Abstract
BACKGROUND:
More than 65 million Americans suffer from arthritis, which is the primary cause of disability in older adults. Arthritis is also a leading disease, with more than $600 billion in medical expenses each year. There is, however, little research on health care expenditure by race and ethnicity among older adults with arthritis.
OBJECTIVE:
To examine the racial and ethnic differences in health care expenditures among older adults with arthritis in the United States.
METHODS:
A retrospective multiyear cross-sectional study using the Medical Expenditure Panel Survey (MEPS) data (2018-2022) analyzed health care expenditures of adults aged 65 years and older with arthritis across different races and ethnicities. All-cause expenditures (total, office-based visits, hospital inpatient visits, prescription medicine, and outpatient visits) were compared between Hispanic patients, non-Hispanic Black (NHB) patients, and non-Hispanic White (NHW) patients, adjusting for covariates using SAS version 9.4.
RESULTS:
The study analyzed 15,345 adults (weighted frequency = 29,915,198) with arthritis. The mean total annual health care expenditure was $15,052 (95% CI = $14,435-$15,667) for all adults with arthritis. Although Hispanic patients had the lowest total expenditure ($14,159, 95% CI = $11,955-$16,363), NHB and NHW patients had similar total annual health care expenditures at $15,623 (95% CI = $12,228-$19,015) and $15,237 (95% CI = $14,599-$15,876), respectively. After adjustment for covariates, Hispanic and NHB patients spent 34% (95% CI = 24%-43%) and 31% (95% CI = 22%-39%) less than NHW patients (P < 0.0001). This was largely because of lower office-based expenditures, where both Hispanic and NHB patients spent approximately 52% (95% CI = 42%-60%, P < 0.0001) less than NHW patients. Also, Hispanic patients incurred 23% (95% CI = 1%-41%) lower hospital inpatient expenditure (P = 0.0406) than NHW patients. For outpatient visits, Hispanic patients spent 71% (95% CI = 59%-80%) and NHB patients 50% (95% CI = 34%-62%) (both P < 0.0001) lower than NHW patients. Hispanic and NHB patients differed only in outpatient expenditures, where NHB patients significantly spent 75% more than Hispanic patients (95% CI = 16%-162%, P = 0.007) after adjusting for covariates.
CONCLUSIONS:
Total health care expenditures were substantially lower for Hispanic and NHB patients with arthritis compared with NHW patients after adjusting for various covariates. Specifically, Hispanics and NHB patients had lower office-based and outpatient expenditures. Additionally, Hispanic patients incurred lower hospital inpatient expenditures than NHW patients. There is a need for further studies delving into finding reasons for these differences in expenditures, such as behavioral and belief systems that may limit the use of care among racial and ethnic minority groups.
Plain language summary
Proper treatment of arthritis may contribute to better health outcomes and improved quality of life. We found that for Hispanic and non-Hispanic Black (NHB) patients, total annual health care expenditure was significantly lower than for non-Hispanic White (NHW) patients. Expenditures associated with all-cause office-based and outpatient visits were significantly lower among Hispanic and NHB patients than NHW patients. Also, hospital inpatient expenditures of Hispanic patients were lower than NHW patients.
Implications for managed care pharmacy
This study found that from 2018 to 2022, total annual health care expenditures for arthritis were lower among Hispanic and NHB patients compared with NHW patients, primarily owing to office-based and outpatient visits. Hispanic patients also had lower hospital inpatient expenditures than NHW patients. This highlights the need to address disparities in access, engagement, and care delivery. Ensuring equitable outpatient and preventive services, along with tailored outreach and culturally appropriate care, can improve outcomes and promote value-based, equitable arthritis management.
In the United States, arthritis is a common chronic illness and a major cause of disability, especially in older persons.1,2 It is characterized by the typical symptoms of joint pain, stiffness, swelling, and negative impacts on mental and physical functioning.1,3,4 The most prevalent type of arthritis is osteoarthritis, which is followed by rheumatoid arthritis.5 In the United States, arthritis, including osteoarthritis and rheumatoid arthritis, affects more than 65 million people and costs more than $600 billion in medical expenses each year.6–8 According to estimates, individuals with arthritis spend, on average, $1,000 more on health care each year than people without the condition because they use medical services more frequently.9,10 However, there is not adequate literature on the role race and ethnicity play in this process.
Health inequalities, which are described as “preventable differences in the burden of disease, injury, violence, or opportunities to achieve optimal health,” persist in historically minority populations in the United States despite continuous attempts to achieve health parity.11 These disparities consistently jeopardize the mental, physical, social, and financial wellbeing of marginalized groups, such as older adults, members of racial or ethnic minorities, and residents of rural areas.12 Disparities in rheumatology care access, care quality, and treatment results may affect historically marginalized communities in the United States.13–15 There are well-documented disparities across various groups, including those about patient characteristics, treatment provided by health care practitioners, and access to health care systems, and these disparities are still growing and enduring.16
Racial and ethnic minority groups may use health care services less often than non-Hispanic White (NHW) patients, even though persons with arthritis are consistently interacting with the health care environment.9,17,18 According to some research, non-Hispanic Black (NHB) patients expect worse results from surgical management and prefer alternative therapy modalities, including natural treatments and spirituality.18,19 Additionally, there is evidence that racial and ethnic minority groups have more obstacles to using typical arthritis treatment services, including poorer socioeconomic positions and higher rates of public insurance or uninsured status.10,20,21 Over time, this discrepancy in access may lead to worse health outcomes, more unmet health care needs, and higher health care expenditures.22–24
Although we are aware that racial and ethnic minority groups use health care differently than other groups, little emphasis has been devoted to how this has affected the cost of health care for these groups. A decade-old study examined the racial and ethnic trends and differences in annual health care expenditures among a nationally representative sample of adults with arthritis from 2008 to 2016 and found that Hispanic and NHB patients spent less than NHW patients.10 However, they did not analyze the differences in expenditures by specific service areas, such as office-based visits, outpatient visits, and hospital inpatient visits. Another study looked at trends in health care expenditures among people with arthritis and discovered an increase in spending among individuals with arthritis between 2008 and 2014.9 However, they did not examine the disparities in health care expenditures by different races and ethnicities.
The extent of racial and ethnic disparities may have been affected by shifts in the health care system. Racial and ethnic minority groups have used health care more frequently and are less likely to put off or forego care since the Patient Protection and Affordable Care Act went into full effect in 2014.25–27 Therefore, the goal of this study is to ascertain whether there are any notable differences in health care expenditure across all health care services, including office-based visits, hospital inpatient visits, outpatient visits, prescription medicine, and emergency visits, as well as the racial disparities in overall health care expenditure among older adults with arthritis.
Methods
STUDY DESIGN
This retrospective multiyear cross-sectional study was carried out on older adult patients (aged ≥65 years) diagnosed with arthritis identified from the Medical Expenditure Panel Survey (MEPS) between January 1, 2018, and December 31, 2022 (Supplementary Figure 1 (321KB, pdf) , available in online article). The criteria for Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) were followed in the conduct and reporting of this study. The institute’s institutional review board approved the study protocol.
DATA SOURCE AND SAMPLE
Using the full-year household consolidated data files of the MEPS, we retrospectively analyzed a total sample of 15,345 adults, aged 65 years and older, who self-reported having arthritis between 2018 and 2022. MEPS is an annual nationwide household survey that uses a nationally representative sample of the civilian noninstitutionalized US population to estimate health status, health insurance coverage, and health care usage.28 Every year, the survey is conducted, and a distinct and representative sample is obtained using a sophisticated, stratified sampling technique. This research offers several cross-sectional views throughout time by merging data from several years. There are 2 components to MEPS: medical conditions and household conditions (HCs). Patients with arthritis were identified using the HC component. Health care providers’ details on individual health problems, including diagnosis, dates of visits and services, medical care services rendered, charges, payment sources, amounts, and process codes for visits and encounters, are contained in the medical condition component.29 Furthermore, the Elixhauser comorbidity index, a gauge of the total burden of comorbidities, was computed using this component.30 Individuals were identified using characteristics from the HC component of MEPS, namely ARTHDX, for the diagnosis of arthritis. ARTHDX determined if the individual has ever been diagnosed with arthritis. Respondents were chosen for analysis if they selected “Yes.”
DEPENDENT VARIABLES
All-cause health care expenditure was the dependent variable. The amount spent on health care (by payers or patients) was used to estimate expenditure on the HC. Cumulative costs for each category were included in the total amount spent on health care. The categories of expenditure included office-based visits, hospital inpatient visits, prescription medicine, emergency visits, and outpatient visits. Medical expenditure projections from 2018 to 2022 were all inflated to a uniform 2022 US dollar value using the Bureau of Labor Statistics medical care component of the Consumer Price Index.31
INDEPENDENT VARIABLES
Race and ethnicity were the main independent variables. The sample was classified as Hispanic cohorts, NHB cohorts, and NHW cohorts. We excluded other races and ethnicities from the analysis owing to their limited sample size.
CONCEPTUAL FRAMEWORK AND MEASURES
We divided the independent variables into 3 categories: predisposing, enabling, and need factors, based on the Andersen and Newman Framework of Healthcare Utilization.32,33 Andersen and Newman proposed a methodology to help researchers find pertinent factors in their analysis and postulated that there are several individual drivers of health care usage. According to this model, health care use is driven by predisposing, enabling, and need factors.
PREDISPOSING FACTORS
Predisposing factors are traits that predate disease onset and may be linked to varying service consumption patterns.33 These factors put the individual at risk for the disease. Such factors include demographic traits like age (65-74 or ≥75 years), sex (male or female), and educational attainment (high school diploma or less, a master’s degree, or a doctoral degree), which are predictors of health literacy.34,35 These factors may impact health care spending and use by influencing people’s health behaviors and access to treatment.36
ENABLING FACTORS
The resources at a person’s disposal that enable them to receive medical treatment are known as enabling factors.33 A number of variables were deemed enabling factors that might either help or impede access to health care services, including income categories, insurance coverage, physical limits, pain constraints that prevent one from working, the affordability of drugs, and social limitations.37,38 Using poverty levels from the Current Population Survey, income categories were established in accordance with MEPS principles.29 The annual family income was calculated as a percentage of the poverty line. People were divided into 5 groups: low income (125% to <200%), medium income (200% to <400%), rich income (≥400%), near poor (100% to <125%), and poor/negative (<100%). People with private insurance through their workplaces, unions, or policies acquired through exchanges, marketplaces, or TRICARE/CHAMPVA were included in the “any private” category. Those insured exclusively by public programs, such as Medicare (parts A and B), Medicaid, other public hospital/physician programs, or the Veterans Administration, were included in the “only public” group. Uninsured people were defined as those who lacked any kind of health insurance.
NEED FACTORS
Need considerations are the seen or assessed existence of a disease that would justify seeking medical attention.33 The Elixhauser comorbidity score, the type of arthritis, and perceptions of one’s physical and mental health are examples of need variables affecting health care consumption and costs. Patients are asked to rate their physical and mental health on a scale of excellent, very good, good, fair, and poor to assess their perceived health status in MEPS.28 Patients were asked if they had been diagnosed with osteoarthritis, rheumatoid arthritis, or an unidentified kind of arthritis. All patients with arthritis, regardless of type, were examined for this research.
EXTERNAL AND LIFESTYLE FACTORS
Birthplace (whether born in the United States or not) and geographic areas (Northeast, Midwest, South, and West) are some external determinants that may affect patterns of health care spending and usage.36 Examples of lifestyle factors include smoking frequency and physical activity. These variables aid in explaining behavioral and geographical differences in health care access and shifts in health care service policies over time.
STATISTICAL ANALYSIS
The variables STRA9622 (stratum) and PSU9622 (Primary Sampling Unit) from the HC-036 file on the MEPS website were combined to create a pooled linkage file for the shared variance structure for pooled analysis.29 We used descriptive weighted analyses to compare the patient characteristics of each racial and ethnic group. We then computed the natural log of the total value after adding $1 to the total health care expenditures and each of the expenditure categories to offset the effect of zero expenditure in some patients who had zero spending.39 We conducted univariate analyses and included only variables that were significant to avoid model overfitting and redundancies in the multivariable analyses. Following log transformation, we compared the all-cause health care expenditures across all ethnic groups using a linear regression model that was adjusted for covariates. SAS version 9.4 was used for statistical analysis, with a significance level of P less than 0.05.
Results
PATIENT CHARACTERISTICS
This study includes 15,345 older adults (aged ≥65 years) with arthritis, representing 29.9 million nationwide (Supplementary Figure 2 (321KB, pdf) ). The sample comprised 1,598 Hispanic, 2,252 NHB, and 11,495 NHW patients. The largest age group was aged 65 to 74 years (54.9%), with female patients comprising 60.8% of the cohort and the majority (80.7%) being unemployed. Public insurance was the most common coverage (51.4%). Although Hispanic and NHB patients had a larger percentage in the poor/negative income categories (Hispanic: 35.5% and NHB: 29.9%; P < 0.0001), most NHW patients were high income earners (41.5%). A large percentage of Hispanic patients reported fair health status (31.2%) (P < 0.0001), whereas a greater proportion of NHW patients reported very good (33.0%) and good (33.2%) health status (Table 1 and Supplementary Table 1 (321KB, pdf) ).
TABLE 1.
Selected Sample Characteristics by Racial and Ethnic Groups
| Characteristic | Total, n (%) | Hispanic cohort, n (%) | NHB cohort, n (%) | NHW cohort, n (%) | P value |
|---|---|---|---|---|---|
| N | 15,345 | 1,598 | 2,252 | 11,495 | |
| Sex | |||||
| Male | 5,735 (39.2) | 480 (30.0) | 757 (33.6) | 4,498 (39.1) | 0.0008a |
| Female | 9,610 (60.8) | 1,118 (70.0) | 1,495 (66.4) | 6,997 (60.8) | |
| Age group | |||||
| 65-74 years | 8,449 (54.9) | 916 (57.3) | 1,333 (59.2) | 6,200 (53.9) | 0.005a |
| ≥75 years | 6,896 (45.1) | 682 (42.7) | 919 (40.8) | 5,295 (46.1) | |
| Marital status | |||||
| Never married | 945 (5.1) | 83 (5.2) | 282 (12.5) | 580 (5.0) | <0.0001a |
| Widowed, divorced, or separated | 6,798 (41.1) | 746 (46.7) | 1,211 (53.8) | 4,841 (42.1) | |
| Married | 7,228 (53.9) | 705 (44.1) | 694 (30.8) | 5,829 (50.7) | |
| Education level | |||||
| No degree | 2,219 (11.9) | 816 (51.1) | 511 (22.7) | 892 (7.8) | <0.0001a |
| High school diploma | 7,076 (54.3) | 512 (32.0) | 1,146 (50.9) | 5,418 (47.1) | |
| Bachelor’s degree | 2,361 (7.7) | 102 (6.4) | 183 (8.1) | 2,076 (18.1) | |
| Master’s degree | 1,690 (12.9) | 57 (3.6) | 136 (6.0) | 1,497 (13.0) | |
| Doctorate degree | 419 (3.2) | 10 (6.3) | 42 (1.9) | 367 (3.2) | |
| Income level | |||||
| Poor/negative income | 2,330 (9.7) | 567 (35.5) | 673 (29.9) | 1,090 (9.54) | <0.0001a |
| Near poor | 927 (4.9) | 154 (9.6) | 193 (8.6) | 580 (5.0) | |
| Low income | 2,609 (16.0) | 319 (20.0) | 481 (21.4) | 1,809 (15.7) | |
| Middle income | 4,121 (28.1) | 350 (21.9) | 531 (23.6) | 3,240 (28.2) | |
| High income | 5,358 (41.37) | 208 (13.0) | 374 (16.6) | 4,776 (41.5) | |
| Employment status | |||||
| Unemployed | 12,566 (80.7) | 1,412 (88.4) | 1,897 (84.2) | 9,257 (80.5) | <0.0001a |
| Employed | 2,699 (19.4) | 176 (11.0) | 334 (14.8) | 2,189 (19.0) | |
| Elixhauser comorbidity | |||||
| None | 1,238 (9.3) | 97 (6.1) | 120 (5.3) | 1,021 (8.9) | 0.0795 |
| 1 comorbidity | 3,036 (20.4) | 271 (17.0) | 458 (20.3) | 2,307 (20.1) | |
| 2 comorbidities | 3,112 (20.8) | 314 (19.7) | 444 (19.7) | 2,354 (20.5) | |
| 3 comorbidities | 2,901 (18.9) | 370 (23.1) | 459 (20.4) | 2,072 (18.0) | |
| 4 comorbidities | 2,058 (13.3) | 206 (12.9) | 288 (12.8) | 1,564 (13.6) | |
| ≥5 comorbidities | 2,845 (17.3) | 308 (19.3) | 465 (20.6) | 2,072 (18.0) | |
| Insurance coverage | |||||
| Any private | 6,470 (41.4) | 298 (18.6) | 729 (32.4) | 5,745 (50.0) | <0.0001a |
| Public only | 6,766 (45.8) | 1,282 (80.2) | 1,522 (67.6) | 5,744 (50.0) | |
| Uninsured | 2,096 (12.8) | 18 (1.1) | 1 (0.01) | 6 (0.1) | |
P values denote statistical significance at 95% CI (calculated using chi-square tests).
NHB = non-Hispanic Black; NHW = non-Hispanic White.
UNADJUSTED HEALTH CARE EXPENDITURES
Unadjusted analyses indicated that the mean total annual health care expenditure was $15,052 (95% CI = $14,435-$15,667) for patients with arthritis. Hispanic patients had the lowest total annual expenditures ($14,159; 95% CI = $11,955-$16,363), and NHB patients had the highest ($15,623; 95% CI = $12,228-$19,015) followed by NHW patients ($15,237; 95% CI = $14,599-$15,876). NHW patients had the highest all-cause office-based ($3,770; 95% CI = $3,582-$3,957) and outpatient expenditures ($1,837; 95% CI = $1,636-$2,039). NHB patients had the highest prescription medicine expenditure ($4,024; 95% CI = $3,352-$4,696). Hispanic patients had lower office-based ($2,509; 95% CI = $2,152-$2,866), outpatient ($741, 95% CI = $499-$983), and emergency visit expenditures ($261; 95% CI = $194-$329) but similar inpatient and prescription medicine expenditure to NHW patients (Table 2).
TABLE 2.
Unadjusted Health Care Expenditures by Category Among Hispanic and NHB Cohorts in Comparison With NHW Cohorts Diagnosed With Arthritis
| Expenditure category | Total, mean, $ | Total, 95% CI, $ | Hispanic cohort, mean, $ | Hispanic cohort, 95% CI, $ | >NHB cohort, mean, $ | NHB cohort, 95% CI, $ | NHW cohort, mean, $ | NHW cohort, 95% CI, $ |
|---|---|---|---|---|---|---|---|---|
| Total expenditurea | 15,052 | 14,435-15,667 | 14,159 | 11,955-16,363 | 15,623 | 12,228-19,015 | 15,237 | 14,599-15,876 |
| Office-based visitsb | 3,564 | 3,376-3,752 | 2,509 | 2,152-2,865 | 3,185 | 2,002-4,368 | 3,770 | 3,582-3,957 |
| Hospital inpatient visits | 3,185 | 2,923-3,447 | 3,159 | 1,968-4,348 | 3,102 | 2,471-3,733 | 3,242 | 2,950-3,535 |
| Prescription medicines | 3,575 | 3,295-3,854 | 3,641 | 2,987-4,295 | 4,023 | 3,352-4,696 | 3,537 | 3,195-3,880 |
| Emergency visits | 301 | 276-325 | 261 | 194-329 | 306 | 246-367 | 305 | 276-335 |
| Outpatient visits | 1,732 | 1,489-1,975 | 741 | 499-983 | 2,120 | 337-3,904 | 1,837 | 1,636-2,039 |
Sum of all health services (total office-based visits, total outpatient visits, total emergency department visits, total inpatient visits, total prescription medicines, total dental visits, and total home health care).
Includes physician, nonphysician, and unknown.
NHB = non-Hispanic Black; NHW = non-Hispanic White.
UNADJUSTED TREND IN TOTAL EXPENDITURE
We also performed a trend analysis to determine the total expenditure by race between 2018 and 2020. Between 2018 and 2019, Hispanic patients spent approximately $15,347 (95% CI = $12,054-$18,640), which reduced to $13,471 (95% CI = $10,857-$16,086) between 2020 and 2022. In contrast, the total health care expenditure increased for NHB and NHW patients between these periods. For NHB patients, the total expenditure increased from $14,409 (95% CI = $12,035-$16,784) between 2018 and 2019 to $16,318 (95% CI = $11,225-$21,412) between 2020 and 2022 and increased from $14,855 (95% CI = $14,048-$15,663) between 2018 and 2019 to $15,467 (95% CI = $14,574-$16,361) from 2020 to 2022 for NHW patients (Supplementary Table 2 (321KB, pdf) ).
ADJUSTED HEALTH CARE EXPENDITURES
The adjusted linear regression analysis of racial and ethnic disparities in total health care expenditure revealed significant findings (Table 3). Hispanic and NHB patients spent 34% (95% CI = 24%-43%) and 31% (95% CI = 22%-39%) less than NHW patients, respectively (P < 0.0001). Across different service areas, including office-based visit expenditure, hospital inpatient expenditure, prescription medicine expenditure, emergency visit expenditure, and outpatient visit expenditure, significant disparities in expenditure were observed among Hispanic patients and NHB patients compared with NHW patients (Table 4 and full models in Supplementary Tables 4-8 (321KB, pdf) , respectively). For all-cause office-based visits, Hispanic and NHB patients incurred approximately 52% (95% CI = 42%-60%; P < 0.0001) less expenditures than NHW patients (Table 4; Supplementary Table 4 (321KB, pdf) ). Hispanic patients incurred 23% (95% CI = 1%-41%) lower hospital inpatient expenditures (P = 0.04) than NHW patients (Table 4; Supplementary Table 5 (321KB, pdf) ). For all-cause outpatient visits, Hispanic patients had 71% (95% CI = 59%-80%) and NHB patients had 50% (95% CI = 34%-62%) (both P < 0.0001) lower expenditures than NHW patients (Table 4; Supplementary Table 8 (321KB, pdf) ).
TABLE 3.
Adjusted Total Health Care Expenditures Among Hispanic and Non-Hispanic Black Cohorts as a Percentage of Non-Hispanic White Cohorts Diagnosed With Arthritis
| Variable | Exponent of the estimate, % | 95% CI, % | P value |
|---|---|---|---|
| Race and ethnicity cohorts | |||
| Hispanic | 65.9 | 57.1-76.2 | <0.0001a |
| Non-Hispanic Black | 69.1 | 61.4-77.8 | <0.0001a |
| Non-Hispanic White | Reference | - | - |
| Sex | |||
| Female | 104.3 | 98.0-110.9 | 0.1846 |
| Male | Reference | - | - |
| Age category | |||
| ≥75 years | 114.2 | 107.4-121.4 | <0.0001a |
| 65-74 years | Reference | - | - |
| Marital status | |||
| Never married | 98.1 | 87.3-110.4 | 0.7555 |
| Widowed, divorced, or separated | 100.9 | 94.4-107.9 | 0.7879 |
| Married | Reference | - | - |
| Insurance coverage | |||
| Any private | 124.1 | 116.5-132.2 | <0.0001a |
| Uninsured | 33.1 | 11.8-92.3 | 0.0346 |
| Public only | Reference | - | - |
| Perceived health status | |||
| Excellent | 41.8 | 35.8-48.9 | <0.0001a |
| Fair | 70.5 | 60.9-81.5 | <0.0001a |
| Good | 52.9 | 45.8-61.2 | <0.0001a |
| Very good | 44.6 | 38.3-51.9 | <0.0001a |
| Poor | Reference | - | - |
| Employment status | |||
| Employed | 86.5 | 79.7-93.9 | 0.0006a |
| Unemployed | Reference | - | - |
| Elixhauser comorbidity | |||
| 1 comorbidity | 126.9 | 110.9-145.4 | 0.0006a |
| 2 comorbidities | 187.1 | 162.1-215.8 | <0.0001a |
| 3 comorbidities | 223.9 | 193.9-258.3 | <0.0001a |
| 4 comorbidities | 306.2 | 266.3-351.5 | <0.0001a |
| ≥5 comorbidities | 407.2 | 352.9-469.7 | <0.0001a |
| 0 comorbidities | Reference | - | - |
P values denote statistical significance at 95% CI.
TABLE 4.
Adjusted Total Health Care Expenditures by Category for Hispanic and NHB Cohorts in Comparison With Non-Hispanic White Cohorts Diagnosed With Arthritis
| Expenditure categories | Hispanic cohort, exponent of the estimate, % | Hispanic cohort, 95% CI, % | Hispanic cohort P value | NHB cohort, exponent of the estimate, % | NHB cohort, 95% CI, % | NHB cohort P value |
|---|---|---|---|---|---|---|
| Total expenditurea | 65.9 | 57.1-76.2 | <0.0001 | 69.1 | 57.1-77.8 | <0.0001b |
| Office-based visitsc | 48.0 | 39.7-58.2 | <0.0001 | 48.2 | 40.5-57.5 | <0.0001b |
| Hospital inpatient visits | 76.6 | 59.3-98.9 | 0.0406 | 88.3 | 71.8-108.7 | 0.2396 |
| Prescription medicines | 82.9 | 68.5-100.2 | 0.0523 | 95.5 | 81.7-111.6 | 0.5611 |
| Emergency visits | 95.5 | 82.0-111.2 | 0.5517 | 96.9 | 86.8-108.0 | 0.5614 |
| Outpatient visits | 28.8 | 20.4-40.5 | <0.0001 | 50.2 | 38.3-65.9 | <0.0001b |
Variables controlled for in the model include age, sex, marital status, insurance coverage, employment, perceived health status, and Elixhauser comorbidity.
Sum of all health services (total office-based visits, total outpatient visits, total emergency department visits, total inpatient visits, total prescription medicines, total dental visits, and total home health care).
P values denote statistical significance at 95% CI.
Includes physician, nonphysician, and unknown.
NHB = non-Hispanic Black.
Adjusted analysis comparing expenditures between Hispanic and NHB patients indicated that although Hispanic patients spent less than NHB patients across most of the service categories (Supplementary Table 3 (321KB, pdf) ; full models in Supplementary Tables 9-14 (321KB, pdf) ), only the outpatient expenditure was significant, with NHB patients spending 75% more than Hispanic patients (95% CI = 16%-162%; P = 0.007) (Supplementary Table 14 (321KB, pdf) ).
Discussion
Using nationally representative data from 2018 to 2022, our findings revealed that total health care expenditures were significantly lower for Hispanic and NHB patients than for NHW patients. Significant differences were observed across various health care service areas by racial and ethnic groups. Hispanic and NHB patients had substantially lower all-cause office-based and hospital outpatient expenditures than NHW patients. Additionally, Hispanic patients had lower hospital inpatient expenditure than NHW patients and lower outpatient expenditure than NHB patients.
Our results are consistent with earlier studies. According to Antoinette et al, Hispanic patients had lower unadjusted mean health care costs than NHB and NHW patients, although costs increased with time for all groups.10 They found that between 2008 and 2010, NHW patients spent $8,961 on average; between 2014 and 2016, that amount increased to $11,376. Over the same period, the mean annual expenditures of Hispanic patients, who had the lowest expenditures, went from $7,211 to $9,469, whereas that of NHB patients rose from $8,088 to $10,240.10 Similarly, our study observed increased health care expenditures for NHB and NHW patients between the periods 2018-2019 and 2020-2022. However, although Hispanic patients did experience some increase in expenditures between 2018 and 2019, their total health care spending declined between 2020 and 2022, which deviates from the continued upward trend observed for NHB and NHW patients. This may be attributed to the COVID-19 pandemic, which exacerbated disparities in the use of arthritis-related care. A study by Stronach et al found that the greatest reduction in overall arthroplasty use occurred among the Hispanic population (a 34% decrease), compared with a 19% decrease observed in the White population.40 Although our findings were adjusted for several social determinants of health (SDOH), including age, education, marital status, insurance, and employment status, other unmeasured factors may have contributed to the observed disparities in health care expenditure. These may include additional SDOH such as social support, as well as non-SDOH factors like treatment preferences, provider prescribing patterns, disease severity, health beliefs, and differences in care-seeking behaviors. Lower total expenditure by Hispanic and NHB patients with arthritis may reflect a lower arthritis care utilization rate by minority groups (Hispanics and NHBs), as posited by previous literature.16,41 According to a study by Dorothy et al, older adults from racial and ethnic minority groups reported lower rates of arthritis-related joint replacements compared with their White counterparts. Specifically, annual joint replacement rates were 0.97% for Hispanic patients and 0.98% for Black patients compared with 1.48% for White patients. After adjusting for covariates, the disparity remained statistically significant (odds ratio [OR] = 0.37; 95% CI, 0.20-0.71).42 Cavanaugh et al discovered that after adjusting for socioeconomic variables, Hispanic patients and Black women continued to have lower odds of utilizing total knee arthroplasty than White women (Black: hazard ratio [HR] = 0.75, 95% CI = 0.67-0.89; Hispanic: HR = 0.65; 95% CI = 0.47-0.89).43 Zhang et al determined that racial and ethnic disparities in the utilization rate of total knee arthroplasty were lower in total knee arthroplasty utilization for Hispanic cohorts, Black patients, and mixed racial groups, and it became worse over time.41
Differences in health attitudes and care-seeking behaviors may account for the persistence of these inequities. A study by Dennis et al reported that after controlling for confounders, African American patients were almost 50% (OR = 0.60; 95% CI = 0.42-0.86; P = 0.005) less likely than White patients to perceive that total joint arthroplasty is beneficial or helpful for their arthritis. They also found that African American patients were 70% (OR = 1.7; 95% CI = 1.18-2.44; P = 0.004) more likely than White patients to recognize barriers.20 Some of these reasons may be tied to prescription behavior and preferences for complementary and alternative therapies over traditional medical care by racial and ethnic minority groups (Hispanic and NHB patients) with arthritis, according to previous research.19,44 This preference is typically not included in expenditure analysis.
Hispanic and NHB patients in the United States historically had lower access to health care and used lower health care services than White patients. Daniel et al found that disease-modifying antirheumatic drugs (DMARDs) were prescribed much less often for African American patients than for White patients, especially among visits to nonrheumatologists.45 They stated that part of the reason that the African American race may be associated with reduced DMARD use was that these patients have more limited access to rheumatologists.45 This difference in prescription behavior could influence outcomes.
Lower office-based and outpatient therapy visits by racial and ethnic minority groups (Hispanic and NHB patients) with arthritis have been reported in previous literature. Sandstrom et al in their study found that Hispanic and Black American patients with arthritis have reduced odds of office-based therapy visits (26.5% [95% CI = 7%-42%] and 44.8% [95% CI = 31.9%-55.3%], respectively) than White patients with arthritis.46 This may reflect barriers to care, such as limited insurance coverage or cultural preferences for home remedies, as identified in prior studies.41 Hispanic patients with arthritis had lower outpatient expenditures than NHB patients, which may reflect their differences in the utilization rate of outpatient therapies. This could be because of more visits and fewer barriers by NHB patients than Hispanic patients to outpatient therapies.41 In addition to previous studies, our study also found that Hispanic and NHB patients fall into the near poor and low-income categories more than NHW patients. Significantly lower hospital inpatient expenditure among Hispanic patients compared with White adults with arthritis, after adjusting for covariates, may reflect lower hospitalization rates among these minorities. Despite often presenting with higher disease severity, Hispanic patients usually encounter delays in treatment initiation and lower hospitalization rates compared with non-Hispanic patients with arthritis.12,23,41 Based on the findings of this research, it is noted that after controlling for some factors that may affect health care access, Hispanic and NHB adults with arthritis had lower total expenditures on care than NHW patients. Thus, more research should be done to examine the behaviors and belief systems of these underrepresented groups of people with arthritis. Understanding the health care beliefs and perspectives of Hispanic and NHB patients can help design more effective, culturally tailored interventions that promote engagement in care and the use of evidence-based treatments. Targeted education is crucial for informing the racial and ethnic minority groups, such as Hispanic and NHB patients, about the importance of using conventional medicines in arthritis management, which could help reduce the burden of the condition. Other strategies to close the care gap and guarantee more equitable access to essential medical treatments for Hispanic and NHB populations could be informed by research. Our study adds new insights to the body of literature by looking into specific service categories, such as office-based visits, hospital inpatient visits, prescription medicines, emergency visits, and outpatient visits, among US adults with arthritis. This level of granularity is important because understanding where disparities in spending occur (eg, lower outpatient vs inpatient use) allows for more precise and actionable public health interventions.
LIMITATIONS
Our study does have certain limitations that should be considered. A cohort of patients who self-reported having arthritis was used to select our sample. The reliability of the results may be impacted by recall bias in self-reported data and may present the possibility of inaccurate arthritis diagnosis.10 However, MEPS uses a network of trained interviewers who encourage participants to share objective information, such as medical records, if possible.29 Further, MEPS lacks information on institutionalized patients and patients in nursing homes. Our study could not control disease stage and severity because of the lack of disease-specific information in MEPS.47 Our study was also limited regarding our categorization of racial and ethnic groups. We could not include other minority groups, such as Asian/Native Hawaiian/Pacific Islander, American Indian/Alaska Native, and those of multiple races, because of their low sample size. There is an ongoing need to oversample understudied populations in national surveys and for researchers to use intersectional approaches that can shed light on within-group disparities.29 In addition, although we accounted for several factors related to sociodemographic characteristics, health care access, and health status, this list was not exhaustive, and there are likely other relevant variables that we could have included in our analysis. Moreover, we were limited to the variables available within the MEPS dataset, so we were unable to account for factors related to disease severity or health beliefs and the use of alternative forms of treatment, which could affect expenditure.10 Another key limitation of this study is the absence of data on provider density and physician reimbursement acceptance (eg, Medicaid acceptance) in the MEPS dataset. We recommend that future studies use alternative data sources to explore how variations in physician availability and insurance acceptance may contribute to disparities in arthritis care. Also, future studies should employ clinically validated data sources to enable precise diagnostic classification of arthritis and explore subtype-specific disparities in health care utilization. This approach could provide deeper insights into how treatment patterns, access to care, and health care costs differ between conditions such as osteoarthritis and rheumatoid arthritis, ultimately guiding more targeted and effective interventions.
Conclusions
Hispanics and NHB patients incurred significantly lower total health care expenditures than NHW patients after adjusting for covariates. This difference was largely driven by lower outpatient and office-based expenditures for NHB patients and outpatient, office-based, and inpatient expenditures for Hispanic patients. Further research into the reasons for these inequalities, including attitudes toward health and treatment preferences, is necessary.
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