Impact of Comorbid Conditions on Healthcare Expenditure and Work-related Outcomes in Patients With Rheumatoid Arthritis

Impact of incident rheumatoid arthritis on earnings: a nationwide sibling comparison study

OBJECTIVES

RA is known to impact work ability, but much of this knowledge comes from historical comparisons vs the general population that neither reflects current RA management nor distinguishes between effects of RA and pre-existing socio-economic conditions of patients. We therefore aimed to examine earnings of patients before and after RA diagnosis, using recent data and sibling comparisons.

METHODS

Swedish register data were used including demographic information and healthcare utilization. Participants were patients with RA (aged 30-60 years, diagnosed with RA between 2006 and 2017) identified in the Swedish National Patient Register, and their same-sex siblings (n = 2433:2433; mean 48 years; 72% women). Earnings data for 2001-2019 were retrieved from Statistics Sweden and analysed from 5 years before to 5 years after RA diagnosis.

RESULTS

No differences in average earnings were observed between siblings during the 5 years before diagnosis, but during the 5 years after diagnosis, patients with RA earned on average 5.4% less annually [-1430€ (95% CI -2130, -720)] than same-sexed siblings. The change in earnings for the subgroup diagnosed between 2006 and 2010 was -8.2% [-2020€ (95% CI -2930, -1120)] but for patients diagnosed between 2011 and 2017, there was no statistically significant change in earnings compared with siblings [-1.5%; -420€ (95% CI -1490, 640)]. Subgroup analyses demonstrated a more negative impact on earnings for older individuals and those with lower education level.

CONCLUSION

RA diagnosis was associated with lower earnings in comparison with same-sex siblings, particularly for older individuals and those with lower education level. The negative impact of RA on earnings declined or disappeared over the study period.

Analysis of Longitudinal Lupus Data Using Multivariate t-Linear Models

Analysis of healthcare utilization, such as hospitalization duration and medical costs, is crucial for policymakers and doctors in experimental and epidemiological investigations. Herein, we examine the healthcare utilization data of patients with systemic lupus erythematosus (SLE). The characteristics of the SLE data were measured over a 10-year period with outliers. Multivariate linear models with multivariate normal error distributions are commonly used to evaluate long series of multivariate longitudinal data. However, when there are outliers or heavy tails in the data, such as those based on healthcare utilization, the assumption of multivariate normality may be too strong, resulting in biased estimates. To address this, we propose multivariate t-linear models (MTLMs) with an autoregressive moving-average (ARMA) covariance matrix. Modeling the covariance matrix for multivariate longitudinal data is difficult since the covariance matrix is high dimensional and must be positive-definite. To address these, we employ a modified ARMA Cholesky decomposition and hypersphere decomposition. Several simulation studies are conducted to demonstrate the performance, robustness, and flexibility of the proposed models. The proposed MTLMs with ARMA structured covariance matrix are applied to analyze the healthcare utilization data of patients with SLE.

A systematic review and meta-analysis of circulating adhesion molecules in rheumatoid arthritis

BACKGROUND

The availability of robust biomarkers of endothelial activation might enhance the identification of subclinical atherosclerosis in rheumatoid arthritis (RA). We investigated this issue by conducting a systematic review and meta-analysis of cell adhesion molecules in RA patients.

METHODS

We searched electronic databases from inception to 31 July 2023 for case-control studies assessing the circulating concentrations of immunoglobulin-like adhesion molecules (vascular cell, VCAM-1, intercellular, ICAM-1, and platelet endothelial cell, PECAM-1, adhesion molecule-1) and selectins (E, L, and P selectin) in RA patients and healthy controls. Risk of bias and certainty of evidence were assessed using the JBI checklist and GRADE, respectively.

RESULTS

In 39 studies, compared to controls, RA patients had significantly higher concentrations of ICAM-1 (standard mean difference, SMD = 0.81, 95% CI 0.62-1.00, p < 0.001; I2 = 83.0%, p < 0.001), VCAM-1 (SMD = 1.17, 95% CI 0.73-1.61, p < 0.001; I2 = 95.8%, p < 0.001), PECAM-1 (SMD = 0.82, 95% CI 0.57-1.08, p < 0.001; I2 = 0.0%, p = 0.90), E-selectin (SMD = 0.64, 95% CI 0.42-0.86, p < 0.001; I2 = 75.0%, p < 0.001), and P-selectin (SMD = 1.06, 95% CI 0.50-1.60, p < 0.001; I2 = 84.8%, p < 0.001), but not L-selectin. In meta-regression and subgroup analysis, significant associations were observed between the effect size and use of glucocorticoids (ICAM-1), erythrocyte sedimentation rate (VCAM-1), study continent (VCAM-1, E-selectin, and P-selectin), and matrix assessed (P-selectin).

CONCLUSIONS

The results of our study support a significant role of cell adhesion molecules in mediating the interplay between RA and atherosclerosis. Further studies are warranted to determine whether the routine use of these biomarkers can facilitate the detection and management of early atherosclerosis in this patient group. PROSPERO Registration Number: CRD42023466662.

Employment of patients with rheumatoid arthritis - a systematic review and meta-analysis

BACKGROUND

Patients with rheumatoid arthritis (RA) have difficulties maintaining employment due to the impact of the disease on their work ability. This review aims to investigate the employment rates at different stages of disease and to identify predictors of employment among individuals with RA.

METHODS

The study was carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines focusing on studies reporting employment rate in adults with diagnosed RA. The literature review included cross-sectional and cohort studies published in the English language between January 1966 and January 2023 in the PubMed, Embase and Cochrane Library databases. Data encompassing employment rates, study demographics (age, gender, educational level), disease-related parameters (disease activity, disease duration, treatment), occupational factors, and comorbidities were extracted. Quality assessment was performed employing Newcastle-Ottawa Scale. Meta-analysis was conducted to ascertain predictors for employment with odds ratios and confidence intervals, and test for heterogeneity, using chi-square and I2-statistics were calculated. This review was registered with PROSPERO (CRD42020189057).

RESULTS

Ninety-one studies, comprising of a total of 101,831 participants, were included in the analyses. The mean age of participants was 51 years and 75.9% were women. Disease duration varied between less than one year to more than 18 years on average. Employment rates were 78.8% (weighted mean, range 45.4-100) at disease onset; 47.0% (range 18.5-100) at study entry, and 40.0% (range 4-88.2) at follow-up. Employment rates showed limited variations across continents and over time. Predictors for sustained employment included younger age, male gender, higher education, low disease activity, shorter disease duration, absence of medical treatment, and the absence of comorbidities. Notably, only some of the studies in this review met the requirements for high quality studies. Both older and newer studies had methodological deficiencies in the study design, analysis, and results reporting.

CONCLUSIONS

The findings in this review highlight the prevalence of low employment rates among patients with RA, which increases with prolonged disease duration and higher disease activity. A comprehensive approach combining clinical and social interventions is imperative, particularly in early stages of the disease, to facilitate sustained employment among this patient cohort.

Trends in Incidence of Chronic Heart Failure in Patients With Rheumatoid Arthritis: A Population-Based Study Validating Different Heart Failure Definitions

OBJECTIVE

To assess trends in the incidence of heart failure (HF) in patients with incident rheumatoid arthritis (RA) from 1980 to 2009 and to compare different HF definitions in RA.

METHODS

The study population comprised Olmsted County, Minnesota residents with incident RA (age ≥ 18 yrs, 1987 American College of Rheumatology criteria met in 1980-2009). All subjects were followed until death, migration, or April 30, 2019. Incident HF events were defined as follows: (1) meeting the Framingham criteria for HF, (2) diagnosis of HF (outpatient or inpatient) by a physician, or (3) International Classification of Diseases, 9th revision (ICD-9), or ICD, 10th revision (ICD-10), codes for HF. Patients with HF prior to the RA incidence/index date were excluded. Cox proportional hazards models were used to compare incident HF events by decade, adjusting for age, sex, and cardiovascular risk factors. HF definitions 2 and 3 were compared to the Framingham criteria.

RESULTS

The study included 905 patients with RA (mean age 55.9 years; 68.6% female; median follow-up 13.4 years). The 10-year cumulative incidence of HF events by any chart-reviewed method in the RA cohort in the 1980s was 11.66% (95% CI 7.86-17.29), in the 1990s it was 12.64% (95% CI 9.31-17.17), and in the 2000s it was 7.67% (95% CI 5.36-10.97). The incidence of HF did not change across the decades of RA incidence using any of the HF definitions. Physician diagnosis of HF and ICD-9/10 code-based definitions of HF performed well compared to the Framingham criteria, showing moderate to high sensitivity and specificity.

CONCLUSION

The incidence of HF in patients with incident RA in the 2000s vs the 1980s was not statistically significantly different. Physician diagnosis of HF and ICD-9/10 codes for HF performed well against the Framingham criteria.

Geographic Variation in Disease Burden and Mismatch in Care of Patients With Rheumatoid Arthritis in the United States

OBJECTIVE

Our objective was to evaluate the factors associated with regional variation of rheumatoid arthritis (RA) disease burden in the US.

METHODS

In a retrospective cohort analysis of Rheumatology Informatics System for Effectiveness (RISE) registry data, seropositivity, RA disease activity (Clinical Disease Activity Index [CDAI], Routine Assessment of Patient Index Data-version 3 [RAPID3]), socioeconomic status (SES), geographic region, health insurance type, and comorbidity burden were recorded. An Area Deprivation Index score of more than 80 defined low SES. Median travel distance to practice sites' zip codes was calculated. Linear regression was used to analyze associations between RA disease activity and comorbidity adjusting for age, sex, geographic region, race, and insurance type.

RESULTS

Enrollment data for 184,722 patients with RA from 182 RISE sites were analyzed. Disease activity was higher in African American patients, in those from Southern regions, and in those with Medicaid or Medicare coverage. Greater comorbidity was prevalent in patients in the South and those with Medicare or Medicaid coverage. There was moderate correlation between comorbidity and disease activity (Pearson coefficient: RAPID3 0.28, CDAI 0.15). High-deprivation areas were mainly in the South. Less than 10% of all participating practices cared for more than 50% of all Medicaid recipients. Patients living more than 200 miles away from specialist care were located mainly in Southern and Western regions.

CONCLUSION

A disproportionately large portion of socially deprived, high comorbidity, and Medicaid-covered patients with RA were cared for by a minority of rheumatology practices. Studies are needed in high-deprivation areas to establish more equitable distribution of specialty care for patients with RA.

Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets

OBJECTIVES

To optimise planning of public health services, the impact of high-cost users needs to be considered. However, most of the existing statistical models for costs do not include many clinical and social variables from administrative data that are associated with elevated health care resource use, and are increasingly available. This study aimed to use machine learning approaches and big data to predict high-cost users among people with cardiovascular disease (CVD).

METHODS

We used nationally representative linked datasets in New Zealand to predict CVD prevalent cases with the most expensive cost belonging to the top quintiles by cost. We compared the performance of four popular machine learning models (L1-regularised logistic regression, classification trees, k-nearest neighbourhood (KNN) and random forest) with the traditional regression models.

RESULTS

The machine learning models had far better accuracy in predicting high health-cost users compared with the logistic models. The harmony score F1 (combining sensitivity and positive predictive value) of the machine learning models ranged from 30.6% to 41.2% (compared with 8.6-9.1% for the logistic models). Previous health costs, income, age, chronic health conditions, deprivation, and receiving a social security benefit were among the most important predictors of the CVD high-cost users.

CONCLUSIONS

This study provides additional evidence that machine learning can be used as a tool together with big data in health economics for identification of new risk factors and prediction of high-cost users with CVD. As such, machine learning may potentially assist with health services planning and preventive measures to improve population health while potentially saving healthcare costs.