CORR Insights®: Has the Affordable Care Act Been Associated with Increased Insurance Coverage and Early-stage Diagnoses of Bone and Soft-tissue Sarcomas in Adults? : Clinical Orthopaedics and Related Research®

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CORR Insights®: Has the Affordable Care Act Been Associated with Increased Insurance Coverage and Early-stage Diagnoses of Bone and Soft-tissue Sarcomas in Adults?

Poeran, Jashvant MD, PhD

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Clinical Orthopaedics and Related Research 479(3):p 503-505, March 2021. | DOI: 10.1097/CORR.0000000000001483

Where Are We Now?

In this health policy analysis, Malik et al. [8] used the United States Surveillance, Epidemiology, and End Results database to investigate whether the introduction of the Patient Protection and Affordable Care Act (ACA) in 2010 coincided with increased insurance coverage for adult patients with newly diagnosed bone and soft-tissue sarcomas, and whether this subsequently led to an increased proportion of sarcoma diagnoses at earlier disease stages. The authors found a modest increase in the proportion of patients with Medicaid insurance and a decreased proportion of uninsured patients; this coincided with a significant increase in the proportion of early-stage diagnoses (Stages I and II), a decrease in the proportion of unknown-stage diagnoses, and no significant change in the proportion of late-stage diagnoses (Stages III and IV). In light of the ongoing political discussions about the future of the ACA, Malik et al.’s findings [8] suggest a net benefit of expansions of healthcare insurance coverage in terms of patient access to orthopaedic cancer care. The broader theme highlighted by this study is the importance of continued, detailed monitoring and evaluation of the impact of policies on care and outcomes (such as survival) in patients with orthopaedic conditions.

Indeed, similar to Malik et al. [8], another study linked the ACA’s Medicaid expansion to an increased number of insured patients and lower stages at the time of diagnosis for a variety of cancer types [5]. However, evidence points towards minor or even negative impacts of this expansion in terms of access to orthopaedic care in general [2, 7, 9]. One noted reason is the limited number of orthopaedic surgeons caring for Medicaid patients, and these surgeons are spread out over a much larger pool of newly insured Medicaid enrollees after eligibility expansion [2, 9]. Moreover, even among patients with Medicaid insurance, state-specific reimbursement policies appear to play an important role in access to orthopaedic care [7]. Such nuances are crucial for contextualization because they provide information on the various mechanisms behind the results of policy evaluations. Policy rollouts—specifically those on a national scale—are not an exact science and I believe that thorough assessments of the full spectrum of potential mechanisms behind policy impacts need to be considered in any policy evaluation. This includes assessments of negative, unintended consequences such as paradoxical lower per-patient reimbursements, as has been described for orthopaedic trauma care after implementation of the ACA [2]. At a Level-1, academic trauma center in a Medicaid expansion state, Beck et al. [2] found that despite treating more Medicaid patients post-ACA, they collected less revenue per Medicaid patient post-ACA. Based on the findings by Malik et al. [8], the ACA seems to have positively impacted orthopaedic cancer care; however, further refinements to this study are needed to fully inform policymakers and surgeons on the ACA’s impact.

Where Do We Need To Go?

Future studies should seek to better characterize whether and how improved access to healthcare insurance—specifically Medicaid expansion—influences the care-seeking behaviors and health outcomes of orthopaedic patients, including those with cancer. These include qualitative studies focusing on patient decision-making and an evaluation of the potential downstream effects of earlier cancer diagnosis such as variations in treatments and—importantly—survival. If such studies fail to show an impact on these or other patient-centric outcome measures, the importance of earlier sarcoma diagnoses may shift towards decreased healthcare costs. In addition, with cost reduction and quality enhancement as the main motivations behind the ACA, cost-effectiveness analyses are another avenue of follow-up research. Finally, patients with Medicaid and patients without insurance are among those with the least access to care and are thus likely to benefit more from policies such as the ACA. Here, subgroup analyses may be needed to assess any heterogeneity of effects and identify patients who are more likely to benefit. Indeed, existing evidence suggests that the ACA has a strong, beneficial effect on insurance coverage among specific patient subgroups, such as those with a low income [5].

Importantly, as with any implementation of health policy that includes the simultaneous rollout of various policies or interventions, it is crucial to study the exact role of each separate policy or intervention, whenever possible. Malik et al. [8] state that “it is unknown what healthcare reform that was part of the ACA was directly responsible for the increase in early-stage diagnoses over time.” Although this may be true to an extent, analytical approaches that take advantage of the observed variations in the rollout of policies or interventions—for example, variation in states opting into Medicaid expansions—may provide some insights into the impact of certain policies or interventions. Here, the “difference-in-differences” analytical approach, which was also applied by Malik et al. [8], is one of the most commonly used methods [4]. Basic evaluations of policies apply a pre-post approach in which outcomes after policy implementation are compared with those before; a difference-in-differences analysis adds robustness by additionally incorporating a control group, that is, a group that was not exposed to a specific policy or intervention of interest. Thus, a pre-post comparison of outcomes would be made separately in study and control groups; this will provide an estimate of a “difference-in-differences”. If a policy is associated with changes in an outcome, then the outcomes after implementation will change to a greater extent in the study group than in the control group. In the context of Malik et al.’s [8] study, the latter would represent states that did not opt to expand Medicaid coverage.

Estimates from a difference-in-differences model are derived from a regression model, adding further robustness to this approach. While this approach has not been traditionally used in orthopaedic health services research, it is increasingly applied to evaluating policies such as the Comprehensive Care for Joint Replacement [1], the Bundled Payments for Care Improvement [10], and other programs [3] that directly impact healthcare delivery to orthopaedic patients. Given the current climate of healthcare reform, and given that orthopaedic surgeries such as joint arthroplasty are being selected as testing grounds for various policies, the need for these analyses will increase.

How Do We Get There?

The most-obvious next step in order to address some of the mentioned knowledge gaps would be to reevaluate the Surveillance, Epidemiology, and End Results dataset using more-recent data because two additional years (up to 2017) have become available, with an additional year expected later in 2020. This will bring various advantages including the possibility of excluding the period directly after the Medicaid expansion because healthcare systems and patients require time to adjust to policy changes. Such an approach was not possible in Malik et al.’s [8] study, given the availability of just 2 years of post-Medicaid expansion data. In addition, more states have opted to expand Medicaid coverage since 2015 [6]. These include Indiana and Pennsylvania (the fifth-most-populous state in the United States), thus adding more data and more variation in the policy’s rollout. The latter may facilitate a further refinement in which the separate effect of Medicaid expansion (as opposed to other ACA-related policies) is teased out using a difference-in-differences approach. Finally, a longer follow-up duration using more-recent data may also allow for an assessment of outcomes, mainly survival.


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