Healthcare resource utilization and economic burden of multiple sclerosis in Chinese patients: results from a real-world survey.
Chenhan SunYusheng JiaHainan LiXuanqi QiaoMi TangMeiyan GengEddie JonesJames PikeMia BerryMin HuPublished in: Scientific reports (2024)
Multiple sclerosis (MS) is uncommon in China and the standard of care is underdeveloped, with limited utilization of disease-modifying treatment (DMT). An understanding of real-world disease burden (including direct medical, non-medical, and indirect costs, such as loss of productivity), is currently lacking in this population. To investigate the overall burden of managing patients with MS in China, a cross-sectional survey of physicians and their consulting patients with MS was conducted in 2021. Physicians provided information on healthcare resource utilization (HCRU; consultations, hospitalizations, tests, medication) and associated costs. Patients provided data on changes in their life, productivity, and impairment of daily activities due to MS. Results were stratified by disease severity using generalized linear models, with a p value < 0.05 considered statistically significant. Patients with more severe disease had greater HCRU, including hospitalizations, consultations and tests/scans, and incurred higher direct and indirect costs and productivity loss, compared with those with milder disease. However, the use of DMT was higher in patients with mild disease severity. With the low uptake and limited efficacy of non-DMT drugs, Chinese patients with MS experience a high disease burden and significant unmet needs. Therapeutic interventions could help save downstream costs and lessen societal burden.
Keyphrases
- multiple sclerosis
- healthcare
- mass spectrometry
- ms ms
- climate change
- primary care
- white matter
- risk factors
- end stage renal disease
- computed tomography
- ejection fraction
- physical activity
- palliative care
- magnetic resonance imaging
- emergency department
- health information
- magnetic resonance
- machine learning
- pain management
- peritoneal dialysis
- quality improvement
- big data
- dual energy