Políticas neoliberales en la década de los 90.

Overview and Context

The findings from this dissertation, across three aims, tell a relatively consistent story relating to financial burden from prescription medication and its effects on cancer patients. First, we observed substantial reduction (between 25 and 75%) in the likelihood of delay or omission of prescription medication among low-income subsidy Medicare enrollees relative to persons not on the LIS. Since the LIS provides substantial reduction in the cost of prescription medication, we attribute this finding largely to an expected lower cost for drugs. Confidence in this result is bolstered by a range of qualitatively consistent sensitivity analyses and a robust balancing strategy that seeks to control for underlying differences between LIS and non-LIS populations using machine learning techniques. We further observed heterogeneity in the ability of the LIS program to reduce delay or omission of prescription medication among the ‘treated’ (LIS participants) and the full sample, suggesting that individuals at higher need based on

income/asset eligibility may be more likely to benefit from a reduction in prescription drug costs. Next, we sought to estimate the effect of this delay or omission of prescription

medication due to cost on all-cause and cancer-specific mortality. Again, we find compelling evidence to affirm that cost-related burden affects patients negatively. The hazard for all-cause death was approximately 25% higher for those reporting delay or omission of prescription medication and more than 50% higher for cancer-specific death. Characteristics that predict delay or omission of prescription medication were used in machine learning algorithms to improve balance between groups. Notably, we found important differences when stratifying by

primary cancer site. Patients with bladder breast and lung cancers exhibited the strongest association between delay or omission of prescription medication and mortality, while prostate cancer patients showed almost no difference in all-cause death.

Finally, we use results from the prior two aims on the effectiveness of potential policy interventions mimicking the LIS to reduce OOPC burden and the consequences of high cost burden to simulate the societal-level impact in a specific population: HER2+ breast cancer. In a microsimulation model of 10,000 women followed for 10 years, we found evidence to suggest substantial loss associated with financial burden (more than a year of perfect health lost per person). We also modeled hypothetical interventions targeting populations at differing levels of income, proxied by the Federal Poverty Line (FPL). Covering more of the population was more costly, with diminishing returns to health (assumptions from evidence in Aim 1). Across a range of sensitivity and variable assumptions, covering 150% of FPL (when compared to no coverage) provides value significantly below commonly accepted willingness to pay thresholds, and on par with many traditional oncologic treatment regimens. Covering the full population appears marginally less efficient, but arguably still under a $100,000 per QALY gain threshold when compared to no intervention.

It is important to put these results into context. Estimates of the difference in delay or omission of prescription medication for LIS vs. non-LIS beneficiaries are consistent with a larger literature which relates cost exposure to adherence and use outcomes. If anything, our estimates may be marginally larger than some of the existing literature that shows a 20-50% reduction in adherence-related outcomes due to cost.9,17 _{We attribute this departure from the literature partly}

to the self-reported measure used in this study which captures both delay and omission – not directly relatable to other studies calculating only ‘adherence’ using proportion of days covered

(among users) in claims. Few studies have analyzed the effect of financial burden on mortality. The most directly relatable study used a pathway through bankruptcy and finds estimates larger than those we estimate here.35 _{Bankruptcy, however, is likely to be an extreme form of financial}

burden, especially compared to the pathway used for our study (delay or discontinuation of medication) which could contribute to the difference. We are not aware of any study that examines cancer-specific mortality, suggesting the need for more research to confirm these associative findings. We are also not aware of any study that seeks to quantify the societal burden, though within the context of economic evaluation in cancer our findings compare favorably to a number of more traditional cancer therapeutics.

Implications for Policy

There are several important implications for policy from this work. First, this work provides further evidence of an association between financial burden and adverse health

outcomes to cancer patients. In this case, failing to take prescription medication because of cost can lead to significant mortality risks – an important but intuitive finding. Policy-wise this finding suggests that programs which promote reduction in cost burden to individuals can improve health, even for ‘hard’ clinical outcomes, such as death.

This work also demonstrates that programs that limit the OOPC costs of prescription medication to patients can produce significant reductions in the likelihood of delay or omission of those prescriptions by patients. Importantly, we observe significant heterogeneous effects by population income. Estimates that seek to balance groups to look like the full sample population were 2-3 times less than those balanced to look like the ‘targeted’ population (here, low-income individuals). The difference is substantial; low-income individuals may experience up to a 75%

reduction in the likelihood of delay or omission, while average effects across the full population were closer to 25%. The suggestion for policy is that not all populations may not benefit equally from cost-savings programs. Targeted interventions may produce the most efficient outcomes.

From simulations, we also demonstrate that the value associated with targeted interventions is likely to be favorable (cost-effective) when compared to no intervention in a HER2+ breast cancer population. Against commonly cited willingness to pay values that allow researchers to compare across intervention types and disease areas, the value of a program to reduce the cost of prescription medication to low-income individual (like the LIS) compares favorably to existing clinical cancer interventions. Though the estimates become considerably less precise, with many unknowns, there may also be evidence to suggest programs targeting low-middle income individuals (e.g., 150-400% FPL) provide good value as well. Notably, our estimates focus on the burden associated with prescription drug costs only; the societal benefit to reductions in out-of-pocket cost burden could be greater when accounting for all types of

medical expense.

Overall, this work provides evidence to support targeted policy interventions that reduce the financial burden to individuals with cancer. The LIS, upon which a number of these estimates are based, provides a strong and efficient base solution for Medicare Beneficiaries. But not all cancer patients are eligible. Those on private insurance, uninsured, or not meeting income/asset thresholds may also be at increased risk of delay or omission of prescription medication, and ultimately, death. Expanding eligibility or creating new programs may be viable solutions in certain settings. But LIS expansion has limits, and moving forward, it will be important to consider the costs and tradeoffs associated with providing this type of benefit.

Future Directions

One obvious extension of this work is to continue demonstrate value associated with targeted interventions across different populations. We chose HER2+ breast cancer as a proof of concept, but many other settings are in need of economic evaluation to help guide policy

decisions. Moving forward, as interventions take shape, research should also focus on specific differences between them, such as the value associated with a national LIS-type program, when compared to a local hospital-based patient navigation program, or 340b benefits. Data at the moment on these different strategies are sparse, but as solutions are developed, more and more information about their effectiveness will lead to an ability to compare and contract programs directly.

Another important extension of this work would be to differentiate between cancer and non-cancer specific medication in the estimates of delay/omission and mortality. Part D claims would help to be able to identify specific sets of drugs that different patient groups may be receiving – which would allow for a better understanding of where clinical harm is most

concentrated. Though our estimates of cancer-specific mortality – higher than those for all-cause – may suggest much of the benefit afforded to reduced cost of prescription medication is in cancer drugs, these estimates are very imprecise and should require further study to verify independently, using Part D claims.

Finally, an important critique of the financial burden literature to date is the inability of different studies to standardized their measure of ‘burden’. Though qualitative research has confirmed the inherently subjective nature of financial burden – individuals may feel burdened or affected by different financial circumstances – a lack of standardization makes quantitative conclusions challenging to compare across studies. The field should work towards a set of

agreed-upon measures, developed with substantial patient and stakeholder input – that would allow for future research to build, rather than re-invent each time.

In an ever-changing health policy landscape, the cost of care is one constant. The cost to patients, the cost to payers, and the cost to society all shape decision making. In this dissertation we demonstrate some of the consequences associated with individual patient cost burden, and how effective policy may limit negative effects. More and better evidence-based implementation of policy to limit overly-burdensome cost exposure for patients could improve cancer health outcomes in an efficient and cost-effective way.

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