Background

The Affordable Care Act expanded Medicaid eligibility allowing low-income individuals greater access to health care. However, the uptake of state Medicaid expansion has been variable. It remains unclear how the Medicaid expansion was associated with the temporal trends in use of evidence-based cardiovascular drugs.

Methods

We used the publicly available Medicaid Drug Utilization and Current Population Survey to extract filled prescription rates per 1000 Medicaid beneficiaries of statins, antihypertensives, P2Y12 inhibitors, and direct oral anticoagulants. We defined expander states as those who expanded Medicaid on or before January 1, 2014, and nonexpander states as those who had not expanded by December 31, 2018. Difference-in-differences (DID) analyses were performed to compare the association of the Medicaid expansion with per-capita cardiovascular drug prescription rates in expander versus nonexpander states.

Results

Between 2011 and 2018, the total number of prescriptions among all Medicaid beneficiaries increased, with gains of 89.7% in statins (11.0 to 20.8 million), 76% in antihypertensives (35.3 to 62.2 million), and 37% in P2Y12 inhibitors (1.7 to 2.3 million). Medicaid expansion was associated with significantly greater increases in quarterly prescriptions (per 1000 Medicaid beneficiaries) of statins (DID estimate [95% CI]: 22.5 [16.5-28.6], P<0.001), antihypertensives (DID estimate [95% CI]: 63.2 [47.3-79.1], P<0.001), and P2Y12 inhibitors (DID estimate [95% CI]: 1.7 [1.2-2.2], P<0.001). Between 2013 and 2018, >75% of the expander states had increases in prescription rates of both statins and antihypertensives. In contrast, 44% of nonexpander states saw declines in statins and antihypertensives. The Medicaid expansion was not associated with higher direct oral anticoagulants prescription rates (DID estimate [95% CI] 0.9 [-0.3 to 2.1], P=0.142).

Conclusions

The 2014 Medicaid expansion was associated with a significant increase in per-capita utilization of cardiovascular prescription drugs among Medicaid beneficiaries. These gains in utilization may contribute to long-term cardiovascular benefits to lower-income and previously underinsured populations.

Objective

Certain antihyperglycemic therapies modify cardiovascular and kidney outcomes among patients with type 2 diabetes, but early uptake in practice appears restricted to particular demographics. We examine the association of Medicaid expansion with use of and expenditures related to antihyperglycemic therapies among Medicaid beneficiaries.

Research design and methods

We employed a difference-in-difference design to analyze the association of Medicaid expansion on prescription of noninsulin antihyperglycemic therapies. We used 2012-2017 national and state Medicaid data to compare prescription claims and costs between states that did (n = 25) and did not expand (n = 26) Medicaid by January 2014.

Results

Following Medicaid expansion in 2014, average noninsulin antihyperglycemic therapies per state/1,000 enrollees increased by 4.2%/quarter in expansion states and 1.6%/quarter in nonexpansion states. For sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide 1 receptor agonists (GLP-1RA), quarterly growth rates per 1,000 enrollees were 125.3% and 20.7% for expansion states and 87.6% and 16.0% for nonexpansion states, respectively. Expansion states had faster utilization of SGLT2i and GLP-1RA than nonexpansion states. Difference-in-difference estimates for change in volume of prescriptions after Medicaid expansion between expansion versus nonexpansion states was 1.68 (95% CI 1.09-2.26; P < 0.001) for all noninsulin therapies, 0.125 (-0.003 to 0.25; P = 0.056) for SGLT2i, and 0.12 (0.055-0.18; P < 0.001) for GLP-1RA.

Conclusions

Use of noninsulin antihyperglycemic therapies, including SGLT2i and GLP-1RA, increased among low-income adults in both Medicaid expansion and nonexpansion states, with a significantly greater increase in overall use and in GLP-1RA use in expansion states. Future evaluation of the population-level health impact of expanded access to these therapies is needed.

BACKGROUND: The association of historical redlining policies, a marker of structural racism, with contemporary heart failure (HF) risk among White and Black individuals is not well established. METHODS: We aimed to evaluate the association of redlining with the risk of HF among White and Black Medicare beneficiaries. Zip code-level redlining was determined by the proportion of historically redlined areas using the Mapping Inequality Project within each zip code. The association between higher zip code redlining proportion (quartile 4 versus quartiles 1-3) and HF risk were assessed separately among White and Black Medicare beneficiaries using generalized linear mixed models adjusted for potential confounders, including measures of the zip code-level Social Deprivation Index. RESULTS: A total of 2 388 955 Medicare beneficiaries (Black n=801 452; White n=1 587 503; mean age, 71 years; men, 44.6%) were included. Among Black beneficiaries, living in zip codes with higher redlining proportion (quartile 4 versus quartiles 1-3) was associated with increased risk of HF after adjusting for age, sex, and comorbidities (risk ratio, 1.08 [95% CI, 1.04-1.12]; P<0.001). This association remained significant after further adjustment for area-level Social Deprivation Index (risk ratio, 1.04 [95% CI, 1.002-1.08]; P=0.04). A significant interaction was observed between redlining proportion and Social Deprivation Index (Pinteraction<0.01) such that higher redlining proportion was significantly associated with HF risk only among socioeconomically distressed regions (above the median Social Deprivation Index). Among White beneficiaries, redlining was associated with a lower risk of HF after adjustment for age, sex, and comorbidities (risk ratio, 0.94 [95% CI, 0.89-0.99]; P=0.02). CONCLUSIONS: Historical redlining is associated with an increased risk of HF among Black patients. Contemporary zip code-level social determinants of health modify the relationship between redlining and HF risk, with the strongest relationship between redlining and HF observed in the most socioeconomically disadvantaged communities.