No, I can’t keep up either. Nevertheless, here are some more potentially interesting papers. I seem to be adding a few comments to my citations in these reading lists. When I do so, they’ll always be just after the author list.
Patient cost-sharing and hospitalization offsets in the elderly, by Amitabh Chandra, Jonathan Gruber, and Robin McKnight. Using different data and far different methods, the authors come up with a conclusion qualitatively similar to that of a paper described by Aaron in a prior post. This is, perhaps, the most convincing evidence of offsets to changes in physician and drug cost sharing in an elderly population. As the authors point out, it is important to recognize that it is an out of sample extrapolation to draw conclusions about the elderly from the findings of the RAND HIE as it did not include elderly in its sample. The next time someone relies on the RAND HIE to suggest cost sharing changes to Medicare, remember this paper.
In the Medicare program, increases in cost sharing by a supplemental insurer can exert financial externalities. We study a policy change that raised patient cost sharing for the supplemental insurer for retired public employees in California. We find that physician visits and prescription drug usage have elasticities that are similar to those of the RAND Health Insurance Experiment (HIE). Unlike the HIE, however, we find substantial “offset” effects in terms of increased hospital utilization. The savings from increased cost sharing accrue mostly to the supplemental insurer, while the costs of increased hospitalization accrue mostly to Medicare.
Data Governance and Stewardship: Designing Data Stewardship Entities and Advancing Data Access, by Sara Rosenbaum. Data access! How boring, you might think. But this is a really big issue that very few people outside research understand. And some of those people are making the rules.
U.S. health policy is engaged in a struggle over access to health information, in particular, the conditions under which information should be accessible for research when appropriate privacy protections and security safeguards are in place. The expanded use of health information—an inevitable step in an information age—is widely considered be essential to health system reform. Models exist for the creation of data-sharing arrangements that promote proper use of information in a safe and secure environment and with attention to ethical standards. Data stewardship is a concept with deep roots in the science and practice of data collection, sharing, and analysis. Reflecting the values of fair information practice, data stewardship denotes an approach to the management of data, particularly data that can identify individuals. The concept of a data steward is intended to convey a fiduciary (or trust) level of responsibility toward the data. Data governance is the process by which responsibilities of stewardship are conceptualized and carried out. As the concept of health information data stewardship advances in a technology-enabled environment, the question is whether legal barriers to data access and use will begin to give way. One possible answer may lie in defining the public interest in certain data uses, tying provider participation in federal health programs to the release of all-payer data to recognized data stewardship entities for aggregation and management, and enabling such entities to foster and enable the creation of knowledge through research.
Balancing Access to Health Data and Privacy: A Review of the Issues and Approaches for the Future, by Julia Lane, Claudia Schur.
Background. There has been a dramatic increase in the types of microdata, and this holds great promise for health services research. However, legislative efforts to protect individual privacy have reduced the flow of health care data for research purposes and increased costs and delays, affecting the quality of analysis.
Aim. This paper provides an overview of the challenges raised by concerns about data confidentiality in the context of health services research, the current methodologies used to ensure data security, and a description of one successful approach to balancing access and privacy.
Materials and Methods. We analyze the issues of access and privacy using a conceptual framework based on balancing the risk of reidentification with the utility associated with data analysis. The guiding principle should be to generate released data that are as close to the maximum acceptable risk as possible. HIPAA and other privacy measures can perhaps be seen as having had the effect of lowering the “maximum acceptable risk” level and rendering some data unreleasable.
Results. We discuss the levels of risk and utility associated with different types of data used in health services research and the ability to link data from multiple sources as well as current models of data sharing and their limitations.
Discussion. One particularly compelling approach is to establish a remote access “data enclave,” where statistical protections are applied to the data, technical protections ensure compliance with data-sharing requirements, and operational controls limit researchers’ access to the data they need for their specific research questions.
Conclusion. We recommend reducing delays in access to data for research, increasing the use of remote access data enclaves, and disseminating knowledge and promulgating standards for best practices related to data protection.
Commentary: Assessing the Health Effects of Medicare Coverage for Previously Uninsured Adults: A Matter of Life and Death? by J. Michael McWilliams, Ellen Meara, Alan M. Zaslavsky, John Z. Ayanian. See also the response to this commentary.
In contrast to a previous study we conducted and other evidence, a recent study found no significant effects of Medicare coverage after age 65 on overall health for previously uninsured adults and significant adverse effects on survival for some of these adults. We discuss explanations for these inconsistent findings, particularly the different ways in which deaths were handled, a key methodological challenge in longitudinal analyses of health. We demonstrate that analytic approaches suitable for examining effects of coverage on health measures may not be suitable for effects on mortality. Thus, estimates may be misleading when these different outcomes are jointly modeled. We also present new survival analyses that suggest Medicare coverage significantly attenuated the rising risk of death for previously uninsured adults.
Modeling Health Care Policy Alternatives, by Jeanne S. Ringel, Christine Eibner, Federico Girosi, Amado Cordova, Elizabeth A. McGlynn.
Background. Computer models played an important role in the health care reform debate, and they will continue to be used during implementation. However, current models are limited by inputs, including available data.
Aim. We review microsimulation and cell-based models. For each type of model, we discuss data requirements and other factors that may affect its scope. We also discuss how to improve models by changing data collection and data access procedures.
Materials and Methods. We review the modeling literature, documentation on existing models, and data resources available to modelers.
Results. Even with limitations, models can be a useful resource. However, limitations must be clearly communicated. Modeling approaches could be improved by enhancing existing longitudinal data, improving access to linked data, and developing data focused on health care providers.
Discussion. Longitudinal datasets could be improved by standardizing questions across surveys or by fielding supplemental panels. Funding could be provided to identify causal parameters and to clarify ranges of effects reported in the literature. Finally, a forum for routine communication between modelers and policy makers could be established.
Conclusion. Modeling can provide useful information for health care policy makers. Thus, investing in tools to improve modeling capabilities should be a high priority.