Universal Drug Coverage and Socioeconomic Disparities in Health Care Costs Among Persons With Diabetes

Conclusions

From our analysis, health care costs were up to 41% greater for low-SES Ontarians with diabetes compared with those in the highest-SES group, with a stepwise increase in health care costs with decreasing neighborhood income. However, the incremental costs incurred by lower-SES groups were relatively diminished among seniors, who receive universal drug coverage as an insurable benefit, compared with those under age 65 years, who rely largely on private insurance or pay out of pocket for medications.

Other studies from the same health care setting yielded similar findings with respect to the relationship between SES and adverse diabetes outcomes, including CVD, end-stage renal disease, and all-cause mortality, with more marked associations observed in those under age 65 years (10–12). Taken together, these findings highlight a potential role for prescription drug coverage to help curb adverse health outcomes and excess costs associated with low SES. In the last two decades, premature morbidity and mortality from diabetes have fallen tremendously, due in large part to growing evidence on the effectiveness of glucose-, blood pressure–, and cholesterol-lowering therapies to reduce diabetes complications and their adoption into practice (5–10,28,29). However, in our setting, reductions in mortality appear to be lagging in low-income populations with diabetes, specifically, those under age 65 (10). This group is less likely to have private insurance coverage for prescription drugs as a means of filling the gap in public insurance and therefore are more prone to restrict medications because of high drug costs (19). In contrast, the use of evidence-based therapies in our setting is high among seniors with diabetes irrespective of SES (29).

Canada is currently the only country in the Organisation for Economic Co-operation and Development (OECD) with a publicly funded health care system that does not does include prescription drug coverage for citizens of all ages (30). Moreover, Canada spends more on medications per capita than comparable OECD countries with universal health care. While there are compelling arguments in support of implementing a national pharmacare strategy in Canada, doing so would likely require a shift in the allocation of health care dollars from other non-drug sectors to provincial drug plans. However, this may not be as costly as previously thought. Morgan et al. (31) estimated that expansion of Canada’s Medicare system to include medications could result in increased government drug spending of only $1 billion annually, while decreasing private spending through private drug plans and out-of-pocket costs by $8.2 billion each year, for a net reduction of $7.3 billion annually. These estimates rely on reductions in generic and brand-name drug costs through bulk purchasing, negotiation of drug prices, and product selection but would place Canadian drug spending closer to or on par with other OECD countries, with the exception of the U.S.

Findings from our study are highly relevant to the current discourse on health care reform in the U.S. Americans face a growing barrier to accessing prescription medications due to rising drug prices. The list price of insulins nearly tripled between 2002 and 2013, with further rises in the years since, despite more modest increases in net manufacturers’ pricing (17,18). This phenomenon is due to complex changes in drug pricing resulting in rebates and discounts that are passed on to intermediary stakeholders in the insulin supply chain but not to patients at the point of sale (18). As a direct result, out-of-pocket costs for insulin have risen considerably among the uninsured, those with cost-sharing health plans with high co-payments and deductibles, and those affected by the Medicare Part D coverage gap known as the “donut hole.” While its full impact is not yet known, there is evidence that rising medication costs and loss of insurance coverage may have had detrimental effects on medication adherence and diabetes-related health outcomes (32–36). Prior to 2010, there were substantial declines in rates of hospital admission for hyperglycemia, amputation, and CVD among young and middle-aged Americans with diabetes; however, these rates have since plateaued or risen (32–34). Over this same period, there was a parallel increase in the number of emergency department visits and days spent in hospital among low-income groups with diabetes whose health insurance switched from a low- to high-deductible plan (35). In contrast, natural experiments resulting in increased insurance coverage have had opposite effects. For example, the expansion of Medicare (Part D) in 2006 to include medication coverage was associated with reductions in out-of-pocket spending, medication nonadherence, non-drug expenditures, and overall mortality among Medicare beneficiaries who had limited drug coverage prior to enrollment (37,38). Furthermore, evidence from a randomized controlled trial that enrolled beneficiaries of a large commercial insurer in the U.S. suggested that provision of drug coverage could improve outcomes following acute myocardial infarction, without increasing non-drug-related health care spending (39). Findings from our study and others are even more relevant today given the current situation many Americans are facing. The COVID-19 pandemic has led to a sharp rise in unemployment rates, with resultant loss of employer-sponsored health insurance, leaving increasing numbers of people with and people without diabetes uninsured (40). This has created further barriers for patients to access prescription drugs, as well as ongoing diabetes care, and inevitably will contribute to increases in avoidable and costly hospital admissions, emergency department visits, and other diabetes-related adverse events.

Strengths of this study include its large, population-level design, thus reducing the potential for selection bias and providing sufficient power to examine health care costs within subgroups of the population defined by age and SES. However, there are limitations to our analysis that merit discussion. Firstly, we assumed that providing universal coverage for essential medications to people with diabetes who are not currently receiving these benefits (i.e., younger groups with diabetes) would reduce the relative gap in non-drug-related health care costs to levels commensurate with those of populations who do receive universal drug coverage (i.e., older groups with diabetes). Other factors may contribute to the relatively wider SES differences in younger adults (e.g., fewer cost-related barriers to healthy food and exercise facilities among high-income groups) or relatively narrower SES differences in older adults (survival bias leading lower-SES groups to be relatively healthier; old age security payments; less social support, worse care transitions, and greater need for home care, regardless of SES). However, none of these reasons would cause a sudden shift in the SES gradient at age 65 years. Secondly, it is possible that individuals with diabetes complications suffered a decline in SES from loss of employment due to disability, prior to commencement of this study. However, while we cannot rule out some element of reverse causality, a previous study found the same income gradient in health outcomes among individuals with newly diagnosed diabetes as among those with preexisting diabetes diagnoses (11). Thirdly, we did not examine SES disparities in indirect costs, which may be far greater due to the increased likelihood of disability and premature death among lower-income groups. Since those who died no longer incurred health care costs, the excess mortality observed among lower-income groups provides additional insight into the high cost of low SES. Fourth, SES was based on neighborhood rather than individual income; however, these tend to be closely correlated and neighborhood SES performs well when individual measures are unavailable (41). Furthermore, we did not treat SES as a time-varying covariate in our models. This may have led to some degree of misclassification, particularly for young adults who may have been students or unemployed at the time of cohort entry. However, this bias would be expected to favor our null hypothesis, since the likelihood that wealthier students might live in less wealthy neighborhoods seems greater than the possibility that lower-SES students would live in high-SES areas. Fifth, our diabetes algorithm was unable to discriminate between type 1 and type 2 diabetes. Although type 1 diabetes comprises a small proportion of all diabetes cases, its higher prevalence at younger ages may have contributed to variations in the association between SES and health care expenditures in younger verses older groups. Finally, we accounted for differences in follow-up time between individuals by reporting costs per person-year but did not consider fluctuations in costs over time.

Creating a health system that is both efficient and equitable relies on measures to enhance care and outcomes for the most vulnerable members of society. High out-of-pocket costs for prescription drugs may serve as an important barrier to accessing care for many individuals with diabetes. Low-income groups shoulder a disproportionate burden of these costs, often spending 3%–4% or more of household earnings on drugs and supplies for their management. Our findings demonstrate that SES is a significant predictor of publicly funded health care costs for adults with diabetes in our setting, and the association between SES and health care expenditures appears to be stronger among younger (<65 years) individuals, for whom drug costs are not universally covered as an insurable benefit, than among older (≥65 years) individuals, for whom drug costs are included. Further research is needed to more fully understand the health impact and costs associated with changes in drug policy.