Racial disparities in healthcare expenditures are well-documented, yet the underlying drivers remain complex and require further investigation. This study employs causal and counterfactual path-specific effects to quantify how various factors, including socioeconomic status, insurance access, health behaviors, and health status, mediate these disparities. Using data from the Medical Expenditures Panel Survey, we estimate how expenditures would differ under counterfactual scenarios in which the values of specific mediators were aligned across racial groups along selected causal pathways. A key challenge in this analysis is ensuring robustness against model misspecification while addressing the zero-inflation and right-skewness of healthcare expenditures. For reliable inference, we derive asymptotically linear estimators by integrating influence function-based techniques with flexible machine learning methods, including super learners and a two-part model tailored to the zero-inflated, right-skewed nature of healthcare expenditures.

For instance, according to the Centers for Medicare & Medicaid Services, in December 2021, it was reported that the total national health expenditure in the U.S. increased to USD 4.1 trillion in 2020, which was a growth of 9.7% as compared to the previous year. Thus, a significant number of insurance providers are relying on the service providers to negotiate the drug price with retail pharmacy units and lower the price of the listed drugs in the insurance coverage. Furthermore, increasing initiatives, such as extending mail order delivery services and strengthening distribution network in remote areas, were responsible for the growing adoption of these services. Hence, these initiatives by the major players coupled with increasing demand for specialty drugs boosted the pharmacy benefit management market growth during the COVID-19 pandemic. Request a Free sample to learn more about this report.

Complex distributions of the healthcare expenditure pose challenges to statistical modeling via a single model. Super learning, an ensemble method that combines a range of candidate models, is a promising alternative for cost estimation and has shown benefits over a single model. However, standard approaches to super learning may have poor performance in settings where extreme values are present, such as healthcare expenditure data. We propose a super learner based on the Huber loss, a "robust" loss function that combines squared error loss with absolute loss to down-weight the influence of outliers. We derive oracle inequalities that establish bounds on the finite-sample and asymptotic performance of the method. We show that the proposed method can be used both directly to optimize Huber risk, as well as in finite-sample settings where optimizing mean squared error is the ultimate goal. For this latter scenario, we provide two methods for performing a grid search for values of the robustification parameter indexing the Huber loss. Simulations and real data analysis demonstrate appreciable finite-sample gains in cost prediction and causal effect estimation using our proposed method.

Racial and ethnic disparities in access to healthcare in the United States is well-known and documented [1]. Health disparities are defined to be differences in health ou tcomes and causes among different groups of people. Health equity is achieved when everyone has the same opportunity to be as healthy as possible. We have a very good handle on the types of health disparities i n the US healthcare system, but the causes for these disparities are complex [2, 3] - such as inco me, education, socioeconomic conditions, neighborhood and community influence, public policy, and so cietal structure. Achieving health equity also necessitates a complex set of programs and interventions, a nd several public and private initiatives have tried to address this problem over the past decades.

A small fraction of individuals account for the bulk of population healthcare expenditures in the USA, Denmark and other industrialised countries.1–4 Although many high-cost patients show consecutive high-cost years, the majority experience a'cost bloom', or a surge in healthcare costs that propels them from a lower to the upper decile of population-level healthcare expenditures between consecutive years.4 Proactively identifying and managing care for high-cost patients--especially cost bloomers, who may disproportionately benefit from interventions to mitigate future high-cost years--can be an effective way to simultaneously improve quality and reduce population health costs.5–16 However, since the Centers for Medicare and Services (CMS) commissioned the Society of Actuaries to compare leading prediction tools more than 10 years ago, scant progress has been made in improving cost-prediction tools.17 Overcoming these and other challenges associated with the management and care of high-cost patients is essential to achieving a higher value healthcare system.