Medicaid Might Cut Cardiovascular Risk for This One Group

Getting onto Medicaid might not improve cardiovascular risk factors universally, but it did for some individuals, as secondary analysis of a randomized trial showed.

Previously uninsured, low-income individuals who won a lottery slot into Medicaid in Oregon had significantly lowered systolic blood pressure after becoming insured as compared with the overall population in one subgroup: those predicted to benefit highly based on their baseline characteristics (-4.96 mm Hg, P<0.001).

That same group also had significantly improved glycemic control marked by lower HbA1c, although the effect size was “not clinically meaningful” (-0.12%, 95% CI -0.25% to -0.01%), researchers led by Kosuke Inoue, MD, PhD, of Kyoto University in Japan, reported in The BMJ.

“These findings are important because the Oregon Health Insurance Experiment is one of the few randomized controlled trials that enables us to assess the causal impact of health insurance coverage,” co-author Yusuke Tsugawa, MD, MPH, PhD, of the University of California Los Angeles, said in a statement. “Our results should be informative to policymakers and health policy researchers, as they provide robust evidence that health insurance not only improves mental health, as the original study has found, but also improves physical health, such as lowering blood pressure.”

In the main trial findings, gaining Medicaid coverage on average improved access to care and certain outcomes, including depression, but not physical health, including cardiovascular risk factors.

“Our findings highlight the importance of looking beyond ‘average effects’ to consider that the impact of interventions often varies heterogeneously based on the characteristics of study participants,” said Tsugawa. “By focusing too heavily on average effects without comprehensively evaluating the ‘heterogeneity’ in the effectiveness of the intervention, such as health insurance policies, we risk incorrectly concluding that an intervention is ineffective when, in fact, specific identifiable subgroups are benefiting.”

The trial took uninsured people earning under the federal poverty line who were waitlisted to apply for Medicaid insurance in Oregon and randomized them via lottery to apply for coverage in 2008 or to remain on the waitlist.

Of the total of 12,134 individuals who met the inclusion criteria, 6,338 were assigned to the lottery group and 5,796 to the control group. The two groups had similar baseline characteristics, with a mean systolic blood pressure of 119 mm Hg and mean HbA1c of 5.3% at the in-person interview survey.

For the secondary analysis, the researchers looked at subgroups of patients as identified by a machine-learning algorithm, called causal forest, that estimated the intervention impact based on individuals’ baseline characteristics given the typical impact of Medicaid on health outcomes.

The individuals the algorithm predicted to benefit highly for the cardiovascular risk factors were less likely to have a prior hypertension diagnosis and had lower total and emergency department charges at baseline than those with lower predicted benefit.

After Medicaid enrollment, drug prescriptions and office visits rose in the group predicted to benefit for high blood pressure, although the changes didn’t differ significantly from the overall population.

The difference between the predicted and observed total charges was larger for the high benefit group than for the low benefit group ($3,837 vs -$91, P<0.001), "suggesting that individuals who benefit the most from Medicaid coverage were those who did not have access to healthcare before Medicaid coverage," the researchers wrote.

Study limitations included lack of information on some important cardiovascular risk factors such as drinking, smoking, obesity status, and family disease history. Also, characteristics were self-reported, which could introduce measurement error and misclassification bias.

“By using advanced analytical tools and considering a wider range of patient characteristics, future studies can help develop more personalized treatment approaches,” Tsugawa said. “This will ensure that medical interventions are tailored to the unique needs of different patient groups, ultimately improving healthcare outcomes for everyone.”