Exploring humanistic burden of fatigue in adults with multiple sclerosis: an analysis of US National Health and Wellness Survey data.

BACKGROUND: This retrospective study examined the humanistic burden of fatigue in patients with relapsing-remitting multiple sclerosis (RRMS), compared with adults without MS, using data from the 2017 and 2019 US National Health and Wellness Survey. METHODS: The 5-item Modified Fatigue Impact Scale (MFIS-5) was used to assess level of fatigue (MFIS-5 score <15: low fatigue [LF]; MFIS-5 score ≥15: high fatigue [HF]) in patients with RRMS. Health-related quality of life (HRQoL) measures (Short Form 36-Item Health Survey version 2, Euroqol-5 Dimensions-5 Levels [EQ-5D-5L], Patient Health Questionnaire-9 [PHQ-9], Generalized Anxiety Disorder-7 [GAD-7], Perceived Deficits Questionnaire-5) and treatment-related characteristics were assessed. RESULTS: In total, 498 respondents were identified as RRMS (n=375 RRMS+LF, n=123 RRMS+HF) and compared with 1,494 matched non-MS controls. RRMS+LF and RRMS+HF had significantly lower Short Form 6 Dimensions health utility, Mental and Physical Component Summary, and EQ-5D-5L scores and higher PHQ-9 and GAD-7 scores, compared with matched non-MS controls (all p<0.001); scores were worse for RRMS+HF than RRMS+LF across all measures (all p<0.001). A higher proportion of RRMS+HF reported moderate-to-severe depression and moderate-to-severe anxiety, compared with RRMS+LF and matched non-MS controls (both p<0.001). Fatigue was a significant predictor of poor HRQoL across all measures (all p<0.001). CONCLUSIONS: Patients with RRMS experienced lower HRQoL with higher levels of fatigue, highlighting an unmet need. Results may help to inform physician-patient communication and shared decision-making to address fatigue and its associated impact on patients' HRQoL.

Assessment of economic burden of fatigue in adults with multiple sclerosis: An analysis of US National Health and Wellness Survey data.

BACKGROUND: Fatigue, a common disabling symptom in multiple sclerosis (MS), is reported by the majority of patients. However, evidence on the economic burden of fatigue in MS by fatigue status is limited. This study aimed to evaluate the economic burden of fatigue, including healthcare resource utilization (HCRU), labor force participation, and Work Productivity and Activity Impairment (WPAI), among adults with relapsing-remitting MS (RRMS) by low fatigue (LF) vs high fatigue (HF) and compared with adults without MS. METHODS: This cross-sectional, retrospective, observational study included pooled data from the 2017 and 2019 US National Health and Wellness Survey. The RRMS sample included respondents aged ≥18 years who reported being diagnosed with MS by a healthcare provider (HCP) and reported having RRMS. Non-MS controls included respondents aged ≥18 years who did not report being diagnosed with MS by an HCP. Fatigue was measured using the Modified Fatigue Impact Scale-5 (MFIS-5). Outcomes included HCRU (HCP visits, emergency department visits, and hospitalizations in the past 12 months), labor force participation (yes vs no), WPAI (absenteeism, presenteeism, total work productivity impairment, and activity impairment), and annualized costs (direct medical, indirect, and total). Respondents with RRMS were propensity-score matched to non-MS controls (ratio 1:3). RRMS respondents were categorized as having LF (MFIS-5<15; RRMS+LF) and HF (MFIS-5≥15; RRMS+HF). Bivariate analysis compared matched non-MS controls, RRMS+LF, and RRMS+HF. Multivariable analyses were conducted among RRMS to evaluate associations between fatigue (continuous variable) and outcomes. RESULTS: Overall, 498 respondents with RRMS (RRMS+LF, n=375; RRMS+HF, n=123) and 1494 matched non-MS controls were included. RRMS+HF and RRMS+LF had more HCRU in the past 12 months than non-MS controls, whereas RRMS+HF had greater HCRU than RRMS+LF (all p<0.05). WPAI was also higher among RRMS+HF and RRMS+LF, compared with non-MS controls, as well as higher in RRMS+HF vs RRMS+LF (all p<0.001). RRMS+HF had significantly higher annualized direct medical costs than RRMS+LF and matched non-MS controls ($19,978 vs $10,656, p=0.007; vs $8,048, p<0.001). Among employed respondents, RRMS+HF and RRMS+LF had higher annualized indirect costs than non-MS controls, with RRMS+HF also having higher annualized indirect costs than RRMS+LF ($23,647 vs $13,738 vs $8,001; all p<0.01); total annualized costs were higher in RRMS+HF and RRMS+LF, compared with non-MS controls, as well as RRMS+HF vs RRMS+LF (all p<0.01). In multivariable models, fatigue was significantly and positively associated with the number of HCP visits in the past 12 months (p=0.002); not participating in the labor force (p<0.001); and absenteeism, presenteeism, total work productivity impairment, and activity impairment (all p<0.001). CONCLUSION: RRMS poses a substantial economic burden on patients and society, and this burden is disproportionately associated with HF.

Adult Obesity and Health-Related Quality of Life, Patient Activation, Work Productivity, and Weight Loss Behaviors in the United States.

INTRODUCTION: While it is generally believed that people living with more severe obesity experience greater negative impacts on health-related quality of life (HRQoL), their experience may be impacted by other factors such as age, gender, and type 2 diabetes mellitus (T2DM). METHODS: The 36-Item Short Form Health Survey physical component score and mental component score, Work Productivity and Activity Impairment, and Patient Activation Measure® data from the 2018 National Health and Wellness Survey were analyzed in adults by body mass index (BMI) categories (normal weight: ≥18.5-<25kg/m2, overweight: ≥25-<30kg/m2, class 1 obesity: ≥30-<35kg/m2, class 2 obesity: ≥35-<40kg/m2, class 3 obesity: ≥40kg/m2, combined class 2/3 obesity: ≥35kg/m2). Findings were further stratified across age groups (young: 18-35y, middle-aged: 36-64y, older: ≥65y), by gender, and by T2DM status. RESULTS: Overall, as BMI increased people had greater negative effects on HRQoL, felt less involved with and in control of their healthcare, and had greater work productivity and activity impairments. The largest declines were generally observed between class 2 and 3 obesity categories. Young adults with obesity were more likely to feel less engaged with their health care than middle-aged/older adults with obesity. The effects of obesity on HRQoL and patient activation were generally consistent by gender. People with T2DM and obesity tended to have greater declines in physical functioning and more work and activity impairments than people with obesity without T2DM. The proportion of people trying to lose weight increased with increasing BMI category, and people with T2DM were less likely to exercise and more likely to diet than those without T2DM. CONCLUSION: Increasing levels of obesity tended to have a greater negative impact on HRQoL, patient activation, work productivity, and weight loss behaviors, but some differences in effects by age, gender, and T2DM status were observed.