Objectives: The present study examined differences in neurocognitive outcomes among non-Hispanic Black and White stroke survivors using the NIH Toolbox-Cognition Battery (NIHTB-CB), and investigated the roles of healthcare variables in explaining racial differences in neurocognitive outcomes post-stroke. Methods: One-hundred seventy adults (91 Black; 79 White), who participated in a multisite study were included (age: M=56.4; SD=12.6; education: M=13.7; SD=2.5; 50% male; years post-stroke: 1–18; stroke type: 72% ischemic, 28% hemorrhagic). Neurocognitive function was assessed with the NIHTB-CB, using demographically corrected norms. Participants completed measures of socio-demographic characteristics, health literacy, and healthcare use and access. Stroke severity was assessed with the Modified Rankin Scale. Results: An independent samples t test indicated Blacks showed more neurocognitive impairment (NIHTB-CB Fluid Composite T-score: M=37.63; SD=11.67) than Whites (Fluid T-score: M=42.59, SD=11.54; p=.006). This difference remained significant after adjusting for reading level (NIHTB-CB Oral Reading), and when stratified by stroke severity. Blacks also scored lower on health literacy, reported differences in insurance type, and reported decreased confidence in the doctors treating them. Multivariable models adjusting for reading level and injury severity showed that health literacy and insurance type were statistically significant predictors of the Fluid cognitive composite (p<.001 and p=.02, respectively) and significantly mediated racial differences on neurocognitive impairment. Conclusions: We replicated prior work showing that Blacks are at increased risk for poorer neurocognitive outcomes post-stroke than Whites. Health literacy and insurance type might be important modifiable factors influencing these differences. (JINS, 2017, 23, 640–652)

Opioids are the mainstay of treatment for moderate to severe cancer pain. The variations in average monthly cost can make it difficult for most patients to procure them without adequate insurance coverage. There are increasing numbers of denials of payment and statements made by insurance agents and other sources regarding inappropriate opioid use, resulting in severe pain and emotional distress for cancer patients and their families. This case series describes five events where the insurer was a major barrier to opioid access.

There has been an increased focus on the relationship between health technology assessment (HTA) and regulatory assessments and how regulatory, HTA and coverage bodies, and industry can work better together to improve efficiency and alignment of processes. There is increasingly agreement across sectors that improved communication and coordination could contribute to facilitating timely patient access to effective, affordable treatments that offer value to the health system. Discussions on aspects of this relationship are being held in different forums and various forms of coordination and collaboration are being developed or piloted within several jurisdictions. It is therefore both timely and of value to stakeholders to describe and reflect on current initiatives intended to improve interactions between regulatory, HTA and coverage bodies, and industry. Drawing on 2011 meetings of the HTAi Policy Forum and the Center for Innovation in Regulatory Science (CIRS), this study aims to describe and compare initiatives, and point to success factors and challenges that are likely to inform future work and collaboration.

Objectives: The aim of this study was to describe the current issues surrounding Coverage with Evidence Development (CED). CED is characterized by restricted coverage for a new technology in parallel with targeted research when the stated goal of the research or data collection is to provide definitive evidence for the clinical or cost-effectiveness impact of the new technology.

Methods: Presented here is information summarized and interpreted from presentations and discussions at the 2008 Health Technology Assessment International (HTAi) meeting and additional information from the medical literature. This study describes the differences between CED and other conditional coverage agreements, provides a brief history of CED, describes real-world examples of CED, describes the areas of consensus between the stakeholders, discusses the areas for future negotiation between stakeholders, and proposes criteria to assist stakeholders in determining when CED could be appropriate.

Results: Payers could interpret the evidence obtained from a CED program either positively or negatively, and a range of possible changes to the reimbursement status of the new technology may result. Striking an appropriate balance between the demands for prompt access to new technology and acknowledging that some degree of uncertainty will always exist is a critical challenge to the uptake of this innovative form of conditional coverage.

Conclusions: When used selectively for innovative procedures, pharmaceuticals, or devices in the appropriate disease areas, CED may provide patients access to promising medicines or technologies while data to minimize uncertainty are collected.