Abstract
Objective:
The Centers for Medicare & Medicaid Services tested the Part D Senior Savings Model (“PDSS Model”) in 2021, capping monthly out-of-pocket (OOP) insulin costs at $35. Diabetes disproportionately affects racial/ethnic minorities compared to their non-Hispanic White (White) counterparts, so this study compared the changes in racial/ethnic disparities in healthcare costs among insulin users between Medicare and non-Medicare populations in 2021.
Methods:
This study analyzed Medical Expenditure Panel Survey (2020-2021). The intervention group comprised Medicare beneficiaries aged ≥65, while the comparison group included near-elderly non-Medicare population. The study outcomes included annual OOP/total costs in 2021 dollars for insulin, medication, health services (medical), and overall healthcare. A difference-in-differences-in-differences (DDD) approach was employed to assess the PDSS Model’s effects between White and each racial/ethnic minority group.
Results:
The weighted sample included 1,056,386 insulin users (53.89% intervention). In 2020, among the intervention group, non-Hispanic Black (Black) and Hispanic had similar or higher insulin costs than White patients. Black-White differences in OOP insulin costs were lowered more among the intervention group (cost ratio [CR]=0.12, 95% confidence interval=0.06-0.22) than the comparison group. Black-White differences in OOP costs for medication, health services, and overall healthcare widened more among the intervention group by 61%-64%. These patterns were not seen for other racial/ethnic disparities.
Conclusions:
Among insulin users, Black may have benefited more from the PDSS Model than White patients, which may be associated with enhanced insulin access and lower needs for other healthcare. Future studies should examine the long-term and heterogeneous impact of the PDSS Model.
Keywords: Disparities, Healthcare cost, Medicare, Senior Savings Model, diabetes
Introduction
Diabetes poses a tremendous burden on populations in the United States. In 2021, approximately 38.4 million Americans were living with diabetes, representing 11.6% of the U.S. population [1]. The prevalence of diabetes increases with age, with rates of 29.2% among individuals aged 65 years and older, compared to 18.9% for the 45-64 age group, and 4.8% for the 18-44 age group [1]. The incidence rate of newly diagnosed diabetes among individuals aged 65 years and older was also double that of the 18-44 age group [1]. Diabetes exerts a significant strain on the healthcare system, particularly on Medicare, as 26.5% of Medicare beneficiaries with full or nearly full fee-for-service coverage had diabetes in 2021 [2]. Medical spending per capita is also notably higher for individuals with diagnosed diabetes compared to those without the condition [3]. In recent years, diabetes has increasingly become a significant economic burden, particularly affecting seniors [1, 3]. Adults aged 65 and older exhibit double the annual healthcare spending per capita of younger adult groups [3]. The estimated total costs associated with diagnosed diabetes in the U.S. have risen significantly, from $316 billion in 2012 to $413 billion in 2022 [1].
Diabetes also disproportionately affects racial/ethnic minorities. During 2019-2021, Non-Hispanic Black (Black), Hispanic, Non-Hispanic Asian (Asian), and American Indian/Alaska Native (AI/AN) adults reported elevated age-adjusted prevalence rates of diagnosed diabetes, at 12.1%, 11.7%, 9.1%, and 13.6% respectively, compared to 6.9% for non-Hispanic White (White) adults [1]. In addition to higher prevalence rates, previous studies have shown that racial/ethnic minorities experience worse diabetes management and increased rates of diabetes complications and mortality compared to White patients [4-8]. Black patients with diabetes have triple the rates of diabetes-related emergency department visits and hospital discharges compared to White patients [5, 7]. It has been documented that racial/ethnic minority groups experience increased risks of diabetes-related complications [6, 8]. For instance, Black and Hispanic patients reported diabetic retinopathy prevalence rates of 38.8% and 34.0%, respectively, compared to 26.4% in White patients [8]. One retrospective study, which investigated older Medicare beneficiaries with Type 2 diabetes, found that Blacks and AI/ANs exhibited 15% and 33% higher mortality rates in 2012 compared to Whites [4]. Furthermore, socioeconomic status (SES), encompassing education, income, and occupation status [9, 10], is recognized as a significant factor associated with the prevalence and health outcomes of diabetes [9, 10]. Individuals positioned lower on the SES spectrum are more likely to experience Type 2 diabetes and its complications [9, 10].
Blood glucose control is vital in managing diabetes to prevent or delay health problems associated with diabetes, such as heart disease and kidney disease [11]. Insulin is an essential medication and a common treatment to achieve glucose control and prevent diabetes complications [12]. In 2022, more than 34.1% of all adults with diagnosed diabetes in the U.S. used insulin alone or in combination with other diabetes medications [13]. Total out-of-pocket (OOP) insulin spending for all Medicare Part D enrollees increased from $236 million in 2007 to $1.03 billion in 2020 [14]. Furthermore, one study, which investigated OOP insulin spending trends among Medicare Part D enrollees who did not receive low-income subsidies, reported that average annual OOP insulin spending per capita significantly increased from $324 in 2007 to $572 in 2020, surpassing the 1.7% average annual general inflation growth for the same period [14]. The same study showed that many spent substantially more on insulin than the average values mentioned above [14]. For instance, 10% of insulin users spent over $1300 on insulin in 2020 [14]. Despite efforts to narrow the coverage gap for insulin in recent years, the requirement of deductibles and copays for prescriptions without caps on OOP costs, coupled with price increases on insulin, have led to a significant rise in OOP expenses for insulin [15, 16]. The rise in insulin prices and high OOP insulin spending in recent years may result in insulin underutilization, leading to poor disease control [17]. Cost-related underutilization may be more prominent in certain populations, such as Black individuals [18]. Among Medicare beneficiaries, Black patients were more likely than their White counterparts to report healthcare and medication access issues due to cost [18].
Effective January 1st, 2023, the Inflation Reduction Act was implemented to enhance Medicare beneficiaries’ access to affordable treatments and address financial obstacles by limiting OOP spending on medications [19]. To test the effects of providing lower and more stable cost-sharing for insulin products on patients’ OOP costs and medication utilization, the Centers for Medicare and Medicaid Services (CMS) launched the Part D Senior Savings (PDSS) Model in 2021, prior to the implementation of the Inflation Reduction Act [20]. The PDSS Model caps OOP insulin costs at no more than $35 per month for selected insulin products during 2021-2023 [20]. This initiative received robust interest and participation during its implementation, with 1505 prescription drug plans participating in the model in 2021, including 1195 Medicare Advantage Prescription Drug Plans (MAPDs) and 310 standalone Prescription Drug Plans (PDPs) [20]. Due to the unavailability of the PDSS Model data to research communities, this model has not been comprehensively evaluated, particularly regarding its effects on racial/ethnic disparities. To address this gap, this study aims to evaluate the impact of the PDSS Model on racial/ethnic disparities in various categories of healthcare costs. Because racial/ethnic minorities disproportionately suffer from diabetes more than their White counterparts, racial/ethnic disparities in healthcare costs may be reduced as a result of the PDSS Model implementation [4-8]. The study findings can provide valuable insights into the potential impact of the Inflation Reduction Act on racial/ethnic disparities in healthcare costs.
Methods
Data source
This study was a retrospective analysis of the Household Component in the Medical Expenditure Panel Survey (MEPS) spanning from 2020 to 2021. Sponsored by the Agency for Healthcare Research and Quality, the MEPS is a survey of the U.S. civilian non-institutionalized population representative at a national scale [21]. The MEPS employs an overlapping panel design, introducing a new sample of households each calendar year to create overlapping panels. The Household Component provides annual information on sociodemographic characteristics, health conditions, health insurance coverage, utilization of health services and prescription drugs, as well as expenditures and sources of payments associated with the utilization of all healthcare. The sample of survey participants comprises individuals and families in selected communities across the United States.
Study sample
The study sample consisted of patients who reported maintaining health insurance coverage in all rounds of interviews and had at least one prescription fill of insulin in each study year. All commercially available insulin in the United States, including rapid-acting, short-acting, intermediate-acting, and long-acting varieties, were considered in the current study [22]. Patients without health insurance were excluded to create a more compatible comparison group. Patients with health insurance covered by Medicaid were excluded from the study because the PDSS Model does not apply to those eligible for the Part D low-income subsidy [20]. To further ensure a more homogeneous sample and minimize confounding effects from other social welfare programs, individuals with healthcare coverage from U.S. military healthcare initiatives, including Veteran Affairs and TRICARE, as well as other federal and state programs, were also excluded from the analysis.
To facilitate a difference-in-differences-differences (DDD) analysis, individuals who met the aforementioned sample selection criteria were further categorized into mutually exclusive intervention and comparison groups. The intervention group comprised Medicare beneficiaries aged 65 and older at the end of 2020. The comparison group included near-elderly individuals with private insurance coverage who were between 50 and 63 years of age at the end of 2020. Following prior studies on Medicare Part D, the non-elderly cohort was employed as a comparison group due to their similarity to Medicare beneficiaries [23-28].
Outcomes
The study outcomes included costs for insulin, medication, health services (medical), and overall healthcare. Costs for medications (encompassing all prescription drugs purchased) and health services (covering all physician, inpatient, outpatient, home health, and other health services received) were analyzed separately to gain an understanding of the impact of PDSS on medication and medical costs. Overall healthcare costs were calculated as the sum of costs for medications and health services. All costs were measured annually for the years 2020 and 2021, separately, from both the patient’s and healthcare system’s perspectives. The patient perspective was defined as OOP costs incurred by patients, capturing the direct financial burden on individuals. The healthcare system perspective accounted for the total costs stemming from all payment sources associated with healthcare utilization (including OOP costs and other payment sources), which has direct implications for health policymakers. To account for inflation, all dollar amounts for OOP costs and total costs (from the healthcare system perspective) were adjusted to the 2021 levels based on the Consumer Price Index and the Gross Domestic Product Price Index, respectively, based on recommendations on the analysis of MEPS data [29].
Covariates
Because the study outcomes were associated with the utilization of healthcare, Andersen’s behavioral model of health services use was adopted as the conceptual model for guiding the choice of covariates in regression analysis. According to this model, the realized utilization of healthcare services is a function of predisposing, enabling, and need factors [30]. Predisposing factors influence an individual’s inclination to seek healthcare. Enabling factors either impede or facilitate the use of and access to healthcare. Need factors reflect the perceived or actual health status affecting an individual’s requirements for healthcare service. Predisposing factors in this study encompassed race/ethnicity, age, gender (female/male), marital status (unmarried/married), and education (less than or equal to high school/greater than high school). Racial/ethnic groups included White, Black, Hispanic, and Asian. Enabling factors included personal income and census regions (Northeast, Midwest, South, and West). The need factor consisted of the patient’s self-perceived health status (very good or better, good, fair, and poor).
Empirical approach and statistical analysis
Descriptive analyses were initially conducted to summarize the baseline characteristics and study outcomes for the intervention and comparison groups. Statistical comparisons between the groups were then performed using a t-test for continuous variables and a chi-square test for categorical variables. The differential impact of the PDSS Model on racial/ethnic groups was assessed through a series of steps using multivariable regression models with interaction terms. A generalized linear model with gamma distribution and log link was employed at each step. In the first step, potential disparities in cost outcomes during the baseline year for both the intervention and comparison groups were determined by including a dummy variable for each racial/ethnic minority in the model, with White patients as the reference group. Disparities were defined as significant differences in costs after controlling for the effects of other covariates in the model. A cost ratio (CR) represents the exponentiated coefficient of an explanatory variable in the model. If the CR estimate for a minority dummy variable was significantly greater (less) than one, it would indicate a higher (lower) cost for the respective minority group compared to White patients.
In the second step, using a difference-in-differences approach, interaction terms between dummy variables for the year 2021 and racial/ethnic minorities were included in the model to evaluate changes in disparities over time. This was conducted separately for the intervention and comparison groups. If disparities of a higher (lower) value in a cost outcome for a minority group relative to White patients were detected in the prior step, an estimated CR of less than one for the interaction term for the year 2021 and the minority group would indicate that the disparities decreased (widened) in 2021 compared to 2020.
In the final step, employing a difference-in-differences-in-differences (DDD) approach, the impact of the PDSS Model across race/ethnicity was determined by including three-way interaction terms between dummy variables for Medicare, the year 2021, and racial/ethnic minorities in the model. This approach compared changes in disparities over time between the intervention and the comparison groups. If temporal disparity reductions were identified in both study groups from the second step, a CR estimate significantly lower than one for the three-way interaction term of a minority would indicate that the reduction in disparities between White patients and the minority patients in question was greater in the intervention group than in the comparison group. This would indicate that the minority group benefited more from the PDSS Model implementation compared to White patients. Figure 1 illustrates the empirical approach, highlighting the key elements of the research design to evaluate disparities in healthcare costs across racial/ethnic groups. The structural forms of the multivariable regression models used at each step are provided in Appendix 1 for reference.
Figure 1. Research design.
This figure illustrates the temporal change of difference-in-differences (racial/ethnic disparities) between individuals in the intervention group and those in the comparison group across the study years 2020 and 2021.
Statistical analyses were completed utilizing SAS v.9.4, where statistical significance was determined at a two-sided alpha value of 0.05. All analyses accounted for the complex survey design of the MEPS by incorporating survey weights, sampling strata, and primary sampling units. These design variables were used to generate weighted estimates and compute standard errors using a Taylor-series linearization method. The Institutional Review Board at the institution of the corresponding author provided an exemption approval for this study (approval number 24-09919-NHSR).
Results
The baseline characteristics of the total patients in the intervention and comparison groups were compared in Table 1. The weighted total study sample in 2020 consisted of 1,056,386 insulin-using patients, with 56.06% from the intervention group and 43.94% from the comparison group. Significant differences were observed in several patient characteristics between the two study groups, including race/ethnicity, age, marital status, and personal income (P < 0.05). Compared to those in the comparison group, patients in the intervention group had a higher mean age (73.82 vs. 56.90), a lower proportion of being married (68.14% vs. 78.09%), and a lower mean personal income ($46.32 thousand vs. $73.56 thousand). The racial/ethnic composition of the intervention group was 78.93% White, 17.53% Black, 2.05% Hispanic, and 1.49% Asian. In contrast, the comparison group was 80.22% White, 4.98% Black, 9.79% Hispanic, and 5.01% Asian.
Table 1.
Baseline Characteristics of Study Population by Intervention Status.
| Characteristics | Intervention Group (Weighted n = 592,240; 56.06%) |
Comparison Group (Weighted n = 464,146; 43.94%) |
P-Value | ||
|---|---|---|---|---|---|
| Weighted Number | % | Weighted Number | % | ||
| Predisposing Factors | |||||
| Race/Ethnicity | < 0.0001 | ||||
| Non-Hispanic White | 467,478 | 78.93 | 372,338 | 80.22 | |
| Non-Hispanic Black | 103,810 | 17.53 | 23,101 | 4.98 | |
| Hispanic | 12,128 | 2.05 | 45,456 | 9.79 | |
| Non-Hispanic Asian | 8,823 | 1.49 | 23,252 | 5.01 | |
| Age, mean (SE) | 73.82 (0.58) | 56.90 (0.36) | < 0.0001 | ||
| Male | 354,667 | 59.89 | 274,671 | 59.18 | 0.946 |
| Married | 403,529 | 68.14 | 362,463 | 78.09 | 0.0499 |
| Education > High School | 328,993 | 55.55 | 209,718 | 45.18 | 0.234 |
| Enabling Factors | |||||
| Personal Income (in $1,000), mean (SE) | 46.32 (3.37) | 73.56 (3.36) | < 0.0005 | ||
| Census Regions | 0.593 | ||||
| Northeast | 112,141 | 18.94 | 83,185 | 17.92 | |
| Midwest | 141,730 | 23.93 | 155,460 | 33.49 | |
| South | 215,236 | 36.34 | 143,792 | 30.98 | |
| West | 123,133 | 20.79 | 81,709 | 17.60 | |
| Need Factor | |||||
| Self-Perceived Health Status | 0.225 | ||||
| Excellent/Very Good | 125,948 | 21.27 | 112,036 | 24.14 | |
| Good | 292,490 | 49.39 | 194,612 | 41.93 | |
| Fair | 120,779 | 20.39 | 144,529 | 31.14 | |
| Poor | 53,022 | 8.95 | 12,969 | 2.79 | |
Abbreviation: SE, standard error.
The baseline mean costs by race/ethnicity in the two study groups for 2020 based on the description analysis are illustrated in Figure 2. Generally speaking, when across-group differences were significant, racial/ethnic minority groups tended to incur lower costs than their White counterparts. For instance, the mean total overall healthcare costs for White, Black, Hispanic, and Asian patients in the intervention group were $26,044.55, $11,216.28, $12,759.77, and $12,230.53, respectively. In terms of insulin costs, patients from each racial/ethnic minority group in both study groups generally had similar or lower mean values in both OOP and overall costs compared to their White counterparts. A similar pattern was observed for medication costs except for Hispanic patients in the comparison group. For health services and overall healthcare costs, Black and Hispanic patients consistently exhibited significantly lower mean OOP and total costs compared to White patients in both study groups.
Figure 2. Baseline mean healthcare costs by race/ethnicity in the study groups.
This figure illustrates the mean out-of-pocket (OOP) and total costs across race/ethnicity among both the intervention and comparison groups in 2020. An asterisk indicates that the mean for a cost category is significantly different for a minority group compared to their non-Hispanic White counterparts (p <.05).
Results from the first step of the multivariable regression analyses examining baseline racial/ethnic disparities in 2020 are displayed in the first half of the columns for the intervention and comparison groups, respectively, in Table 2. The patterns are similar to those in the descriptive analysis above, and racial/ethnic minority groups incurred lower costs in many cost categories than White patients, with some exceptions. For example, minority patients in both study groups generally exhibited similar or higher insulin costs than White patients in 2020. Black patients had a lower total medication cost (CR=0.45, 95% CI=0.29-0.70) in the intervention group, and Asian patients had a lower total medication cost (CR=0.28, 95% CI=0.11-0.68) in the comparison group. For health services and overall healthcare costs, Black and Hispanic patients generally incurred lower OOP and total costs than White patients in both study groups.
Table 2.
Racial/Ethnic Disparities in Costs by Study Groups.
| Cost Outcome | Race/Ethnicity | Intervention Group | Comparison Group | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Baseline Year | Changes across Years | Baseline Year | Changes across Years | ||||||
| Cost Ratio | 95% CI | Cost Ratio | 95% CI | Cost Ratio | 95% CI | Cost Ratio | 95% CI | ||
| Out-of-Pocket | |||||||||
| Insulin | Non-Hispanic Black | 1.32 | 0.76 - 2.31 | 0.17 | 0.07 - 0.45 | 5.15 | 2.26 - 11.74 | 1.91 | 1.58 - 2.31 |
| Hispanic | 3.89 | 1.62 - 9.30 | 0.31 | 0.24 - 0.39 | 10.40 | 6.93 - 15.61 | 0.65 | 0.27 - 1.58 | |
| Non-Hispanic Asian | 1.53 | 0.36 - 6.48 | 0.10 | 0.08 - 0.13 | 2.48 | 0.96 - 6.41 | 0.90 | 0.36 - 2.28 | |
| Medications | Non-Hispanic Black | 0.89 | 0.56 - 1.41 | 0.54 | 0.33 - 0.88 | 0.95 | 0.55 - 1.65 | 1.44 | 1.01 - 2.05 |
| Hispanic | 1.18 | 0.32 - 4.35 | 0.27 | 0.26 - 0.29 | 2.90 | 1.59 - 5.29 | 0.22 | 0.07 - 0.70 | |
| Non-Hispanic Asian | 0.57 | 0.18 - 1.78 | 0.28 | 0.18 - 0.44 | 0.92 | 0.45 - 1.90 | 1.28 | 0.88 - 1.85 | |
| Health Services | Non-Hispanic Black | 0.55 | 0.43 - 0.70 | 0.43 | 0.33 - 0.56 | 0.03 | 0.02 - 0.08 | 0.88 | 0.26 - 3.00 |
| Hispanic | 0.10 | 0.02 - 0.54 | 1.89 | 0.29 - 12.21 | 0.10 | 0.04 - 0.22 | 3.93 | 1.63 - 9.48 | |
| Non-Hispanic Asian | 5.66 | 2.13 - 15.00 | 0.27 | 0.11 - 0.64 | 0.73 | 0.24 - 2.25 | 0.09 | 0.05 - 0.16 | |
| Overall Healthcare | Non-Hispanic Black | 0.63 | 0.48 - 0.81 | 0.51 | 0.39 - 0.66 | 0.03 | 0.01 - 0.12 | 1.21 | 0.58 - 2.51 |
| Hispanic | 0.41 | 0.16 - 1.01 | 0.52 | 0.37 - 0.73 | 0.18 | 0.05 - 0.62 | 0.64 | 0.41 - 1.01 | |
| Non-Hispanic Asian | 2.01 | 0.60 - 6.73 | 0.43 | 0.24 - 0.77 | 0.90 | 0.32 - 2.56 | 0.22 | 0.16 - 0.30 | |
| Total | |||||||||
| Insulin | Non-Hispanic Black | 1.69 | 1.35 - 2.12 | 0.37 | 0.22 - 0.60 | 1.29 | 0.24 - 7.03 | 2.30 | 1.56 - 3.39 |
| Hispanic | 2.82 | 1.59 - 5.02 | 0.92 | 0.63 - 1.35 | 0.44 | 0.22 - 0.90 | 1.64 | 0.88 - 3.03 | |
| Non-Hispanic Asian | 2.89 | 0.77 - 10.88 | 0.16 | 0.12 - 0.22 | 0.36 | 0.14 - 0.92 | 0.94 | 0.46 - 1.92 | |
| Medications | Non-Hispanic Black | 0.45 | 0.29 - 0.70 | 0.94 | 0.67 - 1.31 | 0.83 | 0.14 - 4.93 | 1.39 | 0.88 - 2.20 |
| Hispanic | 0.69 | 0.31 - 1.52 | 0.61 | 0.42 - 0.87 | 0.48 | 0.15 - 1.53 | 0.64 | 0.35 - 1.18 | |
| Non-Hispanic Asian | 0.55 | 0.17 - 1.79 | 0.40 | 0.25 - 0.64 | 0.28 | 0.11 - 0.68 | 1.24 | 0.93 - 1.66 | |
| Health Services | Non-Hispanic Black | 0.30 | 0.16 - 0.55 | 2.64 | 1.07 - 6.54 | 0.02 | 0.01 - 0.04 | 1.51 | 0.72 - 3.16 |
| Hispanic | 0.30 | 0.06 - 1.51 | 1.39 | 0.22 - 8.73 | 0.07 | 0.03 - 0.17 | 1.10 | 0.35 - 3.48 | |
| Non-Hispanic Asian | 0.51 | 0.34 - 0.77 | 0.43 | 0.27 - 0.70 | 0.34 | 0.19 - 0.62 | 0.32 | 0.20 - 0.49 | |
| Overall Healthcare | Non-Hispanic Black | 0.33 | 0.26 - 0.42 | 1.36 | 0.93 - 1.99 | 0.08 | 0.04 - 0.20 | 1.88 | 1.33 - 2.65 |
| Hispanic | 0.42 | 0.35 - 0.51 | 0.90 | 0.56 - 1.46 | 0.12 | 0.04 - 0.36 | 1.01 | 0.36 - 2.82 | |
| Non-Hispanic Asian | 0.52 | 0.36 - 0.76 | 0.52 | 0.37 - 0.73 | 0.24 | 0.13 - 0.48 | 0.60 | 0.44 - 0.82 | |
Abbreviation: CI, confidence interval. Reference groups: non-Hispanic White, baseline year (2020), female, unmarried, education ≤ high school, Northeast region, and health status ≥ very good.
Results based on the second step of the multivariable analysis for changes in racial/ethnic disparities across study years are reported in the second half of the columns for the intervention and the comparison groups, respectively, in Table 2. More CRs for the intervention groups were lower than one compared to the comparison group, particularly in the OOP cost categories. For example, the CRs for all OOP costs among the intervention group were generally lower than one, with few exceptions (Table 2). The Black-White, Hispanic-White, and Asian-White differences in OOP insulin costs significantly decreased by 83% (CR=0.17, 95% CI=0.07-0.45), 69% (CR=0.31, 95% CI=0.24-0.39), and 90% (CR=0.10, 95% CI=0.08-0.13), respectively. An interesting pattern emerged when comparing various disparities (Table 3). The CRs for Black-White disparities in the intervention group were more consistently lower than one compared to those in the comparison group, hinting at a greater reduction in Black-White disparities in the intervention group. However, for Hispanic-White and Asian-White disparities, the CRs were similar between the comparison and intervention groups.
Table 3.
Comparing Racial/Ethnic Disparities in Costs across Time between the Intervention and Comparison Groups.
| Cost Outcome | Race/ Ethnicity | Out-Of-Pocket | Total | ||
|---|---|---|---|---|---|
| Cost Ratio | 95% Confidence Interval | Cost Ratio | 95% Confidence Interval | ||
| Insulin | Non-Hispanic Black | 0.12 | 0.06 - 0.22 | 0.22 | 0.09 - 0.50 |
| Hispanic | 1.51 | 0.69 - 3.31 | 1.03 | 0.66 - 1.62 | |
| Non-Hispanic Asian | 0.54 | 0.21 - 1.39 | 0.43 | 0.21 - 0.85 | |
| Medications | Non-Hispanic Black | 0.39 | 0.28 - 0.55 | 0.82 | 0.47 - 1.42 |
| Hispanic | 1.72 | 0.79 - 3.75 | 1.82 | 1.41 - 2.36 | |
| Non-Hispanic Asian | 0.23 | 0.10 - 0.53 | 0.42 | 0.28 - 0.65 | |
| Health Services | Non-Hispanic Black | 0.36 | 0.18 - 0.71 | 1.03 | 0.24 - 4.47 |
| Hispanic | 0.59 | 0.10 - 3.44 | 0.27 | 0.07 - 1.01 | |
| Non-Hispanic Asian | 2.08 | 0.65 - 6.69 | 0.79 | 0.36 - 1.71 | |
| Overall Healthcare | Non-Hispanic Black | 0.39 | 0.23 - 0.66 | 0.73 | 0.43 - 1.25 |
| Hispanic | 0.76 | 0.47 - 1.22 | 0.70 | 0.50 - 0.97 | |
| Non-Hispanic Asian | 1.67 | 0.65 - 4.29 | 0.79 | 0.51 - 1.22 | |
Reference groups: non-Hispanic White, non-Medicare, baseline year (2020), female, unmarried, education ≤ high school, Northeast region, and health status ≥ very good.
The results comparing changes in racial/ethnic disparities over time between the intervention and comparison groups based on the third step of the multivariable analysis are reported in Table 3. The Black-White difference in OOP insulin costs narrowed by 88% more over time (CR=0.12, 95% CI=0.06-0.22) in the intervention group compared to the comparison group. The Black-White differences in OOP costs for medication, health services, and overall healthcare widened more over time among the intervention group than the comparison group by 61% (CR=0.39, 95% CI=0.28-0.55), 64% (CR=0.36, 95% CI=0.18-0.71), and 61% (CR=0.39, 95% CI=0.23-0.66), respectively. These consistent patterns of reductions in disparities for OOP costs were not observed for other racial/ethnic minority groups. In contrast, changes over time in racial/ethnic disparities for total cost categories did not show a consistent pattern between the intervention and comparison groups.
Apart from race/ethnicity, several other patient characteristics also demonstrated significant relationships with the cost outcomes. As indicated in Table 4, some of these characteristics consistently showed a positive association with both OOP and total costs for medications. This included being married (e.g., CR=1.57, 95% CI=1.30-1.90 for OOP cost) and having fair and poor self-perceived health status, as opposed to having very good or better health (e.g., CR=1.59, 95% CI=1.47-1.73; and CR=1.95, 95% CI=1.30-2.93 for OOP cost). Other characteristics only displayed significant relationships with either OOP or total cost outcomes. Personal income (CR=1.002, 95% CI=1.001-1.004) and residing in the Midwest, South, and West, compared to the Northeast, (CR=1.63, 95% CI=1.01-2.63; CR=1.44, 95% CI=1.02-2.02; and CR=1.56, 95% CI=1.28-1.90) were associated with higher OOP medication costs. Conversely, being male (CR=0.67, 95% CI=0.56-0.81) was associated with a lower total medication cost.
Table 4.
Factors Influencing Cost for Medications among Patient Cohorts.
| Characteristics | Out-Of-Pocket | Total | ||
|---|---|---|---|---|
| Cost Ratio | 95% Confidence Interval | Cost Ratio | 95% Confidence Interval | |
| Predisposing Factors | ||||
| Race/Ethnicity (Race) | ||||
| Non-Hispanic Black | 1.10 | 0.57 - 2.12 | 0.49 | 0.28 - 0.83 |
| Hispanic | 3.82 | 2.45 - 5.96 | 0.83 | 0.64 - 1.09 |
| Non-Hispanic Asian | 0.52 | 0.37 - 0.73 | 0.11 | 0.07 - 0.19 |
| Medicare | 3.07 | 2.00 - 4.70 | 1.96 | 0.93 - 4.12 |
| Intervention Year (2021) | 1.43 | 0.98 - 2.08 | 1.82 | 1.18 - 2.80 |
| Medicare × Intervention Year | 0.767 | 0.591 - 0.996 | 0.57 | 0.44 - 0.74 |
| Medicare × Race | ||||
| Non-Hispanic Black | 0.81 | 0.48 - 1.38 | 1.12 | 0.54 - 2.34 |
| Hispanic | 0.19 | 0.07 - 0.52 | 0.61 | 0.50 - 0.73 |
| Non-Hispanic Asian | 0.74 | 0.28 - 1.94 | 3.70 | 2.23 - 6.13 |
| Intervention Year × Race | ||||
| Non-Hispanic Black | 1.34 | 0.91 - 1.96 | 1.18 | 0.73 - 1.90 |
| Hispanic | 0.24 | 0.08 - 0.68 | 0.60 | 0.44 - 0.81 |
| Non-Hispanic Asian | 1.16 | 0.70 - 1.92 | 1.32 | 0.86 - 2.00 |
| Medicare × Intervention Year × Race | ||||
| Non-Hispanic Black | 0.39 | 0.28-0.55 | 0.82 | 0.47-1.42 |
| Hispanic | 1.72 | 0.79-3.75 | 1.82 | 1.41-2.36 |
| Non-Hispanic Asian | 0.23 | 0.10-0.53 | 0.42 | 0.28-0.65 |
| Age | 1.001 | 0.982 - 1.020 | 0.99 | 0.97 - 1.02 |
| Male | 0.92 | 0.75 - 1.13 | 0.67 | 0.56 - 0.81 |
| Married | 1.57 | 1.30 - 1.90 | 1.57 | 1.38 - 1.78 |
| Education > High School | 1.01 | 0.58 - 1.74 | 0.70 | 0.45 - 1.09 |
| Enabling Factors | ||||
| Personal Income | 1.002 | 1.001 - 1.004 | 0.9998 | 0.9984 - 1.0012 |
| Census Regions | ||||
| Midwest | 1.63 | 1.01 - 2.63 | 1.09 | 0.78 - 1.53 |
| South | 1.44 | 1.02 - 2.02 | 1.05 | 0.77 - 1.43 |
| West | 1.56 | 1.28 - 1.90 | 0.75 | 0.51 - 1.11 |
| Need Factor | ||||
| Self-Perceived Health Status | ||||
| Good | 1.09 | 0.92 - 1.29 | 0.87 | 0.68 - 1.10 |
| Fair | 1.59 | 1.47 - 1.73 | 1.26 | 1.07 - 1.48 |
| Poor | 1.95 | 1.30 - 2.93 | 1.40 | 1.04 - 1.89 |
Reference groups: non-Hispanic White, non-Medicare, baseline year (2020), female, unmarried, education ≤ high school, Northeast region, and health status ≥ very good.
Discussion
This study analyzed insulin users in 2020-2021 MEPS data to assess the effects of the PDSS Model on racial/ethnic disparities in OOP and total costs for insulin, medication, health services, and overall healthcare. Generally, compared to White patients, minority patients exhibited similar or lower OOP and total costs in measured cost outcomes in the unadjusted model. Similar trends persisted in the adjusted model, except for similar or higher insulin costs for minority patients compared to White patients. When comparing the intervention group to the comparison group over time, the gap between Black and White patients narrowed in OOP and total insulin costs while widening in OOP costs for medications, health services, and overall healthcare for the intervention group. Such patterns of cost changes between Blacks and Whites were not prominent for other minority groups.
Historically, racial/ethnic minorities have been reported to underutilize medications and health services, resulting in lower incurred costs [31, 32]. The findings from this study, such as the generally lower medication and health services costs of minorities compared to Whites, align with the previous literature [31, 32]. In the adjusted model in this study, minorities exhibited similar or higher OOP and total insulin costs compared to Whites in 2020. These findings could be attributed to the higher prevalence of diabetes and poorer diabetes control among minorities, leading to increased insulin usage and expenses [1, 5, 7]. Previous studies have highlighted racial/ethnic disparities in health insurance coverage, which may also contribute to the disparities in OOP insulin costs [33, 34]. For instance, Black and Hispanic individuals have reported higher uninsured rates compared to White individuals [34]. High OOP costs act as a barrier to appropriate medication utilization, disproportionately affecting racial/ethnic minorities [35]. Previous research indicated that Black and Hispanic individuals reporting higher rates of medication underutilization due to cost compared to Whites [35]. Despite diminishing disparities after adjusting for other factors, Hispanic individuals with diabetes still exhibited higher rates of cost-related medication underutilization compared to White individuals [35].
Based on the results of the DDD analysis, the Black-White difference in OOP and total insulin costs has narrowed for the intervention group since the PDSS Model came into effect in 2021. This suggests that the implementation of the PDSS Model may have played a positive role in reducing racial/ethnic disparities in insulin costs. According to CMS’ first-year evaluation report of the PDSS Model, the model has reduced insulin OOP costs, total OOP costs, and total Part D costs among Medicare Part D beneficiaries who used insulin [20]. This study provides valuable evidence of the model’s effectiveness in reducing racial/ethnic disparities.
The observed narrowing of Black-White disparities in insulin costs aligns with the study hypothesis, as Black patients were more likely to experience cost-related barriers to insulin utilization [18,35]. Improved insulin affordability through the PDSS Model may have addressed these barriers, potentially impacting broader categories of healthcare costs for Black patients. In contrast, the limited impact of the PDSS Model on insulin costs for Hispanic and Asian patients may reflect differences in baseline insulin utilization or health-seeking behaviors that influenced their responses to the policy. Additionally, variations in healthcare provider practices or patterns of diabetes management for these minority groups may have contributed to the observed outcomes, underscoring the complexity of the PDSS Model’s effects.
This study also highlights the potential positive impact of the Inflation Reduction Act in reducing racial/ethnic disparities in insulin utilization. The Assistant Secretary for Planning and Evaluation of the Department of Health & Human Services has projected that the Inflation Reduction Act will save $7.4 billion of annual OOP spending for 18.7 million Medicare enrollees in 2025 when all its provisions take effect [36]. The projected impacts for minority groups are also encouraging; for example, the Inflation Reduction Act could potentially save 36% of OOP costs for Black and 33% for Hispanic Medicare enrollees in 2025 [37]. Future studies should explore the effects of the PDSS Model and the Inflation Reduction Act on racial/ethnic disparities in various health and economic outcomes when the data become available.
This study found that the Black-White difference in OOP costs for medication, health services, and overall healthcare increased more for the intervention group than the comparison group over time. The PDSS Model that has already improved insulin affordability and appropriate utilization may also have a positive impact on patients’ health outcomes, particularly for the Black population, who were more likely to experience cost-related medication underutilization than Whites [18]. Black individuals have been found in this study to incur lower medication and health service costs compared to White individuals at baseline. The increased cost gap may be attributable to the improved diabetes control and related health outcomes experienced by Black patients, potentially leading to reduced medication and health service utilization and associated expenses, as suggested by previous studies [38, 39]. It is also plausible that lower insulin costs did not translate to overall affordability of healthcare. Persistent cost barriers faced by racial/ethnic minority groups, such as Black individuals, in accessing non-insulin healthcare services may have led to reduced utilization, contributing to the observed increased cost gaps. Alternatively, the increased cost gap could reflect higher healthcare expenses incurred by White patients relative to Black patients.
This study has identified several factors that are associated with the study outcomes besides the main variables of interest. Females in the study generally reported higher total medication costs than males, which aligns with previous findings that females were more likely to use one or more medications and spent more on medications [40, 41]. Being married and having poorer self-perceived health status are generally associated with increased OOP and total medication costs in this study. Previous studies have reported that marital status was associated with increased social support for healthy behaviors and medication compliance [42, 43]. Therefore, being married may help patients appropriately utilize medications, resulting in higher medication costs. People with poorer perceived health status may use more medications due to worse health outcomes, which can result in higher medication costs [44]. Higher personal income and residing in a census region other than the Northeast were associated with higher OOP medication costs in this study. Since Medicare Part D was implemented in 2006, disparities in OOP medication spending among people with different income levels have improved [45]. A previous study that investigated prescription medication expenditure trends between 1997 and 2015 using a national representative sample reported that absolute OOP medication spending increased with income levels, which aligns with the findings of this study [45]. The uninsured rates in the Northeast region are generally lower than in other regions, which could explain the disparities in OOP medication spending across geographic regions [46].
This study has several limitations that should be acknowledged. First, the retrospective study design restricted the ability to establish causal inference for the study outcomes, despite efforts to ensure the validity of the results, such as adopting a DDD approach. Second, despite efforts to establish a comparable comparison group, the selection of near-elderly privately insured individuals as the comparison group is associated with a limitation. Individuals nearing Medicare eligibility might postpone expensive treatments in anticipation of gaining Medicare coverage, potentially skewing the study results. Nevertheless, the lack of a notable decline in cost outcomes over time within the comparison group reinforces the reliability of the comparison group. Third, some empirical estimates for racial/ethnic groups had wide and overlapping confidence intervals. This highlights uncertainty and imprecision in the magnitude of the observed disparities, warranting cautious interpretation of the findings. Fourth, adopting eligibility criteria for the study population imposed restrictions on the generalizability and significance of the study findings. The findings cannot be generalized to other populations, such as the uninsured and Medicaid beneficiaries, because the PDSS Model is not applicable to them. Analysis of the Inflation Reduction Act, which provides a broader set of provisions with the potential to address disparities for more population groups, may produce more meaningful findings [19]. Fifth, not all individuals in the intervention group enrolled in the PDSS Model. Due to the unavailability of PDSS enrollment information in the dataset, we were unable to identify PDSS Model enrollees using MEPS. Therefore, this study may have underestimated the effects of the PDSS Model on the measured outcomes. Future research should aim to incorporate datasets with detailed PDSS enrollment information to better evaluate the intervention’s impact. Sixth, the cost information used in the study did not include costs for insurance premiums, which may vary depending on the plans. Lastly, while the current study included a range of covariates guided by Andersen’s behavioral model of health services use, it remains subject to omitted variable bias. Unaccounted factors, such as variations in healthcare provider practices and other social determinants of health, may have influenced the study findings.
Conclusions
This study aimed to assess the impact of the PDSS Model on racial/ethnic disparities in OOP and total costs of insulin, medications, health services, and overall healthcare. The study found that the PDSS Model was effective in reducing Black-White disparities in insulin costs. Therefore, the implementation of the PDSS Model could benefit patients who use insulin in general and advantage a racial and ethnic minority group by addressing potential barriers to insulin utilization, such as cost-related underutilization. The Inflation Reduction Act, with similar provisions but a more comprehensive scope, is expected to produce even more promising results in addressing racial/ethnic disparities in healthcare. Future research should investigate the long-term effects of the PDSS Model and Inflation Reduction Act on healthcare utilization, racial/ethnic disparities, and health outcomes of Medicare beneficiaries who use insulin.
Supplementary Material
Acknowledgments
No assistance in the preparation of this article is to be declared. This paper was previously presented as a poster presentation at the 2024 Pharmacy Quality Alliance Annual Meeting and a podium presentation at the 2024 AcademyHealth Annual Research Meeting.
Declaration of funding
Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Number R01AG040146. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Footnotes
Declaration of financial/other relationships
Chi Chun Steve Tsang, Xiangjun Zhang, Ashley Ellis, and Jessie Jiaqi Zeng declared no potential conflicts of interest concerning the research, authorship, and/or publication of this article. Junling Wang received funding from AbbVie, Curo, Bristol Myers Squibb, Pfizer, and Pharmaceutical Research and Manufacturers of America (PhRMA) and serves as the Co-Chair of the Value Assessment-Health Outcomes Research Advisory Committee of the PhRMA Foundation.
Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Data availability statement
The data that support the findings of this study are available in the Medical Expenditure Panel Survey at https://meps.ahrq.gov/mepsweb/data_stats/download_data_files.jsp, reference number [21].
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available in the Medical Expenditure Panel Survey at https://meps.ahrq.gov/mepsweb/data_stats/download_data_files.jsp, reference number [21].