Disparities across Diverse Populations in the Health and Treatment of Patients with Osteoarthritis

Racial and Ethnic Differences in the Prevalence of Patients With Arthritis and Severe Joint Pain and Who Received Provider Counseling About Physical Activity for Arthritis Among Adults Aged 18 Years or Older-United States, 2019

OBJECTIVE

This study examined the racial and ethnic differences in individuals with self-reported and doctor-diagnosed arthritis, severe joint pain, and provider counseling for physical activity among US adults with arthritis.

METHODS

We estimated prevalence by race and ethnicity among 31,997 adults aged ≥18 years in the 2019 National Health Interview Survey. We used multiple logistic regression models to investigate associations between outcomes and race and ethnicity.

RESULTS

Compared with non-Hispanic White adults (22.9%), we found a significantly higher age-adjusted prevalence of arthritis among American Indian/Alaska Native adults (30.3%). Among adults with arthritis, higher age-adjusted prevalence of severe joint pain among American Indian/Alaska Native (39.1%), non-Hispanic Black (36.4%), and Hispanic adults (35.7% vs 22.5% [White]) and higher provider counseling for physical activity among non-Hispanic Black adults (58.9% vs 52.1% [White]) were observed and could not be fully explained by differences in socioeconomic factors, body mass index, depression history, and comorbid conditions. Additional models also containing inability to pay medical bills and food insecurity did not explain racial and ethnic differences.

CONCLUSION

Our findings highlight a need for multilevel interventions to mitigate social and environmental barriers to physical activity and eliminate disparities in individuals with arthritis and severe joint pain.

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature

The major and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five- year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease identification and prediction, disease classification, patient stratification and disease subtype identification, disease progression and activity, treatment response, and predictors of outcomes. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed, are highlighted in a discussion. Therefore, the present review aims to characterise how researchers are applying data science techniques in the rheumatology medical field. The most immediate conclusions that can be drawn from this work are: multiple and novel data science techniques have been used in a wide range of rheumatic and musculoskeletal diseases including rare diseases; the sample size and the data type used are heterogeneous, and new technical approaches are expected to arrive in the short-middle term.

The Effects of Sociodemographic Factors on Baseline Patient-Reported Outcome Measures in Patients with Foot and Ankle Conditions

BACKGROUND

Racial and ethnic care disparities persist within orthopaedics in the United States. This study aimed to deepen our understanding of which sociodemographic factors most impact patient-reported outcome measure (PROM) score variation and may explain racial and ethnic disparities in PROM scores.

METHODS

We retrospectively reviewed baseline PROMIS (Patient-Reported Outcomes Measurement Information System) Global-Physical (PGP) and PROMIS Global-Mental (PGM) scores of 23,171 foot and ankle patients who completed the instrument from 2016 to 2021. A series of regression models was used to evaluate scores by race and ethnicity after adjusting in a stepwise fashion for household income, education level, primary language, Charlson Comorbidity Index (CCI), sex, and age. Full models were utilized to compare independent effects of predictors.

RESULTS

For the PGP and PGM, adjusting for income, education level, and CCI reduced racial disparity by 61% and 54%, respectively, and adjusting for education level, language, and income reduced ethnic disparity by 67% and 65%, respectively. Full models revealed that an education level of high school or less and a severe CCI had the largest negative effects on scores.

CONCLUSIONS

Education level, primary language, income, and CCI explained the majority (but not all) of the racial and ethnic disparities in our cohort. Among the explored factors, education level and CCI were predominant drivers of PROM score variation.

LEVEL OF EVIDENCE

Prognostic Level IV . See Instructions for Authors for a complete description of levels of evidence.

Complications and Costs Associated With Ethnicity Following Total Hip Arthroplasty: A Retrospective Matched Cohort Study

BACKGROUND

Minority patients often have greater numbers of complications, revisions, and costs after total hip arthroplasty (THA). This study investigates how race correlates with specific surgical complications, revisions, and total costs following THA both before and after propensity matching.

METHODS

Data from 2014-2016 were collected from a large commercial insurance database known as PearlDiver. THA patients were assigned under Current Procedural Terminology (CPT-27130) and International Statistical Classification of Diseases (ICD-9-P-8151) codes and then divided into groups based on racial status in the database. Patients of different ethnicities including White, Black, Asian, and Hispanic patients were compared in regard to age, gender, comorbidities, lengths of stay, and surgical complications and costs at thirty days, ninety days, and one year using unequal variance t-tests. Black, Asian, and Hispanic patients are collectively referred to as minority patients. Patient comparisons were done both before and after matching for age, gender, tobacco use, diabetes, and obesity comorbidities.

RESULTS

A total of 73,688 White (93%), 4,822 Black (6%), 268 Asian (0.3%), and 420 Hispanic (0.5%) THA patients were included. Significantly more minority patients underwent THA under the age of 65 and had higher comorbidity indices and lengths of stay. Black patients had significantly higher complication rates, but there was no significant difference in rates of revision in any minority group. Minority patients were charged 9%-83% more. After matching, Black and Hispanic patients maintained higher comorbidity indices and lengths of stay. Black patients had a spectrum of complication rates but significantly decreased revision rates. Furthermore, after matching, minority patients were charged 5%-65% more.

CONCLUSIONS

Black patients experienced significantly greater rates of complications and higher total costs; whereas, Asian and Hispanic patients did not have significant differences in complications but did have higher costs. Therefore, this study aligns with previous studies and supports our hypothesis that Black ethnicity patients have worse outcomes than White ethnicity patients after THA, advocating for reducing health disparities and establishing more equitable healthcare, but does not support our hypothesis for Asian and Hispanic patients, likely due to a small study population size, warranting further research into the topic.

Development of a personalized shared decision-making tool for knee osteoarthritis and user-testing with African American and Latina women

Background

Patients with chronic knee pain are often unaware of treatment options and likely outcomes-information that is critical to decision-making. A consistent framework for communicating patient-personalized information enables clinicians to provide consistent, targeted, and relevant information. Our objective was to user-test a shared decision-making (SDM) tool for chronic knee pain.

Methods

A cross-functional team developed a Markov-based health economics model and tested the model outputs with patient panels, patient and clinician focus groups, and clinical specialists. The resulting SDM tool was user-tested in a parallel-designed, randomized controlled study with 52 African American and 52 Latina women from geographically representative areas of the US. Participants were randomized to counseling with or without the SDM tool. Feedback was collected at intervention and at 1 month after intervention and analyzed with Student's t-tests and Chi-squared analyses (alpha = 0.05).

Results

Qualitative results indicated patients understood the material, rated the overall experience highly, and were likely to recommend the physician. The SDM group reported high satisfaction with the tool. A greater proportion of the SDM group (56%) reported increased physical activity over baseline at 1 month compared with the control group (33%) (P = 0.0005). New use of medications for knee pain (58% SDM; 49% control) did not differ significantly between groups (P = 0.15).

Conclusion

Use of this innovative SDM tool was associated with high satisfaction and a significant increase in self-reported physical activity level at 1 month. The SDM tool may elicit behavioral changes to promote musculoskeletal health.

Trends and Racial/Ethnic Differences in Health Care Spending Stratified by Gender among Adults with Arthritis in the United States 2011-2019

The purpose of this study was to determine if there were racial/ethnic differences and patterns for individual office-based visit expenditures by gender among a nationally representative sample of adults with arthritis. We retrospectively analyzed pooled data from the 2011 to 2019 Medical Expenditure Panel Survey of adults who self-reported an arthritis diagnosis, stratified by gender (men = 13,378; women = 33,261). Our dependent variable was office-based visit expenditures. Our independent variables were survey year (categorized as 2011-2013, 2014-2016, 2017-2019) and race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, non-Hispanic Asian, non-Hispanic other/multiracial). We conducted trends analysis to assess for changes in expenditures over time. We utilized a two-part model to assess differences in office-based expenditures among participants who had any office-based expenditure and then calculated the average marginal effects. The unadjusted office-based visit expenditures increased significantly across the study period for both men and women with arthritis, as well as for some racial and ethnic groups depending on gender. Differing racial and ethnic patterns of expenditures by gender remained after accounting for socio-demographic, healthcare access, and health status factors. Delaying care was an independent driver of higher office-based expenditures for women with arthritis but not men. Our findings reinforce the escalating burden of healthcare costs among U.S. adults with arthritis across genders and certain racial and ethnic groups.

Health, Psychological and Demographic Predictors of Depression in People with Fibromyalgia and Osteoarthritis

Depression is common in people with fibromyalgia (FM) and osteoarthritis (OA) and has been linked to adverse health outcomes in these conditions. The purpose of this study was to examine differences in predictors of depression among individuals with FM and OA using a range of health, demographic, and psychological variables. Of the total 963 participants, 600 were diagnosed with FM, and 363 with OA. The Quality of Well-Being Scale (QWB) was used to assess health status. The Fibromyalgia Impact Questionnaire (FIQ) and the Arthritis Impact Measurement Scale (AIMS) were used to measure disease-specific impact. Additionally, participants completed self-efficacy and helplessness assessments. Depression was measured using the Center for Epidemiological Studies Scale (CES-D). The results of a moderated linear regression showed that higher depression scores were associated with lower health status and a greater condition impact, especially in the FM group. Self-efficacy and helplessness predicted depression in both groups, but more strongly in FM. White participants with OA were more depressed than their non-White counterparts, while the opposite was true for FM. These findings indicate that improving health status and psychological well-being might alleviate depression in both FM and OA.