Psychiatric comorbidities cluster early after onset in MOGAD: a cross-sectional comparative study with MS and NMOSD.
Authors: Niederschweiberer M, Bakir C, Vorasoot N, Cacciaguerra L, Tillema JM, Vilaseca A, Thakolwiboon S, Aboseif A, Syc-Mazurek SB, Rees WA, Basso MR, Lopez-Chiriboga AS, Wingerchuk DM, Tobin WO, Croarkin PE, Sagen J, Pittock SJ, Chen JJ, Flanagan EP
Journal: Journal of neurology, neurosurgery, and psychiatry
mental health
psychology
open access
Abstract
Health and social care systems face growing pressure to improve outcomes with limited resources, increasing the need to evaluate service effectiveness. The secondary use of health data enables more efficient health care and data-driven value solutions, but its success depends on the availability and quality of contextual data.
The emerging European Health Data Space (EHDS) is reshaping how data can be accessed and reused.
Improved data availability enables more systematic assessment of effectiveness and increases the need for frameworks and information requirements that guide such evaluation.
The World Health Organization (WHO) emphasizes data infrastructures that support personalized care and the purposeful secondary use of health information.
Effectiveness evaluation should be based on dynamic, system-level knowledge management that incorporates the citizen and client perspective and examines service processes through their overall impact on well-being, with individual visits or service episodes informing this broader evaluation. Health and social care registries generate extensive and heterogeneous data, making structured, interoperable, and high-quality data essential for effective research.
Analytical choices must align with the structure and provenance of available data to ensure meaningful results, as modeling frameworks vary in suitability, assumptions, and interpretability, and many are not designed for heterogeneous registry data.
In this study, we selected an analytical approach suitable for registry-based data and transparent variable-level interpretation. We apply a structured association-analysis framework that supports the systematic assessment of individual-level variables and their combinations across the different service use contexts defined by reason-for-encounter categories. Digitalization has become a strategic foundation for health and social care development.
The COVID-19 pandemic accelerated digital adoption, expanding the use of video consultations.
Before the pandemic, evidence on digital primary care focused mainly on telephone-based models with limited evaluation of effectiveness or patient needs.
Post-pandemic research shows that digital services expanded across population groups while digital inequality increased, particularly among older adults and socioeconomically disadvantaged populations.
Today, digital health services span video consultations, remote monitoring, the transmission of patient-generated data, online information seeking, and alert systems supporting continuity of care.
The WHO digital health intervention classification further systematizes this landscape by delineating capability-based categories that encompass targeted and untargeted communication, personal health tracking and self-monitoring, on-demand access to health information, and platform-based functionalities for reporting, financial transactions, and the management of consent.
The emerging European Health Data Space (EHDS) is reshaping how data can be accessed and reused.
Improved data availability enables more systematic assessment of effectiveness and increases the need for frameworks and information requirements that guide such evaluation.
The World Health Organization (WHO) emphasizes data infrastructures that support personalized care and the purposeful secondary use of health information.
Effectiveness evaluation should be based on dynamic, system-level knowledge management that incorporates the citizen and client perspective and examines service processes through their overall impact on well-being, with individual visits or service episodes informing this broader evaluation. Health and social care registries generate extensive and heterogeneous data, making structured, interoperable, and high-quality data essential for effective research.
Analytical choices must align with the structure and provenance of available data to ensure meaningful results, as modeling frameworks vary in suitability, assumptions, and interpretability, and many are not designed for heterogeneous registry data.
In this study, we selected an analytical approach suitable for registry-based data and transparent variable-level interpretation. We apply a structured association-analysis framework that supports the systematic assessment of individual-level variables and their combinations across the different service use contexts defined by reason-for-encounter categories. Digitalization has become a strategic foundation for health and social care development.
The COVID-19 pandemic accelerated digital adoption, expanding the use of video consultations.
Before the pandemic, evidence on digital primary care focused mainly on telephone-based models with limited evaluation of effectiveness or patient needs.
Post-pandemic research shows that digital services expanded across population groups while digital inequality increased, particularly among older adults and socioeconomically disadvantaged populations.
Today, digital health services span video consultations, remote monitoring, the transmission of patient-generated data, online information seeking, and alert systems supporting continuity of care.
The WHO digital health intervention classification further systematizes this landscape by delineating capability-based categories that encompass targeted and untargeted communication, personal health tracking and self-monitoring, on-demand access to health information, and platform-based functionalities for reporting, financial transactions, and the management of consent.