies in its ability to "depict the epidemic curve during outbreaks of infectious diseases." Conversely, a notable weakness was the "insufficient collaboration from non-academic sectors (e.g., clinics, private laboratories) in registering and reporting infectious diseases. The present study's findings suggest that the issue lies not in the framework itself, but rather in the execution and functionality of the strategies. We can cultivate a repository of reliable and beneficial data by incorporating initiatives like training programs, enforcing regulations with consequences for inadequate data documentation, offering both material and motivational rewards, and streamlining all data collection and reporting systems.

[Testing an algorithm to identify disabilities using current health and social healthcare database: the experience of the Agency for Health Protection of the Metropolitan Area of Milan]. / La classificazione delle disabilità utilizzando i flussi sanitari e sociosanitari correnti: l'algoritmo sviluppato dall'Agenzia di Tutela della Salute della Città Metropolitana di Milano.

OBJECTIVES: to describe prevalence of disability in the population of the Agency for Health Protection of Milan (ATS Milan), integrating current administrative healthcare, socio-healthcare, and social data; to classify disability with a diagnosis into a predominant structural and functional category according to the International Classification of Functioning, Disability and Health (ICF), supplementing it with additional levels of detail. DESIGN: retrospective observational study. SETTING AND PARTICIPANTS: subjects residing in the territory of ATS Milan in the years from 2018 to 2022.  Main outcomes measures: prevalence of disability in the population of ATS Milan from 2018 to 2022; average annual costs since disability diagnosis of the entire population and stratified by the most common ICF classifications. RESULTS: the prevalence of disability ranges from 5.8% in 2018 to 8.4% in 2022. In general, women have a higher prevalence than men. However, there are significant differences in the gender distribution depending on the considered age group. The main disabilities (32.2%) affect the structures of the nervous system and mental functions, followed by disabilities identified solely by major prosthetic devices (9.4%) and sensory disabilities with alterations in sensory functions with the presence of a major device (5.2%). Analysis of average total annual per capita costs shows an upward trend with increasing years since the diagnosis. CONCLUSIONS: the definition of standardized tools, such as the selection from several available healthcare data provided by service suppliers, can be helpful in obtaining reliable data on the prevalence of disability in the population. This evidence can be useful in planning public health interventions to address the needs of this population. The work developed by ATS Milan has been carried out in alignment with the activities outlined in Mission 5 of the National Recovery and Resilience Plan (PNRR), in particular for the reform of disability legislation, which foresees the definition of standardized tools for the in-depth study of the epidemiological aspects of the phenomenon.

Lessons learned from a pay-for-performance scheme for appropriate prescribing using electronic health records from general practices in the Netherlands.

INTRODUCTION: A nationwide pay-for-performance (P4P) scheme was introduced in the Netherlands between 2018 and 2023 to incentivize appropriate prescribing in general practice. Appropriate prescribing was operationalised as adherence to prescription formularies and measured based on electronic health records (EHR) data. We evaluated this P4P scheme from a learning health systems perspective. METHODS: We conducted semi-structured interviews with 15 participants representing stakeholders of the scheme: general practitioners (GPs), health insurers, pharmacists, EHR suppliers and formulary committees. We used a thematic approach for data analysis. RESULTS: Using EHR data showed several benefits, but lack of uniformity of EHR systems hindered consistent measurements. Specific indicators were favoured over general indicators as they allow GPs to have more control over their performance. Most participants emphasized the need for GPs to jointly reflect on their performance. Communication to GPs appeared to be challenging. Partly because of these challenges, impact of the scheme on prescribing behaviour was perceived as limited. However, several unexpected positive effects of the scheme were mentioned, such as better EHR recording habits. CONCLUSIONS: This study identified benefits and challenges useful for future P4P schemes in promoting appropriate care with EHR data. Enhancing uniformity in EHR systems is crucial for more consistent quality measurements. Future P4P schemes should focus on high-quality feedback, peer-to-peer learning and establish a single point of communication for healthcare providers.

An Unsupervised Error Detection Methodology for Detecting Mislabels in Healthcare Analytics.

Medical datasets may be imbalanced and contain errors due to subjective test results and clinical variability. The poor quality of original data affects classification accuracy and reliability. Hence, detecting abnormal samples in the dataset can help clinicians make better decisions. In this study, we propose an unsupervised error detection method using patterns discovered by the Pattern Discovery and Disentanglement (PDD) model, developed in our earlier work. Applied to the large data, the eICU Collaborative Research Database for sepsis risk assessment, the proposed algorithm can effectively discover statistically significant association patterns, generate an interpretable knowledge base for interpretability, cluster samples in an unsupervised learning manner, and detect abnormal samples from the dataset. As shown in the experimental result, our method outperformed K-Means by 38% on the full dataset and 47% on the reduced dataset for unsupervised clustering. Multiple supervised classifiers improve accuracy by an average of 4% after removing abnormal samples by the proposed error detection approach. Therefore, the proposed algorithm provides a robust and practical solution for unsupervised clustering and error detection in healthcare data.

Guidance of development, validation, and evaluation of algorithms for populating health status in observational studies of routinely collected data (DEVELOP-RCD).

BACKGROUND: In recent years, there has been a growing trend in the utilization of observational studies that make use of routinely collected healthcare data (RCD). These studies rely on algorithms to identify specific health conditions (e.g. diabetes or sepsis) for statistical analyses. However, there has been substantial variation in the algorithm development and validation, leading to frequently suboptimal performance and posing a significant threat to the validity of study findings. Unfortunately, these issues are often overlooked. METHODS: We systematically developed guidance for the development, validation, and evaluation of algorithms designed to identify health status (DEVELOP-RCD). Our initial efforts involved conducting both a narrative review and a systematic review of published studies on the concepts and methodological issues related to algorithm development, validation, and evaluation. Subsequently, we conducted an empirical study on an algorithm for identifying sepsis. Based on these findings, we formulated specific workflow and recommendations for algorithm development, validation, and evaluation within the guidance. Finally, the guidance underwent independent review by a panel of 20 external experts who then convened a consensus meeting to finalize it. RESULTS: A standardized workflow for algorithm development, validation, and evaluation was established. Guided by specific health status considerations, the workflow comprises four integrated steps: assessing an existing algorithm's suitability for the target health status; developing a new algorithm using recommended methods; validating the algorithm using prescribed performance measures; and evaluating the impact of the algorithm on study results. Additionally, 13 good practice recommendations were formulated with detailed explanations. Furthermore, a practical study on sepsis identification was included to demonstrate the application of this guidance. CONCLUSIONS: The establishment of guidance is intended to aid researchers and clinicians in the appropriate and accurate development and application of algorithms for identifying health status from RCD. This guidance has the potential to enhance the credibility of findings from observational studies involving RCD.

Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives.

Disease trajectories, defined as sequential, directional disease associations, have become an intense research field driven by the availability of electronic population-wide healthcare data and sufficient computational power. Here, we provide an overview of disease trajectory studies with a focus on European work, including ontologies used as well as computational methodologies for the construction of disease trajectories. We also discuss different applications of disease trajectories from descriptive risk identification to disease progression, patient stratification, and personalized predictions using machine learning. We describe challenges and opportunities in the area that eventually will benefit from initiatives such as the European Health Data Space, which, with time, will make it possible to analyze data from cohorts comprising hundreds of millions of patients.

Nationwide Electronic Prescription Services in Finland in 2010-2023.

This research aimed to follow up a 14-year period (2010-2023) public and private healthcare service organizations' and community pharmacies' entries to and exits from the centralized, interoperable and shared electronic Prescription Services in Finland. Our material were the official Social Welfare and Healthcare Organization Registry and the official Pharmacy Registry; their data were extracted in January 2024. Outcomes were continuous registration of services or registered exist from the services. In addition, we used information from the Kanta Services for presenting monthly and annual number of electronic prescriptions and medicine dispensations on national level. In 2010-2023, totally 838 community pharmacies' and their subsidiary pharmacies' entries to and 24 exits from the nationwide Prescription Services took place, and in total 814 pharmacy outlets had the Prescription Services in production in 2023. Totally, 1980 public and private healthcare service organizations' entries to and 494 exits from the Prescription Service took place, and 1486 organizations had the Prescription Services in production in 2023. Healthcare service organizations recorded totally 303.8 million electronic prescriptions into the Prescription Services. Recorded numbers were lower during the Covid-19 epidemic in Finland in 2020-2021. We also observed seasonal effects in the time series. Pharmacies recorded totally 660.4 million medicine dispensations (purchases) into the Prescription Services with an increasing trend year after year. We also observed seasonal effects in the dispensation time series.

Enhancing Clinical Relevance of Pretrained Language Models Through Integration of External Knowledge: Case Study on Cardiovascular Diagnosis From Electronic Health Records.

BACKGROUND: Despite their growing use in health care, pretrained language models (PLMs) often lack clinical relevance due to insufficient domain expertise and poor interpretability. A key strategy to overcome these challenges is integrating external knowledge into PLMs, enhancing their adaptability and clinical usefulness. Current biomedical knowledge graphs like UMLS (Unified Medical Language System), SNOMED CT (Systematized Medical Nomenclature for Medicine-Clinical Terminology), and HPO (Human Phenotype Ontology), while comprehensive, fail to effectively connect general biomedical knowledge with physician insights. There is an equally important need for a model that integrates diverse knowledge in a way that is both unified and compartmentalized. This approach not only addresses the heterogeneous nature of domain knowledge but also recognizes the unique data and knowledge repositories of individual health care institutions, necessitating careful and respectful management of proprietary information. OBJECTIVE: This study aimed to enhance the clinical relevance and interpretability of PLMs by integrating external knowledge in a manner that respects the diversity and proprietary nature of health care data. We hypothesize that domain knowledge, when captured and distributed as stand-alone modules, can be effectively reintegrated into PLMs to significantly improve their adaptability and utility in clinical settings. METHODS: We demonstrate that through adapters, small and lightweight neural networks that enable the integration of extra information without full model fine-tuning, we can inject diverse sources of external domain knowledge into language models and improve the overall performance with an increased level of interpretability. As a practical application of this methodology, we introduce a novel task, structured as a case study, that endeavors to capture physician knowledge in assigning cardiovascular diagnoses from clinical narratives, where we extract diagnosis-comment pairs from electronic health records (EHRs) and cast the problem as text classification. RESULTS: The study demonstrates that integrating domain knowledge into PLMs significantly improves their performance. While improvements with ClinicalBERT are more modest, likely due to its pretraining on clinical texts, BERT (bidirectional encoder representations from transformer) equipped with knowledge adapters surprisingly matches or exceeds ClinicalBERT in several metrics. This underscores the effectiveness of knowledge adapters and highlights their potential in settings with strict data privacy constraints. This approach also increases the level of interpretability of these models in a clinical context, which enhances our ability to precisely identify and apply the most relevant domain knowledge for specific tasks, thereby optimizing the model's performance and tailoring it to meet specific clinical needs. CONCLUSIONS: This research provides a basis for creating health knowledge graphs infused with physician knowledge, marking a significant step forward for PLMs in health care. Notably, the model balances integrating knowledge both comprehensively and selectively, addressing the heterogeneous nature of medical knowledge and the privacy needs of health care institutions.

Unlocking the Power of Real-World Data: A Framework for Sustainable Healthcare.

Real-world data (RWD) has the potential to revolutionize healthcare by offering valuable insights into patient outcomes and treatment efficacy. However, leveraging RWD effectively presents challenges, including its inherent limitations, diverse stakeholders, and insufficient data management pipelines. A proposed framework advocates three essential elements: adherence to FAIR principles (Findable, Accessible, Interoperable, and Reusable), stakeholder engagement and education, and highlighting the need for inclusive, pragmatic federated hybrid pipelines. By employing these strategies, healthcare organizations can overcome obstacles to RWD utilization and foster sustainable progress in patient care.

Challenges and Lessons Learned in Mapping HL7 v2 Data to openEHR: Insights from UKSH Medical Data Integration Center.

This paper explores the challenges and lessons learned during the mapping of HL7 v2 messages structured using custom schema to openEHR for the Medical Data Integration Center (MeDIC) of the University Hospital, Schleswig-Holstein (UKSH). Missing timestamps in observations, missing units of measurement, inconsistencies in decimal separators and unexpected datatypes were identified as critical inconsistencies in this process. These anomalies highlight the difficulty of automating the transformation of HL7 v2 data to any standard, particularly openEHR, using off-the-shelf tools. Addressing these anomalies is crucial for enhancing data interoperability, supporting evidence-based research, and optimizing clinical decision-making. Implementing proper data quality measures and governance will unlock the potential of integrated clinical data, empowering clinicians and researchers and fostering a robust healthcare ecosystem.

Optimizing Integration with the Italian EHR: The Use of LOINC Codes.

According to the regulation "Decreto del Presidente del Consiglio dei Ministri" (DPCM) of September 29, 2015, n.178, the Logical Observation Identifiers Names and Codes (LOINC) system is included among the coding systems adopted in the Italian Electronic Health Record (EHR). As part of the Digital Health Solutions in Community Medicine (DHEAL-COM) project, one key goal is to categorize parameters using international classification systems. This enables the identification of appropriate Information and Communication Technology (ICT) solutions tailored to support people's health needs. Our objective is to incorporate LOINC codes for parameter categorization, thus anticipating the future use of EHR.

Standardizing X-Ray and Angiography Procedure Coding in Estonia: Validating TermX Through the Implementation of SNOMED CT.

BACKGROUND: The existence of multiple code systems and standards has highlighted the necessity for innovative solutions to bridge these discrepancies. OBJECTIVES: This research investigates the utilisation of TermX to tackle the challenges of interoperability in radiology procedures, with a specific emphasis on angiography and X-ray modalities. RESULTS: The study produced a revised RadLex data model and mapping guide, designed to classify radiology services using TermX. In total, 380 concepts were required to comprehensively describe all 622 procedures examined. CONCLUSIONS: Our study demonstrates the effectiveness of TermX in simplifying the process of mapping between code systems, thus enabling more efficient analysis, and reporting of data.

The Cancer Clinical Library Database (CCLD) from the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) Project.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea's cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

GP consultations for menstrual disorders after COVID-19 vaccination - A self-controlled cohort study based on routine healthcare data from the Netherlands.

INTRODUCTION: Several studies described that COVID-19 vaccinations can cause menstrual disorders. Our study aimed to describe whether this also resulted in more general practitioner (GP) consultations for menstrual disorders after COVID-19 vaccination, based on a large cohort study. METHODS: A retrospective self-controlled cohort study was performed including vaccinated women in 2021 aged 12-49 years from two large, representative GP databases in the Netherlands. Incidence rates and incidence rate ratio's (IRR) were calculated using Poisson regression, adjusting for SARS-CoV-2 infection as time-varying confounder. The exposed period was set at maximum six months after each COVID-19 vaccination and the non-exposed period was defined as all-time outside the exposed period. RESULTS: The cohort included 631,802 women, of which 18,986 (3 %) consulted the GP for a menstrual disorder during 2021. Increased GP consultations were observed among 12-14 year olds for amenorrhea/hypomenorrhea/oligomenorrhea (IRR: 1.85, 95 % CI: 1.30-2.65) and irregular/frequent menstruation (IRR: 1.33, 95 % CI: 1.06-1.69) after COVID-19 vaccination in general, and after Pfizer/BioNTech vaccination (IRR: 1.87, 95 % CI: 1.31-2.67 for amenorrhea/hypomenorrhea/oligomenorrhea and IRR: 1.35, 95 % CI: 1.06-1.70 for irregular/frequent menstruation). Persons from this age group were in general also vaccinated with Pfizer/BioNTech. No increase in the frequency of GP consultations were observed for older age groups, other vaccine brands, and potential risk groups. CONCLUSION: For the majority of women, no increased GP consultations for menstrual disorders was found. Solely for the youngest age group (12-14 year olds) increased GP consultations for specific types of menstrual disorders was found after Pfizer/BioNTech vaccination.

Exploring the Impact of Artificial Intelligence and Machine Learning in the Diagnosis and Management of Esthesioneuroblastomas: A Comprehensive Review.

Esthesioneuroblastomas (ENBs) present unique diagnostic and therapeutic challenges due to their rare and complex clinical presentation. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as promising tools in various medical specialties, revolutionizing diagnostic accuracy, treatment planning, and patient outcomes. However, their application in ENBs remains relatively unexplored. This comprehensive literature review aims to evaluate the current state of AI and ML technologies in ENB diagnosis, radiological and histopathological imaging, and treatment planning. By synthesizing existing evidence and identifying gaps in knowledge, this review aims to showcase the potential benefits, limitations, and future directions of integrating AI and ML into the multidisciplinary management of ENBs.

Adoption of high-sensitivity cardiac troponin for risk stratification of patients with suspected myocardial infarction: a multicentre cohort study.

Background: Guidelines recommend high-sensitivity cardiac troponin to risk stratify patients with possible myocardial infarction and identify those eligible for discharge. Our aim was to evaluate adoption of this approach in practice and to determine whether effectiveness and safety varies by age, sex, ethnicity, or socioeconomic deprivation status. Methods: A multi-centre cohort study was conducted in 13 hospitals across the United Kingdom from November 1st, 2021, to October 31st, 2022. Routinely collected data including high-sensitivity cardiac troponin I or T measurements were linked to outcomes. The primary effectiveness and safety outcomes were the proportion discharged from the Emergency Department, and the proportion dead or with a subsequent myocardial infarction at 30 days, respectively. Patients were stratified using peak troponin concentration as low (<5 ng/L), intermediate (5 ng/L to sex-specific 99th percentile), or high-risk (>sex-specific 99th percentile). Findings: In total 137,881 patients (49% [67,709/137,881] female) were included of whom 60,707 (44%), 42,727 (31%), and 34,447 (25%) were stratified as low-, intermediate- and high-risk, respectively. Overall, 65.8% (39,918/60,707) of low-risk patients were discharged from the Emergency Department, but this varied from 26.8% [2200/8216] to 93.5% [918/982] by site. The safety outcome occurred in 0.5% (277/60,707) and 11.4% (3917/34,447) of patients classified as low- or high-risk, of whom 0.03% (18/60,707) and 1% (304/34,447) had a subsequent myocardial infarction at 30 days, respectively. A similar proportion of male and female patients were discharged (52% [36,838/70,759] versus 54% [36,113/67,109]), but discharge was more likely if patients were <70 years old (61% [58,533/95,227] versus 34% [14,428/42,654]), from areas of low socioeconomic deprivation (48% [6697/14,087] versus 43% [12,090/28,116]) or were black or asian compared to caucasian (62% [5458/8877] and 55% [10,026/18,231] versus 46% [35,138/75,820]). Interpretation: Despite high-sensitivity cardiac troponin correctly identifying half of all patients with possible myocardial infarction as being at low risk, only two-thirds of these patients were discharged. Substantial variation in the discharge of patients by age, ethnicity, socioeconomic deprivation, and site was observed identifying important opportunities to improve care. Funding: UK Research and Innovation.

Association between Gastric Cancer and Osteoporosis: A Longitudinal Follow-Up Study Using a National Health Sample Cohort.

Gastric cancer (GC) survivors may be more likely to develop osteoporosis. However, few studies on the relationship between GC and osteoporosis have been conducted on large patient populations. We aimed to determine the incidence of osteoporosis and identify related factors by comparing patients with GC and matched controls using the Korean National Health Insurance Service-National Sample Cohort (KNHIS-NSC). This study included 9078 patients with GC and 36,312 controls (1:4 propensity score-matched for sex, age, residence, and income). The hazard ratio (HR) for osteoporosis was significantly greater for GC patients than for controls according to Charlson Comorbidity Index (CCI) score-adjusted models (adjusted HR = 1.13). Kaplan-Meier analysis revealed that the cumulative incidence of osteoporosis during the follow-up period commencing from the index date was significantly greater in GC patients than in the controls (p = 0.0087). A positive correlation of osteoporosis with GC was detected for those aged < 65 years, males, and those with CCI scores = 0. In conclusion, the study findings suggest that men with GC aged < 65 years may be at an increased risk for osteoporosis. Research into additional risk factors and the optimal timing of interventions are needed to prevent fractures and minimize bone loss in GC survivors.

Efficient differential privacy enabled federated learning model for detecting COVID-19 disease using chest X-ray images.

The rapid spread of COVID-19 pandemic across the world has not only disturbed the global economy but also raised the demand for accurate disease detection models. Although many studies have proposed effective solutions for the early detection and prediction of COVID-19 with Machine Learning (ML) and Deep learning (DL) based techniques, but these models remain vulnerable to data privacy and security breaches. To overcome the challenges of existing systems, we introduced Adaptive Differential Privacy-based Federated Learning (DPFL) model for predicting COVID-19 disease from chest X-ray images which introduces an innovative adaptive mechanism that dynamically adjusts privacy levels based on real-time data sensitivity analysis, improving the practical applicability of Federated Learning (FL) in diverse healthcare environments. We compared and analyzed the performance of this distributed learning model with a traditional centralized model. Moreover, we enhance the model by integrating a FL approach with an early stopping mechanism to achieve efficient COVID-19 prediction with minimal communication overhead. To ensure privacy without compromising model utility and accuracy, we evaluated the proposed model under various noise scales. Finally, we discussed strategies for increasing the model's accuracy while maintaining robustness as well as privacy.

Model correction of diagnostic coding-based RSV incidence for children 0-4 years in the US.

BACKGROUND: Although administrative claims data have a high degree of completeness, not all medically attended Respiratory Syncytial Virus-associated lower respiratory tract infections (RSV-LRTIs) are tested or coded for their causative agent. We sought to determine the attribution of RSV to LRTI in claims data via modeling of temporal changes in LRTI rates against surveillance data. METHODS: We estimated the weekly incidence of LRTI (inpatient, outpatient, and total) for children 0-4 years using 2011-2019 commercial insurance claims, stratified by HHS region, matched to the corresponding weekly NREVSS RSV and influenza positivity data for each region, and modelled against RSV, influenza positivity rates, and harmonic functions of time assuming negative binomial distribution. LRTI events attributable to RSV were estimated as predicted events from the full model minus predicted events with RSV positivity rate set to 0. RESULTS: Approximately 42% of predicted RSV cases were coded in claims data. Across all regions, the percentage of LRTI attributable to RSV were 15-43%, 10-31%, and 10-31% of inpatient, outpatient, and combined settings, respectively. However, when compared to coded inpatient RSV-LRTI, 9 of 10 regions had improbable corrected inpatient LRTI estimates (predicted RSV/coded RSV ratio < 1). Sensitivity analysis based on separate models for PCR and antigen-based positivity showed similar results. CONCLUSIONS: Underestimation based on coding in claims data may be addressed by NREVSS-based adjustment of claims-based RSV incidence. However, where setting-specific positivity rates is unavailable, we recommend modeling across settings to mirror NREVSS's positivity rates which are similarly aggregated, to avoid inaccurate adjustments.