Tools for the Assessment of Comorbidity Burden in Rheumatoid Arthritis

Aslam, Fawad; Khan, Nasim A.

doi:10.3389/fmed.2018.00039

Cited by 43 publications

(30 citation statements)

References 63 publications

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“…This comparison arm however is difficult to find, since patients recruited at primary care clinics may be offered higher screening than the general population, just for the reason of being medically followed. Finally, we didn't use common validated comorbidity indices, because they are usually validated only in one type of RMD, mostly RA 62,63 . Instead, we preferred using the EULAR points to consider as they cover all RMD simultaneously.…”

Section: Discussionmentioning

confidence: 99%

Prevalence and pattern of comorbidities in chronic rheumatic and musculoskeletal diseases: the COMORD study

Ziadé

Khoury

Zoghbi

et al. 2020

Sci Rep

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increased risk of comorbidities has been reported in Rheumatic and Musculoskeletal Diseases (RMD). We aimed to evaluate the prevalence and pattern of comorbidities in RMD patients nationwide, to identify multimorbidity clusters and to evaluate the gap between recommendations and real screening. Cross-sectional, multicentric nationwide study. Prevalence of comorbidities was calculated according to six EULAR axes. Latent Class Analysis identified multimorbidity clusters. Comorbidities' screening was compared to international and local recommendations. In 769 patients (307 RA, 213 OA, 63 SLE, 103 axSpA, and 83 pSA), the most frequent comorbidities were cardiovascular risk factors and diseases (CVRFD) (hypertension 36.5%, hypercholesterolemia 30.7%, obesity 22.7%, smoking 22.1%, diabetes 10.4%, myocardial infarction 6.6%), osteoporosis (20.7%) and depression (18.1%). Three clusters of multimorbidity were identified: OA, RA and axSpA. The most optimal screening was found for cVRf (> = 93%) and osteoporosis (53%). For malignancies, mammograms were the most optimally prescribed (56%) followed by pap smears (32%) and colonoscopy (21%). Optimal influenza and pneumococcus vaccination were found in 22% and 17%, respectively. Comorbidities were prevalent in RMD and followed specific multimorbidity patterns. Optimal screening was adequate for CVRFD but suboptimal for malignant neoplasms, osteoporosis, and vaccination. The current study identified health priorities, serving as a framework for the implementation of future comorbidity management standardized programs, led by the rheumatologist and coordinated by specialized health care professionals. Rheumatic and musculoskeletal diseases (RMD) are universally prevalent chronic non-communicable diseases (NCD) with a significant contribution to the Global Burden of Diseases 1. They are strong determinants of pain, disability 2-4 and years lived with disability (YLDs) worldwide 5. Many patients experience the concurrent presence of more than one NCD, which is a phenomenon known as multimorbidity 6. NCD may aggregate due to chance-depending on their prevalence in the population-, or due to shared pathophysiologic mechanisms 7,8. They are likely to act synergistically 9 , causing an overall burden that is larger than the sum of their individual impacts. In the general population 6 , four distinct patterns of multimorbidity from chronic NCDs were found: low disease probability, cardio-metabolic conditions, respiratory conditions and RMD and depression pattern, with RMD being highly prevalent across all these patterns. All multimorbidity patterns have a direct association with age and are strongly associated with adverse health outcomes such as long-term disability, frequent healthcare utilization, worsened functional status, poorer quality of life 10 and higher mortality 11,12 .

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Section: Discussionmentioning

confidence: 99%

Prevalence and pattern of comorbidities in chronic rheumatic and musculoskeletal diseases: the COMORD study

Ziadé

Khoury

Zoghbi

et al. 2020

Sci Rep

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“…Additionally, a Charlson co-morbidity index (CCI) was also available. The CCI reflects 19 conditions related to the potential for mortality and morbidity [ 24 ]. Finally, a variable was also included to indicate whether the patient was new to the Rheumatology Department at the time of the study visit or whether they were an established patient.…”

Section: Methodsmentioning

confidence: 99%

The influence of collecting patient-reported outcome measures on visit satisfaction in rheumatology clinics

Honomichl¹,

Katzan²,

Thompson³

et al. 2020

Rheumatology Advances in Practice

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Objectives Patient-reported outcome measures (PROMs) can direct patient-centered care and increase patient satisfaction with the visit. The objective of this study was to assess the relationship between the collection of PROMs and visit satisfaction, as measured by the Clinician and Group Practice Consumer Assessment of Healthcare Providers and Systems (CG-CAHPS) survey. Methods An electronic platform for collection of patient-reported information was implemented in Rheumatology clinics between August-September 2016. Adult patients were included in the study if they completed CG-CAHPS after an ambulatory visit. The pre-implementation cohort consisted of patients seen between January–June 2016; the post-implementation cohort consisted of patients seen January-June 2017. CG-CAHPS scores were compared between cohorts. Mixed effect models were constructed to identify predictors of visit satisfaction. Results Characteristics were similar between the 2,117 pre- and 2,380 post-implementation patients. Visit satisfaction was high in both cohorts but did not differ (OR = 0.97 (95% CI: 0.79, 1.19)). Predictors of improved satisfaction included being an established patient, male, older age, and reporting higher quality-of-life. However, sensitivity analyses in the post-implementation cohort suggested that implementing PROMs may convey benefits for new patients, in particular. Conclusion PROMs collection had no effect on visit satisfaction in Rheumatology clinics, though there may be benefits for new patients. These largely null findings may be due to high satisfaction levels in our cohorts or lack of provider review of PROM data with patients. Further research is indicated to determine the impact of provider communication of PROM results to patients on different domains of visit satisfaction.

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“…The CCI allows for adjustment of baseline comorbidity between groups, and is a widely used comorbidity index in studies that use administrative health data. 25 The higher the score, the more likely the predicted outcome will result in mortality or higher resource use. 26,27 MS characteristics included severity of MS, symptoms, fatigue, and perceived cognitive impairment.…”

Section: Methodsmentioning

confidence: 99%

Burden of Relapsing-Remitting Multiple Sclerosis on Workers in the US: A Cross-sectional Analysis of Survey Data

Nicholas

Electricwala

Lee

et al. 2019

Preprint

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Background: MS is prevalent among working age individuals (20-60 years), leading to high burden on work productivity. Few data are available about the absenteeism and presenteeism in employed individuals with MS in comparison to non-MS personnel. This study aimed to quantify the burden of illness of employed US adults with relapsing-remitting multiple sclerosis (RRMS) and examine burden by levels of work impairment. Methods: A retrospective cross-sectional analysis was conducted using patient-reported responses from the US National Health and Wellness Survey (NHWS). Data from NHWS 2015-2016 were analyzed from 196 employed RRMS respondents who were matched 1:4 to employed respondents without multiple sclerosis (MS) based on demographic and general health characteristics. Demographic and general health characteristics for employed RRMS individuals were analyzed by levels of work impairment (none, 1-30%; 31-68%; 69-100%). Work productivity (absenteeism, presenteeism, and work impairment), decrements in health-related quality of life (HRQoL) (short form-36, EQ-5D), and healthcare resource utilization (HCRU) were compared to determine the burden of RRMS. Results: After propensity score matching, the levels of absenteeism and presenteeism were 2 and 1.8 times higher in the employed RRMS population than the employed non-MS population, respectively (P<0.001 for both). HRQoL was significantly lower in employed respondents with RRMS than those without MS (P<0.001 for all). Employed respondents with RRMS had significantly more HCRU over 6 months compared to those without MS (P<0.001). Furthermore, among employed RRMS respondents, greater levels of impairment were associated with increasing disease severity, greater healthcare resource use, fatigue, and cognitive impairment and inversely associated with mental and physical HRQoL (P<0.0001 for all). Conclusions: Among employed individuals, respondents with RRMS had lower, work productivity, HRQoL, and higher HCRU as compared with those without MS. Given the large impact RRMS has on work impairment, a need exists to manage individuals on therapies that improve HRQoL, reduce symptoms, and improve their ability to perform in the workforce.

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Tools for the Assessment of Comorbidity Burden in Rheumatoid Arthritis

Cited by 43 publications

References 63 publications

Prevalence and pattern of comorbidities in chronic rheumatic and musculoskeletal diseases: the COMORD study

Prevalence and pattern of comorbidities in chronic rheumatic and musculoskeletal diseases: the COMORD study

The influence of collecting patient-reported outcome measures on visit satisfaction in rheumatology clinics

Burden of Relapsing-Remitting Multiple Sclerosis on Workers in the US: A Cross-sectional Analysis of Survey Data

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